FINAL REPORT 2018 Baseline Study of Food for Peace Development Food Security Activities (DFSAs) in Uganda FINAL: January 15, 2019 This report was produced for review by the United States Agency for International Development. It was prepared by ICF Macro, Inc. FINAL REPORT 2018 BASELINE STUDY OF FOOD FOR PEACE DEVELOPMENT FOOD SECURITY ACTIVITIES (DFSAs) IN UGANDA Submission Date: 01/15/2019 Contract Number: GS-00F-189CA/7200AA18M00002 Study Team: Dr. Ramu Bishwakarma, Lead Writer and Analyst, ICF Dr. Benita O’Colmain, Senior Survey Methods Specialist, ICF Dr. Daniel Kibuuka, Qualitative Study Research Lead, IRC Weicheng Chen, Field Coordinator, ICF Genevieve Dupuis, Senior Data Management Specialist, ICF Kirsten Zalisk, Analyst, ICF Dr. Mark Langworthy, TANGO Dr. Lisa Woodson, TANGO Dr. Victoria Brown, TANGO Submitted by: ICF Macro, Inc. 530 Gaither Rd, Suite 500 Rockville, MD 20850 DISCLAIMER The authors’ views expressed in this publication do not necessarily reflect the views of the United States Agency for International Development or the United States Government. 2018 Baseline Study of Food for Peace Development Food Security Activities in Uganda i CONTENTS Executive Summary .......................................................................................................................................................... iv Study Design ................................................................................................................................................................. iv Study Limitations ......................................................................................................................................................... iv Key Findings.................................................................................................................................................................. iv Conclusions ................................................................................................................................................................ viii I. Introduction...................................................................................................................................................................1 1.1 Overview of the Baseline Study ......................................................................................................................1 1.2 Overview of the Current Food Security Situation .....................................................................................2 2. Methodology and Limitations ....................................................................................................................................7 2.1 Methods for the Population-Based Household Survey..............................................................................7 2.2 Methods for Qualitative Study ......................................................................................................................12 2.3 Data Limitations and Fieldwork Challenges ...............................................................................................15 3. Findings .........................................................................................................................................................................17 3.1 Characteristics of the Study Population ......................................................................................................17 3.2 Household Food Security and Poverty........................................................................................................18 3.3 Agriculture..........................................................................................................................................................27 3.4 Water, Sanitation, and Hygiene .....................................................................................................................34 3.5 Women’s Health and Nutrition ....................................................................................................................39 3.6 Children’s Health and Nutrition ...................................................................................................................44 3.7 Gender ................................................................................................................................................................52 3.8 Resilience Analysis Summary .........................................................................................................................56 4. Conclusions ................................................................................................................................................................. 60 4.1 Food Security and Poverty .............................................................................................................................60 4.2 Agriculture..........................................................................................................................................................60 4.3 Water, Sanitation, and Hygiene .....................................................................................................................61 4.4 Maternal Health and Nutrition ......................................................................................................................61 4.5 Children’s Health and Nutrition ...................................................................................................................62 4.6 Gender and Household Decision-Making ...................................................................................................63 4.7 Shocks and Resilience ......................................................................................................................................63 ANNEXES 1. Uganda Baseline Study Statement of Work 2. Uganda Joint Baseline/Endline Population-Based Survey Protocol 3. Population-Based Household Survey Questionnaire 4. Population-Based Survey Data Treatment and Analysis Plan 5. Baseline Qualitative Study Protocol 6. Tabular Summary of Baseline Indicator Estimates 7. List of Variables for Correlation or Regression Analyses 8. Descriptive Tables 9. Multivariate Analyses Tables 10. Uganda Detailed Resilience Analyses 2018 Baseline Study of Food for Peace Development Food Security Activities in Uganda ii List of Tables Table 1: Sampled Households by Implementing Partner, Uganda 2018 ...............................................................8 Table 2: Joint Baseline/Endline PBS Response Rates, Uganda 2018 .....................................................................11 Table 3.2.1: Food Security and Poverty Indicators, FFP Baseline Study, Uganda 2018 ...................................18 Table 3.2.2: Variables Significantly Associated with HDDS Based on Regression Models .............................23 Table 3.2.3: Variables Significantly Associated with FIES Based on Regression Models ................................. 24 Table 3.2.4: Variables Significantly Associated with Poverty Based on Regression Models ...........................25 Table 3.2.5: Variables Significantly Associated with Depth of Poverty Among the Poor Based on Regression models ...........................................................................................................................................................26 Table 3.3.1: Land Ownership and Farm Size by DFSA Area, Uganda 2018 .......................................................29 Table 3.3.2: Agriculture Indicators, Uganda 2018 ....................................................................................................29 Table 3.4.1: WASH Indicators, Uganda 2018............................................................................................................34 Table 3.4.2. Water Use by Household Poverty Status ...........................................................................................36 Table 3.5.1: Women’s Health and Nutrition Indicators, Uganda 2018 ..............................................................39 Table 3.6.1: Children’s Health and Nutrition Indicators, Baseline PBS, Uganda 2018....................................44 Table 3.6.2: 2018 FFP DFSA Children’s Anthropometry Indicators Compared with 2016 DHS for Rural Households .......................................................................................................................................................................47 Table 3.7.1: Cash Decision-Making, Uganda 2018 ...................................................................................................53 Table 3.7.2: Maternal and Child Health Decision-Making, Uganda 2018............................................................55 Table 3.8.1: Resilience Capacity Indexes and Their Components, by DFSA Area, Uganda 2018 ................57 List of Figures Figure 3.1a: Select Characteristics of the Population in the DFSA Areas, Uganda 2018 ...............................17 Figure 3.1b: Education Levels of Household Heads in the DFSA Areas, Uganda 2018 ..................................18 Figure 3.2.1: Percentage of Households Consuming HDDS Food Groups in the DFSA Areas, Uganda 2018 ....................................................................................................................................................................................20 Figure 3.2.2: Sources of Average Per Capita Expenditures, Uganda, 2018 ........................................................21 Figure 3.2.3: Relationship between HDDS and Depth of Poverty of Poor, Uganda, 2018 ............................22 Figure 3.3.1: Livestock Farmers by Type of Livestock Raised and DFSA Area, Uganda 2018 ......................28 Figure 3.3.2: Percentage of Farmers Using Financial Services by DFSA Area, Uganda 2018.........................30 Figure 3.5.1: BMI Levels of Non-Pregnant Women of Reproductive Age in the Combined DFSA Areas, Uganda 2018 .....................................................................................................................................................................40 Figure 3.5.2: Food Groups Consumed by Women 15–49 Years of Age by DFSA Area, Uganda 2018 .....41 Figure 3.6.1: Breastfeeding Status for Children 0–23 Months by Age in Months, CRS, Uganda 2018 ........48 Figure 3.6.2: Breastfeeding Status for Children 0–23 Months by Age in Months, MC, Uganda 2018 .........49 Figure 3.6.3: Components of MAD by Age Group and Breastfeeding Status, CRS, Uganda 2018 ..............50 Figure 3.6.4: Components of MAD by Age Group and Breastfeeding Status, MC, Uganda 2018 ...............50 2018 Baseline Study of Food for Peace Development Food Security Activities in Uganda iii ACRONYMS ANC antenatal care BMI body mass index CHN child health and nutrition CRS Catholic Relief Services DFAP development food assistance project DFSA development food security activity DHS Demographic and Health Survey EBF exclusive breastfeeding FAO Food and Agriculture Organization of the United Nations FEWS NET Famine Early Warning Systems Network FFP Food for Peace FGD focus group discussion FIES Food Insecurity Experience Scale GPS Global Positioning System HDDS household dietary diversity score IP implementing partner IRC International Research Consortium of Uganda IT information technology JMP Joint Monitoring Program KII key informant interview MAD minimum acceptable diet MC Mercy Corps MCHN maternal and child health and nutrition MDD-W minimum dietary diversity—women MHN maternal health and nutrition NGO nongovernmental organization NRM natural resource management ORT oral rehydration therapy PBS population-based survey PPP purchasing power parity RWANU Resiliency through Wealth, Agriculture, and Nutrition in Karamoja ToT Training of trainers UNICEF United Nations Children’s Fund USAID United States Agency for International Development USG United States Government WASH water, sanitation, and hygiene WHO World Health Organization 2018 Baseline Study of Food for Peace Development Food Security Activities in Uganda iv EXECUTIVE SUMMARY This report describes the baseline study for two recently initiated Food for Peace (FFP) Development Food Security Activities (DFSAs) in Uganda. The DFSAs aim to build resilience to shocks, enhance livelihoods and improve food security and nutrition for vulnerable rural families. Two implementing partners (IPs)—Catholic Relief Services (CRS), and Mercy Corps (MC)—are implementing the new FFP-funded DFSAs in the Karamoja Region of Uganda. CRS and its partners are implementing the Nuyok DFSA in the three western districts of Karamoja: Abim, Nakapiripirit, and Napak. MC and its partners are implementing the Apolou DFSA in the four eastern districts of Karamoja: Kaabong, Kotido, Moroto, and Amudat. The purpose of the baseline study is to: (1) provide a baseline for key impact and outcome indicators as a point of comparison for a final evaluation; (2) gain a better understanding of the prevailing conditions and perceptions of the populations in the DFSA implementation areas; and (3) inform program targeting and, where possible, program design. The users will be FFP and its IPs, as well as other key stakeholders. Study Design The baseline study included a representative population-based survey (PBS) of 2,460 households and a qualitative study. Field work for the PBS was conducted from June 7 through July 6, 2018, during the rainy season and toward the end of the lean season in the Karamoja Region. The PBS sample was selected using a multistage clustered sampling design to provide a statistically representative sample of the two DFSA implementation areas. The PBS questionnaire was developed through a series of consultations with FFP, the Food and Nutrition Technical Assistance III Project, FFP awardees, and the United States Agency for International Development in Uganda/Uganda. The qualitative study design was based on a purposively drawn sample of seven data collection sites, one in each of the seven districts of Karamoja. Fieldwork for the qualitative study took place from September 30 through October 7, 2018. Findings from the qualitative data were used to provide contextual information and explanations for significant statistical findings from the PBS. Study Limitations Effects of prior FFP and other donor programs. There are several other ongoing programs in the DFSA implementation areas that may have direct effects on some indicators. These effects are not directly measurable and may contribute to the overall change in indicators measured over time when the endline evaluation is conducted. In addition, prior FFP development food assistance programs operated in a substantial portion of the new DFSA areas, which may influence baseline indicator results for those households that participated in prior program activities, a fact which partners may want to take into account in their program planning. Validity and reliability of self-reported data. Much of the data collected for the household PBS were self-reported, which has several limitations, such as the possibility of exaggeration or omission of information, inaccurate recollection of experiences or events, social desirability bias or reporting of untruthful information, and reduced validity when respondents do not fully understand a question. These same limitations may apply to qualitative data collected through key informant interviews and focus group discussions. To reduce the likelihood of these potential effects on validity and reliability, the PBS findings were triangulated with the qualitative study findings and other data sources. Key Findings Food Security The Household Dietary Diversity Score (HDDS), an average count of food groups consumed by a household and a proxy for socioeconomic status, indicates that households in the DFSA areas access and consume an average of 3 of 12 food groups, reflecting poor access to diverse foods. Nine out of ten 2018 Baseline Study of Food for Peace Development Food Security Activities in Uganda v households experienced moderate to severe food insecurity in the 30 days prior to the survey (91 percent) and in the past 12 months (94 percent) across both DFSA areas. Poverty Based on the World Bank’s extreme poverty line of USD $1.90 per capita per day [2011 Purchasing Power Parity (PPP)], 89 percent of households in the combined DFSA areas are living below the poverty line, with average daily per capita expenditures at USD $1.04. In both DFSA areas, about two thirds of daily per capita consumption expenditures (65 percent) is devoted to food, followed by non-food expenditures (26 percent) and housing (6 percent). Poverty is not only widespread, it is also deep; on average, poor individuals consume 61 percent less than the daily per capita poverty threshold of USD $1.90 (PPP 2011) indicating that poor households1 need to increase their daily per capita expenditures by USD $1.27 (PPP 2011) to rise above the extreme poverty line. Relationship between Poverty and Household Food Security Food security is strongly associated with poverty levels. In both DFSA areas, the HDDS is significantly lower in households below the poverty line, compared to households at and above the poverty line. Households that own more durable assets, participate in local decision-making bodies, and whose household members have a higher level of education tend to have a higher HDDS in both DFSA areas. The Food Insecurity Experience Scale (FIES) indicators are significantly higher in poor households, defined as households living below the $1.90 per day threshold for extreme poverty, indicating that household food insecurity increases as household poverty levels increase. Agriculture Three out of four farmers (more than 73 percent) in the DFSA areas reported owning the farmland over which they make decisions and about one-third of farmers have small plots (less than 0.5 hectares). Adult female-only households are more likely to be sharecroppers or have no land in both project areas, compared to adult male and female households. About one in five farmers in the DFSA areas use financial services through cash savings, agricultural credit, or agricultural insurance. Male and female farmer focus group participants reported the biggest factor contributing to the low level of use of financial services by farmers and other traders for agricultural produce in the community is ignorance about the availability, use, and importance of agricultural financial services. Farmers who use any type of agricultural financial services tend to be less poor in both DFSA areas. About 43 percent of farmers in the CRS area and 34 percent of farmers in the MC area reported planting crops or raising and buying livestock with the specific intention to sell or resell to earn income; and 31 percent of these farmers reported practicing at least one of the value chain activities promoted by the DFSAs. Qualitative interviews suggest that women in general do most of the farming but men tend to make key decisions on value chain activities. Preparation of soil by hand (79 percent in the CRS area, 74 percent in the MC area), broadcasting seed (74 percent in the CRS area, 68 percent in the MC area), and weed control (60 percent in the CRS area, 56 percent in the MC area) are the three most commonly used crop practices in the DFSA areas. Improved shelters (44 percent), vaccinations (39 percent), deworming (35 percent), and kraals (33 percent) are the most commonly used livestock practices in the CRS area. Kraals (61 percent), vaccination and animal shelters (both about 42 percent), and deworming (37 percent) are the most commonly used livestock practices in the MC area. About half of all farmers use improved storage practices in the combined DFSA areas (mainly granaries or super grain bags). 1 Poor households are defined as those with daily per capita expenditures below the World Bank international poverty line of $1.90 per day. 2018 Baseline Study of Food for Peace Development Food Security Activities in Uganda vi Relationship between Agriculture, Food Insecurity and Poverty Households using financial services tend to be less food insecure in the CRS area when households’ background characteristics, such as members’ education, household size, and shocks and stresses, are not considered. When these variables are controlled, households using financial services tend to have lower food insecurity in the MC area. Households in which farmers use agricultural value chain practices are likely to have a lower prevalence of poverty in both DFSA areas and a lower depth of poverty in the CRS area. Households practicing sustainable agricultural practices also tend to have a lower prevalence of poverty in both CRS and MC areas, and a lower depth of poverty in the CRS area. Farmers who use improved agricultural storage practices are likely to have a lower depth of poverty and vice versa in the CRS area, but no such association was found in the MC area. Water, Sanitation, and Hygiene In the combined DFSA areas, only about two in five households (41 percent) use an improved drinking water source (defined by type of source and water availability). About 86 percent of households in the CRS area and 73 percent of households in the MC area obtain their water from boreholes, but water availability from this source is a major concern. Only 55 percent of households in both DFSA areas reported water being available at source year round, and 30 percent of households reported that water was unavailable for a day or more in the past two weeks. About half of all households can obtain drinking water (regardless of whether the source is improved or not) in less than 30 minutes round trip and about 8 percent of households in the CRS area and 12 percent of households in the MC area reported use of recommended household water treatment technologies. Sanitation conditions in the DFSA areas are very poor, only about 8 percent of households in the combined DFSA areas have access to a basic sanitation facility and 6 of 10 households practice open defecation. Poor households are less likely to use basic sanitation facilities compared to non-poor households. Other variables, such as household wealth, ownership of durable assets, receipt of humanitarian assistance from nongovernmental organizations (NGOs) or the government, and availability of basic services, are positively associated with basic sanitation, suggesting that households with higher values for any of these variables are more likely to have access to a basic sanitation service in both DFSA areas. A handwashing station was observed in 29 percent of households in the combined DFSA areas (37 percent in the CRS area and 20 percent in the MC area), but very few households (3 in 100) in the combined DFSA areas were observed with soap or another cleansing agent and water at the handwashing station. Women’s Health and Nutrition About 39 percent of non-pregnant women 15-49 years of age are underweight in the CRS area, and 31 percent of non-pregnant women 15- 49 years of age are underweight in the MC area. About 13 percent of women 15-49 years of age in the CRS area and 20 percent in the MC area consume a diet of minimum diversity, defined as consuming 5 of 10 nutritionally diverse food groups in the 24 hours preceding the survey. Women mostly consumed grains, roots, and tubers, and other vegetables in both DFSA areas. Few women consume eggs, nuts and seeds, and the consumption of dairy and flesh foods is low in both DFSA areas, and particularly low in the CRS area. Few women (about 7 percent in the CRS area and 11 percent in the MC area) consume targeted nutrient-rich commodities (bio-fortified beans, bio-fortified maize or sorghum, and orange-flesh sweet potatoes). A majority of women (about 78 percent) who had a live birth in the past five years attended at least four antenatal care (ANC) visits with a skilled health worker during their last pregnancy. Despite a high uptake of ANC services, a small proportion of women (11 percent in the CRS area and 7 percent in the MC area) use modern contraceptive methods in both DFSA areas. Women in poor households are less likely to use modern contraceptive methods and women in educated households are more likely to use modern contraceptive methods, regardless of household poverty status. Women in households that 2018 Baseline Study of Food for Peace Development Food Security Activities in Uganda vii have linkages to government officials or NGOs (also called linking social capital) are more likely to use modern contraceptives in the MC area, which could be due to knowledge or information transfer from such institutions to women. Women in households that participate in local community groups are more likely to use modern contraceptives in the CRS area. Qualitative study participants indicated that family planning education messages, especially on child spacing, are provided as part of health education during the ANC visits. Despite the high uptake of ANC services, use of family planning is still low mainly due to barriers such as fear of side effects either experienced or hearsay; lack of male partner support and involvement; and inadequate comprehensive knowledge about family planning methods as well as the persistent myths and misconceptions about family planning. Children’s Health and Nutrition About 29 percent of children under five years of age in the combined DFSA areas are underweight, 38 percent are stunted, and 11 percent are wasted.2 Less than 1 in every 10 children between 6-23 months (8 percent) in the combined DFSA areas receives a minimal acceptable diet (MAD). The prevalence of MAD also did not differ between girls and boys. The female household heads from Kotido who participated in the qualitative study emphasized that there was inadequate food to feed the children, and they normally depend on one or two meals just like the adults. As a result, the children are usually hungry and sickly. In addition, qualitative study participants reported that food distributed by food programs targeting malnourished children is often sold to meet more immediate household needs such as paraffin or firewood; or consumed by the male household heads. In the combined DFSA areas, about 7 in 10 children under 6 months of age (74 percent) are exclusively breastfed. Exclusive breastfeeding is practiced widely in both DFSA areas, especially in the first 3 months after birth but drops significantly for children 4–5 months of age when complimentary foods and other liquids are introduced. Ideally, mothers should exclusively breast feed their babies up to six months but qualitative study participants reported that because they cannot produce enough milk due to inadequate dietary intake, they often give babies four months and older semi solid foods in addition to the little breast milk. About 32 percent of children under 5 years of age in the combined DFSA areas had diarrhea in the two weeks prior to the survey. Oral rehydration therapy (ORT) is being used to prevent dehydration among most children with diarrhea; about 84 percent of children under 5 years of age with diarrhea were treated with ORT in the combined DFSA areas. Children under 5 years of age in food insecure households are more likely to experience diarrhea in both DFSA areas. In contrast, children under 6 months of age who are exclusively breastfed are less likely to experience diarrhea in both DFSA areas. Community health workers reported that many children under 5 years of age in their communities are affected by diarrhea because of poor hygiene among the households, lack of clean water for domestic use, open defecation, as well as consumption of unsafe food and water. Gender About 41 percent of men and women in a union earn cash in both the CRS and MC areas, which signifies the lack of cash-earning opportunities. There are no gender differences in terms of the proportion of men and women in a union earning cash in both DFSA areas.3 Most women participate in decisions related to the use of their self-earned cash (85 percent), and about 58 percent of women participate in decisions related to the use of their spouse’s self-earned cash. More women have knowledge of maternal and child health and nutrition practices than men, and more women make maternal and child health and nutrition related decisions alone in both DFSA areas. More men than women reported making joint decisions with their spouses on maternal and child health and 2 Note: The baseline study data were collected toward the end of the lean season. 3 Men and women have approximately the same share of the workforce in agriculture in Uganda. See: UNDP-Uganda Country Gender Assessment, October, 2015. Available at: http://www.ug.undp.org/content/dam/uganda/docs/UNDPUg2016%20- UNDP%20Uganda%20-%20Country%20Gender%20Assessment.pdf 2018 Baseline Study of Food for Peace Development Food Security Activities in Uganda viii nutrition practices. Although use of contraceptives is low, almost all men and women (97 percent) could state at least one health benefit of waiting at least two years after last live birth before attempting the next pregnancy. Resilience Households in both DFSA areas experienced an average of five shocks over the past 12 months. The shock experienced most by households was excessive rains (81.8 percent), followed by flooding (59.5 percent), drought (54.4 percent), increasing food prices (49.1 percent), and crop diseases (45.5 percent). Households with higher levels of absorptive, adaptive, and transformative capacity achieve better economic (higher per capita expenditures and lower likelihood of poverty) and food security outcomes. These capacities, however, are not a significant predictor of a household’s ability to recover. Specific components of resilience capacity that are associated with increased expenditures, lower prevalence of poverty, and greater dietary diversity are cash savings, durable assets, livestock assets, human capital (education/training), exposure to information, access to agricultural extension services, shock preparedness and mitigation, access to infrastructure, aspirations/confidence to adapt, and participation in local decision-making. With respect to the types of coping strategies adopted, households with higher levels of resilience capacities are more likely to use money from savings and sell livestock. Conclusions Factors identified by the baseline PBS data that may lead to improvements in widespread poverty and food insecurity include education levels of household members, livelihood diversification, use of financial services, use of improved storage practices, ownership of durable assets, ownership of land and participation in local decision-making bodies. Development activities with a focus on these factors may have more success in in reducing poverty and food insecurity in the DFSA areas. Poor sanitation and hygiene practices are precipitated by low availability of water due to drought, poor infrastructure for latrines and low levels of knowledge among community members regarding proper water, sanitation, and hygiene (WASH) practices despite significant health and WASH campaigns. Participants in the qualitative study suggested there should be WASH awareness campaigns, trainings and sensitization on the impacts of good hygiene, and that by-laws should be put in place for all households to own latrines. They also suggested that more boreholes should be constructed and that additional health facilities be constructed in communities which are lacking. Poor dietary diversity is a significant contributor to poor nutrition in women and children. Participants in the qualitative study reported that the poor and unreliable weather in the Karamoja Region does not support the growth of many crops, including cereals and fruits. The respondents suggested that the Government should introduce micronutrient supplemented seeds and promote production of bio￾fortified, short cycle crops such as beans. In addition, more programs are needed that target climate change in addition to modern fast yielding crops since the major challenge affecting proper yield of the various crops planted is related to weather patterns. More trainings on livelihood and how to generate income in a semi-arid region like Karamoja can raise the income levels of households thus making them self-reliant and more food secure, and able to afford more dietary diverse food for the families. Traditional economic development activities that are directed to increase household income and wealth—increasing human capital, promotion of value chains, and investment in infrastructure—are also means to enhance household and community resilience capacities. The importance of savings on household economic status and dietary diversity suggests that supporting savings and loans groups and other mechanisms to promote savings can have important impacts on resilience. Strategies that promote bonding and linking social capital formation, for example, through savings and loans groups and other community-based or collective organizations, can also be beneficial. Reduction in poverty can also be supported by investments in shock preparedness and mitigation efforts. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 1 I. INTRODUCTION 1.1 Overview of the Baseline Study In fiscal year 2017, the United States Agency for International Development (USAID) Office of Food for Peace (FFP), the United States Government (USG) leader in international food assistance, awarded funding for two multi-year development food security activities (DFSAs) in the Karamoja Region of Uganda. The goal of the 2017 DFSA awards is to enhance resilience to shocks and livelihoods, and improve food security and nutrition for rural households vulnerable to food insecurity. FFP contracted ICF and its partner TANGO International to conduct a baseline study in the DFSA implementations areas (see Annex 1 for the Scope of Work). The baseline study includes a population￾based survey (PBS) and a qualitative component to add context to the PBS findings. ICF is leading overall implementation of the survey—from the initial planning phase through the dissemination of results—and subcontracted International Research Consortium of Uganda (IRC) as the local data collection firm to support field implementation of the PBS and to lead the qualitative component. In addition to a baseline study, the ICF team conducted a final evaluation, including an endline PBS, in the target areas for the prior development food assistance projects (DFAPs) that recently ended.4 The joint baseline/endline PBS was designed to represent the implementation areas for the new DFSAs and prior DFAPs, which overlap to a large extent. The baseline study is the first phase of a pre-post survey cycle for the new DFSA awards. The purpose of the baseline study is to assess the current status of key indicators, serve as a point of comparison with the same indicators that will be collected in a future endline PBS, and gain a better understanding of the prevailing conditions and perceptions of the populations in the DFSA implementation areas. The baseline study results will also be used to further refine program targeting and, where possible, understand the relationship between variables to inform program design. Implementing Partners (IPs) for the DFSAs (1) Catholic Relief Services (CRS) and its partners are implementing the Nuyok DFSA in the three western districts of Karamoja: Abim, Nakapiripirit, and Napak Districts. (2) Mercy Corps (MC) and its partners are implementing the Apolou DFSA in the four eastern districts of Karamoja: Kaabong, Kotido, Moroto, and Amudat Districts. 4 The GHG DFAP ended in January 2018, and the RWANU DFAP ended in August 2017. FFP contracted with ICF to conduct a final performance evaluation of the two DFAPs. The results from the endline PBS will be provided as part of the Final Evaluation Report for the DFAPs. Figure 1: DFSA Implementation Areas, Uganda 2018 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 2 The DFSAs build on the prior FFP-funded DFAP awards that were implemented from 2012 through 2017/2018 in the same areas. The current DFSAs are fostering resilience to shocks, enhancing livelihoods, and improving food security and nutrition for vulnerable rural families. The USG global food security strategy established the following definition for food security: “access to–– and availability, utilization, and stability of––sufficient food to meet caloric and nutritional needs for an active and healthy life” (USG, 2016; 10). This underscores the four pillars of food security: availability, access, utilization, and stability defined by the Food and Agriculture Organization of the United Nations (FAO).5 The PBS, designed to provide information on all four elements of food security, investigates the following: food insecurity and food access; expenditures and assets; water, sanitation, and hygiene (WASH) practices; agriculture; women’s and children’s health and nutrition; gender differences in decision-making for cash earners and parents of children under two; and resilience. This report begins with an overview of the current food security situation in Uganda, followed by a description of the methods used for the population-based household survey and qualitative study. The findings from the PBS are presented and integrated with the qualitative study results, followed by conclusions and recommendations based on key findings. 1.2 Overview of the Current Food Security Situation Uganda is a landlocked country in eastern Africa that is bordered to the north by South Sudan, to the west by the Democratic Republic of Congo, to the east by Kenya, and to the south by Rwanda and Tanzania. The country includes a sizable portion of Lake Victoria in the southeast and sits in the Nile Basin, with much of the country on a plateau at an average altitude of 1,400 meters. The climate is tropical and rainy with two dry seasons (December to February and June to August) throughout most of the country, but it is semiarid in the northeastern Karamoja sub-region where the FFP-funded projects operate, with one rainy season that runs from April through October. Uganda, including the Karamoja sub-region, has a number of natural resources, such as copper, cobalt, hydropower, limestone, salt, gold, and arable land.6,7 Uganda is also working to tap its oil reserves and start commercial oil production by 2020.8 Yet the country is among the poorest in the world and is ranked 163 out of 188 countries in the United Nations Development Program’s human development index.9 Uganda is one of the most densely populated countries on mainland Africa and is estimated to have a population of nearly 45 million in 2018, yet comprises only 197,100 km2 of land. Nearly 80 percent of Uganda’s population resides in rural areas, although most of the population is concentrated in the central and southern parts of the country. The Karamoja sub-region has the smallest percentage of the population (3 percent) and also the lowest population density (35 individuals per km2). Uganda has one of the youngest and fastest growing populations in the world, with a median age of 15.9 years and a 3.0 percent population growth rate according to the national 2014 census.10 The 5 Food and Agriculture Organization of the United Nations (FAO). 2009. Declaration of the World Food Summit on Food Security. Rome: FAO. http://www.fao.org/fileadmin/templates/wsfs/Summit/Docs/Final_Declaration/WSFS09_Declaration.pdf 6 CIA. 2018. Accessed on: August 7, 2018. 7 Baleke TS. 2015. Uganda’s Karamoja; where abundant natural resources haven’t transformed lives. Afrika Reporter. Retrieved at: http://www.afrikareporter.com/ugandas-karamoja-where-abundant-natural-resources-havent-transformed-lives/ 8 Musisi F. 2018 June 5. Uganda’s slow pace towards oil production. Daily Monitor. Retrieved at: http://www.monitor.co.ug/Business/Prosper/Uganda-slow-pace-towards-oil-production/688616-4594748- 742undz/index.html 9 United Nations Development Programme. 2016. 2016 Human Development Report: Human Development for Everyone. Retrieved at: http://hdr.undp.org/sites/default/files/2016_human_development_report.pdf 10 UBOS. (2017b). 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 3 average life expectancy in the country is 60 years,11 with the leading causes of death across all age groups being communicable diseases, such as HIV/AIDS, malaria, and lower respiratory infections, and with malnutrition and WASH being two of the top four risk factors driving death and disability.12 The country’s total fertility rate in 2014 was 5.8 children per woman—a decrease from 7.0 children per woman in 2002,13 but still one of the highest rates globally. In the Karamoja sub-region, the fertility rate (7.5 children per woman in 2014) is much higher than the national average.14 Other indications that Karamoja lags behind the rest of the country developmentally include the sub-region’s higher-than-average poverty and child mortality rates and low literacy rate. The sub-region has the highest prevalence of poverty (61 percent compared to 27 percent nationally) and the highest under-5 mortality rate in the country (102 deaths per 1,000 live births compared to 64 deaths per 1,000 live births nationally).15 Although nearly 7 in 10 females (68 percent) and 8 in 10 males (77 percent) from age 10 years and above are literate in Uganda,16 only 25 percent of them are literate in Karamoja, which is the lowest among all the sub-regions.17 The sex disaggregated data shows that about 31 percent of males and 19 percent of females are literate in Karamoja. Approximately 70 percent of land in Uganda is used for agriculture, including 34 percent that is arable, 11 percent that has permanent crops, and 25 percent that is permanent pasture.18 Agriculture employs 70 percent of the workforce19 and accounts for about one-quarter of the gross domestic product.20 Subsistence farming is the main source of earnings for 43 percent of households.21 Cassava, cooking bananas, dry beans, maize, millet, rice, sweet potatoes, and sorghum are the main staple foods in Uganda.22 Coffee, cotton, cut flowers, maize, processed fish, and tea are among the main agricultural products exported.23 Because Uganda has two harvest seasons and benefits from decent amounts of rain throughout much of the country, the country is able to produce sufficient amounts of staple foods and has a large role in regional food supply and trade. However, agricultural production in Karamoja is more 11 The World Bank Group. 2018. Uganda. World Bank Open Data. Retrieved at: https://data.worldbank.org/country/uganda. Accessed on: August 8, 2018. 12 Institute for Health Metrics and Evaluation. 2018. Uganda Country Profile. Retrieved at: http://www.healthdata.org/uganda. Accessed on: August 7, 2018. 13 Uganda Bureau of Statistics (UBOS). 2017. Uganda National Household Survey 2016/2017. Kampala Uganda; UBOS. https://www.ubos.org/onlinefiles/uploads/ubos/pdf%20documents/UNHS_VI_2017_Version_I_%2027th_September_2017.pdf 14 Uganda Bureau of Statistics (UBOS). 2017. Uganda National Household Survey 2016/2017. Kampala Uganda; UBOS. https://www.ubos.org/onlinefiles/uploads/ubos/pdf%20documents/UNHS_VI_2017_Version_I_%2027th_September_2017.pdf 15 Uganda Bureau of Statistics. 2017a. The National Population and Housing Census 2014 – National Analytical Report, Kampala, Uganda. https://www.ubos.org/onlinefiles/uploads/ubos/2014CensusProfiles/National_Analytical_Report_nphc%202014.pdf 16 Uganda Bureau of Statistics (UBOS) and ICF. 2018. Uganda Demographic and Health Survey 2016. Kampala, Uganda and Rockville, Maryland, USA: UBOS and ICF. https://www.ubos.org/onlinefiles/uploads/ubos/pdf%20documents/Uganda_DHS_2016_KIR.pdf 17 Uganda Bureau of Statistics. 2017. The National Population and Housing Census 2014 – National Analytical Report, Kampala, Uganda. https://www.ubos.org/onlinefiles/uploads/ubos/2014CensusProfiles/National_Analytical_Report_nphc%202014.pdf 18 Central Intelligence Agency (CIA). 2018. The World Factbook 2018. Washington, DC: CIA. Retrieved at: https://www.cia.gov/library/publications/the-world-factbook/index.html. Accessed on: August 7, 2018. 19 The World Bank Group. 2018. Employment in agriculture (% of total employment) (modeled ILO estimate) Indicator. Retrieved at: https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS. Accessed on: August 10, 2018. 20 The World Bank Group. 2018. Agriculture, forestry, and fishing, value added (% of GDP) in 2017. Retrieved at: https://data.worldbank.org/indicator/NV.AGR.TOTL.ZS. Accessed on: August 10, 2018. 21 Uganda Bureau of Statistics. 2017. The National Population and Housing Census 2014 – National Analytical Report, Kampala, Uganda. https://www.ubos.org/onlinefiles/uploads/ubos/2014CensusProfiles/National_Analytical_Report_nphc%202014.pdf 22 Famine Early Warning Systems Network (FEWS NET). 2017. Uganda Staple Food Market Fundamentals. Retrieved at: http://fews.net/east-africa/uganda/market-fundamentals/march-2017. Accessed August 7, 2018. 23 Famine Early Warning Systems Network (FEWS NET). 2017. Uganda Staple Food Market Fundamentals. Retrieved at: http://fews.net/east-africa/uganda/market-fundamentals/march-2017. Accessed August 7, 2018. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 4 limited because of its climate; it has only one main harvest annually, and inhabitants rely on surpluses cultivated in other areas of the country.24 The Karamoja sub-region makes up 10 percent of the total land in Uganda, and 50 percent of the land is reserved for wildlife reserves and other protected areas, including areas in the wetter “green belt”. 25 It is reported that the area of land under cultivation increased by tenfold from 2001 to 2014.26 Households owning livestock have been reported to be better in dealing with shocks and stresses. Despite this, livestock ownership is decreasing in the region. Between 1959 and 2002, the per capita livestock holdings decreased, from 2.7 Tropical Livestock Units per person to 1.3 Tropical Livestock Units per person. The decrease in livestock ownership could be attributed to various factors, such as conflict and shortage of water. Shortage of water has led to scarcity of pasture, especially in the dry season.27 Although Uganda can produce sufficient amounts of staple foods, the country still experiences food security challenges, which are reflected in the country’s hunger and nutrition indicators.28 Nearly one in three women (32 percent) have anemia.29 Almost one-third (29 percent) of children who are under 5 years of age are chronically malnourished (stunted), 11 percent are underweight, and 4 percent suffer acute malnutrition (wasting).30 In Karamoja, children are more likely to be malnourished than in other parts of Uganda. The 2016 Demographic and Health Survey (DHS) showed that 35 percent of children under 5 years of age in surveyed areas of Karamoja were stunted and 26 percent were underweight. WASH practices in Karamoja contribute to malnutrition through infectious disease. Almost two-thirds of households (65 percent) reported using the bush for human waste disposal; and only 2 percent of households had water and soap at a handwashing station. 31 Food security in Uganda is also affected by the substantial number of refugees in the country. Although the number of internally displaced persons has decreased substantially over recent years after a period of violent civil unrest, estimates indicate that the country has some 1.15 million refugees, primarily from South Sudan.32 Although good weather and market prices contributed to a substantial portion of the decrease in poverty between 2006 and 2013,33 poverty increased from 20 percent in 2012/13 to 27 percent in 24 Famine Early Warning Systems Network (FEWS NET). 2017. Uganda Staple Food Market Fundamentals. Retrieved at: http://fews.net/east-africa/uganda/market-fundamentals/march-2017. Accessed August 7, 2018. 25 Cullis, A. 2018. Agricultural Development in Karamoja, Uganda: Recent Trends in Livestock and Crop Systems, and Resilience Impacts. Karamoja Resilience Support Unit, USAID/Uganda, UK aid, and Irish Aid, Kampala. 26 https://resilience.igad.int/index.php/hierarchical-list/studies/18-krsu-karamoja-livestock-review-1/file, retrieved on December 5, 2018 27 Aklilu, Y. 2016. Livestock in Karamoja: A Review of Recent Literature. Karamoja Resilience Support Unit, USAID/Uganda, Kampala. Available at https://resilience.igad.int/index.php/hierarchical-list/studies/18-krsu-karamoja-livestock-review-1/file-- citation. 28 USAID. 2017. Climate Risk Screening for Food Security, Karamoja Region, Uganda. Retrieved at: https://www.climatelinks.org/resources/climate-change-risk-profile-climate-risk-screening-food-security-karamoja￾region-uganda 29 Uganda Bureau of Statistics (UBOS) and ICF. 2018. Uganda Demographic and Health Survey 2016. Kampala, Uganda and Rockville, Maryland, USA: UBOS and ICF. https://www.ubos.org/onlinefiles/uploads/ubos/pdf%20documents/Uganda_DHS_2016_KIR.pdf 30 Uganda Bureau of Statistics (UBOS) and ICF. 2018. Uganda Demographic and Health Survey 2016. Kampala, Uganda and Rockville, Maryland, USA: UBOS and ICF. https://www.ubos.org/onlinefiles/uploads/ubos/pdf%20documents/Uganda_DHS_2016_KIR.pdf 31 Uganda Bureau of Statistics (UBOS) and ICF. 2018. Uganda Demographic and Health Survey 2016. Kampala, Uganda and Rockville, Maryland, USA: UBOS and ICF. https://www.ubos.org/onlinefiles/uploads/ubos/pdf%20documents/Uganda_DHS_2016_KIR.pdf 32 The World Factbook 2018. Washington, DC: Central Intelligence Agency, 2018. https://www.cia.gov/library/publications/the￾world-factbook/index.html, https://data2.unhcr.org/en/country/uga 33 The World Bank Group. 2016. The Uganda Poverty Assessment Report 2016. Washington, DC, USA: The World Bank. Retrieved at: http://pubdocs.worldbank.org/en/381951474255092375/pdf/Uganda-Poverty-Assessment-Report-2016.pdf 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 5 2016/17,34 which may be at least partially attributed to severe drought and army worm infestations among crops. Food production is challenged by the use of unimproved cultivating and post-harvest management practices, crop and livestock pest and disease infestations, a weak market information system, and limited market access.35 Because of Karamoja’s climate, the sub-region has three main livelihood zones: agricultural in the west, agro-pastoral in the center, and pastoral in the east.36 It is prone to climate-related shocks that include droughts, floods, outbreaks in livestock disease and changing crop pest dynamics, high food prices, and livelihood insecurity.37 In the 2014 FFP household survey, most farmers (91 percent) reported growing crops, and one-quarter reported raising animals.38 Farmers rely on rain to raise their crops—primarily sorghum, maize, and beans, although famers also grow groundnuts, cassava, and sweet potatoes.39 Most farming is subsistence farming. Only 17 percent of farmers reported using at least two sustainable crop practices, and 12 percent reported using at least two sustainable livestock practices (for goats and cattle).40 In Karamoja in particular, challenges to crop farming include poor soil fertility, poor soil moisture, reliance on rainfall, small plot sizes, lack of draft animals (oxen), lack of improved seeds and tools, general lack of water, transportation challenges, and inefficient crop drying and storage practices.41 The November/December 2017 harvest season was above average and resulted in a decrease in staple food prices, which resulted in large amounts of exports to neighboring countries, although exports to South Sudan decreased due to trade-related insecurities and lower demand.42 Prices were also low across markets in Karamoja, and the retail price of sorghum is approximately 60 percent below 2016 and 30 percent lower than the five-year average. Information from the Famine Early Warning Systems Network (FEWS NET) indicates average-to-above average harvests throughout much of Uganda through January 2019 because the country benefited from above-average rainfall from March to May.43 In some areas, however, heavy rainfall has led to flooding and waterlogging, which delayed planting or destroyed crops and may substantially decrease yields. In Karamoja, the lean season was extended because harvests have been delayed until September, but relative to other lean seasons in the sub-region, very poor households have better food access.44 Surplus staple production has increased overall market supply, keeping prices below 2017 prices and 34 Uganda Bureau of Statistics (UBOS). 2017b. Uganda National Household Survey 2016/2017. Kampala Uganda; UBOS. https://www.ubos.org/onlinefiles/uploads/ubos/pdf%20documents/UNHS_VI_2017_Version_I_%2027th_September_2017.pdf 35 Famine Early Warning Systems Network (FEWS NET). 2017. Uganda Staple Food Market Fundamentals. Retrieved at: http://fews.net/east-africa/uganda/market-fundamentals/march-2017 36 USAID. 2017. Climate Risk Screening for Food Security, Karamoja Region, Uganda. https://www.climatelinks.org/resources/climate-change-risk-profile-climate-risk-screening-food-security-karamoja-region-uganda 37 USAID. 2017. Climate Risk Screening for Food Security, Karamoja Region, Uganda. https://www.climatelinks.org/resources/climate-change-risk-profile-climate-risk-screening-food-security-karamoja-region-uganda 38 ICF International. 2014. Baseline Study for the Title II Development Food Assistance Programs in Uganda. https://www.usaid.gov/sites/default/files/documents/1866/Uganda%20Baseline%20Study%20Report%2C%20March%202014.pdf 39 ICF International. 2014. Baseline Study for the Title II Development Food Assistance Programs in Uganda. https://www.usaid.gov/sites/default/files/documents/1866/Uganda%20Baseline%20Study%20Report%2C%20March%202014.pdf 40 ICF International. 2014. Baseline Study for the Title II Development Food Assistance Programs in Uganda. https://www.usaid.gov/sites/default/files/documents/1866/Uganda%20Baseline%20Study%20Report%2C%20March%202014.pdf 41 USAID. 2017. Climate Risk Screening for Food Security, Karamoja Region, Uganda. https://www.climatelinks.org/resources/climate-change-risk-profile-climate-risk-screening-food-security-karamoja-region-uganda 42 FEWS NET. 2018. Uganda Food Security Outlook June 2018 to January 2019. Retrieved at: http://fews.net/east￾africa/uganda/food-security-outlook/june-2018 43 FEWS NET. 2018. Uganda Food Security Outlook June 2018 to January 2019. Retrieved at: http://fews.net/east￾africa/uganda/food-security-outlook/june-2018 44 FEWS NET. 2018. Uganda Key Message Update July 2018. Retrieved at: http://fews.net/east-africa/uganda/key-message￾update/july-2018 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 6 contributing to above-average food access for purchase-dependent households. Livestock body conditions and milk production are better than usual. Government Policy and Programs The Government of Uganda has developed a range of key policies, strategies, and guidelines that aim to strengthen food availability and access. The foundational legal document is the Uganda Food and Nutrition Policy, which was adopted in 2003 to promote the nutritional status of the people of Uganda through multi-sectoral and coordinated interventions that focus on food security, improved nutrition, and increased incomes. The country benefits from many international organizations, projects, and donors intervening in several fields. These international organizations and donors are intensifying efforts to meet the goals of the “Zero Hunger” challenge launched by United Nations Secretary-General Ban Ki-moon in 2012. Donors’ and organizations’ activities are aimed at assisting refugees, building resilience among food insecure populations, training smallholder farmers on improved farming techniques, building local capacity to coordinate food security and respond to shocks, and supporting supply chains. A comprehensive list of nongovernmental organizations (NGOs), donors, and projects that were operational in Karamoja in 2016 is available in the Karamoja NGO Mapping Report. 45 Several projects funded by the USG operate in the country in a number of sectors, such as agriculture, education, human rights and governance, health, economic development, environment, and conflict mitigation and prevention. USG-funded food security programs are implemented in Uganda through USAID’s Feed the Future and FFP programs. Other ongoing programs include the following:  Fostering Sustainability and Resilience for Food Security in the Karamoja Region: A five-year Government-led initiative that began in 2018 and focuses on improving food security and environmental sustainability in the sub-region. It is funded by the Global Environment Fund, the United Nations Development Program, and FAO.  Drylands Integrated Development Project: A five-year program, funded by the Islamic Development Bank and the Government of Uganda, that started in 2014 to increase income and reduce poverty among the pastoral populations in Uganda  Driving Youth-led New Agribusiness and Microenterprise: A five-year program funded by the MasterCard Foundation to ensure that out-of-school and economically disadvantaged young people have the skills and access to support systems to secure entry-level jobs or start their own businesses in agriculture  Project for Financial Inclusion in Rural Areas: A three-year program funded by the International Fund for Agricultural Development that started in 2016 to sustainably increase the access to and use of financial services by the rural poor through savings and credit cooperatives and community savings and credit groups 45 https://www.karamojaresilience.org/what-s-new/item/karamoja-ngo-mapping-report 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 7 2. METHODOLOGY AND LIMITATIONS 2.1 Methods for the Population-Based Household Survey 2.1.1 Study Design and Objectives The baseline study serves as the first phase of a pre-post evaluation cycle for the DFSA awards. The second phase will take place at the end of the DFSA cycle, during which a final evaluation, including an endline PBS, will be conducted. This pre-post design allows for the determination of statistically significant change in indicators between the baseline and endline surveys; however, it does not support conclusions about attribution or causation relating to DFSA impact. The objectives of the baseline study for the DFSAs are to assess the current status of key indicators, gain a better understanding of the prevailing conditions and perceptions of the populations in the DFSA implementation areas, and serve as a point of comparison for a future endline final evaluation in the DFSA implementation areas. Results will also be used to further refine program targeting and, where possible, understand the relationship between variables to inform program design. 2.1.2 Sample Design The target population for the joint baseline/endline PBS consists of two components: (1) all households in the areas where the prior DFAPs were implemented, and (2) all households in the areas where the new DFSAs will be implemented. These target populations overlap to a great extent, because the DFSAs are being implemented in most of the same geographic areas where the prior DFAPs were implemented. The sampling frames for the baseline and endline PBSs were constructed to take into account these overlapping geographies. ICF used the list of target areas provided by the IPs and the most recent census data for constructing the sampling frame.46 The sample size for the joint baseline/endline PBS was derived by these steps: (1) calculating the sample size needed for the baseline PBS for the current DFSAs; (2) calculating the sample size needed for the endline PBS for the prior DFAPs; and (3) deriving a joint sample size based on these sample size calculations, taking into account the overlap between the current DFSA and prior DFAP implementation areas. The sample size calculation for (1) and (2) are based on a multi-stage clustered sample designed to adequately power a test of differences between the baseline and endline estimates for the FFP stunting indicator for each DFSA (stunting is a key FFP indicator). Table 1 shows the areas covered and derived sample size by IP for the joint baseline/endline PBS.47 A stratified multi-stage clustered sample design was used with three stages of sampling: (1) selection of clusters (census enumeration areas), (2) selection of households (30 households per enumeration area),48 and (3) selection of individuals. Each DFSA represented one stratum, and the sampled villages were allocated to districts in each stratum based on the number of households in each district (see Annex 2, Uganda Joint Baseline/Endline PBS Protocol, for more details on the sample design and allocations). 46 The most recent Ugandan Census was conducted in 2014 by the Uganda Bureau of Statistics. 47 It is worth noting that the overall sample size for the joint baseline/endline PBS (3,360 households) is substantially less than the sum of the sample sizes of the two individual PBSs (2,440 + 2,460 = 4,900 households); this comparison highlights the sample size (and related cost) savings realized by the joint administration of the two surveys, as compared to that had the two PBSs been administered separately. 48 Because this is a population-based survey, all sampled households will not necessarily participate in the DFSA activities. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 8 Table 1: Sampled Households by Implementing Partner, Uganda 2018 DFSA Districts in DFSA Area* Number of Sampled Households for 2013 BL Study Number of Households Needed for 2018 EL Study Number of Households Needed for 2018 BL Study Joint BL/EL Sample Size Requirement MC Apolou Kaabong, Kotido, Moroto, and Amudat 2,400 1,220 1,230 1,680 CRS Nuyok Abim, Nakapiripirit, and Napak 2,400 1,220 1,230 1,680 TOTAL 4,800 2,440 2,460 3,360 BL=baseline, EL=endline * The 2012 Resiliency through Wealth, Agriculture, and Nutrition in Karamoja DFAP included the southern districts of Amudat, Moroto, Nakapiripirit, and Napak; the 2012 Growth, Health, and Governance DFAP included the northern districts of Abim, Kaabong, and Kotido. The districts were divided differently for the new DFSAs. The CRS Nuyok DFSA included the districts of Abim, Nakapiripirit and Napak, and the MC Apolou DFSA included the districts of Amudat, Kaabong, Kotido, and Moroto. 2.1.3 Questionnaire The questionnaire for the PBS (see Annex 3) was developed based on the core FFP and resilience indicators. All questionnaire modules follow FFP and Feed the Future guidelines, as described in the FFP Indicators Handbook (April 2015)49 and the Feed the Future Indicator Handbook (September 2016).50 The ICF team revised the questionnaire from the 2013 baseline survey so that new indicators could be included for the 2018 PBS. Information was gathered before, during, and after the baseline planning workshop (January 2018) to tailor the newly added questions to the context of Uganda. The resilience module was contextualized by TANGO based on prior work in Uganda. The questionnaire was translated into three local languages (Ngakarimojong, Pokot, and Lepthur) and consisted of separate modules covering the following topics:  Module A: Household Identification and Informed Consent  Module B: Household Roster  Module C: Household Food Security  Module CC: Mobility, Local Government Responsiveness, and Poverty Probability Index51  Module D: Children’s Nutrition and Health  Module E: Women’s Nutrition and Health  Module F: Water, Sanitation, and Hygiene  Module G: Agriculture  Module H: Poverty  Module J: Gender—Cash  Module K: Gender—Maternal and Child Health and Nutrition  Module L. Gender—Household Decision-Making, Access to Credit, and Group Participation  Module R: Resilience 49 Food and Nutrition Technical Assistance III Project. 2015. FFP Indicators Handbook Part I: Indicators for Baseline and Final Evaluation Surveys. April 2015. Washington, DC. A newer version of the FFP Indicators Handbook is pending release in 2018. 50 Available at https://feedthefuture.gov/sites/default/files/resource/files/Feed_the_Future_Indicator_Handbook_Sept2016.pdf 51 The 2018 baseline study will include questions to calculate the Innovations for Poverty Action (IPA) Poverty Probability Index (PPI), which will be used to construct two poverty indicators: prevalence of poverty and depth of poverty. These two indicators will be compared to the same two indicators constructed using the Living Standards Measurement Study (LSMS) methodology. This is a pilot study for the PPI to test its viability as a replacement methodology for the LSMS variants for calculating the poverty indicators. The analyses for the PPI will be conducted by IPA with support from ICF and presented in a separate report. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 9 2.1.4 Field Procedures Listing Exercise The local data collection subcontractor (IRC) conducted a household listing and mapping of the selected clusters. Household listers were trained to locate a cluster, identify its geographical boundaries, draw sketch maps, identify locations of the households on the sketch map, and measure and record Global Positioning System (GPS) coordinates at the center of the cluster. The listing exercise was conducted from January 22 to 25, 2018, by 20 trained listers in the 112 selected clusters located in the 7 districts of Karamoja. The results from the household listing operation were used for the second-stage sampling of households in each selected cluster. Training The ICF team developed training manuals based on FFP and Demographic and Health Survey guidelines for training and for use in the field. These included a supervisor manual, an interviewer manual with a question-by-question guide, and an anthropometry manual. The supervisor and interviewer manuals contained instructions on how to operate the tablet computers in their respective roles. Training of trainers (ToT): Before the interviewer training, a group of 6 field coordinators and 12 selected experienced supervisors received an intensive questionnaire training from January 22 to February 15, 2018, at the IRC office. The purpose was to familiarize this group with the approved questionnaire. After the questionnaire training, this select group of individuals supported the main interviewer training. There was a time lag of about two months between the completion of the ToT session and the commencement of the main interviewer training, so a refresher ToT was conducted for two days during May 9 to 11. This was done to ensure that all the field coordinators understood the questionnaire, how to use the tablets, and all the field procedures.52 Main training: The main survey training took place from May 12 to 31 at Makerere University in Kampala. Combined and separate trainings were conducted for interviewers, supervisors, and anthropometry specialists. The training covered the study design and objectives, key roles and responsibilities for supervisors and interviewers, rules and regulations, ethics, fieldwork preparations, quality control requirements and procedures, guidelines for implementation of the survey, interviewing techniques, procedures for completing the questionnaires, and detailed explanation and instructions for each question. Trainees were also given different quizzes to assess their level of comprehension of the key concepts and modules. In addition, an events calendar was developed for Karamoja to help in estimating ages for the survey respondents for cases in which dates of births could not be ascertained. Staff from USAID/Uganda and the IPs attended parts of the training to provide technical assistance as needed. Anthropometry training and standardization: Twenty anthropometrists were trained from May 16 to 31 at the IRC offices. Training was interactive and participatory with practice, testing, and discussions. The training covered the following topics:  The importance of taking accurate measurements, types of measurement errors, reading and recording measurements, reading and recording systems  Definitions of measurements, derived anthropometry indices (i.e., stunting, wasting, underweight)  Understanding weighing and measuring instruments  Introduction to standing height and weight of children 2–5 years of age  Recumbent length and weight of children under 2 years of age  Hands-on practice to measure height/length and weight of children under 2 years of age, children 2–5 years of age, and women on separate days 52 A pre-test of the survey questionnaire was not conducted since it was essentially the same as that used for the 2013 Baseline Study which was pre-tested in 2013. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 10  Use of the quality control sheet and Z-score tables  Bilateral pedal edema training  Age assessment training  Anthropometry questionnaire training and completion The standardization testing was undertaken to ensure that all anthropometry specialists acquired the skills necessary to collect high-quality data. For standardization testing, anthropometry specialists’ measurements were compared to the measurements taken by the anthropometry trainer. The testing included comparing repeated measurements that were taken by the anthropometry specialists to check the consistency of their own measurements. The purpose of standardization testing was to detect differences in measurements, identify possible causes, and correct them. The exercises were repeated as many times as necessary until none of the measurements recorded by the anthropometry specialists differed significantly from those of the anthropometry trainer. Pilot Test Upon completion of the trainings, all survey staff participated in a pilot exercise in pre-selected, non-sampled villages in the Moroto District from June 2 to 4. The pilot was conducted to test the soundness of the questionnaire and identify any potential problem areas, such as issues with skip patterns, wording, sequencing of questions, instructions to interviewers, and clarity of the questionnaire for coding. The pilot was further used to test the programming of the questionnaire flow and skips, and use of the tablets in the field, including data transmissions. The pilot provided the survey team practice on the following:  Location of selected villages by supervisors  Respondent selection routines by interviewers  Appropriate interviewing behavior  Questionnaire proficiency  Completion of field control sheets by supervisors  GPS data collection at the household level  Team dynamics  Distribution of work assignments and coordination by supervisors Each interviewer completed at least three full interviews using the tablet during the pilot test. The total time for completing the survey in each household was approximately three to four hours, depending on the size of the household. Supervisors observed the interviewers in their teams during the pilot test and took notes on their performance. IRC’s survey management team, the ICF survey coordinator and the local survey monitor, the ICF information technology (IT) expert and the IRC IT counterpart, the anthropometry trainer, and the local anthropometry counterpart also participated in the pilot test. On June 5–6, the management team, together with the supervisors, debriefed the team. They provided feedback and clarified all issues encountered during the pilot study. Fieldwork Data collection started immediately after the pilot study and was conducted from June 7 to July 6. There were 18 teams involved in data collection: 4 consisted of 7 field team members (1 supervisor, 5 interviewers, and 1 anthropometry specialist), and 14 consisted of 6 field team members (1 supervisor, 4 interviewers, and 1 anthropometry specialist). IRC hired a total of 76 interviewers, 18 anthropometry specialists, and 18 supervisors. In addition, IRC’s field staff included the principal investigator, 5 field coordinators, and 2 IT specialists, for a total of 120 field staff for the survey. In addition to the IRC staff, the ICF survey coordinator and local survey monitor were in the country throughout all critical phases of the survey—including the training, pilot test, and fieldwork—to coordinate and supervise activities. Throughout fieldwork, the ICF survey coordinator received frequent updates from the field coordinators assigned to each of the DFSA areas. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 11 2.1.5 Data Processing and Analysis Sampling Weights Sampling weights were computed and used in the data analyses. Weights were computed separately for the baseline PBS data analyses according to the unique sampling scheme that was relevant for the associated sampled household or individual. This involved computing an overall sampling weight for each distinct sampling group by taking the inverse of the product of the probabilities of selection from each stage of sampling. Weights were calculated for the following distinct sampling groups:  Households (used for indicators derived from Modules C, CC, F, H, L, and R)  Children under 5 years of age (Module D and children’s anthropometry)  Women 15–49 years of age (Module E)  Non-pregnant women 15–49 years of age (women’s anthropometry)  Farmers (Module G)  Cash-earning adults (Module J)  Parents of children under 2 years of age (Module K) Weights were calculated separately for each of the DFSA areas and adjusted to compensate for household- and individual-level non-response, as shown in Table 2. Table 2: Joint Baseline/Endline PBS Response Rates, Uganda 2018 Sampling Groups Number Sampled Number Interviewed Response Rate (%) Households (Modules C, CC, F, H, L, and R) 3,326 3,135 94.3 Children 0–59 months of age (Module D)a 3,235 3,098 95.8 Women 15–49 years of age (Module E)b 3,259 2,670 81.9 Non-pregnant women 15–49 years of age (anthropometry) 2,243 2,095 93.4 Farmers (Module G) 4,286 4,122 96.2 Male cash earners, married or in a union (Module J) 975 769 78.9 Female cash earners, married or in a union (Module J) 1,032 954 92.4 Fathers of children under two (Module K) 830 633 79.9 Mothers of children under two (Module K) 1,114 1,034 92.8 a The portion of Module D pertaining to exclusive breastfeeding was administered for children under 6 months of age, and the portion of Module D pertaining to minimum acceptable diet was administered for children under 6–23 months of age. b The portion of Module E relating to contraception was administered only to women 15–49 years of age who are currently married, and the portion related to antenatal care was administered only to women 15–49 years of age who had a live birth in the past five years. Indicator Definitions and Tabulations Definitions and methods for tabulation of all FFP indicators are presented in the Data Treatment and Analysis Plan (see Annex 4). The World Health Organization (WHO) child growth standards and associated software (WHO, 2011) are the basis for tabulating the child stunting, underweight, and wasting indicators. Consumption aggregates for computing the poverty indicators follow the World Bank’s Living Standards Measurement Study methodology. Results for all indicators are weighted to represent the full target population in the DFSA-identified areas of implementation. Variance estimates for each indicator were computed using Taylor series expansion, taking into account the design effect associated with the complex sampling design. Depending on the indictor, differences in estimates by age, sex, and gendered household type were tested for statistical significance, taking into account the clustered sample design.53 Additional bivariate and multivariate analyses were conducted to explore 53 Differences are considered statistically significant based on the p-value for the significance test. P-values of <.10 are considered marginally significant, p-values of <.05 are significant, and p-values <.01 are highly significant. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 12 relationships between indicators. Only those differences that are statistically significant are cited in the report; the results of all analyses are provided in Annexes 8 and 9. Handling of Missing or Erroneous Data Missing data points were excluded from the denominator and numerator for indicator calculations. The denominator included “Don’t Know” responses, recoded to the null value. For example, the denominator for the contraceptive prevalence rate included “Yes,” “No,” and “Don’t Know” responses, but the numerator included only “Yes” responses. For poverty indicators, there are special methods for handling missing data (see Appendix C of Annex 4). 2.2 Methods for Qualitative Study 2.2.1 Study Objectives and Design The overarching objective of the baseline qualitative data collection was to interpret and contextualize data derived from the baseline PBS. The design of the qualitative study draws on key informant interviews (KIIs) and focus group discussions (FGDs) in a purposive sample of data collection sites based on a specific set of guiding research questions designed to help interpret and contextualize specific results from the baseline PBS (see Table 3). The interview questions and topic guides for KIIs and FGDs were related to one or more modules in the PBS questionnaire. Annex 5 provides a detailed description of the study protocol and the interview guides. Table 3: PBS Quantitative Findings and Key Qualitative Study Questions Quantitative PBS Findings Guiding Research Questions Nutrition and Food Security  The average household dietary diversity score (HDDS) is low (less than 4 out of 12 food items) in both the CRS and MC areas.  Why is the HDDS so low in the project areas? What are the issues and challenges associated with low dietary diversity? How could the households improve their dietary diversity?  Nine out of 10 households experience moderate or severe food insecurity in both project areas.  What are the community perceptions about food security in Karamoja region?  Is the food insecurity situation in the project areas as dire as the quantitative PBS data show?  What factors contribute to the high levels of food insecurity in the project areas?  What are the cultural, social, and economic challenges of food security within the households, among children under 5 years of age, and among women of reproductive age?  Nearly 40 percent of children are chronically malnourished (stunted) in both project areas.  What are the barriers of child malnutrition within the households and in the communities? How could they be improved?  Why are male children more stunted than female children?  What kind of foods are typically consumed by male and female children? Are male children more exclusively breastfed than female children?  What are the typical foods provided to children 0–5 months of age, 6–23 months of age, and 24–59 months of age? How adequate are these foods?  What are the differences in feeding practices between male and female children?  What has been done by communities and development agencies in the past five years to improve children’s diets and address child malnutrition in Karamoja? What still needs to be done? What should be done differently?  More male children compared to female children are stunted in both project areas.  Only about 10 percent of children 6–23 months of age consume a minimally accepted diet. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 13 Quantitative PBS Findings Guiding Research Questions  About a quarter of women of reproductive age are underweight.  What factors contribute to malnutrition among adult women?  What has been done to decrease women’s malnutrition in the last three to four years, and what still needs to be done? What should be done differently?  Less than 20 percent of women consume a diet with minimum diversity. Poverty  About 9 in 10 households are below the poverty line in both project areas.  Why do many households fall below the poverty line in spite of many development efforts, including Resiliency through Wealth, Agriculture, and Nutrition in Karamoja (RWANU) and Growth, Health, and Governance (GHG), which have been implemented in the Karamoja region over the last 10 years?  What has been the effect of GHG and RWANU on the livelihoods and income sources of community members and households?  Why are household per capital expenditures still below USD 1.90 per day in spite of previous support from development projects, including the FFP projects?  What are households doing differently to cover their nutritional needs and health care compared to five years ago?  Are there obvious improvements in the capacity of households to cover their nutritional needs, health care needs, and other necessary expenses over the last five years? If so, what are they?  How best can communities be supported to improve their livelihoods and income sources?  The depth of poverty among the poor is also remarkably high, around 60 percent. This indicates that households are not just below the poverty line, but that their per capita expenditures is way below the poverty line of USD 1.90 per day (purchasing power parity 2010).  Despite this, nearly 90 percent of men and women in a union reported earning cash in the past 12 months. WASH  About 65 percent of households practice open defecation in the both project areas.  Why do communities still engage in open defecation?  What innovative approaches should be used to eradicate open defecation in the Karamoja region?  Less than 5 percent of households have soap and water in their handwashing station.  What factors contribute to the low levels of use and availability of WASH services and commodities in the region?  What gender norms influence the use of WASH services in the community?  About 20 percent of households use basic drinking water sources.  Why do many households fail to use basic drinking water sources?  A quarter of children under 5 years of age had diarrhea within two weeks before the survey took place.  What factors contribute to the high prevalence of diarrhea among children under 5 years of age?  What factors contribute to the low levels of hygiene and sanitation practices in the communities?  How can communities address challenges related to access and utilization of WASH services? What kind of support is needed from the local government and implementing partners? Agriculture  About 20 percent of farmers used financial services in the past 12 months.  What factors contribute to the low level of use of financial services by farmers and other traders for agricultural produce in the community?  How can access to financial services be scaled up among farmers?  Less than 50 percent of farmers used improved storage practices in the past 12 months.  What factors contribute to the low level of use of improved storage practices among farmers in the community?  How can the use of improved storage practices be scaled up among farmers? 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 14 2.2.2 Site Selection and Study Participants Qualitative data were collected in seven villages (one in each district) that were purposively selected (see Table 4) based on the following characteristics:  They were included in the joint baseline/endline PBS and are currently targeted by Apolou and Nuyok.  They feature interventions in agriculture/livelihoods, WASH, and maternal and child health and nutrition.  They were located in places that could easily be accessed within the time allocated for the research. Table 4: Villages and Participants for the Baseline Qualitative Study, Uganda 2018 District Village FGDs KIIs Number Planned Number Completed Number Planned Number Completed Kotido Nakapelimoru 8 7 4 2* Kaabong Losongolo 8 8 4 1* Abim Abim S/C 8 8 4 4 Napak Lopeii 8 6 4 4 Moroto Tapac 8 4 4 4 Nakapiripirit Kakomangole 8 8 4 4 Amudat Namosing 8 7 4 1* Total 56 48 28 20 *Missing key informant as not available during the data collection period The study participants included key informants and community members.54 Key informants were purposively selected because of their expert knowledge in food security and nutrition, agriculture/livelihoods, WASH, and maternal and child health and nutrition. For each district, the following key informants were targeted: district production officer, subcounty agriculture extension officer, representative of a micro finance institution (MFI), and maternal and child health nurse or in-charge of a nearby health facility. In addition, FGDs were conducted with the following community members at the household level in each district:  Male head of household: A man who self-identifies or is identified by another household member as head of household and has decision-making authority. This individual may or may not have children, may or may not have a spouse, and may or may not participate in farming activities. Preference will be given to individuals who have children under 5 years of age in the household; however, this will not be a requirement.  Female head of household or lead female in household: A woman who self-identifies or is identified by another household member as a lead female figure in a household and has some decision-making authority over the type of food to eat and items to purchase in the household. The individual may or may not have children, may or may not live with her husband or a male head of household, and may or may not participate in farming activities. Preference will be given to individuals who have children under 5 years of age in the household; however, this will not be a requirement.  Male farmer: A man who undertakes and has decision-making authority over farming activities either on his own property or on someone else’s (community plot). He may participate in the care of animals, preparation of fields, tending to and harvesting crops, or the processing of food stuffs. He may participate in farming either for subsistence or income generation, or both. 54 Key informants are individuals who have important information and insights to offer regarding the local context and socio-economic situation because of their position in the project activities, community, and government or private institutions. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 15  Female farmer: A woman who undertakes and has decision-making authority over farming activities on her own property or someone else’s (community plot). She may participate in the care of animals, preparation of fields, tending to and harvesting crops, or the processing of food stuffs. She may participate in farming either for subsistence or for income generation, or both.  Male and female young people/teenagers: These are male and female young people/teenagers 15–20 years of age.  Community health workers: These are members of the village health teams as identified by the village local council chairpersons.  Members of the village water service committees 2.2.3 Data Collection Procedures Data collection for the qualitative study took place from September 30 to October 7 and was conducted by a team comprising a lead qualitative researcher and six field assistants (three males, three females). The qualitative researcher provided leadership in the design, implementation, analysis, and reporting for the qualitative study. He trained and closely supervised the field assistants in data collection, transcription, and translation. The field assistants were selected based on their experience in conducting KIIs and FGDs and fluency in the local dialects or languages. The entire field team was comprehensively oriented on the study methodology and data collection tools before the start of the actual fieldwork through a pre-test, after which any necessary corrections were made. All field assistants were closely supervised by the qualitative researcher for the entire duration of field data collection. All interviews were conducted using the most suitable language for the study respondents. In addition, all interviews were digitally recorded with permission from the participants. 2.2.4 Data Processing, Management, and Analysis The FGDs and KIIs were audio-recorded and later transcribed verbatim by the field assistants. All typed transcripts were reviewed by the qualitative researcher, double-checked for completeness, and uploaded in a Dropbox folder for storage. Access to the Dropbox folders was restricted with a password that was provided to only those with access rights. In addition, all the collected information was printed and stored as hard copies in a secure locked cabinet at the IRC offices. The audio-taped data were transcribed verbatim by the field assistants and were analyzed manually using content analysis.55 Data were read and re-read by the qualitative researcher to identify emerging themes from the transcripts.56 To provide an understanding of the quantitative indicators derived from the results of the household survey, content analysis was used to identify themes or trends in responses, both within and across respondent groups, and then the findings from the quantitative PBS were triangulated with the findings from the qualitative data collection. Direct quotations from the KIIs and FGDs were also used to support the interpretations. Typical quotes were selected and included in this report to emphasize the reasons for the reported quantitative performance. 2.3 Data Limitations and Fieldwork Challenges 2.3.1 Data Limitations Effects of prior FFP and other donor programs. There are several other ongoing programs in the DFSA implementation areas that may have direct effects on some indicators. These effects are not directly measurable and may contribute to the overall change in indicators measured at endline. In addition, the prior DFAPs operated in a substantial portion of the new DFSA areas, which may influence indicator results for those households that participated in the prior DFAP activities. 55 Riley, J. 1990. “Getting the most from your data.” London, King’s Fund. 56 Glaser, B. G., and Strauss, A. L. 1967. “The discovery of grounded theory.” Chicago IL. Aldine. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 16 Validity and reliability of self-reported data. Much of the data collected for the household PBS were self-reported, which has several limitations, such as the possibility of exaggeration or omission of information, inaccurate recollection of experiences or events, social desirability bias or reporting of untruthful information, and reduced validity when respondents do not fully understand a question. These same limitations may apply to qualitative data collected through KIIs and FGDs. To reduce the likelihood of these potential effects on validity and reliability, the analysts triangulated the PBS findings with the qualitative study findings. 2.3.2 Fieldwork Challenges  The road network in most of the districts was very poor and was worsened by flooding in districts like Amudat, Abim, Napak, and Nakapiripirit. Some teams struggled over long distances, which had an impact on the output per day in some of the clusters. In some cases, teams had to travel for more than 10km to access the clusters. The coordinators and supervisors worked with local authorities to establish areas that were relatively dry and could be accessed. This required modifications to the travel plan. Despite the heavy rains and long distances to some selected clusters, the teams managed to reach out to the clusters and carried out 95 percent of household interviews. A few clusters were replaced because the sampled ones were inaccessible in the rainy season.  Some tablets would freeze during the interview. The IT support team worked in real time to provide support to the interviewer teams that reported challenges. They installed the Team Viewer application on all the tablets to enable them to troubleshoot challenges on specific tablets without necessarily travelling physically everywhere in the field. Replacements were made for a few tablets whose issues could not be resolved in the field. This saved time and prevented data loss.  Supervisors were required to upload data daily, but in some cases this was not possible due to low network bandwidth. Supervisors were advised to move to the nearest towns or elevated areas where they could access the internet and upload the data.  Interviewers experienced difficulties in obtaining the required 30 interviews in a few clusters. Despite these problems the overall PBS household-level response rate was close to 95 percent. o Specific to Napak District, there were floods in Iriri Subcounty, which led to migration in some areas. This presented a challenge because there were 11 vacant households in one of the clusters. o Specific to Nakapiripirit District, in Moruita Subcounty, at the Moruita trading centre, a number of different languages are spoken because of the mixture of people from different districts. The team was only able to conduct 19 household interviews (of 30 needed for the cluster) with the help of translators. Fifteen of the interviews were Bantu-speaking households and four were Pokot-speaking households. o Specific to Kaabong District, the major reason for call backs was the respondents’ engagement in cash for work activities (through a project with the Northern Uganda Social Action Fund), so they left their homes very early in the morning and returned very late in the evening. This was a challenge if a call back fell on the third or fourth day in a cluster, because it would cause logistical challenges and impact on the daily output of the affected team. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 17 3. FINDINGS The baseline PBS findings are described in the following sections: (1) characteristics of the population, (2) food security and poverty, (3) WASH, (4) agriculture, (5) women’s health and nutrition, (6) children’s health and nutrition, (7) gender, and (8) resilience. Each section discusses the findings from the household survey and the qualitative study as related to the relevant FFP indicators. Annex 6 provides a tabular summary of all indicators and indicator disaggregations along with sampling statistics. 3.1 Characteristics of the Study Population The current population of Uganda is estimated to be 44,703,000 people.57 The population of the combined DFSA areas was estimated at 723,726 people,58 or about 1.6 percent of the total population of Uganda. Adults 15 years of age and older comprise 46.2 percent of that population. More than one-half of adults (55.0 percent) are farmers. Women of reproductive age comprise 18.9 percent of the population, and children under 5 years of age comprise 18.6 percent of the population. In addition, 78.2 percent of women of reproductive age are married, and 65.1 percent of all women of reproductive age had a live birth in the past five years. The demographic characteristics of the population are similar across the two DFSA areas, as shown in Figure 3.1a. See Table A7.1, Annex 7 for further details on estimated population sub-groups in the DFSA areas. Figure 3.1a: Select Characteristics of the Population in the DFSA Areas, Uganda 2018 45.7% 18.5% 20.4% 19.7% 5.1% 2.1% 46.6% 19.3% 21.0% 17.7% 4.6% 1.9% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Adults age 15 or older Women of reproductive age (15-49 years) Adolescents 10-19 years of age Children under 5 years of age Children 6-23 months of age Children under 6 months of age CRS MC The combined DFSA areas have an estimated 132,832 households, and the average household includes 5.4 household members. Households have an average of 2.5 adults 15 years of age and older. About three-quarters of households in the combined DFSA areas (77.1 percent) include at least one adult male and female, 18.6 percent of the households include at least one adult female but no adult male, and 3.6 percent of households include at least one adult male but no adult female. Three of five households in the DFSA areas are headed by males. More than 70 percent of all heads of households (71.7 percent) have no formal education, 15.4 percent have a primary education, 7.9 percent have a secondary education, and 5.0 percent have a higher education. Education levels are slightly higher 57 United Nations Department of Economic and Social Affairs: Population Division, 2018 58 This is the estimated number of people based on the weighted results from the baseline PBS. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 18 in the CRS DFSA area compared to the MC DFSA area (see Figure 3.1b). Among all households in the combined DFSA areas, 61.5 percent have children under 5 years of age. See Annex 7, Table A7.2, for more details on household characteristics in the DFSA areas. Figure 3.1b: Education Levels of Household Heads in the DFSA Areas, Uganda 2018 65.9% 19.5% 9.2% 5.4% 76.9% 11.7% 6.8% 4.6% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% No formal education Primary Secondary Higher CRS MC 3.2 Household Food Security and Poverty This section uses data from the household survey and the qualitative study that can provide insights for program targeting and design related to food security and poverty. Food security indicators include the HDDS and prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES) using two reference periods, 30-day recall and 12-month recall.59 Poverty indicators include daily per capita expenditures, prevalence of poverty, and depth of poverty of the poor. Table 3.2.1 provides the baseline indicator estimates for food security and poverty indicators in the combined DFSA areas and for the CRS and MC DFSA areas separately. In the sections that follow, these indicators are examined through bivariate and multivariate analyses to identify factors that are closely associated with food insecurity and poverty and are triangulated with qualitative data where appropriate. 59 FIES is a new indicator recently formulated by FAO and adopted by FFP in 2017. FAO-prescribed methodology was used to compute the FIES indicator. Details about FIES calculation can be found at: The Food Insecurity Experience Scale-Development of a Global Standard for Monitoring Hunger Worldwide. Table 3.2.1: Food Security and Poverty Indicators, FFP Baseline Study, Uganda 2018 Indicator Overall CRS MC Average HDDS 3.3 3.1 3.6 Prevalence of moderate and severe food insecurity based on 30-day recall (FIES) 91.0 90.4 91.6 Male and female adults 91.0 90.7 91.3 Female adults only 92.1 90.7 93.7 Male adults only 85.2+ 80.7* 89.2 Children only (no adults) N/A N/A N/A Prevalence of moderate or severe food insecurity based on 12-month recall (FIES) 93.7 94.0 93.4 Male and female adults 93.6 94.3 93.0 Female adults only 94.7 94.1 95.4 Male adults only 90.4 88.0 92.6 Children only (no adults) N/A N/A N/A 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 19 Indicator Overall CRS MC Per capita expenditures (as a proxy for income) of USG-assisted areas a $1.04 $0.99 $1.09 Male and female adults $1.04 $0.98 $1.10 Female adults only $1.00 $0.99 $1.01 Male adults only $1.64+ NA $1.48 Children only (no adults) N/A N/A N/A Prevalence of poverty: Percent of people living on less than $1.90/day 2011 purchasing power parity b 88.8 88.0 89.5 Male and female adults 88.8 88.1 89.4 Female adults only 90.4 89.3 91.6 Male adults only 74.3** NA 76.6+ Children only (no adults) N/A N/A N/A Depth of poverty of the poor: Mean percent shortfall relative to the $1.90 poverty line c 61.4 60.2 62.5 Male and female adults 61.1 60.5 61.6 Female adults only 62.3 57.9 66.7** Male adults only 67.1+ N/A 68.1 Children only (no adults) N/A N/A N/A Number of responding households 2,770 1,235 1,535 Male and female adults 2,169 953 1,216 Female adults only 499 240 259 Male adults only 98 42 56 Children only (no adults) N/A N/A N/A a Daily expenditures expressed in constant 2010 USD b Poverty estimates are computed based on the purchasing power parity derived from 2011 International Comparison Program market surveys. c Expressed as percentage of poverty line N/A=Not available *** p<0.001, ** p<0.01, * p<0.05, + p<0.10; Reference group=Male and female adults households 3.2.1 Food Security The FIES indicator measures the percentage of households that experienced food insecurity at moderate or severe levels during the reference period. Nine out of ten households in both project areas experienced moderate to severe food insecurity in terms of quality and quantity due to lack of money or other resources in the past 30 days. The poor food security situation is also reflected in the HDDS indicator; on average, households accessed and consumed 3.3 of 12 basic food groups. Among the 12 food groups, cereals (62 percent), vegetables (61 percent), miscellaneous items such as tea, coffee and condiments (57 percent), and oil and fats (28 percent) were the most commonly consumed food groups in the CRS area. In the MC area, cereal (81 percent), vegetables (72 percent), miscellaneous items such as tea, coffee and condiments (57 percent), and oil and fats (34 percent), and milk and milk-products (31 percent) were the most commonly consumed food groups (see Figure 3.2.1 and Table A7.3, Annex 7). Household consumption of meat, poultry and organ meat, eggs, and fruits is minimal in both DFSA areas. Note that the baseline study was conducted during the month of June and the first week of July; at the height of the rainy season and the end of the typical lean season in Karamoja. The FIES 30-day recall and the HDDS thus reflect the food security situation during the time when food is most scarce in the DFSA areas. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 20 Figure 3.2.1: Percentage of Households Consuming HDDS Food Groups in the DFSA Areas, Uganda 2018 Cereals Root and tubers Vegetables Fruits Meat, poultry, organ meat Eggs Fish and seafood Pulses/ legumes/ nuts Milk and milk products Oil/fats Sugar and honey Miscella￾neous (tea, coffee, condi￾ments, etc.) CRS 62.0 21.4 60.8 10.2 4.1 1.6 5.1 24.6 17.3 28.4 11.9 57.3 MC 80.6 9.3 72.4 9.3 13.9 5.4 2.6 24.3 30.9 33.7 19.0 57.4 0 20 40 60 80 100 CRS MC The qualitative study also showed that the food insecurity situation was dire and communities only depend on the limited food which is available in their households for survival. Most respondents in both DFSA areas cited that poor and unreliable weather conditions do not support the growth of many highly nutritious food crops. The Karamoja Region experiences long dry spells that lead to drying up of most food crops and heavy rains which wash away the planted crops in the fields. “The main threats of food security in our community remains to be the poor performance of rainfall in terms of variations, low amounts received and poor distribution in space and time that leads to near total crop failure.” Female youth, Abim In addition, many of the community members do not know how to time the seasons especially when cultivation should start resulting in delayed planting of food crops. “People don’t know how to time the season for the case of Karamoja, rain sometimes come early and disappears. As a result; people will end up not cultivating and this leads to food insecurity.” Male household head, Kotido Many qualitative respondents reported that the seeds which were provided to communities are not drought resistant and are frequently affected by pests and diseases resulting in smaller harvests. “Pests and diseases like the thread worm are very alarming. Immediately you start planting, the thread worm pops up as well. Even last year it was a terrible threat but we reported the issue only that the government takes long to respond. We just advised the farmers to maybe use other simple control measures. The other one which is becoming a threat is stringer weed and it affects mostly the cereals,” Napak local government official Due to high levels of poverty; communities cannot afford modern farming equipment like tractors, or ox ploughs to cultivate larger pieces of land. In addition, the small harvest is usually diverted to make local brew by some of the community members and yet the monetary returns are every limited. A common habit of selling food meant for household consumption to purchase other household items like soap also increases household food insecurity. Another reason cited for food insecurity is inadequate labor at the household level caused by the cultural practice of leaving only women to cultivate land while the men look after cattle. Many qualitative respondents also mentioned that many families have many small children who do not provide labor in land cultivation. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 21 3.2.2 Poverty The main objective of FFP DFSAs is to improve nutrition and food security for vulnerable households in their implementation areas. Poverty indicators, based on household expenditures, are used as a proxy for income.60 Poverty indicators include per capita expenditures calculated in constant USD 2010, percentage of people living on less than USD $1.90/day,61 and depth of poverty of the poor relative to the USD $1.90 poverty line.62 Daily per capita expenditures are calculated using purchasing power parity (PPP) 2011 and adjusted for inflation between 2010 and the survey month.63 Percentage of people living below the poverty line, also called prevalence of poverty, is calculated as the share of the population whose consumption (combining food, non-food, and housing) is below the poverty threshold of USD $1.90 per day. Depth of poverty of the poor is computed by subtracting each poor household’s per capita expenditure value from the poverty threshold of USD $1.90 to obtain the household shortfall from the poverty line. Households that have per capita expenditure values above the poverty threshold are not included in this indicator. The mean depth of poverty for the poor is expressed as a percentage of the per capita per day poverty line. Based on the international poverty line of USD $1.90 per capita per day (2011 PPP), 89 percent of households in the combined DFSA areas are poor, with average daily per capita expenditures at USD $1.04 PPP 2011 (USD $0.99 for CRS and USD $1.09 for MC, see Table 3.2.1). The share of different food and non￾food sources in total per capita expenditures for the combined DFSA areas is presented in Figure 3.2.2. Food is the greatest source of expenditures (65 percent), followed by non-food (non-asset and non-housing) expenditures (26 percent). A large share of household expenditures concentrated on food, especially in the rural context, indicates households’ poor economic status. Depth of poverty among the poor in the combined DFSA areas is 61.4 percent of the USD $1.90 poverty line (PPP 2011), indicating that on average, each poor individual in the combined DFSA areas would need to increase his or her income by 0.614 times USD $1.90 or $1.17 (PPP 2011) in order to move above the poverty line. Table 3.2.1 shows poverty indicators disaggregated by gendered household type. The adult male-only households have a lower prevalence of poverty relative to households with male and female adults and 60 Income in most developing countries and rural areas is difficult to measure due to the limited and often seasonal nature of cash-earning opportunities. In comparison, expenditure data are typically less prone to recall error and more evenly distributed over time than income data. 61 In October 2015, the World Bank announced a new international poverty line of USD $1.90 per capita per day using 2011 purchasing power parity. 62 The methods used to estimate poverty indicators are described in detail in Appendix C of the Data Treatment and Analysis Plan (see Annex 4). 63 Daily per capita expenditures are reported in constant 2010 USD to allow cross-country comparisons because the U.S. consumer price inflation is calculated with 2010 as a base year. International agencies such as the World Bank generally report countries’ indicators, such as per capita expenditure and gross domestic product per capita, in constant 2010 USD. Figure 3.2.2: Sources of Average Per Capita Expenditures, Uganda, 2018 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 22 adult female-only households across both DFSA areas. Among the poor households, the adult female-only households in the MC area have a higher depth of poverty compared to households with male and female adults, but no such difference was found for the male adult-only households. Unlike in the MC area, no significant differences were found between the female adult-only households and households with male and female adults in the CRS area. Relationships between Food Security and Poverty A significant positive relationship between moderate to severe food insecurity and prevalence of poverty was found in the combined DFSA areas, indicating that household food insecurity increases as household poverty level increases. This relationship was found only in the MC area but not in the CRS area. Depth of poverty among the poor was also found to be significantly positively associated with household’s food insecurity experience in the MC area. The HDDS is found to be significantly inversely associated with prevalence of poverty in the combined DFSA areas and in the CRS and MC areas separately. A similar association is found between the depth of poverty among the poor and the HDDS score (see Figure 3.2.3). The results are consistent across the DFSA areas and indicate that an increase in the prevalence of poverty or the depth of poverty may lead to reduced access to diverse foods at the household level. 2 3 4 5 HDDS 1 2 3 4 5 Quintiles of mean depth of poverty of poverty-1-poor, 5=poorest Figure 3.2.3: Relationship between HDDS and Depth of Poverty of Poor, Uganda, 2018 Potential Drivers of Food Insecurity and Poverty The food security and poverty situation in Karamoja region is complex and intertwined with various local, national, and even international political, social, economic and geographic factors. Exploring all those factors is beyond the scope of this report. Nonetheless, some relevant household-level variables available in the baseline data are likely to be associated with food security or poverty indicators. These variables are identified for simple bivariate correlation analyses (see Annex 8 for the list and definition of each variable). Variables found to be statistically significantly associated with indicator variables are considered for multivariate regressions to examine the tenacity and direction of the associations when other background characteristics as represented by other associated variables are controlled for. The bivariate and multivariate regression results are presented in Tables CT10.1–RT10.8, Annex 9. Potential drivers of HDDS: Tables 10.1a–10.1d, Annex 9, present bivariate correlation results between the household HDDS score and select household-level variables. In the CRS area, the HDDS score is moderately64 positively associated with households in which at least one farmer uses agricultural financial 64 Following the standard statistical practice, this report considers all bivariate associations (correlations) with correlation coefficient (r) greater than 0.50 as strong association, moderate if r is equal to or greater than 0.30 and less than 0.50; and weak if r is less than 0.30. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 23 services such as credit, savings, or agricultural insurance; households with cash savings; households with durable assets; households receiving or exposed to external information; and households participating in and making decisions in local community groups. In the MC area, the HDDS score is moderately associated with households owning durable assets. Other variables, such as households with educated household members, an adult cash earner, a higher wealth quintile, and linkages with government officials and NGOs, are also positively associated with the HDDS score in both DFSA areas, but the degree of association is not as strong. All the variables significantly associated with the HDDS score, regardless of their strength of association and direction, were then considered for multivariate regression models developed separately for each DFSA (see Table T10.2, Annex 9).65 The results showed a robust association of three household variables with the HDDS in both DFSA areas even after controlling for other background variables: (1) households with durable asset ownership, (2) households who participate in local decision-making bodies, and (3) households with educated adult members (see Table 3.2.2). These associations are significantly positive, indicating that an improvement in these characteristics would likely lead to an improvement in the HDDS score, regardless of the households’ poverty status, exposure to shocks, and other variables as controlled for in the regression models. The results also showed that the relative advantage of adult household members’ education on HDDS is observed at the secondary and tertiary/university level of education only. The regression results showed a few other variables associated with HDDS, although the associations are not consistent across each DFSA area. Potential drivers of FIES: The same set of variables used to examine the correlation with HDDS is used to examine the correlation with the FIES score (based on 12-month recall).66 The correlation results presented in Table 10.2, Annex 9 indicate that two variables in particular—households with Table 3.2.2: Variables Significantly Associated with HDDS Based on Regression Models Variables Relationship with HDDS CRS MC Households with durable asset ownership + + Households that participate in local decision-making bodies + + Households with educated adult members + + Adult female-only households relative to both male and female adult households + Households with access to land for sharecropping relative to HHs that own the land - Households with access to no land relative to households that own the land - Households with more than three hectares of land compared to households with less than one hectares - Households with at least one adult>=15 years of age and earn cash or cash and in-kind payment + Households in the fourth wealth quintile relative to first wealth quintile (poorest wealth quintile) + Households with higher level life aspirations/confidence to adapt + Households linkages to government officials and NGOs + Households exposed to external information + Households with access to financial institutions - Households experiencing climate shocks + Households experiencing conflict shocks + Households’ ability to recover from shocks + 65 As noted earlier, the purpose of the regression models is to check the tenacity of association shown by the bivariate analysis when other associated variables are controlled for. The multivariate regression models also help examine how much variation in the outcome variable is explained by the select set of variables and how each of the explanatory variables influence the outcome variable. Two sets of Poisson regression models were developed because of the zero truncated ordinal nature of the outcome variable—HDDS. 66 The 12-month recall-based FIES indicator is more realistic to analyze household food insecurity because it encompasses households’ experience throughout the year, including different agricultural seasons—lean and harvest times. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 24 educated adult members and households experiencing biological shocks in the past 12 months—are associated with the FIES score in both the CRS and MC areas. Households with educated adult members tend to have less food insecurity, and households experiencing biological shocks in the past 12 months tend to have more food insecurity in both DFSA areas. Other variables, such as household size (+), households’ access to natural resources (-), economic shock (+), and exposure to conflicts (+), are also associated with FIES score but only in the CRS area in the direction as pointed by the (+) or (-) symbols after each variable. In the MC area, HDDS (-), households with a farmer using agricultural financial services (-), poverty households saving cash (-), access to remittance (+), households receiving humanitarian assistance (+), social bonding (+), bridging (+), higher livelihood strategies (+), using agricultural extension services (-), biological shocks (+), and higher severity of shocks (+) are associated with the FIES. All the variables that are significantly associated with FIES were then included in a multivariate linear regression for each DFSA area separately. Complete regression results are presented in Table T10.4, Annex 9, and a summary of the key findings is provided in Table 3.2.3. After controlling for other variables, regression results show that households with access to communal natural resources tend to be less food insecure in the CRS area and more food insecure in the MC area. Compared to households with no education, households with any member with a primary or secondary education are less food insecure, even after controlling for other household￾level variables in the CRS area. In the MC area, households with any member with a tertiary education are less food insecure. Households experiencing any of the three shocks in the past 12 months— biological, climate, or conflict—are more food insecure in the CRS area, and no such association was found in the MC area. Poor households tend to be more food insecure in the MC area, even after controlling for household members’ education level, exposure to shocks, access to natural resources, and access to agricultural financial services. No such relationships were found in the CRS area. Interestingly, households that have higher livelihoods diversification tend to be more food insecure in the MC area—somewhat counter￾intuitive findings and a contrast to the findings in the CRS area. More diversified livelihoods options, higher poverty, and better access to communal natural resources indicate more food insecure households in the MC area. Table 3.2.3: Variables Significantly Associated with FIES Based on Regression Models Variables Relationship with FIES CRS MC Households with access to communal natural resources - + Household size + Households with at least one member with a primary-level education relative to no education - Households with at least one member with a secondary-level education relative to no education - Households with at least one member with a tertiary-level education relative to no education - Households with a farmer using agricultural financial services - Prevalence of poverty + Availability of humanitarian assistance from government or NGO + Households with higher livelihood diversification - + Households experiencing biological shocks + Households experiencing economic shocks + Households experiencing conflict shocks + 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 25 Potential drivers of poverty: The same set of background variables considered for the HDDS are also used to examine bivariate associations with household poverty in both DFSA areas, and the results are presented in Table CT10.5, Annex 9. Only two variables—households with educated household members and households with ownership of durable assets—are found to have moderate inverse association with poverty in the MC area, and only one variable—households with ownership of durable assets—is found to have a moderate inverse association with poverty in the CRS area. Other variables that are associated with poverty in both the CRS and MC areas include households in which farmers have access to larger plot of agricultural land (-), households with farmers using agricultural financial services (-), save cash (-), prepared to mitigate and adapt shocks (-), have higher aspirations (-), have linkages with government officials and NGOs (-), have exposure to external information (-), use agricultural extension services (-), have linkage to livestock extension services (-), participate in local community groups (-), experience climate shocks (-), experience economic shocks (-), and experience conflict shocks (-). In addition to these, there are variables that are associated with poverty in the CRS area only and variables that are associated with poverty in the MC area only. The bivariate relationships can be affected or moderated by different variables, including, but not limited to, the ones presented in Table CT10.5, Annex 9. To identify which of the variables retain their strength of association with poverty when other variables are controlled for, multivariate logistic regression models were developed for the two DFSA areas separately (see Table RT10.6, Annex 9). Only one variable—households with tertiary-level education—is found to have an association with lower household poverty in both DFSA areas, even when all other variables in the model remain constant. Few other variables retain the strength of association but not consistently across the DFSA areas. Table 3.2.4 summarizes the variables associated with poverty based on the regression results. Potential drivers of the depth of poverty of the poor: Bivariate analyses were conducted between the depth of poverty among the poor and the same sets of variables considered for similar bivariate analyses for HDDS and household poverty. The bivariate analyses followed by multivariate analyses could help identify factors that contribute in reducing the depth of poverty of the poor. The bivariate results are presented in Table CT10.7, Annex 9. Household ownership of durable assets is the only variable that has moderate negative association (r>=.30) with the depth of poverty of the poor in both DFSA areas. Most of the variables that are associated with poverty are also associated with the depth of poverty in both DFSA areas. However, although household with increased livelihoods strategies, households owning livestock, and households with an adult member earning cash or in-kind payments were only associated with poverty in the CRS area, they are associated in both areas with the depth of Table 3.2.4: Variables Significantly Associated with Poverty Based on Regression Models Variables Relationship with Poverty CRS MC Households with at least one member with a tertiary-level education relative to no education - - Household size + Households with at least one member with a primary-level education relative to no education - Households with at least one member with a secondary-level education relative to no education - Households with a farmer using agricultural financial services - Households that are in the second quintile of household wealth index in relation to households in the first quintile + Households with higher livelihoods diversification - Households exposed to external information - Households with access to financial information + Households with access to communal natural resources - Households with access to livestock services - Households experiencing climate shocks - Households experiencing conflict shocks - 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 26 poverty. Households experiencing climate shocks was associated with poverty for both DFSA areas, but it was associated with the depth of poverty in the MC area only. All the variables that are associated with the depth of poverty of the poor in bivariate analyses are included in the multivariate regression. The regression results are presented in Table RT10.8, Annex 9. A summary of the significantly associated variables is presented in Table 3.2.5. Controlling for other variables, households with bigger household size and adult male-only households compared to adult male and adult female households are found to have a higher depth of poverty among the poor in both DFSA areas. In contrast, households with farmers using agricultural financial services are found to have a lower depth of poverty among the poor in both areas. Interesting results are found for households with access to land for sharecropping. These households tend to have a lower depth of poverty among the poor in the CRS area, but a higher depth of poverty among the poor in the MC area relative to the households that own the land. A similar result was found for adult female-only households, which were found to have a lower depth of poverty among the poor in the CRS area and a higher depth of poverty among the poor in the MC area. Several other variables also retain their association with depth of poverty, albeit separately in each DFSA area. Households accessing rented and sharecropping land compared to households owning the land are more likely to have higher depth of poverty in the MC area. These results are also reflected in households’ access to size of land, because the households accessing a larger plot of land (more than three hectares) relative to households accessing less than one hectare are likely to have a lower depth of poverty in the MC area. This relationship was not found for the CRS area. In the CRS area, controlling for other variables in the model, households with a higher level of social bonding, households with exposure to external information, and households with access to livestock services are likely to have a lower depth of poverty among the poor. This was not the case in the MC area. A lower depth of poverty was also found in households experiencing climate shocks in the MC area, and households with improved bridging social capital tend to have higher depth of poverty among the poor in the CRS area. Qualitative respondents were asked to explain why many households are still poor despite the previous investments in the region through the GHG, RWANU and other development projects. Study respondents strongly reported that GHG and RWANU have had notable impacts in their society Table 3.2.5: Variables Significantly Associated with Depth of Poverty Among the Poor Based on Regression models Variables Relationship with Depth of Poverty among Poor CRS MC Adult female-only households relative to adult male and adult female households - + Adult male-only households relative to adult male and adult female households + + Household size + + Households’ access of land for sharecropping relative to owning - + Households with at least one member with a secondary-level education relative to no education - Households with access to rented land relative to owned land + Households with access to sharecropping land relative to owned land + Household with more than 3 hectares of land relative to no land - Households with farmer using agricultural financial services - - Households with higher bonding social capital - Households with higher bridging social capital + HHs exposed to external information - HHs with access to livestock services - HHs experiencing climate shocks - 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 27 ranging from health, women rights, food security and agricultural production. Farmers greatly benefited from these programs through acquisition of farming tools, inputs and knowledge. There has been a great reduction in cases of domestic violence which was rampant in the past. The projects also improved livelihood of community members and also increased access to food and non-food items from the nearby markets. Many respondents cited that these projects significantly contributed to reductions in childhood malnutrition and also equipped individuals with knowledge and skills on how to save using the village saving and loan association (VSLA) strategy. However, according to the community members, these programs have also had some negative impacts. “Food aid supplies have rescued households in emergency. Awareness campaigns, trainings and sensitization on the importance of farming have raised the ability of communities to produce their own food breaking the ugly norm of depending on handouts.” Female youth, Kotido “Most of the effects of RWANU were positive and the beneficiaries appreciated the program because it was contributing a lot to their livelihoods. The officials from the program were close to the beneficiaries because even the facilitators who were trained were just within the area.” District key informant, Napak Most respondents cited that many households still fall below the poverty line despite the previous project investments because of poor attitudes and mindset towards poverty and work. Many people in the region do not value hard work and have become highly dependent on hand outs from development partners. Local leaders also strongly believe that their region is unique and totally different from other regions and therefore, they strongly pointed out the need to review and totally the approach to development work in Karamoja “I think their attitude is the one that makes them fall below the poverty line. They know, some of them believe that they cannot do much beyond what they can do. They think that there is a certain magic which others can do which can make them to go up.” Agricultural extension officer, Moroto “There programs in a way have encouraged the attitude of our community of relying on handouts. This mentality of free support is partly facilitated by these development programs. The programs have also facilitated over producing among women since food aid is only given to pregnant, breastfeeding mother and children under 5 years of age.” Female youth, Kaabong 3.3 Agriculture The agricultural component of the household survey was completed by 3,664 farmers in the combined DFSA areas (1,651 farmers in the CRS area and 2,013 farmers in the MC area). All individuals in the household who met the definition of a farmer67 were interviewed. Of these farmers, about 45 percent were male and 55 percent were female. 3.3.1 Crops, Livestock, and Land Types of Crops Planted by Farmers A majority of farmers (97 percent in the CRS area and 93 percent in the MC area) reported planting at least one crop in the past 12 months. The top three crops planted by farmers in both the CRS and MC areas include sorghum (86 percent in the CRS area, 79 percent in the MC area), maize (43 percent in the CRS area, 66 percent in the MC area), and legumes (44 percent in the CRS area, 36 percent in the 67 Farmers, including herders and fishers, are defined as: (1) men and women who have access to a plot of land (even if very small) over which they make decisions about what will be grown, how it will be grown, and how to dispose of the harvest; or (2) men and women who have animals or aquaculture products over which they have decision-making power; or both. Farmers produce food, feed, and fiber, where “food” includes agronomic crops (crops grown in large scale, such as grains), horticulture crops (vegetables, fruit, nuts, berries, and herbs), and animal and aquaculture products, as well as natural products (e.g., non-timber forest products, wild fisheries). These farmers may engage in the processing and marketing of food, feed, and fiber and may reside in settled communities, mobile pastoralist communities, or refugee or internally displaced person camps. An adult member of the household who does farm work but does not have decision-making responsibility over the plot or animals would not be considered a farmer. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 28 MC area) (see Table A7.4, Annex 7). Oilseeds, which include sunflower, mustard, and sesame, are the fourth most planted crops in the CRS area (29 percent), and groundnuts is the fourth most planted crop in the MC area (12 percent). All other crops, such as wheat, millet, barley, soybean, fruits, potato, vegetables, and chat, are produced by very few farmers in both DFSA areas. No clear pattern is observed about the types of crops grown by male and female farmers in either DFSA area. Among the most popular crops (sorghum, maize, and legumes), more female farmers tend to plant sorghum than male farmers in the CRS area. More female farmers compared to male farmers tend to plant vegetables in the MC area (see Table A7.5, Annex 7). Types of Livestock Raised by Farmers Livestock and livestock production are integral to the household economy and an important source of income, food, and dairy products. Farmers use oxen or cattle for ploughing their fields. About 25 percent of farmers in the CRS area and 42 percent in the MC area reported that they have animals and/or aquaculture products over which they make decisions about. Among the farmers who have animals and/or aquaculture products, goats (69 percent), cattle (66 percent), sheep (29 percent), and poultry (24 percent) are most commonly raised by livestock farmers in the MC area. Cattle (49 percent), goats (48 percent), poultry (20 percent), and sheep (14 percent) are most commonly raised by livestock farmers in the CRS area (see Figure 3.3.1 and Table A7.6, Annex 7). Figure 3.3.1: Livestock Farmers by Type of Livestock Raised and DFSA Area, Uganda 2018 0 10 20 30 40 50 60 70 80 90 100 Cattle Goats Sheep Donkeys Camels Poultry Pigs Other Percentage of livestock farmers raising at least one livestock CRS MC Land Ownership and Farm Size A majority of farmers (more than 73 percent) in all DFSA areas reported owning the farmland over which they make decisions (see Table 3.3.1). Analysis of the size of farmland over which farmers make decisions indicates that almost one-third of farmers have very small plots of farmland (less than 0.5 hectares) in the CRS and MC areas. In the CRS area, farmers tend to have slightly smaller plots; about 32 percent of farmers reported land size greater than one hectare in the CRS area, compared to 39 percent of farmers who reported land size greater than one hectare in the MC area. Status of land ownership and farm size vary significantly by the sex of the farmer. Fewer female farmers use rented land in the CRS area, and more female farmers have no land ownership of any type compared to male farmers. A similar situation is found in the MC area, where fewer female farmers own the land, and more of them are sharecroppers, compared to male farmers (see Table A7.7, Annex 7). Similar results are found when looking at gendered household type. Adult female-only households are more likely to be sharecroppers in both the CRS and MC areas, compared to adult male and female households, and they also tend to have no land, compared to adult male and female households.68 68 Relevant bivariate analyses of these results are available upon request. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 29 Most FGD and KII participants (mainly in the MC area) reported that women have limited land rights in the Karamoja region in general and that land is predominantly owned by men. In addition, decisions on land usage, including renting and sharecropping, are predominantly made by men, as best illustrated by a FGD participant in a Kotido village: “In my community, men own most of the land and they also make decisions on how it will be used by the household.” Female FGD participant, Kotido Table 3.3.1: Land Ownership and Farm Size by DFSA Area, Uganda 2018 Land Ownership and Farm Size Overall CRS MC Status of land ownership Own 74.1 75.2 73.1 Rent 8.3 10.3 6.5 Sharecrop 13.0 10.8 15.1 None of these 4.5 3.7 5.3 Size of land Less than 0.5 hectares 34.8 38.2 31.5 0.5 to 1 hectare 29.7 30.2 29.3 Greater than 1 hectare 35.5 31.6 39.2 Number of responding farmers 3,664 1,651 2,013 3.3.2 Use of Financial Services, Value Chain Activities, Sustainable Agricultural Practices, and Improved Storage Practices The baseline PBS collected agriculture-related data primarily to estimate FFP agricultural indicators for financial services, value chain activities, the use of sustainable agricultural practices [for crops, livestock, and natural resource management (NRM)], and improved storage practices (see Table 3.3.2). These services and practices are expected to directly benefit households and lead to increased food security. The indicator values for value chain activities, sustainable crop practices, sustainable livestock practices, and sustainable NRM practices are derived based on specific value chain and agricultural practices promoted by each DFSA.69 Each DFSA set thresholds for the minimum number of required practices to be included for calculation of the indicator values for sustainable crop, sustainable livestock, and sustainable NRM practices separately; an overall threshold was set to compute the overall sustainable agricultural practice indicator (these thresholds are noted in Table 3.3.2). Table 3.3.2: Agriculture Indicators, Uganda 2018 Indicator Overall CRS MC Percentage of farmers who used financial services in the past 12 monthsa 20.5 21.7 19.4 Male 21.1 21.5 20.7 Female 20.0 21.8 18.3 Percentage of farmers who practiced value chain activities promoted by the project in the past 12 months 31.0 35.0 27.4 Male 35.2 38.7 32.1 Female 27.8*** 32.2* 23.6*** Percentage of farmers who used at least [3 in CRS, 5 in MC] sustainable agriculture (crop, livestock, and NRM) practices and/or technologies in the past 12 months 28.1 41.7 15.4 Male 31.6 46.0 18.5 Female 25.5*** 38.6* 12.9*** 69 These specific project-promoted value chain and sustainable agricultural practices are provided in Annex 7, Tables A7.9 and A7.10. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 30 Indicator Overall CRS MC Percentage of farmers who used at least [3 for both CRS and MC] sustainable crop practices and/or technologies in the past 12 months 43.7 34.1 52.6 Male 46.1 36.4 54.9 Female 41.8** 32.3† 50.9* Percentage of farmers who used at least [3 in CRS, 4 in MC] sustainable livestock practices and/or technologies in the past 12 months 7.6 8.1 7.1 Male 11.9 11.8 12.1 Female 4.2*** 5.3** 3.1*** Percentage of farmers who used at least [2 for both CRS and MC] sustainable NRM practices and/or technologies in the past 12 months 1.8 2.6 1.1 Male 2.2 3.2 1.3 Female 1.5† 2.2 0.9 Percentage of farmers who used improved storage practices in the past 12 monthsb 49.9 50.5 49.3 Male 47.8 48.0 47.6 Female 51.5* 52.4† 50.6 Number of responding farmers 3,664 1,651 2,013 Male 1,631 737 894 Female 2,033 914 1,119 Male/female group differences: *** p<0.001, ** p<0.01, * p<0.05, † p<0.10 a Financial services include savings, agricultural credit, or agricultural insurance. b Improved storage practices include cereal bank, granary, super grain/PICS bags, and manufactured silo. Financial Services Increased use of financial services can help farmers to access inputs and other resources to improve agricultural productivity. About 80 percent of farmers in the combined DFSA areas do not use any financial services (credit, savings, or agricultural insurance). No significant differences were found between farmers’ gender with respect to use of financial services in either of the DFSA areas. In the CRS area, use of credit is 13 percent and use of agricultural insurance services is 3 percent; in the MC area, use of credit is 8 percent and use of agricultural insurance is 0.6 percent (see Figure 3.3.2 and Table A7.8, Annex 7). Figure 3.3.2: Percentage of Farmers Using Financial Services by DFSA Area, Uganda 2018 Credit Savings Agriculture insurance None CRS 12.8 18.1 2.8 78.3 MC 8.1 17.1 0.6 80.6 0 20 40 60 80 100 Relationships between use of agricultural financial services and value chain, sustainable crop, livestock, and NRM practices were further explored for each DFSA. Farmers’ use of financial services is positively associated with the use of value chain activities in both DFSA areas. There is a positive association between the use of financial services and use of sustainable agricultural practices, including sustainable crop, livestock, and NRM practices, in both the CRS and MC areas. This indicates that farmers who use financial services are more likely to use sustainable value chain practices and sustainable agricultural practices, including crop, livestock, and NRM practices, and vice versa. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 31 Male and female farmer FGD participants from the qualitative study reported limited access to financial services like credit, saving and agricultural insurance to boost farming. They cited the following factors which contribute to the low level of utilization of financials services: (1) Ignorance about the availability, use and importance of agricultural financial services by farmers and traders of agricultural produce; (2) Unpredictable seasons; 3) Bureaucratic and tedious process involved in processing loans; (4) Lack of collateral; and (5) Fear of taking risks with financial institutions. “There are many farmers and traders of agricultural produce who reported that they were not aware about where and how to access small loans to boost their activities and yet some were asking for government support to set up milling stations for their produce. Agricultural extension officer, Moroto Representatives of microfinance institutions also cited the failure by many farmers of not being registered as members of financial institutions, as a contributory factor to the limited access and utilization of financial services in the community. They also reported that farmers who accessed loans were able to engage in better livelihood activities and also increased their farm produce during the year of loan acquisition. “We have some farmers here in Moroto who properly used the loans acquired to open up more farmland for simsim and maize production. One farmer bought a small maize mill for commercial flour production and was able to pay his loan back on time.” Microfinance institution representative, Moroto Qualitative study respondents indicated that access to financial services among farmers can be scaled up through the following ways: (1) Creating awareness on the availability and benefits of financial services; (2) Lowering interest rates on loans to support agricultural production; (3) Forming groups (with representatives) that link and network with financial institutions; (4) Using successful farmers to train others; and (5) Helping farmers to access markets for their produce as a way of motivating them to look for financial services. Value Chain Activities Agricultural value chain activities promoted by the DFSAs include, but are not limited to, pre- and post￾harvest activities such as a purchase of inputs for crops, purchase of inputs for livestock, tillage of land, bulk transporting of inputs produced, bulk transporting of animals,70 sorting produce, grading produce, drying or processing produce, trading or marketing, use of supplements, and feed production. The PBS data show that about 43 percent of farmers in the CRS area and 34 percent of farmers in the MC area reported planting crops or raising and buying livestock with the specific intention to sell or resell to earn income (see Table A7.9, Annex 7). Those farmers who reported raising crops or livestock with the specific intention to sell were then asked about the value chain activities in which they participate. Overall, about 31 percent of all farmers reported practicing at least one of the value chain activities that were promoted by the DFSAs. More male farmers, compared to female farmers, practice project-promoted value chain activities in both DFSA areas. Qualitative interviews suggest that women in general do most of the farming but men tend to make key decisions on value chain activities. Among the farmers who practice any value chain activities (either project-promoted or not) in the DFSA areas, tillage of land (27 percent in the CRS area, 20 percent in the MC area), procurement of inputs for crops (27 percent in the CRS area, 13 percent in the MC area), and sorting of produce (18 percent in the CRS area, 9 percent in the MC area) are the most commonly practiced value chain activities (see Table A7.9, Annex 7). Sustainable Agricultural Practices The baseline PBS asked farmers about a series of sustainable crop, livestock, and NRM practices. Depending upon the local context of each implementation area, the IPs selected specific crop, livestock, and NRM practices to promote (see Table A7.10, Annex 7). Approximately 42 percent of farmers in the CRS area reported using at least three project-promoted sustainable agricultural practices or 70 Bulk transporting of animals is not a project-promoted value chain activity for the CRS area. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 32 technologies, and about 15 percent of farmers in the MC area reported using at least five project￾promoted sustainable agricultural practices or technologies (see Table 3.3.2). Fewer female farmers use sustainable agricultural practices or technologies, compared to male farmers in both DFSA areas. Sub-indicators were calculated for crop, livestock, and NRM practices separately according to the project-defined minimums for each type of practice (see Table 3.3.2). Overall, about 44 percent of farmers who raise crops use the project-defined minimum number of sustainable crop practices in the combined DFSA areas (34 percent in the CRS area, 53 percent in MC area). A small percentage of all farmers (about 8 percent in the CRS area and about 7 percent in the MC area) practice the project￾defined minimum number of sustainable livestock practices. Less than 3 percent of farmers in the CRS area and 1 percent of farmers in the MC area practice sustainable NRM practices. Compared to men, fewer women practice sustainable crop and livestock practices in both DFSA areas. For sustainable NRM practices, these gender differences are not observed in either DFSA area. The low levels of use of sustainable agricultural practices are challenges for local agriculture, but also present an opportunity for the IPs to improve these areas over the course of the DFSAs. Crop practices: Proper use of crop practices is important to improve agricultural yield. The PBS asked farmers about 14 different types of sustainable crop practices that they may have used in the past 12 months (most of these practices are promoted by the DFSAs). The data show that almost all farmers used one or more crop practices in the past 12 months (see Table A7.10, Annex 7). Preparation of soil by hand (79 percent in the CRS area, 74 percent in the MC area), broadcasting seed (74 percent in the CRS area, 68 percent in the MC area), and weed control (60 percent in the CRS area, 56 percent in the MC area) are the 3 most commonly used crop practices. Intercropping (43 percent in the CRS are, 29 percent in the MC area) and soil preparation with ox plow (43 percent in the CRS area, 38 percent in the MC area) are also popular sustainable crop practices in the DFSA areas. Each DFSA is promoting a specific set of crop practices in their activity area. In the CRS area, weed control, pest and diseases control, soil preparation with ox plow, and planting seeds in rows are the most popular practices among the nine total promoted crop practices.71 In the MC area, soil preparation by hand, weed control, soil preparation with ox plow, intercropping, and planting seeds in rows are the most popular practices among 11 total promoted practices.72 Livestock practices: The PBS asked farmers who raise livestock about 10 different types of livestock practices that they may have used in the past 12 months—most of these practices are promoted by the DFSAs. The results indicate that about 9 percent of farmers who raise livestock in the CRS area and 13 percent of farmers who raise livestock in the MC area did not use any of these livestock practices in the past 12 months (see Table A7.10, Annex 7).73 Improved shelters (44 percent), vaccinations (39 percent), and deworming (35 percent) are the most commonly used promoted livestock practices in the CRS area. Vaccination and animal shelters (both about 42 percent), and deworming (37 percent) are the most commonly used promoted livestock practices in the MC area.74 NRM practices: Proper management of natural resources that are related to on-farm production is important for sustainable and improved on-farm production. The PBS asked farmers who raise crops or livestock about seven different types of NRM practices that they may have used in the past 12 months. The results indicate that a large majority of farmers (83 percent) do not use NRM practices in the CRS and MC areas (see Table A7.10, Annex 7). Among those who do, construction of water catchments is 71 Soil preparation with ox plow, soil preparation with tractor, planting seeds in rows, crop rotation, fertilizer application, intercropping, pest and diseases control, weed control and mulching are the CRS promoted sustainable crop practices. 72 Soil preparation by hand, soil preparation with ox plow, soil preparation with tractor, planting seeds in rows, crop rotation, fertilizer application, intercropping, pest and diseases control, weed control, mulching, and thinning are the MC promoted sustainable crop practices. 73 As noted in Section 3.3.1, about 25 percent of farmers in the CRS area and 42 percent of farmers in the MC area raise livestock. 74 Table A7.10 in Annex 7 shows the DFSA promoted or not promoted livestock, crop and NRM practices. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 33 the most common promoted NRM practice in the CRS area (8.5 percent), and soil conservation on hillsides is the most common promoted practice in the MC area (6 percent). Given these results, there is a good opportunity for the IPs to promote the use of NRM practices in both DFSA areas. Improved Storage About half of all farmers use improved storage practices in the combined DFSA areas (see Table 3.3.2). More female farmers, compared to male farmers, use improved storage practices in the CRS area, although no such differences were found in the MC area. Granary is the most popular improved storage practice used by the farmers both in the CRS (51 percent) and MC (49 percent) areas, followed by super grain/PICS bags (29 percent in the CRS area, 23 percent in the MC area). Cereal banks and manufactured silos are used by very few farmers in both DFSA areas (see Table A7.11, Annex 7). Maize and legumes are the top edible staple crops produced in both areas, so proper storage could help households meet their food needs, especially during the agricultural lean period. Qualitative study respondents indicated that farmers are aware of modern storage methods such as silos and improved modern granaries but their acquisition was limited by lack of funds and the low scale of commercial farming. Agricultural extension workers noted that improved storage practices could be used more often if farmers were trained and supported to engage in commercial farming. “Our farmers here are still practicing subsistence farming. Very few farmers are for commercial farming. With subsistence farming, which things can you take to the store? There is no bulk. So all of it gets consumed. Next year they also consume all of it. It is only very few people who have gone commercial.” Agricultural extension officer, Abim “Most households store produce in local granaries. Others store in sacks, buckets, or jerry cans. We would like to use silos and improved modern granaries. However, silos are very expensive to purchase and we do not have knowledge on how to make modern granaries with rat guards.” Female farmer, Moroto Many NGOs constructed community stores but these have not been adequately utilized because farmers do not have surplus produce to store (as a result of the poor weather conditions). Farmers also cited lack of leadership over the stores and suggested the need for leadership to manage the community stores and also motivate farmers to store their produce collectively. Relationship Between Agriculture and HDDS, Food Insecurity and Poverty Simple bivariate correlation analyses were implemented to analyze the relationships between HDDS, moderate to severe food insecurity, prevalence of poverty and depth of poverty with the four agricultural indicators (use of financial services, use of value chain practices, use of sustainable agricultural practices, and use of improved storage practices) for each DFSA area (see Table 3.3.3). Table 3.3.3: Relationship of Agricultural Indicators with Food Insecurity and Poverty Indicators Agricultural Indicators Relationship with HDDS Relationship with Food Insecurity (12 months) Relationship with Prevalence of Poverty Relationship with Depth of Poverty of Poor CRS MC CRS MC CRS MC CRS MC Use of financial services Improved Improved Improved Improved Improved Improved Improved Use of value chain practices Improved Improved Improved Improved Improved Use of sustainable agriculture practices Improved Improved Improved Improved Improved Use of improved storage practices Improved 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 34 Households using financial services tend to be less food insecure in the CRS area. Households in which farmers use agricultural value chain practices are likely to have a lower prevalence of poverty in both DFSA areas and a lower depth of poverty in the CRS area (see Annex 9, Tables 10.17a–10.17b). Households practicing sustainable agricultural practices also tend to have a lower prevalence of poverty in both DFSA areas, and a lower depth of poverty in the CRS area. Farmers who use improved agricultural storage practices are likely to have a lower depth of poverty and vice versa in the CRS area. There are a few other interesting relationships with respect to farmers and land ownership (see Tables 10.17a–10.17b, Annex 9). Farmers who own land are less likely to be poor, and farmers who sharecrop are more likely to be poor and their depth of poverty is higher in the MC area. Farmers who have no land are more likely to be poor in both the CRS and MC areas, and farmers who use rented land tend to have a higher depth of poverty in the MC area. This result is also reinforced by the size of land to which farmers have access. For example, farmers who have access to less than 0.5 hectares of land are more likely to be poor in the CRS area and have a higher depth of the poverty in the MC area. In contrast, farmers who have access to one or more hectares of land tend to have a lower depth of poverty in both the CRS and MC areas. 3.4 Water, Sanitation, and Hygiene Adequate WASH practices help improve child and adult nutrition, health, and overall well-being. Conversely, poor WASH practices can lead to increased malnutrition, morbidity, and mortality. A fecal-contaminated environment is associated with chronic undernutrition, poor gut health, and suboptimal absorption of nutrients, especially among the children.75 Worldwide, it is estimated that improved water sources reduce diarrhea morbidity by 21 percent, improved sanitation reduces diarrhea morbidity by 37.5 percent, and the simple act of washing hands at critical times can reduce the number of diarrhea cases by as much as 35 percent.76 Household WASH practices were assessed based on six FFP indicators as shown in Table 3.4.1. Table A7.12 of Annex 7 provides more details on the types of drinking water sources and types of sanitation facilities used by households. Table 3.4.1: WASH Indicators, Uganda 2018 WASH Indicators Overall CRS MC Percentage of households using an improved drinking water source 40.7 40.4 41.0 Available on premises 2.6 3.5 1.8 Available in 30 minutes or less 26.4 25.3 27.3 Available in more than 30 minutes 11.7 11.6 11.8 Percentage of households practicing correct use of recommended household water treatment technologies 10.0 7.9 12.0 Chlorination 1.8 2.0 1.7 Flocculent/disinfectant 0.2 0.2 0.2 Filtration 1.1 1.3 1.0 Solar 0.0 0.0 0.0 Boiling 7.8 5.4 9.9 Percentage of households that can obtain drinking water in less than 30 minutes (round trip)a 45.1 47.8 42.8 Percentage of households with access to a basic sanitation facility 8.4 6.7 10.0 Male and female adults 9.1 6.9 11.1 75 USAID. (January 2015) WASH and Nutrition: Water and Development Strategy Implementation Brief. Available at https://www.usaid.gov/sites/default/files/documents/1865/WASH_Nutrition_Implementation_Brief_Jan_2015.pdf. 76 WHO. (2004). Facts and Figures: Water, Sanitation and Hygiene Links to Health. Available at http://apps.who.int/iris/bitstream/10665/69489/1/factsfigures_2004_eng.pdf. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 35 WASH Indicators Overall CRS MC Adult female, no adult male 5.6+ 5.3 5.8** Adult male, no adult female 9.9 10.2 9.7 Child, no adults N/A N/A N/A Percentage of households in target areas practicing open defecation 65.6 66.9 64.4 Percentage of households with soap and water at a handwashing station commonly used by family members 3.5 3.9 3.1 Number of responding households 2,835 1,259 1,576 *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 N/A=not available a From improved and unimproved drinking water sources 3.4.1 Drinking Water Improved drinking water sources, as defined by the Joint Monitoring Programme (JMP) for Water Supply and Sanitation are sources that are protected by the nature of their construction or through an active intervention from outside contamination, in particular, contamination from fecal matter (WHO/United Nations Children’s Fund [UNICEF], 2016). These sources include the following: water piped into the dwelling, plot, or yard; a public tap or standpipe; a tube well or borehole; a protected dug well; a protected spring; or rainwater collection. An “improved” drinking water source means that a household can access water from one of these sources year-round without experiencing interruptions of a day or longer in a two-week period (USAID, 2015). In the combined DFSA areas, only about two in five households (41 percent) use an improved drinking water source (see Table 3.4.1). These include households that use an improved drinking water source and have access to water year round and have not experienced interruptions in this access in the two weeks preceding the survey. Among those households that use an improved drinking water source, tubewells or boreholes are the most common sources; 86 percent of households in the CRS area and 73 percent of households in the MC area obtain their water from tubewells or boreholes (see Table A7.12, Annex 7). Although most households that have access to improved drinking water use tubewells or boreholes, water availability is a major concern. Only 55 percent of households in both DFSA areas reported water being available at source year round, and 30 percent of households reported that water was unavailable for a day or more in the past two weeks (see Table A7.12, Annex 7).77 The majority (about 75 percent) of households using an improved drinking water source have water on their premises or can obtain their water in 30 minutes or less round trip. Qualitative study participants reported that water was unavailable at many boreholes during the long dry spells when the wells would dry up and during times when boreholes had broken down. About half of all households can obtain drinking water (regardless of whether the source is improved or not) in less than 30 minutes round trip. According to the JMP, if people in rural places can reach a source of water and get back within 30 minutes, they will fetch enough drinking water to satisfy their basic needs for direct ingestion, cooking, and hygiene. Of those households that reported taking more than 30 minutes, 50 percent reported taking anywhere from 30 minutes to one hour, 33 percent reported taking one to two hours, and about 9 percent reported taking two to three hours. Examination of poverty status with drinking water indicators yields some interesting results (see Table 3.4.2). Poor households, compared to non-poor households, are more likely to use an improved water source in the CRS area, and poor households, compared to non-poor households, are more likely to be closer to any kind (improved or unimproved) of drinking water source in the MC area. Disaggregation of water sources by poor and non-poor (see Table 7.20, Annex 7) shows that about 57 percent of poor households in the CRS and MC areas reported availability of water year round, while about 53 and 47 percent of non-poor households reported year round water availability in the CRS and 77 These water sources may or may not be chlorinated for safety. The baseline questionnaire did not ask this question. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 36 MC area respectively. About 26 percent of poor households in the CRS area and 32 percent of poor households in the MC area reported water being unavailable for a day or more in the last two weeks, while 33 and 36 percent of non-poor households reported so in the CRS and MC area respectively. These differences could potentially explain why poor households in the CRS area are positively associated with improved water sources. However, further investigation is needed to confirm this. Separate bivariate analyses conducted between the depth of poverty among the poor with use of an improved water source or availability of water in less than 30 minutes (round trip) showed no significant association in either of the DFSA areas (see Tables CT10.9a and b, Annex 9). This means that all poor households (regardless of how poor they are) are similar in terms of use of an improved drinking water source or whether the water source is available within 30 minutes or more. About 8 percent of households in the CRS area and 12 percent of households in the MC area reported use of recommended household water treatment technologies. Boiling is the most common method of treating water in both DFSA areas (5 percent in the CRS area, 10 percent in the MC area). Examination of the relationship between poverty indicators and use of water treatment technologies indicates that households that use recommended water treatment technologies are likely to be better off economically in both DFSA areas. Although fewer poor households use recommended water treatment technologies in both DFSA areas, the depth of poverty among the poor is found to be associated with households’ use of recommended water treatment technologies in the MC area only. With the exception of Abim, community health workers from the qualitative study reported that most community members typically boil and cool their water before storing it in the water pot. In the past, some communities were using water guard but now it’s no longer seen. The community members also complained about the quality of the water and that borehole water is salty and hard. The community health workers of Abim reported that they believe the water from the borehole is safe for drinking, and so they do not boil it. Table 3.4.2. Water Use by Household Poverty Status Project and Water Sources Non￾poor Poor Sig CRS Use of an improved water source 28.8 42.5 + Proximity (on premise) 2.6 3.7 Proximity (30 minutes or less) 19.5 26.3 Proximity (more than 30 minutes) 6.6 12.5 Households that can obtain drinking water in less than 30 minutes round trip 43.3 48.6 MC Use of an improved water source 33.3 42.0 Proximity (on premise) 2.5 1.8 Proximity (30 minutes or less) 19.9 28.3 Proximity (more than 30 minutes) 10.9 11.9 Households that can obtain drinking water in less than 30 minutes round trip 34.5 43.9 + *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 3.4.2 Sanitation Sanitation conditions in the DFSA areas are very poor, and about 8 percent of households in the combined DFSA areas have access to a basic sanitation facility. Basic sanitation facilities include flush or pour/flush facility connected to a piped sewer system, septic system or a pit latrine with slab, composting toilet, or ventilated improved pit latrine with slab that is not shared with other households (see Table A7.12, Annex 7). No significant differences were found in access to a basic sanitation facility across different gendered household types in either of the DFSA areas, except that in the MC area, where adult female-only households are less likely to have access to a basic sanitation facility compared to households with adult males and females. There could be many socio-economic reasons why fewer of the female-headed households may have access to basic sanitation than the households with adult males and females. For example, adult female-adult only households may not have enough financial resources, 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 37 education, or labor resources to build proper sanitation facilities. Qualitative data suggest that mainly men build the sanitation facilities. Poor sanitation status is also reflected in the high prevalence of open defecation in both DFSA areas. More than 6 in 10 households practice open defecation (use of open field or bush area or hanging toilets). Among those households with access to a sanitation facility, pit latrine with slab and latrine without slab/open pit are most commonly used in both DFSA areas (see Table A7.12, Annex 7). Identifying specific factors that are significantly associated with basic sanitation and open defecation can help design activities for effective targeting of the problem. Hence, a set of relevant household-level characteristics78 were considered for bivariate correlation analyses with basic sanitation and open defecation. Results are presented in Tables CT10.9c and d, Annex 9. A number of variables are found to be associated with households’ access to basic sanitation in the CRS and MC areas. Poverty is inversely associated with basic sanitation in the CRS area, indicating that poor households are less likely to use basic sanitation services there, compared to non-poor households. Other variables, such as ownership of durable assets and participation in local community groups, are positively associated with basic sanitation, suggesting that households with higher values for each of these variables are more likely to have access to a basic sanitation service in the CRS area. In the MC area, households with larger household size, higher education level, and availability of basic service institutions (such as health centers, schools etc.) are positively associated with basic sanitation. The relationships of these variables with basic sanitation remained intact even after controlling for each other (Table RT 10.10, Annex 9). Regarding open defecation, poor households are likely to have a higher prevalence of open defecation in both activity areas, and households with educated household members and with durable assets have a lower prevalence of open defecation in both activity areas. Households that participate in local decision￾making bodies and earning cash tend to have lower open defecation in the CRS area, and households with basic service institutions nearby tend to have lower open defecation in the MC area. Overall, poverty and household ownership of durable assets appear to be important variables in the CRS area, and cash earning, household size, and availability of basic service institutions remain important variables even after controlling for other variables (Table 10.11, Annex 9). Results from the qualitative study indicate a general consensus that the communities in the region still practice open defecation irrespective of the education status of household members. Key informants indicate that open defecation is common; however respondents argued that there has been a marked improvement, and that although cases of open defecation still exist, these are few and isolated. Some people engage in open defecation because of traditional beliefs and customs, out of ignorance, or because they do not have latrines. “Of course they believe that putting feces in a place, it becomes smelly and they look at it as unhygienic. The other thing is that looking at the in-laws, sharing the same latrine is culturally not allowed. Pregnant women fear to go to the pit latrine for fear of them dropping their baby in the pit latrine,” Agricultural extension officer, Moroto “These people are only lazy to construct latrines. Those ones who don’t have latrines are the ones still doing open defecation.” Water service committee member, Kaabong 78 The complete list of characteristics considered for correlational analysis is as follows: household size; household with educated household members (1—one or more family members have attended formal school, 2—one or more family members have completed secondary-level or higher education, 3—one or more family members have completed tertiary-level education); cash earning status (1—adult male or female has earned cash or cash/kind in the past 12 months, 2—household head has earned cash or cash/kind in the past two months); household wealth quintiles; poverty status; ownership of durable assets; receipt of humanitarian assistance in the past 12 months; participation in local community groups; time to obtain any kind of drinking water within 30 minutes; and number of basic services index. Discussion of each of these characteristics is beyond the scope of this report, but all of them can be expected to have an association with use of basic sanitation facilities. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 38 “The percentage of latrine coverage is very low. In the villages, the communities were educated about the use of latrines but utilization of them is very low. Some fear to go to the toilets thinking they would fall into the pit or the toilet would sink.” Nutrition focal person, Napak There were also reports that the geographical nature of the land also places the people of Karamoja at a disadvantage when it comes to constructing latrines. For example, some places are rocky so the households find it hard to dig, and other places have soft soil, which is very porous. “We blame these people but when you look at most sub-counties here in Nakapiripirit, they are very rocky, and even others, a place like Namar is water logged, the water table is very high, you just dig like two three feet and the water is already there. Then other places are just very rocky, you cannot dig deep which is also another big challenge.” Nutrition focal person, Nakapiripirit The district agricultural extension officers confirmed that many community members still feel that digging the latrines is not necessary and is against cultural norms. They would rather go to the bush, despite all the proper WASH communication that they have received. For example, there are beliefs that latrines are muzungu (white man) things and that a pregnant woman should not use the latrine, among others. Although this was reported to be a cultural issue, the respondents added that this was gradually changing and that things are not as bad now as they were before. The Karamoja region has received a lot of health education and meetings with stakeholders targeting improved latrine coverage and use, as well as elimination of open defecation. However, it has been difficult to substantially change behavior. “Even last week we had a meeting and we were asking that ‘why is it that we are having poor latrine coverage?’ We have done health education, everybody knows why latrines are good and so forth, but now we are resorting to using law. We are going to give households without latrines ultimatums of about one month to dig a latrine and if you fail, then we arrest.” Nutrition focal person, Nakapiripirit Qualitative study key informants from the district local government offices proposed the following approaches which can be used to reduce open defecation: (1) Creating by-laws requiring every household to own a latrine and these by-laws should be enforced by local leaders by imposing monetary fines on offenders; (2) Constructing public toilets for the lazy or those that cannot afford to construct latrines for themselves; (3) Orienting poor households on how to construct simple latrines using locally available construction materials; (4) Awareness campaigns, trainings and sensitization; and (5) Conducting more rigorous research to understand why open defecation has persisted despite the investments in health educations and community support. 3.4.3 Handwashing Practices A handwashing station was observed in 29 percent of households in the combined DFSA areas (37 percent in the CRS area and 20 percent in the MC area). Water was observed in only 15 percent of all households in which a handwashing station was observed in the combined DFSA areas (12 percent in the CRS area and 20 percent in the MC area). Soap79 was observed in only 9 percent of all households in which a handwashing station was observed in the combined DFSA areas (7 percent in the CRS area and 12 percent in the MC area). Very few households (3 in 100) were observed in the combined DFSA areas with soap and water at the handwashing station. Lack of adequate hygiene and sanitation practices is pronounced in both DFSA areas, suggesting plenty of opportunities to improve these practices. Qualitative study participants indicated that poor sanitation and hygiene practices can mainly be attributed to low availability and poor access to water and sanitation facilities. However, community members were happy that there has been some relative improvement from the past WASH situation and this has been a result of numerous water and sanitation awareness campaigns. Community members reported that they must pay for water services. At the borehole each household pays a monthly fee of 79 Soap includes any type of soap or detergent or other cleaning agents, such as ash, mud, or sand. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 39 1000/= to cater for maintenance. Once the borehole breaks down, every household is expected to contribute 2000/= towards the repairs. Other commonly used water sources are wells, although these were reported to often dry up during the dry season leading to severe water scarcity. During rainy seasons, households tend to fetch rain water and reduce the burden of paying for water. The qualitative respondents reported that they have a few boreholes which serve as the main source of water to the majority of the population both during the rainy and dry seasons. One borehole is usually shared among up to six villages in some districts. Water is usually fetched in the morning and in the evenings, although sometimes this occurs several times a day depending on the water needs at home. As a result of the large numbers of people being served, the queues at the borehole are often very long and these result in delays to access water which could last up to several hours. Community members also reported that the boreholes break down very frequently, and yet they do not have adequate technical knowledge on the borehole repair and maintenance. “There is also a borehole nearby but there is huge population fetching from that borehole. People who can afford do have taps close to their yards or plots but there is a public tap which is opened between 8 – 10 am and between 5 – 6:30 pm. Sometimes, fights erupt when people are struggling to fetch and sometimes containers are stolen in the process of waiting.” Community health worker, Kotido “Here in Kololo we are almost 900 households, but sharing one water source (borehole) we request and appeal to government and or any development agency to come to our rescue to improvise another alternative source of water or another borehole.” Female household head, Napak 3.5 Women’s Health and Nutrition The women’s module of the household survey was administered to 2,422 women 15–49 years of age (1,062 in the CRS area, 1,360 in the MC area). Valid anthropometry measurements were taken for 1,888 women 15–49 years of age who were not pregnant. Results for all women’s indicators are presented in Table 3.5.1. Table 3.5.1: Women’s Health and Nutrition Indicators, Uganda 2018 Women’s Health and Nutrition Indicators Overall CRS MC Prevalence of underweight women a 34.6 38.6 31.0 Prevalence of women of reproductive age consuming a diet of minimum diversity b 16.4 12.5 19.6 Contraceptive prevalence rate c 13.8 14.5 13.1 Modern methods 9.1 11.3 7.2 Traditional methods 4.9 3.3 6.3 Percentage of births receiving at least four antenatal care visits during pregnancy d 78.4 77.9 78.8 Prevalence of women of reproductive age who consume targeted nutrient-rich commodities 8.7 6.7 10.5 Bio-fortified beans 3.0 1.6 4.1 Bio-fortified maize or sorghum 7.3 5.0 9.3 Orange-flesh sweet potatoes 1.1 1.3 1.0 Number of responding women (15–49 years of age) 2,422 1,062 1,360 Number of responding women (15–49 years of age) who are not pregnant 1,888 872 1016 Number of responding women (15–49 years of age) with live birth in the past five years 1,639 746 893 Number of responding women (15–49 years of age) who are married or in a union 1,381 636 745 a Percentage of non-pregnant women with a body mass index (BMI) less than 18.5. BMI is defined as weight in kilograms divided by height in meters squared (kg/m2). b Minimum dietary diversity is defined as consumption of 5 or more of 10 food groups in the past 24 hours. c The percentage of women of reproductive age (married or in a union) who are currently using, or whose sexual partner is currently using, at least one contraceptive method, regardless of the method used. d Antenatal care visits are reported for the most recent birth for all women who have had a live birth in the last five years. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 40 3.5.1 Women’s Nutritional Status Body mass index (BMI), expressed as the ratio of weight in kilograms to the square of height in meters (kg/m2), was used to evaluate the nutritional status of women. A BMI below 18.5 indicates underweight or acute malnutrition and is associated with increased mortality. A BMI between 18.5 and 24.9 is considered normal, a BMI above 24.9 is considered overweight, and a BMI above 30.0 is considered obese. This indicator frames the extent to which women’s diets meet their caloric requirements. Undernutrition among women of reproductive age could lead to physical weakness, poor immune system, and being prone to infections and diseases. This can also contribute to adverse birth outcomes in future pregnancies. Improvements in women’s nutritional status are expected to increase their productivity, which may also have benefits for agricultural production. As shown in Table 3.5.1, about 35 percent of non-pregnant women 15–49 years of age in the combined DFSA areas have a BMI of less than 18.5 and, therefore, are classified as underweight. In the CRS area, about 39 percent of non-pregnant women are underweight, and in the MC area, about 31 percent of non-pregnant women are underweight. Figure 3.5.1 illustrates the distribution of women’s BMI in the combined DFSA areas. Sixty-one percent of women have a normal BMI, 3 percent are overweight, and 1 percent are obese (see Table A7.13, Annex 7 for distribution of women’s BMI by DFSA area). Figure 3.5.1: BMI Levels of Non-Pregnant Women of Reproductive Age in the Combined DFSA Areas, Uganda 2018 3.5.2 Minimum Dietary Diversity for Women The improvement of women’s dietary diversity and the maintenance of a healthy weight contribute to several other positive factors, including improved pregnancy and child health and nutrition outcomes.80,81 Minimum dietary diversity—women (MDD-W)82 is defined as the proportion of women of reproductive 80 FAO and FHI 360. 2016. Minimum Dietary Diversity for Women: A Guide for Measurement. Rome: FAO. 81 Lee, S.E., Talegawkar, S.A., Merialdi, M. & Caulfield, L.E. 2013. Dietary intakes of women during pregnancy in low- and middle￾income countries. Public Health Nutr. 16(8): 1340–53. 82 Introducing the Minimum Dietary Diversity–Women (MDD-W) Global Dietary Diversity Indicator for Women. Available at: http://www.fao.org/fileadmin/templates/nutrition_assessment/Dietary_Diversity/Minimum_dietary_diversity_-_women__MDD￾W__Sept_2014.pdf. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 41 age who are consuming a minimum dietary diversity, based on consumption of 5 out of 10 nutritionally diverse food groups. About 16 percent of women of reproductive age in the combined DFSA areas consume a diet of minimum diversity (see Table 3.5.1). As shown in Figure 3.5.2, most women (more than 70 percent) consume grains, roots, and tubers, and other vegetables. Followed by these two food groups, vitamin A￾rich dark green leafy vegetables, other vitamin A-rich vegetables and fruits, and legumes and beans are the most commonly consumed food groups. Few women consume nuts and seeds, eggs, and flesh foods, including organ meat and small animal protein (see Table A7.14, Annex 7). Variations in the consumption of animal flesh and dairy products such as milk, yogurt, and cheese is evident between the DFSA areas. The qualitative study showed that the nutrient rich foods such as eggs, chicken, etc. which are supposed to be eaten mainly by children, lactating and pregnant women are either sold off or offered to men. “When you go to other parts of Moroto, Nakapiripirit, or Kaabong, the belief is that the lion’s share should be eaten by the man, then the children and the woman eat the leftover food. Some of the nutrient-rich foods like eggs, chicken, offals, are not given to the children and the mothers, they instead give these foods to the man.” Nutritionist, Moroto, Regional Referral Hospital Figure 3.5.2: Food Groups Consumed by Women 15–49 Years of Age by DFSA Area, Uganda 2018 3.5.3 Consumption of Targeted Nutrient-Rich Commodities In addition to the 10 food groups used to compute the minimum dietary diversity indicator, the baseline PBS also collected information on the consumption of nutrient-rich food items (commodities) that will be promoted by the IPs (see Table 3.5.1). These nutrient-rich food items include bio-fortified beans, bio-fortified maize or sorghum, and orange-flesh sweet potatoes. In the MC area, about 11 percent of women 15–49 years of age consumed targeted nutrient-rich food items, and in the CRS area, about 7 percent of women 15–49 years of age consumed these food items. In the combined DFSA areas, among the nutrient-rich items, foods made from bio-fortified maize or sorghum are consumed by 7 percent of women, foods made from bio-fortified beans are consumed by 3 percent of women, and orange flesh sweet potatoes are consumed by 1 percent of women. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 42 Relationships Between Women’s Dietary Diversity, Household Characteristics, and Other Indicators Analysis of relationships between MDD-W, household demographic variables, and other indicators can help us better understand factors associated with women’s consumption of diverse nutrient-rich foods. The same set of variables considered for correlation analysis with HDDS is used to examine the correlation with MDD-W. Correlation analysis is also conducted with HDDS to examine how closely MDD-W and HDDS are related to each other, and the results are presented in Tables 10.12a–10.12d in Annex 9. Household consumption of diverse foods (HDDS) is strongly positively associated with MDD-W across both DFSA areas, suggesting that increased access to the 12 food groups that comprise the HDDS at the household level would likely mean increased women’s dietary diversity. In the CRS area, a weak but notable positive association is also found with household wealth index, cash saving, ownership of livestock, increased livelihood diversification, and household exposure to outside information. In the MC area, a positive association is found between women’s minimum dietary diversity and households with educated household members, households with aspirations and confidence to adapt to new shocks, higher livelihood strategies, and ability to recover from shocks. Household poverty is inversely associated with MDD-W in both DFSA areas, indicating that women in poor households are more likely to have poor dietary diversity. These results are also corroborated by the qualitative data. Some district production officers reported that communities in which the government promoted micronutrient-supplemented seeds and production of bio-fortified, short-cycle crops such as beans have access to better diets. The community health workers said that they sensitize the community members on how to consume a balanced diet using locally available resources. They also educate the communities and encourage them to farm food that has all food groups, especially vitamins, cereals, and legumes to feed their children and themselves to maintain good health. More practical trainings and awareness sessions on the dangers of malnutrition and how households can fight malnutrition are needed, however. “Communities need to be trained and sensitized on how to use locally available foods to form a balanced diet. In addition, they should be told which foods to prioritize in their gardens.” Health Worker, Abim Many youth suggested that more trainings on livelihoods and how to generate income in a semi-arid region like Karamoja can raise the income levels of households, thus making them self-reliant, more food secure, and able to afford more dietary diverse food for their families. 3.5.4 Antenatal Care and Use of Contraceptives Antenatal Care WHO recommends that pregnant women attend a minimum of four antenatal care (ANC) visits with a trained health care professional during their pregnancy. About 65 percent of women of reproductive age reported having a live birth in the past five years. Of those women, about 78 percent attended at least four ANC visits with a skilled provider during their pregnancy in the combined DFSA areas (see Table 3.5.1).83 About 40 percent of pregnant women receive ANC from nurses in both DFSA areas; 38 percent of pregnant women in the CRS area receive ANC services from midwives, and 32 percent of pregnant women in the MC area receive ANC services from midwives. Correlation analyses between poverty and pregnant women’s minimum number of ANC visits indicate that poor households in the CRS area are more likely to achieve the minimum required ANC visits. This relationship is not found in the MC area. This rather counterintuitive association between poverty and pregnant women’s ANC visits in CRS area could mean that the poor women may be making health 83 The 2016 Demographic and Health Survey reported that 60 percent of women did not attend at least four ANC visits during their pregnancy, but this is for the entire country of Uganda. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 43 center visits more frequently due to other health reasons and may have received ANC care while they are in the health centers. Some of the female FGD participants in both the CRS and MC areas reported that they received a lot of health education from the women’s community groups and also during home visits by community health workers. This has increased their understanding of the importance of receiving ANC during pregnancy and delivering at a health facility. Contraceptive Use The contraceptive prevalence rate among women 15–49 years of age is the most important proximate determinant of total fertility, and contraceptive knowledge and use is positively associated with women’s education (Westoff & Bankole, 2001; Gordon et al., 2011; Buyinza & Hisali 2013). The contraceptive prevalence rate84 serves as a proxy measure of access to reproductive health services and is considered to be useful for tracking progress toward improved access to reproductive health services and the quality of family planning. The baseline survey data show that about 14 percent of women in the combined DFSA areas who are married or in a union reported using either modern or traditional contraceptive methods (see Table 3.5.1). A majority of women who use contraceptives reported using modern contraceptives (14 percent in the CRS area and almost 13 percent in the MC area) such as female sterilization, male sterilization, intrauterine device, injectables, implants, pills, male and female condoms, emergency contraception, standard days method, and the lactational amenorrhea method, among others.85 In the CRS area, 14.2 percent of adolescents (15–19 years of age) and 12.7 percent of youth (20–24 years of age) use contraception, and in the MC area, 18.5 percent of adolescents and 13.4 percent of youth use contraception The most common methods used, which are at the lower end of use, that is about 2 percent of women, are implants and injectables (see Table A7.15, Annex 7). The minimal use of modern contraception could be due to many reasons. Qualitative study participants indicated that family planning education messages, especially on child spacing, are provided as part of health education during the ANC visits. Despite the high uptake of ANC services, use of family planning is still low mainly due to barriers such as fear of side effects either experienced or hearsay; lack of male partner support and involvement; and inadequate comprehensive knowledge about family planning methods as well as the persistent myths and misconceptions about family planning. To further explore factors that may contribute to contraceptive use, a set of bivariate analyses was implemented with different relevant variables for each DFSA: household members’ education, prevalence of poverty, lack of connection to external members outside of one’s own family—bridging social capital and linking social capital, lack of institutions that provide basic services such as health and education, and lack of households’ participation in community activities. The results are presented in Table 10.13, Annex 9. A positive association is found between women’s use of modern contraception and households with educated members and households’ exposure to external information in both DFSA areas. This means that women in households where there are educated household members tend to use modern contraception methods. Similarly, women in households that receive or are exposed to external information tend to use modern contraception. In contrast, women in poor households are less likely to use modern contraceptives in both DFSA areas. In the MC area, women in households that have linkages to government officials or NGOs are more likely to use modern contraceptives, and in the CRS area, women in households that participate in local community groups are more likely to use modern contraceptives. 84 For a given year, contraceptive prevalence rate measures the percentage of women of childbearing age who are married or in a sexual union who use any form of contraception (FFP Indicators Handbook Part I, 2015). 85 The 2014 Demographic and Health Survey reported that 13 percent of women used any modern method and 8 percent used any traditional contraception at the national level. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 44 To test the strength of household members’ education on women’s modern contraception use, a simple set of logistic regression models was implemented.86 The regression results show that in the CRS area, women in the households that have educated adult members at the secondary level are more likely to use a modern contraception method than the women in households in which adult members are not educated, even after controlling for poverty. In the MC area, women in households with educated adult members—either at the primary, secondary, or university level—are more likely to use modern contraceptive methods than women in households with no educated adult members, regardless of whether the household is poor. 3.6 Children’s Health and Nutrition Protein-energy malnutrition is a major contributor to deaths among children under 5 years of age in Uganda.87,88,89 This section reviews child health and nutrition (CHN) indicators, which include stunting, underweight, wasting, healthy weight, diarrhea and oral rehydration therapy (ORT) minimum acceptable diet (MAD), exclusive breastfeeding (EBF), and consumption of nutrient-rich commodities. Table 3.6.1 provides the baseline PBS results for these indicators. Among these, the first three indicators measure the status of chronic and acute child malnutrition. Prevalence of diarrhea is measured based on diarrheal incidences among children under 5 years of age in the past two weeks, and ORT measures the percentage of children with diarrhea treated with ORT. MAD measures both feeding frequency and dietary diversity of children 6–23 months of age by their breastfeeding status. EBF calculates the percentage of children under 6 months of age who are exclusively breastfed in the past 24 hours prior to the baseline survey. Table 3.6.1: Children’s Health and Nutrition Indicators, Baseline PBS, Uganda 2018 Children’s Health and Nutrition Indicators Overall CRS MC Prevalence of healthy weight (WHZ ≤ 2 and ≥ -2) among children under 5 years of age (0-59 months) 87.9 87.6 88.2 Male 86.5 86.7 86.3 Female 89.2+ 88.4 90.0+ Age 0–23 months 84.0 83.5 84.4 Age 24–59 months 90.3 89.9 90.6 Prevalence of underweight children under 5 years of age 29.0 27.8 30.3 Male 34.2 34.8 33.5 Female 24.4*** 21.3*** 27.4** Prevalence of stunted children under 5 years of age 38.1 35.7 40.5 Male 43.2 42.7 43.7 Female 33.5*** 29.2*** 37.7* Prevalence of wasted children under 5 years of age 11.2 11.5 10.8 Male 12.4 11.7 13.2 Female 10.0+ 11.4 8.6* Percentage of children under 5 years of age who had diarrhea in the last two weeks 31.6 31.6 31.5 Male 30.6 31.6 29.7 Female 32.4 31.7 33.1 Percentage of children under 5 years of age with diarrhea treated with ORT 84.1 83.3 84.9 Male 82.1 82.3 81.9 86 Regression results are available upon request. 87 World Bank. 2006. Repositioning Nutrition as Central to Development: A Strategy for Large-Scale Action. Washington, DC. 88 World Food Program, https://reliefweb.int/report/uganda/malnutrition-major-problem-uganda, accessed on October 18, 2018. 89 UNICEF, https://www.unicef.org/uganda/media_16947.html, accessed on October 18, 2018. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 45 Children’s Health and Nutrition Indicators Overall CRS MC Female 85.8 84.2 87.3 Prevalence of exclusive breastfeeding of children under 6 months of age 73.5 72.6 74.3 Male 76.7 82.7 72.4 Female 70.5 65.4+ 76.4 Prevalence of children 6–23 months of age receiving a MAD 8.4 6.6 10.2 Male 8.4 5.6 11.0 Female 8.5 7.6 9.3 Prevalence of children 6–23 months of age who consume targeted nutrient￾rich commodities 11.4 8.6 14.1 Male 12.3 11.6 12.9 Female 10.6 5.6+ 15.5 Bio-fortified beans 3.7 2.2 5.2 Bio-fortified maize or sorghum 10.8 7.5 14.2 Orange-flesh sweet potatoes 1.8 2.5 1.2 Number of children under 5 years of age 2,779 1,264 1,515 Male 1,349 624 715 Female 1,430 640 790 Number of children under 6 months of age 301 135 166 Male 147 61 86 Female 154 74 80 Number of children 6–23 months of age 757 355 402 Male 385 180 205 Female 372 175 197 *** p<0.001, ** p<0.01, * p<0.05, + p<0.10; Reference group= Male WHZ = weight-for-height z-score 3.6.1 Stunting, Underweight, and Wasting Child undernourishment can lead to serious short-term and long-term consequences, impaired cognitive development, and a higher risk for mortality. Children who are stunted (height-for-age), underweight (weight-for-age), or wasted (weight-for-height) are considered undernourished. Underweight status reflects either chronic (as tracked by the stunting indicator) and acute (as tracked by the wasting indicator) malnutrition or both and is often used to monitor nutritional status longitudinally. Child underweight status, especially for newborns, is associated with mortality. Severe linear growth retardation (stunting) reflects the outcome of a failure to receive adequate nutrition over a number of years (chronic malnutrition) and the effect of recurrent and chronic illness. Stunting, therefore, represents a measure of the long-term effects of malnutrition in a population and does not vary appreciably according to the season of data collection. Wasting, on the other hand, varies significantly seasonally and is generally used as an indicator of an acute food shortage or disease. The prevalence of stunted children under 5 years of age is high—38 percent—in the combined DFSA areas, with 36 percent in the CRS area and 41 percent in the MC area (see Table 3.6.1).90 Rates for underweight are similar for children in the CRS (about 28 percent) and MC (about 30 percent) areas.91 90 Stunting rates greater than or equal to 40 percent are considered “very high” by WHO standards. 91 Underweight rates in the range of 20–29 percent are considered “high” by WHO standards, and rates greater than or equal to 30 percent are considered “very high.” 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 46 The prevalence of wasted children under 5 years of age is high, about 12 percent in the CRS area and about 11 percent in the MC area.92,93 The female household heads from Kotido who participated in the qualitative study emphasized that there was inadequate food to feed the children, and they normally depend on one or two meals just like the adults. As a result, the children are usually hungry and sickly. In addition, food distributed by food programs targeting malnourished children is often sold to meet more immediate household needs such as paraffin or firewood; or consumed by the male household heads. “Food distributed by these programs is usually sold off to pay off the debts which the household could have incurred some time back. The other part of it, if the food remained for the child, women tend to value more of their husband; they feed their husband more with the food of the child. And sometimes, the husband dictates. Women cannot control utilization in the household.” Agricultural extension officer, Moroto “Children sometimes stay without food from morning to evening. They eat one meal and, in most cases, they depend on soya from NGOs.” Female household head Soil and weather conditions do not support growth of legumes in many of the DFSA areas, which could be very beneficial for the growth of the children; however, with irrigation practices and modern seeds and fertilizers, there is a high chance that the situation can be improved. This would help alleviate the nutrition status of children within the households. Community health workers in Amudat reported that poor water, sanitation, and hygiene with increased use of unsafe water and absence of sanitary facilities has increased the occurrence of water-borne diseases, which are among the underlying causes of malnutrition among the children in the community. Except for wasting in the CRS area, all three malnutrition indicators—stunting, underweight, and wasting—are more prominent among male children compared to female children in both DFSA areas.94 This is also reflected in the healthy weight indicator in the MC area, and a higher proportion of female children compared to male children have a healthy weight in that area. The female household heads interviewed for the qualitative study in both DFSA areas reported that male children were more stunted than the female children because the former eat a lot more than the latter. Food is generally scarce in the community and therefore, females who eat little get satisfied even with less food while the male children don’t get satisfied with the little food offered. “Male children need a lot of food because they eat a lot but the females eat little because they get satisfied very fast.” Female household head, Kotido Comparisons with the 2016 Demographic and Health Survey (DHS) rates for rural households (see Table 3.6.2) indicate that the 2018 stunting rate of 38 percent in the FFP areas is slightly higher than the 2016 DHS rate of 35 percent in Karamoja.95 The prevalence of wasting and underweight are also slightly higher than the DHS rates in the combined DFSA areas. 92 In the absence of aggravating circumstances, wasting prevalence of 5–9 percent is “poor,” and 10–14 percent is “serious.” 93 Disaggregation by districts showed that except for Abim and Amudat, all other districts—Kotido, Moroto, Nakapiripirit, Kaabong, Napak—have wasting rates of more than 10 percent indicating that wasting is widespread and not concentrated in only one area. 94 Higher rates of stunting in male children compared to female children have been reported in other sub-Saharan African countries. See https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1865375/ 95 Uganda Bureau of Statistics (UBOS) and ICF. 2018. Uganda Demographic and Health Survey 2016. Kampala, Uganda and Rockville, Maryland, USA. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 47 Table 3.6.2: 2018 FFP DFSA Children’s Anthropometry Indicators Compared with 2016 DHS for Rural Households Area Underweight Stunting Wasting FFP CRS DFSA 27.8 35.7 11.5 FFP MC DFSA 30.3 40.5 10.8 Combined 2018 FFP DFSAs 29.0 38.1 11.2 DHS 2016, Karamoja Region 25.8 35.2 10.0 Rates of stunting, underweight, and wasting by six-month age groups are shown in Table A7.16, Annex 7. For stunting and underweight, the lowest rates are found in the first six months of a child’s life, which is typical according to the nutritional literature. The stunting rate in general tends to increase as the age of the child increases. In the CRS area, the highest stunting rate is found in children 54–59 months of age (47 percent), and in the MC area, the highest stunting rate is found in children 30–35 months of age. The prevalence of underweight fluctuates but gradually increases as age increases, with the highest rates found in children more than 54–59 months of age in the CRS area (44 percent) and 12–17 months of age in the MC area (38 percent). In the MC area, rates of wasting are highest between 12 and 17 months of age, and then gradually decrease. In the CRS area, rates of wasting remain constant and even spike after children cross 42–47 months of age. Relationship Between Child Malnutrition Indicators and Other Variables A set of variables (41 total) that may be associated with child malnutrition according to existing literature and also based on theoretical intuition was selected for correlation analyses. These variables come from a wide range of household characteristics, such as food insecurity, poverty, drinking water, cash income, education, shocks, and agricultural practices. Correlation results for stunting are presented in Table T10.14, Annex 9. Only a few variables are associated with child stunting in the CRS and MC areas. In the CRS area, households with educated members and households with access to financial services have fewer stunted children; children with diarrhea have a higher rate of stunting. In the MC area, poverty and diarrhea are positively associated with child stunting; and households accessing larger plots of agricultural land, that are able to save cash, have access to remittances, have ownership of durable assets, and have higher aspirations to adapt with shocks, as well as children consuming a MAD are less likely to have child stunting. Households participating in local community groups are less likely to have child stunting in the MC area, but no such association was found in the CRS area. To determine which variables are associated with wasting (acute malnutrition), the same set of variables considered for stunting is used in correlation models. As in the case of stunting, households with educated members and households with access to financial services are found to have lower child wasting rates in the CRS area. A number of other variables are also found to be associated with wasting in the CRS area: household wealth quintile (-), poverty (+), households saving cash (+), ownership of livestock (-), ownership of durable assets (-), exposure to external information (-), households increased livelihood strategies (-), household’s exposure to conflict (-) and children’s consumption of MAD (-). In the MC area, fewer variables are associated with wasting. Households owning durable assets and households using correct water treatment technologies are likely to have a lower prevalence of child wasting. On the other hand, households receiving humanitarian assistance in the form of emergency food or cash from the government or NGOs, and households with children with diarrhea are likely to have a higher prevalence of child wasting. In summary, household members’ education and households’ access to financial services appear to be important variables to investigate further when considering both stunting and wasting in the CRS area. There are other variables that are associated with stunting and wasting separately in the CRS area as discussed above, which should also be considered when addressing child malnutrition in that area. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 48 Households’ asset ownership and children’s exposure to diarrhea are two important variables that are consistently associated with both stunting and wasting in the MC area. These, along with other variables that are independently associated with stunting and wasting as discussed above, should be further investigated to effectively address malnutrition in the MC area. 3.6.2 Exclusive Breastfeeding Breastfeeding is an important factor in contributing to the future health of children. Research indicates a strong link between breastfeeding and the development of a child’s immune system. Breastfeeding can protect against conditions such as diarrhea that lead to other diseases and respiratory infections, such as pneumonia, and breastfeeding lowers the chances of infant mortality and morbidity (Debes et al., 2013; Khan et al., 2015; Lamberti et al., 2011). Breastfeeding has also been linked to child cognitive development (Kramer et al., 2008). UNICEF and WHO recommend that children be exclusively breastfed, that is, no other liquid or solid food or plain water, during the first six months of life and that children be given solid or semi-solid complementary food, in addition to continued breastfeeding, beginning when the child is 6 months of age and continuing to 2 years of age. Introducing breastmilk substitutes to infants before 6 months of age can contribute to limited breastfeeding, which has negative implications for a child’s health and development. The lack of appropriate complementary feeding may lead to malnutrition, frequent illnesses, and, in some cases, death. The indicator for EBF is based on the last 24 hours and is considered a proxy for long-term behavior. In the combined DFSA areas, about 7 in 10 children under 6 months of age (74 percent) are exclusively breastfed. Compared to male children, fewer female children are exclusively breastfed in the CRS area, and gender differences were not found in the MC area. Table A7.17, Annex 7 provides further details with respect to breastfeeding status. Figures 3.6.1 and 3.6.2 show the breakdown of breastfeeding status by children’s age in months in the CRS and MC areas. EBF is practiced widely in both DFSA areas, especially in the first 3 months after birth. EBF drops significantly for children 4–5 months of age in both DFSA areas as complimentary foods and other liquids are introduced. Ideally, mothers should exclusively breast feed their babies up to six months but qualitative study participants reported that because they cannot produce enough milk due to insufficient dietary intake, they often give babies four months and older semi-solid foods in addition to the little breast milk. Figure 3.6.1: Breastfeeding Status for Children 0–23 Months by Age in Months, CRS, Uganda 2018 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 49 Figure 3.6.2: Breastfeeding Status for Children 0–23 Months by Age in Months, MC, Uganda 2018 Qualitative study respondents did not express any belief to show that male children are more exclusively breastfed than female children. All children are entitled to being breastfed at any time. In Kaabong and Amudat, local communities believe that babies should not be breastfeed for prolonged periods of time because they think it increases the likelihood of acquiring HIV. “Yes, there are some beliefs that can interfere with breast feeding like; people with HIV are not supposed to breastfeed their children for long because of fear of transmitting the virus to their children,” Female household head, Kotido 3.6.3 Minimum Acceptable Diet Adequate nutrition from birth to 2 years of age is critical for a child’s optimal growth, health, and development. During this period, growth faltering, micronutrient deficiencies, and common childhood illnesses, such as diarrhea and acute respiratory infection, are likely to occur. Adequate nutrition requires a minimum dietary diversity, which is measured in seven key food groups. In addition to dietary diversity, feeding frequency—the number of times a child is fed—and the consumption of other types of milk or milk products, apart from breastmilk, are considered. All three dimensions are aggregated in the MAD indicator—which measures the percentage of children 6–23 months of age who receive a MAD— by breastfeeding status. The MAD indicator measures both the minimum feeding frequency and minimum dietary diversity as appropriate for various age groups and whether the child is breastfed, because both of these characteristics will influence how often the child should be fed and what to feed the child. If a child meets the minimum feeding frequency96 and minimum dietary diversity97 for his or her age group and breastfeeding status, the child is considered to be receiving a MAD. The baseline PBS results indicate that only 8 percent of children 6–23 months of age are receiving a MAD in the combined DFSA areas. About 7 percent of children 6–23 months of age receive a MAD in the CRS area, and 10 percent receive a MAD in the MC area (see Table 3.6.1). 96 Minimum meal frequency for breastfed children is defined as two or more feedings of solid, semi-solid, or soft food for children 6–8 months of age and three or more feedings of solid, semi-solid, or soft food for children 9–23 months of age. Minimum meal frequency for non-breastfed children is defined as four or more feedings of solid, semi-solid, or soft food, or milk feeds for children 6–23 months of age, with at least two of these feedings being milk feeds. 97 Minimum dietary diversity for breastfed children 6–23 months of age is defined as four or more food groups out of seven food groups. Minimum dietary diversity for non-breastfed children is defined as four or more food groups out of six food groups. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 50 Figures 3.6.3–3.6.4 and Table A7.18, Annex 7 present the status of minimum meal frequency and minimum dietary diversity, along with consumption of the seven food groups by children’s breastfeeding status. Among breastfed children, those meeting the minimum meal frequency requirement decrease from 51 percent to 16 percent in the CRS area and from 50 percent to 20 percent in the MC area when they age from 6–8 to 9–23 months. In the CRS area, 20 percent of breastfed children 9–23 months of age are found to consume a minimum dietary diversity, which is 7 percentage points higher compared to breastfed children 6–8 months of age. In the MC area, 24 percent of breastfed children 9–23 months of age consume a minimum dietary diversity, which is 4 percentage points higher compared to breastfed children 6–8 months of age. Consumption of a minimum dietary diversity for non-breastfed children 6– 23 months of age is about 20 percent in both the CRS and MC areas. Overall, children in both DFSA areas have low dietary diversity, similar to the poor dietary diversity of women of reproductive age. Among the seven different groups that are considered to be important for children’s dietary diversity, the most popular food items for non-breastfed children 6–23 months of age and for breastfed children 9–23 months of age in the CRS and MC areas are as follows: grains, roots, and tubers; other fruits and vegetables; and vitamin A-rich fruits and vegetables. Protein-rich foods such as eggs, flesh foods, dairy products, and legumes and nuts are least consumed by children in all age groups in both DFSA areas. Figure 3.6.3: Components of MAD by Age Group and Breastfeeding Status, CRS, Uganda 2018 Figure 3.6.4: Components of MAD by Age Group and Breastfeeding Status, MC, Uganda 2018 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 51 The qualitative study explored the different types and adequacy of typical foods provided to children age 0-5 months, 6-23 months and 24-59 months. All the participants acknowledged that there was inadequate food to feed their families including the children. The caretakers mostly buy food from the markets to feed their families, and most times it isn’t enough for the whole family. The children under 5 months were mostly being breastfed, those of 6-23 months were mainly being fed on soup of beans, sorghum porridge and juice in addition to breast-feeding, while those between 24-59 months were fed beans, greens like ‘boo’, and any other food prepared for the family that day. “A child of 0 – 5 months is given only breast milk, 6 – 23 months a child is introduced to semi solid and solid foods beginning with water, soup, porridge and Irish potatoes then a child of 24 – 59 months is given porridge and other nutritious foods to balance the diet,” Community Health Worker, Kotido district. Then from seven months on wards up to twenty-three months there are foods which they give to these children and there is a special nutrient rich food which is called “omunya” in Karamojong. They have fermented milk and they put herbs in order for it not to get spoilt or bad and it can stay up to six months.” Nutritionist, Moroto Regional Referral Hospital The nutritional officers who were interviewed cited inadequate knowledge among community members on the relevancy of diversifying the children’s diet by the mothers/caretakers as one of the contributory factors to the low dietary diversity in both DFSA areas. Many community members think that what is important for the children, is to get satisfied irrespective of what food groups they have consumed. 3.6.4 Targeted Nutrient-Rich Commodities The baseline PBS collected information on targeted locally available, nutrient-rich food items (commodities) to be promoted by the projects. Both DFSAs identified the same set of commodities to be promoted as part of their activities.98 Children’s consumption status on each of the three different commodities within the past 24 hours was collected. Table 3.6.1 provides the baseline PBS results for targeted nutrient-rich commodities for children 6–23 months of age. Only about 11 percent of children consume targeted nutrient-rich foods in the combined DFSA areas. In the CRS area, about 9 percent of children consume targeted nutrient-rich commodities, and about 14 percent do so in the MC area. Compared to female children, a higher proportion of male children consume such food items in the CRS area, and no significant sex-specific differences were observed in the MC area. Among the targeted nutrient-rich foods, foods made with bio-fortified maize or sorghum are the most commonly consumed nutrient-rich items in the CRS and MC areas. 3.6.5 Diarrhea and Oral Rehydration Therapy Dehydration as a result of severe diarrhea is a major cause of morbidity and mortality among young children. Diarrhea can lead to short-term malnutrition among children. About 32 percent of children under 5 years of age in the combined DFSA areas had diarrhea in the two weeks prior to the survey. The rates of diarrhea are almost identical in the MC and CRS areas (see Table 3.6.1). ORT is being effectively used to prevent dehydration among children; more than 83 percent of children under 5 years of age with diarrhea were treated with ORT in the combined DFSA areas (see Table 3.6.1). A set of correlation analyses were implemented to check the association between children’s diarrhea and household hygiene practices, such as open defecation, use of an improved drinking water source, and availability of soap and water at their handwashing station,99 as well as other variables, such as poverty and household members’ education (see Table 10.16, Annex 9). Children under 5 years of age in food insecure households are more likely to experience diarrhea in both DFSA areas. In contrast, 98 Both projects are promoting bio-fortified beans, bio-fortified maize or sorghum, and orange-flesh sweet potatoes. 99 These variables have been identified as some of the main causes of child diarrhea (UNICEF, 2009: https://www.unicef.org/media/files/Final_Diarrhoea_Report_October_2009_final.pdf, accessed on Dec 14, 2016) 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 52 children under 6 months of age who are exclusively breastfed are less likely to experience diarrhea in both DFSA areas. Children in households that have basic service institutions (schools and health posts) nearby are less likely to have diarrhea in the CRS area, but these children are more likely to have diarrhea in the MC area. Households’ socio-economic status, such as poverty, education, and cash earners, produce counter-intuitive associations with children’s diarrhea in the CRS area. For example, household poverty is negatively associated with diarrhea, and households with educated members and households with cash-earning adults are positively associated with diarrhea. These results indicate that there may be other underlying reasons for children’s diarrhea in the CRS area than the households’ socio-economic characteristics as reflected through these variables. Households experiencing economic shocks and conflicts are also likely to have higher rates of diarrhea among their children in the CRS area. No such association was found in the MC area. In general, relative to the CRS area, not many variables are statistically associated with children’s diarrhea in the MC area. This could mean that the drivers of diarrhea in the MC area may not be among the measured variables compared to those in the CRS area. None of the related variables, such as improved water sources, correct use of water treatment technologies, and time to collect water in 30 minutes round trip, are associated with diarrhea. Hygiene and sanitation-related variables, such as open defecation and having a handwashing station with soap and water present, did not show a significant association with diarrhea (see Table C10.16, Annex 9). This is not to suggest that these variables are not important, because they are sensitive to weather seasons, among many other things. For example, open defecation is more likely to contaminate the water sources and increase diarrheal incidence, especially in the rainy seasons.100 Community health workers interviewed for the qualitative study reported that many children under 5 years of age in their communities are affected by diarrhea because of poor hygiene among the households, lack of clean water for domestic use, open defecation, and consumption of unsafe food and water. “Because of the open defecation, flies come from the stool out on the food and causing diarrhea. Others also because of dirty compounds and utensils used at home also gives the diarrhea. Some parents are also very lazy to keep the food of their children clean leading to diarrhea also.” Community health worker, Kaabong “Many of the adults live without bathing and eat without washing their hands. So in such a situation if they cannot wash their hands after using the toilets, then they are not expected to wash the hands of the children.” Nutritionist, Moroto There were also reports that the water quality at the boreholes may be acceptable; however, the containers used for water collection and storage are usually dirty, and this could be one of the causes of diarrhea among children. “People have water, the only challenge we get in these people is a problems concerning collection containers, storage and others. These people use very dirty containers but concerning safe water, safe water is there.” Nutrition focal person, Nakapiripirit 3.7 Gender To evaluate gender equality in the DFSA areas, FFP developed two sets of cross-cutting gender indicators related to self-earned cash and maternal and child health and nutrition (MCHN) decision￾making practices. These indicators are described in this section. Because gender is a cross-cutting theme, gender dimensions are explored for other indicators as appropriate, and significant gender differences are highlighted in the indicator tables and the text throughout the Findings section of the report. 100 The baseline PBS was conducted during the rainy season when diarrhea is more frequent. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 53 3.7.1 Cash Earning and Decision-Making Many FFP DFSAs promote men’s and women’s access to income-generating opportunities and activities that allow them to earn cash. Measuring the extent to which men and women earn cash is important because cash can provide a relatively rapid pathway to women’s empowerment and gender equality. As women gain access to greater income, their household financial contribution increases, potentially resulting in increased household decision-making authority. In a variety of country contexts, women’s control over earned income, or household spending, is associated with expenditure and consumption patterns that tend to favor children, such as increased spending on health care, child care, and children’s clothing and education.101,102 In other contexts, women may be permitted to earn cash but do not have control over spending. In such instances, there is a need to promote women’s control over their income. The FFP indicators on cash earning and decision-making are presented in Table 3.7.1. Table 3.7.1: Cash Decision-Making, Uganda 2018 Indicator Combined CRS MC Percentage of men and women in union who earned cash in the past 12 monthsa 41.4 47.4 36.3 Men 41.7 47.2 37.2 Women 41.0 47.6 35.4 Percentage of women in union and earning cash who report participation in decisions about the use of self-earned cash 85.0 86.6 83.3 Percentage of women in union and earning cash who report participation in decisions about the use of spouse/partner’s self-earned cash 58.0 64.8 50.1 Percentage of men in union and earning cash who report spouse/partner participation in decisions about the use of self-earned cash 48.8 56.6 40.2 Number of men and women (age 15 or older) 7,891 3,142 4,040 Men 3,630 1,431 1,879 Women 4,261 1,711 1,161 Number of men and women in union and earning cash in the past 12 months 4,604 1,993 2,611 Men 2,169 931 1,238 Women 2,435 1,062 1,373 a Includes all household members who are 15 years of age or older, have worked in the past 12 months, and were usually paid in cash (or cash and in-kind) for this work during the 12-month period *** p<0.001, ** p<0.01, * p<0.05, + p<0.10; Reference group= Male Cash-earning opportunities for men and women 15 years of age and older who are in union (or married) are limited in the combined DFSA areas, and only about 41 percent reported working and earning cash in the past 12 months. There are no differences in cash-earning between men and women in either DFSA area (see Table 3.7.1). Among the women in a union who earned cash, 87 percent in the CRS area and 83 percent in the MC area reported participating in decisions about the use of self-earned cash, indicating that most women are involved in the decisions pertaining to their self-earned income. Nearly 65 percent of women in a union participate in decisions about the use of their spouse’s income in the CRS area, and about 50 percent do so in the MC area. Women’s interpretation of their participation in spouse’s income is also corroborated by men’s responses; about 57 percent of men in the CRS area and 40 percent of men 101 Gitter, Seth R., and Bradford L. Barham. 2008. Women's power, conditional cash transfers, and schooling in Nicaragua. The World Bank Economic Review, 22(2):271-90. 102 Quisumbing, Agnes R., and John A. Maluccio. 2000. Intra-household allocation and gender relations: New empirical evidence from four developing countries. Washington, DC: International Food Policy Research Institute. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 54 in the MC area who are in union and earn cash reported their spouse participating in decisions about the use of their own self-earned cash. Compared to women, fewer men felt that their spouse participated in the decision-making of men’s self-earned cash in both DFSA areas. This difference could be due to a difference in perceptions about the meaning of participation by men and women. Regardless, based on these three indicator estimates, women’s participation in decision-making about the use of their self-earned cash is high, and more than half participate in decisions about their spouse’s self-earned cash. Factors Associated with Cash Income To better understand the different factors that may be associated with adults’ cash-earning opportunities, a set of variables likely to be associated with cash earning is selected and used for correlation analysis. These variables include household poverty, farmers’ use of agricultural financial services, value chain activities, access to different types and sizes of agricultural land, household members’ education, access to markets, access to natural resources, livelihoods strategies, and household size. Correlation results are provided in Table 10.18, Annex 9. The correlation results show that two variables—farmers’ use of agricultural financial services and households with a higher number of livelihoods strategies—are consistently positively associated with married or in-union men and women cash earners in both DFSA areas. This means that households using agricultural financial services and households with diverse livelihoods strategies are more likely to have cash-earning couples in both the CRS and MC areas. Other variables that are positively associated with couples’ cash income in the CRS area are agricultural value chain practices and no access to land or access to less than 0.5 hectares of land. This means that households in which farmers use value chain practices and households with little or no access to land are more likely to have a cash-earning couple. In contrast, poor households are less likely to have couples who earn cash. In the MC area, households that have access to markets tend to have a cash-earning couple, and households with higher educated household members also tend to have a cash-earning couple. Households with deeper poverty as defined by the depth of poverty also tend to have fewer cash-earning couples in the MC area. Couples who earn cash tend to use fewer improved storage practices in the MC area. This might indicate a better food security situation in the households using improved storage practices, which could suggest less of a requirement for a couple to look for work. 3.7.2 Maternal and Child Health and Nutrition Decision-Making The DFSAs provide education and training to improve knowledge and skills of specific behaviors and practices, such as MCHN. The four MCHN practices that contribute to components of the knowledge of MCHN practices indicator are as follows: (1) making at least four ANC visits during pregnancy, (2) eating more nutritious foods during pregnancy, (3) initiating breastfeeding early, and (4) introducing complementary foods to children at 6 months of age. The practices in these focus areas are not intended to be comprehensive; instead they represent a selection of practices that are relevant to the 1,000-day window from pregnancy to a child’s second birthday. About 85 percent of in-union or married mothers and fathers of children under 2 years of age have knowledge of MCHN practices in the CRS area, and 76 percent of in-union or married mothers and fathers of children under 2 years of age have knowledge of MCHN practices in the MC are (see Table 3.7.2). This is also reflected through nearly the universal understanding of men and women with children under two years (97 percent in both DFSA areas) about the health benefits of waiting at least two years after the last live birth before attempting the next pregnancy. Knowledge of MCHN practices is higher among women compared to men in both DFSAs, and the gender gap of knowledge is wider in the MC area. This could be partly because women are generally more involved in daily aspects of MCHN than men. Community health workers participating in the 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 55 qualitative study reported that women are the predominant participants in the health education and awareness sessions provided in the community and health facilities which offers them a greater opportunity to acquire more knowledge on MCHN. Table 3.7.2: Maternal and Child Health Decision-Making, Uganda 2018 Gender Indicators Combined CRS MC Percentage of men and women in union with children under 2 who have knowledge of MCHN practices a 80.6 85.0 76.3 Male 70.8 79.0 63.5 Female 88.3*** 89.4** 87.1*** Percentage of men in union with children under 2 who make maternal health and nutrition decisions alone 22.4 26.1 19.0 Percentage of women in union with children under 2 who make maternal health and nutrition decisions alone 42.5*** 45.4*** 39.5*** Percentage of men in union with children under 2 who make maternal health and nutrition decisions jointly with spouse or partner 42.5 41.8 43.2 Percentage of women in union with children under 2 who make maternal health and nutrition decisions jointly with spouse or partner 34.5*** 36.3+ 32.7** Percentage of men in union with children under 2 who make child health and nutrition decisions alone 12.8 15.7 10.3 Percentage of women in union with children under 2 who make child health and nutrition decisions alone 36.8*** 39.7*** 33.8*** Percentage of men in union with children under 2 who make child health and nutrition decisions jointly with spouse or partner 49.0 51.1 47.1 Percentage of women in union with children under 2 who make child health and nutrition decisions jointly with spouse or partner 41.5*** 42.1** 41.0* Number of men and women with children under 2 1,944 821 908 Men 830 342 397 Women 1,114 479 511 Number of men/women in union with children under 2 1,431 664 767 Men 592 269 323 Women 839 395 444 Custom Indicator Percentage of target population (all men and women with children under 2 years) who can state at least one health benefit of waiting at least two years after last live birth before attempting the next pregnancy 96.8 96.8 96.9 Male 96.6 97.0 96.3 Female 97.0 96.6 97.4 a Correctly answered at least three of four MCHN questions *** p<0.001, ** p<0.01, * p<0.05, + p<0.10; Reference group=Male There is not much variation in MCHN (refers to both maternal health and nutrition [MHN] and CHN) decision-making practices across the DFSA areas (see Table 3.7.2). In both DFSAs, twice as many women in union with children under 2 years of age make MCHN decisions alone, compared to men alone. For example, about 26 percent of men and 45 percent of women in a union with children under 2 years of age reported making MHN decisions alone in the CRS area, compared to 19 percent of men and 40percent of women in the MC area. A similar gender pattern is observed for men’s and women’s decisions related to CHN, except that the proportion of men who report making CHN decisions alone is much lower, compared to those who report making MHN decisions alone. In contrast, a higher proportion of men in a union with children under 2 years of age report making joint decisions with their spouse on both MHN and CHN practices, compared to women in a union with children under 2 years of age. For example, about 42 percent of men in the CRS area and 43 percent of men in the MC area 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 56 reported making joint decisions on MHN, and only 36 percent of women in the CRS area and 33 percent of women in the MC area report making similar decisions. Similar patterns are also observed for men’s and women’s joint decision-making on issues related to CHN. The overall findings of this section can be summarized as follows: (1) less than 50 percent of men and women in a union earn cash in both the CRS and MC areas, which signifies the lack of cash-earning opportunities; (2) there are no gender differences in terms of the proportion of men and women in a union earning cash in both DFSA areas, and most women do participate in the decisions related to the use of their self-earned cash and their spouse’s cash income; (3) more women have knowledge of MCHN practices than men, and more women make MHN- and CHN-related decisions alone in both CRS and MC areas; and (4) more men than women reported making joint decisions with their spouses on MHN and CHN. 3.8 Resilience Analysis Summary Resilience and resilience capacity are both key concepts on which the resilience analyses are based. It is thus important to understand what each concept is and, importantly, the distinction between them. According to the USAID definition, resilience is “the ability of people, households, communities, countries, and systems to mitigate, adapt to, and recover from shocks and stresses in a manner that reduces chronic vulnerability and facilitates inclusive growth.” Based on this definition, household resilience is the ability of a household to mitigate, adapt to, and recover from shocks and stresses. Resilience itself is an ability to manage or recover from shocks, and resilience capacities are a set of conditions that are thought to enable households to achieve resilience in the face of shocks. At the household level, resilience capacities can be classified into three categories:  Absorptive capacity is the ability to minimize exposure to shocks and stresses (ex-ante) where possible and to recover quickly when exposed (ex-post).  Adaptive capacity involves making proactive and informed choices about alternative livelihood strategies based on changing conditions.  Transformative capacity relates to governance mechanisms, policies and regulations, infrastructure, community networks, and formal safety nets that are part of the wider system in which households and communities are embedded. Transformative capacity refers to system-level changes that enable more lasting resilience. Given their complexity, measuring the resilience capacities requires combining a variety of indicators of the underlying concepts relevant to a particular setting into one overall indicator. Details on the measurement of absorptive, adaptive, and transformative capacities are described in detail in Annex 10. Key findings from the preliminary quantitative analysis are summarized in this section. The mean values of the resilience capacities (absorptive, adaptive, and transformative) and their individual components for the baseline survey are provided, disaggregated by DFSA area. Key Findings Households across both DFSA areas experienced an average of 5.0 shocks over the past 12 months, as shown in Table 3.8.1. The shock experienced most by households was excessive rains (81.8 percent), followed by flooding (59.5 percent), drought (54.4 percent), increasing food prices (49.1 percent), and crop diseases (45.5 percent). The perceived severity of these shocks is measured by their impact on food consumption and income security at the household level (index ranges from 1–144). The mean impact across both DFSA areas is 27.4. The absorptive capacity index (ranging from 0–100) is constructed using 10 variables and measures the ability of households to minimize their exposure to shocks through preventive measures and appropriate coping strategies to avoid permanent, negative impacts of shocks. The mean absorptive 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 57 capacity across the two DFSA areas is 23.2.103 Of the components, a small percentage of households have access to agricultural insurance, remittances, and humanitarian assistance. Households also report low bonding social capital (defined as the bonds between community or group members), limited ability to prepare for and mitigate the effects of shocks, and relatively low availability of informal safety nets. Asset ownership is also relatively low across the productive, livestock, and durable goods categories. In contrast, one-quarter of households have access to cash savings. In comparing the DFSA areas, the CRS area has the greatest availability of humanitarian assistance, agricultural insurance, and informal safety nets, as well as the greatest bonding social capital and assets (durable and productive). This may account for CRS having a higher level of absorptive capacity at 25.0. Adaptive capacity measures the ability of households to make proactive and informed choices about alternative livelihood strategies based on an understanding of changing conditions. It contains 11 variables.104 With an index ranging from 0–100, the mean adaptive capacity across the two DFSA areas is 37.0, with CRS having the greatest value at 38.1, as shown in Table 3.8.1. Livelihood diversification indicates that households were engaged in an average of 3.1 income-earning activities over the last year. Most households were engaged in farming/crop production and sales (67.0 percent), followed by agricultural wage labor (46.1 percent), and selling of wild/bush products such as charcoal and firewood (40.9 percent). Of the other components, households have low scores for bridging and linking social capital, which measure the bonds between members of other communities or groups and the bonds between households and government officials or NGO leaders. In contrast, households tend to score higher on the aspirations index (which measures the extent to which household members consider that they have some direct control over achieving their aspirations—and are not subject only to fate) and access to financial institutions for credit and/or savings. Finally, nearly all households adopted improved agricultural practices. Table 3.8.1: Resilience Capacity Indexes and Their Components, by DFSA Area, Uganda 2018 Resilience Capacities and Components Overall CRS MC Recovery from most salient shocks a Excessive rain (%) 5.7 3.4 8.4 n 1860 943 917 Flooding (%) 7.4 4.7 11.3 n 1437 801 636 Drought (%) 10.8 12.3 8.7 n 1177 622 555 Increase food price (%) 8.5 10.1 6.2 n 1193 632 561 Crop disease (%) 3.5 2.2 4.6 n 1078 443 635 103 Given the way in which the resilience capacity indexes are computed, it is not meaningful to compare the values across indexes to conclude, for example, that the transformative capacity is “higher” than absorptive capacity because the index value is higher. A high value (greater than 50) means that the distribution of the index is skewed to the right—that there are a higher proportion of the sample with index values above the mean value than below the mean. An overall value below 50 means that a greater proportion of sample values are below the mean. However, values can be compared across project areas. 104 Note that some indicators are components of more than one index (e.g., asset scores are a component of both the absorptive and adaptive capacity indexes). 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 58 Resilience Capacities and Components Overall CRS MC Shock Exposure/Severity Shock exposure index (mean, 0–18) 5.0 5.5 4.4 n 2,799 1,251 1,548 Cumulative impact of shock exposure (mean, 1–144) 27.4 31.9 23.3 n 2,726 1,226 1,500 Absorptive Capacity Index (mean, 0–100) 23.2 25.0 23.2 Availability of informal safety nets (mean, 0–6) 2.0 2.1 1.9 Bonding social capital (mean, 0–6) 2.3 2.4 2.3 Access to cash savings (%) 24.2 23.6 23.5 Access to remittances (%) 9.2 9.2 9.2 Asset ownership Productive asset index (mean, 0–24) 4.9 5.0 4.9 Livestock asset index (mean, 0–7) 1.4 0.8 1.7 Durable goods asset index (mean, 0–22) 2.4 2.5 2.2 Shock preparation and mitigation (mean, 0–4) 0.6 0.7 0.5 Access to agricultural insurance (%) 2.0 3.7 1.0 Availability of humanitarian assistance (%) 8.6 11.5 7.1 Adaptive Capacity Index (mean, 0–100) b 37.0 38.1 36.1 Aspirations/confidence to adapt (mean, 0–16) 10.7 10.8 10.6 Bridging social capital (mean, 0–6) 2.2 2.4 2.2 Linking social capital (mean, 0–4) 0.9 1.0 0.9 Education/training (mean, 0–3) 0.7 0.8 0.6 Livelihood diversification (mean, 0–17) 3.1 3.1 3.0 Exposure to information (mean, 0–19) 4.7 5.4 4.4 Adoption of improved practices (%) 81.1 84.8 79.6 Access to financial institutions (mean, 0–2) 1.2 1.2 1.3 Transformative Capacity Index (mean, 0–100) c 37.2 40.9 33.9 Availability of formal safety nets (mean, 0–3) 0.1 0.1 0.0 Availability of markets (mean, 0–3) 1.7 1.8 1.6 Access to communal natural resources (mean, 0–4) 1.9 1.7 2.0 Access to basic services (mean, 0–3) 1.8 1.7 1.9 Access to infrastructure (mean, 0–4) 1.1 1.2 0.8 Access to ag extension services (%) 25.7 31.3 16.4 Access to livestock services (%) 44.3 53.4 31.8 Collective action (mean, 0–10) 0.3 0.3 0.2 Local government responsiveness (%) 87.2 82.8 91.1 Participation in local decision-making (%) 54.4 50.9 53.6 n 2,779 1,251 1,548 a Only for those households who experienced and were impacted by a shock in the last 12 months. b The asset ownership index is not shown under adaptive capacity because it is listed under absorptive capacity. c Bridging social capital is not shown under transformative capacity because it is listed under adaptive capacity. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 59 The transformative capacity index comprises 12 variables that measure governance mechanisms, policies, infrastructure, community networks, and formal and informal social protections that enable systemic change.105 The mean value for the transformative capacity index across the DFSA areas is 37.2 (on a scale of 0–100). Data from Table 3.8.1 show that communities have little access to formal safety nets and infrastructure; however, nearly all communities report high local government responsiveness, half of communities have access to agricultural extension services, and one-quarter of communities have access to livestock services. Interestingly, households report low levels of engagement in collective activities that benefit the village, but a high percentage report participation in local decision￾making. Annex 10 provides a more detailed analysis of the relationships among resilience capacities and household well-being outcomes (per capita expenditures, poverty, household dietary diversity, ability to recover), and adoption of coping strategies. The key findings of this analysis are as follows:  Households with higher levels of absorptive, adaptive, and transformative capacity achieve better economic (higher per capita expenditures and lower likelihood of poverty) and food security outcomes. Adaptive capacity, however, is the only significant predictor for recovery across the five most salient shocks, whereas greater absorptive capacity is associated exclusively with recovery from excessive rain, flooding and drought.  Specific components of resilience capacity that are associated with increased expenditures, lower prevalence of poverty, and greater dietary diversity are cash savings, durable assets, livestock assets, human capital (education/training), exposure to information, access to agricultural extension services, shock preparedness and mitigation, access to infrastructure, aspirations/confidence to adapt, and participation in local decision-making.  With respect to the types of coping strategies adopted, households with higher levels of resilience capacity are most likely to get food on credit, use money from savings, and take out a loan from MFI or village savings groups. These findings suggest some implications with respect to programming to enhance resilience. First, traditional economic development activities that are directed to increase household income and wealth—increasing human capital, promotion of value chains, and investment in infrastructure—are also means to enhance household and community resilience capacities. The importance of savings on household economic status and dietary diversity suggests that supporting savings and loans groups and other mechanisms to promote savings can have important impacts on resilience. Strategies that promote bonding and linking social capital formation, for example, through savings and loans groups and other community-based or collective organizations, can also be beneficial. Reduction in poverty can also be supported by investments in shock preparedness and mitigation efforts. 105 Note that some indicators are components of more than one index (e.g., bridging social capital is a component of both the adaptive and transformative capacity indexes). 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 60 4. CONCLUSIONS This section provides concluding comments and preliminary recommendations by sector. 4.1 Food Security and Poverty A majority of people in the DFSA areas are food insecure due to lack of financial resources. This reality is reflected in the poverty indicator: nearly 9 in every 10 individuals are below the international extreme poverty line. Poverty is not only widespread, it is also deep; on average, poor individuals consume 61 percent less than the daily per capita poverty threshold of $1.90 (PPP 2011). Bivariate analysis showed a positive association between food insecurity and poverty in the MC area. The other food security indicator, HDDS, is very low (3 in the score of 1–12), indicating a poor socio-economic situation of the households in general. As expected, the HDDS is inversely associated with poverty and depth of poverty in both the CRS and MC areas, suggesting that the higher and deeper the poverty, the lower the HDDS. Households with a tertiary-level educated family member are less likely to be poor in both the CRS and MC areas. Households in which farmers use at least one of the agricultural financial services (credit, savings, or agricultural insurance) tend to be less poor in both DFSA areas. Households’ food insecurity is associated with access to communal natural resources and higher livelihoods diversification in both DFSA areas, but negatively in the CRS area and positively in the MC area. The counterintuitive positive associations in the MC area may indicate a higher concentration of food insecure households near the communal natural resources and their greater need to diversify livelihoods due to poverty or other socio-economic situations. Households with higher durable assets ownership, participation in local decision-making bodies, and higher education of their members tend to have higher HDDS in both DFSA areas. A number of other factors are associated with HDDS, poverty, and food insecurity, but not consistently across the DFSA areas, which suggests that each DFSA area has unique drivers of food security and poverty. The factors that are associated with these three outcome indicators for each DFSA as presented in Section 3 are worth considering for further research and while designing the interventions. 4.2 Agriculture Sustainable Agricultural Practices and Value Chain Practices Slightly less than half of farmers practice the project-defined minimum number of sustainable agricultural practices (crop, livestock, or NRM) in the CRS area, and only about one out of seven farmers practice project-defined practices in the MC area.106 About one in three farmers in the CRS are and one in four farmers in the MC area practice the value chain activities promoted by the project. Both of these practices are used more by male farmers compared to female farmers, although female farmers are generally more involved in day-to-day agricultural activities, as observed through the qualitative data. About 43 percent of farmers in the CRS area and 34 percent of farmers in the MC area plant crops or raise and buy livestock with the specific intention to sell or resell to earn income. The low uptake of value chain practices could indicate poor return on farmers’ investment and lack of efficiency in farming. This could also indicate the lack of awareness among the farmers, lack of markets, and the need for agriculture extension offices to focus more on trade-oriented farming activities. All of these are missed opportunities to improve farmers’ economic well-being because bivariate analyses indicated that households using improved sustainable agricultural practices and practicing value chain activities are less likely to be poor in both DFSA areas. 106 Project-defined minimum number is three for CRS is three and five for MC. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 61 Use of Agricultural Financial Services About 22 percent of farmers in the CRS area and 19 percent of farmers in the MC area used any kind of agricultural financial services (credit, savings, or agricultural insurance). Easy access to and use of credit services are considered critically important for economic development in general, but less than 13 percent of farmers in the CRS area and only about 8 percent of farmers in the MC area used agricultural credit services. Farmers who used any type of financial services were also likely to practice value chain activities and sustainable agricultural practices in both DFSA areas. Farmers using financial services tend to be less poor in both DFSA areas and less food insecure in the CRS area. Use of Improved Storage Practices About half of farmers use at least one improved storage practice in the combined DFSA areas. Non￾short-term perishable crops—sorghum, maize, and legumes—are the most commonly produced crop items in both areas, and proper storage of crops can help households meet their food needs, especially during the agricultural lean period, and can also potentially help them engage in some of the value chain activities (such as drying, processing, and trading or marketing). Bivariate analysis indicated that households using improved storage practices are less likely to be poor in the CRS area. Most farmers (more than 73 percent) in both DFSA areas own the agricultural land that they make decisions over. Among those who have access to land, about 38 percent in the CRS area and 32 percent in the MC area have less than 0.5 hectares. Fewer female farmers compared to male farmers tend to own the land, but in the MC area, more female farmers tend to sharecrop the land, compared to male farmers. Farmers who have no land or less than 0.5 hectares of land are more likely to be poor in the CRS area, and the poverty seems to be deeper among such farmers in the MC area. 4.3 Water, Sanitation, and Hygiene Drinking Water About 41 percent of households in the combined DFSA areas use improved drinking water sources. About 48 percent of households in the CRS area and 43 percent of households in the MC area are able to obtain drinking water in less than 30 minutes. On the whole, the availability of water, regardless of whether the source is improved or not, is a challenge in the DFSA areas. The use of recommended water treatment technologies is fairly low in both areas (8 percent in the CRS area and 12 percent in the MC area), and this could be due to the fact that most households use a borehole or tube well and consider this a safe drinking water source. Sanitation and Hygiene Less than 7 percent of households in the CRS area and about 10 percent of households in the MC area use basic sanitation facilities. Fewer adult female-only households, compared to adult male and female households, have basic sanitation facilities in the MC area, but no such difference was observed in the CRS area. A large percentage of households (more than 64 percent) practice open defecation in both DFSA areas, and about 4 percent of households in the CRS area and 3 percent of households in the MC area have a handwashing station with soap and water available. Poor households are more likely to use open defecation and less likely to have basic sanitation facilities. Interestingly, despite what the existing literature suggests, household members’ education is not associated with open defecation in either of the DFSA areas. 4.4 Maternal Health and Nutrition Nutrition About 39 percent of non-pregnant women between 15 and 49 years of age are underweight in the CRS area, and 31 percent of non-pregnant women between 15 and 49 years of age are underweight in the MC area. About 13 percent of women 15-49 years of age in the CRS area and 20 percent in the MC 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 62 area consume a MDD-W, defined as consuming 5 of 10 nutritionally diverse food groups. Women mostly consumed grains, roots, and tubers, and other vegetables in both DFSA areas. Few women consume eggs and nuts and seeds, and the consumption of dairy and flesh foods is low in both DFSA areas, and particularly low in the CRS area. Few women (about 7 percent in the CRS area and 11 percent in the MC area) consume targeted nutrient-rich commodities (bio-fortified beans, bio-fortified maize or sorghum, and orange-flesh sweet potatoes). Women in poor households tend to consume less diverse foods, and households that consume less diverse foods tend to have less diverse foods for women as well in both DFSA areas. In addition, women in households with diverse livelihoods strategies and exposure to external information tend to have more diverse diets in the CRS area, and women in households with diverse livelihoods strategies and educated household members tend to have more diverse diets in the MC area. Health Care A majority of women (about 78 percent) who had a live birth in the past five years attended at least four ANC visits with a skilled health worker during their pregnancy. Despite a high uptake of ANC services, a small proportion of women (11 percent in the CRS area and 7 percent in the MC area) use modern contraceptive methods in both DFSA areas. Overall, about 1 in 7 women use any kind of contraceptive method. Poor use of modern contraception can lead to large household sizes, which could result in inadequate maternal and child health care and overall economic well-being. Women in poor households are less likely to use modern contraceptive methods in both DFSA areas. Women in educated households, however, tend to use modern contraceptive methods, regardless of household poverty status. Women in households that have linkages to government officials or NGOs are more likely to use modern contraceptives in the MC area, and women in households that participate in local community groups are more likely to use modern contraceptives in the CRS area. Overall, education, exposure to and interaction with external stakeholders such as government officials and NGOs and local community groups should be considered while designing and implementing activities targeted to promote women’s health care and health services. 4.5 Children’s Health and Nutrition About 28 percent of children under 5 years of age are underweight in the CRS area, and about 30 percent are underweight in the MC area. About 36 percent of children under 5 years of age are stunted in the CRS area, and about 41 percent are stunted in the MC area. About 12 percent of children under 5 years of age are wasted in the CRS area, and about 11 percent are wasted in the MC area. More male than female children are underweight and stunted in both areas. Because wasting is more than 10 percent, both DFSA areas have serious acute malnutrition, and immediate services should be delivered to save the critically undernourished children. Less than 1 in every 10 children 6–23 months of age (9 percent) in the combined DFSA areas receives a MAD. A majority (about 74 percent) of the children under 6 months of age are exclusively breastfed in the combined DFSA areas. EBF begins to decrease as early as 2–3 months, and by 4–5 months only about 38 percent of children in the CRS area and 46 percent of children in the MC area are exclusively breastfed. In addition, the introduction of complementary foods begins before the UNICEF/WHO￾recommended age of 6 months and as early as 2 months. Close to one-third (30 percent) of children under 5 years of age in the combined DFSA areas experienced diarrhea in the two weeks preceding the survey, but most of them (84 percent) were treated with ORT. Bivariate analyses suggests that children in households in which there are educated household members are less likely to be stunted. Children in households that have access to financial services are less likely to be stunted and wasted in the CRS area. Households that own durable assets tend to have less stunted and wasted children in the MC area. Children who are exposed to diarrhea tend to be more wasted in the MC area. Based on the results presented in this report, CHN is clearly a cross-cutting issue and is associated with several factors, such as education, poverty, and WASH practices. 2018 Baseline Study of Food of Peace Development Food Security Activities in Uganda 63 4.6 Gender and Household Decision-Making About 47 percent of men and women in a union reported earning cash in the past 12 months in the CRS area, and 36 percent of men and women in union reported earning cash in the past 12 months in the MC area. Adults in a union who are in households that use agricultural financial services and have diverse livelihoods strategies are more likely to have men and women in a union that earn cash in both DFSA areas. No gender differences were found between men and women in cash earning.107 A majority of women (87 percent in the CRS area, 83 percent in the MC area) reported participating in decision-making related to their self-earned cash in the DFSA areas. Women’s participation in decision￾making related to their spouse’s cash is relatively high; 65 percent of women in the CRS area and 50 percent of women in the MC area reported participating in such decisions. This is also corroborated by men; about 57 percent of men in the CRS area and about 40 percent of men in the MC area reported their spouses’ participation in the decision-making of their self-earned cash income. About 85 percent of adults in a union have MCHN knowledge in the CRS area, and 76 percent of adults in a union have MCHN knowledge in the MC area. More women reported having MCHN knowledge than men in both areas, because women are more involved in domestic work, including taking care of children. More women make MCHN decisions alone compared to men in both DFSA areas. Gender inequalities come into play when men and women make joint decisions on MCHN; more men than women reporting making such decisions, despite more women having the MCHN knowledge. 4.7 Shocks and Resilience Households were exposed to an average of five types of shocks in the year prior to the survey. The shock experienced most by households was excessive rains (81.8 percent), followed by flooding (59.5 percent), drought (54.4 percent), increasing food prices (49.1 percent), and crop diseases (45.5 percent). A majority of resilience capacity indicators and sub-indicators are low in both DFSA areas. For example, households’ absorptive capacity score is 25 in the CRS area and 23 in the MC area (out of 0–100). The adaptive capacity index is 38 in the CRS area and 36 in the MC area (out of 0–100), and transformative capacity is 41 in the CRS area and 34 in the MC area (out of 0–100). Analyses suggest that households with higher levels of absorptive, adaptive, and transformative capacities tend to have better economic (higher per capita expenditures and a lower likelihood of poverty) and food security outcomes. Some specific components of resilience capacities are associated with increased expenditures, lower prevalence of poverty, and greater dietary diversity. These components are cash savings, durable assets, livestock assets, human capital (education/training), exposure to information, access to agricultural extension services, shock preparedness and mitigation, access to infrastructure, aspirations/confidence to adapt, and participation in local decision-making. 107 Men and women have approximately the same share of the workforce in agriculture in Uganda. See: UNDP-Uganda Country Gender Assessment, October, 2015. Available at: http://www.ug.undp.org/content/dam/uganda/docs/UNDPUg2016%20- UNDP%20Uganda%20-%20Country%20Gender%20Assessment.pdf Annex 1: Uganda Baseline Study Statement of Work Statement of Work Population-Based Baseline Study of Food for Peace Development Food Security Activities in Uganda Introduction A. General FFP uses a population-based survey for its baseline and endline studies, with the baseline study being the first step in a two-part evaluation process and the final evaluation with an endline survey as the second step. Since 2012, FFP has externally contracted an independent firm to conduct mixed-method PBS. FFP requires a representative PBS to collect data for required impact and outcome indicators for the FFP activity implementation areas. The 2017 PBS comprises 11 modules that explore household food security, nutrition and health, WASH, agriculture, poverty, gender and resilience. There is also a module on anthropometry. The study will also include a purposively sampled qualitative study to capture insights and community perceptions about practices and behaviors, as well as information on risks and resilience capacities. It is preferred that the quantitative survey and qualitative research are conducted sequentially, allowing the quantitative results to help inform qualitative inquiry. Ideally, the baseline study occurs during the hunger season and before the partners have begun significant implementation. Given that the lean season often coincides with the rainy season in many countries, rain may make access to certain areas of data collection difficult. The second part of the evaluation process, the performance evaluation, will be conducted near the time of DFSA expiration, with the endline PBS conducted as close as possible to the timeline of the baseline PBS. B. Overview of 2017 development food security activities (DFSA) The Office of Food for Peace (FFP) uses Title II and/or community development funds to support multi-year development food security activities (DFSA) around the world that improve and sustain the food and nutrition security of vulnerable populations. DFSAs implement activities across technical sectors, layering and sequencing interventions at both the individual and household levels. FFP’s partners improve food access and incomes through agriculture and other livelihoods initiatives; enhance natural resource and environment management; combat under nutrition, especially for children under two and pregnant and lactating women; and mitigate disaster impact through early warning and community preparedness activities. Development activities are intended to build resilience in populations vulnerable to chronic hunger and repeated hunger crises, and to reduce their future need for ongoing or emergency food assistance. FFP’s efforts are increasingly integrated with other USAID efforts to promote resilience and reduce Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 2 of 18 extreme poverty, and the DFSAs support the President’s Feed the Future initiative (FTF), as well as the Global Food Security Strategy (GFSS). Based on analyses of key indicators of food security; nutritional and health status; educational and water and sanitation deficiencies; historical need for humanitarian assistance; and other considerations, FFP selected the seven districts of Karamoja, Uganda as the geographic focus for multi-year assistance beginning in 2017. Catholic Relief Services (CRS) and Mercy Corps (MC) each received DFSA awards, with a combined value of approximately $75 million over five-years, to improve food and nutrition security and economic well-being in the sub-region. CRS and its consortium implement the DFSA Nuyok in the districts Abim, Nakapirit, and Napak. Over the LOA, Nuyok will reach 355,121 people through its efforts in governance, resilience, livelihoods, nutrition and water, sanitation and hygiene (WASH). MC and its consortium implement the DFSA Apolou in Kotido, Kaabong, Morotu and Amudat. Over the LOA, Apolou will reach 310,000 Ugandans through governance, nutrition, WASH and livelihoods activities. C. FFP Approach to PBS in Uganda The approach to the household survey in 2018 will be unique for three reasons. First, the opportunity exists to combine the baseline survey of the new DFSAs with the endline survey for the prior development food security projects (DFAPs). Second, indicator estimates will be calculated at both population and beneficiary levels. Third, poverty estimates will be calculated using the standard DHS format, as well as using the Poverty Probability Index (PPI). FFP has determined that combining the baseline/endline surveys will increase efficiency in data collection, minimize the time burden for respondents, and is financially prudent. In 2013, ICF International carried out a PBS on the Growth, Health, and Governance (GHG) project in northern Karamoja implemented by MC, and the Resiliency through Wealth, Agriculture, and Nutrition (RWANU) project in southern Karamoja implemented by ACDI/VOCA. The geographic overlap between the DFAPs (GHG and RWANU) and the 2017 DFSAs (Nuyok and Apolou) is very high, and as the DFAPs expired in 2017, and the DFSAs are just starting up, the endline survey and baseline survey can occur simultaneously. Therefore, the timeline for the 2018 baseline/endline PBS must align with the timeline of the baseline survey conducted in 2013; data collection must commence as close to mid-February 2018 as possible1. In addition to understanding changes in population-level estimates, FFP seeks to learn about changes in beneficiary-level estimates between baseline and endline. The same household survey will be used to achieve both ends. FFP determined that this will not increase the sample size 1 Baseline Study for the Title II Development Food Assistance Programs Uganda https://www.usaid.gov/sites/default/files/documents/1866/Uganda%20Baseline%20Study%20Report%2 C%20March%202014.pdf. Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 3 of 18 because majority of the people2 in Karamoja is potential project participants or eligible to be a direct project participant. With the goal of minimizing the length of future surveys and the corresponding time burden of survey respondents, poverty estimates will be calculated in the traditional manner, as well as using the PPI. FFP will instruct the firm on data to collect and share with an external contractor to facilitate such calculation. 2018 Uganda PBS A. Purpose and Objectives The Uganda PBS has the following purposes: 1. Provide baseline estimates for population-level impact, outcome, and resilience capacity indicators for the 2017 awards to serve as a point of comparison for a final evaluation; 2. Provide endline estimates and analyze endline survey data to assess the performance of RWANU and GHG3; 3. Provide baseline estimates for direct beneficiary-level impact, outcome, and resilience capacity indicators/indices to serve as a point of comparison for a final evaluation4; and 4. Provide evidence to refine DFSA interventions (where appropriate). The specific objectives of the baseline study are the following: 1. Determine baseline values of key impact, outcome, and resilience capacity indicators/indices stratified by awardee and disaggregated by sex and gendered household type as appropriate in implementation areas. In addition to baseline values, provide demographic information and household composition data from sampled households; 2. Conduct bivariate and multivariate analyses for select impact, outcome, and resilience capacity indicators/indices for the overall area of FFP’s investments and for each award’s implementation area (the plan for bivariate and multivariate analyses should be presented in the data analyses plan); and 3. Interpret data and present an analysis of food insecurity; resilience capacities and social capital; disaster risks and risk management strategies; perceptions about key practices and behaviors; and quality of and access to community infrastructure, extension services, organizations, systems, and safety nets, in order to provide DFSA partners with an 2 Approximately 94 percent of the people are extremely poor, living below $1.90/day, and 76 percent of the households have at least one child age 5 years or below – the two key eligibility criteria to participate in the project 3 The SoW for the final evaluation provides more information about both the specific objectives of the endline survey, as well as the analytical requirements for the endline survey data. 4 FFP and the IPs will compare baseline/endline beneficiary-level estimates to understand the changes in outcomes among direct beneficiaries. Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 4 of 18 understanding of how well their proposed theories of change align – or don’t – with the demonstrated needs of their target populations. While the firm will design and lead the study and report on findings, staff from FFP, the USAID Mission in each country, implementing partners, and the Food and Nutrition Technical Assistance III (FANTA) Project5 will provide input and be involved during all stages. The firm must consult FFP/Uganda and FFP awardees to better understand the country’s food security context, the DFSA approaches, strategies, and theories of change, and properly develop data collection, sampling, and logistics plans. In discussion and coordination with FFP, the firm will provide draft and final versions of specific deliverables to the awardees for review and information. B. Indicators for Collection The firm will be responsible for collecting data on the following 42 FFP indicators: ▪ Prevalence of moderate or severe food insecurity in the households (FIES) ▪ Average Household Dietary Diversity Score (HDDS) ▪ Prevalence of Poverty: Percent of people living on less than $1.90/day ▪ Depth of Poverty: The mean percent shortfall relative to the $1.90 poverty line ▪ Daily per capita expenditures (as a proxy for income) USG-assisted areas ▪ Prevalence of underweight children under five years of age ▪ Prevalence of wasted children under five years of age ▪ Prevalence of stunted children under five years of age ▪ Percentage of children 6-23 months of age receiving a minimum acceptable diet (MAD) ▪ Prevalence of exclusive breastfeeding of children under six months of age ▪ Prevalence of children 6-23 months who consume targeted nutrient-rich value chain and/or non- value chain commodities ▪ Prevalence of underweight women of reproductive age ▪ Prevalence of women of reproductive age consuming a diet of minimum dietary diversity ▪ Prevalence of women of reproductive age who consume targeted nutrient-rich value chain and/or non- value chain commodities ▪ Percentage of births receiving at least 4 antenatal care (ANC) visits during pregnancy ▪ Contraceptive Prevalence Rate (CPR) ▪ Percentage of households using basic drinking water services ▪ Percentage of households using a basic sanitation facility ▪ Percentage of households with soap and water at a handwashing station commonly used by family members ▪ Percentage of households in target areas practicing correct use of recommended household water treatment technologies 5 The Food and Nutrition Technical Assistance III (FANTA) Project provides technical support to FFP on monitoring and evaluation. Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 5 of 18 ▪ Percentage of households that can obtain drinking water in less than 30 minutes (round trip) ▪ Percentage of households in target areas practicing open defecation ▪ Percentage of children under age five who had diarrhea in the prior two weeks ▪ Percent of children under five years old with diarrhea treated with Oral Rehydration Therapy (ORT) ▪ Percentage of men and women who earned cash in the past 12 months ▪ Percentage of men/women in union and earning cash who make decisions alone about the use of self-earned cash ▪ Percentage of men/women in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash ▪ Percentage of men and women with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices ▪ Percentage of men/women in union with children under two who make maternal health and nutrition decisions alone ▪ Percentage of men/women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner ▪ Percentage of men/women in union with children under two who make child health and nutrition decisions alone ▪ Percentage of men/women in union with children under two who make child health and nutrition decisions jointly with spouse/partner ▪ Percentage of farmers who used at least [a project-defined minimum number of] sustainable agriculture (crop, livestock, and/or NRM) practices and/or technologies in the past 12 months ▪ Percentage of farmers who used improved storage techniques in the past 12 months ▪ Percentage of farmers who used financial services (savings, agricultural credit, and/or agricultural insurance) in the past 12 months ▪ Percentage of farmers who practiced the value chain activities promoted by the project in the past 12 months ▪ Absorptive Capacity Index (resilience): o Bonding social capital o Shock preparedness and mitigation o Access to informal safety nets o Availability of hazard insurance o Household ability to recover from shocks ▪ Adaptive Capacity Index (resilience): o Human capital o Bonding social capital o Linking social capital o Exposure to information o Diversity of livelihoods o Access to financial resources o Asset ownership o Aspirations and confidence to adapt Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 6 of 18 ▪ Transformative Capacity Index (resilience): o Access to formal safety nets o Availability of communication networks o Access to markets o Access to infrastructure o Access to basic services o Access to communal natural resources o Access to livestock services o Bridging social capital o Linking social capital ▪ Shock Exposure Index ▪ Cumulative Impact of Shock Exposure Index Note: The list of indicators for each resilience capacity index are illustrative. Data collected on poverty estimates will be shared with an external contractor who will be tasked with conducting poverty estimates using the PPI. In advance of data collection, it will be necessary to contextualize the resilience module to the Karamoja context. The objectives of the contextualization are to: a) identify the shocks and stressors that have the greatest impact on the food security and well-being of most vulnerable individuals, households and communities and/or occur with the greatest frequency, b) identify the capacities that are critical to absorb and adapt to the impact of the shocks and stressors and fill critical gaps in the existing knowledge on resilience along three dimensions: absorptive, adaptive, and transformative capacities, c) determine the minimum set of indicators needed to develop absorptive, adaptive and transformative capacity indices that will be integrated into the baseline survey, and d) to develop appropriate questions that will allow for these indicators and indices to be collected and calculated. Note: an effort should be made to reduce the number of indicators in the indices for each of these capacity measures. Most of the indicators listed above conform to FFP’s September 2017 list of indicators. Performance indicator reference sheets are available in the April 2015 “FFP Indicators Handbook - Part I: Indicators for Baseline and Final Evaluation Surveys,” which should be consulted for definitions, collection methodology, and analysis of the indicators listed above.6 The firm may need to refer to the source documents used to develop the indicator reference sheets for instructions on adapting questionnaires to the local context, and other important details on data collection and tabulation. As the indicators comprising the resilience indices may vary by country, FFP requests that the firm provide the Uganda specific PIRS for resilience indices with the report. In addition to FFP’s standard indicators, the firm may be asked to collect data for a limited number of additional Mission and project-specific (custom) indicators selected by each awardee. 6 The FFP Indicators Handbook can be found at http://www.usaid.gov/what-we-do/agriculture-and-food￾security/food-assistance/guidance/implementation-and-reporting. Several indicators are currently being updated to align with State indicators and the Global Food Security Strategy, and the firm will be apprised of changes to PIRS and the addition of any new indicators on a rolling basis - with the understanding that new questions cannot be added to the household survey following the completion of the second phase of programming (post-pilot). Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 7 of 18 The firm will work closely with the awardees to refine the PIRS, and also develop questionnaires and tabulation instructions for any additional project-specific indicators not specified in the FFP Indicators Handbook. The final list of indicators to be collected must be discussed and agreed upon in consultation with FFP, the USAID Mission in each country, and FFP awardees. Note that this list may evolve in step with USAID’s engagement in the Global Food Security Strategy and other US- or global initiatives. Baseline Study Design and Methodology The baseline study will consist of the following data collection activities: a representative population-based household survey7 and purposively-sampled qualitative research. A. Representative, Population-based Quantitative Survey The firm must design and execute all aspects of a representative, population-based household survey. These include developing a sampling plan, questionnaires, and field procedure manuals for enumerators and supervisors; training enumerators, supervisors, and anthropometrists to administer the questionnaires and take anthropometric measures; piloting and refining questionnaires; arranging logistics for field work; pre-testing the survey rollout; supervising data collection; and ensuring data entry, cleaning, tabulation, and analyses. The PBS must be designed to generalize the results to both the DFAPs – RWANU and GHG – and the DFSAs - Nuyok, and Apolou - target areas. The design should take into account the designs of the 2013 baseline survey as well as the baseline survey for the newly awarded activities. Discrepancies between the two surveys’ sampling frame and questions should be identified and discussed with FFP. Sampling Plan: The firm must consult the FANTA Sampling Guide (1997)8 and Addendum (2012)9 to develop the sampling strategy and estimate sample size. The former provides an overview of design features recommended for FFP baseline and final evaluation surveys, while the 2012 Addendum provides important corrections to the guide, which the firm must follow closely. The sampling frame for the survey is the population living in geographic areas where the development projects will be implemented, i.e., the FFP implementation areas. The firm must use a multi-stage cluster design with each awardee’s implementation area representing a stratum in the design. FFP requires that the final evaluation for most projects— which will be implemented between four and five years after the baseline study—be a performance 7 The BBS will derive from the same questions and survey as the PBS. 8 Although the FANTA Sampling Guide presents random walk as an acceptable sampling method, it is no longer considered acceptable and will not be accepted as a proposed second-stage method. 9 The FANTA Sampling Guide and Addendum can be found at http://www.fantaproject.org/publications/sampling.shtml. Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 8 of 18 evaluation, rather than an impact evaluation. This implies that a simple pre-post design without control groups or randomization will be used at baseline and final evaluation. Since the PBS will be designed to generalize the results to the target communities of RWANU and GHG and the target communities of Nuyok and Apolou, the sampling design must take into account the 2013 sampling frame used for the baseline, the communities in which RWANU and GHG projects were actually implemented, the list of communities in which Nuyok and Apolou will be implemented, and the geographical overlaps between RWANU and GHG implementation communities and Nuyok and Apolou target communities. Even though the firm will be designing and implementing one PBS, to ensure adequacy and required precision, sample sizes must be estimated separately using parameters from the 2013 baseline and parameters for the 2017 baseline. The sample size to produce beneficiary level estimates requires using a different set of parameters, and must consider both the targeting criteria of the two awards and the level of poverty in Karamoja. However, FFP has determined that the sample size for PBS will have enough potential direct beneficiaries to produce beneficiary level estimates with the desired level of precision. Therefore, the firm does not have to make any additional adjustments to its sample size estimation. The firm must submit a sampling design protocol before beginning the survey. The sampling design protocol must include all of the following: • A base sample size at both the awardee and country overall levels. FFP requires that the firm use the equation for proportions presented in the FANTA Sampling Guide and Addendum to estimate sample size. When estimating the sample size for the PBS, the firm should use the prevalence of stunting and the following parameters: o 95 percent confidence level for one tailed test; o 80 percent power; o 6 percentage point reduction to estimate the sample size for the endline; o 8 percentage point reduction to estimate the sample size for the baseline; and o Design Effect of 2. When estimating the sample size for direct-beneficiary level estimates, the firm should use o 95 percent confidence level for one tailed test; o 80 percent power; o 10 percentage point reduction in the prevalence of stunting; and o Design Effect of 2. • A description of how the base sample sizes for target groups of individuals are translated to the number of households that must be visited to ensure that the desired number of target individuals is reached, considering that: o Households may contain more than one or no eligible members from the target group. See the FANTA 2012 Addendum for more details. Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 9 of 18 o Some households will not respond. The firm must indicate by how much the number of households to be sampled will be pre-inflated to account for household non￾response.10 • An explanation on the source of the information for the sampling frame, e.g., census lists or national or internationally sponsored surveys, such as the Demographic Health Surveys (DHS). the firm must indicate the date and perceived reliability of this information. • Geographic or other stratification. At a minimum, the sample will be stratified by awardee in countries where multiple awards are made. Additional strata are not required but may be considered. Note that estimates must be produced at both the awardee and combined FFP country project level. However, when additional stratification is included in the design, sample sizes do not have to allow for precise estimates at the level of the lower strata. • The number of stages of sampling to be used. • An explanation of how the numbers of clusters and households per cluster in the sample will be determined. • The Probability Proportionate to Size (PPS), PPS systematic or another appropriate first￾stage sampling mechanism must be used to randomly select the clusters. • An indication that the firm will use systematic sampling (or another probability-based sampling technique such as Simple Random Sampling) to select dwellings within clusters. This implies that a list of all households, with household and location identifiers, must be obtained for the sampled clusters through either a mapping and listing operation in the cluster prior to interviewing (preferred), or through other existing reliable sources. • An indication of how the firm will treat cases where there are multiple households per dwelling. the firm must include an explanation of how the Census office defines households and how polygamous households will be treated. • An indication that the firm will adopt a “take-all-individuals” approach to collect data for all individuals within sampled households. The firm must undertake a dwelling listing exercise of all sampled clusters within all the project implementation areas, unless recent high-quality cluster level frames already exist and are readily available. The firm must collect geographic information system (GIS) data using GPS equipment to locate dwellings during the listing process. During the household surveys, the firm must use GPS units to capture the precise longitude and latitude of the households. Questionnaire: The firm must develop a questionnaire in English and ensure accurate translation to key local languages in the areas in which the survey will be conducted and back translation from the local languages to English. The questionnaire must incorporate all modules specified in the “FFP Indicators Handbook” (referenced above) that are relevant to the FFP 10 To account for household non-response, the firm must pre-inflate the sample size by 5 percent or the DHS non-response rate in rural areas, whichever is higher. Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 10 of 18 development projects in the country and USAID’s needs. Some of the modules associated with various FFP indicators, such as HDDS, MAD, TNRVCC for women and children, poverty, resilience and agriculture, will require country-specific adaptation, which must be done in consultation with FFP, the USAID Missions, and the awardees. Note that questionnaire modules for most FFP indicators have been developed and are readily available.11 The questionnaire must include an informed consent statement for each respondent and must begin with questions to establish a household roster. The informed consent should note that their identity will not be released publicly, and only shared for professional and learning purposes. The questions within the questionnaire must be organized by respondent type12 and questions must follow international standard format, e.g., DHS, wherever possible. In general, the firm must ensure that questions follow established questionnaire design principles and that rigorous practices are used to collect, tabulate, and analyze indicator data. The firm must ensure that the questionnaire is piloted and validated in communities not included in the sample frame prior to commencement of data collection. Unless otherwise infeasible, FFP requires the use an electronic data collection instrument, e.g., tablets. If paper-based data collection is proposed, the firm must provide a solid justification. If paper-based data collection is used, each page and module of the questionnaire(s) must include sufficient identifiers, such as cluster number, household number, and respondent identification number (line number from household roster). This will help ensure that pages related to a given individual or household can be correctly associated and enable the derivation of household-level sampling weights and household non-response adjustments in all data analyses. Field Procedure Manuals for Enumerators and Supervisors: The firm will develop two field manuals as part of the training materials and as reference material for field staff conducting the survey: one for enumerators, and one for supervisors of enumerators. The enumerator field manual must explain in detail how to properly administer each question in the questionnaire, recommend best practices for conducting interviews, and suggest ways to handle challenging situations. The supervisor field manual may contain some of the same material as the enumerator manual, but must also describe the roles and responsibilities of field staff and outline the chronology of field work, including training, piloting of questionnaires, pre-testing of the survey, field protocol, and data collection. The manual must include instructions for mapping and listing clusters; adding identifiers to dwellings, households, and individuals; using GPS equipment; sampling dwellings within clusters, households within dwellings (if applicable), individuals within households; 11 Questionnaires used in recent baseline studies of FFP development projects conducted in Bangladesh and Mali are available in the final study reports, which can be found at USAID’s Development Experience Clearinghouse (DEC) (dec.usaid.gov). 12 Note that a respondent is an individual or set of individual(s) identified as most appropriate to respond to a set of questions on behalf of a specific target group. Such respondents can be the actual sampled members of the target group themselves (e.g., adults providing direct responses on behalf of themselves) or can be individuals not part of the target group providing proxy responses on behalf of sampled individuals in the target group (e.g., caregivers on behalf of young children). Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 11 of 18 monitoring enumerators for quality assurance; procedures for editing questionnaires and re￾interviewing; and any administrative and logistics responsibilities. If using a paper-based questionnaire, instructions for editors must be included in one of the two manuals or in a separate manual. Note that field procedure manuals and training materials developed for the baseline studies of FFP development projects recently conducted in DRC and Ethiopia are available to the firm. Anthropometry: Prior to quantitative data collection, the firm will ensure that an anthropometry specialist properly trains and conducts standardization testing of the anthropometrists who will be taking the measurements required for the stunting and underweight indicators. The anthropometry expert must provide oversight and feedback to the anthropometrists in the field during a portion or the entire period of data collection. The firm will provide a short guide and/or other materials to support this training. The training will include instructions on how to take measurements on height/length and weight for women and children under five years of age, citing a reference for the methodology to be used. It will also include instructions on developing methods, e.g., event calendars, which will be used to ascertain the age of individuals whose measurements are being taken. The training must include standardization testing of trainees’ measurements, including assessments of each individual trainee’s precision and accuracy of each type of measure. A standard for performance must be established in agreement with FFP, and trainees who are unable to reach high performance standards must not be included in the survey teams. Finally, the training must include procedures for the anthropometrists to identify children suffering from wasting or exhibiting bilateral pitted edema and make referrals to appropriate health clinics. The firm will provide the equipment necessary for accurate anthropometric measures (height/length and weight) of women and children under five years of age. The equipment must be portable and of sufficient durability to withstand the survey process and difficult terrain conditions. Data Treatment and Analysis Plan: The firm will prepare a data treatment and analysis plan to address the following elements: • Indication of how and when data will be entered into the database. (If the firm uses a paper-based questionnaire, double-data entry is required); • Descriptions of data quality checks that will be built into the data entry processes; of tests and edits (data cleaning) planned to ensure logical consistency and coherence within and across records; and of manipulations, conditional tests, and combinations of data to create new variables. A data dictionary must include definitions of all the variables created from the raw data that describe how they were derived; • Sampling weights to be applied in the separate analyses of each awardee’s implementation area and the aggregate data base. The formulae used to calculate the sampling weights must be included as part of a data dictionary document. A household non-response adjustment must be made to the base sampling weights as part of the final weighting system. In addition, there must be a separate adjustment to compensate Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 12 of 18 for individual non-response for all different target groups, e.g., children 0-5 months, children 6-23 months, and children 24-59 months; • Indicator tabulation plan. Estimates must be produced for all indicators including resilience indicators/indices for each awardee stratum and for the overall level; • Descriptions of sub-groups, e.g., age, sex or other geographic breakdowns, if any, for which the firm will produce estimates, with an indication of the associated precision levels; • Planned data analyses. The firm must specify all methods of intended bivariate and multivariate analyses including an overview of model definition for key indicators including resilience indicators/indices. • Computed Design Effect of the survey based on data from field work; • Confidence intervals associated with the indicator estimates that takes into account the design effect; • Identification of the software to be used for all steps of data entry, cleaning, and analysis (either STATA or SPSS are recommended), including the software used to convert anthropometric data into Z-scores (WHO’s Anthro is recommended but not mandatory); • Specification of how location data will be stored to protect personally identifiable information in accordance with the research protocol submitted to the Institutional Review Board (IRB). Note that the firm will be responsible for adhering to and obtaining all necessary US and host country IRB approvals; and • Specification of how the datasets will be anonymized to protect individual confidentiality, given that these datasets will be available to the public through the USAID Open Data warehouse. The firm will ensure that the labeling and architecture of all datasets is consistent to facilitate meta-analyses of datasets across FFP development projects and countries at a later date. FFP will provide the firm specific details about the requested architecture of the datasets. To the extent possible, the firm must follow the same database architecture used for baseline studies conducted in Guatemala, Niger, and Uganda in FY 2013, and in Haiti and Zimbabwe in FY 2014. The meta￾analysis is not part of this SOW. In addition, the firm will provide a report indicating the average completion time for each of the survey modules in each of the countries upon completion of the household surveys. B. Qualitative Research The firm must design the qualitative study as an integral part of a mixed-method baseline study13 that will address a mutually agreed upon topic (e.g. implementation areas’ food insecurity context; 13 Similar to the joint PBS, the qualitative component of the baseline study will draw upon data gathered through the qualitative component of the performance evaluations. This approach necessarily requires close Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 13 of 18 disaster risks; risk-management strategies; perceptions about key practices and behaviors; and quality of and access to community infrastructures, extension services, organizations, systems, and safety nets). The study will use key informant interviews and focus groups discussions to answer identified questions. The firm must submit for approval a proposed plan of inquiry prior to beginning the study. The information from the interviews and discussions will be reviewed and incorporated into the baseline study. It is FFP’s preference that the qualitative study follows the quantitative survey to allow qualitative research to be informed by the quantitative results. However, qualitative research can be implemented concurrently with the quantitative household survey. The qualitative study described in this SOW is not expected to replace any in-depth qualitative assessments or formative research that FFP awardees may conduct to inform specific aspects of project design. The firm is responsible for the design and execution of the qualitative study, including methods and questions to understand the context and community perceptions about key practices and behaviors. The firm must submit for approval a proposed plan of inquiry prior to beginning data collection. Approximate numbers and types of informants who will be addressed to answer each study question, ways in which they will be engaged, e.g., individual or group interviews, focus groups, etc., and how they will be selected; Plans for triangulation to verify the information received from informants; Description of the purposeful sampling approach for selecting the number and locations of sites for interviews, discussions, and observation; Timeline for data collection, and analysis. Sample site selection: The firm must indicate the site selection methodology for the qualitative interviews and group discussions, which is not required to be a probability sampling strategy. Analysis plan: The firm must prepare an analysis plan indicating how the qualitative data will be analyzed and integrated with the quantitative data. The plan is to be submitted along with the proposed plan of inquiry. Baseline Study Deliverables and Report Outline A. Deliverables collaboration with FFP, but also the evaluation team. Additional detail on the qualitative methodology and expectations is presented in the Uganda evaluation SoW. Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 14 of 18 Deliverable Timeline Work Plan • includes a brief synthesis and timeline for the Uganda baseline study, with the timeline including major activities throughout the study, including dates by which field guides and training materials will be completed. Only one work plan detailing both baseline study and final evaluation activities is required TBD PBS Data Treatment and Analysis Plan • details how the data will be cleaned, weighted, and analyzed and must include: programming specifications and editing rules for cleaning data, data dictionary codebook, SPSS syntax and output for all analyses and variable transformations into indicators; and • includes a descriptive, inferential, and econometric analyses plan. Only one DTAP that serves both the baseline study and final evaluation is required, but it must clearly differentiate between the different analytical approaches used for each. TBD PBS Protocol (a combined protocol that discusses both baseline/endline surveys.) • identifies indicators to be collected; • introduces the local partner (data collection firm); • discusses the quantitative analysis methods and plan; • includes sampling frame; • presents PBS sample size, design and plan, survey design, questionnaire design; and • presents the fieldwork plan (including trainings and field support/supervision, data management, quality control, recording, analysis and reporting). TBD Pertinent Permissions and approvals • demonstrate official approval from all relevant institutional review boards and from host country institutions to collect data, conduct the evaluation, and release data and reports, as required, as well as a statement affirming adherence to all requirements specified in USAID’s Scientific Research Policy. TBD PBS Quantitative Survey Instrument • comprises both English and Ngakarimojong versions of the combined baseline/endline household survey (note: the instrument must be back-translated to English via a second translator to ensure accurate translation. Following the pilot of the survey, any modifications based on field experience will again require translation and back translation to ensure accuracy); TBD [preliminary estimates submitted in advance to support qualitative research] Qualitative Sites and Key Questions • Describe site selection methodology and factors used to select sample communities for qualitative interviews and discussions; TBD Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 15 of 18 • list sample communities; • discusses groups to be interviewed; and • explain criteria used to select respondents. Draft Baseline Study • will be no more than 50 pages; • includes as annexes o tables for FFP indicator estimates; and o resilience analysis. October 2018 In-country briefings to USAID/Uganda and FFP stakeholders in Uganda • Two 60-minute presentations of the major findings of the baseline study to provide an opportunity for immediate stakeholder feedback that can be considered for the revision (as appropriate and without compromising the validity or independence of the evaluation): o One presentation to USAID/Uganda; o One presentation to FFP stakeholders in Uganda, including the DFSA partners, donors, and Government of Uganda (invited by USAID/Uganda and the partners), via a 60-minute PowerPoint presentation • One utilization workshop to USAID/Uganda and DFSA partners to discuss baseline indicator estimates, applicability of findings to activity design, and document conclusions/lessons learned November 2018 Briefing to FFP/Washington • comprises a 60-minute PowerPoint presentation on the baseline study, major findings, lessons learned, and recommendations November 2018 Final Baseline Study Report • includes items identified in the draft report, additional annexes, as well as a three- to five-page executive summary of the purpose, background of the activities, methods, findings, conclusions and recommendations, and the following annexes: the scope of work, tools used in conducting the baseline study (questionnaires, checklists, and discussion guides), and any substantially dissenting views by any Team member, USAID or the PVOs on any of the findings or recommendations; and • must be 508 compliant and uploaded to the Development Clearinghouse following COR approval December 2018 Data (to be submitted at the time of the final report*) • include a separate electronic file of all quantitative data in an easily readable format that is organized and fully documented so as to facilitate use by those not fully familiar with the project or the evaluation; • provides cleaned data, sampling weights at each stage, final sampling weights, and all derived indicators; • includes a second final data set in CSV format that has been anonymized to protect individual confidentiality for use as a public data file in the USAID Open Data; and December 2018 Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 16 of 18 • include a separate file detailing GPS coordinates of households that participated in the PBS. *FFP may request data sets earlier for internal use only B. Outline of Baseline Study Report The recommended outline for each country’s baseline study report, which must not exceed 50 pages (excluding the annexes), is the following: Cover page, Table of Contents, List of Acronyms; Executive Summary must be a clear and concise stand-alone document that states the most salient findings, conclusions, and recommendations of the study and gives readers the essential contents of the baseline report in three to five pages. The Executive Summary helps readers to build a mental framework for organizing and understanding the detailed information within the report; Introduction must include purpose, audience, and synopsis of task; Methodology and Study Design must describe the methodology and design of the household survey and qualitative component, constraints and limitations to the study process and rigor, and issues in carrying out the study; Overview of the Current Food Security Situation must provide a brief overview of the current food security situation in each country related to food availability, access, and utilization; current and anticipated programs funded by other USAID offices and donors; and stakeholders. A desk review of information already available will suffice; Tabular summary of quantitative survey results must present findings of the household survey in table form for all the indicators by awardee and for the aggregate FFP development project area in each country. Results of bivariate analysis undertaken must also be included; Resilience analysis: While a summary of key findings should be presented in the main body of the report, the detailed analysis with interpretation should be attached as Annex. Findings must present results from the household quantitative survey and qualitative study. Results must be analyzed and discussed, using findings from the qualitative and quantitative investigations in a complementary fashion. In addition, the findings from the community questionnaires must be analyzed and presented. The source of each finding must be clearly identified; Conclusions must provide high-level conclusions about the food security situation, vulnerabilities, and capacities of the population and sub-groups, and contextual, cultural, and individual factors that influence the current situation. All conclusions must be based solidly on the presented findings. If information from other sources is used to reach these conclusions, references must be provided, and reference documents or Internet links to these included. Limitations must provide a list of key technical and/or administrative limitations, if any, that the FFP projects should consider; and Annexes must document the following and be succinct, pertinent, and readable: a. Resilience Analysis b. Baseline study SOW; Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 17 of 18 c. Quantitative survey instruments in English and applicable local languages; d. Sampling Plan for the PBS surveys; e. Data Treatment and Analysis Plan for the quantitative survey and analysis plan for qualitative research; f. Results of bivariate and multivariate analysis undertaken; g. Quantitative data sets and qualitative data transcripts in electronic format; h. Data dictionary and project files used to process the data in electronic format; i. Qualitative study methodology and questions used; j. Expanded details of qualitative findings, including photos, as appropriate; k. List of data collection activities, including meetings, interviews, focus group discussions, observations, and other activities with dates and numbers of participants; l. References, including bibliographical documentation; and m. Other special documentation identified as necessary or useful. Team Composition and Qualifications For planning purposes, the team for this study will consist of key personnel with defined technical expertise, a mix of consultants with extensive experience in household survey, anthropometric measurements, qualitative research, and support staff. The expertise for PBS needed includes design, implementation, supervision, strategies to minimize non-sampling errors14, and execution of population-based household surveys; analysis of complex survey data. The expertise for qualitative research would include training on and experience in qualitative inquiry/research, hands on experience in in-depth interviews using semi structured tools, group and focus group discussions, key informant interviews, transect walk, and other interactive qualitative tools, understand non probability sampling, deep knowledge about various sources of bias, and analyzing and interpreting qualitative information. Please refer to the SOW in the RFTOP for more information on key personnel. Baseline Study Management and Logistics FFP will provide overall direction to the firm, identify key documents, and assist in facilitating a work plan. Staff from FFP in Washington and the USAID Missions in Uganda will assist in arranging meetings with key stakeholders as identified by USAID prior to the initiation of fieldwork. The firm is responsible for arranging other meetings as identified during the course of this study and advising FFP prior to each of those meetings. The firm is also responsible for making all logistical and administrative arrangements, such as vehicle rental and drivers as needed for site visits and fieldwork, lodging, work/office space, computers, Internet access, printing, and photocopying. The firm will be required to make its own payments. Staff from FFP, FANTA, and the USAID Missions in Uganda will be made available to the team for consultations regarding sampling, geographical 14 Spot check, randomly check the GPS code to ensure that the enumerator interviewed the right households, check the interview time, re-interview a subsample of the households, review the data on a daily basis, perform data consistency checks, and review skip pattern. Statement of Work for Baseline Study of FY 2017 FFP Development Food Security Activities April 27, 2018 Page 18 of 18 targeting, sources, and technical issues before and during the baseline study process. It is strongly recommended that the firm consult with the Mission on reputable car-rental firms. Intellectual Property USAID shall, solely and exclusively, own all rights in and to any work created in connection with this agreement, including all data, documents, information, copyrights, patents, trademarks, trade secrets or other proprietary rights in and to the work. The firm is not allowed to withhold any information related to this agreement, as this will become public information. Annex 2: Uganda Joint Baseline/Endline Population-Based Survey Protocol Uganda Joint Baseline/Endline Population-Based Survey Protocol - FINAL Office of Food for Peace (FFP) Contract #: GS-00F-189CA/7200AA18M00002 June 28, 2018 This publication was produced for review by the U.S. Agency for International Development. It was prepared by ICF Macro, Inc. Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace i Abbreviations BL Baseline CAPI Computer-assisted personal interview CRS Catholic Relief Services DFAP Development food assistance program DHS Demographic and Health Survey DFSA Development food security activity EL Endline FANTA Food and Nutrition Technical Assistance Project III FFP Office of Food for Peace FIES Food insecurity experience scale GHT Gendered household type HDDS Household dietary diversity score IFSS Internet file streaming system IP Implementing partner IPA Innovations for Poverty Action MCHN Maternal and child health and nutrition MC Mercy Corps PBS Population-based survey PPI Poverty probability index PPS Probability proportional to size USAID U.S. Agency for International Development WASH Water, sanitation, and hygiene Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace ii Table of Contents 1. BACKGROUND AND PURPOSE ......................................................................................................................1 2. DESIGN 2 2.1 Indicators to be Measured...........................................................................................................................2 2.2 Sampling Plan...................................................................................................................................................2 2.2.1 Sampling Frame.......................................................................................................................................2 2.2.2 Sample Size ..............................................................................................................................................3 2.2.3 Sample Selection.....................................................................................................................................5 2.3 Questionnaire...................................................................................................................................................7 3. FIELD PROCEDURES ..........................................................................................................................................7 3.1 Data Collection Mode...................................................................................................................................7 3.2 Field Manuals....................................................................................................................................................7 3.3 Training...............................................................................................................................................................8 3.4 Data Collection..............................................................................................................................................10 3.5 Quality Control..............................................................................................................................................10 4. DATA PROCESSING AND ANALYSIS ............................................................................................................11 4.1 Data Transmissions.......................................................................................................................................11 4.2 Data Analysis ...................................................................................................................................................11 4.3 Dissemination of Findings .........................................................................................................................12 5. ETHICAL CONSIDERATIONS .........................................................................................................................12 5.1 Ethics approval...............................................................................................................................................12 5.2 Verbal Informed Consent..........................................................................................................................12 6. TIMELINE ............................................................................................................................................................13 Annex 1 – Joint BL/EL PBS Indicators Annex 2 – Training Agendas Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 1 1. BACKGROUND AND PURPOSE USAID’s Office of Food for Peace (FFP) is the U.S. Government leader in international food assistance. In 2018, FFP awarded ICF International a contract to conduct a joint baseline (BL)/endline (EL) population-based household survey (PBS) in the Karamoja region of Uganda. The BL survey is for two newly funded development food security activities (DFSAs), beginning in fiscal year (FY) 2017 which will be implemented by Catholic Relief Services (CRS) and Mercy Corps (MC) in seven districts of Karamoja. The EL survey is for two development food assistance programs (DFAPs) that were implemented by ACDI VOCA and MC in the same seven districts of Karamoja. These programs started in FY 2012 and ended in FY 2017. The implementation areas for the new DFSAs and prior DFAPs overlap to a large extent. For this reason, ICF will administer a joint BL/EL PBS using a common questionnaire in the overlap and non￾overlap areas encompassed by the prior DFAPs and current DFSAs. The common questionnaire will be driven by the indicators required for the baseline PBS for the new DFSAs, many of which (but not all) overlap with those required for the DFAPs. The purpose of the BL PBS for the DFSAs is to assess the current status of key indicators, to serve as a point of comparison with indicators collected at EL and to have a better understanding of the prevailing conditions and perceptions of the populations in the DFSA implementation areas. The study results will also be used to further refine program targeting and, where possible, to understand the relationship between variables to inform program design. The results of the EL PBS for the prior DFAPs will be used to evaluate change over time in the indicators that were collected at BL as part of a performance evaluation of these DFAPs. The fieldwork for the joint BL/EL PBS will be conducted in June-July 2018. ICF has subcontracted the International Research Consortium of Uganda (IRC), a local data collection firm, to support the field implementation of the joint BL/EL PBS. ICF will work closely with the survey subcontractor in the implementation of the PBS. All deliverables will be approved by ICF and the USAID COR. Implementing partners and their roles From 2012-2017, two implementing partners (IPs) conducted the prior DFAPs in the Karamoja region: (1) ACDI/VOCA and its partners implemented the Resiliency through Wealth, Agriculture and Nutrition in Karamoja (RWANU) Program in Amudat, Moroto, Napak and Nakapiripirit districts. (2) MC and its partners implemented the Growth Health and Governance Program (GHG) Program in Abim, Kotido and Kaabong districts. In 2017, FFP awarded two new DFSAs in the Karamoja region: (1) MC and its partners will implement the Apolou DFSA in Kaabong, Kotido, Moroto, and Amudat districts. (2) CRS and its partners will implement the Nuyok DFSA in Abim, Nakapiripirit and Napak districts. Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 2 2. DESIGN The BL component of the joint BL/EL PBS serves as the first phase of a pre-post survey cycle for the new DFSA awards, and the EL component of the joint BL/EL PBS serves as the second phase of a pre￾post survey cycle for the DFAP awards. The BL survey for the DFAPs was conducted from late February to end of April of 2013. The pre-post design (using the 2013 BL PBS and the 2018 EL component of the joint BL/EL PBS) allows for the determination of statistically significant change in indicators; however, it does not allow statements to be made about attribution or causation relating to program impact. 2.1 Indicators to be Measured The PBS will collect data to measure key FFP impact and outcome indicators, resilience indicators and custom indicators developed by the IPs. The FFP indicators are related to food security: poverty; water, sanitation, and hygiene practices; agricultural practices; women’s and children’s health and nutritional status (including anthropometry); and gender. A definition and full description of each of the key FFP indicators is available in the FFP Indicator Handbook (April, 2015). 1 FFP resilience indicators measure household well-being, exposure to shocks, resilience capacities (absorptive, adaptive, and transformative), and responses. Definitions for resilience and custom indicators are provided in the Uganda PBS Data Treatment and Analysis Plan (DTAP). The full list of FFP, resilience and custom indicators is provided in Annex 1. 2.2 Sampling Plan 2.2.1 Sampling Frame The target population for the joint BL/EL PBS consists of two components: 1) all households in the areas where the prior DFAPs were implemented and 2) all households in the areas where the new DFSAs will be implemented. These target populations overlap since the new DFSAs will be implemented in most of the same areas where the prior DFAPs were implemented. The sampling frames for the BL and EL PBSs were constructed taking into account these overlapping geographies. ICF used the list of target areas provided by the IPs and the most recent census data for constructing the sampling frame. The most recent Ugandan Census was conducted in 2014 by the Uganda Bureau of Statistics. The census administrative geographic levels are as follows:  District  County  Sub-County  Parish  Village  Enumeration Area (EA) Table 2 provides 2014 census estimates of the number of EAs and households included in the BL and EL sampling frame for each program. 1 Food and Nutrition Technical Assistance III Project (FANTA III). 2015. FFP Indicators Handbook Part I: Indicators for Baseline and Final Evaluation Surveys. April 2015. Washington, DC. A newer version of the FFP Indicators Handbook is pending release in 2018. Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 3 Table 2. EAs and Households Included in the Baseline and Endline Sampling Frames Number of EAs Number of Households Rwanu Endline (ACDI/VOCA) 652 49,540 GHG Endline (MC) 740 48,903 Apolou Baseline (MC) 974 68,263 Nuyok Baseline (CRS) 831 54,741 Note: The 2012 Rwanu DFAP included the southern districts of Amudat, Moroto, Nakapiripirit and Napak, while the 2012 GHG DFAP included the northern districts of Abim, Kaabong and Kotido. The districts were divided differently for the new DFSAs. The Nuyok DFSA included the districts of Abim, Nakapiripirit and Napak, while the Apolou DFSA included the districts of Amudat, Kaabong, Kotido and Moroto. 2.2.2 Sample Size The sample size for the joint BL/EL PBS was derived by: 1) calculating the sample size needed for the BL survey for the DFSAs (assuming a statistical test of differences will be implemented at EL five years hence), 2) calculating the sample size needed for the EL survey for the prior DFAPs (assuming a statistical test of differences will be implemented at EL relative to the BL five years earlier), and 3) deriving a joint sample size based on these sample sizes taking into account the overlap between the DFSA and prior DFAP implementation areas. 1) BL Sample Size for the DFSAs The sample size calculation for the BL project areas for the 2017 DFSAs is based on adequately powering a statistical test of differences in the prevalence of stunting because stunting is a key measure of food insecurity. The following criteria were used for deriving the sample size:  Design effect of 2.0  Confidence level of 95 percent  Power level of 80 percent  Expected reduction in stunting over the life of the project of 8.0 percentage points  Use of the Stukel/Deitchler inflation and deflation factors (see Addendum to the Feed the Future Population-Based Survey Sampling Guide2) to determine the number of households needed for the required sample of children under five years of age  5 percent inflation of the household sample size to adjust for estimated household nonresponse The formula used for deriving the sample size is based on a statistical test of the difference of proportions (or prevalence) for an indicator (e.g., from BL to EL), as described in Appendix A of the Feed the Future Population-Based Survey Sampling Guide. Table 3 provides the target sample size derived using estimates from the 2011 Uganda Demographic and Health Survey (DHS) for the two input parameters to the sample size calculation: 1) prevalence of stunting in rural households, and 2) number of children per household. The sample size calculation yields a total of 82 clusters (with 30 households per cluster) and 2,460 households (41 clusters and 1,230 households for each of the two DFSAs). 2 Stukel, Diana, Feed the Future Population-Based Survey Sampling Guide (2018), Washington, DC, FHI 360/ FANTA. Available at: https://www.fantaproject.org/monitoring-and-evaluation/sampling Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 4 Table 3. BL PBS Sample Size for each DFSA and Overall Indicator Prevalence of Stunting (P1)* Number of Children per Household* Number of Children Needed Household Sample Size Needed** Households Needed with 5 Percent Nonresponse Adjustment Clusters per DFSA Overall Sample Size of Households Needed Prevalence of stunting 0.36 0.98 829 1,157 1,230 41 2,460 *Source: 2011 DHS estimates for rural households (where estimated household size is given as 5.05 and estimated proportion of population under five years of age is given as 0.192) **Includes Stukel/Deitchler inflation and deflation factor adjustments Assumptions for all calculations: one-sided test, alpha=0.05, beta=0.80, households per cluster=30 2) EL Sample Size for the DFAPs The sample size for the EL survey was calculated based on the number of children needed to detect a 6 percent reduction in stunting3 over the life of the program cycle (between BL and EL) in the prior DFAP implementation areas. The BL sample size for the prior DFAPs was 2,400 households in each of the two program areas4 and valid height and weight measurements were obtained for a total of 5,335 children under five years of age across both programs. The prevalence of stunting at baseline was 34.5 percent and the design effect for the prevalence of stunting was 4.0.5 Using these parameters, the number of children needed at endline is 1,975 across both programs. Inflating to the household level yields a sample size of 2,440 households across both programs (assuming an average household size of 6.3 persons, 19.4 percent of the population is comprised of children under five years of age6 and a 5 percent nonresponse rate). 3) Sample Size for the Joint BL/EL PBS The joint BL/EL sample size was derived by first identifying the number of households in each of 3 areas: 1) the overlapping area between the prior DFAPs and the new DFSAs, 2) those in the prior DFAP area only (old), and 3) those in the DFSA area only (new). Then, the sample size was proportionately allocated among the districts in these three groups relevant to the particular survey in question (BL versus EL), and based on the proportion of households in each group. To elaborate, this allocation was done separately for the prior DFAPs yielding an EL sample size of 1,220 by allocating to “old” and “overlap” EAs, and for the new DFSAs yielding a BL sample size of 1,230 by allocating to “new” and “overlap” EAs. As a result, there were two sample sizes for the “overlap” EAs that had to be simultaneously satisfied – one in relation to the prior DFAPs and one in relation to the new DFSAs. The overall joint BL/EL sample size requirement was then calculated based on: 1) the sample size requirement for the prior DFAPs only; 2) the sample size requirement for the new DFSAs only; and 3) 3 A 6 percentage point reduction in stunting was used here (rather than the 8 percentage point reduction referred to in the paragraph on BL Sample Size for DFSAs) to be consistent with the percentage point drop posited at BL for the DFAPs. 4 A detailed description of the sampling for the baseline study for the prior DFAPs can be found in Appendix 2 of the “Baseline Study of Title II Development Food Assistance Programs in Uganda” Report, March 2014. Available at: https://www.usaid.gov/opengov/developer/datasets/UgandaBaselineReport-March2014.pdf 5 These parameters were obtained from the 2013 FFP baseline survey in Uganda. 6 Ibid. Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 5 the maximum of the sample size requirement for the overlapping prior DFAP and DFSA implementation areas (see Table 4). This resulted in a total of 56 clusters and 1,680 households per BL DFSA. Table 4. Sampled Households and Clusters by BL DFSA Implementation Area Project Sample Size Requirement (Prior DFAP/EL) Sample Size Requirement (New DFSA/BL) Joint BL/EL Sample Size Requirement Number of Clusters CRS DFSA 1,220 1,230 1,680 56 Overlap 939 916 990 33 Old 281 0 330 11 New 0 314 360 12 MC DFSA 1,220 1,230 1,680 56 Overlap 1,220 820 1,230 41 Old 0 0 0 0 New 0 410 450 15 TOTAL 2,440 2,460 3,360 112 2.2.3 Sample Selection The sample for each project was selected using stratified multi-stage cluster sampling with three stages of sampling: (1) selection of clusters (EAs), (2) selection of households, and (3) selection of individuals. First stage sampling of clusters Within each project, stratification is done at the district level and the number of EAs to be sampled are proportionately allocated at the district level based on the distribution of households across all districts. EAs are then selected from the sampling frame for each project using probability proportional to size sampling (PPS). The total number of EAs sampled for each project for the joint BL/EL PBS is based on the joint sample size requirement for each project as a proportion of the overall joint sample size requirement (Table 5).7 Table 5. EAs and Households Included in the BL and EL Samples Number of EAs Number of Households Rwanu Endline (ACDI/VOCA) 43 1,290 GHG Endline (MC) 42 1,260 Apolou Baseline (MC) 56 1,680 Nuyok Baseline (CRS) 45 1,350 7 The first stage of sampling included two phases due to a recalculation of the sample size after the listing exercise was completed. The original calculation of the sample size did not take into account the number of children with valid height and weight measurements obtained at baseline and was therefore substantially larger than the sample size after adjustment for these children. The first phase of sampling included selection of 95 EAs per project for a total sample size of 2,850 households – this was based on the original sample size calculations. The listing exercise took place in these 95 EAs for each project. The adjusted sample size required 56 EAs per project and these 56 EAs were allocated as described in step 3 above and randomly selected from the previously selected EAs. Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 6 Second stage sampling of households: At the second stage of sampling, 30 households are randomly selected per cluster using systematic sampling. Before the selection of households can take place, a listing exercise is conducted to identify and count each household in the cluster. GPS coordinates are taken for each cluster and the name of the head of household is recorded for each household. For the purposes of the household survey a household is defined as follows: A person or group of people who live together and share meals (“eating from the same pot”). This is not the same as a family. A family includes people who are related, but a household includes any people who live together, whether or not they are related. For example, three unrelated men who live and cook meals together would not be considered one family, but they would be considered one household. For men with more than one wife (polygamous situations), households will be treated in accordance with the below definition: If the wives live in the same homestead (dwelling structures and adjoining land occupied by family members) and also share the same eating arrangements, they will be treated as the same household. But if the wives live independently and do not share the same eating arrangements they will be treated as separate households. Third stage selection of individuals within sampled households: The household roster will be completed at the beginning of the interview, thus identifying all members of the selected household. The selection of individuals within households will be dependent on which questionnaire module (See Section 2.3 below) the individuals are eligible for. The protocol for the selection of individuals within households (and their potential proxy respondents) will be as follows:  For the modules requiring data about the household (C, CC, F, H), no individuals are sampled since the household is the sampling unit. The head of household or any responsible adult will be interviewed on behalf of the household.  For the children’s module (D), data and anthropometry measures will be collected for all eligible children. The mother or caregiver of the selected children under five years of age will be interviewed as a proxy respondent.  For the woman’s module (E), all woman between the ages of 15-49 will be selected. No proxy respondents are allowed. For women’s anthropometry, only non-pregnant women will be measured.  For the agricultural module (G), all farmers within the household who have ownership or decision-making power over all plots of land and/or livestock that are part of the “farm” will be interviewed. If a farmer has migrated for an extended period to work outside of the household, the spouse and/or another responsible adult farmer that can answer the agricultural questions can be interviewed as a proxy respondent.  For the gender modules (J and K), all cash earners that are married or in a union and all parents of children under two years of age that are married or in a union will be interviewed. No proxy respondents are allowed. Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 7 2.3 Questionnaire The questionnaires for the PBS were developed based on the core FFP and resilience indicators. All questionnaire modules follow FFP and Feed the Future guidelines, as described in the FFP Indicators Handbook (April 2015) and the Feed the Future Indicator Handbook (September 2016).8 The ICF team revised the questionnaire from the 2013 BL survey in Uganda, and gathered information before, during, and after the BL planning workshop (January 2018) to tailor the questionnaires to the context of Uganda. The resilience module was contextualized by Tango International based on prior work in Uganda. The questionnaire consists of separate modules covering the following topics:  Module A: Household identification and informed consent  Module B: Household roster  Module C: Household food security  Module CC: Mobility, Local Government Responsiveness and Poverty Probability Index (PPI)  Module D: Children’s nutrition and health  Module E: Women’s nutrition and health  Module F: Water, sanitation, and hygiene  Module G: Agriculture  Module H: Poverty  Module J: Gender – Cash  Module K: Gender – Maternal and Child Health and Nutrition (MCHN)  Module L. Gender – Household Decision-Making, Access To Credit And Group Participation  Module R: Resilience The questionnaire will be translated into three local languages (Karamojong, Pokot, and Lethur). The total time for completing the survey in each household is expected to be approximately two to three hours, depending on the size of the household. 3. FIELD PROCEDURES 3.1 Data Collection Mode The data for the joint BL/EL PBS will be collected with tablets using Computer-Assisted Personal Interviewing (CAPI). Tablets will be loaded with a CSPro data entry application developed at ICF for FFP surveys and tailored to fit the Uganda questionnaire. All data will be entered directly into the tablets and edited while interviewing in the field. 3.2 Field Manuals Prior to the start of training and fieldwork, the ICF team will develop training manuals based on those developed for prior BL surveys and using FFP, Feed the Future, and DHS guidelines. The manuals will be used for household survey training and fielding purposes and will provide guidance to field staff on the survey protocol and procedures. The ICF team will customize the field manuals for Uganda to align with 8 Available at https://feedthefuture.gov/sites/default/files/resource/files/Feed_the_Future_Indicator_Handbook_Sept2016.pdf Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 8 the final questionnaire and Uganda-specific field protocols. The supervisors’ manual will describe the study design and objectives, supervisors’ roles and responsibilities, rules and regulations, ethics, fieldwork preparations, and quality control requirements and procedures. The interviewers’ manual will include guidelines for implementation of the survey and fieldwork procedures, including interviewing techniques and procedures for completing the questionnaires. This latter manual will also include detailed explanations and instructions for each question. The anthropometry training manual will include detailed instructions for all anthropometry specialists on proper procedures for taking accurate anthropometry measures (height/length and weight) along with procedures to conduct anthropometry standardization testing. 3.3 Training Using the manuals described above, the ICF team will work together with IRC, the local data collection sub-contractor, to conduct in-depth trainings for supervisors, interviewers, and anthropometry specialists. Prior to the start of training, the field team (ICF survey coordinator, local survey monitors, IRC’s country operations manager, and the lead anthropometry specialist) will develop a detailed training curriculum and timeline for supervisors and interviewers’ trainings, and the anthropometry training and standardization testing, including local sites where the anthropometry standardization testing activities will take place. These training curriculums are provided in Annex 2. The organization and flow of the training will be adapted to fit the situation and logistics in Uganda.9 The training curriculum and timeline and all training manuals will be submitted to FFP for approval prior to the start of trainings. Interviewer training will involve review of the questionnaire, module by module, along with practical sessions on handling and entering data into the tablets using the CAPI template and transferring data from interviewers’ tablets to supervisors’ tablets. Interviewers will participate in role playing and mock interviews and the questionnaires will be further checked for content, consistency and flow, as well as validity and reliability. Revisions to the questionnaire will be made at the end of the training as needed. Supervisor training will cover the topics of supervisors’ roles and responsibilities; rules, behaviors, and ethics; household and respondent selection; use of the field control sheet, maps, and GPS; and data collection. It will include a detailed review of the CAPI survey procedures for receiving and transmitting completed interviews. The anthropometry training will include instruction on taking accurate measurements, types of possible measurement errors, and reading and recording measurements followed by some practical sessions. Anthropometry training will also include a training session for all interviewers as anthropometry assistants, which require them to hold children two to five years of age to ensure that their feet and knees are in the correct position for standing measurement, and to hold children younger than two years of age to ensure that their heads are correctly positioned for recumbent length measurement. Anthropometry standardization testing will be conducted for all anthropometrists after the anthropometry training is completed. This involves objectively testing anthropometrists’ accuracy (ability to obtain anthropometric weights and measurements as close to the true weight and measurement as 9 Note that the questionnaire was not pretested (to identify problematic questions or contextual changes) because the pretesting was undertaken when the questionnaire was developed at baseline. Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 9 possible with minimal variation in comparison to the trainer’s weights and measurements), and precision (the ability for an anthropometrist to repeat his/her own weights and measurements with minimal variation). Upon completion of the trainings, all survey staff will participate in a pilot study in pre-selected non￾sampled villages near the project areas. The pilot test will provide the survey team practice on:  Locating of selected villages and selected households by supervisors  GPS data collection at the household level  CAPI data entry and respondent selection routines by interviewers  CAPI data editing, survey management by supervisor  CAPI data transmission to control room by interviewers  Appropriate interviewing behavior  Team dynamics  Distribution of work assignments and coordination by supervisors  Completion of field control sheets by supervisors Each interviewer will complete at least two full-interviews on paper and with the tablet during the pilot test. Supervisors will observe the interviewers in their teams during the pilot test and take notes on their performance. IRC’s survey management team, the ICF survey coordinator and the local survey monitors, the ICF anthropometry trainer will also participate in the pilot test. Together with the supervisors, they will debrief the team members the day after the pilot test is completed. They will provide feedback and clarify/troubleshoot any issues encountered during the pilot study. Based on the discussion at the debrief session, ICF will make final modifications to field procedures and manuals, if required. Table 6 summarizes the sequence of field preparation activities. The ICF survey coordinator will oversee all activities. Table 6: Field Preparation Activities Duration Activities Participants 15 days Listing exercise Listers 15 days Interviewer training Interviewers, supervisors 5 days Anthropometry Training Anthropometry specialists, interviewers, supervisors 5 days Anthropometry standardization testing Anthropometry specialists 3 days Supervisor/field procedure training Supervisors, field coordinators 4 days Pilot test and debrief IRC survey management team, ICF survey coordinator, local survey monitor, supervisors, interviewers, and anthropometry specialists Prior to the start of data collection, the field team will ensure that all required permissions and ethical review approvals have been obtained. They will develop a detailed field movement plan that will describe the location and timing for each field team throughout the data collection period. The field movement plan will be submitted to FFP for approval prior to the start of data collection activities. Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 10 3.4 Data Collection Data collection will start immediately after the pilot study. To collect data from the sampled 3,360 households for the joint BL/EL, there will be 18 teams, each consisting of six field team members (one supervisor, four interviewers, and an anthropometry specialist). Accordingly, IRC will hire a total of 72 interviewers, 18 anthropometry specialists, and 18 supervisors. In addition, IRC will engage five field coordinators and two IT Specialists, making the total number of field personnel for the survey to be 115. Given 112 clusters to cover and 18 teams to undertake the work, each team will, on average, collect data from 7 clusters. Estimating each interviewer can complete two household interviews per day, a total of roughly 35 days will be required to complete the data collection from the 3,360 households, including time for travel. 3.5 Quality Control Working in close partnership with IRC, the ICF team will ensure high-quality PBS data through a strong focus on training field staff and monitoring data collection. The ICF team will be using CAPI data collection, which allows for real-time editing of data, frequent uploading of collected data, continuous data quality review, and correction of field staff behavior as data collection proceeds. ICF requires that the field teams upload collected data from completed clusters at minimum on a weekly basis. During critical periods, including training, anthropometry standardization testing, piloting, and at the beginning of fieldwork, the ICF survey coordinator will be in-country to coordinate and oversee these activities. When the ICF survey coordinator leaves the country, the local survey monitors will oversee fieldwork activities and closely update the ICF survey coordinator on fieldwork progress or any issues encountered during data collection. Table 7 provides survey procedures and safeguards for field supervision. Table 7: Procedures and Safeguards for Fieldwork Oversight Goal Procedure or Safeguard Proper fieldwork oversight  Maximum ratio of one supervisor for every four interviewers and one anthropometry specialist. The subcontractor (IRC) will provide one field coordinator to oversee every four-or-five survey teams. Proper selection of households and respondent  Adherence to household and respondent selection methods per ICF protocol Assurance of questionnaire accuracy  Complete review of data immediately after the interview is conducted  In the event of errors or omissions, required corrections will be made before the interviewer proceeds to the next household Prevention of fraud in interviewing  Spot-checks with households on the day of the interview to ensure honesty on the part of the interviewer. Proper spot-checks involve verifying demographic information of the household respondents and other information to make sure that interviewers are recording data that is accurate and truthful. Fifteen percent of the completed interviews should be randomly selected for spot-checks.  In the event of fabrication or falsification of data collected, the interviewer will be fired from the project immediately Completion of interviews  If the entire interview is not completed on the first visit, interviewers will make up to a total of three visits to the household to complete the Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 11 Goal Procedure or Safeguard interview. The interviewer will plan one or two follow-up visits with the respondents to successfully complete the interview.  The supervisor will ensure that each household survey is appropriately completed. All interview items should be 100 percent complete. 4. DATA PROCESSING AND ANALYSIS 4.1 Data Transmissions For transmission of data from the field, IRC will use Internet File Streaming System (IFSS), a cloud-based electronic file delivery web service. The primary objective of the service is to deliver files from one user to another in a way that is fast and secure. The ICF CSPro programmer assigned to the project will work in-country to set up and test the cloud-based data transmission system, and to provide technical support during the first week of data collection to ensure that tablets and the IFSS transmission system are operating smoothly. IRC will upload data to the IFSS on a weekly basis. Data transmissions will be on a weekly basis and will begin after interviewers have completed all interviews in their first assigned cluster. For the final dataset, the CSPro programmer will develop a program to run quality control checks and convert the raw data exported from the CSPro application into the data format needed for analysis using Stata, SPSS or SAS. 4.2 Data Analysis ICF will generate estimates for all FFP and project-specific indicators, along with additional analyses to explore relationships and plausible determinants for key outcome indicators and a select number of resilience indicators. For indicators that were collected at BL in the old DFSA project areas, a statistical comparison of BL (2013) and EL (2018) estimates will be conducted to determine population-level change over time. All descriptive, bivariate, and multivariate analyses to be conducted will be discussed with FFP and clearly defined in the Data Treatment and Analysis Plan (DTAP) while recognizing that after the analysis begins, there may be other interesting analyses to pursue. Tango International will generate the resilience indicators and conduct the detailed resilience analyses. Innovations for Poverty Action (IPA) will lead the analysis for the PPI. The DTAP will be prepared following completion of the PBS data collection protocol and will be submitted to FFP for approval prior to the start of data analysis. Final data files and documentation will be delivered to FFP following the completion of the data analysis and vetting of the PBS results with all stakeholders. All personal identifying information will be removed from the datasets prior to delivery to FFP in order to protect the confidentiality of survey respondents. The final data files will include:  Sampling frames for each DFSA  Raw datasets generated from the CSPro data entry application  Edit rules and programming specifications for data cleaning  Data dictionary/code book for each final dataset  Syntax for all analyses and variable transformations Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 12  Final analytic datasets, including sampling weights and all derived indicators, in STATA format and comparable datasets in CSV format that have been anonymized to protect individual confidentiality, for use as a public data files in the USAID Open Data warehouse. 4.3 Dissemination of Findings All of the data is collected at the regional or sub-regional level, and analysis and dissemination of findings will focus on populations rather than on individuals. ICF will prepare three reports—one Baseline Study Report (for newly awarded DFSA programs) and two Endline Evaluation Reports for the two expired DFSA programs. The reports will be reviewed and finalized after USAID approval. Upon approval, the reports and the datasets will be uploaded to the USAID website and made available to the public. Additionally, USAID/FFP will host one to two day in-country briefing sessions with USAID/Uganda, relevant Uganda government agencies and the IP organizations to present the findings. A data utilization workshop will also be conducted with the BL DFSA IPs to further familiarize them with the baseline study results and how these results can be used to support their program planning and target setting. 5. ETHICAL CONSIDERATIONS 5.1 Ethics approval Ethical approval will be obtained from the ICF Institutional Review Board and the Mildmay Uganda Ethics Review Committee and thereafter the protocol will be registered with the Uganda National Council for Science and Technology. Permission to access the study communities will be obtained after obtaining approval from the offices of the Chief Administrative Officers for the targeted districts as well as the USAID Food for Peace Office in Uganda. 5.2 Verbal Informed Consent Verbal informed consent will be obtained before each interview after explaining to each respondent the objectives and purposes of the study and other information safeguards. Guidelines for requesting verbal consent are included in Module A of the household survey instrument. The interviewer will read to each eligible respondent a statement of informed consent that clearly outlines the subject’s rights. Participation in the interviews is completely voluntary. If there is any question that the respondent does not want to answer, the respondent can choose to skip to the next question. Additionally, respondents can choose to terminate the interviews at any time during the interview. The interviewer is required to certify that (a) each eligible respondent has given his/her informed consent before being personally interviewed, (b) the adult caregiver in the household has given his/her consent before any child under 5 years of age is measured, and (c) adult guardians of eligible respondents of minors under 18 years of age have given consent before they are interviewed. The adult guardians normally will be the parents or other close family member living in the household (e.g. grandparents or aunts/uncles). All children eight years of age and above will be required to assent to participate in the study and this will be done after obtaining the parent’s/guardian’s consent. The child’s assent or dissent will take precedence over the parent’s or guardian’s consent. Risks and benefits: The study presents no direct risks or benefits to the participants as they will not undergo any invasive procedures and they will not be directly compensated for their participation. The study carries a relatively low burden. There are different respondents for each module and therefore no Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 13 individual respondent will be interviewed for more than two hours. In addition, respondents will not incur transportation or other costs as a result of participating in the survey. 6. TIMELINE Table 8 provides the timeline for critical activities for the joint BL/EL PBS. Table 8. Uganda Joint BL/EL PBS Critical Activities Timeline Activity Date Research protocol submitted to the ethics committee Jan 31, 2018 Listing training and listing exercise Jan/Feb, 2018 Questionnaire Finalized for Main Training May 7, 2018 Refresher training May 9-10, 2018 Main Training (interviewer and supervisor) May 11-31, 2018 Anthropometry training and standardization May 16-31, 2018 Field pilot practice June 1-4, 2018 Pilot debriefing and procedural adjustments if needed June 5-6, 2018 Household survey fieldwork starts June 7, 2018 Household survey fieldwork ends July 6, 2018 Final clean dataset delivered to ICF July 15, 2018 Note that the first two activities were completed under the prior EVELYN contract. ANNEX 1 – JOINT BL/EL PBS INDICATORS Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 14 Indicator Disaggregation Level 2018 BL 2018 EL FOOD SECURITY 1. Average Household Dietary Diversity Score (HDDS) None   2. Prevalence of moderate and severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES) [30 day recall] GHT   3. Prevalence of moderate and severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES) [12 month recall] GHT  POVERTY 4. Per capita expenditures (as a proxy for income) of USG-assisted areas GHT   5. Prevalence of Poverty: Percent of people living on less than $1.25/day 2005 PPP (EL) or $1.90/day 2011 PPP (BL) GHT   6. Depth of Poverty: Mean percent shortfall relative to the $1.25/day (EL) or $1.90/day (BL) poverty line GHT   7. Depth of Poverty of the Poor: Mean percent shortfall of the poor relative to the $1.90/day 2011 PPP poverty line GHT  WATER, SANITATION, AND HYGIENE 8. Percentage of households using an improved drinking water source None  9. Percentage of households using basic drinking water services None  10. Percent of households in target areas practicing correct use of recommended household water treatment technologies None  11. Percent of households that can obtain drinking water in less than 30 minutes (round trip) None  12. Percentage of households using an improved sanitation facility None  13. Percentage of households with access to a basic sanitation service GHT  14. Percent of households in target areas practicing open defecation None  15. Percent of households with soap and water at a handwashing station commonly used by family members None   AGRICULTURE 16. Percentage of farmers who used financial services (savings, agricultural credit, and/or agricultural insurance in the past 12 months Sex   17. Percentage of farmers who practiced the value chain activities promoted by the project in the past 12 months Sex   18. Percentage of farmers who used at least [a project-defined minimum] sustainable agriculture (crop, livestock and natural resource management) practices and/or technologies in the past 12 months Sex, type of practice  19. Proportion of producers who have applied targeted improved management practices or technologies* Sex, type of practice, type of commodity  20. Percentage of farmers who used improved storage practices in the past 12 months Sex   WOMEN’S HEALTH AND NUTRITION 21. Prevalence of underweight (BMI < 18.5) women of reproductive age None   22. Prevalence of women of reproductive age consuming a diet of minimum diversity None  23. Percentage of women of reproductive age who are currently using, or whose sexual partner is currently using, at least one contraceptive method, regardless of the method used None  24. Percent of births receiving at least four antenatal care (ANC) visits during pregnancy None   25. Prevalence of women of reproductive age who consume targeted nutrient-rich commodities Sex, type of commodity  CHILDREN’S HEALTH AND NUTRITION 26. Prevalence of healthy weight (WHZ ≤ 2 and ≥ -2) among children under five (0-59 months) Sex  ANNEX 1 – JOINT BL/EL PBS INDICATORS Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 15 Indicator Disaggregation Level 2018 BL 2018 EL 27. Prevalence of underweight children (WAZ < -2) children under five (0-59 months) Sex  28. Prevalence of stunted children (HAZ < -2) children under five (0-59 months) Sex   29. Prevalence of wasted children (WHZ < -2) children under five (0-59 months) Sex   30. Percentage of children under age five who had diarrhea in the past two weeks Sex   31. Percentage of children under five years old with diarrhea treated with oral rehydration therapy Sex   32. Prevalence of exclusive breastfeeding of children under six months of age Sex   33. Prevalence of children 6-23 months receiving a minimum acceptable diet Sex   34. Prevalence of children 6- 23 months who consume targeted nutrient-rich commodities Sex, type of commodity  GENDER 35. Percentage of men and women in union who earned cash in the past 12 months Sex  36. Percentage of women in union and earning cash who report participation in decisions about the use of self-earned cash None  37. Percentage of women in union and earning cash who report participation in decisions about the use of spouse/partner’s self-earned cash None  38. Percentage of men in union and earning cash who report spouse/partner participation in decisions about the use of self-earned cash None  39. Percentage of men and women in union with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices Sex  40. Percentage of men/women in union with children under two who make maternal health and nutrition decisions alone Sex  41. Percentage of men/women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner Sex  42. Percentage of men/women in union with children under two who make child health and nutrition decisions alone Sex  43. Percentage of men/women in union with children under two who make child health and nutrition decisions jointly with spouse/partner Sex  RESILIENCE 44. Shock exposure index None  45. Cumulative impact of shock exposure index None  46. Absorptive capacity index None  47. Adaptive capacity index None  48. Transformative capacity index None  49. Ability to recover from shocks and stresses index None  50. Proportion of households participating in group-based savings, micro-finance or lending programs None  51. Index of Social Capital at the household level None  CUSTOM INDICATORS 52. Percentage of respondents reporting increased movement in areas that were previously not accessible due to insecurity None  53. Percentage of households with access to a sanitation facility – not necessarily improved None  54. Average number of crops produced per farmer in the past 12 months None  55. Percentage of farmers adopting farmer managed natural regeneration practices in the past 12 months None  56. Percentage of livestock owners accessing government or private sector vet care in the past 12 months None  ANNEX 1 – JOINT BL/EL PBS INDICATORS Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 16 Indicator Disaggregation Level 2018 BL 2018 EL 57. Average rating of government's ability to be responsive to citizens' needs (including transparency, inclusivity, effectiveness) as measured on scorecard Sex  58. Percent of target population who can state at least one health benefit of waiting at least two years after last live birth before attempting the next pregnancy Sex, Age  BL = DFSA Baseline Study EL = DFAP Endline Evaluation GHT= Gendered Household Type, FIES = Food Insecurity Experience Scale * Pending confirmation of feasibility with existing data ANNEX 2 - TRAINING AGENDAS Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 17 2018 Food for Peace Population-Based Survey in Uganda Training Agenda for Interviewer and Supervisor (Main) Training Training Venue:  May 11-19: Makerere University Senate Building Level 4, Senate Conference Hall  May 21-31: Makerere University Food Science and Technology Building, Food Science Conference Hall Date Time Discussion Topic Note (Day 1) May 11 08:00-09:00 Registration  Introduction & Overview  Household identification & Consent  Household roster 09:00-10:30 Introduction of the trainees and resource persons Background of FFP Population-Based Survey Detailed explanation of the objectives of the survey  Personal qualities and performance standards in interviewing  Role of pretest interviewers  Dos and don’ts of interviewing 10:30-10:45 Tea-break 10:45-12:30 Discussion on field procedures Discussion on survey methodology including sample design Familiarization with questionnaire (Module-by-Module explanation of questionnaires) 12:30-1:30 Lunch-break 1:30-3:30 Discussion on Module-A (Identification and Consent)) 3:30-3:45 Tea-break 3:45-5:30 Discussion on Module- B (Household roster) (Day 2) May 12 8:30:00-10:30 Review of previous day’s sessions Discussion on Module- B (Household roster)  Household Roster 10:30-10:45 Tea-break 10:45-12:30 Discussion on Module- B (Household roster) 12:30-1:30 Lunch-break 1:30-3:30 Practice and role play: Module A and B 3:30-3:45 Tea-break 3:45-5:30 Practice and role play: Module A, B Debrief (Day 3) May 14 8:30:00-10:30 Review of previous day’s sessions Discussion Module C  Food Access & Food Security  Mobility, Local Government Responsiveness, and Poverty Probability Index 10:30-10:45 Tea-break 10:45-12:30 Discussion on Module C, CC 12:30-1:30 Lunch-break 1:30-3:30 Discussion on Module F 3:30-3:45 Tea-break ANNEX 2 - TRAINING AGENDAS Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 18 Date Time Discussion Topic Note 3:45-5:30 Practice and role play: Module C, CC, F Debrief  Water, Sanitation and Hygiene (Day 4) May 15 8:30:00-10:30 Review of previous day's sessions Discussion of Module D1, D2, E  Children’s Nutritional Status and Feeding Practices  Children’s Diarrhea and Oral Rehydration Therapy  Women’s Nutrition, Breastfeeding, and Antenatal Care 10:30-10:45 Tea-break 10:45-12:30 Discussion of D1, D2, E 12:30-1:30 Lunch 1:30-3:30 Practice and role play: Module D1, D2, E 3:30-3:45 Tea-break 3:45-5:30 Practice and role play: Module D1, D2, E Debrief (Day 5) May 16 8:30:00-10:30 Review of previous day's sessions Discussion of Module G  Agriculture 10:30-10:45 Tea Break 10:45-12:30 Discussion of Module G 12:30-1:30 Lunch 1:30-3:30 Practice and role play: Module G 3:30-3:45 Tea Break 3:45-5:30 Practice and role play: Module G Debrief (Day 6) May 17 8:30:00-10:30 Review of previous day’s session Discussion of Module J, K, L  Gender-Cash  Gender-Maternal Child Health and Nutrition (MCHN)  Gender￾Household Decision-Making, Access to Credit and Group Participation Request participation of Agriculture Specialist from IPs 10:30-10:45 Tea Break 10:45-12:30 Discussion of Module J, K, L 12:30-1:30 Lunch 1:30-3:30 Explanations on Module G by experts from IPs 3:30-3:45 Tea Break 3:45-5:30 Explanations on Module G by experts from IPs (Day 7) May 18 8:30:00-10:30 Review of previous day’s sessions Practice and role play: Module J, K, L  Gender-Cash  Gender-Maternal Child Health and Nutrition (MCHN)  Gender￾Household Decision-Making, Access to Credit and Group Participation  Poverty Measurement 10:30-10:45 Tea Break 10:45-12:30 Practice and role play: Module J, K, L Debrief 12:30-1:30 Lunch 1:30-3:30 Discussion of Module H 3:30-3:45 Tea Break 3:45-5:30 Discussion of Module H (Day 8) 8:30:00-10:30 Review of previous day’s sessions ANNEX 2 - TRAINING AGENDAS Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 19 Date Time Discussion Topic Note May 19 Discussion of Module H  Poverty 10:30-10:45 Tea Break Measurement 10:45-12:30 Practice and role play of Module H 12:30-1:30 Lunch 1:30-3:30 Practice and Role Play: Module H Debrief 3:30-3:45 Tea Break 3:45-5:30 Practice and Role Play: Module H Debrief (Day 9) May 21 8:30:00-10:30 Anthropometry Assistant Training  Training on Anthropometry Assistant THIS IS ONE-DAY SESSION FOR ALL INTERVIEWERS WHO WILL SERVE AS ANTHROPOMETRY ASSISTANTS. PLEASE REFER TO ANTHROPOMETRY TRAINING AGENDA FOR COMPLETE DETAILS ON TRAINING OF ANTHRPOMETRY SPECLAISTS. 10:30-10:45 Tea Break 10:45-12:30 Anthropometry Assistant Training 12:30-1:30 Lunch 1:30-3:30 Anthropometry Assistant Training 3:30-3:45 Tea Break 3:45-5:30 Anthropometry Assistant Training (Day 10) May 22 8:30:00-10:30 Review of previous day’s sessions Discussion: Module R  Resilience 10:30-10:45 Tea Break 10:45-12:30 Discussion: Module R 12:30-1:30 Lunch 1:30-3:30 Practice and role play: Module R 3:30-3:45 Tea Break 3:45-5:30 Practice and role play: Module R Debrief (Day 11) May 23 8:30:00-10:30 Introduction to CAPI Tablet basics  CAPI Training 10:30-10:45 Tea Break 10:45-12:30 Distribution of Tablets Data entry exercise 12:30-1:30 Lunch 1:30-3:30 CAPI menu system Assigning households 3:30-3:45 Tea break 3:45-5:30 Household data entry Demonstration and practice Enter data from paper questionnaire (Day 12) 8:30:00-10:30 Review of previous day’s sessions ANNEX 2 - TRAINING AGENDAS Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 20 Date Time Discussion Topic Note May 24 Household characteristics data entry Continue entering data  CAPI Training 10:30-10:45 Tea Break 10:45-12:30 Listing eligible members for anthropometry Continue entering data 12:30-1:30 Lunch 1:30-3:30 Continue entering data 3:30-3:45 Tea Break 3:45-5:30 Practice transferring data to Supervisor’s tablet (Day 13) May 25 8:30:00-10:30 Review of previous day’s sessions Listing cases for outstanding modules Continue entering data  CAPI Training 10:30-10:45 Tea Break 10:45-12:30 Downloading updated programs 12:30-1:30 Lunch 1:30-3:30 Data review by Supervisor’s tablet. Check and modify data as needed 3:30-3:45 Tea Break 3:45-5:30 Discussion on error after reviewing data. Discussion on CAPI part done so far. (Q/A session). Transfer data to Supervisor’s tablet, Supervisor uploads data (Day 14) May 26 8:30:00-10:30 Review of previous day’s sessions Entering anthropometry data Mock interviews with CAPI  CAPI Training 10:30-10:45 Tea Break 10:45-12:30 Mock interviews with CAPI 12:30-1:30 Lunch 1:30-3:30 Mock interviews with CAPI Transferring data to supervisor’s tablets 3:30-3:45 Tea Break 3:45-5:30 Discussion on error after reviewing data. Discussion on CAPI part done so far. (Q/A session). (Day 15) May 28 8:30:00-10:30 Closing clusters Practice finalizing work in a cluster  CAPI Training 10:30-10:45 Tea Break 10:45-12:30 Mock Interview with CAPI 12:30-1:30 Lunch 1:30-3:30 Mock interviews with CAPI Transferring data to supervisor’s tablets Fixing duplicate households 3:30-3:45 Tea Break 3:45-5:30 Discussion on error after reviewing data. Discussion on CAPI part done so far. (Q/A session). (Day 16) May 29 8:30:00-10:30 Review of previous day’s sessions Mock interviews with CAPI Supervisor Training 10:30-10:45 Tea Break 10:45-12:30 Mock interviews with CAPI ANNEX 2 - TRAINING AGENDAS Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 21 Date Time Discussion Topic Note 12:30-1:30 Lunch 1:30-3:30 Discussion on error after reviewing data. Discussion on CAPI part done so far. (Q/A session). 3:30-3:45 Tea Break 3:45-5:30 Discussion on error after reviewing data. Discussion on CAPI part done so far. (Q/A session). (Day 17) May 30 8:30:00-10:30 Review of previous day’s sessions Mock interviews with CAPI in languages Supervisor Training 10:30-10:45 Tea Break 10:45-12:30 Mock interviews with CAPI in languages 12:30-1:30 Lunch 1:30-3:30 Discussion on error after reviewing data. Discussion on CAPI part done so far. (Q/A session). 3:30-3:45 Tea Break 3:45-5:30 Discussion on error after reviewing data. Discussion on CAPI part done so far. (Q/A session). (Day 18) May 31 8:30:00-10:30 Review of previous day’s sessions Mock interviews with CAPI in languages Supervisor Training 10:30-10:45 Tea Break 10:45-12:30 Mock interviews with CAPI in languages 12:30-1:30 Lunch 1:30-3:30 Discussion on error after reviewing data. Discussion on CAPI part done so far. (Q/A session). 3:30-3:45 Tea Break 3:45-5:30 Discussion on planning of pilot test (Day 19- Day 22) June 1 to June 4 Pilot Test Day 23 June 5 Pilot Debriefing and Deployment plan/logistics ANNEX 2 - TRAINING AGENDAS Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 22 Anthropometry training and standardization testing schedule May 2018 Sunday Monday Tuesday Wednesday Thursday Friday Saturday FIRST WEEK 13 MAY 14 MAY 15 MAY 16 MAY 17 MAY 18 MAY 19 MAY 9am to 1 pm Meeting to define schedule and logistics Equipment checkup including standardization. Take the equipment to the main training center and prepare the anthropometrist’s kit. At main training center: Introduction to Anthropometry At main training center: Introduction to standing height and weight of children 2-5 yrs. old, Recumbent length and weight of children <2 yrs. old At facility for children: Hands on practice, every procedure with special focus on the 2-5 yrs. age category At facility for children: Hands on practice, every procedure with special focus on the < 2yrs age category 2pm to 4pm At main training center: Height and weight of adults (mothers).Training and practice on each other To be used as needed To be used as needed To be used as needed SECOND WEEK 20 MAY 21 MAY 22 MAY 23 MAY 24 MAY 25 MAY 26 MAY 9am to 1pm At main training center: Introduction to anthropometry and practice (ASSISTANT) At main training center. Introduction to standardization testing of mothers. At facility for children: Standing height and weight standardization test of children 2-5 yrs. old At facility for children: Length standardization test of children <2 yrs. old At facility for children: Re￾standardization test At facility for children: Hands on practice, every procedure. 2pm to 4pm At facility for children: Standing height and weight of children 2-5 yrs. old, training and practice (ASSISTANT) To be used as needed To be used as needed To be used as needed At facility for children: Hands on practice, every procedure. At facility for children: Recumbent length and weight training of children <2 yrs. old (ASSISTANT) THIRD WEEK 27 MAY 28 MAY 29 MAY 30 MAY 9am to 1pm Introducing the quality control sheet and Z￾score tables to team supervisors Continued practice Anthropometrists familiarizing with their teams in preparation for pilot 2pm to 4pm To be used as needed To be used as needed ANNEX 2 - TRAINING AGENDAS Uganda Joint Baseline/Endline Population-Based Survey Protocol – USAID Office of Food for Peace 23 NOTES: 1. Interactive Training Training will be interactive and participatory with practice, testing and discussions. 2. Flexibility The above schedule WILL change according to the needs as the training progresses. 3. Introduction to Anthropometry The Introduction to Anthropometry session will include subjects of the measurements taken, the importance of taking accurate measurements, types of measurement errors, reading and recording measurements, reading and recording systems. Also, definitions of measurements, derived anthropometry indices (i.e., stunting, wasting, underweight) will be presented (note—this covers some of the basic science of anthropometry so that the trainees will have a greater understanding of anthropometry, its use in population surveys with the intention of a greater ‘ownership’ of the anthropometry component of the survey). 4. Other subjects to be covered and trained (to be inserted in the above schedule:  Bilateral pedal edema training  Age assessment training  Anthropometry questionnaire training, completion  Team supervisor training that includes the use of growth charts to evaluate acceptable measurements, taking replicate measurements occasionally, use of the Anthropometry Supervisor Checklist and other quality control tasks  Understanding weighing and measuring instruments Annex 3: Population-Based Household Survey Questionnaire Module A. Identification and Informed Consent (Head of HH or Responsible Adult) IDENTIFICATION (1) A01 CLUSTER CODE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A02 HOUSEHOLD NUMBER (HH) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A03 DISTRICT KAABONG 1 KOTIDO 2 ABIM 3 MOROTO 4 NAPAK 5 NAKAPIRIPIRIT 6 AMUDAT 7 INTERVIEWER VISITS SECOND VISIT THIRD VISIT FINAL VISIT A09 DAY A05 DATE A10 MONTH A06 ENUMERATOR A11 YEAR A07 DAY OF VISIT A08 RESULT USE CODES BELOW A12 INT. NUMBER NEXT VISIT: DATE A13 TOTAL NUMBER TIME OF VISITS A14 FINAL OUTCOME OF INTERVIEW (CIRCLE ONE) A17 TOTAL PERSONS 1 COMPLETED 3 ENTIRE HOUSEHOLD ABSENT IN THE HOUSEHOLD 2 NO HOUSEHOLD MEMBER AT HOME FOR EXTENDED PERIOD OF TIME OR NO COMPETENT RESPONDENT 4 POSTPONED/PARTIALLY COMPLETED A18 LINE NO. OF AT HOME AT TIME OF VISIT 5 REFUSED RESPONDENT TO HOUSEHOLD ROSTER 9 OTHER (SPECIFY) A19 TOTAL CHILD￾REN UNDER FIVE A15A. MALE PRIMARY DECISION-MAKER'S NAME AND LINE NUMBER A20 TOTAL ELIG. WOMEN 15-49 YRS A15B. FEMALE PRIMARY DECISION-MAKER'S NAME AND LINE NUMBER A21 TOTAL NO. OF FARMERS A22 SUPERVISOR NAME CODE 2018 UGANDA JOINT BL/EL QUESTIONNAIRE FIRST VISIT 2 0 1 8 INFORMED CONSENT : HOUR MINUTE Do you have any questions about the study or about your participation? You or other respondents can ask any questions you may have about the study at any time. AS APPLICABLE, CHECK AND SIGN THE CONSENT BOX BELOW. 1. Who is the main male adult (15 years or older) decision-maker in the household? [NAME], do you agree to participate in the survey? NAME: __________________ RESPONDENT AGREED ____ RESPONDENT DID NOT AGREE ____ 2. Who is the main female adult (15 years or older) decision-maker in the household? [NAME], do you agree to participate in the survey? NAME: __________________ RESPONDENT AGREED ____ RESPONDENT DID NOT AGREE ____ 3. PRIMARY CAREGIVERS FOR CHILDREN UNDER FIVE YEARS OF AGE [NAME], do you agree to participate in the survey and allow your child to be weighed and measured? NAME: __________________ RESPONDENT AGREED ____ RESPONDENT DID NOT AGREE ____ NAME: __________________ RESPONDENT AGREED ____ RESPONDENT DID NOT AGREE ____ NO CHILDREN UNDER FIVE IN THE HOUSEHOLD ______ ADDITIONAL ELIGIBLE HOUSEHOLD MEMBERS RESPONDENT RESPONDENT AGREED DID NOT AGREE 4. NAME_____________________________ Do you agree to participate in the survey? ____ ____ 5. NAME_____________________________ Do you agree to participate in the survey? ____ ____ My signature affirms that I have read the verbal informed consent statement to the respondent(s), and I have answered any questions asked about the study. INTERVIEWER'S NAME AND CODE DAY MONTH YEAR SIGNATURE AND DATE • • INTERVIEWER'S NAME AND CODE DAY MONTH YEAR SIGNATURE AND DATE • • A26: END TIME : HOUR MINUTE 8 2 0 1 8 A00: START TIME Hello. My name is _______________________________________. I am working with IRC. We are conducting a survey to learn about agriculture, food consumption, nutrition and welfare of households in Karamoja. The study is funded by the United States Agency for International Development Food for Peace Office. It was approved by the Mildmay Uganda Ethics Research Committee (MUREC) and registered by the Uganda National Council of Science and Technology (UNCST). The survey will target 3,360 households and your household is among those that were chosen for the survey. I would like to ask you some questions about your household. These questions can take two to three hours to complete. We can come back tomorrow if we do not have enough time to go through all of the questions today. As part of the survey we would also like your permission to measure and weigh women and children under 5 years of age. These measurments can be used to assess the nutritional status of a population. All the information provided for the survey will be kept confidential and will only be shared for professional and learning purposes. Your identity shall not be disclosed on any publicly available data or reports. The data collected in this survey may be used as part of a study in the future. If your household is selected for the future study then a second survey will be conducted. If you agree to participate in the second study, the data from this study will be used for comparison. You do not have to agree to participate in either study and there will be no penalties if you decide not to participate. If I ask you any question that you don’t want to answer, just let me know and I will go on to the next question. You can stop the interview at any time. There are no direct risks or benefits to you for participating in the study. However, the information we collect will help to improve on the services provided to your community aimed at improving food security, nutrition and welfare of households. Study participants will get feedback on the progress and findings of the study. You can also contact the principal investigator for the study, Dr. Daniel Kibuuka Musoke TEL 0772587094 for Information regarding the progress and findings of the study. If you have any concerns about the study, you can contact the research ethics committee chairperson, Dr. Nabiryo Christine; TEL 0392174236. IT IS NECESSARY TO INTRODUCE THE HOUSEHOLD TO THE SURVEY AND OBTAIN THE CONSENT OF ALL RESPONDENTS. FIRST IDENTIFY THE HEAD OF HOUSEHOLD AND CONDUCT THE INFORMED CONSENT WITH HIM/HER. THEN BEGIN THE INTERVIEW. AS YOU IDENTIFY NEW RESPONDENTS FOR SUBSEQUENT MODULES, RETURN TO THIS PAGE AND OBTAIN THEIR CONSENT BEFORE INTERVIEWING THEM. 2 0 1 MODULE B. HOUSEHOLD ROSTER (HEAD OF HH OR RESPONSIBLE ADULT) NO. QUESTIONS AND FILTERS CODING CATEGORIES C00 INSERT TIME MODULE STARTED HOUR MINUTE C01 CLUSTER CODE AND HOUSEHOLD NUMBER HH C02A LINE NUMBER (B01) C02B YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NOT AVAILABLE 3 C24 HDDS QUESTIONS C03 Was yesterday an unusual or special day (Festival, Funeral, fasting YES . . . . . . . . . . . . . . . . . 1 C16Y etc.) or were most household members absent? NO . . . . . . . . . . . . . . . . . 2 C04 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 C05 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 C06 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 C07 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 C08 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 C09 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 C10 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 C11 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 C12 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 C13 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 C14 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 C15 YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 Any fresh or dried fish or shellfish? Any cheese, yogurt, milk, or other milk products? Any foods made with oil, fat, or butter? Any sugar or honey? Any other foods, such as condiments, coffee or tea? (Person responsible for food preparation) Any bread, biscuits, rice, noodles, posho, porridge, cereals or other foods made from wheat, maize, rice, sorghum, millet? Any fruits? (watermelon, jackfruit, etc.) Any eggs? Any Irish potatoes, yams, sweet potatoes, cassava, matoke, or any other foods made from roots or tubers? Any vegetables? (pumpkin, squash, etc.) PERSON IN CHARGE OF FOOD PREPARATION FROM THE HOUSEHOLD ROSTER (B06) = 1) OBTAIN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? CLUS TER Now I would like to ask you about the types of foods that you or anyone else in your household ate yesterday during the day and at night. READ THE LIST OF FOODS. RECORD “YES” IF ANYONE IN THE HOUSEHOLD ATE THE FOOD IN QUESTION. RECORD “NO” IF NO ONE IN THE HOUSEHOLD ATE THE FOOD. THE FOODS LISTED SHOULD BE THOSE PREPARED IN THE HOUSEHOLD AND EATEN IN THE HOUSEHOLD OR TAKEN ELSEWHERE TO EAT. DO NOT INCLUDE FOODS CONSUMED OUTSIDE THE HOME THAT WERE PREPARED ELSEWHERE. Any beef, pork, lamb, goat, rabbit, field rats, wild game, chicken, duck, or other birds, liver, kidney, heart, or other organ meats or blood? Module C. Food Security Any foods made from beans, peas, lentils, green grams, cowpeas, pigeon peas, nuts, or sunflower seeds? NO. QUESTIONS AND FILTERS CODING CATEGORIES (Person responsible for food preparation) Module C. Food Security FOOD INSECURITY EXPERIENCE SCALE (FIES) C16Y YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C17Y C16M YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C17Y YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C18Y C17M YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C18Y YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C19Y C18M YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C19Y YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C20Y C19M YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C20Y YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C21Y C20M YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C21Y YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C22Y C21M YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C22Y YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C23Y C22M YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C23Y YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C24 C23M YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C24 INSERT TIME MODULE ENDED HOUR MINUTE GO TO MODULE CC Now, I would like to ask you some questions about your food consumption in the past 30 DAYS or 12 MONTHS. During the past 30 DAYS was there a time when you or others in your household were worried you would not have enough food to eat because of a lack of money or other resources? During the past 12 MONTHS, was there a time when you or others in your household were worried you would not have enough food to eat because of a lack of money or other resources? During the past 12 MONTHS was there a time when you or others in your household were unable to eat healthy and nutritious food because of a lack of money or other resources? During the past 30 DAYS was there a time when you or others in your household were unable to eat healthy and nutritious food because of a lack of money or other resources? During the past 12 MONTHS was there a time when you or others in your household went without eating for a whole day because of a lack of money or other resources? During the past 30 DAYS was there a time when you or others in your household went without eating for a whole day because of a lack of money or other resources? During the past 30 DAYS was there a time when you or others in your household ate less than you thought you should because of a lack of money or other resources? During the past 12 MONTHS was there a time when your household did not have food because of a lack of money or other resources? During the past 30 DAYS was there a time when your household did not have food because of a lack of money or other resources? During the past 12 MONTHS was there a time when you or others in your household were hungry but did not eat because there was not enough money or other resources for food? During the past 30 DAYS was there a time when you or others in your household were hungry but did not eat because there was not enough money or other resources for food? During the past 12 MONTHS was there a time when you or others in your household ate only a few kinds of foods because of a lack of money or other resources? During the past 30 DAYS was there a time when you or others in your household ate only a few kinds of foods because of a lack of money or other resources? During the past 12 MONTHS was there a time when you or others in your household had to skip a meal because there was not enough money or other resources to get food? During the past 30 DAYS was there a time when you or others in your household had to skip a meal because there was not enough money or other resources to get food? During the past 12 MONTHS was there a time when you or others in your household ate less than you thought you should because of a lack of money or other resources? NO. QUESTIONS AND FILTERS CODING CATEGORIES CC00 INSERT TIME MODULE STARTED HOUR MINUTE CC01 CLUSTER CODE AND HOUSEHOLD NUMBER HH CC02A LINE NUMBER (B01) CC02B YES . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NOT AVAILABLE 3 CC27 PROJECT PARTICIPATION CC03 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8 CC04 CC03A YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 CC03B YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 CC03C YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 CC03D YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 MOBILITY AND SECURITY CC04 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8 LOCAL GOVERNMENT RESPONSIVENESS CC05 CC06 CC07 CC08 CC09 CC10 CC11 CC12 CC13 CC14 CC15 CC16 Module CC. Mobility, Local Government Responsiveness and PPI (Head of HH or Responsible Adult) CLUS TER HEAD OF HOUSEHOLD OR RESPONSIBLE ADULT (B10 = 1) FROM HOUSEHOLD ROSTER OBTAIN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? Local government are actively working to solve problems and meet needs of people like me and my community. 1 2 3 4 5 6 7 8 9 10 Are there areas in your community that you were unable to visit in 2012 due to insecurity, that you are now able to access, such as grazing land, farmland, markets, or social events? Now, I will ask you about your impressions on the performance of representatives from local government. On a scale of 1 to 10, with 10 being the best and 1 being the worst, how would you rank officials in local government in the following categories? Local government officials share important information that helps my household to make better decisions. 1 2 3 4 5 6 7 8 9 10 Local government officials are available to me if I want to express my opinion or solve a problem. 1 2 3 4 5 6 7 8 9 10 Local government officials are aware of the issues of most concern to people like me. 1 2 3 4 5 6 7 8 9 10 Local government officials speak regularly with people like me and interact with us. 1 2 3 4 5 6 7 8 9 10 Local government officials try their best to listen to what people like me have to say. 1 2 3 4 5 6 7 8 9 10 Local government officials are working in the interest of the people, and not their own self-interest. 1 2 3 4 5 6 7 8 9 10 Local government officials are competent and professional in performing their jobs. 1 2 3 4 5 6 7 8 9 10 Local government officials are accountable to the public for the quality of their job performance and the decisions that they take. 1 2 3 4 5 6 7 8 9 10 Local government officials are open and honest about their work and the decisions that they take. 1 2 3 4 5 6 7 8 9 10 Local government officials are willing to share information about their work with me and my community 1 2 3 4 5 6 7 8 9 10 Local government officials have taken action to improve health and water services in my community. 1 2 3 4 5 6 7 8 9 10 Have you or someone from your household regularly participated in [GHG/Rwanu] activities? USE PROBES. Have you received food rations? Have you regularly participated in nutrition training/ meetings? Have you regularly participated in agriculture related training/meetings? Have you participated in any other activties? NO. QUESTIONS AND FILTERS CODING CATEGORIES Module CC. Mobility, Local Government Responsiveness and PPI (Head of HH or Responsible Adult) PPI CC17 NO ROOF . . . . . . . . . . . . . . . 1 THATCHED . . . . . . . . . . . . . . . 2 IRON SHEETS . . . . . . . . . . . . . . . 3 ASBESTOS . . . . . . . . . . . . . . . 4 TILE . . . . . . . . . . . . . . . 5 CONCRETE . . . . . . . . . . . . . . . 6 TIN . . . . . . . . . . . . . . . 7 OTHER . . . . . . . . . . . . . . . 96 CC18 EARTH . . . . . . . . . . . . . . . 1 CEMENT . . . . . . . . . . . . . . . 2 RAMMED EARTH . . . . . . . . . . . . . . . 3 CONCRETE . . . . . . . . . . . . . . . 4 TILES . . . . . . . . . . . . . . . 5 BRICK . . . . . . . . . . . . . . . 6 STONE . . . . . . . . . . . . . . . 7 WOOD . . . . . . . . . . . . . . . OTHER . . . . . . . . . . . . . . . 96 CC19 FIREWOOD . . . . . . . . . . . . . . . 1 COW DUNG . . . . . . . . . . . . . . . 2 GRASS . . . . . . . . . . . . . . . 3 REEDS . . . . . . . . . . . . . . . 4 ELECTRICITY GRID . . . . . . . . . . . . . . . 5 PARAFFIN LANTERN . . . . . . . . . . . . . . . 6 PARAFFIN TABODA . . . . . . . . . . . . . . . 7 PLYWOOD . . . . . . . . . . . . . . . 8 SOLAR . . . . . . . . . . . . . . . 9 GENERATOR . . . . . . . . . . . . . . . 10 GAS . . . . . . . . . . . . . . . 11 BIOGAS . . . . . . . . . . . . . . . 12 THERMAL . . . . . . . . . . . . . . . 13 OTHER . . . . . . . . . . . . . . . 96 CC20 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 CC21 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 CC22 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 CC23 LESS THAN TWO . . . . . . . . . . . . . . . . . . . . 1 TWO . . . . . . . . . . . . . . . . . . . . 2 MORE THAN TWO . . . . . . . . . . . . . . . . . . . . 3 CC24 THERE IS NO CHILD AGES 6 TO 12 1 AT LEAST ONE CHILD AGES 6 TO 12 IS NOT IN SCHOOL . . . . . . . . . . . . . . . . . 2 ALL CHILDREN AGES 6 TO 12 ARE ATTENDING SCHOOL . . . . . . . . . . . . . . . . . . . . . . . 3 CC25 MORE THAN SIX . . . . . . . . . . . . . . . . . . . . 1 FIVE OR SIX . . . . . . . . . . . . . . . . . . . . 2 FOUR OR LESS . . . . . . . . . . . . . . . . . . . . 3 CC26 NO . . . . . . . . . . . . . . . . . . . . 1 NO FEMALE HEAD/SPOUSE 2 YES . . . . . . . . . . . . . . . . . . . . 3 CC27 INSERT TIME MODULE ENDED HOUR MINUTE GO TO MODULE F What type of material is mainly used for the construction of the floor? What type of material is mainly used for the construction of the roof? Are all household members ages 6 to 12 currently in school? How many usual members does the household have? Can the (oldest) female head/spouse read and write with understanding in any language? What source of energy does this household mainly use for lighting? Does any member of your household own a bicycle at present? Has the household consumed beans (fresh/dry), groundnuts (in shell, shelled, pounded) or peas in the last 7 days? Does every member of the household have at least one pair of shoes? What was the average number of meals taken by household members per day in the last 7 days? Module F. Water, Sanitation and Hygiene (Head of HH or Responsible Adult) NO. QUESTIONS AND FILTERS SKIP F00 INSERT TIME MODULE STARTED HOUR MINUTE F01 CLUSTER CODE AND HOUSEHOLD NUMBER CLUSTER HH F02A HEAD OF THE HOUSEHOLD OR RESPONSIBLE ADULT (B10 = 1) FROM HOUSEHOLD ROSTER LINE NUMBER (B01) YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 F02B NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 NOT AVAILABLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 F17 DRINKING WATER F04 What is currently the main source of drinking water for PIPED WATER members of your household? PIPED INTO DWELLING . . . . . . . . . . . . . . . . . . . . . . . . . . 11 COPY FROM DHS PIPED TO YARD/PLOT . . . . . . . . . . . . . . . . . . . . . . . . . . 12 F07 PUBLIC TAP/STANDPIPE. . . . . . . . . . . . . . . . . . . . . . . . . . 13 TUBEWELL OR BOREHOLE. . . . . . . . . . . . . . . . . . . . . . . . . . 21 DUG WELL PROTECTED WELL . . . . . . . . . . . . . . . . . . . . . . . . . . 31 UNPROTECTED WELL . . . . . . . . . . . . . . . . . . . . . . . . . . 32 WATER FROM SPRING . . . . . . . . . . . . . . . . . . . . . . . . . . 41 UNPROTECTED SPRING. . . . . . . . . . . . . . . . . . . . . . . . . . 42 RAINWATER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 F07 ROCK CATCHMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 TANKER TRUCK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 CART WITH SMALL TANK 71 SURFACE WATER (RIVER/DAM/ LAKE/POND/STREAM/CANAL/IRRIGATION CHANNEL) . 81 BOTTLED WATER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 OTHER 96 (SPECIFY) F05 Where is that water source located? IN OWN DWELLING . . . . . . . . . . . . . . . . . . . . . . . . 1 IN OWN YARD/PLOT . . . . . . . . . . . . . . . . . . . . . . . . 2 F07 ELSEWHERE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 F06 How long does it take to go there, get water, and come back? MINUTES . . . . . . . . . . . . . . . . . . . . . . . . DON'T KNOW . . . . . . . . . . . . . . . . . . . . . . . . . . . . 998 F07 Is water available from this source all year round? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 F08 In the last two weeks, was water unavailable from this YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 source for a day or longer? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 F09 Do you do anything to the water to make it safer to drink? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 F10a F10 What do you usually do to make the water safer CHLORINATION (chemical disinfection) . . . . . . . . . . . . . . . . A to drink? FLOCCULENT/DISNFECTANT (physio-chemical disinfection) B FILTRATION (physical removal) . . . . . . . . . . . . . . . . . . . C Anything else? SOLAR DISINFECTION (UV/heat disinfection) . . . . . . . . . D BOILING (disinfection via heat) . . . . . . . . . . . . . . . . . . . . . . E OTHER X (SPECIFY) DON'T KNOW . . . . . . . . . . . . . . . . . . . . . . . . . . Z F10a. What types of containers do you use for water storage? No containers (water used on delivery, not stored) 1 Open containers (bucket/drum/Jerry-can without lid) 2 RECORD ONE ANSWER ONLY Containers with lid (bucket/drum/Jerry with lid) . . . . . . . 3 Containers with and without lid . . . . . . . . . . . . . . . . . . . . . . D OTHER X (SPECIFY) REFER TO THE MANUAL FOR INSTRUCTIONS ON OBSERVATIONS NEEDED TO VERIFY EACH METHOD. RECORD ALL RESPONSES AFTER VERIFICATION. CODING CATEGORIES PROTECTED SPRING OBTAIN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? Module F. Water, Sanitation and Hygiene (Head of HH or Responsible Adult) NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP SANITATION F11 What kind of toilet facility do members of your FLUSH OR POUR FLUSH TOILET household usually use ? FLUSH TO PIPED SEWER SYTEM . . . . . . . . . . . . . . . . . . 11 FLUSH TO SEPTIC TANK . . . . . . . . . . . . . . . . . . . . . . . . 12 FLUSH TO PIT LATRINE . . . . . . . . . . . . . . . . . . . . . . . . . . 13 FLUSH TO SOMEWHERE ELSE . . . . . . . . . . . . . . . . . . . . 14 FLUSH, DON'T KNOW WHERE . . . . . . . . . . . . . . . . . . . . 15 PIT LATRINE VENTILATED IMPROVED PIT LATRINE. . . . . . . . . . . . . . . 21 PIT LATRINE WITH SLAB . . . . . . . . . . . . . . . . . . . . . . . . 22 PIT LATRINE WITHOUT SLAB/OPEN PIT. . . . . . . . . . . . . . 23 ECOSAN LATRINE . . . . . . . . . . . . . . . . . . . . . . . . . . 31 BUCKET TOILET . . . . . . . . . . . . . . . . . . . . . . . . . . 41 HANGING TOILET/HANGING LATRINE 51 DESIGNATED AREA NOT ALREADY LISTED . . . . . . . . . . . . . . . . . . 61 DIG AND BURY 62 NO FACILITY/BUSH/FIELD . . . . . . . . . . . . . . . . . . . . . . . . . . 71 F13A OTHER 96 (SPECIFY) F12 Does your household share the toilet YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 facility with other households? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 F13A F13 How many households share that toilet facility? NUMBER OF HOUSEHOLDS IF LESS THAN 10 . . . . . . . . . . . . . . . . 10 OR MORE HOUSEHOLDS . . . . . . . . . . . . . . . . . . . . 95 DON'T KNOW . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 F13A Do the children of this household use a different YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 toilet facilitY as the adult members? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 F14 F13B What kind of facility do children use? FLUSH OR POUR FLUSH TOILET FLUSH TO PIPED SEWER SYTEM . . . . . . . . . . . . . . . . . . 11 FLUSH TO SEPTIC TANK . . . . . . . . . . . . . . . . . . . . . . . . 12 FLUSH TO PIT LATRINE . . . . . . . . . . . . . . . . . . . . . . . . . . 13 FLUSH TO SOMEWHERE ELSE . . . . . . . . . . . . . . . . . . . . 14 FLUSH, DON'T KNOW WHERE . . . . . . . . . . . . . . . . . . . . 15 PIT LATRINE VENTILATED IMPROVED PIT LATRINE. . . . . . . . . . . . . . . 21 PIT LATRINE WITH SLAB . . . . . . . . . . . . . . . . . . . . . . . . 22 PIT LATRINE WITHOUT SLAB/OPEN PIT. . . . . . . . . . . . . . 23 ECOSAN LATRINE . . . . . . . . . . . . . . . . . . . . . . . . . . 31 BUCKET TOILET . . . . . . . . . . . . . . . . . . . . . . . . . . 41 HANGING TOILET/HANGING LATRINE 51 DESIGNATED AREA NOT ALREADY LISTED . . . . . . . . . . . . . . . . . . 61 DIG AND BURY 62 NO FACILITY/BUSH/FIELD . . . . . . . . . . . . . . . . . . . . . . . . . . 71 OTHER 96 (SPECIFY) HANDWASHING F14 Please show me where members of your household OBSERVED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 most often wash their hands. NOT OBSERVED, NOT IN DWELLING/YARD/PLOT . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 NOT OBSERVED, NO PERMISSION TO SEE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 NOT OBSERVED, OTHER REASON . . . . . . . . . . . . . . . . . . . . . . . . . . 4 (SKIP TO F17) F15 OBSERVATION ONLY: WATER IS AVAILABLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 OBSERVE PRESENCE OF WATER AT THE WATER IS NOT AVAILABLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 PLACE FOR HANDWASHING. F16 OBSERVATION ONLY: SOAP OR DETERGENT OBSERVE PRESENCE OF SOAP, DETERGENT, (BAR, LIQUID, POWDER, PASTE) . . . . . . . . . . . . . . . . . . . . . . . . . . 1 OR OTHER CLEANSING AGENT AT THE PLACE FOR ASH, MUD, SAND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 HANDWASHING. NONE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 F17 INSERT TIME MODULE FINISHED GO TO HOUR MINUTE MODULE G 0 IF RESPONDENT CANNOT GIVE CLEAR RESPONSE, THEN OBSERVE THE TOILET AND RECORD THE CORRECT RESPONSE. Module G. Agriculture (All Farmers) G00 INSERT TIME MODULE STARTED HOUR MINUTE G01 CLUSTER CODE AND HOUSEHOLD NUMBER CLUSTER NO. QUESTIONS AND FILTERS NAME ______________________ NAME ___________________ NAME ____________________ G02A FARMER FROM THE HOUSEHOLD LINE NO. LINE NO. LINE NO. ROSTER (B14 = 1) (B01) (B01) (B01) G02B FARMER'S SEX FROM THE MALE . . . . . . . . . . . . . . . . . 1 MALE . . . . . . . . . . . 1 MALE . . . . . . . . . . . 1 HOUSEHOLD ROSTER (B04) FEMALE . . . . . . . . . . . . . . . 2 FEMALE 2 FEMALE 2 G02C YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 (SKIP TO G04) (SKIP TO G04) (SKIP TO G04) NO . . . . . . . . . . . . . . . . . . . . 2 NO . .. . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 (SKIP TO G26) (SKIP TO G26) (SKIP TO G26) NOT AVAILABLE . . . . . . 3 NOT AVAILABLE . . . 3 NOT AVAILABLE . . . . . . . 3 G03A YES .. . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 YES .. . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 (SKIP TO G26) (SKIP TO G26) (SKIP TO G26) G03B ALTERNATE RESPONDENT'S LINE LINE LINE LINE NUMBER FROM THE HH ROSTER (B01) NUMBER….. NUMBER….. NUMBER….. G03C ALTERNATE RESPONDENT'S SEX MALE . . . . . . . . . . . . . . . . . 1 MALE . . . . . . . . . . . 1 MALE . . . . . . . . . . . 1 FROM THE HH ROSTER (B04) FEMALE . . . . . . . . . . . . . . . 2 FEMALE 2 FEMALE 2 G03D YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 (SKIP TO G26) (SKIP TO G26) (SKIP TO G26) INSTRUCTION TO RESPONDENT WHEN THE FARMER IS ABSENT: I want to know about all farming activities in this household. Because [NAME OF ABSENT FARMER] is absent, please answer these questions about [HIS/HER] farming. G04 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 (SKIP TO G05) (SKIP TO G05) (SKIP TO G05) G04A OWN . . . . . . . . . . . . . . . . . 1 OWN . . . . . . . . . . . . . 1 OWN. . . . . . . . . . . . . . 1 RENT . . . . . . . . . . . . . . . . . 2 RENT . . . . . . . . . . . 2 RENT . . . . . . . . . . . 2 SHARECROP . . . . . . . . 3 SHARECROP. . . . . . . 3 SHARECR. . . . . . . . . OP 3 NONE OF THESE . . 4 NONE OF THESE 4 NONE OF THES. . . . . E 4 (SKIP TO G05) (SKIP TO G05) (SKIP TO G05) G04B ● ● ● ACRES ACRES ACRES G05 YES .. . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 YES .. . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . 2 NO . .. . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 G06 IF YES, THEN CONTINUE. IF YES, THEN CONTINUE. IF YES, THEN CONTINUE. IF NO, SKIP TO G26. IF NO, SKIP TO G26. IF NO, SKIP TO G26. FIRST FARMER SECOND FARMER THIRD FARMER REGISTER NAME, SEX AND LINE NUMBER FROM THE HOUSEHOLD ROSTER FOR THE FIRST FARMER (B14=1). START WITH QUESTION G02 FOR THE FIRST FARMER. IF THERE IS MORE THAN ONE FARMER IN THE HOUSEHOLD THEN INTERVIEW ALL ADDITIONAL FARMERS AS NEEDED. QUESTIONS G03A-G03D ARE ONLY USED IF THE FARMER IS ABSENT AFTER THREE TRIES AND THERE IS AN ALTERNATE RESPONDENT THAT IS KNOWLEDGABLE ABOUT THE FARMER'S AGRICULTURAL PRACTICES AND DECISIONS. OBTAIN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? ARE YOU INTERVIEWING AN ALTERNATE RESPONDENT ? OBTAIN WRITTEN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? Do you have access to a plot of land (even if very small) over which you make decisions about what will be grown, OR how it will be grown, OR how to dispose/store/sell the harvest? INCLUDES PLOTS OF LAND ALLOCATED TO FARMERS FOR GROWING CROPS BUT NOT OWNED. Do you have animals and/or aquaculture products over which you make decisions about their management OR how to dispose/store/sell of the production? CHECK ANSWERS TO QUESTIONS G04 AND G05. IS THE ANSWER TO QUESTION G04 OR G05 "YES"? Do you own, rent, or sharecrop the land over which you make decisions? What was your farm size (the largest total area of your farmland) in any cropping season in the past 12 months? NOTE: BEEKEEPING IS INCLUDED INCLUDE LAND THAT IS OWNED, RENTED OR SHARE CROPPED NO. QUESTIONS AND FILTERS NAME ______________________ NAME ___________________ NAME ____________________ FIRST FARMER SECOND FARMER THIRD FARMER FINANCIAL SERVICES G07 YES ...................................... 1 YES ........................... 1 YES ............................ 1 NO ...................................... NO ........................... NO ............................ G08 YES ...................................... 1 YES ........................... 1 YES ............................ 1 NO ...................................... NO ........................... NO ............................ G09 YES .. . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 YES .. . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . 2 NO . .. . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 VALUE CHAIN ACTIVITIES G10A YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 (SKIP TO G11) (SKIP TO G11) (SKIP TO G11) G10B Purchase inputs for crops A Purchase inputs for livestock B Tillage of land C Bulk transporting of inputs produced D Bulk transporting of animals (on foot or by vehicle) E Sorting produce F Grading produce G Drying or processing produce H Trading or marketing (wholesale, retail, or export) for either animals or crops I Use of supplements to increase livestock production J Feed production K Other activity Specify________________________________________ L Other activity Specify________________________________________ M DID NOT PRACTICE ANY OF THESE ACTIVITIES IN PAST 12 MONTHS…………………………………………………. Y . CIRCLE ALL ACTIVITIES STATED. AGRICULTURAL PRACTICES G11 REFER TO G04 TO DETERMINE WHETHER "YES" NO "YES" NO "YES" NO THE RESPONDENT HAS ACCESS TO A CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED PLOT OF LAND OVER WHICH HE/SHE MAKES DECISIONS. (SKIP TO G14) (SKIP TO G14) (SKIP TO G14) G12 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO G14) (SKIP TO G14) (SKIP TO G14) DON'T KNOW . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 G13 1 ___________________ 1 ___________________ 1 ___________________ 2 ___________________ 2 ___________________ 2 ___________________ 3 ___________________ 3 ___________________ 3 ___________________ 4 ___________________ 4 ___________________ 4 ___________________ 5 ___________________ 5 ___________________ 5 ___________________ 6 ___________________ 6 ___________________ 6 ___________________ In the past 12 months, did you plant any crops in the plot(S) over which you make decisions? What crops did you plant during the [PAST 12 MONTHS] in the plot(S) over which you make decisions. REGISTER THE NAME OF ALL CROPS NAMED BY THE RESPONDENT CROPS NAMED BY THE REGISTER RED SORGHUM AND WHITE SORGHUM AS TWO DIFFERENT CROPS CROPS NAMED BY THE A B C D E F G H I J K L M Y 2 Did you take any agricultural credit, in cash or in kind, in the [PAST 12 MONTHS]? PROBES: Village savings and credit groups, farmers group, MFI, Bank, RUSACCO etc. 2 Did you save any cash in the [PAST 12 MONTHS]? PROBES: village savings and credit group, MFI, cooperatives, bank, mobile banking, etc. 2 A B C D E F G H I J K L M Y Now I want to ask you about farming and livestock practices about which you make decisions. This includes practices about crops, animals and aquaculture products. Which of the following activities related to farming and/or animal husbandry have you practiced or received services for during [PAST 12 MONTHS]? READ EACH ACTIVITY. RECORD RESPONSES IN THE CELL BELOW THE RESPONSE LIST FOR EACH FARMER. DO NOT CIRCLE THE CODE IN THE RESPONSE LIST. IF NONE OF THESE ACTIVITIES WERE PRACTICED, THEN CIRCLE Y. A B C D E F G H I J K L M Y Some people insure their agricultural production against negative unexpected circumstances, such as drought, floods, and pests by paying for this service. Did you buy agricultural insurance in the [PAST 12 MONTHS] ? 2 Do you plant any crops or raise/buy livestock with the specific intention to sell or resell to earn income? 2 2 NO. QUESTIONS AND FILTERS NAME ______________________ NAME ___________________ NAME ____________________ FIRST FARMER SECOND FARMER THIRD FARMER G13A SOIL PREPARATION BY HAND…………………………………………………………………………….. A SOIL PREPARATION WITH OX PLOW…………………………………………………………………………… B SOIL PREPARATION WITH TRACTOR………………….……………………………………………………. C BROADCASTING SEED………...………………………………………………………………… D PLANTING SEEDS IN ROWS………………………………………………………………………… E CROP ROTATION…………………………………………………………….. F APPLY FERTILIZER…………………………………………………………………………. G INTERCROPPING…………………………………………………………………………. H PEST AND DISEASE CONTROL I WEED CONTROL J MULCHING K THINNING L CONTOURING LAND WITH BERMS AND SWALES M OTHER PRACTICE ________________________________________ ……………..... N (SPECIFY NAME AND TYPE OF PRACTICE) OTHER PRACTICE ________________________________________ ………………….. O (SPECIFY NAME AND TYPE OF PRACTICE) DID NOT USE ANY OF THESE PRACTICES IN PAST 12 MONTHS………….…..… Y CROP #1 CROP #2 CROP #3 CROP #4 CROP #5 CROP #6 G14 CHECK G05: CODE CODE CODE CODE CODE CODE DETERMINE WHETHER THE "YES" "NO" "YES" "NO" "YES" "NO" RESPONDENT HAS ANY ANIMALS OR CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED AQUACULTURAL PRODUCTS OVER WHICH HE/SHE MAKES DECISIONS (SKIP TO G18) (SKIP TO G18) (SKIP TO G18) G15 1 ___________________ 1 ___________________ 1 ___________________ 2 ___________________ 2 ___________________ 2 ___________________ 3 ___________________ 3 ___________________ 3 ___________________ 4 ___________________ 4 ___________________ 4 ___________________ 5 ___________________ 5 ___________________ 5 ___________________ 6 ___________________ 6 ___________________ 6 ___________________ G16A ANIMAL SHELTERS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A KRAALS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B VACCINATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C DEWORMING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D HOMEMADE ANIMAL FEEDS MADE OF LOCALLY AVAILABLE PRODUCTS E USED THE SERVICES OF COMMUNITY ANIMAL HEALTH WORKERS . . . . . . . . . F PURCHASED DRUGS/MEDICINES TO GIVE TO ANIMALS . . . . . . . . . . . . . . . . G ROTATIONAL GRAZING H DEHORNING I CASTRATION J DID NOT PRACTICE ANY OF THESE ACTIVITIES IN PAST 12 MONTHS Y SPECIES #1 SPECIES #2 SPECIES #3 SPECIES #4 SPECIES #5 SPECIES #6 G16B VETERINARIAN . . . . . . . 1 VETERINARIAN . . . . . . . 1 VETERINARIAN . . . . . . . 1 COMMUNITY ANIMAL COMMUNITY ANIMAL COMMUNITY ANIMAL HEALTH WORKER. . 2 HEALTH WORKER. . 2 HEALTH WORKER. . 2 OTHER SOURCE. . . . . 3 OTHER SOURCE. . . . . 3 OTHER SOURCE. . . . . 3 DID NOT PURCHASE DID NOT PURCHASE DID NOT PURCHASE DRUGS/MEDICINES 9 DRUGS/MEDICINES 9 DRUGS/MEDICINES 9 For each crop you planted, did you use any of these practices In the [PAST 12 MONTHS]? CIRCLE ALL PRACTICES THAT ARE MENTIONED FOR EACH CROP PROBE TO IDENTIFY ANY OTHER PRACTICES REGISTER ALL PRACTICES THAT RESPONDENT MENTIONS What animal species did you raise/care for and make decisions about during the [PAST 12 MONTHS]? A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y A B C D E F G H I J K L M N O Y CIRCLE ALL THE PRACTICES THAT ARE MENTIONED FOR EACH SPECIES REGISTER THE NAME OF ALL ANIMAL SPECIES LISTED BY THE RESPONDENT. NOTE: IF THE RESPONDENT MENTIONS BEEKEEPING, DO NOT INCLUDE HERE. IT WILL BE COVERED IN QUESTIONS 17A, 17B, 17C AND 17D. A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y A B C D E F G H I Y CIRCLE ALL THE PRACTICES THAT ARE MENTIONED FOR EACH SPECIES Did you use any of the following practices when you cared for the animals during the [PAST 12 MONTHS]? If you purchased drugs or medicines to give to animals, where did you purchase the drugs? ASK ONLY FOR RESPONDENTS THAT ANSWERED "G" TO QUESTION G16. (SPECIFY) (SPECIFY) (SPECIFY) NO. QUESTIONS AND FILTERS NAME ______________________ NAME ___________________ NAME ____________________ FIRST FARMER SECOND FARMER THIRD FARMER G17A YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO G18) (SKIP TO G18) (SKIP TO G18) G17B KENYAN TOP BAR 1 KENYAN TOP BAR 1 KENYAN TOP BAR 1 TRADITIONAL BEEHIVE 2 TRADITIONAL BEEHIVE 2 TRADITIONAL BEEHIVE 2 LOG HIVE 3 LOG HIVE 3 LOG HIVE 3 TREE SWARM HARVEST 4 TREE SWARM HARVEST 4 TREE SWARM HARVEST 4 OTHER . . . . . 5 OTHER . . . . . . . . . . . 5 OTHER . . . . . . . . . . . 5 G17C SMOKER 1 SMOKER 1 SMOKER 1 BEE SUIT 2 BEE SUIT 2 BEE SUIT 2 HIVE TOOL/KNIFE 3 HIVE TOOL/KNIFE 3 HIVE TOOL/KNIFE 3 OTHER . . . . . 4 OTHER . . . . . . . . . . . 4 OTHER . . . . . . . . . . . 4 DID NOT HARVEST ....... 9 DID NOT HARVEST ............... 9 DID NOT HARVEST .............. 9 G17D USED JERRY CAN 1 USED JERRY CAN 1 USED JERRY CAN 1 USED HONEY BUCKET 2 USED HONEY BUCKET 2 USED HONEY BUCKET 2 OTHER . . . . . 3 OTHER . . . . . . . . . . . 3 OTHER . . . . . . . . . . . 3 DID NOT STORE ........... 9 DID NOT STORE ....................... 9 DID NOT STORE...................... 9 G18 MANAGEMENT OF WATERSHED OR REFORESTATION . . . . . . . . . . . . . . . . . . . . . . A AGRO-FORESTRY OR CULTIVATION OF FRUIT TREES. . . . . . . . . . . . . . . . . . . . . . . B MANAGEMENT OF FOREST PLANTATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C MANAGEMENT OF NATURAL REGENERATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . D COLLECTING PRODUCTS FROM FOREST PLANTS (SUCH AS GUM ARABIC) E SOIL CONSERVATION ON HILLSIDES F CONSTRUCTION OF WATER CATCHMENTS G DID NOT PRACTICE ANY OF THESE ACTIVITIES FOR THE PAST 12 MONTHS Y IMPROVED STORAGE PRACTICES G19 CHECK G04: CODE CODE CODE CODE CODE CODE DETERMINE WHETHER THE "YES" "NO" "YES" "NO" "YES" "NO" RESPONDENT HAS ACCESS TO CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED A PLOT OF LAND OVER WHICH HE/SHE MAKES DECISIONS. (SKIP TO G26 ) (SKIP TO G26 ) (SKIP TO G26 ) G20 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO G26 ) (SKIP TO G26 ) (SKIP TO G26 ) DON'T KNOW . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 G21 Did you store sorghum? YES . . . . . . . . . . . . . . . . . . 1 YES . .. . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO G22) (SKIP TO G22) (SKIP TO G22) G21A CEREAL BANK 1 CEREAL BANK 1 CEREAL BANK 1 GRANARY . . . . . . . . . 2 GRANARY . . . . . . . . . . . . . 2 GRANARY . . . . . . . . . . . . . 2 SUPER GRAIN BAGS/ SUPER GRAIN BAGS/ SUPER GRAIN BAGS/ PICS BAGS 3 PICS BAGS 3 PICS BAGS 3 MANUFACTURED SILO 4 MANUFACTURED SILO 4 MANUFACTURED SILO 4 OTHER METHOD. . . . . 5 OTHER METHOD. . . . . 5 OTHER METHOD. . . . . 5 (SPECIFY) (SPECIFY) (SPECIFY) G22 Did you store maize? YES . . . . . . . . . . . . . . . . . . 1 YES . .. . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO G23) (SKIP TO G23) (SKIP TO G23) G22A MANUFACTURED SILO 1 MANUFACTURED SILO 1 MANUFACTURED SILO 1 GRANARY . . . . . . . . . 2 GRANARY . . . . . . . . . . . . . 2 GRANARY . . . . . . . . . . . . . 2 SUPER GRAIN BAGS/ SUPER GRAIN BAGS/ SUPER GRAIN BAGS/ PICS BAGS 3 PICS BAGS 3 PICS BAGS 3 OTHER METHOD. . . . . 4 OTHER METHOD. . . . . 4 OTHER METHOD. . . . . 4 (SPECIFY) (SPECIFY) (SPECIFY) Did you use any of the following natural resources management practices or techniques that were not related directly to your on-farm production during the [PAST 12 MONTHS]? CIRCLE ALL PRACTICES MENTIONED BY THE RESPONDENT A B C D E F G Y A B C D E F G Y A B C D E F G Y How did you store your honey? Did you keep bees during the [PAST 12 MONTHS]? Which equipment did you use to harvest honey? Which hive did you use? During [THE LAST 12 MONTHS], did you store any crops from the plot over which you make decisions? What was the main method that you used to store sorghum? What was the main method that you used to store maize? NO. QUESTIONS AND FILTERS NAME ______________________ NAME ___________________ NAME ____________________ FIRST FARMER SECOND FARMER THIRD FARMER G23 YES . . . . . . . . . . . . . . . . . . 1 YES . .. . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO G24) (SKIP TO G24) (SKIP TO G24) G23A MANUFACTURED SILO 1 MANUFACTURED SILO 1 MANUFACTURED SILO 1 GRANARY . . . . . . . . . 2 GRANARY . . . . . . . . . . . . . 2 GRANARY . . . . . . . . . . . . . 2 SUPER GRAIN BAGS/ SUPER GRAIN BAGS/ SUPER GRAIN BAGS/ PICS BAGS 3 PICS BAGS 3 PICS BAGS 3 OTHER METHOD. . . . . 4 OTHER METHOD. . . . . 4 OTHER METHOD. . . . . 4 (SPECIFY) (SPECIFY) (SPECIFY) G24 Did you store rice? YES . . . . . . . . . . . . . . . . . . 1 YES . .. . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO G25) (SKIP TO G25) (SKIP TO G25) G24A MANUFACTURED SILO 1 MANUFACTURED SILO 1 MANUFACTURED SILO 1 GRANARY . . . . . . . . . 2 GRANARY . . . . . . . . . . . . . 2 GRANARY . . . . . . . . . . . . . 2 SUPER GRAIN BAGS/ SUPER GRAIN BAGS/ SUPER GRAIN BAGS/ PICS BAGS 3 PICS BAGS 3 PICS BAGS 3 OTHER METHOD. . . . . 4 OTHER METHOD. . . . . 4 OTHER METHOD. . . . . 4 (SPECIFY) (SPECIFY) (SPECIFY) G25 YES . . . . . . . . . . . . . . . . . . 1 YES . .. . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO G26) (SKIP TO G26) (SKIP TO G26) G25A 1 1 1 2 2 2 REGISTER THE NAMES OF THE ADDITIONAL CROPS THAT WERE 3 3 3 STORED BY EACH RESPONDENT 4 4 4 G25B CEREAL BANK . . . . . . . . . . . . . . . . 1 MANUFACTURED SILO . . . . . . . . . . . . . . . . . . . . . 2 GRANARY . . . . . . . . . 3 SUPER GRAIN BAGS/PICS BAGS 4 OTHER METHOD . . . . . . . . . . . 5 ADDITIONAL CROP #1 ADDITIONAL CROP #2 ADDITIONAL CROP #3 ADDITIONAL CROP #4 G26 G27 GO TO INSERT TIME MODULE ENDED HOUR MINUTE MODULE D1 Did you store legumes (beans, cowpeas, pigeon peas, or green grams/mung beans)? What was the main method that you used to store legumes (beans, cowpeas, pigeon peas, or green orams/muno beans)? What was the main method that you used to store rice? In addition to sorghum, maize, rice and legumes, did you store any additional crops from the plot over which you make decisions during the [PAST 12 MONTHS]? What other crops did you store during the [PAST 12 MONTHS] What was the main method that you used to store each of the additional crops? 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 THERE ARE NO MORE QUESTIONS FOR THIS FARMER. GO TO G02 FOR ANOTHER FARMER. IF THERE ARE NO MORE FARMERS, GO TO G27. GO TO G02 FOR ANOTHER FARMER. IF THERE ARE NO MORE FARMERS, GO TO G27. GO TO G02 FOR ANOTHER FARMER. IF THERE ARE NO MORE FARMERS, GO TO G27. CIRCLE THE MAIN METHOD MENTIONED TO STORE ANY ADDITIONAL CROPS (SPECIFY) 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 D00 INSERT TIME MODULE STARTED HOUR MINUTE D01 CLUSTER CODE AND HOUSEHOLD NUMBER HH FIRST ELIGIBLE CHILD SECOND ELIGIBLE CHILD THIRD ELIBIBLE CHILD FROM ROSTER FROM ROSTER FROM ROSTER NO. QUESTIONS AND FILTERS NAME NAME NAME D02 CHILD UNDER 5 YEARS OLD (B07= 1) LINE NO. LINE NO. LINE NO. FROM THE HOUSEHOLD ROSTER CHILD (B01) CHILD (B01) CHILD (B01) D03A CAREGIVER'S LINE NUMBER FROM THE HOUSEHOLD LINE NO. LINE NO. LINE NO. ROSTER (B08) CAREGIVER CAREGIVER CAREGIVER D03B YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 (SKIP TO D65) (SKIP TO D65) (SKIP TO D65) NOT AVAILABLE . 3 NOT AVAILABLE . . . . 3 NOT AVAILABLE . . . . . 3 D04 What is [CHILD NAME]'s sex? MALE . . . . . . . . . . . . 1 MALE . . . . . . . . . . . . 1 MALE . . . . . . . . . . . . 1 FEMALE . . . . . . . . . . 2 FEMALE . . . . . . . . . 2 FEMALE . . . . . . . . . . 2 D05 I would like to ask you some questions about [CHILD'S NAME]. Does [CHILD'S NAME] have a health/vaccination card or other document with the birth date recorded? DAY DAY DAY MONTH MONTH MONTH YEAR YEAR YEAR IF A DOCUMENT WITH THE BIRTHDATE IS NOT SHOWN THEN ASK: In what month and year was [CHILD'S NAME] born? What is [HIS/HER] birthday? RECORD BIRTH DAY, MONTH AND YEAR D06 YEARS YEARS YEARS D07 MONTHS MONTHS MONTHS D08 CHECK D05, D06, AND D07 TO VERIFY CONSISTENCY. A) IS THE YEAR RECORDED IN D05 CONSISTENT WITH THE AGE IN YEARS RECORDED IN D06? OBTAIN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? IF THE CAREGIVER DOES NOT KNOW THE EXACT DAY OF BIRTH, ENTER “98”, INDICATING “DON’T KNOW” FOR DAY. YOU DO NOT NEED TO PROBE FURTHER FOR DAY OF BIRTH. NOTE THAT YOU ARE NOT ALLOWED TO ENTER “DON’T KNOW” FOR MONTH OR YEAR OF BIRTH. IF A DOCUMENT WITH THE BIRTHDATE IS SHOWN RECORD THE DAY, MONTH AND YEAR AS DOCUMENTED. Module D1. Children’s Nutritional Status and Feeding Practices (Primary Caregivers) How old was [CHILD'S NAME] at [HIS/HER] last birthday? RECORD AGE IN COMPLETED YEARS How many months old is [CHILD'S NAME]? RECORD AGE IN COMPLETED MONTHS B) ARE YEAR AND MONTH OF BIRTH RECORDED IN D05 CONSISTENT WITH AGE IN MONTHS RECORDED IN D07? USE BIRTHDATE CONVERSION TABLE TO CHECK. IF THE ANSWER TO A OR B IS “NO‟ RESOLVE ANY INCONSISTENCIES. RECORD AGE IN YEARS IN D06 CLUST ER FIRST ELIGIBLE CHILD SECOND ELIGIBLE CHILD THIRD ELIBIBLE CHILD FROM ROSTER FROM ROSTER FROM ROSTER NO. QUESTIONS AND FILTERS NAME NAME NAME EXCLUSIVE BREAST FEEDING AND MINIMUM ACCEPTABLE DIET D14 CHECK D07: YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 IS THE CHILD UNDER 60 MONTHS (5 YEARS)? NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 (GO TO D02 FOR (GO TO D02 FOR (GO TO D02 ON NEW NEXT CHILD OR TO NEXT CHILD OR TO PAGE FOR NEXT CHILD D66 IF NO MORE D66 IF NO MORE OR TO D66 IF NO CHILDREN) CHILDREN) MORE CHILDREN) DON'T KNOW . . . . 8 DON'T KNOW . . . . 8 DON'T KNOW . . . . . 8 D15 CHECK D07: YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 IS THE CHILD UNDER 24 MONTHS (2 YEARS)? NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 (SKIP TO D54) (SKIP TO D54) (SKIP TO D54) DON'T KNOW . . . . 8 DON'T KNOW . . . . 8 DON'T KNOW . . . . . 8 D16 Has [CHILD'S NAME] ever been breastfed? YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 (SKIP TO D18) (SKIP TO D18) (SKIP TO D18) DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D17 Was [CHILD'S NAME] breastfed yesterday during YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 the day or at night? (SKIP TO D19) (SKIP TO D19) (SKIP TO D19) NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D18 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D19 Now I would like to ask you about some medicines and vitamins that are sometimes given to infants. YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D20 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 Next I would like to ask you about some liquids that [CHILD'S NAME] may have had yesterday during the day or at night. Did [CHILD'S NAME] have: D21 Plain water? YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D22 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 (SKIP TO D24) (SKIP TO D24) (SKIP TO D24) DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D23 How many times yesterday during the day or at night did [CHILD'S NAME] consume any formula? TIMES . . . . TIMES . . . . TIMES . . . . D24 Did [CHILD'S NAME] have any milk such as tinned, YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 powdered or fresh animal milk? NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 (SKIP TO D26) (SKIP TO D26) (SKIP TO D26) DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D25 How many times yesterday during the day or at night did [CHILD'S NAME] consume any milk? TIMES . . . . TIMES . . . . TIMES . . . . D26 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 Did [CHILD'S NAME] have any juice or juice drinks? Any kind of Infant formula like Nani, SMA, Nestle? Sometimes babies are breastfed by another woman or given breast milk from another woman by spoon, cup, bottle, or some other way. This can happen if a mother cannot breastfeed her own baby for various reasons, such as the mother is sick or away, mastitis, etc. Did [CHILD'S NAME] consume breast milk in any of these ways yesterday during the day or at night? Was [CHILD'S NAME] given any vitamin drops or other medicines as drops yesterday during the day or at night? Was [CHILD'S NAME] given oral rehydration solution yesterday during the day or at night? FIRST ELIGIBLE CHILD SECOND ELIGIBLE CHILD THIRD ELIBIBLE CHILD FROM ROSTER FROM ROSTER FROM ROSTER NO. QUESTIONS AND FILTERS NAME NAME NAME D27 Clear broth? YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D28 Yogurt? YES . .. . . . . . . . . . . . . . . 1 YES ................................ 1 YES . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 (SKIP TO D30) (SKIP TO D30) (SKIP TO D30) DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D29 How many times yesterday during the day or at night did [CHILD'S NAME] consume any yogurt? TIMES . . . . TIMES . . . . TIMES . . . . D30 Did [CHILD'S NAME] have any thin porridge? YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D31 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D32 Yesterday, during the day or at night, did [CHILD'S NAME] eat any (ASK QUESTIONS D33A-D49)? D33 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D34 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D35 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D36A YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D36B YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D36C YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D37A YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D37B YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D37C YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D38A YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D38B YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 Any indgenous vegetables such as eboo, alilote, ekamalakwang. ekoreete seeds and/or leaves, ngadekela seeds and/or leaves? Any other fruits like watermelon, tamarind, or jackfruit? PROBES: EUGI Bread, biscuits, cereals/porridge, noodles, rice, chapati, posho, sorghum mash/residue or other foods made ​from grains such as maize, millet, sorghum, wheat, bullrush? Please do not include any food used in a small amount for seasoning or condiments (like chilies, spices, herbs, or fish powder), I will ask you about those foods separately. Any other liquids? Any meat from domesticated animals, such as beef, pork, lamb, goat, chicken, or duck? White irish potatoes, white yams, white sweet potato, cassava, matoke, or any other foods made from roots? Any dark green leafy vegetables such as spinach, lettuce, chard, dodo (amaranthis), pumpkin leaves, cassava leaves, bean leaves, kales/sukumawiki, cowpea leaves or okra? Any other vegetables, like cucumbers, tomatoes, cauliflower, cabbage, broccoli, eggplant, etc.? Ripe mangoes, ripe papaya, melon, passionfruit or other fruits that are dark yellow or orange inside? Any indigenous fruits like ekoreete, ngadekela (white watermelon), ngimongo, ngakamuria, ngikajika, hgikaruka or ngalam? Pumpkin, carrots, squash, orange flesh sweet potatoes or or any other dark yellow or orange fleshed roots, tubers and vegetables? Now I would like to ask you about (other) liquids or foods that (NAME) ate yesterday during the day or at night. I am interested in whether your child had the item even if it was combined with other foods. For example, if (NAME) ate a millet porridge made with a mixed vegetable sauce, you should reply yes to any food I ask about that was an ingredient in the porridge or sauce. LIMIT TO PORRIDGE MIXED VERY THIN OR THICK DRINKS MADE FROM CEREAL. THICKER LESS LIQUID PORRIDGE IS INCLUDED UNDER ITEM D33. Any liver, kidney, heart, blood or other organ meats from domesticated animals such as cow, pig, goat, chicken or duck? FIRST ELIGIBLE CHILD SECOND ELIGIBLE CHILD THIRD ELIBIBLE CHILD FROM ROSTER FROM ROSTER FROM ROSTER NO. QUESTIONS AND FILTERS NAME NAME NAME D39A YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D39B YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D40 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D41 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D42 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D43 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D44 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D45 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D46 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D47 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D48 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D49 YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 CHECK QUESTIONS D33-D49: IF "NO" TO ALL D50 IF "NO" TO ALL D50 IF "NO" TO ALL D50 IF AT LEAST IF AT LEAST IF AT LEAST ONE "YES" OR ONE "YES" OR ONE "YES" OR "DK" TO ALL D51 "DK" TO ALL D51 "DK" TO ALL D51 D50 Did [CHILD'S NAME] eat any solid, semi-solid, or YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 or soft foods yesterday during the day or at night? GO BACK TO D33- GO BACK TO D33- GO BACK TO D33- D47 AND RECORD D47 AND RECORD D47 AND RECORD IF "YES" PROBE: What kind of solid, semi-solid, FOODS EATEN. FOODS EATEN. FOODS EATEN. or soft foods did [CHILD'S NAME] eat? THEN CONTINUE THEN CONTINUE THEN CONTINUE WITH D51. WITH D51. WITH D51. NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 GO TO D52 GO TO D52 GO TO D52 DON'T KNOW . . . . 8 DON'T KNOW . . . . 8 DON'T KNOW . . . . . 8 D51 How many times did [CHILD'S NAME] eat solid, semi-solid, or soft foods other than liquids yesterday TIMES . . . . TIMES . . . . TIMES . . . . during the day or at night? DON'T KNOW . . . . . . . . . . . . 98 DON'T KNOW . . . . . . . . . . . . 98 DON'T KNOW . . . . . . . . . . . . 98 SPECIFIC TARGETED NUTRIENT-RICH COMMODITIES D52a YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D52b YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 D52c YES . .. . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . . 8 GO TO D54 GO TO D54 GO TO D54 FIRST COLUMN SECOND COLUMN THIRD COLUMN Condiments for flavor, such as chilies, spices, herbs, or fish powder? Fresh or dried fish, shellfish or seafood? Any flesh from wild animals, such as game meat, bush rats, birds, wild pigeons, guinea fowl, deer, wild boar, wild goat? Any sugary foods such as chocolates, sweets, candies, pastires, cakes or biscuits? Any foods made from beans, peas, lentils, peanuts or other legumes such as cowpeas, pigeon peas, green grams or simsim? Any foods made from nuts and seeds such as pumpkin, sunflower seeds? Eggs? Any organs from wild animals, such as game meat, bush rats, birds, wild pigeons, guinea fowl, deer, wild boar, wild goat? Any shea nut oils, other oils, fats, butter or foods made with any of these? Cheese, yogurt or other milk products? Did [CHILD'S NAME] eat any foods made from bio-fortified beans yesterday during the day or at night? Did [CHILD'S NAME] eat any orange flesh sweet potatoes (OFSP) or foods made with OFSP yesterday during the day or at night? Did [CHILD'S NAME] eat any foods made from bio-fortified maize or sorghum yesterday during the day or at night? Foods made with red palm oil, red palm nut, or red palm nut pulp sauce? Grubs, snails or insect? Module D2. Children’s Diarrhea and Oral Rehydration Therapy (Primary Caregivers) FIRST ELIGIBLE CHILD SECOND ELIGIBLE CHILD THIRD ELIGIBLE CHILD FROM ROSTER FROM ROSTER FROM ROSTER NO. QUESTIONS AND FILTERS NAME _________________ NAME _________________ NAME __________________ D54 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 (GO TO D02 FOR (GO TO D02 FOR (GO TO D02 ON NEW NEXT CHILD OR NEXT CHILD OR PAGE FOR NEXT CHILD DIARRHEA IS DEFINED AS 3 OR TO D66 IF NO TO D66 IF NO OR TO D66 IF NO MORE WATERY STOOLS IN A DAY. MORE CHILDREN) MORE CHILDREN) MORE CHILDREN) DON'T KNOW . . . . . . 8 DON'T KNOW . . . . . . 8 DON'T KNOW . . . . . . 8 D62 Was he/she given any of the following to drink at any time since he/she started having the diarrhea: YES NO DK YES NO DK YES NO DK a) FLUID FROM FLUID FROM FLUID FROM ORS PKT………1 2 8 ORS PKT………1 2 8 ORS PKT………1 2 8 b) RECONSITUTED ORS RECONSITUTED ORS RECONSITUTED ORS FROM GOVT……….. 1 2 8 FROM GOVT……….. 1 2 8 FROM GOVT……….. 1 2 8 c) HOMEMADE 1 2 8 HOMEMADE 1 2 8 HOMEMADE 1 2 8 FLUID……….. FLUID……….. FLUID……….. D63 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 (GO TO D02 FOR (GO TO D02 FOR (GO TO D02 ON NEW NEXT CHILD OR NEXT CHILD OR PAGE FOR NEXT CHILD TO D66 IF NO TO D66 IF NO OR TO D66 IF NO MORE CHILDREN) MORE CHILDREN) MORE CHILDREN) DON'T KNOW . . . . . . 8 DON'T KNOW . . . . . . 8 DON'T KNOW . . . . . . 8 D64 PILL OR SYRUP PILL OR SYRUP PILL OR SYRUP ANTIBIOTIC . . . . . . . . A ANTIBIOTIC . . . . . . . . A ANTIBIOTIC . . . . . . . . . A ANTIMOTILITY . . . . . . B ANTIMOTILITY . . . . . . B ANTIMOTILITY . . . . . . B OTHER (NOT ANTIBIO- OTHER (NOT ANTIBIO- OTHER (NOT ANTIBIO￾RECORD ALL TREATMENTS TIC, ANTIMOTILITY, TIC, ANTIMOTILITY, TIC, ANTIMOTILITY, GIVEN. OR ZINC) . . . . . . . . C OR ZINC) . . . . . . . . C OR ZINC) . . . . . . . . . C UNKNOWN PILL UNKNOWN PILL UNKNOWN PILL OR SYRUP . . . . . . D OR SYRUP . . . . . . D OR SYRUP . . . . . . D UPDATED FROM DHS INJECTION INJECTION INJECTION ANTIBIOTIC . . . . . . . . E ANTIBIOTIC . . . . . . . . E ANTIBIOTIC . . . . . . . . . E NON-ANTIBIOTIC . . . F NON-ANTIBIOTIC . . . F NON-ANTIBIOTIC . . . . F UNKNOWN UNKNOWN UNKNOWN INJECTION . . . . . . G INJECTION . . . . . . G INJECTION . . . . . . G (IV) INTRAVENOUS (DRIPS) (IV) INTRAVENOUS (DRIPS) (IV) INTRAVENOUS (DRIPS) .............................. H .............................. H ............................... H HOME REMEDY/ HOME REMEDY/ HOME REMEDY/ HERBAL MEDICINE . I HERBAL MEDICINE . I HERBAL MEDICINE . I OTHER X OTHER X OTHER X (SPECIFY) (SPECIFY) (SPECIFY) D65 GO TO D02 GO TO D02 GO TO D02 ON NEW PAGE FOR NEXT CHILD OR, FOR NEXT CHILD OR, FOR NEXT CHILD OR, IF NO MORE CHILDREN, IF NO MORE CHILDREN, IF NO MORE CHILDREN, GO TO D66 GO TO D66 GO TO D66 D66 INSERT TIME MODULE ENDED HOUR MINUTE GO TO MODULE E (1) The term(s) used for diarrhea should encompass the expressions used for all forms of diarrhea, including bloody stools (consistent with dysentery), watery stools, etc. Has [CHILD'S NAME] had diarrhea in the last 2 weeks? (1) THERE ARE NO MORE QUESTIONS FOR THIS CHILD. Was anything (else) given to treat the diarrhea? What (else) was given to treat the diarrhea? A government-recommended homemade fluid? A reconstituted ORS liquid provided through government health facilities? A fluid made from a special packet called ORS sachet such as Zinkid or RESTORE? WOMAN'S NAME WOMAN'S NAME WOMAN'S NAME NO. QUESTIONS AND FILTERS _________________________ _________________________ _________________________ E00 INSERT TIME MODULE STARTED HOUR HOUR HOUR MINUTE MINUTE MINUTE E01 CLUSTER CLUSTER CLUSTER HH HH HH E02A LINE LINE LINE NUMBER (B01) NUMBER (B01) NUMBER (B01) E02B YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 SKIP TO E49A SKIP TO E49A SKIP TO E49A NOT AVAILABLE . . . . . . . . 8 NOT AVAILABLE . . . . . . . . 8 NOT AVAILABLE . . . . . . . . 8 E03 In what month and year were you born? MONTH . . . . . . . . MONTH . . . . . . . . MONTH . . . . . . . . IF DON'T KNOW MONTH RECORD "98" IF DON'T KNOW YEAR RECORD "9998" YEAR YEAR YEAR E04 Please tell me how old you are. What was your age at your last birthday? AGE IN YEARS AGE IN YEARS AGE IN YEARS RECORD AGE IN COMPLETED YEARS AND SKIP TO E06. (SKIP TO E06) (SKIP TO E06) (SKIP TO E06) DON'T KNOW . . . . . . . . . . 98 DON'T KNOW . . . . . . . . . . 98 DON'T KNOW . . . . . . . . . . 98 E05 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E06 IF YES, THEN CONTINUE. IF YES, THEN CONTINUE. IF YES, THEN CONTINUE. IF NO, THEN GO TO E49A IF NO, THEN GO TO E49A IF NO, THEN GO TO E49A WOMAN'S DIETARY DIVERSITY E07 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E08 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E09 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E10 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E10A YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E11 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E12 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E12A YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E13 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E14 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E15 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E16 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E17 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 CLUSTER CODE AND HOUSEHOLD NUMBER IF ANSWER IS 'NO' AND ANOTHER WOMAN IS INCLUDED, THAN QUESTIONS E02-E05 MUST BE REPEATED FOR THE NEW WOMAN. IF THE INFORMATION IN E03, E04 AND E05 CONFLICTS, DETERMINE WHICH IS MOST ACCURATE. IF RESPONDENT CANNOT REMEMBER HOW OLD SHE IS, CIRCLE 98 AND ASK QUESTION E05. Are you between the ages of 15 and 49 years old? CHECK E03, E04 AND E05 (IF APPLICABLE): IS THE RESPONDENT BETWEEN THE AGES OF 15 AND 49 YEARS? LINE NUMBER OF WOMAN 15-49 YEARS OF AGE FROM ROSTER (B09=1) Yesterday during the day or night did you drink/eat any [ASK QUESTIONS E07 to E25]? Any other vegetables, like cucumbers, tomatoes, cauliflower, cabbage, broccoli, eggplant, etc.? Bread, biscuits, cereals/porridge, noodles, rice, chapati, posho, sorghum mash/residue or other foods made ​from grains such as maize, millet, sorghum, wheat, bullrush? Any flesh from wild animals, such as game meat, bush rats, birds, wild pigeons, guinea fowl, deer, wild boar, wild goat? Pumpkin, carrots, squash, orange flesh sweet potatoes or or any other dark yellow or orange fleshed roots, tubers and vegetables? Any dark green leafy vegetables such as spinach, lettuce, chard, dodo (amaranthis), pumpkin leaves, cassava leaves, bean leaves, kales/sukumawiki, cowpea leaves or okra? Ripe mangoes, ripe papaya, melon, passionfruit or other fruits that are dark yellow or orange inside? Any indigenous fruits like ekoreete, ngadekela (white watermelon), ngimongo, ngakamuria, ngikajika, hgikaruka or ngalam? Module E. Women's Nutrition, Breastfeeding and Antenatal Care (Women 15-49) White irish potatoes, white yams, white sweet potato, cassava, matoke, or any other foods made from roots? Any meat from domesticated animals, such as beef, pork, lamb, goat, chicken, or duck? Any organs from wild animals, such as game meat, bush rats, birds, wild pigeons, guinea fowl, deer, wild boar, wild goat? Any liver, kidney, heart, blood or other organ meats from domesticated animals such as cow, pig, goat, chicken or duck? Now I would like to ask you about liquids or foods that you ate yesterday during the day or at night. I am interested in whether you had the item even if it was combined with other foods. For example, if you ate a millet porridge made with a mixed vegetable sauce, you should reply yes to any food I ask about that was an ingredient in the porridge or sauce. Please do not include any food used in a small amount for seasoning or condiments (like chilies, spices, herbs, or fish powder), I will ask you about those foods separately. OBTAIN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? Any indgenous vegetables such as eboo, alilote, ekamalakwang. ekoreete seeds and/or leaves, ngadekela seeds and/or leaves? Any other fruits like watermelon, tamarind, jackfruit, ngimongo, ngakamuria, ngikajika, hgikaruka, or ngalam? WOMAN'S NAME WOMAN'S NAME WOMAN'S NAME NO. QUESTIONS AND FILTERS _________________________ _________________________ _________________________ Module E. Women's Nutrition, Breastfeeding and Antenatal Care (Women 15-49) E18 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E19 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E20 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E21 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E22 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E23 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E24 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E25 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E26 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E27 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 SPECIFIC TARGETED NUTRIENT-RICH COMMODITIES E27a YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E27b YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E27c YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 HISTORY OF PREGNANCIES AND BIRTHS E28 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 (SKIP TO E30) (SKIP TO E30) (SKIP TO E30) NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 DON'T KNOW . . . . . . . . . . . 8 E29 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO E45) (SKIP TO E45) (SKIP TO E45) E30 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO E45) (SKIP TO E45) (SKIP TO E45) E31 Date of Last Live Birth Date of Last Live Birth Date of Last Live Birth DAY..................... |___|___| DAY..................... |___|___| DAY..................... |___|___| MONTH................ |___|___| MONTH................ |___|___| MONTH................ |___|___| YEAR............ |___|___|___|___| YEAR............ |___|___|___|___| YEAR............ |___|___|___|___| IF YES, THEN CONTINUE. IF YES, THEN CONTINUE. IF YES, THEN CONTINUE. IF NO, THEN SKIP TO E45 IF NO, THEN SKIP TO E45 IF NO, THEN SKIP TO E45 E32 NAME _____________________ NAME _________________________ NAME _________________________ Any shea nut oils, other oils, fats, butter or foods made with any of these? Milk, cheese, yogurt or other milk products? LINE NUMBER (B01) |___|___| What is the name of your child who was born on (DATE INDICATED IN E31)? Have you ever been pregnant? Have you ever given birth? When was the last time you gave birth to a boy or girl who was born alive? Now I would like to ask you about pregnancies and births you may have had. Are you currently pregnant? Do you have a health/vaccination card for that child with the birthdate recorded? IF THE HEALTH/VACCINATION CARD IS SHOWN, RECORD THE DATE OF BIRTH AS DOCUMENTED ON THE CARD Any foods made from nuts and seeds such as pumpkin, sunflower seeds? If day is not known, enter '98' above CHECK ANSWER TO QUESTION E31. DID THE RESPONDENT'S LAST LIVE BIRTH OCCUR WITHIN THE PAST 5 YEARS, THAT IS, SINCE [INSERT MONTH OF INTERVIEW] 2012? Any sugary foods such as chocolates, sweets, candies, pastires, cakes or biscuits? Condiments for flavor, such as chilies, spices, herbs, or fish powder? ADD LINE NUMBER (B01) FROM HH ROSTER. WRITE 00 IF CHILD NOT IN LINE NUMBER (B01) |___|___| HH. If day is not known, enter '98' above LINE NUMBER (B01) |___|___| IF THE RESPONDENT DOES NOT KNOW THE BIRTHDATE ASK: If day is not known, enter '98' above Eggs? Fresh or dried fish, shellfish or seafood? Any foods made from bio-fortified sorghum or maize yesterday during the day or at night? Any orange flesh sweet potatoes (OFSP) or foods made with OFSP yesterday during the day or at night Any foods made from bio-fortified beans yesterday during the day or at night? Grubs, snails or insects? Foods made with red palm oil, red palm nut, or red palm nut pulp sauce? Any foods made from beans, peas, lentils, peanuts or other legumes such as cowpeas, pigeon peas, green grams or simsim? WOMAN'S NAME WOMAN'S NAME WOMAN'S NAME NO. QUESTIONS AND FILTERS _________________________ _________________________ _________________________ Module E. Women's Nutrition, Breastfeeding and Antenatal Care (Women 15-49) ANTENATAL CARE AND CONTRACEPTIVE PREVALENCE YES . . . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO E45) (SKIP TO E45) (SKIP TO E45) HEALTH PERSONNEL HEALTH PERSONNEL HEALTH PERSONNEL Whom did you see? DOCTOR ................................... A DOCTOR ................................... A DOCTOR ................................... A NURSE ...................... B NURSE ...................... B NURSE ...................... B MIDWIFE C MIDWIFE C MIDWIFE C HEALTH OFFICIER ............... D HEALTH OFFICIER ............... D HEALTH OFFICIER ............... D Anyone else? HEALTH EXTENSIO WORKER ........................... HEALTH EXTENSIO WORKER ........................... HEALTH EXTENSIO WORKER ........................... WORKER E WORKER E WORKER E OTHER PERSON OTHER PERSON OTHER PERSON TRADITIONAL BIRTH TRADITIONAL BIRTH TRADITIONAL BIRTH ATTENDANT ........................... F ATTENDANT ........................... F ATTENDANT ........................... F OTHER PERSON OTHER PERSON OTHER PERSON ............... X ............... X ............... X (SPECIFY) (SPECIFY) (SPECIFY) Where did you receive antenatal care for this pregnancy? HOME HOME HOME YOUR HOME…………………………….. A YOUR HOME…………………………….. A YOUR HOME…………………………….. A Anywhere else? OTHER HOME……………………………. B OTHER HOME……………………………. B OTHER HOME……………………………. B PUBLIC SECTOR PUBLIC SECTOR PUBLIC SECTOR GOVT HOSPITAL………………………… C GOVT HOSPITAL………………………… C GOVT HOSPITAL………………………… C GOVT HEALTH GOVT HEALTH GOVT HEALTH CENTER/STATION.......... D CENTER/STATION.......... D CENTER/STATION.......... D GOVT HEALTH GOVT HEALTH GOVT HEALTH POST ........................... E POST ........................... E POST ........................... E OTHER PUBLIC OTHER PUBLIC OTHER PUBLIC F F F (SPECIFY) (SPECIFY) (SPECIFY) NON-GOVT (NGO) SECTOR NON-GOVT (NGO) SECTOR NON-GOVT (NGO) SECTOR HEALTH FACILITY G HEALTH FACILITY G HEALTH FACILITY G OTHER NGO FACILITY OTHER NGO FACILITY OTHER NGO FACILITY H H H (SPECIFY) (SPECIFY) (SPECIFY) PRIVATE MED. SECTOR PRIVATE MED. SECTOR PRIVATE MED. SECTOR PVT. HOSPITAL ............... I PVT. HOSPITAL ............... I PVT. HOSPITAL ............... I PVT. CLINIC ............... J PVT. CLINIC ............... J PVT. CLINIC ............... J OTHER PRIVATE MED. OTHER PRIVATE MED. OTHER PRIVATE MED. K K K (SPECIFY) (SPECIFY) (SPECIFY) OTHER X OTHER X OTHER X (SPECIFY) (SPECIFY) (SPECIFY) MONTHS MONTHS MONTHS How many times did you receive antenatal care during this pregnancy? NUMBER OF TIMES NUMBER OF TIMES NUMBER OF TIMES YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 IF YES, THEN SKIP TO E49A IF YES, THEN SKIP TO E49A IF YES, THEN SKIP TO E49A IF NO, THEN CONTINUE. IF NO, THEN CONTINUE. IF NO, THEN CONTINUE. YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . . . . . 2 (SKIP TO E49A) (SKIP TO E49A) (SKIP TO E49A) Which method are you using? NOTE: MOON BEADS ARE LOCALLY USED FOR STANDARD DAYS METHOD INSERT TIME MODULE ENDED HOUR MINUTE GO TO ANTHROPOMETRY GO TO E02A FOR NEXT WOMAN OR, IF NO MORE WOMEN, GO TO E49B. FEMALE STERILIZATION ................A MALE STERILIZATION ....................B IUD................................................C INJECTABLES ...............................D IMPLANTS.....................................E PILL................................................F CONDOM.......................................G FEMALE CONDOM........................H EMERGENCY CONTRACEPTION ......I STANDARD DAYS METHOD ...........J LACTATIONAL AMEN. METHOD......K RHYTHM METHOD..........................L WITHDRAWAL ..............................M OTHER MODERN METHOD ............N OTHER TRADITIONAL METHOD.......O PROBE TO IDENTIFY EACH TYPE OF FACILITY AND RECORD ALL MENTIONED. FEMALE STERILIZATION ................A MALE STERILIZATION ....................B IUD................................................C INJECTABLES ...............................D IMPLANTS.....................................E PILL................................................F CONDOM.......................................G FEMALE CONDOM........................H EMERGENCY CONTRACEPTION ......I STANDARD DAYS METHOD ...........J LACTATIONAL AMEN. METHOD......K RHYTHM METHOD..........................L WITHDRAWAL ..............................M OTHER MODERN METHOD ............N OTHER TRADITIONAL METHOD.......O FEMALE STERILIZATION ................A MALE STERILIZATION ....................B IUD................................................C INJECTABLES ...............................D IMPLANTS.....................................E PILL................................................F CONDOM.......................................G FEMALE CONDOM........................H EMERGENCY CONTRACEPTION ......I STANDARD DAYS METHOD ...........J LACTATIONAL AMEN. METHOD......K RHYTHM METHOD..........................L WITHDRAWAL ..............................M OTHER MODERN METHOD ............N OTHER TRADITIONAL METHOD.......O GO TO E02A FOR NEXT WOMAN OR, IF NO MORE WOMEN, GO TO E49B. GO TO E02A FOR NEXT WOMAN OR, IF NO MORE WOMEN, GO TO E49B. E43 At this time, do you know of a place where you can go to receive services for family planning? ? E41 How many months pregnant were you when you first received antenatal care during this pregnancy? E42 E39 E40 E49B E45 CHECK ANSWER TO QUESTION E28. IS THE WOMAN CURRENTLY PREGNANT? E47 Are you or your partner currently doing something or using any method to delay or avoid getting pregnant? E48 RECORD ALL MENTIONED. E49A THERE ARE NO MORE QUESTIONS FOR THIS WOMAN. E38 Did you see anyone for antenatal care during the pregnancy? CLUSTER CODE HH NUMBER AN00: START TIME HOUR: MINUTE: . CM . KG . CM . KG . CM . KG . CM . KG . CM . KG . CM . KG . CM . KG . CM . KG . CM . KG . CM . KG ANTHROPOMETRY - Children under 5 years of age CHECK QUESTION D14 IN EACH COLUMN OF MODULE D. IF THE CHILD IS LESS THAN 5 YEARS OLD (D14= YES), THE CHILD SHOULD BE MEASURED. TRANSFER THE INFORMATION FOR EACH CHILD LESS THAN 5 YEARS OLD FROM MODULE D TO QUESTIONS D67 TO D72 BELOW. CHILDREN LESS THAN 5 YEARS OF AGE WEIGHT AND HEIGHT OF CHILDREN D67 D68 D69 D70 D71 D72 LINE NO. FROM HH ROSTER (B01) NAME SEX 1. MALE 2. FEMALE AGE IN MONTHS CHILD’S BIRTH DATE (DDMMYY) EDEMA 1. YES 2. NO D73 D74 D75 D76 D77 SOURCE BIRTH DATE HEIGHT (CM) 9994 = NOT PRESENT 9995 = REFUSED HEIGHT MEASURED: 1. LAYING DOWN 2. STANDING UP WEIGHT (KG) 9994 = NOT PRESENT 9995 = REFUSED RESULT 1. MEASURED 2. NOT PRESENT 3. REFUSED 6. OTHER (explain in comments #1) D78: COMMENTS #1 SOURCE OF BIRTH DATE 1. BIRTH CERTIFICATE 4. HOME RECORD 2. BAPTISMAL/CHURCH RECORD 5. PARENT STATEMENT 3. HEALTH REGISTRATION CARD 6. OTHER ___________ EA CODE HH NUMBER . CM . KG . CM . KG . CM . KG . CM . KG . CM . KG . CM . KG . CM . KG . CM . KG . CM . KG AN01: END TIME MINUTE: SIGNATURE: AN03 2 ID NO. DAY MONTH YEAR SIGNATURE: AN05 2 ID NO. DAY MONTH YEAR ANTHROPOMETRY - Non-pregnant women 15-49 years of age CHECK QUESTIONS E04, E05 AND E28 IN MODULE E. IF THE WOMAN IS 15-49 YEARS OLD AND NOT PREGNANT (E28 = NO OR DK), SHE SHOULD BE MEASURED. TRANSFER THE INFORMATION FOR EACH NON-PREGNANT WOMAN 15-49 YEARS FROM MODULE E TO QUESTIONS E50 TO E52 BELOW. SELECTED WOMAN’S (15-49) INFORMATION WEIGHT AND HEIGHT OF SELECTED WOMAN (15-49) RESULT 1. MEASURED 2. NOT PRESENT 3. REFUSED 6. OTHER (Explain in comment #2) E50 E51 E52 E53 E54 E55 LINE NO. FROM HH ROSTER (B01) NAME AGE IN YEARS HEIGHT (CM) 9994 = NOT PRESENT 9995 = REFUSED WEIGHT (KG) 99994 = NOT PRESENT 99995 = REFUSED E56:COMMENTS #2 GO TO MODULE J ANTHROPOMETRIST PRINT NAME: AN02 0 1 8 SUPERVISOR PRINT NAME: AN04 0 1 8 Module J. Gender - Cash (All Men and Women who Earned Cash) FIRST ELIGIBLE PERSON SECOND ELIGIBLE PERSON THIRD ELIBIBLE PERSON NO. QUESTIONS AND FILTERS FROM ROSTER FROM ROSTER FROM ROSTER J00 INSERT TIME MODULE STARTED HOUR MINUTE J01 CLUSTER CODE AND HOUSEHOLD NUMBER HH J02 MAN/WOMAN WHO EARNED CASH (B12 = 1 OR 2) FROM THE HOUSEHOLD ROSTER J03A YES . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 GO TO J12 GO TO J12 GO TO J12 J03B YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . 1 YES . . . . . . . . . . . . 1 NO . . . . . . . . . . . 2 NO . . . . . . . . . . 2 NO . . . . . . . . . . 2 GO TO J12 GO TO J12 GO TO J12 NOT AVAILABLE 3 NOT AVAILABLE 3 NOT AVAILABLE 3 J04 MALE . . . . . . . . . . . . . 1 MALE . . . . . . . . . . . . 1 MALE . . . . . . . . . . . . 1 FEMALE . . . . . . . . . . . 2 FEMALE . . . . . . . . . . 2 FEMALE . . . . . . . . . . 2 J05 YEARS YEARS YEARS J06 YES . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 GO TO J12 GO TO J12 GO TO J12 J07 CASH ONLY . . . . . . . 1 CASH ONLY . . . . . 1 CASH ONLY . . . . 1 CASH AND KIND . 2 CASH AND KIND . . 2 CASH AND KIND . . 2 IN KIND ONLY . . . . . 3 IN KIND ONLY . . . . . 3 IN KIND ONLY . . . . 3 GO TO J12 GO TO J12 GO TO J12 NOT PAID . . . . . . . . . . 4 NOT PAID . . . . . . . 4 NOT PAID . . . . . . 4 J08 RESPONDENT. . . . . . . 1 RESPONDENT . . . . . . . 1 RESPONDENT . . . . . . . 1 SPOUSE/PARTNER . 2 SPOUSE/PARTNER . . 2 SPOUSE/PARTNER . . 2 SOMEONE ELSE IN HH 3 SOMEONE ELSE IN HH 3 SOMEONE ELSE IN HH 3 (SPECIFY) (SPECIFY) (SPECIFY) OTHER 4 OTHER 4 OTHER 4 (SPECIFY) (SPECIFY) (SPECIFY) J09A YES . 1 YES . . 1 YES . . 1 NO 2 NO 2 NO 2 (SKIP TO J10) (SKIP TO J10) (SKIP TO J10) J09B SPOUSE/PARTNER . A SPOUSE/PARTNER . . AA SPOUSE/PARTNER . . A SOMEONE ELSE IN HH SOMEONE ELSE IN HH SOMEONE ELSE IN HH CIRCLE ALL THAT APPLY. (SPECIFY RELATIONSHIP) (SPECIFY RELATIONSHIP) (SPECIFY RELATIONSHIP) B BB B OTHER C OTHER CC OTHER C (SPECIFY) (SPECIFY) (SPECIFY) J10 YOURSELF . . . . . . . 1 YOURSELF . . . . . . . 1 YOURSELF . . . . . . . 1 SPOUSE/PARTNER . 2 SPOUSE/PARTNER . . 2 SPOUSE/PARTNER . . 2 YOURSELF AND YOURSELF AND YOURSELF AND READ ALL RESPONSES AND SELECT ONLY ONE. SPOUSE/PARTNER SPOUSE/PARTNER SPOUSE/PARTNER JOINTLY . . . . . . . 3 JOINTLY . . . . . . . 3 JOINTLY . . . . . . . 3 YOURSELF AND YOURSELF AND YOURSELF AND OTHER JOINTLY . 4 OTHER JOINTLY . . 4 OTHER JOINTLY . . 4 (SPECIFY) (SPECIFY) (SPECIFY) OTHER 5 OTHER 5 OTHER 5 (SPECIFY) (SPECIFY) (SPECIFY) IF YES CONTINUE IF YES CONTINUE IF YES CONTINUE IF NO GO TO J11. IF NO GO TO J11. IF NO GO TO J11. J10A. YES . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 GO TO J11 GO TO J11 GO TO J11 DON'T KNOW ............... 9 DON'T KNOW............... 9 DON'T KNOW............... 9 GO TO J11 J10B CASH ONLY . . . . . . . 1 CASH ONLY . . . . . 1 CASH ONLY . . . . 1 CASH AND KIND . 2 CASH AND KIND . . 2 CASH AND KIND . . 2 IN KIND ONLY . . . . . 3 IN KIND ONLY . . . . . 3 IN KIND ONLY . . . . 3 GO TO J11 GO TO J11 GO TO J11 NOT PAID . . . . . . . . . . 4 NOT PAID . . . . . . . 4 NOT PAID . . . . . . 4 J10C YOURSELF . . . . . . . 1 YOURSELF . . . . . . . 1 YOURSELF . . . . . . . 1 SPOUSE/PARTNER . 2 SPOUSE/PARTNER . . 2 SPOUSE/PARTNER . . 2 YOURSELF AND YOURSELF AND YOURSELF AND READ ALL RESPONSES AND SELECT ONLY ONE. SPOUSE/PARTNER SPOUSE/PARTNER SPOUSE/PARTNER JOINTLY . . . . . . . 3 JOINTLY . . . . . . . 3 JOINTLY . . . . . . . 3 YOURSELF AND YOURSELF AND YOURSELF AND OTHER JOINTLY . 4 OTHER JOINTLY . . 4 OTHER JOINTLY . . 4 (SPECIFY) (SPECIFY) (SPECIFY) OTHER 5 OTHER 5 OTHER 5 (SPECIFY) (SPECIFY) (SPECIFY) FOR RESPONSES #4 AND #5, SPECIFY THE RELATIONSHIP TO THE RESPONDENT. RESPONDENT'S AGE FROM HOUSEHOLD ROSTER (B05) Have you done any work in the past 12 months? CHECK RESPONSE TO QUESTION J04. IS THE RESPONDENT A FEMALE? Has your spouse/partner done any work in the past 12 months? READ DEFINITION OF WORK FROM MODULE B. During the past 12 months, was he usually paid in cash or kind for this work or you not paid at all? Who usually decides how the cash he earns will be used? LINE NO. (B01) LINE NO. (B01) LINE NO. (B01) CLUST ER CHECK HOUSEHOLD ROSTER QUESTION B15 (MARITAL STATUS). IS RESPONDENT MARRIED OR LIVING TOGETHER (B15=1)? With whom do you usually talk about how the cash you earn will be used? IF RESPONSE IS SOMEONE ELSE IN HH OR OTHER, THEN SPECIFY THE RELATIONSHIP TO THE RESPONDENT. FOR RESPONSES B AND C, SPECIFY THE RELATIONSHIP TO THE RESPONDENT. Do you usually discuss with someone about how the cash you earn will be used? Who usually decides how the cash you earn will be used? OBTAIN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? READ DEFINITION OF WORK FROM MODULE B. During the past 12 months, were you usually paid in cash or kind for this work or were you not paid at all? When you were paid in cash for this work, was the payment usually made directly to you, to your spouse/partner or to someone else in your household? FOR RESPONSES #4 AND #5, SPECIFY THE RELATIONSHIP TO THE RESPONDENT. RESPONDENT'S SEX FROM HOUSEHOLD ROSTER (B04) Module J. Gender - Cash (All Men and Women who Earned Cash) FIRST ELIGIBLE PERSON SECOND ELIGIBLE PERSON THIRD ELIBIBLE PERSON NO. QUESTIONS AND FILTERS FROM ROSTER FROM ROSTER FROM ROSTER J11 YOURSELF . . . . . . . 1 YOURSELF . . . . . . . 1 YOURSELF . . . . . . . 1 SPOUSE/PARTNER . 2 SPOUSE/PARTNER . . 2 SPOUSE/PARTNER . . 2 YOURSELF AND YOURSELF AND YOURSELF AND READ ALL RESPONSES AND SELECT ONLY ONE. SPOUSE/PARTNER SPOUSE/PARTNER SPOUSE/PARTNER JOINTLY . . . . . . . 3 JOINTLY . . . . . . . 3 JOINTLY . . . . . . . 3 YOURSELF AND YOURSELF AND YOURSELF AND OTHER JOINTLY . 4 OTHER JOINTLY . . 4 OTHER JOINTLY . . 4 (SPECIFY) (SPECIFY) (SPECIFY) OTHER 5 OTHER 5 OTHER 5 (SPECIFY) (SPECIFY) (SPECIFY) J12 J13 INSERT TIME MODULE ENDED HOUR MINUTE GO TO MODULE K GO TO J02 FOR NEXT CASH EARNER, OR J13 IF NO MORE CASH EARNERS GO TO J02 FOR NEXT CASH EARNER, OR J13 IF NO MORE CASH EARNERS GO TO J02 FOR NEXT CASH EARNER, OR J13 IF NO MORE CASH EARNERS THERE ARE NO MORE QUESTIONS FOR THIS CASH EARNER. Who usually makes decisions about making major household purchases? FOR RESPONSES #4 AND #5, SPECIFY THE RELATIONSHIP TO THE RESPONDENT. Module K. Gender - MCHN (All Men and Women with Child Under 2 Years) FIRST ELIGIBLE PERSON SECOND ELIGIBLE PERSON THIRD ELIBIBLE PERSON NO. QUESTIONS AND FILTERS FROM ROSTER FROM ROSTER FROM ROSTER K00 INSERT TIME MODULE STARTED HOUR MINUTE K01 CLUSTER CODE AND HOUSEHOLD NUMBER HH K02A MAN/WOMAN WITH A CHILD UNDER 2 YEARS (B13=1) FROM THE HOUSEHOLD ROSTER K02B YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . 1 YES . . . . . . . . . . . . 1 NO . . . . . . . . . . . 2 NO . . . . . . . . . . 2 NO . . . . . . . . . . 2 SKIP TO K17 SKIP TO K17 SKIP TO K17 NOT AVAILABLE 3 NOT AVAILABLE 3 NOT AVAILABLE 3 K03 MALE . . . . . . . . . . . . . 1 MALE . . . . . . . . . . . . 1 MALE . . . . . . . . . . . . 1 FEMALE . . . . . . . . . . . 2 FEMALE . . . . . . . . . . 2 FEMALE . . . . . . . . . . 2 K04A YEARS YEARS YEARS K04B MARITAL MARITAL MARITAL STATUS STATUS STATUS K05 YES . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 GO TO K17 GO TO K17 GO TO K17 K06 NAME NAME NAME ____________________ ______________________ ____________________ LINE NO. (B01) |___|___| LINE NO. (B01) |___|___| LINE NO. (B01) |___|___| K07 NUMBER OF TIMES NUMBER OF TIMES NUMBER OF TIMES DON'T KNOW 98 DON'T KNOW . . 98 DON'T KNOW . K08 MORE . . . . . . . . . . . . . 1 MORE . . . . . . . . . . . . . 1 MORE . . . . . . . . . . . . . 1 LESS . . . . . . . . . . . . . 2 LESS . . . . . . . . . . . . . . 2 LESS . . . . . . . . . . . . . . 2 SAME . . . . . . . . . . . . . 3 SAME . . . . . . . . . . . 3 SAME . . . . . . . . . . . 3 DON'T KNOW 8 DON'T KNOW . . 8 DON'T KNOW . 8 K09 IMMEDIATELY . . . . . . . 1 IMMEDIATELY . . . . . . . 1 IMMEDIATELY . . . . . . . 1 LESS THAN 1 HOUR LESS THAN 1 HOUR LESS THAN 1 HOUR AFTER DELIVERY . 2 AFTER DELIVERY. . . . . 2 AFTER DELIVERY. . . . 2 SOME HRS LATER BUT SOME HRS LATER BUT SOME HRS LATER BUT LESS THAN 24 HRS 3 LESS THAN 24 HRS 3 LESS THAN 24 HRS 3 1 DAY LATER . . . . . . . 4 1 DAY LATER. . . . . . . . . . 4 1 DAY LATER. . . . . . . . . . 4 MORE THAN 1 DAY MORE THAN 1 DAY MORE THAN 1 DAY LATER . . . . . . . . 5 LATER . . . . . . . 5 LATER . . . . . . 5 BABY SHOULD NOT BABY SHOULD NOT BABY SHOULD NOT BE BREASTFED . 6 BE BREASTFED . . . . . 6 BE BREASTFED . . . . 6 DON'T KNOW . . . . . . . 8 DON'T KNOW . . . . . . . 8 DON'T KNOW . . . . . . . 8 K10 AGE IN MONTHS AGE IN MONTHS AGE IN MONTHS DON'T KNOW 98 DON'T KNOW . . 98 DON'T KNOW . K11 REDUCED RISK OF A REDUCED RISK OF A REDUCED RISK OF A MATERNAL DEATH MATERNAL DEATH MATERNAL DEATH REDUCED RISK OF B REDUCED RISK OF B REDUCED RISK OF B CHILD DEATH CHILD DEATH CHILD DEATH REDUCED RISK OF C REDUCED RISK OF C REDUCED RISK OF C MISCARRIAGE MISCARRIAGE MISCARRIAGE REDUCED RISK OF D REDUCED RISK OF D REDUCED RISK OF D PREMATURE DELIVERY PREMATURE DELIVERY PREMATURE DELIVERY CHILD WILL GROW E CHILD WILL GROW E CHILD WILL GROW E HEALTHIER HEALTHIER HEALTHIER HEALTH OF OTHER F HEALTH OF OTHER F HEALTH OF OTHER F CHILDREN CHILDREN CHILDREN ECONOMIC BENEFIT G ECONOMIC BENEFIT G ECONOMIC BENEFIT G INCREASED EDUCATIONH INCREASED EDUCATION H INCREASED EDUCATIONH FOR OTHER CHILDREN FOR OTHER CHILDREN FOR OTHER CHILDREN OTHER X OTHER X OTHER X (SPECIFY) (SPECIFY) (SPECIFY) DON'T KNOW . . . . . . . Y DON'T KNOW. . . . . . . Y DON'T KNOW. Y LINE NO. (B01) RESPONDENT'S MARITAL STATUS FROM HOUSEHOLD ROSTER (B15) 98 How many times should a pregnant woman go for antenatal check-ups during the pregnancy? In your opinion, do you think pregnant women, overall, need to eat more, less or the same amount of food as they did before they got pregnant? At what age should a breast-fed child be introduced to semi￾solid or solid foods? How long after birth should a mother first put her baby to the breast? 98 What is the name of your (youngest) child under 2 years of age? OBTAIN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? Can you please list the benefits of waiting at least two years after the last live birth before attempting the next pregnancy? DO NOT READ THE ANSWERS. IF THE RESPONDEND INDICATES THAT S/HE DOES NOT KNOW DO NOT PROBE FOR ADDITIONAL RESPONSES. CIRCLE ALL THAT APPLY. AFTER RECORDING ALL RESPONSES, PROBE TWICE ASKING FOR ANY OTHER BENEFITS. ADD LINE NUMBER (B01) FROM HH ROSTER LINE NO. (B01) CLUST ER LINE NO. (B01) RESPONDENT'S SEX FROM HOUSEHOLD ROSTER (B04) RESPONDENT'S AGE FROM HOUSEHOLD ROSTER (B05) Do you have a child under 2 years of age living in the household? Module K. Gender - MCHN (All Men and Women with Child Under 2 Years) FIRST ELIGIBLE PERSON SECOND ELIGIBLE PERSON THIRD ELIBIBLE PERSON NO. QUESTIONS AND FILTERS FROM ROSTER FROM ROSTER FROM ROSTER YES . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 GO TO K17 GO TO K17 GO TO K17 K12 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . 2 (SKIP TO K14) (SKIP TO K14) (SKIP TO K14) K13 SPOUSE/PARTNER . A SPOUSE/PARTNER . . A SPOUSE/PARTNER . . A SOMEONE ELSE IN HH SOMEONE ELSE IN HH SOMEONE ELSE IN HH CIRCLE ALL THAT APPLY. (SPECIFY RELATIONSHIP) (SPECIFY RELATIONSHIP) (SPECIFY RELATIONSHIP) B B B OTHER C OTHER C OTHER C (SPECIFY) (SPECIFY) (SPECIFY) K14 Yourself . . . . . . . 1 Yourself . . . . . . . 1 Yourself . . . . . . . 1 Spouse/partner . 2 Spouse/partner . . 2 Spouse/partner . . 2 Yourself and Yourself and Yourself and Spouse/partner Spouse/partner Spouse/partner Jointly . . . . . . . 3 Jointly . . . . . . . 3 Jointly . . . . . . . 3 Yourself and Yourself and Yourself and READ ALL RESPONSES AND SELECT ONLY ONE. other jointly . 4 other jointly . . 4 other jointly . . 4 (SPECIFY) (SPECIFY) (SPECIFY) Other 5 Other 5 Other 5 (SPECIFY) (SPECIFY) (SPECIFY) K15 Yourself . . . . . . . 1 Yourself . . . . . . . 1 Yourself . . . . . . . 1 Spouse/partner . 2 Spouse/partner . . 2 Spouse/partner . . 2 Yourself and Yourself and Yourself and READ ALL RESPONSES AND SELECT ONLY ONE. Spouse/partner Spouse/partner Spouse/partner Jointly . . . . . . . 3 Jointly . . . . . . . 3 Jointly . . . . . . . 3 Yourself and Yourself and Yourself and other jointly . 4 other jointly . . 4 other jointly . . 4 (SPECIFY) (SPECIFY) (SPECIFY) Other 5 Other 5 Other 5 (SPECIFY) (SPECIFY) (SPECIFY) K16 Yourself . . . . . . . 1 Yourself . . . . . . . 1 Yourself . . . . . . . 1 Spouse/partner . 2 Spouse/partner . . 2 Spouse/partner . . 2 Yourself and Yourself and Yourself and READ ALL RESPONSES AND SELECT ONLY ONE. Spouse/partner Spouse/partner Spouse/partner Jointly . . . . . . . 3 Jointly . . . . . . . 3 Jointly . . . . . . . 3 Yourself and Yourself and Yourself and other jointly . 4 other jointly . . 4 other jointly . . 4 (SPECIFY) (SPECIFY) (SPECIFY) Other 5 Other 5 Other 5 (SPECIFY) (SPECIFY) (SPECIFY) K17 K18 INSERT TIME MODULE ENDED HOUR MINUTE GO TO MODULE R GO TO K02A FOR NEXT RESPONDENT, OR K18 IF NO MORE RESPONDENTS GO TO K02A FOR NEXT RESPONDENT, OR K18 IF NO MORE RESPONDENTS With whom do you usually discuss this? FOR RESPONSES B AND C, SPECIFY THE RELATIONSHIP TO THE RESPONDENT. Who usually makes decisions about [NAME OF INDEX CHILD]’s health and nutrition? THERE ARE NO MORE QUESTIONS FOR THIS RESPONDENT. GO TO K02A FOR NEXT RESPONDENT, OR K18 IF NO MORE RESPONDENTS IF MALE RESPONDENT ASK: Who usually makes decisions about your spouse/partner's health and nutrition? FOR RESPONSES #4 AND #5, SPECIFY THE RELATIONSHIP TO THE RESPONDENT. FOR RESPONSES #4 AND #5, SPECIFY THE RELATIONSHIP TO THE RESPONDENT. Who usually makes decisions about making major household purchases? CHECK K04B ABOVE, MARITAL STATUS IS PERSON MARRIED/LIVING TOGETHER (K04B=1)? IF FEMALE RESPONDENT ASK: Is there someone with whom you usually discuss your or [NAME OF INDEX CHILD]’s health and nutrition? IF MALE RESPONDENT ASK: Is there someone with whom you usually discuss your spouse/partner’s or [NAME OF INDEX CHILD]’s health and nutrition? IF FEMALE RESPONDENT ASK: Who usually makes decisions about your health and nutrition? FOR RESPONSES #4 AND #5, SPECIFY THE RELATIONSHIP TO THE RESPONDENT. MODULE H. POVERTY MEASUREMENT HOUSEHOLD NUMBER FROM MODULE A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . INSERT TIME MODULE STARTED HOUR CLUSTER NUMBER FROM MODULE A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MINUTES INFORMANT'S LINE NUMBER IN HOUSEHOLD ROSTER (COLUMN 6) . . . . . . . . . . . . . . . . . . . . MODULE H1. FOOD, BEVERAGES AND TOBACCO CONSUMPTION OVER PAST 7 DAYS ITEM YES = 1 FOOD CONSUMPTION OVER FROM PURCHASES TOTAL FROM AGRICULTURAL CODE NO = 2 PAST 7 DAYS SPENT PRODUCTION IF "NO" SKIP TO NEXT ITEM (IF H1.04A =0 THEN SKIP TO H1.06A) 101 Matooke 1 2 105 Sweet Potatoes 1 2 107 Cassava 1 2 109 Irish Potatoes 1 2 110 Rice 1 2 111 Maize 1 2 114 Bread 1 2 115 Millet 1 2 116 Sorghum 1 2 119 Goat Meat 1 2 120 Other Meat 1 2 121 Chicken 1 2 122 Fish 1 2 124 Eggs 1 2 125 Fresh Milk 1 2 126 Infant Formula Foods 1 2 127 Cooking oil 1 2 129 Margarine, Butter, Ghee, etc 1 2 130 Fruits 1 2 Kilogramme . . . 1 NO.12 PLATE . 7 BASKET (DENGU) LITRE. . . . . . . . 15 BASIN. . . . . . 21 50 kg. Bag . . . .2 BUNCH. . . . . . 8 (SHELLED). . . . .. 12 CUP. . . . . . . . . 16 SATCHET/TUBE. . .22 90 kg. Bag . . . .3 PIECE. . . . . . . 9 BASKET (DENGU) TIN. . . . . . . . . 17 TOTAL ……..23 Pail (small) . . .4 HEAP . . . . . . 10 (UNSHELLED) . . 13 GRAM . . . . . . 18 OTHER _________96 Pail (large) . . .5 BALE . . . . . . 11 OX-CART MILLILITRE . . . 19 (SPECIFY) No. 10 plate . . .6 (UNSHELLED) . . 14 TEASPOON. . . . .20 FROM GIFTS AND OTHER SOURCES How much came from own production? Please tell me how much it would have cost to buy that much [FOOD ITEM] if you had to purchase it in the market today. H1.07C SHILLING/UGX ESTIMATED COST ESTIMATED COST (IF H1.06A =0 THEN SKIP TO H1.07A) (IF H1.07A =0 THEN SKIP TO NEXT ITEM) H1.06C SHILLING/UGX Please tell me how much it would have cost to buy that much [FOOD ITEM] if you had to purchase it in the market today. How much came from gifts and other sources? PRODUCT Over the past one week (7 days), did you or others in your household eat any [ITEM]? INCLUDE FOOD BOTH EATEN COMMUNALLY IN THE HOUSEHOLD AND SEPARATELY BY INDIVIDUAL HOUSEHOLD MEMBERS. DO NOT INCLUDE FOOD OR DRINKS EATEN IN RESTAURANTS. How much in total did your household eat in the past week? How much from [ITEM] came from purchases? How much did you spend on what was eaten last week? If the family ate part but not all of something they purchased, estimate only cost of what was consumed. H1.04A QUANTITY H1.04B UNIT H1.05 SHILLING/UGX H1.06A QUANTITY H1.06B UNIT H1.07A QUANTITY H1.07B UNIT UNIT CODES Bags: Uganda normally uses 100kg bags. H1.01 H1.02 H1.03A QUANTITY H1.03B UNIT 20180605_FFP Uganda 2018 Main Questionnaire.xlsx Page 30 MODULE H1. FOOD, BEVERAGES AND TOBACCO CONSUMPTION OVER PAST 7 DAYS ITEM YES = 1 FOOD CONSUMPTION OVER FROM PURCHASES TOTAL FROM AGRICULTURAL CODE NO = 2 PAST 7 DAYS SPENT PRODUCTION IF "NO" SKIP TO NEXT ITEM (IF H1.04A =0 THEN SKIP TO H1.06A) FROM GIFTS AND OTHER SOURCES How much came from own production? Please tell me how much it would have cost to buy that much [FOOD ITEM] if you had to purchase it in the market today. H1.07C SHILLING/UGX ESTIMATED COST ESTIMATED COST (IF H1.06A =0 THEN SKIP TO H1.07A) (IF H1.07A =0 THEN SKIP TO NEXT ITEM) H1.06C SHILLING/UGX Please tell me how much it would have cost to buy that much [FOOD ITEM] if you had to purchase it in the market today. How much came from gifts and other sources? PRODUCT Over the past one week (7 days), did you or others in your household eat any [ITEM]? INCLUDE FOOD BOTH EATEN COMMUNALLY IN THE HOUSEHOLD AND SEPARATELY BY INDIVIDUAL HOUSEHOLD MEMBERS. DO NOT INCLUDE FOOD OR DRINKS EATEN IN RESTAURANTS. How much in total did your household eat in the past week? How much from [ITEM] came from purchases? How much did you spend on what was eaten last week? If the family ate part but not all of something they purchased, estimate only cost of what was consumed. H1.04A QUANTITY H1.04B UNIT H1.05 SHILLING/UGX H1.06A QUANTITY H1.06B UNIT H1.07A QUANTITY H1.07B UNIT H1.01 H1.02 H1.03A QUANTITY H1.03B UNIT 135 Onions 1 2 136 Tomatoes 1 2 139 Other vegetables 1 2 140 Beans 1 2 142 Ground nuts 1 2 145 Peas 1 2 146 Sim sim 1 2 147 Sugar 1 2 148 Coffee 1 2 149 Tea 1 2 150 Salt 1 2 151 Soda (NOT AT RESTAURANTS) 1 2 152 Alcoholic Drinks (NOT AT RESTAURANTS) 1 2 154 Other drinks 1 2 155 Cigarettes 1 2 156 Other Tobacco 1 2 EXPENDITURE AT RESTAURANTS 157 Food 1 2 158 Drinks 1 2 OTHER FOOD NOT LISTED 161 SPECIFY _______________________ 1 2 161 SPECIFY _______________________ 1 2 161 SPECIFY _______________________ 1 2 Kilogramme . . . 1 NO.12 PLATE . 7 BASKET (DENGU) LITRE. . . . . . . . 15 BASIN. . . . . . 21 50 kg. Bag . . . .2 BUNCH. . . . . . 8 (SHELLED). . . . .. 12 CUP. . . . . . . . . 16 SATCHET/TUBE. . .22 90 kg. Bag . . . .3 PIECE. . . . . . . 9 BASKET (DENGU) TIN. . . . . . . . . 17 TOTAL ……..23 Pail (small) . . .4 HEAP . . . . . . 10 (UNSHELLED) . . 13 GRAM . . . . . . 18 OTHER _________96 Pail (large) . . .5 BALE . . . . . . 11 OX-CART MILLILITRE . . . 19 (SPECIFY) No. 10 plate . . .6 (UNSHELLED) . . 14 TEASPOON. . . . .20 UNIT CODES 20180605_FFP Uganda 2018 Main Questionnaire.xlsx Page 31 MODULE H2. NON-DURABLE GOODS AND FREQUENTLY PURCHASED SERVICES OVER PAST MONTH H2.01 HOUSEHOLD AND CLUSTER NUMBER HH. VN. . . . . . . . H2.02 LINE NUMBER IN THE HOUSEHOLD LISTING (COLUMN 10) OF HEAD OF HOUSEHOLD OR RESPONSIBLE ADULT . . . . . . . . . . . . . . . . CODING CATEGORIES COST IN SHILLING/UGX HOUSE/FUEL/POWER 304 Maintenance and repair expenses? YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 305 Water? YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 306 Electricity? YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 NEXT ITEM) 307 Generators/lawn mower fuels? YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 308 Paraffin (Kerosene)? YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 309 Charcoal? YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 310 Firewood? YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 311 Other expenditures? What? YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 451 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 452 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 453 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 454 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 455 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 456 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 457 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) Matches? ITEM NO. QUESTIONS FOR A REFERENCE PERIOD OF ONE MONTH Over the past one month, did your household use or buy any [ITEM]: How much did you pay (how much did they cost) in total? NON-DURABLE OR PESONAL GOODS Soap? Tooth paste? Cosmetics? Handbags, travel bags, etc? Batteries (Dry cells)? Sanitary towels? 20180605_FFP Uganda 2018 Main Questionnaire.xlsx Page 32 MODULE H2. NON-DURABLE GOODS AND FREQUENTLY PURCHASED SERVICES OVER PAST MONTH H2.01 HOUSEHOLD AND CLUSTER NUMBER HH. VN. . . . . . . . H2.02 LINE NUMBER IN THE HOUSEHOLD LISTING (COLUMN 10) OF HEAD OF HOUSEHOLD OR RESPONSIBLE ADULT . . . . . . . . . . . . . . . . CODING CATEGORIES COST IN SHILLING/UGX ITEM NO. QUESTIONS FOR A REFERENCE PERIOD OF ONE MONTH Over the past one month, did your household use or buy any [ITEM]: How much did you pay (how much did they cost) in total? 458 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 459 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 460 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) TRANSPORT AND COMMUNICATION 461 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 NEXT ITEM) 462 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 463 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 466 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 467 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 469 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 471 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 LIST EXPENDITURE LIST EXPENDITURE (NEXT ITEM) 501 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 502 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 601 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) Stamps, envelopes? Newspapers and Magazines? Security protection (weapons, bows, bullets, etc.) Other non-durable and personal goods? What? Tires, tubes, spares, etc Petrol, diesel etc Transport Fares (taxi, bus, boda boda)? Phone fees (fixed/ mobile phones)? Mobile money fees Other transport and communications expenditures? What? HEALTH AND MEDICAL CARE Health and medical care services? Medicines, etc? OTHER SERVICES Sports, theaters, etc? 20180605_FFP Uganda 2018 Main Questionnaire.xlsx Page 33 MODULE H2. NON-DURABLE GOODS AND FREQUENTLY PURCHASED SERVICES OVER PAST MONTH H2.01 HOUSEHOLD AND CLUSTER NUMBER HH. VN. . . . . . . . H2.02 LINE NUMBER IN THE HOUSEHOLD LISTING (COLUMN 10) OF HEAD OF HOUSEHOLD OR RESPONSIBLE ADULT . . . . . . . . . . . . . . . . CODING CATEGORIES COST IN SHILLING/UGX ITEM NO. QUESTIONS FOR A REFERENCE PERIOD OF ONE MONTH Over the past one month, did your household use or buy any [ITEM]: How much did you pay (how much did they cost) in total? 602 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 603 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 604 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 605 YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 606 Other expenditures? What? YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 LIST EXPENDITURE LIST EXPENDITURE (NEXT MODULE) Expenses in hotels, lodging, etc? Dry cleaning and laundry? Houseboys/ girls, Shamba boys etc? Barber and beauty shops? 20180605_FFP Uganda 2018 Main Questionnaire.xlsx Page 34 MODULE H3. NON-FOOD EXPENDITURES OVER PAST 12 MONTHS NO. QUESTIONS AND FILTERS (ONE YEAR REFERENCE) CODING CATEGORIES TOTAL COST IN SHILLING/UGX CLOTHING AND FOOTWEAR 201 Clothing (mens, womens, childrens) YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 202 Other clothing and clothing materials YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 203 Tailoring and Materials YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 204 Footwear (mens, womens, childrens) YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 205 Other Footwear and repairs YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) FURNITURE, CARPET, FURNISHING, ETC. 301 Furniture Items YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 302 Carpets, mats, etc. YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 303 Bedding YES . . . . . . . . . . . . . . 1 TOTAL COST (curtains, bed sheets, mattresses, blankets, etc.) NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 304 Others and Repairs YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) HOUSEHOLD APPLIANCES AND EQUIPMENT 401 Charcoal and Kerosene Stoves YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 402 Electronic Appliances or Equipment YES . . . . . . . . . . . . . . 1 TOTAL COST (iron, kettle, TV, etc.) NO . . . . . . . . . . . . . . 2 EXCLUDE RADIOS - COVERED UNDER ITEM 404. (NEXT ITEM) 403 Transport equipment YES . . . . . . . . . . . . . . 1 TOTAL COST (bicycles, motor cycles, motors, pick-ups, etc.) NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 404 Radio YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 405 Computers for household use YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) ITEM Over the past twelve months (one year), did your household use or buy any [ITEM]: How much did you pay (how much did they cost) in total? 20180605_FFP Uganda 2018 Main Questionnaire.xlsx Page 35 MODULE H3. NON-FOOD EXPENDITURES OVER PAST 12 MONTHS NO. QUESTIONS AND FILTERS (ONE YEAR REFERENCE) CODING CATEGORIES TOTAL COST IN SHILLING/UGX ITEM Over the past twelve months (one year), did your household use or buy any [ITEM]: How much did you pay (how much did they cost) in total? 406 Phone Handsets (both fixed and mobile) YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 407 Agricultural tools YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 408 Security/protection - weapons, bows, bullets YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 409 Other equipment and repairs YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 410 Jewelry, Watches, etc YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) GLASS/TABLEWARE/UTENSILS, ETC 501 Plastics YES . . . . . . . . . . . . . . 1 TOTAL COST (basins, plates, tumblers, buckets, jerry canes) NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 504 Enamel and metallic utensils YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 505 Switches, plugs, cables, etc YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 506 Others and repairs YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) EDUCATION EXPENDITURES 601 Educational expenses YES . . . . . . . . . . . . . . 1 TOTAL COST (fees, PTA, boarding, uniforms, books & supplies) NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 602 Other educational expenses YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) SERVICES NOT ELSEWHERE SPECIFIED 701 Expenditure on household functions YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 702 Expenditure on agricultural services YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 703 YES . . . . . . . . . . . . . . 1 TOTAL COST Other services N.E.S. NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 20180605_FFP Uganda 2018 Main Questionnaire.xlsx Page 36 MODULE H3. NON-FOOD EXPENDITURES OVER PAST 12 MONTHS NO. QUESTIONS AND FILTERS (ONE YEAR REFERENCE) CODING CATEGORIES TOTAL COST IN SHILLING/UGX ITEM Over the past twelve months (one year), did your household use or buy any [ITEM]: How much did you pay (how much did they cost) in total? NON-CONSUMPTION EXPENDITURES 801 Taxes (income, local services, etc.) YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 802 Property rates (taxes) YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 803 User fees and charges YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 805a Pension and social security payments YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 805b Insurance premiums YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 806 Remittances, gifts, and other transfers YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 807 Funerals and other social functions YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 808 Interest on Loans YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 809 Dowry and/or debt payments YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 810 Animal sales letter/market fee YES . . . . . . . . . . . . . . 1 TOTAL COST NO . . . . . . . . . . . . . . 2 (NEXT ITEM) 811 Other expenditures, what? YES . . . . . . . . . . . . . . 1 TOTAL COST (GO TO NEXT LIST EXPENDITURE MODULE) LIST EXPENDITURE 20180605_FFP Uganda 2018 Main Questionnaire.xlsx Page 37 MODULE H4. HOUSING EXPENDITURES NO. QUESTIONS AND FILTERS SKIP 101 OWN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 01 BEING PURCHASED . . . . . . . . . . . . . . . . . . . 02 EMPLOYER PROVIDES . . . . . . . . . . . . . . . . 03 104 FREE, AUTHORIZED . . . . . . . . . . . . . . . . . . . 04 104 FREE, NOT AUTHORIZED . . . . . . . . . . . . . . 05 104 RENTED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 06 105 OTHER 96 104 (SPECIFY) DON'T KNOW/NO RESPONSE/ NOT APPLICABLE . . . . . . . . . . . . . . . . 98 H5 102 SHILLING/UGX________________________ DON'T KNOW/NO RESPONSE/ NOT APPLICABLE . . . . . . . . . . . . . . . . 98 103 How many years ago was this house built? YEARS. . . . . . . . . . . . . . . . . . . . . . . . How old is it? DON'T KNOW . . . . . . . . . . . . . . . . . . . . . . . . . 98 104 SHILLING/UGX________________________ DAY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 H5 WEEK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 H5 MONTH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 H5 YEAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 H5 DON'T KNOW/NO RESPONSE/ NOT APPLICABLE . . . . . . . . . . . . . . . . 8 H5 105 How much do you pay to rent this dwelling? SHILLING/UGX________________________ DAY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 WEEK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 MONTH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 YEAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 DON'T KNOW/NO RESPONSE/ NOT APPLICABLE . . . . . . . . . . . . . . . . 8 CODING CATEGORIES Do you own or are you purchasing this house, is it provided to you by an employer, do you use it for free, or do you rent this house? If you sold this dwelling today, how much would you receive for it? If you rented this dwelling today, how much rent would you receive? 20180605_FFP Uganda 2018 Main Questionnaire.xlsx Page 38 MODULE H5. VALUE OF ASSETS ITEM YES = 1 NUMBER OF UNITS AGE OF ITEMS PRICE IF SOLD ITEMS BOUGHT AMOUNT PAID FOR ALL CODE NO = 2 OF EACH ITEM IN LAST ITEMS BOUGHT IN THE 12 MONTHS LAST 12 MONTHS Does your household own a [ITEM]? CIRCLE 1 (YES) OR 2 (NO) IN THE FOLLOWING COLUMN. IF THE ANSWER IS "NO" ASK THE QUESTIONS FOR THE FOLLOWING ITEM. IF MORE THAN IF MORE THAN ONE ITEM, ONE ITEM, "NO": CIRCLE "2" AND AVERAGE AGE AVERAGE VALUE GO TO NEXT ITEM. 02 Other Buildings besides House 1 2 1 2 03 Land 1 2 1 2 04 Furniture/Furnishings 1 2 1 2 05 Household Appliances e.g. Kettle, Flat iron, etc. 1 2 1 2 06 Television 1 2 1 2 07 Radio/Cassette 1 2 1 2 08 Generators 1 2 1 2 09 Solar panel/electric inverters 1 2 1 2 10 Bicycle 1 2 1 2 11 Motor cycle 1 2 1 2 12 Motor vehicle 1 2 1 2 13 Boat 1 2 1 2 14 Other Transport equipment 1 2 1 2 15 Jewelry and Watches 1 2 1 2 16 Mobile phone 1 2 1 2 17 Computer 1 2 1 2 18 Internet Access 1 2 1 2 19 Other electronic equipment 1 2 1 2 20 Solar lanterns/chargers 1 2 1 2 21 Fuel efficient stoves (charcoal or kerosene) 1 2 1 2 22 Mosquito nets 1 2 1 2 23 Other household assets e.g. lawn mowers, etc. 1 2 1 2 24 Other, what? ________________________ 1 2 1 2 25 Other, what? ________________________ 1 2 1 2 33 Other, what? ________________________ 1 2 1 2 H5.8 How much did you pay for all these [ITEM]s all together (total) in the last 12 months? PRODUCT How many [ITEMS] do you own? What is the age of these [ITEM]s? If you wanted to sell one of these [ITEM]s today, how much would you receive? Did you purchase or pay for any of these [ITEM]s in the last 12 months? H5.1 H5.2 H5.3 NUMBER OF ITEMS H5.4 NUMBER OF YEARS H5.5 SHILLING/UGX H5.7 SHILLING/UGX INSERT TIME MODULE ENDED HOUR MINUTE H5.6 20180605_FFP Uganda 2018 Main Questionnaire.xlsx Page 39 INTERVIEWER'S OBSERVATIONS TO BE FILLED IN AFTER COMPLETING INTERVIEW COMMENTS ABOUT RESPONDENT: COMMENTS ON SPECIFIC QUESTIONS: ANY OTHER COMMENTS: SUPERVISOR'S OBSERVATIONS NAME OF TEAM LEADER: DATE: EDITOR'S OBSERVATIONS NAME OF EDITOR: DATE: Version 1 – January 29, 2018 Page 1 of 5 MODULE L. GENDER – HOUSEHOLD DECISION-MAKING, ACCESS TO CREDIT AND GROUP PARTICIPATION Enumerator: This questionnaire should be administered separately to the primary and secondary respondents identified in the household questionnaire. You should complete this coversheet for each individual identified in the “selection section” even if the individual is not available to be interviewed for reporting purposes. Please double check to ensure:  You have completed the roster section of the household questionnaire to identify the correct primary and/or secondary respondent(s);  You have noted the household number and line number correctly for the person you are about to interview;  You have gained informed consent for the individual in the household questionnaire;  You have sought to interview the individual in private or where other members of the household cannot overhear or contribute answers.  Do not attempt to make responses between the primary and secondary respondent the same—it is ok for them to be different. MODULE 1. INDIVIDUAL IDENTIFICATION Code Code 1.01. Household Number: .................................................................................. 1.08. Type of household Male and female adult …………….1 Female adult only…………………..2 Male adult only……………………...3 Child only (no adults 18 or older)…4 1.02. Cluster number 1.09a. Name of respondent currently being interviewed (Line number from Module B, Household Roster): Surname, First Name ______________________________________ 1.05 District Number KAABONG ……………….1 KOTIDO …………………..2 ABIM ………………………3 MOROTO………………….4 NAPAK…………………… 5 NAKAPIRITPIRIT……….. 6 AMUDAT…………………. 7 1.09b. Sex of Respondent Male …………………..1 Female …………………..2 1.10. Outcome of interview Completed ............................... 1 Incomplete ............................... 2 Absent ...................................... 3 Refused ................................... 4 Could not locate ....................... 5 1.11. Ability to be interviewed alone: Alone ........................................ …1 With adult females present ...... …2 With adult males present ......... …3 With adults mixed sex present . …4 With children present ............... …5 With adults mixed sex and children present….. ............................... 6 1.06. Primary Decision-Maker Name and ID (from Module A and B) ___________________________________________________ 1.07. Secondary Decision-Maker Name and ID (from Module A and B) ___________________________________________________ The primary and secondary decision makers are those who self-identify as the primary male and female (or female only) members responsible for the decision making, both social and economic, within the household. In Male and Female Adult Households, they are usually the husband and wife; however they can also be other household members as long as they are aged 18 and over. In Female Adult Only households, there will only be one Primary Decision￾Maker -- the principal female decision-maker aged 18 or older. Primary and Secondary Decision-Makers do not need to be noted for Male Adult Only and Child Only Households, and the WEAI should not be applied in Male Adult Only and Child Only Households. Version 1 – January 29, 2018 Page 2 of 5 MODULE 2: ROLE IN HOUSEHOLD DECISION-MAKING AROUND PRODUCTION AND INCOME GENERATION Activity Did you (singular) participate in [ACTIVITY] in the past 12 months? Yes ......... 1 No .......... 2 next activity How much input did you have in making decisions about [ACTIVITY]? No input ......................................... 1 Input into very few decisions ......... 2 Input into some decisions ............. 3 Input into most decisions .............. 4 Input into all decisions .................. 5 No decision made ......................... 6 How much input did you have in decisions on the use of income generated from [ACTIVITY] No input .................................... 1 Input into very few decisions .... 2 Input into some decisions......... 3 Input into most decisions.......... 4 Input into all decisions .............. 5 No decision made .................... 6 Activity Code Activity Description 2.01 2.02 2.03 A Food crop farming: crops that are grown primarily for household food consumption 1 2 1 2 3 4 5 6 1 2 3 4 5 6 B Cash crop farming: crops that are grown primary for sale in the market 1 2 1 2 3 4 5 6 1 2 3 4 5 6 C Livestock raising (including beekeeping) 1 2 1 2 3 4 5 6 1 2 3 4 5 6 D Non-farm economic activities: Small business, self-employment, buy-and-sell 1 2 1 2 3 4 5 6 1 2 3 4 5 6 E Wage and salary employment: in-kind or monetary work both agriculture and other wage work 1 2 1 2 3 4 5 6 1 2 3 4 5 6 F Fishing or fishpond culture 1 2 1 2 3 4 5 6 1 2 3 4 5 6 GO TO MODULE 3 Version 1 – January 29, 2018 Page 3 of 5 MODULE 3: ACCESS TO CREDIT 3.01 Have you taken out a cash loan in the last 12 months? 1. Yes  3.07A 2. No 8 Don’t know  3.07A 3.02 Why not? CONTINUE TO 3.07A 01. Didn’t need 02. Couldn’t find a loan that met my needs” (i.e. “is appropriate” in terms of size, terms, etc.); 03. Afraid I couldn’t pay back 04. No loan providers in my area 05. Other (specify) 98 Don’t know Lending sources Has anyone in your household taken any loans or borrowed cash/in-kind from [SOURCE] in the past 12 months? Yes, cash………………...1 Yes, in-kind……………...2 Yes, cash and in-kind…..3 No…………………………4 Don’t know……………….5 Who made the decision to borrow from [SOURCE]? Who makes the decision about what to do with the money/ item borrow from [SOURCE]? Lending source names 3.07 3.08 3.09 A Non-governmental organization (NGO) 1 2 3  3.08 4 5  next item 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 B Informal lender 1 2 3  3.08 4 5  next item 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 C Formal lender (bank/financial institution) 1 2 3  3.08 4 5  next item 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 D Friends or relatives 1 2 3  3.08 4 5  next item 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 E Group based micro-finance or lending including VSLAs / SACCOs/ merry-go￾rounds 1 2 3  3.08 4 5  next item 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 3.08/3.09: Decision-making and control over credit Self……………………………...........................................1 Partner/Spouse ....................................…………………..2 Self and partner/spouse jointly…….....…………………..3 Other household member ....................…………………..4 Self and other household member(s)…………………….5 Partner/Spouse and other household member(s)…........6 Someone (or group of people) outside the household…7 Self and other outside people...……………….….............8 Partner/Spouse and other outside people……………….9 Self, partner/spouse and other outside people..............10 \ Go to next item GO TO MODULE 4 Version 1 – January 29, 2018 Page 4 of 5 MODULE 4: GROUP MEMBERSHIP Group membership Is there a [GROUP] in your community? Yes...... 1 No ......2  next group Are you an active member of this [GROUP]? Yes ..... 1 No ...... 2 Group Categories 4.04 4.05 A Agricultural / livestock/ fisheries producer’s group (including marketing groups) 1 2  Next group 1 2  Next group B Water users’ group 1 2  Next group 1 2 GO TO 4.06a C Forest users’ group 1 2  Next group 1 2  Next group D Credit or microfinance group (including SACCOs/merry-go-rounds/ VSLAs) 1 2  Next group 1 2  Next group E Savings groups (VSLA, merry-go-rounds, etc.) 1 2  Next group 1 2  Next group F Mutual help or insurance group (including burial societies) 1 2  Next group 1 2  Next group G Trade and business association 1 2  Next group 1 2  Next group H Civic groups (improving community) or charitable group (helping others) 1 2  Next group 1 2  Next group I Local government 1 2  Next group 1 2  Next group J Religious group (e.g. Mother’s Union) 1 2  Next group 1 2  Next group K Mother’s group 1 2  Next group 1 2  Next group L Youth group 1 2  Next group 1 2  Next group M Farmers’/cattle rearing cooperative 1 2  Next group 1 2  Next group N Communal grazing land users’ group 1 2  Next group 1 2 GO TO 4.07 O Communal natural resources group (Area Land Committee) 1 2  Next group 1 2 GO TO 4.08 P Disaster planning /response group (Resilience Action Committee) 1 2  Next group 1 2  Next group Q Peace committee 1 2  Next group 1 2  Next group R Other women’s group (only if it does not fit into one of the other categories) 1 2  Next group 1 2  Next group X Other (specify)________________________________________________________ 1 2  GO TO 5.01 1 2  Next group Version 1 – January 29, 2018 Page 5 of 5 MODULE 4: GROUP MEMBERSHIP AND INFLUENCE IN THE GROUP (continued) ASK ONLY IF 4.04B =Yes 4.06a Does the water user’s group manage communal water for livestock in this village? 1. Yes 2. No 99. Don’t know 4.06b Does the water user’s group manage communal water for irrigation in this village? 1. Yes 2. No 99. Don’t know >> Go to 4.04C ASK ONLY IF 4.04N = Yes 4.07 Does the group decide who in the village can use communal grazing land and when they can use it? 1. Yes – who can use (not when) 2. Yes – who can use and when 3. No 99. Don’t know >> Go to 4.04O ASK ONLY IF 4.04O = Yes 4.08 Does the communal natural resources group decide who in the village can gather firewood and how much? 1. Yes- who can gather (not how much) 2. Yes – who can gather and how much 3. No 99. Don’t know >> Go to 4.04P 1 | P a g e Resilience Module: Combined, May 29, 2018 Cluster code (Module A) HH number (from Module A) Start time: Hour Minute UGANDA PBS - MODULE R – RESILIENCE The resilience module questions will be asked to the household head, or other responsible HH member R02A. Respondent's line number (B01) from Module B, Question B10 R02B. OBTAIN WRITTEN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? 1=Yes 2=No  Go to Module H 3=Not available  Go to Module H MODULE R1: SHOCKS AND STRESSORS R101 R102 R103 R104 R105 R106 Over the last year (12 months) did your household experience [the shock]? 1= Yes 2 = No 99 -= Don’t know >>If 2 or 99, Next shock In which month did [the shock] start? Note: If experienced [the shock] more than once, use the month of the most recent occurrence Enter code from list How severe was the overall impact on your household (income) Enter code from list Only ask if R101=1 How severe was the impact on your household’s food consumption? Enter code from list How did you cope with the [shock]? Enter code from list Select all that apply Only ask if R101=1 To what extent has your household been able to recover? Enter code from list Only ask if R103=2,3, or 4 Climatic shocks a. Excessive rains b. Flooding c. Too little rain/drought d. variable rain (early/late) e. Hail/frost f. Landslides/erosion Biological shocks g. Crop disease (rust on wheat, sorghum) h. Crop pests (locusts) 2 | P a g e Resilience Module: Combined, May 29, 2018 SHOCKS CODE LIST R102 R103, R104 R106 Month in which shock started Severity of impact Ability to recover 1. June 2017 2. July 3. August 4. September 5. October 6. November 7. December 8. January 2018 9. February 10. March 11. April 12. May 13. June 2018 99. Don’t know 1. None (the same) 2. Slight decrease 3. Severe decrease 4. Worst ever happened 99. Don’t know 1. Did not recover 2. Partially recovered 3. Fully recovered, same as before the shock 4. Fully recovered and better than before the shock 5. Not affected by [event] 99. Don’t know i. Weeds (e.g., associated with striga) j. Livestock disease k. Human disease outbreaks (from contaminated water) Conflict shocks l. Theft or destruction of assets m. Theft of livestock (raids) Land conflict Water conflict Gender Based Violence Economic shocks n. Delay in food assistance o. Increasing food prices p. Increased prices of agricultural or livestock inputs q. Decreased prices for agricultural or livestock products r. Loss of land/rental property s. Unemployment t. Death or long-term illness of household member u. Non-function of borehole 3 | P a g e Resilience Module: Combined, May 29, 2018 R105 (How coped with the shock) LIVESTOCK AND LAND HOLDINGS COPING STRATEGIES TO GET MORE FOOD OR MONEY a. Send livestock in search of pasture m. Take up new/additional work (casual labor, wage labor) b. Sell livestock n. Sell household items (e.g., radio, bed) c. Slaughter livestock o. Sell productive assets (e.g., plough, water pump) d. Lease out land p. Take out a loan (with interest) from a (formal) bank MIGRATION q. Take out a loan (with interest) from an MFI or village savings group e. HH member migrated r. Take out a loan (with interest) from a money-lender f. Migrate (the whole family) s. Take out a loan (no interest) from friends or relatives within the community (bonding) g. Send children or an adult to stay with relatives t. Take out a loan (no interest) from friends or relatives outside of the community (bridging) u. Gift of money (not remittances) or food from family, friends, church or other group within community (bonding) COPING STRATEGIES TO REDUCE CURRENT EXPENDITURE v. Gift of money (not remittances) or food from family, friends, church or other group outside of community (bridging) h. Take children out of school (to work, or can’t pay school) fees) w. Send children to work for money (e.g., domestic service) i. Move to less expensive housing x. Receive emergency food aid from the government or NGO j. Reduce food consumption (quantity/meal; # of meals/day) y. Receive emergency cash transfer from the government or NGO k. Reduced non-essential HH expenses z. Participate in government or NGO food-for-work or cash-for-work activities l. Gotten food on credit from a local merchant aa. Use money from savings bb. Remittances from a relative that migrated cc. Other (specify) dd. Did nothing Shock exposure and severity (cont’d) R107 To what extent has your ability to meet food needs returned to the level it was before all the shocks and stressors you experienced in the last 12 months? [PROMPT] Ability to meet food needs is the same as before the shock…………………..1 Ability to meet food needs is better than before the shock…………… ……..2 Ability to meet food needs is worse than before the shock…………………. .3 R108 In light of the shocks and stressors you faced in the last 12 months, to what extent do you believe you will be able to meet your food needs in the next year? [PROMPT] Ability to meet food needs will be the same as before the shock…………………..1 Ability to meet food needs will be better than before the shock…………… ……..2 Ability to meet food needs will be worse than before the shock…………………. .3 Don’t know………………………………………………………………………………………………………………4 4 | P a g e Resilience Module: Combined, May 29, 2018 R109 What have you done to protect your household from the impact of shocks in the future? [Read list; select all that apply] Nothing…………………1 Increased savings……….2 Put aside grains (for HH or animals)……………….3 Put water aside……………4 Planted different crops………………….5 Purchased different animals…………………6 Changed livelihood………………………..7 Added different livelihood activity…………..8 Acquired crop insurance……………9 Relocated temporarily………………….10 Relocated permanently……………….11 Other ………………..12 99 Don’t know MODULE R2. PRODUCTIVE ASSETS R201 Number owned now 99 Don’t know a. Plough (oxen-pulled) b. Mechanical plough c. Sickle d. Pick axe e. Axe f. Pruning/cutting shears g. Hoe h. Spade or shovel i. Water trough j. Traditional beehive k. Modern beehive l. Knapsack chemical sprayer m. Mechanical water pump 5 | P a g e Resilience Module: Combined, May 29, 2018 n. Motorized water pump o. Stone grain mill p. Motorized grain mill q. Broad bed maker (oxen-pulled) r. Small tractor s. Hand-held motorized tiller t. Individual granary (at homestead) traditional u. Modern silo v. Grain bag w. Tarpaulin x. Agricultural land (hectares) MODULE R2A. LIVESTOCK ASSETS R201A Number owned now 9999 Don’t know a. Oxen b. Cattle c. Goats d. Sheep e. Donkey/mule f. Poultry h. Horse i. Honey bees (hives) 6 | P a g e Resilience Module: Combined, May 29, 2018 MODULE R3. ACCESS TO MARKETS, INFRASTRUCTURE, AND SERVICES R301 Are the following services available IN or WITHIN FIVE KM of your village?a 1= yes 2= no 99 Don’t know a. Institutions were people can borrow money If yes, go to R302 b. Institutions were people can save money (incljding VSLA) If yes, go to R302a c. Primary school If yes, go to R303a d. Health services (at least level 3 facility) If yes, go to R304a e. Agricultural extension services If yes, go to R305a f. Veterinary services (mobile vet, vet center, etc.) If yes, go to R306a g. Electricity from public utility (main grid) If yes, go to R307 h. Mobile phone service j. Public transport service (boda/boda, bus) Go to R308 a Interviewer: if respondent cannot estimate distance, ask how long to walk to the location. Assume that 60 minutes walking is equal to 5 KM. ASK ONLY IF R301a = YES R302 Who provides this service? Select all that apply 1. Banks 2. MFI (SACCO) 3. Community savings/loan group 4. Shops/merchants 5. Money lender 6. Other (specify): 99. Don’t know >> Go to R301b ASK ONLY IF R301b = YES 7 | P a g e Resilience Module: Combined, May 29, 2018 R302a Who provides this service? Select all that apply 1. Banks 2. MFI (SACCO) 3. Community savings/loan group 4. Other (specify): 99. Don’t know >> Go to R301c ASK ONLY IF R301c = Yes R303a Are there enough teachers for the primary school that children in this village attend? 1. Yes 2. No 99. Don’t know R303b What is the physical condition of the primary school that the children in this village attend? 1. Very good 2. Good 3. Poor 4. Very poor 99. Don’t know >> Go to R301d ASK ONLY IF R301d = Yes R304a What is the physical condition of the health service used by people in this village? 1. Very good 2. Good 3. Poor 4. Very poor 99. Don’t know R304b In the last year was there a time when your household needed health services but could not get them? 1. Yes 2. No Go to R301e 99. Don’t know R304c If yes, why were you not able to get the health services? Select all that apply 1. No beds, facility was full 2. No staff in the facility 3. Health facility was destroyed 4. Security problem (e.g., armed conflict) 5. No transportation 6. No road or poor road condition 7. No drugs at the health center 8. No money for services 9. Quality of the service is very poor 10. Other (specify): 99. Don’t know >> Go to R301e 8 | P a g e Resilience Module: Combined, May 29, 2018 ASK ONLY IF R301e = Yes R305a In the last year was there a time when you needed agricultural extension services but could not get them? 1. Yes 2. No Go to R301f 99. Don’t know R305b Is yes, why were you not able to get agricultural extension services? Select all that apply 1. No service provider in area 2. No equipment/inputs available from service provider 3. No road or poor road condition 4. Too busy/bad timing of ext agent visit 5. Quality of the services is poor 6. Other (specify): 99. Don’t know >> Go to R301f ASK ONLY IF R301f = Yes R306a In the last year was there a time when you needed veterinary services but could not get them? 1. Yes 2. No Go to R301g 99. Don’t know R306b If yes, why were you not able to get the veterinary services? Select all that apply 1. No service provider (vet center, veterinarian) in area 2. Service provision too expensive 3. No vaccines/medicines available 4. No road or poor road condition 5. No money for services 6. Quality of the services is poor 7. Other (specify): 99. Don’t know >> Go to R301g ASK ONLY IF R301g = Yes R307 Does your household have electricity from a public utility (main grid)? 1. Yes 2. No 99. Don’t know >> Go to R301h 9 | P a g e Resilience Module: Combined, May 29, 2018 ASK AFTER COMPLETING R301j R308 Can the village be reached by a tarmac road all year around? 1. Yes 2. No 99. Don’t know Can the villaged be reache murram (graded) road 1. Yes 2. No 99. Don’t know R309 How far away is the nearest livestock market from this village? _____ km 99. Don’t know R310 How far away is the nearest market for selling agricultural products from this village? _____ km 99. Don’t know R311 How far away is the nearest market for purchasing agricultural inputs from this village? _____ km 99. Don’t know MODULE R6. ACCESS TO FINANCIAL SERVICES/ SAVING R601 Do you or any other household member regularly save cash? 1. Yes 2. No Skip to next module 99. Don’t know R602 Where are the savings primarily held? Select only one 1. At home 2. MFI (SACCO) 3. Village savings/credit group (e.g., VSLA) 4. Bank 5. Mobile banking 6. Other 99. Don’t know 10 | P a g e Resilience Module: Combined, May 29, 2018 MODULE R7. ACCESS TO INFORMATION R701 R702 Did you receive any information on [topic] in the last 12 months? 1. Yes 2. No 99 Don’t know If 2, 99, skip to next topic What was your main source of information about [topic]? See codes below a. Early warning for natural hazards (flooding, hail, landslide) b. Long-term changes in weather patterns c. Rainfall/ weather prospects for coming season d. Water prices and availability in local boreholes, shallow wells etc e. Animal health (e.g., disease, epidemic) threats/prevention f. Crop health (e.g., pest outbreaks, disease) threats/prevention g. Improved crop production practices/technologies (CCA, seeds) h. Improved livestock production practices (fodder, husbandry) i. Current market prices for live animals in the area j. Market prices for animal products (milk, hides, skins, etc.) k. Grazing conditions in nearby areas l. Conflict or security issues m. Business and investment opportunities n. Opportunities for borrowing money o. Market prices of the food that you buy p. Child nutrition and health information q. Equal rights for women and men r. Gender-based violence s. Natural resource management 11 | P a g e Resilience Module: Combined, May 29, 2018 CODES FOR R702 - Main Information sources 1 Relatives, friends, neighbors 8 Local market 2 Gov’t officials 9 Gov’t: rural development agents, health/agriculture ext. 3 Village Development Committee 10 NGOs 4 School teachers 11 Newspaper /Radio / TV 5 Group in community (e.g., savings, forest users, farmers) 12 Internet or SMS 6 Religious leaders 13 Private sector (input supplier, veterinarian, etc.) 7 Clan Elders 99 Don’t know 14 Health facility 15 Other MODULE R9. COLLECTIVE ACTION R901 In the last 12 months, have you worked with others in your village to do something for the benefit of everyone in the village? 1. Yes 2. No skip to next module 99. Don’t know R902 What activities did you participate in that benefit the village? Read list; select all that apply 1. Soil conservation (terracing, bunds, half￾moons, gabions, etc.) 2. Flood diversion activities 3. Repaired/built schools 4. Repaired/built health posts or centers 5. Road maintenance/construction 6. Planted trees on communal land 7. Formed a cooperative 8. Area enclosure 9. Improving community access to drinking water 10. Repaired/built communal irrigation system 11. Other (specify) 99. Don’t know 12 | P a g e Resilience Module: Combined, May 29, 2018 MODULE R10. LIVELIHOOD ACTIVITIES R1001 What were the sources of your household’s food/income over the last 12 months? Read each source a. Farming/crop production and sales b. Livestock production/fattening and sales c. Agricultural wage labor d. Non-agricultural wage labor e. Salaried work f. Sale of wild/bush products (including charcoal, firewood) g. Honey production and sales h. Petty trade (selling other products, e.g., grain, veggies, oil, sugar, etc.) i. Petty trade (selling own products, e.g., local beer, sex work) j. Other self-employment/own business (agricultural, e.g., buying/reselling chat) k. Other self-employment/own business (non-agricultural, e.g., stone cutting, hair braiding, etc. l. Rental of land, house, rooms m. Remittances n. Gifts/inheritance o. Safety net food/cash assistance p. Artisanal mining/quarrying p. Other (specify): Note: Enumerator does not record; number is automatically generated R1003 Total number of sources 13 | P a g e Resilience Module: Combined, May 29, 2018 MODULE R13. SOCIAL AND CAPACITY-BUILDING SUPPORT INFORMAL SOURCES OF SOCIAL SUPPORT R1300 During the drought members of my community have helped each other to cope (Scale:1-5; 1=Strongly disagree to 5=Strongly agree) R1301 Read list, single response 1 Strongly disagree 2 Somewhat disagree 3 No opinion 4 Somewhat agree 5 Strongly agree R1302 During the drought members from different communities have helped each other to cope (Scale:1-5; 1=Strongly disagree to 5=Strongly agree) R1303 Read list, single response 1 Strongly disagree 2 Somewhat disagree 3 No opinion 4 Somewhat agree 5 Strongly agree R1304 If your household had a problem and needed help urgently (e.g., food, money, labor, transport, etc.), who IN THIS VILLAGE could you turn to for help? Read list; select all that apply 1. Relatives 2. Non-relatives in my ethnic group/clan 3. Non-relatives in other ethnic group/clan 4. No one 5. Other (specify) 99. Don’t know R1305 If your household had a problem and needed help urgently (e.g., food, money, labor, transport, etc.), who OUTSIDE THIS VILLAGE could you turn to for help? Read list; select all that apply 1. Relatives 2. Non-relatives in my ethnic group/clan 3. Non-relatives in other ethnic group/clan 4. No one 5. Other (specify) 99. Don’t know R1306 Compared to one year ago has your ability to get this type of help (from someone within or outside of your village): 1. Increased 2. Stayed the same 3. Decreased 99. Don’t know R1307 Who INSIDE THIS VILLAGE would you help if they needed help urgently (e.g., food, money, labor, transport, etc.)? Read list; select all that apply 1. Relatives 2. Non-relatives in my ethnic group/clan 3. Non-relatives in other ethnic group/clan 14 | P a g e Resilience Module: Combined, May 29, 2018 4. No one 5. Other (specify): 99. Don’t know R1308 Who OUTSIDE THIS VILLAGE would you help if they needed help urgently (e.g., food, money, labor, transport, etc.)? Read list; select all that apply 1. Relatives 2. Non-relatives in my ethnic group/clan 3. Non-relatives in other ethnic group/clan 4. No one 5. Other (specify): 99. Don’t know LINKING SOCIAL CAPITAL R1309 Do you or does anyone else in your household personally know an elected government official? 1. Yes 2. No Skip to R1312 99. Don’t know R1310 How do you (or other household member) know the government official? Is he or she a… Read list; select all that apply 1. Family member or relative 2. Friend /neighbor 3. Acquaintance (members of a group, friend of a friend, etc.) 4. Other (specify): 99. Don’t know R1311 Could you ask the official to help your family or village if help was needed? 1. Yes 2. No 99. Don’t know R1312 Do you or does anyone else in your household personally know a staff member of an NGO? 1. Yes 2. No Skip to R1315 99. Don’t know R1313 How do you (or another household member) know the NGO staff member? Is he or she a… Read list; select all that apply 1. Family member or relative 2. Friend /neighbor 3. Acquaintance (members of a group, friend of a friend, etc.) 4. Other (specify): 99. Don’t know R1314 Could you ask the NGO staff member to help your family or community if help was needed? 1. Yes 2. No 15 | P a g e Resilience Module: Combined, May 29, 2018 99. Don’t know EDUCATION AND TRAINING SUPPORT R1327 Have you or anyone in your household ever received any vocational (job) or skills training? 1. Yes 2. No Skip to R1329 99. Don’t know R1329 Have you or anyone in your household ever received any business development training (including financial literacy)? 1. Yes 2. No Skip to R1331 99. Don’t know R1331 Have you or anyone in your household ever received any early warning training? 1. Yes 2. No Skip to R1333 99. Don’t know R1333 Have you ever or anyone in your household received any natural resource management training? 1. Yes 2. No Skip to R1335 99. Don’t know R1335 Have you or anyone in your household ever received adult education (literacy or numeracy or financial education)? 1. Yes 2. No 99. Don’t know Skip to R1338 R1337 Have you or anyone in your household ever received training in how to use your mobile phone to get market information like prices? 1. Yes 2. No Skip to R1340 99. Don’t know 16 | P a g e Resilience Module: Combined, May 29, 2018 MODULE R14. ASPIRATIONS AND CONFIDENCE TO ADAPT R1401 Please tell me which one of these two views you most agree with. 1. “Each person is primarily responsible for his/her success or failure in life”. 2. “One’s success or failure in life is a matter of his/her destiny”. R1402 Please tell me which one of these two views you most agree with. 1. “To be successful, above all one needs to work very hard”. 2. “To be successful above all one needs to be lucky”. R1403 Are you willing to move somewhere else to improve your life? 1. Yes 2. No R1404 Are you hopeful about your children’s future? 1. Yes 2. No R1405 What level of education do you want for your children? 1. No preference 2. Any level of primary (but not graduated) 3. Graduated from primary 4. Graduated from secondary 5. Post-secondary (college, university) R1406 Do you agree that one should always follow the advice of the elders? 1. Yes 2. No R1407 Do you communicate regularly with at least one person outside the village? 1. yes 2. No R1408 During the past week, have you engaged in any economic activities with other villages or clans? For example, farming, trading, employment, borrowing or lending money. 1. Yes 2. No R1409 How many times in the past month have you gotten together with friends, family, neighbors, etc. to discuss issues or share food/drinks, either in someone’s home or in a public place? R1410 How many times in the past month have you attended a church/ mosque or other religious service? R1411 In the last year, how many times have you stayed more than 2 days outside your village? 17 | P a g e Resilience Module: Combined, May 29, 2018 Below is a series of statements that you may agree or disagree with. Using the scales below indicate your agreement with each item. Strongly disagree Disagree Slightly disagree Slightly agree Agree Strongly agree R1412 My experience in my life has been that what is going to happen will happen. 1 2 3 4 5 6 R1413 My life is chiefly controlled by other powerful people. 1 2 3 4 5 6 R1414 It is not always wise for me to plan too far ahead because many things turn out to be a matter of good or bad fortune. 1 2 3 4 5 6 R1415 I can mostly determine what will happen in my life. 1 2 3 4 5 6 R1416 When I get what I want, It is usually because I worked hard for it. 1 2 3 4 5 6 R1417 My life is determined by my own actions. 1 2 3 4 5 6 R1418 Most people are basically honest. 1 2 3 4 5 6 R1419 Most people can be trusted. 1 2 3 4 5 6 R1420 I trust my neighbors to look after my house if I am away. 1 2 3 4 5 6 18 | P a g e Resilience Module: Combined, May 29, 2018 MODULE R15: GOVERNMENT SUPPORT R1501 Are there any government or NGO programs in this village? 1. Yes 2. No Skip to 1503 99. Don’t know R1502 What types of programming do they provide? Read list; Select all that apply 1. Emergency food/cash assistance 2. Food/cash transfers 3. Household materials and non-food items 4. Educational assistance 5. Agricultural inputs (seeds, fertilizer, etc.) 6. Livestock inputs (feed, fodder, medicine, etc.) 7. WASH 8. Disaster planning/response 9. Safety net (FFW/CFW) 10. Child malnutrition/infant feeding 11. Other 99 Don’t know R1503 In the last 12 months, did you or your household receive any government or NGO assistance? 1. Yes 2. No Skip to 1505 99. Don’t know R1504 What type(s) of assistance did you or your household receive? Read list; Select all that apply 1. Emergency food/cash assistance 2. Food/cash transfers 3. Household materials and non-food items 4. Educational assistance 5. Agricultural inputs (seeds, fertilizer, etc.) 6. Livestock inputs (feed, fodder, medicine, etc.) 7. WASH 8. Disaster planning/response 9. Safety net (FFW/CFW) 10. Child malnutrition/infant feeding 11. Install water points 12. Install latrines 13. Other 99 Don’t know 19 | P a g e Resilience Module: Combined, May 29, 2018 R1505 Is there an emergency plan for livestock offtake if a drought hits your village? 1. Yes 2. No 99 Don’t know R1506 Do you have an active Peace Committee in your village? 1. Yes 2. No 99. Don’t know R1506a Do you have an active Area Land Committee in your village? 1. Yes 2. No 99. Don’t know R1507 Does this village have a security or police force? 1. Yes 2. No Skip to next module 99. Don’t know R1508 Who provides the nearest security/police force? 1. Subcounty government 2. District government 3. National government 4. Local militia 5. Community members 6. Other (specify): 99. Don’t know R1509 How long does it take for the nearest security/police force to reach this village? 1. Over one hour 2. About one hour 3. Half an hour 4. Minutes 99. Don’t know 20 | P a g e Resilience Module: Combined, May 29, 2018 MODULE R16: GENDER NORMS R1601 Generally, do adult men and women sit and eat together within households? Select only one 1. Yes, regularly 2. Yes, occasionally 3. No 99. Don’t know R1602 Generally, do you and your spouse sit and eat together? Select only one 1. Yes, regularly 2. Yes, occasionally 3. No 4. No spouse/spouse absent 99. Don’t know R1603 Generally, do adult men and women sit together at public meetings? Select only one 1. Yes, regularly 2. Yes, occasionally 3. No 99. Don’t know R1605 Generally, do men in the village help with childcare around the household? Select only one 1. Yes, regularly 2. Yes, occasionally 3. No 99. Don’t know R1606 Generally, who cares for your children? Select only one 1. Yourself 2. Your spouse/partner 3. You help your spouse/partner 4. Your spouse/partner helps you 5. No children in household 6. Other (specify) 99. Don’t know R1607 Generally, do men in the village help collect firewood or carry water for your household? Select only one 1. Yes, regularly 2. Yes, occasionally 3. No 99. Don’t know R1608 Generally, who collections firewood for your household? 1. Yourself 2. Your spouse/partner 3. You help your spouse/partner 21 | P a g e Resilience Module: Combined, May 29, 2018 Select only one 4. Your spouse/partner helps you 5. No need 6. Other (specify) 99. Don’t know R1609 Generally, who fetches water for your household? Select only one 1. Yourself 2. Your spouse/partner 3. You help your spouse/partner 4. Your spouse/partner helps you 5. No need 6. Other (specify) 99. Don’t know Annex 4: Population-Based Survey Data Treatment and Analysis Plan Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan - FINAL Office of Food for Peace (FFP) Contract #: GS-00F-189CA/7200AA18M00002 Contract #: GS00F189CA/AID-OAA-M-15-00022 August 17, 2018 – REVISED This publication was produced for review by the U.S. Agency for International Development. It was prepared by ICF Macro, Inc. Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan i ACRONYMS ANC Antenatal care BL Baseline CAPI Computer-assisted personal interviewing CHN Child health and nutrition CRS Catholic Relief Services DFAP Development food assistance project DFSA Development food security activity DTAP Data treatment and analysis plan EL Endline FANTA Food and Nutrition Technical Assistance Project III FFP Office of Food for Peace FIES Food insecurity experience scale GHG Growth Health and Governance Program GHT Gendered household type HDDS Household dietary diversity score ICF ICF International IFSS Internet file streaming system IP Implementing partner IRC International Research Consortium of Uganda MAD Minimum acceptable diet MC Mercy Corps MCHN Maternal and child health and nutrition MHN Maternal health and nutrition MDD-W Minimum dietary diversity for women ORT Oral rehydration therapy PBS Population-based survey PE Performance evaluation PPI Poverty Probability Index RWANU Resiliency through Wealth, Agriculture and Nutrition in Karamoja TANGO Tango International USAID U.S. Agency for International Development WASH Water, sanitation and hygiene Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan ii TABLE OF CONTENTS 1. BACKGROUND ..........................................................................................................................................................................1 2. STUDY DESIGN AND SAMPLE ..............................................................................................................................................2 3. QUESTIONNAIRE .......................................................................................................................................................................2 4. DATA COLLECTION AND QUALITY CONTROL .........................................................................................................3 4.1. Data Collection Mode and Data Transmission Procedure......................................................................................3 4.2. CAPI Data Entry Training .................................................................................................................................................4 4.3. Field Quality Control Procedures ..................................................................................................................................4 4.4. Data Processing Quality Control Procedures.............................................................................................................5 5. DATA PREPARATION...............................................................................................................................................................8 5.1. Sampling Weights................................................................................................................................................................8 5.2. FFP Indicator Definitions ..................................................................................................................................................8 5.3. Handling of Missing Data and “Don’t know” Responses........................................................................................13 6. DATA ANALYSIS PLAN ..........................................................................................................................................................13 6.1. Analyses for 2018 BL Study ...........................................................................................................................................14 6.1.1 Household Characteristics ..............................................................................................................................14 6.1.2 FFP, Resilience and Custom Indicators ........................................................................................................15 6.1.3 Additional Analyses ...........................................................................................................................................15 6.2 Analyses for 2018 Performance Evaluations ..............................................................................................................20 6.2.1 Comparison of 2013 BL and 2018 EL Household Characteristics .......................................................20 6.2.2 FFP and Custom Indicators .............................................................................................................................21 6.2.3 Comparison of 2013 BL and 2018 EL FFP and Custom Indicators ......................................................21 6.2.4 Additional Analyses ...........................................................................................................................................21 APPENDIX A: Sampling Weights APPENDIX B: Methodology to Calculate New/Modified or Custom Indicators APPENDIX C: Methodology to Calculate Poverty Indicators APPENIDX D: Overview of PPI Analyses APPENDIX E: Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 1 1. BACKGROUND USAID’s Office of Food for Peace (FFP) is the U.S. Government leader in international food assistance. In 2018, FFP awarded ICF International (ICF) a contract to conduct a joint baseline (BL)/endline (EL) population-based household survey (PBS) in the Karamoja region of Uganda. The BL survey is for two newly funded development food security activities (DFSAs), beginning in fiscal year (FY) 2017 which will be implemented by Catholic Relief Services (CRS) and Mercy Corps (MC) in seven districts of Karamoja. The EL survey is for two development food assistance projects (DFAPs) that were implemented by ACDI VOCA and Mercy Corps in the same seven districts of Karamoja. These projects started in FY 2012 and ended in FY 2017. The project areas for the new DFSAs and prior DFAPs overlap to a large extent. For this reason, ICF will administer a joint BL/EL PBS using a common questionnaire in the overlap and non-overlap areas encompassed by the prior DFAPs and current DFSAs. The common questionnaire will be driven by the indicators required for the BL PBS for the new DFSAs, many of which (but not all) overlap with those required for the DFAPs. The purpose of the BL PBS for the DFSAs is to assess the current status of key indicators, to serve as a point of comparison with indicators collected at EL and to have a better understanding of the prevailing conditions and perceptions of the populations in the DFSA implementation areas. The study results will also be used to further refine program targeting and, where possible, to understand the relationship between variables to inform program design. The results of the EL PBS for the prior DFAPs will be used to evaluate change over time in the indicators that were collected at BL as part of the performance evaluations (PEs) for these DFAPs. The fieldwork for the joint BL/EL PBS will be conducted in June-July 2018. ICF has subcontracted the International Research Consortium of Uganda (IRC), a local data collection firm, to support the field implementation of the joint BL/EL PBS. ICF will work closely with the survey subcontractor in the implementation of the PBS. Implementing partners and their roles From 2012-2017, two implementing partners (IPs) conducted the prior DFAPs in the Karamoja region: (1) ACDI VOCA and its partners implemented the Resiliency through Wealth, Agriculture and Nutrition in Karamoja (RWANU) Program in Amudat, Moroto, Napak and Nakapiripirit districts. (2) MC and its partners implemented the Growth Health and Governance Program (GHG) Program in Abim, Kotido and Kaabong districts. In 2017, FFP awarded two new DFSAs in the Karamoja region: (1) MC and its partners will implement the Apolou DFSA in Kaabong, Kotido, Moroto, and Amudat districts. (2) CRS and its partners will implement the Nuyok DFSA in Abim, Nakapiripirit and Napak districts. Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 2 2. STUDY DESIGN AND SAMPLE This section briefly describes the study design and sample. A more detailed description of the sampling design for the joint BL/EL PBS is available in the “Uganda Joint Baseline/Endline PBS Protocol”, June 2018. A detailed description of the sampling design for the BL PBS for the prior DFAPs can be found in the “Development Food Aid Program in Uganda Baseline Survey” Report, March 2014. The BL component of the joint BL/EL PBS serves as the first phase of a pre-post survey cycle for the DFSA awards, and the EL component of the joint BL/EL PBS serves as the second phase of a pre-post survey cycle for the prior DFAP awards. The pre-post design (using the 2013 BL survey and the 2018 EL component of the joint BL/EL PBS) allows for the determination of statistically significant change in indicators between the BL and EL for the prior DFAPs; however, it does not allow statements about attribution or causation relating to project impact to be made. The target population for the joint BL/EL PBS consists of two components: 1) all households in the areas where the prior DFAPs were implemented and 2) all households in the areas where the DFSAs will be implemented. These target populations overlap to a great extent since the new DFSAs will be implemented in most of the same geographic areas where the prior DFAPs were implemented. The sample size for the joint BL/EL PBS was derived by: 1) calculating the sample size needed for the BL survey for the DFSAs, 2) calculating the sample size needed for the EL survey for the prior DFAPs, and 3) deriving a joint sample size based on these sample sizes, taking into account the overlap between the DFSA and prior DFAP project areas. The sample size calculations for both 1) and 2) are based on a multi-stage clustered sample designed to adequately power a test of differences between the BL and EL estimates for the FFP stunting indicator for each project. Table 2.1 shows the sample size by DFSA implementing partner for the joint BL/EL PBS. See Table 4 in the June 2018 “Uganda Joint Baseline/Endline PBS Protocol” for more details on the sample allocations. Table 2.1. Program Area and Sampled Households by DFSA Implementing Partner DFSA Implementing Partner Districts in Program Area Number of sampled households for 2013 BL Study Number of households needed for 2018 EL study Number of households needed for 2018 BL study Joint BL/EL Sample Size Requirement MC Kaabong, Kotido, Moroto, and Amudat 2,400 1,220 1,230 1,680 CRS Abim, Nakapiripirit and Napak 2,400 1,220 1,230 1,680 TOTAL 4,800 2,440 2,460 3,360 3. QUESTIONNAIRE The joint BL/EL questionnaire was developed through a series of consultations with FFP, the Food and Nutrition Technical Assistance III Project (FANTA), and the IPs before, during, and after the BL planning workshop in January 2018. All questionnaire modules follow FFP and Feed the Future guidelines, as Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 3 described in the FFP Indicators Handbook (April 2015)1 and the Feed the Future Indicator Handbook (September 2016).2 The questionnaire consists of separate modules covering the following topics:  Module A: Household identification and informed consent  Module B: Household roster  Module C: Household food security  Module CC: Mobility, Local Government Responsiveness and Poverty Probability Index  Module D: Children’s nutrition and health  Module E: Women’s nutrition and health  Module F: Water, sanitation, and hygiene  Module G: Agriculture  Module H: Poverty  Module J: Gender – Cash  Module K: Gender – Maternal and Child Health and Nutrition (MCHN)  Module L. Gender – Household Decision-Making, Access To Credit And Group Participation  Module R: Resilience The questionnaire will be translated into three local languages (Karamojong, Swahili, and Lethur). The total time for completing the survey in each household is expected to be approximately two to three hours, depending on the size of the household. The 2013 BL PBS questionnaire did not include modules CC, J, K, L and R listed above since these were introduced to collect data for indicators added after the 2013 BL PBS was conducted. The indicators collected for the 2013 BL study where change over time can be measured using the 2018 EL PBS are noted in Table 5.2 of Section 5.2. 4. DATA COLLECTION AND QUALITY CONTROL 4.1. Data Collection Mode and Data Transmission Procedure The 2018 joint BL/EL PBS data will be collected with tablets using Computer-Assisted Personal Interviewing (CAPI) mode by local data collection subcontractor, IRC. Tablets will be loaded with a CSPro data entry application developed at ICF for FFP surveys and tailored to fit the PBS questionnaire. All data will be entered directly into the tablets and edited dynamically while interviewing in the field. For transmission of data from the field, IRC will use Internet File Streaming System (IFSS), a cloud-based electronic file delivery web service. The primary objective of the service is to deliver files from one user to another in a way that is fast and secure. The ICF CSPro programmer will work in-country to set up and test the cloud-based data transmission system, as well as to provide technical support during the 1 Food and Nutrition Technical Assistance III Project (FANTA III). 2015. FFP Indicators Handbook Part I: Indicators for Baseline and Final Evaluation Surveys. Washington, DC. Available at http://pdf.usaid.gov/pdf_docs/PBAAE201.pdf. A newer version of the FFP Indicators Handbook is pending release in 2018. 2 Available at https://feedthefuture.gov/sites/default/files/resource/files/Feed_the_Future_Indicator_Handbook_Sept2016.pdf Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 4 first week of data collection, to ensure that tablets and the IFSS transmission system are operating smoothly. The subcontractor, IRC, will upload data to the IFSS on a weekly basis. Data transmissions will occur on a weekly basis and will begin after interviewers have completed all interviews in their first assigned cluster. For the final dataset, the CSPro programmer will develop a program to run quality control checks and convert the raw data exported from the CSPro application into the data format needed for analysis using Stata, SPSS or SAS. 4.2. CAPI Data Entry Training All interviewers and supervisors will participate in a CAPI data entry training prior to the start of fieldwork to ensure the successful use of tablets during data collection. ICF IT specialists will lead the CAPI training sessions, which will include:  Basic use of the tablet, including how to check and prepare the tablets, switching off/on, login, touch screen/keyboard, rotating screen, buttons to avoid, change of batteries, power management, click/double click, swiping, basic operating system tasks  Review of different types of responses to questions, including predetermined numeric, open￾ended numeric, predetermined alpha, open text, and multiple response  Trouble spots in the questionnaire and troubleshooting, error messages  Anthropometry data entry with anthropometry measurement exercises  Practice interviews with tablets in pairs, including starting/stopping the interview, reading questions, entering different types of responses, household rosters, use of calendar for age verification  Workflow, including assigning interviews, receiving assignments and sending completed interviews back to supervisors, supervisors transferring updates to interviewers  Bluetooth transfers of data to the central office via the IFSS 4.3. Field Quality Control Procedures ICF ensures high-quality data through a strong emphasis on training field staff, monitoring data collection and quality control at the field level. During critical periods, including training, anthropometry standardization testing, pretesting, piloting, and at the beginning of fieldwork, the ICF survey coordinator will be in-country to coordinate and oversee these activities. When the ICF survey coordinator is not in the country, the local survey monitor will oversee fieldwork activities and closely update the ICF survey coordinator on fieldwork progress or any issues encountered during data collection. The quality control procedures established in the field include: Proper fieldwork oversight: Maximum ratio of one supervisor for every four interviewers, and one anthropometry specialist. Each interviewer will be accompanied by a team supervisor for one full interview, from start to finish, within the interviewer’s first three clusters of households. Inconsistency checks: These will be built into the CSPro data entry application and will include respondent eligibility checks, checks for questionnaire skip patterns and filters, valid response range checks and other quality control checks. Field-check tables: These tables will be run on raw survey data that are uploaded from the survey teams to the central office via the Internet file transfer system. Therefore, they represent a near real-time snapshot of the status of the survey data quality. Field-check tables are designed to flag Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 5 indicators that appear to be lower or higher than anticipated, such as the expected number of eligible women and children per household. The ICF CSPro programmer and supervisors will work together to review the tables and identify any problems. If data collection problems are discovered at the team level, individual-level tabulations can be run to determine whether problems are team￾wide or restricted to one or two of the team members. Immediate action will be taken to address problems, either by contacting the team supervisor by telephone or by visiting the team to review the findings. In cases of serious problems, a brief written report will be produced detailing the teams with problems and the actions that were taken. The supervisors of teams whose data indicate serious problems in data collection will be informed immediately of the specific problems observed. Data review: Supervisors will review the electronic data received from interviewers and resolve error messages identified by the program. The review will be conducted on a daily basis to identify any missing or problematic data items. Supervisors will not be able to close a cluster and transmit the final data to the central office until all error messages identified are resolved. Re-interviews: During fieldwork, ten percent of the households interviewed per cluster (three households) will be randomly selected for a short re-interview by the team supervisor. The supervisor will visit the household and conduct a quick re-interview on paper comprising the first two sections of the household questionnaire (the cover page and the household roster). The team supervisor will then compare the manually collected responses to the responses in the CAPI system. Any significant discrepancies between the two will be followed up by the supervisor. Re-interviews can be effective in detecting issues, such as falsifying interviews and deliberate displacement of ages of household members to reduce workload. Completion of interviews: Interviewers will make up to three visits to the household to interview a respondent, and will plan one to two visits with the respondents to successfully complete the interview, as necessary. Closing the cluster: This is the last step for the field team and supervisors before leaving each cluster. This is an ongoing activity throughout the data collection period. After the supervisor receives all data from the team, s/he will run a program to check all data collected for completeness and structural integrity. The program will generate a report flagging any missing or incomplete data items. The supervisor will make sure that any problems are resolved before leaving the cluster. When there are no issues remaining, the system will archive the data and automatically upload them to the local subcontractor’s (IRC) central office. Data transfers from the field to the central data office in Kampala will take place regularly during the data collection. 4.4. Data Processing Quality Control Procedures The CSPro data capture and processing program is designed to allow only valid data ranges, to check questionnaire logic (skips and filters) and to flag data inconsistencies during data entry. The CSPro program will also make comprehensive reviews of the data at the cluster level. Within CSPro, a hierarchical structure is used to store the survey data; each module corresponds to a unique record within the CSPro dictionary (codebook). For singly-occurring modules (i.e., one set of values per sampled household), such as C, CC, F, H and R, there will be one line of data in the ASCII file corresponding to the CSPro record where those variables have been defined. For modules where more than one person is included (such as the household roster (Module B), the anthropometry modules for children and women, and Modules D, E, G, J, K and L), there will be one line of data per household Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 6 corresponding to each person eligible for that roster/module. For example, if there are five persons in the household, there will be five lines of data in the data file corresponding to the record created to represent Module B. The complete suite of quality control checks used during the data processing cycle include the following: 1) Data Capture (During fieldwork in CAPI mode) a) Range checking for numeric responses: Based on all possible values being listed in the CSPro dictionary, CSPro automatically ensures that values cannot be entered outside that range. For example, once the variable "sex" has been assigned to the codes 1 (male) and 2 (female), no other value can be entered. b) Range checking for alphabetic responses: For questions that allow multiple responses to be selected (corresponding to the alphabetic responses), a specially-programmed function has been added, which ensures that: (1) only the letters listed can be entered; (2) allowable letters only appear once ("A", but not "AA"); (3) responses requiring an "other" text entry (generally indicated with the "X" and sometimes "W" characters) are captured; (4) responses that must appear in isolation from any other response (usually "Y" (no one) or "Z" (don't know)) do not appear in combination with any other letter; and (5) the field cannot be left blank. c) Consistency checks: In selected fields when applicable, answers will be cross-checked against other fields for validity. For example, in Modules D and E and the anthropometry sections, age and date of birth will be compared to one another to ensure agreement. In addition, in any module that asks for a person's age, this will be cross-checked against the age given in the household roster (Module B); if an age difference exists, a warning message is issued and the interviewer must verify the correct age. d) Skips: If a skip is present, then based on the respondent's answer to the question, the skip will be applied by the CAPI system. Responses that are skipped will be designated as missing by the CAPI system. For numeric responses, missing is indicated by filling the entire field with the number "9". For alpha fields, missing is indicated by filling the field with “X" to indicate "text missing". e) Filters: If a question should not be asked, it will be skipped. For example, persons under the age of 15 are not asked their marital status in the household roster. Therefore, the question will be skipped over for those under-age persons. f) Identifier integrity: A file containing the geographic identifiers will be created for each country. The file provides, for any given cluster, all levels of geographic identifiers. This information will be prefilled from the sample files. This step ensures that the correct identifier is associated with each record. 2) Structure Checks (During fieldwork in CAPI mode) a) Files are created at the cluster level. They are concatenated into a single file at the very end of closing the clusters. The final data are then transmitted to the central office. When closing the clusters, the total number of households with complete (result=1) and incomplete (result <> 1) result codes are also logged in. A check is applied that compares the number of households found within their data file against what was expected from the sample file, with an error being Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 7 generated if the two are not the same. Likewise, if the total number of households found is correct, but if there are some partially completed households, an error message will be generated. The cluster cannot be closed until these problems have been resolved. b) In addition to checking for result codes and total number of households, the program will ensure for each household that the required number of individual records exist, based on the eligibility of the persons within Module B. For example, if the household roster indicates three persons should be administered Module D, then three records must exist in the file before the structure check can succeed. The cluster cannot advance to the consistency editing stage until any identified problems have been resolved. 3) Miscellaneous Data Quality Measures (During fieldwork in CAPI mode) a) Field-check tables will be run on a weekly basis during fieldwork that will report on several key items measuring fieldwork quality. These tables will show data at the team level. For example, a table will be generated that shows age distributions of female respondents between 12-18 years that allows survey managers to determine if teams are dropping respondents with ages below 15, in order to disqualify women from Module E. This helps to identify underperforming teams. b) Frequencies will be generated to ensure reasonable distribution of the data and that no out-of￾range values exist. 4) Consistency Checks (After fieldwork is complete) a) More complex issues are handled after fieldwork is complete. Once a cluster has been closed in the field and data have been transmitted to the central office, a secondary (consistency) edit program will be run against the data in the central office. Many of the checks made during the interviewing process will be repeated here. All error messages are assigned a unique number. b) The central office will be provided a secondary editing manual that lists all error messages in numerical order. It will describe the problem that prompted the error, and possible methods to resolve the conflict. In general, the method is to review the data collected, compare the variables (questions) involved, and look for any notes the interviewer may have made, or changes the field supervisor or field coordinators may have made, that created/exacerbated the problem. Checks for missing values are not made at this time, as it is too late for the field team to resolve this type of error. ICF will conduct a quality control review of the raw and edited data as the data is received from the central office in Kampala. Data transfers will take place weekly from the central office to ICF in Rockville, MD, via the IFSS secure file transfer protocol. Data cleaning will take place based on secondary (consistency) editing reports generated in-country, and per ICF feedback. Final review and data cleaning will take place at ICF in Rockville, MD, upon receipt of the final clean datasets. The final raw CSPro datasets will be accompanied by a data dictionary/codebook with all variables clearly labeled. The raw CSPro datasets will be converted to facilitate data analysis using SAS, Stata or SPSS Statistical Software. Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 8 5. DATA PREPARATION 5.1. Sampling Weights Sampling weights will be computed and used in the data analyses. Weights will be computed separately for the 2018 BL and EL PBS data analyses, according to the unique sampling scheme that is relevant to the associated sampled household or individual. This will involve computing an overall sampling weight for each distinct sampling group by taking the inverse of the product of the probabilities of selection from each stage of sampling (cluster selection and household selection). Weights will be calculated for the following distinct sampling groups:  Households (used for indicators derived from Modules C, CC, F, H, L and R)  Children under five years of age (Module D and Children’s Anthropometry)  Women 15-49 years of age (Module E)  Non-pregnant women 15-49 years (Women’s anthropometry)  Farmers (Module G)  Cash-earning adults (Module J)  Parents of children under two years of age (Module K) Weights will be calculated separately for each of the project areas and will be adjusted to compensate for household- and individual-level non-response, where appropriate. The household level nonresponse adjustment, relevant for all modules, is based on the total number of households with completed interviews and the total number of households in each cluster from the listing exercise. Individual level non-response adjustments for Modules D, E, G, J, K and the anthropometry data are based on the total number of completed interviews for each group of individuals and the total number of eligible individuals from the household roster.3 A more detailed description of the calculation for sampling weights is provided in Appendix A. 5.2. FFP Indicator Definitions The FFP required indicators and the resilience indicators to be included in the data analysis are listed in Table 5.2. Definitions of the FFP indicators are provided in the FFP Indicator Handbook, and definitions for resilience indicators are described in this section. Of the full set of indicators included in the joint BL/EL PBS, the 25 indicators highlighted in yellow are those that were calculated for the 2013 DFAP BL study and can be compared with 2018 DFAP EL indicators to assess change over time. Table 5.2. Joint BL/EL PBS Indicators Indicator Disaggregation Level 2018 BL 2018 EL FOOD SECURITY 1. Average Household Dietary Diversity Score (HDDS) None   2. Prevalence of moderate and severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES) [30 day recall] GHT   3. Prevalence of moderate and severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES) [12 month recall] GHT  3 Strictly speaking, a separate non-response adjustment should be made for all indicator subgroups, e.g., children 0-5 months, children 6-23 months, women married in a union, etc. However, nonresponse for these subgroups very closely mirrors nonresponse for the entire group, so one nonresponse adjustment for the entire group is used. Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 9 Indicator Disaggregation Level 2018 BL 2018 EL POVERTY 4. Per capita expenditures (as a proxy for income) of USG-assisted areas GHT   5. Prevalence of Poverty: Percent of people living on less than $1.25/day 2005 PPP (EL) or $1.90/day 2011 PPP (BL) GHT   6. Depth of Poverty: Mean percent shortfall relative to the $1.25/day (EL) or $1.90/day (BL) poverty line GHT   7. Depth of Poverty of the Poor: Mean percent shortfall of the poor relative to the $1.90/day 2011 PPP poverty line GHT  WATER, SANITATION, AND HYGIENE 8. Percentage of households using an improved drinking water source None  9. Percentage of households using basic drinking water services None  10. Percent of households in target areas practicing correct use of recommended household water treatment technologies None  11. Percent of households that can obtain drinking water in less than 30 minutes (round trip) None  12. Percentage of households using an improved sanitation facility None  13. Percentage of households with access to a basic sanitation service GHT  14. Percent of households in target areas practicing open defecation None  15. Percent of households with soap and water at a handwashing station commonly used by family members None   AGRICULTURE 16. Percentage of farmers who used financial services (savings, agricultural credit, and/or agricultural insurance in the past 12 months Sex   17. Percentage of farmers who practiced the value chain activities promoted by the project in the past 12 months Sex   18. Percentage of farmers who used at least [a project-defined minimum] sustainable agriculture (crop, livestock and natural resource management) practices and/or technologies in the past 12 months Sex, type of practice  19. Proportion of producers who have applied targeted improved management practices or technologies* Sex, type of practice, type of commodity  20. Percentage of farmers who used improved storage practices in the past 12 months Sex   WOMEN’S HEALTH AND NUTRITION 21. Prevalence of underweight (BMI < 18.5) women of reproductive age None   22. Prevalence of women of reproductive age consuming a diet of minimum diversity None  23. Percentage of women of reproductive age who are currently using, or whose sexual partner is currently using, at least one contraceptive method, regardless of the method used None  24. Percent of births receiving at least four antenatal care (ANC) visits during pregnancy** None   25. Prevalence of women of reproductive age who consume targeted nutrient-rich commodities Sex, type of commodity  CHILDREN’S HEALTH AND NUTRITION 26. Prevalence of healthy weight (WHZ ≤ 2 and ≥ -2) among children under five (0-59 months) Sex  27. Prevalence of underweight children (WAZ < -2) children under five (0-59 months) Sex  28. Prevalence of stunted children (HAZ < -2) children under five (0-59 months) Sex   29. Prevalence of wasted children (WHZ < -2) children under five (0-59 months) Sex   30. Percentage of children under age five who had diarrhea in the past two weeks Sex   31. Percentage of children under five years old with diarrhea treated with oral rehydration therapy Sex   32. Prevalence of exclusive breastfeeding of children under six months of age Sex   33. Prevalence of children 6-23 months receiving a minimum acceptable diet Sex   Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 10 Indicator Disaggregation Level 2018 BL 2018 EL 34. Prevalence of children 6- 23 months who consume targeted nutrient-rich commodities Sex, type of commodity  GENDER 35. Percentage of men and women in union who earned cash in the past 12 months Sex  36. Percentage of women in union and earning cash who report participation in decisions about the use of self-earned cash None  37. Percentage of women in union and earning cash who report participation in decisions about the use of spouse/partner’s self-earned cash None  38. Percentage of men in union and earning cash who report spouse/partner participation in decisions about the use of self-earned cash None  39. Percentage of men and women in union with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices Sex  40. Percentage of men/women in union with children under two who make maternal health and nutrition decisions alone Sex  41. Percentage of men/women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner Sex  42. Percentage of men/women in union with children under two who make child health and nutrition decisions alone Sex  43. Percentage of men/women in union with children under two who make child health and nutrition decisions jointly with spouse/partner Sex  RESILIENCE 44. Shock exposure index None  45. Cumulative impact of shock exposure index None  46. Absorptive capacity index None  47. Adaptive capacity index None  48. Transformative capacity index None  49. Ability to recover from shocks and stresses index None  50. Proportion of households participating in group-based savings, micro-finance or lending programs None  51. Index of Social Capital at the household level None  CUSTOM INDICATORS 52. Percentage of respondents reporting increased movement in areas that were previously not accessible due to insecurity None  53. Percentage of households with access to a sanitation facility – not necessarily improved None  54. Average number of crops produced per farmer in the past 12 months None  55. Percentage of farmers adopting farmer managed natural regeneration practices in the past 12 months None  56. Percentage of livestock owners accessing government or private sector vet care in the past 12 months None  57. Average rating of government's ability to be responsive to citizens' needs (including transparency, inclusivity, effectiveness) as measured on scorecard Sex  58. Percent of target population who can state at least one health benefit of waiting at least two years after last live birth before attempting the next pregnancy Sex, Age  BL = DFSA Baseline Study EL = DFAP Endline Evaluation GHT= Gendered Household Type, FIES = Food Insecurity Experience Scale * Pending confirmation of definition and feasibility with existing data **2018 BL indicator includes last birth within the past 5 years. 2018 EL indicator includes last birth within the past 2 years. Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 11 Below we provide an introduction to the newly added or modified FFP indicators and definitions for those indicators that require country-specific adaptations. 5.2.1 Prevalence of moderate or severe food insecurity (FIES) This indicator captures both physical and psychological experiences of hunger in the past 30 days and 12 months. A draft PIRS developed by ICF based on guidance from the Food and Agriculture Organization (FAO) and Bureau of Food Security (BFS) is included in Appendix B. 5.2.2 Percentage of households with access to basic sanitation services This is a modified indicator which is essentially the same as the original improved sanitation facilities indicator but no longer includes “flushed to a pit latrine” as an improved facility. The PIRS for this indicator is included in Appendix B. 5.2.3 Prevalence of women of reproductive age who consume targeted nutrient￾rich commodities This indicator is recently updated by FFP. The updated PIRS is included in Appendix B. This indicator is computed based on the sample weighted women of reproductive age (15-49 years old) in the implementation areas that in the previous day ate one or more nutrient-rich commodities promoted by a FFP-funded activity or one or more product made from a nutrient-rich commodity promoted by a FFP-funded activity. The IPs identified the following targeted nutrient-rich commodities that they would be promoting in their projects: orange flesh sweet potatoes (OFSP) and foods made with bio-fortified beans, bio-fortified maize or bio-fortified sorghum, 5.2.4 Prevalence of children 6-23 months who consume targeted nutrient-rich commodities This indicator is also recently updated by FFP. The updated PIRS is included in Appendix B. It is computed based on the sample weighted children of age (6-23 months) in the implementation areas that in the previous day ate one or more nutrient-rich commodities promoted by a FFP-funded activity or one or more product made from a nutrient-rich commodity promoted by a FFP-funded activity. The identified targeted nutrient-rich commodities under 5.2.3 for women are also applicable for this indicator. 5.2.5 Prevalence of healthy weight children under five years of age This is a newly added indicator. It is the sample-weighted number of children 0-59 months of age in the sample with (WHZ>=-2 and WHZ<= 2) divided by the sample-weighted number of children 0-59 months in the sample with WHZ data. The PIRS for this indicator is included in Appendix B. 5.2.6 Agricultural Indicators The agricultural indicators for the 2018 EL study will be calculated using the same definitions that were used for the 2013 BL. 4 The following tabulation instructions will be used to calculate the agricultural indicators for the BL study: 4 These definitions can be found in Appendix B of the “Data Treatment and Analysis Plan for the Title II Quantitative Baseline Surveys in Uganda, Niger and Guatemala”, June 28, 2013. Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 12  Percentage of farmers who used financial services (savings, agricultural credit, and/or agricultural insurance) in the past 12 months is calculated based on the sample weighted number of farmers who reported using at least one financial service divided by the sample weighted total number of farmers.  Percentage of farmers who practiced the value chain activities promoted by the project in the past 12 months is calculated based on the sample weighted number of farmers who reported using at least one value chain activity5 to be promoted by the project divided by the sample weighted total number of farmers.  Proportion of producers who have applied targeted improved management practices or technologies in the past 12 months is a new indicator to be computed for 2018 BL only. The PIRS for this indicator is attached in Appendix B. FFP plans to use this indicator to replace Percentage of farmers who used a project-defined minimum number of sustainable agricultural practices indicator in future BL surveys. This indicator is calculated based on the sample weighted number of producers6 who have applied promoted improved management practices and/or technologies anywhere within the food and fiber system in the reporting year divided by the sample weighted number of producers with application of any improved management practices or technologies.7  Percentage of farmers using improved storage practices is calculated based on the sample weighted number of farmers who reported using at least one improved storage practice and/or technology divided by the sample weighted total number of farmers. 5.2.7 Custom Indicators (2013 BL and 2018 EL) The 2013 BL PBS included five custom indicators as noted in Table 5.2. These indicators will be computed for 2018 EL PBS in order to compare with the 2013 BL PBS values. Computation methodologies for all five indicators are presented in Appendix B. 5.2.8 Custom Indicators (2018 BL)  Average rating of government's ability to be responsive to citizens' needs (including transparency, inclusivity, and effectiveness) as measured on scorecard is the sample weighted average score for each of 12 questions related to government responsiveness. Estimates of this indicator will be presented as a scorecard. The PIRS for this indicator is presented in Appendix B.  Percent of target population who can state at least one health benefit of waiting at least two years after last live birth before attempting the next pregnancy is calculated as sample weighted number of individuals who can state at least one health or social benefit of waiting at least two years after 5 Only those farmers who raise crops or livestock with the intent to sell for income are asked about value chain activities. 6 The definition of a producer as stated in the PIRS “(e.g. farmers, ranchers and other primary sector producers of food and nonfood crops, livestock products, fish and other fisheries/aquaculture products, agro-forestry products, and natural resource￾based products, etc.)” is similar to the definition of a farmer in Module G of the questionnaire. 7 This indicator will be included pending confirmation of definition and feasibility with existing data. It requires each implementing partner to determine their targeted improved management practices or technologies. Similarly, to disaggregate the indicator by prioritized value chain commodities as suggested in the PIRS, the implementing partners will also need to determine and provide a list of such commodities to ICF. Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 13 last live birth before attempting the next pregnancy divided by total sample weighted number of individuals. The indicator PIRS is presented in Appendix B. 5.2.9 Poverty Indicators Calculation of the four poverty indicators involves a complex and time-consuming methodology that follows guidance from USAID and the World Bank. A detailed description of this methodology is provided in Appendix C. The 2018 BL study will include questions to calculate the Innovations for Poverty Action (IPA) Poverty Probability Index (PPI) which will be used to construct two poverty indicators: prevalence of poverty and depth of poverty. These two indicators will be compared to the same two indicators constructed using the LSMS methodology as described in Appendix C. This is a pilot study for the PPI in order to test its viability as a replacement methodology for the LSMS variants for calculating the poverty indicators. The analyses for the PPI will be conducted by IPA with support from ICF. An overview of the analyses is provided in Appendix D. 5.2.10 Resilience Indicators The resilience questionnaire module and indicators were developed by TANGO and are described in Appendix E. TANGO will calculate the resilience indicators and conduct all further analyses of these indicators. 5.3. Handling of Missing Data and “Don’t know” Responses Missing data points will be assessed and excluded from both the denominator and the numerator for the calculation of all indicators as applicable. “Don’t know” and “Refused” responses will be excluded from the numerators used in the calculation of the indicators. For example, for responses to questions relating to consumption of the various food groups in the HDDS component, “Yes,” “No,” and “Don’t know” responses will be included in the denominator, but only “Yes” responses will be counted in the numerator. For poverty indicators, there are special instructions for handling missing data (see Appendix B). 6. DATA ANALYSIS PLAN Separate datasets will be prepared for the 2018 BL and EL samples and data analyses will be conducted separately for each. For the BL study, analyses will include examination of key demographic characteristics of the study population, calculation of all FFP and resilience indicators, bivariate analyses and multivariate analyses as appropriate. TANGO will conduct the resilience analyses, as described in Appendix E. For the PEs, analyses of the 2018 EL PBS data will include comparisons between the 2013 BL and 2018 EL key demographic characteristics and comparisons between the 2013 BL and 2018 EL overlapping FFP indicators. Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 14 Indicators will be calculated separately for each project, as well as for the combined project areas, and all analyses will be weighted to reflect the full target population. Stata version 148 will be used for all analyses and statistical testing. A more detailed data analysis plan for the 2018 BL PBS and EL PEs is provided below. 6.1. Analyses for 2018 BL Study Data analysis for the BL PBS includes examination of key demographic characteristics of the study population, calculation of all FFP and resilience indicators, and bivariate analysis of indicators that can help inform program targeting and program design where possible. Bivariate analyses including disaggregation by key sub-populations will be conducted for each DFSA area separately. They will not be performed for the combined DFSA areas since the combined estimates will mask differences by DFSA area; and program targeting and the design of interventions are DFSA-specific. Additional multivariate analyses will be conducted as appropriate to provide further insights on relationships between key indicators. In some cases, it may not be possible to conduct the proposed analyses due to sample size limitations. 6.1.1 Household Characteristics The BL report will provide an overview of the size and sociodemographic characteristics of the population in the DFSA areas and an explanation for why or how these characteristics may influence the BL indicators and the achievement of DFSA targets over time. This includes the percentage of individuals in the following key target population groups in the combined DFSA areas and by DFSA:  Adults (15+ years), total and by sex  Cash earners (15+ years), total and by sex  Farmers (15+ years), total and by sex  Women of reproduction age (15-49 years) o Non-pregnant o Married or in a union o With a live birth in the past 5 years o Pregnant and lactating women  Children under 5 years, total and by sex  Children under 2 years, total and by sex  Children under 6 months, total and by sex  Children 6-23 months, total and by sex This analysis also includes the following household-level statistics for the combined DFSA areas and by DFSA:  Average household size (Number of persons)  Average number of adults (15+ years) per household  Percent of households with at least one child under 5 years of age  Percent of households with at least one child 6-23 months of age  Percent of households with at least one child under 6 months of age 8 StataCorp. 2014. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP. Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 15  Gendered household type (Percent of households)  Highest level of education achieved by any adult household member 6.1.2 FFP, Resilience and Custom Indicators All indicators will be generated using relevant sampling weights to represent the full target population and tabulated for the combined DFSA areas and for each DFSA separately. All indicators will be disaggregated, as specified in Table 5.2. Variance estimation (derived using Taylor series expansion) will take into account the design effect associated with the complex sampling design; 95 percent confidence intervals will be provided for all FFP indicators at the aggregated DFSA level and for each DFSA separately. 6.1.3 Additional Analyses Select bivariate and multivariate analyses will be conducted to explore relationships between indicators and other important household/individual characteristics, and to explore associations between outcome and impact indicators. These analyses are intended to provide useful information to help identify particular sub-groups on which to focus or to help inform program design by illustrating the factors that are associated with the outcome and impact indicators. Differences in means or proportions between groups or correlations will be tested using appropriate statistical test of differences (such as t-test, chi square test). The BL study will include both quantitative and qualitative information though the study will be heavily dependent on quantitative data and analyses. The qualitative information to be collected for this BL study will be collected at the same time as the larger qualitative data collection effort for the PEs. The qualitative data to be collected for the BL study will be guided by the quantitative findings and its need for further explorations and interpretations. As such, a summary of the findings or excerpts from the qualitative data derived from key informant interviews and focus group discussions will be used to explain the “why” and “how” of the quantitative findings. If time and resources allow, outside literature will be also be consulted and used to explain or confirm the quantitative findings as necessary. The rationale and explanations for select additional bivariate analyses are discussed below, followed by a description of the specific proposed analyses by content area. Rationale for Analyses:  Gendered household type: Based on other FFP countries’ data, certain gendered household types, particularly those with adult females only, seem to have higher food insecurity and poverty. As such, it would be interesting to explore how gendered household type may influence households’ food insecurity, poverty, and child nutrition indicators in the Karamoja Region.  Education: The level of education attained by adult household members can influence the choices and decisions made by their household which in-turn can affect their socio-economic wellbeing. Hence, it would be interesting to conduct relational analyses especially between the highest education attained by any adult member and some select FFP indicators such as child stunting, underweight, FIES, and WASH practices. The results can help IPs to design further studies to investigate the barriers to adult education and also to educate project participants about the importance of continued education for adults in the project areas. Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 16  Gender bias in intra-household resource allocation: Gender bias can affect women and children. Analysis of children’s health and nutrition by sex of the child and sex of the household head might shed an important light on gender bias with respect to household food security indicators (HDDS and FIES). Increased household food security, in theory, could mean improved dietary intake for children and women. The non-existence of such relationships could mean several things, including a lack of adequate distribution of food among the children and women within the household.  Agriculture: Agriculture is the backbone of peoples’ livelihoods in Karamoja. Understanding the types of crops produced, types of animals owned, farmers’ use of financial services, and types of modern/improved technologies practiced in farming and livestock rearing can help the project IPs systematically diagnose the status of agriculture in the project areas. Similarly, exploring the relationships between whether farmers plant (or raise) crops (or livestock) and their intention to sell and size or type of land owned (own, rent, sharecrop etc.) can shed some lights about agriculture constraints.  WASH practices: Household WASH practices can affect household members’ health and nutrition outcomes. Unsafe drinking water, poor or non-existent handwashing practices, and/or inadequate disposal of human feces can cause diarrhea and other diseases. While some of the common causes of these situations are known in the literature, it is still useful to examine the relationships between the WASH indicators and various household and community level characteristics such as, household poverty status, availability of relevant WASH project interventions in the area, availability of a communal water users’ group, and whether or not households receive health and nutritional information. These analyses could be very useful for the project implementers to design or prioritize WASH interventions.  Women’s empowerment: Women’s ability to earn cash and control over household assets can give them leverage to participate in household decision making, such as decisions about household consumption and expenditures, which may contribute to improved nutrition among children and women and improve the food security of the overall household. The proposed analyses will investigate perceived control over decisions about cash and assets and control over decisions about child health and nutrition.  Local context: Households access to important institutions or services such as markets, educational institutions, health services, agricultural inputs and services, and existing external development program, as well as incurring shocks and stresses can influence food insecurity and health and nutrition situations. Where relevant, bivariate analysis will be conducted between such contextual variables (available in the resilience module) and women’s and children’s health and nutritional indicators.  Poverty: Poverty can be a cause of many subpar living conditions such as poor health and malnutrition, poor education, and poor water and sanitation practices. It can also be an outcome of such conditions. As the FFP projects aim to target the neediest households in the project areas, it will be useful to examine the relationships between WASH indicators, FIES, child stunting and underweight, children’s minimum acceptable diet (MAD), and women’s dietary Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 17 diversity; with household’s poverty status. Such analyses may help the IPs to design or prioritize interventions that can effectively target the poor. Proposed specific analyses: Household Food Security, Poverty, and Livelihood Activities  Analysis of food security, poverty and livelihood activities will be presented in the same section because of their interrelationship. The FFP indicators for food security and poverty will be disaggregated by gendered household type. The results of these analyses are intended to help focus targeting on subgroups that may be more vulnerable to food insecurity and poverty.  Bivariate analyses will be conducted between food insecurity and poverty indicators. These can include correlation analysis (1) between average daily per capita consumption and household hunger status; (2) between average household dietary diversity score and household poverty status; and (3) between prevalence of poverty and prevalence of hunger. These analyses are intended to empirically test hypothesized relationships in the theory of change on the interrelationship between food security and poverty and to establish a BL profile of the economic status of food-insecure households.  Both food insecurity and poverty can be attenuated by a host of many internal factors such as household ownership of productive assets including land, ownership of livestock assets, and external factors such as access to markets, infrastructure, and services, and external shocks. A series of correlation analysis will be conducted to identify association between the various internal and external factors with both food insecurity and poverty. The results of this analyses will help IPs to design program interventions that moderate the factors that contribute to food insecurity and poverty.  The analysis of livelihood activities will look at the percentage of households that engage in more than one livelihood activity or have more than one source of income. It will also identify primary livelihood activities by food insecurity and poverty status. The primary livelihood activities of the households will be disaggregated by gendered household type. Bivariate analyses will be conducted between households bearing different set of livelihood activities with the food security and poverty indicators. The results of these analyses are intended to inform program targeting and design by (1) identifying primary livelihoods that households are currently pursuing, and (2) identifying the types of program assistance that can help households focus on livelihood activities that can potentially contribute to higher income and reduce vulnerability to food insecurity and poverty. Agriculture  The types of crops and livestock produced in each of the project areas will be presented and analyzed by sex of the farmer, land ownership and size of land. The types of crops planted will also be analyzed in relation to whether the land is used for sharecropping. The results of the analyses are intended to highlight whether certain subgroups are more likely to focus on food crops versus cash crops. Univariate analyses will be provided for the following variables to provide a BL understanding of some of the factors that may be related to agricultural productivity: land ownership and size of land, availability and accessibility of agricultural Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 18 extension services, availability and accessibility of veterinary services, and types of improved practices for crop and livestock management. The BL estimates of shocks impacting agriculture and its severity experienced by the households will be presented and discussed in relation to agriculture status and prospects in the project areas. Qualitative insights will also be offered to provide a better understanding about evolving nature of shocks and stresses for agriculture. These analyses are helpful for IPs to understand important shocks related to agriculture challenges in the project areas.  The BL estimates for use of financial services, targeted improved management practices or technologies, value chain activities and improved storage methods will be presented and disaggregated by sex of farmer. To better understand the factors that open pathways to the use of financial services, bivariate analyses of the use of financial services will be conducted with the availability of institutions where people can save money within five miles of respondents’ villages and the availability of institutions where people can borrow money within five miles of respondents’ villages. Further, to identify important institutions used by farmers for credit and saving, correlation analyses will be conducted between farmers’ use of financial services and each type of financial service institution used by farmers within the past 12 months.  Bivariate analyses will be conducted for farmers’ use of financial services and their use of applied targeted improved management practices or technologies with household education status and household’s poverty status. This analysis can help assess if poverty and education are important defining factors for use of financial services and improved agriculture management practices.  Both projects’ theories of change emphasize market systems development and promote sales through activities such as access to financial services, entrepreneurship and business development training. Distance to markets can become critical for many farmers to be able to sell their produce. To understand the relationship in the context of project areas, bivariate analysis will be conducted between farmers’ intent to sell their agricultural products and the distances from their household to nearest markets, and the types of road that connect to villages. Water, Sanitation and Hygiene (WASH)  Household WASH practices can be influenced by many factors such as lack of adequate water, lack of education, and lack of proper norms or habits of following basic personal hygiene practices. To explore some of the possible factors in the project area, select WASH indicators will be disaggregated by gendered household type and households’ status of participating in different community groups.  Households’ use of basic water services, use of water treatment technologies, use of basic sanitation facilities and adoption of handwashing practices can be influenced by whether or not they are informed about proper health and hygiene practices. To examine this, a set of bivariate analyses will be conducted between the WASH indicators and households’ status of receiving information on child health and nutrition. While the information on child health and nutrition may not necessarily cover all WASH aspects, it could serve as a proxy to basic WASH information such as safe drinking water, basic hygiene practices are usually part of the child health and nutrition information provided by public health professionals. Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 19  Households’ WASH conditions can be affected by natural and socio-economic shocks. Thus, associations between the select WASH indicators and households’ exposure to shocks will also be explored. The results of these analyses are intended to inform program targeting, for example, by highlighting certain subgroups whose BL estimates are significantly lower than the project area average. Bivariate analyses of WASH practices and children’s health and nutrition status are covered in the section on Child Health and Nutrition. Women’s Health and Nutrition (WHN)  Select WHN indicators will be disaggregated by women’s age groups. This will help better understand if women’s health and nutrition indicators vary by their age group. Also, such disaggregation may help target the women beneficiaries who are in need of health and nutrition support the most.  Bivariate analyses will be conducted for the prevalence of underweight women and MDD-W with the household poverty status and type of development food security programming available in the village. Results can illustrate the types of program interventions that are statistically associated with women’s nutrition and potentially serve as a basis for discussing how and whether to align future interventions with existing ones.  Bivariate analyses will be conducted for women’s underweight, MDD-W and ANC indicators with the household’s access to child and health information, equal rights for women and men, and gender-based violence information, and existing gender norms within the households. Similarly, bivariate analyses will be conducted with households’ involvement in community groups especially mothers’ and women’s groups. These analyses will help assess whether targeting households with certain information or women’s involvement in the community groups make difference in women’s food consumption and health care behavior especially during pregnancy.  Additionally, bivariate analyses will be conducted for the following: (1) antenatal care and availability of health services within five miles of respondents’ villages; (2) antenatal care and physical condition of health service used by people in respondents’ villages; and (3) contraceptive use and availability of health services within five kilometers of respondents’ villages. Results can help shed light on the factors associated with women’s sexual and reproductive health care practices.  Additional multivariate analyses that control for confounding variables may be conducted to illustrate the potential pathways for improvements in women’s health and nutrition. These analyses should control for households’ use of improved agriculture management practices or technologies, maternal health decision making, and female-level and household-level factors that can influence women’s health and nutrition. The results of these analyses may help inform program design and empirically validate relationships in the underlying theory of change. Children’s Health and Nutrition (CHN)  Children’s health and nutrition indictors will be disaggregated by sex and age group. Bivariate analyses will be conducted for children’s malnutrition indicators (stunting, underweight, and wasting) and the prevalence of diarrhea with the following: (1) use of basic water services, (2) Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 20 use of basic sanitation facility, (3) correct water treatment; and (4) availability of a handwashing facility with soap and water. Results are intended to highlight any gender bias and suggest factors that are associated with children’s health and nutrition and inform program design where possible.  Additional multivariate analyses that control for confounding variables may be conducted to illustrate the potential pathways for improvements in children health and nutrition. These analyses could control for households’ use of improved agriculture management practices or technologies, value chain activities, decision making over children’s health, and a host of child￾level and household-level factors that can influence children’s health and nutrition. The results of these analyses could help inform program design and empirically validate relationships in the underlying theory of change. These analyses will be conducted if warranted by the results of the bivariate analyses or the qualitative study. Gender  Gender analysis will be conducted throughout the report, for example by looking at differences by sex in the indicators. In addition, the following will be disaggregated by sex: (1) ownership of agricultural land, (2) plot size, and (3) participation in cash-earning opportunities.   Bivariate gender analyses will be conducted for the following: (1) decision making on use of cash and household hunger, (2) decision making on use of cash and household dietary diversity; (3) maternal health decision making and use of contraception, (4) maternal health decision making and antenatal care, (5) child health decision making and children’s nutritional status (stunting, wasting and underweight), and (6) child health decision making and feeding practices of children (MAD). Results are intended to highlight existing gender gaps in access to and control over resources, and to shed light on decision-making processes within the household that could impact women and children’s health and nutrition and household food security. 6.2 Analyses for 2018 Performance Evaluations Analysis of the PBS data for the PEs includes a comparison of key demographic characteristics of the study population at BL in 2013 and at EL in 2018 along with comparisons of BL and EL indicator estimates for all indicators as noted in Table 5.2. In addition, where relevant, bivariate and multivariate analyses will be performed to support the PEs and explore the plausible determinants for key outcome indicators. 6.2.1 Comparison of 2013 BL and 2018 EL Household Characteristics A comparison of household characteristics between the BL and EL samples will be conducted to determine if differences exist. The same demographic and socioeconomic characteristics described in Section 6.1.1 will be evaluated. If differences are found between BL and EL, an explanation for why or how these differences may influence the change in indicators over time and the achievement of program targets will be provided. Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 21 6.2.2 FFP and Custom Indicators All 24 indicators that were constructed in 2013 will be calculated using 2018 PBS endline data and weighted to represent the full target population. Indicators will be tabulated for the combined DFAP areas and for the two prior DFAPs separately. All indicators will be disaggregated, as specified in Table 5.2. Variance estimation (derived using Taylor series expansion) will take into account the design effect associated with the complex sampling design; 95 percent confidence intervals will be provided for all FFP and custom indicators at the aggregated DFAP level and for each prior DFAP separately. 6.2.3 Comparison of 2013 BL and 2018 EL FFP and Custom Indicators The 2013 BL and 2018 EL indicator estimates will be compared using a statistical test of differences to determine if significant change occurred over time. Raw differences and p-values to test for significant differences will be provided for each comparison. Although the results from these comparisons will provide an indication of whether change occurred over time, the change cannot be directly attributed to the DFAP activities. 6.2.4 Additional Analyses Relevant bivariate analyses from the list as described in section 6.1.3 will be conducted to explore relationships between indicators as appropriate in support of the PEs. Additional select multivariate analyses may be conducted if warranted by the BL-EL comparisons or bivariate analyses findings, to explore plausible determinants of key outcome indicators. The PBS data will be interpreted based in part on FFP’s conceptual model/framework, secondary data from other studies as available, and within the context of the PEs. Uganda PBS Data Treatment and Analysis Plan APPENDIX A – SAMPLING WEIGHTS Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 22 HOUSEHOLD WEIGHTS Household weights will be applied for household level indicators derived from Modules C, CC, F, H, L, and R and included in the construction of individual weights for all other modules. Household design weights are calculated based on the separate sampling probabilities for each sampling stage and for each cluster (kebele). 𝑃1ℎ𝑖= first-stage sampling probability of the i-th cluster in stratum h 𝑃2ℎ𝑖= second-stage sampling probability within the i-th cluster (household selection). The probability of selecting cluster i in the sample is: 𝑃1ℎ𝑖= 𝑚ℎ × 𝑁ℎ𝑖 𝑁ℎ × 𝑏ℎ𝑖 The second-stage probability of selecting households in cluster i is: 𝑃2ℎ𝑖 = 𝑛ℎ𝑖 𝐿ℎ𝑖 ⁄ Where: 𝑚ℎ= number of sample clusters selected in stratum h. 𝑁ℎ𝑖= total households in the frame for the i-th sample cluster in stratum h (obtained from Census or other external information). 𝑁ℎ= total households in the frame in stratum h. 𝑏ℎ𝑖= the number of selected segments divided by the total number of segments in the i-th sample cluster in stratum h 𝑛ℎ𝑖 = number of sample households selected for the i-th sample cluster in stratum h. 𝐿ℎ𝑖= number of households from the household listing exercise for the i-th sample cluster in stratum h (ideally, this is the same as 𝑁ℎ𝑖, but most often is not in practice). The overall selection probability of each household in cluster i of stratum h is the product of the selection probabilities of the two (or three) stages: 𝑃ℎ𝑖 = 𝑃1ℎ𝑖 x 𝑃2ℎ𝑖 = 𝑚ℎ × 𝑁ℎ𝑖 𝑁ℎ × 𝑏ℎ𝑖 × 𝑛ℎ𝑖 𝐿ℎ𝑖 ⁄ The household design weight for each household in cluster i of stratum h is the inverse of its overall selection probability: 𝑊ℎ𝑖 = 1 𝑃ℎ𝑖 = 𝑁ℎ×𝐿ℎ𝑖 𝑚ℎ×𝑁ℎ𝑖×𝑛ℎ𝑖×𝑏ℎ𝑖 The household sampling weight is calculated using the household design weight corrected for household non-response in each of the selected clusters. Weighted response rates are calculated at the cluster level as ratios of the weighted number of interviewed households divided by the weighted number of eligible households, where the weights used are the household design weights. The household sampling weight is calculated by dividing the household design weight by the weighted household response rate. Separate baseline and endline household weights Household weights will be computed separately for the 2018 baseline and endline PBS samples. Because the baseline and endline were jointly administered, only the appropriate component geographic areas relating to each of the two embedded PBSs are used in the formation of the respective set of weights. The steps for calculation of these separate weights are as follows: 1. Divide the sampled households into two groups – one for 2018 baseline (overlap and new areas) and one for 2018 endline (overlap and old areas) Uganda PBS Data Treatment and Analysis Plan APPENDIX A – SAMPLING WEIGHTS Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 23 2. Create a separate set of weights for the 2018 BL group and another set of weights for the 2018 endline group following the two stages of sampling described above 3. Apply an adjustment to the weights if there are more households then are needed for a specific stratum (old, new or overlap) to ensure that the strata contribute with the same proportion as they were in the original allocations. The original allocations are shown in the table below. Project Sample Size Requirement (Old Project/EL) Sample Size Requirement (New Project/BL) Joint BL/EL Sample Size Requirement Number of Clusters CRS DFSA 1,220 1,230 1,680 56 Overlap 939 916 990 33 Old 281 0 330 11 New 0 314 360 12 MC DFSA 1,220 1,230 1,680 56 Overlap 1,220 820 1,230 41 Old 0 0 0 0 New 0 410 450 15 TOTAL 2,440 2,460 3,360 112 INDIVIDUAL WEIGHTS Individual sampling weights will be applied for indicators derived from Modules D (children), E (women of reproductive age), G (farmers), J (cash earners), and K (parents of children under two years). Since all eligible individuals will be selected for each Module, the probability of selecting eligible individuals within sampled households is always one. Therefore, the individual weights will consist of an individual non-response adjustment only. The weighted individual nonresponse adjustment will be applied using the inverted proportion of the weighted total number of completed interviews for each group divided by the weighted total number of eligible individuals for each group. This non-response adjustment is calculated at the project level. The final individual weights will then be computed as the product of the household weights and the weighted individual nonresponse adjustment. Uganda PBS Data Treatment and Analysis Plan APPENDIX B – METHODOLOGY TO CALCULATE NEW/MODIFIED OR CUSTOM INDICATORS Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 24 Provided as separate document. Uganda PBS Data Treatment and Analysis Plan APPENDIX C - METHODOLOGY TO DERIVE POVERTY INDICATORS Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 25 The World Bank defines poverty as whether households or individuals have enough resources or abilities today to meet their needs. Poverty is usually measured based on consumption expenditures rather than income. Consumption expenditures are more closely related to well-being because households adopt strategies to meet their current basic needs. Also, in poor agrarian economies and in urban economies with large informal sectors, income may be difficult to estimate. It may be seasonal and erratic, and it may be difficult to estimate particularly for agricultural households whose income may not be monetized. The prevalence of household poverty will be measured using information on household consumption expenditures to compute a household consumption aggregate. The consumption aggregates will be constructed following guidelines from Deaton & Zaidi (2002)9 and Grosh & Muñoz (1996)10 by adding together the various goods and services consumed by each household during a period of 12 months. The various components of consumption will be grouped together into 6 main categories, including food, usual expenses (expenses in the last 7 days), occasional expenses (expenses in the last 30 days), unusual expenses (expenses in the last 12 months), housing and durable assets. In general, consumption will be calculated by adding the value in local currency units (LCU) of the items consumed by the household, as reported by household informants. These items will be collected according to different time horizons, but will be then transformed into a daily per capita consumption expenditure aggregate. Whenever a household is missing data on the monetary value of an item it has consumed, that value will be imputed using the closest local median value for that item. That is, if a household is missing consumption information on a given item, it will be assigned the median value reported by other households in the vicinity. Whenever the item is reported frequently enough, this imputation will be done at the cluster level. However, some items may be consumed by few households. In those cases, the level of imputation would be at a higher level, depending on how rare the item is. These imputed amounts will be subject to checks that the imputed prices are plausible to avoid undue influence from outliers. The reported values for each item and each consumption component will be checked for outliers to detect possible coding errors or extreme values. Depending on the distribution of variable, values that are 1 to 5 standard deviations (SD) over the average will be flagged and checked for plausibility. Values deemed implausible will be imputed using the methodology described above. Besides this general methodology, some components require specific computations.  Food Consumption Computation of food consumption is complex because it involves products that are purchased in the market, where price information is available, and products that are home-produced or received as a gift, where price information is not available. Even when products are purchased, it is often difficult for 9 Deaton, A. and S. Zaidi (2002), A Guide to Aggregating Consumption Expenditures, Living Standards Measurement Study, Working Paper 135. Available at: http://siteresources.worldbank.org/INTPA/Resources/429966-1092778639630/deatonZaidi.pdf 10 Margaret Grosh and Juan Muñoz (1996). A Manual for Planning and Implementing the Living Standards Measurement Study Surveys. LSMS Working Paper #126, The World Bank. Available at: http://documents.worldbank.org/curated/en/1996/05/438573/manual-planning-implementing-living-standards-measurement￾study-survey Uganda PBS Data Treatment and Analysis Plan APPENDIX C - METHODOLOGY TO DERIVE POVERTY INDICATORS Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 26 household informants to report the precise market value of the amounts consumed by the household over the reference period, which often results in missing data. The value of non-purchased food (and of any food missing value information), will be imputed by first transforming the amounts consumed by the household to a common reference unit, and multiplying the local median value of that unit times the amount consumed. If a product is reportedly consumed, but information on the quantity consumed is missing, the median daily per capita amount consumed by local households will be imputed.  Assets Purchases of durable goods represent large and relatively infrequent expenses. While almost all households incur relatively large expenditures on these at some point, only a small proportion of all households are expected to make such expenditures during the reference period covered by the survey. As indicated by Deaton & Zaidi (2002) “From the point of view of household welfare, rather than using expenditure on purchase of durable goods during the recall period, the appropriate measure of consumption of durable goods is the value of services that the household receives from all the durable goods in its possession over the relevant time period” (p. 33). Consumption of durable goods will be calculated as the annual rental equivalent of owning the asset. This rental equivalent is computed as the price of the asset in its current shape multiplied by the sum of the real interest rate and the depreciation rate: 𝑆𝑡𝑃𝑡 (𝑟𝑡−𝜋𝑡 + 𝛿) Where 𝑆𝑡𝑃𝑡 is the current price of the asset, 𝑟𝑡−𝜋𝑡 is the real rate of interest, and 𝛿 is the depreciation rate for the durable good. Each of these components will be computed separately. 1. Current value of the asset (𝑆𝑡𝑃𝑡 ): This will be obtained from household reports of the value of the asset in its current shape (second-hand). 2. Real rate of interest (𝑟𝑡−𝜋𝑡 ): In theory, 𝑟𝑡 is the general nominal rate at time t, and 𝜋𝑡 is the specific rate of inflation for each asset at time t. However, in practice this is calculated as a single real rate of interest that is used for all goods, taken as an average over several years (see Deaton & Zaidi, 2002 p. 33). Data on real interest rates will be obtained from the World Bank11 and averaged for the appropriate period to obtain a single real rate of interest. 3. Rate of depreciation (𝛿): The rate of depreciation for each of the items is given by the formula: 1 − ( 𝑃𝑡 𝑃𝑡−𝑇 ) 1 𝑇 ⁄ Where 𝑃𝑡 is the current value of the item at current time t, 𝑃𝑡−𝑇 is the value of the item when purchased, and 𝑇 is the age of the item in years. Inflation-adjusted rates of depreciation will be obtained using the local median price of an item at the time of purchase. In order to minimize 11 Data on the real interest rates for Uganda are available for the period 1995 - 2018. Estimates are based on the average real interest rate during 1995-2018, which is 13.39%. Source: https://data.worldbank.org/indicator/FR.INR.RINR?locations=UG&view=chart Uganda PBS Data Treatment and Analysis Plan APPENDIX C - METHODOLOGY TO DERIVE POVERTY INDICATORS Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 27 the influence of outliers, the median 𝛿 will be used for each of the durable assets for which data are collected (i.e. rather than using household-specific values of 𝛿 calculated from the data). A rental equivalent estimating the daily per capita flow of services from the durable goods is then derived by dividing the annual rental equivalent over the number of members in the household and the 365 days of the year.  Housing The case of housing is similar to other durable goods, in that it is better measured as an annual consumption of housing services, either annual rent expenditures for renters, or an annual rental equivalent for non-renters. The household survey will collect information on rent paid among renters, and an estimated rental equivalent for non-renters. It is likely that the housing rental market is small and a significant amount of non-renters are unable to provide an estimated rental equivalent. These missing responses will be imputed using two approaches. First, the age of the house and its current replacement value will be used to estimate a housing rental equivalent, using the methodology described above for durable goods. For those cases where the estimated current value or age of the house are not available, a hedonic OLS (Ordinary Least Squares) regression model will be used (where “hedonic” regression is a preference method of estimating demand or value), as suggested by Grosh & Muñoz (1996). The model will be built on the sample of households reporting non-zero rent or rental equivalents, with the log of rent paid by renters as a dependent variable, and several sets of independent variables, that may include: - Housing characteristics: number of members, type of water access, type of sanitation services, asset ownership. - Location: District The final model will be estimated based on the following regression equation, log(𝑅𝑖) = 𝛽0 + 𝛽𝑋𝑖 + 𝜀𝑖 where 𝑅𝑖 represents the reported non-zero rent paid by household i, 𝛽0 is the constant term, 𝑋𝑖 is the final vector of independent variables and 𝜀𝑖 is the error term accounting for unexplained variance. The initial model will contain consumption variables in log form and a set of dummies for all categorical variables. In order to avoid problems with multi-collinearity, a forward stepwise regression approach will be used to exclude variables that do not contribute to model fit and were thus statistically redundant. The unstandardized beta weights resulting from this regression equation will be applied to the vector of independent variables among non-renting households to estimate their annual rent equivalent.  Average daily per capita consumption expenditures In October 2015, the World Bank raised the poverty line to USD $1.90 using 2011 purchasing power parity (PPP) rates. To facilitate the transition between the 2011 PPP rates and the prior framework based on 2005 PPP rates, this indicator will be computed as the average daily per capita consumption expenditures in constant 2010 US dollars, using the 2011 Purchasing Power Parity (PPP) exchange rates adjusted to 2010 US prices.12 12 For the endline survey, poverty will be computed using the USD $1.25 threshold using 2005 PPP rates. The poverty calculation methodology for the endline will be same as the 2014 baseline in order to facilitate baseline-endline comparisons. Uganda PBS Data Treatment and Analysis Plan APPENDIX C - METHODOLOGY TO DERIVE POVERTY INDICATORS Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 28 o 2011 PPP rates: The steps to convert daily per capita consumption expenditures collected in local currency units (LCU) to constant 2010 US$ (2011 PPP adjusted to 2010 US prices) are: 1) Convert LCU at the time of the survey (June 2018) to LCU at 2011 prices, by dividing by the ratio of the CPI for the survey month (171.77) to the average annual CPI in 2011 for Uganda (116.19).13 2) Convert 2011 LCU to 2011 US$ by dividing by the 2011 PPP conversion rate of 946.89.14 3) Convert US$ in 2011 prices to US$ in 2010 prices by dividing by 1.032, which is the ratio of the US CPI in 2011 (224.94) to the US CPI in 2010 (218.06).15 Note that average daily per capita consumption expenditure is expressed in US$ in 2010 prices in order to enable comparisons with other countries – so a common standard is essential.  Prevalence of Poverty The prevalence of poverty, or poverty headcount ratio, is the proportion of the population in the survey area living in extreme poverty, defined as per capita consumption of less than US$1.90 at 2011 prices. 1) Consumption data in the joint baseline and endline PBS will be collected in Ugandan Shilling. In order to compare the Uganda consumption expenditure data in Ugandan Shilling to the international poverty lines, the poverty lines first need to be converted into the LCU. However, if we use current market exchange rates we would underestimate consumption. One Ugandan Shilling can buy more products and services in Uganda than the equivalent amount in US$ (1 Ugandan Shilling = US $0.00026) 16 can purchase in the US. The conversion of LCUs to US$ should use an exchange rate that takes into account the differences in purchasing power of different currencies. This exchange rate is referred to as the Purchasing Power Parity (PPP) exchange rate. The poverty line will need to be further adjusted for cost of living differences in the FFP survey area since the PPP rates are constructed for entire country. Specifically, the poverty line to estimate the proportion of the population living in extreme poverty will be computed as following: The $1.90 line will be converted into LCU by multiplying it by the 2011 PPP conversion factor for private consumption for Uganda (946.89). 2) The resulting figure ($1.90 * 946.89= 1,799.09) will be adjusted for cumulative price inflation since 2011. The adjustment will be done using the consumer price index (CPI) for the survey month as the numerator, and the average annual CPI for 2011 for Uganda as the base factor. The US$1.90 poverty line is equal to 1,799.09* (171.77/116.19) = 2,659.69 in May 2018 Ugandan Shilling. 3) Finally, resulting figure will be adjusted by the factor of cost of living difference in Karamoja since the cost of basic needs required to live Karamoja could be different than the rest of the country and urban centers in particular. The CPIs in Uganda are constructed based on the prices in urban centers, they don’t take an account of price differences in Karamoja. The price adjustment process involves multiple steps. First, the key food items (food bundle) that contribute to major share of overall households’ food consumption will be identified from the survey data. Second, 13 CPI for the months of June 2018 for Uganda were not available therefore the CPI for the nearest available month (May 2018) is used here. The CPI for May 2017 for Uganda is 171.8. During the actual data analysis which will happen in July, the June CPI are expected to be publicly available and would be used. CPI 2011: http://data.imf.org/?sk=6ac22ea7-e792-4687-b7f8- c2df114d9fdc&sId=1390030341854; CPI for May 2018: https://www.ubos.org/publications/statistical/30/ 14 PPP conversion factor, private consumption (LCU per international$), 2011 International Comparison Program. Source: https://data.worldbank.org/indicator/PA.NUS.PRVT.PP 15 Source: https://www.bls.gov/cpi/cpi_dr.htm 16 https://www.xe.com/currencyconverter/convert/?Amount=1&From=UGX&To=USD, accessed on June 4, 2018. Uganda PBS Data Treatment and Analysis Plan APPENDIX C - METHODOLOGY TO DERIVE POVERTY INDICATORS Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 29 average per standard unit (KG) price of the food bundle will be computed for the Karamoja region and for the rest of the country using the price data from the Uganda National Household Survey 2016/17. The ratio of the food bundle price of Karamoja over rest of the country will then be used to adjust poverty line (2,659.69, 2011 PPP) for the Karamoja region. Cost of living could also vary by the type of dwelling structures and assets owned by the households. Such adjustment is not possible due to lack of price information in UNHS survey. Further, since a major share of household consumptions in poor rural communities go to food consumption, adjustment by food bundle prices would likely suffice for this study.  Depth of Poverty of the poor: Mean percent shortfall relative to the $1.90/day 2011 PPP poverty line This indicator is useful to understand the average gaps between poor people’s living standards and the poverty line. It indicates the extent to which individuals fall below the poverty line (if they do). Depth of poverty is sometimes also called the poverty gap index (PGI). The PGI is computed as the average of the differences between an individual’s total daily per capita consumption and the poverty line, divided by the poverty line, with individuals over the poverty line excluded from the calculation. The PGI is given by the formula: PGI = ( 1 𝑁 ∑ ( 𝑧− 𝑦𝑖 𝑧 ) 𝑁 𝑖=1 ) × 100 Where N is the total number of poor individuals in the population, z is the poverty line and yi is the daily per capita consumption of poor individual i. As noted in previous paragraph, all the individuals above the poverty line will be excluded from the numerator and denominator. Uganda PBS Data Treatment and Analysis Plan APPENDIX D – OVERVIEW OF PPI ANALYSES Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 30 Prevalence of Poverty i) Prevalence of Poverty Scorecard: Innovations for Poverty Action (IPA) has developed household’s PPI score using Ugandan National Household Survey 2012-2013 (UNHS 2012/13), which is then used with a look-up table to estimate the likelihood that the household is poor. This scorecard is calibrated to the 1.90 US PPP 2011 per person per day poverty line. Each question provides a reference to the question number in the UNHS 2012/13 instrument. ii) Prevalence of Poverty Look-up Table: IPA has also developed a look-up table to be used in conjunction with the prevalence of poverty scorecard. A household’s cumulative score is mapped to a corresponding probability that a household is below the 1.90 US PPP poverty line. Depth of Poverty i) Depth of Poverty Scorecard: Using the UNHS 2012/13, IPA has developed household’s Poverty Depth score, which is then used with a look-up table to estimate the depth of poverty, which is also termed the poverty gap (the percentage by which the household’s consumption is below a poverty line, with households at or above the line being assigned a gap of zero). This scorecard is calibrated to the 1.90 US PPP 2011 per person per day poverty line. For each question, a reference to the question number in the UNHS 2012/13 instrument is provided. The interview guidance provided in that instrument should be used when asking those questions. ii) Depth of Poverty Look-up Table: IPA has also developed the depth of poverty look-up table which should be used in conjunction with the depth of poverty scorecard. A household’s cumulative score is mapped to a predicted poverty depth with respect to the 1.90 US PPP poverty line. Approach to Test the Accuracy of the PPI based Poverty Indicators i) When the 2018 joint BL/EL PBS data are available, ICF through TANGO will provide IPA with a Stata data file that contains just the responses to the relevant PPI questions from the PPI Module along with (anonymized) household identifiers. ii) The IPA team will return: a) A Stata do file (and related documents) demonstrating how the PPI scorecards and look up tables can be used to calculate the probability that a household is poor (or a household’s poverty depth). ICF will review the code, scorecard, and the look up tables prepared by IPA. b) IPA will also develop and share a separate Stata file with poverty predictions (along with household identifiers) based on an alternative non-linear approach to estimate poverty (that involves interactions between the responses), that uses responses to the questions previously requested and made available to PPI for the purpose of computing a). iii) Subsequently, ICF will calculate three groups of accuracy measures for each poverty category. a) ICF will use the mean predicted poverty headcount (or mean predicted poverty depth) and compare it to the actual poverty rate (or actual mean poverty depth). This could be positive or negative. Error = Proportion of poor households predicted by the PPI - proportion of households that are actually poor (based on World Bank Living Standards Measurement Study (LSMS) computation methodology). Uganda PBS Data Treatment and Analysis Plan APPENDIX D – OVERVIEW OF PPI ANALYSES Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 31 This error will be calculated for the full sample and each project separately. This set of accuracy measures is useful to estimate poverty outreach. b) Accuracy will also be measured in terms of whether the PPI is able to accurately classify poor and non-poor households. This will use the poverty headcount. For each of these measures, ICF will set a cut-off consistent with reaching all the poor households and none of the non-poor households, and then calculate true positives (predicted poor when poor), true negatives (predicted non-poor when non-poor), false negatives (predicted non-poor when poor), false positives (predicted poor when non-poor). IPA will provide ICF with the Stata code. The cut-off can be either consistent with reaching the number of poor households that matches the poverty rate from the previous (i.e. baseline) survey, or the current survey (which IPA will not see). Alternatively, errors can be estimated for both cut-offs. These inclusion and exclusion errors will be calculated for the full sample and for each representative sub sample. This set of accuracy measures is useful to quantify targeting accuracy which can be compared to perfect targeting. With perfect targeting, in this case, there will be no false negatives (exclusion errors) and false positive (inclusion errors). The other benchmark that is useful here is universal coverage, where everyone is reached. Having these benchmarks will make the targeting efficiency measures easier to interpret. c) Accuracy will also be measured in terms of whether the PPI is able to accurately classify the bottom 20 and bottom 40 quintiles (based on spatially adjusted consumption per capita) of each population as being poor and the top 20 as not being poor. For each of these measures, we will calculate True Positives (e.g. predicted poor when poor) and False Negatives (e.g. predicted non-poor when poor). This error will be calculated for the full sample and for each representative sub sample. This set of accuracy measures is useful to estimate targeting accuracy for the most vulnerable segments of the population as well as leakage to the least vulnerable. Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 32 MEASURING RESILIENCE AND RESILIENCE CAPACITY Resilience is viewed as a set of capacities that enable households and communities to effectively function in the face of shocks and stresses and still meet a set of well-being outcomes. The ability to measure resilience involves measuring the relationship between shocks, capacities, responses, and future states of well-being. Thus, there is no single indicator that measures resilience. There is a need for a number of variables to be used as part of a measurement framework. There are four key factors to consider in measuring resilience:  Identify the well-being outcomes to be achieved and measure resilience in relation to these outcomes.  Identify the shocks and stresses that individuals, households, communities and systems are exposed to and the severity and duration of these shocks and stresses.  Measure the absorptive, adaptive and transformative capacities in relation to these shocks and stresses at different levels.  Identify the responses of individuals, households, communities and systems to these shocks and stresses and trajectory of well-being outcomes. The key questions to be explored through measurement of resilience are:  Does shock exposure have a negative impact on food security and child nutritional status?  Does greater resilience capacity have a positive impact on these outcomes? Resilience and Resilience Capacity Indicators o Well-being Outcomes A number of outcome indicators can be used for measuring well-being: 1. Depth of Poverty: The mean percent shortfall relative to the $1.25 poverty line 2. Prevalence of households with moderate or severe hunger (Household Hunger Scale ‐ HHS) 3. Prevalence of wasted children under five years of age 4. Average Household Dietary Diversity Score (HDDS) 5. Prevalence of stunted children under five years of age 6. Ability to recover from shocks/stressors o Shocks and Stresses The shock exposure index measures the overall degree of shock exposure for each household. The shocks should be those that are experienced by the target population and may include: flooding /excessive rainfall; landslides/erosion; drought or unpredictable or insufficient rain; hail or frost; pests or disease outbreak (crop or livestock); human disease outbreaks (e.g., cholera); death in the HH; unemployment for youths; market price fluctuation; and theft/ conflict. The index is based on household data regarding:  Number of shocks to which a HH is exposed in the past 12 months  Perceived severity of the shocks Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 33 o Resilience capacities Resilience capacities are measured as a set of indices, one for each of the three dimensions of resilience capacity—absorptive capacity, adaptive capacity, and transformative capacity—and one overall index combining these three indexes. Absorptive capacity index. Absorptive capacity is the ability to minimize exposure to shocks and stresses through preventative measures and appropriate coping strategies to avoid permanent, negative impacts. The absorptive capacity index will be constructed from eight variables, some of which are themselves indices. The variables to be used include:  Availability of informal safety nets  Bonding social capital  Access to cash savings  Access to remittances  Asset ownership  Shock preparedness and mitigation  Access to insurance  Availability of humanitarian assistance Adaptive capacity index. Adaptive capacity is the ability to make proactive and informed choices about alternative livelihood strategies based on an understanding of changing conditions. This index is constructed from the following ten variables, again some of which are themselves indices. The variables are:  Bridging social capital  Linking social capital  Social network index  Education/training  Livelihood diversification  Exposure to information  Adoption of improved practices  Asset ownership  Availability of financial services  Aspirations/confidence to adapt index Transformative capacity index. Transformative capacity involves the governance mechanisms, policies/ regulations, infrastructure, community networks, and formal and informal social protection mechanisms that constitute the enabling environment for systemic change. This index is constructed from fourteen variables, including some that are indices. The variables are:  Availability of formal safety nets  Availability of markets  Access to communal natural resources  Access to basic services  Access to infrastructure  Access to agricultural services  Access to livestock services  Bridging social capital  Linking social capital  Collective action Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 34  Gender equitable decision-making index  Participation in local decision-making  Local government responsiveness  Gender index Resilience capacity variables and their corresponding questions Table 1 presents the resilience capacity variables and their respective survey questions. Questions sourced from the FFP/FTF core household baseline questionnaire are preceded by “BL” and those from the household resilience module are preceded by “R”. Table 1. Resilience capacity variables and sources. Resilience capacity variable Questions Ability to recover R107, R108 Shock exposure index Exposure: Number of shocks experienced in the past 12 months R101 Shock severity: Impact of shock on income security Impact of shock food consumption R103 R104 Absorptive capacity index Availability of informal safety nets R801, R802 Bonding social capital R1304, R1307 Access to cash savings R601 Access to remittances R1001 (m) Asset ownership BL H7.02, H7.03, R201, R201A Shock preparedness and mitigation R901, R902, R109, R1502,R1505 Access to insurance BL G09 Availability of humanitarian assistance R1501, R1502 (1,2) Adaptive capacity index Bridging social capital R1305, R1308 Linking social capital R1309-R1314 Social network index R801, R807-R809 Education/training BL B21, R1327, R1329, R1331, R1333, R1335, R1337 Livelihood diversification R1001 Adoption of improved practices BL G13b, G16, G18, G21 Exposure to information R701, R702 Asset ownership See above Availability of financial institutions R301 Aspirations/confidence to adapt R1401-R1405, R1407-R1412, R1413, R1415, R1416, R1417 Transformative capacity index Availability of formal safety nets R1502 Availability of markets R309-R311 Access to communal natural resources R801a-R801d, R803, R804, R806 Access to basic services R301a-R301d, R302, R303a, R303b, R304a-R304c, R1506, R1507 Access to infrastructure BL F04, R301h-R301j, R307, R308 Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 35 Resilience capacity variable Questions Access to agricultural services R301e, R305a, R305b Access to livestock services R301f, R306a, R306b Bridging social capital See above Linking social capital See above Collective action R901, R902 Gender equitable decision making index R603, BL J07, J10, J11, K05, K14, K15 Participation in local decision-making R801, R802 Local government responsiveness R801c, R801d, R805, R806, R1504, R1506, R1507 In order to eliminate duplication of questions between the FFP/FTF core questionnaire and resilience modules, Table 2 maps specific changes to the FFP/FTF household questionnaire assumed as part of this analysis plan. If questions in the FFP/FTF core questionnaire are deleted that should be included, then these questions need to be added to the relevant section in the resilience module. Similarly, those sections/questions identified as not necessary in the FFP/FTF core questionnaire must be deleted in order to not duplicate those in the resilience modules, which are designed specifically with a resilience focus. Table 2. Assumptions regarding FFP/FTF household questionnaire. Includes:17 Does not include:18 FFP/FTF modules/sections Questions FFP/FTF modules/sections Questions Identification and Informed Consent Module A HHS C16-C21 Household roster, with maximum level of education B21 Humanitarian Assistance C22-C24 HDDS C3-C15 Shocks/stresses C25 Main source of drinking water F04 Livestock care/raising G15 Improved practices for crops G13B Access to hazard insurance G09 Improved practices for livestock G16 Improved practices for natural resources G18 Improved practices for crop storage G21 Gender - Cash J07, J10, J11 Gender - MCHN K05, K14, K15 Durable goods expenditures H7.02, F7.03 Calculation of shock exposure and measures of resilience capacity Throughout this document, the explanation for how each index or variable is calculated is followed by the relevant questions from the baseline survey and proposed resilience modules used for each index (in 17 If the FFP/FTF questionnaire does NOT include modules/questions listed here, they need to be added in the resilience module or elsewhere. 18 Items listed here are preferred in the resilience module and need to be removed from the FFP/FTF questionnaire. Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 36 red print). Those from the baseline household questionnaire are preceded by “BL” and those from the household resilience module are preceded by “R”. It should be noted that the specific calculations for how each resilience element is calculated can change slightly, depending on the data. Thus, this document outlines the basic construction of the three resilience capacity indices but may vary slightly once the data have been collected and cleaned. o Ability to recover 1. Ability to recover index. Ability to recover index is based on estimation of the ability of households to recover from the typical types of shocks that occur in the Title II program areas based on data regarding the shocks households experienced in the year prior to the survey. The index is calculated based on responses to two questions: “To what extent has your ability to meet food needs returned to the level it was before the shocks and stressors you experienced in the last 12 months?” With possible responses and weighted values:  Ability to meet food needs is the same as before the shocks (= value of 2)  Ability to meet food needs is better than before the shocks (= value of 3)  Ability to meet food needs is worse than before the shocks (= value of 1) AND “In light of the shocks you faced in the last 12 months, to what extent do you believe you will be able to meet your food needs in the next year?”, with possible responses and weighted values:  Ability to meet food needs will be the same as before the shocks (= value of 2)  Ability to meet food needs will be better than before the shocks (= value of 3)  Ability to meet food needs will be worse than before the shocks (= value of 1) The responses to the two questions are combined into one variable that has a minimum value of 2 and a maximum value of 6. Survey questions: R107, R108 o Index of shock exposure A measure of shock/ stressor exposure and severity is created that takes into account the shocks or stressors to which a household is exposed out of the total number of shocks or stressors (e.g., 18), and the perceived severity of the shock on household income and food consumption. Perceived severity is measured using two variables: impact on income security and impact on food consumption. The variables are based on respondents’ answers to the questions, “How severe was the impact on your income?” and “How severe was the impact on household food consumption?” which are asked of each shock or stressor experienced. The possible responses are:  No impact = value of 1 Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 37  Slight decrease = value of 2  Severe decrease = value of 3  Worst ever = value of 4 The responses to the two questions are combined into one variable that has a minimum value of 2 and a maximum value of 8. The shock exposure measure is then a weighted average of the incidence of experience of each shock (a variable equal to 1 if the shock was experienced and zero otherwise), weighted by the perceived severity of the shock. The shock exposure index ranges from 1 to 144 (i.e., 8*total number of shocks). Survey questions: R103, R104 o Proportion of HH participating in group-based savings, micro-finance, or lending programs This Indicator (EG.4.2) is calculated from the responses to questions BL 3.07 A, E and R602. The indicator value has a value of ‘1’ if BL3.07A or BL3.07E has value of 1-3, or if R602 has value of 2 or 3. Survey questions: BL3.07A, BL3.07E, R602 o Absorptive capacity index The absorptive capacity index is constructed from eight variables, some of which are themselves indices. The variables and explanations of their calculation are as follows. 1. Availability of informal safety nets. This variable is the total number of community organizations that typically serve as informal safety nets that are available and have been active within the community during the 12 months prior to the survey. The six groups are:  Credit or micro-finance group  Savings group  Mutual help group (e.g., ritban, afoosha, ofera/webera, burial, eqqub, etc.)  Religious group  Mothers’ group  Women’s group Survey question: R801, R802 2. Bonding social capital index. The bonding social capital index is based on the responses to two questions:  whether the household indicates it would be able to get help from various categories of people living WITHIN their community if they needed it;  whether the household indicates it would be able to give help to people living WITHIN their community who needed it. The possible responses for whom a household could get help from or to whom they would give help are: “relatives”, “non-relatives/neighbors within my ethnic group/clan”, “non-relatives/neighbors of other Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 38 ethnic groups/clan” and “no one”. An additive index ranging from 0 to 6 is calculated based on these responses. Survey questions: R1304, R1307 3. Access to cash savings. This is a binary (dummy) variable equal to 1 if the respondent reported that a household member regularly saves cash. Survey questions: R601 4. Access to remittances. This is a binary (dummy) variable equal to 1 if the respondent reported that receiving remittances as a source of livelihood. Survey questions: R1001 (m) 5. Asset ownership index. Asset ownership is measured using the number of consumer durables, productive assets, and livestock owned. Survey questions: BL H7.02, H7.03, R201, R201A 6. Shock preparedness and mitigation. Summary variable ranging from 0 to 4 based on the following:  There is a government and/or NGO disaster planning and/or response program in the village (1); Survey question: R1502 (8)  There is an emergency plan for livestock off-take in the village if a drought hits (1); Survey question: R1505  Household reports participating in any of the following activities: soil conservation activities, flood diversion structures (i.e., protection of land/infrastructure from flooding), planting trees on communal land, or improving access to health services (1); Survey questions: R901, R902  Household reports engaging in any of the following ways of protecting their household from the impact of future shocks: increasing savings, putting aside grains/fodder, switching to different crops/livestock, added ag activity to non-ag activity, added non-ag activity to ag activity, acquiring crop insurance (1); Survey question: R109 7. Access to insurance. This is a binary (dummy) variable equal to 1 if the household has agricultural insurance. Survey question: BL G09 8. Availability of humanitarian assistance. This is a binary (dummy) variable equal to 1 if government or NGO emergency food or cash assistance is available in the respondent’s village OR the household reported receiving emergency food or cash assistance from the government or NGO during the 12 months prior to the survey. Survey questions: R1501, R1502 (1,2) Combine the eight variables described into an absorptive capacity index using polychoric factor analysis. Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 39 o Adaptive capacity index The adaptive capacity index is constructed from ten variables, including some which are indices. The variables and calculations are as follows. 1. Aspirations/confidence to adapt index. This index is based on variables of the underlying concepts around people’s aspirations, confidence to adapt, and a sense of control over one’s life. The aspirations component is based on questions regarding an absence of fatalism and belief in the future. The absence of fatalism is based on two sets of binary variables: the first is based on two yes/no questions about whether the respondent agrees that:  Each person is responsible for his/her own success or failure in life.  To be successful one needs to work very hard rather than rely on luck. The second set of variables regarding fatalism is based on a 6-point agreement scale regarding the statements:  My experience in life has been that what is going to happen will happen.  It is not always good for me to plan too far ahead because many things turn out to be a matter of good or bad fortune. Belief in the future is based on two binary variables regarding the respondent’s view of the future.  Whether they are hopeful for their children’s future.  The level of education they want for their children. Survey questions: R1401, R1402, R1412, R1414, R1404, R1405 The confidence to adapt component is based on six variables regarding the degree to which the respondent is exposed to alternatives. Three binary variables involve whether the respondent:  Is willing to move somewhere else to improve his/her life.  Communicates regularly with at least one person outside of the village.  Engaged in any economic activities with members of other villages or clans during the week prior to the survey. The remaining three variables are based on answers to the following:  How many times in the past month have you gotten together with people to have food or drinks, either in their home or in a public place?  How many times in the past month have you attended a church/mosque or other religious service?  How many times in the past month have you stayed more than two days outside of this kebele? Survey questions: R1403, R1407, R1408-R1411 The locus of control component is based on four variables constructed from a 6-point agreement scale regarding the following:  My life is chiefly controlled by other powerful people.  I can mostly determine what will happen in my life.  When I get what I want, it is usually because I worked hard for it.  My life is determined by my own actions. Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 40 Survey questions: R1413, R1415, R1416, R1417 The variables are combined into an index using polychoric factor analysis. 2. Bridging social capital. The bridging social capital index is based on the responses to two questions:  whether the household indicted it would be able to get help from various categories of people living OUTSIDE OF their community if they needed it;  whether the household indicated it would be able to give help to people living OUTSIDE OF their community who needed it. The possible responses for whom a household could get help from or to whom they would give help are: “relatives”, “non-relatives within my ethnic group/clan”, “non-relatives of other ethnic groups/clan” and “no one”. An additive index ranging from 0 to 6 is calculated based on these responses. Survey questions: R1305, R1308 3. Linking social capital. The linking social capital index is based on answers to questions regarding whether household members know a government official and/or NGO leader, how well they know them, and whether they believe the official/leader would help their family or community if help was needed. The index ranges from 0 to 6. Survey questions: R1309-R1314 4. Social network index. This index is a sum ranging from 0 to 6 based on a series of binary (dummy) variables as follows:  There is a savings group in the village (1);  There is a mutual help group in the village (1);  There is a women’s group in the village (1);  The HH reports that any household member participated in a group that provided food to someone in that village at least once in the last 12 months (1);  The HH reports that any household member participated in a group that provided labor to someone in that village at least once in the last 12 months (1);  The HH reports that any household member participated in a group that provided some other type of help to someone in that village at least once in the last 12 months (1); Survey questions: R801, R807-R809 5. Education/training. A summary variable ranging from 0 to 8 as follows:  A binary (dummy) variable is equal to 1 if any household adult has a primary or higher education (1) Survey question: BL B21  The total number of trainings (ranging from 0 to 6) the respondent or any adult household member has had, where the possibilities are: vocational (job) training, business development training (including financial literacy), early warning training, natural resources management training, adult education (literacy or numeracy), or how to use your cell phone to get market information (e.g., prices) Survey questions: R1327, R1329, R1331, R1333, R1335, R1337 Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 41 6. Livelihood diversification. The total number of livelihood activities engaged in over the last year. The question asked to identify these livelihoods is “What were the sources of your household’s food/income over the last 12 months?” The possible options are:  Own farming/crop production and sales  Own livestock production and sales  Ag wage labor (within the village)  Ag wage labor (outside the village)  Non-ag wage labor (within the village)  Non-ag wage labor (outside the village)  Salaried work  Sale of wild/bush products (e.g., charcoal, firewood)  Honey production  Petty trade (reselling other products, e.g., grains, veggies, oil, sugar, etc.)  Petty trade (own products, e.g., local beer, sex work)  Other self-employment/own business (agricultural, e.g., buying/selling chat)  Other self-employment/own business (non-agricultural, e.g., stone cutting, hair braiding, etc.)  Rental of land, house, rooms  Remittances  Gifts/inheritance  Safety net food assistance  Other Survey questions: R1001 7. Exposure to information. The number of topics the respondent has received information on in the last year. Survey questions: R701, R702 8. Adoption of improved practices. This binary (dummy) variable is equal to 1 if respondents report adopting three or more improved practices for crop production (including vegetables) OR respondents report adopting three or more improved practices for livestock production OR respondents report following one natural resource management practice or technique not related directly to on-farm production OR respondents report using any improved storage method. Survey questions: BL G13b, G16, G18, G21 9. Asset ownership index. See above. 10. Availability of financial institutions. The variable is equal to zero if there is no institution in a village that provides credit or savings support, to one if there is one only, and to two if there are both types of support. Survey questions: R301 The overall adaptive capacity index is calculated using polychoric factor analysis. Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 42 o Transformative capacity index The transformative capacity index is constructed from fourteen variables, some of which are indexes. The variables and calculations are as follows. 1. Availability of formal safety nets. This variable is a sum ranging from 0 to 9 of the number of formal safety nets available in a household’s village. Survey question: R1502 (excluding ‘WASH’) 2. Availability of markets. A summary variable based on the number of markets available within 5 kms of a village:  Markets for selling agricultural products  Markets for purchasing agricultural inputs  Livestock market Survey questions: R309-R311 3. Access to communal natural resources. This variable is a sum ranging from 0 to 4 based on the number of communal natural resources that are managed by the community as follows:  A water users’ group who manages the community’s communal water for livestock (1) Survey questions: R801a, R803  A water users’ group who manages the community’s communal water for irrigation (1) Survey questions: R801a, R804  A group who manages the community’s communal grazing lands (1) Survey questions: R801c, R805  A group who manages the community’s firewood resources (1) Survey questions: R801d, R806 4. Access to basic services. This variable is the number of basic services available in a village and that were either in good condition or accessible during the 12 months prior to the survey.  Primary schools. A 4-point scale is constructed as follows:  No primary school within 5 km (0)  A primary school within 5 km but its physical condition is “poor” or “very poor” AND there are not enough teachers (1)  A primary school within 5 km but its physical condition is “poor” or “very poor” OR there are not enough teachers (2)  A primary school within 5 km and its physical condition is “good” or “very good” AND there are enough teachers (3) Survey questions: R301c, R303a, R303b  Health services (post, clinic, center). A 4-point scale is constructed as follows:  No health services within 5 km (0)  Health services within 5 km but its physical condition is “poor” or “very poor” AND there was time over the last year that people needed health services but could not get them because of problems with the quality of service (1) Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 43  Health services within 5 km but its physical condition is “poor” or “very poor” OR there was time over the last year that people needed health services but could not get them because of problems with the quality of service (2)  Health services within 5 km and its physical condition is “good” or “very good” AND there were no problems accessing services over the last year (3) Survey questions: R301d, R304a, R304b, R304c  Police/security force. A binary (dummy) variable regarding the presence of government security forces (local or national) that can reach a village within one hour. Survey questions: R1506, R1507  Financial services. A binary (dummy) variable equal to 1 if there are formal institutions (i.e., government regulated banks) in a village where people can borrow or save money. Survey questions: R301a, R301b, R302 5. Access to infrastructure. This variable is the number of types of infrastructure available in the respondent’s village or accessed by the respondent’s household, as determined by the following conditions:  At least one-half of households in the village have access to piped water;  At least one-half of households in the village have electricity from the main grid;  The village either has mobile phone service/network coverage OR a public telephone/kiosk;  The village can be reached with a paved road all year round OR is served by a public transportation system Survey questions: BL F04, R301h, R301i, R301j, R307, R308 6. Access to agricultural extension services. This variable is based on whether agricultural extensions services are available in a village and were accessible over the 12 months prior to the survey. A 3-point scale is constructed as follows:  No agricultural extension services within 5 km (0)  Agricultural extension services available within 5 km but there was a time in the last year when people were unable to get extension services when they needed them (1)  Agricultural extension services available within 5 km and people were able to get the services they needed over the last year (2) Survey questions: R301e, R305a, R305b 7. Access to livestock services. This variable is based on whether livestock veterinary services are available in a village and were accessible over the 12 months prior to the survey. A 3-point scale is constructed as follows:  No veterinary services within 5 km (0)  Veterinary services available within 5 km but there was a time in the last year when people were unable to get veterinary services when they needed them (1)  Veterinary services available within 5 km and people were able to get the services they needed over the last year (2) Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 44 Survey questions: R301f, R306a, R306b 8. Bridging social capital. See above. 9. Linking social capital. See above. 10. Collective action. A household-level summary variable based on the number of types of collective action a household engaged in over the last 12 months to benefit the entire community. Survey questions: R901, R902 11. Gender equitable decision-making index. Recent experience in Bangladesh, Mali, and Nepal suggest data used to construct this index may be too limited (i.e., respondent restrictions result in a large reduction in sample size). Thus, the following analysis may not be possible, depending on the actual data collected. This community-level variable19 is based on binary (dummy) variables created regarding four types of decision-making control within households: control of income, control over use of savings, control over household purchases and control over health and nutrition decisions. The first variable, gender-equitable control of income, uses responses from the first male and female eligible persons from the roster who state they have been paid in “cash only” or “cash and kind” for work done in the past 12 months. Households without a male and female responding to Module J are excluded. The variable is equal to 1 if male respondents report they participate (solely or jointly) in decisions on how cash they themselves have earned is used AND female respondents also report they participate (solely or jointly) in decisions on how cash they themselves have earned is used. The variable is equal to 0 if either males or females in a household report that “spouse/partner” or “other person” makes this decision. Survey questions: BL J07, J10 The variable gender-equitable decision-making control over savings is equal to 1 if respondents report that males and females jointly determine how savings will be used. Survey questions: R603 The variable gender-equitable control over health and nutrition decisions uses responses from the first male and female from the household roster who state they have a child under 2 years (K05). Households without a male and female responding “yes” to K05 are excluded. The variable is equal to 1 if female respondents report they make decisions about their own health and nutrition (response 1 “yourself” is only valid response) AND female respondents also report they participate jointly in decisions about their child’s health and nutrition AND male respondents report they participate jointly in decisions about their child’s health and nutrition. The variable is equal to 0 if all three conditions are not met. 19 This variable cannot be calculated at the household level because all households do not satisfy the conditions for inclusion. For example, not all households have male and female adults, and not all households have both male and female adults who earn cash income. After the data are collected, it will become clearer whether the proposed method of measuring gender-equitable decision￾making at the community level will be viable in practice. Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 45 Survey questions: BL K05, K14, K15 The variable gender-equitable household decision-making uses responses from the first male and female eligible persons from the roster who state they have been paid in “cash only” or “cash and kind” for work done in the past 12 months. Households without a male and female responding to Module J are excluded. The variable is equal to 1 if male respondents report they participate (solely or jointly) in decisions on major household purchases AND female respondents also report they participate (solely or jointly) in decisions on major household purchases. The variable is equal to 0 if either males or females in a household report that “spouse/partner” or “other person” makes this decision. Survey questions: BL J07, J11 The information from the survey households in each community is used to create the community-level index as follows: The four dummy variables are employed to calculate the percentage of eligible households (i.e., who the dummy variable can be calculated for) in each community satisfying the condition for gender-equitable decision making. Subsequently, the mean of the four indexes is used as the measure of gender-equitable decision-making control for each community. 12. Local government responsiveness. Summary variable ranging from 0 to 2 as follows:  A security/police force provided by the local government that can reach the village in less than one hour (1) Survey questions: R1506, R1507  A conflict resolution committee (1) Survey question: R1504 13. Gender index. This index is a summary variable ranging from 0 to 3 based on binary (dummy) variables regarding gender-neutral practices at the community level. Each binary variable is equal to 1 if there are no constraints to gender-neutral behavior at the community level:  Men and women regularly sit and eat together within their households (1)  Men and women regularly sit together at public meetings (1)  Men in the village help with childcare (1) Survey questions: R1601, R1603, R1605 A household-level gender variable may also be calculated.20 For those households with husband and wife, the household-level component is a summary variable ranging from 0 to 6 based on the degree to which the household engages in gender-neutral behavior. A 3-point scale is constructed for whether the respondent and his/her spouse/partner sit and eat together within their household and whether they sit together at public meetings as follows:  Not culturally acceptable = 0  Culturally acceptable and the household engages in the behavior = 1  Not culturally acceptable but the household engages in the behavior = 2 One binary (dummy) variable is based on who helps with childcare as follows:  Male respondents 20 It might be possible to combine the community and household gender variables into a single gender index, depending on the sample size of households with both husband and wife, etc. but can only be explored during analysis of the data. Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 46  report they themselves care for OR help their spouse/partner care for the children (1);  Female respondents  report their spouse/partner cares for OR helps them care for the children (1); Survey questions: R1602, R1604, R1606 14. Participation in local decision-making. A binary (dummy) variable equal to 1 if the respondent reports any household member’s level of participation in any group’s decision-making as “leader”, “very active”, or “somewhat active”. Survey questions: R801, R802 Combine the variables into a transformative capacity index using polychoric factor analysis. o Index of household resilience capacity The overall index of resilience capacity is calculated using polychoric factor analysis, with the indexes of absorptive capacity, adaptive capacity, and transformative capacity as inputs. Responses to Shocks and Stresses Program interventions that focus on resilience strengthening should be designed and implemented so that they lead to intermediate outcomes (e.g., strengthened resilience capacity of the target population), which themselves should then lead to appropriate response outcomes. Fundamentally, resilience interventions are about strengthening the ability of households (or society) to choose – from a whole 'portfolio' of options – what they perceive at that time as the “right” response(s). An appropriate response (e.g., using social capital, accessing savings) increases the chances of positive well-being outcomes, while an inappropriate or ill-chosen one often leads to vulnerability. Resilience analysis should measure the effect of different resilience responses at multiple levels (i.e., households, communities, local, provincial and national authorities). The current analysis involves only the household level. In the context of food security, the Coping Strategies Index (CSI) represents a viable response indicator as it measures the occurrence of specific detrimental coping strategies. However, the CSI focuses on short-term consumption-related behavior after a shock or stressor. Other short-term ex-post responses might also be relevant such as those focusing on cash or money-borrowing strategies, easily measured by variables that capture access to or utilization of financial services (e.g., savings groups, credit). Improved resilience capacity, however, is not simply about avoiding detrimental short-term response strategies. It is also about nurturing or fostering the ability of actors to engage in positive and sustainable responses that improve all three resilience capacities, i.e., absorptive, adaptive, and transformative capacity. Thus, a reduction in the adoption of detrimental coping strategies (i.e., a lower CSI) might serve as one universal indicator in resilience programs for improving absorptive responses. However, resilience response variables should also measure changes in adaptive and transformative behavior (Table 3). These responses have to be understood in relation to the specific social and ecological contexts and constraints within which these households are operating. Table 3. Resilience response variables and sources. Uganda PBS Data Treatment and Analysis Plan APPENDIX E – Resilience Indicators and Analyses Uganda Joint Baseline/Endline PBS Data Treatment and Analysis Plan 47 Resilience response variables Questions Absorptive responses Coping Strategy Index (CSI) R1201 Use of savings to deal with shocks R106 (aa), R604 Use of remittances to deal with shock R106 (bb), R1108 Use of hazard insurance BL G09 Use of bonding social capital R106 (s,u), R1315-R1320 Receipt of humanitarian assistance R106 (x,y) Adaptive Responses Application of information R703 Adoption of improved agricultural practices BL G13b, G16, G18, G21 Use of bridging social capital R106 (t,v), R1321-R1326 Transformative Responses Participation in local decision-making R802 (3,4,5) Participation in collective action R901, R902 Gender equitable decision making index BL J07, J10, J11, K05, K14, K15, R604 Participation in safety net program R106 (z) Annex 5: Baseline Qualitative Study Protocol D Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda FINAL Study Protocol USAID Office of Food for Peace Contract #: GS-00F-189CA/7200AA18M00002 Principal Investigator: Dr. Daniel Kibuuka Musoke (Consulting Director, IRC) October 2, 2018 This publication was produced for review by the U.S. Agency for International Development. It was prepared by ICF Macro, Inc. Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol i ACRONYMS CRS Catholic Relief Services DFSA development food security activity DFAP development food assistance program FFP Office of Food for Peace FGD focus group discussion GHG Growth, Health, and Governance HDDS household dietary diversity score IRB Institutional Review Board IRC International Research Consortium KII key informant interview MC Mercy Corps MCH maternal and child health MFI microfinance institution PBS population-based survey PPP purchasing power parity RWANU Resiliency through Wealth, Agriculture, and Nutrition UNCST Uganda National Council of Science and Technology WASH water, sanitation, and hygiene Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol ii TABLE OF CONTENTS Acronyms...............................................................................................................................................................................i 1. Background and Purpose .......................................................................................................................................... 1 1.1 Introduction ......................................................................................................................................................1 1.2 Former and Current FFP Investments in the Karamoja Region ..........................................................2 1.3 Study Purpose ..................................................................................................................................................2 2. Study Objective and Questions .............................................................................................................................. 2 2.1 Study Objective................................................................................................................................................2 2.2 Study Questions ..............................................................................................................................................3 3. Methodology................................................................................................................................................................ 5 3.1 Study Design .....................................................................................................................................................5 3.2 Study Respondents..........................................................................................................................................5 3.3 Data Collection Tools ....................................................................................................................................7 3.4 Data Collection and Quality Assurance Procedures ..............................................................................8 3.5 Data Processing, Management, and Analysis.............................................................................................8 3.6 Ethical Considerations....................................................................................................................................9 4. Implementation Workplan .....................................................................................................................................10 Annex A: Key Informant Interview Guide .................................................................................................................11 Annex B: Focus Group Discussion Guide .................................................................................................................14 Annex C: Methodology Matrix.....................................................................................................................................20 Annex D: Informed Statements and Consent Forms..............................................................................................23 Annex E: Curriculum Vitae of Dr. Daniel Kibuuka Musoke ..................................................................................27 Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 1 1. BACKGROUND AND PURPOSE 1.1 Introduction The Karamoja region consists of seven districts in northeastern Uganda (Kaabong, Kotido, Abim, Moroto, Napak, Amudat, and Nakapiripirit). Karamoja is classified as one of the world’s poorest areas, with high rates of malnutrition and a disproportionate number of its 1.3 million inhabitants (82 percent) living in absolute poverty. In addition, hunger, stunting, and lack of access to food are prevalent, with estimates suggesting that about 100 children under five years of age die each week from preventable diseases.1 Food insecurity is a major and ongoing challenge, and heavy reliance on the natural resources renders livelihoods sensitive to climate dynamics. Climate variability and change undermines the already limited resources and development in Karamoja through recurring droughts, flash floods, and prolonged dry spells. The region has high levels of variability in the climate cycle, including unpredictable rainfall patterns. The other vulnerabilities that constrain development in Karamoja stem from the following historical dynamics, which affect governance: private ownership of firearms, cattle raiding, severe environmental degradation, and poor infrastructure and limited delivery of basic services.2,3 The Karamoja region comprises three types of livelihood zones (agro￾ecological zones) that run north to south and have different soils and rainfall patterns as shown in Figure 1.4 The pastoral zone primarily supports livestock production (for cattle, goats, and sheep) and crop cultivation, which is done mainly in the years of adequate rainfall. The agro-pastoral zone supports both crop and livestock production. The agricultural zone (also referred to as the greener belt) primarily supports crop production. This zone supports a wide variety of crops that are grown in two to three planting seasons. 1 World Food Programme. 2014. “Karamoja Food Security Assessment.” Retrieved from http://documents.wfp.org/stellent/groups/public/documents/ena/wfp266332.pdf?iframe 2 Mubiru, D.N. 2010. “Climate Change and Adaptation Options in Karamoja.” Rome: FAO. Retrieved from http://www.fao.org/fileadmin/user_upload/drought/docs/Karamoja percent20Climate percent20Change percent20and percent20Adaptation percent20Options.pdf. 3 Netherlands Commission for Environmental Assessment. 2015. “Climate Change Profile: Uganda.” Retrieved from http://api.commissiemer.nl/docs/os/i71/i7152/climate_change_profile_uganda.pdf. 4 Ayoo, S., Opio, R., & Kakisa, O. 2012. “Karamoja Situational Analysis.” CARE International in Uganda. Northern Uganda Women’s Empowerpoint Programme. Retrieved from http://www.careevaluations.org/Evaluations/Karamoja percent20Situational percent20Analysis percent20- percent20Final percent20Report percent2029.01.2013.pdf. Figure 1: Karamoja Region, Uganda Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 2 1.2 Former and Current FFP Investments in the Karamoja Region The United States Agency for International Development’s Office of Food for Peace (FFP) supported two partners that implemented development food assistance programs (DFAPs) in the Karamoja region from 2012 to 2017:  ACDI/VOCA and its partners implemented the Resiliency through Wealth, Agriculture, and Nutrition in Karamoja (RWANU) DFAP in Amudat, Moroto, Napak, and Nakapiripirit Districts.  Mercy Corps (MC) and its partners implemented the Growth, Health, and Governance (GHG) DFAP in Abim, Kotido, and Kaabong Districts of Karamoja. FFP awarded two new development food security activities (DFSAs) to begin in 2018 in the Karamoja region, commonly referred to as Apolou and Nuyok:  MC and its partners are implementing the Apolou DFSA in Kaabong, Kotido, Moroto, and Amudat districts.  Catholic Relief Services (CRS) and its partners are implementing the Nuyok DFSA in Abim, Nakapiripirit, and Napak districts. Other organizations working in the Karamoja region offer food assistance to the most vulnerable households. Cash-for-work and food-for-work programs are also widespread in the region, and they are funded through the Northern Uganda Social Action Fund and implemented by the World Food Programme and other partners. 1.3 Study Purpose ICF International, with support from FFP, conducted a joint baseline/endline quantitative population￾based survey (PBS) in the Karamoja region in June and July 2018. The endline component was for the two prior DFAPs implemented by ACDI/VOCA and MC, and the baseline component was for the two new DFSAs that are being implemented by CRS and MC. The purpose of the baseline PBS was to assess the current status of key indicators, which will serve as points of comparison with the same indicators that will be collected in a future endline evaluation. ICF will partner with Dr. Daniel Kibuuka Musoke of the International Research Consortium (IRC) to collect qualitative data in selected DFSA implementation areas to supplement the PBS data. The qualitative data will provide a better understanding of the prevailing conditions and perceptions of the populations in the DFSA implementation areas. These data will also be used to refine program targeting and, where possible, to interpret relationships between some quantitative variables. 2. STUDY OBJECTIVE AND QUESTIONS 2.1 Study Objective The overarching objective of the baseline qualitative data collection is to interpret and contextualize data derived from the FFP baseline PBS. The qualitative study will explore the following key quantitative findings, which are listed under different technical sectors: Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 3 Food Security and Nutrition  The average household dietary diversity score (HDDS) is low (less than 4 of 12 food groups are consumed by households on average) in both the CRS and MC areas.  Nine out of 10 households experience moderate or severe food insecurity in both project areas.  Nearly 40 percent of children are chronically malnourished (stunted) in both project areas. More male children compared to female children are stunted in both areas.  Only about 10 percent of children ages 6–23 months consume a minimally acceptable diet.  About a quarter of women of reproductive age are underweight, and less than 20 percent of women consume a diet with minimum diversity. Poverty  About 9 in 10 households are below the poverty line in both project areas.  The depth of poverty among the poor is remarkably high, at around 60 percent. This indicates that daily per capita expenditures are well below the poverty line of USD 1.90 per day (purchasing power parity [PPP] 2010). Water, Sanitation, and Hygiene (WASH)  About 65 percent of households practice open defecation in both project areas, and very few (less than 5 percent) have soap and water at their hand-washing station.  About 20 percent of households use a basic drinking water service.  A quarter of children under five years of age had diarrhea within two weeks before the survey took place. Agriculture  About 20 percent of farmers used financial services in the past 12 months.  Less than 50 percent of farmers used improved storage practices in the past 12 months. 2.2 Study Questions Table 1 shows the key study questions that will guide the qualitative study. They are elaborated further in the focus group discussion (FDG) and key informant interview (KII) guides (see Annexes A and B). Table 1: Key Study Questions for the PBS Quantitative Findings Quantitative PBS Findings Guiding Research Questions Nutrition and Food Security  The average HDDS is low (less than 4 out of 12 food items) in both the CRS and MC areas.  Why is the HDDS so low in the project areas? What are the issues and challenges associated with low dietary diversity? How could the households improve their dietary diversity?  Nine out of 10 households experience moderate or severe food insecurity in both project areas.  What are the community perceptions about food security in Karamoja region?  Is the food insecurity situation in the project areas as dire as the quantitative PBS data show?  What factors contribute to the high levels of food insecurity in the project areas? Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 4 Quantitative PBS Findings Guiding Research Questions  What are the cultural, social, and economic challenges of food security within the households, among children under five and among women of reproductive age?  Nearly 40 percent of children are chronically malnourished (stunted) in both project areas.  What are the barriers of child malnutrition within the households and in the communities? How could they be improved?  Why are male children more stunted than female children?  What kind of foods are typically consumed by male and female children? Are male children more exclusively breastfed than female children?  What are the typical foods provided to children ages 0–5 months, 6–23 months, and 24–59 months? How adequate are these foods?  What are the differences in feeding practices between male and female children?  What has been done by communities and development agencies in the past five years to improve children’s diets and address child malnutrition in Karamoja? What still needs to be done? What should be done differently?  More male children compared to female children are stunted in both the areas.  Only about 10 percent of children ages 6–23 months consume a minimally accepted diet.  About a quarter of women of reproductive age are underweight.  What factors contribute to malnutrition among adult women?  What has been done to decrease women’s malnutrition in the last three to four years, and what still needs to be done? What should be done differently?  Less than 20 percent of women consume a diet with minimum diversity. Poverty  About 9 in 10 households are below the poverty line in both project areas.  Why do many households fall below the poverty line in spite of many development efforts, including RWANU and GHG, which have been implemented in the Karamoja region over the last 10 years?  What has been the effect of GHG and RWANU on the livelihoods and income sources of community members and households?  Why are household per capital expenditures still below USD 1.90 per day in spite of previous support from development projects, including the FFP projects?  What are households doing differently to cover their nutritional needs and health care compared to five years ago?  Are there obvious improvements in the capacity of households to cover their nutritional needs, health care needs, and other necessary expenses over the last five years? If so, what are they?  How best can communities be supported to improve their livelihoods and income sources?  The depth of poverty among the poor is also remarkably high, around 60 percent. This indicates that households are not just below the poverty line, their per capita expenditures is way below the poverty line of USD 1.90 per day (PPP 2010).  Despite this, nearly 90 percent of men and women in a union reported earning cash in the past 12 months. WASH  About 65 percent of households practice open defecation in the both project areas.  Why do communities still engage in open defecation?  What innovative approaches should be used to eradicate open defecation in the Karamoja region?  Less than 5 percent of households have soap and water in their hand-washing station.  What factors contribute to the low levels of utilization and availability of WASH services and commodities in the region?  What gender norms influence the utilization of WASH services in the community?  About 20 percent of households use basic drinking water sources.  Why do many households fail to use basic drinking water sources? Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 5 Quantitative PBS Findings Guiding Research Questions  A quarter of children under five had diarrhea within two weeks before the survey took place.  What factors contribute to the high prevalence of diarrhea among children under five?  What factors contribute to the low levels of hygiene and sanitation practices in the communities?  How can the community address challenges related to access and utilization of WASH services? What kind of support is needed from the local government and implementing partners? Agriculture  About 20 percent of farmers used financial services in the past 12 months.  What factors contribute to the low level of utilization of financial services by farmers and other traders for agricultural produce in the community?  How can access to financial services be scaled up among farmers?  Less than 50 percent of farmers used improved storage practices in the past 12 months.  What factors contribute to the low level of utilization of improved storage practices among farmers in the community?  How can the utilization of improved storage practices be scaled up among farmers? 3. METHODOLOGY 3.1 Study Design The qualitative study will use FGDs and KIIs. Qualitative data will be collected in seven villages (one in each district) that were purposively selected based on the following characteristics:  They were included in the joint baseline and endline PBS and are currently targeted by Apolou and Nuyok.  They feature interventions in agriculture/livelihoods, WASH, and maternal and child health and nutrition.  They are located in places that can easily be accessed within the time allocated to this research. Table 2 shows the list of villages that have been selected for the baseline qualitative data collection. Table 2: List of Villages That Will Be Visited for Qualitative Data Collection District Sub-county Village To Be Visited (Supported Groups) Kaabong Losongolo Naporukolong governance Abim Abim S/C Geregere East Napak Lopeii Lomusia Nakapiripirit Kakomangole Kilimanjaro Kotido Nakapelimoru Longelep Amudat Namosing Loroo Moroto Tapac Katikekile 3.2 Study Respondents The study respondents will include key informants and community members. Key informants are individuals who have important information and insights to offer regarding the local context and Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 6 socio-economic situation because of their position in the project activities, community, and government or private institutions. They will be purposively selected because of their expert knowledge in food security and nutrition, agriculture/livelihoods, WASH, and maternal and child health and nutrition. Four KIIs will be conducted in each district with the district production officer, sub-county agriculture extension officer, representative of a microfinance institution (MFI), and maternal and child health (MCH) nurse or in-charge of a nearby health facility. The guide for the KIIs is shown in Annex A. In addition, eight FGDs will be conducted with community members at the household level in each district. The following categories of community members will be recruited for the FGDs:  Male head of household: A man who self-identifies or is identified by another household member as head of household and has decision-making authority. This individual may or may not have children, may or may not have a spouse, and may or may not participate in farming activities. Preference will be given to individuals who have children under five in the household; however, this will not be a requirement.  Female head of household or lead female in household: A woman who self-identifies or is identified by another household member as a lead female figure in a household and has some decision-making authority over the type of food to eat and items to purchase in the household. The individual may or may not have children, may or may not live with her husband or a male head of household, and may or may not participate in farming activities. Preference will be given to individuals who have children under five in the household; however, this will not be a requirement.  Male farmer: This will be a male who undertakes and has decision-making authority over farming activities either on his own property or on someone else’s (community plot). 5 He may participate in the care of animals, preparation of fields, tending to and harvesting crops, or the processing of food stuffs. He may participate in farming either for subsistence or income generation, or both.  Female farmer: This will be a female who undertakes and has decision-making authority over farming activities on her own property or someone else’s (community plot). She may participate in the care of animals, preparation of fields, tending to and harvesting crops, or the processing of food stuffs. She may participate in farming either for subsistence or for income generation, or both.  Male and female young people/teenagers: These will be restricted to male and female young people/teenagers ages 15–20.  Community health workers: These will include members of the village health teams as identified by the village local council chairpersons.  Members of the village water service committees 5 FFP definition of a farmer: Farmers include (1) herders and fishers and are men and women who have access to a plot of land (even if very small) over which they make decisions about what will be grown, how it will be grown, and how to dispose of the harvest; AND/OR (2) men and women who have animals and/or aquaculture products over which they have decision-making power. Farmers produce food, feed, and fiber, where “food” includes agronomic crops (crops grown in large scale, such as grains), horticulture crops (vegetables, fruit, nuts, berries, and herbs), animal and aquaculture products, as well as natural products (e.g., non-timber forest products, wild fisheries). These farmers may engage in processing and marketing food, feed, and fiber and may reside in settled communities, mobile pastoralist communities, or refugee/internally displaced person camps. Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 7 Table 3 summarizes the types of qualitative methods that will be used and the kinds of participants proposed for interviews. Table 3: Type and Number of Interviews To Be Done at Study Site Interview Types and Tentative Participants Naporukolong Geregere East Lomusia Kilimanjaro Longelep Katikekile Loro FGDs (six to eight participants each) Male head of household 1 1 1 1 1 1 1 Female head of household or lead female in household: 1 1 1 1 1 1 1 Male farmer 1 1 1 1 1 1 1 Female farmer 1 1 1 1 1 1 1 Male young people/teenagers ages 15–20 1 1 1 1 1 1 1 Female young people/teenagers ages 15–20 1 1 1 1 1 1 1 Member of village water service committees 1 1 1 1 1 1 1 Community health workers 1 1 1 1 1 1 1 Subtotal (FGDs) 8 8 8 8 8 8 8 KIIs District production officer 1 1 1 1 1 1 1 Sub-county agriculture extension officer 1 1 1 1 1 1 1 Representative of an MFI 1 1 1 1 1 1 1 In-charge or MCH nurse at nearby health facility or hospital 1 1 1 1 1 1 1 Subtotal (KIIs) 4 4 4 4 4 4 4 3.3 Data Collection Tools Two interview guides will be used (see Annexes A and B). They have been designed with questions under each of the following technical sectors: food security and nutrition, WASH, agriculture/livelihoods, and poverty and socio-cultural community context.  The KII guide will be used for the following respondents: district production officer, sub-county agriculture extension officer, representative of an MFI, and MCH nurse or in-charge of a nearby health facility.  The FGD guide will be used for the following respondents: male and female heads of households, male and female farmers, male and female young people/teenagers ages 15–20, community health workers, and members of village water service committees. Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 8 3.4 Data Collection and Quality Assurance Procedures Data collection will be conducted by a team comprising a qualitative researcher and four field assistants (two male, two female). The qualitative researcher will provide leadership in the design, implementation, analysis, and reporting for the qualitative data collection. He will train and closely supervise the field assistants in data collection, transcription, and translation. The field assistants will be selected based on their experience in conducting KIIs and FGDs and fluency in the local dialects or languages. All interviews will be conducted using the most suitable language for the study respondents. In addition, all interviews will be digitally recorded with permission from the participants. Our quality control and quality assurance plan will involve the following:  Field interviewers with the skills and abilities to follow and implement the field process as stated in the protocol will be recruited.  A training will be organized for field assistants to carefully go through the protocol, interview guides, and data collection procedures. The training will be facilitated by the qualitative researcher.  Consistency will be ensured in terms of interpretation and contextual presentation of information.  The entire study team will be comprehensively oriented on the study methodology and data collection tools before the start of the actual fieldwork through a pretest. Any necessary corrections will be made following the pretest.  Adequate and proper supervision during fieldwork will be done to ensure quality data collection. In addition, ICF technical managers will supervise and guide the qualitative data collection on all key study tasks.  After each day of data collection, key findings will be discussed among the study team in a debriefing session, after which the data collected will be edited, cleaned, and summarized. The study team will follow up with key informants for any missing information or inconsistencies in each KII, within one day or at a more convenient time for the respondents. In some cases, this follow-up process will be done through a telephone conversation. 3.5 Data Processing, Management, and Analysis The FGDs and KIIs will be audio-recorded and later transcribed verbatim by the field assistants. All typed transcripts will be reviewed by the lead qualitative researcher to identify emerging themes from the transcripts. They will be validated and uploaded to a Dropbox folder for storage. Access to the Dropbox folders will be restricted, and the qualitative researcher will be responsible for approving access rights. All the collected information will also be printed and stored as hardcopies in a secure, locked cabinet at the IRC offices. All audio recorders will be given to the qualitative researcher, and the tapes will later be destroyed after transcription validation so as not to retain recordings with identifiers. All data will be analyzed manually using content analysis.6 Data will be read and re-read by the qualitative researcher in order to identify emerging themes from the transcripts.7 To provide an understanding of 6 Riley, J. 1990. “Getting the most from your data.” London, King’s Fund. 7 Glaser, B. G., and Strauss, A. L. 1967. “The discovery of grounded theory.” Chicago IL. Aldine. Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 9 the quantitative indicators derived from the results of the household survey, content analysis will be used to identify themes or trends in responses, both within and across respondent groups so that the findings from the quantitative PBS are triangulated with the findings from the qualitative data collection. Direct quotations from the KIIs and FGDs will also be used to support the interpretations. Quotes will also be selected and included in the reports to emphasize the reasons for the reported quantitative performance. 3.6 Ethical Considerations Institutional Review Board (IRB) approval: This study will involve interviewing human subjects, and therefore, the study team will make every effort to address ethical considerations during the planning and implementation of the study. Permission to conduct the study will be sought from the Mildmay Uganda Institutional Review Committee, and later the protocol will be registered at the Uganda National Council of Science and Technology (UNCST). In addition, permission will be sought from the chief administrative officers of the districts where communities will be selected for the interviews. Consent process and documentation: Prior to each interview, the objective of the study will be explained to the respondents to enable them to provide verbal informed consent. Interviewers will obtain verbal informed consent using consents forms, which will be inserted at the beginning of every interview guide. During training, interviewers will be instructed on how to use the consent forms to obtain verbal informed consent from the study respondents. All KIIs and FGDs will be recorded for consistency in data collection unless a participant refuses to do so. Verbal informed consent to participate as well as consent to be audio-recorded will be captured on the audiotape as appropriate. After participants have been screened, recruited, and brought to the interview site, the interviewer will review the informed consent document in full. If the participant agrees to participate and indicates a willingness to be recorded, the interviewer will then turn on the recorder. The audio-recording will begin with a note to indicate the interview number or other code, briefly restate the main points of the informed consent, and then ask the participant if he or she is willing to participate. After the participant consents verbally, the interviewers will reaffirm that he or she is willing to be audio-recorded. Confidentiality and privacy: The study will include a number of measures to protect the respondents’ confidentiality. Personal identifiers will not be collected from participants. Only a study code will be included on the transcript to identify the respondent. To further protect respondent confidentiality, interviewers will be forbidden from interviewing respondents who are known to them. The interviewers will also be trained in research ethics, including the importance of ensuring subject confidentiality. All study team members will sign a confidentiality statement that confirms their commitment to keep verbal and electronic information collected from respondents safe and confidential. Interviewers’ contractual letters of agreement will explicitly state that they will safeguard the confidentiality of the information to be collected and will not discuss it with anyone outside the study team. Interviewers will ensure that the KIIs are conducted in a confidential setting. KIIs should be conducted one-on-one with a respondent and no one should be able to hear the respondent’s answers. Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 10 Interviewers will ensure respondents that their participation is voluntary and that they can choose to stop the interview at any time. The FGDs with community members will also be conducted in a private setting that will be identified in the community. Risks: The study will present minimal risks to the participants because they will not undergo any invasive procedures. As indicated above, the study will not include any identifying information. Further, because the study team will use interviewers who do not know the participants, the risk of breach of confidentiality will be reduced significantly. In addition, the study will carry a relatively low time commitment on the part of the respondents because the interviews will not exceed two hours. Respondents also will not incur transportation or other costs as a result of participating in the study because interviewers will be travelling to their communities and work sites. 4. IMPLEMENTATION WORKPLAN Key Activities Duration (weeks) Deliverables 1 2 3 4 5 6 Inception stage Develop study protocol Final narrative Develop research tools Pretest research tools Tested research tools Ethical approval stage Prepare IRB submission IRB and UNCST research approval letters Obtain IRB approval Prepare UNSCT submission Obtain UNCST approval Fieldwork stage Training of field teams One-day training workshop Data collection (field) Audio files for all completed interviews Data management stage Transcription Fully typed transcripts Data analysis Analysis report Reporting stage Technical report writing Draft technical report Finalization of study report Final technical report Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 11 ANNEX A: KEY INFORMANT INTERVIEW GUIDE Before we begin our conversation around food security, I want to learn a little bit about who you are and the nature of your position. 1. What organizations do you work with? 2. What is your current title? 3. What are the roles and responsibilities related with that position?  Tell me specifically about the roles and responsibilities related to food security and nutrition?  In which districts, sub-counties, villages, etc., do you work? 4. What is your past work experience in the Karamoja region of Uganda? Nutrition and Food Security [district production officer, agriculture extension officer, and health worker or in-charge at nearby health facility or hospital] 5. What is your view or opinion about the food insecurity situation among households in you district? Is it very bad as portrayed by current statistics? 6. Are there any customs, traditions, or beliefs that involve food in your community? For example, is there a period in which people fast, or eat a particular food type, or avoid a particular food type? Are there beliefs that interfere with breastfeeding? Are there beliefs as to the kinds of foods children need when they are sick? 7. To what extent is food security influenced by gender? 8. Why is the household dietary diversity score so low in the project areas? What are the issues and challenges associated with low dietary diversity? How could the households improve their dietary diversity? 9. How do the households understand food security? How do they assess their food security situation? If they are concerned and/or have experienced the inadequacy or lack of food in the past 30 days or 12 months, why is that the case? What factors trigger their food insecurity? How could this situation be improved? 10. How do the caregivers assess the child nutrition situation in their households and in the communities? Do they think that malnutrition is prevalent or that it is not a big problem? If they realize the existence of this problem, do they understand the implications of child malnutrition, especially the implications of chronic malnutrition like stunting for children under five? 11. What are the barriers to child malnutrition within the households and in the communities? How could it be improved? What has been done in recent years (in the last three to four years) to address this problem by development agencies, and what needs to be done to address this problem more effectively? 12. Why are male children more stunted than female children? What kinds of foods are typically consumed by male and female children? 13. Are male children more exclusively breastfed than female children? Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 12 14. What are the typical foods provided to this age group of children (0–5months, 6–23 months, and 24–59months) in addition to breastfeeding?  How do you assess the adequacy of foods consumed by children of the following ages?  What are barriers to adequate diet for children of this age group?  Is there a difference in feeding practices between the male and female children?  What has been done by the respondents or by their community to improve children’s diet in recent years (in the last three to four years), and what needs to be done? 15. Do the study participants think that adult women in the project area are generally malnourished? What kind of households are likely to have malnourished women? What are the factors that might be contributing to poor nutrition among the adult women? Do the participants think that malnourishment among adult women is a serious problem? Why or why not? What has been done to improve women’s malnutrition in recent years (last three to four years), and what needs to be done? 16. What are the social and economic challenges of food security within the households, among children under five, and among the women of reproductive age? 17. What has been done to address those challenges at the household and/or community level in recent years (in the last three to four years)? Have the previous efforts worked? Why or why not? What needs to be done to address the existing food insecurity challenges? Is this a recurring problem? If so, is there something that could be done to address it? 18. Have there been food security programs implemented in the past by the government, foreign donors, or community-based organizations? If so, please tell me a little bit about your experiences with those programs. What were some of the strengths of those programs? And weaknesses? Water, Sanitation, and Hygiene (WASH) [maternal and child health nurse or in-charge of nearby health facility] 19. What is the main source of drinking water for members of the community? In the dry season? In the rainy season? Please tell me about the quality of water in the dry and rainy seasons. 20. What is your view or opinion about the availability and utilization of WASH services and commodities in the community? 21. What factors contribute to the low levels of utilization and availability of WASH services and commodities in the region? 22. Why do communities still engage in open defecation? 23. Why do many households fail to use basic drinking water sources? 24. What do you think about the use of latrines in this community? Probe for why communities have not been able to use them. 25. What factors contribute to the high prevalence of diarrhea among children under five? 26. Are there major differences in access to and utilization of WASH services in the community by males and females? 27. What are the major challenges related to access to and use of WASH services in the community? 28. How can the community address these challenges, and what kind support do they need from the local government and implementing partners? Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 13 Agriculture and Livelihoods [district production officer, representative of micro-finance institution, and agriculture extension officer] 29. What are your views and opinions about financial services available in your community? Probe for profiling practices, minimum threshold, repayment rates. 30. What factors contribute to the low level of utilization of the financial services by farmers and other traders for agricultural produce in the community? 31. What prevents farmers from accessing the available financial services? 32. How can access to financial services be scaled up among farmers? 33. How do households store produce? What improved storage practices are currently used in your district? 34. What factors contribute to the low level of utilization of improved storage practices among farmers in the community? 35. What prevents farmers from using the improved storage practices? 36. How can the utilization of improved storage practices be scaled up among farmers? Poverty [district production officer and sub-county agriculture extension officer] 37. What would you say are the primary sources of income for the majority of households in this community? Agriculture, livestock, trading? Services? Combination? Others (e.g., selling wild food, firewood, charcoal)? And who is involved in those activities? 38. Are there business development opportunities that you believe would help build food security in this area? Please explain your thoughts. 39. Is it common for individuals or families to save money? Why or why not? And if so, what are savings commonly directed towards? Are people members of saving groups? What is the composition of male versus female versus mixed groups in the community? 40. Is it possible to secure business loans in your community that may help inspire development? What are some of the roadblocks to securing loans? Are the screening process and requirements the same for men and women? 41. Are there other structural features in the community that may prevent successful economic growth? Please explain. 42. Why do many households fall below the poverty line in spite of many development programs, including Food for Peace (FFP) projects, which have been implemented in Karamoja region over the last 10 years? 43. What has been the effect of previous FFP development programs on the livelihoods and income sources of community members and households? 44. Why is the household per capital expenditures still below USD 1.9 per day in spite of previous support from development projects, including the FFP projects? 45. Are there obvious improvements in the capacity of households to cover their nutritional needs, health care needs, and other necessary expenses over the last five years? 46. How best can communities be supported to improve on their livelihoods and income sources? Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 14 ANNEX B: FOCUS GROUP DISCUSSION GUIDE Food Security and Nutrition [male and female household heads, male and female youth, community health workers] 1. Please tell me what you understand by food security in your community. 2. Do you think food insecurity is a problem in your community? Probe for why. 3. How do you assess your food security situation? 4. Are you concerned and/or have experienced the inadequacy or lack of food in the past 30 days or 12 months, and if so, why is that the case? What factors trigger the food insecurity? How could this situation be improved? 5. Please tell me a little bit about the typical eating habits here in your community. PROBES:  What kinds of foods do you typically eat? (Think about the foods that you and the members of your household ate over the last week.)  What times of day do people eat?  Who prepares the food? 6. Please tell me a little bit about the liquids individuals in your community typically drink.  What do they typically drink? Probe for alcohol or local brew; probe for reasons as to why they take the mentioned drinks; probe for the local brew and residues fed to children and why they do so.  What times of day? 7. Where do most of the foods and beverages you consume come from? 8. Are there particular special events or holidays you celebrate that effect your food choices? Tell me about those occasions. How frequently do these events occur? 9. Are there any beliefs or customs that involve food in your community? For example, is there a period in which people fast, or eat a particular food type, or avoid a particular food type? 10. Are there beliefs that interfere with breastfeeding? Are there beliefs as to the kinds of foods children need when they are sick? 11. How do caregivers of children assess the child nutrition situation in their households and in the communities? Do they think that malnutrition is prevalent or that it is not a big problem? If they realize the existence of this problem, do they understand the implications of child malnutrition, especially the implications of chronic malnutrition like stunting for children under five? 12. Do you know of anything which has been done by development agencies in the recent years (in the last three to four years) to address this problem, and what needs to be done to address this problem more effectively? 13. What kind of foods are typically consumed by male and female children? 14. Do you think male children are more stunted that female children? If so, why? 15. What kinds of foods typically consumed by male and female children? 16. What are the typical foods provided children of the following age groups in your community: 0–5 months, 6–23 months, and 24–59 months? Probe for the following:  How do you assess the adequacy of foods consumed?  What are barriers to an adequate diet for children? Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 15  Probe for any differences in feeding practices between male and female children.  What has been done by the respondents or by your community to improve children’s diet in recent years (in the last three to four years)?  What else needs to be done? 17. How do you assess the adequacy of foods consumed by children ages 6–23 months? Probe for:  What are the typical foods provided to this age group of children in addition to breastfeeding?  What are barriers to adequate diet for children of this age group?  Is there a difference in the way male and female children are fed?  What has been done by your community to improve children’s diet in recent years (in the last three to four years), and what needs to be done? 18. Do you think that adult women in the project area are generally malnourished? Probe for:  What kind of households are likely to have malnourished women?  What are the factors that might be contributing to poor nutrition among the adult women?  Do the participants think that malnourishment among the adult women is a serious problem? Why or why not?  What has been done to decrease women’s malnutrition in recent years (in the last three to four years), and what still needs to be done? 19. What has been done to address food insecurity in the community? Probe for what has been done and by whom. 20. Are there any food security programs implemented in the past by the government, foreign donors, or community-based organizations? If so, please tell me a little bit about your experiences with those programs. What were some of the strengths of those programs? And weaknesses? Agriculture and Livelihoods [male and female farmers] Farming at the community level 21. What are some of the most common products that are farmed in this community? a. For sale? b. For consumption? 22. What type of farming do members of the community do? For food to consume? For food to sell? Or both? If both, what percentage for each? Does this vary by time of year? Or do people typically farm for some other purpose? If so, what is that other purpose? 23. I would like to learn a little bit more about the type of farming families do here for subsistence.  What type of products are farmed? (particular plant or animal?)  Who typically makes the primary decisions about the farming in a household in your community? Tell me a little bit about the typical roles and responsibilities of individuals in households in your community for farming as well as household work.  Please tell me a little bit about the processes that occur once food has been harvested for consumption. What is the process for storing it? How is it processed? Who makes the decisions regarding the production and storage of the food that has been harvested? Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 16 24. Would like to learn a little bit more about the type of farming that happens in this community to generate income.  What type of products are farmed? (particular plant or animal?)  Who typically makes the primary decisions about the farming in a household in your community? Tell me a little bit about the typical roles and responsibilities of individuals in households in your community for farming as well as household work.  Please tell me a little bit about the processes that occur once food has been harvested for income generation. What is the process for storing it? How do is it processed? Who makes the decisions regarding the production and storage of the food that has been harvested? 25. If community members are selling any part of the goods produced, please describe that process for me.  Does the farming occur here locally or do community members go elsewhere to farm?  What part of the process do household members in this community typically undertake in the preparation and sale of foods?  Do you work with other community members?  Who makes decisions regarding how the money will be allocated if farming and sales are a communal process? 26. Has the community experienced any events in the past that have impacted the ability to farm either for sustenance or for income? (illness, environmental episode, accident, community event, national event?) How did members of the community get through that event? 27. Where do community members typically learn their farming techniques? Are there techniques you or others in the community would like to learn, but have not had access to?  Techniques for farming for consumption?  Techniques for farming for income generation? 28. How often do agriculture extension workers visit your household to provide support? 29. What are some of the customs, traditions, and beliefs related to work in the household? What differences are there in men’s versus women’s work roles? Who owns livestock? Who is responsible for processing different kinds of crops and livestock? Are there specific gender issues that affect food security? 30. What is your primary source of water in this community? Is this water used both for consumption and for farming? 31. What are some of the biggest challenges faced in this community with farming? Poverty [male and female household heads, male and female youth] 32. What would you say are the primary sources of income for the majority of households in this community? Agriculture, livestock, trading? Services? Combination? Others (e.g., selling wild food, firewood, charcoal)? And who is involved in those activities? 33. Are there business development opportunities that you believe would help build food security in this area? Please explain your thoughts. 34. Is it common for individuals or families to save money? Why or why not? And if so, what are savings commonly directed towards? Are people members of saving groups? Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 17 35. Is it possible to secure business loans in your community that may help inspire development? What are some of the roadblocks to securing loans? 36. What do you think are some of the greatest needs for your community? 37. Have there been programs implemented in the past by the government, foreign donors, or community-based organizations? If so, please tell me a little bit about your experiences with those programs. 38. What were some of the strengths of those programs? And weaknesses? How has the overall context and living situation changed within the last two years? Especially relating to security, food, health, women’s rights, youth rights, and agricultural production? 39. What has been the effect of previous Food for Peace development programs on the livelihoods and income sources of community members and households? 40. Are there obvious improvements in the capacity of households to cover their nutritional needs, health care needs, and other necessary expenses over the last five years? 41. How best can communities be supported to improve their livelihoods and income sources? Access to and Use of Financial Services [male and female farmers] 42. What are your views and opinions about the access and utilization of financial services in your district? 43. What factors contribute to the low level of utilization of the financial services by farmers and other traders for agricultural produce in the community? 44. What prevents farmers from accessing the available financial services? 45. How can access to financial services be scaled up among farmers? 46. What motivates or inhibits farmers from using financial services? 47. How can access to financial services be scaled up in the community? 48. What factors affect the utilization of the financial services provided to farmers in the community? How can access to financial services be scaled up among farmers? Storage Practices [male and female farmers] 49. How does your household store the produce? 50. Do you know of other methods you want to try? Why? 51. What prevents you from using your preferred storage methods for your produce? Water, Sanitation, and Hygiene [members of the community water service committees and community health workers) 52. What is the main source of water for members in your community? 53. Tell me about the typical daily routine for fetching water. Does this activity happen individually for each household? Or is there a community system in place for fetching water? 54. How long does it take to fetch water and return, including travel and waiting time? How is water carried?  What time does it occur? And how often in the week? Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 18  Who in the family is responsible for that activity?  Who makes the decision regarding who will be responsible for fetching the water?  Do those who fetch water face any risks? What are these risks and what steps have been taken, if any, to reduce the risk?  Do you typically sanitize your water before use? If so, what process do you follow? If not, why not?  Do community members pay for water services? How is it done? Are community members willing to pay for water if they know that they will get it? If they do not pay for water, what are some the solutions identified by the community members themselves to help improve access to water in the community? 55. Does this change in the dry versus the rainy season? 56. Are there times when water is not available to you? If so, what do you do when this happens? 57. What is the main source of drinking water for members of the community? In the dry season? In the rainy season? Please tell me about the quality of water in the dry and rainy seasons. 58. What is your view or opinion about the current levels of sanitation and hygiene in your community? Do you think it is bad or good? Probe for availability of water and sanitation commodities; use of latrines, open defecation, garbage disposal, and hand washing. 59. Why do communities still engage in open defecation? 60. Why do many households fail to use improved drinking water sources? Definition: Basic drinking water services are defined as improved sources or delivery points that by nature of their construction or through active intervention are protected from outside contamination, in particular from outside contamination with fecal matter, and where collection time is no more than 30 minutes for a roundtrip, including queuing. Drinking water sources meeting these criteria include: piped drinking water supply on premises; public tap/standpost; tube well/borehole; protected dug well; protected spring; rainwater; and/or bottled water (when another basic service is used for hand washing, cooking, or other basic personal hygiene purposes). All other services are considered to be “unimproved,” including: unprotected dug well, unprotected spring, cart with small tank/drum, tanker truck, surface water (river, dam, lake, pond, stream, canal, irrigation channel), and bottled water (unless basic services are being used for hand washing, cooking, and other basic personal hygiene purposes). 61. Why are many children under five affected by diarrhea? 62. What can be done to improve sanitation and hygiene in your community? Socio-Cultural and Political Community Context [male and female household heads, male and female youth, male and female farmers, member of community water service committees, community health workers] 63. In this last part of our interview, I would like to learn a little bit more about your community as a whole. 64. What do you think are some of the greatest needs for your community? Have there been programs implemented in the past by the government, foreign donors, or community-based organizations? If Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 19 so, please tell me a little bit about your experiences with those programs. What were some of the strengths of those programs? And weaknesses? 65. How has the overall context and living situations changed within the last two years? Especially relating to security, food, health, women’s rights, and agricultural production? Also probe for market systems development. Are there more agriculture and livestock products available in the market now than two years ago? More drugs, vets, agriculture extension workers? 66. Are there locations or resources in your community that members would wish to access but have not for the past year due to insecurity or avoidance of disputes? How has the level of access to this resource changed? How free are you to move around? Has this changed over time? 67. How often do members of your community interact with individuals from other communities? What is the nature of interaction? What types of economic interactions are associated with good or bad relationships? Are there variations by sex? Who are the aggravators of conflict? 68. Is there any other additional information you would like to share with us about your access to food, your consumption of food and beverages, your work and livelihoods, or your health care practices? Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 20 ANNEX C: METHODOLOGY MATRIX Key Findings from the Population-based Household Survey Research Questions and Sub-questions Data Collection Method and Respondents Nutrition and Food Security  The average household dietary diversity score (HDDS) is low (less than 4 out of 12 food items) in both the Catholic Relief Services and Mercy Corps areas.  Why is the household dietary diversity score so low in the project areas? What are the issues and challenges associated with low dietary diversity? How could the households improve their dietary diversity? Key informant interviews with the following respondents:  District production officer  Health worker at nearby health facility or hospital Focus group discussion with the following respondents:  Male household head  Female household head  Male young people/ teenagers ages 15–20  Female young people/ teenagers ages 15–20  Male farmers  Female farmers  Community health workers  Members of the water service committees  Nine out of 10 households experience moderate or severe food insecurity in both project areas.  What are the community perceptions about food security in the Karamoja region?  Is the food insecurity situation in the project areas as dire as the quantitative population-based data show?  What factors contribute to the high levels of food insecurity in the project areas?  What are the cultural, social, and economic challenges of food security within the households, among children under five and among women of reproductive age?  Nearly 40 percent of children are chronically malnourished (stunted) in both project areas.  What are the barriers of child malnutrition within the households and in the communities? How could these be improved?  Why are male children more stunted than female children?  What kind of foods are typically consumed by male and female children? Are male children more exclusively breastfed than female children?  What are the typical foods provided to children ages 0–5months, 6–23 months, and 24–59 months? How adequate is it?  What are the differences in feeding practices between male and female children?  Has been done by communities and development agencies in the past five years to improve children’s diets and address child malnutrition in Karamoja? What still needs to be done? What should be done differently?  More male children compared to female children are stunted in both project areas.  Only about 10 percent of children ages 6–23 months consume a minimally accepted diet.  About a quarter of women of reproductive age are underweight.  What factors contribute to malnutrition among adult women?  What has been done to decrease women’s malnutrition in recent years (last three to four years), and what still needs to be done? What should be done differently?  Less than 20 percent of women consume a diet with minimum diversity. Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 21 Key Findings from the Population-based Household Survey Research Questions and Sub-questions Data Collection Method and Respondents Poverty  About 9 in 10 households are below the poverty line in both project areas.  Why do many households fall below the poverty line in spite of many development efforts, including the Resiliency through Wealth, Agriculture, and Nutrition (RWANU) project and the Growth, Health, and Governance (GHG) project, which have been implemented in the Karamoja region over the last 10 years?  What has been the effect of previous GHG and RWANU efforts on the livelihoods and income sources of community members and households?  Why are the household per capital expenditures still below USD 1.90 per day in spite of previous support from development projects, including Food for Peace projects?  What are households doing differently to cover their nutritional needs and health care compared to five years ago?  Are there obvious improvements in the capacity of households to cover their nutritional needs, health care needs, and other necessary expenses over the last five years? If so, what are they?  How best can communities be supported to improve on their livelihoods and income sources? Key informant interviews with the following respondents:  District production officer  Community development officer Focus group discussion with the following respondents  Male household head  Female household head  Male young people/ teenagers ages 15–20  Female young people/ teenagers ages 15–20  Male farmers  Female farmers  The depth of poverty among the poor is remarkably high, around 60 percent. This indicates that households are not just below the poverty line, their per capita expenditures are way below the poverty line of USD 1.90 per day (purchasing power parity 2010).  Despite all this, nearly 90 percent of men and women in union reported earning cash in the past 12 months. Water, Sanitation, and Hygiene (WASH)  About 65 percent of households practice open defecation in both project areas.  Why do communities still engage in open defecation?  What innovative approaches should be used to eradicate open defecation in Karamoja region? Key informant interviews with the following respondents:  Health worker at nearby health facility or hospital Focus group discussion with the following respondents:  Members of the water service committees  Male household head  Female household head  Male young people/ teenagers ages 15–20  Female young people/ teenagers ages 15–20  Community health workers  Less than 5 percent of households have soap and water in their hand￾washing station.  What factors contribute to the low levels of utilization and availability of WASH services and commodities in the region?  What gender norms influence the utilization of WASH services in the community?  About 20 percent of households use basic drinking water sources.  Why do many households fail to use basic drinking water sources?  A quarter of children under five had diarrhea within two weeks before the survey took place.  What factors contribute to the high prevalence of diarrhea among children under five?  What factors contribute to the low levels of hygiene and sanitation practices in the communities?  How can the community address challenges related to access and utilization of WASH services? What kind of support is needed from the local government and implementing partners? Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 22 Key Findings from the Population-based Household Survey Research Questions and Sub-questions Data Collection Method and Respondents Agriculture  About 20 percent of farmers used financial services in the past 12 months.  What factors contribute to the low level of utilization of financial services by farmers and other traders for agricultural produce in the community?  How can access to financial services be scaled up among farmers? Key informant interviews with the following respondents:  Representative of a microfinance institution  Agricultural extension officer Focus group discussion with the following respondents  Male farmers  Female farmers  Male household head  Female household head Less than 50 percent of farmers used improved storage practices in the past 12 months.  What factors contribute to the low level of utilization of improved storage practices among farmers in the community?  How can the utilization of improved storage practices be scaled up among farmers? Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 23 ANNEX D: INFORMED STATEMENTS AND CONSENT FORMS Study Title: USAID Food for Peace Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda This study has been approved by an accredited Ugandan Research Ethics Committee (MUREC). This study is funded by the United States Agency for International Development (USAID) Office of Food for Peace. About 588 people will be interviewed during the collection of qualitative baseline data from the communities targeted for the implementation of NUYOK and APOLOU projects. INFORMED CONSENT STATEMENT and CONSENT FORM for INDIVIDUALS SCHEDULED to PARTICIPATE in a KEY INFORMANT INTERVIEW The same statement should be used for all key informant at the district, sub-county and village levels. This statement must be read at the beginning of each interview by the person leading the interview. Introductions and duration of interview Thank you for very much for agreeing to meet us for an interview today. My name is X and I am from the International Research Consortium. (Each other study team member present will introduce him/herself.) This interview will take no more than one hour of your time. Purpose of the study, and purpose of this interview Duration Before we begin I would like to tell you about the purpose of this study. We have been hired by ICF International to collect information from areas that will benefit from NUYOK and APOLOU projects. The information to be collected will be used to devise better ways of serving your communities with project services. The reason why we want to interview you is because of your knowledge/experience with (X insert the specific intervention/activity that is the focus of the interview). Potential risks, our procedures to reduce potential risk, and confidentiality measures Alternatives for notes taken during the interview and for recording the interview There should not be any risk to you for agreeing to be interviewed. Many other people in this district and in other villages will be interviewed, too, for the same purpose. The way we reduce any risk that might possibly occur because of your answers to our questions, is by our guarantee to you personally that everything you say during our meeting will be kept confidentially. We will not tell other people in your district/village or in any other places in this district, what we talked about and what you said. We plan to take notes during this interview so that we will not forget this conversation, but these notes will not be shared with any other persons. Your name will not be included in these notes. I will type up these notes into my computer, and the notes we take from other people we interview, to better understand the food security situation in your community. When we finish studying the notes from all the interviews we hold with people in this district, I will erase the notes from my computer so that no one else will be able to read the notes. I will also destroy the notes we take down on paper today. Alternative for instances where we also plan to record the interviews. I would like to use this recorder to record our discussion to make sure I do not forget any important information. Do you have any objections? (plan to take notes if the person objects to being recorded) Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 24 I will listen to the recording of the interview and type it up into my personal laptop/computer to make it easier for me to study. No one else is allowed to listen to the recording or to use my computer to read the notes from the recording of our interview today. I will study these notes, and the notes we take from other people we interview, to determine if there are ways in which future activities can be improved for the benefit of people in this area. When we finish studying the notes from all the interviews we hold with people in this district, I will erase the recording and delete the notes I typed up into my computer so that no one else will be able to listen to this interview or to read the notes. Benefits from consenting to interview There are no specific benefits to you for agreeing to be interviewed today. We can say that the information you give us today will make a contribution to these types of activities in the future to improve X (insert one of these phrases depending on the focus of this interview: the health of children, the local economy, the livelihoods of people, peace) in this area. We value your contribution. Contact Information for any questions You can contact Dr. Daniel Kibuuka Musoke TEL 0772587094 if you have any questions about this research after we leave here today and also for information regarding the progress and findings of the study. If you have any concerns about the study, you can contact the MUREC chairperson, Ms. Harriet Chemusfo; Tel: 0392174236. Participation is Voluntary. No penalty involved in leaving any time during interview. Before I begin asking questions, we want to assure you that your participation in this interview today is completely voluntary. If after hearing what I just explained you decide that you do not want to be interviewed, you are free to leave. I will not ask you for an explanation. There is no penalty involved. If there are any questions I ask that you do not want to answer, please let me know. We will skip to the next question. If at any time during the interview you wish to stop, please let me know. I will not ask you for an explanation. Individual’s questions Do you have any questions? Ask for consent Do you agree to be interviewed? (If yes) Will you sign this consent form? Participant Name, Signature and Date: Interviewer Name, Signature and Date: Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 25 INFORMED CONSENT STATEMENT AND FORM FOR INDIVIDUALS SCHEDULED to PARTICIPATE IN FOCUS GROUP DISCUSSIONS The same statement should be used for all focus group discussions at the village level. This statement must be read before the discussion begins by the person leading the interview. Introductions and duration of interview Thank you for very much for agreeing to meet us for an interview today. My name is X and I am from the International Research Consortium. (Each other study team member present will introduce him/herself.) This interview will take no more than 60-75 minutes of your time. Purpose of the study, and purpose of this interview Duration Before we begin I would like to tell you about the purpose of this study. We have been hired by ICF International to collect information from areas that will benefit from NUYOK and APOLOU projects. The information to be collected will be used to better understand the food security situation in your community. The reason why we want to interview you is because of your knowledge/experience with (X insert the specific intervention/activity that is the focus of the interview). Potential risks, our procedures to reduce potential risk, and confidentiality measures Alternative statements for notes taken during the discussion and for recording the discussion There should not be any risk to anyone here for agreeing to participate in this discussion. The way we try to reduce any risks to you that might happen because of what you say during this discussion, is by our guarantee to each one of you personally that everything you say during our meeting will be kept confidential. We will not tell other people in your village or in any other place in this district, what we talked about and what anybody said here today. We plan to take notes during this interview so that we will not forget this conversation, but we will not write down your names. These notes will not be shared with any other people. I will type up these notes into my computer to make them easier for me to study later on. I will be studying these notes and the notes we take from all other interviews in this district to see if there are ways in which future activities can be improved for the benefit of communities. When I finish studying these notes, I will erase them from my computer. I will also destroy the notes we write down today. Alternative for instances when a recorder is used during the discussion. I would like to use this recorder to record our discussion to make sure I do not forget any important information. Does anyone object? (plan to take notes if there are objections) I will listen to the recording and type up what we said today into my computer to make it easier for me to study. No one will be allowed to listen to the recording or to use my computer to read the notes from the recording of our discussion today. I will study these notes, and the notes we take all other people we interview in this village and in this district, to see if there are ways in which Mercy Corps’s future activities can be improved for the benefit of people living in this area. When I finish studying the notes from our discussion today, I will erase the recording so no one will be able to listen to what anyone said. I will also destroy the notes from the recording that I typed into my computer so that no one will be able to read what anyone in this group said. Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 26 Benefits from consenting to participate in the discussion There are no specific benefits to anybody for agreeing to be interviewed today. We can say that the information you give us today will make a contribution to these types of activities in the future to improve X (insert one of these phrases depending on the focus of this interview: the health of children, the local economy, the livelihoods of people, peace) in this area. We value everybody’s contribution to the discussion. Contact Information for any questions You can contact Dr. Daniel Kibuuka Musoke TEL 0772587094 if you have any questions about this research after we leave here today and also for information regarding the progress and findings of the study. If you have any concerns about the study, you can contact the MUREC chairperson, Ms. Harriet Chemusfo; Tel: 0392174236. Participation is Voluntary. No penalty involved in leaving any time during interview. Before we begin asking questions, I want to assure you that your participation in this discussion today is completely voluntary. If after hearing what I just explained you decide that you do not want to participate, you are free to leave. We will not ask you for an explanation. There is no penalty involved. If there are any questions we ask that you do not want to answer, please let us know. We will skip to the next question. If at any time during the interview you wish to leave, please let us know. We will not ask you for an explanation. Ask for questions Does anyone have any questions? Ask for consent Do you agree to participate? Please raise your hand if you want to participate in this discussion. Participant Name, Signature and Date: Interviewer Name, Signature and Date: Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 27 ANNEX E: CURRICULUM VITAE OF DR. DANIEL KIBUUKA MUSOKE Career Summary Daniel is a Senior Research and Evaluation Specialist and the Consulting Director of the International Research Consortium, a multidisciplinary agency offering research services in the East and Southern African Countries. He has over 13 years of experience in the design and implementation of a wide range research and evaluation projects in collaboration with top researchers from different universities and research institutions around the world. Daniel is uniquely skilled and has a wealth of experience in the following: (i) Survey methodology and qualitative approaches; (ii) Project/program evaluations including baseline assessments for rapid project start-ups, mid-term reviews, end of project evaluations and field based impact evaluations; (iii) Development of monitoring and evaluation systems, including design of conceptual, logical and results frameworks, indicators and tools using participatory approaches; (iv) Data quality audits/assessments; (v) Development of policies, strategies and guidelines; (vi)Provision of technical support in strategic planning and reviews through the use of participatory approaches; and (vii) Data analysis and synthesis of complex information in to user friendly formats. Daniel has considerable experience in heading multidisciplinary research teams having done this for several client assignments at both the local and international scenes. He has successfully accomplished several research projects with USAID Uganda, DFID, UNFPA, UNICEF Uganda, World Bank, Uganda MoH, Management Systems International, JSI Research and Training Institute, Belgium Technical Cooperation, Mitchell Group, Inc., TANGO International, Westat, FHI360, and other Ugandan based HIV/AIDS service organizations. He is a very hardworking and energetic individual with exceptional communication, leadership, consulting and presentation skills. He is highly analytical and good at conceptualization, logical thinking, and problem solving. He also has excellent report writing and documentation skills, and promptly meets deadlines. In 2010, he was among the recipients of the Young Achiever’s Award for excellence and professionalism in the in the Provision of Research and Evaluation Services. He holds an International Master’s Degree in Health Economics and Pharma-Economics from the University of Pompeu Fabra, Barcelona, Spain; Master’s Degree in Health Services Research from the University of South Africa and also holds a bachelor of medicine and bachelor of surgery from Makerere University. He has also attended several short courses at recognized international centers of learning. Tertiary Education Aug 2010: International Masters in Health Economics and Pharma-Economics, University of Pompeu Fabra, Barcelona, Spain. December 2004: Master of Science (Health Service Research) - University of South Africa. June 1999/2000: Bachelor of Medicine and Bachelor of Surgery (MBChB), Makerere University Kampala, Uganda. Other Courses Attended March 2004: Public Health Management, Costing and Budgeting, Case Western Reserve University (CWRU), USA and the Uganda Joint Clinical Research Centre (JCRC). Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 28 January 2004: Monitoring and Evaluation of Community Health Program by Center for Infectious Disease Research in Zambia May 2003: Survey Methodology for Community Medicine by the Makerere University Institute of Public Health. April 2003: Further Practical Statistics, University of South Africa. April 2000: Certificate in HIV/AIDS care and Prevention for Medical Doctors in Africa by the Academic alliance on HIV/AIDS care and prevention between North America Infectious Disease Society and Makerere University Medical School. Evaluation and research experience for Nutrition and livelihood projects 1. International Evaluator, Evaluation of the Swaziland Food by Prescription Development Programme Supported by World Food Programme (April-August 2016). This evaluation was commissioned by the World Food Programme (WFP) Office of Evaluation (OEV) and focused on the appropriateness of the operation, results of the operation, why and how the operation produced the observed results. The evaluation findings informed a hand over strategy which was used by WFP to hand over the food by prescription programme to the Swaziland National Nutritional Council. 2. Project head, End-of-Project Evaluation for “Climate Change Adaptation for improved Food Security and Applied Nutrition (CCA)” Project in Ngora District (January-June 2016). Heifer International-Uganda and International Institute of Rural Reconstruction (IIRR) were supported to conduct a final project evaluation which was guided by the following standard evaluation criteria: Relevance, effectiveness, efficiency and sustainability. The evaluation documented project achievements and lessons learnt from implementation that guided scale up of similar projects in Uganda and the rest of Sub Saharan Africa. 3. Co-Principal Investigator of the Impact Evaluation Study for the Kuroiler Distribution Project in Uganda (January-December 2014). In collaboration with Arizona State University and TANGO International, the 2012, The Bill and Melinda Gates Foundation was supported to undertake an impact evaluation of the Kuroiler Distribution project in Uganda. Using a quasi￾experimental design, the evaluation assessed the impact of Kuroiler chickens versus local chickens on household nutrition, household income, and gender equality. 4. Project head, Impact evaluation of School Health and Nutrition projects funded by USAID through UPHOLD to Save the children/US in Nakaseke and Luwero districts (December 2005). Using a quasi-experimental design and school based methodology, this evaluation assessed the impact of school health and nutrition interventions on key learning outcomes. Evaluation findings were used by the Save the Children/US to inform the design of similar projects in other settings/countries. 5. Project head, End of project evaluation for the Easier Living Project implemented by the International Rescue Committee (IRC) with funding from Stichtling Vlutcheling [Jan-March 2013]. The International Rescue Committee and its stakeholders were supported to understand the overall project performance towards achieving its stated goal and objectives, and also learned from the experiences of the project. Since the project worked at zonal levels to provide business skills training and support to women through the VSLA methodology, the evaluation assessed the VSLA group experience for the 9 VSLA groups and the kind of social capital assets that were created as a result of participation. The evaluation used both quantitative and qualitative data collection approaches. Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 29 6. Project head, Baseline assessment of food and nutrition interventions for PLHIV in Uganda (October 2008- January 2009). University Research Co., LLC/USAID and the Uganda Ministry of Health were supported to conduct a combined health facility and household baseline nutritional assessment in 32 health facilities and 1450 HHs. This helped to document baseline indices of project performance that were compared with the end of project results in order to determine the outcome of effect of the project interventions. The household survey component was done using a two-stage stratified random design and covered nutritional status and dietary intake. 7. Project head, Monitoring and Evaluation of Food for Peace programs implemented by ACDI VOCA and Mercy Corps in Uganda using the Layers Survey (October 2010-June 2011): The USAID supported Food and Nutritional Technical Assistance Project (FANTA-2) was supported to implement the Layer survey (using PDA technology) which involved tracking the quality of the following activities that were implemented by Mercy Corps and ACDI/VOCA at their activity sites in Uganda: Warehouse management, Food for work - road rehabilitation, Institutional Strengthening, Agriculture package- post harvest handling, market training, farming as a business, agronomy training, nutrition and hygiene training, savings and credit group formation, Food distribution - assistance to PLHIV, NTIHCHN- health centers, care groups, Village health teams (VHTs), Agriculture- producer groups, women's garden groups, demo plots Food for work- road construction, wells, primary school latrines and Food distribution- (NTIHCHN, FFW). 8. Project head, Joint endline and baseline population based surveys in Karamoja region, May –September 2018. The IRC team was contracted by ICF International to support the training and field activities for the joint baseline and endline PBS. The purpose of the surveys is to improve understanding of how FFP projects are functioning, whether the projects are achieving targeted results, how they are perceived by primary stakeholders and whether the approaches, methods and interventions promoted by FFP are efficient and cost-effective. 9. Project head, Feed the Future (FTF) Feedback Zone of Influence (ZOI) Uganda Survey (September 2014- April 2015). Feed the Future is the US government’s global food security initiative that seeks to reduce poverty, hunger, and under-nutrition among women and children; and to increase income, women’s empowerment, dietary diversity, and appropriate feeding practices. Representative cluster sample surveys were done to track progress in achieving Feed the Future’s objectives. IRC was contracted to plan, conduct, and supervise the fieldwork, including training and managing the interviewers; with support provided throughout all phases of the survey by the USAID-funded FTF FEEDBACK project (led by Westat). 10. Project head, 2012 Uganda Feed the Future Zone of Influence Baseline Survey. Technical support was provided to TANGO and Uganda Bureau of Statistics (UBoS) during the training of enumerators and field supervisors, review of the English and local versions of the questionnaires, anthropometry training, training in the use of tablets and on human subjects protection and procedures for maintaining confidentiality. The training and field pre-test was done using the 2012 Uganda Feed the Future Zone of Influence Baseline Survey Questionnaire which covered the following areas: agriculture, food security, food consumption, nutrition and wellbeing of households. 11. Project head, Feed the Future (FTF) Feedback Pilot Quantitative Household Survey (August 2012). TANGO International and Westat were supported to conduct a pilot survey that tested the hardware, software and data file transfer protocols in Uganda for the 2012 Uganda Feed the Future Zone of Influence Baseline Survey. There were four modes of data entry: three hardware/software combinations plus paper-based data entry. The pilot survey was used to test; three models of tablet computers (Nexus 7, HTC Flyer and the Fujitsu Q550) for field data collection; two data entry templates operating on two different operating systems and the feasibility of five different modalities of data backup/synchronization – The pilot test covered 500HHs that were randomly selected in Mukono district in Central Uganda. Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 30 Evaluation and research experience for health programmes 1. Project head Final Evaluation of the Pioneer Project (December 2013). The Pioneer project was a five-year special malaria prevention and control initiative project which was funded by Comic Relief and implemented by Malaria Consortium in five districts of Hoima, Buliisa, Kyankwanzi, Kibaale and Kiboga districts from May 2009 to December 2013. The evaluation assessed the project’s overall performance and determined the extent to which project achieved its intended objectives. Data collection and analysis was partly done using the Most Significant Change technique. 2. Project head, Evaluation of the Effectiveness and sustainability of the VHT model of Depo Provera provision and its impact on Family Planning switching behaviour (June￾November 2013). Marie Stopes International was supported to assess the challenges in the supply chain management for injectable contraceptives both at the community and health facility levels, and also explored the feasibility of different mechanisms that were utilized to address stock outs of key supplies and commodities. In addition, the evaluation determined factors that contributed to prolonged Depo-Provera usage and the most feasible approach which is currently used by Marie Stopes Uganda in strengthening the VHT supervision and monitoring techniques. 3. Project Head, Second Annual Performance review of DFID funded mTrac project. (August- October 2013). The United Kingdom, Department for International Development UNICEF and WHO Uganda were supported to conduct the second annual performance review of the mTrac project. The project involved procurement of ACTs and development and roll out of a national SMS based system for monitoring availability of ACTs and generating community action for improved health system accountability. Progress in implementing the three key components of the mTrac project were assessed, namely: (i) (use of SMS to transmit weekly reports; (ii) U-Reporters; and (iii) anonymous hotline). Progress was assessed against the project implementation plans and log frame matrix and the review findings were used to make evidence based decisions at national and district level. 4. Project Head, First Annual Performance review of DFID funded mTrac project. (August- October 2013). The United Kingdom, Department for International Development UNICEF and WHO Uganda were supported to conduct the second annual performance review of the mTrac project. The project involved procurement of ACTs and development and roll out of a national SMS based system for monitoring availability of ACTs and generating community action for improved health system accountability. Progress in implementing the three key components of the mTrac project were assessed, namely: (i) (use of SMS to transmit weekly reports; (ii) U-Reporters; and (iii) anonymous hotline). Progress was assessed against the project implementation plans and log frame matrix and the review findings were used to make evidence based decisions at national and district level. 5. Project head, Evaluation and documentation of the CSO capacity building interventions that were supported by the SCIPHA project (April-August 2013). Joint Clinical Research Centre was supported to conduct a process and outcomes based evaluation of the capacity building initiatives for Strengthening Civil Society for Improved HIV &AIDS and OVC service delivery (SCIPHA) project in Uganda. The evaluation determined the effects of the SCIPHA project capacity￾building efforts on HIV service delivery results of supported CSOs as well as their overall institutional sustainability. The evaluation also identified links to the capacity-building inputs that were provided by SCIPHA and lessons learned in capacity building. 6. Project head, Evaluation of the Child Status Index (CSI) tool and database (July-August 2012). The Child Status Index (CSI) was developed in order to contribute to the improvement in the delivery of services for Orphans and Vulnerable Children (OVC). The Ministry of Gender, Labour and Social Development (MGLSD), the Civil Society Fund (CSF) and TPO-Uganda adapted the CSI tool from MEASURE Evaluation after field pretests that were conducted in August 2011. Additionally, a database for the CSI was developed for tracking and monitoring the changes in the Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 31 wellbeing of OVC supported by CSF. After one year of CSI implementation, an evaluation of the use of the CSI tool and database was done which identified successes/best practices as well as the critical bottlenecks and gaps that faced CSI application and determined possible options for rolling it out to other OVC service providers in the country. 7. Project head, final evaluation of the strategic plan for Baylor College of Medicine Children Foundation Uganda (2007-2012). The evaluation assessed the effectiveness of the Baylor Uganda strategic plan 2007-2012 in meeting its operational objectives and identified issues that emerged in the changing external and internal environment that required substantial modification of approach and institutional changes in the Strategic Plan for the subsequent period (2012-2017). 8. Project head, Final evaluation of the USAID supported New Partners Technical Assistance (NuPITA) project that was implemented by John Snow Inc. (May -June 2012). This evaluation was done in Uganda, Kenya and South Africa and determined the extent to which technical support that was provided by the NuPITA project to HIV service delivery organizations led to improvement in service delivery results and institutional sustainability. The final evaluation findings were disseminated to all key stakeholders. 9. Project head, Final evaluation of the 5-year TB/HIV project implemented by AIC, and funded by Centre for Diseases Control and Prevention (CDC) (October-December 2011). The evaluation assessed the extent to which the project achieved its intended objectives with a focus on relevancy, efficiency, effectiveness and sustainability of project activities. 10. Project head, Final evaluation of the ‘Saving Women’s Lives: Tearing down the Barriers to Unsafe Abortion in Uganda’ project [also called Safe Abortion Action Fund (SAAF) project] – March –April 2011. With funding from the International Planned Parenthood Federation (IPPF), Reproductive Health Uganda (RHU) was supported to evaluate the SAAF project. The evaluation assessed the extent to which the project achieved its intended objectives with a focus on relevancy, efficiency, effectiveness, and sustainability of the project activities. The evaluation assessed the appropriateness of the strategies which were used in project implementation and also documented lessons learnt which were used as the basis for instituting improvements to the planning, design, and management of similar projects or for scale up. 11. Project head, Final Evaluation of Expansion of National Pediatric HIV&AIDS Prevention, Care and Treatment and Training of Service Providers in the Republic of Uganda under President’s Emergency Plan for AIDS Relief (PEPFAR) [March –May 2011]. With funding from CDC Uganda, Baylor Uganda was supported to evaluate key project achievement with a focus of relevancy, effectiveness, efficiency and sustainability. The evaluation also determined whether the project achievements and failures were influenced by factors outside Baylor-Uganda’s control, and whether Baylor-Uganda partnership strategy was appropriate and effective. 12. Project head, Final evaluation of the HOPE project initiative (October 2009 – March 2010). The HOPE project was funded by DFID ($7m) for a period of five years. It was implemented by Interact Worldwide, UK and National Community of Women Living with HIV/AIDS in Uganda (NACWOLA). The evaluation used both qualitative and quantitative methods of data collection and assessed the relevancy, equity, efficiency, effectiveness, impact and sustainability. The evaluation findings informed strategies for engaging people living with HIV/AIDS in service delivery. 13. Project head, Mid-term evaluation of the Northern Uganda Malaria, AIDS and Tuberculosis (NUMAT) program (July - August 2009). USAID Uganda monitoring and evaluation services project (managed by the Mitchell Group, Inc.) was supported to conduct a mid￾term review of the NUMAT program. The evaluation determined whether the program was on track to achieve its objectives within the existing time frame and funding parameters and recommended changes in program or management strategies for the remaining two years of the Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 32 project which helped to increase its impact in Northern Uganda in light of the changing political and social context. 14. Project head, Evaluation of HIV/AIDS service networks in Uganda (January 2008-March 2008). The USAID Uganda monitoring and evaluation management services project (managed by Management Systems International) was supported to undertake and in-depth assessment of the role of “networked approaches” in the delivery of HIV/AIDS services. Study findings were used by the USAID Uganda and supported implementing partners to develop better strategies for supporting networked HIV/AIDS services for improved efficiency and sustainability. 15. Project head, Evaluation of the Africa Dialogue on AIDS Care (ADAC); an African AIDS Experts led initiative managed at the JCRC (July 2007-August 2008). The Joint Clinical Research Centre in Uganda was supported to conduct a rapid participatory review of the achievements of the African Dialogue in AIDS Care in Africa. 16. Project head, Provision of technical assistance to the USAID AIDS Capacity Enhancement Project in strengthening monitoring and evaluation services at Joint Clinical Research Centre (JCRC) in Uganda (2007-2008). Chemonics International was supported to conducting a rapid participatory assessment of the monitoring and evaluation systems at the Joint Clinical Research Centre. Thereafter, additional supported was extended to JCRC to develop an organizational wide monitoring and evaluation framework and plan, integrated monitoring and evaluation database, data collection tools and reporting formats. This assignment was done in close collaboration with JCRC senior management as well as interaction with the JCRC’s centers of excellence and ART sites. 17. Project head, Provision of technical assistance to UNICEF/MoH in the Evaluation of the National PMTCT programme 2000/01-2006/07. This evaluation was conducted in partnership with Health Consult and Mbarara University Department of Community Health and also involved working closely with the Ministry of Health. The evaluation assessed the PMTCT programme coordination, organization and management; access, utilization and quality of services; procurement and supply chain of PMTCT services; quality and integration of services, and lastly funding mechanisms and cost of PMTCT implementation. Additionally, a retrospective cost analysis was done in order to determine the cost of PMTCT implementation at the national, district, health facility and community levels. 18. Co-principal investigator, Public Expenditure Review (PER) of the Health Sector (November 2006-February 2007). The assessment of public expenditures for health generated evidence that informed the strategic implementation of Uganda’s health program through improved planning, budgeting and management of public funds for equitable and efficient allocation and utilization of public resources for health. 19. Project head, Assessment of the Uganda’s progress in the implementation of the UNGASS Declaration of commitment to HIV/AIDS (November 2005 - January 2006). The assessment was done for the Uganda AIDS Commission using the national agreed indicators entailed in the National M&E Framework and the UNAIDS UNGASS Guidelines on construction of core indicators. The exercise involved largely analysis of data from secondary sources, consultation with sectors mandated to report on various indicators and interviews with key informants. Household and health facility surveys 20. Project head, The Uganda Diarrhea prevention and treatment survey (October￾December, 2011). Abt Associates was supported to conduct a household survey in 20 project districts including Masaka, Rakai, Luwero, Mukono, Bugiri, Kayunga, Mbale, Gulu, Lira, Amuru, Arua, Nebbi, Dokolo, Soroti, Kaberamaido, Kibaale, Masindi, Hoima, Kyankwanzi and Kiboga. The survey was done using a multi-stage stratified random design and covered 4500 households. The survey was comprised of 3 components, namely; diarrhea prevention and treatment survey for caregiver of SFG staff undertaking survey field data collection Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 33 children 6-59months, a survey on water treatment for caregivers of children in the same age range, and a survey of service providers regarding diarrhea treatment within the selected enumeration areas. The survey included house holding listing and was done using PDAs that were programmed using Pocket PC creations. 21. Project head Baseline survey of the Strengthening Civil Society for Improved HIV & AIDS and Orphans and Vulnerable Children (OVC) service delivery project in Uganda (SCIPHA project - (September- November, 2011). Joint Clinical Research Centre (JCRC) and Uganda Health Marketing Group were supported to conduct a quantitative household survey using Lot Quality Assurance Sampling methodology in 5 regions of Uganda (West Nile, North, Central, Midwest, and Eastern) covering a total of 19 districts – The survey covered 1805 Households and mainly investigated HIV/AIDS prevention and treatment. 22. Annual survey of reproductive health and family planning services in 15 districts in Uganda [October-December 2010]. Management Sciences for Health (MSH) was supported to conduct a combined health facility and household surveys which tracked performance of the PMP indicators. It covered 2400 households and mainly investigated reproductive health and utilization of family planning services. 23. Baseline assessment of HoPE Lake Victoria Basin project in Uganda and Kenya (March –September 2012). Pathfinder International was supported to conduct baseline household surveys that helped to gain an in-depth understanding of the socio cultural, environmental and institutional settings of project sites. This study also helped to assess relevant factors that affected ecosystem threats and health status in the communities and service delivery points, as well as existing capacities and areas that needed to be strengthened. The survey was done using a multi-stage stratified random design and covered 1245 households. The survey included house holding listing and was done using PDAs that were programmed using Pocket PC creations. 24. Project head, Baseline Evaluation of the “Preserving the African family in the face of HIV/AIDS through prevention initiative (January-July 2007). Children AIDS Fund was supported to conduct a baseline survey on the abstinence and be faithful project in six districts of Kampala, Mukono, Kayunga, Luwero, Wakiso and Mpigi. It was done using two stage stratified random design and covered 12 households. It involved house hold listing and rigorous training of field interviewers on human subjects protection. Research on Malaria 25. Co-Investigator, Perceptions about the value of malaria rapid diagnostic tests among private sector providers in Uganda (January-May 2015). Collaborated with John Hopkins University to conduct a qualitative study which was aimed at: (i) Understanding factors that influence malaria rapid diagnostic test (mRDT) use and associated treatment at private health facilities in and around the Wakiso district of Uganda; (ii) Understanding how structural market factors, including price, supply chain structure, and alternative treatment sources act to facilitate or pose a barrier to adoption of malaria rapid diagnostic tests (mRDT) at private health facilities in and around the Wakiso district of Uganda; and (iii) Identifying opportunities for addressing barriers to malaria rapid diagnostic test (mRDT) use and treatment according to mRDT results at private health facilities in and around the Wakiso district of Uganda. 26. Co-Investigator, Net care and repair formative research in Uganda (January-June 2012). Collaborated with the Johns Hopkins University Center for Communication Programs (JHU·CCP) to carry out carry out qualitative research in Lira and Soroti districts in Uganda which informed NetWorks/JHU-CCP’s interventions that focused on promoting LLIN care and repair behaviors among households. The study identified protective, adaptive and repair behaviors around mosquito net ownership and use in Uganda, including the barriers and motivations for these behaviors. The study findings informed the development of net care and repair interventions in Uganda Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 34 27. Principal investigator, Post Insecticide Treated Nets (ITN) distribution survey in Karamoja sub-region (March-December 2009). Supported UNICEF Uganda and MoH to conduct a post ITN distribution survey in Karamoja sub region which determined ITN household coverage, retention and use, factors that affect ITN retention and use and the effectiveness of the behavior campaigns which were conducted as part of the campaign distribution. The survey was done using two stage stratified random design and targeted 1,550 households in the districts of Moroto, Napak, Amudat, Kotido, Kaabong and Nakapiripirit. 28. Principal Investigator, Post Insecticide Treated Nets (ITN) distribution survey in Lango sub-region (March-December 2009). Supported UNICEF Uganda and MoH to conduct a post ITN distribution survey which determined ITN household coverage, retention and use, factors that affect ITN retention and use and the effectiveness of the behavior campaigns which were conducted as part of the campaign distribution. The survey was done using a two stage stratified random design and targeted 1,550 households in the districts of Lira, Oyam, Apac, Dokolo and Pader. 29. Principal Investigator, Baseline and final household surveys for the “Hang Up’ project in Eastern Uganda (October 2009-December 2010). Supported the Uganda Red Cross Society (URCS) to conduct a baseline and final household surveys which assessed household knowledge and practices about malaria prevention among the communities in the districts of Mbale, Manafwa and Kaberamaido. The surveys were done using a two stage stratified random design and targeted 850 households in each survey rounds. Baseline and final indices for key performance indicators were compared in order to determine changes in key project performance indicators. 30. Project head, Baseline survey of medical laboratory facilities in Uganda (March-July 2005). Provided technical support to the World Health Organization to conduct a baseline survey of medical laboratory facilities in Uganda. The study documented major strengths and weaknesses in the provision of medical laboratory in Uganda. The study findings informed major policy reforms and strategic actions that helped to improve medical laboratory services in Uganda. Research on key populations 31. Project head, Rapid assessment on access to and utilization of HIV/AIDS care and treatment services for key and priority populations in Uganda (August 2014-March 2015). Supported USAID Uganda and Ministry of Health to conduct a rapid key population assessment using mixed methods. The study determined which MARPs are served; identified current services available for MARPs; described services delivery models used and best service delivery practices. In addition, the study also determined gaps in service delivery and opportunities for scaling up HIV/AIDS services targeted to key populations. The findings from the rapid assessment informed the roll out of the revised national ART guidelines with a strong focus on the test and treat strategy for key populations. 32. Project head, Rapid assessment and response analysis for HIV prevention services for MARPS in the Eastern Region (July- August 2013). Supported the USAID supported The STAR-E project to review the current MARPs interventions using mixed methods in the Eastern region. The study focused on the following key populations: Fisher folk, commercial sex workers (CSWs) and their clients, long distance truck drivers, discordant couples, and men having sex with men (MSM). The study findings informed the design of better strategies for scaling up MARPs intervention in the Eastern region. 33. Project head, IRAPP/IGAD End line Behavioural Surveillance Survey (BSS) at Bibia and Oraba Hotspots in Uganda (January 2015). Supported the Inter-Governmental Agency for Development (IGAD) supported Regional HIV/AIDS Partnership Project (IRAPP) to conduct an end￾line Behavioural surveillance survey (BSS) at two HIV hot spots in Uganda (Bibia/Elegu and Koboko/Oraba). The study assessed the extent to which the IRAPP had progressed in meeting its intended results. Specifically, the end line survey was done using both qualitative and quantitative Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 35 methods and provided data which was used to track changes in the key project performance indicators. 34. Project head, IRAPP/IGAD Baseline Behavioural Surveillance Survey (BSS) at Bibia and Oraba Hotspots in Uganda (January 2012). ). Supported the Inter-Governmental Agency for Development (IGAD) supported Regional HIV/AIDS Partnership Project (IRAPP) to conduct a baseline behavioural surveillance survey (BSS) at two HIV hot spots in Uganda (Bibia/Elegu and Koboko/Oraba). The IGAD Regional HIV/AIDS Partnership Programme (IRAPP) was aimed at reducing HIV transmission along the major transport routes in the targeted countries including Uganda. Specifically, the survey collected information on HIV/AIDS knowledge, attitudes, practices and behaviours among the high risk and vulnerable target groups (commercial sex workers, truck drivers and youth). The survey established baseline data for the IRAPP projects in the two hot spots which was used to set project benchmarks and targets. 35. Project head, Rapid HIV/AIDS situation analysis among long distance truck drivers and their sexual networks in West Nile region of Uganda (April 2011). Supported Uganda Health Marketing Group to conduct a rapid HIV/AIDS situation analysis among the truckers and their sexual networks. This study generated strategic information which informed a new HIV prevention project for truckers and their sexual networks in West Nile region. Data quality assessments 36. Project head, USAID Uganda DO3 Data Quality Assessment for FY2013 (April 2014). Supported USAID Uganda to conduct the Data Quality Assessment (DQA) on a set of 27 performance indicators. The DQA help USAID Uganda to ensure that the Development Objective (DO3) team was aware of the strengths and weaknesses of the project performance data which by applying the five data quality standards (validity, integrity, precision, reliability and timeliness). The DQA also determined the extent to which the data integrity could be trusted to influence management decisions, and also ensured that all data reported to USAID/Washington or data that was reported externally on USAID performance had a recent data quality assessment done within three years before submission. The DQA exercise was conducted from July 8th 2014 to September15th 2014, covering 27 DO3 indicators and 18 implementing partners (IPs). 37. Project head, Independent verification of Marie Stopes Uganda’s outreach service numbers (2012-2014). Supported Marie Stopes International to conduct an independent assessment of the accuracy of service data which was reported by the 25 mobile outreach teams to MSU management, and subsequently to USAID, between 1st January 2013 and 30th September 2014. The verification exercise involved cross-checking of a sample of selected data and ascertained its quality based on the crosschecked and verified that the data was of reasonable quality, based on the five data quality standards; validity, reliability, integrity, precision, and timeliness. The key findings from the verification exercise were used to eventually strengthen the MSU M&E system at the both the regional centers and headquarters, which in turn helped to improve management and programmatic decisions based on data generated through the M&E system. 38. Project head, Comprehensive audit of the health communication interventions in Uganda (Nov 2013-Jan 2014). Supported FHI360 to conduct a comprehensive audit of health communication interventions in Uganda over the period 2007-2012. The audit findings informed GOU and partners to design and implement quality health communication interventions which have contributed to reduction in HIV infections, total fertility, maternal & child mortality, malnutrition, malaria & tuberculosis. 39. Project head, Development of planning and evaluation frameworks for implementation of combination HIV prevention strategies in Rakai and Mayuge Districts (October 2012- Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 36 February 2013). Supported UNFPA Uganda and Ministry of Health to conduct a baseline programmatic assessment for combination HIV prevention in Mayuge and Rakai District using mixed methods. The study findings informed the development of district planning and evaluation frameworks which were used to roll out combination HIV prevention activities in the districts. 40. Project head, Evaluation of the five-year strategic plan for Baylor College of Medicine Children Foundation Uganda and development of a new strategic plan for the period 2013/17. Baylor Uganda was supported to evaluate the organization strategic plan, and also engaged senior and middle level managers to craft a new strategic plan that was discussed and harmonized with all key stakeholders and collaborating partners. 41. Project head, Development of alternative distribution strategies for the distribution of contraceptives in Uganda (July- August 2011). Reproductive Health Uganda (RHU) and UNFPA Uganda were supported to develop and build national level consensus on an alternative strategy for distribution of contraceptives in Uganda. This activity involved review of key project documents, interviews with key partners in reproductive health service delivery and a national level consensus building workshop. 42. Project head, Design of a five year 60 million US$ project for National Medical Stores; for the purchase, distribution and tracking of cotrimoxazole, HIV/AIDS Related Laboratory Commodities and Supplies in the Republic of Uganda under the Presidents’ Emergency Plan for AIDS Relief (PEPFAR). CDC Uganda and National Medical Stores were supported to design a new project which strengthened the supply chain management for HIV related laboratory commodities and supplies in Uganda. 43. Project head, Development of a Costed National HIV Drug Resistance Prevention, Monitoring and Surveillance plan (March 2007). Provided technical support to WHO, Ministry of Health and other HIV/AIDS service institutions to craft a costed national plan for HIV Drug Resistance Prevention, Surveillance and Monitoring. The information for incorporation in the country plan was garnered from desk reviews and selected key informant interviews. The key output was a Costed National HIVDR Prevention, Monitoring and Surveillance Plan for the FYs 2006/07-2010/11, which was costed, prioritized and fully harmonized with key HIV implementing partners in Uganda. 44. Project Head: Evaluation of St. Francis Health Care Services in Jinja District of Uganda and Development of a five year costed strategic plan (December 2006-January 2007). This was a cross-sectional descriptive and analytical impact evaluation that was done using both rapid participatory assessment and economic appraisal techniques. The evaluation assessed the outcomes and impact of the different project activities that were undertaken by St. Francis Health Care Services programme in Jinja and Mukono District. 45. Project head, Joint annual evaluation of HIV/AIDS activities in Uganda and development of a Costed National HIV/AIDS Priority Action Plan for FY 2006/07 (November 2005 - January 2005). The joint annual evaluation exercise was done for the Uganda AIDS Commission HIV/AIDS Partnership and UNAIDS. It was implemented through a collaborative arrangement between International Research Consortium, Mbarara University Department of Community Health and HealthConsult. The evaluation documented progress in the implementation of the national multi-sectoral response to HIV/AIDS in Uganda for the period June 2004 to July 2005. Progress, challenges and constraints were reported on the goals and objectives of the Revised National Strategic Framework 2003/04-2005/6 and the Monitoring and evaluation framework for HIV/AIDS activities in Uganda. 46. Project Head, Provision of technical support to the Uganda Programme for Human and Holistic Development (UPHOLD/USAID) to strengthen integrated management for HIV, Tuberculosis, Malaria and STIs with in 26 UPHOLD supported health facilities in the districts of Mbarara, Isingiro, Kiruhura, Bushenyi, Ibanda and Rukungiri (December 2007 to July 2008). The USAID supported UPHOLD programme was supported to Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 37 conduct a baseline assessment to determine the extent to which the targeted health facilities were providing services in an integrated manner. This was followed with monthly support supervision visits which were used to mentor and also update the skills of health facility staffs in the management of HIV, TB, Malaria and STIs in an integrated manner. Health workers at all supported sites were mentored on how to develop work plans for the integrated delivery of HIV, TB, Malaria and STI services. Employment Record January 2005 – Current: Consulting Director, International Research Consortium (IRC): The International Research Consortium is a Ugandan based development consulting firm that focuses on health and social sector development. The firm is dedicated to improving human condition by offering strategic advice and technical assistance to both the public and private sector agencies in the design, implementation, monitoring and evaluation of public health and social development programmes in resource poor settings. Major duties include:  Coordinates a team of 15 consultants (8 full time and 7 part-time) in providing technical assistance in executing different consultancy assignments  Provides technical assistance in the design, implementation and Monitoring and Evaluation of different projects  Provides technical assistance to different companies in Uganda in developing and implementing Workplace HIV/AIDS programs and policies  Takes proactive steps in strategic business developments  Develops terms of reference for areas requiring technical expertise January 2002 - Dec 2004: Regional technical Advisor for HIV and Cancer Programs – Axios International, Uganda Office  Increased institutional enrolment for HIV and Cancer philanthropy programs by 68% in 65 countries  Successfully carried out a baseline assessment for ARV drug logistics management systems, diagnostic and monitoring capacities for HIV/AIDS management in four regional hospitals in Tanzania (Muhilimbili Regional Referral Hospital, Kilimanjaro Christian Medical Centre. Mawenzi and Morogoro Regional Referral Hospitals) and three provinces in Zambia (Lusaka, Mongu and Livingstone provinces)  Developed an network of 123 institutions for widening access to cancer and HIV treatments with the support of five pharmaceutical companies  Coordinated a highly dedicated international team that established the antiretroviral drug logistics system in Zambia  Carried out a baseline assessment of the cancer treatment sites in Ethiopia and developed a strategic business plan for the implementation of cancer donations program by Astra Zeneca in Ethiopia  Provide technical assistance to districts health Management teams in planning needs assessments, designing and implementation of VCT & PMTCT programs of in AXIOS’s programs in Tanzania and Burkina Faso 2001: Program Manager, Uganda Police HIV Drug Treatment Program: The Police HIV drug treatment program was started in 2001 as one of the pioneers ART programs in Uganda with the support of the Ford Foundation, to rapidly expand access to treatments for HIV infection using highly active antiretroviral treatment (HAART) for a large number of HIV infected police officers and their families. Baseline Qualitative Study for the Development Food Security Activities (NUYOK and APOLOU) in the Karamoja Region of Uganda—Final Study Protocol 38  Carried out intensive mobilization of the police community for VCT, PMTCT and ARVS and increased patient enrolment by 54 percent.  Increased enrolment for VCT and PMTCT services by 56 percent Certification I, the undersigned, certify that to the best of my knowledge and belief, these bio-data correctly describe my qualification, my professional experience, and me. Signature: Dr. Daniel Kibuuka Musoke Date: July 16th, 2018 Annex 6: Tabular Summary of Baseline Indicator Estimates Lower Upper FOOD SECURITY INDICATORS Average Household Dietary Diversity Score (HDDS) 3.3 3.1 3.5 2,259 106,169 2.0 0.11 2.7 Prevalence of moderate and severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES) [30 day recall] 91.0 89.2 92.9 2,770 129,500 23.4 0.93 2.1 Male and female adults 91.0 89.2 92.9 2,169 100,215 23.3 0.94 1.9 Adult female, no adult male 92.1 89.4 94.9 499 24,484 22.0 1.42 1.5 Adult male, no adult female 85.2 78.9 91.6 98 4,603 32.4 3.24 1.0 Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A Prevalence of moderate and severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES) [one year recall] 93.7 92.4 95.0 2,770 129,500 18.8 0.65 1.9 Male and female adults 93.6 92.2 95.0 2,169 100,215 19.1 0.70 1.7 Adult female, no adult male 94.7 92.9 96.6 499 24,484 16.3 0.94 1.3 Adult male, no adult female 90.4 84.9 95.9 98 4,603 25.1 2.80 1.1 Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A POVERTY INDICATORS Per capita expenditures (as a proxy for income) of USG-assisted areas $1.04 $0.90 $1.19 15,469 723,726 2.8 0.07 1.4 Male and female adults $1.04 $0.88 $1.20 13,122 607,772 2.8 0.08 1.4 Adult female, no adult male $1.00 $0.77 $1.24 2,073 101,713 2.9 0.12 0.9 Adult male, no adult female $1.64 $0.94 $2.35 248 12,953 4.4 0.35 0.8 Child, no adults N/A N/A N/A 26 1,289 N/A N/A N/A Prevalence of poverty: Percentage of people living on less than $1.90/day 88.8 86.4 91.2 15,469 723,726 31.5 1.23 2.1 Male and female adults 88.8 86.3 91.3 13,122 607,772 30.4 1.26 1.9 Adult female, no adult male 90.4 87.2 93.7 2,073 101,713 33.0 1.64 1.1 Adult male, no adult female 74.3 60.2 88.5 248 12,953 61.9 7.12 1.2 Child, no adults N/A N/A N/A 26 1,289 N/A N/A N/A Depth of Poverty: Mean percentage shortfall relative to the $1.90 poverty line 54.6 52.1 57.0 15,469 723,726 26.8 1.22 2.4 Male and female adults 54.3 51.8 56.8 13,122 607,772 25.8 1.24 2.3 Adult female, no adult male 56.4 52.9 59.8 2,073 101,713 29.0 1.74 1.3 Adult male, no adult female 49.9 39.8 59.9 248 12,953 48.2 5.05 1.1 Child, no adults N/A N/A N/A 26 1,289 N/A N/A N/A Depth of Poverty of the Poor: Mean percentage shortfall relative to the $1.90 poverty line 61.4 60.1 62.8 13,655 642,665 19.7 0.69 1.7 Male and female adults 61.1 59.7 62.6 11,603 539,751 18.9 0.72 1.7 Adult female, no adult male 62.3 60.0 64.7 1,859 91,995 21.2 1.19 1.2 Adult male, no adult female 67.1 60.9 73.3 167 9,630 23.7 3.12 0.9 Child, no adults N/A N/A N/A 26 1,289 N/A N/A N/A WASH INDICATORS Percentage of households using an improved drinking water source 40.7 35.9 45.6 2,835 132,832 49.1 2.45 2.7 Available on premises 2.6 1.5 3.7 2,835 132,832 16.0 0.55 1.8 Available in 30 minutes or less (round trip) 26.4 21.4 31.4 2,835 132,832 44.1 2.50 3.0 Available in more than 30 minutes (round trip) 11.7 8.7 14.7 2,835 132,832 32.2 1.53 2.5 Percentage of households practicing correct use of recommended household water treatment technologies 10.0 8.0 12.1 2,835 132,832 30.1 1.04 1.8 Chlorination 1.8 0.9 2.7 2,835 132,832 13.3 0.43 1.7 Flocculent/Disinfectant 0.2 0.0 0.4 2,835 132,832 4.3 0.09 1.1 Filtration 1.1 0.6 1.7 2,835 132,832 10.6 0.27 1.4 Solar 0.0 2,835 132,832 0.0 0.0 Boiling 7.8 6.1 9.5 2,835 132,832 26.8 0.86 1.7 Percentage of households that can obtain drinking water in less than 30 minutes (round trip) 45.1 39.0 51.3 2,835 132,832 49.8 3.10 3.3 Table A6.1. FFP Uganda Baseline Indicators - Combined Project Areas Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Lower Upper Table A6.1. FFP Uganda Baseline Indicators - Combined Project Areas Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of households with access to a basic sanitation facility 8.4 5.7 11.1 2,835 132,832 27.8 1.35 2.6 Male and female adults 9.1 6.1 12.2 2,216 102,392 29.0 1.54 2.5 Adult female, no adult male 5.6 2.4 8.8 503 24,734 22.4 1.61 1.6 Adult male, no adult female 9.9 3.5 16.4 102 4,819 29.8 3.24 1.1 Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A Percentage of households in target areas practicing open defecation 65.6 59.7 71.5 2,835 132,832 47.5 2.96 3.3 Percentage of households with soap and water at a handwashing station commonly used by family members 3.5 1.9 5.1 2,835 132,832 18.3 0.80 2.3 AGRICULTURAL INDICATORS Percentage of farmers who used financial services (savings, agricultural credit, and/or agricultural insurance) in the past 12 months 20.5 17.3 23.6 3,664 182,782 40.4 1.58 2.4 Male 21.1 17.9 24.2 1,631 79,667 41.2 1.59 1.6 Female 20.0 16.2 23.8 2,033 103,116 39.7 1.91 2.2 Percentage of farmers who practiced value chain activities promoted by the project in the past 12 months 31.0 27.6 34.5 3,664 182,782 46.3 1.71 2.2 Male 35.2 31.1 39.3 1,631 79,667 48.3 2.07 1.7 Female 27.8 24.2 31.4 2,033 103,116 44.4 1.82 1.8 Percentage of farmers who used at least [project-defined minimum number] 1 sustainable agriculture (crop, livestock, and NRM) practices and/or technologies in the past 12 months 28.1 24.6 31.6 3,664 182,782 45.0 1.76 2.4 Male 31.6 27.7 35.4 1,631 79,667 47.0 1.91 1.6 Female 25.5 21.5 29.4 2,033 103,116 43.2 1.98 2.1 Percentage of farmers who used at least [project-defined minimum number] 1 sustainable crop practices and/or technologies in the past 12 months 43.7 39.4 48.0 3,664 182,782 49.6 2.16 2.6 Male 46.1 41.6 50.6 1,631 79,667 50.4 2.26 1.8 Female 41.8 37.2 46.4 2,033 103,116 48.9 2.31 2.1 Percentage of farmers who used at least [project-defined minimum number] 1 sustainable livestock practices and/or technologies in the past 12 months 7.6 6.1 9.0 3,664 182,782 26.4 0.75 1.7 Male 11.9 9.5 14.4 1,631 79,667 32.8 1.22 1.5 Female 4.2 2.9 5.5 2,033 103,116 19.8 0.65 1.5 Percentage of farmers who used at least [project-defined minimum number] 1 sustainable NRM practices and/or technologies in the past 12 months 1.8 0.8 2.8 3,664 182,782 13.3 0.51 2.3 Male 2.2 1.0 3.4 1,631 79,667 14.8 0.61 1.7 Female 1.5 0.5 2.5 2,033 103,116 12.1 0.49 1.8 Percentage of farmers who used improved storage practices in the past 12 months 49.9 45.4 54.3 3,664 182,782 50.0 2.23 2.7 Male 47.8 43.1 52.4 1,631 79,667 50.5 2.34 1.9 Female 51.5 46.7 56.2 2,033 103,116 49.6 2.38 2.2 WOMEN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight (BMI < 18.5) women of reproductive age 34.6 31.6 37.7 1,888 94,089 47.6 1.54 1.4 Prevalence of women of reproductive age consuming a diet of minimum diversity 16.4 13.4 19.4 2,422 137,039 37.0 1.52 2.0 Contraceptive Prevalence Rate 13.8 10.9 16.7 1,381 77,607 34.5 1.47 1.6 Modern methods 13.5 10.6 16.4 1,381 77,607 34.2 1.46 1.6 Traditional methods 0.3 0.1 0.6 1,381 77,607 5.8 0.14 0.9 Percentage of births receiving at least four antenatal care (ANC) visits during pregnancy 78.4 75.8 80.9 1,639 92,945 41.2 1.28 1.3 Prevalence of women of reproductive age who consume targeted nutrient-rich commodities 8.7 6.1 11.4 2,422 137,039 28.3 1.31 2.3 Bio-fortified beans 3.0 1.8 4.2 2,422 137,039 17.1 0.60 1.7 Bio-fortified maize or sorghum 7.3 4.7 9.9 2,422 137,039 26.1 1.30 2.5 Orange-flesh sweet potatoes 1.1 0.4 1.8 2,422 137,039 10.5 0.36 1.7 CHILDREN'S HEALTH AND NUTRITION INDICATORS Prevalence of healthy weight (WHZ ≤ 2 and ≥ -2) among children under five (0-59 months) 87.9 86.4 89.4 2,577 124,483 32.6 0.74 1.1 Male 86.5 84.4 88.6 1,259 59,475 34.6 1.06 1.1 Lower Upper Table A6.1. FFP Uganda Baseline Indicators - Combined Project Areas Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Female 89.2 87.2 91.2 1,318 65,008 30.8 1.01 1.2 Age 0-23 months 84.0 81.4 86.5 1,001 46,741 36.7 1.27 1.1 Age 24-59 months 90.3 88.5 92.0 1,576 77,742 29.6 0.87 1.2 Prevalence of underweight children (WAZ < -2) children under five (0-59 months) 29.0 26.5 31.6 2,596 125,643 45.4 1.30 1.5 Male 34.2 30.2 38.1 1,269 60,045 48.0 1.98 1.5 Female 24.4 21.5 27.2 1,327 65,599 42.5 1.42 1.2 Prevalence of stunted children (HAZ < -2) children under five (0-59 months) 38.1 35.9 40.3 2,572 124,397 48.6 1.11 1.2 Male 43.2 39.7 46.7 1,254 59,299 50.2 1.76 1.2 Female 33.5 30.7 36.3 1,318 65,098 46.7 1.41 1.1 Prevalence of wasted children (WHZ < -2) children under five (0-59 months) 11.2 9.7 12.7 2,577 124,483 31.5 0.76 1.2 Male 12.4 10.3 14.5 1,259 59,475 33.4 1.05 1.1 Female 10.0 8.0 12.0 1,318 65,008 29.7 1.00 1.2 Percentage of children under age 5 who had diarrhea in the last two weeks 31.6 28.9 34.2 2,779 134,915 46.5 1.33 1.5 Male 30.6 27.2 33.9 1,349 64,041 46.6 1.68 1.3 Female 32.4 29.2 35.6 1,430 70,874 46.3 1.61 1.3 Percentage of children under age 5 with diarrhea treated with ORT 84.1 81.0 87.2 862 42,571 36.6 1.57 1.3 Male 82.1 77.5 86.8 404 19,591 38.7 2.33 1.2 Female 85.8 81.7 89.9 458 22,980 34.2 2.07 1.3 Prevalence of exclusive breast-feeding of children under six months of age 73.5 66.8 80.2 301 14,436 44.2 3.38 1.3 Male 76.7 69.7 83.8 147 6,927 43.3 3.55 1.0 Female 70.5 60.3 80.7 154 7,509 45.1 5.13 1.4 Prevalence of children 6-23 months of age receiving a minimum acceptable diet (MAD) 8.4 5.6 11.3 757 35,315 27.8 1.46 1.4 Male 8.4 4.8 12.0 385 17,589 28.7 1.83 1.2 Female 8.5 4.9 12.1 372 17,726 27.8 1.82 1.3 Prevalence of children 6-23 months who consume targeted nutrient-rich commodities 11.4 8.3 14.5 757 35,315 31.8 1.58 1.4 Male 12.3 7.6 16.9 385 17,589 33.9 2.32 1.3 Female 10.6 7.0 14.1 372 17,726 30.7 1.77 1.1 Bio-fortified beans 3.7 1.9 5.5 679 31,704 18.8 0.91 1.3 Bio-fortified maize or sorghum 10.8 7.8 13.9 679 31,704 31.1 1.52 1.3 Orange-flesh sweet potatoes 1.8 -0.2 3.9 679 31,704 13.4 1.04 2.0 GENDER INDICATORS Percentage of men and women in union who earned cash in the past 12 months 41.4 37.1 45.6 4,604 211,675 49.3 2.12 2.9 Male 41.7 37.2 46.2 2,169 99,142 49.9 2.26 2.1 Female 41.0 36.4 45.7 2,435 112,533 49.3 2.33 2.3 Percentage of women in union and earning cash who report participation in decisions about the use of self-earned cash 85.0 81.4 88.6 867 45,498 35.7 1.81 1.5 Percentage of women in union and earning cash who report participation in decisions about the use of spouse/partner’s self-earned cash 58.0 51.8 64.3 576 30,076 49.4 3.14 1.5 Percentage of men in union and earning cash who report spouse/partner participation in decisions about the use of self￾earned cash 48.8 43.7 53.9 703 41,465 50.0 2.59 1.4 Percentage of men and women in union with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices 80.6 77.2 83.9 1,431 72,873 39.6 1.67 1.6 Male 70.8 65.7 75.9 592 32,171 44.0 2.58 1.4 Female 88.3 85.3 91.2 839 40,702 33.0 1.49 1.3 Percentage of men in union with children under two who make maternal health and nutrition decisions alone 22.4 18.5 26.2 592 32,171 41.7 1.96 1.1 Percentage of women in union with children under two who make maternal health and nutrition decisions alone 42.5 37.7 47.2 839 40,702 49.5 2.39 1.4 Percentage of men in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 42.5 37.4 47.7 592 32,171 49.5 2.58 1.3 Lower Upper Table A6.1. FFP Uganda Baseline Indicators - Combined Project Areas Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 34.5 30.3 38.8 839 40,702 47.6 2.13 1.3 Percentage of men in union with children under two who make child health and nutrition decisions alone 12.8 9.4 16.3 592 32,171 33.5 1.75 1.3 Percentage of women in union with children under two who make child health and nutrition decisions alone 36.8 31.2 42.3 839 40,702 48.2 2.78 1.7 Percentage of men in union with children under two who make child health and nutrition decisions jointly with spouse/partner 49.0 43.9 54.1 592 32,171 50.0 2.56 1.2 Percentage of women in union with children under two who make child health and nutrition decisions jointly with spouse/partner 41.5 36.5 46.5 839 40,702 49.3 2.51 1.5 RESILIENCE INDICATORS Shock exposure index 5.0 4.6 5.3 2,799 130,970 2.9 0.19 3.5 Cumulative impact of shock exposure index (severity weighted shock exposure) 27.4 25.3 29.5 2,726 127,687 16.9 1.05 3.3 Absorptive capacity index 21.1 19.8 22.4 2,799 130,970 12.7 0.66 2.8 Adaptive capacity index 38.2 36.5 39.9 2,799 130,970 15.1 0.87 3.1 Transformative capacity index 40.4 35.8 45.1 2,799 130,970 21.9 2.34 5.6 Ability to recover from shocks and stressors index 4.1 4.0 4.3 2,332 109,042 1.3 0.07 2.6 Proportion of households participating in group-based savings, micro-finance or lending 37.2 31.8 42.6 1,682 79,370 0.5 0.27 2.3 Index of Social Capital at the household level 54.0 51.8 56.3 2,799 130,970 24.1 1.14 2.5 CUSTOM INDICATORS Average rating of government's ability to be responsive to citizens' needs (including transparency, inclusivity, effectiveness) as measured on 12 item scorecard 4.7 4.6 4.9 2,787 130,295 2.0 0.08 2.2 Percent of target population who can state at least one health benefit of waiting at least two years after last live birth before attempting the next pregnancy 96.8 95.1 98.6 1,513 76,810 17.5 0.89 2.0 Male 96.6 94.7 98.5 594 32,244 17.5 0.96 1.3 Female 97.0 95.2 98.9 919 44,566 17.4 0.92 1.6 1 Project-defined minimums for CRS: 3 for overall, 3 for crops, 3 for livestock and 2 for NRM. Project-defined minimums for MC: 5 for overall, 3 for crops, 4 for livestock and 2 for NRM N/A = Not available Lower Upper FOOD SECURITY INDICATORS Average Household Dietary Diversity Score (HDDS) 3.1 2.7 3.4 1,036 51,883 1.9 0.18 3.0 Prevalence of moderate and severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES) [30 day recall] 90.4 87.6 93.1 1,235 61,006 24.4 1.41 2.1 Male and female adults 90.7 88.1 93.4 953 46,484 23.5 1.34 1.8 Adult female, no adult male 90.7 86.0 95.3 240 12,348 24.7 2.37 1.5 Adult male, no adult female 80.7 71.9 89.5 42 2,174 36.6 4.49 0.8 Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A Prevalence of moderate and severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES) [one year recall] 94.0 92.3 95.7 1,235 61,006 18.2 0.88 1.7 Male and female adults 94.3 92.7 95.9 953 46,484 17.8 0.82 1.4 Adult female, no adult male 94.1 91.3 96.9 240 12,348 17.5 1.41 1.3 Adult male, no adult female 88.0 79.3 96.7 42 2,174 28.3 4.44 1.0 Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A POVERTY INDICATORS Per capita expenditures (as a proxy for income) of USG-assisted areas $0.99 $0.86 $1.13 6,831 337,266 1.5 0.07 1.6 Male and female adults $0.98 $0.83 $1.12 5,764 280,735 1.4 0.07 1.6 Adult female, no adult male $0.99 $0.80 $1.18 983 51,245 1.8 0.09 0.8 Adult male, no adult female $1.88 $0.73 $3.03 84 5,286 4.2 0.57 0.9 Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A Prevalence of poverty: Percentage of people living on less than $1.90/day 88.0 85.0 91.1 6,831 337,266 32.5 1.51 1.7 Male and female adults 88.1 84.6 91.6 5,764 280,735 31.2 1.73 1.7 Adult female, no adult male 89.3 85.1 93.6 983 51,245 34.7 2.11 0.9 Adult male, no adult female 71.1 48.0 94.2 84 5,286 66.9 11.44 1.1 Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A Depth of Poverty: Mean percentage shortfall relative to the $1.90 poverty line 53.0 49.8 56.2 6,831 337,266 27.4 1.57 2.0 Male and female adults 53.3 49.9 56.8 5,764 280,735 26.5 1.70 2.0 Adult female, no adult male 51.7 47.0 56.3 983 51,245 29.7 2.30 1.2 Adult male, no adult female 46.6 29.1 64.0 84 5,286 47.0 8.62 1.2 Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A Depth of Poverty of the Poor: Mean percentage shortfall relative to the $1.90 poverty line 60.2 58.1 62.2 5,994 296,895 20.5 1.01 1.6 Male and female adults 60.5 58.4 62.7 5,067 247,361 19.7 1.04 1.5 Adult female, no adult male 57.9 54.3 61.4 882 45,776 22.8 1.77 1.1 Adult male, no adult female N/A N/A N/A 45 3,758 N/A N/A N/A Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A WASH INDICATORS Percentage of households using an improved drinking water source 40.4 32.4 48.4 1,259 62,225 49.1 3.95 2.9 Available on premises 3.5 1.5 5.5 1,259 62,225 18.5 0.99 1.9 Available in 30 minutes or less (round trip) 25.3 18.0 32.6 1,259 62,225 43.5 3.63 3.0 Available in more than 30 minutes (round trip) 11.6 7.0 16.2 1,259 62,225 32.0 2.26 2.5 Percentage of households practicing correct use of recommended household water treatment technologies 7.9 5.3 10.4 1,259 62,225 26.9 1.28 1.7 Chlorination 2.0 0.5 3.4 1,259 62,225 13.9 0.71 1.8 Flocculent/Disinfectant 0.2 -0.1 0.4 1,259 62,225 4.1 0.12 1.1 Filtration 1.3 0.4 2.2 1,259 62,225 11.3 0.44 1.4 Solar 0.0 1,259 62,225 0.0 0.0 Boiling 5.4 3.5 7.3 1,259 62,225 22.6 0.93 1.5 Percentage of households that can obtain drinking water in less than 30 minutes (round trip) 47.8 38.5 57.1 1,259 62,225 50.0 4.61 3.3 Table A6.2. FFP Uganda Baseline Indicators - Catholic Relief Services (CRS) Nuyok Project Area Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Lower Upper Table A6.2. FFP Uganda Baseline Indicators - Catholic Relief Services (CRS) Nuyok Project Area Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of households with access to a basic sanitation facility 6.7 3.5 9.8 1,259 62,225 25.0 1.56 2.2 Male and female adults 6.9 3.2 10.6 974 47,485 25.5 1.82 2.2 Adult female, no adult male 5.3 0.0 10.7 242 12,508 22.0 2.66 1.9 Adult male, no adult female 10.2 -0.1 20.5 43 2,231 29.6 5.11 1.1 Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A Percentage of households in target areas practicing open defecation 66.9 59.6 74.2 1,259 62,225 47.1 3.60 2.7 Percentage of households with soap and water at a handwashing station commonly used by family members 3.9 1.3 6.5 1,259 62,225 19.3 1.29 2.4 AGRICULTURAL INDICATORS Percentage of farmers who used financial services (savings, agricultural credit, and/or agricultural insurance) in the past 12 months 21.7 16.6 26.7 1,651 88,266 41.2 2.49 2.5 Male 21.5 17.1 25.9 737 37,867 41.9 2.15 1.4 Female 21.8 15.5 28.1 914 50,399 40.7 3.11 2.3 Percentage of farmers who practiced value chain activities promoted by the project in the past 12 months 35.0 29.7 40.2 1,651 88,266 47.7 2.59 2.2 Male 38.7 32.3 45.1 737 37,867 49.7 3.17 1.7 Female 32.2 26.5 37.9 914 50,399 46.0 2.81 1.8 Percentage of farmers who used at least 3 sustainable agriculture (crop, livestock, and NRM) practices and/or technologies in the past 12 months 41.7 35.5 48.0 1,651 88,266 49.3 3.09 2.5 Male 46.0 39.4 52.6 737 37,867 50.9 3.26 1.7 Female 38.6 31.3 45.8 914 50,399 48.0 3.61 2.3 Percentage of farmers who used at least 3 sustainable crop practices and/or technologies in the past 12 months 34.1 28.1 40.1 1,651 88,266 47.4 2.98 2.6 Male 36.4 30.1 42.7 737 37,867 49.1 3.12 1.7 Female 32.3 25.8 38.8 914 50,399 46.1 3.21 2.1 Percentage of farmers who used at least 3 sustainable livestock practices and/or technologies in the past 12 months 8.1 5.6 10.6 1,651 88,266 27.3 1.22 1.8 Male 11.8 7.9 15.7 737 37,867 32.9 1.92 1.6 Female 5.3 3.1 7.6 914 50,399 22.1 1.10 1.5 Percentage of farmers who used at least 2 sustainable NRM practices and/or technologies in the past 12 months 2.6 0.6 4.6 1,651 88,266 15.9 0.99 2.5 Male 3.2 0.8 5.5 737 37,867 17.8 1.16 1.8 Female 2.2 0.3 4.1 914 50,399 14.3 0.94 2.0 Percentage of farmers who used improved storage practices in the past 12 months 50.5 44.3 56.7 1,651 88,266 50.0 3.07 2.5 Male 48.0 41.1 54.9 737 37,867 51.0 3.42 1.8 Female 52.4 45.9 58.9 914 50,399 49.2 3.22 2.0 WOMEN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight (BMI < 18.5) women of reproductive age 38.6 34.3 42.8 872 45,075 48.7 2.11 1.3 Prevalence of women of reproductive age consuming a diet of minimum diversity 12.5 8.9 16.1 1,062 62,450 33.1 1.77 1.7 Contraceptive Prevalence Rate 14.5 9.9 19.1 636 36,582 35.3 2.27 1.6 Modern methods 14.2 9.7 18.7 636 36,582 34.9 2.24 1.6 Traditional methods 0.4 -0.1 0.8 636 36,582 6.0 0.22 0.9 Percentage of births receiving at least four antenatal care (ANC) visits during pregnancy 77.9 74.4 81.4 746 44,505 41.5 1.71 1.1 Prevalence of women of reproductive age who consume targeted nutrient-rich commodities 6.7 4.0 9.4 1,062 62,450 25.0 1.32 1.7 Bio-fortified beans 1.6 0.6 2.7 1,062 62,450 12.7 0.51 1.3 Bio-fortified maize or sorghum 5.0 2.8 7.2 1,062 62,450 21.8 1.08 1.6 Orange-flesh sweet potatoes 1.3 0.0 2.6 1,062 62,450 11.2 0.65 1.9 CHILDREN'S HEALTH AND NUTRITION INDICATORS Prevalence of healthy weight (WHZ ≤ 2 and ≥ -2) among children under five (0-59 months) 87.6 85.5 89.6 1,188 61,974 33.0 1.00 1.0 Male 86.7 83.8 89.6 590 29,509 34.9 1.43 1.0 Lower Upper Table A6.2. FFP Uganda Baseline Indicators - Catholic Relief Services (CRS) Nuyok Project Area Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Female 88.4 85.3 91.4 598 32,465 31.4 1.50 1.2 Age 0-23 months 83.5 80.0 86.9 461 22,561 37.2 1.71 1.0 Age 24-59 months 89.9 87.1 92.7 727 39,414 30.1 1.38 1.2 Prevalence of underweight children (WAZ < -2) children under five (0-59 months) 27.8 23.5 32.0 1,196 62,570 44.8 2.11 1.6 Male 34.8 27.9 41.7 595 29,808 48.9 3.42 1.7 Female 21.3 16.8 25.8 601 32,763 40.1 2.21 1.4 Prevalence of stunted children (HAZ < -2) children under five (0-59 months) 35.7 32.2 39.1 1,186 61,936 47.9 1.71 1.2 Male 42.7 36.8 48.7 589 29,480 50.8 2.94 1.4 Female 29.2 25.6 32.9 597 32,456 44.6 1.82 1.0 Prevalence of wasted children (WHZ < -2) children under five (0-59 months) 11.5 9.4 13.7 1,188 61,974 31.9 1.06 1.1 Male 11.7 8.9 14.4 590 29,509 33.0 1.37 1.0 Female 11.4 8.3 14.5 598 32,465 31.1 1.52 1.2 Percentage of children under age 5 who had diarrhea in the last two weeks 31.6 28.0 35.3 1,264 66,557 46.5 1.80 1.4 Male 31.6 27.3 35.9 624 31,256 47.7 2.13 1.1 Female 31.7 27.0 36.4 640 35,301 45.5 2.32 1.3 Percentage of children under age 5 with diarrhea treated with ORT 83.3 79.9 86.8 400 21,063 37.3 1.70 0.9 Male 82.3 76.5 88.2 197 9,866 39.6 2.89 1.0 Female 84.2 78.7 89.7 203 11,197 35.2 2.72 1.1 Prevalence of exclusive breast-feeding of children under six months of age 72.6 60.7 84.5 135 6,945 44.8 5.87 1.5 Male 82.7 71.9 93.4 61 2,897 40.6 5.31 1.0 Female 65.4 48.8 82.1 74 4,048 45.8 8.22 1.5 Prevalence of children 6-23 months of age receiving a minimum acceptable diet (MAD) 6.6 2.1 11.2 355 17,354 24.9 2.24 1.7 Male 5.6 0.8 10.4 180 8,487 24.5 2.35 1.3 Female 7.6 1.9 13.4 175 8,867 26.5 2.84 1.4 Prevalence of children 6-23 months who consume targeted nutrient-rich commodities 8.6 4.6 12.6 355 17,354 28.0 1.98 1.3 Male 11.6 4.5 18.8 180 8,487 34.1 3.54 1.4 Female 5.6 2.0 9.3 175 8,867 23.0 1.78 1.0 Bio-fortified beans 2.2 0.2 4.1 319 15,738 14.6 0.96 1.2 Bio-fortified maize or sorghum 7.5 4.2 10.7 319 15,738 26.3 1.61 1.1 Orange-flesh sweet potatoes 2.5 -1.5 6.5 319 15,738 15.7 1.98 2.3 GENDER INDICATORS Percentage of men and women in union who earned cash in the past 12 months 47.4 39.4 55.5 1,993 96,385 49.9 3.97 3.6 Male 47.2 38.5 55.9 931 44,777 50.7 4.31 2.6 Female 47.6 39.2 56.1 1,062 51,608 50.0 4.18 2.7 Percentage of women in union and earning cash who report participation in decisions about the use of self-earned cash 86.6 82.2 91.1 435 24,013 34.1 2.20 1.3 Percentage of women in union and earning cash who report participation in decisions about the use of spouse/partner’s self-earned cash 64.8 55.6 74.0 299 16,282 47.8 4.56 1.6 Percentage of men in union and earning cash who report spouse/partner participation in decisions about the use of self￾earned cash 56.6 49.5 63.6 359 21,849 49.6 3.47 1.3 Percentage of men and women in union with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices 85.0 80.2 89.8 664 35,769 35.8 2.38 1.7 Male 79.0 71.2 86.8 269 15,153 39.8 3.85 1.6 Female 89.4 85.1 93.6 395 20,616 31.3 2.10 1.3 Percentage of men in union with children under two who make maternal health and nutrition decisions alone 26.1 19.3 32.9 269 15,153 44.0 3.36 1.3 Percentage of women in union with children under two who make maternal health and nutrition decisions alone 45.4 38.5 52.3 395 20,616 49.9 3.42 1.4 Percentage of men in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 41.8 34.7 48.9 269 15,153 49.4 3.50 1.2 Lower Upper Table A6.2. FFP Uganda Baseline Indicators - Catholic Relief Services (CRS) Nuyok Project Area Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 36.3 29.9 42.7 395 20,616 48.1 3.16 1.3 Percentage of men in union with children under two who make child health and nutrition decisions alone 15.7 10.1 21.3 269 15,153 36.5 2.78 1.3 Percentage of women in union with children under two who make child health and nutrition decisions alone 39.7 31.1 48.2 395 20,616 49.0 4.23 1.7 Percentage of men in union with children under two who make child health and nutrition decisions jointly with spouse/partner 51.1 44.0 58.2 269 15,153 50.1 3.50 1.1 Percentage of women in union with children under two who make child health and nutrition decisions jointly with spouse/partner 42.1 34.5 49.7 395 20,616 49.4 3.75 1.5 RESILIENCE INDICATORS Shock exposure index 5.5 4.9 6.2 1,251 61,809 2.8 0.31 3.8 Cumulative impact of shock exposure index (severity weighted shock exposure) 31.9 28.6 35.3 1,226 60,992 16.7 1.66 3.5 Absorptive capacity index 22.0 19.5 24.4 1,251 61,809 13.7 1.21 3.1 Adaptive capacity index 39.2 36.3 42.0 1,251 61,809 14.3 1.41 3.5 Transformative capacity index 44.7 38.0 51.4 1,251 61,809 17.8 3.32 6.6 Ability to recover from shocks and stressors index 4.1 3.9 4.4 1,060 52,954 1.3 0.10 2.5 Proportion of households participating in group-based savings, micro-finance or lending 40.1 30.1 50.1 775 38,475 0.5 0.05 2.9 Index of Social Capital at the household level 55.4 51.5 59.4 1,251 61,809 24.9 1.94 2.8 CUSTOM INDICATORS Average rating of government's ability to be responsive to citizens' needs (including transparency, inclusivity, effectiveness) as measured on 12 item scorecard 4.7 4.5 5.0 1,250 61,655 1.8 0.13 2.5 Percent of target population who can state at least one health benefit of waiting at least two years after last live birth before attempting the next pregnancy 96.8 93.8 99.8 706 37,917 17.7 1.49 2.2 Male 97.0 93.9 100.2 270 15,175 16.7 1.56 1.5 Female 96.6 93.5 99.7 436 22,743 18.3 1.53 1.7 N/A = Not available Lower Upper FOOD SECURITY INDICATORS Average Household Dietary Diversity Score (HDDS) 3.6 3.3 3.8 1,223 54,286 2.0 0.12 2.1 Prevalence of moderate and severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES) [30 day recall] 91.6 89.2 94.1 1,535 68,495 22.6 1.23 2.2 Male and female adults 91.3 88.7 93.9 1,216 53,730 23.1 1.32 2.0 Adult female, no adult male 93.7 90.5 96.9 259 12,137 18.9 1.63 1.4 Adult male, no adult female 89.2 81.0 97.5 56 2,429 28.0 4.21 1.1 Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A Prevalence of moderate and severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES) [one year recall] 93.4 91.6 95.3 1,535 68,495 19.3 0.96 2.0 Male and female adults 93.0 90.9 95.1 1,216 53,730 20.1 1.09 1.9 Adult female, no adult male 95.4 92.9 97.9 259 12,137 15.0 1.27 1.4 Adult male, no adult female 92.6 86.8 98.3 56 2,429 22.0 2.93 1.0 Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A POVERTY INDICATORS Per capita expenditures (as a proxy for income) of USG-assisted areas $1.09 $0.84 $1.34 8,638 386,461 3.6 0.13 1.4 Male and female adults $1.10 $0.83 $1.37 7,358 327,037 3.5 0.13 1.3 Adult female, no adult male $1.01 $0.57 $1.45 1,090 50,468 3.8 0.22 0.9 Adult male, no adult female $1.48 $0.49 $2.47 164 7,667 4.5 0.49 0.8 Child, no adults N/A N/A N/A 26 1,289 N/A N/A N/A Prevalence of poverty: Percentage of people living on less than $1.90/day 89.5 85.7 93.3 8,638 386,461 30.7 1.88 2.4 Male and female adults 89.4 85.8 93.1 7,358 327,037 29.7 1.81 2.2 Adult female, no adult male 91.6 86.7 96.5 1,090 50,468 31.3 2.43 1.3 Adult male, no adult female 76.6 57.2 96.0 164 7,667 58.2 9.62 1.3 Child, no adults N/A N/A N/A 26 1,289 N/A N/A N/A Depth of Poverty: Mean percentage shortfall relative to the $1.90 poverty line 55.9 52.3 59.6 8,638 386,461 26.2 1.83 2.8 Male and female adults 55.1 51.5 58.7 7,358 327,037 25.2 1.79 2.5 Adult female, no adult male 61.1 56.6 65.7 1,090 50,468 27.5 2.27 1.3 Adult male, no adult female 52.1 38.6 65.7 164 7,667 48.5 6.73 1.1 Child, no adults N/A N/A N/A 26 1,289 N/A N/A N/A Depth of Poverty of the Poor: Mean percentage shortfall relative to the $1.90 poverty line 62.5 60.6 64.4 7,661 345,770 18.9 0.95 1.9 Male and female adults 61.6 59.6 63.6 6,536 292,390 18.2 1.00 1.8 Adult female, no adult male 66.7 63.9 69.6 977 46,220 18.3 1.41 1.2 Adult male, no adult female 68.1 58.5 77.6 122 5,871 28.3 4.74 1.0 Child, no adults N/A N/A N/A 26 1,289 N/A N/A N/A WASH INDICATORS Percentage of households using an improved drinking water source 41.0 34.9 47.0 1,576 70,607 49.2 3.01 2.4 Available on premises 1.8 0.7 2.9 1,576 70,607 13.4 0.54 1.6 Available in 30 minutes or less (round trip) 27.3 20.4 34.3 1,576 70,607 44.6 3.45 3.1 Available in more than 30 minutes (round trip) 11.8 7.6 16.0 1,576 70,607 32.3 2.08 2.6 Percentage of households practicing correct use of recommended household water treatment technologies 12.0 8.9 15.1 1,576 70,607 32.5 1.55 1.9 Chlorination 1.7 0.6 2.7 1,576 70,607 12.8 0.54 1.7 Flocculent/Disinfectant 0.2 -0.1 0.5 1,576 70,607 4.5 0.13 1.1 Filtration 1.0 0.3 1.7 1,576 70,607 10.0 0.33 1.3 Solar 0.0 1,576 70,607 0.0 0.0 Boiling 9.9 7.2 12.7 1,576 70,607 29.9 1.36 1.8 Percentage of households that can obtain drinking water in less than 30 minutes (round trip) 42.8 34.5 51.0 1,576 70,607 49.5 4.11 3.3 Table A6.3. FFP Uganda Baseline Indicators - Mercy Corps (MC) Apolou Project Area Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Lower Upper Table A6.3. FFP Uganda Baseline Indicators - Mercy Corps (MC) Apolou Project Area Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of households with access to a basic sanitation facility 10.0 5.6 14.3 1,576 70,607 30.0 2.16 2.9 Male and female adults 11.1 6.2 16.0 1,242 54,907 31.6 2.44 2.7 Adult female, no adult male 5.8 2.1 9.4 261 12,226 22.8 1.80 1.3 Adult male, no adult female 9.7 1.4 18.1 59 2,588 29.9 4.15 1.1 Child, no adults N/A N/A N/A N/A N/A N/A N/A N/A Percentage of households in target areas practicing open defecation 64.4 55.2 73.6 1,576 70,607 47.9 4.58 3.8 Percentage of households with soap and water at a handwashing station commonly used by family members 3.1 1.2 5.1 1,576 70,607 17.4 0.97 2.2 AGRICULTURAL INDICATORS Percentage of farmers who used financial services (savings, agricultural credit, and/or agricultural insurance) in the past 12 months 19.4 15.3 23.4 2,013 94,516 39.5 2.01 2.3 Male 20.7 16.1 25.3 894 41,800 40.6 2.31 1.7 Female 18.3 13.7 23.0 1,119 52,716 38.6 2.30 2.0 Percentage of farmers who practiced value chain activities promoted by the project in the past 12 months 27.4 23.3 31.5 2,013 94,516 44.6 2.03 2.0 Male 32.1 26.8 37.4 894 41,800 46.8 2.63 1.7 Female 23.6 19.9 27.3 1,119 52,716 42.4 1.84 1.4 Percentage of farmers who used at least 5 sustainable agriculture (crop, livestock, and NRM) practices and/or technologies in the past 12 months 15.4 12.4 18.4 2,013 94,516 36.1 1.49 1.8 Male 18.5 14.4 22.6 894 41,800 38.9 2.04 1.6 Female 12.9 10.3 15.5 1,119 52,716 33.5 1.29 1.3 Percentage of farmers who used at least 3 sustainable crop practices and/or technologies in the past 12 months 52.6 47.1 58.2 2,013 94,516 49.9 2.76 2.5 Male 54.9 48.9 60.9 894 41,800 49.9 2.97 1.8 Female 50.9 45.1 56.7 1,119 52,716 49.9 2.88 1.9 Percentage of farmers who used at least 4 sustainable livestock practices and/or technologies in the past 12 months 7.1 5.2 8.9 2,013 94,516 25.6 0.93 1.6 Male 12.1 9.0 15.2 894 41,800 32.7 1.54 1.4 Female 3.1 1.6 4.6 1,119 52,716 17.2 0.75 1.5 Percentage of farmers who used at least 2 sustainable NRM practices and/or technologies in the past 12 months 1.1 0.3 1.9 2,013 94,516 10.4 0.39 1.7 Male 1.3 0.3 2.4 894 41,800 11.6 0.51 1.3 Female 0.9 0.2 1.6 1,119 52,716 9.4 0.37 1.3 Percentage of farmers who used improved storage practices in the past 12 months 49.3 42.8 55.8 2,013 94,516 50.0 3.23 2.9 Male 47.6 41.1 54.1 894 41,800 50.0 3.22 1.9 Female 50.6 43.6 57.6 1,119 52,716 49.9 3.49 2.3 WOMEN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight (BMI < 18.5) women of reproductive age 31.0 26.7 35.4 1,016 49,014 46.3 2.16 1.5 Prevalence of women of reproductive age consuming a diet of minimum diversity 19.6 15.0 24.3 1,360 74,589 39.7 2.32 2.2 Contraceptive Prevalence Rate 13.1 9.2 17.0 745 41,026 33.8 1.93 1.6 Modern methods 12.9 9.0 16.8 745 41,026 33.6 1.93 1.6 Traditional methods 0.3 0.0 0.7 745 41,026 5.6 0.18 0.9 Percentage of births receiving at least four antenatal care (ANC) visits during pregnancy 78.8 75.0 82.6 893 48,440 40.9 1.89 1.4 Prevalence of women of reproductive age who consume targeted nutrient-rich commodities 10.5 6.1 14.8 1,360 74,589 30.6 2.16 2.6 Bio-fortified beans 4.1 2.1 6.2 1,360 74,589 19.9 1.01 1.9 Bio-fortified maize or sorghum 9.3 4.8 13.8 1,360 74,589 29.1 2.23 2.8 Orange-flesh sweet potatoes 1.0 0.3 1.7 1,360 74,589 9.9 0.36 1.3 CHILDREN'S HEALTH AND NUTRITION INDICATORS Prevalence of healthy weight (WHZ ≤ 2 and ≥ -2) among children under five (0-59 months) 88.2 86.1 90.4 1,389 62,509 32.2 1.08 1.2 Male 86.3 83.2 89.5 669 29,965 34.4 1.56 1.2 Lower Upper Table A6.3. FFP Uganda Baseline Indicators - Mercy Corps (MC) Apolou Project Area Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Female 90.0 87.4 92.6 720 32,543 30.0 1.29 1.2 Age 0-23 months 84.4 80.7 88.2 540 24,181 36.3 1.87 1.2 Age 24-59 months 90.6 88.6 92.7 849 38,328 29.1 1.02 1.0 Prevalence of underweight children (WAZ < -2) children under five (0-59 months) 30.3 27.2 33.5 1,400 63,073 46.0 1.56 1.3 Male 33.5 29.5 37.5 674 30,237 47.3 1.99 1.1 Female 27.4 24.0 30.8 726 32,836 44.6 1.69 1.0 Prevalence of stunted children (HAZ < -2) children under five (0-59 months) 40.5 37.5 43.6 1,386 62,461 49.1 1.50 1.1 Male 43.7 39.7 47.6 665 29,819 49.7 1.97 1.0 Female 37.7 33.6 41.7 721 32,642 48.5 2.02 1.1 Prevalence of wasted children (WHZ < -2) children under five (0-59 months) 10.8 8.7 13.0 1,389 62,509 31.1 1.08 1.3 Male 13.2 10.0 16.4 669 29,965 33.9 1.59 1.2 Female 8.6 6.3 11.0 720 32,543 28.1 1.18 1.1 Percentage of children under age 5 who had diarrhea in the last two weeks 31.5 27.5 35.4 1,515 68,358 46.5 1.95 1.6 Male 29.7 24.5 34.8 725 32,786 45.6 2.57 1.5 Female 33.1 28.7 37.6 790 35,572 47.1 2.22 1.3 Percentage of children under age 5 with diarrhea treated with ORT 84.9 79.6 90.2 462 21,508 35.9 2.63 1.6 Male 81.9 74.5 89.3 207 9,726 37.9 3.68 1.4 Female 87.3 81.2 93.4 255 11,783 33.0 3.04 1.5 Prevalence of exclusive breast-feeding of children under six months of age 74.3 67.0 81.5 166 7,491 43.8 3.61 1.1 Male 72.4 63.2 81.7 86 4,030 43.9 4.61 1.0 Female 76.4 65.8 87.0 80 3,461 43.3 5.29 1.1 Prevalence of children 6-23 months of age receiving a minimum acceptable diet (MAD) 10.2 6.4 13.9 402 17,961 30.3 1.86 1.2 Male 11.0 5.7 16.4 205 9,102 31.5 2.67 1.2 Female 9.3 4.7 13.9 197 8,859 29.1 2.28 1.1 Prevalence of children 6-23 months who consume targeted nutrient-rich commodities 14.1 9.1 19.2 402 17,961 34.9 2.50 1.4 Male 12.9 6.7 19.0 205 9,102 33.7 3.04 1.3 Female 15.5 9.2 21.7 197 8,859 36.3 3.11 1.2 Bio-fortified beans 5.2 2.2 8.2 360 15,967 22.2 1.50 1.3 Bio-fortified maize or sorghum 14.2 9.0 19.3 360 15,967 34.9 2.57 1.4 Orange-flesh sweet potatoes 1.2 0.0 2.3 360 15,967 10.7 0.58 1.0 GENDER INDICATORS Percentage of men and women in union who earned cash in the past 12 months 36.3 32.1 40.4 2,611 115,290 48.1 2.05 2.2 Male 37.2 32.7 41.7 1,238 54,365 48.8 2.25 1.6 Female 35.4 30.8 40.1 1,373 60,925 47.9 2.31 1.8 Percentage of women in union and earning cash who report participation in decisions about the use of self-earned cash 83.3 77.2 89.4 432 21,485 37.4 3.04 1.7 Percentage of women in union and earning cash who report participation in decisions about the use of spouse/partner’s self-earned cash 50.1 42.1 58.0 277 13,794 50.1 3.93 1.3 Percentage of men in union and earning cash who report spouse/partner participation in decisions about the use of self￾earned cash 40.2 33.8 46.5 344 19,616 49.1 3.14 1.2 Percentage of men and women in union with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices 76.3 71.9 80.7 767 37,104 42.6 2.19 1.4 Male 63.5 57.0 70.0 323 17,018 46.1 3.22 1.3 Female 87.1 82.9 91.4 444 20,086 34.6 2.11 1.3 Percentage of men in union with children under two who make maternal health and nutrition decisions alone 19.0 14.8 23.3 323 17,018 39.3 2.11 1.0 Percentage of women in union with children under two who make maternal health and nutrition decisions alone 39.5 33.0 45.9 444 20,086 48.9 3.21 1.4 Percentage of men in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 43.2 35.7 50.7 323 17,018 49.6 3.74 1.4 Lower Upper Table A6.3. FFP Uganda Baseline Indicators - Mercy Corps (MC) Apolou Project Area Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 32.7 26.8 38.7 444 20,086 47.0 2.95 1.3 Percentage of men in union with children under two who make child health and nutrition decisions alone 10.3 6.1 14.5 323 17,018 30.4 2.07 1.2 Percentage of women in union with children under two who make child health and nutrition decisions alone 33.8 26.8 40.7 444 20,086 47.3 3.45 1.5 Percentage of men in union with children under two who make child health and nutrition decisions jointly with spouse/partner 47.1 39.5 54.7 323 17,018 50.0 3.77 1.4 Percentage of women in union with children under two who make child health and nutrition decisions jointly with spouse/partner 41.0 34.2 47.7 444 20,086 49.2 3.36 1.4 RESILIENCE INDICATORS Shock exposure index 4.4 4.1 4.8 1,548 69,161 2.8 0.18 2.6 Cumulative impact of shock exposure index (severity weighted shock exposure) 23.3 21.2 25.3 1,500 66,695 15.9 1.03 2.5 Absorptive capacity index 20.3 19.0 21.5 1,548 69,161 11.6 0.63 2.1 Adaptive capacity index 37.3 35.2 39.5 1,548 69,161 15.7 1.06 2.7 Transformative capacity index 36.6 29.8 43.4 1,548 69,161 24.7 3.39 5.4 Ability to recover from shocks and stressors index 4.1 3.9 4.3 1,272 56,088 1.3 0.10 2.6 Proportion of households participating in group-based savings, micro-finance or lending 34.5 29.6 39.4 907 40,895 0.5 0.02 1.5 Index of Social Capital at the household level 52.8 50.4 55.1 1,548 69,161 23.1 1.17 2.0 CUSTOM INDICATORS Average rating of government's ability to be responsive to citizens' needs (including transparency, inclusivity, effectiveness) as measured on 12 item scorecard 4.8 4.5 5.0 1,537 68,640 2.1 0.11 2.0 Percent of target population who can state at least one health benefit of waiting at least two years after last live birth before attempting the next pregnancy 96.9 94.9 98.9 807 38,893 17.3 1.01 1.7 Male 96.3 93.9 98.6 324 17,069 18.2 1.17 1.2 Female 97.4 95.4 99.4 483 21,823 16.4 1.01 1.4 N/A = Not available All CRS MC FOOD SECURITY INDICATORS Average Household Dietary Diversity Score (HDDS) 3.3 3.1 3.6 Prevalence of moderate and severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES) [30 day recall] 91.0 90.4 91.6 Male and female adults 91.0 90.7 91.3 Adult female, no adult male 92.1 90.7 93.7 Adult male, no adult female 85.2 80.7 89.2 Child, no adults N/A N/A N/A Prevalence of moderate and severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES) [one year recall] 93.7 94.0 93.4 Male and female adults 93.6 94.3 93.0 Adult female, no adult male 94.7 94.1 95.4 Adult male, no adult female 90.4 88.0 92.6 Child, no adults N/A N/A N/A POVERTY INDICATORS Per capita expenditures (as a proxy for income) of USG-assisted areas $1.04 $0.99 $1.09 Male and female adults $1.04 $0.98 $1.10 Adult female, no adult male $1.00 $0.99 $1.01 Adult male, no adult female $1.64 $1.88 $1.48 Child, no adults N/A N/A N/A Prevalence of poverty: Percentage of people living on less than $1.90/day 88.8 88.0 89.5 Male and female adults 88.8 88.1 89.4 Adult female, no adult male 90.4 89.3 91.6 Adult male, no adult female 74.3 71.1 76.6 Child, no adults N/A N/A N/A Depth of Poverty: Mean percentage shortfall relative to the $1.90 poverty line 54.6 53.0 55.9 Male and female adults 54.3 53.3 55.1 Adult female, no adult male 56.4 51.7 61.1 Adult male, no adult female 49.9 46.6 52.1 Child, no adults N/A N/A N/A Depth of Poverty of the Poor: Mean percentage shortfall relative to the $1.90 poverty line 61.4 60.2 62.5 Male and female adults 61.1 60.5 61.6 Adult female, no adult male 62.3 57.9 66.7 Adult male, no adult female 67.1 N/A 68.1 Child, no adults N/A N/A N/A WASH INDICATORS Percentage of households using an improved drinking water source 40.7 40.4 41.0 Available on premises 2.6 3.5 1.8 Available in 30 minutes or less (round trip) 26.4 25.3 27.3 Available in more than 30 minutes (round trip) 11.7 11.6 11.8 Percentage of households practicing correct use of recommended household water treatment technologies 10.0 7.9 12.0 Chlorination 1.8 2.0 1.7 Flocculent/Disinfectant 0.2 0.2 0.2 Filtration 1.1 1.3 1.0 Solar 0.0 0.0 0.0 Boiling 7.8 5.4 9.9 Percentage of households that can obtain drinking water in less than 30 minutes (round trip) 45.1 47.8 42.8 Percentage of households with access to a basic sanitation facility 8.4 6.7 10.0 Male and female adults 9.1 6.9 11.1 Adult female, no adult male 5.6 5.3 5.8 Adult male, no adult female 9.9 10.2 9.7 Child, no adults N/A N/A N/A Percentage of households in target areas practicing open defecation 65.6 66.9 64.4 Percentage of households with soap and water at a handwashing station commonly used by family members 3.5 3.9 3.1 AGRICULTURAL INDICATORS Percentage of farmers who used financial services (savings, agricultural credit, and/or agricultural insurance) in the past 12 months 20.5 21.7 19.4 Male 21.1 21.5 20.7 Female 20.0 21.8 18.3 Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] BASELINE INDICATOR VALUES Table A6.4. FFP Uganda Baseline Indicators - Comparison Across Project Areas All CRS MC Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] BASELINE INDICATOR VALUES Table A6.4. FFP Uganda Baseline Indicators - Comparison Across Project Areas Percentage of farmers who practiced value chain activities promoted by the project in the past 12 months 31.0 35.0 27.4 Male 35.2 38.7 32.1 Female 27.8 32.2 23.6 Percentage of farmers who used at least [project-defined minimum number] 1 sustainable agriculture (crop, livestock, and NRM) practices and/or technologies in the past 12 months 28.1 41.7 15.4 Male 31.6 46.0 18.5 Female 25.5 38.6 12.9 Percentage of farmers who used at least [project-defined minimum number] 1 sustainable crop practices and/or technologies in the past 12 months 43.7 34.1 52.6 Male 46.1 36.4 54.9 Female 41.8 32.3 50.9 Percentage of farmers who used at least [project-defined minimum number] 1 sustainable livestock practices and/or technologies in the past 12 months 7.6 8.1 7.1 Male 11.9 11.8 12.1 Female 4.2 5.3 3.1 Percentage of farmers who used at least [project-defined minimum number] 1 sustainable NRM practices and/or technologies in the past 12 months 1.8 2.6 1.1 Male 2.2 3.2 1.3 Female 1.5 2.2 0.9 Percentage of farmers who used improved storage practices in the past 12 months 49.9 50.5 49.3 Male 47.8 48.0 47.6 Female 51.5 52.4 50.6 WOMEN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight (BMI < 18.5) women of reproductive age 34.6 38.6 31.0 Prevalence of women of reproductive age consuming a diet of minimum diversity 16.4 12.5 19.6 Contraceptive Prevalence Rate 13.8 14.5 13.1 Modern methods 13.5 14.2 12.9 Traditional methods 0.3 0.4 0.3 Percentage of births receiving at least four antenatal care (ANC) visits during pregnancy 78.4 77.9 78.8 Prevalence of women of reproductive age who consume targeted nutrient-rich commodities 8.7 6.7 10.5 Bio-fortified beans 3.0 1.6 4.1 Bio-fortified maize or sorghum 7.3 5.0 9.3 Orange-flesh sweet potatoes 1.1 1.3 1.0 CHILDREN'S HEALTH AND NUTRITION INDICATORS Prevalence of healthy weight (WHZ ≤ 2 and ≥ -2) among children under five (0-59 months) 87.9 87.6 88.2 Male 86.5 86.7 86.3 Female 89.2 88.4 90.0 Age 0-23 months 84.0 83.5 84.4 Age 24-59 months 90.3 89.9 90.6 Prevalence of underweight children (WAZ < -2) children under five (0-59 months) 29.0 27.8 30.3 Male 34.2 34.8 33.5 Female 24.4 21.3 27.4 Prevalence of stunted children (HAZ < -2) children under five (0-59 months) 38.1 35.7 40.5 Male 43.2 42.7 43.7 Female 33.5 29.2 37.7 Prevalence of wasted children (WHZ < -2) children under five (0-59 months) 11.2 11.5 10.8 Male 12.4 11.7 13.2 Female 10.0 11.4 8.6 Percentage of children under age 5 who had diarrhea in the last two weeks 31.6 31.6 31.5 Male 30.6 31.6 29.7 Female 32.4 31.7 33.1 Percentage of children under age 5 with diarrhea treated with ORT 84.1 83.3 84.9 Male 82.1 82.3 81.9 Female 85.8 84.2 87.3 Prevalence of exclusive breast-feeding of children under six months of age 73.5 72.6 74.3 Male 76.7 82.7 72.4 Female 70.5 65.4 76.4 Prevalence of children 6-23 months of age receiving a minimum acceptable diet (MAD) 8.4 6.6 10.2 Male 8.4 5.6 11.0 All CRS MC Indicators, 95% Confidence Intervals and Base Population [Uganda, 2018] BASELINE INDICATOR VALUES Table A6.4. FFP Uganda Baseline Indicators - Comparison Across Project Areas Female 8.5 7.6 9.3 Prevalence of children 6-23 months who consume targeted nutrient-rich commodities 11.4 8.6 14.1 Male 12.3 11.6 12.9 Female 10.6 5.6 15.5 Bio-fortified beans 3.7 2.2 5.2 Bio-fortified maize or sorghum 10.8 7.5 14.2 Orange-flesh sweet potatoes 1.8 2.5 1.2 GENDER INDICATORS Percentage of men and women in union who earned cash in the past 12 months 41.4 47.4 36.3 Male 41.7 47.2 37.2 Female 41.0 47.6 35.4 Percentage of women in union and earning cash who report participation in decisions about the use of self-earned cash 85.0 86.6 83.3 Percentage of women in union and earning cash who report participation in decisions about the use of spouse/partner’s self￾earned cash 58.0 64.8 50.1 Percentage of men in union and earning cash who report spouse/partner participation in decisions about the use of self￾earned cash 48.8 56.6 40.2 Percentage of men and women in union with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices 80.6 85.0 76.3 Male 70.8 79.0 63.5 Female 88.3 89.4 87.1 Percentage of men in union with children under two who make maternal health and nutrition decisions alone 22.4 26.1 19.0 Percentage of women in union with children under two who make maternal health and nutrition decisions alone 42.5 45.4 39.5 Percentage of men in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 42.5 41.8 43.2 Percentage of women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 34.5 36.3 32.7 Percentage of men in union with children under two who make child health and nutrition decisions alone 12.8 15.7 10.3 Percentage of women in union with children under two who make child health and nutrition decisions alone 36.8 39.7 33.8 Percentage of men in union with children under two who make child health and nutrition decisions jointly with spouse/partner 49.0 51.1 47.1 Percentage of women in union with children under two who make child health and nutrition decisions jointly with spouse/partner 41.5 42.1 41.0 RESILIENCE INDICATORS Shock exposure index 5.0 5.5 4.4 Cumulative impact of shock exposure index (severity weighted shock exposure) 27.4 31.9 23.3 Absorptive capacity index 21.1 22.0 20.3 Adaptive capacity index 38.2 39.2 37.3 Transformative capacity index 40.4 44.7 36.6 Ability to recover from shocks and stressors index 4.1 4.1 4.1 Proportion of households participating in group-based savings, micro-finance or lending 37.2 40.1 34.5 Index of Social Capital at the household level 54.0 55.4 52.8 CUSTOM INDICATORS Average rating of government's ability to be responsive to citizens' needs (including transparency, inclusivity, effectiveness) as measured on 12 item scorecard 4.7 4.7 4.8 Percent of target population who can state at least one health benefit of waiting at least two years after last live birth before attempting the next pregnancy 96.8 96.8 96.9 Male 96.6 97.0 96.3 Female 97.0 96.6 97.4 N/A = Not available 1 Project-defined minimums for CRS: 3 for overall, 3 for crops, 3 for livestock and 2 for NRM. Project-defined minimums for MC: 5 for overall, 3 for crops, 4 for livestock and 2 for NRM Annex 7: List of Variables for Correlation or Regression Analyses Overall CRS MC Total population 723,726 337,266 386,461 Male 333,182 155,960 177,222 Female 390,544 181,305 209,239 Adults age 15 or older 334,085 154,061 180,024 Male 151,563 68,619 82,944 Female 182,522 85,443 97,079 Cash earners (age 15 or older) 120,345 61,933 58,413 Male 53,600 27,280 26,320 Female 66,745 34,652 32,093 Farmers (age 15 or older) 183,844 89,424 94,420 Male 80,644 38,297 42,347 Female 103,200 51,127 52,073 Women of reproductive age (15-49 years) 137,039 62,450 74,589 Women 15-49 years who are not pregnant 103,505 48,798 54,707 Women 15-49 years who are married or in a union 107,223 49,162 58,061 Women 15-49 years with a live birth within the past five years 89,197 43,132 46,065 Young female 15-24 years 55,203 25,211 29,992 Young female 15-24 years who are married or in a union 26,202 12,453 13,749 Adolescents 10-19 years of age 150,096 68,821 81,274 Males 10-19 years of age 71,769 33,817 37,952 Females 10-19 years of age 78,327 35,005 43,322 Children under 5 years of age 134,915 66,557 68,358 Males under 5 years of age 64,041 31,256 32,786 Females under 5 years of age 70,874 35,301 35,572 Children 6-23 months of age 35,315 17,354 17,961 Males 6-23 months of age 17,589 8,487 9,102 Females 6-23 months of age 17,726 8,867 8,859 Children under 6 months of age 14,436 6,945 7,491 Males under 6 months of age 6,927 2,897 4,030 Females under 6 months of age 7,509 4,048 3,461 Parents of children under 2 years of age 77,267 38,367 38,901 Male 32,392 15,360 17,032 Female 44,875 23,007 21,868 Source: FFP 2018 baseline survey weighted population estimates Table A7.1. Estimated Population in the Baseline Survey Project Areas [Uganda, 2018] Overall CRS MC Total households (Number of households)1 132,832 62,225 70,607 Male and female adults 102,392 47,485 54,907 Female adult(s) only 24,734 12,508 12,226 Male adult(s) only 4,819 2,231 2,588 Child(ren) only (no adults) 887 0 887 Gendered household type (Percent of households) 100.0 100.0 100.0 Male and female adults 77.1 76.3 77.8 Female adult(s) only 18.6 20.1 17.3 Male adult(s) only 3.6 3.6 3.7 Child(ren) only (no adults) 0.7 0.0 1.3 Average household size (Number of persons) 5.4 5.4 5.5 Average number of adults 15 and older per household 2.5 2.5 2.5 Average number of farmers 15 and older per household 1.4 1.4 1.4 Percent of households with children under 5 years of age 61.5 62.7 60.5 Percent of households with a child 6-23 months of age 24.5 26.1 23.0 Percent of households with a child under 6 months of age 10.0 10.3 9.8 Household headship (Percent male) 60.6 62.1 59.2 Education level of head of household (Percent of households) No formal education 71.7 65.9 76.9 Primary 15.4 19.5 11.7 Secondary 7.9 9.2 6.8 Higher 5.0 5.4 4.6 Number of responding households 2,835 1,259 1,576 Male and female adults 2,216 974 1,242 Female adult(s) only 503 242 261 Male adult(s) only 102 43 59 Child(ren) only (no adults) 14 0 14 1 Adults are defined as individuals 18 or older. Table A7.2. Household Characteristics in the in the Baseline Survey Project Areas [Uganda, 2018] Table A7.3. Household Dietary Diversity Overall CRS MC Cereals 71.5 62.0 80.6 Root and tubers 15.2 21.4 9.3 Vegetables 66.7 60.8 72.4 Fruits 9.8 10.2 9.3 Meat, poultry, organ meat 9.1 4.1 13.9 Eggs 3.5 1.6 5.4 Fish and seafood 3.9 5.1 2.6 Pulses/legumes/nuts 24.5 24.6 24.3 Milk and milk products 24.2 17.3 30.9 Oil/fats 31.1 28.4 33.7 Sugar/honey 15.6 11.9 19.0 Miscellaneous (tea, coffee, condiments, etc.) 57.4 57.3 57.4 Number of responding households 2,303 1,053 1,250 [Uganda, 2018] Overall CRS MC 1. MAIZE 55.1 43.4 65.9 2. WHEAT 0.5 0.2 0.7 3. MILLET 6.4 4.6 8.0 4. BARLEY 0.1 0.0 0.2 5. SORGHUM 82.4 85.9 79.0 6. SOYBEAN 1.8 2.6 1.1 7. LEGUME (BEAN, LENTIL) 39.8 43.9 36.0 8. OILSEED (SUNFLOWER, MUSTARD, SESAME) 18.6 29.3 8.7 9. FRUITS 0.2 0.4 0.0 10. POTATO 2.7 5.4 0.2 11. CHAT 0.0 0.1 0.0 13. GROUNDNUTS 10.2 8.7 11.6 15. VEGETABLES 7.9 7.1 8.6 97. OTHER1 6.2 7.0 5.4 98. OTHER2 1.2 1.9 0.6 Number of farmers that raised crops 3664 1651 2013 Table A7.4. Crops Raised by Farmers Percentage of farmers by type of crops and project area [Uganda, 2018] Male Female Sig. Male Female Male Female 1. MAIZE 57.0 52.8 * 46.6 40.5 66.3 64.5 2. WHEAT 0.5 0.5 0.4 0.1 0.5 0.8 3. MILLET 6.1 6.4 4.1 5.0 7.9 7.9 4. BARLEY 0.2 0.1 0.1 0.0 0.3 0.2 5. SORGHUM 80.4 82.7 * 83.7 86.6 † 77.4 78.9 6. SOYBEAN 2.2 1.5 2.8 2.4 1.6 0.6 † 7. LEGUME (BEAN, LENTIL) 40.3 38.8 43.9 43.5 37.1 34.4 8. OILSEED (SUNFLOWER, MUSTARD, SESAME) 17.0 19.6 27.9 30.0 7.3 9.6 9. FRUITS 0.3 0.2 0.6 0.3 0.0 0.1 10. POTATO 3.1 2.4 6.0 4.8 0.4 0.1 11. CHAT 0.0 0.1 † 0.0 0.2 † 0.0 0.0 13. GROUNDNUTS 10.7 9.6 8.7 8.6 12.5 10.6 15. VEGETABLES 6.8 8.7 † 6.2 7.7 7.2 9.6 † 97. OTHER1 6.9 5.5 † 7.6 6.4 6.3 4.6 98. OTHER2 1.5 0.9 † 2.5 1.4 † 0.6 0.5 Male/Female group differences: *** p<0.001, ** p<0.01, * p<0.05, † p<0.10 MC Table A7.5. Crops Raised by Farmers Percentage of male and female farmers by type of crop and project area [Uganda, 2018] Overall CRS Overall CRS MC Cattle 60.2 49.1 66.3 Goats 61.8 47.9 69.4 Sheep 23.5 13.9 28.8 Donkeys 3.0 1.3 4.0 Camel 0.1 0.0 0.2 Poultry 22.7 19.9 24.2 Pigs 1.4 2.5 0.9 Other 0.5 0.8 0.3 Number of farmers that raised livestock 1,294 437 857 Table A7.6. Livestock Raised by Farmers Percentage of livestock farmers by type of livestock and project area [Uganda, 2018] Own Rent Share None CRS Male 77.8 12.5 9.7 3.0 Female 78.4 9.0* 12.3 4.0+ Farmers with a yes response 1,258 190 145 58 MC Male 84.3 6.0 9.8 5.0 Female 75.0*** 7.6 21.0*** 5.5 Farmers with a yes response 1,477 139 285 112 Male/Female group differences: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 Table A7.7. Land Ownership by farmer's gender [Uganda, 2018] Overall CRS MC Credit Yes 10.4 12.8 8.1 No 89.6 87.2 91.9 Savings Yes 17.6 18.1 17.1 No 82.4 81.9 82.9 Agricultural Insurance Yes 1.7 2.8 0.6 No 98.3 97.2 99.4 Did not use any financial services 79.5 78.3 80.6 Number of responding farmers 3,664 1,651 2,013 Table A7.8. Financial Services Percentage of farmers by type of financial service and project area [Uganda, 2018] Overall CRS MC Percentage of farmers who plant crops or raise/buy livestock with the specific intention to sell or resell to earn income 38.2 42.7 34.1 Value Chain Activities Procurement of inputs for crops 19.7 27.4 12.5 Procurement of inputs for livestock 3.7 3.8 3.6 Tillage of land 23.4 27.1 20.0 Bulk transporting of inputs produced 0.5 0.4 0.6 Bulk transporting of animals (on foot or by vehicle) 0.2 0.1 0.3 Sorting produce 13.2 17.6 9.1 Grading produce 4.5 7.0 2.1 Drying and processing produce 12.0 14.9 9.3 Trading or marketing (wholesale, retail, or export) for either animals or crops 4.3 6.8 2.0 Use of supplements to increase livestock production 0.7 0.6 0.7 Feed production 0.3 0.2 0.4 Other activity 0.3 0.3 0.2 Total number of farmers who plant crops or raise/buy livestock 3664 1651 2013 Note: highlighted cells represent project promoted value chain practices or technologies Table A7.9. Value Chain Activities Percentage of farmers by value chain activity and project area [Uganda, 2018] Overall CRS MC Crops Soil preparation by hand 76.5 79.2 73.9 Soil preparation with ox plow 40.4 42.5 38.4 Soil preparation with tractor 2.7 1.7 3.7 Broadcasting seed 70.7 74.0 67.6 Planting seeds in rows 22.5 22.1 22.9 Crop rotation 8.3 12.2 4.7 Fertilizer application 1.4 1.5 1.3 Intercropping 36.0 43.1 29.3 Pest and disease control 6.3 4.6 7.9 Weed control 57.9 59.9 56.0 Mulching 7.4 9.0 5.9 Thinning 15.4 15.2 15.6 Contouring land with berms and swales 1.8 0.4 3.1 Other 1.6 3.1 0.1 None of these practices 2.5 0.2 4.6 Number of farmers that raised crops 3,646 1,649 1,997 Livestock Animal shelters 42.4 43.8 41.7 Kraals 50.9 32.8 60.9 Vaccinations 40.7 39.0 41.7 Deworming 36.1 35.1 36.7 Homemade animal feeds made of locally available products 7.1 9.6 5.7 Used the services of community animal health workers 9.4 9.2 9.5 Purchased drugs/medicines to give to animals 26.5 22.3 28.8 Rotational grazing 5.8 10.6 3.2 Dehorning 4.1 3.5 4.4 Castration 10.3 9.7 10.6 Did not use any of these practices in the past 12 months 11.6 9.2 12.9 Number of farmers that raised livestock 1,294 437 857 Natural resource management Management of watersheds or reforestation 3.7 1.5 5.8 Agro-forestry or cultivation of fruit trees 2.1 2.8 1.5 Management of forest plantation 1.6 1.3 2.0 Management of natural regeneration 4.1 5.8 2.5 Collecting products from forest plants 0.4 0.2 0.6 Soil conservation on hillsides 3.0 1.9 4.0 Construction of water catchments 6.2 8.5 4.0 Did not use any of these practices in the past 12 months 83.4 83.4 83.3 Number of farmers that raised crops or livestock 3,664 1,651 2,013 Table A7.10. Sustainable Agricultural Practices Percentage of farmers by type of agricultural practice and project area [Uganda, 2018] Overall CRS MC Storage practices Cereal bank 0.4 0.1 0.6 Granary 49.8 50.5 49.1 Super grain / PICS bags 26.0 28.9 23.3 Manufactured silo 0.4 0.3 0.5 Other 5.2 4.9 5.5 Did not use any of these methods 27.7 25.9 29.4 Number of responding farmers 3,646 1,649 1,997 Table A7.11. Improved Storage Practices Percentage of farmers by storage practice and project area [Uganda, 2018] Overall CRS MC Improved, not shared sanitation facility Flush to piped sewer system 0.2 0.0 0.4 Flush to septic tank 0.0 0.0 0.1 Ventilated improved pit latrine 1.0 0.3 1.6 Pit latrine with slab 6.7 6.2 7.2 Ecosan Latrine 0.5 0.2 0.7 Improved, shared sanitation facility Flush to piped sewer system 0.0 0.0 0.1 Flush to septic tank 0.0 0.0 0.0 Ventilated improved pit latrine 2.9 2.0 3.7 Pit latrine with slab 9.6 12.3 7.3 Ecosan Latrine 0.4 0.3 0.5 Non-improved sanitation facility Flush to somewhere else 0.0 0.0 0.0 Flush to don't know where 0.0 0.0 0.0 Latrine Without Slab/Open Pit 10.3 10.5 10.2 Latrine with Open Pit/Hole (previously called Flush to pit latrine1 ) 0.1 0.0 0.2 Bucket toilet 0.4 0.4 0.4 Dig and bury 1.4 1.0 1.7 No Facility/Bush/Field 65.2 66.5 64.1 Other 0.3 0.3 0.4 Improved source of drinking water Piped into dwelling 0.3 0.1 0.6 Piped into yard/plot 0.4 0.0 0.8 Piped to public tap/standpipe 4.8 1.4 7.8 Tubewell or borehole 79.2 86.6 72.7 Protected well 0.4 0.2 0.6 Protected spring 0.1 0.1 0.2 Rainwater 0.2 0.0 0.3 Bottled water 0.0 0.0 0.0 Non-improved source of drinking water Unprotected well 3.0 0.8 5.0 Unprotected spring 1.8 1.5 2.0 Rock catchments 0.3 0.0 0.5 Surface water (river/dam/ lake/ponds/stream/canal/irrigation channel) 7.9 8.1 7.6 Other 0.5 0.9 0.2 Water availability Water is generally available from this source year round (% 'Yes') 55.5 56.3 54.8 Water was unavailable for a day or more during the last two weeks (% 'Yes') 29.9 27.1 32.4 Water treatment prior to drinking Chlorination 1.8 2.0 1.7 Flocculent/Disinfectant 0.2 0.2 0.2 Filtration 1.1 1.3 1.0 Solar Disinfection 0.0 0.0 0.0 Boiling 7.8 5.4 9.9 Other 0.1 0.1 0.2 No treatment 90.0 92.1 88.0 Number of households 2,835 1,259 1,576 Table A7.12. Household Sanitation and Drinking Water Sanitation facility, source of drinking water and treatment for drinking water by project area [Uganda, 2018] Overall CRS MC Percent less than 145 cm 0.4 0.4 0.5 Mean Body Mass Index (BMI) 19.7 19.5 19.8 Normal 18.5-24.9 (total normal) 61.2 57.8 64.3 Underweight <18.5 (total underweight) 34.6 38.6 31.0 17.0-18.4 (mildly underweight) 22.4 23.8 21.2 <17 (moderately and severely underweight) 12.2 14.8 9.8 Overweight/obese ≥25 (total overweight or obese) 4.2 3.6 4.7 25.0-29.9 (overweight) 3.3 2.5 4.0 ≥30.0 (obese) 0.9 1.1 0.7 Number of non-pregnant women of reproductive age 1,888 872 1,016 Women's height and BMI levels by project area [Uganda, 2018] Table A7.13. Nutritional Status of Non-pregnant Women of Reproductive Age Overall CRS MC Grains, roots and tubers 75.8 76.6 75.0 Legumes and beans 25.5 29.3 22.4 Nuts and seeds 4.6 4.0 5.1 Dairy products (milk, yogurt, cheese) 22.8 16.4 28.2 Eggs 4.0 2.4 5.4 Flesh foods including organ meat and misc. small animal protein 16.1 11.1 20.3 Vitamin A dark green leafy vegetables 42.5 35.6 48.3 Other Vitamin A rich vegetables and fruits 32.4 33.1 31.8 Other vegetables 72.0 73.8 70.5 Other fruits 13.5 9.3 16.9 Number of responding women 15-49 years 2,422 1,062 1,360 Table A7.14. Women's Dietary Diversity Percentage of women 15-49 years of age consuming 10 MDD-W food groups by project area [Uganda, 2018] Overall CRS MC Modern Methods Female sterilization 0.3 0.6 0.0 Male sterilization 0.0 0.0 0.0 Inter-uterine device 0.7 1.1 0.4 Injectables 2.1 2.0 2.1 Implants 2.2 2.7 1.7 Pill 0.7 0.6 0.7 Condom 1.1 1.6 0.6 Female condom 0.2 0.2 0.2 Emergency contraception 0.0 0.0 0.0 Other modern method 2.4 3.2 1.8 Any modern method 9.2 11.3 7.3 Traditional Methods Standard days method 3.2 2.2 4.1 Lactational amenorrhea method 1.6 0.7 2.4 Rhythm 0.2 0.1 0.2 Withdrawal 0.2 0.2 0.1 Other traditional method 0.0 0.0 0.0 Any traditional method 4.9 3.3 6.3 Any Method 13.8 14.5 13.2 Number of women 15-49 years married or in a union 1,381 636 745 Table A7.15. Contraceptive Prevalence Percentage of women 15-49 years married or in a union that used a contraceptive method by type of contraceptive method and project area [Uganda, 2018] Overall CRS MC Prevalence of stunted children 0-59 months <6 17.5 11.5 22.7 6-11 28.8 18.4 37.8 12-17 47.1 45.7 48.4 18-23 41.6 39.6 43.8 24-29 43.8 40.7 46.9 30-35 45.9 39.4 51.8 36-41 42.1 39.5 44.0 42-47 36.9 39.8 32.6 48-53 35.2 35.8 34.4 54-59 46.3 47.2 43.8 Number of children 0-59 months with valid height measurement 2,572 1,186 1,386 Prevalence of underweight children 0-59 months <6 15.6 14.0 17.0 6-11 31.9 30.4 33.2 12-17 38.7 39.5 38.0 18-23 28.9 25.3 33.0 24-29 32.5 32.4 32.6 30-35 27.8 20.4 34.6 36-41 31.6 27.4 34.7 42-47 22.9 20.8 26.2 48-53 25.1 26.6 23.5 54-59 39.4 43.7 26.6 Number of children 0-59 months with valid weight measurement 2,596 1,196 1,400 Prevalence of wasted children 0-59 months <6 9.4 10.9 8.1 6-11 18.0 19.8 16.5 12-17 20.8 21.1 20.5 18-23 9.9 9.3 10.6 24-29 10.1 12.2 8.0 30-35 6.7 6.0 7.5 36-41 9.8 8.6 10.6 42-47 3.7 2.5 5.4 48-53 11.2 12.1 10.2 54-59 10.8 13.0 4.0 Number of children 0-59 months with valid measurements 2,577 1,188 1,389 Table A7.16. Children's Nutritional Status Prevalence of stunted, underweight, and wasted children by age in months and project area [Uganda, 2018] NOTE: The results for these subgroup analyses are based on small sample sizes and may be unreliable. Overall CRS MC Not breastfeeding <2 0.0 0.0 0.0 2-3 0.0 0.0 0.0 4-5 1.2 0.0 2.2 6-8 0.0 0.0 0.0 9-11 4.0 2.0 5.8 12-17 8.8 10.1 7.5 18-23 28.2 35.2 19.9 Exclusively breastfed <2 90.9 90.5 91.4 2-3 89.9 91.8 88.3 4-5 42.0 38.2 45.5 6-8 15.3 13.1 17.0 9-11 0.5 0.4 0.5 12-17 2.3 1.9 2.7 18-23 0.3 0.6 0.0 Breastfed and plain water only <2 5.8 7.5 4.1 2-3 6.2 2.5 9.3 4-5 15.5 10.5 20.1 6-8 7.0 8.3 6.0 9-11 5.5 5.9 5.1 12-17 0.2 0.0 0.4 18-23 1.5 1.7 1.2 Breastfed and non-milk liquids <2 0.0 0.0 0.0 2-3 0.0 0.0 0.0 4-5 6.9 10.5 3.5 6-8 6.8 8.0 5.9 9-11 4.7 8.4 1.5 12-17 2.0 1.5 2.3 18-23 0.0 0.0 0.0 Breastfed and other milk <2 0.0 0.0 0.0 2-3 2.6 5.7 0.0 4-5 6.5 2.1 10.5 6-8 3.3 2.3 4.1 9-11 0.0 0.0 0.0 12-17 0.5 0.4 0.5 18-23 0.8 0.0 1.7 Breastfed and complementary foods <2 3.3 2.0 4.5 2-3 1.3 0.0 2.4 4-5 28.0 38.7 18.1 6-8 67.5 68.3 67.0 9-11 85.3 83.3 87.0 12-17 86.4 86.0 86.7 18-23 69.3 62.6 77.2 Number of children 1,058 490 568 Table A7.17. Breastfeeding Status Breastfeeding status for children 0-23 months by age in months and project area [Uganda, 2018] NOTE: The results for these subgroup analyses are based on small sample sizes and may be unreliable. Overall CRS MC Breastfed children 6-8 months Percent with minimum meal frequency (2 or more) 50.5 51.1 49.9 Percent with minimum dietary diversity (4 or more) 16.8 12.5 20.2 Grains, roots, and tubers 52.3 44.1 58.9 Legumes and nuts 17.9 22.2 14.4 Dairy products (milk, yogurt, cheese) 28.5 20.0 35.2 Flesh foods (meat, fish, poultry, and liver/organ meats) 5.8 2.1 8.7 Eggs 7.7 5.3 9.5 Vitamin A-rich fruits and vegetables 26.3 24.0 28.2 Other fruits and vegetables 33.3 38.8 28.9 Number of children 116 53 63 Breastfed children 9-23 months Percent with minimum meal frequency (3 or more) 18.2 16.1 20.2 Percent with minimum dietary diversity (4 or more) 22.3 20.1 24.4 Grains, roots, and tubers 83.4 82.6 84.2 Legumes and nuts 28.3 30.3 26.4 Dairy products (milk, yogurt, cheese) 34.5 27.8 40.6 Flesh foods (meat, fish, poultry, and liver/organ meats) 13.6 9.4 17.5 Eggs 7.8 4.8 10.6 Vitamin A-rich fruits and vegetables 45.4 42.2 48.4 Other fruits and vegetables 60.4 66.0 55.1 Number of children 554 254 300 Non-breastfed children 6-23 months Percent with minimum meal frequency (4 or more + 2 milk) 17.5 14.2 22.9 Percent with minimum dietary diversity (4 or more) 19.6 19.5 19.6 Grains, roots, and tubers 81.3 90.7 66.0 Legumes and nuts 26.9 25.6 29.0 Dairy products (milk, yogurt, cheese) 30.1 26.1 36.4 Flesh foods (meat, fish, poultry, and liver/organ meats) 15.1 11.0 21.8 Eggs 4.1 0.0 10.8 Vitamin A-rich fruits and vegetables 52.5 59.5 41.1 Other fruits and vegetables 53.5 60.4 42.3 Number of children 87 48 39 Table A7.18. Minimum Acceptable Diet (MAD) Components of MAD indicator for children 6-23 months by project area [Uganda, 2018] NOTE: The results for these subgroup analyses are based on small sample sizes and may be unreliable. Overall CRS MC Livelihood activities Farming/crop production and sales 65.6 73.1 58.8 Livestock production/fattening and sales 21.9 15.0 28.0 Agricultural wage labor 43.7 60.6 28.6 Non-agricultural wage labor 30.0 32.4 27.9 Salaried work 9.1 8.9 9.3 Sale of wild/bush products (including charcoal, firewood) 39.2 39.6 38.7 Honey production and sales 3.1 2.4 3.7 Petty trade (selling other products, e.g., grain, veggies, oil, sugar, etc.) 8.4 6.4 10.2 Petty trade (selling own products, e.g., local beer, sex work) 21.5 17.4 25.2 Other self-employment/own business (agricultural, e.g., buying/reselling chat) 7.0 5.5 8.3 Other self-employment/own business (non-agricultural, e.g., stone cutting, hair braiding, etc. 4.0 3.2 4.8 Rental of land, house, rooms 2.6 2.0 3.2 Remittances 8.6 8.8 8.4 Gifts/inheritance 24.9 24.2 25.6 Safety net food/cash assistance 12.3 10.6 13.9 Artisanal mining/quarrying 4.9 2.8 6.8 Other (specify): 1.8 1.1 2.5 Number of households 2799 1251 1548 Table A7.19. Livelihood Activities Percentage of households by type of livelihood activities and DFSA area [Uganda, 2018] Poor Non-poor Poor Non-poor Piped into dwelling 0.0 0.5 0.1 3.9 Piped into yard/plot 0.0 -- 0.3 4.3 Piped to public tap/standpipe 0.9 3.8 6.0 22.5 Tubewell or borehole 87.0 85.5 76.3 56.0 Protected well 0.3 -- 0.7 -- Unprotected well 0.8 0.7 4.7 8.0 Protected spring 0.1 -- 0.2 -- Unprotected spring 1.6 0.5 2.2 1.1 Rainwater -- -- 0.4 -- Rockcatchments -- -- 0.6 -- Tanker Truck -- -- -- -- Cart with small tank -- 0.9 0.0 0.6 Surface water (river/dam/ lake/ponds/stream/canal/irrigation channel) 8.2 7.7 8.4 3.0 Bottled Water -- -- -- -- Other 1.0 0.4 0.2 0.6 Number of households 1063 195 1354 205 Water availability Water is generally available from this source year round (% 'Yes') 56.9 53.3 56.8 47.1 Water was unavailable for a day or more during the last two weeks (% 'Yes') 25.9 32.9 31.8 36.3 Table A7.20. Sources of drinking water by poverty status Sources of drinking water by DFSA area and poverty status [Uganda, 2018] Highlighted drinking water sources are considered improved sources CRS MC Annex 8: Descriptive Tables Annex 8: List of variables considered for correlation and/or regression analyses on different outcome variables Variable name Definition Variable type HDDS Household Dietary Diversity Score (0-12) Discrete Score (0=min, 12=max) Fies_score12m FIES score for 12 months recall questions Continuous Score Genhhtype Adult male and female, 2= Adult female only, 3= adult male only, 4= child only Nominal Hhsize Household size Discrete La_typeacc 1=Own, 2=Rent, 3= Sharecrop, 4=None Nominal La_hecsize Access to land, 0 is no land, 1 is <=1 hector, 2 is >1 & <=2, 3 is >2 & <=3, 4 is >3 Ordinal Hhmem_edu Household members' highest level of education: 1= preschool or no schooling, 2= primary level, 3= secondary level, 4= university/tertiary level Ordinal Agri_finance Farmers who used financial services (saving) in the past 12 months Dichotomous Hh_cashearner HH with at least 1 adult>15age who earn cash or cash/kind Dichotomous Wealthq HH wealth quintile- 1- poorest, 5 richest Ordinal (1=poorest, 5=richest) Poverty Prevalence of poverty- percentage of people living below US$ 1.90/day (PPP 2011) Dichotomous Povgap_index1 Depth of poverty of the poor based on US$ 1.90 poverty line (PPP 2011) Continuous score Svcash Household regularly saves cash (0-1) Dichotomous Acctorem Access to remittances (0-1) Dichotomous Asset_prod Productive assets count (0-24) Discrete score (min=0, max=24) Asset_lvsk Livestock assets count (0-7) Discrete score (min=0, max=7) Asset_durable Durable assets count (0-22) Discrete score (min=0, max=22) I_prepmit Index of shock preparedness and mitigation (0-4) Discrete score (min=0, max=4) Human_asst Availability of humanitarian assistance from gov't and/or NGO (0-1) Dichotomous I_aspiration Index of aspirations/confidence to adapt (0-16) Discrete score (min=0, max=16) I_bonding Index for Bonding Social Capital (0-6) Discrete score (min=0, max=6) I_bridging Index for bridging social capital (0-6) Discrete score (min=0, max=6) I_linking Index for linking social capital- government officials and NGO (0-4) Discrete score (min=0, max=4) I_lvhdiv Index of livelihood diversification (0-17) Discrete score (min=0, max=17) Variable name Definition Variable type I_infoexp Index for information exposure (0-19) Discrete score (min=0, max=19) I_fsn Access to formal safety nets (0-3) Discrete score (min=0, max=3) I_accfinc Index for access to financial institutions (0-2) Discrete score (min=0, max=2) Accb_market Index for access to markets within 5km of a village (0-3) Ordinal (min=0, max=3) Accb_natres Index for access to communal natural resources (0-4) Ordinal (min=0, max=4) I_bserv Index for access to basic services (0-3) Discrete score (min=0, max=3) I_agext Index for access to agricultural extension services (0-1) Dichotomous I_lvskserv Index for access to livestock services (0-1) Dichotomous Asset_lvsk Livestock assets count (0-7) Discrete score (min=0, max=7) Partic_loc Participation in local decision making (0-1) Dichotomous Shock_clim Household experienced climate shocks (0-1) Dichotomous Shock_biol Household experienced biological shocks (0-1) Dichotomous Shock_econ Household experienced economic shocks (0-1) Dichotomous Shock_confl Household experienced conflict shocks (0-1) Dichotomous I_shocksev Index of shock severity impact - none, slight decrease, severe decrease etc. (2-8) Discrete score (min=0, max=8) Atr Ability to recover shock severity index Continuous Agri_finance Farmers who used financial services (saving) in the past 12 months Dichotomous Agri_valuechain Farmers who practiced >=1 value chain activities promoted by the project in the Dichotomous Agri_sustagri Farmers who used >=3 sustainable agriculture practices &/or technologies in the past 12 Dichotomous Agri_storage Farmers who used improved storage practices in the past 12 months Dichotomous Basic_sanitation % of HHs using an basic sanitation facility Dichotomous Cash_earned Men & women who earned cash in the past 12 months Dichotomous Time_water % of households that can obtain drinking water in less than 30 minutes round trip) Dichotomous Correct_watertre at Percent of HHs practicing correct use of recommended household water treatment technology Dichotomous Water_improved Percentage of HHs using improved water sources Dichotomous Open_defecation % of HHs practicing open defecation Dichotomous Proper_handwashi ng % of HHs with soap and water at a handwashing station commonly used by family members Dichotomous Chn_diarrhea % of children 0-59 months (1,825 days) of age who had diarrhea in the prior two weeks Dichotomous Chn_mad children 6-23 months receiving a minimum acceptable diet Dichotomous Chn_nutrich Prevalence of children (6-23m) who consume targeted nutrient-rich commodities Dichotomous Annex 9: Multivariate Analyses Tables Table 10.1a: Correlation results on HDDS Variables HDDS CRS MC genhhtype 0.02 -0.09 * (0.74) (0.04) 1004 1215 hhsize -0.02 0.07 * (0.69) (0.04) 1004 1215 la_typeacc -0.04 -0.14 *** (0.36) (0.00) 868 1043 la_hecsize 0.02 0.09 * (0.73) (0.04) 843 993 hhmem_edu 0.29 *** 0.29 *** (0.00) (0.00) 868 1043 agri_finance 0.33 *** 0.25 *** (0.00) (0.00) 866 1040 hh_cashearner 0.21 *** 0.19 *** (0.00) (0.00) 868 1043 Wealth Index Quintile 0.17 *** 0.11 * (0.00) (0.03) 998 1196 poverty -0.26 *** -0.26 *** (0.00) (0.00) 1004 1215 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.2b: Correlation results on HDDS Variables HDDS CRS MC svcash 0.33 *** 0.27 *** (0.00) (0.00) 998 1206 acctorem 0.06 + 0.01 (0.06) (0.74) 998 1206 asset_prod 0.14 * 0.04 (0.01) (0.38) 998 1206 asset_lvsk 0.22 *** 0.13 * (0.00) (0.01) 998 1206 asset_durable 0.38 *** 0.36 *** (0.00) (0.00) 998 1206 i_prepmit 0.20 *** 0.23 *** (0.00) (0.00) 998 1206 human_asst 0.00 0.03 (0.99) (0.30) 998 1206 i_aspiration 0.16 *** 0.28 *** (0.00) (0.00) 998 1206 i_bonding 0.02 0.04 (0.65) (0.32) 998 1206 i_bridging 0.00 0.06 (0.98) (0.12) 998 1206 i_linking 0.23 *** 0.14 * (0.00) (0.01) 998 1206 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Variables CRS MC i_lvhdiv 0.17 *** 0.14 ** (0.00) (0.01) 998 1206 i_infoexp 0.32 *** 0.16 *** (0.00) (0.00) 998 1206 i_fsn -0.03 -0.05 (0.80) (0.18) 998 1206 i_accfinc 0.28 *** 0.07 (0.00) (0.22) 998 1206 accb_market 0.08 0.09 (0.44) (0.13) 998 1206 accb_natres -0.04 0.05 (0.70) (0.50) 998 1206 i_bserv 0.06 0.05 (0.48) (0.55) 998 1206 i_agext 0.11 0.22 * (0.17) (0.01) 998 1206 i_lvskserv 0.19 * 0.18 * (0.03) (0.01) 998 1206 partic_loc 0.30 *** 0.13 *** (0.00) (0.00) 998 1206 Table 10.2c: Correlation results on HDDS HDDS Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.2d: Correlation results on HDDS Variables HDDS CRS MC shock_clim 0.04 0.12 *** (0.19) (0.00) 998 1206 shock_biol 0.10 + 0.02 (0.06) (0.62) 998 1206 shock_econ 0.17 *** 0.10 * (0.00) (0.01) 998 1206 shock_confl 0.22 *** 0.09 + (0.00) (0.05) 998 1206 i_shocksev 0.01 0.03 (0.80) (0.45) 979 1162 atr 0.02 0.16 *** (0.79) (0.00) 867 998 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.2: Poisson Regression on HDDS with other variables VARIABLES OR se OR se Household Dietary Diversity Score (0-12) . (.) . (.) Gendered household type = 2, FemaleOnly 1.16* (0.06) 0.96 (0.06) Gendered household type = 3, MaleOnly 1.07 (0.18) 0.95 (0.17) (sum) hhsize 0.99 (0.01) 1.00 (0.01) 1=Own, 2=Rent, 3= Sharecrop, 4=None = 2 1.07 (0.10) 0.83* (0.07) 1=Own, 2=Rent, 3= Sharecrop, 4=None = 3 0.96 (0.08) 0.90* (0.04) 1=Own, 2=Rent, 3= Sharecrop, 4=None = 4 0.90 (0.17) 0.77 (0.12) Access to land, 0 no land, 1 is <=1 hector, 4 is >3 = 2 0.97 (0.05) 1.03 (0.05) Access to land, 0 no land, 1 is <=1 hector, 4 is >3 = 3 1.01 (0.07) 0.98 (0.06) Access to land, 0 no land, 1 is <=1 hector, 4 is >3 = 4 0.71* (0.09) 0.99 (0.08) Household members' highest level of education = 2, Primary 1.03 (0.05) 1.02 (0.06) Household members' highest level of education = 3, Secondary 1.13+ (0.08) 1.15+ (0.08) Household members' highest level of education = 4, Tertiary/University 1.28** (0.10) 1.22** (0.08) (max) agri_finance 1.06 (0.07) 1.07 (0.06) HH with at least 1 adult>15age who earn cash or cash/kind 1.15* (0.06) 1.07 (0.05) HH wealth quintile- 1- poorest, 5 richest = 2 1.00 (0.06) 1.04 (0.08) HH wealth quintile- 1- poorest, 5 richest = 3 1.11 (0.09) 1.09 (0.09) HH wealth quintile- 1- poorest, 5 richest = 4 1.15 (0.12) 1.17* (0.08) HH wealth quintile- 1- poorest, 5 richest = 5 1.13 (0.18) 1.16 (0.12) Prevalance of poverty- percentage of people living below US$ 1.90/day poverty li 0.93 (0.06) 0.94 (0.05) Household regularly saves cash (0-1) 0.99 (0.07) 1.09 (0.06) Access to remittances (0-1) 1.00 (0.06) 1.05 (0.07) Productive assets count (0-24) 1.01 (0.01) 1.00 (0.01) Livestock assets count (0-7) 1.01 (0.03) 1.00 (0.02) Durable assets count (0-22) 1.03** (0.01) 1.04** (0.01) Index of shock preparedness and mitigation (0-4) 0.98 (0.02) 1.03 (0.02) Index of aspirations/confidence to adapt (0-16) 1.00 (0.02) 1.04*** (0.01) Index for linking social capital- gov officials and NGO (0-4) 1.06** (0.02) 1.00 (0.01) Index of livelihood diversification (0-17) 1.01 (0.01) 1.02 (0.01) Index for information exposure (0-19) 1.02* (0.01) 1.01 (0.01) Index for access to financial institutions (0-2) 1.07 (0.07) 0.94* (0.02) Index for access to agricultural extension services (0-1) 0.93 (0.08) 1.03 (0.07) Index for access to livestock services (0-1) 1.03 (0.10) 1.07 (0.06) Participation in local decision making (0-1) 1.23** (0.09) 1.07+ (0.04) Household experienced climate shocks (0-1) 0.80 (0.17) 1.39*** (0.12) Household experienced biological shocks (0-1) 1.10 (0.12) 1.03 (0.07) Household experienced economic shocks (0-1) 0.94 (0.08) 0.98 (0.04) Household experienced conflict shocks (0-1) 1.16* (0.07) 0.98 (0.05) Ability to recover shock severity index 1.01 (0.02) 1.03* (0.01) Constant 1.71+ (0.52) 1.20 (0.25) Observations 748 823 F-statistic 11.17 8.934 Prob>F 0.0855 0.000125 seEform in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 CRS MC HDDS -0.06 -0.14 * (0.16) (0.02) 1004 1215 genhhtype -0.04 0.03 (0.13) (0.28) 1235 1535 hhsize 0.07 † -0.02 (0.05) (0.55) 1235 1535 la_typeacc -0.05 -0.01 (0.45) (0.84) 1072 1276 la_hecsize -0.06 -0.01 (0.17) (0.78) 1041 1212 hhmem_edu -0.10 * -0.19 *** (0.04) (0.00) 1072 1276 agri_finance -0.02 -0.10 * (0.48) (0.03) 1070 1271 hh_cashearner 0.02 -0.01 (0.60) (0.80) 1072 1276 Wealth Index Quintile -0.03 -0.01 (0.28) (0.69) 1228 1511 poverty 0.01 0.21 *** (0.63) (0.00) 1235 1535 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Variables Food Insecurity Experience Score (FIES- 12 month) CRS MC Table 10.2a: Correlation results on FIES Score (12 Month Recall) svcash -0.03 -0.10 * (0.39) (0.07) 1228 1522 acctorem -0.03 0.04 + (0.45) (0.08) 1228 1522 asset_prod 0.02 0.03 (0.55) (0.26) 1228 1522 asset_lvsk -0.02 -0.03 (0.62) (0.36) 1228 1522 asset_durable -0.03 -0.15 (0.55) (0.02) 1228 1522 i_prepmit 0.00 0.00 (0.92) (0.99) 1228 1522 human_asst -0.03 0.03 * (0.41) (0.33) 1228 1522 i_aspiration 0.03 -0.03 (0.41) (0.44) 1228 1522 i_bonding -0.02 0.06 * (0.73) (0.04) 1228 1522 i_bridging -0.02 0.06 + (0.75) (0.06) 1228 1522 i_linking 0.00 -0.03 (0.96) (0.43) 1228 1522 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.2b: Correlation results on FIES Score (12 Month Recall) Variables Food Insecurity Experience Score (FIES- 12 month) CRS MC Variables CRS MC i_lvhdiv -0.01 0.11 * (0.73) (0.01) 1228 1522 i_infoexp 0.01 0.05 (0.74) (0.32) 1228 1522 i_fsn 0.04 -0.01 (0.43) (0.68) 1228 1522 i_accfinc -0.05 0.08 (0.39) (0.12) 1228 1522 accb_market -0.01 -0.04 (0.78) (0.32) 1228 1522 accb_natres -0.09 + 0.03 (0.05) (0.46) 1228 1522 i_bserv -0.02 -0.08 (0.61) (0.13) 1228 1522 i_agext 0.00 -0.11 + (0.94) (0.07) 1228 1522 i_lvskserv -0.04 -0.04 (0.50) (0.42) 1228 1522 partic_loc -0.02 0.01 (0.49) (0.71) 1228 1522 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.2c: Correlation results on FIES Score (12 Month Recall) Food Insecurity Experience Score (FIES- 12 month) shock_clim 0.07 0.02 (0.14) (0.50) 1228 1522 shock_biol 0.13 * 0.15 *** (0.01) (0.00) 1228 1522 shock_econ 0.08 * 0.06 (0.04) (0.15) 1228 1522 shock_confl 0.07 * 0.05 (0.02) (0.10) 1228 1522 i_shocksev 0.04 0.09 * (0.40) (0.01) 1204 1467 atr 0.04 -0.03 (0.41) (0.55) 1043 1251 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.2d: Correlation results on FIES Score (12 Month Recall) Variables Food Insecurity Experience Score (FIES- 12 month) CRS MC Table 10.4: Regression on FIES-12 month with other variables VARIABLES coeff se coeff se FIES score for 12 months recall questions . (.) . (.) Household Dietary Diversity Score (0-12) 0.01 (0.01) 0.00 (0.00) (sum) hhsize 0.01+ (0.00) -0.00 (0.00) Household members' highest level of education = 2, Primary -0.06** (0.02) -0.00 (0.01) Household members' highest level of education = 3, Secondary -0.07+ (0.04) -0.06 (0.04) Household members' highest level of education = 4, Tertiary/University -0.04 (0.02) -0.16*** (0.04) (max) agri_finance -0.01 (0.02) -0.03+ (0.02) Prevalance of poverty- percentage of people living below US$ 1.90/day poverty li -0.01 (0.02) 0.05+ (0.03) Household regularly saves cash (0-1) -0.02 (0.02) 0.01 (0.02) Access to remittances (0-1) -0.01 (0.02) -0.00 (0.02) Availability of humanitarian assistance from gov't and/or NGO (0-1) -0.00 (0.02) 0.03* (0.01) Index for Bonding Social Capital (0-6) 0.00 (0.01) 0.00 (0.01) Index for bridging social captial (0-6) -0.01 (0.01) 0.00 (0.01) Index of livelihood diversification (0-17) -0.01* (0.00) 0.00 (0.00) Index for access to communal natural resources (0-4) -0.01+ (0.01) 0.01* (0.01) Index for access to agricultural extension services (0-1) 0.01 (0.01) -0.02 (0.02) Household experienced biological shocks (0-1) 0.05* (0.02) 0.02 (0.02) Household experienced economic shocks (0-1) 0.03* (0.02) 0.02 (0.01) Household experienced conflict shocks (0-1) 0.02+ (0.01) 0.02 (0.01) Index of shock severity impact - none, slight decrease, severe decrease, worst e -0.00 (0.01) 0.00 (0.00) Constant 0.95*** (0.07) 0.84*** (0.04) Observations 890 1,033 R-squared 0.07 0.09 F-statistic 2.54 2.059 Prob>F 0.0206 0.0359 Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 CRS MC genhhtype -0.02 -0.01 (0.44) (0.73) 1259 1576 hhsize 0.13 ** -0.04 (0.00) (0.43) 1259 1576 la_typeacc 0.02 0.04 (0.76) (0.24) 1092 1299 la_hecsize -0.17 *** -0.08 * (0.00) (0.03) 1061 1233 hhmem_edu -0.22 *** -0.33 *** (0.00) (0.00) 1092 1299 agri_finance -0.20 *** -0.22 *** (0.00) (0.00) 1090 1293 hh_cashearner -0.07 + -0.06 (0.09) (0.15) 1092 1299 Wealth Index Quintile -0.14 ** 0.00 (0.00) (0.97) 1247 1537 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Variables Poverty CRS MC Table 10.5a: Correlation results on Poverty svcash -0.21 *** -0.24 *** (0.00) (0.00) 1251 1548 acctorem -0.12 ** 0.02 (0.01) (0.48) 1251 1548 asset_prod -0.06 0.04 (0.10) (0.20) 1251 1548 asset_lvsk -0.17 *** -0.03 (0.00) (0.43) 1251 1548 asset_durable -0.33 *** -0.36 *** (0.00) (0.00) 1251 1548 i_prepmit -0.18 *** -0.14 ** (0.00) (0.00) 1251 1548 human_asst -0.02 0.02 (0.71) (0.60) 1251 1548 i_aspiration -0.08 * -0.14 *** (0.02) (0.00) 1251 1548 i_bonding -0.13 *** 0.03 (0.00) (0.33) 1251 1548 i_bridging -0.11 * 0.02 (0.01) (0.43) 1251 1548 i_linking -0.13 * -0.13 * (0.02) (0.02) 1251 1548 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.5b: Correlation results on Poverty Variables Poverty CRS MC Variables CRS MC i_lvhdiv -0.21 *** 0.00 (0.00) (0.91) 1251 1548 i_infoexp -0.25 *** -0.09 * (0.00) (0.03) 1251 1548 i_fsn -0.04 0.02 (0.45) (0.54) 1251 1548 i_accfinc -0.14 * -0.08 (0.01) (0.14) 1251 1548 accb_market -0.02 -0.09 (0.68) (0.10) 1251 1548 accb_natres 0.09 -0.13 + (0.11) (0.09) 1251 1548 i_bserv -0.02 -0.16 + (0.70) (0.05) 1251 1548 i_agext -0.10 + -0.26 ** (0.08) (0.00) 1251 1548 i_lvskserv -0.16 ** -0.19 ** (0.00) (0.01) 1251 1548 partic_loc -0.14 ** -0.06 * (0.00) (0.02) 1251 1548 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.5c: Correlation results on Poverty Poverty shock_clim -0.04 * -0.05 + (0.02) (0.07) 1251 1548 shock_biol -0.06 + 0.01 (0.15) (0.83) 1251 1548 shock_econ -0.14 *** -0.08 * (0.00) (0.01) 1251 1548 shock_confl -0.16 ** -0.08 + (0.00) (0.05) 1251 1548 i_shocksev -0.06 * 0.04 (0.02) (0.21) 1226 1491 atr -0.01 -0.09 * (0.88) (0.03) 1060 1272 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.5d: Correlation results on Poverty Variables Poverty CRS MC Table 10.6: Logistic Regression on Prevalance of Poverty with other variables VARIABLES OR se OR se Prevalance of poverty- percentage of people living below US$ 1.90/day poverty li . (.) . (.) (sum) hhsize 1.39*** (0.10) 1.04 (0.07) Access to land, 0 no land, 1 is <=1 hector, 4 is >3 = 2 0.82 (0.24) 0.92 (0.29) Access to land, 0 no land, 1 is <=1 hector, 4 is >3 = 3 0.56 (0.29) 0.85 (0.39) Access to land, 0 no land, 1 is <=1 hector, 4 is >3 = 4 0.19** (0.10) 0.77 (0.41) Household members' highest level of education = 2, Primary 1.05 (0.44) 0.52* (0.17) Household members' highest level of education = 3, Secondary 0.94 (0.40) 0.22** (0.10) Household members' highest level of education = 4, Tertiary/University 0.09*** (0.05) 0.14*** (0.07) (max) agri_finance 0.61 (0.22) 0.50** (0.12) HH with at least 1 adult>15age who earn cash or cash/kind 1.34 (0.35) 1.19 (0.37) HH wealth quintile- 1- poorest, 5 richest = 2 0.86 (0.36) 2.62* (1.24) HH wealth quintile- 1- poorest, 5 richest = 3 0.84 (0.32) 1.27 (0.81) HH wealth quintile- 1- poorest, 5 richest = 4 0.94 (0.67) 1.97 (1.14) HH wealth quintile- 1- poorest, 5 richest = 5 0.74 (0.73) 2.43 (2.06) Household regularly saves cash (0-1) 0.93 (0.32) 0.63 (0.22) Access to remittances (0-1) 1.06 (0.46) 1.00 (0.73) Livestock assets count (0-7) 0.88 (0.17) 0.80 (0.14) Index of shock preparedness and mitigation (0-4) 0.81 (0.16) 0.73 (0.15) Index of aspirations/confidence to adapt (0-16) 1.01 (0.06) 0.89 (0.07) Index for Bonding Social Capital (0-6) 0.86 (0.12) 1.31 (0.28) Index for bridging social captial (0-6) 1.08 (0.17) 0.85 (0.19) Index for linking social capital- gov officials and NGO (0-4) 0.96 (0.19) 1.02 (0.12) Index of livelihood diversification (0-17) 0.69*** (0.07) 1.15 (0.10) Index for information exposure (0-19) 0.95+ (0.03) 0.97 (0.04) Index for access to financial institutions (0-2) 0.88 (0.14) 1.54+ (0.36) Index for access to communal natural resources (0-4) 1.29 (0.24) 0.73+ (0.13) Index for access to basic services (0-3) 0.90 (0.25) 1.01 (0.39) Index for access to agricultural extension services (0-1) 0.95 (0.36) 0.40 (0.24) Index for access to livestock services (0-1) 0.52+ (0.20) 0.94 (0.45) Participation in local decision making (0-1) 0.88 (0.33) 0.88 (0.24) Household experienced climate shocks (0-1) 0.86 (1.16) 0.15* (0.14) Household experienced biological shocks (0-1) 1.34 (0.61) 0.88 (0.32) Household experienced economic shocks (0-1) 1.05 (0.30) 0.61 (0.18) Household experienced conflict shocks (0-1) 0.61+ (0.17) 1.09 (0.43) Index of shock severity impact - none, slight decrease, severe decrease, worst e 0.97 (0.11) 1.13 (0.15) Ability to recover shock severity index 0.82 (0.11) 0.93 (0.12) Constant 77.77* (163.62) 284.85** (485.20) Observations 913 1,015 F-statistic 5.83 5.379 Prob>F 0.0285 0.000555 seEform in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 CRS MC genhhtype -0.01 0.19 ** (0.82) (0.00) 1064 1371 hhsize 0.10 * 0.06 (0.04) (0.19) 1064 1371 la_typeacc 0.03 0.12 *** (0.46) (0.00) 926 1142 la_hecsize -0.06 + -0.11 * (0.18) (0.02) 897 1088 hhmem_edu -0.20 *** -0.15 * (0.00) (0.01) 926 1142 agri_finance -0.29 *** -0.14 *** (0.00) (0.00) 925 1139 hh_cashearner -0.07 + -0.15 *** (0.06) (0.00) 926 1142 Wealth Index Quintile -0.07 + -0.09 * (0.09) (0.02) 1053 1334 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Variables Depth of Poverty of Poor CRS MC Table 10.7a: Correlation results on Depth of Poverty of Poor genhhtype -0.02 -0.01 (0.44) (0.73) 1259 1576 hhsize 0.13 ** -0.04 (0.00) (0.43) 1259 1576 la_typeacc 0.02 0.04 (0.76) (0.24) 1092 1299 la_hecsize -0.17 *** -0.08 * (0.00) (0.03) 1061 1233 hhmem_edu -0.22 *** -0.33 *** (0.00) (0.00) 1092 1299 agri_finance -0.20 *** -0.22 *** (0.00) (0.00) 1090 1293 hh_cashearner -0.07 + -0.06 (0.09) (0.15) 1092 1299 Wealth Index Quintile -0.14 ** 0.00 (0.00) (0.97) 1247 1537 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size CRS MC Variables Poverty svcash -0.29 *** -0.10 ** (0.00) (0.00) 1056 1344 acctorem -0.08 * -0.03 (0.04) (0.50) 1056 1344 asset_prod -0.13 * 0.01 (0.02) (0.79) 1056 1344 asset_lvsk -0.10 * -0.13 *** (0.03) (0.00) 1056 1344 asset_durable -0.38 *** -0.30 *** (0.00) (0.00) 1056 1344 i_prepmit -0.25 *** -0.07 + (0.00) (0.06) 1056 1344 human_asst -0.09 + -0.03 (0.09) (0.49) 1056 1344 i_aspiration -0.12 ** -0.11 ** (0.00) (0.00) 1056 1344 i_bonding -0.16 ** -0.06 + (0.00) (0.08) 1056 1344 i_bridging -0.11 * -0.05 (0.04) (0.17) 1056 1344 i_linking -0.13 * -0.05 (0.02) (0.29) 1056 1344 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.7b: Correlation results on Depth of Poverty of Poor Variables Depth of Poverty of Poor CRS MC svcash -0.21 *** -0.24 *** (0.00) (0.00) 1251 1548 acctorem -0.12 ** 0.02 (0.01) (0.48) 1251 1548 asset_prod -0.06 0.04 (0.10) (0.20) 1251 1548 asset_lvsk -0.17 *** -0.03 (0.00) (0.43) 1251 1548 asset_durable -0.33 *** -0.36 *** (0.00) (0.00) 1251 1548 i_prepmit -0.18 *** -0.14 ** (0.00) (0.00) 1251 1548 human_asst -0.02 0.02 (0.71) (0.60) 1251 1548 i_aspiration -0.08 * -0.14 *** (0.02) (0.00) 1251 1548 i_bonding -0.13 *** 0.03 (0.00) (0.33) 1251 1548 i_bridging -0.11 * 0.02 (0.01) (0.43) 1251 1548 i_linking -0.13 * -0.13 * (0.02) (0.02) 1251 1548 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size CRS MC Variables Poverty Variables CRS MC i_lvhdiv -0.16 * -0.12 * (0.01) (0.02) 1056 1344 i_infoexp -0.33 *** -0.07 (0.00) (0.10) 1056 1344 i_fsn 0.02 0.05 (0.70) (0.26) 1056 1344 i_accfinc -0.18 * 0.04 (0.02) (0.40) 1056 1344 accb_market 0.01 -0.04 (0.88) (0.40) 1056 1344 accb_natres 0.16 * 0.06 (0.02) (0.34) 1056 1344 i_bserv -0.01 0.02 (0.87) (0.74) 1056 1344 i_agext -0.09 + -0.13 * (0.22) (0.02) 1056 1344 i_lvskserv -0.19 ** -0.10 + (0.00) (0.08) 1056 1344 partic_loc -0.22 *** -0.06 + (0.00) (0.09) 1056 1344 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.7c: Correlation results on Depth of Poverty of Poor Depth of Poverty of Poor Variables CRS MC i_lvhdiv -0.21 *** 0.00 (0.00) (0.91) 1251 1548 i_infoexp -0.25 *** -0.09 * (0.00) (0.03) 1251 1548 i_fsn -0.04 0.02 (0.45) (0.54) 1251 1548 i_accfinc -0.14 * -0.08 (0.01) (0.14) 1251 1548 accb_market -0.02 -0.09 (0.68) (0.10) 1251 1548 accb_natres 0.09 -0.13 + (0.11) (0.09) 1251 1548 i_bserv -0.02 -0.16 + (0.70) (0.05) 1251 1548 i_agext -0.10 + -0.26 ** (0.08) (0.00) 1251 1548 i_lvskserv -0.16 ** -0.19 ** (0.00) (0.01) 1251 1548 partic_loc -0.14 ** -0.06 * (0.00) (0.02) 1251 1548 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Poverty shock_clim -0.01 -0.08 + (0.77) (0.05) 1056 1344 shock_biol -0.15 ** -0.03 (0.00) (0.39) 1056 1344 shock_econ -0.23 *** -0.07 + (0.00) (0.06) 1056 1344 shock_confl -0.21 ** -0.02 (0.00) (0.69) 1056 1344 i_shocksev -0.06 -0.02 (0.22) (0.59) 1032 1293 atr 0.04 -0.06 (0.49) (0.17) 873 1100 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.7d: Correlation results on Depth of Poverty of Poor Variables Depth of Poverty of Poor CRS MC shock_clim -0.04 * -0.05 + (0.02) (0.07) 1251 1548 shock_biol -0.06 + 0.01 (0.15) (0.83) 1251 1548 shock_econ -0.14 *** -0.08 * (0.00) (0.01) 1251 1548 shock_confl -0.16 ** -0.08 + (0.00) (0.05) 1251 1548 i_shocksev -0.06 * 0.04 (0.02) (0.21) 1226 1491 atr -0.01 -0.09 * (0.88) (0.03) 1060 1272 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Variables Poverty CRS MC Table 10.8: Linear Regression on Depth of Poverty of the Poor with other variables VARIABLES coeff se coeff se Depth of poverty of the poor based on US$ 1.90 poverty line (PPP 2011) . (.) . (.) Gendered household type = 2, FemaleOnly -3.74+ (1.93) 2.81+ (1.61) Gendered household type = 3, MaleOnly 4.03+ (2.36) 8.03+ (4.00) Household members' highest level of education = 2, Primary -1.29 (1.61) -2.46 (1.58) Household members' highest level of education = 3, Secondary -2.72 (2.36) -12.90* (4.85) Household members' highest level of education = 4, Tertiary/University -9.43 (8.64) -12.85 (8.19) (sum) hhsize 0.90* (0.44) 1.09*** (0.31) 1=Own, 2=Rent, 3= Sharecrop, 4=None = 2 -1.03 (2.24) 5.80* (2.60) 1=Own, 2=Rent, 3= Sharecrop, 4=None = 3 -8.64** (2.80) 6.03** (1.91) 1=Own, 2=Rent, 3= Sharecrop, 4=None = 4 -0.63 (2.40) 4.62 (2.92) Access to land, 0 no land, 1 is <=1 hector, 4 is >3 = 2 -2.83 (1.72) -1.34 (1.71) Access to land, 0 no land, 1 is <=1 hector, 4 is >3 = 3 -4.28 (2.88) -3.28 (3.04) Access to land, 0 no land, 1 is <=1 hector, 4 is >3 = 4 1.18 (3.77) -6.07* (2.69) (max) agri_finance -5.00** (1.71) -3.40+ (1.88) HH with at least 1 adult>15age who earn cash or cash/kind 2.96+ (1.62) -5.01** (1.54) HH wealth quintile- 1- poorest, 5 richest = 2 0.18 (1.87) 3.82+ (2.03) HH wealth quintile- 1- poorest, 5 richest = 3 -1.42 (2.29) -0.96 (2.52) HH wealth quintile- 1- poorest, 5 richest = 4 -0.02 (4.04) -0.92 (3.55) HH wealth quintile- 1- poorest, 5 richest = 5 -7.25 (5.43) -0.13 (4.99) Household regularly saves cash (0-1) -3.57 (2.48) -0.47 (1.87) Access to remittances (0-1) 4.90 (2.91) -3.26 (2.95) Livestock assets count (0-7) 0.48 (1.27) -0.94 (1.03) Index of shock preparedness and mitigation (0-4) -0.57 (1.11) -0.96 (0.89) Index of aspirations/confidence to adapt (0-16) -0.35 (0.31) -0.12 (0.31) Index for Bonding Social Capital (0-6) -2.87*** (0.72) 0.76 (1.14) Index for bridging social captial (0-6) 1.95* (0.75) -0.59 (1.02) Index for linking social capital- gov officials and NGO (0-4) -0.64 (0.81) 0.01 (0.69) Index of livelihood diversification (0-17) -1.13 (0.68) -0.17 (0.41) Index for information exposure (0-19) -0.74** (0.22) 0.13 (0.16) Index for access to financial institutions (0-2) -1.30 (1.05) 1.69 (1.01) Index for access to communal natural resources (0-4) 0.65 (0.73) 0.98 (1.13) Index for access to agricultural extension services (0-1) 3.39 (2.91) -4.04 (4.28) Index for access to livestock services (0-1) -6.82* (2.75) -1.56 (2.97) Participation in local decision making (0-1) -0.30 (1.87) -0.46 (1.47) Household experienced climate shocks (0-1) -1.33 (6.91) -6.99*** (1.77) Household experienced biological shocks (0-1) -2.83 (1.71) -0.50 (1.67) Household experienced economic shocks (0-1) -1.16 (2.09) -0.23 (1.53) Household experienced conflict shocks (0-1) -1.47 (2.29) -2.59 (2.10) Constant 79.53*** (9.00) 65.58*** (5.02) Observations 891 1,071 R-squared 0.28 0.19 F-statistic 5.26 10.92 Prob>F 0.0974 4.34e-05 Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 CRS MC povgap_index1 -0.01 -0.01 (0.87) (0.86) 1064 1371 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size hhsize 0.03 0.08 * (0.46) (0.03) 1259 1576 hhmem_edu 0.19 0.18 * (0.00) (0.02) 1092 1299 hh_cashearner 0.04 -0.06 (0.43) (0.14) 1092 1299 Wealth Index Quintile 0.02 0.00 (0.52) (0.90) 1247 1537 poverty -0.16 ** -0.08 (0.00) (0.18) 1259 1576 asset_durable 0.24 *** 0.05 (0.00) (0.31) 1251 1548 human_asst 0.02 0.03 (0.50) (0.54) 1251 1548 partic_loc 0.12 * 0.01 (0.01) (0.72) 1251 1548 water_time -0.04 0.03 (0.31) (0.65) 1259 1576 i_bserv 0.07 0.13 * (0.15) (0.03) 1251 1548 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size water_time is time to obtain any kind of drinking water within 30 minutes Table 10.9c: Correlation results on Basic Sanitation Variables Basic Sanitation CRS MC Table 10.9a: Correlation results on Improved Water Sources Variables Percentage of HHs with access to CRS MC povgap_index1 0.07 0.05 (0.17) (0.16) 1064 1371 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size hhsize 0.01 -0.01 (0.70) (0.72) 1259 1576 hhmem_edu -0.28 *** -0.37 *** (0.00) (0.00) 1092 1299 hh_cashearner -0.09 + -0.03 (0.09) (0.56) 1092 1299 Wealth Index Quintile -0.02 0.07 (0.43) (0.15) 1247 1537 poverty 0.26 *** 0.19 ** (0.00) (0.00) 1259 1576 asset_durable -0.41 *** -0.15 + (0.00) (0.05) 1251 1548 human_asst -0.04 -0.02 (0.39) (0.63) 1251 1548 partic_loc -0.16 *** -0.02 (0.00) (0.62) 1251 1548 water_time 0.10 + 0.00 (0.09) (0.95) 1259 1576 i_bserv -0.07 -0.26 * (0.47) (0.01) 1251 1548 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.9d: Correlation results on Open Defecation Variables Open defecation CRS MC Table 10.9b: Correlation results on time to obtain drinking water Variables Percentage of HHs who can obtain CRS MC Table 10.10: Logistic regression on basic sanitation services VARIABLES OR se OR se % of HHs using an basic sanitation facility . (.) . (.) (sum) hhsize 1.05 (0.05) 1.07+ (0.04) HH with at least 1 adult>15age who earn cash or cash/kind 1.06 (0.51) 0.54* (0.15) HH wealth quintile- 1- poorest, 5 richest = 2 0.58 (0.26) 0.85 (0.38) HH wealth quintile- 1- poorest, 5 richest = 3 0.91 (0.39) 1.98+ (0.75) HH wealth quintile- 1- poorest, 5 richest = 4 0.60 (0.26) 1.46 (0.48) HH wealth quintile- 1- poorest, 5 richest = 5 0.61 (0.33) 1.44 (0.59) Prevalance of poverty- percentage of people living below US$ 1.90/day poverty li 0.49* (0.16) 0.54 (0.27) Durable assets count (0-22) 1.36*** (0.09) 0.99 (0.07) Participation in local decision making (0-1) 1.69 (0.87) 0.76 (0.21) Availability of humanitarian assistance from gov't and/or NGO (0-1) 0.75 (0.37) 1.93 (1.16) % of hhs that can obtain drinking water in less than 30 minutes round trip) 0.92 (0.27) 1.09 (0.51) Index for access to basic services (0-3) 2.28+ (1.10) 1.96* (0.51) Constant 0.01*** (0.01) 0.03*** (0.02) Observations 1,085 1,280 F-statistic 3.79 2.938 Prob>F 0.0022 0.00562 seEform in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 CRS MC Table 10.11: Logistic regression on Open Defacation services VARIABLES OR se OR se % of HHs practicing open defecation . (.) . (.) (sum) hhsize 1.02 (0.03) 0.95 (0.04) HH with at least 1 adult>15age who earn cash or cash/kind 0.90 (0.23) 1.00 (0.21) HH wealth quintile- 1- poorest, 5 richest = 2 0.76 (0.19) 1.03 (0.28) HH wealth quintile- 1- poorest, 5 richest = 3 0.89 (0.16) 0.85 (0.27) HH wealth quintile- 1- poorest, 5 richest = 4 0.98 (0.29) 1.04 (0.32) HH wealth quintile- 1- poorest, 5 richest = 5 2.00* (0.67) 1.11 (0.40) Prevalance of poverty- percentage of people living below US$ 1.90/day poverty li 2.05* (0.61) 2.51** (0.70) Durable assets count (0-22) 0.58*** (0.05) 0.88* (0.06) Availability of humanitarian assistance from gov't and/or NGO (0-1) 0.89 (0.32) 0.57 (0.23) % of hhs that can obtain drinking water in less than 30 minutes round trip) 1.36 (0.29) 0.97 (0.27) Constant 4.09** (1.82) 1.95 (0.90) Observations 1,085 1,280 F-statistic 5.22 1.740 Prob>F 0.0002 0.105 seEform in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 CRS MC hdds 0.36 *** 0.42 *** (0.00) (0.00) 913 1118 fies_score12m 0.00 -0.05 (0.98) (0.18) 1056 1354 genhhtype 0.00 -0.07 * (1.00) (0.01) 1062 1360 hhsize 0.02 0.02 (0.72) (0.68) 1062 1360 la_typeacc 0.00 -0.08 + (0.93) (0.05) 955 1168 la_hecsize 0.06 0.06 (0.26) (0.20) 926 1111 hhmem_edu 0.07 0.15 * (0.25) (0.01) 955 1168 agri_finance 0.02 0.11 + (0.65) (0.07) 644 825 hh_cashearner 0.03 -0.04 (0.42) (0.43) 955 1168 Wealth Index Quintile 0.15 *** 0.08 * (0.00) (0.04) 1052 1341 poverty -0.15 *** -0.12 ** (0.00) (0.01) 1062 1360 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.12a: Correlation results on MDD_W Variables MDD_W CRS MC svcash 0.15 ** 0.12 ** (0.00) (0.01) 1054 1350 acctorem -0.02 0.08 (0.59) (0.13) 1054 1350 asset_prod 0.13 ** 0.10 + (0.00) (0.06) 1054 1350 asset_lvsk 0.17 *** 0.09 + (0.00) (0.06) 1054 1350 asset_durable 0.13 ** 0.14 * (0.00) (0.01) 1054 1350 i_prepmit 0.07 + 0.10 * (0.09) (0.03) 1054 1350 human_asst 0.05 0.11 + (0.36) (0.07) 1054 1350 i_aspiration 0.05 0.17 *** (0.19) (0.00) 1054 1350 i_bonding 0.06 0.06 (0.15) (0.18) 1054 1350 i_bridging 0.05 0.08 + (0.21) (0.06) 1054 1350 i_linking 0.05 0.14 ** (0.34) (0.00) 1054 1350 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.12b: Correlation results on MDD_W Variables MDD_W CRS MC Variables CRS MC i_lvhdiv 0.16 *** 0.18 ** (0.00) (0.00) 1054 1350 i_infoexp 0.20 *** 0.07 + (0.00) (0.10) 1054 1350 i_fsn 0.03 0.08 (0.57) (0.38) 1054 1350 i_accfinc 0.01 0.02 (0.86) (0.65) 1054 1350 accb_market 0.00 0.01 (0.98) (0.89) 1054 1350 accb_natres 0.03 0.05 (0.55) (0.34) 1054 1350 i_bserv -0.04 0.09 (0.38) (0.04) * 1054 1350 i_agext 0.05 0.08 (0.34) (0.19) 1054 1350 i_lvskserv 0.09 + 0.08 (0.10) (0.19) 1054 1350 partic_loc 0.08 + 0.14 *** (0.08) (0.00) 1054 1350 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.12c: Correlation results on MDD_W MDD_W shock_clim -0.02 0.08 ** (0.68) (0.00) 1054 1350 shock_biol 0.05 0.00 (0.24) (0.96) 1054 1350 shock_econ 0.04 -0.05 (0.34) (0.22) 1054 1350 shock_confl 0.11 + 0.02 (0.06) (0.50) 1054 1350 i_shocksev 0.05 0.07 * (0.24) (0.04) 1036 1309 atr -0.03 0.16 ** (0.62) (0.00) 907 1124 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.12D: Correlation results on MDD_W Variables MDD_W CRS MC hhmem_edu 0.11 ** 0.32 ** (0.00) (0.00) 573 663 poverty -0.08 + -0.12 + (0.07) (0.07) 636 741 partic_loc 0.11 ** 0.03 (0.00) (0.39) 632 736 i_bserv 0.06 0.08 (0.17) (0.26) 632 736 i_bridging 0.07 -0.01 (0.23) (0.78) 632 736 i_linking 0.07 0.17 ** (0.14) (0.00) 632 736 i_infoexp 0.13 * 0.10 * (0.02) (0.03) 632 736 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size CRS MC Table 10.13: Correlation results with use of modern contraception Variables Use of modern contraception hdds -0.03 -0.04 (0.46) (0.20) 1012 1155 fies_score12m 0.02 0.01 (0.73) (0.81) 1180 1379 genhhtype 0.05 0.00 (0.27) (0.94) 1186 1386 hhsize 0.04 -0.02 (0.20) (0.35) 1186 1386 la_typeacc -0.01 0.03 (0.69) (0.41) 1071 1209 la_hecsize 0.04 -0.11 *** (0.39) (0.00) 1040 1159 hhmem_edu -0.11 *** -0.04 (0.00) (0.29) 1071 1209 hh_cashearner 0.02 -0.01 (0.54) (0.73) 1071 1209 Wealth Index Quintile 0.02 -0.08 * (0.46) (0.02) 1179 1368 poverty 0.00 0.09 * (0.89) (0.03) 1186 1386 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.14a: Correlation with Stunting Variables Stunting CRS MC svcash -0.03 -0.07 * (0.55) (0.04) 1181 1379 acctorem -0.04 -0.07 * (0.44) (0.04) 1181 1379 asset_prod 0.01 0.02 (0.68) (0.59) 1181 1379 asset_lvsk -0.01 -0.03 (0.65) (0.37) 1181 1379 asset_durable -0.06 -0.08 + (0.14) (0.05) 1181 1379 i_prepmit 0.00 0.01 (0.94) (0.69) 1181 1379 human_asst 0.01 0.00 (0.60) (0.91) 1181 1379 i_aspiration 0.00 -0.06 + (0.97) (0.05) 1181 1379 i_bonding -0.01 0.02 (0.76) (0.54) 1181 1379 i_bridging -0.04 0.03 (0.23) (0.37) 1181 1379 i_linking -0.04 0.00 (0.29) (0.98) 1181 1379 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.14b: Correlation with Stunting Variables Stunting CRS MC Variables CRS MC i_lvhdiv -0.01 0.01 (0.85) (0.64) 1181 1379 i_infoexp 0.02 0.05 (0.61) (0.22) 1181 1379 i_fsn -0.03 0.00 (0.16) (0.95) 1181 1379 i_accfinc -0.08 * 0.03 (0.02) (0.46) 1181 1379 accb_market 0.01 0.03 (0.88) (0.33) 1181 1379 accb_natres -0.05 0.01 (0.20) (0.82) 1181 1379 i_bserv -0.03 0.05 (0.36) (0.17) 1181 1379 i_agext 0.04 -0.06 (0.27) (0.14) 1181 1379 i_lvskserv -0.02 -0.06 (0.56) (0.11) 1181 1379 partic_loc -0.05 -0.08 ** (0.16) (0.00) 1181 1379 water_improved -0.04 0.06 * (0.22) (0.04) 1186 1386 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.14c: Correlation with Stunting Stunting shock_clim 0.03 -0.01 (0.26) (0.70) 1181 1379 shock_biol 0.01 0.05 (0.72) (0.18) 1181 1379 shock_econ -0.06 0.02 (0.16) (0.66) 1181 1379 shock_confl -0.01 -0.05 (0.73) (0.16) 1181 1379 i_shocksev 0.01 0.04 (0.69) (0.25) 1164 1339 atr -0.05 -0.04 (0.20) (0.24) 1030 1141 chn_diarrhea 0.08 * 0.09 * (0.01) (0.02) 1186 1386 chn_mad 0.04 -0.09 + (0.67) (0.09) 335 380 chn_nutrich 0.04 0.01 (0.56) (0.87) 335 380 chn_exbrfeed 0.05 0.00 (0.52) (1.00) 122 155 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size CRS MC Table 10.14D: Correlation with Stunting Variables Stunting hdds -0.05 -0.05 (0.29) (0.22) 1015 1157 fies_score12m -0.01 0.03 (0.86) (0.19) 1182 1382 genhhtype 0.01 0.00 (0.89) (0.96) 1188 1389 hhsize 0.02 0.01 (0.45) (0.70) 1188 1389 la_typeacc 0.00 -0.03 (0.93) (0.45) 1072 1213 la_hecsize 0.03 0.00 (0.40) (0.98) 1041 1163 hhmem_edu -0.06 * 0.01 (0.02) (0.73) 1072 1213 hh_cashearner 0.02 0.00 (0.52) (0.96) 1072 1213 Wealth Index Quintile -0.07 * -0.04 (0.01) (0.16) 1181 1371 poverty 0.07 ** 0.03 (0.00) (0.40) 1188 1389 chn_exbrfeed -0.19 -0.12 (0.19) (0.24) 123 156 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.15a: Correlation with Wasting Variables Wasting CRS MC svcash -0.08 * -0.04 (0.04) (0.33) 1183 1382 acctorem -0.05 0.02 (0.14) (0.57) 1183 1382 asset_prod 0.03 0.04 (0.33) (0.14) 1183 1382 asset_lvsk -0.07 * -0.03 (0.01) (0.28) 1183 1382 asset_durable -0.09 ** -0.06 * (0.00) (0.03) 1183 1382 i_prepmit -0.10 *** 0.05 (0.00) (0.23) 1183 1382 human_asst -0.03 0.09 * (0.26) (0.03) 1183 1382 i_aspiration -0.02 -0.02 (0.46) (0.52) 1183 1382 i_bonding -0.03 0.02 (0.36) (0.50) 1183 1382 i_bridging -0.02 0.01 (0.25) (0.59) 1183 1382 i_linking -0.02 -0.03 (0.48) (0.34) 1183 1382 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.15b: Correlation with Wasting Variables Wasting CRS MC Variables CRS MC i_lvhdiv -0.08 * 0.04 (0.02) (0.27) 1183 1382 i_infoexp -0.06 + 0.02 (0.09) (0.53) 1183 1382 i_fsn 0.04 -0.01 (0.35) (0.53) 1183 1382 i_accfinc -0.07 * 0.00 (0.03) (0.94) 1183 1382 accb_market 0.03 0.04 (0.29) (0.33) 1183 1382 accb_natres 0.02 -0.05 (0.70) (0.30) 1183 1382 i_bserv 0.02 0.04 (0.47) (0.16) 1183 1382 i_agext -0.03 0.01 (0.35) (0.71) 1183 1382 i_lvskserv -0.07 0.03 (0.04) (0.45) 1183 1382 partic_loc -0.03 -0.02 (0.46) (0.65) 1183 1382 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.15c: Correlation with Wasting Wasting shock_clim -0.02 0.02 (0.60) (0.31) 1183 1382 shock_biol -0.01 0.01 (0.82) (0.79) 1183 1382 shock_econ -0.07 -0.05 (0.11) (0.10) 1183 1382 shock_confl -0.05 + -0.03 (0.09) (0.39) 1183 1382 i_shocksev 0.03 0.03 (0.42) (0.20) 1166 1343 atr 0.01 -0.03 (0.83) (0.45) 1030 1142 correct_watertreat 0.01 -0.06 + (0.85) (0.02) 1188 1389 water_improved 0.01 -0.02 (0.77) (0.48) 1188 1389 chn_diarrhea 0.00 0.12 *** (0.95) (0.00) 1188 1389 chn_mad -0.09 + 0.11 (0.07) (0.13) 336 382 chn_nutrich 0.09 0.09 (0.40) (0.13) 336 382 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.15D: Correlation with Wasting Variables Wasting CRS MC fies_score12m 0.09 * 0.06 * (0.01) (0.03) 1257 1506 genhhtype 0.03 0.03 (0.55) (0.51) 1264 1515 hhsize -0.02 0.06 (0.58) (0.10) 1264 1515 la_typeacc 0.00 0.11 * (0.98) (0.01) 1142 1326 la_hecsize 0.04 0.03 (0.39) (0.37) 1109 1271 hhmem_edu 0.11 * 0.04 (0.02) (0.38) 1142 1326 hh_cashearner 0.08 * 0.05 (0.03) (0.18) 1142 1326 Wealth Index Quintile 0.06 -0.06 (0.11) (0.10) 1255 1492 poverty -0.12 ** -0.01 (0.00) (0.76) 1264 1515 chn_exbrfeed -0.28 * -0.27 * (0.02) (0.02) 135 166 i_bserv -0.09 * 0.10 * (0.02) (0.01) 1257 1504 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.16a: Correlation with Diarrhea Variables Diarrhea CRS MC shock_clim 0.02 0.01 (0.51) (0.67) 1257 1504 shock_biol 0.03 0.04 (0.41) (0.22) 1257 1504 shock_econ 0.08 + 0.02 (0.08) (0.63) 1257 1504 shock_confl 0.10 * 0.02 (0.01) (0.48) 1257 1504 i_shocksev 0.03 0.05 (0.40) (0.20) 1240 1463 human_asst 0.06 0.05 (0.22) (0.23) 1257 1504 correct_watertreat -0.01 0.02 (0.85) (0.58) 1264 1515 water_improved -0.04 0.00 (0.20) (0.91) 1264 1515 time_water 0.04 0.06 (0.27) (0.16) 1264 1515 open_defecation -0.04 -0.05 (0.32) (0.21) 1264 1515 proper_handwashing -0.02 0.01 (0.80) (0.75) 1264 1515 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size MC Table 10.16b: Correlation with Diarrhea Variables Diarrhea CRS agri_finance -0.16 *** -0.18 ** (0.00) (0.00) 1651 2013 agri_valuechain -0.08 + -0.12 * (0.10) (0.01) 1651 2013 agri_storage -0.08 0.08 (0.15) (0.10) 1651 2013 la_own 0.01 -0.07 * (0.91) (0.03) 1670 2035 la_rent 0.03 0.03 (0.30) (0.21) 1670 2035 la_sharecr -0.08 0.08 ** (0.31) (0.00) 1670 2035 la_none 0.06 * -0.02 (0.03) (0.59) 1670 2035 la_hecpt5 0.09 * 0.02 (0.04) (0.41) 1670 2035 la_hecpt5to1 -0.01 0.00 (0.89) (0.90) 1670 2035 la_hecpt1more -0.09 -0.02 (0.26) (0.53) 1670 2035 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.17a: Correlation with Poverty Variables Poverty CRS MC agri_finance -0.24 *** -0.15 *** (0.00) (0.00) 1383 1782 agri_valuechain -0.10 + -0.05 (0.08) (0.23) 1383 1782 agri_storage -0.12 ** 0.01 (0.00) (0.77) 1383 1782 la_own -0.01 -0.15 *** (0.81) (0.00) 1399 1802 la_rent 0.04 0.11 ** (0.29) (0.00) 1399 1802 la_sharecr -0.08 0.07 + (0.11) (0.08) 1399 1802 la_none 0.08 + 0.07 + (0.01) (0.05) 1399 1802 la_hecpt5 0.06 0.10 * (0.26) (0.01) 1399 1802 la_hecpt5to1 0.03 0.01 (0.48) (0.74) 1399 1802 la_hecpt1more -0.09 + -0.10 * (0.07) (0.03) 1399 1802 Pearson's correlation coefficient, P value in parenthesis †p<0.10, *p<0.05, **p<0.01, *** p<0.001. ^ small sample size Table 10.17b: Correlation with Depth of Poverty Variables Depth of Poverty CRS MC Annex 10: Uganda Detailed Resilience Analyses 1 UGANDA RESILIENCE ANALYSIS: BASELINE STUDY OF THE FOOD FOR PEACE DEVELOPMENT FOOD ASSISTANCE PROJECTS IN UGANDA TANGO International, 2018 1. INTRODUCTION 1.1 OBJECTIVES The objective of this research is to provide implementing partners, Catholic Relief Services (CRS), Mercy Corps (MC), and the United States Agency for International Development (USAID) Office of Food for Peace (FFP) with insights into factors that strengthen household and community resilience in Uganda. This research complements the baseline study prepared by ICF International. It examines factors that can serve as the foundation for an evidence base for improving resilience programming across the two DFSA areas of the implementing partners. The research aims to address the following questions: 1. Which resilience capacities are associated with positive well-being outcomes, including expenditures, poverty, dietary diversity, and ability to recover from shock? 2. Which resilience capacity components are significant drivers of positive well-being outcomes? 3. Which coping strategies are more likely to be adopted based on level of resilience capacity? 1.2 ORGANIZATION OF THE REPORT The report is organized to provide both context and understanding of the projects in relation to how the resilience capacities and well-being indicators are measured and analyzed. To begin, Section 2 describes the methodology used to conduct this research. Sections 3 and 4 describe the types of shocks households experienced in the past 12 months and the extent to which households utilized coping strategies to recover from shock. Section 5 provides baseline estimates for select well-being outcome indicators used in this study. These include: per capita daily expenditures, prevalence of poverty, Household Dietary Diversity Scores (HDDS), and ability to recover from shock. Section 6 presents the findings for the absorptive, adaptive, and transformative resilience capacity index scores, and their respective individual components. Section 7 demonstrates the predicted effects of each resilience capacity and their respective components on well-being outcomes. Section 8 examines the association of shock coping strategies and well-being outcomes. Finally, Section 9 provides an overview of the report findings. 2. METHODOLOGY This section briefly outlines the methodology utilized in the descriptive and multivariate analyses that explore the objectives of this deep dive. 2 2.1 DESCRIPTIVE ANALYSIS The descriptive analysis is conducted on the following indicators: household exposure to shocks and their perceived level of severity, coping strategies, resilience capacities, and well-being outcomes. Household exposure to shocks is the total number of shocks (out of a possible 18) that a household experienced in the 12 months prior to the survey. The cumulative impact of those shocks on a household’s food and income indicates the perceived level of severity experienced and can range from 1 to 144. The three resilience capacities (absorptive, adaptive and transformative) are indexes constructed from a number of indicators (see Figure 1) at the household and community levels. The indexes range in value from 0-100. The indicators used to measure well-being at the household level include per capita expenditure, poverty, dietary diversity and recovery. Per capita expenditure is a proxy for income and is measured using USD, prevalence of poverty is based on USD $1.90 daily per capita income threshold, the Household Dietary Diversity Score (HDDS) is a count of 12 food groups consumed by a household in the previous 24 hours, and ability to recover is an index that measures a household’s ability to recover from the most salient shocks exposed to in the previous 12 months. Figure 1: Resilience capacity components 3 2.2 MULTIVARIATE ANALYSIS The methodology used for deriving the resilience capacity indexes is principle components factor analysis, which normalizes the index so that the factor scores, which are the basis for computing the indexes, have a mean of zero and standard deviation of one. Thus, it is not meaningful to compare the values across indexes to conclude, for example, that the transformative capacity is ‘higher’ than absorptive capacity because the index value is higher. Rather, the value of the index score is a measure of the skewness of the distribution of the resilience capacity index. A value close to 50 means that the distribution of the index is quite symmetric, with about half the values above the mean and half the values below the mean. A high value (greater than 50) means that the distribution of the index is skewed to the right – that there is a higher proportion of the sample with index values above the mean value than below the mean. Similarly, an overall value less than 50 means that there are a greater proportion of sample values are below the mean. The multiple regression analyses explore the relationships between resilience capacity and the four well￾being outcomes, per capita expenditure, poverty, HDDS, and ability to recover. Different estimators, appropriately chosen, are utilized across the analyses depending on the particular specification and distribution of the dependent variable or well-being outcome. For the purposes of the regression analyses, recovery across the five most salient shocks was dichotomized so that a value of 1 indicates the household reports having recovered fully or better and a 0 indicates the household has not recovered or has only partially recovered. To begin, the resilience capacity indexes are analyzed separately across the four well-being outcomes. The model is designed to capture the ability of the resilience capacity index (absorptive, adaptive or transformative) to explain the variation in the well-being outcomes of interest. Other determinants, used as controls, include cumulative impact of shock exposure, structural household characteristics, and project area (CRS, MC). 𝑶𝒖𝒕𝒄𝒐𝒎𝒆𝒔 = 𝑓 [ 𝑹𝒆𝒔𝒊𝒍𝒊𝒆𝒏𝒄𝒆 𝒄𝒂𝒑𝒂𝒄𝒊𝒕𝒚 𝒊𝒏𝒅𝒆𝒙𝒆𝒔 (𝐴𝑏𝑠𝑜𝑟𝑝𝑡𝑖𝑣𝑒, 𝐴𝑑𝑎𝑝𝑡𝑖𝑣𝑒, 𝑇𝑟𝑎𝑛𝑠𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑣𝑒) 𝑪𝒖𝒎𝒖𝒍𝒂𝒕𝒊𝒗𝒆 𝒊𝒎𝒑𝒂𝒄𝒕 𝒐𝒇 𝒔𝒉𝒐𝒄𝒌 𝒆𝒙𝒑𝒐𝒔𝒖𝒓𝒆 𝑯𝒐𝒖𝒔𝒆𝒉𝒐𝒍𝒅 𝒄𝒉𝒂𝒓𝒂𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔 (𝑔𝑒𝑛𝑑𝑒𝑟𝑒𝑑 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑡𝑦𝑝𝑒, 𝑠𝑖𝑧𝑒 𝑜𝑓 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑎𝑛𝑑 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑑𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑠) 𝑷𝒓𝒐𝒋𝒆𝒄𝒕 𝒂𝒓𝒆𝒂 ] 4 In the second model, the resilience capacities indicators (see Figure 1) are analyzed across the well-being outcomes. 𝑶𝒖𝒕𝒄𝒐𝒎𝒆𝒔 = 𝑓 [ 𝑹𝒆𝒔𝒊𝒍𝒊𝒆𝒏𝒄𝒆 𝒄𝒂𝒑𝒂𝒄𝒊𝒕𝒚 𝒊𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓𝒔 (𝑖. 𝑒. , 𝑏𝑜𝑛𝑑𝑖𝑛𝑔 𝑠𝑜𝑐𝑖𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙, 𝑎𝑐𝑐𝑒𝑠𝑠 𝑡𝑜 𝑟𝑒𝑚𝑖𝑡𝑡𝑎𝑛𝑐𝑒𝑠, 𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 𝑙𝑜𝑐𝑎𝑙 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑚𝑎𝑘𝑖𝑛𝑔, 𝑒𝑡𝑐. ) 𝑪𝒖𝒎𝒖𝒍𝒂𝒕𝒊𝒗𝒆 𝒊𝒎𝒑𝒂𝒄𝒕 𝒐𝒇𝒔𝒉𝒐𝒄𝒌 𝒆𝒙𝒑𝒐𝒔𝒖𝒓𝒆 𝑯𝒐𝒖𝒔𝒆𝒉𝒐𝒍𝒅 𝒄𝒉𝒂𝒓𝒂𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔 (𝑔𝑒𝑛𝑑𝑒𝑟𝑒𝑑 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑡𝑦𝑝𝑒, 𝑠𝑖𝑧𝑒 𝑜𝑓 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑎𝑛𝑑 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑑𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑠) 𝑷𝒓𝒐𝒋𝒆𝒄𝒕 𝒂𝒓𝒆𝒂 ] To answer the third and final objective of this analysis, multivariate regression analyses are performed with key coping strategies as the dependent variable. Coping strategies are selected if they are adopted by at least five percent of the sample (Table 3). The specification above helps us determine which shock coping strategies are utilized by wealthier and poorer households when using per capita expenditures as a proxy for wealth. 𝑨𝒅𝒐𝒑𝒕𝒊𝒐𝒏 𝒐𝒇 𝑪𝒐𝒑𝒊𝒏𝒈 𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚 = 𝑓 [ 𝑷𝒆𝒓 𝒄𝒂𝒑𝒊𝒕𝒂 𝒆𝒙𝒑𝒆𝒏𝒅𝒊𝒕𝒖𝒓𝒆 𝑪𝒖𝒎𝒖𝒍𝒂𝒕𝒊𝒗𝒆 𝒊𝒎𝒑𝒂𝒄𝒕 𝒐𝒇𝒔𝒉𝒐𝒄𝒌 𝒆𝒙𝒑𝒐𝒔𝒖𝒓𝒆 𝑯𝒐𝒖𝒔𝒆𝒉𝒐𝒍𝒅 𝒄𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔 (𝑔𝑒𝑛𝑑𝑒𝑟𝑒𝑑 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑡𝑦𝑝𝑒, 𝑠𝑖𝑧𝑒 𝑜𝑓 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑎𝑛𝑑 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑑𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑠) 𝑷𝒓𝒐𝒋𝒆𝒄𝒕 𝒂𝒓𝒆𝒂 ] The formula below illustrates the model used to analyze the propensity to adopt, or not, coping strategies as a function of resilience. 𝑨𝒅𝒐𝒑𝒕𝒊𝒐𝒏 𝒐𝒇 𝑪𝒐𝒑𝒊𝒏𝒈 𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚 = 𝑓 [ 𝑹𝒆𝒔𝒊𝒍𝒊𝒆𝒏𝒄𝒆 𝒄𝒂𝒑𝒂𝒄𝒊𝒕𝒚 (𝑎𝑏𝑠𝑜𝑟𝑝𝑡𝑖𝑣𝑒, 𝑎𝑑𝑎𝑝𝑡𝑖𝑣𝑒, 𝑡𝑟𝑎𝑛𝑠𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑣𝑒) 𝑪𝒖𝒎𝒖𝒍𝒂𝒕𝒊𝒗𝒆 𝒊𝒎𝒑𝒂𝒄𝒕 𝒐𝒇 𝒔𝒉𝒐𝒄𝒌 𝒆𝒙𝒑𝒐𝒔𝒖𝒓𝒆 𝑯𝒐𝒖𝒔𝒆𝒉𝒐𝒍𝒅 𝒄𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔 (𝑔𝑒𝑛𝑑𝑒𝑟𝑒𝑑 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑡𝑦𝑝𝑒, 𝑠𝑖𝑧𝑒 𝑜𝑓 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑎𝑛𝑑 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑑𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑠) 𝑷𝒓𝒐𝒋𝒆𝒄𝒕 𝒂𝒓𝒆𝒂 ] Finally, the last model as illustrated below explores the relationship between the select coping strategies and the resilience capacity indicators. 5 𝑨𝒅𝒐𝒑𝒕𝒊𝒐𝒏 𝒐𝒇 𝑪𝒐𝒑𝒊𝒏𝒈 𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚 = 𝑓 [ 𝑹𝒆𝒔𝒊𝒍𝒊𝒆𝒏𝒄𝒆 𝒄𝒂𝒑𝒂𝒄𝒊𝒕𝒚 𝒊𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓𝒔 (𝑖. 𝑒. , 𝑏𝑜𝑛𝑑𝑖𝑛𝑔 𝑠𝑜𝑐𝑖𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙, 𝑎𝑐𝑐𝑒𝑠𝑠 𝑡𝑜 𝑟𝑒𝑚𝑖𝑡𝑡𝑎𝑛𝑐𝑒𝑠, 𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 𝑙𝑜𝑐𝑎𝑙 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑚𝑎𝑘𝑖𝑛𝑔, 𝑒𝑡𝑐. ) 𝑪𝒖𝒎𝒖𝒍𝒂𝒕𝒊𝒗𝒆 𝒊𝒎𝒑𝒂𝒄𝒕 𝒐𝒇𝒔𝒉𝒐𝒄𝒌 𝒆𝒙𝒑𝒐𝒔𝒖𝒓𝒆 𝑯𝒐𝒖𝒔𝒆𝒉𝒐𝒍𝒅 𝒄𝒉𝒂𝒓𝒂𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔 (𝑔𝑒𝑛𝑑𝑒𝑟𝑒𝑑 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑡𝑦𝑝𝑒, 𝑠𝑖𝑧𝑒 𝑜𝑓 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑎𝑛𝑑 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑑𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐𝑠) 𝑷𝒓𝒐𝒋𝒆𝒄𝒕 𝒂𝒓𝒆𝒂 ] 2.3 PREDICTED VALUE OF OUTCOME In Sections 7 and 8 of this report, the relationships between resilience capacities, coping strategies, and well-being outcomes are presented using percent change as the predicted values or probabilities of the outcome. The predicted values of the outcome are computed by comparing the “low” and “high” values in the sample. In general, the “low” and “high” values are set at the 25th and 75th percentiles for continuous variables, and 0 and 1 for binary variable while holding all values of other explanatory variables constant at their means. The results are presented as a percent change from “high” to “low”, and provides a depiction of the strength, or magnitude, of the relationship between the well-being outcome and explanatory variables of interest. Further, the percent change allows for comparability across variables that use different scales. 3. HOUSEHOLD EXPOSURE TO SHOCK This section describes the types of shocks experienced by surveyed households in the 12 months prior to the baseline survey. As shown in Table 1, households across all the two DFSA areas experienced an average of 5.0 shocks over the past 12 months. The perceived severity of these shocks is measured by their impact on food consumption and income security at the household level (index ranges from 1-144). The mean impact across all DFSA areas is 27.4. Table 1. Shock exposure and severity, by DFSA area SHOCK EXPOSURE / SEVERITY Overall CRS MC Shock Exposure Index (mean, 0-18) 5.0 5.5 4.4 N 2799 1251 1548 Cumulative impact of shock exposure index a/ (mean, 1-144) 27.4 31.9 23.3 n 2726 1226 1500 a/ Only includes households experiencing a shock in previous 12 months Table 2 provides a list of shocks households experienced in the 12 months preceding the baseline survey. The most salient shock was excessive rains (81.8 percent); this is especially true for the CRS project area, where 86.6 percent of households reported this particular shock. The second most experienced shock 6 was flooding (59.5 percent), followed by drought (54.4 percent), increasing food prices (49.1 percent), and crop disease (45.5 percent). Table 2: Shocks experienced in past 12 months, by DFSA area Type of shock Overall CRS MC Climate shocks (%) Excessive rains 81.8 86.6 77.5 Flooding 59.5 72.1 48.2 Drought 54.4 61.3 48.2 Variable rain (early/late) 27.6 32.0 23.6 Hail/frost 10.4 20.0 1.9 Landslides/Erosion 8.0 14.0 2.7 Biologic shocks (%) Crop disease (e.g., rust on wheat, sorghum) 45.5 41.4 49.3 Crop pests (e.g., locusts) 37.0 45.9 29.0 Weeds (e.g., associated with Strega) 37.0 40.7 33.8 Livestock disease 23.2 21.4 24.8 Human disease outbreaks (from contaminated water) 19.3 15.7 22.5 Conflict shocks (%) Theft or destruction of assets 8.1 7.9 8.2 Theft of livestock (raids) 7.0 4.9 8.9 Land conflict 9.2 10.3 8.1 Water conflict 2.4 2.2 2.6 Gender-based violence 5.8 7.5 4.2 Economic shocks (%) Delay in food assistance 10.0 12.8 7.5 Increasing food prices 49.1 57.8 41.4 n 2799 1251 1548 7 4. COPING STRATEGIES This section provides data on coping strategies utilized by households across all shocks, and across the five most salient shocks as reported in Table 2. These include excessive rain, flooding, drought, increase food prices, and crop disease. The findings presented are a summary of the overall results; data across shocks and DFSA areas that can be found in the Supplementary Annex: Table 11-Table 13. Table 3 shows that fifty percent of households reduced food consumption when faced with any shock. This is mostly driven by households experiencing increases in food prices, especially in the CRS project area where 65.6 percent adopted this strategy. Households are also likely to take up new/additional work and to sell livestock when faced with any shock at 33.6 and 16.8 percent, respectively. When comparing across DFSA areas, CRS households were more likely to take up new/additional work whereas MC households are more likely to sell livestock as a coping mechanism. The strategy of taking up new/additional work is most often utilized when households are faced with landslides or erosion (49.1 percent), increases in food prices (28.3 percent), variable rain (25.0 percent), and drought (24.3 percent). Households were also more likely to sell livestock when faced with livestock disease (37.1 percent) and theft of livestock (32.3 percent) Table 3: Coping strategies adopted to recover from ANY shock, by DFSA area COPING STRATEGIES a/ Overall CRS MC Livestock and land holdings (%) Send livestock in search of pasture 4.8 2.1 7.4 Sell livestock 16.8 9.9 23.3 Slaughter livestock 8.9 6.2 11.3 Lease out land 10.2 10.5 9.9 Migration (%) HH member migrated for work 8.0 7.8 8.3 Migrate (the whole family) 3.7 3.5 3.9 Send children or an adult to stay with relatives 8.4 7.4 9.4 Coping strategies to reduce current expenditures (%) Take children out of school 3.9 4.6 3.2 Move to less expensive housing 2.5 2.8 2.2 Reduce food consumption (quantity/meal; # of meals/day) 50.0 62.5 38.4 Reduce non-essential HH expenses 16.2 17.9 14.6 Got food on credit from a local merchant 6.1 7.8 4.6 Coping strategies to get more food or money (%) Take up new/additional work (causal labor, wage labor) 33.6 40.4 27.2 Sell household items (e.g., radio, bed) 1.1 0.4 1.7 Sell productive assets (e.g., plough, water pump) 1.9 1.6 2.1 Take out a loan (with interest) from a (formal) bank 0.7 0.3 1.0 Take out a loan (with interest) from an MFI or village savings group 5.1 4.9 5.2 Take out a loan (with interest) from a money‐lender 1.7 2.0 1.4 Take out a loan (no interest) from friends or relatives within the community (bonding) 3.6 3.5 3.7 8 Take out a loan (no interest) from friends or relatives outside of the community (bridging) 1.5 1.7 1.4 Gift of money (not remittances) or food from family, friends, church or other group within community (bonding) 3.6 3.4 3.7 Gift of money (not remittances) or food from family, friends, church or other group outside of community (bridging) 2.9 2.5 3.4 Send children to work for money (e.g., domestic service) 2.6 1.5 3.7 Receive emergency food aid from the government or NGO 2.1 1.8 2.4 Receive emergency cash transfer from the government or NGO 3.1 3.7 2.6 Participate in government or NGO food‐for‐work or cash‐for‐work activities 2.8 1.1 4.4 Use money from savings 5.1 5.1 5.2 Remittances from a relative that migrated 3.0 3.0 3.0 Other 26.8 21.7 31.6 Did nothing 11.0 9.4 12.5 n 2677 1213 1464 a/ Only for those households who experienced and were impacted by a shock in the last 12 months. Table 4 provides a summary of coping strategies by the most salient shocks. Strategies with adoption at greater than five percent across all shocks are highlighted below. Overall, reduction in food consumption is the most adopted strategy across the five most salient shocks, followed by taking up new/additional work and selling livestock. However, households facing increases in food prices are more likely to reduce non-essential household expenses in comparison to those faced with excessive rain, flooding, drought, and crop disease. Table 4: Adoption of coping strategies, by most salient shocks COPING STRATEGIES a/ ALL shock Excessive Rain Flooding Drought Increase Food Prices Crop Disease Reduce food consumption (quantity/meal; # of meals/day) 50.0 23.5 23.5 30.9 54.7 24.1 Take up new/additional work (causal labor, wage labor) 33.6 16.4 16.4 24.3 28.3 20.7 Sell livestock 16.8 2.8 1.2 3.4 5.4 2.2 Reduce non-essential HH expenses 16.2 6.8 6.8 6.9 18.6 3.5 Lease out land 10.2 3.1 3.1 3.0 1.3 3.8 Slaughter livestock 8.9 0.6 0.5 0.9 0.6 0.8 Send children or an adult to stay with relatives 8.4 2.3 2.3 3.3 3.2 1.8 HH member migrated for work 8.0 3.6 3.6 5.0 1.1 1.5 Got food on credit from a local merchant 6.1 1.7 1.7 2.5 4.5 1.5 Use money from savings 5.1 1.7 1.7 0.9 2.1 1.8 Take out a loan (with interest) from an MFI or village savings group 5.1 1.7 1.7 1.1 3.0 2.6 Other (specify) 26.8 5.8 5.8 3.5 3.9 6.5 Did nothing 11.0 5.2 5.2 3.1 2.6 6.4 9 n 2677 2251 1591 1459 1314 1229 a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 5. HOUSEHOLD WELL-BEING OUTCOMES As shown in Table 5, the overall mean per capita expenditure, as a proxy for income, is $1.05 USD1 . Households in the MC project area have higher expenditures compared to those in the CRS region. Prevalence of poverty measures the percentage of those households living on less than $1.90 USD per day. A majority of households in this sample are considered “poor” across all DFSA areas (88.7 percent). As expected, based on the results from per capita expenditures, MC has the highest prevalence of poverty while CRS has the lowest. The food security measure used in this study is HDDS. On a scale from 0 to 12, the overall mean dietary diversity is 3.3. MC households have the highest HDDS at 3.6. Recovery is the final well-being outcome used in this analysis. When comparing across the five most salient shocks (excessive rain, flooding, drought, increase food price, and crop disease), households reported recovering to the same level or better on a range from 3.5 percent for crop disease to 10.8 percent for drought. CRS households had better recovery than MC households for drought and increase food prices. Table 5: Well-being outcome indicators, by DFSA area Well-being indicator Overall CRS MC Per capita expenditures (USD) a/ 1.05 1.00 1.10 n 2799 1251 1548 Prevalence of poverty (%) a/ 88.7 87.9 89.4 n 2799 1251 1548 HDDS (mean, 0-12) 3.3 3.0 3.6 n 2287 1047 1240 Recovery from salient shocks b/ Excessive rain (%) 5.7 3.4 8.4 n 1860 943 917 Flooding (%) 7.4 4.7 11.3 n 1437 801 636 Drought (%) 10.8 12.3 8.7 n 1177 622 555 Increase food price (%) 8.5 10.1 6.2 n 1193 632 561 Crop disease (%) 3.5 2.2 4.6 n 1078 443 635 a/ Values vary slightly from the baseline report because the sample is restricted to the household level. b/ Only for those households who experienced and were impacted by a shock in the last 12 months. 1 This value varies slightly from the baseline report because the sample is restricted to the household level. 10 6. HOUSEHOLD RESILIENCE CAPACITIES This section presents the descriptive results for the three resilience capacity indexes (absorptive, adaptive and transformative), disaggregated by project area (CRS and MC), and includes the indicators that constitute each index. Note that some indicators are components of more than one index (e.g., asset scores are a component of both the absorptive and adaptive capacity indexes). All resilience capacity indicators included in this section are presented in their original scales to facilitate understanding of the disparate factors – and their differing measurement – contributing to resilience capacities. 6.1 ABSORPTIVE CAPACITY The absorptive capacity index (ranging from 0-100) is constructed using 10 indicators and measures the ability of households to minimize their exposure to shocks through preventive measures and appropriate coping strategies to avoid permanent, negative impacts of shocks. As shown in Table 6, the mean absorptive capacity across the two DFSA areas is 23.2. Of the indicators, a small percentage of households have access to agricultural insurance, remittances, and/or humanitarian assistance. Households also report low bonding social capital, defined as the bonds between community or group members. They are also limited in their ability to prepare and mitigate the effects of shocks, indicate a relatively low availability of informal safety nets, and possess relatively few assets. In contrast, one-quarter of households have access to cash savings. Across DFSA areas, CRS has the greatest availability of humanitarian assistance, agricultural insurance, and informal safety nets, as well as the greatest bonding social capital and assets (durable and productive). This likely accounts for CRS having the highest level of absorptive capacity across the DFSA areas (CRS value of 25.0 and MC value of 23.2). 6.2 ADAPTIVE CAPACITY Adaptive capacity measures the ability of households to make proactive and informed choices about alternative livelihood strategies based on an understanding of changing conditions. It is comprised of 11 indicators. With an index ranging from 0-100, the mean adaptive capacity across the two DFSA areas is 37.0 with CRS having the greatest value at 38.1. Livelihood diversification indicates that households were engaged in an average of 3.1 activities over the last year. Of the other indicators, households have low scores for bridging and linking social capital, which measure the bonds between members of other communities or groups and the bonds between households and government officials and/or NGO leaders, respectively. In contrast, households tend to score higher on the aspirations index and access to financial institutions for credit and/or savings. Finally, nearly all households adopted one or more improved agricultural practices for crop and livestock production, natural resource management, and/or crop storage. 6.3 TRANSFORMATIVE CAPACITY The transformative capacity index is computed on the basis of 13 indicators that measure governance mechanisms, policies, infrastructure, community networks and formal and informal social protections that enable systemic change. The mean value for the transformative capacity index across the DFSA areas is 37.2 (on a scale of 0-100). Included within the transformative capacity index is a gender norms index that measures gender dimensions of socially acceptable practices within the household and community. Data from Table 6 show that the mean gender norm is 2.6 out of a possible score of 4 across the DFSA areas. 11 This index takes into account gender-neutral practices (e.g., men helping with childcare and/or carrying water) at the community level. Of the other indicators within transformative capacity, communities have little access to formal safety nets and infrastructure; however, nearly all communities report high local government responsiveness, half of communities have access to agricultural extension services, and one￾quarter have access to livestock services. Interestingly, households report low levels of engagement in collective activities that benefit the village, but more than half report participation in local decision￾making. Table 6: Resilience capacity indexes and their indicators, by DFSA area Resilience capacities and indicators Overall CRS MC Absorptive capacity index (mean, 0-100) 23.2 25.0 23.2 Availability of informal safety nets (mean, 0-6) 2.0 2.1 1.9 Bonding social capital (mean, 0-6) 2.3 2.4 2.3 Access to cash savings (%) 24.2 23.6 23.5 Access to remittances (%) 9.2 9.2 9.2 Asset ownership Productive asset index (mean, 0-24) 4.9 5.0 4.9 Livestock asset index (mean, 0-7) 1.4 0.8 1.7 Durable goods asset index (mean, 0-22) 2.4 2.5 2.2 Shock prep and mitigation (mean, 0-4) 0.6 0.7 0.5 Access to agricultural insurance (%) 2.0 3.7 1.0 Availability of humanitarian assistance (%) 8.6 11.5 7.1 Adaptive capacity index (mean, 0-100) a/ 37.0 38.1 36.1 Aspirations/confidence to adapt (mean, 0-16) 10.7 10.8 10.6 Bridging social capital (mean, 0-6) 2.2 2.4 2.2 Linking social capital (mean, 0-4) 0.9 1.0 0.9 Education/training (mean, 0-3) 0.7 0.8 0.6 Livelihood diversification (mean, 0-17) 3.1 3.1 3.0 Exposure to information (mean, 0-19) 4.7 5.4 4.4 Adoption of improved practices (%) 81.1 84.8 79.6 Access to financial institutions (mean, 0-2) 1.2 1.2 1.3 Transformative capacity index (mean, 0-100) b/ 37.2 40.9 33.9 Availability of formal safety nets (mean, 0-3) 0.1 0.1 0.0 Availability of markets (mean, 0-3) 1.7 1.8 1.6 Access to communal natural resources (mean, 0-4) 1.9 1.7 2.0 Access to basic services (mean, 0-3) 1.8 1.7 1.9 Access to infrastructure (mean, 0-4) 1.1 1.2 0.8 Access to ag extension services (%) 25.7 31.3 16.4 Access to livestock services (%) 44.3 53.4 31.8 Collective action (mean, 0-10) 0.3 0.3 0.2 Local government responsiveness (%) 87.2 82.8 91.1 Participation in local decision making (%) 54.4 50.9 53.6 Gender Indicators Gender norms index (mean, 0-4) 2.6 2.8 2.5 Gender equitable decision-making (mean, 0-3) c/ 0.4 0.4 0.3 n 2779 1251 1548 a/ The asset ownership index is not shown under adaptive capacity as it is previously listed under absorptive capacity. b/ Bridging social capital is not shown under transformative capacity as it is previously listed under adaptive capacity. c/ Gender indicators are not used when computing the overall index due to missing values. 12 7. RESILIENCE CAPACITIES AND WELL-BEING OUTCOMES This section presents results that compare the three resilience capacity index scores against key well￾being outcome measures of per capita daily expenditures, poverty, food security, and ability to recover from shock, including the direction and magnitude of any statistically significant relationships. These relationships are assessed using regression analysis, with the well-being outcome variables as dependent variables and the resilience capacities, cumulative impact of shock exposure, and other household characteristics as explanatory variables. The findings inform our understanding of the kinds of outcomes we can expect given investments in a particular resilience capacity, and give some idea of the magnitude of this influence. It is important to emphasize that all of the following regression results are based on statistical methods exploring the relationships between resilience capacity and well-being outcomes while controlling for the cumulative impact of shock exposure. Relationships found between the resilience capacities and well-being outcomes suggest an association between the two, and does not give insight into the causality in one direction or another. 7.1 RESILIENCE CAPACITIES AND WELL-BEING OUTCOMES Table 7 presents key information for purposes of discussion, and full analysis results are available in Supplementary Annex A: Table 14-Table 21. 7.1.1 Per capita expenditure Data in Table 7 show a significant and positive relationship between per capita daily expenditure and the three resilience capacities (absorptive, adaptive and transformative). Increases in expenditures across the capacities from the lowest to the highest quartiles averages around 34 percent, indicating that households who are more resilient are that much more likely to have more money to spend compared to those who are less resilient. 7.1.2 Poverty As expected, the results for poverty mirror the results for per capita expenditures. Results in Table 7 show that poverty and the three resilience capacities are negatively associated at the 0.01 level. This indicates that households who are more resilient are less likely to live in poverty. Across the capacities, the reduction in poverty ranges from 10 to 12 percent. 7.1.3 Dietary diversity In Table 7, dietary diversity and the three resilience capacities are positively associated and significant at the 0.01 level. These results imply that households with more resilience are 16.6 to 25.4 percent more likely to consume more food groups compared to those with lower levels of resilience. 7.1.4 Recovery The recovery variable is dichotomized such that a value of ‘0’ indicates a household has not recovered or has only recovered partially and a value of ‘1’ if it has recovered fully for each of the five most salient shocks.2 Results in Table 7 show that household absorptive capacity is a significant predictor of their ability to recover from excessive rain, flooding, and increased food prices. Similarly, greater adaptive 2 Refer to Table 2. 13 capacity is associated with recovery from all five salient shocks. Transformative capacity, conversely, is negatively associated with recovery from drought. Table 7: Relationship between resilience capacity and well-being outcomes Absorptive Adaptive Transformative Outcome Coef. % change Coef. % change Coef. % change Expenditure 0.022*** 34.7 0.024** 35.6 0.011*** 33.3 Poverty -0.028*** -10.1 -0.035*** -12.3 - 0.015*** -11.2 HDDS 0.012*** 20.6 0.016*** 25.4 0.005*** 16.6 Recovery a/ Excessive rain 0.012** 36.5 0.015*** 41.9 0.003 22.9 Flooding 0.010** 31.6 0.009* 27.2 0.004 23.3 Drought -0.002 -5.5 0.013*** 33.1 -0.009* -67.4 Increase food prices 0.011** 32.4 0.022*** 51.3 -0.003 -23.4 Crop disease 0.008 29.4 0.016*** 46.7 -0.001 -4.9 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (absorptive, adaptive, transformative). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 7.2 DECOMPOSING ABSORPTIVE, ADAPTIVE AND TRANSFORMATIVE CAPACITIES This section of the report explores the relationship between the well-being outcomes and the indicators that comprise the three resilience capacity indexes. Table 8 shows the results from that analysis, including the percent change of values from “low” to “high” for each indicator. For continuous variables, with the exception of access to basic services and collective action, the values of the indicators are set at the 25th (“low”) and 75th (“high”) percentiles of the sample. For access to basic services and collective action, the “low” and “high” range values are set at the first and 99th percentiles of the sample, respectively, which allows for differences in the percent change from “high” to “low” to be observed. The percent change for binary variables is defined as the difference between the absence of (a value of ‘0’) and presence of that indicator (a value of ‘1’) (e.g., not having verses having access to remittances). This permits comparability across variables that use different scales.3 7.2.1 Per capita expenditures As shown in Table 8, a number of household-level indicators are significantly associated with increases in per capita expenditures. The strongest indicators, those that are associated with the largest percent change in expenditures, are durable assets, access to cash savings, and exposure to information. Other significant contributors are livestock assets and education/training. At the community level, access to agricultural extension services accounts for a 22.5 percent increase in daily expenditures. Interestingly, improved practices, access to financial institutions, and productive assets are inversely related to expenditures. 3 Although availability of formal safety nets ranges from 0-3, respondents identified no more than one formal safety nets within their communities and thus, it is treated within this analysis as a binary variable. 14 7.2.2 Poverty Findings for the analysis of predicting poverty by resilience indicators follow a similar pattern seen for the expenditure analysis, except in the opposite direction, as is expected. The following household-level indicators are most strongly associated with reduction in poverty: access to cash savings, durable and livestock assets, shock preparation and mitigation, and education/training. Access to infrastructure is the only community-level indicator shown to significantly reduce the likelihood of poverty. It has the greatest impact at 6.7 percent. However, similar to the results for expenditures, productive assets have a significant, but negative, relationship with poverty. 7.2.3 Dietary diversity Improvement in food security is significantly predicted by several resilience capacity components presented in Table 8. At the household level, access to cash savings, livestock and durable assets, aspirations/confident to adapt, and education/training are significantly associated with greater access to a wider variety of foods. Community-level results show that access to infrastructure and participation in local decision making also result in households consuming more food types. Of these capacity indicators, access to infrastructure has the greatest positive impact on food security at 12.0 percent change. However, access to basic services is negatively related to HDDS with a percent change of -33.6. Thus, households with less access to basic services such as primary schools, and health and financial services have greater dietary diversity. Also contrary to expectation, cumulative impact of shock exposure is positively related to food security, implying that households with greater cumulative exposure to shocks are more likely to consume more food types. This may be a result of households having greater reliance on more varied food sources due to lack of availability and/or resources in times of shocks. 7.2.4 Recovery The data in Table 8 show the increasing likelihood of a household to recover from the five most salient shocks (excessive rain, flooding, drought, increase food prices, and crop disease). Access to agricultural insurance and exposure to information are positively associated with household’s ability to recover across four of the five shocks. Local government responsiveness is also significant across most of the shocks, however the relationship is positive for excessive rain and flooding but negative for increase food prices and crop disease. The negative relationship may be a result of program targeting households who are considered less able to recover. Overall, the ability of a household to recover is largely dependent on a variety of resilience indicators and thus, trends could not easily be established across the shocks. 15 Table 8: Relationship between resilience capacity indicators and well-being outcomes CAPACITY COMPONENTS Expenditure Poverty HDDS Recovery (Excessive rain) b/ Recovery (Flooding) b/ Recovery (Drought) b/ Recovery (Increase food prices) b/ Recovery (Crop disease) b/ Percent change (%) Absorptive capacity indicators Availability of informal safety nets (0-6) -1.1 0.5 -0.012 -14.8 -16.8 -10.8 6.5 -36.0 Bonding social capital (0-6) 6.2 -1.0 -0.013 -4.0 12.8 -43.8** 24.1** -20.4 Access to cash savings (0-1) 19.6* -4.1** 0.110*** 26.1 59.0*** -21.0 5.8 16.1 Access to remittances (0-1) 10.1 0.0 -0.017 8.8 -19.0 -266.4*** -2.4 -24.0 Productive assets index (0-24) -10.7** 2.1*** 0.003 -6.3 -6.4 -2.4 -7.4 -95.6*** Durable assets index (0-22) 41.4*** -5.4*** 0.053*** 12.8 -17.4 11.8 12.5*** 18.9* Livestock assets index (0-7) 14.5** -3.6*** 0.038*** 15.3 3.7 2.3 5.3 38.3** Shock prep and mitigation (0-4) 5.0 -2.6*** 0.019 -1.7 17.3 6.4 -31.1** -7.6 Access to agricultural insurance (0-1) 24.2 -2.6 0.076 59.2* 61.0* 62.6** 69.0*** 56.9 Availability of humanitarian assistance (0-1) -16.8 2.8 -0.070 38.4 -82.8 -87.2 -81.1 -47.1 Adaptive capacity indicators Aspirations/confidence to adapt (0-16) -4.8 -1.0 0.028*** 9.8 15.7 -1.5 3.3 -2.3 Bridging social capital (0-6) 0.0 0.0 0.009 -2.9 -14.0 6.4 -34.1*** -2.1 Linking social capital (0-4) -14.9 1.0 0.013 4.7 20.5 17.8 12.1 3.1 Education/training (0-3) 13.7** -3.8*** 0.076*** 18.1 28.0* 13.8 -5.0 -8.7 Livelihood diversification (0-17) 2.0 -0.9 0.013 -18.3 -27.8** 10.5 10.3 -14.0 Improved practices (0-1) -48.9* 0.6 -0.056 -3.2 22.2 40.3* -21.7 87.3** Exposure to information (0-19) 17.0** -2.1 0.004 49.8*** 22.6 51.3*** 56.2*** 72.2*** Access to financial institutions (0-2) -27.7** 2.6 0.014 -29.6 -257.3*** 32.5 34.5 7.2 Transformative capacity indicators Availability of formal safety nets (0-3) -6.5 -4.3 -0.044 -286.1 -49.4 -39.0 -430.2* 48.5 Availability of markets (0-3) 3.8 -0.3 0.026 53.0*** 26.2 -22.2 -20.6 -13.8 Access to communal natural resources (0-4) -14.7 0.0 -0.004 11.5 20.8 -57.9* -9.6 -20.2 Access to basic services (0-3) a/ 26.1 2.3 -0.097** -2.6 -86.5 -14.1 38.1 74.6 Access to infrastructure (0-4) 13.9 -6.7*** 0.064** -21.7 52.9*** -94.7 -15.5 -119.9* Access to ag extension services (0-1) 22.5** -3.4 0.009 -6.4 11.6 34.6 -50.8 8.5 Access to livestock services (0-1) -12.1 1.3 -0.029 -10.1 -5.3 -146.2** -43.0 12.1 16 Collective action (0-10) a/ -25.0 2.9 0.004 43.6 -27.0 -81.6 50.1* -73.2 Local government responsiveness (0-1) -21.9 3.2 0.021 52.0* 56.9*** -20.5 -128.1** -211.9*** Participation in local decision making (0-1) -10.4 -1.8 0.109*** -69.8 -19.1 23.4 -17.3 -4.0 Observations 2799 2797 2287 1859 1436 1177 1191 1078 Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables. For binary variables, the change is defined as the difference between 0 and 1. Formal safety nets is also used as a binary predictor because responses only ranged from 0-1. a/ This represents the percent change from the 1st to the 99th percentile of the sample. b/ Only for those households who experienced and were impacted by a shock in the last 12 months. 17 8. SHOCK COPING STRATEGIES, EXPENDITURES, AND RESILIENCE CAPACITIES This section explores the relationship among coping strategies, expenditures, and resilience capacities. Coping strategies were selected for this analysis if they were adopted by at least five percent of the sampled households.4 These include: reduce food consumption, take up new/additional work, sell livestock, reduce non-essential household expenses, lease out land, slaughter livestock, send children or adult to stay with relatives, household member migrated for work, got food on credit from a local merchant, use money from savings, and take out a loan from MFI or village savings groups. The findings presented in this section are a summary of the results that can be found in the Supplementary Annex: Table 23-Table 34. 8.1 EXPENDITURES AND ADOPTION OF COPING STRATEGIES Table 9 shows which coping mechanisms are more likely to be adopted by wealthier and poorer households. Using per capita expenditures as a proxy for income, results show that compared to poorer households, wealthier households are more likely to use money from savings, take out a loan with interest from MFI or village savings groups, get food on credit from a local merchant, reduce non-essential household expenses, and sell livestock. This makes intuitive sense since wealthier households have significantly more assets and better access to savings.5 Wealthier households may also have greater accessibility to food and money through credit and/or loans because they are assumed to have the ability to reimburse those costs. Findings in Table 9 show no significant relationship between poorer households and utilization of coping strategies. 8.2 RESILIENCE AND UTILIZATION OF COPING STRATEGIES In this section, coping is analyzed as a function of resilience in order to measure the likelihood households will engage in select coping strategies depending on their levels of absorptive, adaptive or transformative capacity. Households are more likely to get food on credit, use money from savings, and take out a loan from MFI or village savings groups across all three resilience capacities. These show the greatest positive percent changes across the selected coping strategies. It is interesting to note that wealthier households are also more likely to engage in these coping mechanisms. Other strategies in which absorptive and adaptive are positively associated include slaughtering livestock, reducing non-essential expenses, and selling livestock. However, households with greater resilience capacity are also more likely to reduce household consumption which is assumed to be an intuitively negative strategy because it has a direct negative impact on the household’s well-being. Table 9 shows that transformative capacity is negatively associated with selling livestock, leasing out land, and migrating for work with percent changes ranging from -35.7 to -74.4 percent. These large negative percent changes may be accounted for by the fact that transformative 4 Refer to Table 3 above. 5 Refer to Table 8 above. 18 capacity is a community-level index derived from indicators measuring availability and accessibility to community resources and services. Thus, household members are less likely to engage in these strategies if there are more abundant resources and means to support themselves within their communities. Table 9: Use of coping strategy predicted by expenditures and resilience capacity Coping strategies Expenditure Absorptive Adaptive Transformative % change % change % change % change Reduce food consumption 0.9 13.1*** 16.4*** -3.5 Take up new/additional work -1.3 -5.0 2.4 -11.1 Sell livestock 1.5* 21.7** 32.8*** -49.5*** Reduce non-essential HH expenses 2.2* 37.6*** 49.0*** 0.9 Lease out land -1.4 11.4 18.6 -35.7** Slaughter livestock 1.3 31.6*** 52.7*** -8.6 Send child or adult to stay with relatives 0.1 16.2 -23.9 7.7 HH member migrate for work -2.2 7.0 -35.5 -74.4*** Gotten food on credit from local merchant 4.2*** 53.1*** 57.0*** 40.0*** Use money from savings 7.7*** 70.1*** 73.3*** 56.3*** Take out a loan with interest from MFI or village savings group 4.5*** 68.5*** 66.2*** 28.0* Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables. For binary variables, the change is defined as the difference between 0 and 1. *The model includes shock exposure, household characteristics, and projects as independent variables. 19 8.3 RESILIENCE CAPACITY INDICATORS AND UTILIZATION OF COPING STRATEGIES This section explores the relationship between resilience capacity indicators and key coping strategies identified in the previous sections. Table 10 provides the level of significance and percent change of values from “low” to “high” for each indicator. The complete table can be found in the Supplementary Annex: Table 35. The results from Table 10 explore in detail which resilience capacity indicators either promote or discourage households from using certain strategies. Overall, the results exploring the relationship between resilience capacity indicators and coping vary across the strategies, and thus, no specific trends could be identified. However, when comparing the resilience capacity indicators of the coping mechanisms that were positively associated with the three resilience capacities in Table 9 (getting food on credit from a local merchant, using money from savings, and taking out a loan from MFI or village saving groups), durable assets is the only commonly associated indicator for household adoption of these strategies. In addition to durable assets, households were also more likely to get food on credit with greater shock preparation and mitigation, aspirations/confidence to adapt, livelihood diversity, and education/training. Households who use savings or take out a loan, on the other hand, had greater access to cash savings, financial institutions, and collective action. In the previous section 8.2, selling livestock, leasing out land, and migrating for work are negatively associated with transformative capacity. In Table 10, the resilience capacity indicators that drive these results at the community level vary across these strategies. For example, households are less likely to sell livestock if they have greater access to basic services and infrastructure with percent changes at -127.6 and -36.5 percent, respectively. Leasing out land, in comparison, is negatively associated with availability of markets and agriculture extension services. Household members are also less likely to migrate for work with greater access to infrastructure at -91.9 percent change. 20 Table 10: Relationship between resilience capacity indicators and key coping strategies, percent change (%) CAPACITY INDICATORS Reduce food New work Sell livestock Reduce expenses Lease land Slaughter livestock Child stay with relatives Migrate for work Food credit Use savings Loan from MFI/VSG Percent change (%) Absorptive capacity indicators Availability of informal safety nets (0-6) -5.1 -8.5 -2.2 -26.1** 0.9 -4.6 5.7 13.4 -3.2 -0.1 1.5 Bonding social capital (0-6) 2.3 8.1* 2.6 12.9** 9.7 -4.3 0.3 13.7 -2.4 8.7 5.7 Access to cash savings (0-1) 9.4* 1.8 -4.2 28.4** -29.6 6.9 24.2 -9.6 4.2 63.8*** 78.6*** Access to remittances (0-1) -11.5 -33.1* 5.0 -18.2 -46.1 25.1 16.4 33.9 11.0 -8.1 17.4 Durable assets index (0-22) 4.2 -9.1* 6.6 26.8*** 12.0 -3.5 -10.9 -39.6*** 22.1* 26.8** 19.1* Livestock assets index (0-7) -9.3*** -26.3*** 55.5*** -13.9* 2.1 48.5*** -15.9* 15.0 -20.1 11.3 -39.2** Productive assets (0-24) 1.7 -0.8 -17.2** 3.6 2.5 -19.1* 6.8 11.2 -38.9** -20.5* 18.4* Shock prep and mitigation (0-4) 15.5*** 11.3** -2.8 25.0*** 8.1 -14.3* 9.7 14.5 31.7*** 16.6 0.8 Access to agricultural insurance (0-1) -8.7 -39.4 -10.3 -7.5 34.6 -4.9 0.5 36.6 20.7 57.3** 36.9 Availability of humanitarian assistance (0-1) -51.8*** -14.0 -3.8 -1.9 -10.1 -5.7 14.9 -17.1 2.9 -1.0 -2.3 Adaptive capacity indicators Aspirations/Confidence to adapt (0-16) -3.3 9.6 0.6 -2.7 -2.0 27.2*** -13.3 -5.2 21.0** 1.2 15.1 Bridging social capital (0-6) 2.5 -17.6*** -14.3*** 14.7** -29.1*** -7.6 -17.4* -21.3* -2.9 -34.3** -9.7 Linking social capital (0-4) 9.7** -19.3* 23.0*** 22.6** 12.6 23.4** -22.4 -41.5* -37.2** 16.5 -26.2 Education/training (0-3) -1.5 -8.3* -6.6 -4.8 10.2 -5.6 -1.1 19.5* 26.8** -0.4 15.4 Livelihood diversification (0-17) 4.0 26.5*** 12.4* -7.5 20.3** 20.8*** 11.8 -0.3 32.9*** 5.1 19.2* Improved practices (0-1) -8.5 17.7** -23.2 23.3 -88.2*** 21.3 -143.0*** -72.1** 18.0 3.6 -22.7 Exposure to information (0-19) 15.7*** 10.3 21.2** 14.8 -15.7 9.0 -22.9 -27.2 -6.1 26.0 -19.6 Access to financial institutions (0-2) 4.3 17.8* -34.4** 43.3*** 14.6 19.5 -44.1** -92.5*** 22.7 44.2* 49.8* Transformative capacity indicators Availability of formal safety nets (0-3) -3.7 26.6** 24.0 -70.8 -83.5 -17.8 31.5 58.7*** 3.4 -223.6** -826.2** Availability of markets (0-3) 1.1 24.3** -0.3 -53.0** -100.7*** -20.6 -12.4 -20.0 -20.1 -9.3 -43.4 Access to communal natural resources (0-4) -8.7* -3.5 -16.3 -6.3 7.0 11.4 9.7 -38.0 -0.3 3.7 -57.5** Access to basic services (0-3) a/ -95.1*** -92.5** -127.6** 4.6 -14.6 1.9 37.3 71.5* -3.1 3.2 51.9 Access to infrastructure (0-4) 7.5 -43.7** -36.5** 25.2 34.1 -15.5 16.7 -91.9** 24.8 43.0** -37.9 Access to ag extension services (0-1) 17.2*** 14.3 -0.2 2.7 -108.6*** -67.5*** -21.1 37.6 11.8 -84.0** -26.9 Access to livestock services (0-1) -15.5** 12.5 15.3 -58.7* -9.2 31.3** 7.7 -47.3 -10.4 21.5 -9.6 21 Collective action (0-10) a/ -7.2 11.5 13.7 -30.6 36.4** 50.5*** 63.5*** 33.6 28.8 46.2** 51.3*** Local government responsiveness (0-1) 15.3 -22.7 17.4 52.6** 35.0 -11.4 49.5** 2.8 11.3 18.2 -5.8 Participation in local decision making (0-1) -5.7 -7.2 6.4 -8.3 -1.1 -5.4 -26.5 -1.7 14.1 -6.9 36.6 Observations 2675 2677 2675 2675 2675 2675 2675 2675 2675 2675 2675 Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. Component indicators that are not significant across the seven identified coping strategies are not included (productive assets, linking social capital, aspirations/confidence to adapt); refer to the Annex for the complete table. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables. For binary variables, the change is defined as the difference between 0 and 1. Formal safety nets is also used as a binary predictor because responses only ranged from 0-1. a/ This represents the percent change from the 1st to the 99th percentile of the sample. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. 22 9. CONCLUSIONS Using data from the 2018 baseline (BL) population-based survey (PBS) of two Food for Peace (FFP) Development Food Security Activities (DFSAs) in Uganda, this study identifies various factors that strengthen household and community resilience in Uganda. Following are summary conclusions that address the three research questions posed for this study. Research question 1: Which resilience capacities are associated with positive well-being outcomes, including expenditures, poverty, dietary diversity, and recovery from shock? Based on the analysis for this report, findings strongly suggest that high levels of resilience are linked with improved well-being. For per capita expenditures, poverty, and dietary diversity, the three resilience capacities are significantly associated in the anticipated direction, even when controlling for different degree of shock exposure. Data for recovery indicate that adaptive capacity improves household ability to recover from all five salient shocks (excessive rain, flooding, drought, increased food prices, and crop disease). Comparatively, absorptive capacity is only positively associated with recovery from excessive rain, flooding and drought whereas transformative capacity is negatively associated with recovery from drought. Research question 2: Which resilience capacity indicators are significant drivers of positive well-being outcomes? For expenditures, poverty, and dietary diversity, there is evidence of opportunities available for improved well-being outcomes directly through four significant resilience indicators, including increases in access to cash savings, durable assets, livestock assets, and human capital (education/training). Additional indicators that directly increase expenditures include exposure to information and access to agricultural extension services. A reduction in poverty is also predicted by shock preparedness and mitigation, and access to infrastructure. Improvements in dietary diversity are driven, in addition to the four indicators mentioned above, by increases in aspirations/confidence to adapt, and participation in local decision making. It is interesting to note certain indicators are inversely related to the well-being outcomes. These include productive assets (for expenditures and poverty), improved practices (for expenditures), and access to basic services (dietary diversity). Resilience capacity indicator results for recovery vary across the shocks; however, access to information is significantly associated with recovery from excessive rain, drought, increase food prices, and crop disease. Additionally, when comparing the community-level capacity indicators for the negative association between transformative capacity and drought recovery, it is largely impacted by the availability to community natural resources and livestock services. This may be attributed to programming efforts within these DFSA areas and need further exploration. Research question 3: Does resilience capacity determine which coping strategies households are more likely to adopt? Coping strategies adopted by wealthier households include: selling livestock, reducing non￾essential expenses, getting food on credit from a local merchant, using money from savings, and 23 taking out a loan from MFI or village savings groups. The results, however, show no significant negative relationship between expenditures and coping, and thus, the adoption of coping strategies by poorer households could not be established. Similar to the per capita expenditure results above, households with higher levels of resilience capacity are most likely to get food on credit, use money from savings, and take out a loan from MFI or village savings groups across all three resilience capacities. Other strategies in which absorptive and adaptive capacities are positively associated include slaughtering livestock, reducing non-essential expenses, and selling livestock. However, households with greater resilience capacity are also more likely to reduce household consumption which is assumed to be an intuitively negative strategy because it has a direct negative impact on the household’s well-being. Transformative capacity, on the other hand, is negatively associated with selling livestock, leasing out land, and migrating for work and may be accounted for by the fact that transformative capacity is a community-level index derived from indicators measuring availability and accessibility to community resources and services. Thus, household members are less likely to engage in these strategies if there are more abundant resources and means to support themselves within their communities. The results exploring the relationship between resilience capacity indicators and selected coping strategies varied. Among those indicators that showed greater adoption by wealthier households and were positively associated with all three resilience capacities (getting food on credit, using money from savings, and taking out a loan from MFI or village savings groups), durable assets was the only significant indicator positively associated between these strategies. In addition to durable assets, households were also more likely to get food on credit with greater shock preparation and mitigation, aspirations/confidence to adapt, livelihood diversity, and education/training. Households who use savings or take out a loan, on the other hand, had greater access to cash savings, financial institutions, and collective action. For selling livestock, leasing out land, and migrating for work (shown to have a significant negative association with transformative capacity), the resilience capacity indicator analysis also varied at the community-level across these strategies. For example, households are less likely to sell livestock if they have greater access to basic services and infrastructure whereas leasing out land is negatively associated with availability of markets and ag extension services. Household members are also less likely to migrate for work with greater access to infrastructure. Programming implications These findings suggest some implications with respect to programming to enhance resilience. First, traditional economic development activities that are directed to increase household income and wealth—increasing human capital, promotion of value chains, and investment in infrastructure—are also means to enhance household and community resilience capacities. The importance of savings on household economic status and dietary diversity suggests that supporting savings and loans groups and other mechanisms to promote savings can have important impacts on resilience. Strategies that promote bonding and linking social capital formation, for example, through savings and loans groups and other community-based or collective organizations, can also be beneficial. Reduction in poverty can also be supported by investments in shock preparedness and mitigation efforts. 24 Secondly, strengthening community access to markets, agricultural extension services, and livestock services would work to promote livestock production and ownership at the household level, which has been shown to enhance both household economic status and dietary diversity. The results also point to the importance of intervention strategies that promote access to information, especially for recovery efforts. However, interventions that focus on recovery must be tailored specifically to the type of shock as resilience indicators vary across recovery well-being outcomes. This is also true for coping strategies where investments may vary depending the type of coping adoption promoted within program areas. 25 10. SUPPLEMENTARY ANNEX Table 11: Coping strategies adopted to recover from most salient shocks, Overall COPING STRATEGIES a/ Excessive Rain Flooding Drought Increase Food Prices Crop Disease Livestock and land holdings (%) Send livestock in search of pasture 0.9 0.4 1.9 0.4 0.9 Sell livestock 2.8 1.2 3.4 5.4 2.2 Slaughter livestock 0.6 0.5 0.9 0.6 0.8 Lease out land 3.1 3.1 3.0 1.3 3.8 Migration (%) HH member migrated for work 3.6 3.6 5.0 1.1 1.5 Migrate (the whole family) 0.9 0.9 1.1 0.3 1.1 Send children or an adult to stay with relatives 2.3 2.3 3.3 3.2 1.8 Coping strategies to reduce current expenditures (%) Take children out of school 0.6 0.6 1.3 1.8 0.8 Move to less expensive housing 0.4 0.4 0.7 1.0 1.0 Reduce food consumption (quantity/meal; # of meals/day) 23.5 23.5 30.9 54.7 24.1 Reduce non-essential HH expenses 6.8 6.8 6.9 18.6 3.5 Got food on credit from a local merchant 1.7 1.7 2.5 4.5 1.5 Coping strategies to get more food or money (%) Take up new/additional work (causal labor, wage labor) 16.4 16.4 24.3 28.3 20.7 Sell household items (e.g., radio, bed) 0.3 0.3 0.1 0.1 0.5 Sell productive assets (e.g., plough, water pump) 0.2 0.2 0.4 0.2 0.7 Take out a loan (with interest) from a (formal) bank 0.1 0.1 0.2 0.0 0.3 Take out a loan (with interest) from an MFI or village savings group 1.7 1.7 1.1 3.0 2.6 Take out a loan (with interest) from a money‐lender 0.3 0.3 0.1 1.4 0.2 Take out a loan (no interest) from friends or relatives within the community (bonding) 0.6 0.6 0.7 2.2 0.7 Take out a loan (no interest) from friends or relatives outside of the community (bridging) 0.1 0.1 0.3 0.6 0.5 Gift of money (not remittances) or food from family, friends, church or other group within community (bonding) 0.8 0.8 0.8 1.0 1.6 Gift of money (not remittances) or food from family, friends, church or other group outside of community (bridging) 0.8 0.8 0.8 1.0 1.0 Send children to work for money (e.g., domestic service) 0.7 0.7 0.9 0.9 1.1 Receive emergency food aid from the government or NGO 0.3 0.3 1.2 0.4 0.9 Receive emergency cash transfer from the government or NGO 2.0 2.0 1.6 1.0 1.1 Participate in government or NGO food‐for‐work or cash‐ for‐work activities 0.6 0.6 1.5 0.7 2.3 Use money from savings 1.7 1.7 0.9 2.1 1.8 Remittances from a relative that migrated 0.7 0.7 0.9 0.4 1.1 26 Other 5.8 5.8 3.5 3.9 6.5 Did nothing 5.2 5.2 3.1 2.6 6.4 n 2231 1589 1459 1310 1225 a/ Only for those households who experienced and were impacted by a shock in the last 12 months. Table 12: Coping strategies adopted to recover from most salient shocks, CRS COPING STRATEGIES a/ Excessive Rain Flooding Drought Increase Food Prices Crop Disease Livestock and land holdings (%) Send livestock in search of pasture 0.3 0.0 0.7 0.2 0.2 Sell livestock 1.5 1.2 0.8 1.8 1.7 Slaughter livestock 0.5 0.4 0.2 0.3 0.2 Lease out land 2.1 5.1 1.8 0.5 1.9 Migration (%) HH member migrated for work 1.5 3.2 4.2 1.8 1.3 Migrate (the whole family) 1.5 1.9 0.7 0.4 0.2 Send children or an adult to stay with relatives 2.1 1.8 3.1 3.2 0.8 Coping strategies to reduce current expenditures (%) Take children out of school 1.0 1.2 1.2 2.5 0.3 Move to less expensive housing 0.5 1.2 0.7 1.4 0.5 Reduce food consumption (quantity/meal; # of meals/day) 29.3 27.9 35.0 65.6 34.8 Reduce non-essential HH expenses 6.8 5.1 5.9 19.6 4.3 Got food on credit from a local merchant 2.3 2.7 2.9 6.0 1.6 Coping strategies to get more food or money (%) Take up new/additional work (causal labor, wage labor) 22.5 22.8 26.8 31.8 24.2 Sell household items (e.g., radio, bed) 0.2 0.0 0.0 0.1 0.0 Sell productive assets (e.g., plough, water pump) 0.2 0.2 0.1 0.3 0.4 Take out a loan (with interest) from a (formal) bank 0.1 0.0 0.1 0.0 0.0 Take out a loan (with interest) from an MFI or village savings group 1.5 0.8 0.4 2.9 1.6 Take out a loan (with interest) from a money‐lender 0.2 0.1 0.0 1.6 0.2 Take out a loan (no interest) from friends or relatives within the community (bonding) 0.6 0.8 0.5 2.9 0.6 Take out a loan (no interest) from friends or relatives outside of the community (bridging) 0.1 0.0 0.4 1.0 0.2 Gift of money (not remittances) or food from family, friends, church or other group within community (bonding) 0.7 0.7 0.4 1.5 0.7 Gift of money (not remittances) or food from family, friends, church or other group outside of community (bridging) 0.5 0.2 0.4 1.1 0.3 Send children to work for money (e.g., domestic service) 0.4 0.3 0.0 0.7 0.3 Receive emergency food aid from the government or NGO 0.2 0.2 0.9 0.7 0.4 Receive emergency cash transfer from the government or NGO 2.8 2.7 2.3 1.6 1.0 27 Participate in government or NGO food‐for‐work or cash‐ for‐work activities 0.3 0.2 0.2 0.0 0.7 Use money from savings 1.6 0.7 0.8 1.8 2.2 Remittances from a relative that migrated 0.9 0.4 0.2 0.4 0.5 Other 4.8 8.1 3.0 2.1 4.8 Did nothing 4.4 7.4 3.0 1.2 4.8 n 1044 842 703 672 477 a/ Only for those households who experienced and were impacted by a shock in the last 12 months. Table 13: Coping strategies adopted to recover from most salient shocks, MC COPING STRATEGIES a/ Excessive Rain Flooding Drought Increase Food Prices Crop Disease Livestock and land holdings (%) Send livestock in search of pasture 1.5 0.8 3.4 0.5 1.4 Sell livestock 4.2 1.3 6.4 10.1 2.6 Slaughter livestock 0.8 0.7 1.6 1.1 1.2 Lease out land 4.1 1.8 4.4 2.4 5.3 Migration (%) HH member migrated for work 5.7 8.5 6.0 0.3 1.7 Migrate (the whole family) 0.4 0.6 1.5 0.1 1.8 Send children or an adult to stay with relatives 2.6 4.5 3.5 3.1 2.5 Coping strategies to reduce current expenditures (%) Take children out of school 0.2 1.5 1.3 0.9 1.2 Move to less expensive housing 0.4 0.4 0.6 0.5 1.4 Reduce food consumption (quantity/meal; # of meals/day) 17.7 16.4 26.1 41.0 16.0 Reduce non-essential HH expenses 6.8 6.6 8.0 17.2 2.9 Got food on credit from a local merchant 1.2 0.6 2.1 2.7 1.4 Coping strategies to get more food or money (%) Take up new/additional work (causal labor, wage labor) 10.2 9.0 21.4 24.0 18.0 Sell household items (e.g., radio, bed) 0.4 0.0 0.3 0.0 0.9 Sell productive assets (e.g., plough, water pump) 0.3 0.1 0.7 0.0 0.9 Take out a loan (with interest) from a (formal) bank 0.2 0.0 0.3 0.0 0.5 Take out a loan (with interest) from an MFI or village savings group 1.8 1.0 1.9 3.1 3.4 Take out a loan (with interest) from a money‐lender 0.5 0.7 0.1 1.1 0.3 Take out a loan (no interest) from friends or relatives within the community (bonding) 0.7 0.6 1.0 1.3 0.7 Take out a loan (no interest) from friends or relatives outside of the community (bridging) 0.1 0.2 0.3 0.0 0.6 Gift of money (not remittances) or food from family, friends, church or other group within community (bonding) 0.9 0.2 1.2 0.4 2.3 Gift of money (not remittances) or food from family, friends, church or other group outside of community (bridging) 1.1 0.8 1.3 0.9 1.5 Send children to work for money (e.g., domestic service) 1.0 0.7 1.8 1.2 1.6 Receive emergency food aid from the government or NGO 0.4 0.1 1.4 0.0 1.3 28 Receive emergency cash transfer from the government or NGO 1.2 0.5 0.7 0.3 1.2 Participate in government or NGO food‐for‐work or cash‐ for‐work activities 1.0 0.3 2.9 1.6 3.5 Use money from savings 1.8 1.1 0.9 2.5 1.6 Remittances from a relative that migrated 0.5 0.6 1.6 0.4 1.6 Other 6.9 14.1 4.0 6.1 7.9 Did nothing 6.0 13.6 3.2 4.3 7.7 n 1187 747 753 638 748 a/ Only for those households who experienced and were impacted by a shock in the last 12 months. Table 14: Relationship between resilience capacity indexes and per capita expenditure (in constant USD 2010) Dependent Variable: Per Capita Expenditure OLS estimator Resilience Capacity Indexes (Absorptive) (Adaptive) (Transformative) Absorptive capacity 0.022*** Adaptive capacity 0.024** Transformative capacity 0.011*** Cumulative shock exposure index (1-144) -0.006 -0.006* 0.002 Household demographics (/Percent 30+) Percent 0-15 -0.911** -0.951* -0.910** Percent 16-30 0.538 0.425 0.454 Gender HH type (/Adult Male and Female) Adult Female Only 0.179 0.219 0.071 Adult Male Only 0.634* 0.509 0.442 Child Only -0.824*** -0.334 -0.955*** Project Area (/CRS) MC 0.059 0.067 0.165 Constant 0.997*** 0.691*** 0.883*** Observations 2799 2799 2799 R2 0.019 0.019 0.017 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. 29 Table 15: Relationship between resilience capacity indexes and prevalence of poverty (based on USD $1.90 daily per capita income threshold) Dependent Variable: Poverty Probit estimator Resilience Capacity Indexes (Absorptive) (Adaptive) (Transformative) Absorptive capacity -0.028*** Adaptive capacity -0.035*** Transformative capacity -0.015*** Cumulative shock exposure index (1-144) 0.002 0.003 -0.009*** Household size 0.028 0.034* 0.035* Household demographics (/Percent 30+) Percent 0-15 0.707*** 0.700*** 0.629** Percent 16-30 -0.510** -0.388 -0.413* Gender HH type (/Adult Male and Female) Adult Female Only -0.101 -0.168 0.079 Adult Male Only -0.647** -0.478* -0.317 Child Only 0.000 0.000 0.000 Project Area (/CRS) MC 0.012 0.037 -0.063 Constant 1.516*** 2.065*** 1.674*** Observations 2797 2797 2797 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. 30 Table 16: Relationship between resilience capacity indexes and HDDS Dependent Variable: HDDS Poisson estimator Resilience Capacity Indexes (Absorptive) (Adaptive) (Transformative) Absorptive capacity 0.012*** Adaptive capacity 0.016*** Transformative capacity 0.005*** Cumulative shock exposure index (1-144) 0.002** 0.001 0.007*** Household size -0.004 -0.005 -0.002 Household demographics (/Percent 30+) Percent 0-15 0.162 0.151 0.184* Percent 16-30 0.374*** 0.302*** 0.370*** Gender HH type (/Adult Male and Female) Adult Female Only -0.055 -0.009 -0.104** Adult Male Only 0.121 0.037 0.005 Child Only -0.019 0.311 -0.049 Project Area (/CRS) MC 0.209*** 0.198*** 0.237*** Constant 0.577*** 0.335*** 0.562*** Observations 2287 2287 2287 R2 N/A N/A N/A Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. 31 Table 17: Relationship between resilience capacity indexes and recovery from excessive rain Dependent Variable: Recovery (excessive rain) Probit estimator Resilience Capacity Indexes (Absorptive) (Adaptive) (Transformative) Absorptive capacity 0.012** Adaptive capacity 0.015*** Transformative capacity 0.003 Cumulative shock exposure index (1-144) -0.012** -0.013** -0.008* Household size 0.003 -0.000 0.007 Household demographics (/Percent 30+) Percent 0-15 -0.010 0.006 -0.025 Percent 16-30 0.300 0.226 0.309 Gender HH type (/Adult Male and Female) Adult Female Only 0.022 0.050 -0.006 Adult Male Only -0.071 -0.138 -0.174 Child Only 0.000 0.000 0.000 Project Area (/CRS) MC 0.377*** 0.376*** 0.402*** Constant -1.805*** -2.011*** -1.781*** Observations 1859 1859 1859 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. Table 18: Relationship between resilience capacity indexes and recovery from flooding Dependent Variable: Recovery (flooding) Probit estimator Resilience Capacity Indexes (Absorptive) (Adaptive) (Transformative) Absorptive capacity 0.010** Adaptive capacity 0.009* Transformative capacity 0.004 Cumulative shock exposure index (1-144) -0.010** -0.010** -0.006 Household size 0.052* 0.052* 0.053* Household demographics (/Percent 30+) Percent 0-15 -0.113 -0.090 -0.096 Percent 16-30 0.201 0.181 0.198 Gender HH type (/Adult Male and Female) Adult Female Only -0.146 -0.145 -0.193 Adult Male Only 0.235 0.186 0.175 Child Only 0.000 0.000 0.000 Project Area (/CRS) MC 0.425*** 0.421*** 0.452*** Constant -1.863*** -1.937*** -1.885*** Observations 1436 1436 1436 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. 32 Table 19: Relationship between resilience capacity indexes and recovery from drought Dependent Variable: Recovery (drought) Probit estimator Resilience Capacity Indexes (Absorptive) (Adaptive) (Transformative) Absorptive capacity -0.002 Adaptive capacity 0.013*** Transformative capacity -0.009* Cumulative shock exposure index (1-144) 0.002 -0.004 0.001 Household size -0.071*** -0.080*** -0.059** Household demographics (/Percent 30+) Percent 0-15 0.523 0.533 0.479 Percent 16-30 0.034 -0.116 0.139 Gender HH type (/Adult Male and Female) Adult Female Only -0.075 -0.028 -0.051 Adult Male Only 0.652** 0.674** 0.750*** Child Only 0.000 0.000 0.000 Project Area (/CRS) MC -0.169 -0.178 -0.220 Constant -1.093*** -1.361*** -0.836*** Observations 1177 1177 1177 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. Table 20: Relationship between resilience capacity indexes and recovery from increase food prices Dependent Variable: Recovery (increase food prices) Probit estimator Resilience Capacity Indexes (Absorptive) (Adaptive) (Transformative) Absorptive capacity 0.011** Adaptive capacity 0.022*** Transformative capacity -0.003 Cumulative shock exposure index (1-144) -0.009* -0.012** -0.007* Household size -0.068** -0.084** -0.044 Household demographics (/Percent 30+) Percent 0-15 0.197 0.329 0.123 Percent 16-30 0.636** 0.560** 0.730*** Gender HH type (/Adult Male and Female) Adult Female Only -0.538*** -0.502*** -0.538*** Adult Male Only 0.283 0.183 0.260 Child Only 0.000 0.000 0.000 Project Area (/CRS) MC -0.309** -0.305* -0.363** Constant -1.118*** -1.613*** -0.810*** Observations 1191 1191 1191 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. 33 Table 21: Relationship between resilience capacity indexes and recovery from crop disease Dependent Variable: Recovery (crop disease) Probit estimator Resilience Capacity Indexes (Absorptive) (Adaptive) (Transformative) Absorptive capacity 0.008 Adaptive capacity 0.016*** Transformative capacity -0.001 Cumulative shock exposure index (1-144) -0.013** -0.016*** -0.011** Household size 0.003 0.001 0.010 Household demographics (/Percent 30+) Percent 0-15 0.044 0.002 0.039 Percent 16-30 -0.006 -0.105 0.046 Gender HH type (/Adult Male and Female) Adult Female Only -0.214 -0.167 -0.237 Adult Male Only 0.409 0.364 0.334 Child Only 0.000 0.000 0.000 Project Area (/CRS) MC 0.185 0.169 0.184 Constant -1.703*** -1.995*** -1.569*** Observations 1078 1078 1078 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. 34 Table 22: Relationship between resilience capacity indicators and well-being outcomes CAPACITY INDICATORS Expenditure Poverty HDDS Coef. % change Coef. % change Coef. % change Absorptive capacity indicators Availability of informal safety nets (0-6) -0.006 -1.1 0.014 0.5 -0.012 -2.5 Bonding social capital (0-6) 0.068 6.2 -0.058 -1.0 -0.013 -1.3 Access to cash savings (0-1) 0.242* 19.6 -0.227** -4.1 0.110*** 10.4 Access to remittances (0-1) 0.117 10.1 0.000 0.0 -0.017 -1.7 Productive assets index (0-24) -0.054** -10.7 0.062*** 2.1 0.003 -0.9 Durable assets index (0-22) 0.248*** 41.4 -0.176*** -5.4 0.053*** 10.1 Livestock assets index (0-7) 0.080** 14.5 -0.115*** -3.6 0.038*** 7.3 Shock prep and mitigation (0-4) 0.054 5.0 -0.164*** -2.6 0.019 1.9 Access to agricultural insurance (0-1) 0.332 24.2 -0.145 -2.6 0.076 7.3 Availability of humanitarian assistance (0-1) -0.153 -16.8 0.189 2.8 -0.070 -7.2 Adaptive capacity indicators Aspirations/confidence to adapt (0-16) -0.016 -4.8 -0.019 -1.0 0.028*** 8.0 Bridging social capital (0-6) 0.052 0.0 0.020 0.0 0.009 0.9 Linking social capital (0-4) -0.073 -14.9 0.030 1.0 0.013 2.5 Education/training (0-3) 0.149** 13.7 -0.249*** -3.8 0.076*** 7.3 Livelihood diversification (0-17) 0.011 2.0 -0.027 -0.9 0.013 2.5 Improved practices (0-1) -0.479* -48.9 0.036 0.6 -0.056 -5.8 Exposure to information (0-19) 0.024** 17.0 -0.016 -2.1 0.004 3.1 Access to financial institutions (0-2) -0.131** -27.7 0.077 2.6 0.014 2.7 Transformative capacity indicators Availability of formal safety nets (0-3) -0.065 -6.5 -0.227 -4.3 -0.044 -4.5 Availability of markets (0-3) 0.013 3.8 -0.006 -0.3 0.026 7.5 Access to communal natural resources (0-4) -0.071 -14.7 0.001 0.0 -0.004 -0.7 Access to basic services (0-3) a/ 0.102 26.1 0.046 2.3 -0.097** -33.6 Access to infrastructure (0-4) 0.078 13.9 -0.200*** -6.7 0.064** 12.0 35 Access to ag extension services (0-1) 0.285** 22.5 -0.194 -3.4 0.009 0.9 Access to livestock services (0-1) -0.119 -12.1 0.077 1.3 -0.029 -2.9 Collective action (0-10) a/ -0.072 -25.0 0.064 2.9 0.004 1.3 Local government responsiveness (0-1) -0.224 -21.9 0.180 3.2 0.021 2.1 Participation in local decision making (0-1) -0.104 -10.4 -0.112 -1.8 0.109*** 10.3 Household characteristics Cumulative shock exposure index (1-144) -0.004 -0.001 0.003*** Household size N/A 0.046** -0.007 Household demographics (/Percent 30+) Percent 0-15 -0.982* 0.942*** 0.111 Percent 16-30 0.123 0.107 0.142** Gender HH type (/Adult Male and Female) Adult Female Only 0.168 -0.186* -0.007 Adult Male Only 0.187 -0.380 0.005 Child Only -1.050* 0.000 0.340 Project (/CRS) MC 0.187* -0.114 0.222*** Constant 1.418*** 1.692*** 0.371*** Observations 2799 2797 2287 R2 0.056 N/A N/A 36 CAPACITY INDICATORS Recovery from excessive rain b/ Recovery from flooding b/ Recovery from drought b/ Recovery from increase food prices b/ Recovery from crop disease b/ Coef. % change Coef. % change Coef. % change Coef. % change Coef. % change Absorptive capacity indicators Availability of informal safety nets (0-6) -0.039 -14.8 -0.049 -16.8 -0.024 -10.8 0.016 6.5 -0.055 -36.0 Bonding social capital (0-6) -0.022 -4.0 0.087 12.8 -0.250** -43.8 0.194** 24.1 -0.099 -20.4 Access to cash savings (0-1) 0.170 26.1 0.574*** 59.0 -0.132 -21.0 0.041 5.8 0.095 16.1 Access to remittances (0-1) 0.052 8.8 -0.107 -19.0 -0.797*** -266.4 -0.017 -2.4 -0.115 -24.0 Productive assets index (0-24) -0.017 -6.3 -0.020 -6.4 -0.006 -2.4 -0.025 -7.4 -0.126*** -95.6 Durable assets index (0-22) 0.038 12.8 -0.051 -17.4 0.044 11.8 0.089*** 12.5 0.112* 18.9 Livestock assets index (0-7) 0.046 15.3 0.012 3.7 0.008 2.3 0.019 5.3 0.126** 38.3 Shock prep and mitigation (0-4) -0.009 -1.7 0.117 17.3 0.046 6.4 -0.197** -31.1 -0.040 -7.6 Access to agricultural insurance (0-1) 0.552* 59.2 0.661* 61.0 0.791** 62.6 0.926*** 69.0 0.488 56.9 Availability of humanitarian assistance (0-1) 0.284 38.4 -0.354 -82.8 -0.409 -87.2 -0.390 -81.1 -0.204 -47.1 Adaptive capacity indicators Aspirations/confidence to adapt (0-16) 0.029 9.8 0.035 15.7 -0.005 -1.5 0.006 3.3 -0.006 -2.3 Bridging social capital (0-6) -0.016 -2.9 -0.081 -14.0 0.046 6.4 -0.206*** -34.1 -0.011 -2.1 Linking social capital (0-4) 0.013 4.7 0.072 20.5 0.068 17.8 0.045 12.1 0.009 3.1 Education/training (0-3) 0.109 18.1 0.197* 28.0 0.103 13.8 -0.034 -5.0 -0.045 -8.7 Livelihood diversification (0-17) -0.047 -18.3 -0.077** -27.8 0.038 10.5 0.025 10.3 -0.024 -14.0 Improved practices (0-1) -0.018 -3.2 0.153 22.2 0.341* 40.3 -0.139 -21.7 0.963** 87.3 Exposure to information (0-19) 0.047*** 49.8 0.018 22.6 0.060*** 51.3 0.076*** 56.2 0.083*** 72.2 Access to financial institutions (0-2) -0.073 -29.6 -0.420*** -257.3 0.137 32.5 0.142 34.5 0.020 7.2 Transformative capacity indicators Availability of formal safety nets (0-3) -0.669 -286.1 -0.240 -49.4 -0.221 -39.0 -0.979* -430.2 0.380 48.5 Availability of markets (0-3) 0.138*** 53.0 0.063 26.2 -0.047 -22.2 -0.043 -20.6 -0.023 -13.8 Access to communal natural resources (0-4) 0.034 11.5 0.073 20.8 -0.155* -57.9 -0.064 -9.6 -0.101 -20.2 Access to basic services (0-3) a/ -0.005 -2.6 -0.132 -86.5 -0.031 -14.1 0.110 38.1 0.242 74.6 37 Access to infrastructure (0-4) -0.055 -21.7 0.236*** 52.9 -0.228 -94.7 -0.050 -15.5 -0.219* -119.9 Access to ag extension services (0-1) -0.035 -6.4 0.077 11.6 0.314 34.6 -0.281 -50.8 0.048 8.5 Access to livestock services (0-1) -0.054 -10.1 -0.032 -5.3 -0.604** -146.2 -0.250 -43.0 0.070 12.1 Collective action (0-10) a/ 0.112 43.6 -0.049 -27.0 -0.130 -81.6 0.173* 50.1 -0.071 -73.2 Local government responsiveness (0-1) 0.387* 52.0 0.488*** 56.9 -0.133 -20.5 -0.622** -128.1 -0.669*** -211.9 Participation in local decision making (0-1) -0.297* -69.8 -0.109 -19.1 0.187 23.4 -0.111 -17.3 -0.021 -4.0 Household characteristics Cumulative shock exposure index (1-144) -0.017*** -0.011* -0.009* -0.016*** -0.021*** Household size -0.009 0.057** -0.073** -0.048 0.030 Household demographics (/Percent 30+) Percent 0-15 0.006 -0.372 0.396 0.284 -0.070 Percent 16-30 0.152 -0.115 -0.182 0.716** -0.120 Gender HH type (/Adult Male and Female) Adult Female Only -0.022 -0.036 0.075 -0.564*** -0.142 Adult Male Only -0.475* -0.071 0.977*** 0.014 0.158 Child Only 0.000 0.000 0.000 0.000 0.000 Project (/CRS) MC 0.392*** 0.626*** -0.214 -0.406** 0.017 Constant -2.115*** -2.283*** -0.391 -0.684 -1.401* Observations 1859 1436 1177 1191 1078 R2 N/A N/A N/A N/A N/A Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables. For binary variables, the change is defined as the difference between 0 and 1. Formal safety nets is also used as a binary predictor because responses only ranged from 0-1. a/ This represents the percent change from the 1st to the 99th percentile of the sample. b/ Only for those households who experienced and were impacted by a shock in the last 12 months. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. 38 Table 23: Relationship between expenditures and coping strategies Dependent variable: Coping strategies Probit estimator a/ Reduce food consumption Take up new/additional work Sell livestock Reduce non￾essential expenses Coef. % change Coef. % change Coef. % change Coef. % change Per capita expenditures 0.0224 0.9 -0.0236 -1.3 0.0208* 1.5 0.0262* 2.2 Receive formal assistance (food/cash transfer/food-for￾work) -0.321* -0.00791 0.414*** -0.100 Household characteristics Cumulative Shock Exposure Index(2-144) 0.0300*** 0.0203*** 0.0221*** 0.0181*** Household size 0.00416 -0.0131 -0.0161 -0.00599 Household demographics (/Percent 30+) Percent 0-15 -0.0716 0.466** 0.428* -0.129 Percent 16-30 -0.0607 0.285* -0.0275 0.0517 Gender HH type (/Adult Male and Female) Adult Female Only -0.138 -0.279*** -0.637*** -0.129 Adult Male Only -0.366* -0.628*** -0.451** -0.0716 Child Only 0 1.536* 0 0 Project (/CRS) MC -0.431*** -0.239* 0.786*** 0.0163 Constant -0.540*** -1.022*** -2.159*** -1.459*** Observations 2675 2677 2675 2675 Note: Non-significant coping strategies are not included. Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (expenditures). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 39 Table 23: Relationships between expenditures and coping strategies Dependent variable: Coping strategies Probit estimator a/ Lease out land Slaughter livestock Send child/adult to stay with relatives HH member migrate for work Coef. % change Coef. % change Coef. % change Coef. % change Per capita expenditures -0.0126 -1.4 0.0147 1.3 0.00312 0.1 -0.0188 -2.2 Receive formal assistance (food/cash transfer/food￾for-work) 0.276 0.138 0.372* 0.369* Household characteristics Cumulative Shock Exposure Index (2-144) 0.00807*** 0.0268*** 0.0160*** -0.000232 Household size 0.0145 -0.00897 0.000130 0.0138 Household demographics (/Percent 30+) Percent 0-15 -0.157 0.209 0.554** -0.409 Percent 16-30 -0.200 0.139 -0.877*** -0.306 Gender HH type (/Adult Male and Female) Adult Female Only -0.308** -0.346** -0.0587 -0.0381 Adult Male Only 0.0466 -0.234 0.271 0.393** Child Only 0 0 0 0 Project (/CRS) MC 0.0243 0.561*** 0.294*** 0.0467 Constant -1.443*** -2.587*** -2.210*** -1.252*** Observations 2675 2675 2675 2675 Note: Non-significant coping strategies are not included. Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (expenditures). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 40 Table 23: Relationships between expenditures and coping strategies Dependent variable: Coping strategies Probit estimator a/ Gotten food on credit Use money from savings Take out loan from MFI/VSG Coef. % change Coef. % change Coef. % change Per capita expenditures 0.0415*** 4.2 0.0717*** 7.7 0.0439*** 4.5 Receive formal assistance (food/cash transfer/food-for-work) 0.217 0.546*** 0.719*** Household characteristics Cumulative Shock Exposure Index (2-144) 0.0168*** 0.00779** 0.0110*** Household size 0.0179 0.0249 0.0362* Household demographics (/Percent 30+) Percent 0-15 0.376 0.535* 0.355 Percent 16-30 0.0617 0.785*** 0.690** Gender HH type (/Adult Male and Female) Adult Female Only -0.183 -0.0930 -0.146 Adult Male Only -0.113 0.126 -1.359*** Child Only 0 0 0 Project (/CRS) MC -0.132 0.0381 0.0706 Constant -2.367*** -2.624*** -2.644*** Observations 2675 2675 2675 Note: Non-significant coping strategies are not included. Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (expenditures). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 41 Table 24: Relationship between resilience capacity indexes and reducing food consumption Dependent Variable: Reducing food consumption Probit estimator a/ Absorptive Adaptive Transformative Coef. % change Coef. % change Coef. % change Absorptive capacity 0.0105*** 13.1 Adaptive capacity 0.0134*** 16.4 Transformative capacity -0.00137 -3.5 Receive formal assistance (food/cash transfer/ food-for-work) -0.362** -0.366** -0.335* Cumulative Shock Exposure Index (2- 144) 0.0272*** 0.0262*** 0.0300*** Household demographics (/Percent 30+) Percent 0-15 -0.0843 -0.0930 -0.108 Percent 16-30 -0.0920 -0.154 -0.0386 Household size -0.00412 -0.00801 0.00552 Gender HH type (/Adult Male and Female) Adult Female Only -0.113 -0.0833 -0.128 Adult Male Only -0.291 -0.353* -0.325 Child Only 0 0 0 Project Area (/CRS) MC -0.442*** -0.437*** -0.440*** Constant -0.627*** -0.814*** -0.454** Observations 2675 2675 2675 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (absorptive, adaptive, transformative). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 42 Table 25: Relationship between resilience capacity indexes and taking up new/additional work Dependent Variable: Take up new/additional work Probit estimator a/ Absorptive Adaptive Transformative Coef. % change Coef. % change Coef. % change Absorptive capacity -0.00257 -5.0 Adaptive capacity 0.00125 2.4 Transformative capacity -0.00281 -11.1 Receive formal assistance (food/cash transfer/food-for-work) 0.00479 -0.00647 -0.0191 Cumulative Shock Exposure Index (2-144) 0.0210*** 0.0197*** 0.0201*** Household demographics (/Percent 30+) Percent 0-15 0.495** 0.497** 0.480** Percent 16-30 0.289* 0.272 0.304* Household size -0.0104 -0.0130 -0.00745 Gender HH type (/Adult Male and Female) Adult Female Only -0.287*** -0.275*** -0.268*** Adult Male Only -0.663*** -0.650*** -0.631*** Child Only 1.537* 1.592* 1.526* Project Area (/CRS) MC -0.237* -0.240* -0.262** Constant -1.035*** -1.092*** -0.971*** Observations 2677 2677 2677 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (absorptive, adaptive, transformative). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 43 Table 26: Relationship between resilience capacity indexes and sell livestock Dependent Variable: Sell livestock Probit estimator a/ Absorptive Adaptive Transformative Coef. % change Coef. % change Coef. % change Absorptive capacity 0.00952** 21.7 Adaptive capacity 0.0153*** 32.8 Transformative capacity -0.00807*** -49.5 Receive formal assistance (food/cash transfer/food-for-work) 0.384*** 0.367*** 0.380*** Cumulative Shock Exposure Index (2- 144) 0.0192*** 0.0176*** 0.0221*** Household demographics (/Percent 30+) Percent 0-15 0.413* 0.409* 0.300 Percent 16-30 -0.0409 -0.115 0.00842 Household size -0.0247 -0.0277* 0.000364 Gender HH type (/Adult Male and Female) Adult Female Only -0.606*** -0.564*** -0.593*** Adult Male Only -0.387* -0.479** -0.368 Child Only 0 0 0 Project Area (/CRS) MC 0.792*** 0.793*** 0.718*** Constant -2.249*** -2.521*** -1.861*** Observations 2675 2675 2675 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (absorptive, adaptive, transformative). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 44 Table 27: Relationship between resilience capacity indexes and reduce non￾essential household expenses Dependent Variable: Reduce non-essential HH expenses Probit estimator a/ Absorptive Adaptive Transformative Coef. % change Coef. % change Coef. % change Absorptive capacity 0.0162*** 37.6 Adaptive capacity 0.0228*** 49.0 Transformative capacity 0.000165 0.9 Receive formal assistance (food/cash transfer/food-for-work) -0.132 -0.160 -0.107 Cumulative Shock Exposure Index (2-144) 0.0133*** 0.0114*** 0.0182*** Household demographics (/Percent 30+) Percent 0-15 -0.134 -0.148 -0.160 Percent 16-30 0.0122 -0.0990 0.0594 Household size -0.0215 -0.0249 -0.00807 Gender HH type (/Adult Male and Female) 0 0 Adult Female Only -0.0865 -0.0244 -0.127 Adult Male Only 0.0267 -0.0712 -0.0447 Child Only 0 0 0 Project Area (/CRS) MC 0.0347 0.0257 0.0163 Constant -1.632*** -2.005*** -1.410*** Observations 2675 2675 2675 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (absorptive, adaptive, transformative). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 45 Table 28: Relationship between resilience capacity indexes and lease land Dependent Variable: Lease land Probit estimator a/ Absorptive Adaptive Transformative Coef. % change Coef. % change Coef. % change Absorptive capacity 0.00365 11.4 Adaptive capacity 0.00623 18.6 Transformative capacity -0.00475** -35.7 Receive formal assistance (food/cash transfer/food-for-work) 0.271 0.260 0.250 Cumulative Shock Exposure Index (2-144) 0.00681** 0.00603* 0.00772*** Household demographics (/Percent 30+) Percent 0-15 -0.134 -0.140 -0.197 Percent 16-30 -0.218 -0.257 -0.168 Household size 0.0120 0.0100 0.0242 Gender HH type (/Adult Male and Female) Adult Female Only -0.302** -0.285** -0.276** Adult Male Only 0.0556 0.0233 0.0797 Child Only 0 0 0 Project Area (/CRS) MC 0.0217 0.0233 -0.0122 Constant -1.508*** -1.612*** -1.305*** Observations 2675 2675 2675 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (absorptive, adaptive, transformative). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 46 Table 29: Relationship between resilience capacity indexes and slaughter livestock Dependent Variable: Slaughter livestock Probit estimator a/ Absorptive Adaptive Transformative Coef. % change Coef. % change Coef. % change Absorptive capacity 0.0118*** 31.6 Adaptive capacity 0.0224*** 52.7 Transformative capacity -0.00138 -8.6 Receive formal assistance (food/cash transfer/food-for-work) 0.107 0.0684 0.127 Cumulative Shock Exposure Index (2-144) 0.0233*** 0.0203*** 0.0268*** Household demographics (/Percent 30+) Percent 0-15 0.205 0.207 0.174 Percent 16-30 0.122 0.0104 0.154 Household size -0.0199 -0.0260 -0.00671 Gender HH type (/Adult Male and Female) Adult Female Only -0.310** -0.245* -0.336** Adult Male Only -0.165 -0.278 -0.208 Child Only 0 0 0 Project Area (/CRS) MC 0.574*** 0.573*** 0.545*** Constant -2.728*** -3.177*** -2.508*** Observations 2675 2675 2675 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (absorptive, adaptive, transformative). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 47 Table 30: Relationship between resilience capacity indexes and send child/adult to stay at relatives Dependent Variable: Send child/adult to stay at relatives Probit estimator a/ Absorptive Adaptive Transformative Coef. % change Coef. % change Coef. % change Absorptive capacity 0.00535 16.2 Adaptive capacity -0.00669 -23.9 Transformative capacity 0.00126 7.7 Receive formal assistance (food/cash transfer/ food-for-work) 0.357* 0.398* 0.381* Cumulative Shock Exposure Index (2-144) 0.0143*** 0.0181*** 0.0161*** Household demographics (/Percent 30+) Percent 0-15 0.563** 0.550** 0.562** Percent 16-30 -0.888*** -0.828*** -0.887*** Household size -0.00406 0.00392 -0.00211 Gender HH type (/Adult Male and Female) Adult Female Only -0.0439 -0.0908 -0.0663 Adult Male Only 0.303 0.269 0.265 Child Only 0 0 0 Project Area (/CRS) MC 0.293*** 0.296** 0.303*** Constant -2.274*** -2.044*** -2.249*** Observations 2675 2675 2675 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (absorptive, adaptive, transformative). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 48 Table 31: Relationship between resilience capacity indexes and household member migrate for work Dependent Variable: HH member migrate for work Probit estimator a/ Absorptive Adaptive Transformative Coef. % change Coef. % change Coef. % change Absorptive capacity 0.00207 7.0 Adaptive capacity -0.00871 -35.5 Transformative capacity -0.00802*** -74.4 Receive formal assistance (food/cash transfer/ food-for-work) 0.368* 0.406** 0.327 Cumulative Shock Exposure Index (2- 144) -0.000980 0.00255 -0.000378 Household demographics (/Percent 30+) Percent 0-15 -0.387 -0.392 -0.485* Percent 16-30 -0.326* -0.245 -0.262 Household size 0.0132 0.0212 0.0296 Gender HH type (/Adult Male and Female) Adult Female Only -0.0328 -0.0825 -0.00222 Adult Male Only 0.391* 0.380** 0.459** Child Only 0 0 0 Project Area (/CRS) MC 0.0444 0.0484 -0.0147 Constant -1.306*** -1.089*** -1.027*** Observations 2675 2675 2675 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (absorptive, adaptive, transformative). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 49 Table 32: Relationship between resilience capacity indexes and gotten food on credit from local merchant Dependent Variable: Gotten food on credit Probit estimator a/ Absorptive Adaptive Transformative Coef. % change Coef. % change Coef. % change Absorptive capacity 0.0195*** 53.1 Adaptive capacity 0.0219*** 57.0 Transformative capacity 0.00732*** 40.0 Receive formal assistance (food/cash transfer/ food-for-work) 0.175 0.159 0.257 Cumulative Shock Exposure Index (2- 144) 0.00993*** 0.0100*** 0.0176*** Household demographics (/Percent 30+) Percent 0-15 0.322 0.342 0.350 Percent 16-30 -0.0158 -0.0938 0.00885 Household size -0.000118 0.00147 0.00386 Gender HH type (/Adult Male and Female) Adult Female Only -0.120 -0.0693 -0.205 Adult Male Only 0.0187 -0.112 -0.137 Child Only 0 0 0 Project Area (/CRS) MC -0.130 -0.148 -0.0782 Constant -2.514*** -2.866*** -2.552*** Observations 2675 2675 2675 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (absorptive, adaptive, transformative). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 50 Table 33: Relationship between resilience capacity indexes and use money from savings Dependent Variable: Use money from savings Probit estimator a/ Absorptive Adaptive Transformative Coef. % change Coef. % change Coef. % change Absorptive capacity 0.0293*** 70.1 Adaptive capacity 0.0319*** 73.3 Transformative capacity 0.0110*** 56.3 Receive formal assistance (food/cash transfer/ food-for-work) 0.490*** 0.460*** 0.606*** Cumulative Shock Exposure Index (2- 144) -0.00239 -0.00219 0.00919** Household demographics (/Percent 30+) Percent 0-15 0.371 0.447 0.492 Percent 16-30 0.679*** 0.570*** 0.680*** Household size -0.00511 -0.00370 -0.00526 Gender HH type (/Adult Male and Female) Adult Female Only 0.0266 0.0648 -0.130 Adult Male Only 0.350 0.163 0.124 Child Only 0 0 0 Project Area (/CRS) MC 0.0494 0.0228 0.105 Constant -2.831*** -3.319*** -2.838*** Observations 2675 2675 2675 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (absorptive, adaptive, transformative). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 51 Table 34: Relationship between resilience capacity indexes and take out a loan (with interest) from MFI or village savings group Dependent Variable: Take out a loan from MFI/VSG Probit estimator a/ Absorptive Adaptive Transformative Coef. % change Coef. % change Coef. % change Absorptive capacity 0.0282*** 68.5 Adaptive capacity 0.0269*** 66.2 Transformative capacity 0.00457* 28.0 Receive formal assistance (food/cash transfer/ food-for-work) 0.679*** 0.662*** 0.737*** Cumulative Shock Exposure Index (2- 144) 0.00189 0.00295 0.0114*** Household demographics (/Percent 30+) Percent 0-15 0.327 0.323 0.278 Percent 16-30 0.604** 0.489* 0.613** Household size 0.00915 0.0156 0.0253 Gender HH type (/Adult Male and Female) Adult Female Only -0.0433 0.0108 -0.151 Adult Male Only -1.172*** -1.425*** -1.368*** Child Only 0 0 0 Project Area (/CRS) MC 0.0958 0.0701 0.101 Constant -2.992*** -3.311*** -2.673*** Observations 2675 2675 2675 Note: Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables (absorptive, adaptive, transformative). a/ Only for those households who experienced and were impacted by a shock in the last 12 months. 52 Table 35: Relationship between resilience capacity indicators and key coping strategies CAPACITY INDICATORS a/ Reduce food consumption Take up new/additional work Sell livestock Reduce HH non￾essential expenses Coef. % change Coef. % change Coef. % change Coef. % change Absorptive capacity indicators Availability of informal safety nets (0-6) -0.0404 -5.1 -0.0461 -8.5 -0.0101 -2.2 -0.0969** -26.1 Bonding social capital (0-6) 0.0366 2.3 0.0958* 8.1 0.0255 2.6 0.107** 12.9 Access to cash savings (0-1) 0.162* 9.4 0.0199 1.8 -0.0382 -4.2 0.272** 28.4 Access to remittances (0-1) -0.169 -11.5 -0.305* -33.1 0.0477 5 -0.133 -18.2 Durable assets index (0-22) 0.0340 4.2 -0.0494* -9.1 0.0325 6.6 0.117*** 26.8 Livestock assets index (0-7) -0.0723*** -9.3 -0.133*** -26.3 0.299*** 55.5 -0.0533* -13.9 Productive assets index (0-24) 0.00929 1.7 -0.00293 -0.8 -0.0529** -17.2 0.00981 3.6 Shock prep and mitigation (0-4) 0.257*** 15.5 0.130** 11.3 -0.0293 -2.8 0.220*** 25.0 Access to agricultural insurance (0-1) -0.130 -8.7 -0.344 -39.4 -0.0901 -10.3 -0.0581 -7.5 Availability of humanitarian assistance (0-1) -0.592*** -51.8 -0.143 -14 -0.0348 -3.8 -0.0153 -1.9 Adaptive capacity indicators Aspirations/confidence to adapt (0-16) -0.0174 -3.3 0.0373 9.6 0.00301 0.6 -0.00710 -2.7 Bridging social capital (0-6) 0.0407 2.5 -0.183*** -17.6 -0.125*** -14.3 0.124** 14.7 Linking social capital (0-4) 0.0813** 9.7 -0.0981* -19.3 0.115*** 23.0 0.101** 22.6 Education/training (0-3) -0.0238 -1.5 -0.0901* -8.3 -0.0595 -6.6 -0.0383 -4.8 Livelihood diversification (0-17) 0.0323 4 0.160*** 26.5 0.0567* 12.4 -0.0296 -7.5 Improved practices (0-1) -0.135 -8.5 0.209** 17.7 -0.200 -23.2 0.206 23.3 Exposure to information (0-19) 0.0328*** 15.7 0.0150 10.3 0.0263** 21.2 0.0157 14.8 Access to financial institutions (0-2) 0.0353 4.3 0.109* 17.8 -0.141** -34.4 0.219*** 43.3 Transformative capacity indicators Availability of formal safety nets (0-3) -0.0581 -3.7 0.375** 26.6 0.264 24 -0.404 -70.8 Availability of markets (0-3) 0.00582 1.1 0.103** 24.3 0.00577 -0.3 -0.115** -53.0 Access to communal natural resources (0-4) -0.0660* -8.7 -0.0195 -3.5 -0.0752 -16.3 -0.0246 -6.3 Access to basic services (0-3) b/ -0.372*** -95.1 -0.251** -92.5 -0.253** -127.6 0.0127 4.6 53 Access to infrastructure (0-4) 0.0623 7.5 -0.204** -43.7 -0.134** -36.5 0.116 25.2 Access to ag extension services (0-1) 0.316*** 17.2 0.178 14.3 -0.00212 -0.2 0.0221 2.7 Access to livestock services (0-1) -0.232** -15.5 0.151 12.5 0.150 15.3 -0.376* -58.7 Collective action (0-10) b/ -0.0363 -7.2 0.0471 11.5 0.0463 13.7 -0.0700 -30.6 Local government responsiveness (0-1) 0.250 15.3 -0.243 -22.7 0.173 17.4 0.539** 52.6 Participation in local decision making (0-1) -0.0888 -5.7 -0.0776 -7.2 0.0566 6.4 -0.0650 -8.3 Household characteristics Receive formal assistance (food/cash transfer/ food-for-work) -0.311** -0.0754 0.418*** -0.135 Cumulative shock exposure index (1-144) 0.0230*** 0.0156** * 0.0178*** 0.0125*** Household size 0.0220 -0.0131 0.00566 -0.00778 Household demographics (/Percent 30+) Percent 0-15 -0.250 0.393* 0.194 -0.169 Percent 16-30 -0.199 0.340* -0.0110 -0.0966 Gender HH type (/Adult Male and Female) Adult Female Only -0.0920 -0.292*** -0.402*** -0.0710 Adult Male Only -0.457** -0.670*** -0.451* -0.382 Child Only 0 1.510* 0 0 Project (/CRS) MC -0.369*** -0.136 0.524*** 0.0371 Constant -0.0624 -0.909** -1.648*** -2.726*** Observations 2675 2677 2675 2675 Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables. For binary variables, the change is defined as the difference between 0 and 1. Formal safety nets is also used as a binary predictor because responses only ranged from 0-1. a/ Only for those households who experienced and were impacted by a shock in the last 12 months. b/ This represents the percent change from the 1st to the 99th percentile of the sample. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. 54 CAPACITY INDICATORS a/ Lease land Slaughter livestock Send child/adult to stay with relatives HH member migrate for work Coef. % change Coef. % change Coef. % change Coef. % change Absorptive capacity indicators Availability of informal safety nets (0-6) 0.00290 0.9 -0.0161 -4.6 0.0189 5.7 0.0438 13.4 Bonding social capital (0-6) 0.0655 9.7 -0.0304 -4.3 0.00171 0.3 0.0901 13.7 Access to cash savings (0-1) -0.162 -29.6 0.0515 6.9 0.181 24.2 -0.0553 -9.6 Access to remittances (0-1) -0.232 -46.1 0.210 25.1 0.116 16.4 0.260 33.9 Durable assets index (0-22) 0.0399 12 -0.0125 -3.5 -0.0334 -10.9 -0.101*** -39.6 Livestock assets index (0-7) 0.00673 2.1 0.210*** 48.5 -0.0475* -15.9 0.0489 15 Productive assets index (0-24) 0.00535 2.5 -0.0435* -19.1 0.0150 6.8 0.0237 11.2 Shock prep and mitigation (0-4) 0.0527 8.1 -0.0990* -14.3 0.0640 9.7 0.0941 14.5 Access to agricultural insurance (0-1) 0.283 34.6 -0.0345 -4.9 0.00340 0.5 0.292 36.6 Availability of humanitarian assistance (0-1) -0.0605 -10.1 -0.0395 -5.7 0.104 14.9 -0.0948 -17.1 Adaptive capacity indicators Aspirations/confidence to adapt (0-16) -0.00410 -2 0.0732*** 27.2 -0.0269 -13.3 -0.0102 -5.2 Bridging social capital (0-6) -0.159*** -29.1 -0.0528 -7.6 -0.102* -17.4 -0.118* -21.3 Linking social capital (0-4) 0.0422 12.6 0.0934** 23.4 -0.0649 -22.4 -0.105* -41.5 Education/training (0-3) 0.0673 10.2 -0.0392 -5.6 -0.00724 -1.1 0.130* 19.5 Livelihood diversification (0-17) 0.0698** 20.3 0.0779*** 20.8 0.0397 11.8 -0.000777 -0.3 Improved practices (0-1) -0.425*** -88.2 0.168 21.3 -0.604*** -143.0 -0.343** -72.1 Exposure to information (0-19) -0.0117 -15.7 0.00830 9 -0.0169 -22.9 -0.0184 -27.2 Access to financial institutions (0-2) 0.0495 14.6 0.0771 19.5 -0.119** -44.1 -0.196*** -92.5 Transformative capacity indicators Availability of formal safety nets (0-3) -0.359 -83.5 -0.116 -17.8 0.251 31.5 0.591*** 58.7 Availability of markets (0-3) -0.145*** -100.7 -0.0447 -20.6 -0.0251 -12.4 -0.0369 -20 Access to communal natural resources (0-4) 0.0231 7 0.0439 11.4 0.0328 9.7 -0.0968 -38 Access to basic services (0-3) b/ -0.0287 -14.6 0.00453 1.9 0.0989 37.3 0.247* 71.5 Access to infrastructure (0-4) 0.133 34.1 -0.0516 -15.5 0.0588 16.7 -0.190** -91.9 55 Access to ag extension services (0-1) -0.439*** -108.6 -0.359*** -67.5 -0.121 -21.1 0.302 37.6 Access to livestock services (0-1) -0.0557 -9.2 0.275** 31.3 0.0516 7.7 -0.228 -47.3 Collective action (0-10) b/ 0.0997** 36.4 0.175*** 50.5 0.231*** 63.5 0.0857 33.6 Local government responsiveness (0-1) 0.260 35 -0.0786 -11.4 0.407** 49.5 0.0170 2.8 Participation in local decision making (0-1) -0.00695 -1.1 -0.0381 -5.4 -0.152 -26.5 -0.0101 -1.7 Household characteristics Receive formal assistance (food/cash transfer/ food-for-work) 0.156 -0.0945 0.320* 0.312* Cumulative shock exposure index (1-144) 0.00648* 0.0231*** 0.0166*** -0.00150 Household size 0.0234 -0.0234 -0.00477 0.0199 Household demographics (/Percent 30+) Percent 0-15 -0.221 0.234 0.722** -0.334 Percent 16-30 -0.413** 0.144 -0.936*** -0.131 Gender HH type (/Adult Male and Female) Adult Female Only -0.283** -0.168 -0.239* -0.0576 Adult Male Only 0.0885 -0.384 0.239 0.625*** Child Only 0 0 0 0 Project (/CRS) MC -0.0502 0.275** 0.289** -0.0681 Constant -1.235*** -3.507*** -1.834*** -0.810* Observations 2675 2675 2675 2675 Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables. For binary variables, the change is defined as the difference between 0 and 1. Formal safety nets is also used as a binary predictor because responses only ranged from 0-1. a/ Only for those households who experienced and were impacted by a shock in the last 12 months. b/ This represents the percent change from the 1st to the 99th percentile of the sample. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group. 56 CAPACITY INDICATORS a/ Gotten credit for food from local merchant Use money from savings Take out loan from MFI or village saving groups Coef. % change Coef. % change Coef. % change Absorptive capacity indicators Availability of informal safety nets (0-6) -0.00951 -3.2 -0.000192 -0.1 0.00506 1.5 Bonding social capital (0-6) -0.0148 -2.4 0.0586 8.7 0.0389 5.7 Access to cash savings (0-1) 0.0260 4.2 0.593*** 63.8 0.922*** 78.6 Access to remittances (0-1) 0.0710 11 -0.0492 -8.1 0.129 17.4 Durable assets index (0-22) 0.0726* 22.1 0.0908** 26.8 0.0673* 19.1 Livestock assets index (0-7) -0.0560 -20.1 0.0380 11.3 -0.114** -39.2 Productive assets index (0-24) -0.0690** -38.9 -0.0405* -20.5 0.0438* 18.4 Shock prep and mitigation (0-4) 0.210*** 31.7 0.112 16.6 0.00542 0.8 Access to agricultural insurance (0-1) 0.144 20.7 0.584** 57.3 0.319 36.9 Availability of humanitarian assistance (0-1) 0.0178 2.9 -0.00656 -1 -0.0150 -2.3 Adaptive capacity indicators Aspirations/confidence to adapt (0-16) 0.0464** 21.0 0.00254 1.2 0.0353 15.1 Bridging social capital (0-6) -0.0173 -2.9 -0.186** -34.3 -0.0615 -9.7 Linking social capital (0-4) -0.0983** -37.2 0.0561 16.5 -0.0791 -26.2 Education/training (0-3) 0.179** 26.8 -0.00268 -0.4 0.107 15.4 Livelihood diversification (0-17) 0.111*** 32.9 0.0167 5.1 0.0678* 19.2 Improved practices (0-1) 0.119 18 0.0233 3.6 -0.139 -22.7 Exposure to information (0-19) -0.00455 -6.1 0.0229 26 -0.0153 -19.6 Access to financial institutions (0-2) 0.0772 22.7 0.175* 44.2 0.215* 49.8 Transformative capacity indicators Availability of formal safety nets (0-3) 0.0208 3.4 -0.675** -223.6 -1.224** -826.2 Availability of markets (0-3) -0.0375 -20.1 -0.0189 -9.3 -0.0807 -43.4 Access to communal natural resources (0-4) -0.00102 -0.3 0.0120 3.7 -0.147** -57.5 Access to basic services (0-3) b/ -0.00624 -3.1 0.00681 3.2 0.158 51.9 57 Access to infrastructure (0-4) 0.0855 24.8 0.171** 43.0 -0.109 -37.9 Access to ag extension services (0-1) 0.0768 11.8 -0.389** -84.0 -0.157 -26.9 Access to livestock services (0-1) -0.0606 -10.4 0.152 21.5 -0.0612 -9.6 Collective action (0-10) b/ 0.0696 28.8 0.134** 46.2 0.164*** 51.3 Local government responsiveness (0-1) 0.0724 11.3 0.126 18.2 -0.0375 -5.8 Participation in local decision making (0-1) 0.0914 14.1 -0.0430 -6.9 0.288 36.6 Household characteristics Cumulative shock exposure index (1-144) 0.200 0.619*** 0.639*** Receive formal assistance (food/cash transfer/ food-for-work) 0.00871*** 0.00180 0.00693* Household size -0.0108 -0.0176 0.00809 Household demographics (/Percent 30+) Percent 0-15 0.249 0.558 0.260 Percent 16-30 -0.479* 0.443* 0.278 Gender HH type (/Adult Male and Female) Adult Female Only -0.0801 -0.107 -0.0133 Adult Male Only -0.105 -0.0411 -1.462*** Child Only 0 0 0 Project (/CRS) MC -0.0281 0.0573 0.0945 Constant -3.017*** -3.129*** -3.556*** Observations 2675 2675 2675 Asterisks represent statistical significance at the 0.01 (***), 0.05 (**), and 0.10 (*) levels. “% change” represents the percent change from the lowest to highest quartile (25th to 75th percentile) of the sample for indicators measured as continuous variables. For binary variables, the change is defined as the difference between 0 and 1. Formal safety nets is also used as a binary predictor because responses only ranged from 0-1. a/ Only for those households who experienced and were impacted by a shock in the last 12 months. b/ This represents the percent change from the 1st to the 99th percentile of the sample. (/Percent 30+), (/Adult Male and Female), and (/CRS) indicate the comparison group.