February 13, 2018 This report was produced for review by the United States Agency for International Development. It was prepared by the Evaluation and Learning Mechanism (EVELYN) Task Order. FINAL REPORT Ethiopia Endline Study: Quantitative Assessment of Food-for-Peace Development Food Assistance Projects (DFAPs) FINAL REPORT Ethiopia Endline Study: Quantitative Assessment of Food-for-Peace Development Food Assistance Projects (DFAPs) Submission Date: 02/13/2018 Contract Number: AID-OAA-TO-17-00005 Study Team: Dr. Patricia Vondal, Senior Evaluation Specialist, ME&A Dr. Benita O’Colmain, Senior Survey Methods Specialist, ICF International Dr. Gheda Temsah, Senior Data Analyst, ICF International Dr. Ramu Bishwakarma, Senior Data Analyst, ICF International Dr. Nizam Khan, Senior Data Analyst, ICF International Submitted by: ME&A 4300 Montgomery Ave. Suite 103 Bethesda, MD 20814 Tel: 301-652-4334 Email: ethomas@engl.com 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. CONTENTS EXECUTIVE SUMMARY................................................................................................................................................... i Study Design.................................................................................................................................................................... i Study Limitations........................................................................................................................................................... ii Key Findings.................................................................................................................................................................... ii I. INTRODUCTION...................................................................................................................................................1 1.1 Overview of the Endline Study ....................................................................................................................1 1.2 Overview of Food Security Situation from 2010-2016..........................................................................2 2. METHODOLOGY AND LIMITATIONS ...........................................................................................................6 2.1 Methods for the Endline Population-Based Household Survey............................................................6 2.2 Methods For Qualitative Baseline Study and the Performance Evaluation .....................................12 2.3 Data Limitations and Issues Encountered................................................................................................17 3. FINDINGS................................................................................................................................................................18 3.1 Characteristics of the Study Population ..................................................................................................18 3.2 Household Food Security, Poverty, and Livelihood Activities ...........................................................19 3.3 Agriculture ......................................................................................................................................................27 3.4 Water, Sanitation, and Hygiene .................................................................................................................31 3.5 Women’s Health and Nutrition.................................................................................................................34 3.6 Children’s Health and Nutrition................................................................................................................38 3.7 Gender.............................................................................................................................................................47 4. CONCLUSIONS ....................................................................................................................................................52 REFERENCES....................................................................................................................................................................56 ANNEXES (submitted under separate cover) 1. EVELYN Statement of Work 2. Protocol for Population-Based Household Survey 3. Population-Based Household Survey Questionnaire—English version 4. Population-Based Survey Data Treatment and Analysis Plan 5. Protocol for Qualitative Study 6. Data Collection Sites Selected by Implementing Partners and Rationale 7. Qualitative Data Collections Instruments 8a. Tabular Summary of Endline Indicator Estimates 8b. Comparison of Baseline and Endline Indicator Estimates 9. Descriptive and Bivariate Tables 10. Multivariate Analyses for Stunting LIST OF TABLES Table 1. Program Area and Sampled Households by Implementing Partner......................................................7 Table 2. Endline PBS Response Rates, Ethiopia 2017..............................................................................................10 Table 3. 2012-2016 Indicators Collected at Baseline and Endline, Ethiopia 2017 ...........................................11 Table 4. Qualitative Data Collection Sites Used for the Endline Study by Site Classification and Data Source................................................................................................................................................................13 Table 5. Baseline-Endline Comparison of Household Head Characteristics, Ethiopia 2012 & 2017 ..........19 Table 6a. Baseline-Endline Comparison of HDDS and Food Groups, Ethiopia 2012 & 2017.......................20 Table 6b. Prevalence of Moderate or Severe Food Insecurity (FIES), Ethiopia 2017......................................22 Table 6c. Endline Estimates of Poverty Indicators, Ethiopia 2017 .......................................................................24 Table 6d. Baseline-Endline Comparison of the Share of Food Consumption Expenditures, Ethiopia 2012 & 2017 .............................................................................................................................................................25 Table 7. Households by Type of Livelihood Activity and Other Sources of Income in the Year Preceding the Survey (percentage), Ethiopia 2017...................................................................................................26 Table 8. Agricultural Indicators, Ethiopia 2017 ........................................................................................................28 Table 9. Baseline-Endline Comparison of WASH Indicators, Ethiopia 2012 & 2017......................................32 Table 10a. Women's Health and Nutrition Indicators, Endline Survey..............................................................34 Table 10b. Baseline-Endline Comparison of Antenatal Care Visits.....................................................................37 Table 11. Baseline-Endline Comparison of Children's Health and Nutrition Indicators................................38 Table 12. Self-Earned Cash Decision-Making, Ethiopia 2017 ................................................................................48 Table 13. Maternal and Child Health Knowledge and Decision-Making ............................................................50 LIST OF FIGURES Figure 1. Distribution of Average Daily Per Capita Expenditures, Combined Project Areas, Ethiopia 2017 ...........................................................................................................................................................................25 Figure 2. Percentage of Households by Type of Livestock (at least one) by DFAP Area..............................28 Figure 3. Percentage of Farmers That Plant Crops or Raise/Buy Livestock with the Specific Intention to Sell or Resell...................................................................................................................................................30 Figure 4. BMI Levels of Non-Pregnant Women of Reproductive Age, Combined DFAP Areas, Ethiopia 2017..................................................................................................................................................................35 Figure 5a. Comparison of Baseline and Endline Food Groups Consumed by Women 15-49 Years of Age in the CRS Implementation Area, Ethiopia 2012 & 2017....................................................................36 Figure 5b. Comparison of Baseline and Endline Food Groups Consumed by Women 15-49 Years of Age in the FH Implementation Area, Ethiopia 2012 & 2017.......................................................................36 Figure 6. Components of MAD Among Children 6-23 Months of Age by Age and Breastfeeding Status, Combined DFAP Areas, Ethiopia 2017 ...................................................................................................45 Figure 7. Breastfeeding Status for Children 0-23 Months by Age in Months, Combined DFAP Areas, Ethiopia 2017 .................................................................................................................................................46 Figure 8. Self-Earned Cash Decision-Making, Combined DFAP Areas, Ethiopia 2017 ...................................49 Figure 9. Maternal Health and Nutrition Decision-Making, Combined DFAP Areas, Ethiopia 2017..........51 Figure 10. Child Health and Nutrition Decision-Making, Combined DFAP Areas,.........................................51 Ethiopia 2017 .................................................................................................................................................51 ACRONYMS ANC Antenatal Care BL Baseline BMI Body Mass Index CAPI Computer-Assisted Personal Interviewing Software CFI Chronically Food Insecure CHN Children’s Health and Nutrition CMAM Community Management of Acute Malnutrition CRS Catholic Relief Services DA Development Agent DFAP Development Food Assistance Project DFSA Development Food Security Activity DHS Demographic and Health Survey DRM Disaster Risk Management EBF Exclusive Breastfeeding EL Endline EVELYN Evaluation and Learning Mechanism FAO Food and Agriculture Organization of the United Nations FANTA Food and Nutrition Technical Assistance III Project FCo-HHH Female Co-Heads of Household FEWS NET Famine Early Warning Systems Network FGD Focus Group Discussion FFP Food for Peace FH Food for the Hungry FIES Food Insecurity Experience Scale FY Fiscal Year GI Group Interview GOE Government of Ethiopia GPS Global Positioning System HDDS Household Dietary Diversity Score HEW Health Extension Worker HH Household HHS Household Hunger Scale ICF ICF International IP Implementing Partner IRB International Review Board KFSTF Kebele Food Security Task Force KII Key Informant Interview LSMS Living Standards Measurement Study MAD Minimum Acceptable Diet MCHN Maternal and Child Health and Nutrition MDD-W Minimum Dietary Diversity-Women ME&A Mendez England & Associates MHHH Male Heads of Household MHN Maternal Health and Nutrition MIC5 Mothers with Infants and Children Under Age Five NRM Natural Resources Management ORT Oral Rehydration Therapy PBS Population-based Survey PDSB Permanent Direct Support Beneficiary PE Performance Evaluation PIM Program Implementation Manual PPP Purchasing Power Parity PSNP Productive Safety Net Program QS Qualitative Study REST Relief Society of Tigray SAM Severe Acute Malnutrition SCUS Save the Children USA SD Standard Deviation SNNPR Southern Nations, Nationalities and Peoples’ Region SOW Statement of Work UNICEF United Nations Children’s Fund USAID United States Agency for International Development USD United States Dollars USG United States Government WASH Water, Sanitation and Hygiene WDDS Women’s Dietary Diversity Score WFSTF Woreda Food Security Task Force WHO World Health Organization WV World Vision WHHH Women Heads of Household i EXECUTIVE SUMMARY This report provides results from a mixed-methods study of recently completed United States Agency for International Development (USAID) Food for Peace (FFP) Development Food Assistance Projects (DFAPs) in Ethiopia. The DFAPs were designed to: 1) enhance resilience to shocks and livelihoods; and 2) improve food security and nutrition for rural households vulnerable to food insecurity. Four non￾governmental organizations (NGOs), Catholic Relief Services (CRS), Food for the Hungry (FH), Ethiopia/Relief Society of Tigray (REST), and Save the Children USA (SCUS), implemented the FFP￾funded DFAPs in selected woredas of Ethiopia, respectively, in the Oromia Region and Dire Dawa Administrative Unit, the Amhara Region, the Tigray Region, and in Somali and Oromia Regional States. Unfortunately, for security reasons it was not possible to include an assessment of the SCUS DFAP in this study. The endline (EL) study was conducted by a team assembled by Mendez England & Associates (ME&A), under the Evaluation and Learning Mechanism (EVELYN). It was conducted as part of a joint baseline (BL) and EL population-based survey (PBS). The qualitative data were drawn from Tufts University’s qualitative performance evaluation report of the four DFAPS (2017). Additional qualitative data were used from the qualitative study (QS) conducted in 2017 for the BL report for newly-awarded Development Food Security Activities (DFSAs) in Ethiopia. The primary purpose of the EL study is to provide endline estimates for key FFP impact and outcome indicators drawn from communities assisted by the DFAPs, and compare these indicators where appropriate with those collected at baseline. The intended audiences are FFP and its implementing partners (IPs), as well as other key stakeholders. STUDY DESIGN The EL study included a representative PBS of 5,400 households and a qualitative data from the Tufts DFAP Performance Evaluation (PE) report (2017) based on key informant interviews (KIIs) and focus group discussions (FGDs) with woreda and kebele officials and village residents. Data were also drawn from the Qualitative Study conducted in 2017 for the DFSA BL study based on KIIs and group interviews (GIs). In addition, secondary data sources were used to provide important background information covering the DFAP implementation period. Mixed-method analytical techniques were used to integrate qualitative and quantitative findings for the EL study report. Field work for the PBS was conducted from July 4-August 21, 2017. The PBS sample was selected using a multistage clustered sampling design to provide a statistically representative sample of three of the four DFAP areas.1 The PBS questionnaire was developed through a series of consultations with FFP, the Food and Nutrition Technical Assistance III (FANTA) Project, FFP awardees, and USAID/Ethiopia. The design of the qualitative DFAP PE conducted by Tufts was based on a purposive sample of 15 woredas covering the four DFAP implementation areas. Field work was conducted in October and November 2016. The data collection instruments were based on evaluation questions in the scope of work for the DFAP PE. The QS design for the BL study was based on a purposively drawn sample of eight data collection sites, two in each of the four DFSA implementation areas. Site selection criteria were developed by the IPs. Data collection instruments were based on modules used in the questionnaire for conducting the PBS. Findings from the qualitative data were used to provide contextual information and explanations for significant statistical findings for each topic. Field work was conducted from July 4-August 5, 2017. Findings from this 2017 QS for the BL were used to augment those from the DFAP PE report because the Statement of Work (SOW) did not specifically focus on qualitative data collection for FFP impact and outcome indicators. Secondary data sources used to provide important background information and context include the Famine Early Warning System 1 Due to security reasons, the EL study could not cover the SCUS DFAP in the Somali and Oromia Regional States. ii Network (FEWS NET) food security outlook reports and fact sheets, the USAID Complex Emergency Fact Sheets, situation reports from the Food and Agriculture Organization of the United Nations (FAO) and the United Nations Children’s Fund (UNICEF), the 2010 Government of Ethiopia (GOE) Growth and Transformation Plan, and the 2016 Program Implementation Manual for Productive Safety Net Program 4 (PSNP 4). STUDY LIMITATIONS Effects of other donor programs. There were several other ongoing programs in the DFAP implementation areas during the implementation of the DFAPS that may have had a direct effect on some indicators. The attribution of effects to any specific program in the area are not able to be discerned. Limitations of the qualitative data used for the EL study. There was no specific qualitative study designed for the EL study because a qualitative PE of the DFAPs had been conducted in October￾November 2016. The SOW and focus of the DFAP PE were developed to respond to a wide range of FFP questions and to evaluate the performance of the DFAPs. As such, the primary focus was not on collecting qualitative data linked to the PBS Household Questionnaire Modules for FFP impact and outcome indicators. The team used the data from the PE to provide whatever insight could be used to contextualize and explain findings from the PBS EL data. To augment the qualitative findings from the DFAP PE Report, the team drew on some of the findings from the QS, which was designed and conducted to provide qualitative data from the BL study for the four DFSAs. Secondary data from USAID, UNICEF, and FAO reports were also used to provide additional context to the PBS findings. Validity and reliability of self-reported data. Much of the data collected for the household survey were self-reported, which has 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 GIs. To mitigate these limitations, the team triangulated the PBS findings with the qualitative findings from the QS. The QS minimized the possibilities of these effects through a triangulation process by asking the same or very similar questions during GIs with woreda and kebele officials and, at the village level, during separate GIs with Male Heads of Households (MHHH), Female Co-Heads of Households (FCo-HHH), and Women Heads of Households (WHHH). Responses from each GI conducted in villages were internally compared, and then compared with responses from woreda officials and kebele officials. KEY FINDINGS Food security: The Household Dietary Diversity Score (HDDS) improved in all three DFAP areas, but the EL estimates of hunger also underscore moderate access to food. Although a BL-EL comparison of the prevalence of hunger cannot be performed because the BL used the Household Hunger Scale (HHS) while the EL used the Food Insecurity Experience Scale (FIES), the FIES-based EL estimates indicate that food insecurity remains a persistent challenge. Overall, more than one-third of households (36.9 percent) experienced moderate or severe food insecurity in the 30 days prior to the EL survey and close to one-half (53.5 percent) did so in the past 12 months as measured by the FIES. Conclusion: It is possible that further increases in HDDS and improvements in the prevalence of food security may have been achieved, but the severity of the 2015-2016 drought, policy changes related to content and amount of food in the PSNP 4 food transfers, and the 3.5 million PSNP 3 households graduated based on quotas were all limiting factors. Increases in dietary diversity were attained by some households who were able to establish backyard gardens given sufficient rain. Poverty: There are several methodological differences in the calculation of the BL and EL poverty indicators; therefore, a direct comparison is not feasible. The EL estimate for per capita consumption iii expenditures are markedly higher than the BL, and subsequently the EL estimates for the prevalence of poverty are markedly lower. More comprehensive information on household consumption expenditures, collected at EL, could have contributed to a higher consumption aggregate at the per capita level, which could, in turn, have led to a lower EL estimate of the prevalence of poverty. The EL poverty estimates indicate variability in the economic well-being of households by DFAP implementation area. At EL the prevalence of poverty was highest in the CRS implementation area (76.7 percent). The prevalence of poverty was similar in FH area (50.4 percent) and the REST area (46 percent). With the exception of the REST implementation area, adult male-only households have the highest daily per capita consumption expenditures, lowest prevalence of poverty, and lowest mean depth of poverty. Sources of income from remittances, gifts, inheritances, and safety net or cash assistance comprise a source of income to 61.3 percent of households in the REST implementation area, 52.7 percent of households in the FH area, and 35.4 percent of households in the CRS implementation area. The most important source of income among these categories is from safety net or cash assistance, 2 clearly showing how critical government assistance is to the food security of these households. Conclusions: Because a direct comparison of BL and EL poverty indicators is not feasible, the team can only draw limited conclusions. The prevalence of poverty seen at EL was affected by crop failures, declines in livestock productivity, livestock death from multiple years of erratic rainfall, and location￾specific incidents of drought and flooding. These effects on household poverty levels were further exacerbated by the 2015-2016 drought. The drought also caused a reversal of food self-sufficiency achieved by some households. Households frequently search for wage-earning opportunities in times of crop failure, but the daily wage rate paid for agricultural labor declined significantly because of the few commercial farms unaffected by drought conditions and the large number of people seeking opportunities on these farms. Agriculture: Overall, more than one-third (37.5 percent) of farmers in the DFAP areas used at least one financial service. Use of financial services varies widely by DFAP area. Farmers’ use of financial services is lowest in the CRS implementation area (17.3 percent). Compared to CRS, use of financial services is about three times higher in the FH implementation area (46.9 percent) and about two times higher in the REST project area (37.5 percent). Commercial farming is more commonly practiced by male farmers than female farmers. In all three DFAP areas, the majority of farmers engaged in commercial farming practiced at least one value chain activity (CRS, 81.7 percent; FH, 87.7 percent; and REST, 86 percent). The majority of farmers in DFAP areas used at least three sustainable agriculture practices. In the combined DFAP areas, farmers are more likely to use at least three sustainable crop practices (92.1 percent) and at least three sustainable livestock practices (71.5 percent) than three sustainable natural resource management (NRM) practices (44.1 percent). With the exception of use of sustainable livestock practices in CRS areas, in each of the DFAP areas male farmers compared to female farmers are generally more likely to use at least three sustainable crop practices, three sustainable livestock practices, or at least three sustainable NRM practices. Overall, 26.1 percent of farmers used at least one of the improved storage practices in the past 12 months. Water, Sanitation, and Hygiene (WASH): The percentage of households using improved sanitation facilities declined in the CRS and FH implementation areas. Open defecation increased in the FH area. The percentage of households with soap and water at a handwashing station declined in the FH and REST implementation areas. About one-quarter of households can access drinking water in less than 2 There are several sources of safety net food/cash resources. These include PSNP, emergency assistance provided during or immediately after severe weather events such as droughts or flooding, and assistance from GOE contingency funds. Reportedly the major source of emergency assistance is from GOE contingency funds, but contingency funds have also been used as Transitory Assistance for PSNP graduates and other chronically food insecure households that are not enrolled as PSNP beneficiaries. The PBS household survey did not ask respondents to indicate the specific source of safety net food/cash resources. iv 30 minutes. There was no significant difference in the use of water improvement technologies across all DFAP areas. Conclusion: Data from FEWS NET, USAID Complex Emergency Fact Sheets for Ethiopia in 2015 and 2016, and findings from the DFAP PE Report show that years of insufficient rain and particularly the major drought of 2015-2016 created severe water shortages in many of Ethiopia’s hotspot woredas. Steps were taken by the GOE, donors, and relief organizations to address the resulting crisis of insufficient drinking water. These water shortages explain why only one-quarter of households can access drinking water in less than 30 minutes. Despite the widespread messaging and growing understanding of the importance of handwashing at critical times and the training provided by DFAP IPs and health extension workers (HEWs), shortages of water coupled with the prioritization of available water for drinking purposes resulted in the decline of handwashing practices across the three DFAP areas. Lack of durability of the latrine model introduced and its high replacement cost are two key factors that contributed to the decline in using improved sanitation facilities as well as the increase in open defecation found in the FH implementation area. The team has no qualitative data to help explain the low use of water improvement technologies. Women’s Health and Nutrition (WHN): A comparison of BL and EL estimates of overlapping food groups indicates continued reliance on grains, roots, and tubers. In CRS’s implementation area, women’s consumption of other vitamin A-rich fruits and vegetables declined since BL. In the FH area, the consumption of legumes, beans, and nuts increased since BL, but there was decline in the consumption of eggs and other vegetables. Women’s food consumption patterns parallel the overall household consumption patterns and are also consistent with the types of crops planted by DFAP area. Most women in the combined DFAP areas have a normal body mass index, implying they have a normal weight. About one-third (36.2) of non-pregnant women 15-49 years are underweight. Results indicate no significant changes in the receipt of at least four antenatal care (ANC) visits between BL and EL in the FH and REST implementation areas. Contraceptive prevalence rate in the combined DFAP areas at EL is 37.3 percent, and the most commonly used method among women in the three DFAP areas is injectables. Although there are no BL estimates for the contraceptive prevalence rate, a comparison of average household size indicates a decline in the implementation areas since BL. Conclusion: At EL about one-third (36.2 percent) of women in the combined DFAP areas are underweight indicating the need for further improvements in women’s nutritional status. The low levels of dietary diversity as evidenced by the women’s dietary diversity score (WDDS)—on average women consume two or less of nine nutritional rich food groups—at BL and the minimum dietary diversity￾women (MDD-W)—on average, less than 10 percent of women consume 5 of 10 nutritionally rich food groups—at EL are related to multiple factors described in the findings that taken together create barriers to women’s food access and availability. These barriers explain women’s consumption of cheaper and locally grown foods, specifically grains, roots, and tubers, as the basis of their diet. The DFAPs were not designed to include family planning and the promotion of modern contraceptive use. Among the reasons for the relatively low prevalence of contraceptive use are religious beliefs against the concept of family planning, women’s fears that contraceptives may harm them, and, in some areas, the domination of men in all areas of decision-making in predominantly Muslim communities. The team does not have data to form conclusions on the lack of statistically significant change between BL and EL in prevalence of women making at least four ANC visits during their last pregnancy in the REST and FH implementation areas, or why EL ANC results in REST are higher than in CRS and FH areas. The data do suggest that ongoing efforts tailored to specific locational features and sociocultural norms and practices within each DFAP area are required for family planning and health care seeking during pregnancy. v Children’s Health and Nutrition (CHN): Children’s malnutrition (underweight, stunting, and wasting), the prevalence of minimum acceptable diet (MAD), and the prevalence of exclusive breastfeeding improved in all three DFAP areas. Results of multivariate analyses indicated the odds of being stunted are lower for children living in female-head-households compared to male-headed households and lower for children living in households whose head has a primary education or some primary education compared to children living in households whose head has had no schooling. Mother’s participation in paid work is associated with lower odds of stunting but the effect washes out when the model controls for the sociodemographic and economic characteristics of the household. The odds of a child under five being stunted are twice as high in the FH area compared to the CRS area. Children living in the REST area are more likely to be stunted than children in the CRS area. The prevalence of diarrhea in the REST area declined markedly by almost 50 percentage from 68.9 percent to 21 percent despite the deterioration in the use of a proper handwashing station. In FH, the prevalence of children 0-23 months with diarrhea remained stable at around 26 percent despite the deterioration in the use of a proper handwashing station, decline in the use of an improved sanitation facility, the increase in practice of open defecation and no change in the use of an improved water source. Use of a correct water practice or technology is associated with lower prevalence of diarrhea in all three DFAP areas. In the CRS area, the prevalence of diarrhea among children 0-23 months is 8.6 percent of children in households that use correct water treatment but it is twice (19.8 percent) as high among children in households that do not use correct water treatment. Conclusions: There have been moderate improvements in CHN indicators (malnutrition) in all three DFAP areas. The improvement in the CHN indicators (malnutrition, MAD) is supported by moderate improvements in HDDS. The finding from the multivariate analyses that children living in female-headed households are less likely to be stunted suggests differences in decision-making and resource allocation for households where women are the sole decision-makers. Based on bivariate analysis of the prevalence of diarrhea and WASH indicators, the use of water treatment technologies is one of the important factors associated with the decrease in diarrhea in REST between 2012 and 2017. Additional analysis could be conducted to identify other factors that contributed to this decrease. Gender: Men are more likely to partake in cash-earning activities (65.2 percent) compared to women (44.6 percent). Joint decision-making on use of self-earned cash is more common than deciding alone. About one-half of men (57.6 percent) and women (55.1 percent) decide with their spouses on the use of self-earned cash. Approximately one-third of men (33.1 percent) and one-quarter of women (26.1 percent) decide jointly. A total of 17.6 percent of women have no say on how their self-earned cash will be spent compared to 8.9 percent of men. Across the three DFAP areas it is more common for maternal health and nutrition (MHN) and CHN decisions to be made alone and usually by the woman than to be made jointly by spouses. In the combined DFAP areas about 56.6 percent of women decide alone on MHN issues compared to 23.9 percent of men. A total of 59.4 percent of women decide alone on CHN matters compared to only 13.2 percent of men. Joint MHN and CHN decision-making is less common, but nonetheless, approximately one-third of decision-making is done together. The majority of women in the combined implementation areas (83.9 percent) have some participation in decisions having to do with their own health and nutrition and 57 percent make this decision alone. 3 A total of 16.1 percent of women do not participate in MHN decision-making because their husbands decide alone. Relatedly, about 24.2 percent of men do not engage their wives in MHN decision-making. Similar to MHN decision-making, the majority of women (89 percent) in the combined DFAP areas participate in decisions having to do with the health and nutrition of their children. Only 10.9 percent of women have no input into CHN decisions and a similar percentage of men (13.3 percent) report that they decide alone (13.2 percent) on CHN issues or 3 This includes women who reported deciding alone, women who reported deciding with their spouse, and women who reported deciding with someone else on their own health and nutrition. vi let someone else make the decision (0.1 percent). On the other hand, about one-half of fathers (54.1 percent) are not involved in CHN decision-making. Conclusions: The predominance of women’s participation in Maternal and Child Health and Nutrition (MCHN) decisions is likely the result of the ongoing focus on gender equity and the importance of women’s participation promoted by GOE PSNP officials and each IP over the duration of the DFAP implementation period. 4 Each IP used a variety of techniques to promote these changes including: different forms of messaging, such as role playing; holding community conversations on gender; and the establishment of gender clubs in schools. IPs provided gender training of government officials including HEWs to sensitize them on gender issues and to engage them in promoting changes and direct support to village women. 4 The various approaches and methods used by IPs are described in the DFAP PE Report (2017) on under the findings on gender equity and empowerment. 1 I. INTRODUCTION 1.1 OVERVIEW OF THE ENDLINE STUDY In Fiscal Year (FY) 2012, the United States Agency for International Development (USAID) Office of Food for Peace (FFP) awarded funding for four multi-year development food assistance projects (DFAPs) in Ethiopia. The goal of the FY 2012-2016 DFAP awards was to enhance food security among targeted chronically food insecure (CFI) households. Under the Evaluation and Learning Mechanism (EVELYN) umbrella contract, FFP contracted Mendez England & Associates (ME&A) and its subcontractors ICF International (ICF) and TANGO International (TANGO) to conduct an endline (EL) study of three of the four DFAPs recently completed in Ethiopia [see Annex 1 for the Statement of Work (SOW)].5 Kimetrica was subcontracted as the local data collection firm. The EL study was conducted as part of a joint baseline (BL) and EL population-based survey (PBS) and qualitative data collection effort designed to serve the purposes of the EL study as well as a BL study for four newly-awarded development food security activities (DFSA) in FY 2016 in the same regions of Ethiopia. This study focuses on the results of the EL study only; the results of the BL study are provided in a separate report. The EL study is the second phase of a pre-post evaluation cycle. The first phase involved a BL study at the beginning of the DFAP implementation cycle in 2012. The EL study includes: 1) a representative population-based household survey to collect data for key FFP impact and outcome indicators; 2) qualitative data collected from interviews with woreda and kebele officials and village residents in a purposive sample of data collection sites; and 3) review of secondary data sources to provide background information. The objectives of the EL study for the DFAPs are to:  Assess the EL status of key FFP impact and outcome indicators;  Compare EL indicators with BL indicators where appropriate and conduct a statistical test of differences; and  Provide qualitative and secondary data to add context to the PBS findings. The United States Government (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 [Food and Agriculture Organization of the United Nations (FAO, 2009)]. The EL study, designed to provide information on all four elements of food security, investigates: food insecurity and food access; expenditures and assets; water, sanitation, and hygiene (WASH) practices; agriculture; women’s and children’s health and nutrition; and gender differences in decision-making for cash earners and parents of children under two years of age. Implementing Partners (IPs) for the DFAPs Four non-governmental organizations (NGOs), Catholic Relief Services (CRS), Food for the Hungry (FH), Relief Society of Tigray (REST), and Save the Children USA (SCUS), implemented the FFP-funded DFAPs in Ethiopia: 1. CRS and its partners implemented its DFAP in the Oromia Region and Dire Dawa Administrative Unit. 2. FH and its partners implemented its DFAP in the Amhara Region. 3. REST and its partners implemented its DFAP in the Tigray Region. 5 The various approaches and methods used by IPs are described in the DFAP PE Report (2017) on under the findings on gender equity and empowerment. 2 4. SCUS and its partners implemented its DFAP in the Somali and Oromia Regional States. Due to security concerns, this EL study did not collect data for the SCUS DFAP and thus will be limited to results for the three DFAPs implemented by CRS, FH, and REST. Goals and Overall Approach of the Three DFAPs The USAID/FFP DFAPs implemented respectively by CRS, FH, and REST were USAID’s contribution to the Government of Ethiopia (GOE) Protective Safety Net Program (PSNP). As such, they were designed to align with and support PSNP policy and programs in each region. The DFAPs had a common goal (albeit with slight variations) aligned with the goal of PSNP: to enhance food security among targeted CFI households. Key FFP program expectations for each DFAP to achieve increased food security included: reaching targeted objectives in Maternal and Child Health and Nutrition (MCHN) and WASH, resiliency, enhanced gender equity, and capacity building. The following summary provides information about each IP’s specific goal, and the major approaches from their results framework designed to achieve the goal. 6 CRS: The goal of the CRS DFAP was “reduced food insecurity of chronically food-insecure households (HHs) in seven woredas in Oromia Region (six woredas) as well as Dire Dawa City Administration (one woreda) of Ethiopia.” To achieve this goal, CRS focused on two major areas. One was on increasing resilience through asset development and protection as well as through using PSNP transfers of food packages and cash. The other major approach focused on increasing health and nutrition status through programming in WASH, improved diets, health and nutrition services, and improved behaviors. Gender empowerment was included as a strong emphasis in implementing each programmatic area. FH: The goal of FH’s DFAP was “to improve the food security status for all members of food-insecure households in 12 woredas of Amhara Region (415,031 beneficiaries).” To achieve this goal, FH focused on two major sets of activities. One focused on improving resilience by activities that would reduce food gaps, protect assets, improve natural resource management (NRM), and improve local capacity building. The second set of activities were focused on improving health and nutrition by incorporating MCHN and WASH components. Cross-cutting theme incorporated into these two programmatic areas were improving gender relations, capacity building, and disability inclusion. REST: The goal of the REST DFAP was to “sustainably increase the food-security status of chronically food￾insecure households in targeted woredas of Tigray.” Their program was based on three major areas of emphasis: 1) watershed management approaches to improve production, smooth consumption, and increase availability and accessibility of food; 2) complementary support to food security through the development of health and nutrition; and 3) capacity building at the household, community, and institutional levels. This report begins with an overview of the food security situation in Ethiopia during the period 2010- 2016, followed by a description of the methods used for the PBS and qualitative study and for using qualitative findings from secondary sources. The findings from the PBS are presented and integrated with the results of the qualitative study and secondary sources, followed by conclusions based on key findings. 1.2 OVERVIEW OF FOOD SECURITY SITUATION FROM 2010-2016 This section includes an overview of some of the key events that influenced levels of food security between 2010 and 2016, and likely impacted DFAP FFP EL indicator values. These include drought and insufficient and erratic rainfall leading to successive years of crop shortage and livestock mortality between 2010-2014, the onset of an unusually strong El Nino in 2015 bringing a severe and prolonged 6 The various approaches and methods used by IPs are described in the DFAP PE Report (2017) on under the findings on gender equity and empowerment. 3 drought of historic proportions (and other severe weather events) during 2015 and 2016, and two major changes in the GOE PSNP, one in 2011, and one in 2016. 1.2.1 Background of Factors Affecting Food Security Situation 2012-2016 Major Climate Events The FFP Ethiopia DFAP IPs began program implementation in 2012 during a time period of ongoing drought in Ethiopia covering all countries in the Horn of Africa. Delayed and insufficient rain in some areas of Ethiopia and heavy flooding and hail in others led to reduced harvests and in some areas significant crop failures in 2010. Food prices increased in 2010 and, in 2011, food inflation of staple food prices was at 40.7 percent [Famine Early Warning Systems Network (FEWS NET) 2011]. In 2011, rains arrived late and were insufficient to reduce drought effects. These factors led to an emergency food insecurity situation in southern Somali, the lowlands of Oromia and the South Omo section of Southern Nations, Nationalities, and Peoples’ Region (SNNPR). The late rains and scarcity of water resources contributed to heavy livestock mortality, poor condition of remaining livestock, and low milk productivity. In significant parts of agro-pastoralist areas in Tigray, Amhara, and Oromia, late rains contributed to farmers delaying sowing their fields and, overall, less land area was cultivated. Significant crop losses and, in some areas, crop failure were predicted for the belg7 harvest because of delayed or failed rain leaving these areas in a food insecurity crisis situation. While later season kiremt rains brought some relief in local areas, water for human and livestock was scarce, in some areas requiring the continuation of emergency water trucking operations. Some 7.4 million people were supported by PSNP, and an additional 4.5 million people required emergency humanitarian assistance because of decreased rainfall and extremely high food prices. FEWS NET’s Climate Trend Analysis of Ethiopia (2012) covering the mid-1970s through the late 2000s showed increases in temperature across most of the country and seasonal rainfall (belg rains) declines from 15-20 percent across southern, southeastern, and southwestern Ethiopia. Their data analysis showed that over the past 20 years (1991-2011) the areas receiving sufficient belg rains for crop production contracted by 16 percent. The authors forecasted that continuing declines in rainfall and rainfall variability could result in a further contraction of 16 percent. During that same time period, the same areas receiving sufficient kiremt rains also contracted. In the eastern highlands, the trend analysis showed that belg rains would similarly be threatened. The authors concluded that the recent rainfall decreases were linked to a warming of the Indian Ocean and likely to persist for “at least the next decade,” along with more frequent occurrences of severe drought (FEWS NET 2012). 8 The pattern of erratic and unpredictable rainy seasons, rainfall variability, and reduced rainfall across agro-pastoralist and pastoralist areas of Ethiopia and, in some locations, either drought, flooding, or hail, continued from 2012-2014. 9 Below average rainfall during the belg rainy season in 2012 and 2013 adversely affected household recovery in regions that experienced high levels of food insecurity and malnutrition in 2011. In the pastoralist areas in some sections of Somali, Oromia, and SNNPR, chronic drought persisted. Erratic, spatially spotty, and insufficient rainfall in many areas of the country in 2014 decreased harvested food crops further and extended the period of food insecurity and above-average food prices. These conditions all contributed to an increased need for support. With the onset of the 7 Refers to crops harvested that were planted at the beginning of the belg rainy season. See FEWS NET Food Security Outlook reports (2012). In a typical year, belg (early rainy season) rains fall from February through April. Kiremt rains fall from June through September. 8 See FEWS NET Fact Sheet 2012-3053. A Climate Trend Analysis for Ethiopia, pg. 6, April 2012. 9 See FEWS NET Ethiopia Food Security Outlook Reports 2012, 2013, 2014. These reports provide information for areas of chronic food insecurity (hotspots susceptible to drought) on prior harvests for staple crops, effect on food prices, and outlook for crop harvest based on most recent and current behavior of belg and kiremt rainy seasons, and forecasts. 4 2015 El Niño, already predicted to be the strongest on record, the ongoing needs for humanitarian assistance were exacerbated.10 In 2015-2016, El Niño conditions created a severe, long-lasting drought, floods, and other extreme weather events with widespread global impacts. Ethiopia suffered its worst drought in 50 years causing successive harvest failures and widespread death of livestock, a key asset and means of livelihood among agro-pastoralist and pastoralist populations. Between March and May 2015, up to 2.9 million people required emergency food assistance and up to 1.6 million people required emergency water support.11 The United Nations Office for the Coordination of Humanitarian Affairs (OCHA) reported migration from Amhara due to water shortages. The number of “priority” hotspot districts, that is, districts at high risk from malnutrition, increased by 98 percent between February and June 2015.12 In March 2015, there was a 15 percent increase in the number of children with severe acute malnutrition (SAM) that were admitted into therapeutic program feeding sites compared to March 2014.13 In 2016, severe drought conditions persisted in some parts of the country, while other areas experienced very heavy spring rainfall and flash floods displacing populations, destroying roads, and increasing the difficulties of providing relief operations and food distribution.14 By May 2016, 10.2 million people required emergency food assistance, up from 2.9 million in May 2015. More than 1.3 million households across Ethiopia required emergency livestock support to prevent further steep declines in the livestock population. Contributing to food insecurity of poor rural households in hotspot areas was the reduction of opportunities for seasonal daily wage work on larger or commercial farms in 2015-2016. Insufficient rainfall affected production on these farms as well. Where seasonal work in plowing, weeding, and harvesting was available, the usual daily wage rates were reduced by as much as 50 percent in some places (e.g., Amhara, Southern Tigray, Oromia) because of the increased number of people looking for work increased the supply of wage laborers on these farms.15 Petty trade that relied on processing agricultural products, locally brewed beer for example, declined because of very low or failed harvests. Petty trade based on non-agricultural products for sale declined as well because the usual consumers no longer had the income for extra purchases. Government of Ethiopia Productive Safety Net Program The GOE PSNP is designed to increase the food security, economic well-being, and resilience of rural households in designated hot spots affected by recurrent drought. Many of the agriculturally-based households in these areas have become CFI and are vulnerable to food shortages, poverty, and malnutrition. PSNP began its third round of assistance (PSNP 3) in 2011 and was in its second year of operation in 2012 when the four FFP DFAPs were awarded to FH, REST, SCUS, and CRS. The PSNP provides qualifying households in woredas covered by the program with either food or a combination of food and cash transfers six times a year. In return, each able-bodied member of a beneficiary household provides their labor for a defined number of days per month to an organized public works program designed to benefit their village. PSNP involves a diverse number of programs, activities, and services in the areas of: agriculture; water and irrigation; NRM; reforestation and soil fertility improvements; financial services; and a livelihood development component to help beneficiaries graduate from the program. Within a village, only those households that meet the beneficiary criteria are enrolled in PSNP. 10 See USAID, 2016. Project El Nino in Ethiopia 2015-2016: A Real Time Review of Impacts and Responses. USAID/Ethiopia Agriculture Knowledge, Learning, Documentation and Policy Project. 11 See USAID, Ethiopia-Complex Emergency Fact Sheet # 3, Fiscal Year 2015. 12 Ibid. Hotspot priority classifications are defined by the level of vulnerability to malnutrition is measured by several indicators including severity of food insecurity, prevalence of moderate to high levels of malnutrition, and admission trends in therapeutic feeding of children. Priority 1 is the most severe hotspot classification. 13 Ibid. 14 See USAID, Ethiopia-Complex Emergency Fact Sheet # 10, Fiscal Year 2016. 15 See USAID, El Nino in Ethiopia 2015-2016: A Real Time Review of Impacts and Responses, USAID/Ethiopia Agriculture Knowledge, Learning, Documentation and Policy Project, 2016 pg. 12. 5 However, it is important to note that the number of qualified CFI households in woredas covered by PSNP exceeds the amount of resources the government has. 16 By design, the DFAPs (and current DFSAs, see Ethiopia Baseline Report 2017) are directly aligned with PSNP policy, programming, and regulations. DFAP programming is for PSNP beneficiary households, and includes capacity development for PSNP officials at the woreda and kebele administrative levels. Two important events were rolled out in PSNP covering the DFAP implementation timeframe (2012- 2016) which, in addition to the effects of weather shocks and recurrent drought, have implications for the EL status of FFP outcome and impact indicators. The first event was the start of the targeted reduction of PSNP 3 beneficiaries, which began in 2011 one year prior to the start of the DFAP. The second event was based on changes in the amount of food transferred through food distribution packages, introduced in 2016 at the beginning of PSNP 4. Targeted Reduction of Beneficiaries. Starting in the first year of PSNP 3, the GOE began graduating beneficiaries in the highlands area to meet a targeted reduction from 5.0 million beneficiaries in 2011/2012 to 1.3 million in 2014/2015.17 To meet the target, each region was given a yearly quota of PSNP beneficiaries to graduate. Woreda officials were given a specific number of beneficiary households to graduate based on the quota for their region. The final evaluation report of the DFAPs describes the graduation of PSNP beneficiaries according to the GOE targeting plan which was implemented without reference to the status of household food security required for beneficiary program graduation. The DFAPs were excluded from the process of selecting beneficiary households to graduate. According to PSNP policy, to graduate, a household must reach food self-sufficiency. As defined by the program, food self-sufficiency is reached when a household has been able to feed its members for a 12-month period. Households that were graduated in fulfillment of the annual regional quota had not reached food self￾sufficiency.18 The process of graduating beneficiaries according to the targeting plan was stopped during the 2015 drought due to the magnitude of need for food support. Many of the households that were either self-graduated or graduated based on achieving food self-sufficiency lost those gains during the drought. According to the DFAP PE, some of these graduates and other CFI households received support from GOE contingency funds as Transitory Beneficiaries. Reportedly, some of these households were incorporated back into PSNP under stage four when retargeting took place in 2016. The DFAP PE report states that, in some areas, food insecure households that were graduated by quota from PSNP 3 have not been included in PSNP 4. The report includes a footnote briefly mentioning it was reported that in some woredas in Tigray and Oromia, as well as in Dire Dawa, forced graduates from PSNP 3 were not allowed to be re-enrolled under PSNP 4.19 PSNP 3 households that were graduated based on quotas could no longer be covered as beneficiaries under DFAP programming either. The DFAP PE states that they were unable to obtain the specific number of households graduated through implementation of the quota system. There were no funds or programming from the GOE or DFAP to follow-up with graduates (Tufts 2017). Standardization of Amount of Food Transfers. The second event entailed a series of important changes introduced in 2016 under PSNP 4, the last year of the FY 2012-2016 DFAPs. 20 One of the key changes with implications for beneficiary food security was the standardization of the food distribution package based on the nutritional and caloric needs of a five-person household. Under previous rounds of PSNP, the food distribution package was based on the number of people living in the household. Under PSNP 4, beneficiary households with over five people received insufficient food to meet 16 Ibid, DFAP PE 2017. 17 The targeted reduction of beneficiaries is described in the GOE Growth and Transformation Plan (GTP), 2010. Targets for each region were set at the national level for woredas to implement. 18 See 2017 DFAP PE, pg. 10, footnote 1: “Genuine graduates are those who have achieved food sufficiency, while forced graduates are those obliged to leave the DFAP due to the application of a quota, without having achieved food sufficiency.” 19 Ibid, footnote # 30, pg. 22. No source is cited for this reported information. 20 See Project Implementation Plan (PIM) for description of changes introduced in 2016 for PSNP 4. 6 household food security needs and the nutritional and caloric requirements of each individual in the family. In these households, food distribution packages were reportedly divided up between family members.21 According to the DFAP PE (2017), approximately 39 percent of all beneficiary households comprised more than five family members. The composition of the food distribution packages was also changed. Protein-fortified oil was eliminated. 2. METHODOLOGY AND LIMITATIONS 2.1 METHODS FOR THE ENDLINE POPULATION-BASED HOUSEHOLD SURVEY This section describes the methods used for the data collection for the EL PBS. The 2012 BL survey was conducted by Dadimos Development Consultants, PLC and funded by USAID. Methods for the BL survey are described in the “USAID Development Food Aid Program in Ethiopia Baseline Survey” Report, October 2012.22 2.1.1 Study Design and Objectives The EL study was conducted as part of a joint BL/EL PBS that was conducted in 2017. The EL component of the joint BL/EL PBS serves as the second phase of a pre-post survey cycle for the FY 2012-2016 DFAP awards. This pre-post design allows for the determination of statistically significant change in indicators between the 2012 BL and EL surveys; however, it does not allow statements about attribution or causation relating to project impact to be made. The objectives of the EL PBS for the DFAPs are to assess the EL status of key FFP impact and outcome indicators and to conduct a statistical test of differences between relevant 2012 BL and EL indicators. 2.1.2 Sample Design The target population for the joint BL/EL PBS consisted of two components: 1) all households in the areas where the FY 2012-2016 DFAPs were implemented; 23 and 2) all households in the areas where the newly awarded DFSAs will be implemented. These target populations overlap to a considerable extent, 24 since the DFSAs will be implemented in some of the same areas where the DFAPs were implemented. The sample size for the joint BL/EL PBS was derived by: 1) identifying the sample size needed for the EL survey for the DFAPs; 2) calculating the sample size needed for the BL survey for the current DFSAs; and 3) deriving a joint sample size based on these sample sizes, taking into account the overlap between the current DFSA and prior DFAP implementation areas. The sample size calculation for the joint BL/EL 21 Reports of insufficient food in PSNP food distribution packages were heard from village respondents participating in focus group discussions (FGDs) for the performance evaluation of the four DFAPs conducted in 2016, and from village respondents and village chiefs participating in the 2017 QS across all data collection sites. Group interviews (GIs) with kebele officials related that this was one of the major complaints brought before kebele appeals committees. Village participants in GIs living in households with over five family members said they divided the food between them. Accordingly, food in these packages did not last as long as intended by PSNP. “What else are we going to do? Give the food to five people in the family and have the other people in the household watch them eat it?” The field researchers do not know if PSNP food was distributed equally between all members of the household. 22 Available at: http://pdf.usaid.gov/pdf_docs/pa00jnt1.pdf 23 The lowland/pastoral areas of Somali (Liben Zone) and Oromia (Borena Zone) were not included. These areas were part of the prior DFAP implemented by SCUS. 24 Thirty-five percent of households in the current CRS BL target areas are in areas where CRS worked before; 81 percent of households in the current FH BL target areas are in areas where FH worked before; 73 percent of households in the current REST BL target areas are in areas where REST worked before; and 41 percent of households in the current WV target area are in areas where the FH project worked before. 7 PBS is 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 both the DFSAs and the DFAPs. Table 1 shows the areas covered and derived sample size by IP for the joint BL/EL. It is worth noting that the overall sample size for the joint BL/EL PBS (8,460 households) is substantially less than the sum of the sample sizes of the two individual PBSs (4,620 + 6,960 = 11,580 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. A stratified multi-stage clustered sample design was used with three stages of sampling: 1) selection of kebeles; 2) selection of households; and 3) selection of individuals. Each DFAP represented one stratum and the sample was allocated to woredas within each stratum based on the number of households in each woreda (see Annex 2, “Ethiopia Joint Baseline/Endline PBS Protocol” for more details on the sample design and allocations). Table 1. Program Area and Sampled Households by Implementing Partner Implementing Partner (DFAP) Program Area Number of sampled households for 2012 BL study Number of households needed for 2017 EL study Number of households needed for 2017 BL study Number of sampled households for 2017 joint BL/EL study Number of sampled kebeles (30 households per kebele) CRS Oromia and Dire Dawa 1,522 1,540 1,740 2,670 89 FH Amhara 1,530 1,540 1,740 1,740 58 REST Tigray 1,542 1,540 1,740 2,190 73 World Vision (WV)* Oromia and Amhara -- -- 1,740 1,860 62 TOTAL 6,097 4,620 6,960 8,460 282 *Although WV was not an IP for the prior DFAPs, some areas covered by the prior FH DFAP are included in the WV DFSA target area. 2.1.3 Questionnaire The questionnaire for the EL study was the same as that used for the 2017 BL study.25 This joint BL/EL PBS questionnaire was developed through a series of consultations with FFP, the Food and Nutrition Technical Assistance III Project (FANTA), and the 2017 IPs before, during, and after the 2017 BL planning workshop in April 2017 (see Annex 3). All questionnaire modules follow FFP and Feed the Future guidelines, as described in the FFP Indicators Handbook (April 2015)26 and the Feed the Future Indicator Handbook (September 2016). 27 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 25 The questionnaire differed from that used for the 2013 BL study in that the 2013 BL questionnaire was not designed to collect data for all of the same EL indicators although some modules were essentially the same as those in the EL questionnaire. See Annex 2 for a description of the differences in the 2013 BL questionnaire. 26 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 2017. 27 Available at https://feedthefuture.gov/sites/default/files/resource/files/Feed_the_Future_Indicator_Handbook_Sept2016.pdf 8  Module D: Children’s Nutrition and Health  Module E: Women’s Nutrition and Health  Module F: Water, Sanitation, and Hygiene (WASH)  Module G: Agriculture  Module H: Poverty  Module J: Gender – Cash  Module K: Gender – Maternal and Child Health and Nutrition (MCHN) Questions for Modules A through G, J, and K were adapted using questions from the FFP Standard Indicators Handbook and the Demographic and Health Survey (DHS) questionnaire.28 Questions for Module H were adapted from the World Bank’s Living Standards Measurement Study (LSMS). Questions requiring adaption for the local context for all modules were discussed and modified based on inputs provided during the BL workshop. The questionnaires were prepared in English first and then translated into three local languages (Amharic, Tigrigna, and Afan Oromo) and pretested in the field. The total time for completing the survey was approximately 2-3 hours per household. 2.1.4 Field Procedures Listing Exercise The local data collection subcontractor (Kimetrica) conducted household listing and mapping in the 282 selected kebeles. Listers and mappers were trained over three days (May 13-15, 2017) to: locate a cluster; identify its geographical boundaries; draw sketch maps; identify locations of the households on the sketch map; and measure global positioning system (GPS) coordinates. The listing exercise was conducted from May 16 to June 11, 2017. Twenty teams consisting of four listers and a mapper carried out the household listing operation. The results from the household listing operation were used for the second stage sampling of households in each sampled kebele. Pretest, Training, and Pilot Test The EVELYN team developed training manuals based on FFP and DHS 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. Both supervisor and interviewer manuals contain instructions on how to operate the tablet computers in their respective roles. The training manuals were translated into Amharic, the national language. Pretest: Kimetrica conducted the pretest training for a select group of experienced interviewers and supervisors. It included an in-depth review of the questionnaire and the use of tablet computer to conduct the interviews. After the training, both a paper and computer-assisted personal interviewing software (CAPI) version of the questionnaire were pretested with non-sampled households in the three regions—Amhara, Oromia, and Tigray—where the four DFAPs operate. The paper-based pretest assessed the soundness of the questionnaire and identified potential problem areas, such as issues with filter questions, wording, sequencing of questions, instructions to interviewers, and the clarity of the questionnaire for coding. The CAPI-based pretests assessed the programming of the questionnaire flow and skips and use of the tablets in the field, including data transmissions. All proposed changes to the questionnaire were reviewed by the EVELYN team and submitted to FFP for approval. The revised questionnaire was used for the subsequent interviewer and supervisor trainings. The pretest training was conducted in Addis Ababa from May 25-30, 2017 and the pretest was conducted from May 31-June 4, 2017. 28 MEASURE DHS. DHS model questionnaire: Phase 6 (2008-2013) (English, French). Available at http://www.measuredhs.com/publications/publication-dhsq6-dhs-questionnaires-and-manuals.cfm 9 Training: The main survey training took place from June 13-28, 2017 at Ethiopian Red Cross Society Training Center in Addis Ababa. The training included combined and separate training for interviewers, supervisors, and anthropometry specialists. Training was conducted at a residential setting in which all participants were provided with accommodations and meals. The training consisted of lectures, classroom practice, group discussions, use of tablet computers, and role play. The trainings are described as follows:  The interviewer training was held from June 13-27, 2017, and covered: roles and responsibilities of interviewers; objectives of the survey; selection of eligible respondents within households; call-back procedures; completion of field forms; a question-by-question review of the household questionnaire; and the use of tablet computer for interviews. The interviewer training was conducted in four groups with 50–60 participants in each group. Several language sessions were conducted in some evenings throughout the interviewer training. Interviewers were divided into three groups based on the three languages of their native fluency (Amharic, Tigrinya, and Afan Oromo). In these language sessions, interviewers discussed and verified the appropriateness and accuracy of the translation and also practiced mock interviews.  The supervisors were selected from the group of interviewer trainees. After the interviewer’s training, they received a one-day training on June 28, 2017, focused on: the roles and responsibilities of supervisors; quality control procedures; identification of sampled households; selection of eligible respondents; and use of field control sheets, maps, and GPS devices for data collection.  The anthropometry training was conducted from June 15-28, 2017, with classroom and hands￾on training on: measurements of recumbent length and weight for children under two years of age and standing height and weight for children between two and five years of age and women 15-49 years of age. The training included standardization testing. One interviewer from each team was also trained to serve as an anthropometry assistant in the field. Supervisors and field coordinators received training on how to use the World Health Organization (WHO) Growth Charts for referral of severely malnourished children. Pilot Test: All interviewers, supervisors, anthropometry specialists, and field coordinators participated in a full-scale pilot test in non-sampled enumeration areas close to Addis Ababa from June 29 to July 3, 2017. The pilot test provided a field practice for the interviewers, supervisors, and the field coordinators. It served as an opportunity to observe the preparedness of the interview teams, their contact strategies, their familiarity with the questionnaires, and their comprehension of the household sampling process. Each interviewer completed at least two survey questionnaires during the pilot test. At the post-pilot test debriefing, all interviewers received feedback on their performance, discussed difficulties, and clarified any questions. Fieldwork Kimetrica collected the data for the PBS from July 4-August 21, 2017, during the rainy season and towards the end of the hunger season. 29 The survey data collection team included the following personnel: the EVELYN survey coordinator, a local research director, 2 local survey monitors, 10 local field coordinators from Kimetrica, 5 local information technology (IT) specialists, 40 local supervisors, 160 local interviewers, and 40 local anthropometry specialists. Each of the 40 interview teams consisted of a supervisor, 4 interviewers, and an anthropometry specialist. In each interview team, interviewers served as anthropometry assistants and assisted taking the anthropometric measurements. Interviews were conducted in three major local languages—Amharic, Tigrinya, and Afan Oromo. Supervisors 29 As a point of comparison, the data collection for BL in support of the FY 2012-2016 DFAPs was conducted in June/July of 2012. 10 managed the teams to ensure that the survey protocol was followed, reviewed each completed questionnaire, and conducted spot checks for at least 15 percent of all completed questionnaires. Each of the 10 field coordinators from Kimetrica conducted quality control checks for 4-5 survey teams. Quality control checks were designed to verify whether the interviewers completed the questionnaires by interviewing the eligible respondents in the selected households and asking the correct modules and questions. Quality control checking was conducted both in the presence of and absence of the survey team, i.e., at times the checks were performed while the survey team collected data and, on other occasions, the checks were conducted after the survey team had completed and left the kebele. In addition to the Kimetrica staff, the EVELYN survey coordinator and two local survey monitors were in the country throughout all critical phases of the survey, including the pretest, training, pilot test, and fieldwork, to coordinate and supervise the activities. Throughout fieldwork, the EVELYN survey coordinator received frequent updates from the survey monitors assigned to each of the DFAP areas. 2.1.5 Data Processing and Analysis Sampling Weights Sampling weights were computed and used in the data analyses. Weights were computed for the EL PBS 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 (cluster/kebele selection and household selection). Weights were calculated for the following distinct sampling groups:  Households (used for indicators derived from Modules C, F, and H);  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); and  Parents of children under two years of age (Module K). Weights were calculated separately for each of the DFAP areas and adjusted to compensate for household- and individual-level non-response as shown in Table 2 for the EL PBS only. Table 2. Endline PBS Response Rates, Ethiopia 2017 Number Sampled Number Interviewed Response Rate (%) Households (Modules C, F, and H) 5,400 5,227 96.8 Children 0-59 months of age (Module D) 3,418 3,403 99.6 Women 15-49 years of age (Module E) 5,042 4,937 97.9 Non-pregnant women 15-49 years of age (Anthropometry) 4,516 4,494 99.5 Farmers (Module G) 6,802 6,766 99.5 Male cash earners, married or in a union (Module J) 3,548 3,424 96.5 Female cash earners, married or in a union (Module J) 1,813 1,771 97.7 Fathers of children under two years of age (Module K) 1,102 1,051 95.4 Mothers of children under two years of age (Module K) 1,302 1,288 98.9 11 Indicator Definitions and Tabulations For the EL study, definitions and methods for tabulation of all FFP indicators are presented in the Data Treatment and Analysis Plan (see Annex 4). The WHO child growth standards and associated software (WHO, 2011) are the basis for tabulating the child stunting and underweight indicator values. Consumption aggregates, for computing the prevalence of poverty, mean depth of poverty, and per capita expenditure indicators, follow the World Bank LSMS methodology. Results for all indicators were weighted to represent the full target population. 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 indicator estimates by age, sex, and gendered household type were tested for statistical significance, taking into account the clustered sample design.30 Additional bivariate and multivariate analyses were conducted to explore relationships between indicators. Throughout the report only those differences that are statistically significant are cited; the results of all analyses are provided in Annex 9. For the 2012 BL study, weighted indicators were calculated based on the 2011 FFP Indicator Handbook definitions. Because some modules of the BL questionnaire were not administered in all BL implementation areas, indicators could only be calculated for those areas where the appropriate data were collected. Table 3 provides a list of the indicators collected at BL for each DFAP area for which data were also collected at EL and for which comparisons between BL and EL can be made. There were some methodological differences in the way indicators were computed at the time of the 2012 BL study (as compared to how they are computed at present) that required recalculation of EL indicator estimates in adherence with the BL methodology. These are footnoted in Table 3. Table 3. 2012-2016 Indicators Collected at Baseline and Endline, Ethiopia 2017 Indicator CRS FH REST Average Household Dietary Diversity Score (HDDS)    Per capita (adults only) expenditures (as a proxy for income) of USG-assisted areas*    Prevalence of poverty: Percent of people (adults only) living on less than $1.25/day*    Mean depth of poverty (expressed as percent of poverty line)*    Percentage of households using an improved source of drinking water   Percentage of households using improved sanitation facilities   Percentage of households practicing open defecation   Percentage of households with soap and water at a handwashing station commonly used by family members   Percentage of births in the last two years receiving at least four antenatal care (ANC) visits during pregnancy**   Prevalence of underweight children under five years of age    Prevalence of stunted children under five years of age    Prevalence of wasted children under five years of age    Percentage of children 0-23 months of age with diarrhea in the last two weeks**   Percentage of children 0-23 months of age with diarrhea treated with ORT**   Prevalence of exclusive breast-feeding of children under six months of age    Prevalence of children 6-23 months of age receiving a minimum acceptable diet (MAD)    *The target population is adults 18 and older. **The target population is children less than two years of age. 30 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. 12 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 anthropometry indicators, the WHO software flagged biologically implausible cases according to WHO criteria (WHO Multicentre Growth Reference Study Group, 2006), which were excluded from the analysis but left in the data set. For poverty indicators, there are special methods for handling missing data (see Appendix B of Annex 4). 2.2 METHODS FOR QUALITATIVE BASELINE STUDY AND THE PERFORMANCE EVALUATION 2.2.1 Qualitative Data Sources and Purpose The qualitative findings used for the Ethiopia EL Study are drawn from two sources: 1. Secondary data reported in the 2017 Report of the Performance Evaluation (PE) of the FFP DFAPs (2012-2016); and 2. Primary data collected for the 2017 Qualitative Baseline Study (QS). A QS was not conducted for the EL report because a qualitative PE of the four DFAPs was conducted in 2016 that could serve as a source. However, the scope of work was developed to answer a wide range of specific FFP questions and there was no systematic data collection related to FFP impact and outcome indicators. To augment the qualitative information that could be used from the DFAP PE, findings were also drawn from the primary data collected for the 2017 Qualitative Baseline Study. Qualitative Baseline Study: The QS was designed specifically to collect BL data for the 2016-2020 DFSAs. These data collection sites were chosen by the current DFSA IPs (see site selection process below in section 2.2.3. and Annex 6: Data Collection Sites Selected by Implementing Partners and Rationale). Data were collected in July and August 2017. The purposes served by the QS were to:  Complement findings from the 2017 BL PBS with contextual information to increase and enrich understanding of BL data;  Provide explanatory information for 2017 BL PBS findings from each sector where appropriate; and  To serve as a comparison with the situation pertaining to the future PBS 2020 EL data. Performance Evaluation of the FFP DFAPs: The qualitative PE of the four DFAPs was the final evaluation of the DFAPs covering the years 2012-2016. The data collection sites were selected by Tufts University. Methods for site selection for the PE of the four DFAPs are summarized in section 2.2.3. Data collection for this evaluation was conducted in October and November 2016. The purposes of the PE were to:  Evaluate the individual effectiveness of each of the four DFAPs with regard to achieving program objectives and targets, including their crosscutting objectives, and evaluate their contribution to USAID’s effort to improve food security of the target population in the project areas;  Evaluate changes (results) produced by the programs—intended and unintended, direct and indirect; and  Provide specific recommendations on aspects of design, sustainability strategies, and implementation approaches that the FFP and Mission should consider in the design and development of future programs in Ethiopia. 13 2.2.2 Qualitative Data Collection Sites Used for the Endline Study There are 11 qualitative data collection sites from the DFAP PE that qualified for incorporation into the EL study. To qualify, the qualitative data sites had to be included as part of the PBS sample used for this EL study either as woredas having overlap with both 2012 BL and 2017 EL data, or as woredas included in the EL sample covered by the DFAPs having no overlap with current DFSAs (see Table 4). Two of the 11 sites were also part of the sample of data collection sites covered by the QS conducted for the DFSA baseline study. Table 4. Qualitative Data Collection Sites Used for the Endline Study by Site Classification and Data Source # IP Region Woreda Kebele Site Classification Data Source (QS 2017, PE 2016) EL Only EL/BL Overlap 1 FH Amhara Lay Gayint Sofiya Meda and Mekubia * QS 2017-Both kebeles were also included in the PBS sample 2 FH Amhara/South Gonder Simada Engudad * PE 2016 – Woreda only 3 REST Tigray/Southern Tigray Raya Azebo Ebo: * PE 2016 – Woreda only 4 REST Tigray/Eastern Tigray Gula Mekeda Marta: * PE 2016 – Woreda only 5 REST Tigray/Eastern Tigray Hawzen Frewoyni and Debreselam: * PE 2016 – Woreda only 6 REST Tigray/Central Tigray Kola Tembien Bega Sheka: * PE 2016 – Woreda only 7 REST Tigray/Central Tigray Tanqua Abergele Sheka Teli * PE 2016 – Woreda only 8 WV Amhara/Wag Hemra Sekota Hamusit * PE 2016 – Woreda only 9 WV Amhara/North Wollo Lasta Bilbala * PE 2016, QS 2017 The kebele was also included in the PBS sample 10 CRS Oromia/East Hararghe Mete Hawi Bilisuma * PE 2016 11 CRS Dire Dawa Dire Dawa Adada * PE 2016 2.2.3 Sample Design, Data Collection Methods, and Participants This section presents a summary of the methodology used for the QS conducted in 2017 and for the PE conducted in 2017.31 QS Sample The QS used a purposive sample of eight data collection sites. Each site is defined as a single woreda, two kebeles within that woreda, and two villages, each one administered by one of the two kebeles selected. In total, qualitative data were collected in 8 woredas, 16 kebeles, and 16 villages. 31 For full details of the methodology used for the QS, see the Ethiopia Baseline Study Report (2018). The Performance Evaluation of the Four DFAPs Report (2017) provides full details of the methodology used for the PE. 14 The purposive sample design was based on site location criteria defined by each of the four IPs for their DFSA implementation area. Budget parameters for the entire QS meant limiting the number of locations for data collection to two woredas in the implementation area covered by each of the four IPs (REST, FH, CRS, and WV). IPs were asked to define their own criteria for selecting two woredas in their DFSA implementation area and they selected two kebeles within each woreda to serve as the primary loci of data collection. The four IPs selected two data collection sites that contrasted along several variables. Three of the four IPs (FH, REST, and CRS) selected woredas with different agro-ecological zones and, within woredas, kebeles with different agro-ecological zones at a sub- or more micro-level. Two of the four IPs (FH and REST) chose one woreda that overlapped with the DFAPs within which they implemented from 2012 to 2016, and a new woreda (where they had not previously worked). As a new IP for the current DFSAs, WV could not select sites on that basis. One of the criteria selected by CRS was based on sites implemented by different partners in the CRS consortium. Each IP’s selection criteria included a high percentage of PSNP beneficiaries. Following the establishment of their criteria, IPs then selected the actual data collection sites (see Annex 6). Villages in each kebele were selected following the identification of randomly selected villages for the PBS. This was done to ensure that interviews for both the PBS and QS were not conducted in the same village to avoid interview overload. QS Data Collection Methods and Participants The qualitative survey was directed by EVELYN’s Senior Evaluation Specialist and supported by Green Professional Services, a local firm located in Ethiopia. Green Professional Services arranged logistics and transportation, and provided personnel for data collection, preparation of transcripts, coding, data entry, and analysis. Team members were trained in data collection protocols and the use of each data collection instrument July 1-2, 2017. The teams pretested each instrument on July 3, 2017, and instruments were adjusted based on the results of the pretesting on July 4, 2017. Data collection began on July 5 and ended by August 4, 2017. Data collection was conducted by two three-person teams comprising a team leader, 32 a senior social scientist, and a junior/mid-level social scientist with qualitative data collection experience in rural areas on food security issues. Each team member had fluency in Amharic and in either Tigrinya or Oromo. Team 1 was headed by EVELYN’s Senior Evaluation Specialist, and she was accompanied full-time by a local interpreter with fluency in English, Amharic, and Tigrinya. This team collected data in two data collection sites in Amhara in the FH implementation area and in two sites in Tigray in the REST implementation area. Team 2 was led by a local senior social scientist originally from Oromia with extensive experience in qualitative and quantitative data collection in rural agricultural areas of Ethiopia. Team 2 collected data in two sites in Oromia in the WV implementation area, and two sites in the CRS implementation area, one in Oromia and one in Amhara. The QS conducted group interviews (GIs) with respondents at three levels: woreda, kebele, and village. At the village level, all participants in GIs were PSNP beneficiaries. At the woreda level, GIs were conducted with government officials associated with oversight and administration of PSNP in the kebeles and villages included in the woreda. At the kebele level, GIs were conducted with government officials associated with management and implementation of PSNP in the villages included in their kebele. At the village level, GIs were conducted with PSNP beneficiaries in four separate groups based on demographic characteristics. The four groups included: male heads of household (MHHH); female co-heads of household (FCo-HHH); women heads of household (WHHH); and mothers with infants and/or children under age five (MIC5). For the purpose of this study, WHHH were defined as women either widowed, divorced, or abandoned who head their own household. These households do not have any adult male family members. MIC5 were defined as young mothers age 29 and under. Key informant interviews (KIIs) were conducted with kebele Health Extension Workers (HEWs) to obtain information on gender issues, a more detailed understanding of WASH and MCHN issues in the villages they serve, and their 32 The team leaders were senior social scientists with team leadership experience. 15 perspectives on how village women understand and use WASH and MCHN practices. KIIs were also conducted with the village chief in each of the 16 villages included in the QS. The QS questionnaires for GIs with woreda officials and kebele officials, for GIs at the village with MHHH, FCo-HHH, and WHHH, and for the KIIs with village chiefs contained many of the same questions. This was for the purposes of strengthening findings related to those questions during the analysis phase through a triangulation process, but also to highlight differences of opinion and attitude between woreda and kebele officials, and between those of these officials and village respondents. Similarly, many of the same questions in the questionnaire for MIC5 GIs on WASH and MCHN topics were also covered in the questionnaire for KIIs with the kebele HEW to cross-check and contrast responses. The questions for GIs with MHHH and FCo-HHH were all identical to obtain information on gender perspectives and experiences. DFAP PE Sample Design The sample for the DFAP PE was based on the selection of woredas. The sample included 20 woredas. In total, data were collected in 20 woredas and 26 kebeles. Information is not provided in the PE Report on the number of villages visited for data collection. The first stage of sample selection took into account the fact that the number of DFAP woredas varied between IPs. Because the qualitative evaluation also sought to compare performance between IPs, a minimum sample size per IP was required in order to generate meaningful results. Based on the evaluation team’s calculus, a minimum number of three woredas had to be chosen per DFAP. 33 The number of woredas selected for the PE by region included 11 in Tigray, 3 in Amhara, and 6 in Oromia/Dire Dawa. The SCUS DFAP was implemented in the Borena Zone of Oromia and in Somali Region. The evaluators noted that they could not choose woredas for data collection in Somali because of security conditions and logistical issues. Accordingly, only one woreda was visited for data collection in the Borena Zone. Woreda agricultural officials were asked to select kebeles for data collection based on what they viewed as “the best and poorest kebeles.” 34 There is no information in the DFAP PE report describing how villages were selected for data collection. DFAP PE Data Collection Methods and Participants The PE was based on a qualitative design. Data were collected through focus group discussions (FGDs), KIIs, and direct observation techniques. At the woreda and kebele levels, KIIs were held with government officials in each location from the Kebele Food Security Task Force (KFSTF), the Woreda Food Security Task Force (WFSTF), the Woreda Disaster Risk Management (DRM) staff, and with HEWs and Development Agents (DAs). KIIs were also held with IP DFAP field agents. In each kebele, FGDs were held (one each) with the following groups: male beneficiaries, female beneficiaries, graduates (male and female together), and Permanent Direct Support Beneficiary (PDSBs) (male and female together). Groups were stipulated to be between 7 and 10 respondents but, in some cases, more were present. Presumably FGD participants were from nearby villages. 35 In addition, the evaluation team conducted observations of public works. A key aspect of the evaluation was the direct observational assessment of the activities that had been undertaken, taking note of the quality and sustainability of community works and activities, as well as their relevance 33 The DFAP PE report does not include information regarding whether the actual woredas visited by the evaluation team were purposively or randomly selected. Additionally, the number of woredas selected for data collection for each DFAP IP is not included in the PE report. 34 PE Report of the Four DFAPs, 2017, pg. 17. 35 The DFAP PE report does not include information on villages represented in FGDs that were conducted in each kebele or how these participants were selected. 16 to different sections of the community. A checklist of questions was prepared, field tested by the entire team working together in the first kebele, and, subsequently, used when assessing such interventions. 2.2.4 Data Preparation, Coding, and Analysis QS Coding, Data Preparation, and Additional Coding EVELYN designed a code book with multiple codes for location, administrative level, type of participant or participant group, topic area, and sub-topical areas corresponding to modules in the PBS HH questionnaire. Additional coding was added based on resilience indicators appropriate to resilience analysis. Each data collection instrument was pre-coded with multiple codes. The Code Book and pre￾coded data collection instruments were transmitted to a senior qualitative data analyst at Green Professional Services to develop a qualitative database using NVivo. During data collection, transcripts were prepared for each interview by location and transmitted to Green Professional Services on an ongoing basis for translation into English. Completed transcripts were reviewed by the Green Professional Services General Manager, the Green Professional Services Senior Qualitative Data Analyst, and the EVELYN Study Director. Green Professional Services contacted team leaders and the senior qualitative data specialist from each team to review the English language transcript against the field notes they transmitted, to add missing responses and correct errors, and provide clarification. Following this process, transcript data were then entered into the database according to the Code Book. The analyst developed and applied additional sub-codes based on topics that emerged during the data collection phase. QS Analytical Procedures Analysis included the identification of an initial set of broad categories of responses to each group of related questions across all respondents, respondent groups, and data collection sites. In the next stage of analysis, the senior analyst identified distinct themes within each category of responses for each topic and sub-topic and developed narratives based on the thematic analysis. In a subsequent stage, ME&A conducted additional analysis of the data on selected topics. A comparative analysis of responses from woreda officials, kebele officials, and village residents participating in the qualitative study within and across data sites on the issues of recent and current climate shocks, yield and productivity outcomes, and food security was conducted. Within data collection sites, this entailed triangulating responses by respondent categories at the woreda and kebele administrative levels from each of the two kebeles selected for the qualitative study with responses from village resident sub-population groups from each of the two villages selected within the data collection site. These responses were then compared across all eight data collection sites. A comparative analysis was also conducted on issues related to adult female households across data collection sites from WHHH respondents, and where data existed, from woreda and kebele officials. Results of the qualitative analysis were subsequently used to develop findings. PE Analytical Procedures According to the PE report, qualitative responses from FGDs and KIIs were analyzed by a group discussion process. Each of the key questions raised in the original statement of work was discussed in turn by the entire evaluation team. “A balanced response based upon all interviews and group discussions was agreed upon and regional differences noted where these were relevant. The draft balanced response to each question was circulated to all team members to ensure that it correctly captured their observations. This process was repeated for each of the findings, conclusions, and recommendations sections of the Evaluation Report.”36 36 See Performance Evaluation of the Four Title II DFAPs, 2017, pg. 17. 17 Mixed-Method Analysis Mixed-method analysis entails a comparison of the analytical findings from two or more data sources to strengthen findings from the primary data source, in this case, from the PBS EL data. The evaluation design for the QS was structured based on modules from the household questionnaire used by the PBS to purposefully support mixed-method analysis drawing on both data sets. This last stage of analysis was begun by the qualitative analyst who reviewed both sets of findings per topical area to determine if findings from the PBS could be corroborated by the QS or DFAP PE findings. In those instances where they could not, the team looked further into their respective data sets and, in some instances, to other data sources. The EVELYN quantitative and qualitative analysts also collaborated on the integration of findings from the PBS, the QS, and DFAP PE report. The qualitative analyst drew on findings from the QS and DFAP PE report to provide contextual information and explanation for significant statistical findings for each topic. The quantitative analysts then reviewed the integration of these findings and held discussions with the qualitative analyst to refine those statements. 2.3 DATA LIMITATIONS AND ISSUES ENCOUNTERED 2.3.1 Data Limitations Effects of other donor programs. There were several other ongoing programs in the project implementation areas that may have direct effects on some project indicators. The attribution of effects to any specific program in the area are not able to be discerned. Limitations of the 2012 baseline study data. The BL study dataset was provided by the prior data collection subcontractor and was used to generate weighted estimates for BL indicators.37 There were some inconsistencies in the dataset with respect to modules administered for children 0-23 months of age. The results for indicators generated for this subgroup do not align with those presented in the BL study report. In addition, weighted estimates could not be generated for BL poverty estimates because the methods used to generate the BL poverty indicators could not be replicated. Due to the complex nature of deriving per capita expenditures and resulting poverty estimates, the BL poverty indicators cannot be directly compared to the EL poverty estimates which makes it difficult to assess changes in poverty status from BL to EL. Limitations of the qualitative data. The SOW for the DFAP PE was developed to evaluate the performance of the DFAPs. The PE primary areas of focus were: 1) DFAP design and effectiveness; 2) PSNP graduation; 3) gender equality and empowerment; and 4) program management, implementation, and sustainability. As such, the primary focus was not on collecting qualitative data relevant to FFP impact and outcome indicators in each sector. To augment the qualitative data from the DFAP PE report, the EL study team drew from findings from the QS for explanations of PBS EL data for topics that the DFAP PE report did not cover. 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 GIs. However, to reduce the likelihood of these potential effects on validity and reliability, the analysts triangulated the PBS findings with the qualitative study findings which were themselves strengthened by a process of triangulating responses from different respondent groups. Specifically, problems with validity and reliability of qualitative data were minimized by the study design which asked the same or very similar questions 37 The 2012 BL study did not use weighted analyses and sampling weights were unavailable. After communication with the BL study authors, sampling frames were provided and weights were constructed based on these sampling frames and the three stage multi-cluster sampling methodology. 18 during GIs with woreda and kebele officials and at the village level with MHHHs, FCo-HHHs, and WHHHs. Incomplete coverage of Oromia and Somali Regions. Coverage of CRS DFAP implementation areas in Oromia woredas selected for the PE was limited by widespread unrest in the region during the field work period in 2016. The national state of emergency imposed by the GOE in response to this unrest exacerbated limited access to certain CRS iareas. Accordingly, Meta was the only woreda in Oromia included in the DFAP PE, placing a strong limitation on the PE team’s ability to produce findings from this region. CRS areas in Somali could not be included because of unrest (DFAP PE Report, 2017). 2.3.2 Issues Encountered Compact schedule. In order to collect EL data during the same season as the prior BL study, it was necessary to complete the PBS data collection for the joint BL/EL by mid-August 2017. Because of the delayed timing of the EVELYN contract award, the timeframe for the survey was compressed, resulting in tight deadlines for the pre-survey activities. These activities included the: EL workshop; ethical review application; listing training and exercises; questionnaire pretest and modifications; CAPI programming and testing; interviewer and supervisor trainings; and pilot testing. Despite the above challenges, data collection was completed on time as planned with a 97 percent household level response rate. The QS faced the same challenges requiring a rigorous seven-day work week throughout the scheduled data collection period to complete all interviews on schedule in eight data collection sites. Data transmission and Internet connectivity issues. There were periods throughout the training and fieldwork where the entire Internet was down due to government restrictions which created problems with transmission of PBS data from the field teams to the central office. Backup storage devices were used to temporarily store data during these Internet outages. The teams collecting data for the qualitative study encountered similar issues. To counter this problem, transcripts were transmitted from regional airports where Internet coverage was stronger to the Green Professional Services headquarters in Addis Ababa. 3. FINDINGS The EL 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; and 7) gender. Each section discusses the findings from the EL PBS, the comparison between EL and BL, where relevant, and the supporting data from the qualitative study and secondary data sources as related to the relevant FFP indicators. Annex 8a provides a tabular summary of all indicators and indicator disaggregation along with sampling statistics. 3.1 CHARACTERISTICS OF THE STUDY POPULATION This section presents the characteristics of the household head, the composition of households, and the educational attainment of individuals in the three DFAPS at BL and EL. Table 5 illustrates BL-EL comparison of the household head characteristics. There was no change in the sex of the household head in FH but the percent of female-headed households increased in CRS from 5 percent at BL to 19.2 percent at EL. There was a modest increase in the percentage of female-headed households in REST from 21.8 percent to 28.9 percent. In all three project areas, most households at EL are headed by males (CRS, 80.8 percent; FH, 75.2 percent; REST, 71.1 percent). The age distribution of the head of households generally remained the same over time in the three DFAP implementation areas. The mean age of household heads remained relatively unchanged since BL in FH but increased in CRS and FH. Average household size decreased in all DFAP areas. 19 Table 5. Baseline-Endline Comparison of Household Head Characteristics, Ethiopia 2012 & 2017 CRS FH REST Baseline 2012 Endline 2017 Baseline 2012 Endline 2017 Sig Baseline 2012 Endline 2017 Sig Sex Male 95.0 80.8 *** 77.6 75.2 ns 78.2 71.1 ** Female 5.0 19.2 *** 22.4 24.8 ns 21.8 28.9 ** Age (years) 16-24 4.1 7.4 *** 3.0 5.1 * 2.1 3.6 ** 25-34 29.2 24.7 † 18.0 18.2 ns 16.9 16.1 ns 35-44 37.6 22.8 *** 22.0 21.5 ns 26.6 21.6 * 45-54 17.9 17.5 ns 24.1 19.9 * 21.7 17.9 * 55-64 11.2 14.5 *** 32.9 17.4 * 32.7 17.9 ns 65+ N/A 13.2 *** N/A 17.9 *** N/A 22.9 *** Mean age (years) 39.0 44.1 *** 47.0 47.7 ns 47.0 50.1 * Marital Status Married/Living Together 94.5 79.9 *** 75.6 73.5 ns 79.6 71.2 *** Divorced/Separated 0.5 5.5 *** 1.8 12.1 ns 0.5 11.8 ns Widowed 1.0 13.6 *** 11.8 11.6 ns 10.1 15.8 *** Never Married/Lived Together 4.0 1.1 † 10.8 2.9 ns 9.8 1.2 ns Educational attainment Never attended N/A 56.9 ns N/A 75.1 *** N/A 65.0 *** Church/mosque school/ traditional 16.6 N/A 46 N/A *** 20.4 n/a *** 1st cycle primary (1st - 4th grade) 39.6 22.3 ** 26.2 13.3 ** 35.1 18.1 ns 2nd cycle primary (5th - 8th grade) 34.5 16.2 ns 20.6 8.6 ns 31.6 12.8 ns Secondary level education (9th - 10th grade) 7.1 4.2 ns 5.8 2.8 ns 10.4 3.8 Above 10th grade 2.2 0.5 ns 1.5 0.2 * 2.5 0.3 * Average household size 5.8 5.0 ns 4.8 4.3 ** 5.5 4.7 * Number of households 1,523 1,497 1,530 2,141 1,542 1,586 *** p<0.001, ** p<0.01, * p<0.05, † p<0.10, ns = not significant Source: FFP Endline Survey, Ethiopia 2017. BL estimates were re-calculated to include sampling weights and therefore differ from the estimates provided in the BL study. 3.2 HOUSEHOLD FOOD SECURITY, POVERTY, AND LIVELIHOODS This section uses data from the BL and EL household survey where possible to highlight change over time in the food security and poverty status of households. Livelihood activities are discussed since they are related to the food security and poverty status of households and can provide additional context related to the vulnerability of households to food insecurity and poverty. 3.2.1 Household Food Security and Poverty The USG global food security strategy defines food security as “access to––and availability, utilization, and stability of–– sufficient food to meet caloric and nutritional needs for an active and healthy life” (U.S 20 Government, 2016; 10). This underscores the four pillars of food security: availability, access, utilization, and stability (FAO 2009). To measure household food security, the BL and EL surveys collected data on household dietary diversity (HDDS), the prevalence of moderate or severe hunger and the prevalence of moderate or severe food insecurity. Household dietary diversity score: The HDDS score is a count of the number of different food groups that households on average consume from a total of 12 food groups. It is both an indicator of food security and a measure of socioeconomic status because wealthier households can afford to grow or consume a wider range of food groups. Table 6a illustrates BL and EL estimates of HDDS and the percentage of households consuming the HDDS food groups by DFAP implementation area. HDDS at EL is statistically significantly higher than at BL in all three DFAP areas, indicating an increase in the number of food groups that households consume but also underscoring moderate access to food. In all three project areas cereals continued to be the top contributors to household diets and remained unchanged from BL to EL. Household intake of animal protein—namely, meat and poultry—increased in the FH and REST project areas but decreased in the CRS area. On the other had consumption of fish and seafood decreased in FH and REST. In all three DFAP areas, the consumption of eggs and milk or milk products increased from BL to EL. In CRS, the percentage of households consuming roots and tubers increased from 23.7 percent to 38.2 percent and more than doubled in FH from 6.2 percent to 14.7 percent, but decreased in REST from 27.4 percent to 10.3 percent. In REST, the consumption of vegetables showed a large increase from 18.8 percent at BL to 73.5 percent at EL. Increases in the consumption of vegetables were more modest in CRS and FH. Consumption of miscellaneous foods such as condiments, coffee, tea, etc. increased in all three DFAP areas. Table 6a. Baseline-Endline Comparison of HDDS and Food Groups, Ethiopia 2012 & 2017 CRS FH REST Baseline 2012 Endline 2017 Sig. Baseline 2012 Endline 2017 Sig Baseline 2012 Endline 2017 Sig Average Household Dietary Diversity Score 3.9 4.8 *** 3.1 4.3 *** 4.8 5.5 *** Household dietary diversity score food groups Cereals 97.4 97.0 ns 98.4 98.5 ns 96.4 96.3 ns Roots and tubers 23.7 38.2 ** 6.2 14.7 *** 27.4 10.3 *** Vegetables 38.8 42.3 ns 5.2 17.6 *** 18.8 73.5 *** Fruits 9.6 7.5 ns 1.4 3.8 ** 11.6 29.4 *** Meat and poultry 2.5 0.9 * 0.7 7.0 *** 3.2 9.5 *** Eggs 6.4 4.0 † 1.5 7.9 ** 7.8 17.0 *** Fish and seafood 2.0 0.1 † 0.8 0.1 * 2.5 0.2 *** Pulses/legumes/nuts 30.7 33.2 ns 63.0 88.6 *** 74.4 65.1 * Milk and milk products 30.5 44.6 *** 4.2 8.8 *** 11.1 14.4 ns Oils/fats 59.2 69.1 † 40.8 68.6 *** 75.3 78.3 ns Sugar/honey 48.1 50.0 ns 13.9 20.3 ** 73.1 59.9 *** Miscellaneous (coffee, tea, condiments, etc.) 35.1 93.9 *** 69.1 97.9 *** 82.6 99.2 *** Number of responding households 1,519 1,463 1,512 2,141 1,510 1,489 *** p<0.001, ** p<0.01, * p<0.05, † p<0.10, ns = not significant NOTE: T-tests were used to assess the statistical significance of the difference between the BL and EL estimates for HDDS. Chi square tests were used to assess the statistical significance of the difference between the BL and EL estimates for household consumption of the food groups. Source: FFP Endline Survey, Ethiopia 2017. BL estimates were re-calculated to include sampling weights and therefore differ from the estimates provided in the BL study. 21 Findings from the Tufts DFAP PE state that one area of clear improvement is dietary diversity, noting that the DFAPs enhanced dietary diversity among some households. In some DFAP implementation areas such as Oromia, CRS promoted household backyard gardens for cultivating vegetables and fruits. Where these were established and maintained, given sufficient rain, they made a contribution to dietary diversity. However, the PE also reported their finding dietary diversity among the poorest remains a problem, especially for large households and for PSNP PDSB. 38 FGD participants from PDSB households noted that prior to PSNP they normally consumed food from only two food groups, but benefited from access to three food groups during the first years of the program. When PSNP 4 reduced food distribution packages to two food groups, the dietary diversity of these households declined. Food Insecurity: The household hunger scale (HHS) was used to measure the prevalence of hunger at BL. It captures household food insecurity in the four weeks prior to the survey based on three questions: 1) In the past [4 weeks/30 days] was there ever no food to eat of any kind in your house because of lack of resources to get food?; 2) In the past [4 weeks/30 days] did you or any household member go to sleep at night hungry because there was no enough food?; and 3) In the past [4 weeks/30 days] did you or any household member go a whole day and night without eating anything at all because there was no enough food? The BL results for the HHS indicate that about one-quarter of households in CRS (22.6 percent) and FH (26. 8 percent) experienced moderate or severe hunger at BL. The prevalence of hunger was lower in REST at 12.1 percent. At EL, the food insecurity experience scale (FIES) was used to measure the prevalence of moderate or severe food insecurity for two different reference periods—30 days and 12 months prior to the survey. The FIES is based on a more diverse battery of questions and due to methodological differences, cannot be compared to the HHS. Although a BL-EL comparison cannot be performed, the FIES-based EL estimates give context to the household food insecurity situation in the DFAP areas at EL. As illustrated in Table 6b, overall, more than one-third of households (36.9 percent) experienced moderate or severe food insecurity in the 30 days prior to the survey and close to one-half (53.5 percent) in the past 12 months. The higher rate of moderate or severe food insecurity experienced in the past 12 months (compared to the 30 days prior to the survey) includes the food gap months which occur between sowing of seeds for crop cultivation and the period before those crops can be harvested, and this period can be extended in areas experiencing poor yields or crop failure because of weather events. Price increases in basic food commodities following poor and failed harvests contributes to the prevalence of hunger.39 The overall average masks differences between DFAP implementation areas. In both reference periods, the CRS area experienced the highest food insecurity (63.7 and 76.7 percent) and the REST area experienced the lowest food insecurity (30.9 and 46 percent). The prevalence of hunger in the FH project area is similar to that of the REST area (30.4 percent and 50.2 percent, respectively). In all three DFAP areas and for both reference periods, the prevalence of hunger was highest among adult female￾only households and lowest among adult male-only households. The high rate of food insecurity among adult female-only households compared to other type of households is corroborated by findings from qualitative data sites included in the QS. An overview of the factors that underlie the higher rate of food insecurity experienced by these women is found in the Ethiopia BL study (2018).40 The significantly higher rate of food insecurity in the CRS DFAP implementation areas of Dire Dawa and Oromia can be explained by the extensiveness of severe drought. The FEWS NET Ethiopia food 38 PDSB households do not have any able-bodied workers. Under PSNP 4, the number of food distribution packages/cash distributions provided to PDSB households increased from six months per year to 12 months per year. 39 See FEWS NET Food Security Outlook Reports from the years 2011, 2012, 2013, 2014, 2015, and 2016. These reports include data on price increases in basic crops consumed by households in rural areas. 40 See Ethiopia Baseline Report, 2018, for a discussion. The Derg Regime recognized the vulnerable status of women headed households and addressed their situation in implementation of a major land reform enacted in 1976. Or else it is through the implementation procedures used by village committee to implement the land reform. 22 insecurity outlook map in the regions of Ethiopia covering February-May 2016 indicates greater surface area covering central and eastern Oromia designated as experiencing high food insecurity, and a greater area designated as extreme food insecurity, compared to the regions of Amhara and Tigray. In 2016, the zones of East Hararghe and West Haraghe were expected to face extreme food insecurity based on poor harvests in 2015 and reduced income from labor and livestock. These two zones were in the CRS DFAP implementation area in Oromia. Dire Dawa was also shown as an area expected to experience extreme food insecurity. These conditions were expected to remain through September 2016.41 In addition to the greater severity of drought in Dire Dawa and Oromia, the Tufts DFAP PE report notes because family sizes in these areas are large, households experience larger food gaps compared to Tigray and Amhara. Table 6b. Prevalence of Moderate or Severe Food Insecurity (FIES), Ethiopia 2017 Overall CRS FH REST Prevalence of moderate or severe food insecurity based on 30-day recall (FIES) 36.9 63.7 30.4 30.9 Male and female adults 35.3 62.6 28.6 28.2 Adult female, no adult male 44.7 72.4 39.4 41.3 Adult male, no adult female 33.9 59.7 24.1 30.6 Child, no adults Prevalence of moderate or severe food insecurity based on 12-month recall (FIES) 53.5 76.7 50.2 46.0 Male and female adults 51.8 76.2 48.4 43.2 Adult female, no adult male 61.8 80.7 60.1 57.8 Adult male, no adult female 48.4 75.6 40.0 41.4 Child, no adults N/A N/A N/A N/A Number of responding households 5,224 1,497 2,139 1,588 Adult female, no adult male 4,170 1,244 1,688 1,238 Adult male, no adult female 857 187 361 309 Male and female adults 187 64 87 36 Child no adults 10 2 3 5 *Too few cases to include estimates for child only households. Source: FFP Endline Survey, Ethiopia 2017. Within DFAP implementation areas there exist a diversity of household situations and agro-ecological zones affecting prevalence of hunger and levels of food security. For example, PSNP beneficiary households with five or fewer members experienced greater food security because the standardization of the amount of food provided under food distribution packages based on a five-member household introduced under PSNP 4 covers their needs for at least part of the year. As mentioned, larger families, more frequently found in Oromia and Dire Dawa, experience greater food insecurity and longer periods of food gaps. Households in PSNP woredas that were graduated by quota under PSNP 3, and especially those that were graduated on this basis that have over five people, were among the most food insecure. Not only did they lose PSNP benefits, but they also lost the benefits from DFAP programming. The 2017 DFAP PE reports a finding based on FGDs with villagers and with kebele food security task forces in indicating that in the last year of the DFAP program, the effects of the severe 2015-2016 drought resulted in households losing many of the gains they made toward their food security over the previous years. The PE also reports that in the highlands area, crop failures extended the months of household food gaps. This effect was compounded in PSNP beneficiary families with over five household 41 The FEWSNET food security outlook map is found in the Ethiopia Complex Emergency Fact Sheet #7, March 30, 2016. 23 members once the size of the food distribution package was reduced and in households that were graduated by the GOE quota-based system between 2012 and the early years of 2015 (see Section 2.1.1, Factors Affecting Food Security Situation 2012-2016). Households that were better able to withstand the effects of this drought included PSNP households with five or fewer members that remained under the PSNP program spanning the five-year period covering 2012-2016. However, even many of the households that graduated from PSNP according to program criteria based on achieving food self￾sufficiency had to rely on emergency relief. The severity of El Nino effects from 2015-2016 also varied by agro-ecological zones in GOE hotspot areas as well as in agro-ecological zones within DFAP implementation areas. In more lowland areas for example, the severity and length of drought leading to crop failure was much more serious compared to midland and highland areas. For example, in the woreda of Lasta (Amhara region), GIs with kebele officials and in villages with MHHH related that because of rain shortages starting before the 2015-2016 drought, they had not been able to cultivate their land for four years, and were totally dependent on government support from PSNP. 42 Major flooding from the highlands led to crop failure or greatly reduced crop yields in the highlands and downstream in midland areas (USAID 2016, Ethiopia Baseline Report 2018). Poverty: FFP projects aim to improve nutrition and food security for vulnerable households in the project area. Poverty indicators are calculated based on household consumption expenditures, which include foods, non-food items, durable goods, and rent or rental equivalence. Food consumption expenditures account for items that are purchased, home-grown, or received in-kind. Typically, the poverty indicators are calculated after adjusting consumption expenditures to the size. Household consumption expenditures are used as a proxy for income and provide a measure of the poverty status for each household relative to the international defined daily per capita threshold for extreme poverty.43 In October 2015 the World Bank announced a shift from the $1.25 line using 2005 purchasing power parity (PPP) rates to a new international poverty line of $1.90 per capita per day using 2011 PPP rates. See Annex 4 for a detailed explanation and methodology for calculating the poverty indicators. There are several methodological differences in the calculation of the BL and EL poverty indicators that makes it difficult to make comparisons between them. The EL poverty estimates are based on a broader range of food and non-food consumption expenditures and a more detailed approach to estimating housing expenditures compared to the BL data. The estimation of the EL poverty indicators is based on the World Bank’s LSMS approach. The approach used to aggregate the BL data on consumption expenditures and to estimate the prevalence of poverty at BL is not fully documented in the BL study report. However, here are some of the major methodological differences in the calculation of the BL and EL poverty indicators: 1. The BL survey gathered information on household consumption of 58 food items while the EL covered 172 food items. The EL collected data on the monetary value of food consumed that came from own production and that was received in-kind. Thus, the EL estimate for daily per capita consumption expenditures, which is the basis for determining the percentage of poor households and depth of poverty, includes the value of food consumed that was purchased on the market, homegrown, or received in-kind. On the other hand, the BL survey did not collect data on the monetary value of food consumed that came from own production or received in kind. 2. The EL survey collected information on a diverse range of non-food consumption expenditures including detailed information on education and health expenses (74 items in total including the breakouts for specific education and health expenses). On the other hand, the BL survey 42 See Ethiopia Baseline Report, 2018. 43 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. 24 collected information on a less diverse range of non-food expenditures (35 items into some of which can be lumped in same group—e.g., clothing and shoes for women/men and boys/girls). 3. The EL survey collected information on rent and detailed information needed to impute a rental equivalent for individuals who own their home. The BL survey collected information on rent only, but not rental equivalence for homeowners. More comprehensive information on household consumption expenditures that was collected at EL can contribute to a higher consumption aggregate at the per capita level, which could lead to a lower estimate of the prevalence of poverty. At BL, the daily per capita consumption expenditures (adults only) was highest in REST [$1.86 constant 2010 United States Dollars (USD)], followed by CRS ($1.71 constant 2010 USD), and lowest in FH ($1.36 constant 2010 USD). Relatedly, the prevalence of poverty was lowest in REST (35 percent) and highest in FH (57 percent). The prevalence of poverty at BL in CRS was 45 percent. Table 6c illustrates variability between the DFAP areas in the poverty estimates at EL. The EL poverty estimates are presented based on adult members in the household only and using the $1.25 threshold for extreme poverty with 2005 PPP rates. Daily per capita consumption expenditures (adults only) in all three DFAP areas was $2.40 (constant 2010 USD) With the exception of the REST area, adult male only households have the highest daily per capita consumption expenditures, lowest prevalence of poverty, and lowest mean depth of poverty. At EL, the prevalence of poverty was highest in FH (24 percent) and REST (21.5 percent) and lowest in CRS (14.4 percent). The depth of poverty across the three DFAPs was 5.4 percent of the poverty line. Table 6c. Endline Estimates of Poverty Indicators, Ethiopia 2017 ALL CRS FH REST Per capita expenditures (adults only) (as a proxy for income) of USG-assisted areas $2.40 $2.80 $2.26 $2.35 Male and female adults $2.37 $2.76 $2.24 $2.31 Adult female, no adult male $2.54 $3.08 $2.22 $2.66 Adult male, no adult female $2.79 $3.48 $2.92 $2.14 Child, no adults* N/A N/A N/A N/A Prevalence of poverty: Percent of people (adults only) living on less than $1.25/day 21.1 14.4 24.0 21.5 Male and female adults 21.2 14.8 23.6 22.1 Adult female, no adult male 20.4 13.2 29.3 15.0 Adult male, no adult female 18.2 1.3 17.0 30.9 Child, no adults* N/A N/A N/A N/A Depth of poverty: Mean percent shortfall relative to the $1.25 poverty line 5.4 3.9 6.3 5.2 Male and female adults 5.4 3.9 6.2 5.4 Adult female, no adult male 5.7 3.8 8.1 4.1 Adult male, no adult female 3.5 0.4 4.1 4.6 Child, no adults* N/A N/A N/A N/A Number of adult household members in responding households 11,655 3,277 4,707 3,671 Male and female adults 10,411 2,992 4,176 3,243 Adult female, no adult male 1,022 213 432 377 Adult male, no adult female 222 72 99 51 Child, no adults* N/A N/A N/A N/A N/A=Not applicable. *Too few cases Source: FFP Endline Survey, Ethiopia 2017. 25 The distribution of average daily per capita consumption expenditures at EL are presented in Figure 1. In the combined DFAP area food consumption expenditures account for the largest share of daily per capita consumption expenditures (69.3 percent), followed by non-food (23.0 percent), housing (6.6 percent), and durable goods (1 percent). These expenditures patterns were similar across the three project areas. Figure 1. Distribution of Average Daily Per Capita Expenditures, Combined Project Areas, Ethiopia 2017 Source: FFP Endline Survey, Ethiopia 2017. The preponderant share of household expenditures on food underscores households’ poor economic status and the allocation of household consumption expenditures to food appears to be unchanged since the baseline (Table 6d). Table 6d. Baseline-Endline Comparison of the Share of Food Consumption Expenditures, Ethiopia 2012 & 2017 CRS FH REST Baseline 2012 Endline 2017 Baseline 2012 Endline 2017 Baseline 2012 Endline 2017 Percentage share of food expenditures 71.9 72.8 77.1 72.3 77.1 65.4 Source: FFP Baseline Study 2012 & Endline Study 2017. NOTE: The BL estimates are reported as average annual per capita consumption expenditures while the EL estimates are reported as average daily per capita consumption expenditures. 3.2.2 Livelihood Activities and Other Sources of Income Household food security and poverty are closely related to livelihood and other income opportunity options available to households. Table 7 presents the distribution of households by types of livelihood activities and other sources of income. Because the BL report did not provide information on livelihood activities, only EL estimates are provided and are intended to add additional context to help explain the economic strategies and status of households in the project areas. The majority of households engage in two or more livelihood activities for sources of income, primarily to purchase food for the household (see Table 7). For analytical purposes, livelihood activities are divided into three broad groups: agriculture-related livelihoods, non-agriculture related livelihoods, and wage labor. The survey data do not distinguish between wage labor done in agriculture and non￾agriculture settings. Qualitative findings indicate that most wage labor households engage in is seasonal work in the agricultural sector on larger farms or on commercial farms outside their immediate areas. 69.3 23.0 6.6 1.0 Food Non-food Housing Assets 26 Remittances, gifts or inheritance, and safety net food or cash assistance are considered as other sources of income. Table 7. Households by Type of Livelihood Activity and Other Sources of Income in the Year Preceding the Survey (percentage), Ethiopia 2017 CRS FH REST Agricultural activities 97.8 91.6 94.5 Farming/Crop production and sales 93.3 90.5 92.8 Livestock production/fattening and sale 56.9 35.0 42.7 Honey production and sales 1.7 2.6 5.7 Other self-employment/Own bus (agri.) 17.0 3.0 1.5 Non-agricultural activities 23.2 29.4 22.2 Petty trade (selling other products) 6.1 5.6 4.8 Petty trade (selling own products) 2.0 6.1 2.6 Sale of wild/bush products 11.6 5.7 0.7 Rental of land, house, rooms 0.9 5.7 3.3 Salaried work 2.2 7.4 5.9 Other self-employment/Own bus (non-agri.) 1.6 2.3 6.6 Wage labor 34.8 22.1 39.8 Wage labor (within the community) 24.6 11.1 23.4 Wage labor (outside the community) 14.8 13.7 21.9 Other sources of income 35.4 52.7 61.3 Remittances 4.6 5.1 4.7 Gifts/Inheritance 5.5 4.1 11.0 Safety net food/Cash assistance 27.1 40.5 54.3 Other (specify): 2.2 7.1 3.5 Number of responding households 1,498 2,141 1,588 Source: FFP Endline Survey, Ethiopia 2017. The majority of rural households in DFAP implementation areas are agro-pastoralists or pastoralists. Unsurprisingly, the overwhelming majority of households in CRS (97.8 percent), REST (94.5 percent), and FH (91.6 percent) project areas are involved in agricultural activities for their livelihood. While the majority of agricultural income is from crop production and sales, Table 7 also shows the importance of livestock to the rural economy. More than one-third of the households in REST (39.8 percent) and in CRS (34.8 percent) also engage in wage labor as a source of income. In FH, the percentage of households engaged in wage labor is also sizeable (22.1 percent), but significantly less than that found among households in CRS and REST. Depending on the season, daily wage labor in the agricultural sector includes preparation of land for cultivation, weeding, and harvesting. These sources of income are pursued after households harvest their own crops and before the next cultivation season. In the non-agriculture sector one of the daily wage labor activities reported by men was working on road crews when opportunities were available in their area. Qualitative findings from the 2017 QS also showed that in Lasta (Amhara) some young unmarried women migrate seasonally for work in factories. Engagement in non-agricultural activities provides an additional source of income for households. Just under 30 percent (29.4) of households in FH, and in CRS and REST similar percentages of households, engage in non-agricultural activities for income (respectively 23.2 percent and 22.2 percent). However, except for FH, the predominant source of livelihood activities after engagement in agricultural activities is from wage labor. In CRS and REST, wage labor activities predominate over non-agricultural activities (34.8 percent versus 23.2 percent in CRS, and 39.8 percent versus 22.2 percent in REST). In contrast, 27 the percentage of household engaged in non-agricultural activities for income in FH is higher than the percentage of households engaged in wage labor (29.4 percent versus 22.1 percent). Sources of income from remittances, gifts, inheritances, and safety net/cash assistance are very important to the household economy. They comprise a source of income to over 60 percent (61.3 percent) of households in REST, over half in FH (52.7 percent), and over 30 percent of households in CRS (35.4 percent). The most important source of income among these categories is from safety net/cash assistance, 44 clearly showing how critical government assistance is to the food security of households in DFAP implementation areas. More than one-half of households in the REST area (54.3 percent) rely on safety net food or cash assistance compared to 40.5 percent of households in FH. Significantly fewer households in the CRS area (27.1 percent) rely on this source. 3.3 AGRICULTURE This section uses data from the EL household survey to describe the agriculture status of households. The EL survey collected agriculture-related data primarily to estimate FFP agricultural indicators for financial services, value chain activities, and the use of sustainable agricultural practices (for crops, livestock, and NRM) and improved storage practices. These services and practices are expected to directly benefit households and lead to increased food security. The 2012 BL survey did not collect this information so BL-EL comparisons cannot be presented. The agricultural component of the EL PBS questionnaire was completed by 6,776 farmers in the combined DFAP areas. All individuals in the household who met the definition of a farmer45 were interviewed. Of these farmers, about 58.4 percent were male and 41.6 percent were female. In addition to reporting on the core agriculture-related FFP outcome indicators, this section provides an overview of the types of crops planted and livestock raised. 3.3.1 Crop and Livestock Production Types of Crops Cultivated Almost all farmers (99.4 percent) planted at least one crop in the 12 months preceding the survey. There is variation in the crops cultivated by project area although farmers in all three DFAP areas planted teff and/or maize. In the CRS area, the major crops cultivated by farmers were maize (75.9 percent), chat (49.4 percent), sorghum (46.1 percent), and millet (24.4 percent). The major crops cultivated by farmers in the FH areas include wheat (73.7 percent), teff (63.2 percent), barley (61.4 percent), legumes (57.9 percent), sorghum (44.5 percent), potato (34.8 percent), and vegetables (23.8 percent). Farmers in the REST area cultivated teff (73.7 percent), maize (60.5 percent), wheat (54.7 percent), sorghum (53.7 percent),/ barley (51.3 percent), millet (39.4 percent), legumes (29.1 percent), and vegetables (23.2 percent). 44 There are several sources of safety net food/cash resources. These include PSNP, emergency assistance provided during or immediately after severe weather events such as droughts or flooding, and assistance from GOE contingency funds. Reportedly the major source of emergency assistance is from GOE contingency funds, but contingency funds have also been used as Transitory Assistance for PSNP graduates and other chronically food insecure households that are not enrolled as PSNP beneficiaries. The PBS household survey did not ask respondents to indicate the specific source of safety net food/cash resources. 45 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); animal and aquaculture products; and 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/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.” 28 Male and female farmers generally do not differ in the types of crops they planted, with a few exceptions. Table A9.1, Annex 9 provides details on the type of crops planted by DFAP implementation area and by sex of the farmer. Types of Livestock Owned Livestock and livestock production are integral to the household economy as an important source of, labor, food, dairy products, and income. In the highlands and midlands areas, livestock are also viewed as an investment and will be sold when cash needs are high. In pastoral areas of the lowlands, livestock production is the predominant means of livelihood. The majority of households in the DFAP areas reported owning at least one livestock (CRS, 90.7 percent; FH, 86.4 percent; REST, 90.4 percent). All DFAP areas showed some livestock diversity (see Figure 2 and Table A9.2, Annex 9). Figure 2. Percentage of Households by Type of Livestock (at least one) by DFAP Area Source: Endline Study, Ethiopia 2017. 3.3.2 Use of Financial Services, Value Chain Activities, Sustainable Agricultural Practices, and Improved Storage Practices Table 8 provides the estimates for the FFP indicators on financial services, value chain activities, sustainable agricultural practices, and improved storage practices by sex of the farmer. Table 8. Agricultural Indicators, Ethiopia 2017 Overall CRS FH REST Percentage of farmers who used financial services (savings, agricultural credit, and/or agricultural insurance) in the past 12 months 37.5 17.3 46.9 37.5 Male 40.3 *** 18.6 * 49.6 ** 42.8 * Female 33.5 13.8 42.5 32.3 Percentage of farmers who practiced the value chain activities promoted by the project in the past 12 months 85.8 81.7 87.7 86.0 Male 87.8 *** 83.3 * 89.8 ** 88.6 * Female 82.3 76.0 83.3 83.1 Percentage of farmers who used at least three sustainable agriculture (crop, livestock, and/or NRM) practices and/or technologies in the past 12 months 94.7 94.7 94.0 95.3 Male 97.6 *** 96.7 *** 97.5 *** 98.0 *** 0 10 20 30 40 50 60 70 80 Cattle Goats Poultry Donkey or mule Oxen Sheep Honey bees (hives) Camels Horse Percentage CRS FH REST 29 Overall CRS FH REST Female 90.8 89.3 88.1 92.5 Percentage of farmers who used at least three sustainable crop practices and/or technologies in the past 12 months 92.1 87.3 91.9 94.0 Male 94.1 *** 88.7 * 95.2 *** 96.0 *** Female 89.0 83.1 86.2 91.8 Percentage of farmers who used at least three sustainable livestock practices and/or technologies in the past 12 months 71.5 45.9 71.1 79.8 Male 75.2 *** 47.6 ns 76.2 *** 86.3 *** Female 66.3 41.4 61.9 73.0 Percentage of farmers who used at least three sustainable NRM practices and/or technologies in the past 12 months 44.1 30.2 43.9 49.3 Male 51.9 *** 33.4 *** 53.8 *** 59.7 *** Female 33.2 21.5 27.0 38.9 Percentage of farmers who used improved storage practices in the past 12 months5 26.1 26.9 23.2 27.9 Male 27.0 ns 28.4 ns 23.8 29.2 * Female 24.7 22.4 22.1 26.6 Number of responding farmers 6,764 1,750 2681 2,335 Male 4,104 1,268 1,663 1,168 Female 2,662 482 1,013 1,167 *** p<0.001, ** p<0.01, * p<0.05, † p<0.10, ns = not significant NOTE: Chi squared tests were used to assess the statistical significance of the relationship between the sex of farmer and core FFP agriculture indicators. Source: FFP Endline Survey, Ethiopia 2017. Financial Services Increased use of financial services can help farmers to access inputs and other resources to improve agricultural productivity. Farmers are considered to have used a financial service in the 12 months prior to the survey if they reported taking agricultural credit in cash or in-kind, 46 saved any cash, 47 or bought agricultural insurance to protect their agricultural production against negative unexpected circumstances such as droughts, floods, or pests. Overall, more than one-third (37.5 percent) of farmers in the project area used at least one financial service (Table 8). Use of financial services varies widely by the project area. Farmers’ use of financial services is lowest in CRS (17.3 percent). Compared to CRS, use of financial services is about three times higher in the FH project area (46.9 percent) and about two times higher in REST (37.5 percent). Table A9.2, Annex 9 shows that in FH and REST the most commonly used type of financial service is making cash savings, followed by taking out agricultural credit. In CRS, farmers were as likely to make cash savings as to borrow. In all project areas, the purchase of agricultural insurance is very limited (CRS, 0.8 percent; FH, 0.6 percent; REST, 3 percent). Value Chain Activities About one-half of farmers in CRS, FH, and REST project areas plant any crops or raise any livestock with the intention to sell or resell them. As illustrated in Figure 3, commercial farming is more 46 The survey considered both formal and informal sources of agricultural credit that are locally available which include the following: village savings and credit groups, farmer groups, MFIs, banks, or Rural Savings and Credit Cooperatives (RUSACCO). 47 The survey considered both formal and informal saving facilities including the following: village savings and credit groups, MFIs, cooperatives, and mobile banking services. 30 commonly practiced by male farmers than female farmers and this difference is statistically significant in each of the project areas. Figure 3. Percentage of Farmers That Plant Crops or Raise/Buy Livestock with the Specific Intention to Sell or Resell *p<0.05, **p<0.01, *** p<0.001 Source: Endline Study, Ethiopia 2017. In all three project areas, the majority of farmers engaged in commercial farming practiced at least one value chain activity (CRS, 81.7 percent; FH, 87.7 percent; REST, 86 percent). As shown in Table 8, use of a value chain activity was more commonly practiced by male farmers than female farmers in each of the project areas and this difference is statistically significant (p<0.01). The most commonly used value chain practice in the project areas is the purchase of inputs through agro-dealers and/or community associations (64.2 percent), followed by use of feed lots or pen feeding (34.3 percent), use of training and extension services (29.5 percent), and use of formal marketing systems (21.8 percent). Annex 9 Table A9.3b provides the details on the percentage of farmers by type of value chain activity and sex of farmer in each of the DFAP areas. Sustainable Agricultural Practices The EL survey asked farmers to report on the use of sustainable agricultural practices or technologies. Sustainable agricultural practices (USAID 2015) were divided into three subcategories: 1) crop practices; 2) livestock practices; and 3) NRM practices (see Table 7). The majority of farmers in the DFAP areas used at least three sustainable agriculture practices. In the combined DFAP implementation areas, farmers are more likely to use at least three sustainable crop practices (92.1 percent) and at least three sustainable livestock practices (71.5 percent) than three sustainable NRM practices (44.1 percent). With the exception of use of sustainable livestock practices in CRS, in each of the project areas male farmers compared to female farmers are generally more likely to use at least three sustainable crop practices three sustainable livestock practices or at least three sustainable NRM practices and these differences are statistically significant. Use of sustainable crop practices: Use of sustainable crop practices is similar across the three project areas. Table A9.5, Annex 9 indicates that the most common crop practices used by farmers in the combined project areas are weed control (88 percent), crop rotation (77.2 percent), use of manure (68.6 percent), and use of improved fertilizer (65.8 percent). Generally, a similar pattern in the use of crop practices by type is observed across the project areas. Annex 9, Table A9.5 provides details on the percentage of farmers by type of crop practice. 31 Use of sustainable livestock practices: Use of at least three sustainable livestock practices is highest in REST (79.8 percent) followed by FH (71.1 percent) and is lowest in CRS (45.9 percent). In the combined DFAP areas, farmers most commonly used vaccinations (70.8 percent) and cut and carry systems (63.6 percent). The patterns of most commonly used sustainable livestock practices are consistent across the three project areas, but, in REST, the use of emergency feed reserve (61.1 percent) and deworming (60.2 percent) are also common practice. See Annex 9, Table A9.5 for details on the percentage of farmers by type of livestock practice. Use of NRM practices: Use of NRM practices ranges between 49.3 percent in REST and 43.9 percent in FH to 30.2 percent in CRS. In the combined DFAP areas, the management or protection of watersheds and catchments is the widely practiced NRM practice (57.3 percent). Table A9.5, Annex 9 illustrates the similarity in the NRM practices used by farmers across the three project areas. Improved Storage Practices Improved storage practices refer to cost-effective methods and procedures to store seeds, grains, and animal feed and aquaculture products for the short and long term. These practices help farmers safely store excess harvest for subsequent sale, consumption, or propagation of plant material, such as seeds for future planting. Specific practices included in the survey were: hermetic storage; improved granaries; warehousing or cereal banks; use of traps for mice; grain bags with pesticides; and diffused light storage. Overall, 26.1 percent of farmers used at least one of the improved storage practices in the past 12 months. With the exception of the REST implementation area, generally, use of improved storage practices did not differ by sex of the farmer. The percentage of farmers that use improved storage practices has little to no variation across the DFAP areas (see Annex 9, Table A9.6). 3.4 WATER, SANITATION, AND HYGIENE Poor WASH practices are associated with increased morbidity and mortality, particularly for diarrheal diseases. In addition, a fecal-contaminated environment is associated with chronic undernutrition, poor gut health, and suboptimal absorption of nutrients.48 Worldwide, it is estimated that improved water sources reduce diarrheal 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. 49 Water Shortages In 2015, the GOE led assessment in May of that year reported that 1.6 million Ethiopians required emergency water support due to major drought related shortages affecting the country at that time.50 As the drought continued, a GOE-led assessment in early 2016 reported approximately 5.8 million people lacked access to WASH services. As of March 2016, relief organizations had provided emergency water trucking services to approximately 1.1 million people with plans to reach up to 3 million people.51 Although the wide spread drought of 2015-2016 ended, water shortages persisted in 2017 in many of the GOE hotspot areas due to insufficient rainfall. According to GIs conducted with woreda and kebele officials in Oromia for the QS, some areas experienced several years of no rainfall at all. It is within this context that the WASH findings must be understood. 48 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. 49 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. 50 Reported in the USAID Ethiopia Complex Emergency Fact Sheet, June 26, 2015. 51 Reported in the USAID Ethiopia Complex Emergency Fact Sheet, Fact Sheet # 6, March 16, 2016. 32 3.4.1 Household WASH Practices Availability and access to water is one of the key determinants of household and community hygienic and sanitation practices. The DFAPs aimed to improve household water, sanitation, and hygiene practices and infrastructure because basic water, hygiene, and sanitation services are vitally important for human health. Both the BL and EL surveys collected information on household use of an improved drinking water source, use of improved sanitation facilities, open defecation, and availability of soap and water at a handwashing station. The EL study also collected data on correct use of water treatment technologies and time to fetch water. This section provides a comparison of BL and EL estimates for indicators measured at both BL and EL (see Table 9). The EL survey collected information on availability of water in less than 30 minutes and correct use of water treatment technologies whereas the BL did not collect this information. Those results are described to provide additional context to better understand the WASH status of households in the project areas at EL. Table 9. Baseline-Endline Comparison of WASH Indicators, Ethiopia 2012 & 2017 CRS FH REST Baseline 2012 Endline 2017 Sig Baseline 2012 Endline 2017 Sig Baseline 2012 Endline 2017 Sig Percentage of households using an improved water source1 27.2 25.8 NS 47.9 49.8 NS N/A 44.9 Percentage of households in target areas practicing correct use of recommended household water treatment technologies N/A 9.4 N/A 9.0 N/A 15.6 Chlorination N/A 4.1 N/A 6.0 N/A 9.0 Flocculent/Disinfectant N/A 2.0 N/A 0.9 N/A 5.0 Filtration N/A 2.6 N/A 0.9 N/A 0.6 Solar disinfection N/A 0.0 N/A 0.0 N/A 0.0 Boiling N/A 1.2 N/A 1.5 N/A 1.1 Percentage of households that can obtain drinking water in less than 30 minutes round trip N/A 20.4 N/A 30.2 N/A 18.8 Percentage of households using improved sanitation facilities2 41.7 6.8 *** 23.0 6.6 *** N/A 8.2 Percentage of households practicing open defecation 38.3 47.5 ns 29.2 44.4 ** N/A 64.8 Percentage of households with soap and water at a handwashing station commonly used by family members3 N/A 0.9 8.2 2.0 *** 7.6 0.3 *** Number of responding households 1,515 1,498 1,502 2,141 1,531 1,588 N/A= Not available *** p<0.001, ** p<0.01, * p<0.05, † p<0.10, ns = not significant NOTE: Chi squared tests were used to assess the statistical significance of the difference between the BL and EL estimates of the WASH indicators. 1 The BL estimates for use of an improved water source are reported for the wet season and dry season separately, and no overall estimate is provided. The EL survey was collected during the wet season so the results are comparable with the BL estimates for the wet season. BL data for the percent of households using an improved water source in the REST area were not collected. 2 BL data for the percent of households using improved sanitation facilities in the REST area were not collected. 3 BL data for the percent of households with soap and water at a commonly used handwashing station in the CRS area were not collected. Source: FFP Endline Survey, Ethiopia 2017. BL estimates were recalculated to include sampling weights and therefore differ from the estimates provided in the BL study report. 33 3.4.2 Use of Improved Water Source and Correct Water Treatment The Joint Monitoring Programme (JMP) for Water Supply and Sanitation defines improved drinking water sources as sources that are protected by the nature of their construction or through an active intervention against outside contamination from fecal matter [WHO/ United Nations Children’s Fund (UNICEF) 2016]. These sources include: 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” water source means that a household can access water from the source year-round without experiencing interruptions of a day or longer in a two-week period (USAID 2015). The BL survey collected information on seasonal differences for drinking water sources during the wet and dry season. The EL survey did not collect information on seasonal differences in the use of improved water sources. Since the EL survey was conducted during the rainy season, the results are comparable to the BL estimates for the wet season only. In the CRS project area, the percentage of households using an improved water source did not change between BL (27.2 percent) and EL (25.8 percent). In the FH project area, the rate remained stable (47.9 percent at BL and 49.8 percent at EL). Table A9.7, Annex 9 illustrates the source of drinking water at BL and EL by type. A sizeable percentage of households in the project areas rely on public taps and tube wells or boreholes to obtain their water. Many households also get their water from unprotected springs. Results show a decline in access to water from a public tap and standpipe in both CRS and FH. At BL, 42.5 percent of households in CRS and 44.1 percent in FH accessed their water from a public tap in the wet season compared to 28.9 percent and 22.9 percent at EL respectively. In CRS, reliance on unprotected springs is highest; at EL 41.3 percent of households get their water from an unprotected spring, compared to 16.3 percent at BL. At EL, about 3 in 10 households in the combined DFAP areas (29.3 percent) reported that water is not available the entire year around, and 13.7 percent indicated that water from the source was unavailable for a day or more in the two weeks prior to the survey (see Annex 9, Table A9.7); reflecting the extent to which drought conditions persisted in affecting water shortages in 2017. The EL estimates for the use of water treatment technologies and access to a drink water source in less than 30 minutes underscore that access to water is a challenge. About one in five households (11.8 percent) in the combined DFAP areas use a correct treatment technology or practice. The most commonly used method is chlorination. About one-quarter of households (23.5 percent) can obtain drinking water in less than 30 minutes. This percentage is highest in FH (30.2 percent) followed by CRS (20.4 percent) and lowest in REST (18.8 percent). 3.4.3 Handwashing Practices In the combined project areas, the percentage of households with soap and water at a commonly used handwashing station is very low at EL (1.1 percent). The BL data, available from FH and REST, for this indicator were also low (respectively 8.2 percent and 7.6 percent), but by 2017 dropped to 2.0 percent in FH and 0.3 percent in REST. EL data for the CRS DFAP area show only 0.9 percent of households had soap and water at a commonly used handwashing station. The key reason that explains this low percentage is the shortage of water. With recurrent drought occurring from 2010 through 2016, in some years more severe and widespread than in others, water sources have dried up or contracted. The Tufts DFAP PE Report (2017) notes that the availability of water for handwashing outside latrines was a constraint in almost all woredas the evaluation team visited covering Amhara, Tigray, Oromio, and Dire Dawa. The report concludes that WASH messages were well received and understood by DFAP communities, but respondents reported difficulties in implementing WASH practices such as handwashing mainly because of the limited availability of water. However, where spring-capture or dam projects had been implemented or water pumps installed, a greater degree of handwashing practices was noted by the evaluation team. Mothers participating in GIs in the QS (2017) reported that, with the severe shortage of water, the first and most important use for water is drinking. 34 3.4.4 Improved Sanitation An improved sanitation facility is defined as a facility that hygienically prevents human contact with human excreta, and must not be shared with other households. This includes: flush to piped sewer system or septic tank or pit latrine; ventilated improved latrine; pit latrine with slab; and composting toilet. Other types of facilities such as flush or pour and flush toilets without a sewer connection, pit latrines without slab, open pits, bucket latrines, and hanging toilets or latrines are considered unimproved. As illustrated in Table 9, sanitation conditions in the implementation areas worsened over time in CRS and FH. Use of an improved sanitation facility declined markedly in CRS from 41.7 percent to 6.8 percent. A sharp decline (but of a smaller magnitude) is also observed in FH where use of an improved sanitation facility decreased from 23.0 percent to 6.6 percent. Although there is a lack of qualitative data from the DFAP PE Report that may explain this decrease, data collected for the QS in 2017 provide findings indicating one of the key factors that most likely contributed to this decline is that the model of toilet used is not durable. They do not stand up to long use before they become broken. Village respondents explained they cannot afford to repurchase the materials necessary for building a replacement toilet (see Ethiopia BL study, 2018). At EL, about half of households in the combined DFAP areas (53.7 percent) practice open defecation. The practice of open defecation in the FH implementation area increased from 29.2 at BL to 44.4 percent at EL. Details on the type of sanitation facility by shared status are provided in Annex 9, Table A9.7. 3.5 WOMEN’S HEALTH AND NUTRITION This section provides the findings on: women’s nutritional status and food consumption practices; contraceptive use and choice of methods; and health care seeking behavior during pregnancy. The EL estimates of women’s health and nutrition indicators for the combined DFAP areas and by each DFAP area are summarized in Table 10a. BL estimates are only available for antenatal care (ANC) visits for women’s most recent live birth in the last 24 months. Table 10b provides a BL-EL comparison of the percentage of births receiving at least four ANC visits and the results of the test of statistical difference. Table 10a. Women's Health and Nutrition Indicators, Endline Survey Overall CRS FH REST Prevalence of underweight women1 36.2 32.2 29.5 43.5 Prevalence of women of reproductive age who are consuming a minimum dietary diversity (MDD-W)2 7.9 7.5 3.1 12.1 Contraceptive Prevalence Rate3 37.3 30.1 40.4 38.0 Modern methods 36.2 27.8 39.9 37.0 Traditional methods 1.1 2.3 0.5 1.1 Number of responding women (15-49 years) 4,937 1,418 1,941 1,578 Number of responding non-pregnant women (15-49 years) 4,494 1,253 1,785 1,456 Number of responding women aged 15-49 who are married or in a union 2,812 845 1,132 835 1 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). 2 A minimum dietary diversity is defined as consumption of 5 or more of 10 food groups in the past 24 hours. 3 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. Source: Endline Study, Ethiopia 2017. 3.5.1 Women’s Nutritional Status Undernutrition among women of reproductive age is associated with increased morbidity, poor food security, and adverse birth outcomes in future pregnancies. Improvements in women’s nutritional status are expected to improve women’s work productivity, which may also have benefits for agricultural production. Figure 4 indicates that the most women in the combined DFAP areas have a normal body mass index (BMI) implying they have a normal weight. About one-third (36.2) of non-pregnant women 35 15-49 years are underweight. The prevalence of underweight women is highest in REST (43.5 percent) and lowest in FH (27.7 percent). Table A9.8, Annex 9 provides details on the height and BMI levels of non-pregnant women 15-49 years of age. Figure 4. BMI Levels of Non-Pregnant Women of Reproductive Age, Combined DFAP Areas, Ethiopia 2017 Source: Endline Study, Ethiopia 2017. 3.5.2 Women’s Minimum Dietary Diversity The women’s minimum dietary diversity indicator (MDD-W) was introduced in 2014 to improve the usefulness of the women’s dietary diversity score (WDDS) indicator.52 The WDDS and MDD-W differ in two ways: 1) the MDD-W is a proportion, compared to the WDDS, which is a quasi-continuous score; and 2) the food groups used to calculate MDD-W are slightly different from those used to calculate the WDDS. The MDD-W uses 10 food groups, and the WDDS uses nine food groups.53 The WDDS reflects the number of food groups that women on average consume over the last 24 hours from a total of nine groups. The MDD-W reflects the percentage of women consuming at least 5 of 10 nutritiously diverse food groups over the last 24 hours. The BL study reported on the women’s WDDS. At BL, women’s dietary diversity was low: the WDDS was 2.48 in CRS, 1.73 in FH, and 2.10 in REST reflecting two or fewer of the nine food groups were consumed by women at BL. The EL study reported on MDD-W. The MDD-W was highest in REST (12.1 percent) followed by CRS (7.5 percent) and FH (3.1 percent). Despite the relatively higher percentage in REST, it is important to note that dietary diversity is very low in each DFAP implementation area at BL and EL. While a direct comparison between the WDDS and MDD-W cannot be made, it is possible to compare the BL and EL estimates of overlapping food groups. Because the BL report provides information on women’s food consumption patterns in CRS and FH only this comparison is restricted to those DFAP areas. Figures 5a and 5b underscore a continued dependence on grains, roots, and tubers.54 Only 8 of the 10 possible food groups are illustrated because whereas the EL survey collected information on 52 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. 53 The following three characteristics summarize the key differences in the Women’s Dietary Diversity Score (WDDS) food groups and the Minimum Dietary Diversity-Women (MDD-W) food groups: 1) the WDDS combines beans, legumes, nuts, and seeds in one category, while the MDDS-W distinguishes between legumes and beans on one hand, and nuts and seeds on the other; 2) the MDD-W combines organ meat and flesh foods into one group, while the WDDS distinguishes between organ meat as one group and flesh foods as another; and 3) the MDD￾W treats other fruits and other vegetables as two separate categories, while the WDDS combines them into one food group. 54 The BL report provided information on women’s food consumption patterns in CRS and FH only, therefore BL data is provided for those project areas only. Additionally, the BL estimates are unweighted, but the EL estimates are weighted. 18.5-24.9 (total normal) 17.0-18.4 (mildly underweight) <17 (moderately and severely underweight) 25.0-29.9 (overweight) ≥30.0 (obese) 36 women’s consumption of other fruits and vegetables in two separate questions, the BL combined these two categories in one question so it is not possible to disaggregate the BL estimate. Figure 5a. Comparison of Baseline and Endline Food Groups Consumed by Women 15-49 Years of Age in the CRS Implementation Area, Ethiopia 2012 & 2017 Source: Endline Study, Ethiopia 2017. Figure 5b. Comparison of Baseline and Endline Food Groups Consumed by Women 15-49 Years of Age in the FH Implementation Area, Ethiopia 2012 & 2017 Source: Endline Study, Ethiopia 2017. The percentage of women consuming the various food groups at EL is illustrated in Annex 9 Table A9.9 for the three DFAPs and these indicates that women’s food consumption patterns parallel the overall household consumption patterns illustrated in Table 6a. Women’s food consumption patterns are also consistent with the types of crops planted by project area as illustrated in Table A9.1, Annex 9, indicating that households rely on foods produced on their own land and locally available food groups. As illustrated in Annex 9 Table A9.9, grains, roots, and tubers are the most commonly consumed food groups by women in all the project areas. Few women consume eggs, flesh foods, nuts and seeds, vitamin A-rich dark green leafy vegetables, or other vitamin A-rich fruits and vegetables. Women in PSNP beneficiary households also have access to pulses and grains three or four times per year from 0 10 20 30 40 50 60 70 80 90 100 Grains, roots and tubers Legumes, beans, nuts and seeds Dairy products Eggs Flesh foods & organ meat Vitamin A dark green leafy vegetables Other Vitamin A rich vegetables & fruits Other vegetables Percentage Baseline 2012 Endline 2017 0 10 20 30 40 50 60 70 80 90 100 Grains, roots and tubers Legumes, beans, nuts and seeds Dairy products Eggs Flesh foods & organ meat Vitamin A dark green leafy vegetables Other Vitamin A rich vegetables & fruits Other vegetables Percentage Baseline 2012 Endline 2017 37 food distribution packages. This provides 15 kg of cereals and 4 kg of pulses based on a five-person household, but, as described earlier in this report, women in larger households would not get the amount of food calculated to fill nutritional and caloric requirements of an individual. The DFAP PE report (2017) concludes that Maternal Health and Nutrition (MHN) messaging and trainings on the importance of dietary diversity and other issues related to ANC, contraceptive use, and child care was effective in terms of promoting awareness and understanding across DFAP project areas. The DFAP PE Report (2017) and QS (2018) findings confirm this conclusion. However, based on FGDs and KIIs with HEWs, there has not been appreciable change in practice partly because of lack of access to required foods.55 QS findings from GIs with MIC5 and HEWS also show access and availability are key factors limiting women from improving their dietary diversity. Several interacting factors underlie limited access and availability. One is the high rates of household food insecurity exacerbated by poor yields and crop failure from consecutive years of rain shortages and drought. The types of food available in local markets are also limited. For example, vegetables, and particularly fruit, requiring more water than grain, roots, and tubers, are not widely available on a consistent basis. Protein-based foods—e.g., milk, dairy, eggs, and meat—are unaffordable by food insecure households, particularly during food gap periods. Another is the high price of basic foods consumed by people in each region following poor harvests (see USAID 2016 and FEWS NET reports from years 2015-2017). The types of food available during food gap periods is also limited. Dietary diversity of PSNP households was reduced by the elimination of vitamin-fortified cooking oils in food distribution packages. Lastly, in some locations women’s dietary diversity is limited by following traditional household feeding practices, reportedly dying out according to GIs with FCo-HHH and MIC5 and KIIs with HEWs conducted for the QS. According to tradition, women feed their husbands first, and the men receive the largest amount of food, and the best types of food. Then the children are fed. The women eat whatever is left over, usually injera, or go to bed without eating. However, the primary reasons for the low rates of women’s dietary diversity are access and availability. 3.5.3 Women’s Antenatal Care and Contraceptive Prevalence Table 10b illustrates the BL and EL percentages of women who had a live birth in the two years prior to the survey and who received the WHO minimum recommended four ANC visits during their pregnancy with a doctor, nurse, midwife, skilled birth attendant, or clinical officer during pregnancy.56 Results indicate no significant changes in the receipt of at least four ANC visits between BL and EL in the FH and REST implementation areas. The BL report did not provide an estimate for CRS therefore a trend analysis is not provided. At EL one-third of births in CRS (33.9 percent) and FH (31.1 percent) received at least four ANC visits compared to one-half in REST (55.8 percent) Table 10b. Baseline-Endline Comparison of Antenatal Care Visits CRS FH REST Baseline 2012 Endline 2017 Sig Baseline 2012 Endline 2017 Sig Baseline 2012 Endline 2017 Sig Percentage of births receiving at least four antenatal care (ANC) visits during pregnancy1 N/A 33.9 34.0 31.1 ns 72.4 55.8 ns Number of responding women aged 15-49 with live birth in the past two years N/A 427 289 488 40 388 55 See DFA PE Report, pg. 30. The evaluators state these findings are confirmed by limited data in some Indicator Performance Tracking Tables (IPTTs) and observations. 56 This indicator does not measure the quality of the ANC visit, and is limited to counting occurrences of visits with a skilled health professional (doctor, nurse, midwife, skilled birth attendant, or clinical officer). 38 CRS FH REST Baseline 2012 Endline 2017 Sig Baseline 2012 Endline 2017 Sig Baseline 2012 Endline 2017 Sig N/A = not applicable *** p<0.001, ** p<0.01, * p<0.05, † p<0.10, ns = not significant NOTE: Chi squared tests were used to assess the statistical significance of the difference between the BL and EL estimates of the percentage of births receiving at least four ANC visits during pregnancy. 1 ANC visits are reported for the most recent live birth in the two years preceding the survey. BL estimates for the percentage of births receiving at least four ANC visits were not reported for the CRS project area. Source: FFP Ethiopia Baseline Study 2012 & FFP Ethiopia Endline Survey 2017. The contraceptive prevalence rate in the combined project areas at EL is 37.3 percent. Table A9.10, Annex 9 provides details on modern and traditional methods of contraception used by women in the project areas and indicates that the most commonly used method among women in the three project areas is injectables (76 percent). The lower contraceptive prevalence rate in the CRS DFSA implementation area compared to the FH and REST areas can be explained in part because of the areas in Oromia that are predominantly Muslim. They believe that the use of contraceptives is not allowed by their religion, and men in these families make all the decisions. Findings from the 2017 QS also indicate that some women fear the use of contraceptives believing that they are bad for one’s health, their own religious beliefs that all children should be welcome, and, in some cases, husbands’ disapproval of the concept of family planning. Furthermore, the DFAP PE report notes that DFAPs were not involved in family planning activities. The promotion of family planning and the use of modern contraceptives were the responsibility of HEWs. 3.6 CHILDREN’S HEALTH AND NUTRITION This section covers the prevalence of underweight, stunted, and wasted children, the prevalence of exclusive breastfeeding (EBF), children’s receipt of a minimum acceptable diet (MAD), the prevalence of diarrhea and use of oral rehydration therapy (ORT). BL and EL estimates of children’s health and nutrition indicators by project area are summarized in Table 11. Table 11. Baseline-Endline Comparison of Children's Health and Nutrition Indicators CRS FH REST Baseline 2012 Endline 2017 Baseline 2012 Endline 2017 Baseline 2012 Endline 2017 Prevalence of underweight children under five years of age1 27.1 23.0 * 50.2 32.0 *** 29.4 25.1 † Male 25.0 23.6 ns 48.8 34.7 *** 28.0 25.4 ns Female 32.0 22.5 ** 55.3 28.8 *** 32.6 24.7 † Prevalence of stunted children under five years of age2 44.6 36.5 * 63.1 54.5 ** 50.9 44.3 * Male 43.8 37.2 * 62.1 57.6 ns 51.2 46.4 ns Female 46.3 35.9 * 66.2 50.9 ** 50.5 41.8 * Prevalence of wasted children under five years of age3 12.8 9.1 * 20.4 7.0 *** 8.6 4.8 ** Male 12.2 9.7 ns 19.2 7.5 *** 7.3 4.1 * Female 14.2 8.4 * 24.5 6.5 *** 11.0 5.7 * Percentage of children under age two who had diarrhea in the last two weeks4 N/A 18.7 26.1 26.7 ns 68.9 21.0 *** 39 CRS FH REST Baseline 2012 Endline 2017 Baseline 2012 Endline 2017 Baseline 2012 Endline 2017 Male N/A 19.7 23.9 30.7 ns N/A 21.4 Female N/A 17.7 27.7 22.2 ns N/A 20.5 Percentage of children under age two with diarrhea treated with ORT N/A 60.7 23.6 24.1 ns N/A 34.2 Male N/A 59.1 N/A 26.7 N/A 30.1 Female N/A 62.7 14.4 20.1 ns N/A 39.4 Prevalence of exclusive breast-feeding of children under six months of age 24.9 67.5 *** 40.0 87.9 *** 66.4 71.0 ns Male 21.2 63.4 *** 28.5 86.2 *** 75.9 69.2 ns Female 29.5 72.7 *** 49.1 89.7 *** 57.6 72.8 ns Prevalence of children 6- 23 months of age receiving a minimum acceptable diet (MAD) 2.8 6.9 * 0.5 6.6 *** 5.1 12.4 ** Male 2.6 5.4 ns 1.2 8.3 ** 8.0 15.2 † Female 3.0 8.2 * 0.0 4.7 ** 3.4 8.9 † Number of children under five 1,491 1,217 679 1,167 850 1,018 Male 1,034 611 523 616 593 545 Female 457 606 156 551 256 473 Number of children under two N/A 432 261 494 42 394 Male N/A 222 109 257 16 214 Female N/A 210 152 237 26 180 Number of children under 6 months 144 132 95 137 104 111 Male 80 72 42 69 50 54 Female 64 60 53 68 54 57 Number of children 6-23 months 463 300 188 357 223 283 Male 229 150 80 188 87 160 Female 234 150 108 169 146 123 N/A = not applicable *** p<0.001, ** p<0.01, * p<0.05, † p<0.10, ns = not significant NOTE: Chi squared tests were used to assess the statistical significance of the difference between the BL and EL estimates of CHN indicators. 1 The EL percentage of underweight children is based on the sample of children with valid weight-for-age measurements. 2 The EL percentage of stunted children is based on the sample of children with valid height-for-age measurements. 3 The EL percentage of underweight children is based on the sample of children with valid weight-for-height measurements. 4 The BL survey did not collect data on the prevalence of diarrhea or treatment with ORT in the CRS Project area. Source: FFP Endline Survey, Ethiopia 2017. BL estimates were re-calculated to include sampling weights and therefore differ from the estimates provided in the BL study report. 40 3.6.1 Underweight, Stunting, and Wasting Child undernutrition can lead to serious short- and long-term consequences, such as increased susceptibility to disease and infection and impaired cognitive development. Children who are stunted (height-for-age), underweight (weight-for-age), or wasted (weight-for-height) are considered undernourished. Weight-for-age takes into account both chronic and acute malnutrition and is often used to monitor nutritional status longitudinally. Children who are below minus two standard deviations (SDs) from the median of the WHO child growth standards population for weight-for-age are considered underweight. The prevalence of underweight among children under five years of age is a strong indicator of undernourishment and food insecurity. The height-for-age index provides an indicator of linear growth retardation (stunting) among children. Children who are below minus two standard deviations from the median of the WHO child growth standards population for height-for-age may be considered short for their age (stunted) or chronically malnourished. Severe linear growth retardation (stunting) reflects the outcome of a failure to receive adequate nutrition over a number of years and the effect of recurrent and chronic illness. Height-for￾age, 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. Weight-for height (wasting) is a robust predictor of under-five mortality and often is a consequence of acute and dire food shortage or disease. Children who are below minus two SDs from the median of the WHO child growth standards population for weight-for-height are considered wasted.57 The prevalence of undernourished children has been a persistent problem among CFI households in GOE hotspot areas, but the seriousness of the problem increased with the onset of widespread drought in 2015 and continued growing through 2016. The UNICEF Ethiopia Humanitarian Situation Report reported that 264,515 children from six months to 59 months will require therapeutic feeding and treatment for severe acute malnutrition in 2015 through the Community Management of Acute Malnutrition (CMAM) program. The number of children admitted into CMAM is used as one of the monitoring mechanisms of children’s nutrition situation.58 As reported in the May 2016 USAID Complex Emergency Fact Sheet, the GOE projected that 2.6 million Ethiopian children would experience acute or severe malnutrition that year resulting from the deteriorated food security situation because of the 2015-2016 drought. 59 Results (Table 11) indicate modest statistically significant improvements from BL in children’s underweight, stunting, and wasting across the project areas. The EL results also highlight variation by project area in the severity of children’s malnutrition. At EL, about one-quarter of children in CRS (23.0 percent) and REST (25.1 percent) were underweight compared to about one-third in FH (32 percent). The prevalence of stunting was highest in FH where more than one-half of children under five (54.5 percent) were chronically malnourished. The prevalence of stunting was lower in CRS (36.5 percent) and REST (44.3 percent). The prevalence of acute malnutrition (wasting) also varied by project area ranging from 4.8 percent in REST to 7 percent in FH. The prevalence of wasting in CRS was 9.1 percent, close to the threshold 10 percent threshold which warrants immediate action according to the United Nations High Commissioner for Refugees (UNHCR). 60 Given the serious threat to food security during the 2015-2016 drought, it is likely that without interventions from PSNP, the DFAPs, UNICEF, and 57 http://www.unicef.org/progressforchildren/2007n6/index_41505.htm. 58 See UNICEF Ethiopia Humanitarian Situation Report, SitRep #3, June-July Reporting Period, 2015. 59 As reported in the USAID Ethiopia Complex Emergency Fact Sheet #10, May 2016. 60 UNHCR. n.d. Guidance on thresholds for child malnourishment. Available at https://emergency.unhcr.org/entry/32605/acute-malnutrition￾threshold. 41 other external assistance, there would have been a deterioration in children’s underweight, stunting, and wasting instead of modest improvements. Although there is a lack specific data to portray the actual situations, differences in these measures will also be found within each DFAP implementation area. Variation in the percentage of underweight, stunted, and wasted children will be affected by the level of household food insecurity and by the severity of effects on household agricultural productivity from insufficient rainfall, drought, and flood in different locations. Gains made in earlier years by many households could not withstand the shock of the severity of the 2015-2016 drought. 3.6.2 Bivariate and Multivariate Analyses of the Prevalence of Stunting Additional analyses were performed to assess the correlates of the prevalence of moderate or severe stunting among children under five in the DFAPs in Ethiopia. Chi squared tests were used to assess differences for categorical variables and t-test of statistical differences were used for continuous variables. Subsequently, multivariate analysis controlling for key factors was used to explore the factors that are associated with stunting. To better understand the relationship between chronic malnutrition (stunting) and children’s dietary diversity and feeding practices additional bivariate analyses were conducted to assess the relationship between stunting and the prevalence of a MAD for children 6-23 months as well as the relationship between stunting and the prevalence of EBF for children under six months. Details on the methodology and the results are provided in Annex 10. The results of the bivariate and multivariate analyses are summarized below. Child’s characteristics and the prevalence of stunting: Results of the bivariate analysis indicated that the sex of the child was related to the prevalence of stunting only in FH; 46.9 percent of females were stunted compared to 53.7 percent of males. In all three project areas, the age of the child was significantly related with the prevalence of stunting and followed a somewhat inverted U-shape implying that stunting peaks by the age of two underscoring that the first two years of a child’s life are critical periods in their development and growth. In FH, the prevalence of stunting decreased with higher order births—that is, second and third-born children are less likely to be stunted compared to first-borns, but in CRS and REST the association was statistically nonsignificant. Mother’s characteristics: Mother’s marital status, age, education, and whether she achieved an MDD￾W were not related to the prevalence of stunting in any of the DFAP areas. In CRS, the prevalence of stunting of children whose mothers engaged in paid work (29.3 percent) was lower than that of children whose mothers did not work (36.1 percent) or whose mothers worked in-kind (40.6 percent).61 Household sociodemographic characteristics: In all three DFAP areas, the prevalence of stunting was not related to the age of the household head. However, in FH, the prevalence of stunting differed markedly by the sex of the household head—30.5 percent of children in female-headed households were stunted compared to 52.5 percent of children in male-headed households. The prevalence of stunting increased with the number of adult males in the household and number of adult females in the household only in FH, but was otherwise unrelated to the prevalence of stunting in CRS and REST. There was an inverse association between number of children under five in the household and the prevalence of stunting only in FH. The prevalence of stunting did not vary by the number of children 5- 17 in the household in all of the DFAPs. Household food security status: There was no association between the prevalence of hunger and the prevalence of stunting in any of the implementation areas. HDDS, an indicator of food security but 61 Work includes jobs in the formal and/or informal sector, full time, part time, or seasonal work that is done within and/or outside the home. It includes, but is not limited to: agricultural daily wage labor, off-farm daily wage labor, income generation activities, sale of goods produced or processed outside the home or at the home, homestead garden or farm (e.g., vegetables, eggs, fish, livestock, artisanal goods), or petty trading. For this indicator, work does not include participating in cash for work, food for work, or conditional transfers and/or productive safety net programs. It does not include: caring for own children, cooking, cleaning or doing other routine chores for own household (e.g., fetching water, collecting firewood), or being involved in agricultural production solely for household consumption. 42 also a proxy for socio economic status, was associated with the prevalence of stunting only in REST; more children who were not stunted reside in households with a higher HDDS (5.93) compared to children who are stunted (5.52). Household WASH status: Bivariate analyses explored the prevalence of stunting in relation to households’ use of an improved water source, correct water treatment, improved sanitation facility, and a proper handwashing station. The results indicated that the difference in the prevalence of stunting by households’ WASH status was statistically nonsignificant across the three DFAPs. Household agriculture practices: In all three project areas, the prevalence of stunting did not differ statistically between households that did not plant any crops, households that planted crops but did not use at least three sustainable crop practices, and households that used three or more crop practices. Similarly, there was no difference in the prevalence of stunting for children among households that did not raise livestock, households that raised livestock but did not use at least three sustainable livestock practices, and households that used at least three sustainable livestock practices. The prevalence of stunting was also compared among households that planted crops and/or raised livestock with the intention of selling, households that did not use a value chain activity, and households that used at least one value chain activity and no statistically significant difference was detected. A similar lack of statistically significance was observed for the relationship of stunting with use of improved storage, use of credit, and farm size. Household poverty status: There was no association between the prevalence of hunger and the daily per capita consumption expenditures except in REST; children who were not stunted reside in households with higher average daily per capita consumption expenditures ($1.19) compared to children who are stunted ($1.04). Receipt of cash and/or food assistance and savings: The prevalence of stunting did not differ statistically between households that relied on cash and/or food assistance as a source of income in the 12 months prior to the survey and those that did not. Region and DFAP area: The prevalence of stunting differs statistically by region and DFAP implementation area. It is highest in Amhara (50.4 percent) followed by Tigray (43 percent) and lowest in Oromia (34.3 percent) and Dire Diwa (35.5 percent). The prevalence of stunting is highest in FH (50.4 percent) followed by REST (43 percent) and lowest in CRS (34.5 percent). Children’s dietary diversity and feeding practices: Additional bivariate analyses were conducted for a subsample of children 6-23 months to explore the association of stunting with dietary diversity and a subsample of children under six months to assess the relationship between stunting and EBF. Table 10.1b indicates that there is no difference in the prevalence of stunting between children 6-23 months who achieve a MAD and children 6-23 months who do not achieve a MAD. Similarly, the prevalence of stunting among children under six months did not differ by EBF status. Because stunting is a measure long-term malnutrition and MAD is based on the last 24 hours, it is possible that differences in MAD status are not reflected in stunting. Similarly, the lack of statistical association between stunting and EBF may be partially explained by the fact that the breastfeeding indicator is measured based on behavior in the last 24 hours as a proxy for long-term behavior. Table 10.4, Annex 10 illustrates the results of the multivariate analysis of stunting for children under five that was performed using the EL data. For the purposes of parsimony, only variables that showed a statistical significant bivariate association were included. Child’s age remains a statistically significant correlate of stunting even after controlling for a host of mother-, household-, region-, and project￾related variables. The odds of stunting of children increases with age and are highest for children 30-35 months compared to children under 6 months, thus underscoring the importance of the first two years in determining the long-term nutritional trajectory of children. The odds of stunting decline after 30-35 months but peak again at 42-47 months and 54-59 months suggesting a cohort effect for children born 43 three years or more prior to the EL survey. The odds of being stunted are lower for children living in female-headed-households compared to male-headed households. Children living in households whose head has a primary education or some primary education are less likely to be stunted compared to children in households headed by someone who never attended any school. Mothers’ participation in any form of work (cash or in-kind) is associated with lower odds of stunting compared to children whose mothers do not engage in economic activities. But the effect of mothers’ work washes out when the model controls for the sociodemographic and economic characteristics of the household. The addition of daily per capita consumption expenditures is likely to have washed out the effect of mother’s work on stunting since household consumption expenditures rolls in expenditures resulting from mother’s work. Regional differences in the prevalence of stunting that were observed in the bivariate analyses wash out in the full model but the effect of DFAP activities remains statistically significant. The odds of a child under five being stunted are twice as high in FH compared to CRS. Children living in REST are more likely to be stunted than children in CRS. To facilitate comparison with the BL study, a simpler model of the prevalence of stunting was conducted using EL data. In most cases, the results are similar for the BL and EL models indicating a consistency in the correlates of stunting.62  Sex of household head: The association is statistically significant in both the BL and EL models.  Age of the mother: The association is statistically significant in the BL model but not in EL model.  Whether the mother (or primary caretaker) is literate: The association is statistically nonsignificant in the BL model. Similarly, in the EL model, mother’s educational attainment was statistically nonsignificant.  Whether the household is a PSNP recipient: This association was statistically nonsignificant in the BL model even though PSNP 3 had started in 2011 and the BL was conducted in 2012. Participation in PSNP was not collected at EL, but, in lieu of this variable, the EL model included a whether the household received cash or food emergency assistance and the analysis shows no association.  Number of cows: This association is statistically nonsignificant in the BL model. The EL did not collect data on the number of cows so this variable was omitted from the EL model.  Number of shoats (sheep or goats): This association is marginally statistically significant in the BL model but nonsignificant at EL.  Children under the age of 15 (dependents): This association is statistically significant in the BL model. The EL model distinguished between the number of children under five and the number of children 5-17 and in the EL model the association of stunting with the number of children under five is statistically significant but not for children 5-17. 3.6.3 Minimum Acceptable Diet Adequate nutrition from birth to two 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 (i.e., breastfed versus non-breastfed). The MAD indicator measures both the 62 The BL study model for the logit regression of stunting did not account for sampling design nor does it include sampling weights. The EL model account for the two-stage clustered sample design and includes sampling weights. 44 minimum feeding frequency and minimum dietary diversity as appropriate for various age groups and whether or not 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 frequency63 and minimum dietary diversity64 for his or her age group and breastfeeding status, the child is considered to be receiving a MAD. Results (Table 11) indicate improvements in the prevalence of children with a MAD but also point to the low levels of children meeting the minimum guidance on children’s feeding practices and the need for continued improvement. The magnitude of improvement was largest in CRS where the prevalence of children with a MAD increased from 2.8 percent at BL to 6.9 percent at EL. In FH, the percentage of children with a MAD increased from the BL level of 0.5 percent to 6.6 percent at EL. At EL. The prevalence of MAD was highest in REST implementation area (12.4 percent) where it more than doubled from BL (4.9 percent). Nevertheless, the rates at both BL and EL are unacceptably low. It is of interest to break down the MAD into its component parts and analyze these separately, as the components provide essential information. Figure 6 indicates that in the combined project areas the percentage of children 6-23 months of age with a minimum meal frequency is highest among breastfed children 9-23 months of age (60.2 percent) and breastfed children 6-8 months of age (48.7 percent). It is lowest among non-breastfed children 6-23 months (15.7 percent). The prevalence of children meeting the threshold for minimum dietary diversity is low for children of all ages and breastfeeding statuses. The prevalence of children with a minimum dietary diversity is highest among breastfed children 9-23 months (13.8 percent). Only 2.6 percent of breastfed children 6-8 months achieve a minimum dietary diversity and 9.9 percent among non-breastfed children 6-23 months. Food groups consumed follow similar patterns among all three groups of children and the data indicate that grains, roots, and tubers are commonly eaten. Annex 9, Table A9.11 provides details on the components of MAD by age group and breastfeeding status, disaggregated by DFAP area. A similar pattern of meal frequency and dietary diversity is observed across the three DFAP implementation areas—namely, that meal frequency is highest among breastfed children of all ages and lowest among non-breastfed children and that dietary diversity is generally low among breastfed children 6-8 months and lowest when compared to other age groups. However, there is variation by DFAP area in the percentage of children achieving the thresholds for appropriate meal frequency and dietary diversity given their breastfeeding status and age. While grains, roots, and tubers are commonly consumed by all children in all three project areas there are some differences in some of the food groups consumed: dairy products are commonly consumed by children in CRS while in FH and REST legumes and nuts are the second most commonly consumed foods after grains, roots, and tubers. 63 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. 64 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. 45 Figure 6. Components of MAD Among Children 6-23 Months of Age by Age and Breastfeeding Status, Combined DFAP Areas, Ethiopia 2017 Source: Endline Study, Ethiopia 2017. There is little qualitative data from the DFAP PE Report to shed light on the increases in the prevalence of MAD, but information on the intensive training of HEWs in mother and child health and nutrition provided by DFAPs and, in turn, the training provided to mothers by HEWs and to mothers by DFAPs in different settings have likely played a role in the achievements gained. Under PSNP 4, pregnant and lactating mothers were excused from providing labor to community public works for some months but, in exchange, were required to attend sessions on CHN requirements, the importance of EBF during the first six months, and how to feed young children with diverse and nutritious locally available foods, as well as practices to increases the health and nutritional status of mothers themselves. The MIC5 interviewed for the QS stated they understood the importance of a diverse, nutritious diet for the healthy development of the child; however, across all data sites said they did the best they could in purchasing foods to fulfill this requirement based on the what money they had at the time and what food was available. Accordingly, young children were fed a diet based on the recommendations on an inconsistent basis. 3.6.4 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, which 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). Longer durations of breastfeeding have been associated with reduced risk of obesity in later life (Harder et al. 2005). 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 six months of age and continuing to two years of age. Introducing breastmilk substitutes to infants before six 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. 0 20 40 60 80 100 Percentage with minimum meal frequency Percentage with minimum dietary diversity Grains, roots, and tubers Legumes and nuts Dairy products (milk, yogurt, cheese) Flesh foods (meat, fish, poultry, and liver/organ meats) Eggs Vitamin A￾rich fruits and vegetables Other fruits and vegetables Percentage Breastfed children 6-8 months of age Breastfed children 9-23 months of age Non-breastfed children 6-23 months of age 46 As illustrated in Table 11, at EL more than two-thirds of children under 6 months in CRS were breastfed exclusively (67.5 percent) and close to three-quarters (71 percent) in REST. In FH, the majority of children under 6 months (87.9 percent) are breastfed exclusively. BL-EL comparison shows marked improvement since BL in all three project areas. This may be attributed to the extensive messaging and training provided to HEWs and to women of reproductive age on the importance of EBF as part of the overall emphasis on improving MCHN described in Section 3.6.3 regarding improvements in MAD. During the QS field work (2017), a young mother in one location proudly noted that after being trained in the importance of EBF by their HEW, she was appointed as a leader in the effort to encourage and promote EBF among lactating mothers in her village. The team does not have information on how widespread this practice was among the DFAP implementation areas, but it is likely that encouraging follow-up from community mothers contributed to this improvement. Across all data collection sites from the 2017 QS, women participating in GIs with young mothers were able to describe the importance of EBF to the health and development of their infant when asked the reason why they follow this practice. Figure 7 illustrates the breastfeeding status of children under two by age in months in the combined project areas and indicates that EBF is pervasive among children under three months. By 4-5 months, only one-half of children are breastfed exclusively. The decline in the prevalence of EBF before the WHO-recommended age of six months appears to be related to the introduction of plain water (26.9 percent), and complimentary foods (11 percent). By 6-8 months about two-thirds of children under two (62.8 percent) are receiving breastmilk and complimentary foods. Qualitative data from the 2017 QS provide several explanations for the introduction of plain water within the first six months. Young mothers interviewed in two villages noted that their breast milk dried up at four months. They attributed this to their poor diet. Young mothers in other locations indicated that their own mothers or aunts fed their infants with water or tea when they had to go out into the field to perform agricultural chores. Annex 9 Table A9.12 provides details on breastfeeding status by DFAP implementation area. Generally, a similar pattern in EBF up until 4-5 months and the introduction of complimentary foods by 6 months is observed across the DFAP areas. However, some differences are noteworthy. In FH, EBF is near universal among children under 2 months (96.2 percent) and 2-3 months (92.5 percent, and most children 4-5 months a fed breastmilk only (73.1 percent). The drop-off in EBF before 6 months is highest in CRS where only 32.7 percent of children 4-5 months are breastfed exclusively. Figure 7. Breastfeeding Status for Children 0-23 Months by Age in Months, Combined DFAP Areas, Ethiopia 2017 Source: Endline Study, Ethiopia 2017. 0 20 40 60 80 100 <2 2-3 4-5 6-8 9-11 12-17 18-23 Percentage Age in months Not breastfeeding Exclusively breastfed Breastfed and plain water only Breastfed and non-milk liquids Breastfed and other milk Breastfed and complementary foods 47 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. The prevalence of diarrhea in the two weeks prior to the survey among children 0-23 months declined markedly in the REST implementation area from 68.9 percent at BL to 21 percent at EL. In FH, there was no change in the percentage of children 0-23 months with diarrhea (26.1 percent at BL and 26.7 percent at EL). In CRS, the prevalence of diarrhea is lowest at EL (18.7 percent). Because the BL did not collect data on diarrhea in CRS, a BL-EL comparison is not possible. There is great variability between project areas in the use of ORT among children with diarrhea. At EL, use of ORT was highest in CRS (60.7 percent) followed by REST (34.2 percent). In FH, less than one quarter (24.1 percent) of children with diarrhea received ORT and did not change significantly from the BL level of 23.6 percent. 3.6.6 Diarrhea and Household Water, Sanitation, and Hygiene Status The prevalence of diarrhea was analyzed by households’ use of improved WASH facilities and practices (Annex 9, Table A9.13). In all three DFAP areas, the prevalence of diarrhea among children 0-23 months does not differ statistically by use of an improved water source. Use of a correct water practice or technology is associated with lower prevalence of diarrhea in all three project areas and these differences are statistically significant. In CRS, the prevalence of diarrhea among children 0-23 months is 8.6 percent of children in households that use correct water treatment but it is twice (19.8 percent) as high among children in households that do not use correct water treatment. The prevalence of diarrhea does not differ by use of improved toilet facilities, but this may be because most children live in households lacking an improved toilet facility. The association of diarrhea with a proper handwashing station was not assessed because of lack of adequate sample size; only 13 of 1,320 children 0-23 months in the combined project areas reside in a household with soap and water at a handwashing station. 3.7 GENDER The USAID Ethiopia Country Development and Cooperation Strategy 2011-2018 (CDCS)65 highlighted key gender issues and challenges such as: gender differences in school enrolment; gender differences in access to land and farm size; gender differences in access to credit; and gender differences in the burden of water shortages and access to clean water. The Ethiopia CDCS 2011-2018 also underscored the persistence of gender-based violence, female-genital mutilation, and early marriage and early childbearing. Gender differences in access to credit are discussed in Section 3.3 on Agriculture. The BL study collected information on: women’s decision-making role in the purchase and sale of household assets; wife beating; female circumcision; women’s decision-making related to health-seeking behavior; and women’s self-reported self-efficacy. This section uses data from the EL survey to address: women’s and men’s participation in cash-earning opportunities; mothers’ and fathers’ correct knowledge of MCHN practices; and men’s and women’s participation in self-earned cash decision-making and MCHN decision-making. These indicators are intended to measure women’s inclusion in processes that impact the overall achievement of poverty reduction and improved food security and nutrition. Since the BL study focused on a different set of gender issues than the EL study, it is not possible to assess improvement in the topics covered at BL but the EL gender-related indicators provide context to better understand the projects’ achievements and remaining challenges. 3.7.1 Participation in Cash-Earning Activities and Self-Earned Cash Decision-Making Table 12 illustrates the EL estimates of participation in cash-earning activities and women’s and men’s control over self-earned cash. About one-half of adults (54.6 percent) in the combined project areas participate in cash-earning opportunities. Men are more likely to partake in cash-earning activities (65.2 65 USAID Ethiopia Country Development and Cooperation Strategy 2011-2018 accessed at https://www.usaid.gov/sites/default/files/documents/1860/CDCS_Ethiopia_December_2018r1.pdf. 48 percent) compared to women (44.6 percent). Joint decision-making on use of self-earned cash is more common than deciding alone. About one-half of men (57.6 percent) and women (55.1 percent) decide with their spouses on the use of self-earned cash. Approximately one-third of men (33.1 percent) and one-quarter of women (26.1 percent) decide alone on the use of self-earned cash. Gender differences in the participation in cash-earning opportunities and decision-making related to self-earned cash are generally consistent across the project areas. There are several reasons contributing to women’s lesser participation in cash-earning activities compared to men. The Ethiopia 2017 BL report provides explanations based on GIs with both women and men in study villages that women with children are less able to partake in cash-earing activities because of their traditional role as child caretaker in the household. Women are also responsible for the household and preparing family meals. Finally, in PSNP households, women with children have less time for engaging in cash earning opportunities compared to men due to program requirements. Unless PSNP women are in the later stages of pregnancy and/or lactating, able-bodied women with children must contribute labor for community public-works program as a pre-condition for receiving food/cash benefits. Under PSNP 4, the GOE recognized the heavy workload of women compared to men based on their childcare and household duties. These women are now permitted to work less hours per day on those days they are scheduled to contribute to public works by starting work later in the morning, and ending earlier in the day. Gender differences in the participation in cash-earning opportunities and decision-making related to self￾earned cash are generally consistent across the DFAP implementation areas. Joint decision-making on self-earned cash is highest in FH (men, 71.7 percent; women, 67.9) and lowest in CRS (men 36.9 percent; women 44 percent). Sole decision-making on self-earned cash is more commonly practiced in CRS (men, 40.8 percent; women, 31 percent). Qualitative findings from the 2017 QS based on GIs with MIC5 and KIIs with HEWs provide a probable interpretation. The CRS project area contains Muslim majority woredas. In these woredas, men have the primary decision-making authority. However, the EL estimates underscore that most women have some participation in self-earned cash decision making. Table 12. Self-Earned Cash Decision-Making, Ethiopia 2017 Overall CRS FH REST Percentage of men and women who earned cash in the past 12 months1 54.6 52.1 50.6 59.1 Male 65.2 67.8 63.7 65.4 Female 44.6 36.5 38.3 53.2 Percentage of men/women in union and earning cash who make decisions alone about the use of self-earned cash Male 33.1 40.8 23.4 38.2 Female 26.1 31.0 22.0 27.2 Percentage of men/women in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash Male 57.6 36.9 71.7 54.8 Female 55.1 44.0 67.9 50.8 Number of men and women (age 15 or older) Men 13,407 3,795 5,373 4,239 Women 6,553 1,893 2,629 2,031 Number of men/women in union and earning cash in the past 12 months 6,854 1,902 2,744 2,208 Men 5,195 1,459 2,057 1,679 Women 3,424 1,028 1,409 987 1 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. Source: FFP Endline Survey, Ethiopia 2017 49 The overall indicator masks the percentage of cash-earning women who do not participate in decision￾making on how to spend their self-earned cash. Figure 8 indicates that 17.6 percent of women have no say on how their self-earned cash will be spent compared to 8.9 percent of men. However, over 50 percent of both men and women respondents reported that how to spend cash is a joint decision made between husband and wife. According to qualitative findings from the QS, joint decision-making is important to the overall well-being of the household given the overwhelming needs of CFI households. Consultations are held on the allocation of cash for expenses on food items to purchase for the family, school fees and school clothes for children, and medical costs. Annex 9, Table A9.14 provides details on self-earned cash decision-making for the combined project areas and by implementation area. Figure 8. Self-Earned Cash Decision-Making, Combined DFAP Areas, Ethiopia 2017 Source: FFP Endline Survey, Ethiopia 2017. 3.7.2 Maternal and Child Health Decision-Making An important part of gender integration and awareness raising is to promote equity between men and women in their access to resources, MCHN information, and knowledge and skills. The EL estimates of MCHN knowledge and decision-making are illustrated in Table 13. Knowledge of the four core MCHN practices implies that mothers and fathers correctly answered at least three of the four questions on core MCHN practices: 1) optimal number of maternal antenatal visits during pregnancy; 2) nutrition during pregnancy; 3) early initiation of breastfeeding; and 4) introduction of complementary foods at six months of age. These four practices do not constitute a comprehensive set of practices, but they are relevant to the 1,000-day window from pregnancy to a child’s second birthday. In the combined DFAP areas, most mothers and fathers of children under two have correct knowledge of MCHN practices, but more women (82.9 percent) with children under two years of age have knowledge of four core MCHN practices than men (71.2 percent). Men and women in a union and with children under two years of age were asked about MCHN decision-making in the household. Female respondents were asked who usually makes decisions about their own health and nutrition and male respondents were asked who usually makes decisions about their wives’ or partners’ health and nutrition. Both female and male respondents were asked who usually makes decisions about the health and nutrition of children under two years of age. Across the three project areas it is more common for MHN and CHN decisions to be made alone and usually by the woman than jointly by spouses. Findings from the DFAP PE Report corroborate this finding across the DFAP implementation areas. Health and nutrition is one area in which women’s decision-making predominates. According to GIs with MIC5, most husbands consider it is the woman’s role to make these decisions. In the combined project areas, about 56.6 percent of women decide alone 33.1 26.3 57.7 55.5 8.9 17.6 0 20 40 60 80 100 Males Females Percentage Respondent alone Respondent with spouse Spouse alone Other Respondent with someone else 50 on MHN issues compared to 23.9 percent of men. A total of 59.4 percent of women decide alone on CHN matters compared to only 13.2 percent of men. Joint MHN and CHN decision-making is less common than joint decision making on self-earned cash, but nonetheless, approximately one-third of men and women agree that decision-making is done together. These statistics are also corroborated by the qualitative findings from the QS. In the combined DFAP areas, a total of 27.2 percent of women decide jointly with their spouse on MHN issues compared to 30.5 percent of men. A similar trend is observed for CHN decisions: 29.3 percent of women decide with men on CHN matters compared to 32.7 percent of men. These results underscore differences in males’ and females’ perceptions of decision-making. Generally, similar patterns of decision making are observed by implementation area. Table 13. Maternal and Child Health Knowledge and Decision-Making Overall CRS FH REST Percentage of men and women with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices1 77.7 77.4 70.7 83.8 Male 71.2 76.2 64.0 74.9 Female 82.9 78.4 76.3 90.8 Percentage of men/women in union with children under two who make maternal health and nutrition (MHN) decisions alone Male 23.9 27.9 21.3 24.0 Female 56.6 50.5 52.8 63.0 Percentage of men/women in union with children under two who make MHN decisions jointly with spouse/partner Male 30.5 20.3 40.2 27.3 Female 27.2 20.7 33.7 25.1 Percentage of men/women in union with children under two who make child health and nutrition (CHN) decisions alone Male 13.2 18.6 10.0 13.1 Female 59.4 54.6 51.8 68.1 Percentage of men/women in union with children under two who make CHN decisions jointly with spouse/partner Male 32.7 22.8 42.1 29.6 Female 29.3 23.3 38.1 25.2 Number of men and women with children under two 2,339 759 887 693 Men 1,051 343 404 304 Women 1,288 416 483 389 Number of men/women in union with children under two 2,225 731 838 656 Men 1,049 343 403 303 Women 1,176 388 435 353 1 Correctly answered at least three of four MCHN questions. The overall indicators mask the percentage of men and women who are not involved in MHN or CHN decisions. Figure 9 illustrates that the majority of women in the combined DFAP areas (83.9 percent) have some participation in decisions having to do with their own health and nutrition and that 57 percent make this decision alone. 66 A total of 16.1 percent of women do not participate in MHN decision-making because their husbands decide alone (15.9 percent) or someone else decides (0.2 percent). Relatedly, about 24.2 percent of men do not engage their wives in MHN decision-making.67 66 This includes women who reported deciding alone, women who reported deciding with their spouse, and women who reported deciding with someone else on their own health and nutrition. 67 This includes men who reported deciding alone or with someone else or “other” on decisions having to do with the health and nutrition of their wives. 51 Annex 9, Table A9.14 provides additional details on MHN decision-making by DFAP area and indicates a similar pattern of MHN decision-making across the three DFAP implementation areas. Figure 9. Maternal Health and Nutrition Decision-Making, Combined DFAP Areas, Ethiopia 2017 Source: FFP Endline Survey, Ethiopia 2017. Figure 10 indicates that the majority of women (89 percent) in the combined project areas participate in decisions having to do with the health and nutrition of their children. 68 Similar to the data on women’s decision-making about their own health and nutrition, most women (59.4 percent) make decisions on behalf of their children by themselves. Only 10.9 percent of women have no input into CHN decisions and a similar percentage of men (13.3 percent) report that they decide alone (13.2 percent) on CHN issues or let someone else make the decision (0.1 percent). On the other hand, about one-half of fathers (54.1 percent) are not involved in CHN decision-making.69 Annex 9, Table A9.15 provides additional details on CHN decision-making by project area. Generally, the pattern of CHN decision-making is similar across the DFAP areas. It is noteworthy however that the percentage of mothers who are excluded from CHN decision-making in CRS is almost double (22.1 percent) the average for the combined project areas (10.9 percent). Figure 10. Child Health and Nutrition Decision-Making, Combined DFAP Areas, Ethiopia 2017 Source: FFP Endline Survey, Ethiopia 2017. 68 This includes women who reported deciding alone, women who reported deciding with their spouse, and women who reported deciding with someone else on their children’s health and nutrition. 69 This includes men who reported their wives or someone else deciding on the health and nutrition of their children. 24 57 31 27 45 16 0 20 40 60 80 100 Males Females Percentage Respondent alone Respondent with spouse Spouse alone Other Respondent with someone else 13.2 32.7 59.4 54 29.3 10.5 0 20 40 60 80 100 Males Females Percentage Respondent alone Respondent with spouse Spouse alone Other Respondent with someone else 52 4. CONCLUSIONS Meta Conclusions about Food Security and Poverty The relatively modest gains in DFAP implementation areas for food security are predominantly because of the devastating drought of 2015-2016. Households reliant on rainfed agriculture experienced ongoing production challenges from successive years of insufficient rain, water shortages, drought, and destructive flooding during the DFAP implementation period, leaving them particularly vulnerable and less able to cope with the severe drought that began in 2015. Some of the gains won toward greater food security and reduction in malnutrition were lost by households that graduated from PSNP once they achieved food self-sufficiency. Households that remained as PSNP and DFAP beneficiaries from 2012-2016 who were seeing improvements in their situation lost some of their gains as well. Despite the adverse effects of these weather patterns and the severity of the 2015-2016 drought, higher levels of food security and improvements in MCHN may have been achieved were it not for several important factors related to changes in the GOE PSNP policies and the amount of resources available to cover all CFI households. These factors, outside of the management influence of the DFAP IPs, include:  Non-PSPN households in the PBS sample included the CFI. The number of CFI households per woreda in each region exceeded the amount of PSNP resources in a given year. Not every household meeting the criteria for PSNP could be included. Because they were not included in PSNP, these households could not be included as DFAP beneficiaries either. While these households may have been indirect beneficiaries of some program activities, they lacked the one of the pillars of PSNP developed to shorten food gaps and maintain or increase assets— specifically, food and cash transfers.  Based on nationally set targets established in the GOE Growth and Transformation Plan (2010), 3.5 million beneficiary households were graduated from PSNP because of the quotas established in the highlands per region to graduate households from the program.70 Many of these households had not achieved self-sufficiency, a key criterion for graduation based on the GOE Program Implementation Manual. These quota-based graduates lost their DFAP beneficiary status as well. Not all of these households were reinstated in the program during the 2015-2016 drought or through the retargeting process which was conducted in 2016.  All PSNP households with more than five family members became more food insecure with the introduction of a standardized package of food based on the nutritional and caloric requirements of a five-person household. Household Dietary Diversity Household dietary diversity has increased overall, but the degree of increase varies in each project area. Not only does the increase in dietary diversity vary across DFAP areas, but also within project areas. This increase is based on a combination of factors that gave some households, across all project areas, the ability to purchase a diversity of nutritious foods. Further increases in dietary diversity may have been achieved if not for the effects of the severe drought of 2015-2016 on agricultural production. The households most likely contributing to the increase in dietary diversity include those households:  With five or fewer household members that were selected as PSNP 3 beneficiaries and remained so throughout the DFAP implementation period carrying over into PSNP 4; 70 The actual target was 3.7 million households but GOE halted the program of graduation by quota given the enormity of humanitarian needs for food, water, and other forms of assistance during the 2015-2016 drought. 53  Located in less drought-affected areas with sufficient good quality land relative to the size of the household that use agro-inputs and pesticides;  Whose crop production benefitted from access to irrigation infrastructure completed through public work efforts;  Who cultivate and maintain backyard vegetable gardens in areas where year-round rainfall occurred; and  Who were able to establish successful agriculture-related micro-enterprises based on small￾scale irrigation for cash crop production of vegetables or fruit. Poverty Because a direct comparison of BL and EL poverty indicators was not feasible, it was only possible to make limited conclusions. The prevalence of poverty seen at EL was affected by crop failures, declines in livestock productivity, and livestock death from multiple years of erratic rainfall, and location-specific incidents of drought and flooding. These effects on household poverty levels were further exacerbated by the 2015-2016 drought. The drought also caused a reversal of food self-sufficiency achieved by some households. Households frequently search for wage earning opportunities in times of crop failure, but the daily wage rate paid for agricultural labor declined significantly because of the few commercial farms unaffected by drought conditions and the large number of people seeking opportunities on these farms. Water, Sanitation, and Hygiene Data from FEWS NET, USAID Complex Emergency Fact Sheets for Ethiopia in 2015 and 2016, and findings from the DFAP PE Report show that years of insufficient rain—and particularly the major drought of 2015-2016—created severe water shortages in many of Ethiopia’s hotspot woredas. Steps were taken by the GOE, donors, and relief organizations to address the resulting crisis of insufficient drinking water such as deploying emergency water delivery trucks. The contraction or disappearance of water sources during this major drought certainly contributed to low percentage of households that can obtain drinking water in less than 30 minutes round trip. Despite the growing understanding reported on the importance of handwashing at critical times from messaging and training provided by DFAP IPs and HEWS, shortages of water coupled with the prioritization of available water for drinking purposes contributed to the decline of handwashing practices across the three project areas. There are two key factors that may have contributed to the decline in use of improved sanitary facilities and the increase in open defecation. The more important factor concerns the quality of the latrine model that was widely introduced. Findings indicate that these latrines break down approximately one year after use. The second factor is that households do not replace the latrines once they have broken because of the high replacement cost. Behavioral issues may contribute to this decline in some of the DFAP implementation areas as suggested by interviews with HEWs and young mothers conducted for the 2017 DFSA BL study. The team does not have data to form conclusions about the low use of water improvement technologies across the three DFAP implementation areas.71 Maternal Health and Nutrition At EL about one-third (36.2 percent) of women in the combined DFAP areas are underweight indicating the need for further improvements in women’s nutritional status. The low levels of dietary diversity as evidenced by the WDDS (on average women consume two or less of nine nutritional rich food groups) at BL and the MDD-W (on average, less than 10 percent of women consume 5 of 10 nutritionally rich food groups) at EL are related to multiple factors described in the findings that, taken together, create barriers to women’s food access and availability. These barriers explain women’s consumption of 71 During the November 2017 Data Utilization Workshop in Addis Ababa, IPs were not able to explain the low use of these technologies to improve water. Some noted plans to conduct assessment to identify the causes. 54 cheaper and locally grown foods, specifically grains, roots, and tubers, as the basis of their diet. Data from the 2017 QS indicate that several factors account for the relatively low use of contraceptives. Among them are: religious beliefs against family planning and the use of contraceptives; women’s lack of decision-making over issues that affect the health and nutrition of themselves and their children among communities that are predominantly Muslim; and women’s fears that contraceptives may be harmful. According to the Tufts DFAP PE report, DFAPs did not include interventions to promote family planning and the use of contraceptives. These efforts are the responsibility of HEWs in kebele health centers and rural health posts. The team does not have data to form conclusions on the lack of statistically significant change between BL and EL in prevalence of women making at least four ANC visits during their last pregnancy in the REST and FH implementation areas, or why EL ANC results in REST are higher than in the CRS and FH implementation areas. The data do suggest that ongoing efforts tailored to specific location features and sociocultural norms and practices within each DFAP area are required for successful family planning and health care seeking during pregnancy as well interventions to supplement messaging. Children’s Health and Nutrition There have been moderate improvements in CHN indicators (malnutrition) in all three DFAP areas despite the deterioration in WASH indicators. The improvement in the CHN indicators (malnutrition, MAD) is supported by moderate improvements in HDDS. The data on malnutrition would have likely deteriorated given the food security situation and serious water shortages during the 2015-2016 drought. However, emergency feeding programs implemented by IPs and the GOE and the treatment of children with severe acute malnutrition in government-designated malnutrition hotspots may have contributed to maintaining some of the increases that were achieved prior to 2015. The finding from the multivariate analyses that children living in female-headed households are less likely to be stunted suggests differences in decision-making and resource allocation for households where women are the sole decision-makers—namely, that decision-making in female-headed households may lead to resource allocations that are favorable to CHN compared to male-headed households. The prevalence of diarrhea in the REST area declined markedly by almost 50 percentage from 68.9 percent to 21 percent even with the decrease in the use of a proper handwashing station. In FH, the prevalence of children 0-23 months with diarrhea remained stable at around 26 percent despite the: deterioration in the use of a proper handwashing station, decline in the use of an improved sanitation facility, and increase in practice of open defecation. Based on bivariate analysis of the prevalence of diarrhea and WASH indicators, the use of water treatment technologies is one of the important factors associated with the decrease in diarrhea in REST between 2012 and 2017. Additional analysis could be conducted to identify other factors that contributed to this decrease. Gender The predominance of women’s participation in MCHN decisions and their perceived participation in other household decisions such as use of self-earned cash are likely the result of the ongoing focus on gender equity and the importance of women’s participation promoted by each IP and by government officials over the duration of the DFAP implementation period. 72 IPs provided gender training of government officials including HEWs to sensitize them on gender issues and to engage them in promoting changes, and direct support to village women. Each IP used a variety of techniques to promote these changes including different forms of messaging such as: role playing, holding community conversations on gender, and through the establishment of gender clubs in schools. Government officials also actively promoted greater gender equity and women’s 72 The various approaches and methods used by IPs and conducted by government officials are described in the DFAP PE Report (2017) on under the findings on gender equity and empowerment. Interviews with woreda officials and HEWS during the QS field work provided examples of the contribution and commitment of GOE officials to promoting gender equity and women’s empowerment. 55 empowerment through similar activities as gender officers or as officials working in Women’s and Children’s Activity Offices. Examples form qualitative data include the arrangement of literacy training for village women and working with woreda legal offices, schools, and local police to stop child marriages. 56 REFERENCES El Nino in Ethiopia 2015-2016: A Real Time Review of Impacts and Responses, USAID/Ethiopia Agriculture Knowledge, Learning, Documentation and Policy Project. Andy Catley, Adrian Cullis and Dawit Abebe, Tufts University, March 2016. FAO, Ethiopia Situation Report, August 13, 2017. Federal Democratic Government of Ethiopia, Growth and Transformation Plan (GTP), 2010/2011- 2014/2015, Ministry of Finance and Economic Development (MoFED), Addis Ababa, 2010. FEWSNET, Informing Climate Change Adaptation Series, Fact Sheet. A Climate Trend Analysis of Ethiopia. April 2012. FEWSNET Food Security Outlook Update, World Food Programme, November 2012. FEWSNET Food Security Outlook, World Food Programme, October 2013-March 2014, March 2014 FEWSNET Horn of Africa: Ethiopia. Special Report. Illustrating the Extent and Severity of the 2015 Drought. December 2015. FEWSNET, Food Security Alert, World Food Programme, December 4, 2015. Performance Evaluation of Title II Funded Development Food Assistance Programs in Ethiopia Agriculture Knowledge, Learning, Documentation and Policy project (AKLDP Ethiopia), Tufts University, May 2017. Productive Safety Net Programme, Phase IV, Programme Implementation Manual Government of Ethiopia, Ministry of Agriculture, Addis Ababa, December 2014. UNICEF Humanitarian Assistance Situation Report # 18: Ethiopia. UN Fund for Children, NY, Reporting Period November 6 – December 5, 2017, December 5, 2017. USAID/Food for Peace, Baseline Study of Development Food Security Activities Report in Ethiopia. EVELYN Contract, Patricia Vondal, Benita O’Colmain, Gheta Temsah, and Ephraim Mebrate Disasa. February 2018. USAID/OFDA, Ethiopia Complex Emergency Fact Sheet # 13, Fiscal Year 2014, January 2014. USAID/OFDA, Ethiopia Complex Emergency Fact Sheet # 1, Fiscal Year 2015, January 21, 2015. USAID/OFDA, Ethiopia Complex Emergency Fact Sheet # 3, Fiscal Year 2015, June 26, 2015. USAID/OFDA, Ethiopia Complex Emergency Fact Sheet # 7, Fiscal Year 2016, March 30, 2016. USAID/OFDA, Ethiopia Complex Emergency Fact Sheet # 10, Fiscal Year 2016, May 13, 2016. The Rising Costs of Nutritious Foods in Ethiopia, Ethiopia Strategy Support Program, Research Note #67. Fantu Bachewe, Kalle Hirvonen, Bart Minten, and Feiruz Yimer, USAID/UKAID/European Union. June 2017. World Bank Development Research Group. Land Governance Policy Brief. Does Large Farm Establishment Create Benefits for Neighboring Smallholders? Evidence from Ethiopia. Daniel Ali, Klaus Deininger, and Anthony Harris. Issue 3, January 2018. ANNEX 1 Statement of Work ANNEX 2 Population-Based Survey Protocol FINAL – November 15, 2017 This publication was produced for review by the U.S. Agency for International Development. It was prepared by the EVELYN contract team. Ethiopia Joint Baseline/End-line PBS Protocol Evaluation and Learning (EVELYN) Mechanism Office of Food for Peace (FFP) Contract #: AID-OAA-I-15-00-24 Order #: AID-OAA-TO-17-00005 Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism Abbreviations ANC Antenatal care BL Baseline CAPI Computer-assisted personal interview CRS Catholic Relief Services CSA Ethiopia Central Statistics Agency DFAP Development Food Assistance Project DFSA Development Food Security Activity DTAP Data treatment and analysis plan EL End-line EVELYN FANTA Evaluation and Learning Mechanism Food and Nutrition Technical Assistance Project III FFP Office of Food for Peace FH Food for the Hungry FIES Food insecurity experience scale GHT Gendered household type HDDS Household dietary diversity score ICF ICF International IFSS Internet file streaming system IP Implementing partner MAD Minimum acceptable diet MCHN ME&A Maternal and child health and nutrition Mendez, England and Associates ORT Oral rehydration therapy PBS Population-based survey PPS Probability proportional to size REST Ethiopia Relief Society of Tigray USAID U.S. Agency for International Development WASH Water, sanitation and hygiene WV World Vision Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism Table of Contents 1. BACKGROUND AND PURPOSE ...................................................................................... 1 2. PBS DESIGN........................................................................................................................ 3 2.1 Indicators to be Measured.................................................................................................. 3 2.2 Sampling Plan ..................................................................................................................... 5 2.2.1 Sampling Frames ..................................................................................................... 5 2.2.2 Sample Size............................................................................................................. 6 2.2.3 Sample Selection ..................................................................................................... 8 2.3 Questionnaire ....................................................................................................................10 3. FIELD PROCEDURES.......................................................................................................11 3.1 Data Collection Mode........................................................................................................11 3.2 Field Manuals ....................................................................................................................11 3.3 Training..............................................................................................................................11 3.4 Data Collection..................................................................................................................13 3.5 Quality Control ..................................................................................................................14 4. DATA PROCESSING AND ANALYSIS...........................................................................15 4.1 Data Transmissions ..........................................................................................................15 4.2 Data Analysis.....................................................................................................................15 5. TIMELINE..........................................................................................................................16 Annex 1 – Indicators for Prior DFAPs Annex 2 – Training Agendas Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 1 1. BACKGROUND AND PURPOSE In Fiscal Year 2016, the U.S. Agency for International Development (USAID) Office of Food for Peace (FFP) awarded funding for four multi-year development food security activities (DFSAs) in Ethiopia. The goal of the 2016-2020 DFSA awards is to enhance resilience to shocks and livelihoods; and improve food security and nutrition for rural households vulnerable to food insecurity. Under the Evaluation and Learning Mechanism umbrella contract (EVELYN), FFP contracted Mendez England & Associates (ME&A) and its subcontractors ICF International (ICF) and TANGO International (TANGO) to conduct population-based surveys (PBSs) and a resilience assessment for the DFSAs in Ethiopia, respectively. In addition to a baseline (BL) study, the EVELYN team will conduct an end-line (EL) PBS in the target areas for the development food assistance projects (DFAPs) that expired in December 2016. The project areas for the DFSAs and prior DFAPs overlap to a large extent. For this reason, EVELYN will administer a joint baseline BL/EL PBS using a common questionnaire in the overlap and non-overlap areas encompassed by the DFSAs and prior DFAPs. The common questionnaire will be driven by the indicators required for the BL PBS for the DFSAs, many of which (but not all) overlap with those required for the prior DFAPs. The purpose of the BL PBS for the DFSAs is to assess the current status of key indicators, to have 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 future EL PBSs. 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 will be used for the final evaluation of the prior DFAPs to evaluate change over time in some of the indicators that were measured in the prior BL study. The fieldwork for the joint BL/EL PBS will be conducted in July-August 2017. Implementing Partners for the DFSAs Four implementing partners (IPs): Catholic Relief Services (CRS), Food for the Hungry (FH), Ethiopia/Relief Society of Tigray (REST), and World Vision (WV), are implementing the new FFP-funded DFSAs in Ethiopia: (1) CRS and its partners implement the Ethiopian Livelihoods & Resilience Project (ELRP) in the Oromia Region and Dire Dawa Administrative Unit. (2) FH and its partners implement the Targeted Response for Agriculture, Income and Nutrition (TRAIN) Project in the Amhara Region. (3) REST and its partners implement its Title II Development Food Assistance Project in the Tigray Region. (4) WV and its partners implement the Strengthen PSNP 4 Institutions and Resilience Project in the Oromia and Amhara Regions. Figure 1 provides a map of the DFSA project areas. Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 2 Figure 1. DFSA Project Areas Implementing Partners for the Prior DFAPs The four prior DFAPs were implemented by prime contractors CRS, FH, REST, and Save the Children United States (SCUS) and their partners. Table 1 describes the regions and zones where the prior DFAPs were implemented. The CSUS Project areas were not included as part of the joint BL/EL PBS. Table 1. Prior DFAP Project Areas Implementing Partner Region/Administrative Area Zone CRS Oromia, Dire Dawa East Harerghe, East Shoa, Dire Dawa FH Amhara North Wollo, South Gondar, Waghimira REST Tigray Central Zone, Eastern Zone, South Eastern Zone, Southern Zone CSUS Oromia, Somali Borena, Gode, Liben Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 3 2. PBS DESIGN 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 BL survey for the prior DFAPs was conducted in June and July of 2012. The pre-post design (using the 2012 BL survey and the 2017 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. 2.1 Indicators to be Measured EVELYN will collect data to measure 38 FFP standard indicators and 5 resilience-related indicators (see Table 2). The 38 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 38 FFP project indicators is available in the 2015 FFP Indicator Handbook.1 FFP resilience indicators measure household well-being, exposure to shocks, resilience capacities (absorptive, adaptive, and transformative), and households’ likely response to shocks. Definitions for resilience indicators are provided in the Ethiopia PBS Data Treatment and Analysis Plan (DTAP). All indicators will be measured for the joint BL/EL PBS, however, all of these indicators were not measured at baseline for the prior DFAPs and there were some indicators measured at baseline for the prior DFAPs that were not included in the joint BL/EL PBS. A complete list of the indicators measured at baseline for the prior DFAPs is included in Annex 1. Although all indicators listed in Table 2 will be measured and reported on separately for the BL study of the DFSAs and the EL evaluation of the prior DFAPs, change over time can only be measured for those indicators that were measured in both the prior BL PBS and the joint BL/EL PBS. These indicators are shown in red font in Table 2. Table 2. Ethiopia Joint BL/EL PBS Indicators Indicator Disaggregation Level FOOD SECURITY 1. Average Household Dietary Diversity Score (HDDS) None 2. Prevalence of moderate to severe or severe food insecurity* GHT** POVERTY 3. Daily per capita expenditures (as a proxy for income) in USG-assisted areas GHT 4. Prevalence of poverty: Percent of people living on less than $1.90 per day GHT 5. Depth of poverty: Mean percent shortfall relative to the $1.90 poverty line GHT WATER, SANITATION, AND HYGIENE 6. Percentage of households using a basic drinking water source Distance from source 7. Percent of households in target areas practicing correct use of recommended household water treatment technologies Technology type 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 2017. Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 4 Indicator Disaggregation Level 8. Percentage of households that can obtain drinking water in less than 30 minutes (round trip) None 9. Percentage of households using a basic sanitation facility None 10. Percentage of households in target areas practicing open defecation Location 11. Percentage of households with soap and water at a handwashing station commonly used by family members Location AGRICULTURE 12. Percentage of farmers who used financial services (savings, agricultural credit, and/or agricultural insurance) in the past 12 months Sex 13. Percentage of farmers who practiced the value chain activities promoted by the project in the past 12 months Sex 14. Percentage of farmers who used at least [a project-defined minimum] sustainable agriculture (crop, livestock and NRM) practices and/or technologies in the past 12 months 15. Percentage of farmers who used at least [a project-defined minimum] sustainable crop practices and/or technologies in the past 12 months Sex 16. Percentage of farmers who used at least [a project-defined minimum] sustainable livestock practices and/or technologies in the past 12 months Sex 17. Percentage of farmers who used at least [a project-defined minimum] sustainable natural resource management practices and/or technologies in the past 12 months Sex 18. Percentage of farmers who used improved storage practices in the past 12 months Sex WOMEN’S HEALTH AND NUTRITION 19. Prevalence of underweight women None 20. Prevalence of women of reproductive age consuming a minimum dietary diversity None 21. Contraceptive Prevalence Rate Modern, traditional 22. Percent of births receiving at least four antenatal care (ANC) visits during pregnancy None 23. Prevalence of women of reproductive age who consume targeted nutrient-rich value chain and non-value chain commodities Value chain and non-value chain commodity CHILDREN’S HEALTH AND NUTRITION 24. Prevalence of underweight children under five years of age Sex 25. Prevalence of stunted children under five years of age Sex 26. Percentage of children under age five who had diarrhea in the prior two weeks Sex 27. Percentage of children under five years old with diarrhea treated with Oral Rehydration Therapy (ORT) Sex 28. Prevalence of exclusive breast-feeding of children under six months of age Sex 29. Prevalence of children 6-23 months receiving a minimum acceptable diet (MAD) Sex 30. Prevalence of children 6- 23 months who consume targeted nutrient-rich value chain and non-value chain commodities Sex, value chain and non￾value chain commodity GENDER 31. Percentage of men and women who earned cash in the past 12 months Sex 32. Percentage of men/women in union and earning cash who make decisions alone about the use of self-earned cash Sex 33. Percentage of men/women in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash Sex 34. Percentage of men and women with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices Sex 35. Percentage of men/women in union with children under two who make maternal health and nutrition decisions alone Sex 36. Percentage of men/women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner Sex 37. Percentage of men/women in union with children under two who make child health and nutrition decisions alone Sex 38. Percentage of men/women in union with children under two who make child health and Sex Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 5 Indicator Disaggregation Level nutrition decisions jointly with spouse/partner RESILIENCE 39. Shock exposure index None 40. Cumulative impact of shock exposure index None 41. Absorptive capacity index None 42. Adaptive capacity index None 43. Transformative capacity index None *Food insecurity is measured using the Food Insecurity Experience Scale (FIES) based on a 12 month and 30 day recall **GHT= Gendered household type Indicators in red font are those that were measured in the baseline survey for the prior DFAPs. 2.2 Sampling Plan 2.2.1 Sampling Frames 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 since the DFSAs will be implemented in some of the same areas where the prior DFAPs were implemented. The sampling frames for the BL PBS and EL PBS were constructed taking into account these overlapping geographies and using 2007 census level data representing the joint BL/EL target areas.2 Table 3 provides 2007 census estimates of the number of households included in the BL and EL sampling frames. The census administration levels are as follows: • Region • Zone • Woreda (District) • Kebele • Enumeration area Table 3. Woredas, Kebeles and Households Included in the BL and EL Sampling Frames Woredas Kebeles Households CRS 14 258 226,211 BL and EL 2 54 46,661 BL only 7 129 109,834 EL only 5 75 69,716 FH 6 119 138,303 BL and EL 3 79 113,740 BL only 3 40 24,562 REST 16 288 384,732 BL and EL 8 163 194,467 BL only 4 61 98,339 2 The data were obtained from the Ethiopia Central Statistics Agency (CSA). Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 6 EL only 4 64 91,926 WV 13 369 374,506 BL and EL 6 135 126,484 BL only 7 234 248,021 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, 2) identifying the sample size needed for the EL survey for the prior DFAPs, and 3) deriving a joint sample size based on these sample sizes and the overlap between the DFSA and prior DFAP project areas. 1) BL Sample Size for the DFSAs The sample size calculation for the BL project areas for the 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 FANTA Sampling Guide3) 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 non-response 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 baseline to final evaluation), controlling for inferential error, as described in Appendix 1 of the Addendum to the FANTA Sampling Guide. Table 4 provides the target sample size using preliminary estimates from the 2016 Ethiopia Demographic and Health Survey (DHS) for the prevalence of stunting in rural households, proportion of children aged 0– 59 months in rural households, and average rural household size. Based on these sample size calculations, a total of 174 clusters (with 30 households per cluster) and 6,960 households should be sampled (58 clusters and 1,740 households for each of the four DFSAs).4 Table 4. BL PBS Sample Size for each Project and Overall Indicator Prevalence of Stunting (P1)* Number of Children per Household* Number of Children Needed Household Sample Size Needed** Households Needed with 5% Nonresponse Adjustment Clusters per Project Overall Sample Size of Households Needed 3 Magnani, Robert. Sampling Guide (1999) and Addendum (2012). Washington, D.C.: FHI 360/FANTA. Available at http://www.fantaproject.org/monitoring-and-evaluation/sampling. 4 To accommodate the selection of 30 households per cluster, 58 clusters of 30 households yielded a sample of 1,740 households, 20 households more than the 1,720 households needed after the nonresponse adjustment. Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 7 Prevalence of stunting 0.42 0.72 908 1,634 1,720 58 6,960 *Source: 2016 Preliminary DHS estimates for rural households (where estimated household size is given as 5.0 and estimated proportion of population under five years of age is given as 0.144) **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 based on the same sample size derived from the BL survey in the prior DFAP project areas. The BL sample size for the prior DFAPs was 1,540 households in each of the four project areas.5 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 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 these three groups relevant to the particular survey in question (BL versus EL), and based on the proportion of households in each group. This allocation was done separately for the prior DFAPs with an EL sample size of 1,540 by allocating to “old” and “overlap” kebeles /woredas, and the DFSAs with BL sample size of 1,740 by allocating to “new” and “overlap” kebeles/woredas.6 The overall joint BL/EL sample size requirement was then calculated based on the sample size requirement for the prior DFAPs only, the sample size requirement for the DFSAs only and the maximum of the sample size requirement for the overlapping prior DFAP and DFSA areas (see Table 5).7 Table 5. Sample Size Requirements for Old, New and Combined DFSA Project Areas Project* Sample Size Requirement for prior DFAPs (EL) Sample Size Requirement for DFSAs (BL) Sample Size Requirement for Joint BL/EL CRS 1,540 1,740 2,670 Overlap (BL and EL) 787 610 787 New (BL only) NA 1,130 1,130 Old (EL only) 753 NA 753 FH 1,540 1,740 1,740 Overlap (BL and EL) 713 1,403 1,403 New (BL only) NA 337 337 REST 1,540 1,740 2,163 Overlap (BL and EL) 1,117 1,172 1,172 New (BL only) NA 568 568 5 A description of the sampling for the baseline study for the prior DFSAs can be found in the “Development Food Aid Program in Ethiopia Baseline Survey” Report, October 2012. Available at https://dec.usaid.gov 6 The EL PBS for the CSUS Project will not be conducted; therefore, there will only be 3 (not 4) EL PBSs required. 7 The prior FH DFAP had 9 woredas; these appear under FH (overlap - 3 woredas) and WV (overlap - 6 woredas); this is because the WV DFSA took over 6 of the 9 FH woredas. Therefore, the EL for FH (prior DFAP project) consists of areas defined by FH (overlap) and WV/FH (overlap). The sample size for the EL for FH is indicated in red font in Table 4. Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 8 Old (EL only) 423 NA 423 WV/FH NA 1,740 1,853 Overlap (BL and EL) 827 714 827 New (BL only) NA 1,026 1,026 TOTAL 4,620 6,960 8,426 *”New” represents the DFSA only areas and “Old” represents the prior DFAP only areas 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, (2) selection of households, and (3) selection of individuals. First stage sampling of clusters: For the Ethiopia PBS, sampling of clusters involved two phases: (1) selection of kebeles and (2) selection of one Gott (sub-kebele) or one group of merged Gotts within each selected kebele.8 A cluster corresponds to the second phase selection of either a Gott (sub￾kebele) or two or more merged Gotts in a selected kebele. This two phase approach was used to reduce the size of the cluster because kebeles are too large. First Phase Selection of Kebeles: Within each project, the sampled kebeles were proportionately allocated at the woreda level based on the distribution of households across all woredas. Kebeles were then selected from the sampling frame for each project using probability proportional to size sampling (PPS). The total number of kebeles sampled for each project for the joint BL/EL PBS was based on the joint sample size requirement for each project as a proportion of the overall joint sample size requirement (see Table 6). Second Phase Selection of Gotts: During the listing exercise the locations of Gotts and number of households for each Gott were determined for each kebele. The number of Gotts vary from kebele to kebele and from region to region. In cases where many small Gotts were found within a kebele, adjacent Gotts were grouped to form larger areas of relatively equal size. Once the list of Gotts and merged Gotts (if applicable) was completed, one Gott or one group of merged Gotts of roughly equal size was randomly selected. Table 6. Sample of Kebeles and Households by Project Area Implementing Partner Combined BL/EL sample size requirements Number of sampled kebeles/Gotts Number of sampled households* CRS 2,670 89 2,670 FH 1,740 58 1,740 REST 2,163 73 2,190 WV 1,853 62 1,860 8 For program and policy implementation purposes, kebeles are subdivided into Gotts (sub-kebeles), having a clear physical demarcation and having a certain number of households. The Gotts have known names and identifiable boundaries known not only to the Gott leader, but also to any common community member in the Gott. The unit for the second phase of the first stage of sampling was defined as a Gott and not a census enumeration area because it was not possible to obtain information on census enumeration areas from the CSA. Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 9 TOTAL 8,426 282 8,460 *The difference between columns 2 and 4 is that column 4 is rounded up to the nearest divisor of 30 – to accommodate for the fact that 30 households per cluster were sampled. Second stage sampling of households: At the second stage of sampling, 30 households were randomly selected per cluster using systematic sampling. Before the selection of households can take place, a listing exercise was conducted to identify and count each household in the cluster. GPS coordinates were taken for each cluster and the name of the head of household was 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 sampling of individuals within sampled households: The PBS is broken into several modules with different individuals eligible to be interviewed, depending on the target groups relevant to the various FFP indicators. These target groups include: • Household head or responsible adults • Person(s) responsible for the preparation of food in the household • Women of reproductive age • Children under five years of age • Farmers • Cash-earning adults • Parents of children under two years of age The household roster will be completed at the beginning of the interview, thus identifying all members of the selected household. The protocol for the selection of individuals within households is as follows: • For the children’s module, data and anthropometry measures will be collected for all eligible children under five. • For the woman’s module, women between the ages of 15-49 will be selected. Data and anthropometry measures will be collected for all eligible women. • For the agricultural module, all farmers within the household who have decision-making power over all plots of land and/or livestock that are part of the “farm” will be selected. Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 10 • For the gender modules, male or female cash earners or parents of children under two years of age will be interviewed. 2.3 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 April 2017. All questionnaire modules follow FFP and Feed the Future guidelines, as described in the FFP Indicators Handbook (April 2015) and Feed the Future Indicator Handbook (September, 2016).9 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 (HDDS and FIES) • 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 – MCHN • Module R: Resilience • ANTHROPOMETRY Questions for Modules A through G, J and K were adapted using questions from the FFP Standard Indicators Handbook and the DHS questionnaire. Questions for Module H were adapted from the World Bank’s Living Standards Measurement Study (LSMS). Questions for Module R were developed by TANGO. The total time for completing the survey is expected to be approximately 2-3 hours. The protocol for the selection of proxy respondents for these modules is as follows: • For the modules requiring data about the household (Modules A, B, F, H, and R), the head of household or any responsible adult will be interviewed. • For the food security module (Module C), the person(s) responsible for the preparation of meals in the household or another family member who was present and ate meals in the household in the past 24 hours will be interviewed. • For the children’s module (Module D), the mother or caregiver for children under five years of age will be interviewed. There should be no substitute respondents for mothers or caregivers. • For the women’s module (Module E), if an eligible woman between the ages of 15-49 is not available after three visits, no alternative respondents should be interviewed. 9 https://feedthefuture.gov/sites/default/files/resource/files/Feed_the_Future_Indicator_Handbook_Sept2016.pdf Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 11 • For the agricultural module (Module G), all farmers within the household 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 who can answer the agricultural questions will be interviewed. • For the gender modules (Modules J and K), if male or female cash earners or parents of children under two years of age are not available, no other individuals should be interviewed to take their place. 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 Ethiopia 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 EVELYN team will develop training manuals based on those developed for prior baseline surveys and using FFP, FTF, 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 EVELYN team will customize the field manuals to align with the final questionnaire and DRC-specific field protocols. The following manuals will be provided: • Supervisor Manual - 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. • Supervisor CAPI Manual - The supervisors’ CAPI manual will describes all procedure needed by supervisors to assign, monitor and transmit the CAPI interviews. • Interviewer Manual - The interviewers’ manual will include guidelines for implementation of the survey and fieldwork procedures, including interviewing techniques and procedures for completing the questionnaires. • Interviewer CAPI Manual – The Interviewers’ CAPI manual will provide detailed instructions for navigating the questionnaires on the tablet, making changes to the questionnaire and for submissions to the supervisor. • Interviewer Question by Question Manual - This manual will include detailed explanations and instructions for completing each question from the questionnaire. • Anthropometry and Standardization Manual - 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 Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 12 Using the manuals described above, the EVELYN team will work together with Kimetrica, 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 (EVELYN survey coordinator, local survey monitors, the Kimetrica’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 Ethiopia. The training curriculum and timeline and all training manuals will be submitted to FFP for approval prior to the start of trainings. Prior to the start of training, a paper- and CAPI-based pretest of the questionnaire will be conducted. The paper-based pretest will test the soundness of the questionnaire and identify potential problem areas, such as issues with skip patterns, wording, sequencing of questions, instructions to interviewers, and the clarity of the questionnaire for coding. The CAPI-based pretest will be conducted to test the programming of the questionnaire flow and skips, and use of the tablets in the field, including data transmissions. All proposed changes to the questionnaire will be reviewed by the EVELYN team and submitted to FFP for approval. The revised questionnaire will be used for interviewer and supervisor 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, transferring data from interviewers’ tablets to supervisors’ tablets, and transferring edited data from supervisors’ tablets to the central office. 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. 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 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. Upon completion of the trainings, all survey staff will participate in a pilot study in pre-selected non￾sampled kebeles 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 Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 13 • 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 during the pilot test. Supervisors will observe the interviewers in their teams during the pilot test and take notes on their performance. Kimetrica’s survey management team, the EVELYN survey coordinator and the local survey monitors, the EVELYN anthropometry trainer and the local anthropometry counterpart 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, EVELYN will make final modifications to field procedures and manuals, if required. Table 7 summarizes the sequence of field preparation activities. The EVELYN survey coordinator will oversee all activities. Table 7: Field Preparation Activities Duration Activities Participants 7 days Pretest Training Experienced interviewers 4 days Questionnaire pretest (paper and CAPI) Experienced interviewers 21 days Interviewer training Interviewers, and supervisors 5 days Anthropometry training Anthropometry specialists, interviewers, supervisors 5 days Anthropometry standardization testing Anthropometry specialists 3 days Supervisor/field procedure training Supervisors and field editors 4 days Pilot test/Field testing of the questionnaire and debrief Interviewers, anthropometry specialists, field editors, and supervisors 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. 3.4 Data Collection Data collection will start immediately after the pilot study. To collect data from the sampled 8,460 households, there will be 40 teams, each consisting of six field team members (one supervisor, four interviewers, and an anthropometry specialist). Accordingly, Kimetrica will hire a total of 160 interviewers, 40 anthropometry specialists, and 40 supervisors. In addition, Kimetrica will engage ten field coordinators and three IT Specialists, making the total number of field personnel for the survey to be 253. Table 8 shows the distribution of the survey personnel by region. Kimetrica may rearrange the regional distribution of the field personnel after the kebeles are selected, to bring equity in workload distribution. Table 8: Main Fieldwork Team Composition Field personnel Tigray Amhara Oromiya/Dire Dawa Total Interviewers 40 52 68 160 Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 14 Supervisors 10 13 17 40 Anthropometry specialists 10 13 17 40 Field Coordinators 2 4 4 10 IT specialists 1 1 1 3 Total 63 83 107 253 Given 282 clusters to cover and 40 teams to undertake the work, each team will, on average, collect data from seven clusters. As can be seen from the description below, a total of roughly 50 days will be required to complete the data collection from the 8,460 households: • 26 days required to conduct interviews. Assuming that an interviewer conducts two interviews a day, a team with four interviewers will complete eight interviews a day and therefore will require a total of 26 days to finish the seven clusters. • 16 days for travel (two days for the back-and-forth travel to the assignment location, 14 days travel to and from the seven clusters, including meeting up with kebele admins, one day per cluster). • 8 days required to cover transportation and implementation delays, as the team will be working during the main rainy season of the country. 3.5 Quality Control Working in close partnership with Kimetrica, the EVELYN team will ensure high-quality PBS data through a strong focus on training field staff and monitoring data collection. The EVELYN 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. During critical periods, including training, anthropometry standardization testing, questionnaire pretests, piloting, and at the beginning of fieldwork, the EVELYN survey coordinator will be in-country to coordinate and oversee these activities. When the EVELYN survey coordinator leaves the country, the local survey monitors will oversee fieldwork activities and closely update the EVELYN survey coordinator on fieldwork progress or any issues encountered during data collection. Table 9 provides survey procedures and safeguards for field supervision. Table 9: 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 (Kimetrica) will provide one field coordinator to oversee every four-or-five survey teams Proper sample selection • Adherence to household and respondent selection methods per EVELYN protocol Assurance of questionnaire accuracy • Complete review of data immediately after the interview is conducted • In the event of errors or omissions, required corrections be made before the interviewer can proceed 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 respondents and critical information on some households to make sure that interviewers are providing data that is accurate and truthful • 15 percent of completed interviews should be randomly selected for spot-checks Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 15 Goal Procedure or Safeguard • In the event of fabrication or falsification of data collected, the interviewer will be fired from the project immediately Completion of interviews • Interviewers will make up to three visits to the household to interview a respondent, and will appropriately plan one or two visits with the respondents to successfully complete the interview • 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, Kimetrica 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 EVELYN 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. Kimetrica will upload data to the IFSS regularly. Data transmission for the household surveys will begin during the second week of fieldwork, when interviewers complete interviews in their first assigned clusters. Currently planned dates for data transmission are presented in Table 10. Table 10. Data Transmission Timeline Activities Dates Data transmission begins July 10 Data transmission for half of households completed August 4 Data transmission for all households completed August 24 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 EVELYN will generate estimates for all 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 baseline in the prior DFAP project areas, a statistical comparison of BL (2012) and EL (2017) estimates will be conducted to determine population-level change over time. Sample weights will be computed and used in the data analyses. This will involve computing an overall sampling weight for each indicator by taking the inverse of the product of the probabilities of selection from each stage of sampling (village selection; household selection; and, when relevant, individual selection). Weights will be calculated separately for each project and will be adjusted to compensate for household- and individual-level non-response, where appropriate. Separate weights will be calculated for: • Households (used for indicators derived from Modules C, F, H, and R) Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 16 • 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) All descriptive and bivariate 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. 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 • 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. 5. TIMELINE Table 11 provides the timeline for critical activities for the joint BL/EL PBS. Table 11. Ethiopia Joint BL/EL PBS Critical Activities Timeline Activity Date Baseline Planning Workshop April 3 – 6, 2017 Research protocol submitted to the ethics committee May 4, 2017 Listing exercise May 16 – June 11, 2017 Pretest training (Paper and CAPI) May 24 – 30, 2017 Questionnaire pretest and debriefing (Paper and CAPI) May 31 – June 5, 2017 Questionnaire Finalized for Main Training June 6 – 12, 2017 Main Training (interviewer and supervisor) June 13 – 28, 2017 Anthropometry training and standardization testing June 19 – 28, 2017 Field pilot practice June 29 – July 1, 2017 Pilot debriefing/questionnaire revisions if needed July 2 – 3, 2017 Household survey fieldwork starts July 4, 2017 Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 17 Activity Date Household survey fieldwork ends August 17, 2017 Final dataset available Sept. 14, 2017 Preliminary indicator estimates Sept. 29, 2017 ANNEX 1 – INDICATORS MEASURED IN PRIOR BASELINE STUDY Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 18 The table below summarizes the modules included in the questionnaire for the baseline study of the prior DFAPs. The modules in red font include indicators that are also collected in the joint BL/EL survey. ETHIOPIA BASELINE SURVEY FOR PRIOR DFAPs – QUESTIONNAIRE MODULES A Identification B Household roster C Food access C1. HDDS C2. HHS C3. Household Food Insecurity Coping Strategy C4. Number of Months with Adequate Food Provisioning D Women's Dietary Diversity (WDDS) E Hygiene (hand washing) E1. Hand washing in HH with 0-23 months old children E2. Hand washing at household level F Improved sanitation facilities G Improved drinking water source H Household economy H1. Staple crop productivity H2. Asset inventory (livestock, prod assets, HH goods, consumer durables) H3. Household consumption expenditure H4. Household access to PSNP (productive safety net program) I Gender and social perspectives I1. Women's decision making on household economic matter I2. Gender based domestic violence: wife beating I3. Gender preference on sending boys and girls to school I4. Female circumcision I5. Women's decision making on seeking health services I6. Women's report on self-efficacy J Persons living with disability benefitting from the program K Social services (primary school, health center, source of water) L Nutritional status of children M Exclusive breastfeeding N MAD O Diarrhea P Access to antenatal care ANNEX 2 – TRAINING AGENDAS Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 19 Training Agenda for Questionnaire Pretesting Date Time Discussion Topic (Day 1) May 24 09:00-11:00 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 11:00-11:15 Tea-break 11:15-13:00 Discussion on field procedures Discussion on survey methodology including sample design Familiarization with questionnaire (Module-by-Module explanation of questionnaires) 13:00-14:00 Lunch-break 14:00-15:30 Familiarization with questionnaire (contd.) (Module-by-Module explanation of questionnaires) 15:30-15:40 Tea-break 15:40-17:00 Discussion on Module-A (Identification and Consent) (Day 2) May 25 Session I 09:00-11:00 Discussion on Module- B (Household roster) 11:00-11:15 Tea-break Session II 11:15-13:00 Discussion on Module-C, F (Food Access and Water, Sanitation & Hygiene) 13:00-14:00 Lunch-break Session III 14:00-15:30 Discussion on Module-G (Agriculture) 15:30-15:40 Tea-break Session IV 15:40-17:00 Discussion on Module-D1, D2 (Children’s Nutritional Status & Feeding Practices, Children’s Diarrhea and Oral Rehydration Therapy) (Day 3) May 26 Session I 09:00-11:00 Review of previous day’s discussion 11:00-11:15 Tea-break Session II 11:15-13:00 Discussion on Module-E, J, K (Women’ nutritional status and dietary diversity, Gender-Cash, Gender-MCHN) 13:00-14:00 Lunch-break Session III 14:00-15:30 Discussion on Module-H1, H2, H3 (Food Consumption over past 7 days, Non-food Consumption over past 7 days, Nonfood expenditures over past 30 days) 15:30-15:40 Tea-break Session IV 15:40-17:00 Discussion on Module-H5, H6, H7 (Nonfood expenditures over past 12 months, Housing expenditures, Durable goods expenditures) (Day 4) May 27 Session I 09:00-11:00 Review of previous day's discussion 11:00-11:15 Tea-break Session II 11:15-13:00 Discussion on Module-R (Resilience) 13:00-14:00 Lunch ANNEX 2 – TRAINING AGENDAS Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 20 Date Time Discussion Topic Session III 14:00-15:30 Discussion on entire questionnaire Role Play (Mock Interview) 15:30-15:40 Tea-break Session IV 15:40-17:00 Role Play (Mock Interview) (Day 5) May 29 Session I 09:00-11:00 Introduction to Tablet Demonstration on Interviewer’s Menu and Data Entry Program Distribution of Tablets. Enter Own’s Mock Interview Questionnaires from role play. Check skip patterns, value labels and QSF, understanding of warnings and error messages 11:00-11:15 Tea Break Session II 11:15-13:00 Role Play (Mock Interview) 13:00-14:00 Lunch Session III 14:00-15:30 Role Play (Mock Interview) 15:30-15:40 Tea Break Session IV 15:40-17:00 Discussion on Problems and Solutions on use of tablets (Day 6) May 30 Session I 09:00–11:00 Role Play (mock interview) 11:00-11:15 Tea Break Session II 11:15-13:00 Role Play (mock interview) 13:00-14:00 Lunch Session III 14:00-15:30 Discussion on Problems and Solutions on entering data 15:30-15:40 Tea Break Session IV 15:40-17:00 Discussion on planning of questionnaire pretest Day 7 to Day 11 May 31 to June 4 Field testing of questionnaire with movement: Three teams will be deployed Interviewers will conduct interviews on both paper questionnaires and tablets. (Day 12) Questionnaire Pretest Debrief June 5 Session I 09:00-11:00 Review of field testing result 11:00-11:15 Tea break Session II 11:15-13:00 Review of field testing result 13:00-14:00 Lunch Session III 14:00-15:30 Review of field testing result 15:30-15:40 Tea break Session IV 15:40-17:00 Review of field testing result ANNEX 2 – TRAINING AGENDAS Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 21 Training Agenda for Interviewer (Main) Training Date Time Discussion Topic Note (Day 1) June 13 09:00-11:00 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 11:00-11:15 Tea-break 11:15-13:00 Discussion on field procedures Discussion on survey methodology including sample design Familiarization with questionnaire (Module-by-Module explanation of questionnaires) 13:00-14:00 Lunch-break 14:00-15:30 Familiarization with questionnaire (contd.) (Module-by-Module explanation of questionnaires) 15:30-15:40 Tea-break 15:40-17:00 Discussion on Module-A (Identification and Consent) (Day 2) June 14 Session I 09:00-11:00 Review of previous day’s sessions Discussion on Module- B (Household roster) 11:00-11:15 Tea-break Session II 11:15-13:00 Discussion on Module- B (Household roster) 13:00-14:00 Lunch-break Session III 14:00-15:30 Practice and role play: Module A and B 15:30-15:40 Tea-break Session IV 15:40-17:00 Practice and role play: Module A, B Debrief (Day 3) June 15 Session I 09:00-11:00 Review of previous day’s sessions Discussion Module C, F 11:00-11:15 Tea-break Session II 11:15-13:00 Discussion on Module C, F 13:00-14:00 Lunch-break Session III 14:00-15:30 Practice and role play: Module C, F 15:30-15:40 Tea-break Session IV 15:40-17:00 Practice and role play: Module C, F Debrief (Day 4) June 16 Session I 09:00-11:00 Review of previous day's sessions Discussion of Module D1, D2, E 11:00-11:15 Tea-break Session II 11:15-13:00 Discussion of D1, D2, E 13:00-14:00 Lunch ANNEX 2 – TRAINING AGENDAS Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 22 Date Time Discussion Topic Note Session III 14:00-15:30 Practice and role play: Module D1, D2, E 15:30-15:40 Tea-break Session IV 15:40-17:00 Practice and role play: Module D1, D2, E Debrief (Day 5) June 17 Session I 09:00-11:00 Review of previous day's sessions Discussion of Module G 11:00-11:15 Tea Break Session II 11:15-13:00 Discussion of Module G Discussion of Module J, K 13:00-14:00 Lunch Session III 14:00-15:30 Discussion of Module J, K Practice and role play: Module G, J, K 15:30-15:40 Tea Break Session IV 15:40-17:00 Practice and role play: Module G, J, K Debrief (Day 6) June 19 Session I 09:00–11:00 Review of previous day’s sessions Discussion of Module H 11:00-11:15 Tea Break Session II 11:15-13:00 Discussion of Module H 13:00-14:00 Lunch Session III 14:00-15:30 Practice and Role Play: Module H 15:30-15:40 Tea Break Session IV 15:40-17:00 Practice and Role Play: Module H Debrief (Day 7) June 20 Session I 09:00–11:00 Review of previous day’s sessions Discussion: Module R 11:00-11:15 Tea Break Session II 11:15-13:00 Discussion: Module R 13:00-14:00 Lunch Session III 14:00-15:30 Practice and role play: Module R 15:30-15:40 Tea Break Session IV 15:40-17:00 Practice and role play: Module R Debrief (Day 8) June 21 Session I 09:00–11:00 Review of previous day’s sessions Mock interviews 11:00-11:15 Tea Break Session II 11:15-13:00 Mock interviews 13:00-14:00 Lunch Session III 14:00-15:30 Mock interviews 15:30-15:40 Tea break Session IV 15:40-17:00 Mock interviews (Day 9) June 22 Session I 09:00–11:00 Review of previous day’s sessions Mock interviews ANNEX 2 – TRAINING AGENDAS Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 23 Date Time Discussion Topic Note 11:00-11:15 Tea Break Session II 11:15-13:00 Mock interviews 13:00-14:00 Lunch Session III 14:00-15:30 Mock interviews 15:30-15:40 Tea break Session IV 15:40-17:00 Mock interviews (Day 10) June 23 Session I 09:00–11:00 Introduction to Tablet Demonstration on Interviewer’s Menu and Data Entry Program 11:00-11:15 Tea Break Session II 11:15-13:00 Distribution of Tablets. Enter Own’s Questionnaire from Paper based mock interviews in Group(2x2). Check skip patterns, value labels and QSF, understanding of warnings and error messages. 13:00-14:00 Lunch Session III 14:00-15:30 Enter own’s data from paper based questionnaire from mock interviews 15:30-15:40 Tea Break Session IV 15:40-17:00 Continue entering data. Discussion on Problems and Solutions on entering data. (Day 11) June 24 Session I 09:00–11:00 Review of previous day’s sessions Mock interviews with CAPI Supervisor Training (Day 1) 11:00-11:15 Tea Break Session II 11:15-13:00 Mock interviews with CAPI 13:00-14:00 Lunch Session III 14:00-15:30 Mock interviews with CAPI Transferring data to supervisor’s tablets 15:30-15:40 Tea Break Session IV 15:40-17:00 Discussion on error after reviewing data. Discussion on CAPI part done so far. (Q/A session). (Day 12) June 26 Session I 09:00–11:00 Review of previous day’s sessions Mock interviews with CAPI Supervisor Training (Day 2) 11:00-11:15 Tea Break Session II 11:15-13:00 Mock interviews with CAPI 13:00-14:00 Lunch Session III 14:00-15:30 Mock interviews with CAPI Transferring data to supervisor’s tablets 15:30-15:40 Tea Break Session IV 15:40-17:00 Discussion on error after reviewing data. Discussion on CAPI part done so far. (Q/A session). (Day 13) June 27 Session I 09:00–11:00 Review of previous day’s sessions Mock interviews with CAPI Supervisor Training (Day 3) 11:00-11:15 Tea Break Session II 11:15-13:00 Mock interviews with CAPI 13:00-14:00 Lunch Session III Discussion on error after reviewing data. Discussion on ANNEX 2 – TRAINING AGENDAS Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 24 Date Time Discussion Topic Note 14:00-15:30 CAPI part done so far. (Q/A session). 15:30-15:40 Tea Break Session IV 15:40-17:00 Discussion on planning of pilot test Day 14 to Day 17 (June 28- July 1) Pilot Test Day 18 (July 2) Pilot Debriefing ANNEX 2 – TRAINING AGENDAS Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 25 SUPERVISOR TRAINING AGENDA SUPERVISOR / FIELD PROCEDURE TRAINING Day 1 June 24 Review of terminology, team members’ roles, supervisors’ responsibilities, rules/behavior/ethics, training and fieldwork schedules. Review of field procedures including: a) Sampling, household selection, callbacks, recording HH location and details b) GPS data collection c) Organizing and supervising fieldwork d) Maintaining fieldwork control sheets e) Monitoring interviewer performance LUNCH BREAK Review of field procedure (cont’d) Day 2 June 26 Review of the questionnaire with Tablet LUNCH BREAK Review of the questionnaire with Tablet Day 3 June 27 Introduction and planning of field piloting LUNCH BREAK Fieldwork prep for pilot continued ANNEX 2 – TRAINING AGENDAS Ethiopia Baseline/End-line PBS Protocol – FFP Evaluation & Learning (EVELYN) Mechanism 26 Anthropometry training and standardization schedule June 16- 29, 2017 Sunday Monday Tuesday Wednesday Thursday Friday FIRST WEEK Meeting with Kimetrica to define schedule and logistics Equipment check up 9am to 1 pm 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 At facility for children: Hands on practice, every procedure 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 To be used as needed SECOND WEEK 9am to 12 noon 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: Standing height and weight of children 2-5 yrs old, training and practice (ASSISTANT) 2pm to 4pm To be used as needed To be used as needed To be used as needed At main training center: Introduction to anthropometry and practice (ASSISTANT) At facility for children: Recumbent length and weight training of children <2 yrs old (ASSISTANT) 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, which is an expected phenomenon of training sessions. 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 REGION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . OROMIA DIRE DAWA AMHARA TIGRAY 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 A15 HEAD OF HOUSEHOLD NAME & LINE NUMBER (B01) A20 TOTAL ELIG. WOMEN 15-49 YRS A21 TOTAL NO. OF FARMERS A22 SUPERVISOR NAME CODE 1201 FIRST VISIT 2 1202 1203 1204 017 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 ____ 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? ____ ____ 6. 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 •• INTERVIEWER'S NAME AND CODE DAY MONTH YEAR SIGNATURE AND DATE •• A26: END TIME : HOUR MINUTE A00: START TIME 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. Hello. My name is _______________________________________. I am working with Kimetrica. WE ARE CONDUCTING A SURVEY TO LEARN ABOUT AGRICULTURE, FOOD SECURITY, FOOD CONSUMPTION, NUTRITION AND WELFARE OF HOUSEHOLDS IN ETHIOPIA. YOUR HOUSEHOLD HAS BEEN 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 QUESTIONS TODAY. ALL THE ANSWERS PROVIDED BY YOU WILL BE CONFIDENTIAL AND WILL ONLY BE SHARED FOR PROFESSIONAL AND LEARNING PURPOSES. YOUR IDENTITY SHALL NOT BE DISCLOSED ON ANY PUBLICALLY AVAILABLE DATA OR REPORTS. The data collected in this baseline survey may be used as part of a study in the future. If your household is selected for the study then a second survey will be conducted, and if you agree, the data from this study will be used for comparison. You don't have to agree to participate in either study, but we hope you will agree to answer the questions for this study since your views are important. IF I ASK YOU ANY QUESTION YOU DON'T WANT TO ANSWER, JUST LET ME KNOW AND I WILL GO ON TO THE NEXT QUESTION OR YOU CAN STOP THE INTERVIEW AT ANY TIME. IN CASE YOU NEED MORE INFORMATION ABOUT THE SURVEY, YOU MAY CONTACT THE PERSON LISTED ON THIS CARD. 2017 2017 2017 MODULE B. HOUSEHOLD ROSTER (HEAD OF HH OR RESPONSIBLE ADULT) IF AGE 0-17 YEARS IF AGE 5 YEARS OR OLDER LINE EVER ATTENDED CURRENT/RECENT NO. USUAL RESIDENTS SURVIVORSHIP AND RESIDENCE OF SCHOOL SCHOOL ATTENDANCE BIOLOGICAL PARENTS B01 B06 B07 B09 B10 B13 B14 IF 95 *SEE OR MORE, DEFINITION 1 = MARRIED SEE CODES SEE CODES RECORD BELOW OR LIVING BELOW. BELOW. '95'. TOGETHER 2 = DIVORCED/ SEE CODES '98'=DON'T SEPARATED BELOW. KNOW. USE 3 = WIDOWED ONLY FOR 4 = NEVER- IF "YES": IF YES: PERSONS MARRIED What is her What is his AFTER LISTING NAMES, WHO ARE ENTER LINE AND name? name? RELATIONSHIP, SEX, AGE, ≥ 50. NUMBER OF NEVER RECORD RECORD CASTE FOR EACH PERSON PRIMARY LIVED MOTHER'S FATHER'S ASK QUESTIONS 2A-2C USE '00' CAREGIVER TOGETHER LINE LINE TO BE SURE THAT THE IF CHILD NUMBER. NUMBER. LISTING IS COMPLETE. IS LESS THEN ASK QUESTIONS THAN IF "NO", IF NO, B06 TO B23 FOR EACH 1 YEAR RECORD RECORD PERSON '00'. '00'. M F IN YEARS Y N Y N Y N Y N Y N Y N Y N Y N DK Y N DK Y N LEVEL GRADE YN LEVEL GRADE 01 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 02 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 03 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 04 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 05 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 06 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 07 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 08 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 09 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE CODES FOR B03: RELATIONSHIP TO HEAD OF HOUSEHOLD CODES FOR Qs. B21 AND B23: EDUCATION 01 = HEAD 07 = PARENT-IN-LAW 02 = WIFE OR HUSBAND 08 = BROTHER OR SISTER LEVEL GRADE 03 = SON OR DAUGHTER 09 = OTHER RELATIVE 0 = PRESCHOOL 04 = SON-IN-LAW OR 10 = ADOPTED/FOSTER/ 1 = PRIMARY DAUGHTER-IN-LAW STEPCHILD 2 = SECONDARY 05 = GRANDCHILD 11 = NOT RELATED 3 = TECHNICAL/VOACATIONAL 06 = PARENT 98 = DON'T KNOW 4 = HIGHER 8 = DON'T KNOW 98 = DON'T KNOW RELATIONSHIP TO HEAD OF HOUSEHOLD SEX AGE MODULE C, H1 MODULE D PRIMARY CAREGIVER MODULE E B00: START TIME HOUR MINUTE IF AGE 15 OR OLDER IF UNDER 5 YEARS IF AGE 15 OR OLDER IF AGE 15 OR OLDER MODULE F, H2-H7, R MODULE J MODULE J MODULE K MODULE G MARITAL STATUS IF AGE 5-24 YEARS ELIGIBILITY B20 B21 B22 B23 Please tell me the name and sex of each person who lives here, starting with the head of the household. For our purposes today, members of a household are adults or children that live together and eat from the "same pot". It should include anyone who has lived in your house for at least 6 of the last 12 months, but it does not include anyone who lives here but eats separately. What is the relationship of (NAME) to the head of the household? Is (NAME) male or female? How old is (NAME)? Is [NAME] responsible for food preparation in the household? IS THIS PERSON UNDER 5 YEARS OF AGE? B02 B03 B04 B05 B08 B11 B12 B15 B16 B17 B18 B19 During this school year, what grade is (NAME) attending? **READ DEFINITION OF "WORK" BELOW TO RESPON￾DENT. 1= CASH ONLY 2= CASH AND KIND 3= IN KIND ONLY 4= NOT PAID ***READ DEFINI￾TION OF FARMER BELOW TO RESPON￾DENT. Is (NAME) a farmer? What is (NAME)'s current marital status? Is (NAME)'s natural mother alive? Does (NAME)'s natural mother usually live in this household? Is (NAME)'s natural father alive? Does (NAME)'s natural father usually live in this household? Has (NAME) done any work in the last 12 months? During the last 12 months, was (NAME) usually paid in cash or kind for this work or was (NAME) not paid at all? Is (NAME) the parent of a child under 2 years of age who is living in this household? 01 GO TO 13 GO TO 13 GO TO 13 GO TO 13 Has (NAME) ever attended school? What is the highest grade (NAME) has completed? Did (NAME) attend school at any time during the 2016/17 school year? Who is the primary caregiver of [NAME]? IS THIS A WOMAN 15-49 YEARS OF AGE? IS THIS PERSON THE HEAD OF THE HH OR A RESPON￾SIBLE ADULT IF HEAD OF HH IS ABSENT? *The primary caregiver is the person who knows the most about how and what the child is fed. Usually, but not always, this will be the child‟s mother. **Work includes jobs in the formal and/or informal sector, full time, part time, or seasonal work that is done within and/or outside the home. It includes, but is not limited to agricultural daily wage labor, off￾farm daily wage labor, income generation activities, sale of goods produced or processed outside the home or at the home, homestead garden or farm (e.g., vegetables, eggs, fish, livestock, artisanal goods), or petty trading. For this indicator, work does not include participating in cash for work, food for work, or conditional transfers and/or productive safety net programs. It does not include either caring for own children, cooking, cleaning or doing other routine chores for own household (e.g., fetching water, collecting firewood) or being involved in agricultural production solely for household consumption. ***Farmers, including herders and fishers, are: 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; 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 of food, feed, and fiber and may reside in settled communities, mobile pastoralist communities, or refugee/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." For instance, a woman working on her husband's land who does not control a plot of her own would not be interviewed. GO TO 13 GO TO 13 GO TO 13 GO TO 13 GO TO 13 DEFINITIONS 00 = LESS THAN 1 YEAR COMPLETED (USE '00' FOR B21 ONLY. THIS CODE IS NOT ALLOWED FOR B23.) IF AGE 0-17 YEARS IF AGE 5 YEARS OR OLDER LINE EVER ATTENDED CURRENT/RECENT NO. USUAL RESIDENTS SURVIVORSHIP AND RESIDENCE OF SCHOOL SCHOOL ATTENDANCE BIOLOGICAL PARENTS B01 B06 B07 B09 B10 B13 B14 IF 95 *SEE OR MORE, DEFINITION 1 = MARRIED SEE CODES SEE CODES RECORD BELOW OR LIVING BELOW. BELOW. '95'. TOGETHER 2 = DIVORCED/ SEE CODES '98'=DON'T SEPARATED BELOW. KNOW. USE 3 = WIDOWED ONLY FOR 4 = NEVER- IF "YES": IF YES: PERSONS MARRIED What is her What is his AFTER LISTING NAMES, WHO ARE ENTER LINE AND name? name? RELATIONSHIP, SEX, AGE, ≥ 50. NUMBER OF NEVER RECORD RECORD CASTE FOR EACH PERSON PRIMARY LIVED MOTHER'S FATHER'S ASK QUESTIONS 2A-2C USE '00' CAREGIVER TOGETHER LINE LINE TO BE SURE THAT THE IF CHILD NUMBER. NUMBER. LISTING IS COMPLETE. IS LESS THEN ASK QUESTIONS THAN IF "NO", IF NO, B06 TO B23 FOR EACH 1 YEAR RECORD RECORD PERSON '00'. '00'. RELATIONSHIP TO HEAD OF HOUSEHOLD SEX AGE MODULE C, H1 MODULE D PRIMARY CAREGIVER MODULE E IF AGE 15 OR OLDER IF UNDER 5 YEARS IF AGE 15 OR OLDER IF AGE 15 OR OLDER MODULE F, H2-H7, R MODULE J MODULE J MODULE K MODULE G MARITAL STATUS IF AGE 5-24 YEARS ELIGIBILITY B20 B21 B22 B23 Please tell me the name and sex of each person who lives here, starting with the head of the household. For our purposes today, members of a household are adults or children that live together and eat from the "same pot". It should include anyone who has lived in your house for at least 6 of the last 12 months, but it does not include anyone who lives here but eats separately. What is the relationship of (NAME) to the head of the household? Is (NAME) male or female? How old is (NAME)? Is [NAME] responsible for food preparation in the household? IS THIS PERSON UNDER 5 YEARS OF AGE? B02 B03 B04 B05 B08 B11 B12 B15 B16 B17 B18 B19 During this school year, what grade is (NAME) attending? **READ DEFINITION OF "WORK" BELOW TO RESPON￾DENT. 1= CASH ONLY 2= CASH AND KIND 3= IN KIND ONLY 4= NOT PAID ***READ DEFINI￾TION OF FARMER BELOW TO RESPON￾DENT. Is (NAME) a farmer? What is (NAME)'s current marital status? Is (NAME)'s natural mother alive? Does (NAME)'s natural mother usually live in this household? Is (NAME)'s natural father alive? Does (NAME)'s natural father usually live in this household? Has (NAME) done any work in the last 12 months? During the last 12 months, was (NAME) usually paid in cash or kind for this work or was (NAME) not paid at all? Is (NAME) the parent of a child under 2 years of age who is living in this household? Has (NAME) ever attended school? What is the highest grade (NAME) has completed? Did (NAME) attend school at any time during the 2016/17 school year? Who is the primary caregiver of [NAME]? IS THIS A WOMAN 15-49 YEARS OF AGE? IS THIS PERSON THE HEAD OF THE HH OR A RESPON￾SIBLE ADULT IF HEAD OF HH IS ABSENT? M F Y N Y N Y N Y N Y N Y N Y N Y N DK Y N DK Y N LEVEL GRADE Y N LEVEL GRADE 10 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 11 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 12 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 13 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 14 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 15 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 16 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 17 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE 18 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 8 1 2 8 1 2 1 2 GO TO 18 GO TO 20 NEXT LINE NEXT LINE YES → ADD TO TABLE CODES FOR Qs. B21 AND B23: EDUCATION NO LEVEL GRADE YES → ADD TO TABLE 0 = PRESCHOOL NO 1 = PRIMARY 2 = SECONDARY YES → ADD TO TABLE 3 = TECHNICAL/VOACATIONAL NO 4 = HIGHER 8 = DON'T KNOW 98 = DON'T KNOW CODES FOR B03: RELATIONSHIP TO HEAD OF HOUSEHOLD 01 = HEAD OF HOUSEHOLD 07 = PARENT-IN-LAW B24: END TIME 02 = WIFE OR HUSBAND 08 = BROTHER OR SISTER 03 = SON OR DAUGHTER 09 = OTHER RELATIVE 04 = SON-IN-LAW OR 10 = ADOPTED/FOSTER/ HOUR MINUTE DAUGHTER-IN-LAW STEPCHILD 05 = GRANDCHILD 11 = NOT RELATED GO TO MODUE C 06 = PARENT 98 = DON'T KNOW ***Farmers, including herders and fishers, are: 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; 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 of food, feed, and fiber and may reside in settled communities, mobile pastoralist communities, or refugee/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." For instance, a woman working on her husband's land who does not control a plot of her own would not be interviewed. 00 = LESS THAN 1 YEAR COMPLETED (USE '00' FOR B21 ONLY. THIS CODE IS NOT ALLOWED FOR B23.) **Work includes jobs in the formal and/or informal sector, full time, part time, or seasonal work that is done within and/or outside the home. It includes, but is not limited to agricultural daily wage labor, off-farm daily wage labor, income generation activities, sale of goods produced or processed outside the home or at the home, homestead garden or farm (e.g., vegetables, eggs, fish, livestock, artisanal goods), or petty trading. It can also include participating in cash for work, food for work, or conditional cash transfers and/or productive safety net programs. For this indicator, work does not include caring for own children, cooking, cleaning or doing other routine chores for own household (e.g., fetching water, collecting firewood) or being involved in agricultural production solely for household consumption. 2B) Are there any other people who may not be members of your family, such as domestic servants, lodgers, or friends who usually live here? 2C) Does anyone else live here even if they are not at home now? INCLUDE CHILDREN IN SCHOOL OR HOUSEHOLD MEMBERS AT WORK OR MIGRATED. GO TO 13 GO TO 13 GO TO 13 GO TO 13 2A) Just to make sure that I have a complete listing: are there any other persons such as small children or infants that we have not listed? DEFINITIONS *The primary caregiver is the person who knows the most about how and what the child is fed. Usually, but not always, this will be the child‟s mother. IN YEARS GO TO 13 GO TO 13 GO TO 13 GO TO 13 GO TO 13 Module C. Food Access 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 C16 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 (Person responsible for food preparation) Maize, bread, rice, millet, barley, bulgar wheat, porridge, buckwheat, noodles, teff, nifro, or other foods made from cereals/grains? Any fruits? Including apples, oranges, banana, guava, papaya, mangoes, pineapple, berries, watermelon, avocado, cactus Any eggs? (chicken, ostrich, guinea fowl/jigra) Cassava, potatoes, sweet potatoes, yams, taro, false banana/enset, or any other foods made from roots? Any vegetables (leaves)? Such as spinach, lettuce, beetroot, kale, moringa, carrots, pumpkin leaves, okra, pumpkin, squash, gourds (including bitter & bottle), mushrooms, raddish, tomato, cucumber, cabbage, cauliflower, green leafy vegetables, skus, broad beans, brinjals, green peas PERSON IN CHARGE OF FOOD PREPARATION FROM THE HOUSEHOLD ROSTER (B06) = 1) OBTAIN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? CLUS TER Any foods made with oil, animal fat or butter? Any sugar or honey, granulated sugar, sugar cane,sweet reed/tinksh/ageda? Any other foods, such as condiments, salt, pepper, chili, giner, garlic, cardimon, cumin, cinnamon, spices, coffee, or tea? 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 meat? Lamb, camel, goat, rabbit, chicken, kok, jigra (guinea fowl), or other birds, beef, liver, kidney, heart, or other organ meats or blood? Any fresh or dried fish? Any foods made from beans, peas, lentils, cowpeas, pigeon peas, groundnuts, peanuts, soyabeans, chickpeas, haricot beans? Any cheese, yogurt, milk, sour milk, skimmed milk, or other dairy products? Module C. Food Access NO. QUESTIONS AND FILTERS CODING CATEGORIES (Person responsible for food preparation) FOOD INSECURITY EXPERIENCE SCALE (FIES) C16 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 C17 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C16A YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C17 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1C18 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C17A YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C18 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1C19 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C18A YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C19 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1C20 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C19A YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C20 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1C21 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C20A YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C21 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1C22 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C21A YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C22 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1C23 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C22A YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C23 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1C24 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C23A YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 C24 INSERT TIME MODULE ENDED HOUR MINUTE GO TO MODULE F 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 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 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 went without eating for a whole day 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 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? 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 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 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 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 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 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 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 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 ate less than you thought you should because of a lack of money or other resources? 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 NUNBERING FROM DHS PIPED TO NEIGHBOR 13 PUBLIC TAP/STANDPIPE. . . . . . . . . . . . . . . . . . . . . . . . . . 14 TUBEWELL OR BOREHOLE. . . . . . . . . . . . . . . . . . . . . . . . . . 21 DUG WELL PROTECTED WELL . . . . . . . . . . . . . . . . . . . . . . . . . . 31 UNPROTECTED WELL . . . . . . . . . . . . . . . . . . . . . . . . . . 32 WATER FROM SPRING . . . . . . . . . . . . . . . . . . . . . . . . . . 41 UNPROTECTED SPRING. . . . . . . . . . . . . . . . . . . . . . . . . . 42 RAINWATER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 F07 TANKER TRUCK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 CART WITH SMALL TANK 62 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 F11 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 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 LATRIN. . . . . . . . . . . . . . 21 PIT LATRINE WITH SLAB . . . . . . . . . . . . . . . . . . . . . . . . 22 PIT LATRINE WITHOUT SLAB/OPEN PIT. . . . . . . . . . . . . 23 COMPOSTING TOILET . . . . . . . . . . . . . . . . . . . . . . . . . . 31 BUCKET TOILET . . . . . . . . . . . . . . . . . . . . . . . . . . 41 HANGING TOILET/HANGING LATRINE 42 NO FACILITY/BUSH/FIELD . . . . . . . . . . . . . . . . . . . . . . . . . . 51 F14 OTHER 96 (SPECIFY) F12 Does your household share the toilet YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 facility with other households? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 F14 F13 How many households share that toilet facility? NUMBER OF HOUSEHOLDS IF LESS THAN 10 . . . . . . . . . . . . . . . . 10 OR MORE HOUSEHOLDS . . . . . . . . . . . . . . . . . . . . 95 DON'T KNOW . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 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 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 G22) (SKIP TO G22) (SKIP TO G22) NOT AVAILABLE . . . . . 3 NOT AVAILABLE . . . 3 NOT AVAILA. . . . . . . 3 G03A YES . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 (SKIP TO G22) (SKIP TO G22) (SKIP TO G22) 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 G22) (SKIP TO G22) (SKIP TO G22) 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 SHARECRO . . . . . . . . 3 SHARECR. . . . . . . . . . 3 NONE OF THESE . 4 NONE OF THESE 4 NONE OF THE. . . . . 4 (SKIP TO G05) (SKIP TO G05) (SKIP TO G05) G04B ●● ● HECTARES HECTARES HECTARES 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 G22. IF NO, SKIP TO G22. IF NO, SKIP TO G22. FINANCIAL SERVICES G07 YES .................................... 1 YES ............................ 1 YES ............................ 1 NO .................................... NO ............................ 2 NO ............................ 2 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 ADD 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. OBTAIN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? ARE YOU INTERVIEWING AN ALTERNATE RESPONDENT ? 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. 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? INCLUDE LAND THAT IS OWNED, RENTED OR SHARE CROPPED NO. QUESTIONS AND FILTERS NAME ____________________ NAME ___________________ NAME ___________________ FIRST FARMER SECOND FARMER THIRD FARMER 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 through agro-dealers, cooperatives, community associations or government A Use of mobile financial services…………………………………………………………………………… B Use of financial services other than mobile………………………………………………………. C Use of training and extension services…………………………………………………………………… D Contract farming………………………………………………………………………………………………E Use of feed lots or pen feeding…………………………………………………………………………… F Drying, processing and packaging for selling/storage…………………………………………………… G Trading or marketing produce through agrodealers/vets, community associations/cooperatives… H Use of formal marketing systems for livestock and/or vegetables and/or fruits and/or spices, honey organic coffee, etc……………………………………………………………………. I DID NOT PRACTICE ANY OF THESE ACTIVITIES IN PAST 12 MONTHS………………………… Y CIRCLE ALL ACTIVITIES STATED. AGRICULTURAL PRACTICES FOR CROPS G11 IF YES, THEN CONTINUE IF YES, THEN CONTINUE IF YES, THEN CONTINUE IF NO, SKIP TO G14 IF NO, SKIP TO G14 IF NO, 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 KN. . . . . . . . . . 8 G13A TEFF TEFF TEFF MAIZE………………………… MAIZE…………………… MAIZE………………… WHEAT……………………… WHEAT………………… WHEAT………………… MILLET……………………… MILLET………………… MILLET………………… BARLEY……………………… BARLEY………………… BARLEY………………… SORGHUM…………………… SORGHUM……………… SORGHUM…………… SOYBEAN…………………… SOYBEAN……………… SOYBEAN……………… LEGUMES (BEAN/LENTIL) LEGUMES (BEAN/LEN LEGUMES (BEAN/LEN OILSEED (SUNFLOWER, OILSEED (SUNFLOWER, OILSEED (SUNFLOWER, MUSTARD, SESAME)…… MUSTARD, SESAME MUSTARD, SESAME FRUITS……………………… FRUITS………………… FRUITS………………… POTATO…………………. L POTATO…………………. L POTATO……………… CHAT M CHAT M CHAT COFFEE N COFFEE N COFFEE GROUNDNUTS O GROUNDNUTS O GROUNDNUTS SPICES P SPICES P SPICES VEGETABLES……………… VEGETABLES………… VEGETABLES………… OTHER 1___________ OTHER 1___________ OTHER 1___________ OTHER 2____________ OTHER 2___________ OTHER 2___________ 2 2 A B C D E F G H I Y EEE WWW (SPECIFY) K Did you save any cash tin 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 Y REFER TO G04 TO DETERMINE WHETHER THE RESPONDENT HAS ACCESS TO A PLOT OF LAND OVER WHICH HE/SHE MAKES DECISIONS 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? AAA BB 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 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 IF NONE OF THESE ACTIVITIES WERE PRACTICED, THEN CIRCLE Y. A B C D E F G H I 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] ? B CCC DDD M N III JJJ KK L QQQ REGISTER ALL CROPS NAMED BY THE RESPONDENT. FFF HHH O P XXX (SPECIFY) Do you plant any crops or raise/buy livestock with the specific intention to sell or resell to earn income? NO. QUESTIONS AND FILTERS NAME ____________________ NAME ___________________ NAME ___________________ FIRST FARMER SECOND FARMER THIRD FARMER G13B Micro dosing………………………………………………………………………… A Manure ……………………………………………………………………………… B Compost………………………………………………………………………………C Planting basins……………………………………………………………………… D Mulching………………………………………………………………………………E Weed control…………………………………………………………………………F Dry planting……………………………………………………………………………G Ripping into residues…………………………………………………………………H Clean ripping………………………………………………………………………… I Tied ridges…………………………………………………………………………… J Pot-holing………………………………………………………………………………K Crop rotations…………………………………………………………………………L Intercropping/Agroforestry……………………………………………………………M Integrated Pest Management (IPM)……………………………………………… N Early planting or planting with first rains……………………………………………O Use of improved crop varieties………………………………………………………P Contour planting……………………………………………………………… Q Terracing………………………………………………………………………………R Land leveling………………………………………………………………….. S Micro-irrigation technology (MIT)……………………………………………. U Crop thinning V Row planting W Sequential or double cropping X Commercial fertilzer Z DID NOT USE ANY OF THESE PRACTICES IN PAST 12 MONTHS …………Y CIRCLE ALL PRACTICES STATED. AGRICULTURAL PRACTICES FOR LIVESTOCK G14 CHECK G05: IF YES, THEN CONTINUE IF YES, THEN CONTINUE IF YES, THEN CONTINUE DETERMINE WHETHER THE RESPONDENT HAS ANY ANIMALS OR IF NO, SKIP TO G18 IF NO, SKIP TO G18 IF NO, SKIP TO G18 AQUACULTURAL PRODUCTS OVER WHICH HE/SHE MAKES DECISIONS G16 Improved animal shelters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Vaccinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B Deworming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C Castration D Dehorning E Homemade animal feeds made of locally available products . . . . . . . . . . F Animal feed supplied by stockfeed manufacturer . . . . . . . . . . . . . . . . . . . . . G Artificial insemination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H Pen feeding or improved feeding practice I Fodder production and/or veld reinforcement with legumes . . . . . . . . . . . . . J Used the services of community animal health workers/paravets . . . . . . . K Emergency feed reserve L Cut and carry system M Controlled grazing N Improved beekeeping O DID NOT PRACTICE ANY OF THESE ACTIVITIES IN PAST 12 MONTHS . . . Y CIRCLE ALL PRACTICES STATED. M N O Y M N O Y M N O Y For the crops (including vegetables) that you planted, did you use any of these practices in the [PAST 12 MONTHS]? READ EACH PRACTICE. 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 PRACTICES WERE USED, THEN CIRCLE Y. A B C D E F G H I J K A B C D E F G H I J K A B C D E F G H I J K READ EACH PRACTICE. 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 PRACTICES WERE USED, THEN CIRCLE Y. A B C D E F G H I J K l A B C D E F G H I J K l A B C D E F G H I J K l L M N O P Q R S T U L M N O P Q R S T U L M N O P Q R S T U V W X Z Y V W X Z Y V W X Z Y Did you use any of the following practices when you cared for the livestock during the [PAST 12 MONTHS]? NO. QUESTIONS AND FILTERS NAME ____________________ NAME ___________________ NAME ___________________ FIRST FARMER SECOND FARMER THIRD FARMER G18 Management or protection of watersheds or water catchments . . . . . . . . . . . . . . . . . . . . Agro-forestry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Management of forest plantation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regeneration of natural landscapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sustainable harvesting of forest products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rotational grazing or trans-humane system of livestock keeping . . . . . . . . . . . . . . . . . . . . Hedge-row planting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Water resource management (irrigation, water harvesting, etc.) H DID NOT PRACTICE ANY OF THESE ACTIVITIES FOR THE PAST 12 MONTHS CIRCLE ALL PRACTICES STATED. IMPROVED STORAGE PRACTICES G19 CHECK G04: IF YES, THEN CONTINUE IF YES, THEN CONTINUE IF YES, THEN CONTINUE DETERMINE WHETHER THE RESPONDENT HAS ACCESS TO IF NO, SKIP TO G22 IF NO, SKIP TO G22 IF NO, SKIP TO G22 A PLOT OF LAND OVER WHICH HE/SHE MAKES DECISIONS. G20 YES . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 NO . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 (SKIP TO G22) (SKIP TO G22) (SKIP TO G22) DON'T KNOW . . . . . . . 8 DON'T KNOW. . . . . . . . 8 DON'T KN. . . . . . . . . . 8 G21 Hermetic storage… ........... A Hermetic storage…....... A Hermetic sto............... A Improved granary ... B Improved granary B Improved grana........... B Warehousing or Warehousing or Warehousing or MULTIPLE RESPONSES POSSIBLE. cereal banks ........... C cereal banks ....... C cereal ban............... C Use of traps for mice……… D Use of traps for mice……D Use of traps for mice… D Grain bags with Grain bags with Grain bags with bio-pesticides ........... E bio-pesticides ....... E bio-pestic ............... E Diffused light storage Diffused light storage Diffused light storage (potatoes, onions, (potatoes, onions, (potatoes, onions, etc.) ................................ G etc.) ........................ G etc.) ........................ G Did not use any of Did not use any of Did not use any of these methods ... Y these methods Y these metho ........... Y G22 G23 GO TO INSERT TIME MODULE ENDED HOUR MINUTE MODULE D1 IF NONE OF THESE METHODS WERE USED, THEN CIRCLE Y. THERE ARE NO MORE QUESTIONS FOR THIS FARMER. GO TO G02A FOR ANOTHER FARMER. IF THERE ARE NO MORE FARMERS, GO TO G23. GO TO G02A FOR ANOTHER FARMER. IF THERE ARE NO MORE FARMERS, GO TO G23. GO TO G02A FOR ANOTHER FARMER. IF THERE ARE NO MORE FARMERS, GO TO G23. A B C D E F G H Y A B C D E F G H Y A B C D E F G H Y During [THE LAST 12 MONTHS], did you store any crops from the plot(s) over which you make decisions? Did you use any of the following improved methods to store the crops? READ EACH METHOD AND CIRCLE ALL THAT APPLY. E F READ EACH PRACTICE. RECORD RESPONSES IN THE CELL BELOW THE RESPONSE LIST FOR EACH G IF NONE OF THESE PRACTICES WERE USED, THEN CIRCLE Y. Y 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]? A B C D 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 cow/goat,tinned, YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . 1 or powdered 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, juice drinks, or any soft drink ? Any kind of Infant formula like Plan, S-26, Nann, Bay Lac, Liptomin? 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 and night, did [CHILD'S NAME] eat any (ASK QUESTIONS D33A-D48)? 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 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 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 PROBES: gruel, Cerelac, Cerefam, Fafa, Mother Choice Bread, biscuits, porridge, Enjera, noodles(Indomin), rice, or other foods made from grains such as Teff, corn, millet, sorghum, wheat, oats, barley? 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 Any other liquids? PROBES: fenugreek, sugar water, camomila water, tea, tea with milk? Pumpkin, carrots, squash, sweet potatoes or or any other dark yellow or orange fleshed roots, tubers and vegetables? Any meat from domesticated animals, such as beef, pork, lamb, goat, chicken, or duck? White potatoes, potato chips, white yams, cassava, bulla, kocho, manioc, or any other foods made from roots? Any dark green leafy vegetables such as spinach, pumpkin leaves, kale, mustard leaves, moringa? Any other vegetables, like green beans, tomatoes, cauliflower, cabbage, broccoli, eggplant, etc.? Ripe mangoes, ripe papaya, or other fruits that are dark yellow or orange inside? Any other fruits like bananas, apples, avocados, guava, pineapple, plum, orange, any berries, etc.? 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, or other organ from domesticated animals such as cow, pig, goa, 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 CHECK QUESTIONS D33-D47: 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 GO TO D54 GO TO D54 GO TO D54 FIRST COLUMN SECOND COLUMN THIRD COLUMN Condiments for flavor, such as chilies, spices, herbs, or fennel grain, corainder, cumin, ginger, turmeric, garlic, cardamon? Fresh or dried fish, shellfish? Any flesh from wild animals, such as birds, wild pigeons, guinea fowl, deer, wild boar, wild goat? Any sugary foods such as chocolates, sweets (halawa, mushebek), candies, doughnuts, cakes, honey? Any foods made from beans, peas, lentils, peanuts or other legumes? Any foods made from nuts and seeds such as pumpkin seeds? Any organs from wild animals, such as birds, wild pigeons, guinea fowl, deer, wild boar, wild goat? Eggs? Any oils, fats, butter,ghee, or foods made with any of these? Cheese, yogurt, skim milk (arera), whey (aguat), cottage￾cheese, or other milk products? 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 28 b) HOMEMADE HOMEMADE HOMEMADE FLUID………. 1 2 8 FLUID………. 1 2 8 FLUID………. 1 2 8 C) Zinc tablets or syrup ZINTABLETS 1 2 8 ZINTABLETS 1 2 8 ZINTABLETS 1 2 8 OR SYRUP OR SYRUP OR SYRUP 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 fluid made from a special packet called Lemlem/ORS? 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 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 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 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 Yesterday during the day or night did you drink/eat any [ASK QUESTIONS E07 to E25]? Any other vegetables, like green beans, tomatoes, cauliflower, cabbage, broccoli, eggplant, etc.? 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) Bread, biscuits, porridge, Enjera, noodles(Indomin), rice, or other foods made from grains such as Teff, corn, millet, sorghum, wheat, oats, barley? Any flesh from wild animals, such as birds, wild pigeons, guinea fowl, deer, wild boar, wild goat? Pumpkin, carrots, squash, sweet potatoes or or any other dark yellow or orange fleshed roots, tubers and vegetables? Any dark green leafy vegetables such as spinach, pumpkin leaves, kale, mustard leaves, moringa? Ripe mangoes, ripe papaya, or other fruits that are dark yellow or orange inside? Any other fruits like bananas, apples, avocados, guava, pineapple, plum, orange, any berries, etc.? Module E. Women's Nutrition, Breastfeeding and Antenatal Care (Women 15-49) White potatoes, potato chips, white yams, cassava, bulla, kocho, manioc, or any other foods made from roots? Eggs? Any meat from domesticated animals, such as beef, pork, lamb, goat, chicken, or duck? Any organs from wild animals, such as birds, wild pigeons, guinea fowl, deer, wild boar, wild goat? Any liver, kidney, heart, or other organ from domesticated animals such as cow, pig, goa, 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? 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) 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 INITIATION OF BREASTFEEDING AND PRELACTAL FEEDS 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 oils, fats, butter,ghee, or foods made with any of these? Milk, cheese, yogurt, skim milk (arera), whey (aguat), cottage-cheese, 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? 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 seeds? Any foods made from beans, peas, lentils, peanuts or other legumes? 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? 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 (halawa, mushebek), candies, doughnuts, cakes, honey? Condiments for flavor, such as chilies, spices, herbs, or fennel grain, corainder, cumin, ginger, turmeric, garlic, cardamon? 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 Fresh or dried fish, shellfish? 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 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 EXTEN........................... HEALTH EXTEN........................... HEALTH EXTEN........................... 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 FFF (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 HHH (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. KKK (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 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? INSERT TIME MODULE ENDED HOUR MINUTE GO TO ANTHROPOMETRY 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. 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 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 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 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? E41 How many months pregnant were you when you first received antenatal care during this pregnancy? E42 E39 E40 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 017 SUPERVISOR PRINT NAME: AN04 017 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) BBB 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) 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 RESPONDENT'S AGE FROM HOUSEHOLD ROSTER (B05) Have you done any work in the past 12 months? MINUTE GO TO MODULE K LINE NO. (B01) LINE NO. (B01) LINE NO. (B01) 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 HOUR CLUST ER CHECK HOUSEHOLD ROSTER QUESTION B15 (MARITAL STATUS). IS RESPONDENT MARRIED OR LIVING TOGETHER (B15=1)? THERE ARE NO MORE QUESTIONS FOR THIS CASH EARNER. Who usually makes decisions about making major household purchases? 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? FOR RESPONSES #4 AND #5, SPECIFY THE RELATIONSHIP TO THE RESPONDENT. 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 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 LATE . . . . . . . . . . 4 1 DAY LATE . . . . . . . . . . 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 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 BB OTHER C OTHER C OTHER C (SPECIFY) (SPECIFY) (SPECIFY) 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 With whom do you usually discuss this? FOR RESPONSES B AND C, SPECIFY THE RELATIONSHIP TO THE RESPONDENT. What is the name of your (youngest) child under 2 years of age? ADD LINE NUMBER (B01) FROM HH ROSTER LINE NO. (B01) OBTAIN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? 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? 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 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 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? 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. HOUSEHOLD CONSUMPTION EXPENDITURE EA code (from Module A) Household number (from Module A) H0.01. Respondent line number (B01) from Module B, Question B06 H0.02. OBTAIN WRITTEN CONSENT. DOES [NAME] AGREE TO PARTICIPATE IN THE SURVEY? 1 = Yes 2 = No  End of Survey 3 = Not available  End of Survey ASK THESE QUESTIONS ABOUT ALL HOUSEHOLD MEMBERS. FOR MODULE H1, ASK WHOEVER IS MOST KNOWLEDGEABLE ABOUT THE FOOD THE HOUSEHOLD MEMBERS HAVE EATEN IN THE PAST WEEK. FOR MODULES H2 THROUGH H7, ASK THE PERSON WHO IS MOST KNOWLEDGEABLE ABOUTOTHER HOUSEHOLD EXPENDITURES, INCLUDING NON￾FOOD ITEMS THAT HOUSEHOLD MEMBERS HAVE BOUGHT. “Now I would like to ask you about the kinds of foods that you and other members of your household have eaten over the past week. I’d also like to ask you about items that you or members of your household may have bought in the past week. Please include foods in meals that are shared with other members of the household, as well as foods that individual members of the household may have consumed independently of other family members. First we will ask about foods that were eaten at your home, or at the home of friends or other family. Later we will ask about foods that were purchased already prepared from a restaurant or a vendor.” MODULE H1. FOOD CONSUMPTION OVER PAST 7 DAYS FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Grains and Cereals Teff 1 YES.........1 NO………2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Rice 2 YES ......... 1 NO ....... 2 NEXT ITEM Maize 3 YES.........1 NO ....... 2 NEXT ITEM Millet 4 YES.........1 NO 2 NEXT ITEM Sorghum 5 YES.........1 NO ....... 2 NEXT ITEM Barley 6 YES.........1 NO 2 NEXT ITEM Wheat 6 YES.........1 NO ....... 2 NEXT ITEM Maize flour 8 YES.........1 NO 2 NEXT ITEM Local wheat flour 9 YES.........1 NO 2 NEXT ITEM Imported wheat flour 10 YES.........1 NO 2 NEXT ITEM Other cereals flour 11 YES.........1 NO 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Pasta 12 YES.........1 NO 2 NEXT ITEM Other cereals (specify) ___________________ 13 YES.........1 NO 2 NEXT ITEM Nuts and pulses Beans 14 YES.........1 NO ....... 2 NEXT ITEM Horsebeans 15 YES.........1 NO2 NEXT ITEM Peanuts 16 YES.........1 NO ....... 2 NEXT ITEM Navy beans 17 YES.........1 NO………2 NEXT ITEM Peas 18 YES.........1 NO2 NEXT ITEM Cowpeas 19 YES.........1 NO2 NEXT ITEM Pigeon peas 20 YES.........1 NO2 NEXT ITEM Soya beans 21 YES.........1 NO2 NEXT ITEM Chick peas 22 YES.........1 NO2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Field pea 23 YES.........1 NO2 NEXT ITEM Haricot beans 24 YES.........1 NO2 NEXT ITEM Ground nuts 25 YES.........1 NO2 NEXT ITEM Lentils 26 YES.........1 NO2 NEXT ITEM Other pulses or nuts (specify) _____________________ __ 27 YES ......... 1 NO ....... 2 NEXT ITEM Other pulses or nuts (specify) 28 YES ......... 1 NO2 NEXT ITEM Other pulses or nuts (specify) 29 YES ......... 1 NO2 NEXT ITEM Other pulses or nuts (specify) 30 YES ......... 1 NO2 NEXT ITEM Eggs and Milk Products Eggs 31 YES.........1 NO ....... 2 NEXT ITEM Milk liquid 32 YES.........1 NO ....... 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Milk powder 33 YES.........1 NO2 NEXT ITEM Sour milk 34 YES.........1 NO ....... 2 NEXT ITEM Skimmed milk 35 YES.........1 NO2 NEXT ITEM Cheese 36 YES.........1 NO ....... 2 NEXT ITEM Yogurt 37 YES.........1 NO ....... 2 NEXT ITEM Other dairy (specify) _____________________ 38 YES.........1 NO2 NEXT ITEM Other dairy (specify) _____________________ 39 YES.........1 NO2 NEXT ITEM Other dairy (specify) _____________________ 40 YES.........1 NO ....... 2 NEXT ITEM Cooking Oils Palm oil 41 YES.........1 NO ....... 2 NEXT ITEM Peanut oil 42 YES.........1 NO ....... 2 NEXT ITEM Sunflower oil 43 YES.........1 NO2 NEXT ITEM Lean seed oil 44 YES.........1 NO2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Vegetable oil 45 YES.........1 NO2 NEXT ITEM Cotton oil 46 YES.........1 NO2 NEXT ITEM Niger seed oil 47 YES.........1 NO2 NEXT ITEM Linseed oil 48 YES.........1 NO2 NEXT ITEM Sesame oil 49 YES.........1 NO2 NEXT ITEM Butter 50 YES.........1 NO 2 NEXT ITEM Ghee 51 YES.........1 NO2 NEXT ITEM Other oils (specify)_____________ 52 YES.........1 NO2 NEXT ITEM Other oils (specify)_____________ 53 YES.........1 NO2 NEXT ITEM Other oils (specify)_____________ 54 YES.........1 NO ....... 2 NEXT ITEM Tubers Potatoes 55 YES.........1 NO ....... 2 NEXT ITEM Boye/Yam 56 YES.........1 NO ....... 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Cassava 57 YES.........1 NO ....... 2 NEXT ITEM Sweet potato 58 YES.........1 NO ....... 2 NEXT ITEM Enset 59 YES.........1 NO2 NEXT ITEM Kocho/Bula 60 YES.........1 NO2 NEXT ITEM Godere 61 YES.........1 NO2 NEXT ITEM Cassava flour (attieke, gari…) 62 YES.........1 NO 2 NEXT ITEM Other tubers (specify) ___________________ 63 YES.........1 NO2 NEXT ITEM Other tubers (specify) ___________________ 64 YES.........1 NO2 NEXT ITEM Other tubers (specify) ___________________ 65 YES.........1 NO ....... 2 NEXT ITEM Vegetables and leaves Onions 66 YES.........1 NO 2 NEXT ITEM Okra 67 YES.........1 NO 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Fresh tomato 68 YES.........1 NO 2 NEXT ITEM Canned tomatoes 69 YES.........1 NO 2 NEXT ITEM Green pepper 70 YES.........1 NO 2 NEXT ITEM Eggplant 71 YES.........1 NO 2 NEXT ITEM Carrot 72 YES.........1 NO 2 NEXT ITEM Green beans 73 YES.........1 NO 2 NEXT ITEM Cucumber 74 YES.........1 NO 2 NEXT ITEM Peas 75 YES.........1 NO 2 NEXT ITEM Zucchini 76 YES.........1 NO 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Bean leaves 77 YES.........1 NO 2 NEXT ITEM Spinach 78 YES.........1 NO2 NEXT ITEM Lettuce 79 YES.........1 NO2 NEXT ITEM Kale 80 YES.........1 NO2 NEXT ITEM Cabbage 81 YES.........1 NO2 NEXT ITEM Beetroot 82 YES.........1 NO2 NEXT ITEM Pumpkin leaves 83 YES.........1 NO2 NEXT ITEM Green chili pepper (kariya) 84 YES.........1 NO2 NEXT ITEM Red pepper (berbere) 85 YES.........1 NO2 NEXT ITEM Other vegetables and leaves (specify)______________ __ 86 YES ......... 1 NO 2 NEXT ITEM Other vegetables and leaves (specify)______________ __ 87 YES ......... 1 NO 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Other vegetables and leaves (specify)______________ __ 88 YES ......... 1 NO 2 NEXT ITEM Fruit and Nuts Mango 89 YES.........1 NO ....... 2 NEXT ITEM Banana 90 YES.........1 NO ....... 2 NEXT ITEM Orange 91 YES.........1 NO ....... 2 NEXT ITEM Lemon 92 YES.........1 NO ....... 2 NEXT ITEM Watermelon 93 YES.........1 NO ....... 2 NEXT ITEM Papaya 94 YES.........1 NO 2 NEXT ITEM Date 95 YES.........1 NO 2 NEXT ITEM Pineapple 96 YES.........1 NO 2 NEXT ITEM Sugar cane 97 YES.........1 NO 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Wild fruits 98 YES.........1 NO ....... 2 NEXT ITEM Roka 99 YES.........1 NO2 NEXT ITEM Other fruits (specify) _____________________ 100 YES.........1 NO2 NEXT ITEM Other fruits (specify) _____________________ 101 YES.........1 NO2 NEXT ITEM Other fruits (specify) _____________________ 102 YES.........1 NO ....... 2 NEXT ITEM Fish and Meat Beef 103 YES.........1 NO 2 NEXT ITEM Camel 104 YES.........1 NO 2 NEXT ITEM Mutton 105 YES.........1 NO 2 NEXT ITEM Goat 106 YES.........1 NO 2 NEXT ITEM Chicken 107 YES.........1 NO 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Lamb 108 YES.........1 NO2 NEXT ITEM Deer 109 YES.........1 NO2 NEXT ITEM Organ meat 110 YES.........1 NO 2 NEXT ITEM Canned meat 111 YES.........1 NO 2 NEXT ITEM Fresh fish 112 YES.........1 NO 2 NEXT ITEM Smoked fish 113 YES.........1 NO 2 NEXT ITEM Dried fish 114 YES.........1 NO 2 NEXT ITEM Canned fish 115 YES.........1 NO 2 NEXT ITEM Other meat (specify) _____________________ 116 YES.........1 NO 2 NEXT ITEM Other meat (specify) _____________________ 117 YES.........1 NO 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Other meat (specify) _____________________ 118 YES.........1 NO 2 NEXT ITEM Spices and Condiments Salt 119 YES.........1 NO ....... 2 NEXT ITEM Hot pepper 120 YES.........1 NO ....... 2 NEXT ITEM Turmeric 121 YES.........1 NO2 NEXT ITEM Garlic 122 YES.........1 NO2 NEXT ITEM Cinnamon 123 YES.........1 NO2 NEXT ITEM Cumin 124 YES.........1 NO2 NEXT ITEM Cardamon 125 YES.........1 NO2 NEXT ITEM Ginger 126 YES.........1 NO2 NEXT ITEM Other spices, condiments, etc. (specify)_____________ 127 YES ......... 1 NO ....... 2 NEXT ITEM Other spices, condiments, etc. (specify)_____________ 128 YES.........1 NO 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Other spices, condiments, etc. (specify)_____________ 129 YES.........1 NO 2 NEXT ITEM Sweets and Confectionery Sugar 130 YES.........1 NO ....... 2 NEXT ITEM Chocolate 131 YES.........1 NO 2 NEXT ITEM Honey 132 YES.........1 NO 2 NEXT ITEM Candy 133 YES.........1 NO 2 NEXT ITEM Halua (rice, milk, sugar paste eaten as dessert) 134 YES.........1 NO2 NEXT ITEM Sugarcane 135 YES.........1 NO2 NEXT ITEM Sweet reed/tinksh 136 YES.........1 NO2 NEXT ITEM Other sweets (specify)_____________ 137 YES.........1 NO ....... 2 NEXT ITEM Other sweets (specify)_____________ 138 YES.........1 NO 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Other sweets (specify)_____________ 139 YES.........1 NO 2 NEXT ITEM Non-Alcoholic Beverages Tea (dried leaves) 140 YES.........1 NO ....... 2 NEXT ITEM Coffee (ground, instant) 141 YES.........1 NO ....... 2 NEXT ITEM Chat/Kat 142 YES.........1 NO2 NEXT ITEM Fruit juices 143 YES.........1 NO2 NEXT ITEM Fruit juices/Carbonated drinks (Coca cola, pepsi, etc.) 144 YES.........1 NO 2 NEXT ITEM Bottled water 145 YES.........1 NO 2 NEXT ITEM Soft drinks/soda 146 YES.........1 NO2 NEXT ITEM Berth 147 YES.........1 NO2 NEXT ITEM Bukre/keribo 148 YES.........1 NO2 NEXT ITEM Besso juice/shameta/borde 149 YES.........1 NO2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Other non-alcoholic drinks (specify)______________ _ 150 YES ......... 1 NO ....... 2 NEXT ITEM Other non-alcoholic drinks (specify)______________ _ 151 YES.........1 NO 2 NEXT ITEM Other non-alcoholic drinks (specify)______________ _ 152 YES.........1 NO 2 NEXT ITEM Alcoholic Beverages Areki 153 YES.........1 NO 2 NEXT ITEM Tella 154 YES.........1 NO 2 NEXT ITEM Beer 155 YES.........1 NO 2 NEXT ITEM Thej beer 156 YES.........1 NO 2 NEXT ITEM Imported alcoholic beverages 157 YES.........1 NO ....... 2 NEXT ITEM Locally produced alcohol 158 YES.........1 NO ....... 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Other alcoholic beverages (specify)______________ __ 159 YES ......... 1 NO ....... 2 NEXT ITEM Other alcoholic beverages (specify)______________ __ 160 YES.........1 NO 2 NEXT ITEM Other alcoholic beverages (specify)______________ __ 161 YES.........1 NO 2 NEXT ITEM Cooked Foods from Vendors Cakes 162 YES.........1 NO 2 NEXT ITEM Bread/biscuit 163 YES.........1 NO 2 NEXT ITEM Grilled meat 164 YES.........1 NO 2 NEXT ITEM Sambusa 165 YES.........1 NO 2 NEXT ITEM Potato chips/fries 166 YES.........1 NO 2 NEXT ITEM FOOD ITEM ITEM CODE Over the past one week (7 days), did you or others in your household eat any [FOOD ITEM]? How much in total did your household eat in the past week? How much of what you ate came from purchases? (IF H1.04A =0 THEN SKIP TO H1.06A) How much did you spend on what was eaten last week? If your family ate part but not all of something you purchased, estimate what you spent only on the part that was consumed. How much of what you ate came from your household’s own production? (IF H1.06A =0 THEN SKIP TO H1.07A) 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 of what you ate came from gifts or other sources? (IF H1.07A =0 THEN SKIP TO NEXT ITEM) 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. H1.01 H1.02 H1.03A QUANTIT Y 999.9 H1.03B UNIT H1.04A QUANTIT Y 999.9 H1.04B UNIT H1.05 BIRR 99999 H1.06A QUANTIT Y 999.9 H1.06B UNIT H1.06C ESTIMATE BIRR 99999 H1.07A QUANTIT Y 999.9 H1.07B UNIT H1.07C ESTIMATE BIRR 99999 Bombolino 167 YES.........1 NO 2 NEXT ITEM Purchased injera 168 YES.........1 NO 2 NEXT ITEM Pasta/Maccraoni 169 YES.........1 NO 2 NEXT ITEM Other cooked food from vendors (specify) ________________ 170 YES.........1 NO 2 NEXT ITEM Other cooked food from vendors (specify) ________________ 171 YES.........1 NO 2 NEXT ITEM Other cooked food from vendors (specify) ________________ 172 YES ......... 1 NO ........ 2 H1.08 RESPONSE CATEGORIES FOR H1.03b/1.04b/1.06b/1.07b – UNITS KILOGRAMME ......................... 01 GRAM ...................................... 02 LITER ....................................... 03 CENTILITER ............................ 04 JOG .......................................... 05 MELEKIYA…................................ 06 BIRCHIKO (SMALL) ................ 07 BIRCHIKO (MEDIUM) .............. 08 BIRCHIKO (LARGE) ................ 09 ESIR (SMALL) ..................... …..10 ESIR (MEDIUM) ........................ 11 ESIR (LARGE) ........................... 12 FESTAL (SMALL) .................... 13 FESTAL (MEDIUM) ................. 14 FESTAL (LARGE) .................... 15 KERCHAT/KEMBA (SMALL) ... 16 KERCHAT/KEMBA (MEDIUM) 17 KERCHAT/KEMBA (LARGE) .. 18 KUBAYA/CUP (SMALL) ................. 19 KUBAYA/CUP (MEDIUM) .............. 20 KUBAYA/CUP (LARGE) ................. 21 KUNNA/MISHE/KEFER/ENKIB (SMALL) .......................................... 22 KUNNA/MISHE/KEFER/ENKIB (MEDIUM) ....................................... 23 KUNNA/MISHE/KEFER/ENKIB (LARGE) ......................................... 24 MEDEB (SMALL) ............. 25 MEDEB (MEDIUM) ........... 26 MEDEB (LARGE) ............. 27 PIECE/NUMBER ............. 28 SAHIN (SMALL) ............... 29 SAHIN (MEDIUM) ............ 30 SAHIN (LARGE) ................. 31 SINI (SMALL)………………32 SINI (LARGE)………………33 TASA/TANIKA/SHEMBER/SELEM ON (SMALL)………………34 TASA/TANIKA/SHEMBER/SELEM ON (MEDIUM)……………35 TASA/TANIKA/SHEMBER/SELEM ON (LARGE)………………36 ZORBA/AKARA (SMALL) ........... 37 ZORBA/AKARA (MEDIUM) ........ 38 ZORBA/AKARA (LARGE) .......... 39 OTHER ___________________ 40 NOTE: ANY UNIT LISTED MUST BE ABLE TO BE CONVERTED TO A STANDARDIZED UNIT. THIS CONVERSION WILL HAPPEN DURING DATA ANALYSIS; IT SHOULD NOT BE DONE IN THE FIELD BY THE INTERVIEWER. MODULE H2. NON-FOOD EXPENDITURES OVER PAST 7 DAYS (Head of Household or Responsible Adult) Respondent line number (B01) from Module B, Question B10 “Now I would like to ask you about items that you or members of your household may have bought in the past week.” ONE WEEK RECALL ITEM ITEM CODE Over the past one week (7 days), did your household purchase or pay for any [ITEM]? How much did you pay (how much did they cost) in total? H2.01 H2.02 H2.03 BIRR (99999) Cigarettes 173 YES ........ 1 NO ........... 2 NEXT ITEM Tobacco 174 YES ........ 1 NO ........... 2 NEXT ITEM Batteries 175 YES ........ 1 NO 2 NEXT ITEM Candles (tua’af), incense 176 YES ........ 1 NO 2 NEXT ITEM Wood 177 YES ........ 1 NO ........... 2 NEXT ITEM Petrol 178 YES ........ 1 NO ........... 2 NEXT ITEM Diesel 179 YES ........ 1 NO 2 NEXT ITEM Kerosene 180 YES ........ 1 NO 2 NEXT ITEM Coal/ Charcoal 181 YES ........ 1 NO ........... 2 NEXT ITEM Matches, lighters 182 YES ........ 1 NO ........... 2 NEXT ITEM Newspapers or magazines 183 YES ........ 1 NO ........... 2 NEXT ITEM Public transportation - buses, taxis, horse cart, donkey ride, Bajaj, camel rickshaws, train tickets, etc. (include any used for school under education costs; include any used for obtaining health care under health expenditures) 184 YES ........ 1 NO ........... 2 NEXT ITEM Other (specify) _____________________________________ 186 YES ........ 1 NO2 NEXT ITEM Other (specify) _____________________________________ 187 YES ........ 1 NO2GO TO MODULE H3 MODULE H3. NON-FOOD EXPENDITURES OVER PAST ONE MONTH (Head of Household or Responsible Adult) “Next I would like to ask you about items that you or members of your household may have bought over the past month.” ONE MONTH RECALL ITEM ITEM CODE Over the past one month, did your household purchase or pay for any [ITEM]? How much did you pay (how much did they cost) in total? H3.01 H3.02 H3.03 BIRR (99999) Toilet soap 188 YES ........ 1 NO ........... 2  NEXT ITEM Household cleaning articles (soap, bleach, washing powder, etc.) 189 YES ........ 1 NO ........... 2  NEXT ITEM Toothpaste, tooth powder, toothbrush, etc. 190 YES ........ 1 NO ........... 2  NEXT ITEM Other personal products (shampoo, combs, cosmetics, etc.) 191 YES ........ 1 NO ........... 2  NEXT ITEM Personal services (haircuts, shaving, shoeshine, etc.) 192 YES ........ 1 NO ........... 2  NEXT ITEM Light bulbs 193 YES ........ 1 NO ........... 2  NEXT ITEM Postal expenses 194 YES ........ 1 NO ........... 2  NEXT ITEM Music or video cassette or CD/DVD 195 YES ........ 1 NO ........... 2  NEXT ITEM Telephone or mobile phone service 196 YES ........ 1 NO ........... 2  NEXT ITEM Donation - to church, temple, charity, beggar, etc. 197 YES ........ 1 NO ........... 2  NEXT ITEM Gifts 198 YES ........ 1 NO ........... 2  NEXT ITEM Repair and other expenses for personal vehicle, bicycle, motor bicycle (registration, fines) 199 YES ........ 1 NO ........... 2  NEXT ITEM Repairs to household and personal items (radios, , TV, Telephone, watches, etc., excluding battery purchases) 200 YES ........ 1 NO ........... 2  NEXT ITEM Utilities: Electricity 201 YES ........ 1 NO ........... 2  NEXT ITEM Utilities: Water 202 YES ........ 1 NO ........... 2  NEXT ITEM Membership fees (for the use of natural resources; water, forest) 203 YES ........ 1 NO ........... 2  NEXT ITEM MODULE H5. NON-FOOD EXPENDITURES OVER PAST 12 MONTHS (Head of Household or Responsible Adult) “Now I would like to ask you about items that you or members of your household may have bought over the past one year.” ONE YEAR (12 MONTH) RECALL ITEM ITEM CODE Over the past one year (twelve months), did your household purchase or pay for any [ITEM]? How much did you pay (how much did they cost) in total? H5.01 H5.02 H5.03 BIRR (99999) Ready-made clothing and apparel (excluding school related) 204 YES ........ 1 NO ........... 2 NEXT ITEM Cloth, wool, yarn, and thread for making clothes and sweaters 205 YES ........ 1 NO ........... 2 NEXT ITEM Tailoring expenses 206 YES ........ 1 NO ........... 2 NEXT ITEM Footwear (shoes, slippers, sandals, etc.) 207 YES ........ 1 NO ........... 2 NEXT ITEM Washing expenses 208 YES ........ 1 NO ........... 2 NEXT ITEM Crockery, cutlery and kitchen utensils (household use) 209 YES ........ 1 NO ........... 2 NEXT ITEM Stationery items (excluding school related) 210 YES ........ 1 NO ........... 2 NEXT ITEM Books (excluding school related) 211 YES ........ 1 NO ........... 2 NEXT ITEM Tickets for cinema / entertainment events 212 YES ........ 1 NO ........... 2 NEXT ITEM Pocket money to children 213 YES ........ 1 NO ........... 2 NEXT ITEM Excursion, holiday (including travel and lodging; excluding school or health related) 214 YES ........ 1 NO ........... 2 NEXT ITEM Carpet, rugs, drapes, curtains 215 YES ........ 1 NO ........... 2 NEXT ITEM Pillows, mattresses, blankets, towels, etc. 216 YES ........ 1 NO ........... 2 NEXT ITEM Jewelry, watches 217 YES ........ 1 NO ........... 2 NEXT ITEM Sports & hobby equipment, musical instruments, toys 218 YES ........ 1 NO ........... 2 NEXT ITEM Cement/ Sand 219 YES ........ 1 NO ........... 2 NEXT ITEM Building woods 220 YES ........ 1 NO ........... 2 NEXT ITEM ONE YEAR (12 MONTH) RECALL ITEM ITEM CODE Over the past one year (twelve months), did your household purchase or pay for any [ITEM]? How much did you pay (how much did they cost) in total? H5.01 H5.02 H5.03 BIRR (99999) Taxes, land taxes, housing and property taxes 221 YES ........ 1 NO ........... 2 NEXT ITEM Rentals (agricultural equipment like ploughs, tractors, combine harvesters, knapsack sprayers) 222 YES ........ 1 NO 2 NEXT ITEM Dowry/Tilosh 223 YES ........ 1 NO ........... 2 NEXT ITEM Marriages, births, and other ceremonies 224 YES ........ 1 NO ........... 2 NEXT ITEM Funeral and death related expenses 225 YES ........ 1 NO ........... 2 NEXT ITEM Expenditure on religious ceremonies 226 YES ........ 1 NO ........... 2 NEXT ITEM Torch 227 YES ........ 1 NO 2 NEXT ITEM Other social events (outside home) 228 YES ........ 1 NO ........... 2 NEXT ITEM HEALTH EXPENDITURES over last 12 months (include estimated value of any in-kind payments or borrowed amounts) Anything related to illnesses and injuries, including for medicine, tests, consultation, & in-patient fees 229 YES ........ 1 NO2 NEXT ITEM Medical care not related to an illness - preventative health care, pre￾natal visits, check-ups, medical insurance etc. 230 YES ........ 1 NO2 NEXT ITEM Non-prescription medicines, for example, Paracetamol, etc. 231 YES ........ 1 NO2 NEXT ITEM Health service charge from a traditional healer 232 YES ........ 1 NO2 NEXT ITEM Transportation used to access health-related services or care that did not require an overnight stay in a health facility or at a traditional healer’s dwelling 233 YES ........ 1 NO2 NEXT ITEM Hospitalizations or overnight stay in any hospital – total cost for treatment 234 YES ........ 1 NO ........... 2 NEXT ITEM Travel to and from the medical facility for any overnight stay(s) or hospitalization 235 YES ........ 1 NO ........... 2 NEXT ITEM Food costs during overnight stay(s) at the medical facility or hospitalization (if not already included above) 236 YES ........ 1 NO ........... 2 NEXT ITEM Over-night(s) stay at a traditional healer's or faith healer's dwelling – total costs for treatment 237 YES ........ 1 NO ........... 2 NEXT ITEM Travel costs to the traditional healer's or faith healer's dwelling for overnight stay(s) 238 YES ........ 1 NO ........... 2 NEXT ITEM ONE YEAR (12 MONTH) RECALL ITEM ITEM CODE Over the past one year (twelve months), did your household purchase or pay for any [ITEM]? How much did you pay (how much did they cost) in total? H5.01 H5.02 H5.03 BIRR (99999) Food costs during overnight stay(s) at the traditional healer's or faith healer's dwelling 239 YES ........ 1 NO ........... 2 NEXT ITEM EDUCATION EXPENDITURES over last 12 months (include estimated value of any in-kind payments or borrowed amounts) Tuition, including extra tuition fees 240 YES ........ 1 NO ........... 2 NEXT ITEM Expenditures on after school programs and tutoring 241 YES ........ 1 NO ........... 2 NEXT ITEM School books and stationery 242 YES ........ 1 NO ........... 2 NEXT ITEM School uniform 243 YES ........ 1 NO ........... 2 NEXT ITEM Boarding fees 244 YES ........ 1 NO ........... 2 NEXT ITEM Contribution to school building maintenance 245 YES ........ 1 NO ........... 2 NEXT ITEM Transport to and from school 246 YES ........ 1 NO ........... 2 NEXT ITEM Parent/Teacher Association and other related fees 247 YES ........ 1 NO ........... 2 NEXT ITEM Other: Specify_____________________________________ 248 YES ........1 NO ........... 2 NEXT ITEM Other: Specify_____________________________________ 249 YES ........1 NO2 NEXT ITEM Other: Specify_____________________________________ 250 YES ........1 NO2 NEXT ITEM MODULE H6. HOUSING EXPENDITURES (Head of Household or Responsible Adult) “Now I’d like to ask you some questions about your home.” QNO. QUESTION RESPONSE CATEGORIES H6.01 Do you own or are purchasing this house, is it provided to you by an employer, do you use it for free, or do you rent this house? OWN........................................................1 BEING PURCHASED .............................. 2 EMPLOYER PROVIDES ......................... 3 FREE ....................................................... 4 H6.04 RENTED ...................................................... 5 H6.05 DON’T KNOW ........................................ 8 H6.02 If you sold this dwelling today, how much would you receive for it in [BIRR]? DON’T KNOW…….9999998 H6.03 How old is this house, in years? USE ‘000’ IF HOUSE IS LESS THAN ONE YEAR. DON’T KNOW …….998 SKIP TO H6.06 H6.04 If you rented this dwelling out today, how much rent would you receive in [BIRR]? H6.04A BIRR H6.04B UNIT DON’T KNOW…….99998  SKIP TO H6.09 DAY ............... 1 WEEK ............ 2 MONTH .......... 3 YEAR ............. 4 DON’T KNOW.8 SKIP TO H6.09 H6.05 How much do you pay to rent this dwelling in [BIRR]? H6.05A BIRR H6.05B UNIT DON’T KNOW…….99998  SKIP TO H6.09 DAY ............... 1 WEEK ............ 2 MONTH .......... 3 YEAR ............. 4 DON’T KNOW….8 H6.06 Do you pay a mortgage on this house, that is, a regular payment towards purchasing the house? YES .............. 1 NO ............... 2 SKIP TO H6.09 H6.07 How often do you make mortgage payments? ONCE A MONTH....................................... 1 ONCE EVERY 3 MONTHS ....................... 2 ONCE EVERY 6 MONTHS ....................... 3 ONCE A YEAR .......................................... 4 OTHER (SPECIFY) ................................... 6 H6.08 How much do you pay each time you make a payment on your mortgage in [BIRR]? AMOUNT IS VARIABLE…………………999995 DON’T KNOW…..…..……………………999998 H6.09 In the past 12 month, how much did you spend on major repairs, renovations & maintenance to this house in [BIRR]? DON’T KNOW…...…….…………………999998 SKIP TO H6.09 MODULE H7. DURABLE GOODS EXPENDITURES “Now I’d like to ask you some questions about items that may be owned by your household.” ITEM Item Code Does your household own a [ITEM]? How many [ITEM]s do you own? What is the age of these [ITEM]s in years? IF MORE THAN ONE ITEM OWNED, AVERAGE AGE. If you wanted to sell one of these [ITEM]s today, how much would you receive? IF MORE THAN ONE ITEM OWNED, AVERAGE VALUE. Did you purchase or pay for any of these [ITEM]s in the last 12 months? How much did you pay for one of these [ITEM]s in the last 12 months?” IF MORE THAN ONE ITEM OWNED, AVERAGE VALUE H7.01 H7.02 H7.03 NUMBER H7.04 AGE IN YEARS H7.05 BIRR (999999) H7.06 H7.07 BIRR (999999) Furniture and fixtures 251 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Electric fan 252 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Cassette/CD recorder or player, radio, etc. 253 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Television/ DVD player/ VCR 254 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Sewing machine 255 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Kitchen appliances (refrigerator, cooking range, blenders, etc.) 256 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Bicycle 257 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Motorcycle 258 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Motor car or other such vehicle 259 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Mobile phone 260 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Clock 261 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Iron (for pressing clothes; electric or other) 262 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Computer, including equipment & accessories 263 YES ........ 1 NO ....... 2 NEXT ITEM YES ........ 1 NO ..... 2 NEXT ITEM Solar panels 264 YES ........1 NO 2 NEXT ITEM Carpentry tools 265 YES ........1 NO 2 NEXT ITEM Other, specify _________________________ 266 YES ........ 1 NO2 NEXT ITEM YES ........ 1 NO2 NEXT ITEM Other, specify _________________________ 267 YES ........ 1 NO2 NEXT ITEM YES ........ 1 NO2 NEXT ITEM H7.08 Do you have a cell phone? YES ........ 1 NO 2 H08 H7.09 What is your cell number? IF THE RESPONDENT REFUSES TO PROVIDE A CELL PHONE NUMBER, THEN ENTER ZEROS PHONE |___|___|___| |___|___| |___|___|___| |___|___|___|___|___| COUNTRY CODE H7.10 Do you use your cell phone to [READ EACH OPTION AND CIRCLE YES OR NO]? YES= 1 NO=2 RECEIVE PAYMENTS/REMITTANCES 1 2 MAKE PAYMENTS 1 2 SAVE MONEY 1 2 GET PRICE INFORMATION 1 2 ACCESS SERVICES 1 2 TABLE OF CONTENTS SHOCKS Module R1. Shocks ASSETS Module R2. Productive assets (excluding livestock) Module R2a. Livestock assets ACCESS TO MARKETS, SERVICES AND INFORMATION Module R3. Access to markets, infrastructure and services Module R5. Access to financial services: credit Module R6. Access to financial services: savings Module R7. Access to information RESILIENCE CAPACITIES Module R8. Group participation Module R9. Collective Action Module R10. Livelihood activities Module R11. Migration and use of remittances Module R12. Food insecurity coping strategies Module R13. Social and capacity‐building support Module R14. Aspirations and confidence to adapt Module R15. Government support Module R16. Gender norms 2 MODULE R1: SHOCKS AND STRESSORS R103 R104 R105 R106 R107 Over the past year (12 months) did your household experience [the shock]? 1= Yes 2 = No 99 ‐= Don’t know >>If 2 or 99, Next event How severe was the impact on your household’s income? Enter code from list 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) To what extent has your household been able to recover? Enter code from list Climatic shocks 1. Excessive rains/ flooding 2. Variable rain/drought 3. Hail/frost 4. Landslides/erosion Biological shocks 5. Crop disease (rust on wheat, sorghum) 6. Crop pests (locusts) 7. Weeds (e.g., associated with striga) 8. Livestock disease 9. Human disease outbreaks (from contaminated water) Conflict shocks 10. Theft or destruction of assets 11. Theft of livestock (raids) Economic shocks 12. Delay in PSNP food assistance 13. Increasing food prices 3 SHOCKS CODE LIST R104, R105 R107 Severity of impact Ability to recover 1. None (the same) 2. Slight decrease 3. Severe decrease 4. Worst ever happened 99. Don’t know 1. Did not recover 2. Fully recovered, same as before the shock 3. Fully recovered and better than before the shock 4. Partially recovered 5. Not affected by [event] 99. Don’t know 14. Increased prices of agricultural or livestock inputs 15. Decreased prices for agricultural or livestock products 16. Loss of land/rental property 17. Unemployment for youths 18. Death of household member 4 R106 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 for work 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 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 a. Use money from savings b. Remittances from a relative that migrated c. Other (specify) d. Did nothing 5 Shock exposure and severity (cont’d) R108 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 R109 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 R110 What have you done to protect your household from the impact of shocks in the future? [Read list; select all that apply] Nothing……………………………………..…………A Increased savings………………………………….B Put aside grains (for HH or animals)……..C Switched to different crops…………………..D Switched to different animals…………….…E Added additional ag activity …………….…..F Added additional non‐ag activity…………..G Diversified into ag livelihood………………...H Diversified into non‐ag livelihood………….I Changed from ag to non‐ag livelihood….J Changed from non‐ag to ag livelihood….K Acquired crop insurance………………….……L Acquired livestock insurance………………..M Acquired other insurance (e.g., health)..N Relocated temporarily………………………….O Relocated permanently……………………..….P Other …………………………………………………..Q Don’t know……………………………………………Y 6 MODULE R2. PRODUCTIVE ASSETS R201 R202 Number owned now 99 Don’t know Did you sell any of these items in the past 12 months because your household was in distress from a shock or stress (not enough money to cover normal expenses)? 1. Yes 2. No 99 Don’t know 1. Plough (oxen‐pulled) 2. Mechanical plough 3. Sickle 4. Pick axe 5. Axe 6. Pruning/cutting shears 7. Hoe 8. Spade or shovel 9. Traditional beehive 10. Modern beehive 11. Knapsack chemical sprayer 12. Mechanical water pump 13. Motorized water pump 14. Stone grain mill 15. Motorized grain mill 16. Broad bed maker (oxen‐pulled) 17. Small tractor 18. Hand‐held motorized tiller 19. Agricultural land (hectares) 7 MODULE R2A. LIVESTOCK ASSETS R201A R202A Number owned now 99 Don’t know Did you sell any of this item in the past 12 months because your household was in distress from a shock or stress (not enough money to cover normal expenses)? 1. Yes 2. No 99 Don’t know 1. Oxen 2. Cattle 3. Goats 4. Sheep 5. Donkey/mule 6. Poultry 7. Camels 8. Horse 9. Honey bees (hives) 8 MODULE R3. ACCESS TO MARKETS, INFRASTRUCTURE , AND SERVICES R301 Are the following services available IN or WITHIN FIVE KM of your village? 1= yes 2= no 99 Don’t know a. Institutions where people can borrow money If yes, go to R302 b. Institutions where people can save money c. Primary school If yes, go to R303a d. Health services (post, clinic, or center) 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 i. A public telephone j. Public transport service Go to R308 ASK ONLY IF R301a = YES R302 Who provides this service? Select all that apply A. Banks B. MFI C. NGO D. Savings/loan group E. Friends/relatives F. Shops/merchants G. Money lender 9 H. Other (specify): Y. Don’t know >> Go to R301b 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 A. No beds, facility was full B. No staff in the facility C. Health facility was destroyed D. Security problem E. No transportation F. No road or poor road condition G. No drugs at the health center H. No money for services I. Quality of the service is very poor J Other (specify): Y. Don’t know >> Go to R301e ASK ONLY IF R301e = Yes 10 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 A. No service provider (woreda office, ag agent) in area B. No equipment/inputs available from service provider C. No road or poor condition into or out of village D. Bad timing of ext agent visit E. Quality of the services is poor F. Other (specify): Y. 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 A. No service provider (vet center, veterinarian) in area B. Service provision too expensive C. No vaccines/medicines available D. No road or poor condition into or out of village E. No money for services F Quality of the services is poor G. Other (specify): Y. 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 11 ASK AFTER COMPLETING R301j R308 Can the village be reached by a paved road all year around? 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 5. ACCESS TO FINANCIAL SERVICES/ CREDIT R501 Have any household members taken out a cash loan in the last 12 months? 1. Yes Skip to R503 2. No 99 Don’t know Skip to next module R502 If no, why not? 1. Didn’t need 2. Couldn’t find a loan that met my needs” (i.e. “is appropriate” in terms of size, terms, etc); 3. Afraid I couldn’t pay back 4. No loan providers in my area 5. Other (specify) Skip to next module 99 Don’t know R503 Did you or any other household member take out a loan in the last 12 months to deal specifically with a shock or stress? 1. Yes 2. No Skip to next module 99 Don’t know R504 What is the primary source of loan taken out in the last year? 1. Friend/family within the village 2. Friend/family outside of the village 3. Money‐lender 4. MFI 5. RuSACCO 6. Bank 12 7. NGO 8. Village‐based savings group 9. Religious group 10. Input supplier 11. Local trader/merchant 12. Other 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 3. Village savings/credit group (e.g., RuSACCO) 4. Bank 5. Mobile banking 6. Other 99. Don’t know R603 Who primarily decides how savings are used? Select only one 1. Yourself 2. Your spouse/partner 3. You and your spouse/partner 4. Yourself and other HH member jointly 5. Your spouse/partner and other HH member jointly 6. Other (specify): 99. Don’t know R604 Did you or any other household member use savings specifically to deal with a shock or stress in the last 12 months? 1. Yes 2. No 99. Don’t know 13 MODULE R7. ACCESS TO INFORMATION R701 R702 R703 Did you or anyone in the household 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 Did the information influence any decisions made by household members? 1. Yes, result of decision benefitted HH 2. Yes, result of decision had a negative effect on HH 3. Yes, no effect on HH 4. No, did not influence decisions 5. No decisions made 99 Don’t know 1. Early warning for natural hazards (flooding, hail, landslide) 2. Long‐term changes in weather patterns 3. Rainfall/ weather prospects for coming season 4. Water prices and availability in local boreholes, shallow wells etc 5. Animal health (e.g., disease, epidemic) threats/prevention 6. Crop health (e.g., pest outbreaks, disease) threats/prevention 7. Improved crop production practices/technologies (CA, seeds) 8. Improved livestock production practices (health, husbandry) 9. Current market prices for live animals in the area 10. Market prices for animal products (milk, hides, skins, etc.) 11. Grazing conditions in nearby areas 14 CODES FOR R702 ‐ Main Information sources 1 Relatives, friends, neighbors 8 Local market 2 Kebele leaders 9 Gov’t: rural development agents, health/agriculture ext. 3 Village Development Army 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 Elders 14 Policy and security people 99 Don’t know 12. Conflict or security issues 13. Business and investment opportunities 14. Opportunities for borrowing money 15. Market prices of the food that you buy 16. Child nutrition and health information 17. Equal rights for women and men 18. Gender‐based violence 19. Natural resource management 15 MODULE R8. GROUP PARTICIPATION R801 R802 Are any of the following groups active in this village? Read list 1= yes 2= no 99 Don’t know If =2 or 99, skip to next topic For any HH member who is in the group, how active is s/he in the group’s decision‐making? 1. No HH member in group 2. HH member does not participate in decision‐making 3. Somewhat active 4. Very active 5. HH member is a leader 99. Don’t know 1. Communal water users’ group If yes, go to R803 2. Communal grazing land users’ group If yes, go to R805 3. Communal natural resources group If yes, go to R806 4. Credit or micro‐finance group 5. Savings groups (VLSA, merry‐go‐round, etc.)` 6. Mutual help group (e.g., ritban, afoosha, ofera/webera, burial, etc.) 7. Religious group 8. Mothers’ group 9. Women’s group 10. Youth group 11. Other (specify) 16 ASK ONLY IF R801a = Yes R803 Does the water user’s group manage communal water for livestock in this village? 1. Yes 2. No 99. Don’t know R804 Does the water user’s group manage communal water for irrigation in this village? 1. Yes 2. No 99. Don’t know >> Go to R802a ASK ONLY IF R801c = Yes R805 Does the group decide who in the village can use communal grazing land and when they can use it? 1. Yes 2. No 99. Don’t know >> Go to R802c ASK ONLY IF R801d = Yes R806 Does the communal natural resources group decide who in the village can gather firewood and how much? 1. Yes 2. No 99. Don’t know >> Go to R802d ASK AFTER COMPLETING R801 and R802 FOR ALL GROUPS R807 Over the last 12 months, how often have you been a part of a group that provided labor to someone in the village who needed help? 1. None, no one needed help 2. None, I wasn’t part of a group 3. Once or twice 4. 3‐5 times 5. 6 or more times 99. Don’t know R808 Over the last 12 months, how often have you been a part of a group that provided food to someone in the village who needed help? 1. None, no one needed help 2. None, I wasn’t part of a group 3. Once or twice 4. 3‐5 times 5. 6 or more times 99. Don’t know R809 Over the last 12 months, how often have you been a part of a group that provided some other type of help to someone else in the village? 1. None, no one needed help 2. None, I wasn’t part of a group 17 3. Once or twice 4. 3‐5 times 5. 6 or more times 99. Don’t know R810 Has the amount of help you can provide to others in your village changed over the last five years? 1. No (stayed the same) 2. Yes, decreased slightly 3. Yes, decreased greatly 4. Yes, increased slightly 5. Yes, increased greatly 99. Don’t know 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 A. Soil conservation (terracing, bunds, half‐ moons, gabions, etc.) B Flood diversion activities C. Repaired/built schools D. Repaired/built health posts or centers E. Road maintenance/construction F. Planted trees on communal land G. Area enclosure H Other (specify) Y. Don’t know 18 MODULE R10. LIVELIHOOD ACTIVITIES R1001 R1002 What were the sources of your household’s food/income over the last 12 months? Read each source Add up number of sources and enter into R1003 Rank these sources based on the proportion of food/income they provide for your household 1 = highest 1. Farming/crop production and sales 2. Livestock production/fattening and sales 3. Wage labor (WITHIN THE COMMUNITY) 4. Wage labor (OUTSIDE THE COMMUNITY) 5. Salaried work 6. Sale of wild/bush products (including charcoal, firewood) 7. Honey production and sales 8. Petty trade (selling other products, e.g., grain, veggies, oil, sugar, etc.) 9. Petty trade (selling own products, e.g., local beer, sex work) 10. Other self‐employment/own business (agricultural, e.g., buying/reselling chat) 11. Other self‐employment/own business (non‐agricultural, e.g., stone cutting, hair braiding, etc. 12. Rental of land, house, rooms 13. Remittances 14. Gifts/inheritance 15. Safety net food/cash assistance 16. Other (specify): 17. Other (specify): 19 R1003 Total number of sources MODULE R11. MIGRATION AND USE OF REMITTANCES R1101 Over the last two years, has anyone who was living in your household migrated to SOMEWHERE ELSE IN ETHIOPIA looking for work? 1. Yes 2. No 99. Don’t know Skip to R1104 R1101a Did the person(s) migrate SOMEWHERE ELSE IN ETHIOPIA permanently or temporarily for work? 1. Permanent 2. Temporary 99 Don’t know R1102 Does the person(s) living SOMEWHERE ELSE IN ETHIOPIA send money back to your household? 1. Yes, regularly 2. Yes, irregularly 3. No 99. Don’t know R1103 Who migrated to SOMEWHERE ELSE IN ETHIOPIA within the last two years looking for work? Select all that apply A. Male HHH B. Female HHH C. Other adult males in HH D. Other adult females in HH E. Youths Y Don’t know R1104 Over the last two years, has anyone who was living in your household migrated to ANOTHER COUNTRY looking for work? 1. Yes 2. No 99. Don’t know Skip to R1107 R1104a Did the person(s) migrate to ANOTHER COUNTRY permanently or temporarily for work? 1. Permanent 2. Temporary 99 Don’t know R1105 Does the person living in ANOTHER COUNTRY send money back to your household? 1. Yes, regularly 2. Yes, irregularly 3. No 99. Don’t know R1106 Who migrated to ANOTHER COUNTRY within the last two years looking for work? A. Male HHH B. Female HHH C. Other adult males in HH 20 Select all that apply D. Other adult females in HH E. Youths Y Don’t know R1107 CHECK ANSWERS TO R1102 AND R1105: IF 1102 AND 1105 = 3 or 99, END OF MODULE R1108 Did you or any other household member use remittances specifically to deal with a shock or stress in the last 12 months? 1. Yes 2. No 99. Don’t know MODULE R12. FOOD INSECURITY COPING STRATEGIES R1201 Over the past 7 days, how many days did your household: Read list Number of days out of the past seven Use 0 – 7 to answer number of days. 1. Rely on less preferred and less expensive foods? 2. Borrow food, or rely on help from a friend or relative? 3. Purchase food on credit? 4. Gather wild food, hunt, or harvest immature crops? 5. Consume seed stock held for next season? 6. Send household members to eat elsewhere? 7. Limit portion size at mealtimes? 8. Restrict consumption by adults in order for small children to eat? 9. Feed working members of HH at the expense of non‐working members? 10. Reduce number of meals eaten in a day? 11. Skip entire days without eating? 21 MODULE R13. SOCIAL AND CAPACITY‐BUILDING SUPPORT INFORMAL SOURCES OF SOCIAL SUPPORT R1304 If your household had a problem and needed money or food urgently, who IN THIS VILLAGE could you turn to for help? Read list; select all that apply A. Relatives B. Non‐relatives C. No one D. Other (specify) Y. Don’t know R1305 If your household had a problem and needed money or food urgently, who OUTSIDE THIS VILLAGE could you turn to for help? Read list; select all that apply A. Relatives B. Non‐relatives C. No one D. Other (specify) Y. Don’t know R1306 Compared to one year ago has your ability to get this type of assistance (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 food or money urgently? Read list; select all that apply A. Relatives B. Non‐relatives C. No one D. Other (specify) Y. Don’t know R1308 Who OUTSIDE THIS VILLAGE would you help if they needed food or money urgently? Read list; select all that apply A. Relatives B. Non‐relatives C. No one D. Other (specify) Y. Don’t know 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 22 R1310 How do you (or other household member) know the government official? Is he or she a… Read list; select all that apply A. Family member or relative B. Friend /neighbor C. Acquaintance (members of a group, friend of a friend, etc.) D. Other (specify): Y. 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 A. Family member or relative B. Friend /neighbor C. Acquaintance (members of a group, friend of a friend, etc.) D. Other (specify): Y. 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 99. Don’t know R1315 Has your household given assistance to anyone WITHIN THIS VILLAGE in the past 12 months? 1 Yes 2 No Skip to 1318 99. Don’t know R1316 What types of assistance has your household given to anyone WITHIN THIS VILLAGE in the past 12 months? Read list; select all that apply A. Labor sharing (weeding, plowing, construction, etc.) B. Gifts (donation) of cash, animals, materials/supplies or food C. Loan of cash, labor, seeds, animals D. Other (specify): Y. Don’t know R1317 In the past 12 months, who IN THIS VILLAGE have you given assistance to? A. Relatives B. Non‐relatives 23 Read list; select all that apply C. No one D. Other (specify) Y. Don’t know R1318 Within the last 12 months, has your household received assistance from anyone WITHIN THIS VILLAGE? 1 Yes 2 No Skip to 1321 99. Don’t know R1319 Who WITHIN THIS VILLAGE provided you with assistance over the last 12 months? Read list; select all that apply A. Relatives B. Non‐relatives C. Other (specify) Y. Don’t know R1320 What types of assistance has your household received from anyone WITHIN THIS VILLAGE in the past 12 months? Read list; select all that apply A. Labor sharing (weeding, plowing, construction, etc.) B. Gifts (donation) of cash, animals, materials/supplies or food C. Loan of cash, labor, seeds, or animals D. Other (specify): Y. Don’t know R1321 Within the last 12 months, has your household given assistance to anyone OUTSIDE THIS VILLAGE? 1 Yes 2 No Skip to 1324 99. Don’t know R1322 Who OUTSIDE THIS VILLAGE did you give assistance to over the last 12 months? Read list; select all that apply A. Relatives B. Non‐relatives C. Other (specify): Y. Don’t know R1323 What types of assistance did you give to someone OUTSIDE THIS VILLAGE in the past 12 months? Read list; select all that apply A. Labor sharing (weeding, plowing, construction, etc.) B. Remittances C. Gifts (donation) of cash, animals, materials/supplies, or food D. Loan of cash, labor, seeds, or animals E. Other (specify): 24 Y. Don’t know R1324 Within the past 12 months, has your household received assistance from anyone OUTSIDE THIS VILLAGE? 1 Yes 2 No Skip to 1327 99. Don’t know R1325 Who OUTSIDE THIS VILLAGE provided you with assistance over the past 12 months? Read list; select all that apply A. Relatives B. Non‐relatives C. Other (specify): Y. Don’t know R1326 What types of assistance did you receive from someone OUTSIDE THIS VILLAGE in the past 12 months? Read list; select all that apply A. Labor sharing (weeding, plowing, construction, etc.) B. Remittances C. Gifts (donation) of cash, animals, materials/ supplies, or food D. Loan of cash, labor, seeds, or animals E. Other (specify): Y. 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 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 99. Don’t know R1331 Have you or anyone in your household ever received any early warning training? 1. Yes 2. No 99. Don’t know R1333 Have you ever or anyone in your household received any natural resource management training? 1. Yes 2. No 99. Don’t know 25 R1335 Have you or anyone in your household ever received seed packets/starter packets from the government or NGOs? 1. Yes 2. No 99. Don’t know R1336 Have you or anyone in your household ever received adult education? 1. Yes 2. No 99. Don’t know R1338 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 99. Don’t know R1340 Can you or any other adult in your household read or write? 1. Yes 2. No 99. Don’t know 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 R1403a Are you hopeful about your children’s future? 1. Yes 2. No R1403b 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 26 5. Post‐secondary (college, university) R1404 Do you agree that one should always follow the advice of the elders? 1. Yes 2. No R1405 Do you communicate regularly with at least one person outside the village? 1. yes 2. No R1406 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 R1407 How many times in the past month have you gotten together with friends, family, neighbors, etc. to discuss issues, or have food or drinks, either in their home or in a public place? R1408 How many days in the past month have you attended a church/ mosque or other religious service? R1409 In the last year, how many times have you stayed more than 2 days outside this kebele? 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 Strongl y agree R1411 My experience in my life has been that what is going to happen will happen. 1 2 3 4 5 6 R1412 My life is chiefly controlled by other powerful people. 1 2 3 4 5 6 R1413 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 R1414 I can mostly determine what will happen in my life. 1 2 3 4 5 6 R1415 When I get what I want, It is usually because I worked hard for it. 1 2 3 4 5 6 27 R1416 My life is determined by my own actions. 1 2 3 4 5 6 R1417 Most people are basically honest. 1 2 3 4 5 6 R1418 Most people can be trusted. 1 2 3 4 5 6 R1419 I trust my neighbors to look after my house if I am away. 1 2 3 4 5 6 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 A. Emergency food assistance B. Emergency cash assistance C. Conditional cash transfers (e.g., CFW) D. Conditional food transfers (e.g., FFW) E. Unconditional cash transfers (non‐emergency) F. Unconditional food transfers (non‐emergency) G. Household materials and non‐food items H. Educational assistance I. Agricultural inputs J. Livestock inputs K. WASH L. Disaster planning/response M. Safety net (PSNP) N. Child malnutrition/infant feeding O. Other Y Don’t know R1503 Is there an emergency plan for livestock offtake if a drought hits your village? 1. Yes 2. No 99 Don’t know R1504 Do you have a conflict resolution committee in your village? 1. Yes 2. No 28 99. Don’t know R1506 Who provides the nearest security/police force for your village? 1. Kebele government 2. Woreda government 3. National government 4. Local militia 5. Community members 6. No one 7. Other (specify): 99. Don’t know R1507 How long does it take for the nearest security/police force to reach this village? Only ask this question if R1506 = 1,2,3 or 4 1. Over one hour 2. About one hour 3. Half an hour 4. Minutes 99. Don’t know MODULE R16: GENDER NORMS R1601 Generally, do adult men and women sit and eat together within households? 1. Yes, regularly 2. Yes, occasionally 3. No 99. Don’t know R1602 Generally, do you and your spouse sit and eat together? 1. Yes, and it is culturally acceptable 2. Yes, but it is not culturally acceptable 3. No, but it is culturally acceptable 4. No, and it is not culturally acceptable 5. Only for special occasions 6. No spouse/spouse absent 99. Don’t know R1603 Generally, do adult men and women sit together in public? 1. Yes, regularly 2. Yes, occasionally 3. No 29 99. Don’t know R1604 Generally, do you and your spouse sit together in public? 1. Yes, and it is culturally acceptable 2. Yes, but it is not culturally acceptable 3. No, but it is culturally acceptable 4. No, and it is not culturally acceptable 5. Only for special occasions 6. No spouse/spouse absent 99. Don’t know R1605 Generally, do men in the village help with childcare around the household? 1. Yes, regularly 2. Yes, but rarely 3. Yes, occasionally 4. No 99. Don’t know R1606 Who primarily cares for your children? 1. Yourself 2. Your spouse/partner 3. You help your spouse/partner 4. Your spouse/partner helps you 5. Not applicable 6. Other (specify) 99. Don’t know 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: ANNEX 4 Population-Based Survey Data Treatment and Analysis Plan DRAFT – September 14, 2017 This publication was produced for review by the U.S. Agency for International Development. It was prepared by the EVELYN PBS team. Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan Evaluation and Learning (EVELYN) Mechanism Office of Food for Peace (FFP) Contract #: AID-OAA-I-15-00-24 Order #: AID-OAA-TO-17-00005 DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan ACRONYMS ANC Antenatal care BL Baseline CAPI Computer-assisted personal interviewing CHN Child health and nutrition CRS Catholic Relief Services DFSA Development food security activity DTAP Data treatment and analysis plan EVELYN Evaluation and Learning Mechanism EL End-line FANTA Food and Nutrition Technical Assistance Project III FFP Office of Food for Peace FH Food for the Hungry FIES Food insecurity experience scale GHT Gendered household type HDDS Household dietary diversity score ICF ICF International IFSS Internet file streaming system IP Implementing partner MAD Minimum acceptable diet MCHN Maternal and child health and nutrition MHN Maternal health and nutrition MDD-W Minimum dietary diversity for women ME&A Mendez, England and Associates ORT Oral rehydration therapy PBS Population-based survey REST Ethiopia Relief Society of Tigray USAID U.S. Agency for International Development WASH Water, sanitation and hygiene WV World Vision DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan TABLE OF CONTENTS 1. BACKGROUND...................................................................................................................................................... 1 2. STUDY DESIGN AND SAMPLE .......................................................................................................................... 1 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 5.2.1 Anthropometry Indicators ........................................................................................................................................10 5.2.2 Agricultural Indicators ...............................................................................................................................................11 5.2.3 Poverty Indicators.......................................................................................................................................................12 5.2.4 Resilience Indicators ..................................................................................................................................................13 5.3. HANDLING OF MISSING DATA AND “DON’T KNOW” RESPONSES.........................................................13 6. DATA ANALYSIS PLAN......................................................................................................................................13 6.1. ANALYSES FOR 2017 BL PBS.........................................................................................................................13 6.1.1 Household Characteristics.......................................................................................................................................14 6.1.2 Calculation and Tabulation of Indicators.............................................................................................................14 6.1.3 Bivariate Analyses.......................................................................................................................................................14 6.2 ANALYSES FOR 2017 EL PBS .........................................................................................................................18 6.2.1 Comparison of 2012 BL and 2017 EL Household Characteristics.............................................................19 6.2.2 Calculation and Tabulation of Indicators.............................................................................................................19 6.2.3 Comparison of 2012 BL and 2017 EL Indicators............................................................................................19 6.2.4 Additional Analyses....................................................................................................................................................19 APPENDIX A: Sampling Weights APPENDIX B: Methodology to Calculate Poverty Indicators APPENDIX C: Resilience Indicators and Analyses DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 1 1. BACKGROUND In Fiscal Year (FY) 2016, the U.S. Agency for International Development (USAID) Office of Food for Peace (FFP) awarded funding for four multi-year development food security activities (DFSAs) in Ethiopia. These DFSAs will be implemented by prime contractors Catholic Relief Services (CRS), Food for the Hungry (FH), Relief Society of Tigray (REST), and World Vision (WV) and their partners. Under the Evaluation and Learning Mechanism umbrella contract (EVELYN), FFP contracted with the Mendez England & Associates (ME&A) and its subcontractors, ICF International (ICF) and TANGO International (TANGO), to conduct population-based surveys (PBSs) and a resilience assessment for the DFSAs in Ethiopia. In addition to a baseline (BL) PBS, the EVELYN team will conduct an end-line (EL) PBS in the target areas for four development food assistance projects (DFAPs) awarded in FY 2011 that expired in December 2016. The four prior DFAPs were implemented by prime contractors CRS, FH, REST, and Save the Children United States (SCUS) and their partners. The project areas for the DFSAs and prior DFAPs overlap to a large extent. For this reason, EVELYN will administer a joint BL/EL PBS using a common questionnaire in the overlap and non-overlap areas encompassed by the DFSAs and prior DFAPs. The common questionnaire will be driven by the indicators required for the BL PBS for the DFSAs, many of which (but not all) overlap with those required for the prior DFAPs. The data for the joint BL/EL PBS will be collected in July/August of 2017. The BL data collection for the prior DFAPs was conducted in February and June/July of 2012. The purpose of the BL PBS for the DFSAs is to assess the current status of key indicators, to gain a better understanding of the prevailing conditions and perceptions of the populations in the DFSA implementation areas, and to serve as a point of comparison for future EL PBSs. 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 will be used for the final evaluation of the prior DFAPs to evaluate change over time for some of the indicators that were measured in the prior BL study. This document provides a detailed description of the data treatment and analysis plan (DTAP) for the joint BL/EL PBS in Ethiopia. This report is divided into six sections. The next section provides a brief description of the joint BL/EL PBS design and sample, the third section describes the questionnaire, the fourth section describes quality control and data processing procedures, the fifth section focuses on data preparation measures, and the final section describes the data analysis plan. 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 “Ethiopia Joint Baseline/End-line PBS Protocol”, July 2017. A detailed description of the sampling design for the BL PBS for the prior DFSAs can be found in the “Development Food Aid Program in Ethiopia Baseline Survey” Report, October 2012. 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 2012 BL survey and the 2017 EL component of the joint BL/EL PBS) allows for the determination of statistically significant change in DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 2 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 since the DFSAs will be implemented in some of the same 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) identifying the sample size needed for the EL survey for the prior DFAPs, and 3) deriving a joint sample size based on these sample sizes and the overlap between the DFSA and prior DFAP project areas. The sample size calculation for the joint BL/EL PBS is 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 DFSA. Table 2.1 shows the areas covered and derived sample size by implementing partner for the prior DFAPs and joint BL/EL PBS. Table 2.1. Program Area and Sampled Households by Implementing Partner Implementing Partner Program Area Number of sampled households for 2012 BL Study Number of households needed for 2017 EL study Number of households needed for 2017 BL study Number of sampled households for 2017 joint BL/EL study CRS Oromia Region and Dire Dawa Administrative Unit 1,522 1,540 1,740 2,670 FH Amhara Region 1,530 1,540 1,740 1,740 REST Tigray Region 1,542 1,540 1,740 2,190 SCUS* Somali and Oromia Regions 1,513 -- -- -- WV** Oromia and Amhara Regions -- -- 1,740 1,860 TOTAL 6,097 4,620 6,960 8,460 *The CSUS Project areas were not included as part of the joint BL/EL PBS. **Although WV was not an implementing partner for the prior DFAPs, some areas covered by the prior FH DFAP were included in the WV DFSA target area, thus resulting in more households. 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 April 2017. All questionnaire modules follow FFP and Feed the Future guidelines, as described in the FFP Indicators Handbook (April 2015)1 and the Feed the Future Indicator Handbook (September 2016).2 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 2017. 2 Available at https://feedthefuture.gov/sites/default/files/resource/files/Feed_the_Future_Indicator_Handbook_Sept2016.pdf DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 3 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 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 R: Resilience • ANTHROPOMETRY Questions for Modules A through G, J and K were adapted using questions from the FFP Standard Indicators Handbook and the Demographic and Health Survey (DHS) questionnaire.3 Questions for Module H were adapted from the World Bank’s Living Standards Measurement Study (LSMS). Questions for Module R were developed by TANGO. The questionnaires are prepared in English first and then translated into three local languages (Amharic, Tigrigna and Oromia) and pre-tested in the field. The total time for completing the survey is expected to be approximately 2-3 hours. The 2012 BL PBS questionnaire included modules to collect data for: household food security; women’s dietary diversity; water, sanitation and hygiene; household economy; gender and social perspectives; persons living with disabilities; social services; nutritional status for children; infant and young children’s feeding practices; children’s diarrhea; and access to antenatal care. Some (but not all) of these modules overlap with the joint BL/EL PBS questionnaire. A list of the overlapping indicators where change over time can be measured for the EL PBS are provided in Table 5.2 of section 5.2. 4. DATA COLLECTION AND QUALITY CONTROL 4.1. Data Collection Mode and Data Transmission Procedure The 2017 joint BL/EL PBS data will be collected with tablets using Computer-Assisted Personal Interviewing (CAPI) mode by local data collection subcontractor, Kimetrica. 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 while interviewing in the field. For transmission of data from the field, Kimetrica 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 EVELYN CSPro programmer will work in- 3 MEASURE DHS. DHS model questionnaire: Phase 6 (2008-2013) (English, French). Available at http://www.measuredhs.com/publications/publication-dhsq6-dhs-questionnaires-and-manuals.cfm DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 4 country to set up and test the cloud-based data transmission system, as well as to provide technical support during the first week of data collection, to ensure that tablets and the IFSS transmission system are operating smoothly. The subcontractor will upload data to the IFSS on a regular schedule. 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 OS 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 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 DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 5 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 central office. Data transfers from the field to the central data office in Addis Ababa 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, 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, and K), there will be one line of data for 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. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 6 The complete suite of quality control checks used during the data processing cycle include the following: 1) Data Capture 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 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 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 generated if the two are not the same. Likewise, if the total number of households found is correct, but if there are some partially saved households, an error message will be generated. The cluster cannot be closed until these problems have been resolved. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 7 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 these problems have been resolved. 3) Consistency Checks a) More complex issues are handled at this stage, rather than during fieldwork. 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. 4) Miscellaneous Data Quality Measures a) Field-check tables will be run on a weekly basis 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. ICF will conduct a quality control review of the raw and edited data as the data is received from the central office in Addis Ababa. Data transfers will take place weekly from the central office to ICF 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. DRAFT Ethiopia Joint Baseline/End-line 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 BL PBS data analyses and the 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 indicator 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: • Households (used for indicators derived from Modules C, F, H, and R) • Children under five years of age (Module D) • 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 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, and K are based on the number of completed interviews for each individual and the number of eligible individuals from the household roster.4 A more detailed description of the calculation for sampling weights is provided in Appendix A. 5.2. FFP Indicator Definitions The FFP required 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. Out of the full set of indicators included in the joint BL/EL PBS, 15 indicators, highlighted in green, were included in the 2012 BL PBS. Table 5.2. Joint BL/EL PBS Indicators Indicator Disaggregation Level Target Population FOOD SECURITY 1. Average Household Dietary Diversity Score (HDDS) None Household 2. Prevalence of moderate or severe food insecurity* GHT** Household POVERTY 3. Per capita expenditures (as a proxy for income) of USG-assisted areas GHT Household 4. Prevalence of poverty: Percent of people living on less than $1.25 or $1.90 per day GHT Household 5. Depth of Poverty: Mean percent shortfall relative to the $1.25 or $1.90 poverty line GHT Household 4 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 these separate nonresponse adjustments are not needed. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 9 WATER, SANITATION AND HYGIENE (WASH) 6. Percentage of households using a basic drinking water source Distance from service Household 7. Percent of households in target areas practicing correct use of recommended household water treatment technologies Technology type Household 8. Percentage of households using a basic sanitation facility None Household 9. Percent of households in target areas practicing open defecation None Household 10. Percent of households with soap and water at a handwashing station commonly used by family members None Household AGRICULTURE 11. Percentage of farmers who used financial services (savings) in the past 12 months Sex Farmer 12. Percentage of farmers who practiced the value chain activities promoted by the project in the past 12 months Sex Farmer 13. Percentage of farmers who used at least [a project-defined minimum] sustainable agriculture (crop, livestock and NRM) practices and/or technologies in the past 12 months Sex Farmer 14. Percentage of farmers who used at least [project-defined minimum number] of sustainable crop practices and/or technologies in the past 12 months Sex Farmer 15. Percentage of farmers who used at least [project-defined minimum number] of sustainable livestock practices and/or technologies in the past 12 months Sex Farmer 16. Percentage of farmers who used at least [project-defined minimum number] of sustainable natural resource management practices and/or technologies in the past 12 months Sex Farmer 17. Percentage of farmers who used improved storage practices in the past 12 months Sex Farmer WOMEN’S HEALTH AND NUTRITION 18. Prevalence of underweight women None Women 15-49 years 19. Prevalence of women of reproductive age consuming a diet of minimum diversity None Women 15-49 years 20. Contraceptive Prevalence Rate None Women 15-49 years who are married or in a union 21. Percent of births receiving at least four antenatal care (ANC) visits during pregnancy None Women 15-49 with a live birth in the past 5 years CHILDREN’S HEALTH AND NUTRITION 22. Prevalence of underweight children under five years of age Gender Children 0–59 months 23. Prevalence of stunted children under five years of age Gender Children 0–59 months 24. Prevalence of wasted children under five years of age Gender Children 0-59 months 25. Percentage of children under age five who had diarrhea in the prior two weeks Gender Children 0–59 months 26. Percentage of children under five years old with diarrhea treated with Oral Rehydration Therapy (ORT) Gender Children 0–59 months with diarrhea in the last 2 weeks 27. Prevalence of exclusive breast-feeding of children under 6 months of age Gender Children 0-5 months 28. Prevalence of children 6-23 months receiving a minimum acceptable diet (MAD) Gender Children 6-23 months GENDER 29. Percentage of men and women who earned cash in the past 12 months Sex Adults 15+ years DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 10 Indicator Disaggregation Level Target Population 30. Percentage of men/women in union and earning cash who make decisions alone about the use of self-earned cash Sex Adult cash earners married or in a union 31. Percentage of men/women in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash Sex Adult cash earners married or in a union 32. Percentage of men and women with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices Sex Parents of children under 2 years 33. Percentage of men/women in union with children under two who make maternal health and nutrition (MHN) decisions alone Sex Parents of children under 2 years married or in a union 34. Percentage of men/women in union with children under two who make MHN decisions jointly with spouse/partner Sex Parents of children under 2 years married or in a union 35. Percentage of men/women in union with children under two who make child health and nutrition (CHN) decisions alone Sex Parents of children under 2 years married or in a union 36. Percentage of men/women in union with children under two who make CHN decisions jointly with spouse/partner Sex Parents of children under 2 years married or in a union RESILIENCE 37. Shock exposure index None Households 38. Cumulative impact of shock exposure index None Households 39. Absorptive capacity index None Households 40. Adaptive capacity index None Households 41. Transformative capacity index None Households * Food insecurity is measured using the Food Insecurity Experience Scale (FIES) based on a 12 month and 30 day recall ** Gendered household type 5.2.1 Anthropometry Indicators Children: Children’s nutritional status indicators will be computed following the method used by the DHS. To obtain anthropometric indicators on stunting, underweight, and wasting children, the World Health Organization (WHO) growth reference standards (WHO Multicentre Growth Reference Study Group 2006) will be used to compute three nutritional scores, described as z-scores. These z-scores are the HAZ (height-for-age), WAZ (weight-for-age), and WHZ (weight-for-height), relating to stunting, underweight, and wasting, respectively. Each z-score is calculated by comparing the child’s height/length or weight with the median value of the WHO 2006 reference population. The difference is divided by the standard deviation of the reference population as shown in the following formula: Z-score = (Individual value of the child – median value of children in the reference population) / (standard deviation of the reference population) Using the above formula, each z-score for HAZ, WAZ, and WHZ is calculated as follows: Z-score for HAZ = (Height-for-age of children in the sample – median value of height of children in the reference population having the same age) / (Standard deviation of height of children in the reference population having the same age) DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 11 Z-score for WAZ = (Weight-for-age of children in the sample – median value of weight of children in the reference population having the same age) / (standard deviation of weight of children in the reference population having the same age) Z-score for WHZ = (Weight-for-height of children in the sample – median value of weight of children in the reference population having the same height) / (standard deviation of weight of children in the reference population having the same height) After obtaining the z-scores, datasets will be cleaned by flagging cases with z-scores beyond specified lower or upper cutoffs and excluding them from the computation of all indicators. The purpose of flagging is to eliminate extreme values that are most probably due to measurement or data-entry errors. Flags used in cleaning anthropometric data prior to computing indicators are shown in Table 5.2.1a below. Cases with height-for-age z-scores that are less than -6 standard deviations (SD) from the median or greater than +6 SD above the median will be flagged and excluded from the calculation of the prevalence of stunting. Similarly, cases with weight-for-age z-scores that are less than -6 SD from the median or greater than +5 SD above the median will be flagged and excluded from the calculation of the prevalence of underweight. Finally, cases with weight-for-height z-scores that are less than -5 SD from the median or greater than +5 SD above the mean will be flagged and excluded from the estimation of the prevalence of wasting. Table 5.2.1a: Flags used in cleaning anthropometric data prior to computing indicators Z-score Cut-off point Height-for-age z-scores (HAZ) <-6 SD or >+6 SD Weight-for-age z-scores (WAZ) <-6 SD or >+5 SD Weight-for-height z-scores (WHZ) <-5 SD or >+5 SD Women: For women, body mass index (BMI) will be used to measure nutritional status. The BMI is the ratio of the weight in kilograms to the square of the height in meters (kg/m2). Criteria for measuring women’s nutritional status by BMI levels are shown in Table 5.2.1b. Table 5.2.1b: Indicators of women’s nutritional status by BMI levels Women’s Nutritional Status BMI Moderately & severely underweight < 17 Mildly underweight 17.0 - 18.49 Normal weight 18.5 - 24.9 Overweight 25.0 - 29.9 Obese >=30 5.2.2 Agricultural Indicators Country-specific adaptations of the FFP agricultural indicators were developed after discussions with FFP, FANTA, and the IPs during the BL planning workshop held in April, 2017. Indicators relating to the use of financial services, value chain activities, sustainable agricultural practices, and improved storage practices were defined based on those activities and practices used and promoted by the projects; and DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 12 the minimum thresholds for the sustainable agriculture practices indicators were set separately by each DFSA. The following tabulation instructions will be used to calculate the agricultural indicators: • Percentage of farmers who used financial services (savings, agricultural credit, and 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 activity to be promoted by the project divided by the sample weighted total number of farmers. • Percentage of farmers who used a project-defined minimum number of sustainable crop practices in the past 12 months is calculated based on the sample weighted number of farmers who reported using the minimum number of sustainable crop practices and/or technologies to be promoted by the project divided by the sample weighted total number of farmers who have access to and make decisions over a plot of land. • Percentage of farmers who used a project-defined minimum number of sustainable livestock practices in the past 12 months is calculated based on the sample weighted number of farmers who reported using the minimum number of sustainable livestock practices and/or technologies to be promoted by the project divided by the sample weighted total number of farmers who raise and make decisions about livestock and/or aquaculture. • Percentage of farmers who used a project-defined minimum number of natural resource management (NRM) practices in the past 12 months is calculated based on the sample weighted number of farmers who reported using the minimum number of sustainable NRM practices and/or technologies to be promoted by the project divided by the sample weighted total number of farmers. • 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. 5.2.3 Poverty Indicators Calculation of the three 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 B. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 13 5.2.4 Resilience Indicators The resilience questionnaire module and indicators were developed by TANGO. Resilience indicators will be calculated based on the definitions and methodology provided by TANGO, as described in Appendix C.5 5.3. Handling of Missing Data and “Don’t know” Responses Missing data points will be excluded from both the denominator and the numerator for the calculation of all indicators. “Don’t know” and “Refused” responses will be excluded from the numerators used in the calculation of the indicators. For example, for 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 2017 BL sample and the 2017 EL sample. Data analyses will be conducted separately for the BL and EL PBS. Analyses will include examination of key demographic characteristics of the study population, calculation of all FFP indicators, bivariate analyses and multivariate analyses as appropriate. Analyses of the EL PBS data will include comparisons between the 2012 BL and 2017 EL key demographic characteristics, calculation of all FFP indicators and comparison between 2012 BL and 2017 EL for overlapping indicators. TANGO will conduct the resilience analyses, as described in Appendix C. Indicators will be calculated separately for each project and for the combined project area, and all analyses will be weighted to reflect the full target population. Stata version 146 will be used for analysis and statistical testing. A detailed data analysis plan for the BL PBS and EL PBS is provided below. 6.1. Analyses for 2017 BL PBS Data analysis for the BL PBS includes examination of key demographic characteristics of the study population, calculation of all FFP indicators, and bivariate analysis of indicators that can help inform program targeting and program design where possible. The baseline indicator estimates are presented for the combined project areas and for each project area separately; and will be disaggregated by overlapping versus non-overlapping geographic areas with the prior DFAPs because the overlapping areas will be of most interest for the new DFSAs. Bivariate analyses including disaggregation by key sub-populations will be conducted for each project area. They will not be performed for the combined project areas since the combined estimates will mask differences by project area; and program targeting and the design of interventions are project￾specific. Additional multivariate analyses may be conducted if warranted by the preliminary indicator estimates and bivariate analyses, or to triangulate the results from the qualitative study. In some cases, it may not be possible to conduct the proposed analyses due to sample size limitations. 5 TANGO will calculate resilience indicators and conduct all further analyses of these indicators. 6 StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 14 6.1.1 Household Characteristics The baseline report will provide an overview of the size and sociodemographic characteristics of the population in the project areas and an explanation for why or how these characteristics may influence the baseline indicators and the achievement of program targets over time. This includes the percentage of individuals in the following key target population groups in the combined project areas and by project: • 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) 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 • Gendered household type (Percent of households) This analysis also includes the following household-level statistics for the combined project areas and by project: • Average household size (Number of persons) • Average number of adults (15+ years) per household • Percent of households with children under 5 years of age • Percent of households with a child 6-23 months of age • Percent of households with a child under 6 months of age • Household headship (Percent male) • Education level of head of household (Percent of households) 6.1.2 Calculation and Tabulation of Indicators All indicators will be generated using relevant sampling weights to represent the full target population and tabulated for the combined program areas and for each DFSA separately. All indicators will be disaggregated, as specified in Table 5.2. Point estimates and 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 program level and for each DFSA separately. 6.1.3 Bivariate Analyses Select bivariate analyses will be conducted to explore relationships between indicators and other important household/individual characteristics, and to explore associations between outcome and DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 15 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 (t-test, proportion test, chi square test). The rationale and some plausible explanations of the bivariate analyses are discussed below, followed by a description of the proposed analyses by content area. • Gendered household type: Based on other FFP countries’ data, certain gendered household types, particularly those with adult females only and adult males only, seem to have higher hunger scores. As such, it would be interesting to explore if the gendered household type also affects women and children’s dietary intake indicators such as minimum dietary diversity – woman (MDD-W) and minimum acceptable diet (MAD), and possibly some of the child nutrition indicators. • Education: Since head of the household plays a major role in household decision making, his/her education are expected to affect most of the indicators. Education status of primary caregiver plays an important role in children’s health and nutrition and overall household wellbeing. A significant positive relation could provide evidence for projects to invest more resources in interventions that could improve adult literacy, particularly that of female adults. • Gender bias in intra-household resource allocation: Gender bias in intra-household resource allocation is well-documented in Asia, and recent studies in Africa, including Ethiopia, also provided evidence of gender bias.7 Gender bias can affect both adult women and female children. Analysis of children’s health and nutrition by sex of the child and sex of the household head might shed important light on gender bias. 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. A quick relational analyses of food security indicators (HDDS and FIES) could provide useful insights for the projects to better understand the targeted households’ food dynamics in the project areas. • Sustainable agriculture practices: Use of improved and sustainable agriculture practices, including access to credit and improved grain storage practices, could help improve the households’ food security; in turn, improved access to food may contribute to consumption of more nutritious foods, thereby improving the nutritional status of households. The suggested analysis will provide evidence of these relationships so that the projects can better design their agriculture interventions. • Health behavior: Household WASH practices can affect household nutrition and wellbeing. Unsafe drinking water, poor or non-existent handwashing practices, and/or inadequate disposal of human feces can cause infections and diseases. Analyses in this section will reveal the evidence of such relationships, which could be very useful for the project implementers to prioritize WASH interventions. 7 Feridoon Koohi-Kamali, Intrahousehold Inequality and Child Gender Bias in Ethiopia, The World Bank DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 16 • Women’s empowerment: Women’s ability to earn cash 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 how cash is spent and decisions about child health and nutrition; and FFP indicators related to women and children’s nutrition and household security. 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 and the level of education of the household head. The results of these analyses are intended to help focus targeting on subgroups that may be more vulnerable to food insecurity and poverty. Knowledge of the characteristics of the head of households who are particularly vulnerable to poverty or food insecurity can inform program design. For example, the literacy and level of education may influence how information in meetings or trainings intended to build skills and provide information are rolled out. • Food security and poverty indictors will also be analyzed in relation to the type of development assistance programming available in the village at the time of the survey and prior receipt of education and training support. The results of this analysis are expected to illustrate the type of program assistance or interventions associated with lower vulnerability to food insecurity and poverty. • Bivariate analyses will be conducted for the FFP food security and poverty indicators, such as (1) average daily per capita consumption by household hunger status; (2) average household dietary diversity score by household poverty status; and (3) prevalence of poverty and prevalence of hunger. This analysis is intended to empirically test hypothesized relationships in the theory of change on the interrelationship between food security and poverty and to establish a baseline profile of the economic status of food-insecure households. • 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. These two variables will be disaggregated by gendered household type, prior receipt of business development training, and prior receipt of vocational or skills training. Bivariate analyses of these two variables will be conducted 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, (2) identifying the types of program assistance that can help households diversify their livelihood activities, and (3) identifying livelihood activities that potentially contribute to higher income and reduce vulnerability to food security 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 DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 17 crops versus cash crops. We are unable to discuss crop and livestock productivity using the quantitative data because the survey does not collect this information. However, univariate analyses will be provided for the following variables to provide a baseline understanding of some of the factors that may be related to agricultural productivity: land ownership and size of land, availability and accessibility of agricultural extension services, and availability and accessibility of veterinary services. • The baseline estimates for use of financial services, sustainable agriculture practices, 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. Bivariate analyses will also explore the relationship between use of financial services and use of agro￾inputs for crops or livestock to empirically test a hypothesized relationship in the theory of change on the relationship between access to financial services and use of inputs that enhance productivity. Water, Sanitation and Hygiene (WASH) • The WASH indicators will be disaggregated by gendered household type and level of education of the household head, and the availability of WASH government or NGO programs in the respondents’ villages. The results of these analyses are intended to inform program targeting, for example, by highlighting certain subgroups or villages whose baseline 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. The results of these analyses are intended to highlight whether program interventions should consider focusing on the use of a particular WASH infrastructure. Women’s Health and Nutrition • Bivariate analyses will be conducted for the prevalence of underweight women and MDD-W with the household poverty status and type of development assistance programming available in the village. Results can illustrate the types of program assistance 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. 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 miles 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 sustainable or improved agriculture practices and value chain activities, 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. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 18 These analyses will be conducted if warranted by the results of the bivariate analyses or the qualitative study. Children’s Health and Nutrition • Children’s health and nutrition indictors will be disaggregated by sex. 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 improved water source, (2) use of improved sanitation facility, (3) correct water treatment; and (4) handwashing facility with soap and water. Use of ORT will be analyzed in relation to the availability of health services within five miles of respondents’ villages and the ability to access the health service facility. 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 sustainable or improved agriculture practices and 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 gender differences in the indicators. In addition, the following will be disaggregated by sex: (1) ownership of agricultural land, (2) size of plot of land, and (3) participation in cash-earning opportunities. • Bivariate analyses will be conducted for the following: (1) cash decision making and household hunger, (2) cash decision making 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 on which to focus, 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 2017 EL PBS Data analysis for the EL PBS includes an examination of key demographic characteristics of the study population at BL and EL, calculation of all FFP indicators at EL, and comparisons of BL and EL indicators estimates where possible. In addition, where relevant, bivariate and multivariate analyses will be performed to explore the plausible determinants for key outcome indicators. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 19 6.2.1 Comparison of 2012 BL and 2017 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. 6.2.2 Calculation and Tabulation of Indicators All indicators will be generated using relevant sampling weights to represent the full target population, and will be tabulated for the combined program areas and for the three prior DFAPs separately. All indicators will be disaggregated, as specified in Table 5.2. Point estimates and 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 program level and for each prior DFAP separately. As mentioned in Section 2, data for 15 of the 41 indicators collected in 2017 were also collected in 2012. Although the other 26 indicators were not measured at baseline, all indicators will be calculated for the EL sample. Even though change over time cannot be measured for the non-overlapping indicators, we can learn something about these indicators at EL in the prior program areas, which has implications for the new DFSAs, particularly in the overlapping geographic areas. 6.2.3 Comparison of 2012 BL and 2017 EL Indicators For each prior DFAP, the 15 overlapping indicators will be statistically compared between BL and EL to determine if significant changes occurred over time. Although the results from this comparison 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 Bivariate analyses as described in section 6.1.4 will be conducted to explore relationships between indicators. Additional multivariate analyses may be conducted if warranted by the preliminary indicator estimates and bivariate analyses, 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 (including the Tufts performance evaluation completed in 2017) and, as available, primary information derived from qualitative data collected for the 2017 baseline study in villages that participated in the previous DFAPs. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 20 Ethiopia PBS Data Treatment and Analysis Plan APPENDIX A – SAMPLING WEIGHTS Household and individual weights will be computed separately for the 2017 baseline and end￾line PBS samples. The calculations for these weights are described below. HOUSEHOLD WEIGHTS Household weights will be applied for household level indicators derived from Modules C, F, H 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). 𝑃𝑃1ℎ𝑖𝑖= first-stage sampling probability of the i-th cluster in stratum h 𝑃𝑃2ℎ𝑖𝑖= second-stage sampling probability within the i-th cluster (household selection). The probability of selecting cluster i in the sample is: 𝑃𝑃1ℎ𝑖𝑖= 𝑚𝑚ℎ × 𝑁𝑁ℎ𝑖𝑖 𝑁𝑁ℎ × 𝑏𝑏ℎ𝑖𝑖 The second-stage probability of selecting households in cluster i is: 𝑃𝑃2ℎ𝑖𝑖 = 𝑛𝑛ℎ𝑖𝑖 𝐿𝐿ℎ𝑖𝑖 � Where: 𝑚𝑚ℎ= number of sample clusters selected in stratum h. 𝑁𝑁ℎ𝑖𝑖= total households in the frame for the i-th sample cluster in stratum h. 𝑁𝑁ℎ= total households in the frame in stratum h. 𝑏𝑏ℎ𝑖𝑖= the number of selected segments8 divided by the total number of segments in the i￾th sample cluster in stratum h 𝑛𝑛ℎ𝑖𝑖 = number of sample households selected for the i-th sample cluster in stratum h. 𝐿𝐿ℎ𝑖𝑖= number of households listed in the household listing for the i-th sample cluster in stratum h. 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: 𝑃𝑃ℎ𝑖𝑖 = 𝑃𝑃1ℎ𝑖𝑖 x 𝑃𝑃2ℎ𝑖𝑖 = 𝑚𝑚ℎ × 𝑁𝑁ℎ 𝑁𝑁ℎ × 𝑏𝑏ℎ𝑖𝑖 × 𝑛𝑛ℎ𝑖𝑖 𝐿𝐿ℎ𝑖𝑖 � The household design weight for each household in cluster i of stratum h is the inverse of its overall selection probability: 𝑊𝑊ℎ𝑖𝑖 = 1 𝑃𝑃ℎ𝑖𝑖 = 𝑁𝑁ℎ×𝐿𝐿ℎ𝑖𝑖 𝑚𝑚ℎ×𝑁𝑁ℎ𝑖𝑖×𝑛𝑛ℎ𝑖𝑖×𝑏𝑏ℎ𝑖𝑖 8 In Ethiopia, Kebeles are subdivided into Gotts (sub-Kebeles), having a clear physical demarcation and having a certain number of households. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 21 The household sampling weight is calculated using the household design weight corrected for non-response in each of the selected clusters. Response rates are calculated at the cluster level as ratios of the number of interviewed households divided by the number of eligible households. The household sampling weight is calculated by dividing the household design weight by the household response rate. 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 individuals will be selected for each Module, these weights will include a non-response adjustment only. The nonresponse adjustment will be applied using the inverted proportion of the total number of completed interviews for each group divided by the total number of eligible individuals for each group. Ethiopia PBS Data Treatment and Analysis Plan APPENDIX B - METHODOLOGY TO DERIVE POVERTY INDICATORS DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 22 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 DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 23 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: 𝑆𝑆𝑡𝑡𝑃𝑃𝑡𝑡 (𝑟𝑟𝑡𝑡−𝜋𝜋𝑡𝑡 + 𝛿𝛿) Where 𝑆𝑆𝑡𝑡𝑃𝑃𝑡𝑡 is the current price of the asset, 𝑟𝑟𝑡𝑡−𝜋𝜋𝑡𝑡 is the real rate of interest, and 𝛿𝛿 is the depreciation rate for the durable good. Each of these components will be computed separately. 1. Current value of the asset (𝑆𝑆𝑡𝑡𝑃𝑃𝑡𝑡 ): This will be obtained from household reports of the value of the asset in its current shape (second-hand). 2. Real rate of interest (𝑟𝑟𝑡𝑡−𝜋𝜋𝑡𝑡): In theory, 𝑟𝑟𝑡𝑡 is the general nominal rate at time t, and 𝜋𝜋𝑡𝑡 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 (𝛿𝛿): The rate of depreciation for each of the items is given by the formula: 1 − � 𝑃𝑃𝑡𝑡 𝑃𝑃𝑡𝑡−𝑇𝑇 � 1 𝑇𝑇� Where 𝑃𝑃𝑡𝑡 is the current value of the item at current time t, 𝑃𝑃𝑡𝑡−𝑇𝑇 is the value of the item when purchased, and 𝑇𝑇 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 Ethiopia are available for the period 1985 - 2008. Estimates are based on the average real interest rate during 1988-2008, which is 2.11%. Source: http://data.worldbank.org/indicator/FR.INR.RINR?locations=NE DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 24 the influence of outliers, the median 𝛿𝛿 will be used for each of the durable assets for which data are collected (i.e. rather than using household-specific values of 𝛿𝛿 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 baseline 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: Woreda The final model will be estimated based on the following regression equation, log(𝑅𝑅𝑖𝑖) = 𝛽𝛽0 + 𝛽𝛽𝑋𝑋𝑖𝑖 + 𝜀𝜀𝑖𝑖 where 𝑅𝑅𝑖𝑖 represents the reported non-zero rent paid by household i, 𝛽𝛽0 is the constant term, 𝑋𝑋𝑖𝑖 is the final vector of independent variables and 𝜀𝜀𝑖𝑖 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 $1.90 using 2011 purchasing power parity (PPP) rates. To facilitate the transition between the 2011 PPP rates and the prior framework based on 2005 PPP rates, this indicator will be computed as the average daily per capita consumption expenditures in constant 2010 US dollars, using both the 2005 and the 2011 Purchasing Power Parity (PPP) exchange rates adjusted to 2010 US prices. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 25 o 2005 PPP rates: The steps to convert daily per capita consumption expenditures data collected in local currency units (LCU)12 to constant 2010 US$ (2005 PPP adjusted to 2010 US prices) are: 1) Convert LCU (Ethiopian Birr) at the time of the survey (July-August, 2017) to LCU at 2005 prices, by dividing by the ratio of the CPI for the survey month (242.59)13 to the average annual CPI in 2005 for Ethiopia (44.84).14 2) Convert 2005 LCU to 2005 US$ by dividing by the 2005 PPP conversion rate of 2.75.15 3) Convert US$ in 2005 prices to US$ in 2010 prices by multiplying by 1.1165 which is the ratio of the US CPI in 2010 (218.06) to the US CPI in 2005 (195.30).16 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 (July-August, 2017) to LCU at 2011 prices, by dividing by the ratio of the CPI for the survey month (242.59) to the average annual CPI in 2011 for Ethiopia (133.22).17 2) Convert 2011 LCU to 2011 US$ by dividing by the 2011 PPP conversion rate of 5.44.18 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).19 Note that average daily per capita consumption expenditures 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. To facilitate the transition between the 2011 PPP rates and the prior framework based on 2005 PPP rates, the poverty line will be defined as a daily per capita consumption of less than US$1.25 at 2005 prices, or less than US$1.90 at 2011 prices. Consumption data in the joint baseline and end-line PBS will be collected in Ethiopian Birr. In order to compare the Ethiopia consumption expenditure data in Ethiopian Birr to the international poverty lines, the poverty lines first need to be converted into the LCU. However, if we use current market exchange 12 The local currency unit (LCU) in Ethiopia is the Ethiopian Birr (ETB). 13CPI for the months of July and August 2017 for Ethiopia were not available therefore the CPI for the nearest available month (May 2017 ) was used. The CPI for May 2017 for Ethiopia is 242.59. Source: http://data.imf.org/?sk=6ac22ea7-e792-4687-b7f8-c2df114d9fdc&sId=1390030341854. 14 Source: http://data.imf.org/?sk=6ac22ea7-e792-4687-b7f8-c2df114d9fdc&sId=1390030341854. 15 PPP conversion factor, private consumption (LCU per international$), 2011 International Comparison Program. Source: http://data.worldbank.org/indicator/PA NUS..PRVT.PP 16 Source: http://www.bls.gov/cpi/cpid10av.pdf 17 CPI for the months of July and August 2017 for Ethiopia were not available therefore the CPI for the nearest available month (May 2017 ) was used. The CPI for May 2017 for Ethiopia is 242.59. Source: http://data.imf.org/?sk=6ac22ea7-e792-4687-b7f8-c2df114d9fdc&sId=1390030341854 18 PPP conversion factor, private consumption (LCU per international$), 2011 International Comparison Program. Source: https://data.worldbank.org/indicator/PA.NUS.PRVT.PP 19 Source: https://www.bls.gov/cpi/cpi_dr.htm DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 26 rates we would underestimate consumption. One Ethiopian Birr can buy more products and services in Ethiopia than the equivalent amount in US$ (1 Ethiopian Bir = US $0.043) 20 can purchase in the US. The conversion of LCUs to US$ 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. Poverty lines will be calculated to estimate the proportion of the population living in extreme poverty, defined as: • Average daily per capita consumption expenditures of less than US$1.25 per day, converted into LCU (Ethiopian Birr) at 2005 Purchasing Power Parity (PPP) exchange rates. This is done following two steps: 1) The $1.25 poverty line will be converted into LCU by multiplying it by the 2005 PPP conversion factor for private consumption for Ethiopia (2.75). 2) The resulting figure ($1.25 * 2.75 = 3.44) will be adjusted for cumulative price inflation since 2005. The adjustment will be done using the consumer price index (CPI) for the survey month as the numerator, and the average annual CPI for 2005 for Ethiopia as the base factor.21 The US$1.25 poverty line is equal to 3.44 * (242.59 /44.84) = 18.61 in May 2017 Ethiopian Bir. • Average daily consumption expenditures of less than US$1.90 per day, converted into LCU (Ethiopian Birr) at 2011 Purchasing Power Parity (PPP) exchange rates. This is done following two steps: 1) The $1.90 line will be converted into LCU by multiplying it by the 2011 PPP conversion factor for private consumption for Ethiopia (5.44). 2) The resulting figure ($1.90 * 5.44 = 10.34) 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 Ethiopia as the base factor. The US$1.90 poverty line is equal to 10.34 * (242.59 /133.32) = 18.81 in May 2017 Ethiopian Bir. • Mean depth of poverty This indicator is useful to understand the average, over all people, of the 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). Mean 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 having a contribution to the PGI of 0. The PGI is given by the formula: PGI = � 1 𝑁𝑁 ∑ � 𝑧𝑧− 𝑦𝑦𝑖𝑖 𝑧𝑧 � 𝑁𝑁 𝑖𝑖=1 � × 100 20 http://www.exchange-rates.org/converter/ETB/USD/1 21 CPI for the months of July and August 2017 for Ethiopia were not available therefore the CPI for the nearest available month (May 2017 ) was used. The CPI for May 2017 for Ethiopia is 242.59. Source: http://data.imf.org/?sk=6ac22ea7-e792-4687-b7f8-c2df114d9fdc&sId=1390030341854. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 27 Where N is the total number of individuals in the population, z is the poverty line and yi is the daily per capita consumption of individual i. For individuals above the poverty line, set yi = z so that contribution to PGI is 0 for those individuals. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 28 Ethiopia PBS Data Treatment and Analysis Plan APPENDIX C – Resilience Indicators and Analyses 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: DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 29 • Number of shocks to which a HH is exposed in the past 12 months • Perceived severity of the shocks 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 DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 30 • Bridging social capital • Linking social capital • Collective action • 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 R108, R109 Shock exposure index Exposure: Number of shocks experienced in the past 12 months R103 Shock severity: Impact of shock on income security Impact of shock food consumption R104 R105 Absorptive capacity index Availability of informal safety nets R801, R802 Bonding social capital R1304, R1307 Access to cash savings R601 Access to remittances R1101, R1102, R1104, R1105 Asset ownership BL H7.02, H7.03, R201, R201A Shock preparedness and mitigation R901, R902, R110, R1501-R1503 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, R1336, R1338 Livelihood diversification R1001, R1002 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, R1402, R1403, R1403a, R1403b, R1405- R1409, R1411-R1416 Transformative capacity index Availability of formal safety nets R1501-R1503 DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 31 Resilience capacity variable Questions 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 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 Gender index R1601-R1606 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:22 Does not include:23 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 22 If the FFP/FTF questionnaire does NOT include modules/questions listed here, they need to be added in the resilience module or elsewhere. 23 Items listed here are preferred in the resilience module and need to be removed from the FFP/FTF questionnaire. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 32 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 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: R108, R109 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 DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 33 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 • 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, R105 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 ethnic groups/clan” and “no one”. An additive index ranging from 0 to 6 is calculated based on these responses. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 34 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 the household receives remittances from someone who migrated within the country, OR someone who migrated to another country, OR both. Survey questions: R1101, R1103, R1105, R1107 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 questions: R1501-R1502 (12) • There is an emergency plan for livestock off-take in the village if a drought hits (1); Survey question: R1503 • 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: R110 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. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 35 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, R1403a, R1403b, R1411, R1413 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, R1405, R1406, R1407-R1409 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. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 36 • My life is determined by my own actions. Survey questions: R1412, R1414-R1416 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 equal to 1 if any adults in the household can read or write (1) Survey question: R1340 • A binary (dummy) variable is equal to 1 if any household adult has a primary or higher education (1) Survey question: BL B21 DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 37 • 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, R1336, R1338 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, R1002 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. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 38 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. 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 4 of the number of formal safety nets available in a household’s village. The possible safety nets are: • Places in a village where people can get food assistance • Places in a village where people can get housing materials and other non-food items • Places in a village where people can get assistance due to losses in livestock • The availability of a government or NGO disaster response program Survey questions: R1501-R1503 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. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 39  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) • 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 DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 40 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) 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 variable24 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 24 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. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 41 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. 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: DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 42 • 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.25 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 • 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 25 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. DRAFT Ethiopia Joint Baseline/End-line PBS Data Treatment and Analysis Plan 43 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. 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) $11(; 3URWRFROIRU4XDOLWDWLYH6WXG\ 1 ANNEX 5: QUALITATIVE BASELINE STUDY DATA COLLECTION AND PROTOCOLS EVELYN used key informant interviews (KIIs) and group interviews (GIs) to collect qualitative baseline data based on a set of questions designed to help interpret and contextualize quantitative data from the PBS. The topical areas and interview questions were keyed to one or more modules in the PBS household (HH) survey questionnaire. The HH questionnaire for the PBS was used to interview individuals in a randomly-selected sample of approximately 8,460 HHs covering the regions where DFSAs are working with GOE officials to implement the fourth round of its PSNP. Data collection for the qualitative and quantitative baseline study took place concurrently. Data Collection Sites The qualitative study covered eight data collection sites. Each IP selected two data collection sites based on criteria developed in April and May 2017. A data collection site is defined by a single woreda, two kebeles, and a targeted village selected for interviews in each kebele (see Qualitative Study Methodology, Site Selection Criteria, pg. x.) Data Collection Instruments and Study Participants A total of seven data collection instruments covering group and key informant interviews per type of participant and per woreda, kebele and village were finalized following review by USAID/Ethiopia FFP activity managers and IPs, and reviews and approval from USAID/FFP. The final set of data collection instruments was transmitted to Green Professional Services in Addis Ababa approximately one month in advance for review. Some changes were made to clarify the intent of questions. Woreda Level: A group interview was conducted with government officials associated with PSNP4 administration and oversight. Groups comprised the head woreda official, the head of the Office of Agriculture, and the head of the Office of Water and/or Irrigation, and the heads of the Office of Natural Resource Management and the Office of Women and Children’s Affairs. Other officials (as available) included the chairs of the Woreda Food Security Task Force (WFSTF) and of the Disaster Response Committee, the Development Agent, the Agriculture Extension Agent and the Livestock Extension Agent. Questions corresponding to each topic were covered during these interviews: 1) food insecurity, poverty, livelihoods migration; 2) agriculture; 3) WASH/MCHN; and 4) food gap seasons. Kebele Level: A group interview was conducted with government officials associated with PSNP management and implementation that mirror the woreda level positions. Kebeles are the lowest level government administration unit, and officials are responsible for governance, security, and PSNP in the villages in their coverage area. This group also included the Chair of the PSNP Appeals Committee. A KII was conducted in the kebele health center with the HEW on issues related to MCHN and WASH. AEAs, LEAs, DAs, and Gender, NRM, and Water/Irrigation officers work very closely with PSNP household beneficiaries in areas of their expertise to increase the food security of these households and their villages. 2 Contextual Information from Woreda and Kebele Officials: PSNP beneficiaries in DFSA implementation areas are affected by the prevailing climatic, political, economic, and security environment. To understand the current environment during baseline data collection, data collection instruments for both woreda and kebele officials included questions on the current food security situation and current and recent historical events, such as flooding, drought, political events, security incidents and gender issues. These officials were also queried on PSNP implementation on topics related to the timing and delivery points for PSNP food and cash distributions, public work (Food for Work Program), emergency aid, PSNP beneficiary complaints, and PSNP graduates. Village Level: In each of the selected villages, a KII was conducted with the village chief. Village chiefs are directly in contact with kebele officials, particularly on PSNP issues. GIs were held with four separate village groups based on demographic and household characteristics: 1) male heads of household (MHHH); 2) females who are co-HHH (FCo-HHH); 3) women HHHs (WHHH) who are widowed, divorced, or abandoned; and 4) young mothers with infants and children five years of age and under (MIC5). Each group provides distinct perspectives and experiences related to food security, poverty, agriculture, and health and nutrition issues. A single data collection instrument was used with MHHHs and FCo-HHHs to contrast male and female points of view and experience, and to understand intra-household decision-making and the status of women. The group of WHHHs covered many of the same questions, but focused on understanding their strategies for filling basic household needs. WHHHs are a particularly vulnerable group, lacking able-bodied male labor to help prepare, plant, and harvest crops; and, without an adult partner, these women have fewer means to increase household resiliency to face and recover from shocks. They are among the poorest and most food insecure households. GIs were conducted with a group of MIC5 in each village to gain information on their knowledge, understanding and practices related to maternal and child health and nutrition, use of contraceptives, sanitation and hygiene, and gender issues in the context of family decision-making. GPS hired qualitative data collection specialists for each of the teams, a driver for each team, and a senior qualitative analyst responsible for designing the qualitative database, reviewing and coding approved English language transcripts, data entry, and the first stage of analysis. GPS also hired an experienced senior interpreter for the EVELYN Senior Evaluation Specialist who served the overall Qualitative Study Director and the team leader for one of the two teams. Each team comprised a team leader, a senior qualitative data collection specialist, and a junior/mid-level qualitative data specialist. The latter was responsible for taking notes and recording each KII and GI, and transmitting completed English language transcripts to GPS. EVELYN hired a senior qualitative data collector to lead the second team. Ethiopian team members were all fluent in English and Amharic. Members of Team 1 covering data collection sites in Tigray were also fluent in Tigrayina. Members of Team 2 covering sites in Oromia, were also fluent in Oromiana. The four DFSA IPs provided the QSD with a designated contact person working in the data collection site areas they selected for the qualitative study. In most cases, the IP M&E specialist was assigned. The QSD transmitted full contact information for each of the selected contact persons (total of 8). Per request, each contact person provided GPS advanced information on recommended accommodations in the woredas, distance between the woreda and each kebele and village, information on road quality, and recommended routes. The contact person also informed the woreda and kebele head officials and the village chief about the study, study purpose and dates a team would arrive. They arranged interview 3 dates and scheduled time frames for interviews in each location, and worked with local officials on the selection of study participants for group interviews with different groupings of village residents based on criteria provided by the QSD. Lastly, the contact person arranged to meet the team leader upon arrival in the study woreda, and introduced the team to woreda officials participating in the study. These same individuals also introduced the team to officials in each of the selected kebeles, and to the village chief in each village. In-briefing and Pre-Field Work Preparation EVELYN’s QSD arrived in Addis on June 29th. The QSD (also serving as leader for Team One), the Team Leader for Team Two, and the GPS Managing Director held an in-briefing on July 30 at USAID Ethiopia with FFP Activity Managers and the FFP M&E Specialist to review and discuss the protocols, data collection sites, types of study participants at each administrative level, and field logistics. A two-day team planning meeting and review workshop was held on July 1 and 2 with the QDS, Managing Director of GPS, all team members scheduled to go to the field, the interpreter, and the GPS senior qualitative data analyst responsible for developing the data base and initial stages of analysis. The purpose of the workshop was to review the data collection process and protocols, and to review and discuss questions contained in each data collection instrument. Based on these reviews, each instrument was revised for language suitable to Ethiopian participants and redundant questions were removed. On July 3 members of both data collection teams and the GPS Managing Director pilot-tested the data collection instruments for woredas, kebeles and villages. A pilot-testing debrief was held on July 4 with the QSD to discuss how long each KII and GI took during the pilot testing, the flow of questions, and how well individual questions were understood. Based on the debriefing, each data collection instrument was shortened by selecting the most critical questions associated with each PBS HH topical module, and questions were further clarified for respondent understanding. Field Work and Field Work Protocols The fieldwork portion of the qualitative baseline study was conducted from July 5, 2017 through August 4, 2017 under the overall direction of the QSD. Data collection was conducted in several locations within two woredas per DFSA. Team 1 traveled to two data collection sites selected by FH; and two data sites selected by REST. Team 2 traveled to two data collection sites selected by CRS; and two sites selected by WV. The two team leaders communicated once per week, or more frequently as necessary, to resolve problems and discuss issues. Each team held a full discussion on findings following the completion data collection at each site. Teams began by collecting qualitative data at the first woreda selected by the DFSA IP. They conducted a GI with woreda-level officials, and then traveled to the first kebele selected within that woreda to conduct a GI with selected kebele officials, and a KII with the kebele HEW. Following interviews in the first kebele, the teams then proceeded to conduct a KII with the village chief and GIs in the targeted village selected for that kebele. The team then repeated the same process of conducting interviews in the second kebele and, when completed, moved to the target village in that kebele. At the second site, teams followed the same procedures for interviewing officials at the woreda and kebele level, followed by a KII and GIs in the target villages. The team then traveled to the data collection sites selected by the second DFSA IP to repeat this process. 4 The complete set of interviews for each data collection site took approximately two and one half to three days depending on the distances and road conditions between each location within a given data collection site. One travel day (and occasionally 1 ½ travel days) was required to arrive at each team’s second data collection site. Teams lodged at a facility in the woreda selected by the IP for their “base camps.” Because of the distances required to travel within each data collection site between the woreda and each kebele selected, and between the woreda and each village selected, each team worked a seven￾day work week. In mid-July, each team took one day off before proceeding to their second set of data collection sites. In total, each of the teams collected qualitative data in four woredas, eight kebeles, and eight villages. Across the two teams, qualitative data were collected in a total of eight woredas, 16 kebeles, and 16 villages. ANNEX 6 Data Collection Sites Selected by Implementing Partners and Rationale ANNEX 6 Data Collection Sites Selected per Implementing Partner with Rationale Implementing Partner Data Collection Sites Site Selection Rationale FH-Ethiopia AMHARA: Woreda 1: Lay Gayint This woreda was included in the previous DFAP implementation area and continues to be a beneficiary location for the GOE PSNP. It contains a high number of beneficiaries. Kebele 1.1: Mekubia These two kebeles each contain a high number of beneficiaries and provide contrasts related to average distance from the woreda center and different agroecological zones. Mekubia is a highlands area located four km away from the woreda center. Roads are paved. Sofia-Meda is a lowland area located 67 km. away from the woreda. Roads are unpaved and in very poor condition. Kebele 1.2: Sofia-Meda AMHARA: Woreda 2: Abergelie In contrast to Woreda 1, Lay Gayint, Abergelie was not included in the previous FH DFAP. It also contains a high number of beneficiaries. Kebele 2.1: Niruak Nurak and Debi represent agroecological contrasts. Nurak is characterized by lowlands and is in a malarial area; roads are unpaved and in poor quality, and the location from the woreda is relatively far. Debi is characterized by midlands; roads are unpaved, but in good shape. It is more closely located to the woreda. Kebele 2.2: Debi Relief Society of Tigray TIGRAY: Woreda 1: Werie Leke Werie Leke is a DFSA overlap woreda. Kebele 1.1: May Chekemte These kebeles were selected to contrast highland and lowland agro-ecological zones. Both kebeles have a high caseload of PSNP Kebele 1.2: May Segli beneficiaries TIGRAY: Woreda 2: Hintalo Wajirit New woreda for the REST DFSA. Kebele 2.1: Adi Keyh See description of rationale for kebeles 1.1 and 1.2 in Werie Leke. Kebele 2.2: Metkel World Vision OROMIA: Woreda 1: Gemechis Woredas 1 and 2 were selected to contrast behaviors and practices related to nutrition and WASH, the availability of opportunities for improved livelihoods, and the potential for participating in “nutritious” value chains. Kebele 1.1: Sire Gudo These kebeles were selected because they have a high number of beneficiaries and are often affected by shock. They also provide contrasts in terms of scarcity of water and access to health facilities. Kebele 1.2: Sire Kelo Haro Tate AMHARA: Woreda 2: Lasta See description of rationale for Woreda 1. Kebele 2.1: Bilbala These kebeles were selected for the same reasons as kebeles 1.1 Kebele 2.2: Degosach and 1.2. Catholic Relief Services OROMIA: Woreda 1: Midega Tola The two woredas were chosen based on IP responsibility so each IP can receive data related to one of the woredas where they work, Meki Catholic Secretariat (west) and Harage Catholic Secretariat (east.) Kebele 1.1: Terkan Feta Both kebeles have a high caseload of PSNP beneficiaries and differing agro-ecological zones. Initially Gedo Geri Kebele was selected. However, due to security reasons, Gedo Geri was replaced in July with Berzala. Kebele 1.2: Berzala OROMIA: Woreda 2: Arsi Negele See rationale for Woreda 1 – Midega Tola. Kebele 2.1: Daka Wara Kelo See rationale for Kebeles 1.1 and 1.2 in Midega Tola. Kebele 2.2: Edo Gigessa ANNEX 7 Qualitative Data Collection Instruments ANNEX 6: DATA COLLECTION INSTRUMENTS GUIDE for GROUP INTERVIEW with WOREDA OFFICIALS GROUP COMPOSITION: Head of Woreda, Heads of Office of Agriculture and Office of Water/Irrigation, Chair of Woreda Food Security Task Force, Chair of Disaster Risk Management Committee, Head of Office of NRM, Food Security Representative, Head/Chair of Women and Children Affairs NOTE: In some woredas, the head of the woreda may also serve as the Chair of the Woreda Food Security Task Force INTERVIEW DATA Implementing Partner Facilitator Date of Interview Start Time: Team Leader Recorder End Time: Region Woreda PARTICIPANT INFORMATION List of Participants by Role Interview Recorded (Please check) Yes No Number of verbal (or signature) consents recorded on consent form Total Number of Participants: Number of Female Participants: Number of Male Participants: P6- Women affairs P7- livestock expert P8- Irrigation expert Annex 6 2 General Observations about the Interview Thank you for the opportunity to speak with you. My name is ______________________ and this is __________________. We are from Green Professional Services, and we are working with the US-based company MEA. We are conducting individual/group discussions with people like you in communities across several regions to strengthen and improve the PNSP. These questions in total will take approximately two hours and your participation is entirely voluntary. If you agree to participate, you can choose to stop at any time or to skip any questions you do not want to answer. Your answers will be completely confidential; we will not share information that identifies you with any one. If you choose not to participate it will not change any services or benefits that you receive now or in the future. You can stop participating at any time without penalty and without having to give any reason. You can also decline to answer any specific questions that you do not want to answer, also without penalty and without having to give any reason. We are going to record this discussion only for the purpose of reviewing our notes to make sure the notes correctly capture the contributions of the group. We will destroy the recording after the notes have been checked and finalized. Do you have any questions about what I have said? PRODUCTIVE SAFETY NET PROGRAMME (PSNP), ENVIRONMENTAL/CLIMATE EVENTS, FOOD SECURITY Facilitator introduces the topics for this set of questions and informs the group the interview will begin with the Productive Safety Net Program 1 PSNP 4regulations Do kebeles in this woreda receive cash payments, a mix of cash payments and foot allotments, or all food? What about specifically in kebele x and y? How is the cash transmitted to PSNP beneficiaries? Can you clarify the distribution chain of food allotments for PSNP 4 beneficiary households from the woreda to the kebele and then from the kebele to the villages? Are food allotments provided at extra times during events such as prolonged drought, massive crop failure due to diseases, flooding, etc.? If there is a shock and extra food allotments are needed, what is the source of that extra food (e.g., is it PSNP4 contingency, HRD, other?) 2 Env/Climatic events and food security Is this woreda, and specifically the kebele x and y (insert name of kebeles to be visited for data collection) experienced drought, outbreak of pests or disease, or flooding in the past 12 months? If the answer is no, ask the following questions: • Have you noticed significant rainfall variability in recent years? How has this affected the sources and availability of water people rely on for (household) HH use, crops (and/or livestock)? Annex 6 3 If the answer is yes, currently this area is experiencing drought, outbreak of pests or diseases, or flooding, OR, very recently such events occurred, ask the following questions: • How long has it been going on (or how long did it last)? How severe? Does it cover the entire woreda? Kebele X and Kebele Y? • What effect has this had (or still having) on farmers’ production of crops/or their livestock? (massive crop failure, livestock death, etc.?) 3 Food gaps/food security Do households in this area experience months in which there is a food gaps? What about villages in kebele x and y? • How often does this happen? • What are the typical reasons that HHs experience food gaps? (Probe for reasons, for example: run out of food before the next food distribution, crop ruined, death of livestock, insufficient crops for harvesting, death or migration of a male member of the household, etc.?) • Has there been any change in the length of food gaps villages experience? Or frequency? • How would you contrast the food security situation of PSNP4 beneficiaries between kebele x and kebele y? . • Are some HH becoming more food secure? What are the characteristics of HHs that are becoming more food secure? • Are some HHs that previously were doing well becoming less food secure? Annex 6 4 AGRICULTURE TOPICS Team Leader introduces the next set of questions on agriculture beginning with climate forecasts and early warning information 4 Climate forecasts and use of information Does this woreda receive forecasts about climatic conditions? If the response is no, skip to questions in the next section on early warning information. If the response is yes, ask the following: • Where do the climate forecasts come from? What organization does this? • What is the quality of the information? Is it understandable? • Is it accurate? Reliable? • How is this information transmitted to kebeles? • Does it come at the right time for farmers to use it? 5 Early warning information on outbreaks of pests, disease and use of information Do this woreda receive early warning information for major crops (or livestock) pests and disease outbreaks? If response is no, skip to next set of questions on market information. If response is yes, this woreda does receive early warning information, ask the following questions: • How often do you receive forecasts? • What is the quality of the information? Is it easy to understand? • How do you use this information? • How is this information transmitted to the kebeles? • Do farmers receive the information in time to respond? 6 Price information, use of price information Does the kebeles in this woreda receive price information for the crops they produce for sale? If the answer is no, skip to the questions on storage If the answer is yes, ask the following questions: How often does price information come out? Is it usually up-to-date? Accurate? How does this information get transmitted down to the kebeles? To your knowledge, how is market information on prices benefiting kebele residents in this woreda? 7 Availability and use of Do farmers/livestock owners in the kebeles in this woreda have access to community storage facilities for their harvested crops (or to store dairy products/eggs? What about in kebele x and y? Annex 6 5 storage If the answer is no, skip to set of questions on agriculture extension services. If yes, ask the following questions • What kind of storage facilities are available for farmers (and/or pastoralists, pastoralists) to use? • Do farmers/livestock owners use it? • To your knowledge, what impact has the use of storage had for village residents that use it? GOVERNANCE AND SECURITY TOPICS Team Leader announces that this is the last set of questions. The topic is on security issues in the region. 8 Security issues, incidents of violence Have there been, or are there presently, major security issues or incidents of violence in any of the kebeles in this woreda? In kebele x and kebele y? If the answer is no, end the interview with the closing question. If the answer is yes, there have been in the recent past, ask the following questions: • How long ago? Can you please describe what happened? • Where did it occur? • What was the impact of this incident in those areas? If currently there are major security issues or incidents of violence, ask the following questions: • Can you please describe what the incidents are? • Where is the trouble occurring? • What impact is this incident having on residents? Annex 6 6 Response to security issues, incidents of violence Effect on HH food security Have (or were) any actions been taken to resolve the issues causing these problems? If the answer is yes, ask the following: • What has been/what is being done? By whom? • Has the security situation (or incidence of violence) been effectively resolved? • Is it likely to occur again? • How is (or how did) this affecting the food security of HHs in those areas? (Probe as necessary with the following or other examples, e.g.: restricted travel outside villages, restricted access to water, grazing land, restriction on herding livestock, restricting marketing/sales of crops, livestock or livestock products, restricted or temporarily halted delivery of FFP food allocations) Closing Question Are there any other issues we haven’t covered that anyone would like to bring up? Team Leader Closes the Interview: Thanks all participants for attending the session. Express appreciation given the time taken away from their obligations. Annex 6 7 GUIDE for GROUP INTERVIEW with KEBELE OFFICIALS GROUP COMPOSITION: Head of Kebele, Head of Office of Agriculture, Head of Office of Natural Resource Management, Chair of Kebele Food Security Task Force, Chair of Appeals Committee, Development Agent serving this kebele, Livestock Extension Agent, Agriculture Extension Agent, Head of Office of Water/Irrigation, Head of Office of Women and Children Affairs Note: The Head of the Kebele might also be serving as the Char of the Kebele Food Security Task Force INTERVIEW DATA Implementing Partner Facilitator Date of Interview Start Time: Team Leader Recorder Mele Ayele End Time: Region Woreda Kebele PARTICIPANT INFORMATION List of Participants by Role P1- Agri/ natural resource Consent given to record interview yes/no Number of verbal consents registered on the consent form Total Number of Participants: Eight Number of Female Participants: one Number of Male Participants: Seven P2- crop expert P3- Agricultural Lead P4- Kebele official P5- Kebele Manager P6- Kebele Supervisor P7- Women affairs P8- Justice Introductions and purpose of the interview. Thank you for the opportunity to speak with you. My name is ______________________ and this is __________________. We are from Green Professional Services, and we are working with the US-based company MEA. We are conducting individual/group discussions with people like you in communities across several regions to strengthen and improve the PNSP. These questions in total will take approximately two hours and your participation is entirely voluntary. If you agree to participate, you can choose to stop at any time or to skip any questions you do not want to answer. Your answers will be completely confidential; we will not share information that identifies you with any one. If you choose not to participate it will not change any services or benefits that you receive now or in the future. You can stop participating at any time without penalty and without having to give any reason. You can also decline to answer any specific questions that you do not want to answer, also without penalty and Annex 6 8 without having to give any reason. We are going to record this discussion only for the purpose of reviewing our notes to make sure the notes correctly capture the contributions of the group. We will destroy the recording after the notes have been checked and finalized. Do you have any questions about what I have said? TOPICS ON PSNP: ENVIRONMENTAL/CLIMATE EVENTS, FOOD SECURITY Team Leader introduces topic starting with PSNP 1 Beneficiary Households, Cash/Food distribution What beneficiary HH categories are there in Village X? Approximately how many households are there in village X? What percentage of the households are beneficiary households? Do PSNP beneficiaries in this kebele receive cash transfers, a combination of cash and food, or just food allotments? What about in village x (insert name)? • How many times a year do beneficiary households receive a cash transfer? (or combination of cash and food?) • How does this differ by beneficiary category? How is the cash transfer made to PSNP beneficiary households? What is the method that is used? Can you clarify the distribution chain of food allotments for PSNP beneficiary households from woreda to the kebeles and from the kebele to the village? What community/public work beneficiaries in Village X (insert name) do in return for their cash/food allotment? What have they developed? • How has it benefitted the village residents? Are additional food allotments or cash provided during major disruptive events such as prolonged drought, massive crop failure due to diseases, flooding, etc.? • Is the amount of food provided the same as during usual distributions? • What is the source of additional food or cash during such events? PSNP contingency? Etc.? • Do all residents in the village receive food during such events, or do they have to be a PSNP beneficiary? Annex 6 9 What kind of complaints does the Appeals Committee receive? • What kind of households usually bring up complaints? • How are complaints brought to your attention? • Do you receive many complaints? • How many complaints have received in the past year? (estimate is fine) • How do you resolve these complaints? 2 Crop production, livestock products for sale and consumption and household decision￾making What kind of crops do households (HHs) grow in this kebele, and particularly in village (insert name)? • Which ones are produced primarily for sale for household income? • Are there any crops grown specifically for household consumption? What kind of livestock do people raise in this kebele? In village X? • What kind of livestock products do village residents sell? (e.g., milk, skins, meat, etc.) • Are any of them for household consumption? To what extent are women involved in decision-making with their husbands or partners about the kind of crops to grow for sale (or products to produce for sale)? • Over the past several years, has there been a change in women’s involvement in making major decisions about household well￾being with their husbands overall? 3 Env/Climatic Events/Water and Food Security Is this kebele currently OR in the past 12 months experiencing drought, outbreak of pests or disease, or flooding? • How long has it been going on (or how long did it last)? How severe? Does it cover the entire kebele? Village X? • What effect has this had (or still having) on farmers’ production of crops/or their livestock? (massive crop failure, livestock death, etc.?) • How are farmers/pastoralist coping with the effects of [this event]? What do they do? Have you noticed significant rainfall variability in recent years? • Did the short rains come late? • Is this a light rainy season so far compared to the past two years? • How do Households cope with this variability of rain? Annex 6 10 4 Food Gaps/Food Security Do HHs in this kebele experience months when they have little or no food? • How often does this happen? • What are the typical reasons that HH experience food gaps? (Prompt for reasons, for example: run out of food before the next food distribution, crop ruined, death of livestock, insufficient crops for harvesting, death or migration of a male member of the household? etc.) Has there been any change in length of food gaps villages experience? Or frequency? 5 Are some HH becoming more food secure? • What are the characteristics of PSNP beneficiary HHs that are becoming more food secure? • How have these household become more food secure? • Have HHs that were becoming more food secure gone backwards? What are the reasons? Agriculture Topics Team Leader introduces the topic of agriculture starting with climate forecasts and early warning information 6 Climate Forecasts and use of Information Does this kebele receive forecasts about climatic conditions? • Where does the information come from? • Is it accurate? Reliable? • Is it understandable? • How do you use this information? (meaning how is it used at the kebele level) • How is this information transmitted to village residents? Annex 6 11 Early warning information on outbreaks of pests, disease and use of information • Does the information come in time for farmers to prepare (for flooding, drought, etc.)? • Do farmers use this information to your knowledge? If the response is no farmers (or some farmers) do not use this information, ask the following: • Why do you think farmers do not use the information? If the response is yes, farmers do (or some farmers do) use this information, ask the following: • How do farmers use this information? • Were their responses effective in limiting damage to their crops (or livestock?) 7 Does this kebele receive early warning information for major crops, pests and disease outbreaks? • Where does the information come from? • How accurate is that information? Is it reliable? • Is it understandable? • How do you use it? (at the kebele level) • How does this information get transmitted to village residents? • Does the information come in time for farmers to prepare? • Do farmers/livestock owners use this information to your knowledge? If respondents say NO they do not think farmers/livestock owners use this information, ask the following: • What are the reasons they do not use this information? (Prompt as necessary with these or other examples: e.g., it comes too late to do anything to prepare, farmers/livestock owners don’t understand this information, don’t trust this information, don’t know how to use this information, don’t think anything can be done about the situation) If respondents say they believe farmers do use this information, ask the following questions: • How do they use it? • Were they able to prepare in time? • Were they able to prevent or limit major crop failure/death of livestock? Annex 6 12 8 9 Price information, use of price information Availability and Use of Community Storage Does this kebele receive price information for the crops they produce or for any livestock process they sell? If the answer is no, skip to the question 9 on storage and sales If the answer is yes, they do receive price information, ask the following questions: • Where does this information come from? • How often does price information come out? • Is it usually up-to-date? Accurate? • How does this information get transmitted down to villages in this kebele? • To your knowledge, do farmers/livestock owners use this information? If some answer is no, ask the following: • Why do you think they do not use this information? If some answer is yes, they do use this information, ask the following: • How do they use it? Can you give some examples? (Prompt with the following examples or others as needed: Does it affect where they sell their crops/livestock/livestock products? Who they sell to? Does it affect when they sell? The price they sell the product for?) • Has use of price information helped to increase HH income? Do farmers/livestock owners in this kebele have access to a community storage facility for their harvested crops/dairy products? What about in village x? If the answer is no, skip question 10 on agriculture extension services and adoption of new technologies If the answer is yes, farmers/livestock owners do have access to a community storage facility, ask the following questions: • Do farmers/livestock owners use it? If the answer is no, they are not using it, or only a few are, ask the following: • To your knowledge, why aren’t farmers (and/or livestock owners) using the community storage facility? (Prompt if necessary with the following examples or others as necessary: e.g., need to sell immediately after harvest for the money, costs for storage are not affordable, location of storage is disadvantageous, hard to get to; storage poorly maintained/built/ineffective) Annex 6 13 If the answer is yes, they are using the community storage facility, ask the following questions: • Do all farmers (and/or livestock owners) use the storage facility? (more than 50%? More than 25%? 25% or under?) • Has use of storage changed when households sell their crops (and/or dairy products)? • Has it affected prices paid for crops, livestock products? • How has the availability of a community storage facility benefitted the households that use it in village X? 10 Adoption of improved technologies and practices; Accessibility and use of agricultural loans What agriculture extension services (and/or livestock extension services) are provided in this kebele, and specifically in village X? How accessible are the agriculture (and/or livestock) extensions agents? • Do they provide services to women? If the response is no, probe for the reasons why services are not provided to women. How effectively have these services helped HHs in this kebele? In village x? What recommended changes in crop productions and livestock production have farmers adopted so far? • Are farmers adopting recommendations? • For those farmers who have adopted recommendations, what has been the benefit? If the response by some or all the kebele officials is these services are not effective or not very effective, ask the following: • What limits the effectiveness of the services they provide? . 11 Are there accessible sources of credit that serve PSNP beneficiary households if they want to apply for loans to pay for improved agricultural inputs, technology, feed/medicine for livestock? If the response is no, there are no accessible sources of credit, skip to question 12 If response is yes, there are accessible sources of credit, ask the following: • What are those sources of credit that most farmers use? • How long ago were lending facilities established in this kebele? • To your knowledge are farmers/pastoralists applying for loans? Are women? If response is no, or very few, ask follow-up questions below. • What are the reasons village residents are not applying for loans? • What are the reasons women are not applying for loans? Annex 6 14 If response is yes, they are applying for loans, ask the following: • Are farmers/pastoralists able to pay back per the terms of their loans? • What happens if they default on their loan? Annex 6 15 WAGE EARNING OPPORTUNITIES Team Leader introduces the topic of wage earning opportunities 12 Wage Opportunities and Migration Are there wage-earning opportunities nearby in this kebele or woreda? • Where are those opportunities located? • What kind of work is it? • Is it daily or seasonal? • Which household members usually take advantage of these opportunities? Do people from this kebele migrate outside of the woreda to work for wages? What about from village x? • Where are these opportunities located? • For what kind of work? • Seasonal? Long-term? • What HH members usually migrate for work? • Do women ever migrate for work? • Do they send back money to their households? • How common is migration for work? • What are the primary reasons people migrate for work? GOVERNANCE AND SECURITY TOPICS Team leader introduces the topic of security issues Annex 6 16 13 Security issues, incidents of violence Have there been any major security issues or incidents of violence (or unrest) in this kebele or surrounding areas, or that are happening right now? Interviewer Note: If the answer is no, end the discussion with this group. Go to Closing Question and Team Leader Remarks. Note: If the answer is yes, there have been in the past, ask the following questions: • How long ago? Can you please describe what happened? • Where did it occur? • What impact did it have on HHs in this kebele? What about in village x? Note: If there currently are major security issues or incidents of violence, ask the same questions: • Can you please describe what these are? • Where is this occurring? • What are the likely causes? • How is (or how did) this affecting HHs in the area? (Prompt as necessary, e.g.: restricted travel outside villages, restricted access to water, grazing land, restriction on herding livestock, restricting marketing/sales of crops, livestock or livestock products, restricted or temporarily halted delivery of FFP food allocations) Have (or were) any actions been taken to resolve the issues causing these problems? If the answer is yes, ask the following questions: • What has been/what is being done? By who? • Has the security situation (or incidence of violence) been effectively resolved? • Is it likely to occur again? Closing Question Are there any other issues we haven’t covered that anyone would like to bring up? Team Leader Closes the Interview: Thanks all participants for attending the session. Expresses appreciation given the time taken away from their obligations. Annex 6 17 KEY INFORMANT INTERVIEW for HEALTH EXTENSION WORKER/S (HEW) For HEWs Serving Villages Where Interviews Will Be Conducted INTERVIEW DATA Implementing Partner Facilitator: Date of Interview Start Time: Team Leader Recorder: End Time: Region: Tigray Woreda: Kebele: Permission to Record Interview: Y/N Signed or Verbal Consent Form Obtained: Y/N Facilitator Instructions for Beginning the Key Informant Interview with the Health Extension Agent (HEW) Introductions and purpose of the interview. Thank you for the opportunity to speak with you. My name is ______________________ and this is __________________. We are from Green Professional Services, and we are working with the US-based company MEA. We are conducting individual/group discussions with people like you in communities across several regions to strengthen and improve the PNSP. These questions in total will take approximately two hours and your participation is entirely voluntary. If you agree to participate, you can choose to stop at any time or to skip any questions you do not want to answer. Your answers will be completely confidential; we will not share information that identifies you with any one. If you choose not to participate it will not change any services or benefits that you receive now or in the future. You can stop participating at any time without penalty and without having to give any reason. You can also decline to answer any specific questions that you do not want to answer, also without penalty and without having to give any reason. We are going to record this discussion only for the purpose of reviewing our notes to make sure the notes correctly capture the contributions of the group. We will destroy the recording after the notes have been checked and finalized. Do you have any questions about what I have said? . Summarize the category of questions you will be asking. These include questions on breastfeeding practices for infants; health and nutrition, especially for mothers, infants and children under five; water and sanitation; use of ORS, and gender issues. Inform the HEW that if there are any questions she is uncomfortable with the team will respect that and skip to the next question. Explain how the interview will be run and inform her the meeting will take approximately 1houronce it begins. Annex 6 18 Q # Question Sub￾Category MATERNAL AND CHILD HEALTH AND NUTRITION TOPICS Team Leader introduces topic of child feeding 1 Use of exclusive breast feeding practice for infants under 6 months M: Do most or all mothers in village X practicing exclusive breastfeeding? What about older mothers? Note: If the answer is no, not all mothers practice exclusive breast feeding, ask the following: • What are the reasons some mothers do not practice exclusive breastfeeding? • What other kinds of food do mothers who don’t practice exclusive breastfeeding give to their infants under 6 months? • What is the reason these foods are given to their infants instead of practicing exclusive breast feeding? 2 Minimum acceptable diet: frequency of feeding and food diversity for infants and children between 6-23 months M: Are most or all mothers feeding their children between the ages of 6-23 months with diverse types of food? . If the answer is yes: M: What kinds of food are they giving these children? M: Do most of these mothers understand the importance of feeding their children different types of food? M: Do men understand this, too? 3 M: By tradition, are there certain kinds of food that are prohibited to give to children between 6-23 months? M: Do you see this changing at all? M: Is there a tradition of giving different types of food to boys versus girls in this age group? M: Do you see this changing at all? 4 M: Do husbands (or male partners) of young mothers ever feed their children in this age group? Annex 6 19 M: Do you see this changing at all? M: Do men give these children different food than their mothers give them? M: Do any other family members (grandmothers, aunts, sisters, brothers, etc.) ever feed children between ages 6-23 months? M: Do they feed those children different foods? 5 M: Do mothers give different kinds of food to male children between ages two and five compared to female children? Do they give male children a greater amount of food than they do to female children? Note to interviewer: If the answer is yes to either one or both of the two questions above, probe for the reasons. Why are mothers giving more and/or different kinds of food to their male children compared to their female children? • Do practices about feeding male and female children change depending on whether families are experiencing food gaps? 6 M: To what extent do mothers of children in this age group have control and decision-making authority around what those children eat? 7 Perception and knowledge of stunting, wasting and underweight children under age five M: What do mothers in these villages know about what a healthy child under age five should look like? For example, in terms of weight, height, or any other features? M: Are mother’s ideas of what a healthy child should look like changing? M: What do mothers know about acute and chronic malnutrition? Do they understand the difference? . M: Do mothers in this kebele know how to prevent malnutrition or to treat it? What about in village x? M: Do mothers understand what stunting is and what the signs of stunting are? Annex 6 20 8 Women of reproductive age: knowledge and use of nutritious foods M: What do women of reproductive age know about eating nutritious and diverse foods? • Are most of these women eating the range and type of foods that are promoted for their health? M: Are most pregnant women and new mothers following the recommendations? What about in village X? M: What are the reasons some pregnant women and new mothers do not eat nutritious/diverse foods? 9 Women’s knowledge and use of family planning and reproductive services for pre￾and post-natal care M: What do women of reproductive age know about the use of family planning? M: Do most women using family planning? Does it vary by age group, or other factors such as the number of children they already have? What about in village x? M: Has this been changing? Do men have a say? M: What are some of the reasons women do not use family planning? 10 M: What do women know about the importance of using reproductive services for pre- and post-natal care? M: To what extent are pregnant women and women who have recently delivered using these services? What about in village x? M: Has this been changing? 11 M: Are any women from the villages you serve using ANC delivery services in the kebele, or do they all use the health post? What about in village x? M: Has this been changing? • To the best of your knowledge, what are the reasons mothers have for choosing one or another place to go for delivery service? Annex 6 21 WATER, SANITATION AND HEALTH TOPICS (WASH) Team Leader introduces the topic beginning with child diarrhea 12 Prevalence of diarrhea among children: knowledge of causes and treatment practices and household hygiene and sanitation practices; access to water and soap M: What do mothers in this area know about the connection between open defecation and the frequency with which their children have diarrhea? M: Do most or all HHs in this kebele use either a community latrine or latrines or latrine holes near their homes? What about in village X? M: Do most HHs that use the community (or household) latrines or latrine holes cover them after use? M: What is motivating those households to use latrines and to cover them after use M: To what extent are HHs in the same villages still practicing open defecation? What about in village X? M: Is there anything that hinders some HH from adopting these practices? 13 What do mothers know about the connection between washing their hands at critical times and the frequency with which their children have diarrhea? +M: To what extent are mothers following recommendations about washing their hands? What about in village x? M: Are those that do using soap? M: Is this changing? M: Do most HHs in this kabele have a handwashing station close to their community (or household) latrine? • Do these stations have soap? • M: What are some of the reasons that some mothers in this area do not wash their hands at critical times? M: How does drought affect these practices? 14 M: Do mothers in this area understand what ORS is and how it should be administered to children with diarrhea? What about in village X? M: Do other caretakers in the household (aunts, older children, etc.) understand the importance of ORS and how it should be administered? Annex 6 22 M: Do men understand? M: Are most mothers (and other caretakers including men) in the villages you serve administering ORS to their children who have diarrhea? Do they administer it correctly? M: Is this changing? M: What is motivating mothers and other caretakers to administer ORS? • What are the reasons some mothers do not administer ORS to their children? GENDER TOPICS: INTRA-HOUSEHOLD DECISION-MAKING, EQUITY, and BEHAVIOR CHANGE Facilitator Introduction: We know that part of your work with villages is to promote joint decision-making in households so that women (wives/partners) also have a say on important issues, such as what crops to plant, how to use cash disbursement, FFP/PSNP food, applying for agriculture loans, adopting new technologies. 15 Intra-household decision-making M: In your view, has there been any progress to date in women’s involvement in decision-making on important issues? If the answer is yes: M: Are there certain issues about which you are seeing (or hearing) more joint decision-making in HHs between men and women? What about in village x? M: What accounts for those changes you are seeing? . If the answer is no, there has been no progress OR little progress, ask the following question: • Why do you think there hasn’t been any progress? 16 Intra-household food distribution and allocation M: Have there been any changes in the type of food traditionally eaten by men compared to women? Or in the amount of food eaten by men compared to women? M: Please describe what the changes are. M: If this hasn’t changed, what is the difference in the type and amount of food eaten by men versus women? M: Have there been any changes in the traditional order of eating among household members during family meals? M: In HHs where the woman is pregnant, or has just given birth to a child, does she eat different kinds of food? Annex 6 23 17 Changes in women’s behavior M: Are there certain activities that women are traditionally prohibited from doing by local custom and traditions? (Prompt as needed, for example: selling at markets outside of the village? Working for wages? Using birth control? Contacting agricultural extension agents? Applying for loans? Picking up the FFP/PSNP HH food package?) M: Do you see any changes in these traditions? Are women starting to do some of these things that they were traditionally prohibited from doing? • What has been the reaction from men to these changes in women’s behavior? Husbands, older brothers, uncles, etc.? • Has this been changing? Closing Question Are there any other issues we haven’t covered that you would like to bring up? Team Leader Closes the Interview: Thanks the HEW for participating in the interview. Expresses appreciation given the time taken away from her obligations. Annex 6 24 KEY INFORMANT INTERVIEW GUIDE for VILLAGE LEADER INTERVIEW DATA Team Leader Facilitator: Date of Interview Start Time: Implementing Partner Recorder: End Time: Region Woreda Kebele: May Village: PARTICIPANT INFORMATION Type of Participants by Role if More Than One Village Leader (please check off) Village Leader Consent given to record interview yes/no Signed or verbal consent recorded on consent reform yes/no Total Number of Participants: Team Leader Instructions for Interviewing Village Leaders Type and purpose of this interview. The purpose of this guide is to get an overview of the village status, descriptions, how food secure the village is, etc. Many if not all of the questions will be covered in individual interview guides for the four village interview groups, as well as in the interview with kebele officials, the Development Agent, and the Health Extension Worker. It is important to get the perspective of the village leader/s and their descriptions and explanations. Their views and responses will be compared to responses from the woreda and kebele levels and from village residents during the data analysis phase. This guide starts out with specific questions on a few critical topics, and then reverts to a topic guide where the topic is presented and there is a list of issues to cover rather than specifically worded questions. Annex 6 25 Introductions and purpose of the interview. Thank you for the opportunity to speak with you. My name is ______________________ and this is __________________. We are from Green Professional Services, and we are working with the US-based company MEA. We are conducting individual/group discussions with people like you in communities across several regions to strengthen and improve the PNSP. These questions in total will take approximately two hours and your participation is entirely voluntary. If you agree to participate, you can choose to stop at any time or to skip any questions you do not want to answer. Your answers will be completely confidential; we will not share information that identifies you with any one. If you choose not to participate it will not change any services or benefits that you receive now or in the future. You can stop participating at any time without penalty and without having to give any reason. You can also decline to answer any specific questions that you do not want to answer, also without penalty and without having to give any reason. We are going to record this discussion only for the purpose of reviewing our notes to make sure the notes correctly capture the contributions of the group. We will destroy the recording after the notes have been checked and finalized. Do you have any questions about what I have said? Summarize the category/type of questions you will be asking. (agriculture, wage opportunities, weather, PSNP, etc.) Inform the village leader that the team would like to understand current conditions/situations in the village. Say that if there are any questions he/they are uncomfortable with and do not wish to discuss, be clear that you will respect that and move on to the next question. Explain how the interview will be run and inform the respondent/s the meeting will take approximately one hour once it begins. Stress anonymity. You have not written down the respondent/s’ names and won’t do so during the interview. Explain why you will be taking notes and then request permission to record. If permission is granted, read consent form, and ask for signature/s (or X mark) from each respondent. Thank the respondent/s again, and begin recording after the consent letter is signed. If permission is not granted, explain that the recorder will take notes, and make it clear, once again, that the respondent/s’ name will not be included in the notes. KII with Village Leader Facilitator explains the interview will begin by asking questions about PSNP beneficiaries. 1 PSNP 4 beneficiaries Are there any HHs that have graduated from PSNP? If the answer is yes, ask the following: How are those households doing? Are they able to feed their families and meet other basic needs? Are any of those households re-enrolled in PSNP as a beneficiary? 2 Community Projects What community project are PSNP beneficiaries working on? Annex 6 26 Why was this project chosen? How will it benefit people living in this village? Have PSNP beneficiary households brought up problems or complaints to the Appeals Committee in kebele X? (insert name of kebele) What kind of problems are brought up to the Appeals s Committee? Are they resolved satisfactorily? 4 Food Gaps/Food Security Do HHs in this village experience months where they have very little food to eat? • How often does this happen? • Why does this happen? • What type of PSNP beneficiaries are most likely to experience periods when they have too little food? • (Probe for reasons, for example: run out of food before the next food (or cash) distribution, crop ruined, death of livestock, insufficient crops for harvesting, death or migration of a male member of the household? etc.) • Has there been any changes in length of food gaps villages experience? Or frequency? 5 Are some HH becoming more food secure? • What are the characteristics of HH that are becoming more food secure? • Have any of those HH gone back to becoming food insecure? If the answer is yes, ask the following: • What are the reasons this happened? 6 AEA and LEA services, Adoption of improved crop and livestock technologies/practices Are agricultural extension services (and/or livestock extension services) provided to this village? • How effectively have these services helped HHs in this village? If the response is not effective or not very effective, ask the following: Annex 6 27 Why aren’t their services very effective in helping HHs here? Have HHs adopted any new agricultural technologies/inputs such as seeds, fertilizer? • New agricultural practices in soil or water management, cropping? Do HHs learn from AEA/LEA? Agro-dealers? Other sources? Are HH in the village adopting these practices (especially PSNP beneficiaries). If not, why not? For those HHs who have adopted new practices, has it been beneficial? 7 Availability and use of loans Do HHs apply for loans/credit to purchase any of these new technologies/inputs? From what sources? Are most people able to pay back their loans? 8 Wage earning opportunities Are their wage-earning opportunities in this region that HHs in the village take advantage of? What HH members are engaged in wage earning work (young men? adult men? women? What is the percentage of HHs you can estimate with a family member that temporarily migrates to earn wages? % that have permanently migrated? . 9 Biggest Challenge What is the biggest challenge in relation to achieving food security households in this village face? 10 Most proud of What are you most proud of about the people in this village? Closing Question Are there any other issues we haven’t covered that anyone would like to bring up? Team Leader Closes the Interview: Thanks village leader/leaders. Express appreciation given the time taken away from their obligations. Annex 6 28 GROUP INTERVIEW GUIDE FOR MALE AND FEMALE HHH Guide for Group Interviews with Male Heads of Household (MHHH) and Group Interviews with Female Co-Heads of Household (FCo-HHH) This data collection tool should be used for both male and female groups. Composition of Participants per Group MHHH: Adult men residing in the village who are the head of their households; and married or living with a partner they are not married to. FCo-HHH: Adult women residing in the village, living with a husband or male partner who is the head of their HH. We use the term co-heads of household to distinguish this group from the group formed with women heads of household (WHHH). WHHH are women who are widowed, divorced, or abandoned. Note: Exclude from the FCo-HHH Group all young women age 29 and under. Young women age 29 and under will be asked to volunteer to participate in a separate group called Young Mothers with Infants and/or Children Age 5 and under (MIC5.) INTERVIEW DATA Implementing Partner Date of Interview: Team Leader: Facilitator: Recorder: Start Time: End Time: Region Tigray Woreda Kebele Village PARTICIPANT DATA Type of Group (check one) Type of Participants in Group (check one) Farmers Agro Pastoralists Consent to record interview (check one) Yes No Number of verbal agreements/signatures recorded on consent form Number of Participants: Team Leader and Facilitator Instructions for Beginning the Group Interview Introductions and purpose of the interview. Thank you for the opportunity to speak with you. My name is ______________________ and this is __________________. We are from Green Professional Services, and we are working with the US-based company MEA. We are conducting individual/group discussions with people like you in communities across several regions to strengthen and improve the PNSP. These questions in total will take approximately two hours and your participation is entirely voluntary. If you agree to participate, you can choose to stop at any time or to skip any questions you do not want to answer. Your answers will be completely confidential; we will not share information that identifies you with any one. Annex 6 29 If you choose not to participate it will not change any services or benefits that you receive now or in the future. You can stop participating at any time without penalty and without having to give any reason. You can also decline to answer any specific questions that you do not want to answer, also without penalty and without having to give any reason. We are going to record this discussion only for the purpose of reviewing our notes to make sure the notes correctly capture the contributions of the group. We will destroy the recording after the notes have been checked and finalized. Do you have any questions about what I have said? Facilitator: Summarize the category of questions you will be asking. Inform the group that if there are any questions participants are uncomfortable with to let you know and you will skip to the next questions. Anyone in the group wishing to leave before the interview is over may do so. Explain how the interview will be run and inform participants the meeting will take approximately two hours once it begins. Note on questions: Unless specified, each question can be used for agricultural, or agro-pastoralist villages. Extra questions are sometimes given for those villages where participants are also livestock owners for interviews in agro-pastoralist villages. Agriculture and Livelihoods Topics Facilitator: Introduce the topic of this set of questions on agriculture/livestock 1 Household decision￾making: What crops are planted for sale? For family consumption? Do you own your own land or rent land? FACILITATOR NOTE: If some participants still haven’t responded, ask if anyone sharecrops. IMPORTANT MESSAGE FOR TEAM RECORDER: Record the number of participants that say they own land, the number that rend land, and the number that sharecrop. What crops do most of households in this village plant? • What crops do you plant for sale? What crops do you plant for feeding the family? How do you decide what to plant? (alternative how do households decide what to plant?) (Prompt with examples as needed, such as: land ownership, location of the land, quality of land sharecropped, market prices, etc.) • Who is involved in making the decision on what to plant? • Annex 6 30 1a If some participants say yes, ask the following: • What happens if husband and wife disagree on what to plant? How is the final decision made? What kind of livestock do households in this village own? Do they sell any products from their livestock? • What kind? Do you use some of the livestock as a source of food for the family? If some participants say yes, ask: • What do you feed the family from your livestock (referring to first question)? Eggs, milk, meat, butter, etc. 2 Environmental factors that affect agriculture and livestock productivity and household responses How good was your crop yield from the last harvest of crops? • Was the amount as expected? • How was it compared to the harvest from the last two years? • What accounts for the yield? If some or all participants say yield was good, probe as necessary with examples (e.g., sufficient rain, using improved seeds, fertilizer, new soil management techniques, no outbreaks of pests/diseases?) If some participants say yield was low, bad compared to last year, etc., probe as necessary with examples (e.g., insufficient rain, delayed rain, drought, flooding, outbreak of pests/diseases, poor soil quality/erosion, interference from wildlife, lack of cash to buy inputs). 3 Sources of water: Rainfall variability and drought and HH responses What is/are the source/s of water households use for growing crops (streams, well, river, lake, pond, rainfed) Is it accessible for use? Is it irrigated to your land? Is there sufficient water for crops (crops for sale and/or for consumption) from this source of water for the entire growing season? If some participants say no, ask the following questions: • What is the reason there wasn’t enough water during the entire growing season? Not enough rain? Rain stopped before rainy season? Annex 6 31 Questions for the entire group: Have you noticed changes in rainfall in recent years? If the answer is yes, ask the following questions: • What kind of changes do you notice? (Prompt for responses giving these or other examples as appropriate: short rains come late, rain during the rainy season is lighter, rain comes at unexpected times than usual, etc.) • How does this affect crops? The fact that you can’t predict the short rains, not enough water for the whole growing season? • What do you do? 3a Livestock questions What are the water sources households in this village use) you use for livestock? a. Are these sources (or source) accessible for use of HHs in this village or nearby? If answer is no: • Did it use to be? What has changed? b. Is there enough water year-round for each HH’s livestock? If answer is yes, skip to Q 4 If no: • What is the reason? (Prompt as necessary: Prohibited from using the water hole traditionally used? Dried up? Drought? Delayed short rains? Light rains?) • How does this affect your livestock? 4 Other challenges for planting crops (not Besides water, what other major challenges do households face in planting and harvesting crops (either for sale or consumption)? Prompt to get a full range of all the challenges faced in planting crops using any of these examples as necessary. You are not expected to ask each one. Annex 6 32 related to water) • Poor soil quality, soil erosion? • No choice but to plant crops on ever steeper slopes? • Flooding, hail? • Lack of cash to buy improved seeds, fertilizer, or other inputs? • Outbreak of pests, disease? • Wildlife intrusion? • Do not own land, or sufficient land? Can’t afford to rent enough land? • Sharecroppers- need to give a significant portion of the harvest or cash payment in exchange for lease of land? • Insufficient HH labor? • Security issues? How long has this been a problem, gotten worse over time? Since when? (NOTE: IF MORE THAN ONE CHALLENGE NOTED, ASK THIS QUESTION FOR EACH MAJOR CHALLENGE) • What changes have you made in response to these challenges? Prompt: provide some examples if needed, e.g., plant in different locations if possible, plant different crops, use fertilizer for soil, take preventative measures for outbreak of pests/disease, use improved seeds, improve soil management practices, etc.) 4a Additional questions for livestock holders What major challenges do your households face managing their livestock and keeping them healthy and productive? Interviewer: Prompt to get a full range of all the challenges faced in keeping livestock healthy and productive using any of these examples as necessary. You are not expected to ask each one. • Poor quality of grazing land? • Lack of access to grazing areas? • Conflict with farmers over grazing areas, access to fields? • Grazing areas taken over by farms? • Poor quality of fodder or insufficient fodder? • Outbreak of pests that affect grazing land? • Outbreak of diseases that affect livestock? • Lack of veterinary services, medicines, vaccinations, dry season fodder, etc.? Follow up Questions: Annex 6 33 • How long has this (have these) been a problem? • Since when? Has this (these) situations gotten worse over time? • What do livestock owners do in this village to respond to these challenges/Problems? 5 Availability and use of climate forecasts Where does the information come from on climate forecasts? (Prompt as necessary: TV, radio? Agriculture Extension Agent? Livestock Extension Agent? Do households use this information? A. Is no, B is Yes a. If no: • Why not? (Provide any of the following examples as needed: (use signs we traditionally use to tell if a drought, etc., will come and we trust it more, the forecasted information comes too far in advance or too late, crops already planted, cannot understand the information, the information is not reliable, believable, etc.) b. If yes: • How do you use this information? What do you do to prepare? • Has this information helped you to minimize damage to your crops (or livestock)? 6 Early warning of pest and disease outbreaks Does this village receive early warning information for major crops pests and disease outbreaks? (or livestock diseases?) If response is no, skip to question 7. On INSURANCE If response is yes: • Who provides the early warning information? Where does it come from? • Do HHs here use this information? A. If answer from some is no B. If answer from some is yes. a. No answers • Why don’t they use this information? (Prompt for reasons as necessary: e.g., not reliable, not accurate, use traditional signs, can’t understand it, doesn’t come in time, nothing can be done about it anyway, etc.) • What do those households do when there is an outbreak of pests or diseases? Are they able to limit the damage to their Annex 6 34 7 Use of crop/livestock insurance crops and livestock? b. Yes answers • How do you use this early warning information? What do you do? (Provide examples as necessary, e.g., buy crop insurance/livestock insurance, purchase pesticides, plant a different crop; for livestock, use fortified feeds to increase resilience to disease, isolate those livestock that become diseased) • Were you able to save your crops or limit the damage? • Your livestock? IF No INSURANCE AVAILABLE IN THIS AREA, SKIP to QUESTION 8 Do HHs in this village ever buy crop/livestock insurance based on climate forecasts or early warning information on pest or disease outbreaks? A. No answers; B. yes answers a. No answers • Why not? Prompt for explanations as needed using some of these examples, e.g. cannot afford to pay for insurance, too complicated to apply for insurance, heard it is too hard to get insurance money when needed, etc.) b. Yes answers • Where is the insurance from? • Did those households (or you) receive insurance money after crops were destroyed/livestock died? • Would you be willing to purchase insurance again? No answers: • Why not? What are the reasons? Annex 6 35 Facilitator introduces the topic on use of improved agricultural and/or livestock inputs/technologies/practices 8 Knowledge of improved technologies and practices for crop production and livestock management A. Crops How do HHs here learn about new ways to increase the yield of their crops? What type of products to increase yield have you learned about? (prompt as necessary: types of seeds, fertilizers, or pesticides to protect crops?) Have new practices for working the soil in different ways been suggested? For planting? Application of water? Or for harvesting crops? Livestock. How do HHs here learn about new technologies to improve the health or productivity of their livestock? Have new practices for improving the quality of grazing areas been suggested? Or for improving the quality of pasturage? . 9 Experience with adopting new practices, inputs Have any of you tried any of the (technologies, techniques, practices)? For improving crop yield or your livestock? A. No answers; B. Yes Answers A. No answers • Why not? What are the reasons you have not tried any of them? B. Yes answers Note: Ask first about crops, then follow up by asking about trying any new product, etc. for livestock • Which ones have you tried? • What were the results? Will you continue to use “x” technology or “y” practice for your crops (and/or livestock?) 1. If Yes: 2. If no 1. Yes answers • What are the reasons you will continue with this (technology, practice, etc.)? 2. No answers • What are the reasons you WON’T continue using this (technology, practice)? Annex 6 36 9 Experience with AEA/LEAs/ Agro-dealers Do you ever ask for services from agriculture extension agents, veterinary extension agents, or agro-dealers? 1.Yes Answers; 2. No answers 1. Yes • Which kind of agent (or agro-dealers)? For what reason? • Was it helpful? How? • Who in your household usually seeks out these services? • Can women seek out services from agriculture extension agents (livestock extension agents, agro-dealers)? Probe for reason if participants say women cannot do this. . 2. No • Why haven’t you? (Probe for reasons as necessary: don’t believe they know what they are talking about, bad recommendations, receive services from their coop, distance, no transportation, poor roads, other physical barriers, financial, security issues, don’t feel welcome or treated well, cultural issues related to appropriate gender roles) 10 Experience with loans Have you ever applied for a loan for the purposes of buying agricultural inputs (seeds/fertilizer) or for livestock medicine, vitamins? 1. No Answers 2. Yes Answers 1. No Answers • Why not, what are the reasons you haven’t? (Prompt using following examples as needed: can’t afford to purchase without borrowing money, rates of interest, collateral requirements, terms of repayment, too difficult to pay back loan, prefer to use other sources, other) 2. Yes Answers Where is your loan from? What kind of loan is it? • Was your loan application successful? • Would you apply for a loan again (to buy technologies, improved seeds, medicines, fertilizer, etc.)? If some say no, they would not borrow again, ask: • Why not? What are the reasons? Probe using following prompts or others as needed: have not been able to repay last loan, were not able to get as much as Annex 6 37 needed, etc.) Are women allowed to apply for loans from banks? (Probe for reasons if anyone says No) Is it difficult for most households to repay these loans? Market Information Topics Facilitator introduces the topic of use of market information on sale prices, sale locations, selling decisions, use of available storage 11 Availability and use of market information Interviewer Note: If YOU LEARN IN ADVANCE NO PRICE INFORMATION IS AVAILABLE, SKIP to Q 12 on the topic of storage and timing of sales. For which products you sell do you receive price information on the amount of money that will be paid? (CROPS/LIVESTOCK) • Where does the price information on crops you sell come from? OR LIVESTOCK products you sell (prompt as necessary: from their coop, tv, radio, agriculture extension agent, etc.) • How often do you receive price information? Daily? Weekly? • Does the information cover all the markets and buyers in your area? Do you use this information? 1. No answers 2. Yes Answers 1. No answers: • Why don’t you use this information? What are the reasons? Prompt as necessary: not reliable, covers markets I can’t reach, word of mouth is better, don’t trust it) 2. Yes answers: • How do you use this information? • Was the information reliable/correct when you went to sell your crops/livestock/livestock products? • Have you been able to earn more income from selling your crops/livestock products by using this information? 12 Household decisions on use of storage and timing of sales Community After harvest, do HHs in this village store the crops (livestock products, honey, eggs) they intend to sale in their homes? 1. Yes Answers 2. If no, skip to question below on community storage 1, Yes Answers • What kind of containers are used for storage? What do you store in it/them? • How many days can you store x, y, z before it goes bad? (FOR x, y, z: put in the name of the items they store) Annex 6 38 Storage Questions ATTENTION: SKIP QUESTION ON COMMUNITY STORAGE IF YOU LEARN FROM KABELE THERE IS NONE: go to Q 12a What can you keep in the community storage facility? How long can you keep your crops (livestock products) in this facility before you have to sell them? Do any of you use it? 1. No answers 2. Yes Answers 1. No answers • Why not? What are the reasons you don’t use the storage facility? (Probe for reasons as necessary using examples: it’s too difficult or costly to transport crops (or livestock products) to the facility, the storage facilities are in bad condition and don’t keep harvested crops (and/for livestock products) fresh, a storage price is charged and some cannot afford it, they choose to sell immediately after harvest because they need the money right away, etc.). 2. Yes answers • Why do you use it? What benefit do you receive from storing your crops (or livestock products) after harvest? (Probe for reasons: maintain freshness if there are problems in accessing markets, intermediary buyers due to road conditions, floods, security reasons, get a better price by being able to sell at a later time) 12 a Selling livestock: for Agro-pastoralist and pastoralist villages Do you ever go through times when you decide you must sell some or all of your livestock? If answer is no, Skip to Q 13 1. Yes answers • What was the reason? Why did you have to sell? (Probe for reasons as necessary: insufficient resources for feeding and watering livestock, grazing area no longer accessible, useable, or must now pay to use, no accessible water sources, need for immediate cash, livestock is too old to produce anymore and are sold for skins, pay back debts on loans, etc.) 13 Household decision making on where to sell products (crop and livestock) Where do HHs usually bring their farm produce and/or livestock or livestock products for sale? (Probe as necessary: to the coop they are members of to sell on their behalf? directly to buyers at the nearest market? To sell it themselves at the nearest market? Directly to intermediary buyers who then bring products to market for sale? Other locations, etc.) Do you have options on where to sell? 1. Ask question below; If yes, skip to 2. 1. Answer is no: • Does every household bring their crops (or livestock products) to sell there at the same time? Annex 6 39 2. Yes answer • How do you decide where to sell? (Probe as necessary: always through the coop, transportation issues, road quality/accessibility, price, easy location for transportation, distance, etc.) • Who in the household decides where to sell? 14 Household decision￾making: use of income from crop sales, sale of livestock products and other sources of income How do you decide to use the money you earn from selling crops/livestock products? (Probe as necessary: food to feed the HH, invest in improving livestock condition, rent additional land, purchase improved seeds, fertilizers, purchase or rent additional land or yearly rent of same land, HH goods, school fees, uniforms, school books and supplies, medicine or other health costs, marriages, funerals, other religious ceremonies/events, loan repayment, etc.) • Who in your household decides how to use the money? (Probe if necessary: joint decision-making between adult male and female partners?) • How do you decide how to use the money if husband and wife don’t agree? Do HH here have other sources of income? (list them in your notes) 1. If no, Skip to Question 15 (Probe as necessary using examples, e.g.: cash transfers, selling part of the HH food allotment package, remittance from HH members who have migrated, selling firewood, charcoal, making/selling clothes, handicrafts, selling forest products, selling fish, chicken eggs) Employment Topics Facilitator explains the next section of questions is about wage earning opportunities 15 Wage earning opportunities, HH wage earners Does anyone in your household earn wages? 1. Yes Answers If no, Skip to Question 16 1. Yes Answers • What members of the family? • Where do they go? • What kind of work is it? • Is it seasonal? Daily? Annex 6 40 • Can women earn wages too? Unmarried girls in the family? Do some of your HH members migrate seasonally or long term? 1. Yes answers. If NO, Skip to Question 16 1. Yes Answers • Which household members? • Where do they go? • Do they send back money they earn to your household? • What are the reasons those family members migrate to earn wages (either seasonally or long-term)? (Probe as necessary: lack of land or lack of money to rent land, poor conditions of soil and/or location of land they have for planting/grazing, no or limited access to grazing land for livestock, crop failure or disease/death of livestock from prolonged drought, flooding, pests, insufficient income from sales of crops/livestock products to meet household needs for food, etc.) Poverty and Food Security Topics Facilitator introduces the topics on HH meals, cost of food, and food from FFP/PSNP HH food allotment 16 Intra-household food allocation and distribution a. What type of food does each member of the HH eat? If answer is all the same, skip to question 16b on amount • What is the reason different members of the HH eat different types of food? b. Does the amount of food each household member eats differ? • What is the reason different members of the HH eat different amounts of food? Is there an order of eating among household members during mealtimes? How do households decide what type of food and/or the amount of food each family member eats? Is it a joint decision between husband and wife? 17 Food for work and use of FFP/PSNP food, cash transfers What type of community project do beneficiary members of this village do in return for your food allotment (or cash transfer)? Is the community project good for the village in your view? 1. Yes answer 2. No answer 1. Yes Answer • Why is it good for the village? What benefits does it bring? 2. No Answer • Why don’t you think it is good (or won’t be good) for the households in this village? CASH TRANSFER QUESTIONS How do households receive their cash transfers? • Who does it come to? Annex 6 41 • How do you use it? FOOD DISTRIBUTION/ALLOTMENT QUESTIONBS Who in the HH is eligible to pick up the family food allotment? • How do you decide who will pick it up? Do HHs ever sell any of the food items provided to buy other types of food available at the market? Or to buy anything else (e.g., or to repay loans, etc.)? If yes, ask questions below, if no, skip to Q 18 • Which items are usually sold? • Why sell those items? 18 HH strategies during periods of food gaps Do HHs in this village ever experience months when they have no food or too little food? • How often does this happen? Does this happen certain times of year? • What are the reasons this happens? (Probe for reasons, using prompts as necessary: ran out of food before the next food distribution, crop ruined, death of livestock, insufficient crops for harvesting, death or migration of a male member of the household, have to sell crops when prices are low to pay off debts, or because the money is needed right away for other reasons, etc.) • What do you (or what do HHs in this village) do when you go through times when you don’t have enough food for yourself and the family? . (Probe for reasons, using examples as necessary, e.g.: borrow from family neighbors in other households? Sell household items? Sell livestock? Sell land or rent land? Send some HH members out for wage employment to send back to the HH? Take children out of school to eliminate the need for paying school fees? Reduce the amount of food eaten or the number of meals eaten per day? Eat cheaper, less desirable food? Use FFP/PNSP cash distributions? Request additional food? Inform the IP (insert name), inform the village gott, the DA, HEW) 19 Household food purchasing decisions What kinds of food do you buy for your household for family meals? Where do you buy the food? • Are there any times when you don’t or cannot buy those types of food you usually purchase? If answer is no, go to closing question • What is the reason that happens sometimes? Prompts as necessary (e.g., the types of food usually purchased at the market is too high, not available based on time of year or Annex 6 42 some other reason, only a small amount, food for sale is sometimes spoiled, etc.) • What do you do during the times you cannot buy those foods for your household? Closing Question Are there any other issues you would like to bring up before we close the interview? Team Leader closes meeting. Thanks everyone for participating in the group. Expresses appreciation for the time they spent in the meeting. Annex 6 43 GUIDE FOR GROUP INTERVIEW with YOUNG MOTHERS with INFANTS and CHILDREN AGE FIVE AND UNDER (MIC5) Group Composition: Young mothers age 29 and under with infants and children age 5 and under. IMPORTANT INTERVIEWER NOTE: None of these women should be included as participants in the FCo-HHH Group or in the WHHH Group. INTERVIEW DATA Implementing Partner Facilitator: Date of Interview Start Time: Team Leader Recorder: End Time: Region: Woreda: Kebele: Village: PARTICIPANT INFORMATION Type of Participants in Group (check one) Agro-Pastoralists Consent to record interview (check one Consent to record not given Number of Participants Facilitator Instructions for Beginning the Group Interview Introductions and purpose of the interview. Facilitator; Thank you for the opportunity to speak with you. My name is ______________________ and this is __________________. We are from Green Professional Services, and we are working with the US-based company MEA. We are conducting individual/group discussions with people like you in communities across several regions to strengthen and improve the PNSP. These questions in total will take approximately two hours and your participation is entirely voluntary. If you agree to participate, you can choose to stop at any time or to skip any questions you do not want to answer. Your answers will be completely confidential; we will not share information that identifies you with any one. If you choose not to participate it will not change any services or benefits that you receive now or in the future. You can stop participating at any time without penalty and without having to give any reason. You can also decline to answer any specific questions that you do not want to answer, also without penalty and without having to give any reason. We are going to record this discussion only for the purpose of reviewing our notes to make sure the notes correctly capture the contributions of the group. Do we have your permission to record the session/interview? Do you have any questions about what I have said? Note Taker; Annex 6 44 Please mark the received number of verbal agreements (or signatures) on the consent form # Question Sub- Category MATERNAL and CHILD HEALTH and NUTRITION FACILITATOR: Introduce topic on maternal and child health and nutrition, explain that we will begin with questions about feeding their infants and young children 1 Use of exclusive breast feeding practice for infants under 6 months M: Do you exclusively breast feed your infant under 6 months of age. Follow-up for those who say yes: M: How often? M: What is the reason you breastfeed your infant exclusively? Follow up for those participants who say no, they do not breastfeed their infant exclusively • What is the reason you don’t breast feed your infant? • What do you feed your infant? Follow-up for those participants who say they do breastfeed exclusively: M: Do you ever give your infants any other foods in addition to breast feeding? If some participants in the group say yes, for mothers who give their children other foods in addition to breast-feeding: • What kinds of food do you give your infant? (Prompt as needed with following and any other examples, e.g., water, juices, milk from livestock, porridge) • What is the reason you give your infant other foods instead of just breast feeding them? For those mothers who say no, they do not give their infants and other food, ask: • What is the reason you don’t give your infants any other food? 2 M: Are there any kinds of food that are prohibited to give infants that are under six months of age? Annex 6 45 Follow-up if the answer is yes: M: What kinds of food are prohibited to give to infants? • What are the reasons these foods are prohibited? 3 M: Is the Health Extension Worker or the IP (insert the name of the partner: e.g., REST/CRS/FH-ETHIOPIA/World Vision) trying to encourage you to feed your infant through exclusive breast feeding? If some participants in the group say yes, ask the following: • Do you know why? What is the reason? Facilitator: Urge participants to tell you what they know – from mothers who do practice exclusive breast feeding and, also, from mothers who do not practice exclusive breast-feeding. Check to see if their knowledge and understanding differ. FACILITATOR: Now I would like to ask you some questions about what kinds of food you give to your children that are between six and 23 months old: 4 Minimum acceptable diet: frequency of feeding and food diversity for infants and children between 6-23 months M: What kinds of food do you give to your children in this age group between 6-23 months old? M: Why do you feed them these kinds of foods? M: Do you give the same kind of food to both girl and boy children? If some participants say no, ask the following: • What is the reason you give different kinds of food to boys than to girls? • What kinds of food do you give to boys that you don’t give to girls? 5 M: Do you feed your children of this age (between 6-23 months) the types of foods that the Health Extension Worker or the IP encourages you to give them? M: What types of food does she want you to feed your children? Do you know why? • Are there any types of food the Health Extension Worker (or the IP) wants you to give your children that you are not feeding them? If the answer is yes, ask the following: Annex 6 46 M: What kind of food aren’t you giving your children that she (or the IP) recommends? M: What is the reason you are not feeding your children x (insert the names of the actual food in the question)? 6 M: Are there any kinds of food that are prohibited to give to children in this age group? If the answer is yes, ask the following: M: What kinds of foods are prohibited? What is the reason? 7 M: At what age do you think it is important to have your children start eating meat? Probe for explanation. M: Is that the same for both boys and girls of this age? Probe for explanation. M: At what age do you think it is important to have your children start eating eggs? Probe for explanation. M: Is that the same for both boys and girls of this age? Probe for explanation. 8 M: Do men (husband, father, grandfather, uncle, brother) ever feed children between ages 6-23 months? If the answer is yes, ask the following: M: Do they feed those children different foods? (PROBE if the answer is yes) 9 M: Do any other family members (grandmothers, aunts, sisters, brothers, etc.) ever feed children between ages 6-23 months? M: Which family members feed your children in this age group? M: Do they feed those children different foods than you do? (probe if the answer is yes) Annex 6 47 10 M: Who in the family decides what children between ages 6-23 months old will eat? Can you explain why? 11 Perception and knowledge of stunting, wasting and underweight children under age five INTERVIEWER NOTE: Please use terms for acute and chronic malnutrition that the health extension worker recommends when you interviewed her at the kebele M: How do you know if your child is healthy? What should a healthy child under five years old look like? (probe for weight, height, other features) 12 M: How do you know if any of your children under five years of age have acute malnutrition? Do any of your children have chronic malnutrition? How do you know? (how can you tell?) What is chronic malnutrition? Facilitator: Now I would like to ask you some questions about the types of food you eat. (This switches the topic to mother’s health and nutritional practices) 13 Women of reproductive age: knowledge and use of nutritious foods M: What kinds of food do you eat? Why do you eat those foods? Probe for reasons M: Are these the same types of foods that the Health Extension Worker (and/or the IP) are encouraging you to eat? M: Are there any foods they would like you to eat that you are not eating? If some participants say yes, there are some recommended foods they are not eating, ask the following: M: What kinds of food are those? M: Why aren’t you eating X (insert the name/s of the food that mothers are not eating as recommended) (Prompt using these or other examples, e.g., affordability, distaste, availability, cultural prohibition, cannot be grown) 14 Women’s knowledge and use M: Do you know where a family planning service is provided Annex 6 48 of family planning and reproductive services for pre￾and post-natal care M: Do you go there to get family planning services or products? Is there a clinic or health post nearby where you can go for family planning information and methods/technologies? FACILITATOR: Please use terms recommended by the Health Extension Worker If some participants say no, they don’t go there, ask the following: • Why don’t you go there? What is the reason? Facilitator: ask the following questions for the entire group: M: Do you use family planning products/methods? Probe for why and why not -- what for reasons some participants in the group say they DO use family planning products/methods? What are the reasons some in the group say they DO NOT use family planning? M: Do your husbands support the use of family planning? 15 M: Does the clinic/health post give you instructions on how to use it? (insert the term for family planning products) If some participants say yes, ask the following: M: Are the instructions understandable? If some mothers say no, they are not given instructions on how to use the family planning product, ask the following: M: Do you understand how to use it; when to use it? . 16 M: Did you go to the clinic/health post for nearby for medical services before your baby was born? M: For those who said yes, ask why they went. What was the purpose? For those who said no, they didn’t go for services before their baby was born, ask why they did not. What are the reasons? Annex 6 49 For those participants who said yes, they went for pre-natal visits, ask the following: • How many times did you go before your baby was born? 17 M: Did you go to the clinic/facility for medical services after your baby was born? For participants who say no, they did not go for (ante-natal) visits, ask the following: • Why didn’t you go? • For participants who say yes, they did go for (ante-natal) visits, ask the following: M: Why did you go? M: How many times did you go for services after your baby was born? WATER, HEALTH and SANITATION TOPICS Facilitator introduces the set of question on health of their children 18 19 Prevalence of diarrhea among children: knowledge of causes and treatment practices; household hygiene and sanitation practices; access to water and soap M: Do any of your children suffer from frequent diarrhea? If some participants say yes, ask the following questions: • Why do you think this happens so often? • What causes diarrhea? M: Is there anything you do to prevent your children from getting frequent diarrhea? If some participants say yes, ask the following: M: What are some of the things you do? If some participants say: “washing hands”, ask the following: M: Are there certain times when it is especially important to wash your hands? If some say yes, they wash their hands, ask the following: M: What are those times when it is important to wash hands? M: Why then? M: Where is your water source for washing hands? Where do you have to go? Annex 6 50 M: Do you have hand washing stands nearby? M: Do you use soap when you wash your hands? If washing hands with soap and water is not mentioned by anyone in the group, ask the following: What about washing your hands with soap and water? Do you wash your hands with soap and water all the time? If some say no, ask the following: • Why not? What is the reason? If some say yes, they do wash their hands, ask the following: M: Are there certain times when it is especially important to wash your hands? If some of those same mothers say yes, there are certain times when it is important to wash your hands, ask the following? M: What are those times when you do wash your hands? Why then? • Where is your water source for washing hands? Where do you have to go? • Do you have hand washing stands nearby? • Do you have soap when you wash your hands 20 M: Is there a community latrine here? M: Do you use it? Follow-on: Why? And for those who participants who say no: Why not? M: Does your household have its own latrine nearby? M: Do some of your family members still defecate outside instead of using the community (or household) latrine? Follow-on if some participants say yes, some members defecate outside: M: What are the reasons why some family members do not use the community (or household) latrine? M: Do you put a cover on top of the latrine hole after using it each time? Do other family members? Annex 6 51 Follow-on: M: Why? And for those who participants who say no: Why not? M: Do you have a hand washing station near your community (or household) latrine? Follow-on if some participants say yes: M: Do you and other family members use it to wash hands with soap and water after going to the bathroom? 21 M: What do you do when your children have diarrhea? (Prompt as necessary e.g., bring the child to the nearest health facility for treatment, give the child ORS) If the mothers don’t mention ORS, ask the following: • Do you give your children ORS when they have diarrhea? Follow-up: M: Why do you give (or don’t give) your children ORS when they have diarrhea? Closing question M: Are there other issues anyone would like to bring up before we close this meeting? Team Leader Closing Remarks: Thanks all the mothers for agreeing to participate in the interview. Expresses appreciation for giving of their time. Annex 6 52 Guide for Group Interview with Women-Headed Households (WHHH) Composition of Group Participants: Households headed by women who are widowed, divorced, or abandoned; and have no male able-bodied labor living in the households. Some of the women may have young adult male children who have migrated for wage earning opportunities. INTERVIEW DATA Implementing Partner Date of Interview: Team Leader: Facilitator: Recorder: Start Time: End Time: Region: Woreda: Kebele: Village: PARTICIPANT DATA Type of Participants in Group (check one) Farmers Agro-Pastoralists Consent to record interview (check one Consent to record not given Consent given Number of Participants Introductions and purpose of the interview. Thank you for the opportunity to speak with you. My name is ______________________ and this is __________________. We are from Green Professional Services, and we are working with the US-based company MEA. We are conducting individual/group discussions with people like you in communities across several regions to strengthen and improve the PNSP. These questions in total will take approximately two hours and your participation is entirely voluntary. If you agree to participate, you can choose to stop at any time or to skip any questions you do not want to answer. Your answers will be completely confidential; we will not share information that identifies you with any one. If you choose not to participate it will not change any services or benefits that you receive now or in the future. You can stop participating at any time without penalty and without having to give any reason. You can also decline to answer any specific questions that you do not want to answer, also without penalty and without having to give any reason. We are going to record this discussion only for the purpose of reviewing our notes to make sure the notes correctly capture the contributions of the group. We will destroy the recording after the notes have been checked and finalized. Annex 6 53 Do you have any questions about what I have said? Team Leader and Facilitator Instructions for Beginning the Group Interview Team Leader: Introductions and purpose of the interview. Introduce yourself, what company you represent, and explain why you are here and for what purpose. Introduce the other two members of the team and their roles. Thank them for agreeing to participate. Asks participants to introduce themselves. Do not write down the names of participants as they introduce themselves. Facilitator: summarize the category of questions you will be asking. Inform the group that if there are any questions participants are uncomfortable with and do not wish to discuss, be clear that you will respect that and move on to the next question. Explain how the interview will be run and inform participants the meeting will take approximately two hours once it begins. Stress anonymity. You have not written down their names and won’t do so during the interview. Explain why you will be taking notes and then request permission to record. If permission is granted, read consent form, and ask for signatures (or X mark) from each participant. Thank participants again, and begin recording after the consent letter is signed. If permission is not granted, explain that the recorder will take notes, and make it clear, once again, that names of individuals will not be included in the notes. Note on questions: Unless specified, each question can be used for agricultural, agro-pastoralist, and pastoralist villages. Sometimes additional questions are added for livestock owners Remember to get responses from those in the group who respond yes to a question and from those in the group who say no to a question. Rarely will there be a unified group response. Land Ownership/Use/Agriculture and Livestock Topics Facilitator Introduces the topic of this set of questions on growing crops/raising agriculture 1 Household decision￾making: What crops are planted for sale? For family consumption? M: Do you own or rent land for growing crops? If some participants say they rent their land (sharecrop), ask the following: M: What arrangements do you make with the person who sharecrops your land? How do you get paid back? M: After harvest, do you usually receive the amount of money/and/or crops from the harvest as per your arrangement? If some participants say no, they neither own or rent land for growing crops, ask the following: Annex 6 54 Environmental factors that affect agriculture and livestock productivity and household responses M: Do you plant crops on land that other households own as a sharecropper? What crops do you plant? M: What crops do you plant for sale? What crops do you plant for family food? • How do you decide what crops to plant? • Do you have sons or any male relatives nearby that grow and harvest crops for you? (Alternatively, to graze and water your livestock?) M: What types of livestock do you raise? M: What types of livestock products you use for sale or food? 2 M: How was your crop yield from the last harvest of crops? • Was the amount as expected? • What accounts for the yield? M: How does the yield compare to the last two or three years? M: What is the reason? 3 Sources of Water: Rainfall variability and drought and HH responses Additional M: What is the source/s of water you use for growing crops (streams, well, river, lake, pond, rainfed, etc.)? M: Is there sufficient water for crops (crops for sale and/or for consumption) from this source of water all during the last growing season? If some participants say no, ask the following questions: M: What do you do when there isn’t enough water? 1. What do you do when there is a prolonged drought? Do you plant different types of crops? Annex 6 55 3a questions for agro￾pastoralists and pastoralists and for pastoralists that own livestock M: What is the source of water for your livestock? M: Is there sufficient water year-round to your livestock? M: What do you do if there is no water? 4 Availability and use of climate forecasts Availability and use of early warning information on pest and disease outbreaks Use of insurance for M: Do you use information from climate forecasts? 1. No Answer 2. Yes Answer 1. Why not 2. Why? M: Do you use the early warning information for major crops pests and disease outbreaks and livestock diseases? M: Why don’t you use this information? M: What do you usually do when there is a major outbreak of crop/livestock pests or disease? M: Are there ways to prepare so that you won’t lose all your crops/livestock? M: Were you able to save your crops (or livestock) or limit the damage? M: How do you use this information? How did you prepare? Annex 6 56 crops/livestock M: Have you ever bought crop (or livestock) insurance based on climate forecasts or early warning information on upcoming outbreaks of pests or diseases? 1. No 2. Yes • What are the reasons? Prompt for explanations as needed using some of these examples, e.g. cannot afford to pay for insurance, too complicated to apply for insurance, heard it is too hard to get insurance money when needed, etc.) a. Yes answers M: Where is the insurance from? • Did you receive insurance money after crops were destroyed/livestock died? M: Would you be willing to purchase insurance again? No answers: • Why not? What are the reasons? 6 Adoption and use of improved technologies and practices for crop production and livestock management 1. Crops M: How do you learn about ways to improve the yield of your crops? M: What type of products to increase yield have you learned about? (prompt as necessary: types of seeds, fertilizers, or pesticides to protect crops?) 2. Livestock. M: How do HHs here learn about new technologies to improve the health or productivity of their livestock? M: Have new practices for improving the quality of grazing areas been suggested? Or for improving the quality of pasturage? Annex 6 57 Experience with AEA/VEAs Agro-dealers Experience with and use of agricultural loans for buying improved technologies M: Have you tried any of the new technologies or practices to improve your crop or livestock? 1. No 2. Yes Was it helpful? No answers • Why not? • What are the reasons you have not tried any of them? Will you continue to use x technology or y practice for your crops? For your livestock? 7 M: Do you ever ask for services from agriculture extension agents (or livestock extension agents), or agro￾dealers? 1. Yes 2. No M: What services do you ever ask for? M: Was it helpful? 8 M: Have you ever applied for a loan for the purposes of buying agricultural inputs (seeds/fertilizer) or for livestock medicines/vaccines? M: Where is your loan from? M: What kind of loan is it? M: Was your loan application successful? M: Would you apply for a loan again (to buy technologies, seeds, medicines, fertilizer, etc.) Is it difficult for most households to repay these loans? Annex 6 58 Market Information and Sales Facilitator introduces the topic of use of market information on sale prices, sale locations, selling decisions, use of available storage Annex 6 59 9 10 10a Availability and use of market information Household Decisions on use of storage and timing of sales for crops and livestock products Community Storage Events triggering sale of livestock NOTE: IF YOU LEARN IN ADVANCE NO PRICE INFORMATION IS AVAILABLE, Skip to Q 10 on storage For which products you sell do you receive price information on the amount of money that will be paid? (crops or livestock) • Where does the price information on crops (or livestock products) you sell come from? Do you use the information? 1. No 2. Yes • No answers • Why don’t you use this information? What are the reasons • Yes answers • How do you use this information? • Was the information reliable/correct when you went to sell your crops/livestock/livestock? • Have you been able to earn more income from selling your crops/livestock by using this information? After harvest, do you store the crops (livestock products you intend to sell? 1. Yes. If no, skip to question below on community storage. M: What kind of containers are used for storage? What do you store in it/them? How many days can you store x, y, z before it goes bad? ATTENTION: SKIP THESE QUESTIONS ON COMMUNITY STORAGE IF YOU FIND THERE IS NONE IN THE AREAS Skip to Question 10a. What can you keep in the community storage facility? How long can you keep your crops (and/or livestock products) in this facility before you have to sell them? Do you use it? 1. No 2. Yes 1, No answers b. Why not? What are the reasons you don’t use the storage facility? Have there ever been any times when you have had to sell most or all of your livestock? What was the reason? Have you bought any replacement livestock since you had to sell all your livestock? Annex 6 60 11 Household decision making on where to sell products (crop and livestock) Household decision￾making: use of income from crop sales, sale of livestock products and other sources of income M: Where do you usually bring your farm produce and/or livestock or livestock products for sale? (Probe as necessary: to the coop where you are a member? directly to buyers at the nearest market? To sell themselves at the nearest market? Directly to intermediary buyers who then bring products to market for sale? Give to sharecroppers or others to sell, etc.) M: Do you have options on where to sell? 1. Yes 2. No How do you decide where to sell? (Probe as necessary: transportation issues, road quality/accessibility, price, easy location for transportation, distance, etc.) Annex 6 61 12 M: Where do you go to bring your products for sale? M: How do you decide to use the money you earn from selling crops/livestock products? (Probe as necessary: food to feed the HH, invest in improving livestock condition, rent additional land, purchase of improved seeds, fertilizers, purchase or rent of additional land or yearly rent of same land, HH goods, school fees, loan repayment, etc.) M: Do you have other sources of income? If the answer is no, skip to Question 13 (Probe as necessary using examples, e.g.: cash transfers, remittances, making/selling clothes, handicrafts, selling forest products, selling fish, honey, chicken eggs) WAGE EARNING OPPORTUNITIES Wage earning opportunities Facilitator describes next Section of questions is about wage earning opportunities 13 M: Do you or does anyone else in your household earn wages? 1. Yes. If no, continue below on question about migrating for work • What members of the family? • Where do they go? • What kind of work is it? • Is it seasonal? Daily? M: Do some of your members of your household migrate seasonally or long term? 1. Yes If no, skip to Question 14 • Yes answers M: Which household members? M: Where do they go? M: Do they send back money to you? Annex 6 62 M: What are the reasons those family members migrate to earn wages (either seasonally or long-term)? Poverty and Food Security Topics Team Leader introduction for topics on HH Meals, Cost of Food, and food from FFP/PSNP distribution 14 15 Intra￾household food allocation and distribution Food for Work and Use of FFP/PSNP food M: What type of food does each member of the HH eat? NOTE: If answer is all the same, skip to next question on AMOUNT of food M: Does the amount of food each member of the HH eats differ? • What is the reason different family members eat different amounts of food? M: Is there an order of eating among member of your household during mealtimes? M: Do you or any members of your household work in return for cash (or cash and food) 1. No 2. Yes M: What type of community project do beneficiary members of this village do in return for your food allotment (or cash transfer)? .M: Which household members? How do you receive your transfer? M: Where do you receive your payment from? M: How do you use it? FOOD ALLOTMENT M: Do you pick up your own food allotment? Annex 6 63 M: Do you ever sell any of the food items provided to buy other types of food available at the market? 1. Yes, If no, skip to the next question 16 HH strategies during periods of food gaps M: Does your household ever have months when there is no food or too little food? M: How often does this happen? Are there any times during the year when it usually happens? M: What are the reasons this happens? M: What do you do when you go through those times when there is not enough food for you and your family? 17 Household food purchasing decisions M: What kinds of food do you usually buy for yourself or for your household for family meals? M: Where do you usually buy it? Are there any times when you don’t or can’t buy those types of food from the market? NOTE: If answer is NO, skip to closing questions • What is the reason that happens sometimes? M: What do you do during the times you cannot buy those foods you usually purchase? 18 Closing Question Team leader asks if there are any other issues participants would like to bring up before the interview ends. Closing Remarks. Team leader thanks everyone for participating in the interview. Expresses appreciation for spending the time to do so. ANNEX 8a Tabular Summary of Endline Indicator Estimates Lower Upper FOOD SECURITY INDICATORS Average Household Dietary Diversity Score (HDDS) 4.9 4.8 5.0 4,936 652,080 1.7 0.05 2.1 Prevalence of moderate or severe food insecurity based on 30 day recall (FIES) 36.9 35.1 38.8 5,224 693,937 39.8 0.93 1.7 Male and female adults 35.3 33.4 37.1 4,170 545,246 39.7 0.94 1.5 Adult female, no adult male 44.7 41.2 48.1 857 123,088 39.0 1.75 1.3 Adult male, no adult female 33.9 28.2 39.6 187 24,351 39.9 2.88 1.0 Child, no adults 10 1,251 Prevalence of moderate or severe food insecurity based on 12 month recall (FIES) 53.5 51.5 55.4 5,224 693,937 41.5 0.97 1.7 Male and female adults 51.8 49.7 53.8 4,170 545,246 41.9 1.03 1.6 Adult female, no adult male 61.8 58.6 65.0 857 123,088 38.0 1.60 1.2 Adult male, no adult female 48.4 41.2 55.6 187 24,351 43.8 3.64 1.1 Child, no adults 10 1,251 POVERTY INDICATORS Per capita (adults only) expenditures (as a proxy for income) of USG-assisted areas $2.40 $2.33 $2.47 11,655 1,544,933 1.3 0.03 2.0 Male and female adults $2.37 $2.30 $2.44 10,411 1,368,794 1.2 0.04 1.9 Adult female, no adult male $2.54 $2.42 $2.66 1,022 147,021 1.8 0.06 1.0 Adult male, no adult female $2.79 $2.54 $3.03 222 29,108 2.1 0.13 0.8 Child, no adults Prevalence of poverty: Percentage of people (adults only) living on less than $1.25/day 21.1 19.1 23.1 11,655 1,544,933 40.8 1.00 1.8 Male and female adults 21.2 19.1 23.4 10,411 1,368,794 38.8 1.08 1.8 Adult female, no adult male 20.4 16.9 24.0 1,022 147,021 52.9 1.80 1.0 Adult male, no adult female 18.2 11.4 25.0 222 29,108 53.2 3.45 0.9 Child, no adults Depth of Poverty: Mean percentage shortfall relative to the $1.25 poverty line 5.4 4.6 6.2 11,655 1,544,933 13.2 0.40 2.2 Male and female adults 5.4 4.5 6.2 10,411 1,368,794 12.5 0.43 2.2 Adult female, no adult male 5.7 4.4 7.0 1,022 147,021 18.4 0.65 1.0 Adult male, no adult female 3.5 2.1 4.8 222 29,108 13.1 0.68 0.7 Child, no adults WASH INDICATORS Percentage of households using an improved drinking water source 43.1 39.0 47.3 5,227 694,492 49.5 2.12 3.1 Percentage of households practicing correct use of recommended household water treatment technologies 11.8 9.8 13.9 5,227 694,492 32.3 1.05 2.3 Chlorination 6.9 5.3 8.5 5,227 694,492 25.4 0.81 2.3 Flocculent/Disinfectant 2.9 2.1 3.7 5,227 694,492 16.7 0.42 1.8 Filtration 1.1 0.6 1.6 5,227 694,492 10.4 0.23 1.6 Solar 0.0 0.0 0.0 5,227 694,492 1.2 0.01 0.9 Boiling 1.2 0.9 1.6 5,227 694,492 11.1 0.19 1.2 Percentage of households that can obtain drinking water in less than 30 minutes (round trip) 23.5 20.9 26.1 5,227 694,492 42.4 1.32 2.3 Percentage of households using a basic sanitation facility 7.3 5.8 8.8 5,227 694,492 26.0 0.76 2.1 Percentage of households in target areas practicing open defecation 53.7 50.0 57.3 5,227 694,492 49.9 1.87 2.7 Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - Combined Project Areas Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 1 Lower Upper Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - Combined Project Areas Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of households with soap and water at a handwashing station commonly used by family members 1.1 0.6 1.5 5,227 694,492 10.4 0.23 1.6 AGRICULTURAL INDICATORS Percentage of farmers who used financial services (savings, agricultural credit and/or agricultural insurance in the past 12 months 37.5 35.1 39.8 6,764 911,377 48.4 1.21 2.0 Male 40.3 37.7 42.9 4,104 531,976 50.0 1.31 1.7 Female 33.5 30.5 36.5 2,660 379,401 45.9 1.52 1.7 Percentage of farmers who practiced value chain activities promoted by the project in the past 12 months 85.8 83.9 87.7 3,354 430,626 34.9 0.96 1.6 Male 87.8 86.1 89.5 2,219 274,502 34.1 0.87 1.2 Female 82.3 79.3 85.3 1,135 156,124 37.3 1.53 1.4 Percentage of farmers who used at least three sustainable agriculture (crop, livestock, and NRM) practices and/or technologies in the past 12 months 94.7 93.9 95.6 6,764 911,377 22.3 0.43 1.6 Male 97.6 96.9 98.2 4,104 531,976 15.8 0.33 1.3 Female 90.8 89.1 92.4 2,660 379,401 28.1 0.84 1.5 Percentage of farmers who used at least three sustainable crop practices and/or technologies in the past 12 months 92.1 91.1 93.2 6,418 860,889 26.9 0.53 1.6 Male 94.1 93.1 95.2 4,005 520,157 23.9 0.54 1.4 Female 89.0 87.3 90.7 2,413 340,731 30.4 0.86 1.4 Percentage of farmers who used at least three sustainable livestock practices and/or technologies in the past 12 months 71.5 69.5 73.6 5,943 808,967 45.1 1.04 1.8 Male 75.2 73.0 77.3 3,640 477,728 44.0 1.08 1.5 Female 66.3 63.5 69.2 2,303 331,239 45.7 1.44 1.5 Percentage of farmers who used at least three sustainable NRM practices and/or technologies in the past 12 months 44.1 41.9 46.3 6,764 911,377 49.7 1.11 1.8 Male 51.9 49.6 54.3 4,104 531,976 50.9 1.18 1.5 Female 33.2 30.5 35.9 2,660 379,401 45.8 1.37 1.5 Percentage of farmers who used improved storage practices in the past 12 months 26.1 23.3 28.8 6,452 865,906 43.9 1.38 2.5 Male 27.0 24.4 29.5 4,017 521,814 45.2 1.30 1.8 Female 24.7 21.3 28.0 2,435 344,092 42.0 1.70 2.0 WOMEN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight women 36.2 34.1 38.2 4,494 608,899 48.1 1.06 1.5 Prevalence of women of reproductive age who are consuming a minimum diertary diversity (MDD-W) 7.9 6.6 9.1 4,937 676,503 26.9 0.65 1.7 Contraceptive Prevalence Rate 37.3 34.8 39.8 2,812 377,633 48.4 1.27 1.4 Modern methods 36.2 33.7 38.7 2,812 377,633 48.1 1.26 1.4 Traditional methods 1.1 0.7 1.6 2,812 377,633 10.6 0.22 1.1 Percentage of births in the past 2 years receiving at least four antenatal care (ANC) visits during pregnancy 42.1 38.5 45.7 1,303 177,853 49.4 1.81 1.3 CHILDREN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight children under 5 years of age 26.9 25.1 28.8 3,381 450,575 44.4 0.93 1.2 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 2 Lower Upper Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - Combined Project Areas Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Male 28.2 25.8 30.6 1,760 239,734 44.5 1.23 1.2 Female 25.5 22.7 28.3 1,621 210,842 44.1 1.41 1.3 Prevalence of stunted children under 5 years of age 45.9 43.6 48.2 3,370 448,998 49.8 1.15 1.3 Male 48.1 45.0 51.3 1,756 238,938 49.4 1.61 1.4 Female 43.4 40.6 46.2 1,614 210,060 50.2 1.41 1.1 Prevalence of wasted children under 5 years of age 6.6 5.6 7.6 3,371 449,373 24.8 0.52 1.2 Male 6.5 5.1 7.9 1,757 239,409 24.4 0.70 1.2 Female 6.6 5.2 8.0 1,614 209,964 25.2 0.71 1.1 Percentage of children 0-23 months of age who had diarrhea in the last two weeks 22.5 19.8 25.2 1320 176,585 41.8 1.35 1.2 Male 24.3 21.0 27.7 693 93,696 42.4 1.70 1.1 Female 20.5 16.8 24.2 627 82,889 40.8 1.87 1.1 Percentage of children 0-23 months of age with diarrhea treated with ORT 34.8 28.1 41.4 275 39,763 47.7 3.35 1.2 Male 33.6 25.5 41.7 154 22,802 44.5 4.09 1.1 Female 36.4 27.1 45.6 121 16,961 47.5 4.69 1.1 Prevalence of exclusive breast-feeding of children under six months of age 76.6 71.9 81.2 380 50,682 42.4 2.36 1.1 Male 74.2 67.5 80.8 195 26,204 43.2 3.36 1.1 Female 79.1 73.2 85.0 185 24,478 41.2 2.99 1.0 Prevalence of children 6-23 months of age receiving a minimum acceptable diet (MAD) 9.2 7.0 11.3 940 125,903 28.9 1.10 1.2 Male 10.8 7.7 14.0 498 67,491 30.6 1.57 1.1 Female 7.2 4.7 9.7 442 58,412 26.2 1.27 1.0 GENDER INDICATORS Percentage of men and women who earned cash in the past 12 months 54.6 53.1 56.1 13,407 1,775,031 49.8 0.75 1.7 Male 65.2 63.9 66.5 6,553 860,685 47.7 0.65 1.1 Female 44.6 42.3 47.0 6,854 914,346 49.5 1.18 2.0 Percentage of men in union and earning cash who make decisions alone about the use of self-earned cash 33.1 31.0 35.1 3,424 457,274 47.3 1.03 1.3 Percentage of women in union and earning cash who make decisions alone about the use of self-earned cash 26.1 23.2 29.1 1,771 245,303 43.5 1.48 1.4 Percentage of men in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash 57.6 55.5 59.7 3,424 457,274 49.7 1.05 1.2 Percentage of women in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash 55.1 52.4 57.8 1,771 245,303 49.2 1.37 1.2 Percentage of men and women with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices 77.7 75.0 80.4 2,339 322,251 41.7 1.37 1.6 Male 71.2 67.9 74.5 1,051 144,749 45.0 1.69 1.2 Female 82.9 79.8 86.0 1,288 177,502 37.3 1.57 1.5 Percentage of men in union with children under two who make maternal health and nutrition decisions alone 23.9 20.9 26.8 1,049 144,538 42.3 1.50 1.1 Percentage of women in union with children under two who make maternal health and nutrition decisions alone 56.6 53.7 59.5 1,176 161,221 49.3 1.46 1.0 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 3 Lower Upper Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - Combined Project Areas Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of men in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 30.5 27.4 33.6 1,049 144,538 45.7 1.55 1.1 Percentage of women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 27.2 24.4 29.9 1,176 161,221 44.2 1.40 1.1 Percentage of men in union with children under two who make child health and nutrition decisions alone 13.2 11.0 15.4 1,049 144,538 33.6 1.11 1.1 Percentage of women in union with children under two who make child health and nutrition decisions alone 59.4 56.2 62.5 1,176 161,221 48.8 1.59 1.1 Percentage of men in union with children under two who make child health and nutrition decisions jointly with spouse/partner 32.7 29.6 35.8 1,049 144,538 46.6 1.58 1.1 Percentage of women in union with children under two who make child health and nutrition decisions jointly with spouse/partner 29.3 26.3 32.3 1,176 161,221 45.3 1.50 1.1 NA = Not available Items both highlighted in gray and italicized do not appear in the SAPQ 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 4 Lower Upper FOOD SECURITY INDICATORS Average Household Dietary Diversity Score (HDDS) 4.8 4.6 5.0 1,463 128,812 1.8 0.12 2.5 Prevalence of moderate or severe food insecurity based on 30 day recall (FIES) 63.7 60.1 67.3 1,497 131,721 41.8 1.79 1.7 Male and female adults 62.6 59.0 66.3 1,244 109,709 42.1 1.80 1.5 Adult female, no adult male 72.4 65.4 79.3 187 16,407 38.5 3.46 1.2 Adult male, no adult female 59.7 48.5 70.9 64 5,456 43.4 5.54 1.0 Child, no adults 2 149 Prevalence of moderate or severe food insecurity based on 12 month recall (FIES) 76.7 73.6 79.9 1,497 131,721 37.1 1.57 1.6 Male and female adults 76.2 72.9 79.5 1,244 109,709 37.4 1.64 1.5 Adult female, no adult male 80.7 74.5 86.8 187 16,407 34.5 3.06 1.2 Adult male, no adult female 75.6 64.6 86.5 64 5,456 37.9 5.42 1.1 Child, no adults 2 149 POVERTY INDICATORS Per capita (adults only) expenditures (as a proxy for income) of USG-assisted areas $2.80 $2.62 $2.98 3,277 287,946 1.5 0.09 2.4 Male and female adults $2.76 $2.58 $2.95 2,992 263,137 1.4 0.09 2.3 Adult female, no adult male $3.08 $2.79 $3.37 213 18,713 2.2 0.15 0.9 Adult male, no adult female $3.48 $2.98 $3.98 72 6,094 2.4 0.25 0.8 Child, no adults Prevalence of poverty: Percentage of people (adults only) living on less than $1.25/day 14.4 11.0 17.9 3,277 287,946 35.2 1.70 1.9 Male and female adults 14.8 11.4 18.3 2,992 263,137 33.9 1.71 1.8 Adult female, no adult male 13.2 6.1 20.2 213 18,713 46.9 3.48 1.0 Adult male, no adult female 1.3 -1.0 3.6 72 6,094 15.9 1.14 0.6 Child, no adults Depth of Poverty: Mean percentage shortfall relative to the $1.25 poverty line 3.9 2.9 4.8 3,277 287,946 11.7 0.48 1.6 Male and female adults 3.9 2.9 4.9 2,992 263,137 11.2 0.50 1.6 Adult female, no adult male 3.8 1.7 6.0 213 18,713 16.3 1.06 0.9 Adult male, no adult female 0.4 -0.3 1.1 72 6,094 5.0 0.36 0.6 Child, no adults WASH INDICATORS Percentage of households using an improved drinking water source 25.8 19.8 31.8 1,498 131,835 43.8 2.98 2.6 Percentage of households practicing correct use of recommended household water treatment technologies 9.4 5.4 13.4 1,498 131,835 29.2 1.98 2.6 Chlorination 4.1 1.6 6.6 1,498 131,835 19.8 1.24 2.4 Flocculent/Disinfectant 2.0 0.8 3.3 1,498 131,835 14.1 0.61 1.7 Filtration 2.6 0.7 4.5 1,498 131,835 15.9 0.93 2.3 Solar 0.0 1,498 131,835 0.0 0.0 Boiling 1.2 0.6 1.8 1,498 131,835 10.7 0.30 1.1 Percentage of households that can obtain drinking water in less than 30 minutes (round trip) 20.4 14.3 26.5 1,498 131,835 40.3 3.02 2.9 Percentage of households using a basic sanitation facility 6.8 3.7 9.9 1,498 131,835 25.2 1.53 2.4 Percentage of households in target areas practicing open defecation 47.5 39.9 55.2 1,498 131,835 50.0 3.78 2.9 Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - CRS Project Area Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 5 Lower Upper Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - CRS Project Area Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of households with soap and water at a handwashing station commonly used by family members 0.9 0.4 1.4 1,498 131,835 9.5 0.24 1.0 AGRICULTURAL INDICATORS Percentage of farmers who used financial services (savings, agricultural credit and/or agricultural insurance in the past 12 months 17.3 14.1 20.5 1,750 153,829 37.8 1.58 1.8 Male 18.6 14.9 22.3 1,268 112,161 38.8 1.84 1.7 Female 13.8 9.9 17.7 482 41,668 34.8 1.95 1.2 Percentage of farmers who practiced value chain activities promoted by the project in the past 12 months 81.7 78.1 85.2 938 85,231 38.7 1.77 1.4 Male 83.3 79.8 86.8 726 66,133 36.9 1.75 1.3 Female 76.0 69.3 82.8 212 19,098 42.4 3.36 1.2 Percentage of farmers who used at least three sustainable agriculture (crop, livestock, and NRM) practices and/or technologies in the past 12 months 94.7 93.3 96.1 1,750 153,829 22.4 0.67 1.3 Male 96.7 95.3 98.1 1,268 112,161 17.8 0.68 1.4 Female 89.3 85.9 92.7 482 41,668 31.2 1.68 1.2 Percentage of farmers who used at least three sustainable crop practices and/or technologies in the past 12 months 87.3 84.6 90.0 1,668 146,839 33.3 1.33 1.6 Male 88.7 85.7 91.8 1,245 110,350 31.5 1.53 1.7 Female 83.1 78.4 87.8 423 36,489 37.8 2.34 1.3 Percentage of farmers who used at least three sustainable livestock practices and/or technologies in the past 12 months 45.9 41.7 50.2 1,390 121,876 49.9 2.11 1.6 Male 47.6 42.2 53.1 1,003 88,476 49.8 2.71 1.7 Female 41.4 36.6 46.2 387 33,400 49.7 2.38 0.9 Percentage of farmers who used at least three sustainable NRM practices and/or technologies in the past 12 months 30.2 25.8 34.5 1,750 153,829 45.9 2.17 2.0 Male 33.4 28.5 38.3 1,268 112,161 47.0 2.43 1.8 Female 21.5 15.6 27.4 482 41,668 41.5 2.93 1.6 Percentage of farmers who used improved storage practices in the past 12 months 26.9 20.8 33.0 1,679 147,765 44.4 3.01 2.8 Male 28.4 22.4 34.5 1,251 110,841 45.0 3.00 2.4 Female 22.4 14.5 30.2 428 36,924 42.1 3.89 1.9 WOMEN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight women 32.2 28.8 35.6 1,253 110,945 46.7 1.69 1.3 Prevalence of women of reproductive age who are consuming a minimum diertary diversity (MDD-W) 7.5 5.2 9.8 1,418 127,802 26.4 1.15 1.6 Contraceptive Prevalence Rate 30.1 26.2 33.9 845 76,082 45.9 1.91 1.2 Modern methods 27.8 24.1 31.4 845 76,082 44.8 1.80 1.2 Traditional methods 2.3 1.0 3.6 845 76,082 15.0 0.66 1.3 Percentage of births in the past 2 years receiving at least four antenatal care (ANC) visits during pregnancy 33.9 28.4 39.4 427 39,144 47.4 2.72 1.2 CHILDREN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight children under 5 years of age 23.0 20.2 25.9 1,212 107,931 42.1 1.42 1.2 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 6 Lower Upper Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - CRS Project Area Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Male 23.6 20.4 26.7 608 54,296 42.4 1.56 0.9 Female 22.5 18.7 26.3 604 53,635 41.8 1.90 1.1 Prevalence of stunted children under 5 years of age 36.5 32.9 40.2 1,208 107,563 48.2 1.81 1.3 Male 37.2 32.7 41.6 607 54,196 48.3 2.19 1.1 Female 35.9 31.2 40.7 601 53,368 48.1 2.37 1.2 Prevalence of wasted children under 5 years of age 9.1 6.7 11.5 1,210 107,755 28.7 1.19 1.4 Male 9.7 6.6 12.9 608 54,296 29.6 1.58 1.3 Female 8.4 5.2 11.6 602 53,459 27.8 1.59 1.4 Percentage of children 0-23 months of age who had diarrhea in the last two weeks 18.7 13.2 24.2 432 38,886 39.1 2.73 1.5 Male 19.7 12.3 27.2 222 19,934 39.7 3.71 1.4 Female 17.7 11.9 23.5 210 18,952 38.0 2.89 1.1 Percentage of children 0-23 months of age with diarrhea treated with ORT 60.7 48.6 72.8 76 7,286 49.2 5.92 1.1 Male 59.1 39.7 78.4 41 3,937 47.4 9.60 1.3 Female 62.7 44.7 80.7 35 3,349 46.8 8.92 1.1 Prevalence of exclusive breast-feeding of children under six months of age 67.5 57.7 77.3 132 11,558 47.0 4.85 1.2 Male 63.4 51.4 75.5 72 6,466 48.0 5.96 1.1 Female 72.7 60.1 85.4 60 5,092 45.6 6.28 1.1 Prevalence of children 6-23 months of age receiving a minimum acceptable diet (MAD) 6.9 3.8 9.9 300 27,328 25.3 1.52 1.0 Male 5.4 1.7 9.2 150 13,468 22.6 1.87 1.0 Female 8.2 3.5 13.0 150 13,860 27.1 2.34 1.1 GENDER INDICATORS Percentage of men and women who earned cash in the past 12 months 52.1 49.0 55.1 3,795 333,087 50.0 1.51 1.9 Male 67.8 65.2 70.3 1,893 165,796 46.8 1.27 1.2 Female 36.5 31.4 41.7 1,902 167,291 48.2 2.55 2.3 Percentage of men in union and earning cash who make decisions alone about the use of self-earned cash 40.8 36.4 45.3 1,028 94,518 49.0 2.21 1.4 Percentage of women in union and earning cash who make decisions alone about the use of self-earned cash 31.0 25.9 36.0 431 37,589 47.2 2.51 1.1 Percentage of men in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash 36.9 33.3 40.5 1,028 94,518 48.1 1.80 1.2 Percentage of women in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash 44.0 38.8 49.2 431 37,589 50.7 2.59 1.1 Percentage of men and women with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices 77.4 72.8 82.0 759 70,765 41.8 2.29 1.5 Male 76.2 70.4 82.0 343 32,268 41.9 2.89 1.3 Female 78.4 73.3 83.6 416 38,498 40.8 2.56 1.3 Percentage of men in union with children under two who make maternal health and nutrition decisions alone 27.9 21.4 34.3 343 32,268 44.1 3.20 1.3 Percentage of women in union with children under two who make maternal health and nutrition decisions alone 50.5 46.3 54.7 388 35,960 49.5 2.09 0.8 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 7 Lower Upper Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - CRS Project Area Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of men in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 20.3 14.9 25.7 343 32,268 39.5 2.69 1.3 Percentage of women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 20.7 16.8 24.7 388 35,960 40.2 1.97 1.0 Percentage of men in union with children under two who make child health and nutrition decisions alone 18.6 13.9 23.4 343 32,268 38.3 2.35 1.1 Percentage of women in union with children under two who make child health and nutrition decisions alone 54.6 48.7 60.5 388 35,960 49.3 2.94 1.2 Percentage of men in union with children under two who make child health and nutrition decisions jointly with spouse/partner 22.8 18.2 27.4 343 32,268 41.3 2.28 1.0 Percentage of women in union with children under two who make child health and nutrition decisions jointly with spouse/partner 23.3 18.8 27.8 388 35,960 41.9 2.24 1.1 NA = Not available Items both highlighted in gray and italicized do not appear in the SAPQ 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 8 Lower Upper FOOD SECURITY INDICATORS Average Household Dietary Diversity Score (HDDS) 4.3 4.2 4.5 1,984 246,547 1.4 0.08 2.5 Prevalence of moderate or severe food insecurity based on 30 day recall (FIES) 30.4 27.6 33.3 2,139 267,804 37.0 1.42 1.8 Male and female adults 28.6 25.8 31.4 1,688 206,205 36.5 1.40 1.6 Adult female, no adult male 39.4 34.6 44.2 361 49,291 38.1 2.40 1.2 Adult male, no adult female 24.1 16.0 32.2 87 12,060 31.8 4.06 1.2 Child, no adults 3 249 Prevalence of moderate or severe food insecurity based on 12 month recall (FIES) 50.2 46.7 53.6 2,139 267,804 41.0 1.72 1.9 Male and female adults 48.4 44.8 51.9 1,688 206,205 41.3 1.80 1.8 Adult female, no adult male 60.1 55.9 64.4 361 49,291 37.8 2.13 1.1 Adult male, no adult female 40.0 29.1 51.0 87 12,060 40.6 5.49 1.3 Child, no adults 3 249 POVERTY INDICATORS Per capita (adults only) expenditures (as a proxy for income) of USG-assisted areas $2.26 $2.17 $2.34 4,707 578,215 1.2 0.04 1.7 Male and female adults $2.24 $2.16 $2.33 4,176 505,863 1.1 0.04 1.5 Adult female, no adult male $2.22 $2.06 $2.39 432 58,684 1.6 0.08 1.0 Adult male, no adult female $2.92 $2.53 $3.30 99 13,666 1.9 0.19 0.9 Child, no adults Prevalence of poverty: Percentage of people (adults only) living on less than $1.25/day 24.0 21.9 26.1 4,707 578,215 42.7 1.06 1.1 Male and female adults 23.6 21.3 25.9 4,176 505,863 40.3 1.15 1.2 Adult female, no adult male 29.3 23.3 35.2 432 58,684 58.7 2.97 1.0 Adult male, no adult female 17.0 8.9 25.1 99 13,666 49.3 4.05 0.8 Child, no adults Depth of Poverty: Mean percentage shortfall relative to the $1.25 poverty line 6.3 5.3 7.3 4,707 578,215 14.4 0.48 1.5 Male and female adults 6.2 5.2 7.1 4,176 505,863 13.4 0.46 1.4 Adult female, no adult male 8.1 5.7 10.5 432 58,684 21.6 1.21 1.1 Adult male, no adult female 4.1 1.9 6.3 99 13,666 15.2 1.10 0.7 Child, no adults WASH INDICATORS Percentage of households using an improved drinking water source 49.8 44.4 55.2 2,141 268,245 50.0 2.70 2.5 Percentage of households practicing correct use of recommended household water treatment technologies 9.0 5.7 12.2 2,141 268,245 28.6 1.61 2.6 Chlorination 6.0 3.0 9.1 2,141 268,245 23.8 1.54 3.0 Flocculent/Disinfectant 0.9 0.4 1.4 2,141 268,245 9.7 0.25 1.2 Filtration 0.9 0.4 1.4 2,141 268,245 9.6 0.26 1.3 Solar 0.0 0.0 0.1 2,141 268,245 1.9 0.04 0.9 Boiling 1.5 0.8 2.2 2,141 268,245 12.0 0.35 1.3 Percentage of households that can obtain drinking water in less than 30 minutes (round trip) 30.2 25.5 34.9 2,141 268,245 45.9 2.36 2.4 Percentage of households using a basic sanitation facility 6.6 4.8 8.3 2,141 268,245 24.8 0.87 1.6 Percentage of households in target areas practicing open defecation 44.4 37.2 51.6 2,141 268,245 49.7 3.61 3.4 Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - FH Project Area Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 9 Lower Upper Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - FH Project Area Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of households with soap and water at a handwashing station commonly used by family members 2.0 0.9 3.1 2,141 268,245 14.0 0.56 1.9 AGRICULTURAL INDICATORS Percentage of farmers who used financial services (savings, agricultural credit and/or agricultural insurance in the past 12 months 46.9 42.6 51.3 2,679 323,695 49.9 2.19 2.3 Male 49.6 45.3 53.8 1,668 203,563 49.8 2.11 1.7 Female 42.5 36.7 48.3 1,011 120,132 49.9 2.90 1.9 Percentage of farmers who practiced value chain activities promoted by the project in the past 12 months 87.7 84.5 90.8 1,421 169,389 32.9 1.58 1.8 Male 89.8 87.2 92.4 960 114,457 30.3 1.31 1.3 Female 83.3 78.1 88.4 461 54,932 37.7 2.58 1.5 Percentage of farmers who used at least three sustainable agriculture (crop, livestock, and NRM) practices and/or technologies in the past 12 months 94.0 92.5 95.5 2,679 323,695 23.7 0.74 1.6 Male 97.5 96.3 98.6 1,668 203,563 15.6 0.57 1.5 Female 88.1 84.7 91.5 1,011 120,132 32.7 1.68 1.6 Percentage of farmers who used at least three sustainable crop practices and/or technologies in the past 12 months 91.9 90.2 93.7 2,571 309,905 27.2 0.89 1.7 Male 95.2 93.5 96.8 1,621 198,548 21.4 0.84 1.6 Female 86.2 83.0 89.5 950 111,357 34.9 1.64 1.4 Percentage of farmers who used at least three sustainable livestock practices and/or technologies in the past 12 months 71.1 67.8 74.5 2,435 293,552 45.3 1.67 1.8 Male 76.2 72.6 79.8 1,556 189,248 42.4 1.81 1.7 Female 61.9 57.4 66.5 879 104,303 49.0 2.29 1.4 Percentage of farmers who used at least three sustainable NRM practices and/or technologies in the past 12 months 43.9 40.5 47.3 2,679 323,695 49.6 1.70 1.8 Male 53.8 50.3 57.4 1,668 203,563 49.6 1.80 1.5 Female 27.0 22.1 31.9 1,011 120,132 44.8 2.45 1.7 Percentage of farmers who used improved storage practices in the past 12 months 23.2 20.0 26.4 2,576 310,556 42.2 1.62 1.9 Male 23.8 20.6 26.9 1,623 199,014 42.3 1.58 1.5 Female 22.1 17.6 26.6 953 111,542 42.1 2.24 1.6 WOMEN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight women 29.5 26.5 32.4 1,785 228,520 45.6 1.49 1.4 Prevalence of women of reproductive age who are consuming a minimum diertary diversity (MDD-W) 3.1 1.8 4.3 1,941 250,554 17.2 0.63 1.6 Contraceptive Prevalence Rate 40.4 36.2 44.5 1,132 144,321 49.1 2.08 1.4 Modern methods 39.9 35.8 43.9 1,132 144,321 49.0 2.03 1.4 Traditional methods 0.5 0.0 1.0 1,132 144,321 7.2 0.26 1.2 Percentage of births in the past 2 years receiving at least four antenatal care (ANC) visits during pregnancy 31.1 26.1 36.1 488 63,928 46.3 2.50 1.2 CHILDREN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight children under 5 years of age 32.0 28.2 35.7 1,164 152,781 46.7 1.87 1.4 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 10 Lower Upper Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - FH Project Area Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Male 34.7 30.1 39.3 614 81,880 47.2 2.31 1.2 Female 28.8 23.3 34.3 550 70,900 45.7 2.74 1.4 Prevalence of stunted children under 5 years of age 54.5 50.2 58.8 1,159 152,066 49.8 2.16 1.5 Male 57.6 52.8 62.3 613 81,678 49.1 2.38 1.2 Female 50.9 45.0 56.9 546 70,388 50.4 2.97 1.4 Prevalence of wasted children under 5 years of age 7.0 5.3 8.8 1,157 151,944 25.6 0.87 1.2 Male 7.5 5.0 10.0 612 81,745 26.1 1.26 1.2 Female 6.5 4.2 8.8 545 70,199 24.8 1.16 1.1 Percentage of children 0-23 months of age who had diarrhea in the last two weeks 26.7 22.5 30.8 494 63,314 44.3 2.09 1.0 Male 30.7 25.4 36.0 257 33,229 46.1 2.66 0.9 Female 22.2 16.0 28.5 237 30,085 42.3 3.13 1.1 Percentage of children 0-23 months of age with diarrhea treated with ORT 24.1 13.8 34.3 117 16,887 42.9 5.09 1.3 Male 26.7 14.7 38.6 68 10,207 41.4 5.99 1.2 Female 20.1 7.9 32.2 49 6,680 39.6 6.09 1.1 Prevalence of exclusive breast-feeding of children under six months of age 87.9 81.7 94.1 137 19,040 32.8 3.08 1.1 Male 86.2 78.3 94.1 69 9,860 32.9 3.94 1.0 Female 89.7 81.7 97.7 68 9,180 30.3 4.01 1.1 Prevalence of children 6-23 months of age receiving a minimum acceptable diet (MAD) 6.6 3.7 9.4 357 44,273 24.8 1.42 1.1 Male 8.3 3.9 12.7 188 23,368 28.0 2.19 1.1 Female 4.7 1.5 7.8 169 20,905 21.7 1.58 0.9 GENDER INDICATORS Percentage of men and women who earned cash in the past 12 months 50.6 48.3 52.9 5,373 659,803 50.0 1.17 1.7 Male 63.7 61.5 65.8 2,629 320,152 48.5 1.09 1.2 Female 38.3 34.6 42.0 2,744 339,651 48.5 1.86 2.0 Percentage of men in union and earning cash who make decisions alone about the use of self-earned cash 23.4 20.6 26.3 1,409 175,577 42.8 1.44 1.3 Percentage of women in union and earning cash who make decisions alone about the use of self-earned cash 22.0 18.0 26.0 648 76,411 42.6 2.02 1.2 Percentage of men in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash 71.7 68.7 74.7 1,409 175,577 45.5 1.52 1.3 Percentage of women in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash 67.9 63.2 72.5 648 76,411 48.1 2.33 1.2 Percentage of men and women with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices 70.7 66.0 75.4 887 116,659 45.6 2.36 1.5 Male 64.0 58.9 69.1 404 53,429 47.2 2.56 1.1 Female 76.3 70.4 82.2 483 63,230 41.8 2.96 1.6 Percentage of men in union with children under two who make maternal health and nutrition decisions alone 21.3 16.1 26.5 403 53,355 40.2 2.61 1.3 Percentage of women in union with children under two who make maternal health and nutrition decisions alone 52.8 47.0 58.6 435 56,522 49.2 2.90 1.2 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 11 Lower Upper Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - FH Project Area Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of men in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 40.2 34.9 45.5 403 53,355 48.2 2.64 1.1 Percentage of women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 33.7 27.7 39.7 435 56,522 46.6 3.02 1.4 Percentage of men in union with children under two who make child health and nutrition decisions alone 10.0 6.6 13.4 403 53,355 29.5 1.72 1.2 Percentage of women in union with children under two who make child health and nutrition decisions alone 51.8 46.0 57.6 435 56,522 49.2 2.91 1.2 Percentage of men in union with children under two who make child health and nutrition decisions jointly with spouse/partner 42.1 36.6 47.5 403 53,355 48.6 2.74 1.1 Percentage of women in union with children under two who make child health and nutrition decisions jointly with spouse/partner 38.1 32.3 44.0 435 56,522 47.9 2.92 1.3 NA = Not available Items both highlighted in gray and italicized do not appear in the SAPQ 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 12 Lower Upper FOOD SECURITY INDICATORS Average Household Dietary Diversity Score (HDDS) 5.5 5.4 5.7 1,489 276,722 1.6 0.08 1.8 Prevalence of moderate or severe food insecurity based on 30 day recall (FIES) 30.9 27.7 34.0 1,588 294,412 36.1 1.55 1.7 Male and female adults 28.2 24.9 31.5 1,238 229,332 34.9 1.65 1.7 Adult female, no adult male 41.3 35.7 46.9 309 57,390 38.7 2.79 1.3 Adult male, no adult female 30.6 19.2 42.0 36 6,836 37.3 5.65 0.9 Child, no adults 5 854 Prevalence of moderate or severe food insecurity based on 12 month recall (FIES) 46.0 43.1 49.0 1,588 294,412 40.1 1.48 1.5 Male and female adults 43.2 39.8 46.5 1,238 229,332 39.8 1.66 1.5 Adult female, no adult male 57.8 52.3 63.4 309 57,390 39.4 2.75 1.2 Adult male, no adult female 41.4 28.4 54.4 36 6,836 39.7 6.44 1.0 Child, no adults 5 854 POVERTY INDICATORS Per capita (adults only) expenditures (as a proxy for income) of USG-assisted areas $2.35 $2.23 $2.46 3,671 678,772 1.2 0.06 1.9 Male and female adults $2.31 $2.19 $2.43 3,243 599,795 1.1 0.06 1.9 Adult female, no adult male $2.66 $2.48 $2.85 377 69,624 1.8 0.09 0.9 Adult male, no adult female $2.14 $1.77 $2.52 51 9,348 1.6 0.19 0.7 Child, no adults Prevalence of poverty: Percentage of people (adults only) living on less than $1.25/day 21.5 17.5 25.5 3,671 678,772 41.1 1.97 1.9 Male and female adults 22.1 17.8 26.4 3,243 599,795 39.0 2.13 1.9 Adult female, no adult male 15.0 9.8 20.2 377 69,624 49.2 2.57 0.9 Adult male, no adult female 30.9 13.1 48.8 51 9,348 59.3 8.82 0.9 Child, no adults Depth of Poverty: Mean percentage shortfall relative to the $1.25 poverty line 5.2 3.6 6.8 3,671 678,772 12.7 0.78 2.5 Male and female adults 5.4 3.6 7.1 3,243 599,795 12.1 0.87 2.5 Adult female, no adult male 4.1 2.4 5.8 377 69,624 16.0 0.83 0.9 Adult male, no adult female 4.6 1.8 7.4 51 9,348 10.9 1.39 0.8 Child, no adults WASH INDICATORS Percentage of households using an improved drinking water source 44.9 36.6 53.2 1,588 294,412 49.8 4.13 3.3 Percentage of households practicing correct use of recommended household water treatment technologies 15.6 12.1 19.1 1,588 294,412 36.3 1.75 1.9 Chlorination 9.0 6.7 11.3 1,588 294,412 28.6 1.14 1.6 Flocculent/Disinfectant 5.0 3.2 6.9 1,588 294,412 21.9 0.92 1.7 Filtration 0.6 0.1 1.1 1,588 294,412 7.6 0.23 1.2 Solar 0.0 1,588 294,412 0.0 0.0 Boiling 1.1 0.5 1.7 1,588 294,412 10.3 0.29 1.1 Percentage of households that can obtain drinking water in less than 30 minutes (round trip) 18.8 15.1 22.5 1,588 294,412 39.1 1.83 1.9 Percentage of households using a basic sanitation facility 8.2 5.3 11.1 1,588 294,412 27.4 1.44 2.1 Percentage of households in target areas practicing open defecation 64.8 60.1 69.5 1,588 294,412 47.8 2.33 1.9 Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - REST Project Area Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 13 Lower Upper Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - REST Project Area Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of households with soap and water at a handwashing station commonly used by family members 0.3 0.0 0.7 1,588 294,412 5.9 0.15 1.0 AGRICULTURAL INDICATORS Percentage of farmers who used financial services (savings, agricultural credit and/or agricultural insurance in the past 12 months 37.5 33.9 41.2 2,335 433,854 48.4 1.81 1.8 Male 42.8 38.2 47.5 1,168 216,252 49.6 2.29 1.6 Female 32.3 28.3 36.3 1,167 217,601 46.7 1.98 1.4 Percentage of farmers who practiced value chain activities promoted by the project in the past 12 months 86.0 82.8 89.3 995 176,007 34.7 1.60 1.5 Male 88.6 85.4 91.8 533 93,912 32.4 1.56 1.1 Female 83.1 78.5 87.6 462 82,095 38.1 2.25 1.3 Percentage of farmers who used at least three sustainable agriculture (crop, livestock, and NRM) practices and/or technologies in the past 12 months 95.3 93.9 96.6 2,335 433,854 21.2 0.67 1.5 Male 98.0 97.1 99.0 1,168 216,252 13.9 0.49 1.2 Female 92.5 90.3 94.8 1,167 217,601 26.2 1.11 1.4 Percentage of farmers who used at least three sustainable crop practices and/or technologies in the past 12 months 94.0 92.4 95.6 2,179 404,145 23.7 0.78 1.5 Male 96.0 94.7 97.4 1,139 211,260 19.6 0.68 1.2 Female 91.8 89.5 94.1 1,040 192,885 27.5 1.15 1.3 Percentage of farmers who used at least three sustainable livestock practices and/or technologies in the past 12 months 79.8 76.8 82.8 2,118 393,540 40.2 1.50 1.7 Male 86.3 83.7 88.9 1,081 200,004 34.4 1.29 1.2 Female 73.0 68.9 77.0 1,037 193,536 44.3 2.01 1.5 Percentage of farmers who used at least three sustainable NRM practices and/or technologies in the past 12 months 49.3 45.6 52.9 2,335 433,854 50.0 1.82 1.8 Male 59.7 55.8 63.7 1,168 216,252 49.1 1.96 1.4 Female 38.9 35.0 42.8 1,167 217,601 48.7 1.94 1.4 Percentage of farmers who used improved storage practices in the past 12 months 27.9 23.1 32.8 2,197 407,585 44.9 2.41 2.5 Male 29.2 24.5 33.9 1,143 211,959 45.5 2.34 1.7 Female 26.6 21.3 31.8 1,054 195,626 44.1 2.60 1.9 WOMEN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight women 43.5 39.7 47.2 1,456 269,434 49.6 1.86 1.4 Prevalence of women of reproductive age who are consuming a minimum diertary diversity (MDD-W) 12.1 9.5 14.6 1,578 298,148 32.6 1.28 1.6 Contraceptive Prevalence Rate 38.0 33.6 42.4 835 157,230 48.6 2.18 1.3 Modern methods 37.0 32.5 41.5 835 157,230 48.3 2.22 1.3 Traditional methods 1.1 0.5 1.8 835 157,230 10.5 0.33 0.9 Percentage of births in the past 2 years receiving at least four antenatal care (ANC) visits during pregnancy 55.8 48.7 62.8 388 74,782 49.7 3.49 1.4 CHILDREN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight children under 5 years of age 25.1 22.3 27.9 1,005 189,864 43.4 1.37 1.0 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 14 Lower Upper Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - REST Project Area Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Male 25.4 21.5 29.4 538 103,557 43.1 1.96 1.1 Female 24.7 20.0 29.4 467 86,306 43.6 2.33 1.2 Prevalence of stunted children under 5 years of age 44.3 40.6 48.1 1,003 189,369 49.7 1.84 1.2 Male 46.4 40.4 52.5 536 103,065 49.4 3.01 1.4 Female 41.8 37.9 45.7 467 86,304 49.9 1.93 0.8 Prevalence of wasted children under 5 years of age 4.8 3.3 6.3 1,004 189,674 21.4 0.74 1.1 Male 4.1 2.1 6.0 537 103,368 19.6 0.96 1.1 Female 5.7 3.5 7.8 467 86,306 23.4 1.08 1.0 Percentage of children 0-23 months of age who had diarrhea in the last two weeks 21.0 16.3 25.6 394 74,385 40.8 2.30 1.1 Male 21.4 15.7 27.0 214 40,533 40.7 2.80 1.0 Female 20.5 14.0 27.0 180 33,852 40.7 3.21 1.1 Percentage of children 0-23 months of age with diarrhea treated with ORT 34.2 24.1 44.4 82 15,589 47.7 4.94 0.9 Male 30.1 17.9 42.3 45 8,658 45.1 6.05 0.9 Female 39.4 24.2 54.6 37 6,931 49.6 7.53 0.9 Prevalence of exclusive breast-feeding of children under six months of age 71.0 62.5 79.5 111 20,083 45.6 4.17 1.0 Male 69.2 55.9 82.4 54 9,877 46.4 6.55 1.0 Female 72.8 62.5 83.2 57 10,206 46.0 5.15 0.8 Prevalence of children 6-23 months of age receiving a minimum acceptable diet (MAD) 12.4 8.1 16.7 283 54,302 33.1 2.13 1.1 Male 15.2 9.1 21.3 160 30,655 35.4 3.03 1.1 Female 8.9 4.1 13.7 123 23,646 28.5 2.39 0.9 GENDER INDICATORS Percentage of men and women who earned cash in the past 12 months 59.1 56.7 61.4 4,239 782,142 49.2 1.17 1.6 Male 65.4 63.3 67.5 2,031 374,737 47.6 1.03 1.0 Female 53.2 49.6 56.8 2,208 407,405 50.0 1.78 1.7 Percentage of men in union and earning cash who make decisions alone about the use of self-earned cash 38.2 34.6 41.8 987 187,178 48.6 1.78 1.2 Percentage of women in union and earning cash who make decisions alone about the use of self-earned cash 27.2 22.4 32.0 692 131,303 44.6 2.38 1.4 Percentage of men in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash 54.8 51.1 58.5 987 187,178 49.8 1.84 1.2 Percentage of women in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash 50.8 47.0 54.7 692 131,303 50.2 1.91 1.0 Percentage of men and women with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices 83.8 79.3 88.3 693 134,827 36.8 2.22 1.6 Male 74.9 68.9 81.0 304 59,052 43.0 3.01 1.2 Female 90.8 86.0 95.6 389 75,775 28.8 2.38 1.6 Percentage of men in union with children under two who make maternal health and nutrition decisions alone 24.0 19.6 28.4 303 58,915 42.4 2.19 0.9 Percentage of women in union with children under two who make maternal health and nutrition decisions alone 63.0 58.6 67.4 353 68,739 48.0 2.19 0.9 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 15 Lower Upper Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - REST Project Area Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Indicator Value Confidence Interval Number of Records Weighted Population Standard Deviation Standard Error DEFT Percentage of men in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 27.3 22.4 32.2 303 58,915 44.2 2.45 1.0 Percentage of women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 25.1 21.4 28.8 353 68,739 43.2 1.83 0.8 Percentage of men in union with children under two who make child health and nutrition decisions alone 13.1 9.4 16.8 303 58,915 33.5 1.82 0.9 Percentage of women in union with children under two who make child health and nutrition decisions alone 68.1 63.2 73.0 353 68,739 46.4 2.43 1.0 Percentage of men in union with children under two who make child health and nutrition decisions jointly with spouse/partner 29.6 24.5 34.7 303 58,915 45.2 2.53 1.0 Percentage of women in union with children under two who make child health and nutrition decisions jointly with spouse/partner 25.2 20.5 29.9 353 68,739 43.2 2.33 1.0 NA = Not available Items both highlighted in gray and italicized do not appear in the SAPQ 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 16 ALL CRS FH REST FOOD SECURITY INDICATORS Average Household Dietary Diversity Score (HDDS) 4.9 4.8 4.3 5.5 Prevalence of moderate or severe food insecurity based on 30 day recall (FIES) 36.9 63.7 30.4 30.9 Male and female adults 35.3 62.6 28.6 28.2 Adult female, no adult male 44.7 72.4 39.4 41.3 Adult male, no adult female 33.9 59.7 24.1 30.6 Child, no adults NA 0.0 0.0 0.0 Prevalence of moderate or severe food insecurity based on 12 month recall (FIES) 53.5 76.7 50.2 46.0 Male and female adults 51.8 76.2 48.4 43.2 Adult female, no adult male 61.8 80.7 60.1 57.8 Adult male, no adult female 48.4 75.6 40.0 41.4 Child, no adults NA NA NA NA POVERTY INDICATORS Per capita (adults only) expenditures (as a proxy for income) of USG-assisted areas $2.40 $2.80 $2.26 $2.35 Male and female adults $2.37 $2.76 $2.24 $2.31 Adult female, no adult male $2.54 $3.08 $2.22 $2.66 Adult male, no adult female $2.79 $3.48 $2.92 $2.14 Child, no adults Prevalence of poverty: Percentage of people (adults only) living on less than $1.25/day 21.1 14.4 24.0 21.5 Male and female adults 21.2 14.8 23.6 22.1 Adult female, no adult male 20.4 13.2 29.3 15.0 Adult male, no adult female 18.2 1.3 17.0 30.9 Child, no adults Depth of Poverty: Mean percentage shortfall relative to the $1.25 poverty line 5.4 3.9 6.3 5.2 Male and female adults 5.4 3.9 6.2 5.4 Adult female, no adult male 5.7 3.8 8.1 4.1 Adult male, no adult female 3.5 0.4 4.1 4.6 Child, no adults WASH INDICATORS Percentage of households using an improved drinking water source 43.1 25.8 49.8 44.9 Percentage of households practicing correct use of recommended household water treatment technologies 11.8 9.4 9.0 15.6 Chlorination 6.9 4.1 6.0 9.0 Flocculent/Disinfectant 2.9 2.0 0.9 5.0 Filtration 1.1 2.6 0.9 0.6 Solar 0.0 0.0 0.0 0.0 Boiling 1.2 1.2 1.5 1.1 Percentage of households that can obtain drinking water in less than 30 minutes (round trip) 23.5 20.4 30.2 18.8 Percentage of households using a basic sanitation facility 7.3 6.8 6.6 8.2 Percentage of households in target areas practicing open defecation 53.7 47.5 44.4 64.8 Percentage of households with soap and water at a handwashing station commonly used by family members 1.1 0.9 2.0 0.3 AGRICULTURAL INDICATORS Percentage of farmers who used financial services (savings, agricultural credit and/or agricultural insurance in the past 12 months 37.5 17.3 46.9 37.5 Male 40.3 18.6 49.6 42.8 Female 33.5 13.8 42.5 32.3 Percentage of farmers who practiced value chain activities promoted by the project in the past 12 months 85.8 81.7 87.7 86.0 Male 87.8 83.3 89.8 88.6 END-LINE INDICATOR VALUES Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - Comparison Across Project Areas Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 17 ALL CRS FH REST END-LINE INDICATOR VALUES Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - Comparison Across Project Areas Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] Female 82.3 76.0 83.3 83.1 Percentage of farmers who used at least three sustainable agriculture (crop, livestock, and NRM) practices and/or technologies in the past 12 months 94.7 94.7 94.0 95.3 Male 97.6 96.7 97.5 98.0 Female 90.8 89.3 88.1 92.5 Percentage of farmers who used at least three sustainable crop practices and/or technologies in the past 12 months 92.1 87.3 91.9 94.0 Male 94.1 88.7 95.2 96.0 Female 89.0 83.1 86.2 91.8 Percentage of farmers who used at least three sustainable livestock practices and/or technologies in the past 12 months 71.5 45.9 71.1 79.8 Male 75.2 47.6 76.2 86.3 Female 66.3 41.4 61.9 73.0 Percentage of farmers who used at least three sustainable NRM practices and/or technologies in the past 12 months 44.1 30.2 43.9 49.3 Male 51.9 33.4 53.8 59.7 Female 33.2 21.5 27.0 38.9 Percentage of farmers who used improved storage practices in the past 12 months 26.1 26.9 23.2 27.9 Male 27.0 28.4 23.8 29.2 Female 24.7 22.4 22.1 26.6 WOMEN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight women 36.2 32.2 29.5 43.5 Prevalence of women of reproductive age who are consuming a minimum diertary diversity (MDD-W) 7.9 7.5 3.1 12.1 Contraceptive Prevalence Rate 37.3 30.1 40.4 38.0 Modern methods 36.2 27.8 39.9 37.0 Traditional methods 1.1 2.3 0.5 1.1 Percentage of births in the past 2 years receiving at least four antenatal care (ANC) visits during pregnancy 42.1 33.9 31.1 55.8 CHILDREN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight children under 5 years of age 26.9 23.0 32.0 25.1 Male 28.2 23.6 34.7 25.4 Female 25.5 22.5 28.8 24.7 Prevalence of stunted children under 5 years of age 45.9 36.5 54.5 44.3 Male 48.1 37.2 57.6 46.4 Female 43.4 35.9 50.9 41.8 Prevalence of wasted children under 5 years of age 6.6 9.1 7.0 4.8 Male 6.5 9.7 7.5 4.1 Female 6.6 8.4 6.5 5.7 Percentage of children 0-23 months of age who had diarrhea in the last two weeks 22.5 18.7 26.7 21.0 Male 24.3 19.7 30.7 21.4 Female 20.5 17.7 22.2 20.5 Percentage of children 0-23 months of age with diarrhea treated with ORT 34.8 60.7 24.1 34.2 Male 33.6 59.1 26.7 30.1 Female 36.4 62.7 20.1 39.4 Prevalence of exclusive breast-feeding of children under six months of age 76.6 67.5 87.9 71.0 Male 74.2 63.4 86.2 69.2 Female 79.1 72.7 89.7 72.8 Prevalence of children 6-23 months of age receiving a minimum acceptable diet (MAD) 9.2 6.9 6.6 12.4 Male 10.8 5.4 8.3 15.2 Female 7.2 8.2 4.7 8.9 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 18 ALL CRS FH REST END-LINE INDICATOR VALUES Table xxx. FFP/EVELYN Ethiopia ENDLINE Indicators - Comparison Across Project Areas Indicators, 95% Confidence Intervals and Base Population [Ethiopia, 2017] GENDER INDICATORS Percentage of men and women who earned cash in the past 12 months 54.6 52.1 50.6 59.1 Male 65.2 67.8 63.7 65.4 Female 44.6 36.5 38.3 53.2 Percentage of men in union and earning cash who make decisions alone about the use of self-earned cash 33.1 40.8 23.4 38.2 Percentage of women in union and earning cash who make decisions alone about the use of self-earned cash 26.1 31.0 22.0 27.2 Percentage of men in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash 57.6 36.9 71.7 54.8 Percentage of women in union and earning cash who make decisions jointly with spouse/partner about the use of self-earned cash 55.1 44.0 67.9 50.8 Percentage of men and women with children under two who have knowledge of maternal and child health and nutrition (MCHN) practices 77.7 77.4 70.7 83.8 Male 71.2 76.2 64.0 74.9 Female 82.9 78.4 76.3 90.8 Percentage of men in union with children under two who make maternal health and nutrition decisions alone 23.9 27.9 21.3 24.0 Percentage of women in union with children under two who make maternal health and nutrition decisions alone 56.6 50.5 52.8 63.0 Percentage of men in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 30.5 20.3 40.2 27.3 Percentage of women in union with children under two who make maternal health and nutrition decisions jointly with spouse/partner 27.2 20.7 33.7 25.1 Percentage of men in union with children under two who make child health and nutrition decisions alone 13.2 18.6 10.0 13.1 Percentage of women in union with children under two who make child health and nutrition decisions alone 59.4 54.6 51.8 68.1 Percentage of men in union with children under two who make child health and nutrition decisions jointly with spouse/partner 32.7 22.8 42.1 29.6 Percentage of women in union with children under two who make child health and nutrition decisions jointly with spouse/partner 29.3 23.3 38.1 25.2 NA = Not available Items both highlighted in gray and italicized do not appear in the SAPQ 20180119_EVE_Ethiopia_2017 ENDLINE Indicator Estimates_adultpoverty.xlsx Page 19 ANNEX 8b Comparison of Baseline and Endline Indicator Estimates Baseline (BL) End-line (EL) Raw Difference (EL - BL) Significance Level1 BL (N) EL (N) FOOD SECURITY INDICATORS Average Household Dietary Diversity Score (HDDS) 3.9 4.8 0.9 *** 1,524 1,463 WASH INDICATORS Percentage of households using an improved source of drinking water 23.6 25.8 2.2 1,467 1,498 Percentage of households using improved sanitation facilities 41.7 6.8 -34.9 *** 1,520 1,498 Percentage of households practicing open defacation 38.3 47.5 9.2 1,517 1,498 Percentage of households with soap and water at a handwashing station commonly used by family members WOMEN'S HEALTH AND NUTRITION INDICATORS Percentage of births in the past 2 years receiving at least four antenatal care (ANC) visits during pregnancy CHILDREN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight children under 5 years of age 27.1 23.0 -4.1 * 1,491 1,212 Male 25.0 23.6 -1.4 1,034 608 Female 32.0 22.5 -9.5 ** 457 604 Prevalence of stunted children under 5 years of age 44.6 36.5 -8.0 * 1,489 1,208 Male 43.8 37.2 -6.6 * 1,032 607 Female 46.3 35.9 -10.4 * 457 601 Prevalence of wasted children under 5 years of age 12.8 9.1 -3.8 * 1,481 1,210 Male 12.2 9.7 -2.5 1,028 608 Female 14.2 8.4 -5.8 * 453 602 Percentage of children 0-23 months of age who had diarrhea in the last two weeks Male Female Percentage of children 0-23 months of age with diarrhea treated with ORT Male Female Prevalence of exclusive breast-feeding of children under six months of age 24.9 67.5 42.6 *** 144 132 Male 21.2 63.4 42.3 *** 80 72 Female 29.5 72.7 43.2 *** 64 60 Prevalence of children 6-23 months of age receiving a minimum acceptable diet (MAD) 2.8 6.9 4.1 * 463 300 Male 2.6 5.4 2.8 229 150 Female 3.0 8.2 5.3 * 234 150 1 ns = not significant, † p<0.1,* p<0.05, ** p<0.01, *** p<0.001 NA : Not available Ethiopia FFP Development Food Assistance Programs Comparison of 2012 Baseline and 2017 End-line Indicators in the CRS Project Area Baseline (BL) End-line (EL) Raw Difference (EL - BL) Significance Level1 BL (N) EL (N) FOOD SECURITY INDICATORS Average Household Dietary Diversity Score (HDDS) 3.1 4.3 1.3 *** 1,519 1,984 WASH INDICATORS Percentage of households using an improved source of drinking water 47.9 49.8 1.9 1,312 2,141 Percentage of households using improved sanitation facilities 23.0 6.6 -16.4 *** 1,530 2,141 Percentage of households practicing open defacation 29.2 44.4 15.2 ** 1,527 2,141 Percentage of households with soap and water at a handwashing station commonly used by family members 8.2 2.0 -6.2 *** 1,502 2,141 WOMEN'S HEALTH AND NUTRITION INDICATORS Percentage of births in the past 2 years receiving at least four antenatal care (ANC) visits during pregnancy 34.0 31.1 -2.8 289 488 CHILDREN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight children under 5 years of age 50.2 32.0 -18.3 *** 679 1,164 Male 48.8 34.7 -14.0 *** 523 614 Female 55.3 28.8 -26.5 *** 156 550 Prevalence of stunted children under 5 years of age 63.1 54.5 -8.6 ** 679 1,159 Male 62.1 57.6 -4.5 523 613 Female 66.2 50.9 -15.3 ** 156 546 Prevalence of wasted children under 5 years of age 20.4 7.0 -13.3 *** 664 1,157 Male 19.2 7.5 -11.7 *** 514 612 Female 24.5 6.5 -18.0 *** 150 545 Percentage of children 0-23 months of age who had diarrhea in the last two weeks 26.1 26.7 0.6 261 494 Male 23.9 30.7 6.8 109 257 Female 27.7 22.2 -5.5 152 237 Percentage of children 0-23 months of age with diarrhea treated with ORT 23.6 24.1 0.4 68 117 Male NA 26 68 Female 14.4 20.1 5.6 42 49 Prevalence of exclusive breast-feeding of children under six months of age 40.0 87.9 47.9 *** 95 137 Male 28.5 86.2 57.7 *** 42 69 Female 49.1 89.7 40.5 *** 53 68 Prevalence of children 6-23 months of age receiving a minimum acceptable diet (MAD) 0.5 6.6 6.0 *** 188 357 Male 1.2 8.3 7.0 ** 80 188 Female 0.0 4.7 4.7 ** 108 169 1 ns = not significant, † p<0.1,* p<0.05, ** p<0.01, *** p<0.001 NA : Not available Ethiopia FFP Development Food Assistance Programs Comparison of 2012 Baseline and 2017 End-line Indicators in the FH Project Area Baseline (BL) End-line (EL) Raw Difference (EL - BL) Significance Level1 BL (N) EL (N) FOOD SECURITY INDICATORS Average Household Dietary Diversity Score (HDDS) 4.8 5.5 0.7 *** 1,539 1,489 WASH INDICATORS Percentage of households using an improved source of drinking water Percentage of households using improved sanitation facilities Percentage of households practicing open defacation Percentage of households with soap and water at a handwashing station commonly used by family members 7.6 0.3 -7.3 *** 1,531 1,588 WOMEN'S HEALTH AND NUTRITION INDICATORS Percentage of births in the past 2 years receiving at least four antenatal care (ANC) visits during pregnancy 72.4 55.8 -16.6 40 388 CHILDREN'S HEALTH AND NUTRITION INDICATORS Prevalence of underweight children under 5 years of age 29.4 25.1 -4.3 † 849 1,005 Male 28.0 25.4 -2.6 593 538 Female 32.6 24.7 -7.9 + 256 467 Prevalence of stunted children under 5 years of age 51.0 44.3 -6.6 * 848 1,003 Male 51.2 46.4 -4.7 592 536 Female 50.5 41.8 -8.7 * 256 467 Prevalence of wasted children under 5 years of age 8.4 4.8 -3.6 ** 847 1,004 Male 7.3 4.1 -3.3 * 591 537 Female 11.0 5.7 -5.4 * 256 467 Percentage of children 0-23 months of age who had diarrhea in the last two weeks 68.9 21.0 -48.0 *** 42 394 Male 16 214 Female 26 180 Percentage of children 0-23 months of age with diarrhea treated with ORT 29 82 Male 8 45 Female 21 37 Prevalence of exclusive breast-feeding of children under six months of age 66.4 71.0 4.6 104 111 Male 75.9 69.2 -6.8 50 54 Prevalence of children 6-23 months of age receiving a minimum acceptable diet Female 57.6 72.8 15.3 54 57 (MAD) 5.1 12.4 7.3 ** 233 283 Male 8.0 15.2 7.1 + 87 160 Female 3.4 8.9 5.5 + 146 123 1 ns = not significant, † p<0.1,* p<0.05, ** p<0.01, *** p<0.001 NA : Not available Ethiopia FFP Development Food Assistance Programs Comparison of 2012 Baseline and 2017 End-line Indicators in the REST Project Area ANNEX 9 Descriptive and Bivariate Analyses Table A9.1. Percentage of farmers by types of crops planted during the past 12 months and sex [Endline Study, Ethiopia, 2017] All Male farmer Female farmer Sig. All Male farmer Female farmer Sig. All Male farmer Female farmer Sig. Teff 15.1 15.3 14.4 63.2 64.9 60.1 * 73.7 77.3 69.8 *** Maize 75.9 74.9 78.8 35 34.4 36.2 60.5 63.4 57.3 ** Wheat 18.0 17.7 19.0 73.7 77.6 66.8 *** 54.7 54.9 54.4 Millet 24.4 26.1 19.4 * 5.1 6.2 3.2 39.4 42 36.7 ** Barely 8.7 8.9 8.4 61.4 64.5 55.8 *** 51.3 51.8 50.8 Sorghum 46.1 45.3 48.6 44.5 43.9 45.5 53.7 56.9 50.1 *** Soybean 3.9 4.0 3.8 13.1 13.5 12.2 1 1.2 0.9 Legumes (bean, lentils) 10.9 10.0 13.6 * 57.9 63.4 48.1 *** 29.1 30.1 28 Oilseed (sunflower, mustard, sesame) 1.1 1.2 0.8 21.6 22 20.8 9.8 10.8 8.8 *** Fruits 8.4 8.8 7.1 2.5 3.2 1.2 ** 10 10.5 9.4 Potato 10.1 11.4 6.4 ** 34.8 33.9 36.6 2.7 2.8 2.6 Chat 49.4 51.0 44.5 * 1 1.1 0.6 0.7 0.7 0.7 Coffee 11.5 11.1 12.5 1.3 1.3 1.4 2.3 2.5 2 Groundnuts 2.8 2.5 3.5 0.2 0.2 0.2 1.7 2.1 1.2 *** Spices 0.2 0.1 0.6 * 10.1 11 8.6 9.4 9.7 9.2 Vegetables 19.1 20.3 15.5 * 23.8 24.7 22.3 23.2 23.9 22.5 Others 10.4 10.7 9.4 19.6 21.9 15.4 ** 21.6 21.4 21.8 Number of farmers 1,668 1,245 423 2,571 1,621 950 2,179 1,139 1,040 *p<0.05 **p<0.01 *** p<0.001 CRS FH REST CRS FH REST Cattle 65.8 57.2 57.6 Goats 54.1 23.4 30.5 Poultry 54.1 63.8 75.3 Donkey or mule 38.4 45.5 49.9 Oxen 32.2 53.4 64.2 Sheep 30.3 36.9 25.9 Honey bees (hives) 7.0 9.3 15.0 Camels 2.7 0.2 2.4 Horse 1.6 6.1 0.4 Any livestock 90.7 86.4 90.4 Number of households 1,498 2,141 1,588 A9.2 Percentage of households owning livestock (at least one) by project area, FFP Endline Study [Ethiopia, 2017] Table A9.3. Percentage of farmers using financial services by sex and type of financial services [Endline Study, Ethiopia 2017] All Male Female All Male Female All Male Female All Male Female Credit 15.4 17.0 13.1 9.5 10.2 7.7 21.5 24.1 17.1 12.9 14.0 11.9 Savings 31.6 34.3 27.9 10.3 11.3 7.5 42.4 44.7 38.4 31.1 36.3 26.0 Insurance 1.8 1.8 1.6 0.8 0.6 1.3 0.6 0.9 0.1 3.0 3.4 2.6 None 62.5 59.7 66.5 82.7 81.4 86.2 53.1 50.4 57.5 62.5 57.2 67.7 Number of farmers 6,764 4,104 2,660 1,750 1,268 482 2,679 1,668 1,011 2,335 1,168 1,167 Overall CRS FH REST Male 59.0 ** 56.2 *** 43.4 *** Female 45.8 45.7 37.7 All 55.4 52.3 40.6 Number of responding farmers 1,750 2,679 2,335 *p<0.05 **p<0.01 *** p<0.001 Table A9.4a. Proportion of farmers that plant crops or raise/buy livestock with the specific intention to sell or resell CRS FH REST All Male Female All Male Female All Male Female All Male Female Purchase of inputs through agro-dealers and/or community associations 64.2 64.5 63.4 45.2 46.7 40.1 65.4 68.9 58.4 80.2 81.1 79.2 Use of mobile financial services 0.1 0.1 0.1 0.2 0.3 0.0 0.1 0.1 0.2 … … … Use of financial services other than mobile (excluding insurance) 2.1 2.5 1.2 0.4 0.6 0.0 3.0 3.5 2.0 2.2 3.2 1.1 Use of training and extension services 29.5 32.3 23.9 21.2 23.7 12.7 32.7 34.9 28.0 32.7 39.4 24.9 Contract farming 0.9 1.1 0.5 1.0 1.2 0.0 1.4 1.6 1.1 0.1 0.0 0.2 Use of feed lots or pen feeding 34.3 37.3 28.6 34.4 35.3 31.6 52.5 53.5 50.3 8.3 10.7 5.6 Drying, processing and packaging for selling/storage 6.9 8.2 4.2 9.7 11.3 4.2 7.1 7.7 5.9 3.9 5.1 2.6 Trading or marketing produce through agro-vets, community associations and/or cooperatives 6.9 7.3 6.3 5.4 4.7 8.0 12.5 12.9 11.7 0.3 0.6 0.0 Use of formal marketing systems for livestock and/or vegetables and/or fruits, spices, honey, organic coffee, etc. 21.8 23.1 19.2 33.2 32.8 34.4 27.3 26.7 28.6 3.2 3.6 2.8 Did not practice any of these activities 13.0 11.4 16.0 18.1 16.4 24.1 9.2 7.7 12.4 13.5 11.3 16.0 Number of responding farmers 3,354 2,219 1,135 938 726 212 1,421 960 461 995 533 462 Overall CRS FH REST Table A9.4b. Percentage of Farmers by Type of Value Chain Activity and Sex of Farmer Table A9.5. Percentage of farmers by type of sustainable agricultural practice and sex of farmer [Endline Study, Ethiopia 2017] All Male Female All Male Female All Male Female All Male Female Crops A. Micro dosing 14.5 15.8 12.4 17.9 18.4 16.3 15.2 15.9 13.8 12.7 14.3 10.9 B. Manure 68.6 70.2 66.2 71.4 70.3 74.6 62.8 65.7 57.6 72.1 74.4 69.6 C. Compost 46.5 49.6 41.9 27.7 30.1 20.4 52.8 57.6 44.1 48.6 52.2 44.7 D. Planting basins 5.5 6.2 4.4 9.2 9.8 7.2 4.8 5.3 4.0 4.6 5.1 4.0 E. Mulching 7.7 9.3 5.3 11.0 11.1 10.8 8.3 9.6 6.0 6.1 8.1 3.8 F. Weed control 81.0 82.3 79.0 78.3 80.1 72.8 84.9 86.0 82.8 79.0 79.8 78.0 G. Dry planting 31.1 32.8 28.6 16.3 17.0 14.2 31.1 34.9 24.4 36.5 39.1 33.7 H. Ripping into residues 5.2 6.0 3.9 7.5 7.7 6.8 7.5 8.7 5.2 2.6 2.5 2.6 I. Clean ripping 16.4 17.4 14.8 25.6 25.0 27.7 15.5 15.5 15.4 13.8 15.3 12.1 J. Tied ridges 28.1 30.7 24.2 24.7 25.6 21.9 28.1 31.9 21.4 29.3 32.1 26.2 K. Pot-holing 3.0 3.6 2.0 2.7 3.1 1.7 4.2 5.0 2.8 2.1 2.6 1.5 L. Crop rotations 77.2 77.2 77.2 36.9 37.5 35.0 86.5 89.6 81.0 84.8 86.3 83.1 M. Intercropping 24.2 28.2 18.0 45.0 45.8 42.5 31.2 34.9 24.6 11.2 12.7 9.6 N. Integrated Pest Management (IPM) 14.8 15.7 13.3 13.2 14.2 10.0 12.5 13.9 9.9 17.1 18.2 15.8 O. Early planting or planting with first rains 45.3 49.4 39.1 41.3 42.3 38.6 55.3 60.4 46.1 39.1 42.7 35.2 P. Use of improved crop varieties 30.0 30.6 29.1 17.3 17.3 17.2 20.3 22.6 16.2 42.1 45.1 38.9 Q. Contour planting 12.1 13.2 10.4 5.1 5.4 4.3 13.7 14.9 11.5 13.4 15.7 10.9 R. Terracing 52.2 58.0 43.4 49.5 52.6 40.0 66.1 73.8 52.3 42.6 46.0 38.9 S. Land leveling 31.9 34.9 27.5 19.4 20.0 17.5 38.0 41.7 31.2 31.9 36.1 27.3 U. Micro-irrigation technology (MIT) 7.7 8.5 6.3 3.6 4.2 1.8 7.1 8.3 5.1 9.5 10.9 7.9 V. Crop thinning 24.4 27.4 19.7 32.7 34.0 28.9 17.9 20.0 14.2 26.3 30.9 21.2 W. Row Planting 28.5 31.5 23.8 43.1 44.5 38.7 30.2 32.4 26.2 21.9 23.9 19.7 X. Sequential or double cropping 7.8 8.9 6.2 4.5 4.9 3.2 8.8 10.5 5.9 8.3 9.6 6.9 Z. Improved fertilizer 65.8 65.4 66.5 27.9 28.4 26.5 60.7 64.8 53.4 83.5 85.3 81.6 Y. Did not use any of these practices in the past 12 months 1.0 0.4 1.9 0.4 0.4 0.4 1.2 0.3 2.8 1.1 0.6 1.6 Number of responding farmers 6,418 4,005 2,413 1,668 1,245 423 2,571 1,621 950 2,179 1,139 1,040 Livestock A. Improved animal shelters 8.5 9.8 6.6 10.1 11.2 7.0 12.8 13.1 12.1 4.8 6.0 3.6 B. Vaccinations 70.8 73.0 67.5 61.6 61.5 62.1 65.3 69.0 58.6 77.7 82.0 73.2 C. Deworming 45.3 46.7 43.4 35.5 34.9 37.1 29.5 32.4 24.2 60.2 65.4 54.7 D. Castration 11.5 13.9 8.1 4.4 4.3 4.8 12.0 14.7 6.9 13.4 17.3 9.4 E. Dehorning 0.4 0.6 0.1 0.0 0.0 0.0 0.8 1.2 0.1 0.2 0.2 0.2 F. Homemade animal feeds made of locally available products 48.3 50.5 45.2 26.5 26.0 27.7 59.0 62.4 52.8 47.1 50.0 44.1 G. Animal feed supplied by stockfeed manufacturer 8.7 9.0 8.2 11.6 11.9 11.0 6.0 6.7 4.9 9.7 10.0 9.4 H. Artificial insemination 5.2 5.8 4.4 1.1 1.2 0.7 3.5 4.5 1.6 7.8 9.0 6.5 I. Pen feeding 15.2 18.4 10.8 22.9 24.9 17.5 27.9 29.8 24.5 3.4 4.6 2.2 J. Fodder production 11.7 13.1 9.7 11.1 12.0 8.6 14.3 16.1 11.1 10.0 10.9 9.2 Overall CRS FH REST Table A9.5. Percentage of farmers by type of sustainable agricultural practice and sex of farmer [Endline Study, Ethiopia 2017] All Male Female All Male Female All Male Female All Male Female Overall CRS FH REST K. Used the services of community animal health workers/paravets 35.9 38.9 31.5 8.5 9.0 7.4 40.7 45.4 32.2 40.8 46.1 35.3 L. Emergency feed reserve 52.4 55.3 48.3 21.5 22.4 19.2 53.7 58.9 44.3 61.1 66.5 55.4 M. Cut and carry system 63.6 66.5 59.4 45.6 46.9 42.2 58.5 62.4 51.5 73.0 79.0 66.7 N. Controlled grazing 27.7 29.3 25.4 3.6 3.8 2.9 28.1 31.0 22.8 34.9 39.0 30.7 O. improved bee keeping 3.1 3.6 2.4 1.0 1.3 0.0 2.2 2.9 1.0 4.4 5.2 3.5 Y. Did not use any of these practices in the past 12 months 9.3 6.0 14.0 13.1 12.7 14.1 8.8 4.4 16.7 8.4 4.5 12.5 Number of responding farmers 5,943 3,640 2,303 1,390 1,003 387 2,435 1,556 879 2,118 1,081 1,037 Natural resource management A. Management or protection of watersheds or water catchments 57.3 58.8 57.5 59.9 61.3 59.0 60.0 61.3 60.5 54.4 56.1 54.8 B. Agro-forestry 6.6 6.8 6.7 14.0 14.6 15.5 5.4 5.5 5.4 4.8 4.9 4.9 C. Management of forest plantation 45.9 47.1 46.5 27.1 27.6 27.8 48.0 49.3 47.9 51.0 52.4 51.3 D. Regeneration of natural landscapes 48.4 49.8 49.0 33.1 34.1 33.0 40.7 41.9 41.3 59.5 61.2 59.7 E. Sustainable harvesting of forest products 11.7 12.0 12.0 3.5 3.6 3.9 13.4 13.7 13.4 13.3 13.6 13.4 F. Rotational grazing or trans-humane system of livestock feeding 11.8 12.4 12.4 1.6 1.6 1.8 21.8 22.7 22.3 8.0 8.4 8.3 G. Hedge-row planting 25.4 26.1 25.6 17.0 17.3 16.7 25.6 26.4 25.8 28.3 29.1 28.2 H. Water resource management 18.7 19.4 19.3 7.5 7.8 7.4 23.3 24.2 23.6 19.3 19.9 19.7 Y. Did not use any of these practices in the past 12 months 25.7 23.9 25.4 32.1 30.7 33.4 26.1 24.6 25.8 23.0 21.0 22.6 Number of responding farmers 6,766 4,104 2,662 1,750 1,268 482 2,681 1,668 1,013 2,335 1,168 1,167 Table A9.6. Percentage of farmers by type of storage practice [Endline Sutdy, Ethiopia 2017] All Male Female All Male Female All Male Female All Male Female Hermatic storage 8.4 9.9 6.0 23.9 25.4 19.2 9.0 9.6 7.9 2.3 2.2 2.4 Improved granary 1.5 1.5 1.5 0.5 0.6 0.3 1.4 1.6 1.2 2.0 1.9 2.0 Warehousing or cereal banks 0.1 0.1 0.2 0.1 0.1 0.0 0.1 0.1 0.1 0.2 0.1 0.3 Use of trap for mice 9.8 9.8 9.9 0.5 0.6 0.3 7.0 7.3 6.4 15.4 17.0 13.7 Grain bags with bio-pesticides 5.9 5.3 6.8 2.0 1.8 2.5 3.0 2.9 3.1 9.6 9.5 9.7 Diffused light storage 3.6 3.7 3.6 0.1 0.1 0.2 9.9 9.4 10.7 0.2 0.2 0.1 Didn't use any of the above methods 58.6 57.3 60.4 35.5 34.6 38.1 68.0 68.0 68.0 59.7 59.2 60.3 Number of responding farmers 6,452 4,017 2,435 1,679 1,251 428 2,576 1,623 953 2,197 1,143 1,054 Overall CRS FH REST Table A9.7. Household Sanitation and Drinking Water Sanitation facility, source of drinking water and treatment for drinking water [Endline Study, Ethiopia 2017] Overall CRS FH REST Improved, not shared sanitation facility Flush to septic tank 0.0 … 0.1 … Flush to pit latrine 0.2 0.9 … … Ventilated improved pit latrine 0.4 0.0 0.2 0.7 Pit latrine with slab 6.7 5.7 6.3 7.4 Composting toilet 0.0 0.1 … 0.1 Improved, shared sanitation facility Flush to piped sewer system … … … … Flush to septic tank … … … … Flush to pit latrine 0.1 1.8 0.1 0.1 Ventilated improved pit latrine 0.1 … 0.1 0.0 Pit latrine with slab 2.6 … 3.7 2.0 Composting toilet 0.0 … 0.0 … Non-improved sanitation facility Flush to somewhere else … … … … Flush to don't know where … … … … Latrine Without Slab/Open Pit 36.1 43.4 45.0 24.8 Bucket toilet 0.0 … … 0.0 Hanging toilet/latrine 0.0 0.2 … … No Facility/Bush/Field 53.7 47.5 44.4 64.8 Other 0.1 0.3 0.1 0.1 Improved source of drinking water Piped into home 0.1 … … 0.2 Piped into yard/plot 0.3 0.5 0.1 0.3 Piped to neighbor 1.5 5.0 0.1 1.2 Piped to public tap/standpipe 20.8 28.9 22.9 15.3 Tubewell or borehole 29.1 8.2 23.4 43.6 Protected well 3.4 0.2 8.2 0.5 Protected spring 8.5 6.1 15.3 3.5 Rainwater 1.4 1.7 0.1 2.4 Bottled water 0.0 0.1 … … Non-improved source of drinking water Unprotected/dug well 1.8 0.2 1.1 3.1 Unprotected spring 24.8 41.3 17.9 23.6 Tanker truck … … … … Cart with small tank 0.1 0.1 0.0 0.1 Surface water (river/dam/ lake/ponds/stream/canal/irrigation channel) 8.3 7.6 10.8 6.3 Other 0.1 0.1 0.2 … Water availability Water is generally available year round (% yes) 70.7 68.1 71.9 70.8 Water is generally unavailable for a day or more during the last 2 weeks (% no) 87.3 80.2 92.5 85.7 Number of responding households 5,227 1,498 2,141 1,588 Overall CRS FH REST BMI Percent less than 145 cm 3.3 2.0 4.3 3.1 Mean Body Mass Index (BMI) 19.5 19.7 19.7 19.2 Normal (%) 18.5-24.9 (total normal) 61.3 63.5 68.5 54.2 Underweight (%) <18.5 (total underweight) 36.1 32.2 29.4 43.5 17.0-18.4 (mildly underweight) 21.7 20.7 18.9 24.6 <17 (moderately and severely underweight) 14.4 11.5 10.5 18.9 Overweight/obese (%) ≥25 (total overweight or obese) 2.6 4.3 2.0 2.3 25.0-29.9 (overweight) 2.3 4.0 1.8 1.9 ≥30.0 (obese) 0.3 0.3 0.2 0.4 Number of non-pregnant women of reproductive age 4,494 1,253 1,785 1,456 Table A9.8. Height and BMI levels of non-pregnant women 15-49 years of age [Endline Study, Ethiopia 2017] REST Baseline 2012 Endline 2017 Baseline 2012 Endline 2017 Endline 2017 Grains, roots and tubers 97.0 98.1 92.2 99.7 99.8 Legumes, beans, nuts and seeds1 34.9 32.1 61.1 88.0 65.2 Dairy products (milk, yogurt, cheese) 32.2 40.8 4.2 6.4 12.8 Eggs 6.8 5.4 1.7 6.9 13.9 Flesh foods, including organ meat and misc. small animal protein1 4.5 1.4 3.4 8.9 13.9 Vitamin A dark green leafy vegetables 16.0 10.2 5.8 0.9 22.3 Other Vitamin A rich vegetables and fruits 29.0 10.5 5.7 3.7 6.5 Other fruits and vegetables2 30.2 49.7 3.4 20.8 62.9 Number of responding women 15-49 years 366 1,418 76 1,941 1,578 NOTE: The baseline report provided information on women’s food consumption patterns in CRS and FH only, therefor baseline data is provided for those project areas only. Additionally, the baseline estimates are unweighted, but the endline estimates are weighted. The baseline report uses the nine food groups that comprise the women's dietary diversity score (WDDS) while the endline report used the 10 groups that comprise the indicator for minimum dietary diversity (MDD-W). The WDDS combines beans, legumes, nuts, and seeds in one category, while the MDDS-W distinguishes between legumes and beans on one hand, and nuts and seeds on the other. (2) The MDD-W combines organ meat and flesh foods into one group, while the WDDS distinguishes between organ meat as one group and flesh foods as another. (3) The MDD-W treats other fruits and other vegetables as two separate categories, while the WDDS combines them into one food group. 2 The baseline report provides the estimates for the consumption of flesh foods and organ meat separately. In order to facilitate comparison with the endline the baseline estimates for the two categories were summed. 3 The baseline report provides a combined average for the consumption of fruits and vegetables. The endline distinguishes between these two groups. To facilitate comparison, the endline estimate for other fruits and other vegetables are combined. At endline 44.6 percent of women in CRS consumed other fruits and 5.1 percent consumed other vegetables. In FH, 17.2 percent consumed other fruits and 3.6 percent consumed other vegetables at endline. Table A9.9. Percentage of food groups consumed by women 15-49 years of age [Endline Study, Ethiopia 2017] CRS FH 1 The baseline report combines beans, legumes, nuts, and seeds in one category, while the endline distinguishes between legumes and beans on one hand, and nuts and seeds on the other. To facilitate comparison the endline estimates for the percent of women consuming nuts and seeds and the endline estimate for the consumption of legumes and beans were summed together. The endline estimate for the consumption of legumes and beans was 29.1 percent in CRS, 87.4 percent in FH and 64.7 percent in REST. The endline estimate for the consumption of nuts and seeds was 3 percent in CRS, 0.6 percent in FH and 0.5 percent in REST. Overall CRS FH REST Female sterilization 0.8 0.4 0.4 1.5 Male sterilization 0.0 0.0 0.0 0.0 Inter-uterine device 3.9 3.0 0.4 7.6 Injectables 76.0 70.7 85.2 69.1 Implants 15.0 14.2 11.7 18.6 Pill 2.3 4.6 1.2 2.6 Condom 0.0 0.0 0.0 0.0 Female condom 0.0 0.0 0.0 0.0 Emergency contraception 0.0 0.0 0.0 0.0 Standard days method 0.2 0.6 0.0 0.3 Lactational amen. Method 0.9 2.8 0.9 0.3 Rhythm 1.1 4.3 0.0 0.8 Withdrawal 0.1 0.0 0.0 0.3 Other modern methods 0.0 0.0 0.1 0.0 Other traditional methods 0.7 0.0 0.4 1.3 Number of women using any contraceptive method 1,069 251 497 321 Prevalence of women 15-49 years married or in a union using any contraceptive method 37.3 30.1 40.4 38.0 Number of women 15-49 years married or in a union Table A9.10 Percentage of women 15-49 years who are married or in a union and using a contraceptive method by type of contraceptive method [Endline Study, Ethiopia 2017] Overall CRS FH REST Breastfed children 6-8 months of age Percentage with minimum meal frequency (2 or more) 48.7 53.8 45.6 48.9 Percentage with minimum dietary diversity (4 or more) 2.6 6.5 0.0 2.9 Percentage consuming the following food groups: Grains, roots, and tubers 57.9 57.8 51.2 65.9 Legumes and nuts 23.3 8.8 36.8 17.3 Dairy products (milk, yogurt, cheese) 16.1 48.0 5.9 6.9 Flesh foods (meat, fish, poultry, and liver/organ meats) 2.0 1.2 1.7 2.9 Eggs 9.2 12.8 5.4 11.2 Vitamin A-rich fruits and vegetables 5.7 14.7 0.0 6.3 Other fruits and vegetables 3.9 3.9 4.3 3.4 Number of children 139 46 59 34 Breastfed children 9-23 months of age Percentage with minimum meal frequency (3 or more) 60.2 48.8 62.1 63.6 Percentage with minimum dietary diversity (4 or more) 13.8 10.1 11.3 17.5 Percentage consuming the following food groups: Grains, roots, and tubers 89.6 92.4 91.8 86.5 Legumes and nuts 54.9 17.3 75.9 54.5 Dairy products (milk, yogurt, cheese) 23.3 38.8 17.5 21.1 Flesh foods (meat, fish, poultry, and liver/organ meats) 6.1 0.0 6.1 8.9 Eggs 16.5 6.6 12.6 24.1 Vitamin A-rich fruits and vegetables 10.3 15.3 4.7 12.7 Other fruits and vegetables 22.1 22.6 12.2 29.8 Number of children 734 215 289 230 Non-breastfed children 6-23 months of age Percentage with minimum meal frequency (4 or more + 2 milk) 15.7 24.6 20.1 6.1 Percent with minimum dietary diversity (4 or more) 9.9 0.0 14.7 17.9 Percentage consuming the following food groups: Grains, roots, and tubers 91.7 100.0 93.2 83.5 Legumes and nuts 42.3 14.7 93.2 53.8 Dairy products (milk, yogurt, cheese) 32.5 52.5 26.9 15.2 Flesh foods (meat, fish, poultry, and liver/organ meats) 10.4 0.0 20.1 17.4 Eggs 17.1 9.0 56.4 13.5 Vitamin A-rich fruits and vegetables 3.7 8.7 0.0 0.0 Other fruits and vegetables 30.1 18.4 0.0 49.9 Number of children 67 39 9 19 Table A9.11. Components of MAD indicator for children 6-23 months by breastfeeding status [Endline Study, Ethiopia 2017] NOTE: The results for these subgroup analyses are based on small sample sizes and may be unreliable. Overall CRS FH REST (%) (%) (%) (%) Not breastfeeding <2 0.9 0.0 2.4 0.0 2-3 0.0 0.0 0.0 0.0 4-5 1.2 0.0 0.0 2.6 6-8 1.5 4.6 0.9 0.0 9-11 0.3 1.3 0.0 0.0 12-17 5.0 8.4 1.1 5.8 18-23 13.6 28.3 5.6 13.4 Exclusively breastfed <2 90.5 84.8 96.2 88.5 2-3 82.9 72.4 92.5 80.2 4-5 54.5 32.1 73.1 48.4 6-8 14.9 9.0 21.8 10.7 9-11 0.4 0.0 0.9 0.0 12-17 0.3 1.2 0.0 0.0 18-23 0.9 1.1 1.9 0.0 Breastfed and plain water only <2 4.6 13.4 0.0 3.2 2-3 9.2 10.2 6.9 10.8 4-5 26.9 29.7 20.4 31.0 6-8 18.0 12.0 21.9 17.5 9-11 6.2 3.7 6.7 6.9 12-17 2.6 0.9 5.3 1.8 18-23 0.5 2.3 0.0 0.0 Breastfed and non-milk liquids <2 1.0 0.0 0.0 2.8 2-3 2.8 1.9 0.0 6.2 4-5 2.1 3.8 4.1 0.0 6-8 2.0 4.7 2.2 0.0 9-11 1.2 0.0 0.8 2.3 12-17 0.4 0.0 1.5 0.0 18-23 0.0 0.0 0.0 0.0 Breastfed and other milk <2 2.0 0.0 0.0 5.6 2-3 2.1 8.5 0.0 0.0 4-5 4.2 8.3 0.0 6.0 6-8 0.8 3.5 0.0 0.0 9-11 0.5 0.0 1.2 0.0 12-17 0.3 1.1 0.0 0.0 18-23 0.0 0.0 0.0 0.0 Table A9.12. Breastfeeding status for children 0-23 months by age in months [Endline Study, Ethiopia 2017] Overall CRS FH REST (%) (%) (%) (%) Table A9.12. Breastfeeding status for children 0-23 months by age in months [Endline Study, Ethiopia 2017] Breastfed and complementary foods <2 1.0 1.9 1.4 0.0 2-3 3.0 7.0 0.6 2.8 4-5 11.0 26.0 2.4 12.0 6-8 62.8 66.2 53.1 71.8 9-11 91.5 94.9 90.4 90.8 12-17 91.4 88.4 92.1 92.4 18-23 85.1 68.3 92.5 86.6 Number of children 0-23 months <2 140 53 51 36 2-3 121 47 41 33 4-5 119 32 45 42 6-8 142 48 60 34 9-11 146 45 62 39 12-17 342 117 113 112 18-23 310 90 122 98 NOTE: The results for these subgroup analyses are based on small sample sizes and may be unreliable. Breastfeeding status refers to a 24 hour period (yesterday during the day or night). Children who are categorized as breastfeeding and consuming water only consumed no liquid or solid supplements. The categories are mutually exclusive and their percentages sum to 100 percent of children 0-23 months. Children who received breastmilk and non-milk liquids but did not receive other milk or complimentary food are categorized in the non-milk category, though they may have received plain water. Non-milk liquids include juice, juice drinks, porridge, and other liquids such as glucose water or sugar water. Number of children % P -value Number of children % P -value Number of children % P -value Number of children % P -value Basic drinking water source ns ns ns ns Household does not use a basic drinking water 800 22.4 324 19.3 266 29.7 210 18.9 Household uses a basic drinking water source 520 22.6 108 17.1 228 23.5 184 23.5 Correct use of recommended water treatment *** * * * Household does not use a correct water treatment practice 1,162 24.2 392 19.8 449 27.8 321 23.3 Household uses a correct water treatment practice 158 11.2 40 8.6 45 13.9 73 10.9 Improved sanitation facility ns ns ns ns Household does not use an improved sanitation facility 1,227 23.1 401 19.6 453 27.1 373 21.5 Household uses an improved sanitation facility 93 15.2 31 6.5 41 21.5 21 12.6 Handwashing station with water and soap or another cleansing agent ns n/a n/a n/a Household does not have a handwashing station with water and soap or another cleanising agent 1,307 22.7 429 18.9 484 27.2 394 21.0 Household has a handwashing station with water and soap or another cleanising agent 13 0.0 3 n/a 10 n/a 0 n/a All households 1,320 22.5 432 18.7 494 26.7 394 21.0 * p<0.05, ** p<0.01, ***0.001 N/A: Not appliciable; results not reported due to small sample size (n<30). NOTE: Chi squared tests were used to examine the statistical signficance of the relationship between the prevalence of diarrhea and household WASH status. Table A9.13. Prevalence of diarrhea among children under two by household WASH status [Endline Study, Ethiopia 2017] Overall CRS FH REST Males Females P-value Males Females P-value Males Females P-value Males Females P-value Self-earned cash decisionmaking *** * ** *** Respondent alone 33.1 26.3 40.8 31.0 23.5 22.2 38.2 27.4 Spouse alone 8.9 17.6 21.9 24.8 4.4 8.9 6.6 20.5 Respondent with spouse 57.7 55.5 36.9 44.1 71.8 68.5 54.9 51.2 Respondent with someone else 0.3 0.4 0.3 0.0 0.3 0.2 0.2 0.6 Other 0.1 0.3 … … 0 0.2 0.1 0.4 Number of responding males/females 3,422 1,762 1,028 430 1,408 645 986 687 † p<0.1,* p<0.05, ** p<0.01, ***0.001 NOTE: Includes all household members who are 15 years or older, have worked in the past 12 months and were usually paid in cash or a combination of cash and in-kind for this work during the 12-month period. Chi squared tests were used to examine the statistical signficance of the difference between male and females perceptions of self earned cash decisionmaking. Table A9.14. Self-earned cash decision-making among males and females married or in union who work and are usually paid in cash or a combination of cash and in-kind [Endline Study, Ethiopia 2017] Overall CRS FH REST Number % Number % P-value Number % Number % P-value Number % Number % P-value Number % Number % P-value Maternal health and nutrition decision making *** *** *** *** Respondent alone 246 23.9 659 56.6 98 27.9 197 50.5 79 21.3 236 52.8 69 24.0 226 63.0 Spouse alone 481 45.3 199 15.9 175 51.8 111 28.8 152 38.0 49 13.2 154 48.4 39 11.4 Respondent with spouse 320 30.5 315 27.2 70 20.3 80 20.7 171 40.2 149 33.7 79 27.3 86 25.1 Respondent with someone else 0 0.0 1 0.1 … … … … 0 0.0 1 0.3 1 0.3 2 0.5 Other 2 0.3 2 0.2 …. …. … … 1 0.5 0 0.0 … … … Number of responding males/females 1,049 100.0 1,176 100.0 343 100.0 388 100.0 403 100 435 100.0 303 100.0 353 100.0 Males Females † p<0.1,* p<0.05, ** p<0.01, ***0.001 NOTE: Chi squared tests were used to examine the statistical significance of the difference between male and females perceptions of maternal health and nutrition decision making. Males Females Males Females Males Females Table A9.15. Maternal health and nutrition decision-making among males and females married or in union with children under the age of two by sex of respondent [Baseline Study, Ethiopia 2017] Overall CRS FH REST Number % Number % P-value Number % Number (%) P-value Number % Number % P-value Number % Number (%) P-value Child health and nutrition decision making *** *** *** *** Respondent alone 141 13.2 685 59.4 67 18.6 213 54.6 38 10.0 227 51.8 36 13.1 245 68.1 Spouse alone 562 54.0 136 10.5 198 58.6 86 22.1 185 47.9 30 8.6 179 57.1 20 6.1 Respondent with spouse 345 32.7 347 29.3 78 22.8 89 23.3 180 42.1 172 38.1 87 29.6 86 25.2 Respondent with someone else 0 0.0 4 0.3 … … … … 0 0 4 1 … … … … Other 1 0.1 4 0.4 … … … … 0 0 2 1 1 0.2 2 1.0 Number of responding males/females 1,049 100.0 1,176 100.0 343 100.0 388 100.0 403 100.0 435 100.0 303 100.0 353 100.0 Males Females † p<0.1,* p<0.05, ** p<0.01, ***0.001 NOTE: Chi squared tests were used to examine the statistical significance of the difference between males and females perceptions of child health and nutrition decision making. Males Females Males Females Males Females Table A9.16. Child health and nutrition decision-making among males and females married or in union with children under the age of two by sex of respondent [Baseline Study, Ethiopia 2017] Overall CRS FH REST ANNEX 10 Multivariate Analysis for Stunting ANNEX Methodology and Results of the Bivariate and Multivariate Analyses Of Moderate-to-Severe Stunting Additional analyses were performed to assess the correlates of the prevalence of moderate-to-severe stunting among children under five in the three FY 2012 Food for Peace (FFP) development food assistance projects (DFAP) in Ethiopia: 1) the Ethiopian Livelihoods & Resilience Project (ELRP) in the Oromia Region and Dire Dawa Administrative Unit, implemented by CRS; 2) Targeted Response for Agriculture, Income and Nutrition (TRAIN) Project in the Amhara Region implemented by FH and its partners; and 3) Development Food Security Activity in the Tigray Region implemented by REST and its partners. Multivariate analyses controlling for key child and mother-level factors, socioeconomic characteristics, and household agriculture status and water, hygiene and sanitation practices as covariates was used to explore the factors that are associated with stunting to help inform learning on a key impact indicator that improved over the project lifetime. Data Used in the Analysis The data used in these analyses come from population-based household surveys (PBS) implemented at project baseline endline (2017). The survey collected standard information on household and respondent characteristics; food security and poverty; agricultural practices; children’s health and nutrition; and women’s health and nutrition. The analyses is restricted to the most recent birth in the household to avoid intrahousehold correlation and to cases with nonmissing information on the dependent and explanatory variables. The final sample size for the analyses of children’s stunting is 2,117 children under five (CRS, 709; FH, 797; and REST, 611). Definitions of Variables Dependent variables The main outcome of interest is the prevalence of moderate-to-severe stunting. Stunting is an indicator of severe linear growth retardation and chronic undernutrition among children under age 5. Stunting reflects the effects of a systematic lack of adequate nutrition over a number of years and recurrent and chronic illness. It is, therefore, a measure of the long-term effects of malnutrition and does not vary significantly according to the season of data collection. The survey collected anthropometric data (weight and height) for all children under five in the household. Recumbent length is measured for children under age 2 years; standing height is measured for all other children. Height-for-age z-scores based on the 2006 WHO Child Growth standards population are assigned to each child based on sex, age in days, and height in centimeters. Children whose height-for-age z-scores are less than –6 SD below the median or more +6 SD above the median are flagged and excluded from the computation of the prevalence of stunting. Children whose height was not measured or responses with missing height information are excluded from the numerator and denominator. Children whose day of month of birth is missing or unknown are assigned day 15. Children missing valid month and year of birth are excluded from the numerator and denominator. Cases with out-of-range or invalid z-scores are excluded from the numerator and denominator. Explanatory variables The analyses included a number of child, mother, household and project-related factors that can influence the prevalence of stunting. The selection of covariates is based on the projects’ goals and availability of data collected at endline. The common goal across the three DFAPs is that food security will be enhanced among targeted chronically food insecure households. If access to diverse and nutritious food by vulnerable households is increased and vulnerability to food security shocks is decreased and community resilience is increased; and the status of women is improved; then improvements in food security and nutrition among poor households should be achieved. Variables considered in the analyses of stunting included child’s age, sex, and birth order; mother’s characteristics; household sociodemographic characteristics; household food security status; household poverty status and economic wellbeing; household water and sanitation status; and household agriculture status. The sex, age, and birth order of children can influence their likelihood of being stunted. In cultures where boys receive preferential treatment in food and health care, this may lead to higher percentages of stunting among females. Sex differentials in child malnutrition are more common in South East Asia and generally not observed in the African context. Because stunting is a measure of chronic or long-term malnutrition it may be higher among older children. Children’s nutritional may be poorer among higher order births because of the distribution of household resources among other older children. Mothers’ characteristics are important predictors of children’s nutrition. Children born to older mothers may have lower birth weight. Mother’s educational attainment impacts their knowledge of critical child health practices and health-seeking behavior. Illiterate mothers may be less likely to obtain and understand basic health information, and to access basic health services. Mother’s participation in paid work results with income for the household, and assuming mothers have some participation in self-earned cash decision making this can translate into better health outcomes because mothers may choose to allocate greater resources to children’s nutrition and health care. Children’s nutrition is closely related to and can be influenced by the sociodemographic characteristics of their households. The following household sociodemographic characteristics were included: age, sex and educational attainment of the household head, number of adult males, number of adult females, number of children 0-4, number of children 5-17. Households with working age males and male-headed households, compared to adult female only households or female-headed households, may be more likely to engage in income generating opportunities and may be more likely to access credit to purchase productivity-enhancing agricultural inputs and equipment, make investments in household water and sanitation infrastructure, and purchase food items that cannot be home grown, and this can indirectly open up pathways to better health outcomes for children. The size and composition of the household may also impact the food intake and health of children; children in larger families may be fed less or less often or both. Household food security status can influence the prevalence of stunting because children living in food insecure households are likely to receive less food, eat less frequently, or both. Therefore the household dietary diversity score (HDDS) and whether or not the household experienced hunger are included in the analysis. Household socioeconomic status was captured using daily per capita consumption expenditures. Since daily per capita consumption expenditures is skewed it was transformed by taking its natural logarithm to avoid the influence of outliers on the outcome. Households’ water and sanitation status can influence nutritional status since the use of unhygienic practices can lead to diarrheal disease and loss of important minerals and vitamins and contribute to weight loss. The following variables were considered: use of a basic water service, correct water treatment, improved sanitation facility and a proper handwashing station. The analyses also included a number of household agriculture status variables because several key activities promoted by the DFAPs, and undertaken by farmers in the household, aimed to increase household food security through increased food production, food availability, and economic resources. These variables included: farm size; use of credit; use sustainable crop practices; use sustainable livestock practices; use of improved storage methods; and use of a value chain activity. The underlying aim for the inclusion of such variables is to better understand their potential role in eliciting improvements in food security and children’s nutritional status. Region dummies are added to capture variations in agro-ecological zones and other unobserved regional factors. The models also include project dummies in order to capture the relationship between potential differences in program implementation and the outcomes. The DFAP activities were designed to align with and support the Government of Ethiopia Protective Safety Net Program (PSNP). The endline survey did not collect information on whether or not households were direct beneficiaries of the PSNP interventions, but households were asked about receipt of cash or food emergency assistance so this variable is included in the analysis. Statistical Methods The analyses used logistic regression models to analyze the correlates of the prevalence moderate-to￾severe stunting. The analyses accounts for the two-stage stratified cluster sampling design. All analyses were conducted using STATA 14. Results Tables 10.1, 10.2 and 10.3 show the results of the bivariate analyses of the prevalence of hunger and the prevalence of underweight women, respectively. The results are presented for the combined sample and by project area. Generally, the associations between the covariates and the outcomes were similar across areas with a few exceptions described below. Child’s characteristics and the prevalence of stunting: As illustrated in table 10.1, the sex of the child was related to the prevalence of stunting only in FH; 46.9 percent of females were stunted compared to 53.7 percent of males. In all three project areas the age of the child was significantly related with the prevalence of stunting and followed a someway inverted U-shape. In FH the prevalence of stunting decreased with higher order births but in CRS and REST the association was statistically nonsignificant. Mother’s characteristics: Mother’s marital status, age, educational attainment and whether she achieved an MDD-W were not related to the prevalence of stunting in any of the project areas (Table 10.1). In CRS the prevalence of stunting of children whose mothers’ engaged in paid work (29.3 percent) was lower than that of children whose mothers did not work (36.1 percent) or whose mother’s worked in-kind (40.6 percent).1 Household sociodemographic characteristics: In all three project areas the prevalence of stunting was not related to the age of the household head. In FH the prevalence of stunting differed markedly by the sex of the household head – 30.5 percent of children in female-headed households were stunted compared to 52.5 percent of children in male-headed households. The prevalence of stunting increased with the number of adult males and number of adult females only in FH but was otherwise unrelated to the prevalence of stunting in CRS and REST. There was a positive association between number of children under five and the prevalence of stunting only in FH. The prevalence of stunting did not vary by the number of children 5 -17 in all of the DFAPs. Household poverty status: There was no association between the prevalence of hunger and the daily per capita consumption expenditures except in REST (Table 10.2); children who were not stunted 1 Work includes jobs in the formal and/or informal sector, full time, part time, or seasonal work that is done within and/or outside the home. It includes, but is not limited to agricultural daily wage labor, off-farm daily wage labor, income generation activities, sale of goods produced or processed outside the home or at the home, homestead garden or farm (e.g., vegetables, eggs, fish, livestock, artisanal goods), or petty trading. For this indicator, work does not include participating in cash for work, food for work, or conditional transfers and/or productive safety net programs. It does not include either caring for own children, cooking, cleaning or doing other routine chores for own household (e.g., fetching water, collecting firewood) or being involved in agricultural production solely for household consumption. reside in households with higher average daily per capita consumption expenditures ($1.19) compared to children who are stunted ($1.04). Household food security status: There was no association between the prevalence of hunger and the prevalence of stunting in any of the project areas. HDDS, an indicator of food security but also a proxy for socio economic status was associated with the prevalence of stunting only in REST (Table 10.3); children who were not stunted reside in households with a higher HDDS (5.93) compared to children who are stunted (5.52). Household WASH status: Bivariate analyses explored the prevalence of stunting in relation to households’ use of a basic water service, correct water treatment, improved sanitation facility and a proper handwashing station. The results indicated that the difference in the prevalence of stunting by households’ WASH status was statistically nonsignificant across the three DFAPs (Table 10.1). Household agriculture practices: In all three project areas the prevalence of stunting did not differ statistically between households that did not plant any crops, that planted crops but did not use at least three sustainable crop practices, and households that used three or more crop practices. Similarly, there was no difference in the prevalence of stunting for children among households that did not raise livestock, households that raised livestock but did not use at least three sustainable livestock practices, and those that used at least three sustainable livestock practices. The prevalence of stunting was also compared among households that planted crops and/or raised livestock with the intention of selling, households that did not use a value chain activity and households that used at least one value chain activity and no statistically significant difference was detected. Similarly, there was no statistical significance observed for the relationship of stunting with use of improved storage, use of credit, and farm size. Receipt of cash and/or food assistance and savings: The prevalence of stunting did not differ statistically between households that relied on cash and/or food assistance as a source of income in the 12 months prior to the survey and those that did not. There was no difference in the prevalence of stunting between households that save regularly and those that do not. Region and project: The prevalence of stunting differs statistically by region and project area. It is highest in Amahara (50.4 percent) followed by Tigray (43 percent) and lowest in Oromoai (34.3 percent) and Dire Diwa (35.5 percent). The prevalence of stunting is highest in FH (50.4 percent) followed by REST (43 percent) and lowest in CRS (34.5 percent). Table 10.4 shows the results of the multivariate analysis of the prevalence of children’s stunting. For the purposes of parsimony only variables that showed a statistical significant bivariate association were included. The baseline model (Model 1) controls for the project to illustrate any differences between DFAPs in the prevalence of children’s stunting. Model 2 control for child and mother’s characteristics, Model 3 controls for household sociodemographic and economic characteristics. The full model (Model 4) controls for region to account agro-ecological differences and unobserved differences by region factors. As shown in Model 4, after controlling for a number of child, mother, household, region and project factors, the odds of being stunted are about lower for children living in female-head-households compared to male-headed households (AOR = 0.47, CI = 0.237 – 0.931, p<0.05). Net of other factors, children living in households whose head has a little as a primary education or some primary education are less likely to be stunted compared to children in households headed by someone who never attended any school (AOR= 0.8, 95% CI = 0.656 – 0.976, p< 0.05). As shown in Model 2, mother’s participation in paid work is associated with lower odds of stunting compared to children whose mothers do not engage in an economic activities (AOR= 0.742, 95% CI = 0.587 – 0.939, p< 0.05). But the effect of mother’s paid work washes out when the model controls for the sociodemographic and economic characteristics of the household (Model 3). Regional differences in the prevalence of stunting that were observed in the bivariate analyses wash out in the full model (Model 4). Child’s age remains a statistically significant correlate of stunting even after controlling for a host of mother, household, region and project variables. The odds of stunting of children who are two years old are about six times that of children under one (AOR = 6.487, 95% CI = 4.785 -8.796, p<0.001). The odds of stunting of children who are 4 years old and just under five are about three times that of children under one (AOR = 3.173, 95% CI = 2.151-4.680, p<0.001). After controlling for other factors, including region, the association between project and the prevalence of stunting is statistically significant. The odds of a child under five being stunted are twice as high in FH compared to CRS (AOR = 2.141, 95% CI = 1.555-2.948, p<0.001). Children living in REST are more likely to be stunted than children in CRS (AOR = 1.556, 95% CI = 1.148-2.109, p<0.01). Post-estimation model specification and goodness of fit tests were conducted and the results did not indicate any model misspecification and indicated that the model fit the data. Discussion and Conclusions The objective of the multivariate analyses was to better understand the correlates of stunting in the DFAP implementation areas. The results of these additional analyses may help inform learning for future programming by identifying potential correlates that are associated with improvements in stunting, which may subsequently help shape future programming of project activities and/or beneficiary targeting on the basis of household demographic and socioeconomic factors. The odds of stunting of children who are two years old are about six times that of children under one, underscoring the importance of the first 2 years (1000 day window) in determining the long-term nutritional trajectory of children. Mother’s participation in paid work is no longer statistically significant after the model controls for household socioeconomic and demographic factors. The addition of HDDS and daily per capita consumption expenditures is likely to have washed out the income effect of mother’s work on stunting. The finding that children living in female-headed households are less likely to be stunted suggests differences in decision making and resource allocation for households where women are the sole decision makers. The relationship between stunting and women’s paid work and female-headed households suggest a need to continue to support women’s engagement in cash-earning opportunities and support for enhancing women’s participation in household decision making. The relationship between level of education of the household head and stunting underscores the importance of making investments in eradicating illiteracy and improving school enrolment, and for future programming to consider different approaches for effective behavior and social change communication that include oral messaging, for example. The results show a statistically significant effect of project even after the inclusion of region dummies which are intended to control for agro-ecological differences, as well as other unobserved regional factors that can impact food security and livelihood opportunities. However, the study is unable to assess the association of program design because of the pre-post design of data collection which does not allow statements to be made about attribution or causation relating to project impact. % N Chi2 % N % N % N Child's characteristics Sex Pr = 0.110 Pr = 0.892 Pr = 0.045 Pr = 0.499 Male 45.6 1,103 34.8 361 53.7 406 44.4 336 Female 41.7 1,014 34.3 348 46.9 392 41.2 275 Child's age (years) Pr = 0.000 Pr = 0.000 Pr = 0.000 Pr = 0.000 0 21.6 568 14.3 206 30.4 220 16.9 142 1 49.6 551 38.5 185 56.2 195 50.6 171 2 59.1 471 50.1 162 66.4 171 58.1 138 3 50.7 302 42.2 98 52.9 113 53.3 91 4 42.6 225 35.8 58 56.1 99 32.2 69 Birth order Pr = 0.000 Pr = 0.245 Pr = 0.018 Pr = 0.139 1st 46.3 1,407 36.3 379 52.8 620 44.2 409 2nd 39.9 646 33.4 286 44.1 173 41.6 187 3rd 20.7 62 23.8 43 0 5 20.9 14 4th 100 2 100 1 … … 100 1 Mother's characteristics Marital status Pr = 0.869 Pr = 0.808 Pr = 0.196 Pr = 0.755 Married/Living Together 44.1 1,945 34.7 658 51.6 733 42.8 554 Divorced/Separated 40.6 121 35.2 35 38.7 50 45.1 36 Widowed 39.5 34 27 16 26.2 8 58.8 10 Never Married/Lived Together 39 18 52.4 7 33.2 11 Educational attainment Pr = 0.305 Pr = 0.982 Pr = 0.282 Pr = 0.309 Never attended school 45 1,403 34.7 479 50.3 559 45.7 365 Primary or less 40.7 637 34.1 219 49 211 38.9 207 Secondary, vocational, or higher 45.6 78 35.1 11 66 28 38.1 39 Age (years) Pr = 0.704 Pr = 0.095 Pr = 0.704 Pr = 0.897 15-19 42.7 107 30.9 37 56.5 45 36.2 25 20-24 41.2 357 37.7 130 45.7 119 39.7 108 25-29 41.8 529 27.2 209 51.8 189 43.4 131 30/34 44.9 578 38.5 202 48.3 211 45.7 165 35-39 46.6 268 43.6 70 50.6 109 44.5 89 40-49 46 279 32 61 54.8 125 42 93 Work participation Pr = 0.393 Pr = 0.049 Pr = 0.086 Pr = 0.162 Not working 46.4 594 36.1 266 55.7 224 43.9 104 Paid work 42.5 1,005 29.3 281 45.3 355 45.3 369 Unpaid work 43.3 519 40.6 162 52.4 219 35.8 138 Minimum dietary diversity (MDD-W) Pr = 0.037 Pr = 0.159 Pr = 0.192 Pr = 0.384 Mother does not achieve a MDD-W 44.4 1,984 35.2 662 51.0 774 43.5 548 Mother achieves a MDD-W 34.4 134 24.8 47 35.0 24 37.7 63 Household sociodemographic characteristics Household head age (years) Pr = 0.125 Pr = 0.317 Pr = 0.295 Pr = 0.582 15-19 29.9 5 0 3 0 1 100 1 20-29 40.4 375 31.2 164 45.7 130 43.5 81 30-39 41.9 749 34 292 47.6 246 42.8 211 40-49 44.4 613 35.3 170 52.5 268 39.7 175 50+ 49.4 376 43.4 80 55.8 153 46.5 143 Table A10.1. Percentage of children under five stunted by child characteristics, mother's characteristics, household sociodemographic characteristics, and household WASH and agriculture status [Endline Study, Ethiopia 2017] Overall CRS FH REST % N Chi2 % N % N % N Table A10.1. Percentage of children under five stunted by child characteristics, mother's characteristics, household sociodemographic characteristics, and household WASH and agriculture status [Endline Study, Ethiopia 2017] Overall CRS FH REST Household head sex Pr = 0.018 Pr = 0.264 Pr = 0.010 Pr = 0.599 Male 44.8 1,927 35.3 646 52.5 733 43.4 548 Female 33.7 191 27.3 63 30.4 65 39.4 63 Household head educational attainment Pr = 0.035 Pr = 0.781 Pr = 0.768 Pr = 0.030 Never attended school 46.6 1,140 33.6 326 50.2 508 49.1 306 Primary or less 40.3 851 35.7 337 50.2 247 37.2 267 Secondary, vocational, or higher 40.1 127 33.3 46 56.2 43 33.4 38 Number of working age males (15+ years) Pr = 0.166 Pr = 0.329 Pr = 0.019 Pr = 0.597 No Adult males 35.5 124 31.6 37 31.3 43 40.1 44 One adult male 43.9 1,681 33.8 588 50.1 650 44.1 443 More than one adult male 46.4 313 41.3 84 61.1 105 39.9 124 Number of adult females (15+ years) Pr = 0.428 Pr = 0.036 Pr = 0.390 Pr = 0.730 No Adult females 24.3 10 0 3 23.3 5 50 2 One adult female 43.6 1,783 33 611 51.2 685 42.6 487 More than one adult female 45.3 325 45.8 95 47.1 108 44.2 122 Number of children under five Pr = 0.002 Pr = 0.522 Pr = 0.033 Pr = 0.302 One 46.2 1,401 35.9 376 52.9 617 44.3 408 More than 2 38.8 716 33 333 42.9 180 40.5 203 Number of children 5 -17 years Pr = 0.491 Pr = 0.233 Pr = 0.743 Pr = 0.577 None 44 382 35.5 132 52.3 150 40.8 100 1 44.9 387 34.7 114 48.2 162 46.6 111 2 43.2 459 27.9 136 51 198 43.3 125 3 40 448 32.7 136 47.3 186 37.2 126 More than 3 46.7 442 40 191 55.1 102 46.4 149 Household food security status HH experiencing moderate or severe food insecurity based on 12 months recall Pr = 0.419 Pr = 0.514 Pr = 0.528 Pr = 0.687 0 46 244 34.6 57 48.5 91 47.7 96 0.0083056 44.6 137 29.2 17 50 70 41.9 50 0.0485599 43.8 207 37.3 36 51.4 99 37.9 72 0.2640326 41 272 38.3 40 45 123 38.2 109 0.6843159 46.5 320 37.3 61 49.2 146 46.7 113 0.9502408 44 298 41.1 75 51 122 39.9 101 0.9968399 41.5 184 35.8 99 45.7 51 45.2 34 0.9996385 47.4 275 36.7 176 61.5 73 53.9 26 0.9999726 33.6 181 25.5 148 69.7 23 44.9 10 Household water, sanitation and hygiene status Access to basic water services Pr = 0.497 Pr = 0.455 Pr = 0.614 Pr = 0.155 No 44.4 1,296 35.4 538 51.2 425 45.8 333 Yes 42.8 822 32 171 49.6 373 39.4 278 Use of correct water treatment technologies Pr = 0.972 Pr = 0.765 Pr = 0.364 Pr = 0.800 No 43.7 1,869 34.7 640 49.9 721 43.3 508 Yes 43.9 249 32.9 69 56.1 77 41.6 103 Use of an improved sanitation facility Pr = 0.597 Pr = 0.551 Pr = 0.994 Pr = 0.653 No 43.9 1,960 34.8 653 50.4 734 43.2 573 Yes 41.5 158 31.1 56 50.4 64 39.6 38 % N Chi2 % N % N % N Table A10.1. Percentage of children under five stunted by child characteristics, mother's characteristics, household sociodemographic characteristics, and household WASH and agriculture status [Endline Study, Ethiopia 2017] Overall CRS FH REST Has soap and water at a handwashing station Pr = 0.520 Pr = 0.271 Pr = 0.118 Pr = 0.380 No 43.7 2,096 34.7 703 50.1 783 43.1 610 Yes 50.5 22 14.2 6 66.4 15 0 1 Household Agriculture Practices Status Household owns any livestock Pr = 0.008 Pr = 0.435 Pr = 0.239 Pr = 0.032 No 30.2 105 29.8 49 39.5 35 19.7 21 Yes 44.4 2,012 34.9 660 50.9 762 43.8 590 Household owns shoats (sheep or goats) Pr = 0.923 Pr = 0.160 Pr=0.794 Pr=0.595 No 43.6 832 30.6 223 49.3 311 43.8 298 Yes 43.8 1,285 36.3 486 51.2 486 42.2 313 Use of sustainable crop practices Pr = 0.351 Pr = 0.110 Pr = 0.182 Pr = 0.543 Did not plant any crops 100 1 … … … … 100 1 Planted crops but did not use at least 3 sustainable crop practices 40.2 135 39.7 85 38.2 29 43.3 21 Planted crops and used at least 3 sustainable crop practices 43.9 1,982 33.8 624 51.1 769 42.9 589 Use of sustainable livestock practices Pr = 0.027 Pr = 0.264 Pr = 0.344 Pr = 0.803 Did not raise any livestock 34 217 29.3 143 39.6 46 38.2 28 Raised livestock but did not use at least 3 sustainable livestock practices 43.4 507 36.8 298 51.1 123 44.8 86 Raised livestock and used at least 3 sustainable livestock practices 45.1 1,394 34.9 268 51.2 629 42.9 497 Use of an improved storage practice Pr = 0.242 Pr = 0.075 Pr = 0.382 Pr = 0.381 Did not store any crops 40.1 405 31 273 60 59 44.2 73 Stored crops but did not use and improved method 43.7 1,070 32.3 232 50 471 40.9 367 Stored crops and used an improved method 46.1 643 41.5 204 48.6 268 46.8 171 Use of a value chain activity Pr = 0.401 Pr = 0.247 Pr = 0.130 Pr = 0.612 Did not grow crops or raise livestock with the intention of selling 43 952 32.9 315 47.9 324 44.1 313 Did not use at least one value chain activity 38.9 127 44.8 65 34.9 27 36.3 35 Used at least one value chain activity 45.1 1,039 34 329 53.9 447 42.4 263 Use of credit Pr = 0.932 Pr = 0.234 Pr = 0.810 Pr = 0.370 No 43.7 1,674 35.4 623 50.1 550 43.8 501 Yes 44 444 27.5 86 51.2 248 39.1 110 Farm size (hectares) Pr = 0.268 Pr = 0.202 Pr = 0.161 Pr = 0.268 Less than 0.5 hectares 38 140 38.3 345 50.8 104 38 140 0.5 hectares to less than 1 hectare 43.2 283 30.7 254 46.1 305 43.2 283 1 hectare and above 46.3 188 31.1 110 53.7 389 46.3 188 Household resilience-related factors Household relied on food and/or cash assistance (12 months) Pr=0.905 Pr = 0.733 Pr = 0.476 Pr = 0.942 No 43.8 1,307 34.9 524 51.2 477 43.2 306 Yes 43.5 810 33.5 185 49 320 42.8 305 Do you or any other household member regularly save cash? Pr = 0.657 Pr = 0.194 Pr = 0.794 Pr = 0.249 No 44.4 778 28.1 100 50.8 466 39.5 212 Yes 43.3 1,339 35.7 609 49.9 331 44.8 399 Region and Project variables Region Pr = 0.000 Pr = 0.819 Amhara 50.4 798 n/a n/a 50.4 798 Dire Dawa 35.5 147 35.5 147 n/a n/a n/a n/a Oromia 34.3 562 34.3 562 n/a n/a n/a n/a % N Chi2 % N % N % N Table A10.1. Percentage of children under five stunted by child characteristics, mother's characteristics, household sociodemographic characteristics, and household WASH and agriculture status [Endline Study, Ethiopia 2017] Overall CRS FH REST Tigray 43 611 n/a n/a n/a n/a 43 611 Project Pr = 0.000 CRS 34.5 709 n/a n/a n/a n/a n/a n/a FH 50.4 798 n/a n/a n/a n/a n/a n/a REST 43 611 n/a n/a n/a n/a n/a n/a Total 43.7 2,117 34.5 709 50.4 797 43 611 Not stunted Stunted Not stunted Stunted Not stunted Stunted Not stunted Stunted Average daily per capita consumption expenditures (constant 2010 USD) $1.21 $1.12 Pr=0.0037 $1.33 $1.28 Pr=0.2868 $1.13 $1.13 Pr=0.9099 $1.19 $1.04 Pr0.0012 Table A10.2. Relationship between prevalence of stunting and average daily per capita consumption expenditures [Endline Study, Ethiopia, 2017] Overall CRS FH REST Not stunted Stunted Not stunted Stunted Not stunted Stunted Not stunted Stunted HDDS 5.19 4.94 Pr=0.0025 4.89 4.71 Pr=0.1986 4.47 4.46 Pr=0.9568 5.93 5.52 Pr=0.0037 Table A10.3. Relationship between the prevalence of stunting and average household dietary diversity score (HDDS) [Endline Study, Ethiopia 2017] Overall CRS FH REST OR 95% CI OR 95% CI OR 95% CI OR 95% CI Project (ref.: CRS) FH 1.925*** 1.481 - 2.501 2.232*** 1.657 - 3.007 2.068*** 1.537 - 2.782 2.141*** 1.555 - 2.948 REST 1.429** 1.108 - 1.843 1.484** 1.133 - 1.945 1.503** 1.138 - 1.985 1.556** 1.148 - 2.109 Child's sex (ref.:male) Female 0.836+ 0.683 - 1.023 0.837+ 0.683 - 1.027 0.835+ 0.680 - 1.024 Child's age in years (ref. less than 1) 1 4.003*** 3.108 - 5.156 4.111*** 3.172 - 5.328 4.116*** 3.177 - 5.333 2 6.181*** 4.576 - 8.350 6.486*** 4.785 - 8.791 6.487*** 4.785 - 8.796 3 4.505*** 3.136 - 6.471 4.553*** 3.151 - 6.578 4.538*** 3.144 - 6.551 4 3.070*** 2.091 - 4.506 3.172*** 2.150 - 4.679 3.173*** 2.151 - 4.680 Child's birth order (ref.: 1st born) 2nd 1.239 0.941 - 1.630 1.187 0.906 - 1.555 1.186 0.905 - 1.554 3rd 0.860 0.414 - 1.788 0.842 0.403 - 1.758 0.840 0.403 - 1.752 4th - - - - - - Mother's work participation (ref.: not currently working) Paid work 0.742* 0.587 - 0.939 0.817+ 0.642 - 1.039 0.813+ 0.640 - 1.034 Unpaid work 0.759+ 0.573 - 1.006 0.774+ 0.576 - 1.042 0.763+ 0.565 - 1.031 Household head sex (ref. male) Female 0.469* 0.237 - 0.929 0.470* 0.237 - 0.931 Household head educational attainment (ref. never attended school) Primary or less 0.797* 0.653 - 0.972 0.800* 0.656 - 0.976 Secondary, vocational/technical or higher 0.763 0.476 - 1.224 0.766 0.477 - 1.231 Number of adult males (ref. none) One adult male 0.877 0.389 - 1.978 0.875 0.387 - 1.977 Two or more adult males 0.968 0.438 - 2.138 0.968 0.438 - 2.141 Average household dietary diversity score 0.939 0.865 - 1.019 0.939 0.865 - 1.019 Natural logarithm of daily per capita consumption expenditures in constant 2010 USD 0.888 0.705 - 1.119 0.884 0.700 - 1.115 Region (ref. Amhara) Dire Dawa 1.178 0.766 - 1.811 Oromia - - Tigray - - 0.528*** 0.436 - 0.639 0.196*** 0.134 - 0.286 0.344* 0.130 - 0.910 0.336* 0.126 - 0.891 Constant N 2,117 2,115 2,115 2,115 Model 1 Model 2 Model 3 Model 4 Table A10.4. Results of logistic regression of the prevalence of moderate-to-severe stunting [Endline Study, Ethiopia 2017]