DISCLAIMER: This publication was produced at the request of the United States Agency for International Development. It was prepared independently by Leslie G. Hodel and Basab Dasgupta of Social Impact, Inc. 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. MALAWI CDCS INTEGRATED DEVELOPMENT IMPACT EVALUATION ENDLINE REPORT April 2019 PHOTO CREDIT: USAID/MALAWI i | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV ACKNOLWEDGEMENTS We are grateful for the work that many people have contributed to the completion of this report. We thank Invest in Knowledge, Limited (IKI) for their diligent work on data collection; specifically Sydney Lungu, Nancy Mulauzi, James Mkandawire, and Hastings Honde. Social Impact’s field supervisor Felix Maulidi provided very helpful guidance and oversight during endline data collection. The USAID/Malawi team—particularly Archanjel Chinkunda and Ryan Walther— has provided consistent thoughtful input into each evaluation phase. Their responsiveness to the evaluation team’s needs has been incredibly helpful throughout the five-year process. SI also extends sincere gratitude to USAID’s implementing partners for responding to information and meeting requests and especially to household survey and focus group respondents for graciously giving their time to meet with us across three rounds of data collection. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | ii CONTENTS Acknolwedgements i Acronyms v Executive Summary vii Evaluation Purpose, Questions, and Core Indicators vii Evaluation Design, Methods, and Limitations viii Limitations ix Findings ix Conclusions xv Recommendations xvi Background 1 USAID/Malawi CDCS Strategic Approach 1 Evaluation Purpose 2 Evaluation Question 2 Core Indicators 3 Methodology 4 Evaluation design 4 Treatment and comparison arms 4 Design assumptions and changes 5 Household sampling 6 Household survey data collection 7 Focus group discussions 8 Data analysis 9 Household survey 9 Focus group discussions 11 Limitations 11 Results 14 Activities and integration in study area 15 Summary of status of integration activities at baseline 15 Status of integration activities at midline 15 Status of integration activities at endline 16 Household Demographics 16 Quality of Life Impact: Complete regression results 16 Welfare: Poverty Status 19 Perceived Well-being 21 Health Behaviors 23 Health Service Quality 27 Education 31 Food Insecurity 34 Nutrition 36 Agriculture 39 Natural Resources and Environment 46 iii | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV Local government participation and use of services 49 Women’s Empowerment 53 Discussion 55 Interpretation of results 55 Summary of impacts 56 Conclusions 61 Recommendations 61 Annexes 63 Annex A. USAID/Malawi Activity Matrix 63 Annex B. Household Survey - English & Chichewa 72 Annex C. Focus Group Discussion Guide 273 Annex D. Qualitative Codebook 282 Annex E. Complete Comparative Data Tables (SEPARATE DOCUMENT) Annex F. Complete Regression Analysis Tables (SEPARATE DOCUMENT) TABLES Table 1. Baseline and Endline Summary Statistics for HSO, PI, and FI Groups................................................ xi Table 2. Sample districts included in the impact evaluation.................................................................................... 4 Table 3. Household survey sample size........................................................................................................................ 7 Table 4. Logistic regression results: Estimated impact of PI and FI for key outcomes................................... 17 Table 5. Linear OLS regression results: Estimated impact of PI and FI for key outcomes............................ 18 Table 6. Regression results: Estimated impact of PI and FI on poverty (BL to EL) ......................................... 19 Table 7. Percentage distribution of households’ likelihood of being under national, $1.25, and $1.90 PPP poverty lines...................................................................................................................................................................... 21 Table 8. Comparison of poverty rates of Malawi’s four poverty-line regions based on GoM's definition of international 2005 and 2011 PPP poverty lines and poverty rates...................................................................... 21 Table 9. Regression results: Estimated impact of PI and FI on overall and financial well-being (BL to EL) 22 Table 10. Regression results: Estimated impact of PI and FI on well-being (BL to EL)................................... 23 Table 11. Regression results: Estimated impact of PI and FI on health behaviors (BL to EL)....................... 24 Table 12. Health behaviors: descriptive means at endline and baseline............................................................. 24 Table 13. Regression results: Estimated impact of PI and FI on health service quality (BL to EL)............... 28 Table 14. Health service quality: descriptive means at EL and BL ....................................................................... 30 Table 15. Regression results: Estimated impact of PI and FI on education (BL to EL) ................................... 32 Table 16. Regression results: Estimated impact of PI and FI on food insecurity (BL to EL).......................... 35 Table 17. Regression results: Estimated impact of PI and FI on nutrition (BL to EL)..................................... 37 Table 18. Nutrition: descriptive means at endline and baseline........................................................................... 38 Table 19. Regression results: Estimated impact of PI and FI on crop outcomes (BL to EL)......................... 40 Table 20. Crop outcomes: descriptive means at endline and baseline............................................................... 41 Table 21. Regression results: Estimated impact of PI and FI on farming practices (BL to EL)...................... 43 Table 22. Farming practices: descriptive means at endline and baseline............................................................ 44 Table 23. Crop contamination: descriptive means at endline and baseline....................................................... 46 Table 24. Regression results: Estimated impact of PI and FI on climate resilience practices (BL to EL).... 46 Table 25. Environment: descriptive means at endline and baseline..................................................................... 48 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | iv Table 26. Regression results: Estimated impact of PI and FI on government knowledge, citizen engagement (BL to EL)........................................................................................................................................................................... 50 Table 27. Local government knowledge and participation: descriptive means at endline and baseline...... 50 Table 28. Utilization of government services: descriptive means at endline and baseline............................. 52 Table 29. Regression results: Estimated impact of PI and FI on women's participation (BL to EL)............. 54 Table 30. Summary of statistically significant impacts for Partial and Full Integration approaches.............. 57 FIGURES Figure 1. USAID/Malawi targeted districts and integration levels...................................................................... xvii Figure 2. Perceived financial and overall well-being (% change BL to EL).......................................................... 22 Figure 3. Received VCT in past 12 months (% change BL to EL)......................................................................... 26 Figure 4. Current contraceptive use among women 15–49 (% change BL to EL)........................................... 27 Figure 5. Reported all children <5 sleep under bednets (% change BL to EL).................................................. 27 Figure 6. Frequently experienced problems with public health facilities in past 12 months (% change BL to EL)........................................................................................................................................................................................ 29 Figure 7. Reported waiting time at health center/hospital at last visit (minutes) (% change BL to EL)...... 31 Figure 8. 2nd graders who can read Chichewa (self-reported, % change BL to EL)....................................... 33 Figure 9. 2nd graders who can read Chichewa, by gender (self-reported, % change BL to EL) .................. 33 Figure 10. complaints of frequent problems with education services (% change BL to EL).......................... 34 Figure 11. food insecurity (% change from BL to EL) ............................................................................................. 36 Figure 12. Food insecurity over time, by district (scale of 0–18)......................................................................... 36 Figure 13. Meets minimum acceptable diet for breastfed child 6–23 months.................................................. 38 Figure 14. Main woman in HH ate groundnuts yesterday ..................................................................................... 39 Figure 15. Individual level gross margin for soya (USD per Ha, excluding land reported in sq. meters)... 42 Figure 16. Individual level gross margin for groundnuts (USD per Ha, excluding land reported in sq. meters) .............................................................................................................................................................................................. 42 Figure 17. Farmer discarded all/portion of crop during grading process due to contamination.................. 45 Figure 18. All/portion of crop rejected at market/warehouse due to contamination .................................... 45 Figure 19. knowledge and engagement with local government (% change BL to EL) ..................................... 51 Figure 20. dissatisfaction with local government services (% change BL to EL)............................................... 53 Figure 21. Females in control of decisions (% change BL to EL).......................................................................... 54 v | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV ACRONYMS 3Cs Colocation, Coordination and Collaboration CDCS Country Development Cooperation Strategy CPR Contraceptive Prevalence Rate CSO Civil Society Organization DFAP Development Food Assistance Program DG Democracy and Governance DHS Demographic and Health Survey DO Development Objective EA Enumeration Area EGRA Early Grade Reading Assessment FI Full Integration FP/RH Family Planning and Reproductive Health FEWSNET Famine and Early Warning Systems Network GDP Gross Domestic Product GoM Government of Malawi Ha Hectare HFIAS Household Food Insecurity Access Scale HH Household HoH Head of Household HSO Health-Sector Only IE Impact Evaluation INVC Integrating Nutrition in Value Chains IP Implementing Partner IR Intermediate Result IRB Institutional Review Board Kg Kilogram MAD Minimum Acceptable Diet MEDA Malawi Electoral and Decentralization Activity MISST Malawi Improved Seed Systems and Technologies MVAC Malawi Vulnerability Assessment Committee NCST National Commission for Science and Technology ODK Open Data Kits OLS Ordinary Least Squares OR Odds Ratio PAT Poverty Assessment Tool PCA Principal Component Analysis USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | vi PI Partial Integration PMP Performance Management Plan PPP Purchase Power Parity QOL Quality of Life QSC Qualitative Score Card RCT Randomized Control Trial SHA Stakeholder Analysis SI Social Impact SIR Sub-Intermediate Result SSDI Support for Service Delivery Integration TB Tuberculosis USAID United States Agency for International Development USD US Dollars VCT Voluntary Counseling and Testing VDC Village Development Committee VSL Village Savings and Loan WEAI Women’s Empowerment in Agriculture Index vii | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV EXECUTIVE SUMMARY Through its 2013–2018 Country Development Cooperation Strategy (CDCS), USAID/Malawi has adopted an integrated approach that focuses investments, integrates activities within and across all sectors, and places greater emphasis on building host country capacity to lead and manage its own development. Part of USAID/Malawi’s development hypothesis posits that “if assistance is integrated then development results will be enhanced, more sustainable, and lead to achievements of our CDCS goal: Malawians’ quality of life improved”. To achieve this, USAID/Malawi adopted a 3C approach to (1) colocate interventions from different sectors geographically where sensible, (2) coordinate more effectively within and across sectors within USAID and with other donors, and (3) collaborate to foster linkages among implementing partners, other donors, and district authorities to improve results and sustainability of USAID investments. USAID/Malawi promoted this 3C strategy in three focus districts: Balaka, Lilongwe Rural, and Machinga (referenced as Full Integration [FI] districts). These focus districts received increased investment across all sectors through colocated projects. In addition, USAID required implementing partners in these districts to collaborate and coordinate on integrating their work plans so that activities across sectors would deliberately work together to boost development outcomes. Several other districts were targeted for Partial Integration (PI), with colocated activities across sectors but no requirement for implementing partners to coordinate or collaborate. Other districts were slated to receive investment in the health sector only (HSO), with no colocated activities in other sectors and no requirement for implementer collaboration or coordination. USAID/Malawi commissioned Social Impact (SI) to complete an independent impact evaluation to test the CDCS integrated hypothesis. This endline report is the culmination of that five-year evaluation. It details methodology, findings, and conclusions in response to USAID/Malawi learning priorities. EVALUATION PURPOSE, QUESTIONS, AND CORE INDICATORS The purpose of this evaluation is to determine the validity of USAID/Malawi’s CDCS integration hypothesis and assist it in determining whether an integrated programming approach leads to improvement in quality of life for beneficiaries. This evaluation also offers an opportunity for USAID to evaluate a broad country￾wide strategy using rigorous methods. The evaluation team addressed the following evaluation question: What impact has the integration of USAID investments through the CDCS Development Objectives had on improving the quality of life for targeted communities? Quality of life is defined for this study as a multi-scale, multi-dimensional concept comprising interacting objective elements of socio-economic indicators to reflect the extent to which human needs are met and subjective elements that capture the self-reported levels of happiness, pleasure, fulfillment, and psychological security. The core indicators used in this evaluation measure various aspects of quality of life and include Development Objectives (DOs) and Intermediate Results (IR) indicators within the Malawi Mission’s Performance Management Plan (PMP). This endline study captures changes in indicators across the CDCS time period, from baseline (2014) to endline (2018), with additional acknowledgement of midline (2016) trends. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | viii EVALUATION DESIGN, METHODS, AND LIMITATIONS The evaluation follows a quasi-experimental mixed methods design. The three districts USAID/Malawi had targeted for the FI approach were grouped together into the primary study arm. Districts having colocated projects within the PI zone were eligible for selection into a secondary study arm. HSO districts targeted for only health sector projects were eligible for a single-sector comparison arm to which PI and FI would be compared. Selection of PI and HSO districts to target for the impact evaluation was narrowed by two eligibility criteria. First, SI identified the Support for Service Delivery Integration (SSDI) health flagship project as a common program across all districts at baseline that addressed a large number of diverse health indicators. This would ensure a point of consistent comparison. The second criterion focused on comparable district-level characteristics such as poverty, adult literacy, HIV prevalence, and fertility rates. Based on these criteria, three comparable PI districts (Zomba Rural, Mangochi, and Nsanje1) and two HSO districts (Karonga and Nkhotakota) were chosen purposively, matching the three FI districts on selected characteristics. At baseline, the evaluation team used USAID geographic information system (GIS) data to restrict random sampling of census enumeration areas to only those located within the eight-kilometer catchment area of health facilities supported by SSDI. Within PI and FI districts, additional criteria required visible colocation with either areas served by USAID’s major agriculture and nutrition sector Integrating Nutrition in Value Chains (INVC) activity or schools supported by USAID's Early Grade Reading Activity (EGRA). Within these boundaries, enumeration areas and villages were randomly sampled, and houses were systematically sampled and revisited at midline and endline, with limited replacements. Trained enumerators administered a comprehensive household survey using electronic tablets. The survey captured conditions, behaviors, and perceptions across all sectors. At endline the total sample, excluding Nsanje District, was 4,595 households. Two focus group discussions in each district complemented the household survey by providing qualitative community input about how quality of life has changed over time and why. Analysis compared incremental and per-unit changes in quality of life metrics between each treatment arm (PI and FI) and HSO districts. To test the impact of integration, SI used Stata 15 software to estimate the difference-in-difference between treatment and comparison groups using a random effects panel regression model for each household-level outcome, controlling for overall time trends, observable factors unique to regions and households, and other characteristics associated with quality of life. In this way, comparisons over time help to “cancel out” these other factors that could otherwise explain part of the changes observed to better isolate the effect of integration. 1 Nsanje was dropped from analysis due to important changes in USAID activities between midline and endline. The SSDI activity ended in November 2016 after the midline data collection phase and was replaced at the same time by USAID’s new flagship health activity: Organized Network of Services for Everyone’s Health (ONSE). Prior to endline, the evaluation team confirmed that ONSE continued to support all the same health facilities and nearly all the districts targeted by the evaluation, thereby allowing the evaluation design—rooted in the presence of a “common thread” major health activity across all treatment arms—to remain intact. However, one exception to this was Nsanje District, which ONSE did not include. This violated evaluation design assumptions, so the district was removed from analysis. The team confirmed that its elimination would not significantly affect the statistical power of the evaluation. ix | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV LIMITATIONS The evaluation team employed the most rigorous design and methods possible while remaining flexible to USAID/Malawi programmatic priorities and constraints. This flexibility introduced several important limitations. Most importantly, differences in outcomes between FI, PI, and HSO districts cannot be attributed to coordination and collaboration alone with a high level of confidence, as these three types of districts also differed in the amount of investment made, with full integration districts having the highest investments across more than four sectors in most areas. In light of these limitations, SI’s analysis of health￾related indicators offers the best estimate of the impact of integration. Every household in the sample is, by design, within the catchment areas (eight km) of health facilities supported by USAID’s SSDI and subsequent ONSE activities. The consistency of these activities allowed the team to examine whether households benefitting from SSDI and ONSE had even greater health outcomes if they also benefitted from additional activities in other sectors (PI treatment arm) or if they benefitted from additional activities that also collaborated and cooperated with each other (FI treatment arm). Another limitation was that, because districts were not randomly assigned to each treatment group, numerous factors could influence outcomes measured in each district, whether they be external factors (e.g. general district government capacity, climatic conditions, other donor engagement), or internal factors (e.g. presence or absence of an intervention, intensity or effectiveness of an individual intervention, intensity of activity integration). While regression models controlled for several observable characteristics, it was not feasible to measure or control for all of these factors. As a mitigation strategy the evaluation team used random effect models to tease out such unobserved influences as much as possible from estimated parameter values of interest. FINDINGS Regression results estimating the impact of PI and FI treatment on core indicators between baseline and endline, as compared to the HSO group, are shown in Table 1. Values shaded in green reflect statistically significant positive improvements for the PI or FI group whereas red shading reflects statistically significant worsening, compared to HSO. Box 1 below provides guidance on how to interpret these statistics. While only core indicators are shown in the tables, this section discusses overall findings that include additional indicators not shown here. Compared to HSO districts, and controlling for other factors, USAID’s Full Integration approach significantly improved perceived well-being as well as several outcomes related to health behavior change, health service quality, and nutrition and food security. In particular, the FI zone had a large impact on improving care-seeking for ill children, reducing drug stockouts, and increasing the number of children 6- 23 months that received a diet meeting minimum criteria for meal frequency and diversity. The FI approach did not exert measurable impacts on poverty or key agricultural outcomes. Likewise, improvements in education were limited to increased accessibility of household reading materials for children. Improvements in governance and citizen responsibility were limited to increased volunteerism, although one could consider public health service improvements to be in part reflective of improved governance. Compared to the HSO districts, the Partial Integration approach exerted positive impacts on even more outcomes than FI districts. The PI approach significantly improved perceived well-being and several outcomes related to health behavior change, health service quality, and nutrition and food security. The PI treatment group exerted a particularly large impact on improving perceived income sufficiency, child USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | x bed net use, care-seeking for an ill child, and relative reductions at public health facilities in doctor absence, waiting times, and dirtiness. The PI approach did have two significant agricultural or environmental impacts: groundnut farmers had higher gross margins, and people were more likely to adopt climate resilience measures. PI districts had the same general effect on education and governance/citizen responsibility outcomes as FI districts. xi | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV TABLE 1. BASELINE AND ENDLINE SUMMARY STATISTICS FOR HSO, PI, AND FI GROUPS BASELINE ENDLINE % CHANGE (DIFFERENCE-IN￾DIFFERENCE)* QUALITY OF LIFE CHARACTERISTICS TOTAL HSO PI FI TOTAL HSO PI FI PI-HSO FI-HSO POVERTY AND PERCEIVED WELL￾BEING Primary: Poverty status - living on less than PPP$1.90/day 83.4% 81.4% 84.6% 84.6% 83.6% 81.7% 84.8% 84.7% -0.04% -0.19% Perceived overall well- being index score 2.6 2.9 2.6 2.5 2.2 2.3 2.1 2.2 0.08 0.20 Perceived financial well￾being index score 2.2 2.4 2.2 2.1 1.9 1.9 1.8 1.8 0.12 0.28 HEALTH DO 1. Child <5 in HH died in past 12 months 4.5% 3.6% 4.8% 5.1% 3.6% 3.3% 4.2% 3.6% -4.0% -19.7% SIR 4. Woman age 15- 49 currently uses any contraceptives 61.6% 57.8% 60.6% 65.9% 73.0% 75.4% 69.4% 72.9% -15.9% -19.8% IR 1.1. Respondent received VCT in past 12 months 61.3% 62.9% 57.8% 62.3% 71.7% 70.8% 73.6% 71.3% 14.8% 1.9% SIR 4. Both partners received VCT in past 12 months 80.5% 78.3% 82.2% 81.6% 84.4% 83.3% 84.7% 85.2% -3.3% -2.0% IR 1.1. Used public hospital/clinic in past 12 months 79.7% 80.6% 79.0% 79.4% 87.5% 89.7% 89.2% 84.1% 1.6% -5.4% USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | xii BASELINE ENDLINE % CHANGE (DIFFERENCE-IN￾DIFFERENCE)* QUALITY OF LIFE CHARACTERISTICS TOTAL HSO PI FI TOTAL HSO PI FI PI-HSO FI-HSO IR 1.2. Affected by drug stockout in past 12 months 65.5% 61.1% 62.2% 72.5% 71.0% 73.6% 68.2% 70.1% -10.8% -23.8% NUTRITION IR 2.3. Meets min. acceptable diet for breastfed child 6-23 months 11.6% 18.9% 10.0% 6.0% 13.5% 13.9% 10.2% 15.5% 28.5% 184.4% SIR 4. Child 0-5 months old is exclusively breast fed 84.2% 77.4% 93.0% 83.9% 82.9% 77.3% 93.2% 83.0% 0.3% -0.9% IR 2.3. Main woman in HH ate soy yesterday 6.2% 8.3% 4.3% 5.6% 7.6% 10.0% 5.3% 6.8% 1.8% -0.3% IR 2.3. Main woman in HH ate groundnuts yesterday 22.5% 22.2% 21.5% 23.5% 24.8% 22.9% 29.5% 23.6% 34.1% -2.7% AGRICULTURE & ENVIRONMENT IR 2.2. Groundnuts - average gross margin (USD/ha) 269.2 368.5 180.2 264.8 181.7 184.7 161.5 192.8 39.5% 22.7% IR 2.2. Soy - average gross margin (USD/ha) 199.9 287.1 201.2 193.0 236.4 1339.0 176.6 216.5 -378.6% -354.2% IR 2.2. Hectares under soya cultivation 0.03 0.01 0.03 0.05 0.02 0.00 0.03 0.04 51.3% 30.9% IR 2.2. Hectares under groundnut cultivation 0.2 0.2 0.2 0.3 0.1 0.1 0.2 0.1 39.8% 10.9% xiii | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV BASELINE ENDLINE % CHANGE (DIFFERENCE-IN￾DIFFERENCE)* QUALITY OF LIFE CHARACTERISTICS TOTAL HSO PI FI TOTAL HSO PI FI PI-HSO FI-HSO IR 2.4. Groundnuts - average yield (Kg/ha) in past cropping season 611.3 756.5 510.1 595.2 452.9 590.4 337.8 456.0 -11.8% -1.4% IR 2.4. Soy - average yield (Kg/ha) in past cropping season 499.8 436.4 395.1 556.3 592.0 529.5 505.2 637.8 6.5% -6.7% Degree of food insecurity (scale of 0-18) 6.4 5.0 7.1 7.5 7.8 6.8 8.1 8.6 -20.6% -21.2% IR 2.1. HH adopted climate resilience measures in past 12 months 11.9% 12.2% 11.5% 11.8% 12.8% 15.1% 12.1% 10.9% -18.6% -31.4% SIR 4. Farming HHs reported use of improved management practice in past 12 months 12.5% 13.1% 12.0% 12.3% 13.6% 15.7% 12.7% 12.1% -14.0% -21.5% GOVERNANCE & CITIZEN RESPONSIBILITIES IR 3.2. Knows what local/district government does 44.8% 44.6% 45.5% 44.4% 53.6% 55.3% 54.0% 51.8% -5.3% -7.3% SIR 2. Used phone for business or to send/receive government service info 13.4% 14.7% 10.9% 13.3% 13.9% 20.4% 8.9% 8.4% -57.5% -75.9% SIR 4. Volunteered in last 6 months 51.2% 46.3% 56.6% 52.3% 55.7% 47.4% 63.5% 58.9% 9.8% 10.2% USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | xiv BASELINE ENDLINE % CHANGE (DIFFERENCE-IN￾DIFFERENCE)* QUALITY OF LIFE CHARACTERISTICS TOTAL HSO PI FI TOTAL HSO PI FI PI-HSO FI-HSO EDUCATION DO 1. Percentage of 2nd graders who can read Chichewa 7.1% 5.0% 8.8% 7.7% 4.8% 6.8% 2.1% 5.1% -112% -70% WOMEN'S EMPOWERMENT Women's participation in HH decisions (% of decisions) 55.1% 55.7% 55.9% 53.8% 65.2% 65.6% 65.3% 64.7% -1.0% 2.5% * Difference-in-difference is calculated by subtracting the baseline-to-endline change in HSO districts from the baseline-to-endline change in PI and FI districts, showing the relative change over time. xv | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV For a limited number of outcomes, PI and FI treatment arms fared significantly worse than HSO districts. Compared to HSO, both PI and FI had significantly lower likelihood of improving the prevalence of 2nd grade literacy, lower likelihood of using mobile phone technology to report/receive government service or business information, and lower likelihood of respondents feeling they were able to control improvements to well-being. Those in the FI districts were also significantly less likely than in HSO to improve use of contraceptives and use a public clinic or hospital in the past year. Those in PI districts were also significantly less likely to have improved satisfaction with Malawian democracy. CONCLUSIONS These findings suggest that USAID/Malawi’s 2013-2018 CDCS integration strategy to colocate interventions geographically, coordinate within and across sectors, and collaborate to foster linkages among implementing partners, other donors, and district authorities, may have effectively improved several aspects of quality of life for Malawians when compared to a single sector, non-integrated development approach. The impact was particularly evident in the health sector. However, this conclusion cannot be made with high certainty, given the study’s limited ability to control for other explanations such as the number, intensity, or effectiveness of interventions in a given area, quality and depth of collaboration, and other factors that might have varied according to district. Nonetheless, results demonstrate at a minimum that some combination of integration and USAID activities have conferred added quality of life benefits in targeted communities. The finding that Partial Integration districts—which were purported to feature colocation alone— experienced slightly more quality of life improvements than in fully integrated districts begs the question of whether colocation alone is sufficient to reap the potential benefits of integration. This question cannot be easily answered in light of the strong possibility that collaboration and coordination were occurring, albeit to a lesser degree, in PI districts. However, it is reasonable to assume that among the 3Cs, colocation would confer the most measurable benefits at the community level by addressing multiple needs simultaneously. As a theoretical example, more in the line of general equilibrium models of development, if one development activity addresses health service improvements, the presence of another unassociated activity that improves local government capacity might lead to additional health service improvements due to improved government responsiveness to health service quality. Or, if one activity is able to reduce food insecurity, beneficiaries would be better able to attend schools that benefit from education sector support. Prior stakeholder analyses complementary to this impact evaluation2 have confirmed that, from the perspective of implementers and USAID/Malawi, integration has led to process-level efficiencies in delivering development aid, including cost savings, organizational efficiencies, diversification of activities and expertise, expansion of geographic and population scope, improved goal alignment, and reduced duplication of effort. Likewise, stakeholder consultations with beneficiaries have documented perceived improvements in message consistency, reduced time burden, improved program quality, and community unity. These advantages from collaboration or coordination might not always translate to measurable improvements to quality of life for beneficiaries, but they nonetheless reflect operational value in how 2 USAID/Malawi, 2018 Malawi Stakeholder Analysis: Identifying and Sustaining Process-level Benefits of Integration in Malawi, by Social Impact, Inc. (February 2019), https://pdf.usaid.gov/pdf_docs/PA00TKDM.pdf. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | xvi development is done, from the perspective of those delivering aid in Malawi. The overall valuation of integration should consider benefits at this level and not just at the level of beneficiaries. RECOMMENDATIONS Drawing on findings from the impact evaluation as well as conclusions of prior stakeholder analyses, we offer the following recommendations: 1. USAID/Malawi should continue to practice integration in some form in its development strategy. The positive impacts over and above the single sector approach reflect value in the concentration of investments (colocation) at a minimum. Results also suggest the added value of collaboration and coordination, but given this impact evaluation’s limited ability to pinpoint specific advantages, how USAID/Malawi should operationalize other elements of integration should be informed more by recommendations within SI’s stakeholder analyses. 2. USAID/Malawi should work to improve local government transparency and platforms for citizen engagement. Though knowledge of local government's role is improving, data confirmed growing dissatisfaction and mistrust of local representatives, particularly due to lack of transparency and responsiveness. Improvements in this area can restore trust and promote more effective service provision. 3. USAID/Malawi should work to aggressively address food insecurity to provide a secure foundation for other well-being improvements. xvii | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV Impact evaluation district FIGURE 1. USAID/MALAWI TARGETED DISTRICTS AND INTEGRATION LEVELS 1 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV BACKGROUND USAID/Malawi’s 2013–2018 Country Development Cooperation Strategy (CDCS) aims to improve the quality of life (QOL) of Malawians through three Development Objectives (DOs): (i) improvement in social development, (ii) increase in sustainable livelihoods, and (iii) assurance that citizen rights and responsibilities are exercised. To achieve this, the CDCS focuses its investments and integrates activities within and across all sectors and places greater emphasis on building host country capacity to lead and manage its own development.3 Part of USAID/Malawi’s development hypothesis posits that “if assistance is integrated then development results will be enhanced, more sustainable, and lead to achievements of our CDCS goal: Malawians’ quality of life improved.” In May 2014, USAID/Malawi awarded Social Impact, Inc. (SI), a five-year contract to evaluate the impact of the CDCS integrated development approach on the QOL of Malawians. The impact evaluation intends to determine the validity of USAID/Malawi’s CDCS development hypothesis and to inform USAID/Malawi in further integration efforts and future planning. This endline report represents the culmination of this impact evaluation (IE), which addresses comparative changes in key QOL indicators across three treatment arms from baseline (2014) through midline (2016) to endline (2018). This IE is complemented by several annual stakeholder analyses and policy briefs that SI completed as part of its contract. These reports share qualitative findings that further investigate how development integration has been operationalized in Malawi over time and the nature of integration’s benefits and challenges according to the perspectives of various stakeholders. These reports are available on USAID’s Development Experience Clearinghouse.4 USAID/MALAWI CDCS STRATEGIC APPROACH Between 2013 and 2018, USAID/Malawi supported a variety of development activities in all sectors: education, health, nutrition, agriculture, economic growth, food security, democracy and governance, and environment. Since the inception of its CDCS in 2013, USAID/Malawi adopted a 3C approach to (1) colocate interventions geographically where sensible, (2) coordinate more effectively within and across sectors within USAID and with other donors, and (3) collaborate to foster linkages among implementing partners, other donors, and district authorities to improve results and sustainability of USAID investments. USAID/Malawi promoted this strategy in three focus districts: Balaka, Lilongwe Rural, and Machinga (referenced throughout this report as full integration [FI] districts). These focus districts received increased investment across all sectors through colocated projects. In addition, USAID required implementing 3 USAID/Malawi, Country Development Cooperation Strategy: 2013–2019 (2013), https://www.usaid.gov/sites/default/files/documents/1860/CDCS_Malawi_September_2019_rev508comp.pdf. 4 See https://dec.usaid.gov/dec/search/SearchResults.aspx?q=KERvY3VtZW50cy5Db250cmFjdF9HcmFudF9OdW1iZXI6K CJBSUQtNjEyLUMtMTQtMDAwMDIiKSk=&qcf=ODVhZjk4NWQtM2YyMi00YjRmLTkxNjktZTcxMjM2NDBmY2 Uy. USAID/MALAWI CDCS DEVELOPMENT HYPOTHESIS If assistance is integrated then development results will be enhanced, more sustainable, and lead to achievement of our CDCS goal: Malawians’ quality of life improved. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 2 partners in these districts to collaborate and coordinate on integrating their work plans so that activities across sectors would deliberately work together to collectively boost development outcomes. Several other districts were targeted for partial integration (PI), with colocated activities but with no requirement for implementing partners to coordinate or collaborate. Other districts were slated to receive only health sector investment, with no colocated activities in other sectors and no requirement for implementer collaboration or coordination (see Figure 1). USAID/Malawi also aimed to increase investment and build Malawian capacity to lead and manage its own development by building the capacity of the Government of Malawi (GoM) to manage, implement, and sustain development programs; by empowering citizens to become more involved and informed in order to exercise their rights and responsibilities; and by increasing the share of USAID programming through local civil society organizations (CSOs). Given widespread capacity issues, USAID intended to strengthen all aspects of implementing partners’ organizational capacities in both public and non-governmental institutions to create a more capable cadre of local implementers. Together, these initiatives aimed to improve the QOL for Malawians by increasing the government’s capacity to provide services, positively impacting people’s health, economic prospects, and ability to demand services and participate in the decision-making process. EVALUATION PURPOSE USAID/Malawi’s theory of change suggested that through colocation, coordination, and collaboration, synergies would develop among USAID projects and activities, thereby increasing the likelihood and magnitude of impacts across activities through a reinforcing effect on all programming efforts. The purpose of this evaluation is to determine the validity of USAID/Malawi’s CDCS integration hypothesis and assist USAID/Malawi in determining whether an integrated programming approach produces sustainable development results leading to improvement in QOL of Malawians. This evaluation also offers an opportunity for USAID to evaluate a broad country-wide strategy using rigorous methods. This evaluation is expected to inform strategic implementation, contribute to USAID/Malawi’s learning, and assist in adapting USAID/Malawi’s CDCS Results Framework. As USAID/Malawi learns (through this evaluation) whether and how integration was successful in achieving desired outcomes, it will have the opportunity to adjust and adapt its next five-year CDCS approach. Evaluation results will be shared with USAID/Malawi staff, the larger Agency, implementing partners, sub-partners, government counterparts, and external stakeholders. Results will also inform other USAID missions on the validity of the integration hypothesis and how the overall process of synthesizing complementary sector activities in select areas can be strengthened to obtain better development results. EVALUATION QUESTION USAID/Malawi identified the following question (and sub-question) to be addressed through this evaluation: • What impact has the integration of USAID investments through the CDCS Development Objectives had on improving the quality of life for targeted communities? • Is there a combination of programs or activities that resulted in greater impact on the quality of life of targeted communities? 3 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV CORE INDICATORS The primary goal of the Malawi CDCS approach is improvement in QOL of Malawians. USAID/Malawi’s definition of QOL relates to the opportunities citizens are afforded to meet human needs built in the forms of human, social, and natural capital, plus the policy options that are available to enhance these opportunities.5 This evaluation considers QOL to be a multi-scale, multi-dimensional concept composed of interacting objective and subjective elements, estimated by combined measures in the following categories: (1) human well-being—health, knowledge, and understanding; freedom and security; relationships, work and play; and subjective well-being; and (2) economy, governance, and culture— income and wealth, democratic participation, access to services, order and safety, political rights, responsiveness, and transparency.6 While this evaluation report addresses many indicators across sectors, the primary impact analysis focuses on several “core indicators” that are reflected in USAID/Malawi’s Performance Management Plan (PMP) to represent development objectives (DOs) and intermediate results (IRs). At the evaluation design stage, SI selected PMP indicators that are feasible to measure through a household survey. In cases where a PMP indicator could not be measured directly, the evaluation offers the closest feasible proxy.7 These core evaluation indicators focus on outcomes that can in theory be directly affected by integration efforts. They address Malawians’ access to and quality of health and education services; changes in their economic security through household welfare and food security through nutrition status; and ability to exercise their civil rights through participation and actions. Beyond the core indicators, this report presents a variety of indicators reflecting access to and usage of services. Here, access refers to availability (including geographical proximity) and awareness (including knowledge), while use refers to adoption and actual utilization of the services (including practices), which is typically affected by affordability and quality of services. While affordability is primarily related to demand-side aspects, and quality of services is primarily a supply-side aspect, affordability can affect quality of services. 5 Costanza, Robert, et al. 2007. “Quality of life: An approach integrating opportunities, human needs, and subjective well-being,” Ecological Economics, 61(2–3): 267-276. 6 Hall, Jon. 2009. “The Global Project: on measuring the progress of societies.” OECD presentation. 7 For example, the indicator for IR 1.1: “Percent of population with access to essential health services,” defined in the PMP as those living within 8 km of a health facility, was impossible to capture based on this evaluation design, since the sampling frame included only households living within eight kilometers of SSDI-supported facilities. Rather, the baseline survey provides a supplementary measure of “Percent of sampled households reporting use of public clinics or hospitals in the past 12 months” to contribute to USAID’s understanding of health access related to this indicator. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 4 METHODOLOGY This impact evaluation is designed as a quasi-experiment using treatment and comparison districts to assess changes in quantitative indicators of quality of life of Malawians that can be attributed to the CDCS integration approach. Qualitative data analysis supplements the impact evaluation, offering contextual information about community perceptions of changes in QOL. EVALUATION DESIGN When USAID/Malawi first engaged SI to design the impact evaluation, districts had already been targeted for various integration or implementation approaches, making a randomized evaluation design impossible. Because USAID/Malawi implemented its integration approaches across selected districts, treatment and comparison groups were selected at the district level. The impact evaluation utilized USAID’s original definition of fully integrated programs (more than one implementer, more than one sector, colocated programs, and implementers must deliberately link work plan activities) to categorize districts into treatment arms. TREATMENT AND COMPARISON ARMS The three districts USAID/Malawi had targeted for this full integration (FI) approach were grouped together into the primary study arm, while districts having colocated projects without the other components comprised a partial integration (PI) secondary study arm. Districts targeted with projects in only one sector were eligible for a single-sector comparison arm to which PI and FI would be compared. Because there was an ongoing impact evaluation of USAID-funded primary education projects and because health sector activities are generally carried out in all zones, this study selected the comparison arm to include districts with activities in the health sector only (HSO). All three FI districts of Balaka, Machinga, and Lilongwe Rural comprised the FI treatment arm. Selection of PI and HSO districts to target for the impact evaluation was narrowed by two eligibility criteria. First, SI identified the Support for Service Delivery Integration (SSDI) flagship project under the health sector as a common program across all districts at baseline that addressed a large number of diverse health indicators and that would ensure a point of consistent comparison. The second criterion focused on comparable district-level characteristics such as poverty, adult literacy, HIV prevalence, and fertility rates. Based on these criteria, five comparable PI and HSO districts were chosen purposively, matching the three FI districts on selected characteristics. Table 2 shows the sample districts. TABLE 2. SAMPLE DISTRICTS INCLUDED IN THE IMPACT EVALUATION HEALTH-SECTOR ONLY (HSO) PARTIAL INTEGRATION (PI) FULL INTEGRATION (FI) 1. Karonga 2. Nkhotakota 1. Mangochi 2. Zomba Rural 3. Nsanje (removed from all analyses at endline because of non-continuation of health flagship activity) 1. Lilongwe Rural 2. Balaka 3. Machinga The impact evaluation compares incremental and per-unit changes in quality of life metrics between the three study arms to understand impacts. If the integration hypothesis is correct, greater improvements to 5 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV quality of life indicators over a five-year period in the full integration zone would occur compared to the single sector-specific zone. Likewise, one might expect to observe greater quality of life improvement in the partial integration zone compared to the single sector zone, albeit more muted than what may be observed in the FI zone. DESIGN ASSUMPTIONS AND CHANGES During a 2014 evaluation design scoping exercise, USAID/Malawi indicated that during the five-year evaluation, several activities would begin and end, potentially changing the integration levels in the eight study districts in response to USAID’s internal sector-specific requirements, external pressure from local Malawi governments and the education, agriculture, and heath ministries, and the start-up of new programs with similar objectives by other donors that could alter the current landscape. USAID/Malawi stated that it was not feasible to restrict entry of new programs and/or restrict collaboration efforts in the PI and HSO study areas and that SI should evaluate the totality of USAID investments, existing and future. Therefore, in the event of activity developments that threatened the evaluation design logic, the design would need to be adjusted. For example, if more sectors, such as education sector programs, were to enter the health-only study districts, they could have begun to mimic partial integration districts, and the treatment arms would have required adjustment. This quasi-experimental design relied on the following assumptions: (1) the three integration levels—full, partial, and low/none in health-only districts—would remain largely unchanged over the five-year evaluation, even with the end of existing programs and start of new programs supported by USAID and other donors; (2) new programs would have similar objectives as existing programs and would utilize similar structures and processes to engage in integrated activities; (3) sampled areas at baseline where projects were slated to end prior to endline data collection could be targeted for continuation of projects with similar sectoral activities, as anticipated by USAID; (4) the intensity and type of basic intervention activities undertaken under similar project umbrellas would be reasonably similar across areas in partial integration, single sector, and full integration zones; and (5) the IPs would adhere to the integration requirements explained to them by USAID at various semi-annual partners meetings since April 2014. The evaluation team annually assessed the validity of these assumptions and determined that they have generally borne out over time, with a few exceptions. For example, the SSDI activity ended in November 2016 after the midline data collection phase and was replaced at the same time by USAID’s new flagship health activity: Organized Network of Services for Everyone’s Health (ONSE). Prior to endline, the evaluation team confirmed that ONSE continued to support all the same health facilities and nearly all the districts targeted by the evaluation, thereby allowing the evaluation design—rooted in the presence of a “common thread” major health activity across all treatment arms—to remain intact. However, one exception to this was Nsanje District. While Nsanje was included in the PI treatment arm at baseline and midline data collection periods, ONSE does not operate in this district, nor does any other health activity targeting outcomes other than HIV/AIDS or nutrition. Seeing that the lack of the “common thread” health activity would disrupt the evaluation logic at endline, this district was removed from the analysis. The team confirmed that its elimination would not significantly affect the statistical power of the evaluation. The evaluation team noted other changes in sectoral activity locations between midline and endline that, though they do not have a major effect on the overall design assumptions, do have implications for the magnitude of changes one might expect for sector-specific outcomes in particular districts. Annex A USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 6 displays changes in USAID/Malawi activities by sector and district and shows how these changes relate to evaluation data collection timeframes. For example, in the education sector, USAID/Malawi’s flagship Early Grade Reading Activity (EGRA) ended in Fall 2016 and was scaled up to the national level through the Malawi Early Grade Reading Improvement Activity (MERIT) beginning mid-2016. Therefore, since mid-2017, all districts, including HSO areas, are now receiving education benefits through a USAID-supported national early grade reading program. Overall, the team did not find national-scale activities to be a major threat to the evaluation’s validity, as they were assumed to have affected districts across all treatment arms equally. However, this particular change means that educational outcomes might be expected to improve in HSO districts between midline and endline. HOUSEHOLD SAMPLING At baseline, following the selection of districts, eligible areas expected to receive varying levels of integration treatment after baseline were identified using mapping data provided by USAID. SI created maps with eight-kilometer (km) buffers around each SSDI-supported health facility to reflect the expected service area of each facility. In FI and PI districts, buffer areas were selected as eligible when they had visible colocation with either USAID’s flagship Integrating Nutrition in Value Chains (INVC) project (with supported extension planning areas or nearby supported group village head activities) or EGRA (with nearby schools designated by the separate EGRA impact evaluation as treatment “phase 1 schools” or “neutral”). Within the buffer areas, census enumeration areas (EAs) and villages were delineated using 2008 Malawi census data. All EAs within all SSDI buffers were eligible for the HSO study arm—Nkhotakota and Karonga Districts—as no colocation or integration had been identified for these comparison districts at the time of the baseline. A random sample of EAs was then drawn from all eligible pooled EAs. Within each sampled EA, up to two villages were selected randomly, provided that they were located within the SSDI buffer. At the village level, complete household listings were not available, so households were selected systematically using a random start and skip pattern proportional to the community size, as reported by village leadership. Because the impact evaluation study design requires sample concentration in specific USAID-targeted areas of each district, it is important to note that results presented in this report are not representative of an entire district or groups of districts named. Rather, they are representative of sub-parts of these districts with a nearby SSDI (later, ONSE) health facility and, in case of PI and FI districts, with colocation of SSDI and other flagship interventions. The baseline sample of districts, EAs, and villages was revisited at midline and endline. For household-level midline and endline data collection, SI aimed to recontact and interview the same households and within￾household respondents from baseline. Enumerators used extensive contact information provided at baseline, combined with hand-drawn maps and assistance from community members, to relocate households. Households or respondents that could not be reinterviewed after three attempts were replaced. Successfully recontacted households in which the previously surveyed respondent was no longer living (e.g., death in the family, migration, marriage) were retained. In those cases, another adult member of the household was surveyed. Households that could not be recontacted at midline or endline were replaced, with the first preferred replacement option being a household occupying the same dwelling or utilizing the land that the originally surveyed household cultivated. If such a replacement was not available, 7 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV SI surveyed the next nearest neighbor. This nearest neighbor replacement strategy was also used to replace original respondents who refused to participate. The composition of the final sample is shown in Table 3. TABLE 3. HOUSEHOLD SURVEY SAMPLE SIZE BASELINE MIDLINE ENDLINE District #EAs # Households (HH) # HH revisited # HH replaced in same EA # HH revisited # HH replaced in same EA FI Balaka 38 571 518 53 516 53 Lilongwe Rural 39 586 511 75 520 63 Machinga 39 571 495 59 500 50 PI Mangochi 39 587 519 68 538 49 Zomba 38 695 555 55 505 60 Nsanje* 38 571 517 46 518 41 HSO Karonga 58 866 792 78 813 57 Nkhotakota 58 867 791 82 780 91 Subtotal 4,698 516 4690 464 Total 347 5,314 5,214 5,154 Total without Nsanje 309 4,743 4,651 4,595 *Nsanje was removed from analysis in this report, as discussed above. However, data were still collected at endline and are available in the annex tables. HOUSEHOLD SURVEY DATA COLLECTION SI designed the household survey instrument (Annex B) to track quality of life indicators and other household characteristics for each household at three time points. The advantage of this longitudinal panel design is that community and household characteristics independent of outside interventions will remain constant over time, allowing better isolation of the effects of USAID activities. Some parts of the survey were modeled after standard tools, including the Poverty Assessment Tool, Malawi Demographic and Health Survey (DHS), the Women’s Empowerment in Agriculture Index (WEAI), and the World Values Survey. The SI survey tool included questions to measure changes in all major PMP indicators that capture outcomes, such as access to and quality of services (including local governments), food security, and poverty status, in all USAID-focused sectors. At midline, SI modified the baseline instrument to add three standard questions to better assess food insecurity according to the Household Food Insecurity Access Scale (HFIAS) scale and to add questions assessing the extent of the aflatoxin crop contamination problem, which had become an issue of increasing concern. To calculate poverty status using an updated USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 8 methodology (discussed below), the team added questions to the endline survey about household roof material, possession of a table, and whether one’s toilet is shared. Household survey data were collected using Android tablets by trained enumerators hired by local subcontractor Invest in Knowledge (IKI). SI and IKI staff collaboratively programmed the electronic survey using the Survey CTO application of Open Data Kits (ODK) software. At each data collection round, SI led a five-day enumerator training in Zomba City with IKI supervisors, enumerators, and management staff, many of whom had participated in the baseline or midline studies. Endline data were collected from October 8 to November 2, 2018, which matched the general timeframe of prior data collection rounds. To ensure local language capabilities, enumerators were men and women from parts of Malawi where data were collected. The household survey was available on the tablet in English, Chichewa, Ciyawo, Cisena, and Citumbuka languages. Interviews were typically conducted in Chichewa or in an alternative language according to the preference of the respondent. The preferred respondent for the baseline household survey was the adult woman most responsible for care of the household, since she would be best positioned to answer all sections of the survey and most likely to be available to respond to the survey, as confirmed during enumerator training. The study refers to this person as the “main woman” of the household. The second preferred respondent was the male most responsible for the household. Other knowledgeable adults were also accepted as a third choice if neither of the first two options was feasible. To allow gender disaggregation of responses related to personal opinion and experience in community participation and governance, enumerators requested that the main male in the household respond to that specific section at every other randomly selected household, if it was possible to locate him. At midline and endline, enumerators requested the same respondent who participated in the prior data collection round as the first choice, but other adults were accepted if the preferred respondent was not available. The Malawi National Commission for Science and Technology (NCST) and SI’s internal Institutional Review Board (IRB) both approved instruments, consents, and protocols prior to data collection. All enumerators were trained to protect the rights of research participants and trained in appropriate informed consent procedures and protection of confidentiality. All survey and FGD respondents provided oral informed consent to participate in the study. FOCUS GROUP DISCUSSIONS To provide further insight into the quantitative findings, the evaluation team completed focus group discussions (FGDs) in two villages per district (16 total). For each FGD, which included ten participants (half men and half women), a trained facilitator invited group discussion about the following topics: food availability and quality, health service availability and quality, education, local government capacity, environment, and poverty. Discussion centered on the degree to which each topical area had changed within the past two years in the village and factors leading to those outcomes. Following the Qualitative Score Card methodology, the facilitator concluded each topic with a vote and tally of participants’ views on whether each topical area had improved, worsened, or stayed the same. The discussion guide is available in Annex C. A notetaker supported each interview. SI purposively selected two village locations per sampled district through visual review of maps to ensure the discussions were conducted within an SSDI buffer sampled for household data collection but not 9 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV within the same village(s) where household (HH) data were collected, to prevent survey exhaustion and to obtain views from residents of other parts of the district. Efforts were made to capture a variety of villages exposed to different types of USAID interventions, including areas near forests or lakes that might be more sensitive to climate change, so that the discussion could revolve around the context of USAID programs. The same villages were revisited at midline and endline. These FGDs are not representative of district populations and do not capture causality or association between the programs and the expressed views but rather are meant to provide better context for understanding changes in the sampled areas observed in the household survey. DATA ANALYSIS HOUSEHOLD SURVEY Endline household survey data were cleaned and analyzed using Stata 15 software. Similar to baseline and midline, potential outliers or illogical values were identified and investigated. In some cases, households were recontacted by phone to verify unusual responses. Means and standard deviations were calculated for each variable, with differences calculated from baseline through midline to endline within each treatment arm to illustrate the magnitude of change in each area. All data from Nsanje District (in the PI treatment arm) were dropped from the dataset prior to analysis, given the absence of the ONSE activity or any other comprehensive health sector service activity and the consequent lack of comparability to other districts. To maintain a balanced panel of the same households who were present in all three survey rounds, households from Nsanje were also dropped from the baseline and midline. As a result, baseline and midline results presented in this report differ from those shared in prior evaluation reports. At a basic level, any evaluation of impact requires comparison of changes over time in each treatment arm compared to changes over time in the comparison group. However, a simple “difference-in-difference” (DID) comparison is not sufficient for this evaluation, owing to inherent differences in districts because USAID/Malawi purposively selected full integration districts and because of other geographic trends that rendered FI, PI, and HSO districts unique at baseline. The effects of this selection bias must be accounted for in analysis of household-level QOL measures across the three study arms. To address this, SI employed a panel data regression difference-in-difference approach to estimate program effects on the same sampled households. This approach allowed SI to control for selection bias to the extent possible, since data for the same respondents were collected across time. SI analyzed key quality of life outcomes (core indicators referenced above) and some additional indicators by testing for statistically significant changes in program outcomes and estimating program effects after controlling for overall time trends, observable factors unique to regions and households, and other characteristics associated with quality of life. In this way, comparisons over time “cancel out” these other factors that could otherwise explain part of the changes observed so that the effect of the intervention can be isolated with higher precision. Unfortunately, through this modeling approach, SI was unable to control for unobserved factors such as district-level changes in policies on development investment, other development investment occurring across the district, or climate conditions. Keeping this important limitation in mind, the evaluation team estimated a random effects panel regression model for each household-level outcome of interest as follows: 𝑌𝑌𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + ∑ 𝛽𝛽𝑗𝑗 𝑗𝑗 𝑗𝑗=1 𝑋𝑋𝑗𝑗 + 𝜃𝜃1𝑅𝑅 + 𝛿𝛿1𝐹𝐹𝐹𝐹 + 𝛿𝛿2𝑃𝑃 + 𝛾𝛾1(𝑅𝑅 ∗ 𝐹𝐹𝐹𝐹) + 𝛾𝛾2(𝑅𝑅 ∗ 𝑃𝑃 ) + 𝛼𝛼𝑖𝑖 + 𝜖𝜖𝑖𝑖𝑖𝑖 (Eq. 1) USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 10 Where: 𝑌𝑌𝑖𝑖𝑖𝑖 is outcome Y for individual i at time t, 𝑋𝑋𝑗𝑗 is a set of j vectors of control variables, 𝑅𝑅 is a time dummy equal to 1 at midline or endline and 0 at baseline, 𝑃𝑃 is a dummy equal to 1 for each household in the PI treatment group, 𝐹𝐹𝐹𝐹 is a dummy equal to 1 for each household in the FI treatment group, 𝛼𝛼𝑖𝑖 is individual-level random effect, and 𝜖𝜖𝑖𝑖𝑖𝑖 is the random error term. Parameters 𝛾𝛾1 and 𝛾𝛾2 are the estimates of the average treatment effect for the FI and PI treatment arms over time, respectively. Program effects were estimated separately for each treatment group: comparing FI to HSO households and comparing PI to HSO. The main treatment effect is measured as the interaction between treatment arm and time (e.g., change from baseline to endline). Panels were defined at the household and time levels, and standard errors were clustered by census enumeration area. SI estimated Equation 1 using linear ordinary least squares (OLS) regression for continuous outcomes and logistic regression for binary outcomes. In its vector of control variables, SI included a range of household characteristics that may have some influence over outcomes. These include gender and age of household head, age, disability status, education level, household composition and density, home characteristics, livelihood activities, access to water and market, and proportion of children in the household under five years of age. The models were estimated for all core indicators and some additional indicators of interest to USAID/Malawi. Certain indicators could not be modeled effectively at the household level (e.g., mortality rate, fertility rate) while others had too little variance to model outcomes (e.g., percent of children aged 6–23 months consuming a minimum acceptable diet apart from breast milk). The team used the same approach as was used at midline. In this endline report, SI presents random effects results for the balanced panel of households in the sample to ensure that all households included in final analysis have information available for all three survey rounds. The random effects models are used because many of the outcomes are binary, the team can retain a greater set of observations (as it is not limited to those households that experienced a change in the outcome), and the team can control for demographic variables that do not vary substantially across time. In addition, the districts under each arm are purposively chosen for different treatment groups without matching or covariate balancing, meaning a group of districts under each treatment arm may not be characteristically similar. As a result, it is not improbable to think that districts might not have shared equal platform at the time of the baseline study. SI presents results for the balanced sample to maximize the number of observations used in the analysis, to increase power, and because the robustness of results to sample subsetting suggests that attrition of households from baseline to endline did not introduce any bias (i.e., there is no evidence for systematic differences between successfully resurveyed households after controlling for household demographics). The full set of regression outputs is presented in Annex F and includes random effects models. The annex includes odds ratios and standard errors for binary outcomes (e.g., yes/no) and coefficients and standard errors for continuous outcomes (e.g., scales). The significance level of the program effect estimates is also indicated by asterisks. A guide to simple interpretation of these statistics is found in Box 1. For indicators for which regression was not feasible, simple differences in means between treatment arms are described to illustrate trends over time. 11 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV FOCUS GROUP DISCUSSIONS Each Qualitative Score Card was digitally recorded and then transcribed from local languages to English. Transcripts were then coded according to a thematic codebook (Annex D) using Dedoose software. Analysis was carried out deductively and entailed searches for occurrences and co-occurrences among codes across all transcripts. LIMITATIONS The impact evaluation adheres to rigorous industry standards, is flexible to accommodate USAID’s ongoing and future programs that focus on improving QOL, is not intrusive to or limiting of any IP activities, builds in learning and adaptation in the design through adaptive categorization of integration levels achieved over time, and could be the first of several impact evaluations (IEs) of such a CDCS approach. However, the evaluation design has a few important limitations. INABILITY TO DISENTANGLE THE EFFECT OF COORDINATION/COLLABORATION FROM INCREASED INVESTMENT ACROSS SECTORS Differences in outcomes between FI, PI, and HSO districts cannot be confidently attributed to coordination/collaboration alone, as these three types of districts also differed in the amount of investment made, with full integration districts having the highest investments across more than four sectors in most areas. Observed differences might in fact have been caused by the increased cross-sectoral investment rather than coordination/collaboration or might be attributable to the combination of both. Because it was not feasible to identify enough comparable locations that were to receive one without the other, the impact evaluation could only examine both aspects of the CDCS together. Also, through the empirical modeling approach used in the evaluation where treatment was assigned at the district level, SI could not control for unmeasured factors that also typically occur at the district level, such as the amount of development investment in the district or climate conditions. This important limitation should be kept in mind when interpreting results. In light of these limitations, SI’s analysis of health-related indicators offers the best estimate of the impact of integration. For example, changes in an agriculture-related outcome in an FI area compared to HSO might be largely due to the simple presence of USAID investment in agriculture in FI districts, whereas there was none in HSO districts. In contrast, every household in the sample is, by design, within the catchment areas (eight km) of health facilities supported by USAID’s SSDI and subsequent ONSE activities. The consistency of these activities allowed the team to examine whether households benefitting from SSDI and ONSE had even greater health outcomes if they also benefitted from additional activities in other sectors (PI treatment arm) or if they benefitted from additional activities that also collaborated and cooperated with each other (FI treatment arm). INSUFFICIENT POWER TO IDENTIFY COMBINATIONS OF PROJECTS THAT RESULT IN GREATER QUALITY OF LIFE BENEFITS Responding to the evaluation sub-question, the empirical measurement of the impact of various combinations of programs or activities requires a sufficient sample size representing each unique combination of interest. Given the high number of combinations across up to six sectors and three or more levels of activity integration and high costs incurred in gathering data from a large sample to represent each combination, it was not feasible to measure each combination with sufficient statistical power. In light of these limitations, SI’s analysis of health￾related indicators offers the best estimate of the impact of integration. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 12 INSTABILITY IN PROGRAM AND INTEGRATION ROLLOUT It was important to ensure that sampled locations within FI districts had similar types of projects as those within PI and HSO districts, so that QOL differences observed among the study arms could be linked to the CDCS strategy rather than to a specific project in a particular study arm that was especially successful. To mitigate this threat, evaluation site selection criteria included creating consistency in types of projects, with SSDI being a common thread through all three study arms and EGRA and INVC being common project approaches through the full and partial integration study arms. However, to prevent “contamination” over time of comparison areas within the evaluation (PI and HSO districts), it was important to ensure that program intensity, types, and integration levels remained relatively constant. USAID/Malawi could not fully control this without substantial program planning shifts that were not in its interest. Many new activities started in the study areas during the study period, making it highly challenging to pinpoint or hold constant the intensity and types of investment or the timing or nature of integration within a given district throughout the study period, as would be ideal for an IE design. The intensity of programmatic approaches and focus of different implementers also probably differed across sampled areas. Furthermore, some programs (and, consequently, the local presence of an IP) ended at varying times prior to the final evaluation of the CDCS, causing more instability. In addition, while the CDCS targeted the three focus districts in particular, there were several indications that integration would become increasingly common throughout other partial and perhaps even HSO districts. While USAID/Malawi representatives of these portfolios suggested that intentional non-integration could potentially be arranged in select locations, these were not likely to be sufficient for the evaluation sample. OTHER DONOR INITIATIVES Malawi is somewhat saturated with initiatives from external donors. While national-level activities are not of concern, as all Malawians would theoretically be equally exposed, programs that targeted specific districts or sub-district locations could “contaminate” the evaluation’s ability to attribute effects to USAID programming. This is a common issue for development evaluation. Most large-scale donor initiatives appeared to be nationally focused, and limited information was available regarding the specific sub-district locations of other, smaller programs. INABILITY TO GENERALIZE FINDINGS TO THE DISTRICT LEVEL Given the purposive restriction of data collection to implementation areas meeting the criteria for non-, partial, and full integration, it is worth noting that results are only representative of these sampled areas and cannot be generalized to represent an entire district or Malawi as a whole. For this reason, sampling weights were not applied to analysis. This approach is necessary and in line with the evaluation questions, which require that respondents in both treatment and comparison groups must be within USAID-targeted intervention areas. LIMITED ABILITY TO DIRECTLY MEASURE DO AND IR INDICATORS Any endeavor to capture outcomes across all DO indicators will certainly be limited. PMP guidance for some indicators required extensive survey modules that would have lengthened the survey beyond reason. In such cases, SI opted to remove or alter the type of indicator collected. SI strived to balance the need to include core indicators with appropriate survey length and content that would ensure truthful responses and respondent retention across multiple data collection rounds. STANDARD LIMITATIONS OF SURVEY METHODS AND SELF-REPORT Survey methods used for this evaluation relied on accurate and truthful reporting of household characteristics. Variables like commodity yields and sales estimates might have been challenging for respondents to recall from the prior cropping season, and some positive behaviors may have been overreported. While these issues are common in surveys and 13 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV expected to some degree, SI worked to improve survey techniques to improve recall and truthful reporting to the extent possible. It is not expected that inaccuracies would differ significantly by study arm and therefore may not pose a large problem to result interpretation. The evaluation team discovered one problem with many farmers misreporting their farming land size. Respondents were asked how much land they use for farming key USAID-supported crops: soy, groundnuts, and orange-fleshed sweet potatoes. They were allowed to report this in either square meters, acres, hectares, or football pitches, which the data analyst standardized into hectares for key indicators such as yield per hectare. Seeing many anomalies and impossible values, the evaluation team investigated and learned that many farmers had reported only the length of their field in meters rather than square meters. The team was not able to verify specific cases of misreporting but estimated it to be a widespread problem. Therefore, SI opted to eliminate from analysis any cases where land was reported in square meter units. This was the best course of action to ensure that invalid data did not affect estimates; however, this means that results for three variables for each crop (hectares under cultivation, yield per hectare, and profit margin per hectare) do not capture results for most smallholder farmers. While the evaluation design has limitations, they do not diminish the utility of this evaluation. The study adds considerable value in that determining the impact of increased multi-sector investment in focused areas alongside the integration approach for service delivery (i.e., the “full CDCS package”) is an important question of great interest to the development community and something that smaller-scale programs such as Millennium Villages have explored in a narrower sense, yet without much rigorous evaluation. This study will help the development community to understand the impacts of the full package of the 3C-based CDCS approach compared to a partial CDCS package. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 14 RESULTS This section details results for selected key indicators. Full analytical tables containing several additional analyses are presented by study arm in Annex E and F. Results in this section are organized by subject area and contain analysis of data from the household survey and the FGDs for each topic, as each method complements the other to allow a deeper understanding of QOL through both quantitative measures and perceptions of community members. Discussion of results is reserved for the Discussion and Conclusion sections of the report. Box 1. Interpretation of results • Multiple regression: Regression containing multiple variables measures the association of each factor with the outcome while adjusting for all other variables in the model by holding them constant. In this way, the relationship between the intervention and outcome can be isolated from the influence of other variables included. • Odds ratios (OR): An odds ratio is a measure of relationship between a binary outcome (e.g., using contraception or not) and a given characteristic (e.g., living in an FI district). It is measured in terms of probability ranging from 0 to infinity. An OR of 1 represents no relationship between the two variables, meaning one has the same odds of experiencing the outcome regardless of which treatment arm they reside in. An OR less than 1 shows that the factor is associated with a reduced likelihood of having the outcome (e.g., a variable with OR=0.5 means a person in the FI treatment arm is half as likely to have the outcome than someone who lives in an HSO area). An OR above 1 represents an increased likelihood of having the outcome (e.g., OR=3 means a person in the treatment group is 3 times more likely to have the outcome than someone in an HSO area). • Beta coefficient: For outcomes measured on a continuous scale, regression coefficients, or beta coefficients, also assess the relationship between each characteristic (predictive variable such as treatment group) and the outcome. The beta coefficient is a measure of how strongly each predictor variable influences the outcome (dependent) variable and allows comparisons across these relationships. The higher the size of the beta coefficient, the greater the impact of the predictor variable on the outcome. For example, a coefficient of 0.8 shows that for a one-unit change in the predictor variable (e.g., moving from an HSO to FI treatment arm), there is a 0.8 unit increase in the outcome (e.g., well-being index score). The sign of the coefficient indicates the direction of the relationship between variables: a positive sign means the relationship of this variable with the outcome is positive (e.g., the more years of education one has, the higher the well-being score); a negative sign means that the relationship is negative (e.g., the more female-headed households, the lower the well-being score). • P value: The level of marginal significance of the relationship between variables tested. This reflects the probability of the observed relationship being due to random chance. Traditionally, a p value <0.05 is considered statistically significant; however, factors with p<0.1 are often worth noting as potentially important associations as well, particularly if the magnitude is large. We use the following notation for significance level: * = p<0.1; ** = p<0.05; *** = p<0.01. An odds ratio or coefficient that is not statistically different from 0 indicates no true relationship between the variables. • Difference-in-difference (DiD): Tables depict this as the percent change from baseline to endline in a treatment zone (PI or FI), after subtracting the percent change in the non-treatment zone (HSO). This reflects the assumption that change in the HSO zone is what would have happened absent integration. Subtracting this better isolates the potential effect of the work in the PI and FI zones. However, DiD in tables are meant to be illustrative. They do not depict any significance testing and cannot be taken as evidence of attributable impact. Only regression results are meant to estimate attributable impact. 15 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV ACTIVITIES AND INTEGRATION IN STUDY AREA Annex A displays the names, sectors, and timelines of USAID activities that were targeted at specific districts within this study (rather than nationally-targeted activities). This demonstrates activity colocation. SI’s annual Stakeholder Analysis, which included review of integration workplans, assessed the level of coordination and collaboration between these activities, as described below. SUMMARY OF STATUS OF INTEGRATION ACTIVITIES AT BASELINE SI’s interviews conducted between November 2014 and January 2015 with IP representatives knowledgeable of program activities, integration plans, and location targeting revealed the following insights on the state of integrated activities in the study districts at baseline as well as likely areas of integration in the impact evaluation districts in the future: • Integration activities across IPs and sectors had been tentative and remained in the very early stages, with most IPs having conducted only a few meetings on the subject. Though several USAID activities had already begun in evaluation districts before 2014, the lack of true integration before the evaluation began supported the validity of the baseline. • Most of the ongoing and planned collaboration involved IPs within the same sector, most frequently within the health sector, where integration is expected as part of their scope of the project. • The majority of integrated activities in the study districts were being conducted by projects supported by the health and sustainable economic growth sectors at USAID. The projects included SSDI (health sector) and INVC (sustainable economic growth sector), and they worked in conjunction with another implementing partner from the same or, at times, with an IP from another sector. Otherwise, very little coordination and/or collaboration was underway across sectors or implementing partners. STATUS OF INTEGRATION ACTIVITIES AT MIDLINE SI conducted a Stakeholder Analysis (SHA) activity at the same time as midline data collection as well as one year prior, which assisted in determining the level of integration occurring across each study district. Complete SHA results are available in the 2016 SHA report and are partially summarized here. • Implementers operating in Balaka, Machinga, and Lilongwe Rural districts were developing annual integration work plans that described several intended collaborative activities with other implementers. • Implementers working in FI focus districts had largely embraced integration, and coordination and collaboration were occurring between IPs both across and within sectors. • Within USAID/Malawi, coordination and collaboration increasingly occurred through meetings between technical offices, as well as development of new cross-cutting activities such as the Local Government Accountability and Performance (LGAP) activity, with funding and objectives from various technical offices. • In 2016, USAID/Malawi facilitated additional workshops to encourage better coordination and collaboration with district government in the three focus districts, as well as a workshop that encouraged implementers to develop cross-sectoral solutions to the El Niño drought crisis. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 16 STATUS OF INTEGRATION ACTIVITIES AT ENDLINE SI’s 2017 and 2018 SHA found the following: • The nature of integration observed at midline continued at endline and took on similar forms over time. With some exceptions, implementers remained positive overall about the value of integration and their ability to integrate effectively. • Whereas at baseline USAID/Malawi emphasized the importance of cross-sectoral integration, this became more and more relaxed over time, and implementing partners were equally encouraged to seek collaboration and coordination with activities in their own sector if it offered a “win-win” benefit to the outcomes of both activities. HOUSEHOLD DEMOGRAPHICS Demographic characteristics of the sample remained stable over time. This was expected, since the same households were tracked from baseline to endline, with relatively few replacements. At endline, on average, households had 5.5 people in a three-room house without electricity (7% reported having electricity), located nearly an hour from the nearest market (53-minute average distance), and near a water source that allowed 27 minutes for round-trip collection time. Nearly all practiced farming (99%), and most spoke Chichewa regularly at home (73%). Just over half had a child under five years old (55%), and 19% reported having a household member with a disability. Most heads of household (85%) had some level of education. The percentage of never-married heads of household was cut in half from baseline to endline (2%), and youth-headed households (age 10–29) dropped from 20% at baseline to 9% at endline, both likely due to the cohort aging by four years over this time period. The proportion of female-headed households was 24%, representing a 23% increase from baseline. However, the proportion of households with no adult male at all (considered a stronger indicator of vulnerability) stayed relatively stable throughout the evaluation (14% at endline). Demographic characteristics are presented in Annex E. QUALITY OF LIFE IMPACT: COMPLETE REGRESSION RESULTS Table 4 and Table 5 present the estimates of program effects for the complete household balanced sample for each time point: baseline (BL) to midline (ML), midline to endline (EL), and overall change from baseline to endline. The PI and FI treatment arms are each compared to the Health Sector Only arm at each round to parametrically estimate the relative difference in changes to capture impact of each level of integration. This was done for each outcome indicator of interest using random effects panel models at the household level, controlling for demographic characteristics, including gender of household head, age, education, and other household characteristics. The complete set of regression results including covariates is in Annex F. Table 4 presents integration effect estimates and their robust standard errors first for binary outcome variables (e.g., yes/no questions), modeled via logit regression. These results are reported as odds ratios. Table 5 presents results for continuous outcome variables, modeled via ordinary least squares (OLS) linear regression. These results are reported as beta coefficient estimates. For ease of interpretation, values Odds Ratio interpretation Greater than 1: Odds of having that outcome are that many times greater than odds for HSO Less than 1: Odds of having that outcome are that many times less than odds for HSO Non-significant results (without ** or ***) are not sufficiently precise to consider the odds ratio to be true and meaningful. 17 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV shaded in green reflect statistically significant positive improvements for the PI or FI group whereas red shading reflects statistically significant worsening, compared to HSO. Regression results for all key indicators are presented here in table form, and subsequent topical sections of the report discuss them in greater detail, with some additional exploration of other indicators. Whereas Table 4 and Table 5 reflect changes at each time point, the remainder of this report focuses on changes that occurred only between baseline and endline, reflecting the overall effect of integration over the five-year CDCS period. Comprehensive data tables featuring midline results, several additional indicators not included in this report, as well as descriptive means disaggregated by district can be referenced in Annex E. TABLE 4. LOGISTIC REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI FOR KEY OUTCOMES Odds ratios (BL to ML) Odds ratios (ML to EL) Odds ratios (BL to EL) Quality of life characteristics PI FI PI FI PI FI Health DO 1. Child <5 in HH died in past 12 months 0.921 0.753 1.33 1.099 1.199 0.841 SIR 4. Woman age 15-49 currently uses any contraceptives 0.888 0.730* 0.792 0.865 0.729* 0.672** Woman age 15-49 currently uses modern contraceptives 0.916 0.741* 0.774 0.891 0.736 0.701** IR 1.1. Respondent received VCT in past 12 months 0.879 0.578*** 1.892*** 1.860*** 1.649*** 1.08 SIR 4. Both partners received VCT in past 12 months 0.854 0.791 1.069 1.197 0.914 0.958 Female respondent received VCT in past 12 months 0.838 0.634** 1.975*** 1.999*** 1.570** 1.253 Male respondent received VCT in past 12 months 1.029 0.485*** 1.735** 1.733** 1.694** 0.792 Reported all children <5 sleep under bed nets 2.245*** 1.523** 1.450* 1.158 3.342*** 1.713*** IR 1.1. Used public hospital/clinic in past 12 months 1.302 0.996 0.821 0.571*** 1.067 0.586*** Takes child to health facility if child needs medical care 2.812*** 2.657*** 0.938 0.975 2.352*** 2.376*** IR 1.2. Affected by drug stockout in past 12 months 0.640*** 0.442*** 0.983 0.999 0.654*** 0.467*** Experienced problems with public clinic/hospital often in past year: Lack of medicine or other supplies 0.571*** 0.603*** 1.144 1.079 0.696** 0.681*** Lack of attention or respect from staff 0.686** 0.817 1.095 1.107 0.764* 0.917 Absent doctors 0.616** 0.827 0.779 0.785 0.497*** 0.681** Long waiting time 0.776* 0.979 0.928 0.942 0.734** 0.918 Dirty facilities 0.921 0.799 0.380*** 0.739 0.372*** 0.616** Nutrition USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 18 Odds ratios (BL to ML) Odds ratios (ML to EL) Odds ratios (BL to EL) Quality of life characteristics PI FI PI FI PI FI IR 2.3. Meets min. acceptable diet for breastfed child 6-23 months 0.506 1.856 4.959** 3.059* 1.798 4.106*** SIR 4. Child 0-5 months old is exclusively breast fed 0.644 0.739 1.878 1.39 1.971 1.533 IR 2.3. Main woman in HH ate soy yesterday 0.852 2.204*** 1.203 0.479*** 1.028 1.054 IR 2.3. Main woman in HH ate groundnuts yesterday 0.899 1.021 1.672*** 0.917 1.492*** 0.958 Agriculture & Environment IR 2.1. HH adopted climate resilience measures in past 12 months 0.878 1.053 1.816*** 1.165 1.606*** 1.225* SIR 4. Farming HHs reported use of improved management practice in past 12 months 0.698** 0.966 1.241 0.913 0.885 0.855 Governance & citizen responsibilities IR 3.2. Knows what local/district government does 0.87 1.101 1.06 0.775** 0.93 0.871 SIR 2. Used phone for business or to send/receive government service info 1.254 1.114 0.406*** 0.342*** 0.522** 0.426*** SIR 4. Volunteered in last 6 months 0.600*** 1.122 2.393*** 1.193 1.466*** 1.310** Note: Coefficient estimates are odds ratios from the respective logistic model. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. Values shaded in green reflect statistically significant positive improvements for the PI or FI group whereas red shading reflects statistically significant worsening, compared to HSO. TABLE 5. LINEAR OLS REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI FOR KEY OUTCOMES OLS Coefficient (BL to ML) OLS Coefficient (ML to EL) OLS Coefficient (BL to EL) Quality of life characteristics PI FI PI FI PI FI Poverty and perceived well-being Primary: Poverty status - living on less than PPP$1.90/day -0.00489 -0.00285 -0.00157 0.000202 -0.00523 -0.00236 Perceived overall well-being index score 0.0878** 0.179*** 0.0173 0.0638* 0.104** 0.245*** Perceived financial well-being index score 0.0976*** 0.196*** 0.0456 0.115*** 0.137*** 0.312*** Agriculture & Environment IR 2.2. Groundnuts - average gross margin (USD/ha) -125.6 -56.28 291.0* 179.4 190.2*** 101.4* IR 2.2. Soy - average gross margin (USD/ha) 62.84 172.8* -1,124 -1,227 -1,153 -1,095 IR 2.2. Hectares under soya cultivation 0.128 0.0175 -0.12 -0.0195 0.00504 -0.00275 IR 2.2. Hectares under groundnut cultivation -0.0135 0.109 0.0794*** -0.145* 0.0693 -0.0424 19 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV OLS Coefficient (BL to ML) OLS Coefficient (ML to EL) OLS Coefficient (BL to EL) Quality of life characteristics PI FI PI FI PI FI IR 2.4. Groundnuts - average yield (Kg/ha) in past cropping season 163.3** 8.58 -112.1* 56.34 27.15 46.25 IR 2.4. Soy - average yield (Kg/ha) in past cropping season -13.55 205.2** 97.96 -174 36.04 -20.97 Degree of food insecurity (scale of 0-18) 0.0198 -0.450** -0.905*** -0.244 -0.894*** -0.71*** Education DO 1. Percentage of 2nd graders who can read Chichewa -0.0378* -0.0132 -0.0556*** -0.041** -0.089*** -0.051** Women's empowerment Women's participation in HH decisions (% of decisions) -0.0122 0.00458 0.000379 -0.0003 -0.0141 0.00545 Note: Coefficient estimates are odds ratios from the respective logistic model. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. Values shaded in green reflect statistically significant positive improvements for the PI or FI group whereas red shading reflects statistically significant worsening, compared to HSO. WELFARE: POVERTY STATUS Based on the OLS linear regression approach, Table 6 reports the estimated effect of the integrated development approach on poverty status between baseline and endline. In a linear regression framework, changes over time in PI and FI districts were compared with those in HSO districts to estimate the impact attributable to the integration strategy. SI found no meaningful change in poverty level as a result of the integrated development approach. This is further discussed below. TABLE 6. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON POVERTY (BL TO EL) PI FI Variable description OLS Coef. SE OLS Coef. SE Quality of life: Poverty status - living on less than PPP$1.90/day -0.00523 -0.00545 -0.00236 -0.00494 Note: Estimated coefficients are derived from OLS regression. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. The calculation of poverty status in the endline report is based on the 2011 international poverty line of purchase power parity (PPP) of $1.90 per day. In 2013, when the CDCS was initiated in Malawi, the overall goal was set to improve quality of life of Malawians. Achievement of this goal in USAID/Malawi’s PMP was OLS Coefficient interpretation Negative OLS coefficient: Outcome for selected treatment arm is that many units lower than outcome for HSO (e.g. Coef. = -37 means PI households are likely to wait at the health facility 37 minutes less than HSO households). Positive OLS coefficient: Outcome for selected treatment arm is that many units higher than outcome for HSO Non-significant results (without ** or ***) are not sufficiently precise to consider the coefficient to be true and meaningful. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 20 captured by a primary indicator: “percent of sampled households living under international PPP $1.25 a day” (established as “extremely poor” poverty line by Ravallion et al.).8 At baseline and midline, SI measured the indicator using data gathered through the Malawi-specific Poverty Assessment Tool (PAT), developed by the Institutional Reform and Informal Sector (IRIS) center of the University of Maryland with funding from USAID. The Malawi PAT is a short survey that measures indicators selected as predictors of whether a given set of households is poor or non-poor according to $1.25 a day line based on 2005 prices in Malawi. The PAT questions were incorporated into the baseline and midline household survey instruments and were used to predict the share of respondent households living below the international $1.25 a day per capita poverty line (2005 prices) in the sample.9 Per USAID’s request to update the poverty calculation at endline from PPP $1.25 a day to PPP $1.90 a day, SI used the ‘Simple Poverty Scorecard-brand poverty-assessment tool’ that uses ten low-cost indicators featured in Malawi’s 2010–11 Integrated Household Survey to estimate the likelihood that a household has consumption below a given poverty line.10 This particular method identifies the household’s vulnerability to poverty or likelihood of being poor on a 0 to 100 percent scale, instead of assigning them to a binary category of poor or not poor. Due to the change in methodology and poverty line, the team recalculated the baseline and midline poverty incidence as well so that the poverty rate is comparable across three rounds of data collection. Table 7 presents poverty rates calculated for all three survey rounds based on the PBM definition of poverty lines, as reported in Schreiner (2015).11 This shows that despite poverty rates in HSO districts being almost unchanged between baseline and endline (at 81%), a marginal decline in the integrated areas over time led to no discernable improvement in poverty in PI and FI treatment arms as compared to HSO districts, when controlling for other factors. The positive direction (indicated by the sign associated with the coefficient), however, shows that poverty is declining in the integrated areas, albeit at a very slow rate. 8 M. Ravallion, S. Chen, and P. Sangraula, “Dollar a Day Revisited,” World Bank Economic Review 23, no. 2 (2009): 163–84. 9 The PAT provides a statistic that calculates the poverty rate for a sample or sub-sample/segment of the population with a high level of confidence. The tool, however, does not yield individual expenditure levels for each household. Rather, it is designed to accurately predict overall poverty rates within a sample or a sub-sample, even if the individual household predictions may be less robust. 10 The 10 low-cost indicators included in the ‘Simple Poverty Scorecard-brand poverty-assessment tool’ are information on household size; sex of the household head; local language reading and writing skill of the head of household; materials used for floor, wall, and roof; access to improved latrine/toilet; type of lighting fuel; use of mosquito net; and possession of bed and table. 11 Schreiner, “Simple Poverty Score Card and Poverty Assessment Tools Malawi” (2015), also available at SimplePovertyScorecard.com. PBM refers to the initials of the authors of this method for estimating poverty: Karl Pauw, Ulrik Beck and Richard Mussa, WIDER for Malawi Poverty (2015). 21 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV TABLE 7. PERCENTAGE DISTRIBUTION OF HOUSEHOLDS’ LIKELIHOOD OF BEING UNDER NATIONAL, $1.25, AND $1.90 PPP POVERTY LINES Baseline Midline Endline HSO PI FI HSO PI FI HSO PI FI Sample size (n) 1,733 1,282 1,728 1,743 1,197 1,711 1,741 1,152 1,702 National poverty line 53% 58% 59% 53% 59% 58% 54% 59% 59% Poverty $1.25 PPP 73% 77% 77% 72% 77% 76% 73% 77% 77% Poverty $1.90 PPP 81% 85% 85% 81% 84% 84% 82% 85% 85% Note: Poverty rates are calculated from three rounds of survey data and PBM-based poverty lines. Although this report bases the poverty rate on PPP $1.90, SI calculated poverty rates based on two other poverty lines—PPP $1.25 per day and the national poverty line. These additional two poverty rates were calculated for validation of poverty rates calculated using this methodology as compared to the Malawi Government’s own calculation of international 2005 and 2011 PPP poverty lines, poverty rates for all of Malawi, and for each of Malawi’s four poverty-line regions based on construction/validation samples, by households and people, for 2004–5 and 2010–11. These poverty rates are based on daily per-capita consumption and measured according to the Government definition.12 Table 8 compares average poverty rates of sample households at baseline, midline, and endline with average rural poverty rates reported in Schreiner (2015) across all regions. The one-to-one comparison shows that average rural poverty rates (average of north, south, and central regions) from GoM’s estimation based on household level per-capita consumption data is very close to SI’s calculation using the PBM-based definition of national and international poverty lines, particularly the rates calculated for baseline. Since the baseline survey is the closest time point to the survey used for poverty assessment (2010–11) reported in Schreiner (2015), the comparison between the rate based on GoM’s definition of poverty lines and SI’s baseline rates is most relevant for validation and consistency verification. TABLE 8. COMPARISON OF POVERTY RATES OF MALAWI’S FOUR POVERTY-LINE REGIONS BASED ON GOM'S DEFINITION OF INTERNATIONAL 2005 AND 2011 PPP POVERTY LINES AND POVERTY RATES Baseline Midline Endline Based on Government definition Sample size (n) 5,314 5,214 5,154 5,087 National poverty line 58% 56% 56% 57% Poverty $1.25 PPP 76% 75% 75% 76% Poverty $1.90 PPP 84% 83% 83% 85% Source: Government-definition international 2005 and 2011 PPP poverty lines and poverty rates for all of Malawi and for each of Malawi’s four poverty-line regions and for construction/validation samples, by households and people, for 2004/5 and 2010/11 (pp. 161, 162). PERCEIVED WELL-BEING This study considers well-being to be multi-dimensional, involving both objective and subjective measures. The household survey included a battery of questions about satisfaction with one’s health, finances, and 12 Ibid. See p. 161 for rates based on national poverty line and p. 162 for rates based on international poverty lines. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 22 food security, as well as improvement in one’s financial stability relative to last year and to neighbors. As perception-based measures, these in part reflect true conditions and in part reflect one’s level of optimism or pessimism. SI used factor analysis to create a composite metric for financial well-being from eight variables and a composite metric of overall well-being using 13 variables. Table 9 displays the estimated impact of integration on perceived overall and financial well-being using linear regression results that compare changes in PI and FI treatment arms to HSO. In contrast to the finding of no effect for the $1.90 poverty metric above, these perception indices provide evidence that partial and fully integrated development might have a significant positive impact on well-being in terms of personal life satisfaction. In the PI districts, change in quality of life between baseline and endline, measured in terms overall well-being, shows a significant improvement by more than 10% as compared to the HSO districts. The improvement in the full integration districts over time is almost 25% more than in HSO districts. A stronger program effect is also evident when we consider perceived financial well-being as a measure of quality of life. For this indicator, the partially integrated districts observed a 14% improvement between baseline and endline while the fully integrated districts recorded a 31% improvement. TABLE 9. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON OVERALL AND FINANCIAL WELL-BEING (BL TO EL) PI FI OLS Coef. SE OLS Coef. SE Quality of life: Overall Well-Being Index Score 0.104** -0.0424 0.245*** -0.0385 Quality of life: Overall Financial Well-Being Index Score 0.137*** -0.0386 0.312*** -0.0351 Note: Estimated coefficients are derived from OLS regression. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. Both composite metrics decreased over time in all study arms, meaning overall well-being perceptions declined between 2014 and 2018; however, HSO districts decreased by the largest margin, making PI and FI households relatively better off (Figure 2). This suggests that fully and partially integrated districts have significantly arrested the slide in perceived well-being of poor households. FIGURE 2. PERCEIVED FINANCIAL AND OVERALL WELL-BEING (% CHANGE BL TO EL) SI further examined selected individual well-being variables using regression analysis to better understand which aspects of life are perceived to be getting better or worse (Table 10). Between baseline and endline, significantly fewer respondents from PI (33% reduction) and FI (77% reduction) districts, as compared to HSO districts, thought their income was insufficient to meet expenses, meaning these groups felt more -21% -18% -18% -17% -11% -13% -25% -20% -15% -10% -5% 0% Self-reported Financial Well Being Score Self-reported Overall Well Being Score HSO PI FI 23 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV financially secure. Those in PI districts also reported significant reductions in the perception that their household was in poor or fair health compared to changes in HSO districts. With respect to dissatisfaction with Malawian democracy, a significantly higher number of respondents from PI districts were increasingly dissatisfied as compared to the respondents from HSO districts. Analysis did not identify significant differences in perceived dissatisfaction with life as a whole or with optimism about being financially better off next year. The household survey also included a question about whether the respondent felt she or he was personally able to make positive changes in their lives (as opposed to all circumstances being up to fate or some other force). This attempted to tap into one small facet of citizen agency. Compared to changes in HSO districts, a significantly smaller number of respondents from PI (69%) and FI (65%) districts reported that they felt they were in control. TABLE 10. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON WELL-BEING (BL TO EL) PI FI Quality of life characteristic Odds Ratio SE Odds Ratio SE Income not sufficient to meet expenses 0.331*** -0.0428 0.770** -0.0838 General health of HH members is poor/fair 0.727** -0.0916 0.830* -0.0934 Dissatisfied with financial situation 1.134 -0.142 0.762** -0.0843 Dissatisfied with Malawi democracy 1.355** -0.176 0.877 -0.0981 Dissatisfied with life as a whole 1.145 -0.151 0.998 -0.117 Optimistic HH will be financially better off next year 0.913 -0.122 0.997 -0.116 Believes personally able to control improvements to own well￾being in life 0.687*** -0.0966 0.649*** -0.0792 Note: Estimated odds ratios are derived from logit regression. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. HEALTH BEHAVIORS As noted in the Limitations section, health indicator analysis is likely the most reliable way to isolate the impact of integration from the effect of increased cross-sectoral investment. In this section, we examine the relative change in health behavior outcomes among treatment arms. The DiD regression estimates for health behavior outcomes suggest the likelihood of significant improvements in PI districts over time as compared to HSO districts (Table 11). Households in PI districts are more than twice as likely to take their children to health facilities when needed and more than 1.6 times as likely to receive voluntary counseling and testing for HIV (VCT) (both overall and for each gender). Children below five years of age were more than three times as likely to sleep under bed nets. These changes over time were strongly significant. The comparative changes over time between FI and HSO districts varied. While households from FI districts were 2.4 times more likely to take their children to health facilities and 1.7 times more likely to put their children to sleep under a bed net, they were significantly less likely to use public hospitals or clinics in the past year and to use contraceptives. There was no significant difference in terms of both partners receiving VCT or child mortality rates. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 24 TABLE 11. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON HEALTH BEHAVIORS (BL TO EL) PI FI Variable description Odds Ratio SE Odds Ratio SE DO 1. Child <5 in HH died in past 12 months 1.199 -0.369 0.841 -0.232 IR 1.1. Used public hospital/clinic in past 12 months 1.067 -0.207 0.586*** -0.0942 Takes child to health facility if child needs medical care 2.352*** -0.781 2.376*** -0.678 IR 1.1. Respondent received VCT in past 12 months 1.649*** -0.233 1.08 -0.134 SIR 4. Both partners received VCT in past 12 months 0.914 -0.197 0.958 -0.183 Female respondent received VCT in past 12 months 1.570** -0.302 1.253 -0.219 Male respondent received VCT in past 12 months 1.694** -0.431 0.792 -0.173 SIR 4. Woman age 15-49 currently uses any contraceptives 0.729* -0.138 0.672** -0.111 Woman age 15-49 currently uses modern contraceptives 0.736 -0.139 0.701** -0.116 Reported all children <5 sleep under bed nets 3.342*** -0.632 1.713*** -0.291 Note: Estimated odds ratios are derived from logit regression. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. Descriptive means provide further insight into trends in health behaviors over time. All key health behaviors examined through the household survey showed improvement over time in all study arms (Table 12). The overall crude birth rate dropped from 29% to 23% at endline. Differences between treatment arms were modest. Contraceptive use increased, with nearly all using modern forms of contraception (72% of women age 15–49), and rates of VCT were up for both men and women. Respondents also reported fewer deaths of children under five years old in the past year. TABLE 12. HEALTH BEHAVIORS: DESCRIPTIVE MEANS AT ENDLINE AND BASELINE Endline Baseline Difference-in￾difference* Total HSO PI FI Total HSO PI FI PI￾HSO FI￾HSO FI￾HSO Variable description n mea n mean mean mean n mean mean mean mean Crude birth rate (live births in past year per 1,000 in sampled population) 25,444 23% 22% 23% 24% 24,953 29% 28% 32% 27% -5% 8% Child <5 in HH died in past 12 months 4,58 8 4% 3% 4% 4% 4,739 4% 4% 5% 5% -4% - 20% Reported all children <5 sleep under bednets 2,74 8 75% 71% 79% 77% 3,042 68% 72% 61% 69% 31% 13% Used public hospital/clinic in past 12 months 4,59 5 88% 90% 89% 84% 4,743 80% 81% 79% 79% 2% -5% Takes child to hospital, health center, or clinic if 4,45 0 97% 96% 98% 97% 4,428 95% 95% 95% 94% 2% 3% 25 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV child needs medical care Receipt of VCT in past 12 months by: Respondent 4,59 1 72% 71% 74% 71% 4,738 61% 63% 58% 62% 15% 2% Female respondent 2,21 2 72% 70% 73% 73% 3,329 62% 64% 60% 63% 13% 6% Male respondent 2,09 9 71% 72% 73% 68% 1,409 59% 61% 53% 60% 18% -5% Both partners 2,62 0 84% 83% 85% 85% 2,429 81% 78% 82% 82% -3% -2% Woman age 15–49 currently uses: Any contraceptive s 2,63 3 73% 75% 69% 73% 3,200 62% 58% 61% 66% - 16% - 20% Modern contraceptive s 2,63 1 72% 74% 68% 72% 3,191 61% 57% 59% 65% - 15% - 19% Any contraceptive (among married) 2,47 0 74% 76% 72% 75% 2,841 65% 60% 64% 70% - 14% - 19% Any contraceptive (among non￾married) 163 51% 59% 46% 52% 351 39% 32% 37% 44% - 62% - 66% * Difference-in-difference in this table (change in treatment group minus change in comparison group) is only illustrative and does not control for other factors or reflect any statistical significance testing. See regression tables for more accurate difference-in-difference modeling. To complement USAID/Malawi’s PMP indicator for access to a health facility within eight km, the household survey examined whether respondents had actually utilized such public hospitals or clinics within the past year. Results were similar across treatment arms at each data collection phase and gradually increased from a baseline average of 80% to 88% at endline (Table 12). The regression results for overall change from baseline to endline show that households in FI districts were significantly less likely to use the public hospitals or clinics in the past year as compared to the households from HSO districts. The odds of a household visiting a public hospital or clinic in FI districts was slightly more than half (0.585) when compared with households from HSO districts. In other words, those living in HSO districts were 1.7 times more likely to have visited a public health facility in the past year than households from FI districts. The pattern is similar to what we found when comparing endline incidence with midline. In contrast, those living in PI districts were nearly equally likely as those in HSO districts to have used a public health facility. It is possible that those not using these facilities are seeking care somewhere other than a public health facility or that they are healthier and felt they had no need for health care services in the past 12 months, but the evaluation team was not able to confirm the true reason. The lack of health care visits does not appear to be related to disappointment in the quality of health care, given the finding that people in both FI and PI areas had more favorable impressions of the quality of health care as compared to those in HSO areas (see Table 14 below). The survey also confirmed that care-seeking intent was high in all study arms, with 97% of caregivers saying they take their child to a health facility when sick, up slightly from 95% at baseline. Trends in health facility utilization and care-seeking for children were relatively consistent across treatment arms, evidenced by the small magnitude of change in both PI and FI zones once accounting for the change in the HSO treatment arm (see difference-in-difference in Table 12). Still, after controlling for other factors in regression models, both PI and FI treatment arms were found to be more than twice as likely to seek care for children, compared to HSO. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 26 Each study arm saw improvements in the rate of VCT in the past year, with PI districts having the greatest gain relative to baseline for both male and female individuals (Figure 3). Each study arm had more modest improvements (3–6%) in having both members of a couple seeking voluntary counseling and testing (VCT). FIGURE 3. RECEIVED VCT IN PAST 12 MONTHS (% CHANGE BL TO EL) The regression results for the respondent (across genders) having received VCT in the last 12 months suggests that respondents in FI areas were almost equally likely to receive VCT at endline when compared to HSO respondents, controlling for other factors. In contrast, respondents from PI districts were 1.7 times more likely to have been tested. This result was strongly significant. There was, however, no evidence of significant program effects on the probability of both sexual partners receiving VCT in the past year (Sub-intermediate result [SIR] 4), either for FI or PI arms, as compared to HSO. For contraceptive use, HSO districts showed the greatest improvement over time in several categories (Figure 4), with a remarkable 85% increase in contraceptive usage among non-married women (reaching 59% of women at endline), compared to a 23% and 18% improvement in PI and FI, respectively. The regression results suggest that the likelihood of using a contraceptive (any type or modern) was significantly lower for respondents in FI districts as compared to HSO districts between baseline and endline. The use of contraceptives grew more than 1.5 times in HSO districts over time as compared to FI districts. Though likelihood of contraceptive use in PI districts was lower than HSO at each data collection round, this difference was not significant. Injectable methods were the most popular forms of contraception at endline for all groups (52% of all contraceptive users) followed by implants (20%). These were also the most popular methods at baseline. Whereas male condoms were the third most common contraception at baseline, female sterilization surpassed this at endline to become the third most common (14% of contraception users). 9% 18% 6% 22% 36% 3% 15% 13% 4% 0% 5% 10% 15% 20% 25% 30% 35% 40% Female respondent Male respondent Both partners received VCT HSO PI FI 27 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV FIGURE 4. CURRENT CONTRACEPTIVE USE AMONG WOMEN 15–49 (% CHANGE BL TO EL) Use of bednets to protect children under five against malaria improved in PI and FI districts by 30% and 12%, respectively, whereas this behavior remained relatively unchanged in the HSO zone (Figure 5). Regression results reflect this difference, as those in PI districts were 3.3 times more likely to report having all children under five sleep under a bed net the prior night compared to HSO districts (Table 11). Those in the FI zone were 1.7 times more likely than those in HSO to practice this behavior. Both results were highly significant, suggesting an impact attributable to USAID work in the integrated zones. FIGURE 5. REPORTED ALL CHILDREN <5 SLEEP UNDER BEDNETS (% CHANGE BL TO EL) HEALTH SERVICE QUALITY Health-seeking behaviors can be shaped in part by one’s experience with the healthcare system. Both of USAID’s flagship health activities have worked to build health facilities’ capacities to provide better services in all evaluation districts. At endline, 82% of respondents reported that the nearby USAID-supported health facility was the one they visited most often. Household survey respondents discussed how frequently they faced selected problems with health services in the past year. Nearly all indicators of perceived healthcare quality improved more in FI and PI zones compared to HSO. Table 13 reports the regression estimates of program effects in PI and FI districts as compared to the HSO districts. Comparing changes in each treatment arm between baseline and endline, both FI and PI zones reported significant improvement in health service quality across several indicators. Significantly fewer people in FI and PI zones reported that they had experienced drug stockouts, absent doctors, or dirty facilities. Those in PI 30% 30% 26% 85% 15% 15% 12% 23% 11% 12% 7% 18% 0% 20% 40% 60% 80% 100% Uses any contraceptives Uses modern contraceptives Uses any contraceptive (among married women) Uses any contraceptive (among non-married women) FI PI HSO -1% 30% 12% -5% 0% 5% 10% 15% 20% 25% 30% 35% HSO PI FI USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 28 districts also reported significant reductions in long waiting times. These findings are further discussed below. TABLE 13. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON HEALTH SERVICE QUALITY (BL TO EL) Panel logistic regression results PI FI Variable description Odds Ratio SE Odds Ratio SE IR 1.2. Affected by drug stockout in past 12 months 0.654*** -0.0945 0.467*** -0.0621 Experienced problems with public clinic/hospital often in past year: Lack of medicine or other supplies 0.696** -0.0993 0.681*** -0.0854 Lack of attention or respect from staff 0.764* -0.117 0.917 -0.122 Absent doctors 0.497*** -0.108 0.681** -0.123 Long waiting time 0.734** -0.0978 0.918 -0.11 Dirty facilities 0.372*** -0.104 0.616** -0.121 Panel OLS regression results OLS Coef. SE OLS Coef. SE Reported waiting time at health center/hospital at last visit (minutes) -37.03*** -6.106 -5.848 -5.511 Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. In all treatment arms, there was increased reporting that facilities were frequently dirty, with long waiting times, lack of attention or respect from staff, lack of medicine or supplies, and unaffordable services (Figure 6). All conditions except unaffordable services were reportedly worse over time in HSO districts. Notably, we found a 116% increase in reports of facilities in HSO districts often being dirty. Those in HSO districts also reported a 36% increase in absence of doctors, whereas PI districts experienced an 18% reduction in this problem and FI districts stayed nearly the same. Residents in PI districts reported a 100% increase in the inability to pay for health services, or feeling they were too expensive. This stands in contrast to findings described above regarding PI district respondents’ perception of being more financially stable over time than those in HSO districts. In comparison, this increased by only 16% and 15% in FI and HSO zones, respectively. 29 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV FIGURE 6. FREQUENTLY EXPERIENCED PROBLEMS WITH PUBLIC HEALTH FACILITIES IN PAST 12 MONTHS (% CHANGE BL TO EL) Comparing complaints of these problems from baseline to endline using regression models (Table 13), the evaluation team found that PI and FI districts had fewer problems over time in all categories compared to HSO districts. As compared to households from HSO districts, those in PI districts were significantly less likely to report frequently experiencing lack of medicine or other supplies over time (.7), lack of attention or respect (.76), absent doctors (.5), long waiting time (.73), and experience of dirty facilities (.37) for every respective increase in incidence in HSO districts. These faster improvements suggest that the integrated approach in PI districts may have led to these changes. The FI districts experienced similar improvements, although the impact was somewhat lower than that in PI districts. All results were significant at p<0.05 except for lack of staff attention or respect and, in the case of FI districts, long waiting time. In addition to reports of how frequently respondents experienced these conditions, the survey also inquired simply whether each respondent had personally experienced a drug stockout in the past year. This was true for 71% of people at endline (Table 14). The logistic regression results show that the odds of an average household experiencing a drug stockout in the last 12 months between baseline and endline were significantly less in PI (.65 times) and FI (.47 times) districts as compared to households from HSO districts. In other words, households in HSO districts were 1.5 times more likely than those in PI to experience a stockout and were 2.1 times more likely to experience a stockout than those in FI, after controlling for other factors. These results suggest that something about the combination of USAID’s activities and integrated approach in both FI and PI districts had an impact on supply chain improvements relative to USAID’s single-sector approach in HSO districts. This may relate to USAID’s nationally-scaled health commodity support activities such as the Global Health Supply Chain-Procurement and Supply Management Project collaborating and coordinating activities with other health activities such as ONSE, Support for International Family Planning Organizations II, and Health Policy Plus. It may be that information sharing and joint planning meetings between these implementers enhanced drug availability outcomes in PI and FI areas. 15% 38% 16% 36% 25% 116% 100% 17% 5% -18% 12% 3% 16% 9% 8% -1% 18% 40% -40% -20% 0% 20% 40% 60% 80% 100% 120% 140% Services are too expensive/unable to pay Lack of medicine or other supplies Lack of attention or respect from staff Absent doctors Long waiting time Dirty facilities FI PI HSO USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 30 In spite of this relative improvement, according to FGD participants, unavailability of drugs was a pervasive and universal problem of great concern. Across all districts, participants shared stories of being sent to other health facilities, or most often private pharmacies to get necessary medication, as public health facilities did not keep regular stock of anything other than HIV medication (which is a national government priority). Many said medication sold privately is not affordable, and the additional travel to get it poses a large inconvenience. Some spoke of health care workers who personally sold medication on the side, suggesting they were stealing medicine from the hospitals for personal gain. TABLE 14. HEALTH SERVICE QUALITY: DESCRIPTIVE MEANS AT EL AND BL Variable description n mean mean mean mean n mean mean mean mean PI-HSO FI￾HSO Reported travel time to closest clinic (minutes) 4,576 89 85 105 83 4,699 98 100 108 88 12% 10% Reported waiting time at health center/hospital at last visit (minutes) 4,009 129 129 115 138 3,758 117 108 127 120 -30% -5% Affected by drug stockout in past 12 months 4,001 71% 74% 68% 70% 3,752 66% 61% 62% 73% -11% -24% Services are too expensive/unable to pay 3,999 4% 3% 5% 4% 3,719 3% 3% 3% 4% 85% 1% Lack of medicine or other supplies 4,001 37% 42% 31% 37% 3,752 30% 30% 26% 34% -22% -29% Lack of attention or respect from staff 3,998 28% 28% 26% 30% 3,742 26% 25% 25% 28% -11% -8% Absent doctors 3,966 13% 14% 9% 13% 3,696 12% 11% 11% 13% -54% -37% Long waiting time 4,008 49% 51% 46% 50% 3,758 42% 41% 42% 42% -13% -7% Dirty facilities 3,976 13% 20% 4% 11% 3,718 7% 9% 4% 8% -113% -76% * Difference-in-difference in this table (change in treatment group minus change in comparison group) is only illustrative and does not control for other factors or reflect any statistical significance testing. See regression tables for more accurate difference-in-difference modeling. Total HSO PI FI Endline Baseline Difference-in￾Total HSO PI FI difference* Frequently experienced problems with public health facilities in past 12 months: On average, people traveled nearly 90 minutes to reach the closest clinic, with PI districts having the highest average travel times. However, those in PI districts also reported the shortest wait times once at the facility, compared to other treatment arms. Compared to baseline, waiting times decreased in PI districts, whereas they increased in FI and especially HSO districts (Figure 7). Regression results reflect this, as those in PI districts were likely to wait 37 minutes less than those in HSO districts, and this result was highly significant. The reduced waiting time found in FI districts was not significantly different from HSO. 31 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV FIGURE 7. REPORTED WAITING TIME AT HEALTH CENTER/HOSPITAL AT LAST VISIT (MINUTES) (% CHANGE BL TO EL) Beyond complaints of drug stockouts, many FGD participants noted disrespectful treatment by doctors at public health facilities. Doctors often arrived late to the clinic or left early. In several FGDs, participants expressed disappointment at a seeming lack of care and consideration during their medical visits. One respondent in Lilongwe District described a recent experience: "My child was sick, and I went there. I went at the health center and we tried to wake the doctor up because he was sleeping. He did not wake up. The watchman went to wake him up, but to no avail. He said that he was tired, and we must go to another heath center. He said that I should go to Demera. I stayed right there until it was dawn. I started off to Demera. I was not welcomed there, too. Then my mother is a friend to a certain doctor there, and she talked with her friend, that was when we were helped. All this was happening while my child was unconscious. For you to be assisted at the hospital, you need to be known to a health worker, else you should have money." EDUCATION USAID/Malawi supported improvements to early grade reading as well as child and adult access to literary materials through its flagship EGRA activity, which was implemented in all FI and PI districts through the midline data collection round. Upon the end of this activity, USAID scaled its early grade reading support to a national-level government support program called MERIT. To assess changes in literacy, the household survey respondent was asked to report each household member’s level of education attained as well as basic literacy through four simple questions about whether the member could read or write a one-page letter in the Chichewa language or English. This measure is likely prone to bias, as it is based on the perception of the respondent. Direct literacy measurement was not feasible within the scope of this study. A separate independent impact evaluation of USAID/Malawi’s flagship EGRA activity provides a direct 20% -9% 15% -15% -10% -5% 0% 5% 10% 15% 20% 25% HSO PI FI USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 32 measurement of child literacy through standardized testing and should be considered a more reliable measure of this indicator.13 The OLS regression estimation of DiD for reading outcomes of 2nd graders shows that both PI and FI districts performed significantly worse than the HSO districts between baseline and endline, though PI districts were more likely to have improved availability of reading materials at home for children and adults (Table 15). Those in the PI zone were more likely to report overcrowded classrooms being a common problem whereas FI respondents complained more of absent teachers. These and other educational outcomes are discussed below. TABLE 15. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON EDUCATION (BL TO EL) PI FI Panel OLS regression results Variable description Coef. SE Coef. SE Percentage of 2nd graders who can read Chichewa -0.0894*** -0.0218 -0.0512** -0.0212 Panel logistic regression results Odds Ratio SE Odds Ratio SE Literacy: Reading materials kids can read at home 1.374** -0.187 1.042 -0.144 Literacy: Reading materials adults can read at home 1.413*** -0.173 1.201 -0.148 Experienced problems with public school often in past year: Fees too expensive/unable to pay 0.973 -0.189 0.955 -0.158 Lack of textbooks or other supplies 1.119 -0.154 0.989 -0.123 Poor teaching 1.052 -0.168 1.238 -0.171 Absent teachers 0.832 -0.149 1.378** -0.214 Overcrowded classrooms 1.352** -0.183 1.153 -0.139 Poor conditions of facilities 1.188 -0.181 1.185 -0.165 Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. Overall, endline respondents reported that only 5% of the 1,852 children in second grade were able to read a one-page letter in Chichewa. This reflects a decline from 7% at baseline. Literacy varied among treatment arms. Whereas reported 2nd grade literacy increased by 36% in the HSO zone, it decreased by 76% and 34% in PI and FI zones, respectively (Figure 8). In all treatment arms, female students fared better, as literacy improved for 70% of girls in HSO districts and only 14% of boys (Figure 9). Declines in literacy for other treatment arms were slightly worse for boys than for girls. 13 USAID/Malawi, Impact Evaluation of the Early Grade Reading Activity (EGRA): Final Report, by Geetha Nagarajan, Pedro Carneiro, and Andrea Hur, of Social Impact, Inc. (May 2018), https://pdf.usaid.gov/pdf_docs/PA00T3Q6.pdf. 33 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV FIGURE 8. 2ND GRADERS WHO CAN READ CHICHEWA (SELF-REPORTED, % CHANGE BL TO EL) FIGURE 9. 2ND GRADERS WHO CAN READ CHICHEWA, BY GENDER (SELF-REPORTED, % CHANGE BL TO EL) After controlling for other factors that might explain changes in literacy and comparing the changes in HSO to each treatment arm, the regression model confirms the trend in the figure. The findings in the regression results table illustrate that 2nd graders in HSO districts had nearly a 9% improvement in basic literacy compared to those in PI districts (Table 15). HSO children had nearly a 5% improvement over children in FI districts. This is despite the significant finding that both children and adults in PI districts were 1.4 times more likely to have access to reading materials at home. The survey also inquired about the degree to which respondents observed problems at their public schools. Across all study arms, complaints about poor teaching or lack of textbooks and supplies decreased over time (Figure 10). People in PI and HSO zones reported reductions in teacher absences, whereas those in the FI zone reported a 17% increase in this problem. Regression analysis confirmed those in FI districts were 1.4 times more likely to experience this issue. HSO respondents complained less about large classroom size, whereas this increased in the other treatment groups. Regression results confirmed the increase in the PI group was significant. Relative to baseline, all treatment arms reported increased challenges with paying school fees, and PI and FI respondents noted slight increases in observing poor school facility conditions. 36% -76% -34% -100% -80% -60% -40% -20% 0% 20% 40% 60% HSO PI FI 14% -81% -37% 70% -71% -31% -100% -50% 0% 50% 100% HSO PI FI Male Female USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 34 FIGURE 10. COMPLAINTS OF FREQUENT PROBLEMS WITH EDUCATION SERVICES (% CHANGE BL TO EL) FGDs confirmed that gender equality in schooling is improving, and most FGDs had participants note that there was no difference in boys' and girls' reading abilities. Some noted that fewer girls drop out of school compared to past years. Some discussions emphasized the importance of school feeding programs for education. One FGD participant in Nkhotakota complained about the state of education in the village and highlighted the lack of food as a barrier to learning: "In the past we used to go to school but knock off at 10, but now the government increased the time [to] 2 o’clock. Here we are in the village, we don’t have money to give to kids for them to buy foods at school, so when it’s 12:00, students are already hungry, so they cannot learn effectively. So here as we have said we have problems earning money, so for us to buy sugar so children should drink tea before going [to school], it’s a hard task. So students go to school with empty stomach. Imagine a standard 3 or 2 kid-- how can he learn at 2 o’clock without eating? Hence comparing to students in town-- their organisation, we hear they give porridge to students. They should also help us." FOOD INSECURITY With high poverty rates and much of its population dependent on subsistence farming, many Malawi households have experienced some level of food insecurity, particularly during recurrent droughts or periods of flooding. Several USAID/Malawi activities have worked to combat food insecurity through agricultural value chain support to improve productivity and income, nutrition education, and food aid in response to crises. Between baseline and endline, food insecurity worsened in all treatment arms, with insecurity peaking at midline due to a prolonged drought in 2015–16. Linear regression results in Table 16 below show that PI and FI districts were significantly better off at endline than at baseline when compared to HSO districts. We discuss these results in context below. -40% -30% -20% -10% 0% 10% 20% 30% 40% School fees too expensive/unable to pay Lack of textbooks or other supplies Poor teaching Absent teachers Overcrowded classrooms Poor conditions of facilities FI PI HSO 35 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV TABLE 16. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON FOOD INSECURITY (BL TO EL) PI FI Variable description OLS Coef. SE OLS Coef. SE Degree of food insecurity (scale of 0-18) -0.894*** -0.24 -0.713*** -0.217 Note: Estimated coefficients are derived from OLS regression. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. Food insecurity in this study was measured using a modified version of the standard Household Food Insecurity Access Scale (HFIAS) questionnaire, which identifies the frequency with which a household recently ate undesirable food or insufficient food due to lack of resources. Respondents were asked whether a particular negative condition occurred in the past 30 days and, if so, whether it happened rarely, sometimes, or often. A household is considered to have suffered from a facet of food insecurity if any member of the household ate a limited variety of foods for lack of resources, ate less than needed for lack of food, ate fewer meals for lack of food, went to sleep hungry for lack of food, or went a full day without eating for lack of food. Responses to each question were assigned a score of 0 (did not happen) to 3 (happened often). The scores for all questions were then added to calculate an overall estimate of food insecurity. Thus, a higher score estimated using this methodology corresponds to greater food insecurity. Figure 11 displays food insecurity trends by treatment arm, and Figure 12 trends by district on a scale of 0 to 18, 14 with 18 being most insecure. Machinga was the only district whose food insecurity level remained the same from baseline to endline. Compared to other treatment arms, HSO districts Karonga and Nkhotakota were least food insecure at both baseline and endline; however, they worsened at a higher rate such that PI and FI districts were comparatively better off over time (Figure 11). This was confirmed through regression results. Being in these treatment arms was associated with a significant reduction in food insecurity, after controlling for change in HSO and other factors (PI districts -0.89 and FI districts -0.71). Along with the trend analysis above, the data suggest that integration may have had a positive impact on arresting the growth of food insecurity in the PI and FI districts. 14 While the standard HFIAS contains nine sets of questions, the baseline survey captured six of these, owing to a clerical oversight. The resulting scale, though based on a smaller number of questions, was still able to provide a comparative measure of food insecurity across treatment zones. The full nine-question questionnaire was used at midline and endline only. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 36 FIGURE 11. FOOD INSECURITY (% CHANGE FROM BL TO EL) FIGURE 12. FOOD INSECURITY OVER TIME, BY DISTRICT (SCALE OF 0–18) Participants in all FGDs noted that food security had worsened in the past two years due to poor harvests brought on by drought, with the exception of one Zomba village that agreed food security had stayed the same. Food aid was an important mitigating factor that arose in discussions in Lilongwe and Machinga, where participants said the food support greatly reduced the impact of the scarcity. According to USAID activity records shown in Annex A, USAID supported World Food Program (WFP) programs between midline and endline data collection in Zomba and Mangochi, with additional WFP school feeding programs in Lilongwe and Zomba. However, food aid extended beyond these districts. NUTRITION USAID/Malawi has implemented several activities through either its Health, Population, and Nutrition or its Sustainable Economic Growth Technical Offices to address nutrition outcomes in all evaluation districts. Table 17 presents regression results for key PMP nutrition indicators. According to regression results, the FI intervention had a strong, significant positive impact on children receiving a minimum acceptable diet, and the PI intervention had a significant positive impact on women having groundnuts in their diet. We discuss these results further below. 36% 15% 15% 0% 5% 10% 15% 20% 25% 30% 35% 40% HSO PI FI 0.0 2.0 4.0 6.0 8.0 10.0 12.0 Baseline Midline Endline F:Balaka F:Lilongwe F:Machinga P:Mangochi P:Zomba H:Karonga H:Nkhotakota 37 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV TABLE 17. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON NUTRITION (BL TO EL) PI FI Odds Ratio SE Odds Ratio SE IR 2.3. Meets min. acceptable diet for breastfed child 6-23 months 1.798 -0.83 4.106*** -1.784 SIR 4. Child 0-5 months old is exclusively breast fed 1.971 -2.696 1.533 -1.553 IR 2.3. Main woman in HH ate soy yesterday 1.028 -0.269 1.054 -0.214 IR 2.3. Main woman in HH ate groundnuts yesterday 1.492*** -0.222 0.958 -0.127 Note: Estimated odds ratios are derived from logit regression. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. Overall trends in key nutrition indicators did not change to a large degree since baseline (Table 18). Nearly the same proportion of caregivers of children under six months of age reported exclusively breastfeeding them (83% at endline). Evidence from difference in difference regression suggests that there is no discernable difference across treatment arms in exclusive breastfeeding of infants below six months. The survey assessed whether children aged 6–23 months met infant and young child feeding (ICYF) criteria for a minimum acceptable diet (MAD) according to standard guidelines,15 which includes indicators of sufficient food variety and meal frequency for both breastfed and non-breastfed children. Among breastfed children in this age group, this practice was relatively uncommon, with only 14% meeting all criteria at endline, representing only a small increase from baseline. However, there was a remarkable increase relative to baseline in FI districts. Breastfed children in FI districts had a 158% improvement in MAD feeding practices, whereas those in HSO districts decreased by 26% (Figure 13). PI districts did not see any major change over time. The FI zone also saw an increase in MAD for non-breastfed children from 0% to 13%, whereas other treatment arms stayed at zero over time (Table 18). The number of non￾breastfed children in this group was so small that regression analysis was not feasible. For the breastfed group of children 6-23 months, between baseline and endline, the likelihood of meeting minimum acceptable diet improved more than four times in FI districts as compared to the HSO districts, and results were highly significant. 15 World Health Organization, “Indicators for assessing infant and young child feeding practices. Part 1 Definitions” (2008), https://www.unicef.org/nutrition/files/IYCF_updated_indicators_2008_part_1_definitions.pdf. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 38 TABLE 18. NUTRITION: DESCRIPTIVE MEANS AT ENDLINE AND BASELINE Variable description n mean mean mean mean n mean mean mean mean PI-HSO FI￾HSO Child 0-5 mo is exclusively breast fed 257 83% 77% 93% 83% 336 84% 77% 93% 84% 0% -1% Child 6-23 months is breast fed 783 91% 89% 92% 94% 908 93% 91% 96% 93% -2% 3% Meets min. acceptable diet for breastfed child 6-23 months 630 14% 14% 10% 16% 758 12% 19% 10% 6% 28% 184% Meets min. acceptable diet for non-breastfed child 6-23 months 61 3% 0% 0% 13% 54 0% 0% 0% 0% 0% 0% Number of food groups consumed yesterday by child 6-23 months (breastfed standard) 801 2.7 2.6 2.6 2.7 977 2.5 2.7 2.4 2.4 11% 18% Number of feedings of solid or semi-solid food for child 6- 23 months 785 2.2 2.2 2.2 2.1 912 2.2 2.5 2.3 1.9 9% 24% Main woman in HH ate soy yesterday 4,465 8% 10% 5% 7% 4,688 6% 8% 4% 6% 2% 0% Main woman in HH ate groundnuts yesterday 4,460 25% 23% 30% 24% 4,682 23% 22% 22% 24% 34% -3% Total HSO Endline Baseline Difference-in￾Total HSO PI FI PI FI difference* * Difference-in-difference in this table (change in treatment group minus change in comparison group) is only illustrative and does not control for other factors or reflect any statistical significance testing. See regression tables for more accurate difference-in-difference modeling. FIGURE 13. MEETS MINIMUM ACCEPTABLE DIET FOR BREASTFED CHILD 6–23 MONTHS To reflect USAID/Malawi’s support of soy and groundnut cultivation, meant to improve both livelihoods and nutritional diversity, SI tracked PMP indicators regarding consumption of soy and groundnuts among women. In this case, SI examined the main woman of the household’s consumption the prior day. This timeframe was meant to reduce recall bias and reflect the likelihood that these foods are a regular part of -26% 2% 158% -40% -20% 0% 20% 40% 60% 80% 100% 120% 140% 160% HSO PI FI 39 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV her diet. At endline, 8% had eaten soy and 25% had eaten groundnuts (Table 18). Both represent very small increases since baseline. Relative change between treatment arms was minimal except for PI districts, which had a 37% increase in recent groundnut consumption, compared to a 3% increase for HSO districts and none for FI (Figure 14). The DiD estimates between baseline and endline reported in Table 17 corroborate that inter-temporal changes in the likelihood of the main woman in households from PI districts eating groundnuts is almost 1.5 times higher than HSO households. In contrast, there is no significant difference between the main women from FI and HSO districts in terms of groundnut consumption. Regression results also suggest that the likelihood of the main woman eating soy was almost identical across treatment arms. FIGURE 14. MAIN WOMAN IN HH ATE GROUNDNUTS YESTERDAY AGRICULTURE Throughout the CDCS time period, various USAID activities have supported improvements in cultivation of soy, groundnuts, and orange-fleshed sweet potatoes. The evaluation examined cultivation trends and profitability for these crops over time. The study also assessed trends in improved farming practices. As noted in the Limitations section above, many farmers who reported the size of their land in square meters had difficulty understanding this concept and instead reported the length of their field in meters. Given the inability to identify each case where this confusion occurred, we excluded all farmers who reported their land size in square meters. At endline this resulted in exclusion of 10% of soy farmers, 7% of groundnut farmers, and 23% of sweet potato smallholder farmers. At midline, 11% of soy, 10% groundnut, and 32% of sweet potato farmers were lost. Baseline exclusions were 6% of soy farmers, 5% groundnut, and 31% of sweet potato farmers. Additionally, it is possible some farmers had difficulty recalling or estimating other factors such as yields or income from crop sales. Data analysts set parameters to restrict outliers reflecting impossible values; however, some misreported data may have gone undetected, thereby affecting estimates. These crop outcomes in particular should be interpreted with caution. CULTIVATION OF USAID-SUPPORTED CROPS Table 19 presents linear regression results for groundnut and soy production. From baseline to endline, farmers in the PI districts reported a significantly higher gross margin for groundnuts than farmers in HSO districts. This and other results related to USAID-supported crops are discussed below. 3% 37% 0% 0% 5% 10% 15% 20% 25% 30% 35% 40% HSO PI FI USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 40 TABLE 19. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON CROP OUTCOMES (BL TO EL) PI FI Variable description Coef. SE Coef. SE IR 2.2. Groundnuts - average gross margin (USD/ha)† 190.2*** -64.48 101.4* -59.88 IR 2.2. Soy - average gross margin (USD/ha)† -1,153 -1,079 -1,095 -1,076 IR 2.2. Hectares under soya cultivation† 0.00504 -0.00562 -0.00275 -0.00472 IR 2.2. Hectares under groundnut cultivation† 0.0693 -0.0892 -0.0424 -0.0853 IR 2.4. Groundnuts - average yield (KG/ha) in past cropping season† 27.15 -66.9 46.25 -67.08 IR 2.4. Soy - average yield (KG/ha) in past cropping season† 36.04 -170.6 -20.97 -156.9 Note: Estimated coefficients are derived from OLS regression. † All variables exclude farmers who reported land size in square meters. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. At endline, 10% of the sampled households cultivated soy in the last cropping season, a slight decrease in all treatment groups (Table 20). Soy yields increased in all areas, resulting in minimal differences for both treatment arms once subtracting the change in HSO districts. The overall average yield was 592 kg per hectare, with similar increases across all treatment arms. HSO districts saw a marked improvement in the gross margin for soy from $287 to $1,339 US dollars per hectare, whereas the PI zone saw only a slight improvement and FI a slight decline. 41 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV TABLE 20. CROP OUTCOMES: DESCRIPTIVE MEANS AT ENDLINE AND BASELINE Variable description n mean mean mean mean n mean mean mean mean PI-HSO FI-HSO Grows soya 4,420 10% 2% 11% 16% 4,696 12% 5% 12% 19% 53% 38% Hectares under soya cultivation (among full sample, excluding land reported in sq. meters) 4,549 0.02 0.00 0.03 0.04 4,657 0.03 0.01 0.03 0.05 51% 31% Yield of soy last season in KG/Ha (among those who grew soy, sq meter land units ex 368 592 530 505 638 460 500 436 395 556 7% -7% Individual level gross margin for soya (USD per Ha, excluding land reported in sq. meters) 249 $236 $1,339 $177 $217 294 $200 $287 $201 $193 -379% -354% Grows groundnuts 4,470 47% 33% 61% 52% 4,719 41% 32% 46% 45% 29% 14% Hectares under groundnut cultivation (full sample, excluding land reported in sq. meters) 4,317 0.1 0.1 0.2 0.1 4,500 0.2 0.2 0.2 0.3 40% 11% Yield of groundnuts last season in KG/Ha (among those who grew it, excluding land reported in sq. meters) 1,804 453 590 338 456 1,583 611 757 510 595 -12% -1% Individual level gross margin for groundnuts (USD per Ha, excluding land reported in sq. meters) 904 $182 $185 $162 $193 846 $269 $369 $180 $265 40% 23% Grows orange fresh sweet potatoes 4,451 26% 27% 26% 26% 4,670 23% 26% 22% 19% 18% 29% Hectares under O.F. sweet potato cultivation (full sample, excluding land reported in sq. meters) 4,251 0.1 0.0 0.1 0.1 4,452 0.1 0.1 0.1 0.0 61% 243% Yield of sweet potato last season in KG (among those who grew it, excluding land reported in sq. meters) 775 3449 3792 2547 3751 621 1935 2274 1321 1998 26% 21% Individual level gross margin for sweet potatoes(USD per Ha, excluding land reported in sq. meters) 403 $266 $384 $155 $240 302 $311 $440 $185 $235 -4% 15% Endline Baseline Difference-in￾Total HSO PI FI Total HSO PI FI difference* * Difference-in-difference in this table (change in treatment group minus change in comparison group) is only illustrative and does not control for other factors or reflect any statistical significance testing. See regression tables for more accurate difference-in-difference modeling. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 42 Households engaged in groundnut cultivation reached 47% at endline, representing an increase in PI and FI districts (Table 20). Average yields were down, from 611 kg/ha at baseline to 453 at endline, with declines in all study arms. Gross profit margin also decreased in all study arms, but with the greatest decline in HSO areas (Figure 16). This outcome improved in PI and FI districts after controlling for the HSO decline in the difference-in-difference calculation (Table 20). FGD respondents in both Karonga and Lilongwe noted that while the groundnut harvest was good, the market had basically bottomed out, leaving farmers without favorable prices. This along with higher fertilizer costs helps to explain the drop in gross margin for groundnuts. A Mangochi FGD respondent noted a similar problem with sweet potatoes. FIGURE 15. INDIVIDUAL LEVEL GROSS MARGIN FOR SOYA (USD PER HA, EXCLUDING LAND REPORTED IN SQ. METERS) FIGURE 16. INDIVIDUAL LEVEL GROSS MARGIN FOR GROUNDNUTS (USD PER HA, EXCLUDING LAND REPORTED IN SQ. METERS) At endline, 26% of households raised orange-fleshed sweet potatoes, up slightly from baseline in all study arms (Table 20). Farmers’ yields sharply increased, with greatest improvements in PI and FI districts. The 366% -12% 12% -50% 0% 50% 100% 150% 200% 250% 300% 350% 400% HSO PI FI -50% -10% -27% -60% -50% -40% -30% -20% -10% 0% HSO PI FI 43 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV gross profit margin increased by 15% for FI district farmers, after controlling for the change in HSO districts. PI district farmers did not see a dramatic change, with a 4% reduction in margin. Regression estimates (Table 19 and Table 20) that compare changes in HSO to changes in each treatment arm show that the change in margin from groundnut production is significantly higher in PI areas than in the HSO areas. Since gross margin in groundnut production declined for all treatment arms, the significantly higher change in margin in PI areas reflects the higher drop in HSO areas as compared to PI areas. The same result from groundnut production in FI also suggests significant improvement over the HSO areas, although this improvement is relatively lower than PI areas and weakly significant. The change in average margin over time from soy cultivation, however, was relatively worse in PI and FI areas as compared to HSO areas. The relative changes in area allocation under groundnut and soy and respective yields in PI and FI areas show no significant difference over the HSO areas. FGDs confirmed that 2018 was a tough year for farmers in Malawi. Across many districts, FGD participants complained that harvests were worse than they had been two years ago due primarily to a shortage of rainfall. This was especially true for maize, the most important staple crop in Malawi. Another pervasive comment related to challenges with the limited supply of affordable fertilizer, which has become a key input and barrier for many to ensuring a better harvest. A Lilongwe participant summarized the issue: "We had no food. The little we got, we used to buy food and not fertilizers. How could we buy fertilizer when we had no food in our households? We depend much on the subsidy fertilizers that for those who managed to get coupons to buy the fertilizers, no one managed to get a coupon without sharing it with someone. So, they got portions of fertilizers, which were far from being enough to apply to the crops." Some said an organization taught them to create their own fertilizer from manure, which was helpful, but not nearly as effective as common fertilizers. FARMING PRACTICES The household survey asked farmers if they had adopted any improved agricultural practices in the past 12 months. These included using improved seeds, crop rotation, mixed cropping, irrigation or methods to use less water, use of fertilizer or earthworms, or changing the type of crop grown. Logistic regression results found no significant difference between HSO and either FI or PI farmers in terms of adopting improved agricultural management practices in the past year (Table 21). This and other findings regarding farming practices are discussed below. TABLE 21. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON FARMING PRACTICES (BL TO EL) PI FI Variable description Odds Ratio SE Odds Ratio SE SIR 4. Farming HHs reported use of improved management practice in past 12 months 0.885 -0.16 0.855 -0.134 Note: Estimated odds ratios are derived from logit regression. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. Nineteen percent of farmers said they made changes to their agricultural practices in the past year. This was a minimal change from baseline in all study arms (Table 22). Among those who did make changes, the most common difference was practicing crop rotation (26%), using improved seeds (19%), using non￾organic fertilizer (13%), or growing a different type of crop (12%) (not shown in table). Farmers’ club USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 44 participation was similar to baseline, with 23% of households and 14% of women in those households being members of such organizations. TABLE 22. FARMING PRACTICES: DESCRIPTIVE MEANS AT ENDLINE AND BASELINE Variable description n mean mean mean mean n mean mean mean mean PI-HSO FI-HSO Household members have participated in farmers' club 4,499 23% 20% 26% 24% 4,743 27% 24% 28% 29% 9% 0% Female in household participates in farmers' club 4,595 14% 12% 15% 15% 4,743 14% 13% 14% 16% 12% -3% Changed any agriculture practice in past 12 months 4,499 19% 19% 18% 19% 4,557 18% 18% 18% 18% -9% -4% Total HSO PI FI Total HSO PI FI Endline Baseline Difference-in￾difference* * Difference-in-difference in this table (change in treatment group minus change in comparison group) is only illustrative and does not control for other factors or reflect any statistical significance testing. See regression tables for more accurate difference-in-difference modeling. Given increasing concern about aflatoxin fungal contamination of crops in Malawi,16 SI added questions to the midline and endline household surveys to assess the degree to which this problem may have affected farmers. For each key USAID-supported crop plus maize, which is often affected by aflatoxin, the survey inquired whether the farmer had discarded any portion of his/her crop during the grading process due to suspected contamination (whether known to be aflatoxin or any other problem). The survey also asked whether the farmer had had any portion of his/her crop rejected at market or warehouse delivery due to suspected contamination with aflatoxin or any other disease. The questions did not specify only aflatoxin, as all farmers may not have known how to identify the specific cause of their crop problems. Therefore, though results suggest potential changes in aflatoxin contamination, they likely include other crop diseases as well. Given the emerging threat of fall armyworm crop infestation,17 it is possible that some portion of farmers captured with these indicators are reporting this type of problem as well. Indeed, in several FGDs, participants confirmed that they had major problems with pests damaging crops. For example, Karonga respondents said armyworms had destroyed much of their maize. Over the two-year time period from 2016 to 2018, there was a sharp increase in the number of farmers affected. Figure 17 and Figure 18 reflect the relative changes in each treatment arm since the midline survey. Market rejection was highest for maize and became a much greater problem for farmers in PI and FI districts over time compared to those in HSO districts. Overall, at endline, farmers who discarded at least a portion of their groundnut harvest jumped to 43%, soya to 28%, sweet potatoes to 32%, and maize to 55% (Table 23). Market or warehouse rejection of at least a part of their crop affected 33% of groundnut farmers, 28% of soybean farmers, 17% of sweet potato farmers, and 27% of maize farmers. 16 Christopher Jimu, “Aflatoxins cost Malawi k8bn yearly,” The Nation (April 24, 2017), https://mwnation.com/aflatoxins-cost-malawi-k8bn-yearly/. 17 “Malawi's new reality: Fall armyworm is here to stay,” International Food Policy Research Institute Blog (Fbruary 26, 2018), http://www.ifpri.org/blog/malawis-new-reality-fall-armyworm-here-stay. 45 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV FIGURE 17. FARMER DISCARDED ALL/PORTION OF CROP DURING GRADING PROCESS DUE TO CONTAMINATION FIGURE 18. ALL/PORTION OF CROP REJECTED AT MARKET/WAREHOUSE DUE TO CONTAMINATION 482% 459% 211% 278% 441% 162% 842% 719% 333% 254% 228% 444% 0% 100% 200% 300% 400% 500% 600% 700% 800% 900% Groundnuts Soya Orange fleshed sweet potato Maize HSO PI FI 1524% 0% 1380% 1358% 3718% 731% 1381% 6467% 2295% 2050% 1872% 4998% 0% 1000% 2000% 3000% 4000% 5000% 6000% 7000% Groundnuts Soya Orange fleshed sweet potato Maize HSO PI FI USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 46 TABLE 23. CROP CONTAMINATION: DESCRIPTIVE MEANS AT ENDLINE AND BASELINE Variable description n mean mean mean mean n mean mean mean mean PI-HSO FI-HSO Groundnuts 595 43% 35% 50% 43% 0 n/a n/a n/a n/a -42% -149% Soya 128 28% 13% 32% 29% 0 n/a n/a n/a n/a -297% -204% Maize 1,256 55% 56% 53% 54% 0 n/a n/a n/a n/a 441% 166% Groundnuts 117 33% 28% 32% 35% 0 n/a n/a n/a n/a 2194% 770% Soya 25 28% n/a 17% 32% 0 n/a n/a n/a n/a n/a n/a Maize 264 27% 30% 18% 29% 0 n/a n/a n/a n/a 5109% 3640% Endline Baseline Difference-in￾Total HSO PI FI Total HSO PI FI difference* Farmer discarded all/portion of crop during grading process due to contamination (aflatoxin or other): All/portion of crop rejected at market/warehouse due to contamination (aflatoxin or other): * Difference-in-difference in this table (change in treatment group minus change in comparison group) is only illustrative and does not control for other factors or reflect any statistical significance testing. See regression tables for more accurate difference-in-difference modeling. NATURAL RESOURCES AND ENVIRONMENT Since baseline, USAID has supported activities addressing natural resource and environmental protection in Balaka and Machinga districts in the FI treatment arm and Mangochi and Zomba districts in the PI treatment arm. The household survey assessed knowledge and practice of climate change and mitigation strategies as well as the degree to which people rely on natural resources for their livelihoods or are vulnerable to the effects of climate change. Any household that had adopted improved farming practices noted above or had planted trees in the past year was considered to have adopted some small measure to show resilience to climate change. Regression results identified that the PI treatment arm had a significant positive impact on adoption of climate resilience measures in the past year (Table 24). We explore this and other environment and natural resource-related data below. TABLE 24. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON CLIMATE RESILIENCE PRACTICES (BL TO EL) PI FI Variable description Odds Ratio SE Odds Ratio SE IR 2.1. HH adopted climate resilience measures in past 12 months 1.611*** -0.214 1.222* -0.143 Note: Estimated odds ratios are derived from logit regression. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. The proportion of respondents who had heard of climate change increased to 88% at endline (Table 25). There was a decline in the number of people who claimed they did not know how to prepare for climate change from 36% to 20%, with the greatest improvement in the PI zone. Households in the PI zone reported improvements in several environmental practices. Relative to the HSO zone, PI households increased their tree planting by 30%, were less dependent on forest materials and timber products for income, experienced 28% less farm soil erosion, and increased adoption of measures to improve resiliency to climate change by 20% (Table 25). Compared to HSO households, those in FI districts were less reliant 47 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV on forest products over time and experienced 24% less soil erosion. Fishing became a more important source of income for residents of FI districts, whereas income from fishing decreased in HSO and PI zones. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 48 TABLE 25. ENVIRONMENT: DESCRIPTIVE MEANS AT ENDLINE AND BASELINE Variable description n mean mean mean mean n mean mean mean mean PI-HSO FI-HSO Has heard of climate change 4,595 88% 90% 88% 87% 4,743 74% 73% 72% 76% -1% -8% Planted trees in past year 4,595 47% 40% 49% 52% 4,743 28% 24% 25% 33% 30% -4% Don't know how to prepare for climate change 4,595 20% 20% 15% 24% 4,739 36% 38% 35% 34% -11% 16% Household gathers materials from forest (e.g. wood, fruit) 4,595 84% 85% 88% 80% 4,743 65% 63% 70% 62% -10% -4% Wood/timber is important source of income 4,595 11% 14% 6% 10% 4,743 7% 8% 6% 8% -86% -57% Income derived from fish sales last month (MWK, among full sample) 4,595 1,169 2,748 84 287 4,743 1,834 4,519 388 215 -39% 73% Income derived from fish sales last month (MWK, among those who fished) 208 25,818 26,732 10,778 24,400 280 31,070 39,160 14,609 8,062 6% 234% Experienced loss or severe reduction of arable land due to erosion in past year 4,595 50% 61% 41% 44% 4,743 40% 43% 37% 38% -28% -24% Respondent saw demonstrations in the past year related to planting or preserving 4,595 44% 36% 48% 50% 4,743 31% 28% 26% 38% 54% 0% HH adopted measure in past year that may improve resiliency to climate change 4,595 51% 46% 53% 56% 4,743 34% 32% 32% 38% 20% 2% Changed ag. practice in past year that may improve resiliency to climate change 4,595 13% 15% 12% 11% 4,743 12% 12% 12% 12% -19% -31% Changed water use in past year that may improve resiliency to climate change 4,595 3% 4% 3% 2% 4,743 2% 2% 3% 3% -109% -116% Endline Baseline Difference-in￾Total HSO PI FI Total HSO PI FI difference* * Difference-in-difference in this table (change in treatment group minus change in comparison group) is only illustrative and does not control for other factors or reflect any statistical significance testing. See regression tables for more accurate difference-in-difference modeling. 49 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV As noted above, logistic regression shows that the likelihood of farming households from the PI districts adopting climate resilience measures over time was significantly higher between baseline and endline as compared to the households from HSO district. The jump in rates of tree planting is likely a large driver of this change, as it is the distinguishing difference between this indicator and that of improved agricultural practices discussed above, which was not significantly different. Although similar evidence is observed in the case of households from FI district, the likelihood was much weaker. According to FGD data, communities were keenly aware of climate change, and respondents across each district had observed its effects with concern. The most common action taken across locations was to plant trees and plant grasses like vetiva to curtail soil erosion. Several FGD participants were able to articulate the interconnected effects of climate change on everyday life. One Balaka FGD participant noted: "We used to have wild animals such as hares and ngulube (wild pig) and others. Over the past two years with my own eyes, I have never seen a hare, ngulube, even mbawala (deer). This means that wild life is slowly disappearing. Coming to fish species in water. The number has been reduced. Summer like this, we used to have a lot of fish from the lake. As of now, it has lowered. I think it’s because of climate change. Bushes where the hares were hiding, and some wild animals, we have turned that land to a cultivation field. We have no longer enough forest for the wild animals to hide. This is what I see...Due to scarcity of trees, we are very concerned about this because even rainfall that used to come, it is not the same that we are receiving. Instead of us having good rains to harvest enough we are harvesting little. The rainfall, it’s not as it was then. It falls today than a week and another week without failing. For us to harvest, we are unable. This is because environment is no more. To talk about the wild animals, the children we have today, they will never even see what, a hyena. They will know because they will be reading and seeing pictures but in real sense they will not. They will not see a hare because we have them not. No forest for these wild animals to live." LOCAL GOVERNMENT PARTICIPATION AND USE OF SERVICES In support of the decentralization process in Malawi, USAID has worked to encourage citizen awareness of and engagement with local government such as Village Development Committees (VDCs) and District Councilors. USAID has also worked to improve government capacity to meet citizens’ needs. The household survey examined knowledge of and participation in local government. Regression analysis found no significant difference across treatment arms in knowledge of what one’s local or district government does (Table 26). People living in PI and FI districts were significantly less likely than those in HSO to use their mobile phone to send or receive government service information or for business purposes. Both treatment arms were significantly more likely to have volunteered in their community in the past six months. These and other results are discussed below. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 50 TABLE 26. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON GOVERNMENT KNOWLEDGE, CITIZEN ENGAGEMENT (BL TO EL) PI FI Variable description Odds Ratio SE Odds Ratio SE IR 3.2. Knows what local/district government does 0.93 -0.116 0.871 -0.0972 SIR 2. Used phone for business or to send/receive government service info 0.522** -0.136 0.426*** -0.0887 SIR 4. Volunteered in last 6 months 1.466*** -0.184 1.310** -0.144 Note: Estimated odds ratios are derived from logit regression. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. At endline, 83% of people were aware of the VDC, and 59% claimed to know what roles it plays (Table 27). A similar number (54%) claimed to understand the roles of local and district government such as the District Councilor. The evaluation found that all study arms similarly improved in awareness of local government and its roles (Figure 19), which explains the lack of significant difference between study groups in regression analysis. Volunteerism was up in all study arms, but relative to HSO districts, PI and FI districts showed a 10% increase in the proportion of respondents who said they volunteered their time for various community services or events in the past six months. TABLE 27. LOCAL GOVERNMENT KNOWLEDGE AND PARTICIPATION: DESCRIPTIVE MEANS AT ENDLINE AND BASELINE Variable description n mean mean mean mean n mean mean mean mean PI-HSO FI-HSO Aware of Village Development Committee (VDC) 4,595 83% 81% 87% 83% 4,743 70% 69% 72% 70% 2% 1% Knows what VDC does 4,595 59% 55% 62% 60% 4,743 47% 45% 47% 49% 10% 0% Knows what local government does 4,595 54% 55% 54% 52% 4,743 45% 45% 46% 44% -5% -7% Participation: In VDC meetings/activities 4,595 43% 38% 49% 44% 4,743 37% 32% 43% 39% -8% -8% Participation: Volunteered in last 6 months 4,595 56% 47% 64% 59% 4,743 51% 46% 57% 52% 10% 10% * Difference-in-difference in this table (change in treatment group minus change in comparison group) is only illustrative and does not control for other factors or reflect any statistical significance testing. See regression tables for more accurate difference-in-difference modeling. Difference-in￾Total HSO PI FI Total difference* Endline Baseline HSO PI FI 51 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV FIGURE 19. KNOWLEDGE AND ENGAGEMENT WITH LOCAL GOVERNMENT (% CHANGE BL TO EL) The DiD estimation using logistic regression suggests that the change in likelihood of households from PI and FI districts to volunteer in the past six months was almost 1.5 times higher than the households from HSO districts. This DiD measure was strongly significant. In contrast, use of technology (cell phone) for business and service information (whether sending information or receiving information about government services) was significantly lower (almost half in PI and 40% in FI). Overall usage of government services increased from baseline to endline (Table 28). Eighty-three percent had a child in public school, and 88% had used a public hospital or clinic, with similar improvement across study arms. Fewer people benefitted from government-run agriculture training (17%), though people in PI and FI districts showed greater usage over time relative to those in HSO districts. Usage of nutrition assistance programs decreased in HSO districts and increased in FI and especially PI zones (Table 28). Though nutrition assistance such as food aid went to a small proportion of households overall, the greater access to or use of this service in PI and FI districts may provide partial explanation of the more muted increase in food insecurity over time compared to HSO districts as noted above. Households in the PI study group also increased their level of benefit from government-run school feeding programs well above the other study arms (Table 28). 18% 22% 24% 21% 20% 31% 19% 13% 19% 22% 17% 13% 0% 5% 10% 15% 20% 25% 30% 35% Aware of VDC Knows what VDC does Knows what local government does Participation: In VDC meetings/activities HSO PI FI USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 52 TABLE 28. UTILIZATION OF GOVERNMENT SERVICES: DESCRIPTIVE MEANS AT ENDLINE AND BASELINE Variable description n mean mean mean mean n mean mean mean mean PI-HSO FI-HSO Nutrition assistance 4,595 8% 7% 12% 7% 4,743 6% 8% 6% 5% 125% 49% Government run school feeding program 4,595 28% 23% 46% 20% 4,743 12% 13% 16% 9% 119% 58% Training related to agriculture 4,595 17% 18% 16% 17% 4,743 15% 17% 14% 14% 13% 12% Public schooling 4,595 83% 83% 84% 82% 4,743 70% 72% 69% 69% 5% 4% Public hospital/clinic 4,595 88% 90% 89% 84% 4,743 80% 81% 79% 79% 2% -5% Road maintenance 4,540 34% 35% 32% 33% 4,384 28% 29% 25% 30% 10% -13% Local policing 4,488 32% 30% 30% 35% 4,317 32% 32% 30% 34% 7% 8% Management of land use 4,549 36% 39% 27% 40% 4,400 36% 38% 30% 39% -13% 1% Maintenance of local markets 4,446 43% 47% 39% 40% 4,284 41% 42% 41% 38% -17% -6% Consulting citizens before making decisions 4,248 50% 55% 46% 49% 3,856 46% 49% 40% 48% 0% -11% Keeping corruption in check 4,295 59% 64% 56% 56% 4,074 48% 51% 44% 47% 1% -5% Managing land use 4,164 42% 43% 37% 43% 3,854 36% 38% 31% 36% 8% 5% * Difference-in-difference in this table (change in treatment group minus change in comparison group) is only illustrative and does not control for other factors or reflect any statistical significance testing. See regression tables for more accurate difference-in-difference modeling. Dissatisfied with government services: Utilized government services: Endline Baseline Difference-in￾Total HSO PI FI Total HSO PI FI difference* Citizens’ level of satisfaction with several selected government services declined in all treatment groups (Figure 20). The proportion of respondents who expressed dissatisfaction with the government’s handling of the following services increased in all study arms: management of land, controlling corruption, consulting citizens, and maintaining local roads. Those in FI districts did not report as high a change in dissatisfaction with consulting citizens or road maintenance, compared to other study arms. Over time, fewer people living in PI districts complained about local market maintenance and provision of water and sanitation services, suggesting that these features were improving. Similarly, those living in HSO districts complained less about local policing as compared to baseline. 53 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV FIGURE 20. DISSATISFACTION WITH LOCAL GOVERNMENT SERVICES (% CHANGE BL TO EL) FGD participants could generally articulate the basic roles of their local and district government representatives (except one village in Machinga), but in nearly all districts, people expressed great frustration with and lack of trust of their District Councilor. Some cited a complete lack of engagement. Others who had consultations with local government representatives to prioritize development projects claimed the projects never happened, with no explanation of why. In some FGDs respondents noted seeing development in nearby villages and wondering why nothing was being done in their own. Complaints about a lack of transparency were pervasive, with community members having no idea what was being done with the district's allocated development budget, or why their projects were not being funded. Many expressed a feeling that corruption must be at play. Several FGDs attributed these failures not to lack of staffing or resources, but lack of political will. Frustration with their ineffectual representatives led one Balaka village to spearhead their own fundraising and planning for a school construction project: “The site where we want to have the secondary school, we have molded bricks…We want to build community center ground so that it should bring money to our community. We have seen that the authorities will never helped us. We believe that after five years we will have our own millions out of this stadium. We will have our own plans...without [the DC office]. We are more than able. We want to develop our community… We had players in December from civil strikers, one from bullets team, and also someone else. They came here at Thundu. They had a play. They made almost half million. People were paying MWK 100 each. So we can have a fence. Come end of the year we will have lots of money. This means government will have nothing to do with the money. We will be the government... We will do it. We will do everything else. We have been by the DC; we have been by the physical planning office. They have given us the design. We have our own ways to source money. We have well-wishers coming up to say ‘we will help you, we will help you.’" WOMEN’S EMPOWERMENT The household survey explored the degree of female participation in agriculture and household decision￾making to better understand potential gendered barriers to development. The primary measure was an -15% -10% -5% 0% 5% 10% 15% 20% 25% 30% 35% Local road maintenance Local policing provision Water and sanitation Local market place maintenance Consulting citizens Keeping corruption in check Managing the use of land FI PI HSO USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 54 additive index of 12 decisions including issues like selecting which crop to grow, getting farming inputs, taking loans, choosing whether to use contraception, and schooling and health care for children. Respondents were asked which household member normally makes the decision about this issue. Any decisions within control of a female in the household were counted toward the percentage of decisions within a female locus of control. Regression analysis found no significant difference between study arms in women’s participation in such decisions using this overall metric (Table 29). TABLE 29. REGRESSION RESULTS: ESTIMATED IMPACT OF PI AND FI ON WOMEN'S PARTICIPATION (BL TO EL) PI FI OLS Coef. SE OLS Coef. SE Women's participation in HH decisions (% of decisions) -0.0141 -0.0146 0.00545 -0.0129 Note: Estimated coefficients are derived from OLS regression. Significance levels are marked as * = p<0.1; ** = p<0.05; *** = p<0.01. Examination of trends in the component variables of this index between baseline and endline show that more women have adopted the locus of control for every type of decision in every study arm (Figure 21). The largest change relative to baseline was in the decision to take loans, where there was a 46% increase in women taking such decisions in FI districts and 39% increase in HSO. Women’s decisions about getting agricultural inputs increased by 38% in both FI and PI districts. Across all topics, respondents claimed that women made the decisions at least 50% of the time (data not shown). FIGURE 21. FEMALES IN CONTROL OF DECISIONS (% CHANGE BL TO EL) 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Inputs for Agriculture Types of crops to grow Taking crops to market Family planning Participation in community activities Taking loans Participation in groups/committees Schooling of boy child Schooling of girl child Health care of boy child Health care of girl child Health care for self FI PI HSO 55 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV DISCUSSION This evaluation aimed to test the validity of USAID/Malawi’s CDCS development hypothesis: “if assistance is integrated then development results will be enhanced, more sustainable, and lead to achievement of our CDCS goal: Malawians’ quality of life improved.” A simple theory of change for integration would posit that colocating to serve the same beneficiaries, coordinating implementer and government efforts, and collaborating on joint projects with other implementers, would improve the quality, efficiency, scale, and/or breadth of USAID implementing partners’ outputs. This, in turn, could be expected to lead to improved quality of life outcomes for those benefitting from these IP activities. Social Impact’s primarily qualitative annual Stakeholder Analysis exercise has indeed confirmed that many implementers and USAID staff have observed that integration has led to cost savings, organizational efficiencies, diversification of activities and expertise, expansion of geographic and population scope, improved goal alignment, and reduced duplication of effort18. The SHA has also documented cases where beneficiaries were aware of integration and claimed it had improved message consistency, reduced their time burden when participating in jointly planned development activities, improved program quality in their view, and helped improve community unity. While these perspectives help to validate the process-level benefits on the implementation side, this impact evaluation tests the ultimate goal of integration: “What impact has the integration of USAID investments through the CDCS Development Objectives had on improving the quality of life for targeted communities?” We considered the impact of two related integration interventions: colocation of development activities from different sectors (Partial Integration), and colocation plus deliberate coordination and collaboration on joint activities (Full Integration). QOL was assessed as multi-dimensional with both objective and subjective elements that relate to issues such as health, income and wealth, access to services, democratic participation, and life satisfaction. INTERPRETATION OF RESULTS As noted previously, health outcomes were considered to be the best-controlled measure of the impact of integration, given that the evaluation sample only included respondents living within the service catchment of health facilities supported by the same USAID project: SSDI, followed by ONSE. This created a consistent point of comparison across HSO, PI, and FI districts, as the evaluation assumed the intensity and content of the SSDI and ONSE activities were similar across treatment arms. In this way, the analysis is better able to isolate additional improvements to health outcomes that might be attributable to the added presence of colocated activities (in PI districts) or colocated activities that also coordinated and collaborated with each other (in FI districts). Though examination of indicators in other sectors is important, health sector outcomes are the best way to identify the added value of integration within the limitations of this study design. Whereas an experimental evaluation design such as a randomized control trial might allow us to confidently attribute impacts to the integration approach, as noted in the Limitations section, this quasi￾experimental design was prone to several important limitations that constrain the evaluation team’s confidence in attribution to integration alone, even for health sector indicators. Districts were not 18 USAID/Malawi, 2018 Malawi Stakeholder Analysis: Identifying and Sustaining Process-level Benefits of Integration in Malawi, by Social Impact, Inc. (February 2019), https://pdf.usaid.gov/pdf_docs/PA00TKDM.pdf. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 56 randomly assigned to each treatment group, and numerous factors can influence outcomes measured in each district, whether they be external factors (e.g. general district government capacity, climatic conditions, other donor engagement), or internal factors (e.g. presence or absence of an intervention, intensity or effectiveness of an individual intervention, intensity of activity integration). It was not feasible to measure or control for all of these factors. Therefore, we conservatively interpret significant results as suggestive of the influence of a combination of USAID’s integrated approach and activities (noting that we have slightly greater confidence in attribution to integration for health outcomes). SUMMARY OF IMPACTS Table 30 summarizes difference-in-difference regression results in narrative form. DID provides a snapshot of relative changes in treatment arms over time as compared to a counterfactual group that did not receive any treatment. As a result, this relative change over time can be considered the impact of the treatment (in this case the integration approach). We found that compared to the two districts with only health sector interventions (HSO), USAID’s Full Integration approach significantly improved perceived well￾being as well as several outcomes related to health behavior change, health service quality, and nutrition and food security. In particular, the FI zone had a large impact on care-seeking for ill children, reducing drug stockouts, and increasing the number of children 6-23 months that received a diet meeting minimum criteria for meal frequency and diversity. The FI approach did not exert measurable impacts on poverty or key agricultural outcomes. Likewise, improvements in education were limited to increased accessibility of household reading materials for children. Improvements in governance and citizen responsibility were limited to increased volunteerism, although one could consider public health service improvements to be in part reflective of improved governance. Compared to the HSO districts, the Partial Integration approach exerted positive impacts on even more outcomes than FI districts. The PI approach significantly improved perceived well-being and several outcomes related to health behavior change, health service quality, and nutrition and food security. The PI treatment group exerted a particularly large impact on improving perceived income sufficiency, child bed net use, care-seeking for an ill child, and reductions at public health facilities in doctor absence, waiting times, and dirtiness. The PI approach did have two significant agricultural impacts: groundnut farmers had higher gross margins, and people were more likely to adopt climate resilience measures. PI districts had the same general effect on education and governance/citizen responsibility outcomes as FI districts. For a limited number of outcomes, PI and FI treatment arms fared significantly worse than HSO districts. Compared to HSO, both PI and FI had significantly lower likelihood of improving the prevalence of 2nd grade literacy, lower likelihood of using mobile phone technology to report/receive government service or business information, and lower likelihood of respondents feeling they were able to control improvements to well-being. Those in the FI districts were also significantly less likely than in HSO to improve use of contraceptives and use a public clinic or hospital in the past year. Those in PI districts were also significantly less likely to have improved satisfaction with Malawian democracy. The potential reasons for and validity of selected results are discussed below. 57 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV TABLE 30. SUMMARY OF STATISTICALLY SIGNIFICANT IMPACTS FOR PARTIAL AND FULL INTEGRATION APPROACHES • Partial Integration • Full Integration Significantly improved, relative to HSO No impact, or (significantly worsened) relative to HSO Significantly improved, relative to HSO No impact, or (significantly worsened) relative to HSO General well-being • Greater perceived overall well-being • Greater perceived financial well-being • Greater perceived income sufficiency • Better perceived health • Poverty status (<$1.90/day) • Under 5 mortality • Greater perceived overall well-being • Greater perceived financial well-being • Greater perceived income sufficiency • Poverty status (<$1.90/day) • Under 5 mortality • Perceived health Health behaviors • More individual VCT adoption • More child bednet use • More care-seeking for children • Contraceptive use • Couples’ VCT adoption • Use of public clinic/hospital • More child bednet use • More care-seeking for children • (Fewer use contraceptives) • (Fewer used public clinic/hospital) • VCT adoption (individual or couple) Health service provision • Fewer drug stockouts • Fewer absent doctors • Shorter waits at public clinic/hospital • Cleaner public clinics/hospitals • Respectful care • Fewer drug stockouts • Fewer absent doctors • Cleaner public clinics/hospitals • Waiting time at public health facility • Respectful care Nutrition • Less food insecurity • More women have groundnuts in diet • Minimum acceptable diet for children • Exclusive breastfeeding • Soy consumption for women • Less food insecurity • More children receiving minimum acceptable diet (breastfed children) • Exclusive breastfeeding • Soy, groundnut consumption for women Education • More homes have child reading materials • (Fewer 2nd graders can read in Chichewa) • (More overcrowded classrooms) • More homes have child reading materials • (Fewer 2nd graders can read in Chichewa) • (More absent teachers) Governance & citizen responsibility • More volunteered • (More people dissatisfied with Malawian democracy) • (Fewer feel they’re able to control • More volunteered • (Fewer used mobile phone technology to report/receive government service or USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 58 improvements to well-being) • (Fewer used mobile phone technology to report/receive government service or business information) • Knowledge of local government role • Women’s participation in decisions • business information) • (Fewer feel they’re able to control improvements to well-being) • Knowledge of local government role • Women’s participation in decisions • Satisfaction with Malawi democracy Agriculture & environment • Higher gross margin for groundnuts • More adopted climate resilience measures • Agriculture management changes • Soy margin • Soy, groundnut cultivation area • Soy, groundnut yield • Soy, groundnut margin • Soy, groundnut cultivation area • Soy, groundnut yield • Agriculture management changes • Climate resilience changes Note: All results noted as improved reflect statistically significant regression results at p<0.05. Outcomes that worsened with statistical significance are noted in parentheses and italics. Those listed as having no effect were not significant. Bold font signifies a large effect size (odds ratio equivalent of 2 or more). FIDELITY TO PARTIAL AND FULL INTEGRATION APPROACHES The evaluation identified more quality of life outcomes with significant positive change in the PI zone than the FI zone. This runs counter to the development hypothesis that the combination of all three elements: colocation, collaboration, and coordination will attain greater quality of life outcomes. In this case the simple presence of colocated activities seems to have achieved the same, if not more, positive results as the full 3C approach. However, PI districts were not completely free from collaboration and coordination. At the evaluation design phase, USAID/Malawi clarified that while it would only insist on full 3C integration in the Full Integration zone, it would not dissuade implementers outside of FI districts from collaborating and coordinating with other implementers if they so chose. Any occurrence of this in PI or HSO districts would naturally dilute the evaluation’s ability to isolate the impact of the full 3C approach. There is evidence from SSDI’s FY15 integration workplan that it practiced collaboration and coordination with other implementers in all of its 15 districts of operation, including those in the evaluation’s PI and HSO study arms. Additional coordination and collaboration across implementers is said to have occurred in PI districts, though the evaluation team could not obtain specific details. Some of USAID/Malawi’s single activities were also inherently integrated, where one implementer or one contract addressed multi￾sectoral outcomes (e.g. INVC targeted agriculture and nutrition) or multi-dimensional outcomes (SSDI had three sub-partners that each addressed health services, health systems, and health communications in close coordination with each other). The advantages of this within-project collaboration and coordination 59 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV might have occurred in all areas. In addition, coordination among donors through working groups or special projects such as the World Bank’s multi-donor trust fund has been the norm in Malawi prior to the CDCS and has continued throughout the past five years. The FI zone certainly had much more concentrated effort to employ the 3C approach, which the evaluation team confirmed occurred in many forms throughout the duration of the evaluation. However, overall interpretation of results should be guided by an understanding that there was some lacking fidelity to the “pure” PI and HSO approaches. The evaluation team sought information on the presence of non-USAID funded projects (especially those outside the health sector) in HSO districts in particular, which might explain some of the better results found in this area. This included outreach to the District Agricultural Development Officers of Nkhotakota and Karonga Districts. No major projects were identified that weren’t also targeted to other districts on a national scale. POVERTY AND PERCEIVED WELL-BEING The evaluation’s measure of poverty was found to be in line with other methods of estimation, as discussed above. Though the evaluation found that the partially or fully integrated districts did not experience any marked reduction in poverty (at PPP $1.90), there were trends toward gradual reduction as compared to HSO districts, albeit at an extremely slow rate of change. Though integration fell short of measurably reducing poverty by this metric, it is important to consider that it was based on household assets and does not address consumption as an important indicator of poverty. If one considers relative decline in food insecurity as a close proxy for consumption-based poverty (food poverty), the evaluation found significant decline in this aspect of poverty in PI and FI districts over time relative to HSO districts (noting all study arms experienced a worsening of food insecurity, but HSO was the worst). Similarly, the less steep decline in the perceived financial well-being index indicator for PI and FI districts, which resulted in a significant improvement relative to HSO, might be in part mirroring community perceptions about relative improvements in consumption. HEALTH USAID/Malawi’s health activities in FI districts have collaborated and coordinated with other implementing partners on a wide variety of activities, ranging from sharing resources to more efficiently distribute supplies or deliver reports to the Ministry of Health, sharing knowledge to combine training efforts or align behavior change messages, developing a unified voice to support government health sector support, and beyond. The findings suggest this integrated approach produced better results in the health sector than when health activities operated alone. Though most in PI and FI districts noted at least a slight decline in health service quality, in nearly all cases, the decline was much worse in HSO districts, suggesting something about USAID’s approach is mitigating service challenges. The significant reduction in drug stockouts for both FI and PI districts relative to HSO is particularly notable, given the resounding complaints during FGDs about how problematic drug availability can be. If these trends continue, one might expect to see even more positive health behavior outcomes, as better services might lead to more people seeking services and information. The reason for the significant drop in usage of a public health facility in the past year for FI residents is not clear. Utilization was still high at 84%, and the majority of FI residents who received contraception or VCT reported doing so at public health facilities, and at the same general rate as other study arms, suggesting no explicit barrier to using services there. It is possible that people in the FI zone were healthier and not as needful of health services, though the evaluation could not confirm this. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 60 AGRICULTURE The problem with inaccurate land size reporting forced the evaluation team to remove many smallholder farmers from analysis of crop outcome indicators. Several smallholder farmers who reported their land size in acres or half acres and football pitches did, however, remain in the dataset. Particularly if USAID’s integration activities targeted smallholder farmers more, the results for crop yields and margin per hectare are perhaps too unreliable, as many of these targeted beneficiaries are not represented. EDUCATION In this evaluation, literacy for 2nd graders was measured not through a reading test, but through the respondent stating whether or not the child was able to read a one-page letter in the Chichewa language. A recent National Reading Program baseline assessment measured literacy through a much more reliable standard method of reading comprehension testing and found that almost no children in 2nd grade (<1%) would qualify as a “reader”19. This suggests respondents overreported this outcome for this impact evaluation survey. However, we have no reason to believe overreporting would differ according to treatment arm. The reason for poorer outcomes in FI and PI districts is unknown. NUTRITION AND FOOD SECURITY Though food insecurity worsened in all study arms, the PI and FI intervention areas had a reduced magnitude of decline, leaving them better off than HSO. All PI and FI districts except Lilongwe Rural have greater vulnerability to hunger, and USAID has supported World Food Programme (WFP) activities in Zomba and Mangochi between midline and endline data collection as well as WFP school feeding programs in Lilongwe and Zomba (see Annex A). FGD participants in Machinga and Lilongwe Rural also noted that they had received helpful food aid support as well from WFP. While WFP had nutrition programs in HSO districts Karonga and Nkhotakota, it is not clear whether the intensity of food support differed across these districts. It is possible that the mitigated food insecurity seen in FI and PI districts could be at least in part reflective of a greater food aid response rather than the integration approach. Nutrition was a common point of intersection between USAID/Malawi’s agriculture and health activities and the focus of several coordination and collaboration activities. This may have led not only to better saturation of nutrition behavior change messaging but also to greater availability of foods to meet those needs. This might explain FI districts’ strong impact on children having a minimum acceptable diet. However, more simply, this outcome could also be in part attributable to the large number of USAID activities targeting nutrition outcomes in FI districts (see Annex A). GOVERNANCE & CITIZEN RESPONSIBILITY Knowledge of local government’s function improved in all study areas over time, but it is not clear why it did not improve at a significantly greater rate in districts targeted by USAID/Malawi democracy and governance activities. FGD participants in all districts noted disappointment with lack of transparency and ineffectual government development processes. This shows a lot remains to be done. The significant decline in satisfaction with Malawian democracy in PI districts, compared to HSO, while certainly reflective of poor government function, could also be capturing improved citizen agency and engagement in 19 USAID. 2018. Malawi National Reading Program Baseline Assessment. Produced by Social Impact. https://pdf.usaid.gov/pdf_docs/PA00T3Q5.pdf 61 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV government’s function in this area, which is an intended outcome of DG activities. Those more engaged are more likely to demand change. It is not clear whether this is the effect behind this number. CONCLUSIONS These findings suggest that USAID/Malawi’s 2013-2018 CDCS integration strategy to co-locate interventions geographically, coordinate within and across sectors, and collaborate to foster linkages among implementing partners, other donors, and district authorities, may have effectively improved several aspects of quality of life for Malawians when compared to a single sector, non-integrated development approach. The impact was particularly evident in the health sector. However, this conclusion cannot be made with high certainty, given the study’s limited ability to control for other explanations such as the number, intensity, or effectiveness of interventions in a given area, quality and depth of collaboration, and other factors that might have varied according to district. Nonetheless, results demonstrate at a minimum that some combination of integration and USAID activities have conferred added quality of life benefits in targeted communities. The finding that Partial Integration districts—which were purported to feature colocation alone— experienced slightly more quality of life improvements than in fully integrated districts begs the question of whether colocation alone is sufficient to reap the potential benefits of integration. This question cannot be easily answered in light of the strong possibility that collaboration and coordination were occurring, albeit to a lesser degree, in PI districts. However, it is reasonable to assume that among the 3Cs, colocation would confer the most measurable benefits at the community level by addressing multiple needs simultaneously. For example, if a development activity is able to improve local government capacity, the government might then improve health and education service quality that in turn leads to better outcomes in these sectors. Or if an activity is able to reduce food insecurity, beneficiaries would be better able to focus on and afford improvements to their health and education. Prior stakeholder analyses complementary to this impact evaluation have confirmed that, from the perspective of implementers and USAID/Malawi, integration has led to process-level efficiencies in delivering development aid, including cost savings, organizational efficiencies, diversification of activities and expertise, expansion of geographic and population scope, improved goal alignment, and reduced duplication of effort. Likewise, stakeholder consultations with beneficiaries have documented perceived improvements in message consistency, reduced time burden, improved program quality, and community unity. These advantages from collaboration or coordination might not always translate to measurable improvements to quality of life for beneficiaries, but they nonetheless reflect operational value in how development is done, from the perspective of those delivering aid in Malawi. The overall value of integration should consider benefits at this level and not just at the level of beneficiaries. RECOMMENDATIONS Drawing on findings from the impact evaluation as well as conclusions of prior stakeholder analyses, we offer the following recommendations: 1. USAID/Malawi should continue to practice integration in some form in its development strategy. The positive impacts over and above the single sector approach reflect value in the concentration of investments (colocation) at a minimum. Results also suggest the added value of collaboration and coordination, but given this impact evaluation’s limited ability to pinpoint specific USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 62 advantages, how USAID/Malawi should operationalize other elements of integration should be informed more by recommendations within SI’s stakeholder analyses. 2. USAID/Malawi should work to improve local government transparency and platforms for citizen engagement. Though knowledge of local government's role is improving, data confirmed growing dissatisfaction and mistrust of local representatives, particularly due to lack of transparency and responsiveness. Improvements in this area can restore trust and promote more effective service provision. 3. USAID/Malawi should work to aggressively address food insecurity to provide a secure foundation for other well-being improvements. 63 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV ANNEXES ANNEX A. USAID/MALAWI ACTIVITY MATRIX This annex displays changes in USAID/Malawi activities by sector and district and shows how these changes relate to impact evaluation data collection timeframes. These tables were used to assess the ongoing validity of the colocation criterion for districts assigned to the Full Integration (FI) and Partial Integration (PI) treatment arms. These tables only address USAID activities targeted at selected districts. National-coverage activities such as MERIT are not included below, as they are assumed to affect all districts equally and not to pose a threat to the validity of a particular treatment arm in comparison to others. Red shading reflects cases where an entire sector (structured according to USAID/Malawi Technical Offices) is absent in a PI or FI district prior to midline or endline data collection. The colocation requirement for FI and PI districts was considered to be met if substantial activities in at least two different sectors were present. Activity acronyms AMAA – Apatseni Mwayi Atsikana Aphunzire (Give Girls a Chance to Learn) APCA – African Palliative Care Association ASPIRE – Girls Empowerment Through Education and Health ASSIST – Applying Science to Strengthen and Improve Systems BCC-CONCERT – Behavior Change Communication - Communicating, Networking and Capacity building to Effectively Respond Together C-SEP - Capacity Support for Early Childhood Development and Psychosocial Support EBT/Prev – Evidence-based, Targeted HIV Prevention Project EGRA – Early Grade Reading Activity EQUIP – Extending Quality Improvement for HIV/AIDS FISH - Fisheries Integration of Society and Habitats FUM – Farmers Union of Malawi HC4L – Health Communications for Life HRH 2030 – Human Resources for Health 2030 IMPACT - Integrated (HIV Effect) Mitigation and Positive Action for Community Transformation INVC – Integrating Nutrition in Value Chains LGAP – Local Government Accountability and Performance MCHIP-VMMC – Maternal and Child Health Integrated Project – Voluntary Medical Male Circumcision MISST – Malawi Improved Seed Systems and Technologies ONSE – Organized Network of Services for Everyone’s Health Pall FtF-AgDiv – Palladium Feed the Future Agriculture Diversification PCI-Em_Prior - Emerging Priorities in Reproductive, Maternal and Newborn Health PERFORM - Protecting Ecosystems and Restoring Forests in Malawi SHOPS – Sustaining Health Outcomes through the Private Sector SIFPO II – Support for International Family Planning Organizations SSDI – Support for Service Delivery Integration UBALE – United in Building and Advancing Life Expectations UofI – University of Illinois’s Feed the Future Malawi Strengthening Agricultural and Nutrition Extension Services Activity WFP – World Food Programme USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 64 Lilongwe Rural (Full Integration) Conclusion: FI designation remained valid throughout the evaluation, with consistent HPN and SEG investment over time and intermittent investment in DG and education. Note: No DG activities between baseline and midline and no district-specific education activities between midline and endline. --> 10/2014 Baseline (Nov.-Dec. 2014) -->10/2015 -->10/2016 Midline (Sept.-Oct. 2016) -->10/2017 -->10/2018 Endline (Oct.-Nov. 2018) Health, Population, and Nutrition (HPN) Malaria SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L Nutrition SSDI, BCC￾CONCERT, INVC SSDI, BCC￾CONCERT, INVC SSDI, BCC￾CONCERT, INVC, UofI FtF Tiwalere II, HC4L, INVC, UofI FtF, ONSE, Pall FtF￾AgDiv Tiwalere II, HC4L, UofI FtF, ONSE, Pall FtF￾AgDiv Family planning (FP)/ Reproductive health (RH) SHOPS, SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L Maternal, newborn, and child health (MNCH) SHOPS, SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L HIV/AIDS SHOPS, APCA, Baylor, IMPACT, Tiwalere, SSDI, BCC￾CONCERT, EBT/Prev, C-SEP Baylor, Tiwalere, SSDI, BCC￾CONCERT Baylor, SSDI, BCC￾CONCERT HRH 2030, EQUIP, 4 Children, HC4L HRH 2030, EQUIP, 4 Children, HC4L Water, sanitation, and hygiene (WASH) HC4L HC4L Sustainable Economic Growth (SEG) Agriculture INVC, Mobile Money INVC, Mobile Money, MISST INVC, Mobile Money, UofI FtF, MISST INVC, UofI FtF, Pall FtF￾AgDiv, MISST UofI FtF, Pall FtF￾AgDiv, MISST Food security WFP￾schools Nat. resources/ Environment (NR/Env) Democracy and governance (DG) LGAP LGAP Education EGRA, Lakeland EGRA, Lakeland EGRA, Lakeland 65 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV Balaka (Full Integration) Conclusion: FI designation remained valid throughout the evaluation, with consistent HPN and SEG investment over time and intermittent investment in DG and education. Note: no DG activities between baseline and midline. Education activities AMAA and ASPIRE target adolescent girls in particular, and core indicators such as second-grade literacy would not likely be impacted by these activities. --> 10/2014 Baseline (Nov.-Dec. 2014) -->10/2015 -->10/2016 Midline (Sept.-Oct. 2016) -->10/2017 -->10/2018 Endline (Oct.-Nov. 2018) Health, Population, and Nutrition (HPN) Malaria SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L Nutrition SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT, Njira SSDI, BCC￾CONCERT, Njira, UofI FtF Tiwalere II, HC4L, Njira, UofI FtF, Pall FtF-AgDiv Tiwalere II, HC4L, Njira, UofI FtF, Pall FtF-AgDiv FP/RH SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT, PCI￾Em_Prior SSDI, BCC￾CONCERT, PCI￾Em_Prior, Pamawa PCI-Em_Prior, ONSE, HC4L, Pamawa PCI-Em_Prior, ONSE, HC4L, Pamawa MNCH SSDI, BCC￾CONCERT SSDI, BCC￾CONCERTPCI￾Em_Prior, Njira SSDI, BCC￾CONCERTPCI￾Em_Prior, Njira PCI-Em_Prior, ONSE, HC4L, Njira PCI-Em_Prior, ONSE, HC4L, Njira HIV/AIDS APCA, IMPACT, Banja, SSDI, BCC￾CONCERT, ASSIST Banja, SSDI, BCC￾CONCERT, ASSIST SSDI, BCC￾CONCERT, ASSIST, One "Community" ASSIST, EQUIP, One "Community", HC4L EQUIP, One "Community", HC4L WASH Njira Njira ONSE, HC4L, Njira ONSE, HC4L, Njira Sustainable Economic Growth (SEG) Agriculture INVC, Mobile Money INVC, Mobile Money, Njira, MISST INVC, Mobile Money, Njira, UofI FtF, MISST Njira, UofI FtF, Pall FtF-AgDiv, MISST Njira, UofI FtF, Pall FtF-AgDiv, MISST Food security Njira Njira Njira Njira NR/Env FISH FISH, Pamawa FISH, Pamawa FISH, Pamawa DG LGAP LGAP Education EGRA EGRA, ASPIRE EGRA, ASPIRE AMAA, ASPIRE AMAA, ASPIRE USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 66 Machinga (Full Integration) Conclusion: FI designation remained valid throughout the evaluation, with consistent HPN and SEG investment over time and intermittent investment in DG and education. Note: No DG activities between baseline and midline. Education activities AMAA and ASPIRE target adolescent girls in particular, and core indicators such as second-grade literacy would not likely be impacted by these activities. --> 10/2014 Baseline (Nov.-Dec. 2014) -->10/2015 -->10/2016 Midline (Sept.-Oct. 2016) -->10/2017 -->10/2018 Endline (Oct.-Nov. 2018) Health, Population, and Nutrition (HPN) Malaria SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L Nutrition SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT, Njira SSDI, BCC￾CONCERT, Njira, UofI FtF Tiwalere II, HC4L, Njira, UofI FtF, ONSE, Pall FtF￾AgDiv Tiwalere II, HC4L, Njira, UofI FtF, ONSE, Pall FtF￾AgDiv FP/RH SHOPS, SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT, Pamawa ONSE, HC4L, Pamawa ONSE, HC4L, Pamawa MNCH SHOPS, SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT, Njira SSDI, BCC￾CONCERT, Njira ONSE, HC4L, Njira ONSE, HC4L, Njira HIV/AIDS SHOPS, IMPACT, JHU BRIDGE II, SSDI, BCC-CONCERT, EBT/Prev SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT, One "Community" EQUIP, One "Community", HC4L EQUIP, One "Community", HC4L WASH Njira Njira ONSE, HC4L, Njira ONSE, HC4L, Njira Sustainable Economic Growth (SEG) Agriculture INVC, Mobile Money INVC, Mobile Money, Njira, MISST INVC, Mobile Money, Njira, UofI FtF, MISST Njira, UofI FtF, Pall FtF-AgDiv, MISST Njira, UofI FtF, Pall FtF-AgDiv, MISST Food security Njira Njira Njira Njira NR/Env PERFORM, FISH PERFORM, FISH, Pamawa PERFORM, FISH, Pamawa PERFORM, FISH, Pamawa DG LGAP LGAP Education EGRA, Lakeland EGRA, ASPIRE, Lakeland EGRA, ASPIRE, Lakeland AMAA, ASPIRE AMAA, ASPIRE 67 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV Nsanje (Partial Integration) Conclusion: PI designation no longer valid due to absence of ONSE between midline and endline data collection and general lack of health activities addressing a variety of health outcomes during this timeframe. Drop from analysis. --> 10/2014 -->10/2015 Baseline (Sept.-Oct. 2015*) -->10/2016 Midline (Sept.-Oct. 2016) -->10/2017 -->10/2018 Endline (Oct.-Nov. 2018) Health, Population, and Nutrition (HPN) Malaria SSDI, BCC-CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT Nutrition SSDI, BCC-CONCERT SSDI, BCC￾CONCERT, UBALE SSDI, BCC￾CONCERT, UBALE, UofI FtF UBALE, UofI FtF UBALE, UofI FtF FP/RH SHOPS, SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT MNCH SHOPS, SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT HIV/AIDS SHOPS, SSDI, MCHIP￾VMMC, JHU BRIDGE II, BCC-CONCERT, C-SEP SSDI, MCHIP￾VMMC, BCC￾CONCERT SSDI, MCHIP￾VMMC, BCC￾CONCERT MCHIP￾VMMC, EQUIP EQUIP WASH Sustainable Economic Growth (SEG) Agriculture UBALE, MISST UBALE, UofI FtF, MISST UBALE, UofI FtF, MISST UBALE, UofI FtF Food security WFP-School NR/Env DG Education USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 68 Zomba Rural (Partial Integration) Conclusion: PI designation remained valid throughout the evaluation, with consistent HPN and SEG investment over time (though SEG’s FISH activity is largely limited to areas with bodies of water) and intermittent investment in DG and education. Note: No DG activities between baseline and midline and no district-specific education activity between midline and endline data collection. --> 10/2014 Baseline (Nov.-Dec. 2014) -->10/2015 -->10/2016 Midline (Sept.-Oct. 2016) -->10/2017 -->10/2018 Endline (Oct.-Nov. 2018) Health, Population, and Nutrition (HPN) Malaria SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT HC4L, ONSE HC4L, ONSE Nutrition SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT HC4L HC4L FP/RH SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L MNCH SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L HIV/AIDS APCA, Banja, IMPACT, Dignitas, SSDI, JHU BRIDGE II, BCC-CONCERT, EBT/Prev Banja, Dignitas, SSDI, BCC￾CONCERT SIFPO II, Dignitas, SSDI, BCC￾CONCERT, One "Community" SIFPO II, HRH 2030, EQUIP, One "Community", HC4L SIFPO II, HRH 2030, EQUIP, One "Community", HC4L WASH ONSE, HC4L ONSE, HC4L Sustainable Economic Growth (SEG) Agriculture Food security WFP, WFP-School WFP NR/Env FISH FISH FISH FISH DG LGAP LGAP Education EGRA, All Children Reading EGRA EGRA 69 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV Mangochi (Partial Integration) Conclusion: PI designation remained valid throughout the evaluation, with consistent HPN and SEG investment over time. Note: No DG activities at any time and no district-specific education activity between midline and endline data collection. --> 10/2014 Baseline (Nov.-Dec. 2014) -->10/2015 -->10/2016 Midline (Sept.-Oct. 2016) -->10/2017 -->10/2018 Endline (Oct.-Nov. 2018) Health, Population, and Nutrition (HPN) Malaria SSDI SSDI SSDI ONSE, HC4L ONSE, HC4L Nutrition SSDI SSDI SSDI, UofI FtF Tiwalere II, HC4L, UofI FtF, Pall FtF￾AgDiv Tiwalere II, HC4L, UofI FtF, Pall FtF￾AgDiv FP/RH SHOPS, SSDI SSDI SSDI HC4L, Pamawa HC4L, Pamawa MNCH SHOPS, SSDI SSDI SSDI HC4L HC4L HIV/AIDS SHOPS, Banja, SSDI, EBT/Prev, C-SEP SSDI SSDI, One "Community" EQUIP, One "Community", HC4L EQUIP, One "Community", HC4L WASH HC4L HC4L Sustainable Economic Growth (SEG) Agriculture INVC, FUM, Mobile Money INVC, FUM, Mobile Money, MISST INVC, FUM, Mobile Money, UofI FtF, MISST UofI FtF, Pall FtF￾AgDiv, MISST UofI FtF, Pall FtF￾AgDiv, MISST Food security WFP WFP NR/Env FISH FISH, Pamawa FISH, Pamawa FISH, Pamawa DG Education EGRA, Timawerenga EGRA EGRA USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 70 Nkhotakota (Health Sector Only) Conclusion: HSO designation remained valid throughout the evaluation. --> 10/2014 Baseline (Nov.-Dec. 2014) -->10/2015 -->10/2016 Midline (Sept.-Oct. 2016) -->10/2017 -->10/2018 Endline (Oct.-Nov. 2018) Health, Population, and Nutrition (HPN) Malaria SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L Nutrition SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT Tiwalere II, HC4L, ONSE Tiwalere II, HC4L, ONSE FP/RH SHOPS, SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L MNCH SHOPS, SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L HIV/AIDS SHOPS, APCA, Banja, Tiwalere, SSDI, BCC￾CONCERT, PIH￾EQUIP, EBT/Prev, C￾SEP Banja, Tiwalere, SSDI, BCC￾CONCERT, PIH￾EQUIP SSDI, BCC￾CONCERT, PIH￾EQUIP EQUIP, HC4L EQUIP, HC4L WASH ONSE, HC4L ONSE, HC4L Sustainable Economic Growth (SEG) Agriculture Food security NR/Env DG Education 71 | MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT USAID.GOV Karonga (Health Sector Only) Conclusion: HSO designation remained valid throughout the evaluation. Note: Lakeland College activity was not widespread in the district and was unlikely to affect education outcomes of interest for young learners. --> 10/2014 Baseline (Nov.-Dec. 2014) -->10/2015 -->10/2016 Midline (Sept.-Oct. 2016) -->10/2017 -->10/2018 Endline (Oct.-Nov. 2018) Health, Population, and Nutrition (HPN) Malaria SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L Nutrition SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT Tiwalere II, HC4L Tiwalere II, HC4L FP/RH SHOPS, SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L MNCH SHOPS, SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT SSDI, BCC￾CONCERT ONSE, HC4L ONSE, HC4L HIV/AIDS SHOPS, APCA, Banja, SSDI, BCC￾CONCERT, PIH￾EQUIP, EBP/Prev, ASSIST Banja, SSDI, BCC￾CONCERT, PIH￾EQUIP, ASSIST SSDI, BCC￾CONCERT, PIH￾EQUIP, ASSIST ASSIST, EQUIP, HC4L EQUIP, HC4L WASH ONSE, HC4L ONSE, HC4L Sustainable Economic Growth (SEG) Agriculture Food security NR/Env DG Education Lakeland College Lakeland College Lakeland College USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 72 ANNEX B. HOUSEHOLD SURVEY - ENGLISH & CHICHEWA CDCS-2018-Replacement Module - English Field Question Answer title USAID/MALAWI - COUNTRY DEVELOPMENT COOPERATION STRATEGY - HOUSEHOLD SURVEY 2018 intro1 Prior to arriving at the house, fill the following: a1 (required) a1: DISTRICT NAME Question relevant when: true () Lilongwe Rural Lilongwe Rural Balaka Balaka Machinga Machinga Mangochi Mangochi Mulanje Mulanje Karonga Karonga Nkhotakota Nkhotakota Zomba Zomba Nsanje Nsanje a2 (required) a2: TRADITIONAL AUTHORITY NAME: Question relevant when: true () Amidu Amidu Chamthunya Chamthunya Kachenga Kachenga Kalembo Kalembo Msamala Msamala Nkaya Nkaya Sawali Sawali -77 Other Chadza Chadza Chiseka Chiseka Chitekwele Chitekwele Kabudula Kabudula Kalolo Kalolo Kalumba Kalumba Kalumbu Kalumbu Khongoni Khongoni Malili Malili Masula Masula USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 73 Field Question Answer Masumbankhunda Masumbankhunda Mazengera Mazengera Mtema Mtema Njewa Njewa Tsabango Tsabango Chikweo Chikweo Chinguza Chinguza Chiwalo Chiwalo Kapoloma Kapoloma Kawinga Kawinga Liwonde Liwonde Mlomba Mlomba NKoola NKoola Ngokwe Ngokwe Nkoola Nkoola Nsanama Nsanama Nyambi Nyambi Sitola Sitola Kilupura Kilupura Kyungu Kyungu Mwakaboko Mwakaboko Mwirang'ombe Mwirang'ombe Wasambo Wasambo Kafuzila Kafuzila Kanyenda Kanyenda Malengachanzi Malengachanzi Mphonde Mphonde Mwadzama Mwadzama Mwansambo Mwansambo Bwana Nyambi Bwana Nyambi Chimwala Chimwala Jalasi Jalasi Katuli Katuli Nankumba Nankumba Chimombo Chimombo Makoko Makoko Malemia Malemia USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 74 Field Question Answer Mbenje Mbenje Mlolo Mlolo Ndamera Ndamera Tengani Tengani Chikowi Chikowi Kumtumanji Kumtumanji M'biza M'biza Mlumbe Mlumbe Mwambo Mwambo a2_otherspecify (required) a2: Specify the TA name Question relevant when: true () a3 (required) a3: EA CODE Question relevant when: true () 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 75 Field Question Answer 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 52 52 53 53 54 54 55 55 56 56 57 57 58 58 67 67 70 70 71 71 73 73 74 74 81 81 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 76 Field Question Answer 51 51 a4 (required) a4: VILLAGE NAME Question relevant when: true () Mdzumila Mdzumila Mndole Mndole -77 Other Chimombo Chimombo Chimongo Chimongo Mpakiza Mpakiza Chiziko Chiziko Kanjira Kanjira Kadise Kadise Menya Menya Mikolo Mikolo Mkaka Mkaka Malama Malama Bvumbwe Bvumbwe Mbawala Mbawala Chagona Chagona Chiwale Chiwale Chabwasa Chabwasa Chigombe Chigombe Chinthochi Chinthochi Lembani Lembani Ingilasi Ingilasi Kazenga Kazenga Binda Binda Chionongera Chionongera Mkuwazi Mkuwazi Mwatselele Mwatselele Sambwaila Sambwaila Kumchakama Kumchakama Malunda Malunda Denie Denie Nachikunga Nachikunga Msilo Msilo Mphamba Mphamba Mphanga Mphanga Milimbo Milimbo USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 77 Field Question Answer Mkongolo Mkongolo Chidothi Chidothi Chimenechi Chimenechi Salima Mpasa Salima Mpasa Chana Chana Chingira Chingira Chimutu Chimutu Khundi 1 Khundi 1 Khundi 2 Khundi 2 Chiuzira Chiuzira Ng'omaikalira Ng'omaikalira Mkulekera Mkulekera Mvululo Mvululo Kasiyafumbi Kasiyafumbi Mwadzalamba Mwadzalamba Chikandwe Chikandwe Msonkho Msonkho Kango Kango Chakakala Chakakala Zapita Zapita Chapumuluka Chapumuluka Gomani Gomani Chimtolo Chimtolo Kapimphi Kapimphi Chitedze Chitedze Msokosela/Kanundu Msokosela/Kanundu Kachitamanja Kachitamanja Chimutha Chimutha Kakopa Kakopa Kwenje 2 Kwenje 3 Chamba Chamba Kutsamba Kutsamba Chipwele Chipwele Misomali Misomali Maliwata Maliwata Mgwira Mgwira Chimatiro Chimatiro USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 78 Field Question Answer Manjanja Manjanja Ngwalo 2 Ngwalo 3 Jambawe Jambawe Saka Saka Ntandiwa Ntandiwa Khwalala Khwalala Chimpakati Chimpakati Tsamba Tsamba Chizinga Chizinga Mankhwala Mankhwala Mpambila Mpambila Tiferakaso Tiferakaso Kainga Kainga Kamowatimwa Kamowatimwa Chilimba Chilimba Chisinkha Chisinkha Gambe Gambe Zidyana Zidyana Mgomwa Mgomwa Zalimu 1 Zalimu 2 Mthawitsa Mthawitsa Njirayagoma Njirayagoma Chiundu Chiundu Msaliwa Msaliwa Peter Kasanga Peter Kasanga Nsulu Nsulu Ntonda Ntonda Muotcha Muotcha Mboga Mboga Pongolani Pongolani Idi Idi Masautso Masautso Helbert Helbert Mawecha Mawecha Kabiyo Kabiyo Mmaniwa Mmaniwa Majikuta Majikuta USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 79 Field Question Answer Kalimira Kalimira M'bawa M'bawa Kefa Kefa Mtende Mtende Chipatala Chipatala Nambazo Nambazo Kalembo 1 Kalembo 2 Tsite Tsite Chilembwe Chilembwe Malihaba Malihaba M'dala Lulanga M'dala Lulanga M'gomba M'gomba Liwonde Liwonde Mahele Mahele Ntepo Ntepo Chisuwi Chisuwi Uthiwa Uthiwa Chilanga Chilanga Matumula Matumula Chiganga Chiganga Maganga Maganga Phwiti Phwiti Nyenje Nyenje Njenjema Njenjema Takataka Takataka M'bobo M'bobo M'bwana M'bwana Mtopa Mtopa Ntopa Ntopa Meja Meja Mlosi Mlosi Ling'ole Ling'ole Salijeni Salijeni Kauma Kauma Maganiza Maganiza Chionga Chionga Mbosongwe Mbosongwe USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 80 Field Question Answer Silika Silika Tebulo Tebulo Kalambo Kalambo Namanja Namanja Ndachi Ndachi Mtholowa Mtholowa Ntholowa Ntholowa Mkapaleya Mkapaleya Mtuwa Mtuwa Machika Machika Sinja Sinja Justin Justin Jastini 1 Jastini 2 Justini 2 Justini 3 Mpelula Mpelula Chikuluma Chikuluma Makawa Makawa Limela Limela Mikundi Mikundi Khungwa Khungwa Nguyeje Nguyeje Chinji Chinji Ntapasyale Ntapasyale Nchou Nchou Samuti Samuti Mkweya Mkweya Wadi Wadi Magombo Magombo Mchaula Mchaula Nsosomela Nsosomela Ntupa Ntupa Kauzu Kauzu Chindunguli Chindunguli Dzoole Dzoole Mtendere Mtendere Songa 2 Songa 3 Kambalame Kambalame USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 81 Field Question Answer Nkaweya Nkaweya Mlongoti Mlongoti Chiutira Chiutira Kwekwere Kwekwere Chisiyana Chisiyana Chitonde Chitonde Leven Leven Mbungo Mbungo Bamusi Bamusi Chingwalu Chingwalu Nasuluma Nasuluma Sinoya Sinoya Nikisi Nikisi Waiti Matenje Waiti Matenje Mvumba Mvumba Mbota Kamsiya Mbota Kamsiya Juma Mbanga Juma Mbanga Sokole A Sokole A Mbusi Makande Mbusi Makande Nkota Nkota Kuchetela Kuchetela Mmatila Mmatila Mkwela kalunga Mkwela kalunga Nakonya Nakonya Namatumbo Namatumbo Mayele Mayele Issa Issa Manduta Manduta Chikauka Chikauka Namaninga Namaninga Mchokola Mchokola Mgawo Mkwepu Mgawo Mkwepu Mlembe Mlembe Ngolojele Ngolojele Mpwakata Mpwakata Msalule Msalule Bakali Bakali USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 82 Field Question Answer Kwitunji Kwitunji Mdala Chilawi Mdala Chilawi Chiwaula Chiwaula Kwilapo Kwilapo Ngalipa Ngalipa Mponda Mponda Gideon Gideon Andrew Andrew Mwenewisi Mwenewisi Yalero Yalero James James Mwakibonja Mwakibonja Mwangalaba Mwangalaba Mwabungulu Mwabungulu Syalisoni Syalisoni Chimalabanthu Chimalabanthu Mwasalano Mwasalano Mwasalano 1 Mwasalano 2 Timothy Timothy Mwakamogho Mwakamogho Mwenengolongo Mwenengolongo Kasebwe Kasebwe Fundi Fundi Peter Mwangalawa Peter Mwangalawa Zindi Gondwe Zindi Gondwe Kayunga Kayunga Mwamatope Mwamatope Mulwa Mulwa Fughala Fughala Kaluwa Kaluwa Mwamasapa Mwamasapa Marko Mwankenja Marko Mwankenja Nayi Nayi Potifala Mwangolera Potifala Mwangolera Yafeti Mwakasungula Yafeti Mwakasungula Mlindaifwa Mlindaifwa USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 83 Field Question Answer Ngosi Ngosi Mwasota Mwasota Kayuni 1 Kayuni 1 Kayuni 2 Kayuni 2 Mwenelupembe 2 Mwenelupembe 3 Mphughu Mphughu Mwambelo Mwambelo Muchenjere Muchenjere Welosi Mwambelo Welosi Mwambelo Mphangweyanjili Mphangweyanjili Wundaninge Wundaninge Zengelanjala Zengelanjala Mwaungulu Mwaungulu Kayelewa Kayelewa Kayerewa Kayerewa Kachaka Kachaka Matambukira Matambukira Mgoyera Mgoyera Mwandovi Mwandovi Mwangamila Mwangamila Chalochamala Chalochamala Charuchamala Charuchamala Maulunge Maulunge Mchekacheka Mchekacheka Galimoto Galimoto Kayaghala Kayaghala Chibwatiko Chibwatiko Mwanyesha Mwanyesha Mwambetania Mwambetania Luhimbo Luhimbo Katesula Katesula Mwangolera Mwangolera Mwaleba Mwaleba Mwambuli Mwambuli Sadala Sadala M'bunthuka M'bunthuka M'buthuka M'buthuka USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 84 Field Question Answer Nkhubasanga Nkhubasanga Mutangala Mutangala Njalayankhunda Njalayankhunda Thangalang'ombe Thangalang'ombe Mwanyongo Mwanyongo Wilson Kalambo Wilson Kalambo Matandala Matandala Mwakhwawa Mwakhwawa Muleleka Muleleka Mwamdimba Mwamdimba Chipembere Chipembere Nowa Nowa Kanthumdende Kanthumdende Sosola Sosola Chindodo Chindodo Mudolo Mudolo Mowe 1 Mowe 2 Chimtumbuka Chimtumbuka Munthanje Munthanje Chiya Chiya Katapila Katapila Chimdima Chimdima Khwayaya Khwayaya Chinkhwangwa Chinkhwangwa Mkwapatira Mkwapatira Nyalubwe Nyalubwe Kambola Kambola Lunda Lunda Mjuwa Mjuwa Malamba Malamba Chimweyo Chimweyo Chigadula Chigadula Kaiwala Kaiwala Mpeta Mpeta Kafuzila Kafuzila Chimbuto Chimbuto Khufi Khufi USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 85 Field Question Answer Chiluma Chiluma Kachuma Kachuma Malengachanzi Malengachanzi Matekenya Matekenya Kawelama Kawelama Shamuti Shamuti Kawelama 2 Kawelama 3 Mchemela Mchemela Chota Chota Mazengera Mazengera Katimba Katimba Sasani 2 Sasani 3 Tandwe 1 Tandwe 2 Chamba 1 Chamba 2 Chanzi Chanzi Kapanga 2 Kapanga 3 Phwetekere Phwetekere Ching'amba Ching'amba Mtanga 2 Mtanga 3 Kawamba Kawamba Funduseni Funduseni Zikomankhani Zikomankhani Chikombe 1 Chikombe 2 Makunganya Makunganya Mphonde Mphonde Kansuli Kansuli Sawawa Sawawa Mng'ongwe Mng'ongwe Mzumara Mzumara Selemani 2 Selemani 3 Chipelela 2 Chipelela 3 Nkhongo 3 Nkhongo 4 Chikwawe 2 Chikwawe 3 Naferanji Naferanji Chalunda Chalunda Chizongwe 1 Chizongwe 2 Ngwata 2 Ngwata 3 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 86 Field Question Answer Chizongwe 2 Chizongwe 3 Chongole 1 Chongole 2 Chizongwe 3 Chizongwe 4 Mzeweza Mzeweza Chikumangala Chikumangala Msamala Msamala Msamala 3 Msamala 4 Nsamala Nsamala Khwapu Khwapu Nambela Nambela Nkhala Nkhala Manondo Manondo Mtachi 2 Mtachi 3 Ndimbwa Ndimbwa Manjawila Manjawila Pembela Pembela Mkukumila Mkukumila Kamongo Kamongo Patsunda 1 Patsunda 2 Chikaluma Chikaluma Mtiku Mtiku Mtutuma Mtutuma Chinangwa Chinangwa Kumchenga Kumchenga Malewa Malewa Chimera/Chidothi Chimera/Chidothi Likapa/Kachulu Likapa/Kachulu Kuntaja Kuntaja Maluwa Maluwa Malajila Malajila Matewere 2 Matewere 3 Magombe Magombe Ganda Ganda Kagaso Kagaso Mteteka Mteteka Chitwanga Chitwanga Chipande 1 Chipande 1 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 87 Field Question Answer Chipande 2 Chipande 2 Bwanausi Bwanausi Mtambo Mtambo Mtambo 2 Mtambo 3 Ndalama 1 Ndalama 2 Namathika Namathika Ehawi Ehawi Mwala 2 Mwala 3 Thumpwa Thumpwa Manyungwa/Mpenda Manyungwa/Mpenda Mulemba Mulemba Machemba Machemba Kadyampakeni Kadyampakeni Steven Steven Steven 2/Kadyampakeni Steven 2/Kadyampakeni Likhomo Muliya Likhomo Muliya Mwala 1 Mwala 2 Ngwelero Ngwelero Chisawa Chisawa Mikundi 2 Mikundi 3 Usumani 2 Usumani 3 Matewe Matewe Matewe 1 Matewe 2 Matewe 1 sinoya Matewe 1 sinoya Petulo Petulo Chimpini Chimpini Mmambo 2 Mmambo 3 Mtiko Mtiko Tung'ande Tung'ande Chisuse Chisuse Ritisan Ritisan Njala Njala Segula Segula Uzingo Uzingo Mwenyali Mwenyali Taibu Taibu USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 88 Field Question Answer Nkupe Nkupe Chombe 1 Chombe 2 Mdalakamuyanja Mdalakamuyanja Fletcher Fletcher Minthanje Minthanje Ntchenyera 2/Farao Ntchenyera 2/Farao Pharaoh/ Ntchenyera Pharaoh/ Ntchenyera Mulira Mulira Nyimbiri Nyimbiri Ng'ambo Ng'ambo Pangeti Pangeti Chinsungwi Chinsungwi Jimu 2 Jimu 3 Makhapha Makhapha Msambokulira Msambokulira Kaitano Kaitano Kasenga Kasenga Mbeta Mbeta Nsikuzakwenda Nsikuzakwenda Nyoza Nyoza Nzondola Nzondola Nyakhavi Nyakhavi Thengothawani Thengothawani Kachaso Kachaso Muyang'anira Muyang'anira Ngala Ngala Makhaza Makhaza Thikiti Thikiti Khambadza Khambadza Sandalamu Sandalamu Chimpilingu Chimpilingu Chilim'madzi Chilim'madzi Mwanavumbe Mwanavumbe Kuyeri Kuyeri Nsangalambe Nsangalambe Melo Melo USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 89 Field Question Answer Guta Guta Madani Madani Chabe Chabe Nsitu Nsitu Nkolimbo Nkolimbo Chinegwe Chinegwe Chipolopolo Chipolopolo Jonikisi Jonikisi Tizola Tizola Tambo 1 Tambo 1 Tambo 3 Tambo 3 Mchacha Mchacha Nthole Nthole Nsabilima Nsabilima Mbang'ombe Mbang'ombe Chambuluka Chambuluka Kadakola Kadakola Chaya Chaya Falamenga Falamenga Leno Leno Dickson Dogo Dickson Dogo Jokonia Jokonia Izeki Izeki Zyuwaki Zyuwaki a4_otherspecify (required) a4: Specify Village Name Question relevant when: true () a6 (required) a6: Enumerator name Question relevant when: true () 1 Annie Manyoni 2 Arthur H. Banda 3 Arthur Banda 4 Aurther Chiumia 5 Aurther Ngwira 6 Babrah Nasala 7 Bertha Loti 8 Billy Kayange 9 Bina Chingwe 10 Blessings Kamoto 11 Blessings Mbwerazino USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 90 Field Question Answer 12 Blessings Nyatepa 13 Caroline Kambalame 14 Charles Sofasi 15 Chifuniro Kalima 16 Chimwemwe Bwanausi 17 Chimwemwe Banda 18 Christina Chabwera 19 Daina Namaluweso 20 Dalitso Mpeketula 21 Daniel Msundwe 22 Darlington kapingasa 23 David Makiyi 24 Davie Scotch 25 Dickson Makwera 26 Dorothy Chirwa 27 Emmanuel .M. kaitano 28 Emmanuel Piseni 29 Esther Chitungu 30 Fanuel Chimbiya 31 Felix Peter 32 Francis Kafa 33 Frank Jumbe 34 Frazer Chafumbwa 35 Fred Bequiet 36 Gabriel Kachigayo 37 Gerald Mhango 38 Gift Kaunga 39 Gift Nsapato 40 Gifton Saizi 41 Gladwell Malunga 42 Gloria Tembo 43 Grace Chiwaya 44 Happy Katuli 45 Happy Nkhoma 46 Hellen Mbutuka 47 Henderson Chagoma 48 Innocent K. Mhango USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 91 Field Question Answer 49 Isaac M. Banda 50 Isaac Masamba 51 Islam Idana 52 Jack Yangairo 53 Jacob Mnkhwamba 54 James Ngwira 55 Jane Banda 56 Jimmy Lungu 57 Jofrey Kamanga 58 John Katete 59 John Munthali 60 Joice Mvula 61 Joseph Juma 62 Joseph Sabola 63 Joshua Bhima 64 Josphat Saidi 65 Kennedy Manda 66 Kenneth Given Limbani 67 Kingsley Manyumba 68 Kondwani Chikondi Munthali 69 Kondwani Wanda 70 Leonard Lakalaka 71 Lonjezo Sekani 72 Lonjezo Jumbe 73 Loveness Chiumula 74 Lusako Mwalwanda 75 Maureen Gwayi 76 Maxwell Phiri 77 Mayamiko Kamwaza 78 Melody Chipoka 79 Memory Kabuya 80 Memory Phiri 81 Mercy Banda 82 Mercy Kalulu 83 Mussa M. Mtayamo 84 Nellie Makanjira 85 Newton Lupoka USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 92 Field Question Answer 86 Noria Mhango 87 Peter Sana Paswell 88 Precious Kadewere 89 Rex Makwinja 90 Sauda Bwanali 91 Shad Banda 92 Shem Yuda 93 Shyma J. Dimu 94 Sikujuwa Nyasulu 95 Siphiwe Kaluwa 96 Steve Gollah 97 Stonald Kumbadzala 98 Stowell Mposa 99 Thamison Mandere 100 Thokozani Makaka 101 Thovise Makamo 102 Timothy Chirwa 103 Tombozgani Mhango 104 Wanangwa Kambondooma 105 Wilfred katunga 106 Wonderful Thindwa 107 Yusuf Ali Ayoub 108 Zynab Njerenga a5 (required) a5: HOUSEHOLD ID − INTERVIWER: Your first Replacement interview of the day = 1, Your Second Replacement interview of the day = 2 e.t.c Question relevant when: true () idnote This Questionnaire ID is: -- hhid (required) Replacement HOUSEHOLD ID − INTERVIWER: Please Enter the correct Household ID from Old Sample provided by your Supervisor. Question relevant when: true () confirmentry (required) Please re-enter Replacement HOUSEHOLD ID as confirmation. Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 93 Field Question Answer a20 (required) a20: Why did you have to replace the old household Question relevant when: true () 1 Moved outside of EA 2 Refused to Participate 3 In eligeble a21 a21: How was this replacement selected 1 Household farms same land 2 Nearest neighbor other Other a21_other Specify other. Question relevant when: selected(${a21}, 'other') a9 (required) a9: Which attempted visit is this? − (verify with log form and record) Question relevant when: true () 1 First visit 2 Second visit 3 Third visit a10 (required) a10: Is any person at this sampled house so you can invite the household to participate? Question relevant when: true () 1 Yes 0 No a11 (required) a11: Are you able to communicate in the same language as someone in the household? Question relevant when: true () 1 Yes 0 No intro2 Read the consent script: Hello. I am working with Invest in Knowledge and Social Impact. We are conducting a study to assess the impact of the USAID/Malawi Country Development Cooperation Strategy. USAID is doing some activities in this area. The results of this study may help to improve the programs offered in Malawi in the future. This household has been randomly selected to participate in this study if you choose to. If you agree to participate in this study, we would like to ask some survey questions. This interview USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 94 Field Question Answer will take about 1 hour and 30 minutes. You will be asked a few questions about yourself and family members, about the work that supports this family, about household goods, your activities in the community, your opinions about local services, your outlook on life, and information about the household's food consumption and health care. For most of the questions we prefer to talk to the head of the household, but if there is another person who knows more about certain topics such as agricultural activities of this household, we would like to invite them to respond to those parts of the survey. Also, for quality assurance, this device may audio record a random one-minute segment of our conversation, to ensure I am doing a good job and being respectful as an interviewer. Your participation is completely voluntary. You can choose not to participate now, or at any time . All information collected in this study is confidential and will be protected to the furthest extent permitted by law. Although anonymous data will be shared publicly, your name and other personal information that identifies you will never be used or shared with anyone outside of this research team. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 95 Field Question Answer There are no known risks of participating in this research project other than losing an hour and 30 minutes of productive time and a very small risk of confidentiality being broken. There are also no direct benefits to you if you participate other than knowing your information will help USAID understand whether its programs are working or whether they need to improve. Should you feel uncomfortable with any question(s), you may refuse to answer it and I will move on to the next question. If you have any questions or concerns now or in the future, you may contact James Mkandawire at 0999-412-756 james.mkandawire@investinknowl edge.org. Or you can contact the Social Impact Institutional Review Board: +1-703-465-1884 irb@socialimpact.com.Do you have any questions? Do you agree to participate in the study? Question relevant when: ${a11} =1 a12 (required) a12: Do you agree to participate in this study? Question relevant when: true () 1 Yes 0 No a30 a30: INTERVIEWER: List the language option you are going to use on the tablet for this interview. Question relevant when: ${a12} =1 1 Chichewa 2 Chitumbuka 3 Chiyawo 4 Chisena 5 English USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 96 Field Question Answer a13 (required) a13: How long have you been living in your current village? − mark no interview due to not living here 2 years and end survey Question relevant when: true () 1 less than 2 years 2 2 years or more a14 (required) a14: Record reason for no interview Question relevant when: true () 1 No interview- Not living here for at least 2 years 2 No interview- No male or female head of household at home 3 No interview- Adult requested reschedule 4 No interview - other reason 5 No Interview -Adults not able to interview (illness/infirmity/mental capacity) 6 Refusal- Adults say reschedule is not possible 7 Refusal - Direct refusal 8 Refusal- other reason 9 Refusal- Recently did long survey with MACRO (DHS or Food For Peace survey) note_end Thank you for your time. Unfortunately, you do not meet the criteria for the survey. Have a nice day. Question relevant when: ${a13} =1 - Group relevant when: ${a12} =1 and ${a13} !=1 a_background Section A: Background − Ensure you are talking to the preferred respondent (The main female in the house is first choice. If not, the head of household. If not, another adult able to speak about the topics.) - > a15_begin1 a15_1 a15_1 What is head of household's surname? a16_1 a16_1 What is the head of household's given name? a17_1 a17_1 What is the sex of head of household? 0 Male 1 Female USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 97 Field Question Answer a18 (required) a18 What is the primary respondent's relationship to the head of the household? Question relevant when: true () 1 Head of household 2 Spouse of head of household 3 Other adult in household - > a15_begin1 > a15_begin Group relevant when: ${a18} !=1 a15 (required) a15 What is main respondent's surname? Question relevant when: true () a16 (required) a16 What is main respondent's first name? Question relevant when: true () a17 (required) a17 What is the main respondent's sex? − (Observe) Question relevant when: true () 0 Male 1 Female b11 (required) b11: Which languages are spoken regularly in your household? Question relevant when: true () 1 Chichewa 2 Chitumbuka 3 Chiyawo 4 Chisena 5 English other Other b11_other Specify other. Question relevant when: selected(${b11}, 'other') b0_ Section B: Household Members num_people B0 How many people do you have in your household? Please include only the people who usually live and eat here and not temporary visitors. Also, include the household head even if he or she has not lived in the household for the past 6 months, as long as he/she is still living and supporting this household. Please do not include children who have already moved out or gotten married. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 98 Field Question Answer − INTERVIWER: Enter 0 if no additional People in the household note_roster Now I would like to make a list of the people starting with the head of household. Question relevant when: ${num_people} >=1 - > Household Roster (1) Group relevant when: ${num_people} >=1 (Repeated group) mem_nm (required) B01 First Name of household member Question relevant when: true () note_member_name_1 Please answer the following questions for [mem_nm] b02 (required) B02: Relationship of [mem_nm] to HH head Question relevant when: true () 1 HOUSEHOLD HEAD 2 SPOUSE 3 SON/DAUGHTER 4 PARENT 5 SIBLING 6 GRANDCHILD 7 GRANDPARENT 8 FOSTER CHILD 9 OTHER RELATIVE 10 NON-RELATIVE b03 (required) B03: Age of [mem_nm] in completed years − Report children under the age of one as zero Question relevant when: true () b03_1 B03_1: How many months old is the child? Question relevant when: ${b03} =0 b04 (required) B04: Gender of [mem_nm] Question relevant when: true () 0 Male 1 Female b05 (required) B05: What is [mem_nm] 's present marital status? Question relevant when: true () 1 MARRIED 2 SINGLE 3 WIDOWED USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 99 Field Question Answer 4 DIVORCED OR SEPARATED 5 N/A (Child) b07 (required) B07: What is [mem_nm] main occupation? Question relevant when: true () 1 FARMING 2 HOUSEWIFE 3 HOUSEHOLD BUSINESS 4 SALARIED PROFESSION 5 WAGE LABOR 6 STUDENT 7 FISHER 8 NONE other Other b07_other Specify other. Question relevant when: selected(${b07}, 'other') - > Household Roster (1) > Education Questions about [mem_nm] Group relevant when: ${b03} >5 - > Household Roster (1) > Education Questions about [mem_nm] > c1_edu2 generated_table_list_label_59 Education Questions about [mem_nm] reserved_name_for_field_list_la bels_60 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer c1_7 (required) C7: Can [mem_nm] read a one￾page letter in Chichewa? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 100 Field Question Answer c1_8 (required) C8: Can [mem_nm] write a one￾page letter in Chichewa? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer c1_9 (required) C9: Can [mem_nm] read a one￾page letter in English? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer c1_10 (required) C10: Can [mem_nm] write a one￾page letter in English? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer c1_11 (required) C11: Has [mem_nm] ever attended school? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer c1_12 (required) C12: Does [mem_nm] currently attend school? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer c1_13 (required) C13: What is the highest educational qualification [mem_nm] has completed? Question relevant when: true () 0 NONE 1 NURSERY/PRESCHOOL 2 STANDARD 1 3 STANDARD 2 4 STANDARD 3 5 STANDARD 4 6 STANDARD 5 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 101 Field Question Answer 7 STANDARD 6 8 STANDARD 7 9 STANDARD 8 10 JUNIOR FORM 1 11 JUNIOR FORM 2 12 SENIOR FORM 3 13 SENIOR FORM 4 14 VOCATIONAL TRAINING 15 DIPLOMA/CERTIFICATE 16 UNIVERSITY UNDERGRADUATE 17 UNIVERSITY GRADUATE/POST-GRADUATE 18 Adult literacy program - 77 OTHER - 99 Don't know b9_0 (required) b9_0: Is there any household member with a mental or physical disability? Question relevant when: true () 1 Yes 0 No - 88 Refused to Answer b9 (required) b9: Have you experienced the death of any member of the household in the past 12 months? Question relevant when: true () 1 Yes 0 No - 88 Refused to Answer b9_1 b10: Was this person aged 5 or younger? Question relevant when: ${b9} =1 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer c2 Section C: Education − Now I have a few general questions about reading. c14 (required) c14: How many minutes does it take to reach the nearest public primary school? Question relevant when: true () - > c15_start USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 102 Field Question Answer c15 (required) c15: Are there any books, magazines, etc. that children can read at home? Question relevant when: true () 1 Yes 0 No - 66 N/A (don't have children) - 88 Refused - 99 Don’t Know c16 (required) c16: Are there any books, magazines, etc. that adults can read at home? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer c16_1 (required) c16_1: Does any member of this household ever go to a community reading center to read? Question relevant when: true () 1 Yes 0 No - 99 Don’t know c17 (required) c17: Does anyone in the household read books, magazines, newspapers, or any materials every day? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer c18 (required) c18: Who reads every day? Question relevant when: true () 1 Adult male(s) 2 Adult female(s) 3 Boy child(ren) 4 Girl child(ren) D_section Section D: Well-Being − Now I would like to ask you about your views on your well￾being d5 (required) d5: Would you say that in general the health of your household members is excellent, very good, good, fair, or poor? Question relevant when: true () 1 Poor 2 Fair 3 Good 4 Very good 5 Excellent USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 103 Field Question Answer - 88 Refused d8 (required) d8: During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation? Question relevant when: true () d2 (required) d2: How satisfied are you with the financial situation of your household? − read options Question relevant when: true () 1 Not at all satisfied 2 Somewhat dissatisfied 3 Neutral 4 Somewhat satisfied 5 Very satisfied - 99 Don't know - 88 Refused d10 (required) d10: Now looking ahead – do you think that a year from now you (and your family living there) will be better off financially, or worse off, or just about the same as now? Question relevant when: true () 1 Worse off 2 Same 3 Better off - 88 Refused d22 (required) d22: Which of the following is true? Your current income... − read options Question relevant when: true () 1 Allows you to build your savings? 2 Allows you to save just a little? 3 Only just meets your expenses? 4 Is not sufficient, so you need to use your savings to meet expenses? 5 Is really not sufficient, so you need to borrow to meet expenses? - 88 Refused d15 (required) d15: Concerning your household's food consumption over the past one month, which of the following is true? − read options 1 It was less than adequate for household needs 2 It was adequate for household needs - 88 Refused USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 104 Field Question Answer Question relevant when: true () - > steps_begin note1 Imagine six steps, where on the bottom, the first step, stand the poorest people within your village and nearby villages, and on the highest step, the sixth, stand the rich. SHOW THE PICTURE OF THE STEPS d19 (required) d19: On which step are you today? Question relevant when: true () 1 Step 1 2 Step 2 3 Step 3 4 Step 4 5 Step 5 6 Step 6 - 88 Refused d21 (required) d21: On which step were you last year? Question relevant when: true () 1 Step 1 2 Step 2 3 Step 3 4 Step 4 5 Step 5 6 Step 6 - 88 Refused d20 (required) d20: On which step are most others in this village today? Question relevant when: true () 1 Step 1 2 Step 2 3 Step 3 4 Step 4 5 Step 5 6 Step 6 - 88 Refused d23 (required) d23: Suppose your household had something unfortunate happen to you, such as an unexpected loss of income or unexpected expense. 1 Yes 0 No USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 105 Field Question Answer Do you have someone you could turn to for help? Question relevant when: true () d26 (required) d26: When you need to leave your home for several hours do you worry about the security of your things? Question relevant when: true () 1 Worry a lot 2 Worry some 3 Do not worry - 88 Refused d4 (required) d4: On the whole, are you very satisfied, fairly satisfied, neutral, not very satisfied, or not at all satisfied with the way democracy works in Malawi? Question relevant when: true () 1 Not at all satisfied 2 Somewhat dissatisfied 3 Neutral 4 Somewhat satisfied 5 Very satisfied - 99 Don't know - 88 Refused d14 (required) d14: Do you believe you are personally able to control whether there can be improvements to your well-being in life? Question relevant when: true () 1 Yes 0 No d1 (required) d1: All things considered, how satisfied are you with your life as a whole these days? Question relevant when: true () 1 Not at all satisfied 2 Somewhat dissatisfied 3 Neutral 4 Somewhat satisfied 5 Very satisfied - 99 Don't know - 88 Refused E_section E. Household Features e13 (required) e13: INTERVIEWER: The outer walls of the main dwelling of the household are prodominantly made of what material? − Prompt Question relevant when: true () 1 GRASS 2 MUD (YOMATA) 3 COMPACTED EARTH (YAMDINDO) 4 MUD BRICK (UNFIRED) 5 BURNT BRICKS USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 106 Field Question Answer 6 CONCRETE 7 WOOD 8 IRON SHEETS - 77 OTHER e13a (required) e13a: The roof of the main dwelling is predominantly made of what material? Question relevant when: true () 1 Grass, plastic sheeting, or other 2 Iron sheets, clay tiles, or concrete e13b (required) e13b: Have you made any changes to your roof material since 2014 (past 4 years)? Question relevant when: true () 1 Yes 0 No - 99 Don’t know e13c (required) e13c: What was the previous roofing material? Question relevant when: true () 1 Grass, plastic sheeting, or other 2 Iron sheets, clay tiles, or concrete e14a (required) e14a: The floor of the main dwelling is predominantly made of what material? Question relevant when: true () 1 SAND 2 SMOOTHED MUD 3 SMOOTHED CEMENT 4 WOOD 5 TILE - 77 OTHER e14b. (required) e14b: Do you have electricity working in your dwelling? Question relevant when: true () 1 Yes 0 No e15 (required) e15: How many separate rooms do the members of your household occupy? − (DO NOT COUNT BATHROOMS, TOILETS, STOREROOMS, OR GARAGE) Question relevant when: true () e16 (required) e16: What is your main source of lighting? Question relevant when: true () 1 Collected firewood 2 Purchased firewood 3 Battery/dry cell (torch) 4 Straw/shrub/grass 5 Paraffin/Kerosene 6 Electricity USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 107 Field Question Answer 7 LPG 8 Natural gas 9 Biogas 10 Candles other Other e16_other Specify other. Question relevant when: selected(${e16}, 'other') e17 (required) e17: What is you main source of cooking fuel? Question relevant when: true () 1 Collected firewood 2 Purchased firewood 3 Straw/shrub/grass 4 Paraffin/Kerosene 5 Electricity 6 LPG 7 Natural gas 8 Biogas 9 Coal, Lignite 10 Charcoal 11 Agricultural crop 12 Animal dung -66 N/A. No food cooked in household other Other e17_other Specify other. Question relevant when: selected(${e17}, 'other') e18 (required) e18: Does someone in the house own a cellular telephone (cell phone) in working condition? Question relevant when: true () 1 Yes 0 No e19a (required) e19a: What kind of toilet facility does your household use? Question relevant when: true () 1 Flush toilet 2 VIP latrine 3 traditional latrine with roof 4 Traditional latrine without roof 5 None/Bush - 77 Other USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 108 Field Question Answer e19b e19b: Do you share this toilet facility with other households? Question relevant when: ${e19a} <5 or ${e19a} =-77 1 Yes 0 No e20 (required) e20: Do the children under 5 in the household sleep under a bed net at those times of the year when there are mosquitoes present? − Only ask if there are children under 5 Question relevant when: true () 1 YES, for all children under 5 2 YES, for some children under 5 3 NO, none of the children under 5 - 66 N/A (does not have children under 5) e20a (required) e20a: Do ANY members of the household sleep under a bed net to protect against mosquitos at some time during the year? Question relevant when: true () 1 Yes 0 No e21 (required) e21: Have you or anyone in your household grow any kind of tobbacco in the past 5 cropping seasons? Question relevant when: true () 1 Yes 0 No e22 (required) e22: Did anyone of your household cultivate a Dimba garden in (last completed dry season)? Question relevant when: true () 1 Yes 0 No e24 (required) e24: Over the past five years, was your household severely affected negatively by the following event: livestock died or were stolen? Question relevant when: true () 1 Yes 0 No - 66 N/A (Never had livestock in past 5 years) - 99 Don’t Know e25a (required) e25a: Over the past one month, did you purchase or pay for any bar soap (body or clothes soap)? Question relevant when: true () 1 Yes 0 No e25b (required) e25b: Over the past one month, did you purchase or pay for any clothes soap (powder or paste)? 1 Yes 0 No USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 109 Field Question Answer Question relevant when: true () e26 (required) e26: What is the main source of drinking water for members of your household in the past month? Question relevant when: true () 1 PIPED WATER INTO DWELLING 2 PIPED TO YARD/PLOT 3 BOREHOLE 4 PUBLIC TAP/STANDPIPE 5 PROTECTED WELL 6 UNPROTECTED WELL 7 PROTECTED SPRING 8 UNPROTECTED SPRING 9 RAINWATER 10 TANKER TRUCK/ WATER VENDOR 11 CART WITH SMALL TANK 12 SURFACE WATER (RIVER/DAM/LAKE/POND/STREAM/CANAL/ IRRIGATION DITCH) 13 BOTTLED WATER oth er Other e26_other Specify other. Question relevant when: selected(${e26}, 'other') - > Water e27_num (required) e27_num: How long does it take to go there, get water, and come back? Question relevant when: true () e27 (required) e27_unit: what is the unit? − RECORD IN UNIT Question relevant when: true () 1 minutes 2 Hours - 99 Don't know e28 (required) e28: Do you usually do anything to the water to make it safer to drink? Question relevant when: true () 0 No 1 Yes - 99 Don't know e29 (required) e29: What do you usually do to make the water safer to drink? Question relevant when: true () 1 BOIL 2 ADD BLEACH/CHLORINE PRODUCT/ WATER GUARD USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 110 Field Question Answer 3 USE WATER FILTER (CERAMIC/SAND/COMPOSITE) 4 STRAIN THROUGH A CLOTH 5 SOLAR DISINFECTION 6 LET IT STAND AND SETTLE - 99 Don't know - 77 OTHER e30 (required) e30: Is the cooking usually done in the house, in a separate building, or outdoors? Question relevant when: true () 1 IN THE HOUSE 2 IN A SEPARATE BUILDING 3 OUTDOORS - 77 OTHER - > Nearest Market e31 (required) e31: How long does it take to reach the nearest market? − RECORD IN UNIT. Put 999 if don't know. Question relevant when: true () e31_unit (required) e31a: what is the unit? Question relevant when: true () 1 minutes 2 Hours - 99 Don't know f F. Assets - > fa2_begin fa2 (required) fa2: How many [OXEN] do you own? − Count baby animals as whole animal ; Write "999" for "don't know/refuse" Question relevant when: true () fb2 (required) fb2: How many [CATTLE(COWS/BULLS)] do you own? − Count baby animals as whole animal ; Write "999" for "don't know/refuse" Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 111 Field Question Answer fc2 (required) fc2: How many [SHEEP] do you own? − Count baby animals as whole animal ; Write "999" for "don't know/refuse" Question relevant when: true () - > fa2b_begin fd2 (required) fd2: How many [GOATS] do you own? − Count baby animals as whole animal ; Write "999" for "don't know/refuse" Question relevant when: true () fe2 (required) fe2: How many [PIGS] do you own? − Count baby animals as whole animal ; Write "999" for "don't know/refuse" Question relevant when: true () ff2 (required) ff2: How many [CHICKEN] do you own? − Count baby animals as whole animal ; Write "999" for "don't know/refuse" Question relevant when: true () - > fg2_begin fg2 (required) fg2: How many [OTHER POULTRY) do you own? − Count any baby animal as whole animal Question relevant when: true () fh2 (required) fh2: How many [BED] do you own? Question relevant when: true () fi2 (required) fi2: Do you own any TABLES? Question relevant when: true () 1 Yes 0 No fj2 (required) fJ2: How many [IRON] do you own? Question relevant when: true () - > fg2b_begin fk2 (required) fk2: How many music players [FLASH/MEMORY CARD; TAPE USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 112 Field Question Answer OR CD PLAYER; HIFI] do you own? Question relevant when: true () fl2 (required) fl2: How many [BICYCLE] do you own? Question relevant when: true () - > fm2_begin fm2 (required) fm2: How many [CHAIR OR SOFA] do you own? Question relevant when: true () fo2 (required) fo2: How many [REFRIGERATOR] do you own? Question relevant when: true () ft2 (required) ft2: How many [RADIO (WIRELESS] do you own? Question relevant when: true () - > fm2_beginx fv2 (required) fv2: How many [WATCH] do you own? Question relevant when: true () fx2 (required) fx2: How many [BEER BREWING DRUM] do you own? Question relevant when: true () - > fy2_begin fy2 (required) fy2: How many [CAR OR TRUCK] do you own? Question relevant when: true () fz2 (required) fz2: How many [MOTORCYCLE OR MOTOR SCOOTER] do you own? Question relevant when: true () faa2 (required) faa2: How many [BOAT/CANOE/RAFT] do you own? Question relevant when: true () - > fy2_beginx fee2 (required) fee2: How many [PANGA] do you own? Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 113 Field Question Answer fgg2 (required) fgg2: How many [AXE] do you own? Question relevant when: true () fhh2 (required) fhh2: How many [SICKLE] do you own? Question relevant when: true () g0 G. Credit. Now I'm going to ask you about your involvement in banks or credit g1 (required) g1: Does any member of this household have a bank account? Question relevant when: true () 1 Yes 0 No - 99 Don't know - 88 Refused to answer g1_5 g1.5: Has anyone in this household received any loan, whether formal or informal, in any form over the last 12 months? 1 Yes 0 No - > loans Group relevant when: ${g1_5} =1 g2_1 (required) g2_1: What form did the loan take? Question relevant when: true () 1 Cash 2 Voucher 3 Materials provided 4 Assistance provided - 99 Don't know - 88 Refused to answer g3 (required) g3: Who provided that loan? Question relevant when: true () 1 Non-governmental organization 2 Formal lender (Bank/financial institution) 3 Informal lender (friends/relatives) 4 VSLAs / SACCOs/ merry-go-rounds 5 Microfinance 6 Friends or relatives 7 A company USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 114 Field Question Answer - 77 Other - 99 Don't know g5 (required) g5: Who made the decision to borrow from [SOURCE]? Question relevant when: true () 1 Main woman 2 Main man 3 Other household member 4 Someone (or group of people) outside the household - 99 Don't know n02 (required) n02: Have you used a mobile phone in the past 12 months to send or receive money or pay a bill? Question relevant when: true () 1 Yes 0 No i i. Food Security [Food Insufficiency] note1_1 READ: Now I would like to ask you about access to food over the past month (30 days) i1a (required) i1a. In the past month (30 days), did you worry that your household would not have enough food? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer i1b (required) i1b: How often did this happen? Question relevant when: true () 1 RARELY (ONCE OR TWICE IN THE PAST MONTH) 2 SOMETIMES (THREE TO TEN TIMES IN THE PAST MONTH) 3 OFTEN (MORE THAN TEN TIMES IN THE PAST MONTH) i1c (required) i1c. In the past month (30 days), were you or any household member not able to eat the kinds of foods you preferred because of a lack of resources? 1 Yes 0 No - 99 Don’t Know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 115 Field Question Answer Question relevant when: true () - 88 Refused to Answer i1d (required) i1d: How often did this happen? Question relevant when: true () 1 RARELY (ONCE OR TWICE IN THE PAST MONTH) 2 SOMETIMES (THREE TO TEN TIMES IN THE PAST MONTH) 3 OFTEN (MORE THAN TEN TIMES IN THE PAST MONTH) i1 (required) i1: In the past month (30 days), did you or any household member have to eat a limited variety of foods due to a lack of resources? − When we say lack of resources, we mean not having means to get food either through growing it, purchasing it or trading for it. Preferred foods might include chicken or rice, nsima, beef, fish Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer i2 (required) i2: How often did this happen? Question relevant when: true () 1 RARELY (ONCE OR TWICE IN THE PAST MONTH) 2 SOMETIMES (THREE TO TEN TIMES IN THE PAST MONTH) 3 OFTEN (MORE THAN TEN TIMES IN THE PAST MONTH) i3 (required) i3: In the past month (30 days), did you or any household member have to eat some foods that you really did not want to eat because of a lack of resources to obtain other types of food? − "A limited variety of foods" might be nsima and salt or beans only Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer i4 (required) i4: How often did this happen? Question relevant when: true () 1 RARELY (ONCE OR TWICE IN THE PAST MONTH) 2 SOMETIMES (THREE TO TEN TIMES IN THE PAST MONTH) 3 OFTEN (MORE THAN TEN TIMES IN THE PAST MONTH) USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 116 Field Question Answer i5 (required) i5: In the past month (30 days), did you or any household member eat less in either the morning or the evening meal than you felt you needed because there was not enough food? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer i6 (required) i6: How often did this happen? Question relevant when: true () 1 RARELY (ONCE OR TWICE IN THE PAST MONTH) 2 SOMETIMES (THREE TO TEN TIMES IN THE PAST MONTH) 3 OFTEN (MORE THAN TEN TIMES IN THE PAST MONTH) i7 (required) i7: In the past month (30 days), did you or any other household member have to eat fewer than your normal number of meals in a day because there was not enough food? − A food you really did not want to eat might include amaranthus Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer i8 (required) i8: How often did this happen? Question relevant when: true () 1 RARELY (ONCE OR TWICE IN THE PAST MONTH) 2 SOMETIMES (THREE TO TEN TIMES IN THE PAST MONTH) 3 OFTEN (MORE THAN TEN TIMES IN THE PAST MONTH) i9 (required) i9: In the past month (30 days), did you or any household member go to sleep at night hungry because there was not enough food? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer i10 (required) i10: How often did this happen? Question relevant when: true () 1 RARELY (ONCE OR TWICE IN THE PAST MONTH) 2 SOMETIMES (THREE TO TEN TIMES IN THE PAST MONTH) USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 117 Field Question Answer 3 OFTEN (MORE THAN TEN TIMES IN THE PAST MONTH) i11 (required) i11: In the past month (30 days), did you or any household member go a whole day and night without eating anything because there was not enough food? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer i12 (required) i12: How often did this happen? Question relevant when: true () 1 RARELY (ONCE OR TWICE IN THE PAST MONTH) 2 SOMETIMES (THREE TO TEN TIMES IN THE PAST MONTH) 3 OFTEN (MORE THAN TEN TIMES IN THE PAST MONTH) i13 (required) i13. In the past month (30 days), was there ever no food to eat of any kind in your household because of lack of resources to get food? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer i14 (required) i14: How often did this happen? Question relevant when: true () 1 RARELY (ONCE OR TWICE IN THE PAST MONTH) 2 SOMETIMES (THREE TO TEN TIMES IN THE PAST MONTH) 3 OFTEN (MORE THAN TEN TIMES IN THE PAST MONTH) i15 (required) i15: In the past 30 days, did anyone in this household eat ground nuts? − This could be any form Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer i16 (required) i16: In the past 30 days, did anyone in this household eat soy beans? − This could be any form Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 118 Field Question Answer j J. Environment j3 (required) j3: Does anyone in this household gather materials from the forest either to sell or use at home? Question relevant when: true () 1 Yes 0 No - > Gathered Materials Group relevant when: ${j3} =1 j4aa j4aa: Do they gather wood or timber? 1 Yes 0 No j4a j4a: What do they use wood/timber for? (read response options) Question relevant when: ${j4aa} =1 1 Use in household only 2 Sell it all 3 Both use in household and sell j5a (required) j5: How important is this as a source of income? Not very important, a little, or very important? Question relevant when: true () 1 Not important 2 A little bit 3 A lot - 99 Don't know j4ca j4ca: Do they gather other materials from the forest such as fruit, leaves, or bark? 1 Yes 0 No j4b j4b: What do they use these materials for? (read response options) Question relevant when: ${j4ca} =1 1 Use in household only 2 Sell it all 3 Both use in household and sell j5c (required) j5: How important are these materials as a source of income? Not very important, a little, or very important? Question relevant when: true () 1 Not important 2 A little bit 3 A lot - 99 Don't know j6 (required) j6: Does anyone in this household practice fishing regularly? Question relevant when: true () 1 Yes 0 No - > Fishing USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 119 Field Question Answer Group relevant when: ${j6} =1 j7 (required) j7: For how many days in the past month has someone in this household fished? Question relevant when: true () - > Fishing > j8_begin j8 (required) j8: How many/much fish would you say are caught in an average day of fishing? − Help them estimate. Don't know is 999 Question relevant when: true () reserved_name_for_field_list_la bels_219 1 Number of fish 2 Number of kilos j8b j8b: Units for fish 1 Number of fish 2 Number of kilos j10_1 j10_1: Do you sell the fish? 1 Yes 0 No j10_2 j10_2: Can you please estimate the amount of income your household made from selling fish in the past month? − Record in Kwacha and put 999 for don’t know Question relevant when: ${j10_1} =1 j11 (required) j11: In the past week how many times (# meals) was fish eaten in this household? Question relevant when: true () j17 (required) j17: In the past 12 months, has this household experienced any loss or severe reduction of arable land due to erosion? Question relevant when: true () 1 Yes 0 No j18 (required) j18: Have you seen any demonstrations in the past year related to planting or preserving trees? 1 Yes 0 No USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 120 Field Question Answer Question relevant when: true () j19 (required) j19: Has anyone in this household planted trees in the past 2 years? Question relevant when: true () 1 Yes 0 No j20 (required) j20: What kinds of trees were planted? Question relevant when: true () 1 Fruit/nut/agriculture trees 2 Other trees -99 Don't know other Other j20_other Specify other. Question relevant when: selected(${j20}, 'other') j21 (required) j21: Have you ever heard of climate change? − Like long-term changes in weather patterns like timing of rains or average temperatures Question relevant when: true () 1 Yes 0 No j22 (required) j22: What kinds of things do you think you can do to prepare for and respond to changes in weather like floods or drought? Question relevant when: true () 1 Planting trees 2 Use less trees/wood 3 Use improved cookstove 4 Use community woodlot 5 Better forest management 6 Conserve water -77 Other -99 Don't know what to do other Other j22_other Specify other. Question relevant when: selected(${j22}, 'other') k1 K. Health - > Closest clinic k02 (required) k02: How long does it take for you to get to the closest clinic? − This should be amout of time, not distance Question relevant when: true () k03 K03: What are the units for closest clinic 1 minutes 2 Hours USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 121 Field Question Answer - 99 Don't know k04 (required) k04: When a child in your household needs medical care, what do you typically do Question relevant when: true () 1 Go to the hospital or health center 2 Go to a clinic 3 Take care of him/her at home 4 Go to a traditional village healer 5 Other, please specify -99 Don’t know -88 Refuse to respond -77 N/A (No Children) other Other k04_other Specify other. Question relevant when: selected(${k04}, 'other') k06 (required) k06: Why do you not go to a clinic or hospital when a child is sick? Question relevant when: true () 1 Can’t afford the clinic fees 2 Can’t afford the transportation 3 It is too far 4 It is difficult to get there 5 Traditional healing/at home care is just as good or better than going to a clinic/hospital 6 Religious beliefs - 77 Other - 99 Don’t know - 88 Refuse to respond k08 (required) k08: What is the name of the clinic/hospital you use most often when it is needed? Question relevant when: true () Chadza Unit 33 Chadza Unit 33 Chikowa Health Centre Chikowa Health Centre Chileka Health Centre Chileka Health Centre Chimbalanga Heath Centre Chimbalanga Heath Centre Chitedze Health Centre Chitedze Health Centre Chiunjiza Health Centre Chiunjiza Health Centre USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 122 Field Question Answer Dickson Health Centre Dickson Health Centre Kabudula Health Centre Kabudula Health Centre Kan'goma Health Centre Kan'goma Health Centre Khongoni Health Centre Khongoni Health Centre Matapila Heath Centre Matapila Heath Centre Mbang'ombe Health Centre Mbang'ombe Health Centre Mbwatalika Health Centre Mbwatalika Health Centre Mitundu Health Centre Mitundu Health Centre Mlale Health Centre Mlale Health Centre Mtenthera Health Centre Mtenthera Health Centre Nathenje Health Centre Nathenje Health Centre Ngoni Health Centre Ngoni Health Centre Nkhoma Hospital Nkhoma Hospital Nsaru health Centre Nsaru health Centre Nthondo Health Centre Nthondo Health Centre Ukwe Health Centre Ukwe Health Centre -77 Other Kalembo Dispensary Kalembo Dispensary Kankao Health Centre Kankao Health Centre Kapile Health Centre Kapile Health Centre USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 123 Field Question Answer Mbera Health Centre Mbera Health Centre Phalula Health Facility Phalula Health Facility Phimbi Health Centre Phimbi Health Centre Ulongwe Health Centre Ulongwe Health Centre Utale I Health Centre Utale I Health Centre Utale II Health Centre Utale II Health Centre Chikweo Halth Centre Chikweo Halth Centre Machinga District Hospital Machinga District Hospital Namanja Health Centre Namanja Health Centre Ngokwe Health Centre Ngokwe Health Centre Nsanama Health Centre Nsanama Health Centre Ntaja Health Centre Ntaja Health Centre Nyambi Health Centre Nyambi Health Centre Chilipa Health Centre Chilipa Health Centre Chilonga Dispensary Chilonga Dispensary Jalasi Health Centre Jalasi Health Centre Katuli/Kasekela Health Centre Katuli/Kasekela Health Centre Luwalika Health Centre Luwalika Health Centre Mkumba Health Centre Mkumba Health Centre USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 124 Field Question Answer Mtimabi Health Centre Mtimabi Health Centre Nagallamu Health Centre Nagallamu Health Centre Namwera Health Centre Namwera Health Centre Nankumba Health Centre Nankumba Health Centre Phirilongwe Health Centre Phirilongwe Health Centre Atupele Community Hospital Atupele Community Hospital Chilumba Rural Hospital Chilumba Rural Hospital Fulirwa Health Centre Fulirwa Health Centre Hara Dispensary Hara Dispensary Iponga Health Centre Iponga Health Centre Kaporo Rural Hospital Kaporo Rural Hospital Karonga District Hospital Karonga District Hospital Kasoba Health Centre Kasoba Health Centre Lupembe Health Centre Lupembe Health Centre Mlare Health Centre Mlare Health Centre Mpata Health Centre Mpata Health Centre Ngana Health Centre Ngana Health Centre Nyungwe Health Centre Nyungwe Health Centre USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 125 Field Question Answer St Annies Health Centre St Annies Health Centre Sangilo Health Centre Sangilo Health Centre Alinafe Rehabilitation Centre Alinafe Rehabilitation Centre Benga Health Centre Benga Health Centre Bua Dispensary Bua Dispensary Chididi Health Centre Chididi Health Centre Dwamadzi Rural Hospital Dwamadzi Rural Hospital Kaongozi Dispensary Kaongozi Dispensary Kapiri Health Centre Kapiri Health Centre Kasitu Health Centre Kasitu Health Centre Lwaladzi Health Centre Lwaladzi Health Centre Malowa Dispensary Malowa Dispensary Matiki Health Centre Matiki Health Centre Mlosa Health Centre Mlosa Health Centre Mpamantha Dispensary Mpamantha Dispensary Msenjere Health Centre Msenjere Health Centre Ngala Health Centre Ngala Health Centre Nkhotakota District Hospital Nkhotakota District Hospital USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 126 Field Question Answer Chingale Health Centre Chingale Health Centre Lambulira Health Centre Lambulira Health Centre Likangala Health Centre Likangala Health Centre Mayaka Health Centre Mayaka Health Centre Mmambo Health Centre Mmambo Health Centre Ngwelelo Health Centre Ngwelelo Health Centre Kalemba Health Centre Kalemba Health Centre Lulwe Health Centre Lulwe Health Centre Makhanga Health Centre Makhanga Health Centre Masenjele Health Centre Masenjele Health Centre Mbenje Health Centre Mbenje Health Centre Ndamera Health Centre Ndamera Health Centre Nsanje District Hospital Nsanje District Hospital Nyamithuthu Health Centre Nyamithuthu Health Centre Phokera Health Centre Phokera Health Centre Sankhulani Health Centre Sankhulani Health Centre Sorgin Health Centre Sorgin Health Centre Tengani Health Centre/Nsanje District Hospital Tengani Health Centre/Nsanje District Hospital Trinity Hospital Trinity Hospital USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 127 Field Question Answer k10 (required) k10: What is your most common source of advice about or treatment for illnesses? Question relevant when: true () 1 GOVERNMENT HOSPITAL 2 GOVERNMENT HEALTH CENTER 3 GOVERNMENT HEALTH POST/OUTREACH 4 HOSPITAL (don't know if government or private) 5 HEALTH CENTER (don't know if government or private) 6 MOBILE CLINIC 7 HSA 8 CBDA/DOOR TO DOOR / Community Health Worker 9 CHAM 10 PRIVATE HOSPITAL/CLINIC/ PRIVATE DOCTOR 11 PHARMACY 12 BLM 13 MACRO 14 YOUTH DROP IN CENTRE 15 SHOP 16 CHURCH 17 FRIEND/RELATIVE 18 WOMEN'S GROUP 19 CARE GROUP - 77 OTHER - 88 REFUSED - 99 N/A k12 (required) k12: Have you heard of any ways or methods that women or men can use to avoid pregnancy? Question relevant when: true () 1 Yes 0 No - 88 Refused to Answer k14 (required) k14: Are you currently doing something or using any method to delay or avoid getting pregnant or getting a woman pregnant? 1 Yes 0 No - 99 Don’t Know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 128 Field Question Answer − Ask if between 15-50 years old Question relevant when: true () - 88 Refused to Answer - 66 N/A (Respondent over 50 years of age) k16 (required) k16: Which methods are you using? Question relevant when: true () 1 FEMALE STERILIZATION 2 MALE STERILIZATION 3 PILL 4 Loop / IUD (Intra-Uterine Device) 5 INJECTABLES 6 IMPLANTS / NORPLANTS 7 MALE CONDOM 8 FEMALE CONDOM 9 PERIODIC ABSTINENCE 10 WITHDRAWAL -99 Don't know -88 Refused to answer other Other k16_other Specify other. Question relevant when: selected(${k16}, 'other') k18 (required) k18: Where did you obtain (CURRENT METHOD) the last time? Question relevant when: true () 1 GOVERNMENT HOSPITAL 2 GOVERNMENT HEALTH CENTER 3 GOVERNMENT HEALTH POST/OUTREACH 4 HOSPITAL (don't know if government or private) 5 HEALTH CENTER (don't know if government or private) 6 MOBILE CLINIC 7 HSA 8 CBDA/DOOR TO DOOR / Community Health Worker 9 CHAM 10 PRIVATE HOSPITAL/CLINIC/ PRIVATE DOCTOR 11 PHARMACY 12 BLM 13 MACRO USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 129 Field Question Answer 14 YOUTH DROP IN CENTRE 15 SHOP 16 CHURCH 17 FRIEND/RELATIVE 18 WOMEN'S GROUP 19 CARE GROUP - 77 OTHER - 88 REFUSED - 99 N/A k20 (required) k20: I don't want to know the results, but have you received HIV counseling, testing and results within the last 12 months? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer k22 (required) k22: INTERVIEWER: If respondent is part of a couple (whether married or not), ASK: Did your spouse also receive HIV counseling, testing and results around the same time? − Ask only if respondent is part of a couple Question relevant when: true () 1 Yes 0 No - 66 N/A (no spouse) - 99 Don’t Know - 88 Refused to Answer k24 (required) k24: Where did you receive these services? Question relevant when: true () 1 GOVERNMENT HOSPITAL 2 GOVERNMENT HEALTH CENTER 3 GOVERNMENT HEALTH POST/OUTREACH 4 HOSPITAL (don't know if government or private) 5 HEALTH CENTER (don't know if government or private) 6 MOBILE CLINIC 7 HSA 8 CBDA/DOOR TO DOOR / Community Health Worker 9 CHAM USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 130 Field Question Answer 10 PRIVATE HOSPITAL/CLINIC/ PRIVATE DOCTOR 11 PHARMACY 12 BLM 13 MACRO 14 YOUTH DROP IN CENTRE 15 SHOP 16 CHURCH 17 FRIEND/RELATIVE 18 WOMEN'S GROUP 19 CARE GROUP - 77 OTHER - 88 REFUSED - 99 N/A k25 k25: INTERVIEWER: Was there a child under 6 months old reported in the roster? 1 Yes 0 No - > Feeding under 6 months Group relevant when: ${k25} =1 k25_1 k25_1 What is the main type of liquid or food that you feed to this child? 1 Breast milk 2 Commercially produced infant formula 3 Mentioned anything other than breast milk or commercial formula (e.g. water, other liquids, semi-solid, or solid foods) - 99 Don't know k25_2 k25_2 Are there any other types of food or liquid that you use sometimes to feed to this child? If so, what else do you feed to the child? 1 Breast milk 2 Commercially produced infant formula 3 Mentioned anything other than breast milk or commercial formula (e.g. water, other liquids, semi-solid, or solid foods) - 99 Don't know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 131 Field Question Answer k26_num k26: INTERVIEWER: Is there a child between ages of 6 and 23 months in this household? 1 Yes 0 No - > Nutrition for 6-23 months Group relevant when: ${k26_num} =1 k26_name Please remind me the name of a child (or the next child) between the ages of 6 and 23 months - > Nutrition for 6-23 months > Liquid and food yesterday notek_1 Now I would like to ask you about liquids or foods [k26_name] had yesterday during the day or at night. (age 6-23 months) − (AUTONAME FROM ROSTER YOUNGEST CHILD AGED 6-23 MONTHS) k26a (required) k26a: Plain water Question relevant when: true () 1 Yes 0 No - 99 Don’t know k26b (required) k26b: Breast milk Question relevant when: true () 1 Yes 0 No - 99 Don’t know k26ba (required) k26ba: How many times did [k26_name] drink breast milk? − IF 7 OR MORE TIMES, RECORD '7'. .Write "-99" if Don't Know or Refused to answer. Question relevant when: true () k26c (required) k26c: Commercially produced infant formula? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k26ca (required) k26ca: How many times did [k26_name] drink infant formula? − IF 7 OR MORE TIMES, RECORD '7'. .Write "-99" if Don't Know or Refused to answer. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 132 Field Question Answer Question relevant when: true () k26d (required) k26d: Milk such as tinned, powdered, or fresh animal milk? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k26da (required) k26da: How many times did [k26_name] drink milk? − IF 7 OR MORE TIMES, RECORD '7'. .Write "-99" if Don't Know or Refused to answer. Question relevant when: true () k26e (required) k26e: Yogurt Question relevant when: true () 1 Yes 0 No - 99 Don’t know k26ea (required) k26ea: How many times did [k26_name] drink/eat yogurt? − IF 7 OR MORE TIMES, RECORD '7'. .Write "-99" if Don't Know or Refused to answer. Question relevant when: true () - > Nutrition for 6-23 months > Liquid and food yesterday > Yesterday during the day or night, did anyone eat aged 6-23 months eat or drink…. k26f (required) k26f: Juice or juice drinks? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k26g (required) k26g: Tea or coffee? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k26h (required) k26h: Soft drink? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k26i (required) k26i: Soup or broth? 1 Yes USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 133 Field Question Answer Question relevant when: true () 0 No - 99 Don’t know - > Nutrition for 6-23 months > Liquid and food yesterday > Yesterday during the day or night, did anyone eat aged 6-23 months eat or drink…. k26j (required) k26j: Any Cerelac (Likuni Phala, Nestum, Purity, Sibusiso)? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k26k (required) k26k: Any thin porridge? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k26l (required) k26l: Thobwa (fermented porridge)? Question relevant when: true () 1 Yes 0 No - 99 Don’t know - > Nutrition for 6-23 months > Liquid and food yesterday > Did anyone eat aged 6-23 months eat or drink…. k26m (required) k26m: ORS (oral rehydration solution)? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k26n (required) k26n: Vitamin or mineral supplements? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k26o (required) k26o: Any other liquids? Question relevant when: true () 1 Yes 0 No - 99 Don’t know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 134 Field Question Answer notek_2 Now I would like to ask you about solid or semi-solid (mushy) foods that [k26_name] may have had 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. - > Nutrition for 6-23 months > Solid and semi-solid (mushy) foods k28_note1 Now I would like to ask you about solid and semi-solid (mushy) foods that [k26_name] may have had 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. k28a (required) k28a: Bread, scone, maize meal (ngaiwa), maize flour (ufawoyera), millet, rice, sorghum, or any other food made from grains? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k28b (required) k28b: Pumpkin, carrots, squash or yams or sweet potatoes that are yellow or orange inside? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k28c (required) k28c: Cocoyams, irish potatoes, white sweet potatoes, white yams, cassava, or other local roots or tubers? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k28d (required) k28d: Any dark green, leafy vegetables such as amaranth, bonongwe, pumpkin leaves, chinese cabbage, greens, kale, cassava leaves, beans, cow peas or sweet potato leaves that are fresh? Question relevant when: true () 1 Yes 0 No - 99 Don’t know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 135 Field Question Answer k28e (required) k28e: Dried pumpkin leaves, beans leaves, cow peas or sweet￾potato leaves Question relevant when: true () 1 Yes 0 No - 99 Don’t know - > Nutrition for 6-23 months > Now I would like to ask you about solid or semi￾solid (mushy) foods that [k26_name] may have had 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. k28f (required) k28f: Ripe mangoes, papayas, guava? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k28g (required) k28g: Any other fruits or vegetables (for example, bananas, apples, green beans, avocados, tomatoes, okra)? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k28h (required) k28h: Liver, kidney, heart or other organ meats? Question relevant when: true () 1 Yes 0 No - 99 Don’t know - > Nutrition for 6-23 months > Now I would like to ask you about solid or semi￾solid (mushy) foods that [k26_name] may have had 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. k28i (required) k28i: Any meat, such as beef, pork, lamb, goat, chicken, duck, 1 Yes 0 No USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 136 Field Question Answer rabbit or rodents (such as mice, moles, etc.)? Question relevant when: true () - 99 Don’t know k28j (required) k28j: Grubs, snails or insects? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k28k (required) k28k: Eggs? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k28l (required) k28l: Fresh or dried fish, nkhanu, crabs or other seafood? Question relevant when: true () 1 Yes 0 No - 99 Don’t know - > Nutrition for 6-23 months > Now I would like to ask you about solid or semi￾solid (mushy) foods that [k26_name] may have had 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. k28m (required) k28m: Any foods made from beans, soybeans, nuts, lentils, pigeon peas, cow peas or ground nut powder (nsinjiro)? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k28n (required) k28n: Cheese or other products made from milk? Question relevant when: true () 1 Yes 0 No - 99 Don’t know - > Nutrition for 6-23 months > Other foods that [k26_name] may have had yesterday USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 137 Field Question Answer k280 (required) k280: Any oil, fats, or butter, or foods made with any of these? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k28p (required) k28p: Any sugary foods such as chocolates, sweets, candies, sugar cane, honey, pastries, cakes, or biscuits? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k28q (required) k28q: Any other solid or semi-solid food? Question relevant when: true () 1 Yes 0 No - 99 Don’t know k30 (required) k30: How many times did [k26_name] eat solid, semi-solid, or soft foods yesterday during the day or at night? Question relevant when: true () - > notek_3 notek_32 Yesterday during the day or at night, did [MAIN WOMAN] eat any of the following foods: − Consider MAIN WOMAN k32a (required) k32a: Groundnuts? − This includes anything made from this Question relevant when: true () 1 Yes 0 No - 99 Don’t know - 66 N/A. not applicable k32b (required) k32b: Soya? − This includes anything made from this Question relevant when: true () 1 Yes 0 No - 99 Don’t know - 66 N/A. not applicable l L. Household Decision Making - > l02: When decisions are made about the following USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 138 Field Question Answer topics, who is it that normally takes the decision: l02a (required) l02a: Getting inputs for agricultural production − select all that apply Question relevant when: true () 1 Main male or husband 2 Main female or wife 3 Another male in the household 4 Another female in the household 5 Someone outside the household/other - 66 N/A. Activity not applicable l02b (required) l02b: The types of crops to grow for agricultural production Question relevant when: true () 1 Main male or husband 2 Main female or wife 3 Another male in the household 4 Another female in the household 5 Someone outside the household/other - 66 N/A. Activity not applicable l02c (required) l02c: When or who would take crops to the market (or not) Question relevant when: true () 1 Main male or husband 2 Main female or wife 3 Another male in the household 4 Another female in the household 5 Someone outside the household/other - 66 N/A. Activity not applicable l02h (required) l02h: Whether or not to use family planning to space or limit births? Question relevant when: true () 1 Main male or husband 2 Main female or wife 3 Another male in the household 4 Another female in the household 5 Someone outside the household/other - 66 N/A. Activity not applicable l02i (required) l02i: Whether or how to participate in community decision making or activities Question relevant when: true () 1 Main male or husband 2 Main female or wife 3 Another male in the household 4 Another female in the household 5 Someone outside the household/other - 66 N/A. Activity not applicable USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 139 Field Question Answer l02j (required) l02j: Taking loans Question relevant when: true () 1 Main male or husband 2 Main female or wife 3 Another male in the household 4 Another female in the household 5 Someone outside the household/other - 66 N/A. Activity not applicable l02k (required) l02k: Whether or how to participate in groups or committees Question relevant when: true () 1 Main male or husband 2 Main female or wife 3 Another male in the household 4 Another female in the household 5 Someone outside the household/other - 66 N/A. Activity not applicable l02l (required) l02l: Decisions about schooling of a boy child Question relevant when: true () 1 Main male or husband 2 Main female or wife 3 Another male in the household 4 Another female in the household 5 Someone outside the household/other - 66 N/A. Activity not applicable l02m (required) l02m: Decisions about schooling of a girl child Question relevant when: true () 1 Main male or husband 2 Main female or wife 3 Another male in the household 4 Another female in the household 5 Someone outside the household/other - 66 N/A. Activity not applicable l02n (required) l02n: Deciding whether to take a boy child to a health center or hospital Question relevant when: true () 1 Main male or husband 2 Main female or wife 3 Another male in the household 4 Another female in the household 5 Someone outside the household/other - 66 N/A. Activity not applicable USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 140 Field Question Answer l02o (required) l02o: Deciding whether to take a girl child to a health center or hospital Question relevant when: true () 1 Main male or husband 2 Main female or wife 3 Another male in the household 4 Another female in the household 5 Someone outside the household/other - 66 N/A. Activity not applicable l02p (required) l02p: Deciding whether to go to health center or hospital for personal illness Question relevant when: true () 1 Main male or husband 2 Main female or wife 3 Another male in the household 4 Another female in the household 5 Someone outside the household/other - 66 N/A. Activity not applicable I05 INTERVIEWER: Is there a main woman in the house to answer the following questions? 0 No main woman exisit in the household 1 Yes, there is a main woman and is available for interview 2 Yes, there is a main woman but she is not available for interview - > I05_mainwoman2 Group relevant when: ${I05} >0 note_k5 Now I would like to know about the agriculture activities of the MAIN WOMAN in the household only l06 (required) l06: Did this household participate in [Food crop farming: crops that are grown primarily for household food consumption] in the past 12 months (that is during the last cropping seasons)? Question relevant when: true () 1 Yes 0 No - > I05_mainwoman2 > I06_begin Group relevant when: ${l06} =1 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 141 Field Question Answer l06a (required) l06a: How much input did [main woman] have in making decisions about [Food crop farming] in the past 12 months (that is during the last cropping season)? Question relevant when: true () 1 No input 2 Input into very few decisions 3 Input into some decisions 4 Input into most decisions 5 Input into all decisions - 66 N/A: Activity not applicable or No decision made l06b (required) l06b: How much input did [main woman] have in decisions on the use of income generated from [Food crop farming]? Question relevant when: true () 1 No input 2 Input into very few decisions 3 Input into some decisions 4 Input into most decisions 5 Input into all decisions - 66 N/A: Activity not applicable or No decision made l08 (required) l08: Did [main woman] participate in [livestock raising] in the past 12 months (that is during the last cropping season)? Question relevant when: true () 1 Yes 0 No l08a (required) l08a: How much input did [main woman] have in making decisions about [livestock raising] in the past 12 months (that is during the last cropping season)? Question relevant when: true () 1 No input 2 Input into very few decisions 3 Input into some decisions 4 Input into most decisions 5 Input into all decisions - 66 N/A: Activity not applicable or No decision made l08b (required) l08b: How much input did [main woman] have in decisions on the use of income generated from [livestock raising]? Question relevant when: true () 1 No input 2 Input into very few decisions 3 Input into some decisions 4 Input into most decisions 5 Input into all decisions - 66 N/A: Activity not applicable or No decision made l10 (required) l10: Did [main woman] participate in [Non-farm economic activities: Small business, self-employment, buy-and-sell] in the past 12 1 Yes 0 No USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 142 Field Question Answer months (that is during the last cropping season)? Question relevant when: true () - > I05_mainwoman2 > I10_begin Group relevant when: ${l06} =1 l10a (required) l10a: How much input did [main woman] have in making decisions about [Non-farm economic activities] in the past 12 months (that is during the last cropping season)? Question relevant when: true () 1 No input 2 Input into very few decisions 3 Input into some decisions 4 Input into most decisions 5 Input into all decisions - 66 N/A: Activity not applicable or No decision made l10b (required) l10b: How much input did [main woman] have in decisions on the use of income generated from [Non-farm economic activities]? Question relevant when: true () 1 No input 2 Input into very few decisions 3 Input into some decisions 4 Input into most decisions 5 Input into all decisions - 66 N/A: Activity not applicable or No decision made l12 (required) l12: Did [main woman] participate in [Wage and salary employment: in-kind or monetary work both agriculture and other wage work] in the past 12 months (that is during the last cropping season)? Question relevant when: true () 1 Yes 0 No - > I05_mainwoman2 > I12_begin Group relevant when: ${l06} =1 l12a (required) l12a: How much input did [main woman] have in making decisions about [Wage and salary employment] in the past 12 months (that is during the last cropping season)? Question relevant when: true () 1 No input 2 Input into very few decisions 3 Input into some decisions 4 Input into most decisions 5 Input into all decisions USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 143 Field Question Answer - 66 N/A: Activity not applicable or No decision made l12b (required) l12b: How much input did [main woman] have in decisions on the use of income generated from [Wage and salary employment]? Question relevant when: true () 1 No input 2 Input into very few decisions 3 Input into some decisions 4 Input into most decisions 5 Input into all decisions - 66 N/A: Activity not applicable or No decision made l14 (required) l14: Did [main woman] participate in [Fishing or fishpond culture] in the past 12 months (that is during the last cropping season)? Question relevant when: true () 1 Yes 0 No - > I05_mainwoman2 > I14_begin Group relevant when: ${l06} =1 l14a (required) l14a: How much input did [main woman] have in making decisions about [Fishing or fishpond culture] in the past 12 months (that is during the last cropping season)? Question relevant when: true () 1 No input 2 Input into very few decisions 3 Input into some decisions 4 Input into most decisions 5 Input into all decisions - 66 N/A: Activity not applicable or No decision made l14b (required) l14b: How much input did [main woman] have in decisions on the use of income generated from [Fishing or fishpond culture]? Question relevant when: true () 1 No input 2 Input into very few decisions 3 Input into some decisions 4 Input into most decisions 5 Input into all decisions - 66 N/A: Activity not applicable or No decision made mnote M. Participation and Governance m00 (required) m00: ENUMERATOR: Please indicate who is responsing to this section Question relevant when: true () 1 Same respondent listed at beginning of survey 2 Different respondent: main male in household 3 Different respondent: other male 4 Different respondent: other female USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 144 Field Question Answer 5 Different respondent: main female in household m02 (required) m02: Do you participate in any groups, organizations, or associations? Question relevant when: true () 1 Yes 0 No m04 (required) m04: Which types of groups, organizations, or associations do you participate in? Question relevant when: true () 1 Farmers/Fishermen's group 2 Village development committee (VDC) or ADC 3 Village Savings and Loan; credit/finance group 4 Traders' Assocation/business group 5 Care group 6 School/education related 7 Health/nutrition related 8 Environment related 9 Community works related (water, waste, roads, etc.) 10 Religious group 11 Professional Association 12 Neighborhood/village association -88 Refused other Other m04_other Specify other. Question relevant when: selected(${m04}, 'other') m22 (required) m22: Have you volunteered your time for an activity in your community in the past 6 months, such as serving on committees, labor for public works, reading, education, health activities, or any other thing? − such as for health, hiv/aids, education help, construction of public works, serving on committees Question relevant when: true () 1 Yes 0 No m34 (required) m34: Are you aware of whether there is a Village Development 1 Yes 0 No USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 145 Field Question Answer Committee (VDC) for this community? Question relevant when: true () m36 (required) m36: Do you know what a Village Development Committee does? Question relevant when: true () 1 Yes 0 No m38 m38: What is their role? Question relevant when: ${m36} =1 1 Consult communityabout development projects to do in the local area 2 Represent local interests at district planning meetings 3 Identify beneficiaires for PWPs (works participation) 4 Identification of beneficiaires for agriculture input subsidy coupons -99 Don't really know other Other m38_other Specify other. Question relevant when: selected(${m38}, 'other') m40 (required) m40: Have you ever participated in the activities or attended meetings of a VDC or Area development Committee (ADC)? Question relevant when: true () 1 Yes 0 No m42 (required) m42: What about local government? I do not mean the national government. I mean your local or district government or district councillor. Do you know what roles your local government plays? Question relevant when: true () 1 Yes 0 No m44 (required) m44: What types of things do you think your local government is responsible for? Question relevant when: true () 1 Consult communityabout development projects to do in the local area 2 Represent local interests at district planning meetings 3 Identify beneficiaires for PWPs (works participation) USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 146 Field Question Answer 4 Identification of beneficiaires for agriculture input subsidy coupons -99 Don't really know other Other m44_other Specify other. Question relevant when: selected(${m44}, 'other') - > m46_begin generated_table_list_label_374 Satisfaction with government − Read Responses m46_note How satisfied are you with how the District Government is: reserved_name_for_field_list_la bels_376 1 Very satisfied 2 Somewhat satisfied 3 Neutral 4 Somewhat dissatisfied 5 Very dissatisfied - 99 Don't know - 88 Refused to answer - 66 N/A m46 (required) m46: MAINTAINING LOCAL ROADS Question relevant when: true () 1 Very satisfied 2 Somewhat satisfied 3 Neutral 4 Somewhat dissatisfied 5 Very dissatisfied - 99 Don't know - 88 Refused to answer - 66 N/A m48 (required) m48: PROVIDING LOCAL POLICING Question relevant when: true () 1 Very satisfied 2 Somewhat satisfied 3 Neutral 4 Somewhat dissatisfied USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 147 Field Question Answer 5 Very dissatisfied - 99 Don't know - 88 Refused to answer - 66 N/A m50 (required) m50: PROVIDING WATER AND SANITATION SERVICES Question relevant when: true () 1 Very satisfied 2 Somewhat satisfied 3 Neutral 4 Somewhat dissatisfied 5 Very dissatisfied - 99 Don't know - 88 Refused to answer - 66 N/A m52 (required) m52: MAINTAINING of LOCAL MARKET PLACES Question relevant when: true () 1 Very satisfied 2 Somewhat satisfied 3 Neutral 4 Somewhat dissatisfied 5 Very dissatisfied - 99 Don't know - 88 Refused to answer - 66 N/A m54_note How satisfied are you with how the District Government is: m54 (required) m54: CONSULTING CITIZENS LIKE YOURSELF BEFORE MAKING DECISIONS Question relevant when: true () 1 Very satisfied 2 Somewhat satisfied 3 Neutral 4 Somewhat dissatisfied 5 Very dissatisfied - 99 Don't know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 148 Field Question Answer - 88 Refused to answer - 66 N/A m56 (required) m56: KEEPING CORRUPTION IN CHECK Question relevant when: true () 1 Very satisfied 2 Somewhat satisfied 3 Neutral 4 Somewhat dissatisfied 5 Very dissatisfied - 99 Don't know - 88 Refused to answer - 66 N/A m58 (required) m58: MANAGING THE USE OF LAND Question relevant when: true () 1 Very satisfied 2 Somewhat satisfied 3 Neutral 4 Somewhat dissatisfied 5 Very dissatisfied - 99 Don't know - 88 Refused to answer - 66 N/A m64 (required) m64: Within the past 12 months, did anyone in your household utilize nutrition assistance from the government? Question relevant when: true () 1 Yes 0 No m64d (required) m64d: How satisfied were you with the quality of service provided? Question relevant when: true () 1 Very satisfied 2 Satisfied 3 Not satisfied 4 Very dissatisfied - 88 Refused USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 149 Field Question Answer m66 (required) m66: Within the past 12 months, did anyone in your household utilize training related to agriculture from the government? Question relevant when: true () 1 Yes 0 No m66d (required) m66d: How satisfied were you with the quality of service provided? Question relevant when: true () 1 Very satisfied 2 Satisfied 3 Not satisfied 4 Very dissatisfied - 88 Refused m62 (required) m62: Within the past 12 months, did anyone in your household utilize a public school? Question relevant when: true () 1 Yes 0 No m62d (required) m62d: How satisfied were you with the quality of service provided? Question relevant when: true () 1 Very satisfied 2 Satisfied 3 Not satisfied 4 Very dissatisfied - 88 Refused - > m68a_begin Group relevant when: ${m62} =1 notedkljs How often have you encountered any of these problems with your local public schools during the past 12 months? reserved_name_for_field_list_la bels_395 0 Never 1 Once or twice 2 A few times 3 Often - 88 Refused m68a (required) m68a: Services are too expensive/unable to pay Question relevant when: true () 0 Never 1 Once or twice 2 A few times 3 Often USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 150 Field Question Answer - 88 Refused m68b (required) m68b: Lack of textbooks or other supplies Question relevant when: true () 0 Never 1 Once or twice 2 A few times 3 Often - 88 Refused m68c (required) m86c: Poor teaching Question relevant when: true () 0 Never 1 Once or twice 2 A few times 3 Often - 88 Refused - > m68d_begin Group relevant when: ${m62} =1 note_mb68d How often have you encountered any of these problems with your local public schools during the past 12 months? reserved_name_for_field_list_la bels_401 0 Never 1 Once or twice 2 A few times 3 Often - 88 Refused m68d (required) m68d: Absent teachers Question relevant when: true () 0 Never 1 Once or twice 2 A few times 3 Often - 88 Refused m68e (required) m68e: Overcrowded classrooms Question relevant when: true () 0 Never 1 Once or twice 2 A few times 3 Often USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 151 Field Question Answer - 88 Refused m68f (required) m68f: Poor conditions of facilities Question relevant when: true () 0 Never 1 Once or twice 2 A few times 3 Often - 88 Refused m60 (required) m60: Within the past 12 months, did anyone in your household utilize a public clinic or hospital? Question relevant when: true () 1 Yes 0 No m60d (required) m60d: How satisfied were you with the quality of service provided? Question relevant when: true () 1 Very satisfied 2 Satisfied 3 Not satisfied 4 Very dissatisfied - 88 Refused - > m70a_begin Group relevant when: ${m60} =1 noted705 noted705: How often have you encountered any of these problems with your local public clinic or hospital during the past 12 months? m70a (required) m70a: Services are too expensive/unable to pay Question relevant when: true () 0 Never 1 Once or twice 2 A few times 3 Often - 88 Refused m70b (required) m70b: Lack of medicine or other supplies Question relevant when: true () 0 Never 1 Once or twice 2 A few times 3 Often - 88 Refused USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 152 Field Question Answer m70c (required) m70c: Lack of attention or respect from staff Question relevant when: true () 0 Never 1 Once or twice 2 A few times 3 Often - 88 Refused - > m70d_begin Group relevant when: ${m60} =1 m70d_note noted705: How often have you encountered any of these problems with your local public clinic or hospital during the past 12 months? reserved_name_for_field_list_la bels_415 0 Never 1 Once or twice 2 A few times 3 Often - 88 Refused m70d (required) m70d: Absent doctors Question relevant when: true () 0 Never 1 Once or twice 2 A few times 3 Often - 88 Refused m70e (required) m70e: Long waiting time Question relevant when: true () 0 Never 1 Once or twice 2 A few times 3 Often - 88 Refused m70f (required) m70f: Dirty facilities Question relevant when: true () 0 Never 1 Once or twice 2 A few times 3 Often USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 153 Field Question Answer - 88 Refused - > m72_begin Group relevant when: ${m60} =1 m72 (required) m72: How long did you wait at your last visit to the health center or hospital? Question relevant when: true () m72_units Units 1 minutes 2 Hours - 99 Don't know m76 (required) m76: Do you or anyone in this household receive food for children from a government-run school feeding program? Question relevant when: true () 1 Yes 0 No m78 (required) m78: Have you ever registered to vote? Question relevant when: true () 1 Yes 0 No m80_2 (required) m80_2: Before the election of 2014, were you aware of any things that your local Councilor promised this community he/she would do if elected? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer m80_3 (required) n80_3: Do you believe the Councilor is working to honor those promises? Question relevant when: true () 1 Yes 0 No - 99 Don’t Know - 88 Refused to Answer m88 (required) m88: Does your district government, VDC, or town council ever hold public meetings to establish development priorities? Question relevant when: true () 1 Yes 0 No - 99 Don’t know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 154 Field Question Answer m90 (required) m90: Have you ever attended such a meeting? Question relevant when: true () 1 Yes 0 No - > Community involvement m92 (required) m92: How much influence do you think the people in this village/town community/neighborhood can have over decisions the local government makes about development projects, such as school buildings, health clinics, irrigation ditches, or roads? Question relevant when: true () 1 A lot 2 Some 3 A little 4 None - 99 Don't Know - 88 Refused to answer m94 (required) m94: When you think of development projects in your district (such as schools, health clinics, electrification, and markets), how much do you think the needs of the community influence where those development projects are located? Question relevant when: true () 1 A lot 2 Some 3 A little 4 None - 99 Don't Know - 88 Refused to answer m110 (required) m110: Do you have confidence in your local government's ability to manage finances? Question relevant when: true () 1 Yes 0 No - 99 Don’t know m118 (required) m118: Have you ever used a mobile phone to access information about public services provided by the government? − PROBE: Such as commodity prices, health statistics, school information Question relevant when: true () 1 Yes 0 No m120 (required) m120: Have you ever used a mobile phone to report information about public services provided by the government? 1 Yes 0 No USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 155 Field Question Answer − PROBE: Such as corruption, teacher absence, drug shortage Question relevant when: true () h H. Farming − Now I would like to ask you about farming last growing season h1_filter Does this household practice farming of any kind either to sell or for personal consumption? 1 Yes 0 No - > h1_filterx Group relevant when: ${h1_filter} =1 h1 (required) h1: During the 2017-2018 growing season, did anyone in your household grow any soybeans, groundnuts, orange fresh sweet potatoes, or tree crops either to sell or for personal consumption? Question relevant when: true () 1 Yes 0 No - > h1_filterx > farming Group relevant when: ${h1} =1 - > h1_filterx > farming > h4_starting Group relevant when: ${h1} =1 reserved_name_for_field_list_la bels_443 1 Yes 0 No - 99 Don’t know h4_soya Did you grow Soyabeans last season? 1 Yes 0 No - 99 Don’t know h4_grcham Groundnuts? 1 Yes 0 No - 99 Don’t know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 156 Field Question Answer h4_sweetpot Orange fresh sweet potatoes? 1 Yes 0 No - 99 Don’t know - > h1_filterx > farming > Production of Soyabeans Group relevant when: ${h4_soya} =1 h3a (required) h3: On how much land did you grow Soyabeans last season? − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h3_unhta (required) h3b: Units − RECORD IN UNIT Question relevant when: true () HECTARES HECTARES ACRES ACRES SQMETERS SQMETERS FOOTBALL PITCHES FOOTBALL PITCHES - > h1_filterx > farming > h4_begina1 Group relevant when: ${h4_soya} =1 and ${h3a} >200000.0 h3a_prompt (required) h3_prompt: Are you sure you grew Soyabeans on [h3a][h3_unhta] of land? − INTERVIWER: If the Answer is NO, Please go back to H3a and Reconcile with respondent the correct amount of land Question relevant when: true () 1 Yes 0 No - > h1_filterx > farming > h4_begina2 Group relevant when: ${h4_soya} =1 h3a_ver (required) h3_ver: Is [h3a][h3_unhta], more, less or about the same land than you used for this crop last time we were here Question relevant when: true () 1 More 2 Less 3 About the same 99 Don’t know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 157 Field Question Answer - 66 N/A. not applicable - > h1_filterx > farming > h4_begina3 Group relevant when: ${h4_soya} =1 h_soytype (required) h_soytype: What type of soyabean seeds did you use? Question relevant when: true () 1 Serenade 2 Tikolore 3 Makwacha -99 Don't know other Other h_soytype_other Specify other. Question relevant when: selected(${h_soytype}, 'other') - > h1_filterx > farming > Production of Soyabeans Group relevant when: ${h4_soya} =1 h4aa (required) h4aa: How many kilograms of this Soyabeans did your household produce (yield) last season? Please include the total amount all harvests of this crop. − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h6aa (required) h6a: How many kilograms of Soyabeans did you keep for your household's own consumption? − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h4ba (required) h4ba: How many kilograms of thisSoyabeans did your household sell last season? Please include the total amount all harvests of this crop. − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 158 Field Question Answer - > h1_filterx > farming > Sales of Soyabeans Group relevant when: ${h4ba} >0 h5a (required) h5: How much did you receive in total for all of Soyabeans sold last season? − RECORD IN MK . Write "- 77" if Don't Know or Refused to answer. Question relevant when: true () h6ba (required) h6b: How long did it take you to sell the Soyabeans harvest? (Days) Question relevant when: true () - > h1_filterx > farming > Crop Methods for Soyabeans Group relevant when: ${h4_soya} =1 h7aa (required) 7a: Did you use Fertilizer on this Soyabeans crop? Question relevant when: true () 1 Yes 0 No - 99 Don’t know h7a8a (required) 7a8c: How much did you spend on [Fertilizer] for this Soyabeans? Please do not include the cost of fertilizer still in stock or paid for but not yet used. − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7a9a (required) 7a9c: How much [Fertilizer] did you "use" on this Soyabeans (record in KG) − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7ba (required) 7ba: Did you use Manure on this Soyabeans? Question relevant when: true () 1 Yes 0 No USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 159 Field Question Answer - 99 Don’t know h7b8a (required) 7b8a: How much did you spend on [Manure] for this Soyabeans? Please do not include the cost of manure still in stock or paid for but not yet used. − RECORD IN MK .Write "- 77" if Don't Know or Refused to answer. Question relevant when: true () h7b9a (required) 7b9a: How much [Manure] did you "use" on this Soyabeans (record in KG) − RECORD IN KGs Question relevant when: true () h7ca (required) h7cc: Did you use Seeds/seedings on this Soyabeans? Question relevant when: true () 1 Yes 0 No - 99 Don’t know h7c8a (required) h7c8c: How much did you spend on [Seeds/seedings] for this Soyabeans? Please do not include the cost of seeds still in stock or paid for but not yet used. (record in MK) − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () - > h1_filterx > farming > Crop Methods for Soyabeans > beginh7a h7c9a (required) h7c9c: How much [Seeds/seedings] did you "use" on thisSoyabeans − RECORD IN KGs or SEEDINGS Question relevant when: true () h7c9a2 (required) Record unit of seedlings Question relevant when: true () 1 KG 2 Seedlings USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 160 Field Question Answer - 99 don't know h7da (required) h7da: Did you use Non-household paid labor for planting, weeding, harvesting, cleaning on this Soyabeans Question relevant when: true () 1 Yes 0 No - 99 Don’t know h7d8a (required) h7d8a: How much did you spend on [Non-household paid labor for planting, weeding, harvesting, cleaning] for this Soyabeans? − RECORD IN MK.Write "- 77" if Don't Know or Refused to answer. Question relevant when: true () h7d9a (required) h7d9a: How much [Non￾household paid labor for planting, weeding, harvesting, cleaning] did you "use" on this Soyabeans? − RECORD IN HOURS..Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7ea (required) h7ea: Did you use Equipment rentals on thisSoyabeans? Question relevant when: true () 1 Yes 0 No - 99 Don’t know h7e8a (required) h7e8a: How much did you spend on [Equipment rentals] for this Soyabeans? − RECORD IN MK Question relevant when: true () - > h1_filterx > farming > Production of GROUNDNUTS Group relevant when: ${h4_grcham} =1 h3b (required) h3b: On how much land did you grow this last season? − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 161 Field Question Answer h3_unhtb (required) h3_unhtb: Units − RECORD IN UNIT Question relevant when: true () HECTARES HECTARES ACRES ACRES SQMETERS SQMETERS FOOTBALL PITCHES FOOTBALL PITCHES - > h1_filterx > farming > h4_beginb1 Group relevant when: ${h4_grcham} =1 and ${h3b} >200000.0 h3b_prompt (required) h3b_prompt: Are you sure you grew groundnuts on [h3b][h3_unhtb] of land? − INTERVIWER: If the Answer is NO, Please go back to H3b and Reconcile with respondent the correct amount of land Question relevant when: true () 1 Yes 0 No - > h1_filterx > farming > h4_beginb2 Group relevant when: ${h4_grcham} =1 h3b_ver (required) h3b_ver: Is [h3b][h3_unhtb], more, less or about the same land than you used for this crop last time we were here Question relevant when: true () 1 More 2 Less 3 About the same 99 Don’t know - 66 N/A. not applicable - > h1_filterx > farming > h4_beginb3 Group relevant when: ${h4_grcham} =1 h_gntype (required) h_gntype: What type of groundnut seeds did you use? Question relevant when: true () 1 CG7 2 Chalimbana 3 Chalimbana 2000 -99 Don't know other Other h_gntype_other Specify other. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 162 Field Question Answer Question relevant when: selected(${h_gntype}, 'other') h4ab (required) h4ab: How many kilograms of this crop did your household produce (yield) last season? Please include the total amount all harvests of this crop. − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h6ab (required) h6ab: How many kilograms of this did you keep for your household's own consumption? − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h4bb (required) h4bb: How many kilograms of this crop did your household sell last season? Please include the total amount all harvests of this crop. − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h5b (required) h5b: How much did you receive in total for all of this sold last season? − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h6bb (required) h6bb: How long did it take you to sell the harvest? (Days) Question relevant when: true () - > h1_filterx > farming > Crop Methods for GROUNDNUTS Group relevant when: ${h4_grcham} =1 h7ab (required) h7ab: Did you use Fertilizer on this crop? Question relevant when: true () 1 Yes 0 No - 99 Don’t know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 163 Field Question Answer h7a8b (required) h7a8c: How much did you spend on [Fertilizer] for this crop? Please do not include the cost of fertilizer still in stock or paid for but not yet used. − RECORD IN MK Question relevant when: true () h7a9b (required) h7a9c: How much [Fertilizer] did you "use" on this crop − RECORD IN KGs Question relevant when: true () h7bb (required) h7bb: Did you use Manure on this crop? Question relevant when: true () 1 Yes 0 No - 99 Don’t know h7b8b (required) h7b8b: How much did you spend on [Manure] for this crop? Please do not include the cost of manure still in stock or paid for but not yet used. − RECORD IN MK .Write "- 77" if Don't Know or Refused to answer. Question relevant when: true () h7b9b (required) h7b9b: How much [Manure] did you "use" on this crop − RECORD IN KGs Question relevant when: true () h7cb (required) h7cb: Did you use Seeds/seedings on this crop? Question relevant when: true () 1 Yes 0 No - 99 Don’t know h7c8b (required) h7c8c: How much did you spend on [Seeds/seedings] for this crop? Please do not include the cost of seeds still in stock or paid for but not yet used. − RECORD IN MK.Write "- 77" if Don't Know or Refused to answer. Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 164 Field Question Answer - > h1_filterx > farming > Crop Methods for GROUNDNUTS > begin_h7b h7c9b (required) h7c9b: How much [Seeds/seedings] did you "use" on this crop − RECORD IN KGs or SEEDINGS.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7c9b2 (required) Record unit of seedlings Question relevant when: true () 1 KG 2 Seedlings - 99 don't know h7db (required) h7da: Did you use Non-household paid labor for planting, weeding, harvesting, cleaning on this crop Question relevant when: true () 1 Yes 0 No - 99 Don’t know h7d8b (required) h7d8a: How much did you spend on [Non-household paid labor for planting, weeding, harvesting, cleaning] for this crop? − RECORD IN MK.Write "- 77" if Don't Know or Refused to answer. Question relevant when: true () h7d9b (required) h7d9a: How much [Non￾household paid labor for planting, weeding, harvesting, cleaning] did you "use" on this crop? − RECORD IN HOURS.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7eb (required) h7ea: Did you use Equipment rentals on this crop? Question relevant when: true () 1 Yes 0 No - 99 Don’t know h7e8b (required) h7e8a: How much did you spend on [Equipment rentals] for this crop? USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 165 Field Question Answer − RECORD IN MK.Write "- 77" if Don't Know or Refused to answer. Question relevant when: true () - > h1_filterx > farming > Production of ORANGE FLESHED SWEET POTATOES Group relevant when: ${h4_sweetpot} =1 h3f (required) h3: On how much land did you grow this last season? − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h3_unhtf (required) h3b: Units − RECORD IN UNIT Question relevant when: true () HECTARES HECTARES ACRES ACRES SQMETERS SQMETERS FOOTBALL PITCHES FOOTBALL PITCHES - > h1_filterx > farming > h4_beginf1 Group relevant when: ${h4_sweetpot} =1 and ${h3b} >200000.0 h3f_prompt (required) h3f_prompt: Are you sure you grew groundnuts on [h3f][h3_unhtf] of land? − INTERVIWER: If the Answer is NO, Please go back to H3f and Reconcile with respondent the correct amount of land Question relevant when: true () 1 Yes 0 No - > h1_filterx > farming > h4_beginf2 Group relevant when: ${h4_sweetpot} =1 h3f_ver (required) h3f_ver: Is [h3f][h3_unhtf], more, less or about the same land than you used for this crop last time we were here Question relevant when: true () 1 More 2 Less 3 About the same 99 Don’t know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 166 Field Question Answer - 66 N/A. not applicable - > h1_filterx > farming > h4_beginf3 Group relevant when: ${h4_sweetpot} =1 h4af (required) h4a: How many kilograms of this crop did your household produce (yield) last season? Please include the total amount all harvests of this crop. − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h4bf (required) h4b: How many kilograms of this crop did your household sell last season? Please include the total amount all harvests of this crop. − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h5f (required) h5: How much did you receive in total for all of this sold last season? − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h6af (required) h6a: How many kilograms of this did you keep for your household's own consumption? − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h6bf (required) h6b: How long did it take you to sell the harvest? (Days) Question relevant when: true () - > h1_filterx > farming > Crop Methods for ORANGE FRESH SWEET POTATOES Group relevant when: ${h4_sweetpot} =1 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 167 Field Question Answer h7af (required) 7a: Did you use Fertilizer on this crop? Question relevant when: true () 1 Yes 0 No - 99 Don’t know h7a8f (required) 7a8c: How much did you spend on [Fertilizer] for this crop? Please do not include the cost of fertilizer still in stock or paid for but not yet used. − RECORD IN MK.Write "- 77" if Don't Know or Refused to answer. Question relevant when: true () h7a9f (required) 7a9c: How much [Fertilizer] did you "use" on this crop − RECORD IN KGs.Write "- 77" if Don't Know or Refused to answer. Question relevant when: true () h7bf (required) 7ba: Did you use Manure on this crop? Question relevant when: true () 1 Yes 0 No - 99 Don’t know h7b8f (required) 7b8a: How much did you spend on [Manure] for this crop? Please do not include the cost of manure still in stock or paid for but not yet used. − RECORD IN MK.Write "- 77" if Don't Know or Refused to answer. Question relevant when: true () h7b9f (required) 7b9a: How much [Manure] did you "use" on this crop − RECORD IN KGs.Write "- 77" if Don't Know or Refused to answer. Question relevant when: true () h7cf (required) h7cc: Did you use Seeds/seedings on this crop? Question relevant when: true () 1 Yes 0 No - 99 Don’t know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 168 Field Question Answer h7c8f (required) h7c8c: How much did you spend on [Seeds/seedings] for this crop? Please do not include the cost of seeds still in stock or paid for but not yet used. − RECORD IN MK.Write "- 77" if Don't Know or Refused to answer. Question relevant when: true () - > h1_filterx > farming > Crop Methods for ORANGE FRESH SWEET POTATOES > h7f h7c9f (required) h7c9c: How much [Seeds/seedings] did you "use" on this crop − RECORD IN KGs or SEEDINGS.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7c9b2 (required) Record unit of seedlings Question relevant when: true () 1 KG 2 Seedlings - 99 don't know h7df (required) h7da: Did you use Non-household paid labor for planting, weeding, harvesting, cleaning on this crop Question relevant when: true () 1 Yes 0 No - 99 Don’t know h7d8f (required) h7d8a: How much did you spend on [Non-household paid labor for planting, weeding, harvesting, cleaning] for this crop? − RECORD IN MK Question relevant when: true () h7d9f (required) h7d9a: How much [Non￾household paid labor for planting, weeding, harvesting, cleaning] did you "use" on this crop? − RECORD IN HOURS Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 169 Field Question Answer h7ef (required) h7ea: Did you use Equipment rentals on this crop? Question relevant when: true () 1 Yes 0 No - 99 Don’t know h7e8f (required) h7e8a: How much did you spend on [Equipment rentals] for this crop? − RECORD IN MK Question relevant when: true () h27 (required) h27: Has anyone in your household participated in any Farmers' Clubs/Groups? Question relevant when: true () 1 Yes 0 No h28 (required) h28: Which household members have participated? Question relevant when: true () 1 Main female or wife 2 Main male or husband 3 Other male in household 4 Other female in household n04 (required) n04: Have you used a mobile phone in the past 12 months for business (such as to check crop prices)? Question relevant when: true () 1 Yes 0 No h30 (required) h30: Where/how did you sell your last harvest? Question relevant when: true () 1 trader comes to door 2 go to market 3 through warehouse 4 through ACE (internet-based commodity exchange) - 77 other - 99 don't know - 66 N/A h30a (required) H30a: In the past 12 months, did you discard any part of your crop during the grading process due to contamination (such as aflatoxin, mold, other)? Question relevant when: true () 1 Yes 0 No - 99 Don’t know USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 170 Field Question Answer h30b (required) H30b: Which crop was this? Question relevant when: true () 1 Groundnuts 2 Soya 3 Sweet potatoes 4 Maize other Other h30b_other Specify other. Question relevant when: selected(${h30b}, 'other') h30c (required) H30c: In the past 12 months, have you experienced rejection at market or warehouse for any part of a crop due to known or suspected contamination (such as aflatoxin, mold, other)? Question relevant when: true () 1 Yes 0 No - 99 Don’t know h30d (required) H30d. Which crop was this? Question relevant when: true () 1 Groundnuts 2 Soya 3 Sweet potatoes 4 Maize other Other h30d_other Specify other. Question relevant when: selected(${h30d}, 'other') h31 (required) h31: Have you changed your agricultural practices in the past 12 months? Question relevant when: true () 1 Yes 0 No h32 (required) h32: Which agricultural practices have you adopted? Question relevant when: true () 1 Using less water 2 Using organic fertilizer 3 Earthworm 4 Different type of crop 5 Mixed Cropping 6 Crop Rotation 7 Using Irrigation 8 Improved Seeds 9 Using non-organic fertilizer 10 Better Storage -77 other USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 171 Field Question Answer -99 Don't know other Other h32_other Specify other. Question relevant when: selected(${h32}, 'other') h33 (required) h33: Has the type of crops that your family grows changed in recent years? Question relevant when: true () 1 Yes 0 No h34 (required) h34: Why has the type of crops your family grows changed? Question relevant when: true () 1 A NGO offered us new seed/told us to change crops 2 A certain type of seed became easier/cheaper to access 3 We gained access to additional land 4 We gained access to a loan and were able to buy new seeds 5 We were trained on the importance of crop diversification 6 We wanted to add a new crop to the farm 7 Rainfall patterns changed - 99 Don’t know o25 o25: INTERVIEWER: Was there a secondary respondent who assisted with any parts of the survey? − Do not ask 1 Yes 0 No - > a21_begin Group relevant when: ${o25} =1 a20 (required) a20 What is secondary respondent's family name? Question relevant when: true () a21_begin (required) a21 What is secondary respondent's given name? Question relevant when: true () a22 (required) a22 INTERVIEWER: What is the secondary respondent's sex? − (observe) 0 Male 1 Female USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 172 Field Question Answer Question relevant when: true () a23 (required) a23 What is the secondary respondent's role in this household? Question relevant when: true () 1 Head of household 2 Spouse of head of household 3 Other adult in household a24 a24 which sections did the person help with? A Background B Household members roster C Education D Well-being E Household features F Assets G Credit/loans H Farming I Food security J Environment K Health L Household decision-making M Participation and governance O GPS, Recontact, Notes o O. Recontact Information o02 (required) o02: Thank you for your time helping this research. For quality assurance, may I please know a phone number that could be used to contact you in case there is need for clarification in the next few weeks? Question relevant when: true () 1 Yes 0 No o04 o04: Best mobile number to use to reach household − Include 9 digits only with no zero in front and no dashes or spaces Question relevant when: ${o02} =1 o06 o06: Alternative mobile number in household − Include 9 digits only with no zero in front and no dashes or spaces USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 173 Field Question Answer Question relevant when: ${o02} =1 o22 o22: Location Notes: Please provide descriptive location of household. (example: near primary school, off main road, etc) Question relevant when: ${o02} =1 o24 o24: Interview Notes: (Please make notes on responsiveness of household, difficulties encountered during interview, etc.) Question relevant when: ${o02} =1 comments GENERAL COMMENTS ON THE INTERVIEW − Only Relevant comments are encouraged o18 o18: GPS Reading. − Wait until accuract is less than 10 meters if possible USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 174 CDCS-2018-Replacement Module - Chichewa Field Question Answer title USAID/MALAWI - COUNTRY DEVELOPMENT COOPERATION STRATEGY - HOUSEHOLD SURVEY 2018 intro1 Prior to arriving at the house, fill the following: a1 (required) a1: DISTRICT NAME Question relevant when: true () Lilongwe Rural Lilongwe Rural Balaka Balaka Machinga Machinga Mangochi Mangochi Mulanje Mulanje Karonga Karonga Nkhotakota Nkhotakota Zomba Zomba Nsanje Nsanje a2 (required) a2: TRADITIONAL AUTHORITY NAME: Question relevant when: true () Amidu Amidu Chamthunya Chamthunya Kachenga Kachenga Kalembo Kalembo Msamala Msamala Nkaya Nkaya Sawali Sawali -77 Other Chadza Chadza Chiseka Chiseka Chitekwele Chitekwele Kabudula Kabudula Kalolo Kalolo Kalumba Kalumba Kalumbu Kalumbu Khongoni Khongoni Malili Malili Masula Masula Masumbankhunda Masumbankhunda Mazengera Mazengera USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 175 Field Question Answer Mtema Mtema Njewa Njewa Tsabango Tsabango Chikweo Chikweo Chinguza Chinguza Chiwalo Chiwalo Kapoloma Kapoloma Kawinga Kawinga Liwonde Liwonde Mlomba Mlomba NKoola NKoola Ngokwe Ngokwe Nkoola Nkoola Nsanama Nsanama Nyambi Nyambi Sitola Sitola Kilupura Kilupura Kyungu Kyungu Mwakaboko Mwakaboko Mwirang'ombe Mwirang'ombe Wasambo Wasambo Kafuzila Kafuzila Kanyenda Kanyenda Malengachanzi Malengachanzi Mphonde Mphonde Mwadzama Mwadzama Mwansambo Mwansambo Bwana Nyambi Bwana Nyambi Chimwala Chimwala Jalasi Jalasi Katuli Katuli Nankumba Nankumba Chimombo Chimombo Makoko Makoko Malemia Malemia Mbenje Mbenje Mlolo Mlolo USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 176 Field Question Answer Ndamera Ndamera Tengani Tengani Chikowi Chikowi Kumtumanji Kumtumanji M'biza M'biza Mlumbe Mlumbe Mwambo Mwambo a2_otherspecify (required) a2: Specify the TA name Question relevant when: true () a3 (required) a3: EA CODE Question relevant when: true () 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24 25 25 26 26 27 27 28 28 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 177 Field Question Answer 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49 50 50 52 52 53 53 54 54 55 55 56 56 57 57 58 58 67 67 70 70 71 71 73 73 74 74 81 81 51 51 a4 (required) a4: VILLAGE NAME Mdzumila Mdzumila USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 178 Field Question Answer Question relevant when: true () Mndole Mndole -77 Other Chimombo Chimombo Chimongo Chimongo Mpakiza Mpakiza Chiziko Chiziko Kanjira Kanjira Kadise Kadise Menya Menya Mikolo Mikolo Mkaka Mkaka Malama Malama Bvumbwe Bvumbwe Mbawala Mbawala Chagona Chagona Chiwale Chiwale Chabwasa Chabwasa Chigombe Chigombe Chinthochi Chinthochi Lembani Lembani Ingilasi Ingilasi Kazenga Kazenga Binda Binda Chionongera Chionongera Mkuwazi Mkuwazi Mwatselele Mwatselele Sambwaila Sambwaila Kumchakama Kumchakama Malunda Malunda Denie Denie Nachikunga Nachikunga Msilo Msilo Mphamba Mphamba Mphanga Mphanga Milimbo Milimbo Mkongolo Mkongolo Chidothi Chidothi USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 179 Field Question Answer Chimenechi Chimenechi Salima Mpasa Salima Mpasa Chana Chana Chingira Chingira Chimutu Chimutu Khundi 1 Khundi 1 Khundi 2 Khundi 2 Chiuzira Chiuzira Ng'omaikalira Ng'omaikalira Mkulekera Mkulekera Mvululo Mvululo Kasiyafumbi Kasiyafumbi Mwadzalamba Mwadzalamba Chikandwe Chikandwe Msonkho Msonkho Kango Kango Chakakala Chakakala Zapita Zapita Chapumuluka Chapumuluka Gomani Gomani Chimtolo Chimtolo Kapimphi Kapimphi Chitedze Chitedze Msokosela/Kanund u Msokosela/Kanund u Kachitamanja Kachitamanja Chimutha Chimutha Kakopa Kakopa Kwenje 2 Kwenje 4 Chamba Chamba Kutsamba Kutsamba Chipwele Chipwele Misomali Misomali Maliwata Maliwata Mgwira Mgwira Chimatiro Chimatiro Manjanja Manjanja USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 180 Field Question Answer Ngwalo 2 Ngwalo 4 Jambawe Jambawe Saka Saka Ntandiwa Ntandiwa Khwalala Khwalala Chimpakati Chimpakati Tsamba Tsamba Chizinga Chizinga Mankhwala Mankhwala Mpambila Mpambila Tiferakaso Tiferakaso Kainga Kainga Kamowatimwa Kamowatimwa Chilimba Chilimba Chisinkha Chisinkha Gambe Gambe Zidyana Zidyana Mgomwa Mgomwa Zalimu 1 Zalimu 3 Mthawitsa Mthawitsa Njirayagoma Njirayagoma Chiundu Chiundu Msaliwa Msaliwa Peter Kasanga Peter Kasanga Nsulu Nsulu Ntonda Ntonda Muotcha Muotcha Mboga Mboga Pongolani Pongolani Idi Idi Masautso Masautso Helbert Helbert Mawecha Mawecha Kabiyo Kabiyo Mmaniwa Mmaniwa Majikuta Majikuta Kalimira Kalimira USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 181 Field Question Answer M'bawa M'bawa Kefa Kefa Mtende Mtende Chipatala Chipatala Nambazo Nambazo Kalembo 1 Kalembo 3 Tsite Tsite Chilembwe Chilembwe Malihaba Malihaba M'dala Lulanga M'dala Lulanga M'gomba M'gomba Liwonde Liwonde Mahele Mahele Ntepo Ntepo Chisuwi Chisuwi Uthiwa Uthiwa Chilanga Chilanga Matumula Matumula Chiganga Chiganga Maganga Maganga Phwiti Phwiti Nyenje Nyenje Njenjema Njenjema Takataka Takataka M'bobo M'bobo M'bwana M'bwana Mtopa Mtopa Ntopa Ntopa Meja Meja Mlosi Mlosi Ling'ole Ling'ole Salijeni Salijeni Kauma Kauma Maganiza Maganiza Chionga Chionga Mbosongwe Mbosongwe Silika Silika USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 182 Field Question Answer Tebulo Tebulo Kalambo Kalambo Namanja Namanja Ndachi Ndachi Mtholowa Mtholowa Ntholowa Ntholowa Mkapaleya Mkapaleya Mtuwa Mtuwa Machika Machika Sinja Sinja Justin Justin Jastini 1 Jastini 3 Justini 2 Justini 4 Mpelula Mpelula Chikuluma Chikuluma Makawa Makawa Limela Limela Mikundi Mikundi Khungwa Khungwa Nguyeje Nguyeje Chinji Chinji Ntapasyale Ntapasyale Nchou Nchou Samuti Samuti Mkweya Mkweya Wadi Wadi Magombo Magombo Mchaula Mchaula Nsosomela Nsosomela Ntupa Ntupa Kauzu Kauzu Chindunguli Chindunguli Dzoole Dzoole Mtendere Mtendere Songa 2 Songa 4 Kambalame Kambalame Nkaweya Nkaweya USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 183 Field Question Answer Mlongoti Mlongoti Chiutira Chiutira Kwekwere Kwekwere Chisiyana Chisiyana Chitonde Chitonde Leven Leven Mbungo Mbungo Bamusi Bamusi Chingwalu Chingwalu Nasuluma Nasuluma Sinoya Sinoya Nikisi Nikisi Waiti Matenje Waiti Matenje Mvumba Mvumba Mbota Kamsiya Mbota Kamsiya Juma Mbanga Juma Mbanga Sokole A Sokole A Mbusi Makande Mbusi Makande Nkota Nkota Kuchetela Kuchetela Mmatila Mmatila Mkwela kalunga Mkwela kalunga Nakonya Nakonya Namatumbo Namatumbo Mayele Mayele Issa Issa Manduta Manduta Chikauka Chikauka Namaninga Namaninga Mchokola Mchokola Mgawo Mkwepu Mgawo Mkwepu Mlembe Mlembe Ngolojele Ngolojele Mpwakata Mpwakata Msalule Msalule Bakali Bakali Kwitunji Kwitunji USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 184 Field Question Answer Mdala Chilawi Mdala Chilawi Chiwaula Chiwaula Kwilapo Kwilapo Ngalipa Ngalipa Mponda Mponda Gideon Gideon Andrew Andrew Mwenewisi Mwenewisi Yalero Yalero James James Mwakibonja Mwakibonja Mwangalaba Mwangalaba Mwabungulu Mwabungulu Syalisoni Syalisoni Chimalabanthu Chimalabanthu Mwasalano Mwasalano Mwasalano 1 Mwasalano 3 Timothy Timothy Mwakamogho Mwakamogho Mwenengolongo Mwenengolongo Kasebwe Kasebwe Fundi Fundi Peter Mwangalawa Peter Mwangalawa Zindi Gondwe Zindi Gondwe Kayunga Kayunga Mwamatope Mwamatope Mulwa Mulwa Fughala Fughala Kaluwa Kaluwa Mwamasapa Mwamasapa Marko Mwankenja Marko Mwankenja Nayi Nayi Potifala Mwangolera Potifala Mwangolera Yafeti Mwakasungula Yafeti Mwakasungula Mlindaifwa Mlindaifwa USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 185 Field Question Answer Ngosi Ngosi Mwasota Mwasota Kayuni 1 Kayuni 1 Kayuni 2 Kayuni 2 Mwenelupembe 2 Mwenelupembe 4 Mphughu Mphughu Mwambelo Mwambelo Muchenjere Muchenjere Welosi Mwambelo Welosi Mwambelo Mphangweyanjili Mphangweyanjili Wundaninge Wundaninge Zengelanjala Zengelanjala Mwaungulu Mwaungulu Kayelewa Kayelewa Kayerewa Kayerewa Kachaka Kachaka Matambukira Matambukira Mgoyera Mgoyera Mwandovi Mwandovi Mwangamila Mwangamila Chalochamala Chalochamala Charuchamala Charuchamala Maulunge Maulunge Mchekacheka Mchekacheka Galimoto Galimoto Kayaghala Kayaghala Chibwatiko Chibwatiko Mwanyesha Mwanyesha Mwambetania Mwambetania Luhimbo Luhimbo Katesula Katesula Mwangolera Mwangolera Mwaleba Mwaleba Mwambuli Mwambuli Sadala Sadala M'bunthuka M'bunthuka M'buthuka M'buthuka USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 186 Field Question Answer Nkhubasanga Nkhubasanga Mutangala Mutangala Njalayankhunda Njalayankhunda Thangalang'ombe Thangalang'ombe Mwanyongo Mwanyongo Wilson Kalambo Wilson Kalambo Matandala Matandala Mwakhwawa Mwakhwawa Muleleka Muleleka Mwamdimba Mwamdimba Chipembere Chipembere Nowa Nowa Kanthumdende Kanthumdende Sosola Sosola Chindodo Chindodo Mudolo Mudolo Mowe 1 Mowe 3 Chimtumbuka Chimtumbuka Munthanje Munthanje Chiya Chiya Katapila Katapila Chimdima Chimdima Khwayaya Khwayaya Chinkhwangwa Chinkhwangwa Mkwapatira Mkwapatira Nyalubwe Nyalubwe Kambola Kambola Lunda Lunda Mjuwa Mjuwa Malamba Malamba Chimweyo Chimweyo Chigadula Chigadula Kaiwala Kaiwala Mpeta Mpeta Kafuzila Kafuzila Chimbuto Chimbuto Khufi Khufi USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 187 Field Question Answer Chiluma Chiluma Kachuma Kachuma Malengachanzi Malengachanzi Matekenya Matekenya Kawelama Kawelama Shamuti Shamuti Kawelama 2 Kawelama 4 Mchemela Mchemela Chota Chota Mazengera Mazengera Katimba Katimba Sasani 2 Sasani 4 Tandwe 1 Tandwe 3 Chamba 1 Chamba 3 Chanzi Chanzi Kapanga 2 Kapanga 4 Phwetekere Phwetekere Ching'amba Ching'amba Mtanga 2 Mtanga 4 Kawamba Kawamba Funduseni Funduseni Zikomankhani Zikomankhani Chikombe 1 Chikombe 3 Makunganya Makunganya Mphonde Mphonde Kansuli Kansuli Sawawa Sawawa Mng'ongwe Mng'ongwe Mzumara Mzumara Selemani 2 Selemani 4 Chipelela 2 Chipelela 4 Nkhongo 3 Nkhongo 5 Chikwawe 2 Chikwawe 4 Naferanji Naferanji Chalunda Chalunda Chizongwe 1 Chizongwe 3 Ngwata 2 Ngwata 4 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 188 Field Question Answer Chizongwe 2 Chizongwe 4 Chongole 1 Chongole 3 Chizongwe 3 Chizongwe 5 Mzeweza Mzeweza Chikumangala Chikumangala Msamala Msamala Msamala 3 Msamala 5 Nsamala Nsamala Khwapu Khwapu Nambela Nambela Nkhala Nkhala Manondo Manondo Mtachi 2 Mtachi 4 Ndimbwa Ndimbwa Manjawila Manjawila Pembela Pembela Mkukumila Mkukumila Kamongo Kamongo Patsunda 1 Patsunda 3 Chikaluma Chikaluma Mtiku Mtiku Mtutuma Mtutuma Chinangwa Chinangwa Kumchenga Kumchenga Malewa Malewa Chimera/Chidothi Chimera/Chidothi Likapa/Kachulu Likapa/Kachulu Kuntaja Kuntaja Maluwa Maluwa Malajila Malajila Matewere 2 Matewere 4 Magombe Magombe Ganda Ganda Kagaso Kagaso Mteteka Mteteka Chitwanga Chitwanga Chipande 1 Chipande 1 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 189 Field Question Answer Chipande 2 Chipande 2 Bwanausi Bwanausi Mtambo Mtambo Mtambo 2 Mtambo 4 Ndalama 1 Ndalama 3 Namathika Namathika Ehawi Ehawi Mwala 2 Mwala 4 Thumpwa Thumpwa Manyungwa/Mpen da Manyungwa/Mpen da Mulemba Mulemba Machemba Machemba Kadyampakeni Kadyampakeni Steven Steven Steven 2/Kadyampakeni Steven 2/Kadyampakeni Likhomo Muliya Likhomo Muliya Mwala 1 Mwala 3 Ngwelero Ngwelero Chisawa Chisawa Mikundi 2 Mikundi 4 Usumani 2 Usumani 4 Matewe Matewe Matewe 1 Matewe 3 Matewe 1 sinoya Matewe 1 sinoya Petulo Petulo Chimpini Chimpini Mmambo 2 Mmambo 4 Mtiko Mtiko Tung'ande Tung'ande Chisuse Chisuse Ritisan Ritisan Njala Njala Segula Segula Uzingo Uzingo Mwenyali Mwenyali USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 190 Field Question Answer Taibu Taibu Nkupe Nkupe Chombe 1 Chombe 3 Mdalakamuyanja Mdalakamuyanja Fletcher Fletcher Minthanje Minthanje Ntchenyera 2/Farao Ntchenyera 2/Farao Pharaoh/ Ntchenyera Pharaoh/ Ntchenyera Mulira Mulira Nyimbiri Nyimbiri Ng'ambo Ng'ambo Pangeti Pangeti Chinsungwi Chinsungwi Jimu 2 Jimu 4 Makhapha Makhapha Msambokulira Msambokulira Kaitano Kaitano Kasenga Kasenga Mbeta Mbeta Nsikuzakwenda Nsikuzakwenda Nyoza Nyoza Nzondola Nzondola Nyakhavi Nyakhavi Thengothawani Thengothawani Kachaso Kachaso Muyang'anira Muyang'anira Ngala Ngala Makhaza Makhaza Thikiti Thikiti Khambadza Khambadza Sandalamu Sandalamu Chimpilingu Chimpilingu Chilim'madzi Chilim'madzi Mwanavumbe Mwanavumbe Kuyeri Kuyeri USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 191 Field Question Answer Nsangalambe Nsangalambe Melo Melo Guta Guta Madani Madani Chabe Chabe Nsitu Nsitu Nkolimbo Nkolimbo Chinegwe Chinegwe Chipolopolo Chipolopolo Jonikisi Jonikisi Tizola Tizola Tambo 1 Tambo 1 Tambo 3 Tambo 3 Mchacha Mchacha Nthole Nthole Nsabilima Nsabilima Mbang'ombe Mbang'ombe Chambuluka Chambuluka Kadakola Kadakola Chaya Chaya Falamenga Falamenga Leno Leno Dickson Dogo Dickson Dogo Jokonia Jokonia Izeki Izeki Zyuwaki Zyuwaki a4_otherspecify (required) a4: Specify Village Name Question relevant when: true () a6 (required) a6: Enumerator name Question relevant when: true () 1 Annie Manyoni 2 Arthur H. Banda 3 Arthur Banda 4 Aurther Chiumia 5 Aurther Ngwira 6 Babrah Nasala 7 Bertha Loti 8 Billy Kayange 9 Bina Chingwe USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 192 Field Question Answer 10 Blessings Kamoto 11 Blessings Mbwerazino 12 Blessings Nyatepa 13 Caroline Kambalame 14 Charles Sofasi 15 Chifuniro Kalima 16 Chimwemwe Bwanausi 17 Chimwemwe Banda 18 Christina Chabwera 19 Daina Namaluweso 20 Dalitso Mpeketula 21 Daniel Msundwe 22 Darlington kapingasa 23 David Makiyi 24 Davie Scotch 25 Dickson Makwera 26 Dorothy Chirwa 27 Emmanuel .M. kaitano 28 Emmanuel Piseni 29 Esther Chitungu 30 Fanuel Chimbiya 31 Felix Peter 32 Francis Kafa 33 Frank Jumbe 34 Frazer Chafumbwa 35 Fred Bequiet 36 Gabriel Kachigayo 37 Gerald Mhango 38 Gift Kaunga 39 Gift Nsapato 40 Gifton Saizi 41 Gladwell Malunga 42 Gloria Tembo 43 Grace Chiwaya 44 Happy Katuli 45 Happy Nkhoma 46 Hellen Mbutuka USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 193 Field Question Answer 47 Henderson Chagoma 48 Innocent K. Mhango 49 Isaac M. Banda 50 Isaac Masamba 51 Islam Idana 52 Jack Yangairo 53 Jacob Mnkhwamba 54 James Ngwira 55 Jane Banda 56 Jimmy Lungu 57 Jofrey Kamanga 58 John Katete 59 John Munthali 60 Joice Mvula 61 Joseph Juma 62 Joseph Sabola 63 Joshua Bhima 64 Josphat Saidi 65 Kennedy Manda 66 Kenneth Given Limbani 67 Kingsley Manyumba 68 Kondwani Chikondi Munthali 69 Kondwani Wanda 70 Leonard Lakalaka 71 Lonjezo Sekani 72 Lonjezo Jumbe 73 Loveness Chiumula 74 Lusako Mwalwanda 75 Maureen Gwayi 76 Maxwell Phiri 77 Mayamiko Kamwaza 78 Melody Chipoka 79 Memory Kabuya 80 Memory Phiri 81 Mercy Banda 82 Mercy Kalulu 83 Mussa M. Mtayamo USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 194 Field Question Answer 84 Nellie Makanjira 85 Newton Lupoka 86 Noria Mhango 87 Peter Sana Paswell 88 Precious Kadewere 89 Rex Makwinja 90 Sauda Bwanali 91 Shad Banda 92 Shem Yuda 93 Shyma J. Dimu 94 Sikujuwa Nyasulu 95 Siphiwe Kaluwa 96 Steve Gollah 97 Stonald Kumbadzala 98 Stowell Mposa 99 Thamison Mandere 100 Thokozani Makaka 101 Thovise Makamo 102 Timothy Chirwa 103 Tombozgani Mhango 104 Wanangwa Kambondooma 105 Wilfred katunga 106 Wonderful Thindwa 107 Yusuf Ali Ayoub 108 Zynab Njerenga a5 (required) a5: HOUSEHOLD ID − 'INTERVIWER: Your first Replacement interview of the day = 1, Your Second Replacement interview of the day = 2 e.t.c Question relevant when: true () idnote This Questionnaire ID is: -- hhid (required) Replacement HOUSEHOLD ID − INTERVIWER: Please Enter the correct Household ID from Old Sample provided by your Supervisor. Question relevant when: true () confirmentry (required) Please re-enter Replacement HOUSEHOLD ID as confirmation. Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 195 Field Question Answer a20 (required) a20: Why did you have to replace the old household Question relevant when: true () 1 Moved outside of EA 2 Refused to Participate 3 Not Eligible a21 a21: How was this replacement selected 1 Household farms same land 2 Nearest neighbor other Other a21_other Specify other. Question relevant when: selected(${a21}, 'other') a9 (required) a9: Which attempted visit is this? − (verify with log form and record) Question relevant when: true () 1 Kubwera koyamba 2 Kubwera kwachiwiri 3 Kubwera kwachitatu a10 (required) a10: Kodi pali munthu woti mucheze naye pa khomo lomwe lasankhidwalo? Question relevant when: true () 1 Inde 0 Ayi a11 (required) a11: Kodi muntha kulumikizana muchiyankhulo chomwe munthu wapakhomopo amalankhula? Question relevant when: true () 1 Inde 0 Ayi intro2 Mulibwanji. Ndikugwira tchito ndi Invest in Knowledge komanso Social Impact. Tikupanga kafukufuku wofuna kuona momwe ntchito za chitukuko zosiyanasiyana za USAID zikuyendera m’Malawi. A USAID akugwira ntchito zina mu dera lino. Zotsatira za kafukufukuyu zitha kuthandiza kupititsa pa tsogolo ma pologalamu omwe amaperekedwa m’Malawi muno mtsogolomu. Khomo lino lasankhidwa mwa mwayi kuti litenge nawo mbali mu kafukufukuyu ngati mungasankhe kutero. Mukavomera kutenga nawo gawo mu kafukufukuyu, tikufunsani mafunso angapo okhudza kafukufukuyu. Kuchezaku kutenga pafupifupi ola USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 196 Field Question Answer limodzi ndi theka [1 hourand 30 minutes]. Mudzafunsidwa mafunso angapo okhudza inuyo ndi anthu apabanja panu, zokhudza ntchito yomwe imakuthandizani pakhomo pano [yobweretsa ndalama], zokhudza katundu wa pakhomo, kutenga kwanu gawo mu zochitika za m’dera lino, maganizo anu okhudza zithandizo zopezeka m’dera lino. M’mene mumaonera zithu pa moyo, komanso uthenga wokhudza madyedwe akhomo lino ndi chithandizo cha zaumoyo. Tingakonde titacheza ndi wankulu wa khomo lino, koma ngati pali munthu wina woti amadziwa bwino zinthu zapakhomo pano monga zaulimi zokhudza khomo lino, tidzakondwa kuti amenewo ayankhe mafunso a gawo limenelo. Komanso, komanso pofuna kuonetsetsa kuti zomwe tikuchita zili mundondomeko yake, chipangizo ichi chitha kujambula magawo ena a zokambirana zanthu mumagawo a mphindi imodzi imodzi. Pofuna kuona kuti ndikugwira ntchito yoyenera komanso kukhala waulemu monga wofunsa mafunso. Kutenga nawo gawo kwa inu mukafukufukuyu ndi kongodzipereka. Mutha kusankha kusatenga nawo gawo pano, kapena nthawi ili yonse. Zonse zomwe titolere mukafukufukuyu zidzasungidwa mwachinsinsi ndipo tidzaonetsetsa kuti chinsinsi chasungidwa bwino zedi polingana ndi malamulo. Ngakhale kuti zotsatira zopanda mayina zidzapezeka mosavuta, dzina lanu ndi chili chonse chokuzindikiritsani sizidzagwiritsidwa USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 197 Field Question Answer ntchito kapena kuti sizidzaperekedwa kwa anthu ena omwe sakukhudzidwa ndi kafukufukuyu. Palibe chowopsa chili chonse chifukwa chotenga nawo mbali mu kafukufukuyu kupatula kuti mutayi nthawi yokwana ola ndi theka la nthawi yomwe munakachita zinthu zina zopindulitsa komanso kuthekera kochepa kwambiri koti chinsinsi chanu chitha kuululika. Palibenso phindu loonekeratu kwa inu ngati mutenge nawo gawo kupatula kuti zomwe mutiuze zidzathandiza a USAID kumvetsa ngati ma pologamu awo akugwira ntchito kapena ngati akuyenera kuwakonza. Ngati simuli omasuka ndi funso lina liri lonse, mutha kukana kuyankha funsolo ndipo ndipita pa funso lotsatira. Ngati muli ndi funso liri lonse kapena dandaulo panopa kapena mtsogolo, mutha kulankhula ndi James Mkandawire at 0999-412-756 james.mkandawire@investinknowledge. org. kapena mutha kulankhulana ndi a Social Impact Institutional Review Board: +1-703-465-1884 irb@socialimpact.com. Question relevant when: ${a11} =1 a12 (required) a12: Kodi mwabvomera kutenga nawo mbali? Question relevant when: true () 1 Inde 0 Ayi a30 a30: INTERVIEWER: Kodi mugwiritsa ntchito fomu ya chilankhulo chanji mu tabuletiyi. Question relevant when: ${a12} =1 1 Chichewa 2 Chitumbuka 3 Chiyawo 4 Chisena 5 Chizungu USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 198 Field Question Answer a13 (required) a13: Ndi kwathawi yayitali bwanji yomwe mwakha mudzi muno ? − mark no interview due to not living here 2 years and end survey Question relevant when: true () 1 Kuchepera zaka ziwiri 2 zaka ziwiri kapena kuposera a14 (required) a14: Record reason for no interview Question relevant when: true () 1 Kucheza kwalepheleka akhala kuno kochepera zaka ziwiri 2 Kucheza kwalepheleka abambo kapena amayi akhomolo palibe 3 kucheza kwalepheleka- apempha kuti tidzabwerenso 4 kucheza kwalephereka- chifukwa china 5 kucheza kwalephereka- anthu akullu akanika kuyankha mafunso[matenda/kulumala/misala] 6 Akana- akuti kubweranso tsiku lina sikutheka 7 Akana-mwachindunji 8 Akana- zifukwa zina 9 Refusal- Recently did long survey with MACRO (DHS or Food For Peace survey) note_end Zikomo chifukwa cha nthawi yanu. Koma pepani kuti sititha kucheza nanu chifukwa chamomwe ndondomeko yakafukufuku yathu ilili. Tikufunirani tsiku labwino. Question relevant when: ${a13} =1 - Group relevant when: ${a12} =1 and ${a13} !=1 a_background Section A: Background − Ensure you are talking to the preferred respondent (The main female in the house is first choice. If not, the head of household. If not, another adult able to speak about the topics.) - > a15_begin1 a15_1 a15_1 Kodi dzina lotsiriza (surname) la mutu wa pabanja pano ndi ndani? USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 199 Field Question Answer a16_1 a16_1 Kodi dzina loyamba la mutu wa pabanja pano ndi ndani? a17_1 a17_1 Kodi mutu wa pa banja pano ndi wamwamuna kapena wa mkazi? 0 wamwamuna 1 Wamkazi a18 (required) a18 Kodi pali ubale wanji pakati pa woyankha mafunsoyo ndi mutu wabanjalo? Question relevant when: true () 1 Mutu wabanja 2 mkazi/mwamuna wa mutu wakhomolo 3 wamkulu wina wakhomopo - > a15_begin1 > a15_begin Group relevant when: ${a18} !=1 a15 (required) a15 Kodi dzina lenileni la banja la oyankha mafunso ndichiyani? Question relevant when: true () a16 (required) a16 Kodi dzina lenileni lomwe oyankha mafunso anapasidwa ndi chiyani? Question relevant when: true () a17 (required) a17 Kodi ndi wamkazi kapena wamamuna? − (Observe) Question relevant when: true () 0 wamwamuna 1 Wamkazi b11 (required) b11: Kodi nthawi zambiri mumayankhula chiyankhulo chanji? Question relevant when: true () 1 Chichewa 2 Chitumbuka 3 Chiyawo 4 Chisena 5 Chizungu other Other b11_other Specify other. Question relevant when: selected(${b11}, 'other') b0_ Section B: Household Members num_people b0: Kodi pakhomo lino mulipo anthu angati? Chonde phatikizani anthu okhawo amene kawirikawiri amakhala ndi kudya pakhomo pano kupatulako alendo. Phatikizaninso mutu wa banja ngakhale sanakhale pakhomopo mumiyezi isanu ndi umodzi yapitayi, koma wakhala akuthandiza banjalo. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 200 Field Question Answer Chonde musaphatikize ana amene anachokapo kapena kukwatiwa. − INTERVIWER: Enter 0 if no additional People in the household note_roster Tsopano ndikufuna kulemba mndandanda wa anthu kuyambira ndi mutu wa banja? Question relevant when: ${num_people} >=1 - > Mndandanda wa anthu (1) Group relevant when: ${num_people} >=1 (Repeated group) mem_nm (required) B01 Dzina loyamba Question relevant when: true () note_member_name_1 Chonde yakhani mafunso otsatilawa okhudzana [mem_nm] b02 (required) B02: Ubale wa [mem_nm] ndi wamkulu wapakhomo Question relevant when: true () 1 Wamkulu wakhomo 2 Mkazake/Mwamunake 3 Mwana wammuna/wamkazi 4 Kholo 5 Mchemwali/Mchimwene 6 Chidzukulu 7 Agogo 8 Mwana wompeza 9 Wachibale wina 10 Palibe chibale b03 (required) B03: Zaka Zakubadwa za [mem_nm] − Report children under the age of one as zero Question relevant when: true () b03_1 B03_1: Kodi mwanayu ali ndi miyezi ingati yakubadwa? Question relevant when: ${b03} =0 b04 (required) B04: [mem_nm] ndi wamkazi kapena wamamuna Question relevant when: true () 0 Wamwamuna 1 Wamkazi b05 (required) B05: Kodi [mem_nm] ali pabanja? Question relevant when: true () 1 Ali pabanja 2 Sali pabanja 3 Wamasiye/Namfedwa 4 Anasiyana/ananyanyalitsana USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 201 Field Question Answer 5 N/A (Mwana) b07 (required) B07: Kodi [mem_nm] amagwira ntchito yanji? Question relevant when: true () 1 Kulima 2 Mayi wapakhomo 3 Bizinesi ya banja 4 Ntchito yolandira malipiro a pamwezi 5 Ganyu 6 Mwana wa sukulu 7 Msodzi 8 Palibe other Other b07_other Specify other. Question relevant when: selected(${b07}, 'other') - > Mndandanda wa anthu (1) > Mafunso amaphunziro a [mem_nm] Group relevant when: ${b03} >5 - > Mndandanda wa anthu (1) > Mafunso amaphunziro a [mem_nm] > c1_edu2 generated_table_list_label_59 Mafunso okhudza maphunziro a [mem_nm] reserved_name_for_field_list_labels_ 60 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha c1_7 (required) C7: Kodi [mem_nm] atha kuwerenga kalata ya tsamba limodzi muchichewa Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha c1_8 (required) C8: Kodi [mem_nm] atha kulemba kalata ya tsamba limodzi muchichewa Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 202 Field Question Answer c1_9 (required) C9: Kodi [mem_nm] atha kuwerenga kalata ya tsamba limodzi muchingerezi Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha c1_10 (required) C10: Kodi [mem_nm] atha kulemba kalata ya tsamba limodzi muchingerezi Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha c1_11 (required) C11: Kodi [mem_nm] anayamba wayimbapo sukulu Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha c1_12 (required) C12: Kodi [mem_nm] akuyimbabe sukulu pakadali pano? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha c1_13 (required) C13: Kodi [mem_nm] anapita nayo patali bwanji sukulu Question relevant when: true () 0 PALIBE 1 SUKULU YA MKAKA 2 STANDARD 1 3 STANDARD 2 4 STANDARD 3 5 STANDARD 4 6 STANDARD 5 7 STANDARD 6 8 STANDARD 7 9 STANDARD 8 10 JUNIOR FORM 1 11 JUNIOR FORM 2 12 SENIOR FORM 3 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 203 Field Question Answer 13 SENIOR FORM 4 14 VOCATIONAL TRAINING 15 DIPLOMA/CERTIFICATE 16 UNIVERSITY UNDERGRADUATE 17 UNIVERSITY GRADUATE/POST￾GRADUATE 18 Sukulu zakwacha - 77 Zina - 99 Sakudziwa b9_0 (required) b9_0: Kodi alipo wina pakhomo pano yemwe ali ndi chilema kapena ali ndi vuto la misala? Question relevant when: true () 1 Inde 0 Ayi - 88 Akana kuyankha b9 (required) b9: Mumiyezi khumi ndi iwiri(12) yapitayi, Kodi alipo wamwalira pa khomo pano? Question relevant when: true () 1 Inde 0 Ayi - 88 Akana kuyankha b9_1 b10: Kodi malemuyu anali wosaposera zaka zisanu? Question relevant when: ${b9} =1 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha c2 Section C: Education − Tsopano ndikufunsani mafunso angapo okhudzana ndi kuwerenga c14 (required) c14: Kodi mumatenga maminitsi angati kuti mukafike pa sukulu ya pulayimale ya boma yapafupi kwambiri Question relevant when: true () - > c15_start c15 (required) c15: Kodi muli ndi mabuku kapena magazine amene ana amatha kuwerenga kunyumba? Question relevant when: true () 1 Inde 0 Ayi - 66 N/A (alibe ana) USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 204 Field Question Answer - 88 Akana - 99 Sakudziwa c16 (required) c16: Kodi muli ndi mabuku kapena magazini amene munthu wamkulu atha kuwerenga kunyumba? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha c16_1 (required) c16_1: Kodi wina aliyense wapakhomo pano alipo amene amapita kumalo owerengera mabuku a mdera lino? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa c17 (required) c17: Kodi alipo wina aliyense wapakhomo pano amene amawerenga mabukhu, magazini, nyuzi, kapena zinthu zina tsiku lililonse? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha c18 (required) c18: Ndi ndani amene amawerenga tsiku lililonseyu? Question relevant when: true () 1 akulu akulu amuna 2 akulu akulu akazi 3 Mwana/ana amuna 4 Mwana/ana akazi D_section Section D: Well-Being − Tsopano ndikufunsani mafunso okhuzana ndi moyo wanu d5 (required) d5: Munganene kuti kuyang'anira zonse umoyo wabanja lanu uli ulibwino kwambiri ndithu, ulibwino kwambiri, uli bwino, siulibwino kwenikweni kapena siulibwino? Question relevant when: true () 1 Siziri bwino 2 Pakatikati 3 Zilibwino 4 Zilibwino kwambiri 5 Zilibwino zedi - 88 Akana d8 (required) d8: pa masiku 30 apitawa, kodi ndi masiku angati amene kupweteka kwa m'thupi mwanu kunakulephelesani inu kugwira ntchito zomwe mumagwira USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 205 Field Question Answer nthawi zonse, monga kuzisamalira nokha, kugwira ntchito, kapena kuchita zinthu zina Question relevant when: true () d2 (required) d2: Ndinu okhutira bwanji pankhani za chuma zokhuza khomo lanu lino? − read options Question relevant when: true () 1 Sakukhutitsidwa nkomwe 2 Sakukhutitsidwa pang'ono 3 Pakatikati 4 kukhutitsidwa pang'ono 5 kukhutitsidwa kwambiri - 99 sakudziwa - 88 Akana d10 (required) d10: Tsopano kuyang'ana kutsogolo mukuonan kuti inuyo ndi banja lanu mudzakhala bwinoko kapena ayi pankhani ya zachuma kapena mudzakhala chimodzimodzi, mu miyezi 12 ikubwerayi? Question relevant when: true () 1 Zidzaipiratu 2 Chimodzimodzi 3 Bolaniko/Nkhasako/Betele - 88 Akana d22 (required) d22: kodi pazinthu izi ndi chiti chomwe chili cholondola? Mapezedwe anu a ndalama padakali pano…. − read options Question relevant when: true () 1 zimatheka kukulolani kumasunga ndalama zina? 2 zimatheka kukulolani kusungako ndalama zochepa? 3 zimangokhala ndalama zokwanira kugwiritsa ntchito? 4 zimakhala ndalama zosakwanira kotero kuti mumayenela kugwiritsanso ntchito ndalama zina zomwe munasunga mmbuyomu? 5 Zimakhala zosakwanira ndithu kotero kuti mumayenera kungongolera ndalama zina kuti mukwaniritse zokhumba zanu? - 88 Akana d15 (required) d15: Kutengela ndi madyedwe a khomo lano mwezi wathawu, ndichiganizo chiti chomwe chili cholondola? 1 zinali zosakwana poyerekeza ndi zofunika zapakhomo USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 206 Field Question Answer − read options Question relevant when: true () 2 Zinali zokwanira kukwaniritsa zofunika zapakhomopo - 88 Akana - > steps_begin note1 [Onetsani Chithunzi chama sitepe] Patakhala kuti pali ma sitepe 6, ndipo sitepe yapansi yoyambilira, ikuimila anthu osaukitsitsa, ndipo sitepe yapamwamba penipeni, yanambala 6, ikuimila olemera a mmudzi wanu uno kapena midzi ina yozungulira. d19 (required) d19: Kodi inu muli ma sitepe iti lero? Question relevant when: true () 1 Sitepe 1 2 Sitepe 2 3 Sitepe 3 4 Sitepe 4 5 Sitepe 5 6 Sitepe 6 - 88 Akana d21 (required) d21: Kodi chaka chatha munali step iti? Question relevant when: true () 1 Sitepe 1 2 Sitepe 2 3 Sitepe 3 4 Sitepe 4 5 Sitepe 5 6 Sitepe 6 - 88 Akana d20 (required) d20: Kodi anthu ena ambiri a mmudzi muno ali pa sitepe iti lero? Question relevant when: true () 1 Sitepe 1 2 Sitepe 2 3 Sitepe 3 4 Sitepe 4 5 Sitepe 5 6 Sitepe 6 - 88 Akana d23 (required) d23: Mutakhala kuti mwakomana ndi mavuto odza mwadzidzi, monga kusowa 1 Inde USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 207 Field Question Answer kwa ndalama kapena kuononga ndalama zomwe simumayembekela. kodi pali munthu amene mungamupemphe chithandizo? Question relevant when: true () 0 Ayi d26 (required) d26: Mukachoka pa khomo pano kwamaola angapo kodi mumadela nkhawa za chitetezo chakatundu wanu? Question relevant when: true () 1 Amadandaula kwambiri 2 Amadandaula pang'ono 3 Sadandaula - 88 Akana d4 (required) d4: Pazonse, ndinu okhutisidwa kwambiri, okhutisidwa pang'ono, palibe kusiyana, osakhutisidwa pang'ono, osakhutisidwa ndi pang'ono pomwe ndi mmene demokalasi imayendela (mudziko lino)? Question relevant when: true () 1 Sakukhutitsidwa nkomwe 2 Sakukhutitsidwa pang'ono 3 Pakatikati 4 kukhutitsidwa pang'ono 5 kukhutitsidwa kwambiri - 99 sakudziwa - 88 Akana d14 (required) d14: Kodi mukukhulupilira kuti mukhoza kukhala ndi ulamuliro wosintha zinthu mmene zimakhalira pa moyo wanu? Question relevant when: true () 1 Inde 0 Ayi d1 (required) d1: Kuyang'nanira zinthu zonse ndinu okhutila bwanji ndi moyo wanu masiku ano? Question relevant when: true () 1 Sakukhutitsidwa nkomwe 2 Sakukhutitsidwa pang'ono 3 Pakatikati 4 kukhutitsidwa pang'ono 5 kukhutitsidwa kwambiri - 99 sakudziwa - 88 Akana E_section E. Household Features e13 (required) e13: INTERVIEWER: Kodi mbali yaikulu ya khoma lakunja ya nyumba yanu inapangidwa ndi chani? − Funsitsani Question relevant when: true () 1 Udzu 2 YOMATA 3 YAMDINDO 4 Zidina zosaotcha 5 Njerwa zowotcha USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 208 Field Question Answer 6 Konkileti 7 Matabwa 8 Malata - 77 Zina e13a (required) e13a: Kodi mbali yayikulu ya denga la nyumba yanu linapangidwa ndi chani? Question relevant when: true () 1 Grass, plastic sheeting, or other 2 Iron sheets, clay tiles, or concrete e13b (required) e13b: kodi mwasinthako zipangizo zofolelera denga la nyumba yanu kuyabila 2014 (muzaka zinayi zapitazi) Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa e13c (required) e13c: Kodi nyumba yanu inafoleledwa ndi chani mbuyomu? Question relevant when: true () 1 Grass, plastic sheeting, or other 2 Iron sheets, clay tiles, or concrete e14a (required) e14a: Kodi mbali yaikulu ya pansi pa nyumba yanu inapangidwa ndi chani? Question relevant when: true () 1 Mchenga 2 Yozira 3 Simenti 4 Matabwa 5 Matailosi - 77 Zina e14b. (required) e14b: Kodi muli ndi magetsi omwe akugwira ntchito pakhomo pano? Question relevant when: true () 1 Inde 0 Ayi e15 (required) e15: Kodi nyumba yanu ili ndi zipinda zingati (kupatula bafa, chimbudzi, chipinda chosungilamo katundu kapena galaja) ? − (DO NOT COUNT BATHROOMS, TOILETS, STOREROOMS, OR GARAGE) Question relevant when: true () e16 (required) e16: Kodi nthawi zambiri mumagwirisa ntchito chani powunikila? Question relevant when: true () 1 Nkhuni zotolera 2 Nhuni zogula 3 Mabatile/Tochi 4 Udzu/Mwatso/Muuni 5 Parafini 6 Magetsi USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 209 Field Question Answer 7 LPG 8 Natural gas 9 Biogas 10 Makandulo other Other e16_other Specify other. Question relevant when: selected(${e16}, 'other') e17 (required) e17: Kodi mumagwiritsa ntchito chani kuphikira kweni kweni? Question relevant when: true () 1 Nkhuni zotolera 2 Nkhuni zogula 3 Udzu/mapesi/zinyalala 4 Parafini 5 Magetsi 6 LPG 7 Natural gas 8 Biogas 9 malasha 10 Makala 11 Zitsononkho/ziguli/mapesi 12 Ndowe -66 N/A. Pakhomopo sipaphikidwa chakudya. other Other e17_other Specify other. Question relevant when: selected(${e17}, 'other') e18 (required) e18: Kodi alipo wapakhomo panu pano amene ali ndi foni ya mmanja (selula) yoti imagwira ntchito? Question relevant when: true () 1 Inde 0 Ayi e19a (required) e19a: Kodi mumagwiritsa ntchito chimbudzi chotani? Question relevant when: true () 1 Chimbudzi cha madzi 2 chimbudzi chokumba chokhala ndi Paipi 3 chimbudzi chokumba chokhala ndi denga 4 Chimbudzi chokumba chopanda denga 5 Palibe/kutchire USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 210 Field Question Answer - 77 zina e19b e19b: Kodi mumagwiritsa ntchito toiletiyi ndi makomo enanso? Question relevant when: ${e19a} <5 or ${e19a} =-77 1 Inde 0 Ayi e20 (required) e20: Kodi ana azaka zochepela zisanu (5) apakhomo pano amagona mu masikito munyengo yomwe kumakhala udzudzu? − Only ask if there are children under 5 Question relevant when: true () 1 Inde, kwa ana onse osakwana zaka zisanu 2 Inde, kwa ana ena osakwana zaka zisanu 3 Ayi, kwa ana onse osakwana zaka zisanu - 66 N/A ( alibe ana osakwana zaka zisanu) e20a (required) e20a: Kodi pali wina aliyense wa pa khomo pano amene amagona mu neti pofuna kuziziteteza ku udzudzu munyengo ina ya pachaka? Question relevant when: true () 1 Inde 0 Ayi e21 (required) e21: Munyengo zolima zisanu(5) zapitazi, Kodi inu kapena wina aliyense wapakhomo pano analimapo fodya? Question relevant when: true () 1 Inde 0 Ayi e22 (required) e22: Kodi alipo wapakhomo pano amene analima mbeu zakudimba (munyengo yachilimwe yapitayi)? Question relevant when: true () 1 Inde 0 Ayi e24 (required) e24: M'zaka zisanu zapitazo, kodi khomo lanu linakhuzidwa ndi kufa kapena kubedwa kwa ziweto? Question relevant when: true () 1 Inde 0 Ayi - 66 N/A (analibe ziweto zaka zisanu zapitazi) - 99 Sakudziwa e25a (required) e25a: M'mwezi umodzi wapitawu, kodi mwagulako kapena kulipila sopo wopaka (Wosambira kapena wochapira)? Question relevant when: true () 1 Inde 0 Ayi USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 211 Field Question Answer e25b (required) e25b: M'mwezi umodzi wapitawu, kodi mwagulako kapena kulipira sopo waufa wochapila? Question relevant when: true () 1 Inde 0 Ayi e26 (required) e26: M'mwezi wangothawu, Kodi khomo lanu lakhala likutunga kuti madzi akumwa kwenikweni? Question relevant when: true () 1 Madzi apampopi amnyumba 2 Madzi apampopi apanja/pabwalo 3 Mjigo 4 Mpopi wa aliyense 5 Chitsime chotetezedwa 6 Chitsime chosatetezedwa 7 Kasupe wotetezedwa 8 Kasupe wosatetezedwa 9 Madzi a mvula 10 Thanki lamadzi/madzi wobwera ndi Venda 11 Ngolo zokhala ndi mathanki ang'ono 12 Madzi amumt'sinje, Damu, Nyanja,Dziwe,Ngalande 13 Madzi am'botolo other Other e26_other Specify other. Question relevant when: selected(${e26}, 'other') - > - e27_num (required) e27: Kodi mumatenga nthawi yaitali bwanji kukafika kumeneku, kutunga madzi ndikubwerako? Question relevant when: true () e27 (required) e27a: Mulingo wake: − RECORD IN UNIT Question relevant when: true () 1 Mphindi 2 Maola - 99 Sakudziwa e28 (required) e28: Kodi pali chomwe mumapanga kuti madziwa akhale awukhondo mukamamwa? Question relevant when: true () 0 Ayi 1 Inde - 99 sakudziwa USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 212 Field Question Answer e29 (required) e29: Kodi mumatani kuti madziwa akhale awukhondo? Question relevant when: true () 1 Kuwiritsa 2 Kuthira kololini/ WATER GUARD 3 Kugwiritsa ntchito sefa monga mchenga 4 Kusefa pogwiritsa ntchito kansalu 5 Kupha magelemusi pogwiritsa ntchito dzuwa 6 Kungowadekhetsa/kungowasiya - 99 Sakudziwa - 77 Zina e30 (required) e30: Kodi nthawi zambiri mumaphikira nyumba, munyumba ina kapena pabwalo? Question relevant when: true () 1 Mnyumba 2 Mnyumba ina yapadera 3 panja - 77 Zina - > Msika woyandikana nawo kwambiri e31 (required) e31: Kodi mumatenga phindi(maminisi) zingati kuti mukafike ku mtsika wapafupi? − RECORD IN UNIT. Put 999 if don't know. Question relevant when: true () e31_unit (required) e31a: Mulingo wake: Question relevant when: true () 1 Mphindi 2 Maola - 99 Sakudziwa f F. Assets - > fa2_begin fa2 (required) fa2: Muli ndi ng'ombe zingati zokoka ngolo? − Count baby animals as whole animal ; Write "999" for "don't know/refuse" Question relevant when: true () fb2 (required) fb2: Muli ndi ng'ombe zingati? − Count baby animals as whole animal ; Write "999" for "don't know/refuse" Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 213 Field Question Answer fc2 (required) fc2: Muli ndi Nkhosa zingati/ abira angati? − Count baby animals as whole animal ; Write "999" for "don't know/refuse" Question relevant when: true () - > fa2b_begin fd2 (required) fd2: Muli ndi Mbuzi zingati? − Count baby animals as whole animal ; Write "999" for "don't know/refuse" Question relevant when: true () fe2 (required) fe2: Muli ndi Nkhumba zingati? − Count baby animals as whole animal ; Write "999" for "don't know/refuse" Question relevant when: true () ff2 (required) ff2: Muli ndi Nkhuku zingati? − Count baby animals as whole animal ; Write "999" for "don't know/refuse" Question relevant when: true () - > fg2_begin fg2 (required) fg2: Kodi muli ndi ziweto zina monga abakha, nkhunda, atsekwe, kapena nkhanga zingati? − Count any baby animal as whole animal Question relevant when: true () fh2 (required) fh2: Muli ndi mabedi angati? Question relevant when: true () fi2 (required) fi2: Kodi muli ndi tebulo? Question relevant when: true () 1 Inde 0 Ayi fj2 (required) fJ2: Kodi muli ndi maayironi angati? Question relevant when: true () - > fg2b_begin fk2 (required) fk2: Muli ndi Mawailesi a flashi, kaseti/CD kapena a memory card angati? Question relevant when: true () fl2 (required) fl2: Muli ndi njiga zakapalasa zingati? Question relevant when: true () - > fm2_begin fm2 (required) fm2: Muli ndi mipando wamba kapena ya sofa ingati? Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 214 Field Question Answer fo2 (required) fo2: Muli ndi Fuliji zingati/ma Fuliji angati? Question relevant when: true () ft2 (required) ft2: Muli ndi Mawayilesi angati? Question relevant when: true () - > fm2_beginx fv2 (required) fv2: Muli ndi Maotchi angati apakhoma? Question relevant when: true () fx2 (required) fx2: Muli ndi Migolo ingati yofululira/kuphikira mowa? Question relevant when: true () - > fy2_begin fy2 (required) fy2: Muli ndi Magalimoto angati? Question relevant when: true () fz2 (required) fz2: Muli ndi Njinga zamoto zingati? Question relevant when: true () faa2 (required) faa2: Muli ndi Mabwato angati? Question relevant when: true () - > fy2_beginx fee2 (required) fee2: Muli ndi Zikwanje zingati? Question relevant when: true () fgg2 (required) fgg2: Muli ndi Nkhwangwa zingati? Question relevant when: true () fhh2 (required) fhh2: Muli ndi zikwakwa/zisikilo zingati? Question relevant when: true () g0 G. Credit. Tsopano ndikufunsani mafunso okhuzana ndi kutenga nawo kwanu gawo mumabanki ndi ngongole g1 (required) g1: Kodi pali munthu wapakhomo panu pano amene ali ndi bukhu kubanki? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha g1_5 g1.5 Kodi pali munthu wa pakhomo pano aliyense amene analandilapo ngongole iliyonse pa miyezi khumi ndi iwili yapitayi? 1 Inde 0 Ayi USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 215 Field Question Answer - > - Group relevant when: ${g1_5} =1 g2_1 (required) g2_1:Kodi munakongola chani? Question relevant when: true () 1 Ndalama 2 Kuponi 3 Zogwirira ntchito zinaperekedwa 4 Chithandizo chinalandiridwa - 99 Sakudziwa - 88 Refused to answer g3 (required) g3: Kodi ngongoleyi anatenga kwandani? Question relevant when: true () 1 Bungwe loyima palokha 2 obwereketsa ndalama odziwika ndi boma[ma Bank] 3 Obwelekesa ndalama osadziwika ku boma (anzanu/achibale) 4 Villaga bank/ SACCOs/ chipereganyo 5 Mabungwe obweleketsa ndalama ang'ono ang'ono 6 Anzanu kapena achibale 7 Kampani - 77 Zina - 99 Sakudziwa g5 (required) g5: Kodi ndi ndani amene anapanga chiganizo chotenga ngongoleyi kuchokela kumeneko? Question relevant when: true () 1 Mzimayi wamkulu/weniweni 2 Mzibambo wamkulu wapakhomopo 3 Wina wapakhomopo 4 Wina[gulu la anthu] lomwe silapakhomopo - 99 Sindikudziwa n02 (required) n02: kodi mwagwiritsako ntchito foni yammanja pochita zinthu izi mumiyezi 12 yapitayi kutumiza ndi kulandila ndalama kapena kulipila ma bilu Question relevant when: true () 1 Inde 0 Ayi i i. Food Security [Food Insufficiency] USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 216 Field Question Answer note1_1 Tsopano ndikufunsani za mapezedwe a zakudya pa masiku makumi atatu apitawa (30) i1a (required) i1a: Mwezi watha (masiku 30), munadandaulapo kuti khomo lanu silikhala ndi chakudya chokwanira Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha i1b (required) i1b: Kodi izi zinachitika pafupipafupi bwanji? Question relevant when: true () 1 Mwa apo ndi apo[kamodzi kapena kawiri pa mwezi wapitawo 2 Nthawi zina [Katatu mpaka kakhumi pa mwezi wapitawu] 3 kawirikawiri[kupitilira ka khumi pa mwezi wapitawo] i1c (required) i1c: Mwezi wathawu (masiku 30), inu kapena wina aliiyense wapakhomo panu simunathe kudya zakudya zamtundu womwe mumafuna chifukwa chosowa zofunikira? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha i1d (required) i1d: Kodi izi zinachitika pafupipafupi bwanji? Question relevant when: true () 1 Mwa apo ndi apo[kamodzi kapena kawiri pa mwezi wapitawo 2 Nthawi zina [Katatu mpaka kakhumi pa mwezi wapitawu] 3 kawirikawiri[kupitilira ka khumi pa mwezi wapitawo] i1 (required) i1: Masiku 30 apitawa, kodi inuyo kapena aliyense wapakhomo pano, alipo amene sanadye chakudya chakasinthasintha chifukwa chosowa chakudyacho? − Chakudya chomwe mukanakonda kuti mudye chitha kukhala nkhuku kapena mpunga, nsima , nyama, nsoma Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha i2 (required) i2: Kodi izi zinachitika pafupipafupi bwanji? Question relevant when: true () 1 Mwa apo ndi apo[kamodzi kapena kawiri pa mwezi wapitawo USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 217 Field Question Answer 2 Nthawi zina [Katatu mpaka kakhumi pa mwezi wapitawu] 3 kawirikawiri[kupitilira ka khumi pa mwezi wapitawo] i3 (required) i3: Pa masiku 30 apitawa kodi inuyo kapena wina aliyense wa pakhomo lanu anadyako chakudya chimodzimodzi/osasintha chifukwa chochepekedwa? − Chakudya chosasintha chikhoza kukhala nsima ndi mchere kapena nyemba zokha Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha i4 (required) i4: Kodi izi zinachitika pafupipafupi bwanji? Question relevant when: true () 1 Mwa apo ndi apo[kamodzi kapena kawiri pa mwezi wapitawo 2 Nthawi zina [Katatu mpaka kakhumi pa mwezi wapitawu] 3 kawirikawiri[kupitilira ka khumi pa mwezi wapitawo] i5 (required) i5: Mwezi wathawu (masiku 30), inu kapena wina aliyense wapakhomo pano anadya mochepa poyelekeza ndi mmene amafunila kudyela chakudya cha mmawa kapena chamadzulo pachifukwa choti panalibe chakudya chokwanila? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha i6 (required) i6: Kodi izi zinachitika pafupipafupi bwanji? Question relevant when: true () 1 Mwa apo ndi apo[kamodzi kapena kawiri pa mwezi wapitawo 2 Nthawi zina [Katatu mpaka kakhumi pa mwezi wapitawu] 3 kawirikawiri[kupitilira ka khumi pa mwezi wapitawo] i7 (required) i7: Masiku 30 apitawa, kodi inu kapena wina aliyense wapakhomo pano anadyapo chakudya chochepa kuyerekeza ndi mmene amafunira chifukwa chakulephela kupeza chakudya chokwanira − - 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 218 Field Question Answer Question relevant when: true () i8 (required) i8: Kodi izi zinachitika pafupipafupi bwanji? Question relevant when: true () 1 Mwa apo ndi apo[kamodzi kapena kawiri pa mwezi wapitawo 2 Nthawi zina [Katatu mpaka kakhumi pa mwezi wapitawu] 3 kawirikawiri[kupitilira ka khumi pa mwezi wapitawo] i9 (required) i9: Mwezi wathawu (masiku 30), inuyo kapena wina aliyense wapakhomo pano, alipo amene anagona ndi njala chifukwa chakudya chinali chosakwanira? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha i10 (required) i10: Kodi izi zinachitika pafupipafupi bwanji? Question relevant when: true () 1 Mwa apo ndi apo[kamodzi kapena kawiri pa mwezi wapitawo 2 Nthawi zina [Katatu mpaka kakhumi pa mwezi wapitawu] 3 kawirikawiri[kupitilira ka khumi pa mwezi wapitawo] i11 (required) i11: Mwezi wathawu (masiku 30), inu kapena wina aliyense wapakhomo pano alipo amene anakhala tsiku lonse mpaka usiku osadya chifukwa chakudya chinali chosakwanira? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha i12 (required) i12: Kodi izi zinachitika pafupipafupi bwanji? Question relevant when: true () 1 Mwa apo ndi apo[kamodzi kapena kawiri pa mwezi wapitawo 2 Nthawi zina [Katatu mpaka kakhumi pa mwezi wapitawu] 3 kawirikawiri[kupitilira ka khumi pa mwezi wapitawo] i13 (required) i13: Mwezi wathawu (masiku 30), inalipo tsiku lina lomwe munalibe chakudya chamtundu wina uliwonse chifukwa chosowa zipangizo zopezera zakudya? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 219 Field Question Answer i14 (required) i14: Kodi izi zinachitika pafupipafupi bwanji? Question relevant when: true () 1 Mwa apo ndi apo[kamodzi kapena kawiri pa mwezi wapitawo 2 Nthawi zina [Katatu mpaka kakhumi pa mwezi wapitawu] 3 kawirikawiri[kupitilira ka khumi pa mwezi wapitawo] i15 (required) i15: Masiku 30 apitawa, kodi pakhomo pano alipo wadya mtedza? − This could be any form Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha i16 (required) i16: Masiku 30 apitawa, kodi pakhomo pano alipo wadya soya? − This could be any form Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha j J. Environment j3 (required) j3: Kodi alipo wapakhomo pano yemwe amakatola zinthu zina zakutchire ndi cholinga chogulitsa kapena kugwiritsa ntchito pakhomo? Question relevant when: true () 1 Inde 0 Ayi - > Zinthu Zotoledwa Group relevant when: ${j3} =1 j4aa j4aa: Kodi amakatola nkhuni kapena matabwa? 1 Inde 0 Ayi j4a j4a: Amagwilitsa ntchito yanji Nkhunizo/matabwawo? (Werengani Mayankho) Question relevant when: ${j4aa} =1 1 Kugwiritsa ntchito pakhomo basi 2 Kugulitsa zonse 3 Zonse, kugwiritsa ntchito pakhomo komanso kugulitsa j5a (required) j5: Kodi nkhuni/matabwa amenewa ndi zofunikira bwanji pa khomo lanu ngati njira yopezera ndalama? Question relevant when: true () 1 sizofunika 2 Zofunika pang'ono 3 Kwambri - 99 sakudziwa USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 220 Field Question Answer j4ca j4ca. Kodi amakatola zinthu zina monga zipatso, masamba kapena makungwa 1 Inde 0 Ayi j4b j4b: Kodi zinthu zimenezi mumagwiritsa ntchito yanji? (werengani mayankho) Question relevant when: ${j4ca} =1 1 Kugwiritsa ntchito pakhomo basi 2 Kugulitsa zonse 3 Zonse, kugwiritsa ntchito pakhomo komanso kugulitsa j5c (required) j5: Kodi zinthu zimenezi ndi zofunikira bwanji pa khomo lanu ngati njira yopezera ndalama? Zosafunika kwenikweni, Zofunika pang'ono, zofunika kwambiri Question relevant when: true () 1 sizofunika 2 Zofunika pang'ono 3 Kwambri - 99 sakudziwa j6 (required) j6: Kodi pakhomo panu pano alipo amene amadalira usodzi kawirikawiri? Question relevant when: true () 1 Inde 0 Ayi - > Usodzi Group relevant when: ${j6} =1 j7 (required) j7: Kodi ndi masiku angati mu mwezi wathawu amene wina wake wapakhomo pano amapita ku usodzi? Question relevant when: true () - > Usodzi > j8_begin j8 (required) j8: Kodi munganene kuti ndi msomba zochuluka bwanji zomwe zamagwida pa tsiku − Help them estimate. Don't know is 999 Question relevant when: true () reserved_name_for_field_list_labels_ 219 1 Chiwerengero cha nsomba 2 Kuchuluka kwa ma kilogalamu j8b j8b: Units for fish 1 Chiwerengero cha nsomba 2 Kuchuluka kwa ma kilogalamu j10_1 j10_1: Kodi mumagulitsa nsombazo? 1 Inde 0 Ayi j10_2 j10_2: Mongoyerekeza ndi ndalama zingati zomwe munapeza pakhomo pano m'mwezi wathawu kuchokera ku malonda a nsomba? USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 221 Field Question Answer − Record in Kwacha and put 999 for don’t know Question relevant when: ${j10_1} =1 j11 (required) j11: M'sabata yathayi, mwadya nsomba kangati pakhomo pano? Question relevant when: true () j17 (required) j17: Mu miyezi 12 yapitayi, kodi khomo lanu linakhudzidwapo ndi mavuto akuchepa kwa malo omwe mumalima kamba ka kukokoloka kwa nthaka? Question relevant when: true () 1 Inde 0 Ayi j18 (required) j18: Mwawonelapo zioneselo za kubyala kapena kusamala mitengo chaka chapitachi? Question relevant when: true () 1 Inde 0 Ayi j19 (required) j19: Nanga pakhomo pano alipo wadzala mitengo Zaka Ziwiri zapitazi? Question relevant when: true () 1 Inde 0 Ayi j20 (required) j20: Kodi ndi mitengo ya mtundu wanji yomwe inadzalidwa? Question relevant when: true () 1 Zipatso/mtedza/ulimi wamitengo 2 Mitengo ina -99 Sakudziwa other Other j20_other Specify other. Question relevant when: selected(${j20}, 'other') j21 (required) j21: Munayamba mwamvapo za kusintha kwa nyengo? − Like long-term changes in weather patterns like timing of rains or average temperatures Question relevant when: true () 1 Inde 0 Ayi j22 (required) j22: Kodi mukuganiza kuti ndi chiyani chimene mungachite pokonzekera kapena kulimbana ndi kusintha kwa nyengo monga kusefukira kwa madzi ndi chilala? Question relevant when: true () 1 kudzala mitengo 2 kugwiritsa ntchito mitengo yochepa/nkhuni 3 kugwiritsa ntchito zophikira zamakono/masitovu 4 kugwiritsa ntchito nkhalango ya mmudzi 5 Kugwiritsa bwino ntchito nkhalango USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 222 Field Question Answer 6 Kusunga madzi -77 Zina -99 Sakudziwa chomwe angachite other Other j22_other Specify other. Question relevant when: selected(${j22}, 'other') k1 K. Health - > - k02 (required) k02: Mumatenga nthawi yayitali bwanji kukafika kuchipatala chapafupi kwambiri? − This should be amout of time, not distance Question relevant when: true () k03 k03: Mulingo 1 Mphindi 2 Maola - 99 Sakudziwa k04 (required) k04: Kodi mwana wanu pakhomo pano akadwala, nthawi zambiri mumatani? Question relevant when: true () 1 Kupita kuchipatala kapena chipatala chaching'ono 2 Kupita ku kiliniki 3 Kusamalira wodwala panyumba 4 Kupita kwa a sing'anga 5 zina, chonde tchulani -99 Sakudziwa -88 Akana kuyankha -77 N/A (Alibe ana) other Other k04_other Specify other. Question relevant when: selected(${k04}, 'other') k06 (required) k06: Kodi ndi chifukwa chiyani simupita kuchipatala mwana wa pakhomo pano akadwala? Question relevant when: true () 1 Sangakwanitse mtengo wakuchipatala 2 Sangakwanitse Mayendedwe 3 Ndikutali 4 Ndikovuta kukafika kumeneko USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 223 Field Question Answer 5 chisamaliro chakwa asing'anga/chapakhomo ndichimodzimodzi ndi chakuchipatala 6 Zikhulupiriro za chipembedzo - 77 Zina - 99 Sakudziwa - 88 Akana kuyankha k08 (required) k08: kodi ndi chipatala chiti chomwe mumapita nthawi zambiri mukafuna kupita kuchipatala? Question relevant when: true () Chadza Unit 33 Chadza Unit 33 Chikowa Health Centre Chikowa Health Centre Chileka Health Centre Chileka Health Centre Chimbalanga Heath Centre Chimbalanga Heath Centre Chitedze Health Centre Chitedze Health Centre Chiunjiza Health Centre Chiunjiza Health Centre Dickson Health Centre Dickson Health Centre Kabudula Health Centre Kabudula Health Centre Kan'goma Health Centre Kan'goma Health Centre Khongoni Health Centre Khongoni Health Centre Matapila Heath Centre Matapila Heath Centre Mbang'ombe Health Centre Mbang'ombe Health Centre Mbwatalika Health Centre Mbwatalika Health Centre Mitundu Health Centre Mitundu Health Centre USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 224 Field Question Answer Mlale Health Centre Mlale Health Centre Mtenthera Health Centre Mtenthera Health Centre Nathenje Health Centre Nathenje Health Centre Ngoni Health Centre Ngoni Health Centre Nkhoma Hospital Nkhoma Hospital Nsaru health Centre Nsaru health Centre Nthondo Health Centre Nthondo Health Centre Ukwe Health Centre Ukwe Health Centre -77 Other Kalembo Dispensary Kalembo Dispensary Kankao Health Centre Kankao Health Centre Kapile Health Centre Kapile Health Centre Mbera Health Centre Mbera Health Centre Phalula Health Facility Phalula Health Facility Phimbi Health Centre Phimbi Health Centre Ulongwe Health Centre Ulongwe Health Centre Utale I Health Centre Utale I Health Centre Utale II Health Centre Utale II Health Centre Chikweo Halth Centre Chikweo Halth Centre USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 225 Field Question Answer Machinga District Hospital Machinga District Hospital Namanja Health Centre Namanja Health Centre Ngokwe Health Centre Ngokwe Health Centre Nsanama Health Centre Nsanama Health Centre Ntaja Health Centre Ntaja Health Centre Nyambi Health Centre Nyambi Health Centre Chilipa Health Centre Chilipa Health Centre Chilonga Dispensary Chilonga Dispensary Jalasi Health Centre Jalasi Health Centre Katuli/Kasekela Health Centre Katuli/Kasekela Health Centre Luwalika Health Centre Luwalika Health Centre Mkumba Health Centre Mkumba Health Centre Mtimabi Health Centre Mtimabi Health Centre Nagallamu Health Centre Nagallamu Health Centre Namwera Health Centre Namwera Health Centre Nankumba Health Centre Nankumba Health Centre Phirilongwe Health Centre Phirilongwe Health Centre Atupele Community Hospital Atupele Community Hospital USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 226 Field Question Answer Chilumba Rural Hospital Chilumba Rural Hospital Fulirwa Health Centre Fulirwa Health Centre Hara Dispensary Hara Dispensary Iponga Health Centre Iponga Health Centre Kaporo Rural Hospital Kaporo Rural Hospital Karonga District Hospital Karonga District Hospital Kasoba Health Centre Kasoba Health Centre Lupembe Health Centre Lupembe Health Centre Mlare Health Centre Mlare Health Centre Mpata Health Centre Mpata Health Centre Ngana Health Centre Ngana Health Centre Nyungwe Health Centre Nyungwe Health Centre St Annies Health Centre St Annies Health Centre Sangilo Health Centre Sangilo Health Centre Alinafe Rehabilitation Centre Alinafe Rehabilitation Centre Benga Health Centre Benga Health Centre Bua Dispensary Bua Dispensary Chididi Health Centre Chididi Health Centre USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 227 Field Question Answer Dwamadzi Rural Hospital Dwamadzi Rural Hospital Kaongozi Dispensary Kaongozi Dispensary Kapiri Health Centre Kapiri Health Centre Kasitu Health Centre Kasitu Health Centre Lwaladzi Health Centre Lwaladzi Health Centre Malowa Dispensary Malowa Dispensary Matiki Health Centre Matiki Health Centre Mlosa Health Centre Mlosa Health Centre Mpamantha Dispensary Mpamantha Dispensary Msenjere Health Centre Msenjere Health Centre Ngala Health Centre Ngala Health Centre Nkhotakota District Hospital Nkhotakota District Hospital Chingale Health Centre Chingale Health Centre Lambulira Health Centre Lambulira Health Centre Likangala Health Centre Likangala Health Centre Mayaka Health Centre Mayaka Health Centre Mmambo Health Centre Mmambo Health Centre Ngwelelo Health Centre Ngwelelo Health Centre Kalemba Health Centre Kalemba Health Centre USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 228 Field Question Answer Lulwe Health Centre Lulwe Health Centre Makhanga Health Centre Makhanga Health Centre Masenjele Health Centre Masenjele Health Centre Mbenje Health Centre Mbenje Health Centre Ndamera Health Centre Ndamera Health Centre Nsanje District Hospital Nsanje District Hospital Nyamithuthu Health Centre Nyamithuthu Health Centre Phokera Health Centre Phokera Health Centre Sankhulani Health Centre Sankhulani Health Centre Sorgin Health Centre Sorgin Health Centre Tengani Health Centre/Nsanje District Hospital Tengani Health Centre/Nsanje District Hospital Trinity Hospital Trinity Hospital k10 (required) k10: Kodi kwenikweni komwe mumapeza uphungu okhuzana ndi chithandizo cha matenda ndi kuti? Question relevant when: true () 1 Chipatala cha boma 2 Chipatala cha boma chaching'ono 3 Chipatala cha boma choyendera 4 Chipatala[ sakudziwa kuti ndi chaboma kapena cholipira] 5 chipatala chaching'ono[sakudziwa ngati ndi chaboma kapena cholipira] 6 Chipatala choyendayenda 7 Alangzi azaumoyo 8 Alangzi otengera kulera Khomo ndi khomo 9 CHAM USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 229 Field Question Answer 10 Zipatala zolipira 11 Malo ogulako mankhwala 12 Banja La Mtsogolo 13 MACRO 14 Malo okumaniranako achinyamata 15 Sitolo 16 Tchalitchi 17 Mzake/wachibale 18 Gulu la amayi 19 Gulu lo samalira - 77 Zina - 88 Akana - 99 N/A k12 (required) k12: Kodi munayamba mwamva za njira zomwe amai kapena abambo amagwiritsa ntchito kuti apewe kukhala ndi pakati? Question relevant when: true () 1 Inde 0 Ayi - 88 akana kuyankha k14 (required) k14: Kodi padakali pano pali njira imene inu mukugwiritsa ntchito pofuna kuchedwesa kapena kupewa kutenga mimba? − Ask if between 15-50 years old Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha - 66 N/A (Aposa zaka 50 zakubadwa) k16 (required) k16: Ndi njira ziti zomwe mukugwiritsa ntchito? Question relevant when: true () 1 Kutseka kwa amayi 2 Kutseka kwa abambo 3 mapilitsi 4 Lupu 5 Jekeseni 6 Nopulanti 7 Mpira wa abambo/makondomu azibambo 8 makondomu achizimayi USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 230 Field Question Answer 9 Njira yowerenga masiku a msambo 10 Kuthira pambali -99 sakudziwa -88 Akana kukyankha other Other k16_other Specify other. Question relevant when: selected(${k16}, 'other') k18 (required) k18: Kodi mumakalandira kuti chithandizo cholera? Question relevant when: true () 1 Chipatala cha boma 2 Chipatala cha boma chaching'ono 3 Chipatala cha boma choyendera 4 Chipatala[ sakudziwa kuti ndi chaboma kapena cholipira] 5 chipatala chaching'ono[sakudziwa ngati ndi chaboma kapena cholipira] 6 Chipatala choyendayenda 7 Alangzi azaumoyo 8 Alangzi otengera kulera Khomo ndi khomo 9 CHAM 10 Zipatala zolipira 11 Malo ogulako mankhwala 12 Banja La Mtsogolo 13 MACRO 14 Malo okumaniranako achinyamata 15 Sitolo 16 Tchalitchi 17 Mzake/wachibale 18 Gulu la amayi 19 Gulu lo samalira - 77 Zina - 88 Akana - 99 N/A USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 231 Field Question Answer k20 (required) k20: Sindikufuna kudziwa zotsatira koma ndikungofuna kudziwa ngati mwalandirapo uphungu okhuzana ndi kachilombo ka edzi, kuyezesa magazi ndikumva zosatila miyezi 12 yapitayi? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha k22 (required) k22: INTERVIEWER: Ngati oyakha ali pabanja kapena ubwenzi: Kodi amuna/akazi wanu analandira uphungu okhuzana ndi kachilombo koyambitsa matenda a edzi, kuyezesa ndi kulandila zosatila nthawi yomweyo pa chaka chapitachi? − Ask only if respondent is part of a couple Question relevant when: true () 1 Inde 0 Ayi - 66 N/A (alibe mwamuna/mkazi) - 99 sakudziwa - 88 Akana kuyankha k24 (required) k24: Kodi chithandizo chimenechi munalandilira kuti? Question relevant when: true () 1 Chipatala cha boma 2 Chipatala cha boma chaching'ono 3 Chipatala cha boma choyendera 4 Chipatala[ sakudziwa kuti ndi chaboma kapena cholipira] 5 chipatala chaching'ono[sakudziwa ngati ndi chaboma kapena cholipira] 6 Chipatala choyendayenda 7 Alangzi azaumoyo 8 Alangzi otengera kulera Khomo ndi khomo 9 CHAM 10 Zipatala zolipira 11 Malo ogulako mankhwala 12 Banja La Mtsogolo 13 MACRO 14 Malo okumaniranako achinyamata 15 Sitolo 16 Tchalitchi 17 Mzake/wachibale 18 Gulu la amayi 19 Gulu lo samalira USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 232 Field Question Answer - 77 Zina - 88 Akana - 99 N/A k25 k25: Ofunsa yankhani funso ili: Kodi pakhomo lino alipo mwana wamiyezi yosakwana 6? 1 Inde 0 Ayi - > - Group relevant when: ${k25} =1 k25_1 k25_1 Kodi mwana wanu wakhanda kwenikweni mumamumwetsa kapena mumamudyetsa chakumwa/chakudya chanji? 1 Mkaka wammawere 2 Mkaka wamchitini wogula 3 Atchula zina osati mkaka wammawere kapena wamchitini - 99 Sakudziwa k25_2 k25_2 Kodi pali chakudya/chakumwa chinanso chimene mumamudyetsa/mumamumwetsa {DZINA} 1 Mkaka wammawere 2 Mkaka wamchitini wogula 3 Atchula zina osati mkaka wammawere kapena wamchitini - 99 Sakudziwa k26_num k26 Kodi muli ndi ana amene ali ndi zaka pakati pa miyezi isanu ndi umodzi (6 months) ndi miyezi makumi awiri nd itatu (23 months)? 1 Inde 0 Ayi - > Nutrition for 6-23 months Group relevant when: ${k26_num} =1 k26_name Tandikumbutsani dzina lamwana uja amene ali pakati pa miyezi 6 ndi 23 yakubadwa - > Nutrition for 6-23 months > Zakudya ndi zamadzi madzi za dzulo notek_1 Tsopano ndikufunsani za zinthu zamadzimadzi kapena zakudya zimene [k26_name] anadya dzulo lonse (miyezi 6-23 yakubadwa) − (AUTONAME FROM ROSTER YOUNGEST CHILD AGED 6-23 MONTHS) USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 233 Field Question Answer k26a (required) k26a: madzi Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k26b (required) k26b: Mkaka wa mmawere Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k26ba (required) k26ba: Kodi [k26_name] anayamwa kangati dzulo mkaka wa mmawere? − IF 7 OR MORE TIMES, RECORD '7'. .Write "-99" if Don't Know or Refused to answer. Question relevant when: true () k26c (required) k26c: Mkaka ogula wa ana akhanda Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k26ca (required) k26ca: Ngati inde, kodi [k26_name] anamwa kangati mkaka ogula wa ana a khanda? − IF 7 OR MORE TIMES, RECORD '7'. .Write "-99" if Don't Know or Refused to answer. Question relevant when: true () k26d (required) k26d: Mkaka ngati wamchitini, waufa kapena mkaka wamadzi wochokera ku nyama. Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k26da (required) k26da: Kodi [k26_name] anamwa kangati mkaka umenewu − IF 7 OR MORE TIMES, RECORD '7'. .Write "-99" if Don't Know or Refused to answer. Question relevant when: true () k26e (required) k26e: Yogati Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k26ea (required) k26ea: Kodi [k26_name] anamwa kangati Yogati? USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 234 Field Question Answer − IF 7 OR MORE TIMES, RECORD '7'. .Write "-99" if Don't Know or Refused to answer. Question relevant when: true () - > Nutrition for 6-23 months > Zakudya ndi zamadzi madzi za dzulo > Kodi dzulo nthawi ya masana kapena madzulo, alipo mwana wa miyezi 6 ndipo osapitilira miyezi 23, amene anadya kapena kumwa … k26f (required) k26f: Juwisi Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k26g (required) k26g: Tiyi kapena Khofi Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k26h (required) k26h: Zakumwa zoziritsa kukhosi monga Fanta Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k26i (required) k26i: Msuzi Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - > Nutrition for 6-23 months > Zakudya ndi zamadzi madzi za dzulo > Kodi dzulo nthawi ya masana kapena madzulo, alipo mwana wa miyezi 6 ndipo osapitilira miyezi 23, amene anadya kapena kumwa … k26j (required) k26j: Zakudya zina monga Seleraki (Likuni phala, Nestum, Purity, Sibusiso) Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k26k (required) k26k: Phala lamadzimadzi Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k26l (required) k26l: Thobwa Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - > Nutrition for 6-23 months > Zakudya ndi zamadzi madzi za dzulo > Kodi dzulo lonse, alipo mwana wa miyezi pakati pa 6 ndi 23 amene anadya kapena kumwa … USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 235 Field Question Answer k26m (required) k26m: Thanzi ORS Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k26n (required) k26n: Mavitamini Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k26o (required) k26o: Zakudya zina zamadzimadzi zilizonse Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa notek_2 Tsopano ndikufunsani za zakudya zolimba kapena zokanya zomwe [k26_name] anadya dzulo lonse. Ndikufuna kudziwa ngati mwana wanu anadya chakudyachi ngakhale zitakhala kuti zakudyazo zinaphatikizidwa ndi zina. - > Nutrition for 6-23 months > Solid and semi-solid (mushy) foods k28_note1 Tsopano ndikufunsani za zakudya zolimba kapena zokanya zomwe [k26_name] anadya dzulo lonse. Ndikufuna kudziwa ngati mwana wanu anadya chakudyachi ngakhale zitakhala kuti zakudyazo zinaphatikizidwa ndi zina. k28a (required) k28a: Buledi, sikono, nsima ya mgaiwa, nsima ya ufa woyera, mapira, mpunga, mchewere, kapena zakudya zina zochokera ku njere. Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k28b (required) k28b: Maungu, karoti, sobo, chilazi, mbatata ya kholowa kapena zina zomwe zili zachikasu kapena olenji mkati Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k28c (required) k28c: Koko wazikhawo, Mbatata ya kachewere, mbatata yeyera mkati, 1 Inde 0 Ayi USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 236 Field Question Answer chilazi, chinangwa, kapena zakudya zina zochokera ku mizu? Question relevant when: true () - 99 Sakudziwa k28d (required) k28d: Masamba obiriwira monga bonongwe, ndi masamba a maungu, kabichi, chiyinizi, chigwada, khwanya, chitambe kapena kholowa/Mtoliro? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k28e (required) k28e: Mfutso wa Nkhwani, khwanya, chitambe kapena Kholowa/Mtoliro? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - > Nutrition for 6-23 months > Tsopano ndikufunsani za zakudya zolimba kapena zokanya zomwe [dzina] anadya dzulo lonse. Ndikufuna kudziwa ngati mwana wanu anadya chakudyachi ngakhale zitakhala kuti zakudyazo zinaphatikizidwa ndi zina. k28f (required) k28f: Mango akupsa, mapapaya, magwafa? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k28g (required) k28g: zakudya zina za zipatso kapena masamba monga nthochi, ma apulo. Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k28h (required) k28h: Chiwindi, mphafa, mtima kapena ziwalo zina za nyama? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - > Nutrition for 6-23 months > Tsopano ndikufunsani za zakudya zolimba kapena zokanya zomwe [dzina] anadya dzulo lonse. Ndikufuna kudziwa ngati mwana wanu anadya chakudyachi ngakhale zitakhala kuti zakudyazo zinaphatikizidwa ndi zina. k28i (required) k28i: Nyama monga ya Ng'ombe, Nkhumba, Nkhosa, Mbuzi, Nkhuku, Bakha, Kalulu, kapena Mbewa? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k28j (required) k28j: Nkhono kapena ziwala? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 237 Field Question Answer k28k (required) k28k: Mazira? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k28l (required) k28l: Nsomba zouma kapena zaziwisi, Nkhanu, kapena zina zammadzi? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - > Nutrition for 6-23 months > Tsopano ndikufunsani za zakudya zolimba kapena zokanya zomwe [k26_name] anadya dzulo lonse. Ndikufuna kudziwa ngati mwana wanu anadya chakudyachi ngakhale zitakhala kuti zakudyazo zinaphatikizidwa ndi zina. k28m (required) k28m: Zakudya zina monga Nyemba, Soya Mtedza Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k28n (required) k28n: Tchizi kapena zina zochokera ku mkaka? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - > Nutrition for 6-23 months > Other foods that [k26_name] may have had yesterday k280 (required) k280: Mafuta, batala, kapena zakudya zina zonga izi? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k28p (required) k28p: zakudya zina za shuga monga chokoleti, siwiti, nzimbe, uchi, makeke, kapena Bisiketi? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k28q (required) k28q: Zakudya zina zolimba kapena zolimbirako Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa k30 (required) k30: Ndikangati komwe[k26_name] anadya zakudya zolimba, zolimbilako kapena zofewa dzulo masana kapena usiku Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 238 Field Question Answer - > notek_3 notek_32 Kodi dzulo lonse mzimayi wamkulu wapakhomo pano anadyako zakudya ngati izi? − Consider MAIN WOMAN k32a (required) k32a: Mtedza − This includes anything made from this Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 66 N/A. not applicable k32b (required) k32b:Soya − This includes anything made from this Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 66 N/A. not applicable l L. Household Decision Making - > l02: Zigamulo zikamapangidwa zokhudza zinthu izi ndi ndani amene amapanga zigamulo zimenezi l02a (required) l02a: Kupeza zipangizo zaullimi − select all that apply Question relevant when: true () 1 Mzibambo wamkulu kapena mwamuna wawo 2 Mzimai wamkulu kapena mkazi wawo 3 Mzibambo wina wa pakhomopo 4 Mzimayi wina wapakhomopo 5 wina wake osati wapakhomopo - 66 N/A. Palibe zomwe zikuyenera l02b (required) l02b: Mbeu zoyenela kulima Question relevant when: true () 1 Mzibambo wamkulu kapena mwamuna wawo 2 Mzimai wamkulu kapena mkazi wawo 3 Mzibambo wina wa pakhomopo 4 Mzimayi wina wapakhomopo 5 wina wake osati wapakhomopo - 66 N/A. Palibe zomwe zikuyenera USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 239 Field Question Answer l02c (required) l02c: Ndiliti kapena ndindani yemwe angatenge mbewu kunsika kokagulisa (kapena ayi) Question relevant when: true () 1 Mzibambo wamkulu kapena mwamuna wawo 2 Mzimai wamkulu kapena mkazi wawo 3 Mzibambo wina wa pakhomopo 4 Mzimayi wina wapakhomopo 5 wina wake osati wapakhomopo - 66 N/A. Palibe zomwe zikuyenera l02h (required) l02h: Kugwiritsa kapena kusagwiritsantchito njira zakulera? Question relevant when: true () 1 Mzibambo wamkulu kapena mwamuna wawo 2 Mzimai wamkulu kapena mkazi wawo 3 Mzibambo wina wa pakhomopo 4 Mzimayi wina wapakhomopo 5 wina wake osati wapakhomopo - 66 N/A. Palibe zomwe zikuyenera l02i (required) l02i: Kutenga kapena kusatenga nawo gawo mukupanga ziganizo kapena muzochitika za mdela Question relevant when: true () 1 Mzibambo wamkulu kapena mwamuna wawo 2 Mzimai wamkulu kapena mkazi wawo 3 Mzibambo wina wa pakhomopo 4 Mzimayi wina wapakhomopo 5 wina wake osati wapakhomopo - 66 N/A. Palibe zomwe zikuyenera l02j (required) l02j: Kutenga ngongole Question relevant when: true () 1 Mzibambo wamkulu kapena mwamuna wawo 2 Mzimai wamkulu kapena mkazi wawo 3 Mzibambo wina wa pakhomopo 4 Mzimayi wina wapakhomopo 5 wina wake osati wapakhomopo - 66 N/A. Palibe zomwe zikuyenera USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 240 Field Question Answer l02k (required) l02k: Kutenga kapena mmene mungatengele nawo mbali mumagulu kapena mumakomiti Question relevant when: true () 1 Mzibambo wamkulu kapena mwamuna wawo 2 Mzimai wamkulu kapena mkazi wawo 3 Mzibambo wina wa pakhomopo 4 Mzimayi wina wapakhomopo 5 wina wake osati wapakhomopo - 66 N/A. Palibe zomwe zikuyenera l02l (required) l02l: Kupanga ziganizo chokhuza sukulu ya mwana wa mamuna Question relevant when: true () 1 Mzibambo wamkulu kapena mwamuna wawo 2 Mzimai wamkulu kapena mkazi wawo 3 Mzibambo wina wa pakhomopo 4 Mzimayi wina wapakhomopo 5 wina wake osati wapakhomopo - 66 N/A. Palibe zomwe zikuyenera l02m (required) l02m: Kupanga ziganizo chokhuza sukulu ya mwana wa mkazi Question relevant when: true () 1 Mzibambo wamkulu kapena mwamuna wawo 2 Mzimai wamkulu kapena mkazi wawo 3 Mzibambo wina wa pakhomopo 4 Mzimayi wina wapakhomopo 5 wina wake osati wapakhomopo - 66 N/A. Palibe zomwe zikuyenera l02n (required) l02n: Kupanga chiganizo ngati kulikoyenela kutengela mwana wammuna kuchipatala Question relevant when: true () 1 Mzibambo wamkulu kapena mwamuna wawo 2 Mzimai wamkulu kapena mkazi wawo 3 Mzibambo wina wa pakhomopo 4 Mzimayi wina wapakhomopo 5 wina wake osati wapakhomopo - 66 N/A. Palibe zomwe zikuyenera USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 241 Field Question Answer l02o (required) l02o: Kupanga chiganizo ngati kulikoyenela kutengela mwana wamkazi kuchipatala Question relevant when: true () 1 Mzibambo wamkulu kapena mwamuna wawo 2 Mzimai wamkulu kapena mkazi wawo 3 Mzibambo wina wa pakhomopo 4 Mzimayi wina wapakhomopo 5 wina wake osati wapakhomopo - 66 N/A. Palibe zomwe zikuyenera l02p (required) l02p: Kupanga chiganizo choti mupite kuchipatala Question relevant when: true () 1 Mzibambo wamkulu kapena mwamuna wawo 2 Mzimai wamkulu kapena mkazi wawo 3 Mzibambo wina wa pakhomopo 4 Mzimayi wina wapakhomopo 5 wina wake osati wapakhomopo - 66 N/A. Palibe zomwe zikuyenera I05 INTERVIEWER: Kodi pakhomo pano alipo mzimayi wamkulu yemwe atha kuyankha mafunso otsatirawa? 0 Ayi, palibe mzimayi wankulu pakhomopa 1 Inde, alipo mzimayi wankulu ndipo ayankha mafunso otsatirawa 2 Inde, alipo mzimayi wankulu koma watalikira palibe sakwanitsa kuyankha mafunso otsatirawa - > I05_mainwoman2 Group relevant when: ${I05} >0 note_k5 Tsopano ndikufuna kudziwa zantchito za ulimi zokhudza mzimayi wamkulu wa khomo lino l06 (required) l06: Kodi m’miyezi 12 yapitayi (nyengo yolima yangothayi) khomo lanu lino linatengapo mbali pa ulimi wa mbeu zomwe zolinga zake zenizeni zinali kudya basi osati kugulitsa? Question relevant when: true () 1 Inde 0 Ayi - > I05_mainwoman2 > I06_begin Group relevant when: ${l06} =1 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 242 Field Question Answer l06a (required) l06a: Kodi [mzimayi wamkulu] anali/munali ndi mphavu yaikulu bwanji popanga ziganizo pa (Mbeu zomwe zimalimidwa ndicholinga chenicheni choti muzadye) munyengo yolima yangothayi? Question relevant when: true () 1 Samaiyikapo zigamulo mpang'ono pomwe 2 Amaikapo zigamulo zochepa kwambili 3 Amaikapo zigamulo mu zinthu zina 4 Amagamula mu zinthu zambiri 5 Amagamula pa zonse - 66 N/A: sipanapangidwe chiganizo chili chonse l06b (required) l06b: Munali/anali mphamvu yaikulu bwanji popanga ziganizo zamene mungagwirisile ntchito ndalama zochokera pa (Mbeu zomwe zimalimidwa ndicholinga chenicheni choti muzadye)? Question relevant when: true () 1 Samaiyikapo zigamulo mpang'ono pomwe 2 Amaikapo zigamulo zochepa kwambili 3 Amaikapo zigamulo mu zinthu zina 4 Amagamula mu zinthu zambiri 5 Amagamula pa zonse - 66 N/A: sipanapangidwe chiganizo chili chonse l08 (required) l08: kodi [mkazi wa wamkulu wapakhomo] anatengako/munatengako mbali pa ulimi wa ziweto pa miyezi 12 yapitayi? Question relevant when: true () 1 Inde 0 Ayi l08a (required) l08a: Kodi munali/anali ndi mphamvu yaikulu bwanji popanga ziganizo pa kuweta ziweto mu nyengo yolima yomwe yathayi? Question relevant when: true () 1 Samaiyikapo zigamulo mpang'ono pomwe 2 Amaikapo zigamulo zochepa kwambili 3 Amaikapo zigamulo mu zinthu zina 4 Amagamula mu zinthu zambiri 5 Amagamula pa zonse - 66 N/A: sipanapangidwe chiganizo chili chonse l08b (required) l08b: Munali/anali mphamvu yaikulu bwanji popanga ziganizo zammene mungagwirisile ntchito ndalama zochokera ku kuweta ziweto? Question relevant when: true () 1 Samaiyikapo zigamulo mpang'ono pomwe 2 Amaikapo zigamulo zochepa kwambili 3 Amaikapo zigamulo mu zinthu zina 4 Amagamula mu zinthu zambiri USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 243 Field Question Answer 5 Amagamula pa zonse - 66 N/A: sipanapangidwe chiganizo chili chonse l10 (required) l10: kodi munatengako/anatengako mbali pa mabizinesi ang'onoang'ono kapena kugula ndi kugulitsa mu miyezi 12 yapitayi? Question relevant when: true () 1 Inde 0 Ayi - > I05_mainwoman2 > I10_begin Group relevant when: ${l06} =1 l10a (required) l10a: Kodi munali/anali mphamvu yaikulu bwanji popanga ziganizopa ndalama zochokera ku mabizinezi ang'onoang'ono? Question relevant when: true () 1 Samaiyikapo zigamulo mpang'ono pomwe 2 Amaikapo zigamulo zochepa kwambili 3 Amaikapo zigamulo mu zinthu zina 4 Amagamula mu zinthu zambiri 5 Amagamula pa zonse - 66 N/A: sipanapangidwe chiganizo chili chonse l10b (required) l10b: Kodi [mkazi wa wamkulu wapakhomo] anali/munali ndi mphamvu yaikulu bwanji popanga ziganizo zamomwe mungagwiritsire ntchito ndalama zochokera kutchito zina osati ulimi? Question relevant when: true () 1 Samaiyikapo zigamulo mpang'ono pomwe 2 Amaikapo zigamulo zochepa kwambili 3 Amaikapo zigamulo mu zinthu zina 4 Amagamula mu zinthu zambiri 5 Amagamula pa zonse - 66 N/A: sipanapangidwe chiganizo chili chonse l12 (required) l12: kodi munatengako/anatengako mbali pa ntchito yolipidwa pamwezi kapena ganyu mu miyezi 12 yapitayi? Question relevant when: true () 1 Inde 0 Ayi - > I05_mainwoman2 > I12_begin Group relevant when: ${l06} =1 l12a (required) l12a: Kodi munali/anali ndi mphamvu yaikulu bwanji popanga ziganizo pa (Zochitika zobweresa ndalama zosakhunzana ndi ulimi: bizinesi 1 Samaiyikapo zigamulo mpang'ono pomwe 2 Amaikapo zigamulo zochepa kwambili USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 244 Field Question Answer yaing'ono, ntchito zopezela ndalama zosalembedwa ndi munthu, kugula ndi kugulitsa)? Question relevant when: true () 3 Amaikapo zigamulo mu zinthu zina 4 Amagamula mu zinthu zambiri 5 Amagamula pa zonse - 66 N/A: sipanapangidwe chiganizo chili chonse l12b (required) l12b: Kodi [mkazi wa wamkulu wapakhomo] anali/munali ndi mphamvu yaikulu bwanji popanga ziganizo zamomwe mungagwiritsire ntchito ndalama zochokera ku malipiro a pamwezi kapena ganyu? Question relevant when: true () 1 Samaiyikapo zigamulo mpang'ono pomwe 2 Amaikapo zigamulo zochepa kwambili 3 Amaikapo zigamulo mu zinthu zina 4 Amagamula mu zinthu zambiri 5 Amagamula pa zonse - 66 N/A: sipanapangidwe chiganizo chili chonse l14 (required) l14: Kodi munatengako mbali pa ulimi wansomba mu miyezi 12 yapitayi? [Munyengo yolima yomwe yangothayi) Question relevant when: true () 1 Inde 0 Ayi - > I05_mainwoman2 > I14_begin Group relevant when: ${l06} =1 l14a (required) l14a: Kodi [mkazi wa wamkulu wapakhomo] anali/munali ndi mphamvu yaikulu bwanji popanga ziganizo zokhudza usodzi kapena ulimi wansomba m'miyezi 12 yapitayi? Question relevant when: true () 1 Samaiyikapo zigamulo mpang'ono pomwe 2 Amaikapo zigamulo zochepa kwambili 3 Amaikapo zigamulo mu zinthu zina 4 Amagamula mu zinthu zambiri 5 Amagamula pa zonse - 66 N/A: sipanapangidwe chiganizo chili chonse l14b (required) l14b: Kodi [mkazi wa wamkulu wapakhomo] anali/munali ndi mphamvu yaikulu bwanji popanga ziganizo zokhudza zamomwe mungagwiritsire ntchito ndalama zochokera muntchito za usodzi kapena ulimi wansomba? Question relevant when: true () 1 Samaiyikapo zigamulo mpang'ono pomwe 2 Amaikapo zigamulo zochepa kwambili 3 Amaikapo zigamulo mu zinthu zina 4 Amagamula mu zinthu zambiri 5 Amagamula pa zonse - 66 N/A: sipanapangidwe chiganizo chili chonse USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 245 Field Question Answer mnote M. Participation and Governance m00 (required) m00: ENUMERATOR: Please indicate who is responsing to this section Question relevant when: true () 1 woyankha mafunso yemwe walembedwa koyambirira kwa mafunso 2 woyankha mafunso wina mzibambo wamkulu wamnyumbamo 3 woyankha wina: munthu wina wamwamuna 4 woyakha wina: munthu wina wa mkazi 5 woyakha wina: mzimayi wamkulu wamyumbamo m02 (required) m02: Kodi mumatenga nawo gawo mumagulu kapena mabungwe Question relevant when: true () 1 Inde 0 Ayi m04 (required) m04: kodi ndimagulu kapena mabungwe ati omwe mumatenga nawo gawo Question relevant when: true () 1 Gulu la asodzi/alimi 2 Komiti ya chitukuko mudzi (VDC) Kapena ADC 3 Magulu obwereketsa ndalama ammudzi 4 Gulu la ochita malonda 5 Gulu losamalira odwala 6 Sukulu/zokhudzana ndi maphunziro 7 Zaumoyo/zokhudzana ndi madyedwe 8 Yokhudzana ndi zachilengedwe 9 Ntchito za mdera[madzi,miseu,zinyalala] 10 Gulu la chipembedzo 11 Gulu lophunzitsa ntchito 12 Gulu la mmudzi -88 Akana other Other m04_other Specify other. Question relevant when: selected(${m04}, 'other') USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 246 Field Question Answer m22 (required) m22: Kodi munagwilitsako ntchito nthawi yanu mongothandiza potenga nawo mbali muzochitika za mu dela lanu pa miyezi 6 yapitayi monga kukonza misewu, kuwerenga, kuphunzitsa, zaumoyo, kapena ina ili yonse? − such as for health, hiv/aids, education help, construction of public works, serving on committees Question relevant when: true () 1 Inde 0 Ayi m34 (required) m34: Kodi mukudziwa ngati dera lino lili ndi komiti ya chitukuko cha mmudzi ya VDC Question relevant when: true () 1 Inde 0 Ayi m36 (required) m36: Kodi mukudziwa zomwe komiti ya chitukuko cha mmudzi ya VDC imapanga Question relevant when: true () 1 Inde 0 Ayi m38 m38: Kodi amagwira ntchito yanji? Question relevant when: ${m36} =1 1 Kufunsa anthu amdera zokhudzana ndi chitukuko choti chichitike mderalo 2 Kuimilila zofuna zawanthu kumisonkhano yamaboma ang'ono 3 Kufufuza anthu oyenera kuti agwire ntchito za mdera 4 Kufufuza anthu oyenera kuti alandire nawo makuponi -99 Sakudziwa kwenikweni other Other m38_other Specify other. Question relevant when: selected(${m38}, 'other') m40 (required) m40: Kodi munayamba mwatengapo gawo muzochitika kapena kukhalanawo pa misonkhano ya komiti ya chitukuko cha m'mudzi [VDC] kapena ya komiti ya chitukuko cha m'dera [ADC]? Question relevant when: true () 1 Inde 0 Ayi USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 247 Field Question Answer m42 (required) m42: Kodi mukudziwa ntchito ya maboma angónoangóno /makhonsolo kapena makhansala? Question relevant when: true () 1 Inde 0 Ayi m44 (required) m44: Kodi mukuganiza kuti ntchito ya maboma angónoangóno/makhonsolo ndichiyani? Question relevant when: true () 1 Kufunsa anthu amdera zokhudzana ndi chitukuko choti chichitike mderalo 2 Kuimilila zofuna zawanthu kumisonkhano yamaboma ang'ono 3 Kufufuza anthu oyenera kuti agwire ntchito za mdera 4 Kufufuza anthu oyenera kuti alandire nawo makuponi -99 Sakudziwa kwenikweni other Other m44_other Specify other. Question relevant when: selected(${m44}, 'other') - > m46_begin generated_table_list_label_374 ndinu okhutira bwanji ndi momwe Boma lino[osati dziko] likugwirira ntchito: − Read Responses m46_note Ndinu okhutira bwanji ndi momwe Boma lino[osati dziko] likugwirira ntchito: reserved_name_for_field_list_labels_ 376 1 Kukhutitsidwa kwambiri 2 Kukhutitsidwa pang'ono 3 Pakatikati 4 kusakhutitsidwa pang'ono 5 kusakhutitsidwa kwambiri - 99 Sakudziwa - 88 Akana kuyankha - 66 N/A m46 (required) m46: zokonza miseu? Question relevant when: true () 1 Kukhutitsidwa kwambiri 2 Kukhutitsidwa pang'ono USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 248 Field Question Answer 3 Pakatikati 4 kusakhutitsidwa pang'ono 5 kusakhutitsidwa kwambiri - 99 Sakudziwa - 88 Akana kuyankha - 66 N/A m48 (required) m48: Kuperekera ntchito za chitetezo zammadera? Question relevant when: true () 1 Kukhutitsidwa kwambiri 2 Kukhutitsidwa pang'ono 3 Pakatikati 4 kusakhutitsidwa pang'ono 5 kusakhutitsidwa kwambiri - 99 Sakudziwa - 88 Akana kuyankha - 66 N/A m50 (required) m50: Ntchito zopereka madzi ndi ukhondo? Question relevant when: true () 1 Kukhutitsidwa kwambiri 2 Kukhutitsidwa pang'ono 3 Pakatikati 4 kusakhutitsidwa pang'ono 5 kusakhutitsidwa kwambiri - 99 Sakudziwa - 88 Akana kuyankha - 66 N/A m52 (required) m52: kukonza misika ya m'madera. Question relevant when: true () 1 Kukhutitsidwa kwambiri 2 Kukhutitsidwa pang'ono 3 Pakatikati 4 kusakhutitsidwa pang'ono 5 kusakhutitsidwa kwambiri USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 249 Field Question Answer - 99 Sakudziwa - 88 Akana kuyankha - 66 N/A m54_note Ndinu okhutitsidwa bwanji ndi mmene Boma lanu monga manisipalite akuperekera ntchito izi ? m54 (required) m54: Ndinu okhutitsidwa bwanji ndi mmene boma lanu monga manicipality limafunsa nzika monga inu asanapange ziganizo? Question relevant when: true () 1 Kukhutitsidwa kwambiri 2 Kukhutitsidwa pang'ono 3 Pakatikati 4 kusakhutitsidwa pang'ono 5 kusakhutitsidwa kwambiri - 99 Sakudziwa - 88 Akana kuyankha - 66 N/A m56 (required) m56: Ndinu okhutira bwanji ndi momwe Boma lino[osati dziko] likugwirira ntchito zothetsa ziphuphu? Question relevant when: true () 1 Kukhutitsidwa kwambiri 2 Kukhutitsidwa pang'ono 3 Pakatikati 4 kusakhutitsidwa pang'ono 5 kusakhutitsidwa kwambiri - 99 Sakudziwa - 88 Akana kuyankha - 66 N/A m58 (required) m58: Ndinu okhutira bwanji ndi momwe Boma lino[osati dziko] likugwirira ntchito zokhudza malo? Question relevant when: true () 1 Kukhutitsidwa kwambiri 2 Kukhutitsidwa pang'ono 3 Pakatikati 4 kusakhutitsidwa pang'ono 5 kusakhutitsidwa kwambiri - 99 Sakudziwa USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 250 Field Question Answer - 88 Akana kuyankha - 66 N/A m64 (required) m64: Kodi mu miyezi 12 yapitayi, pakhomo pano alipo amene walandilapo [Chithandizo cha zakudya chamagulu] Question relevant when: true () 1 Inde 0 Ayi m64d (required) m64d: Kodi munakhutitsidwa bwanji ndi chithandizo chomwe munalandilacho Question relevant when: true () 1 kukhutitsidwa kwambiri 2 Kukhutitsidwa 3 Kusakhutitsidwa 4 Kusakhutitsidwa kwambiri - 88 Akana m66 (required) m66: Kodi mu miyezi 12 yapitayi, pakhomo pano alipo amene walandirapo [Maphunziro okhudza ulimi] kuchokera ku Boma? Question relevant when: true () 1 Inde 0 Ayi m66d (required) m66d: kodi munakhutitsidwa bwanji ndi chithandizo chomwe munalandiracho? Question relevant when: true () 1 kukhutitsidwa kwambiri 2 Kukhutitsidwa 3 Kusakhutitsidwa 4 Kusakhutitsidwa kwambiri - 88 Akana m62 (required) m62: Kodi mu miyezi 12 yapitayi, pakhomo pano alipo anagwiritsapo ntchito [Sukulu zaboma] Question relevant when: true () 1 Inde 0 Ayi m62d (required) m62d: kodi munakhutitsidwa bwanji ndi chithandizo chomwe munalandilacho Question relevant when: true () 1 kukhutitsidwa kwambiri 2 Kukhutitsidwa 3 Kusakhutitsidwa 4 Kusakhutitsidwa kwambiri - 88 Akana - > m68a_begin Group relevant when: ${m62} =1 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 251 Field Question Answer notedkljs Kodi munayambapo mwakumana ndi mavuto awa ku sukulu yanu ya boma mu miyezi 12 yapitayi reserved_name_for_field_list_labels_ 395 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana m68a (required) m68a: Chithandizo chinali chodula kwambiri/analephera kulipila Question relevant when: true () 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana m68b (required) m68b: Kusowa kwamabuku owerenga ndizipangizo zina zophunzilira Question relevant when: true () 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana m68c (required) m86c: Kuphunzitsa kosakwanila Question relevant when: true () 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana - > m68d_begin Group relevant when: ${m62} =1 note_mb68d Kodi munayambapo mwakumana ndi mavuto awa ku sukulu yanu ya boma mu miyezi 12 yapitayi reserved_name_for_field_list_labels_ 401 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 252 Field Question Answer - 88 Akana m68d (required) m68d: kujomba kwa aziphunzitsi Question relevant when: true () 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana m68e (required) m68e: Kuchuluka kwa ana ophunzira mmakalasi Question relevant when: true () 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana m68f (required) m68f: malo ndi zipangizo zophunzilira zosasamalika Question relevant when: true () 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana m60 (required) m60: Kodi mu miyezi 12 yapitayi, pakhomo pano alipo anagwiritsapo ntchito chipatala chaboma? Question relevant when: true () 1 Inde 0 Ayi m60d (required) m60d: Kodi munakhutitsidwa bwanji ndi chithandizo chomwe munalandilacho Question relevant when: true () 1 kukhutitsidwa kwambiri 2 Kukhutitsidwa 3 Kusakhutitsidwa 4 Kusakhutitsidwa kwambiri - 88 Akana - > m70a_begin Group relevant when: ${m60} =1 noted705 noted705: Kodi munayambapo mwakumana ndi mavuto awa muzipatala za bomapa miyezi 12 yapitayi m70a (required) m70a: Chithandizo chinali chodula kwambiri/analephera kulipila 0 palibe USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 253 Field Question Answer Question relevant when: true () 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana m70b (required) m70b: Kusowa kwa mankhwala kapena zipangizo zina zogwiritsira ntchito Question relevant when: true () 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana m70c (required) m70c: Kupelewela kwa chidwi kapena kusowa ulemu kwa ogwira ntchito Question relevant when: true () 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana - > m70d_begin Group relevant when: ${m60} =1 m70d_note noted705: Ndikangati komwe mwakumana ndi ena mwa mavutowa ku chipatala chanu cha boma pa miyezi 12 yapitayi? reserved_name_for_field_list_labels_ 415 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana m70d (required) m70d: kujomba kwa madotolo/anamwino Question relevant when: true () 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana m70e (required) m70e: Nthawi yodikilira yayitali Question relevant when: true () 0 palibe 1 kamodzi kapena kawiri USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 254 Field Question Answer 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana m70f (required) m70f: Malo aumve? Question relevant when: true () 0 palibe 1 kamodzi kapena kawiri 2 Nthawi zochepa 3 Kawirikawiri - 88 Akana - > m72_begin Group relevant when: ${m60} =1 m72 (required) m72: Kodi munatenga nthawi yayitali bwanji podikila chithandizo ku chipatala Question relevant when: true () m72_units - 1 Mphindi 2 Maola - 99 Sakudziwa m76 (required) m76: kodi inuyo kapenawina aliyense wapakhomo pano analandilapo Zakudya za ana kuchokera ku ndondomeko ya zakudya mu sukulu zaboma Question relevant when: true () 1 Inde 0 Ayi m78 (required) m78: Kodi munalembesapo mavoti? Question relevant when: true () 1 Inde 0 Ayi m80_2 (required) m80:Masankho asanachitike a 2014, kodi mukudziwa china chilichonse chomwe Khansala wanu analonjeza kuti adzapanga akadzapambana pa chisankho? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha m80_3 (required) n80_3: Mukukhulupilira kuti khansalayo akugwira ntchito kuti akwaniritse malonjezano ake? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa - 88 Akana kuyankha USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 255 Field Question Answer m88 (required) m88: Kodi boma lanu lalingóno, Khonsolo, komiti ya chitukuko cha m'mudzi, kapena town khanso limapangitsa misonkhano kuti apeze zitukuko zimene zikufunika koyambirira? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa m90 (required) m90: Munayamba mwakhalapo pa nsonkhano wa zimenezi? Question relevant when: true () 1 Inde 0 Ayi - > Community involvement m92 (required) m92: Kodi mukuganiza kuti anthu ammudzi muno/ town ali ndi mphamvu zochuluka bwanji popereka maganizo pankhani zokhudza chitukuko cha madera awo monga ngati , masukulu, zipatala, ngalande zothirira, misewu zomwe zimapangidwa ndi maboma ang'onoangóno. Question relevant when: true () 1 Kwambiri 2 Nthawi zina 3 Pang'ono 4 Palibe - 99 Sakudziwa - 88 Akana kuyankha m94 (required) m94: Mukaganizira ntchito za chitukuko za m’boma lanu (monga sukulu, zipatala, magetsi, ndi misika), kodi mukuganiza kuti zofuna za anthu zimathandizira bwanji kasankhidwe ka malo a chitukukocho Question relevant when: true () 1 Kwambiri 2 Nthawi zina 3 Pang'ono 4 Palibe - 99 Sakudziwa - 88 Akana kuyankha m110 (required) m110: Kodi muli ndi chikhulupiliro ndi kayendesedwe kachuma ka boma lanu la madera? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa m118 (required) m118: Kodi munagwiritsako ntchito foni yammanja kuti mupeze uthenga wokhuza zithandizo zaboma? monga ngati mitengo ya zinthu, chiwerengero cha anthu odwala, uthenga wokhudza sukulu. − PROBE: Such as commodity prices, health statistics, school information Question relevant when: true () 1 Inde 0 Ayi USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 256 Field Question Answer m120 (required) m120: Kodi munayamba mwagwiritsa ntchito foni yammanja kuti muneneze nkhani za ziphuphu, kujomba kwa aphunzitsi, kusowa kwa mankhwala? − PROBE: Such as corruption, teacher absence, drug shortage Question relevant when: true () 1 Inde 0 Ayi h H. Farming − Sopano ndikufunsani mafunso okhudzana ndi ulimi munyengo yoli ma yathayi h1_filter Kodi nkhomo lanu limapanga/limachita ulimi wina uli wonse pofuna kupanga zokolora zogulitsa kapena zokudya? 1 Inde 0 Ayi - > h1_filterx Group relevant when: ${h1_filter} =1 h1 (required) h1: Munyengo yaulimi yangothayi ya 2017-2018, alipo amene pakhomo panu pano anadzala mbeu ya soya, mtedza, mbatata za kholowa za olenji mkati, kapena mitengo kuti agulitse kapena kudya? Question relevant when: true () 1 Inde 0 Ayi - > h1_filterx > farming Group relevant when: ${h1} =1 - > h1_filterx > farming > h4_starting Group relevant when: ${h1} =1 reserved_name_for_field_list_labels_ 443 1 Inde 0 Ayi - 99 Sakudziwa h4_soya Kodi munadzala Soya ulimi wathawu? 1 Inde 0 Ayi - 99 Sakudziwa h4_grcham Mtedza 1 Inde 0 Ayi - 99 Sakudziwa h4_sweetpot mbatata ya kholowa ya olenji mkati? 1 Inde USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 257 Field Question Answer 0 Ayi - 99 Sakudziwa - > h1_filterx > farming > Kakololedwe ka Soya Group relevant when: ${h4_soya} =1 h3a (required) h3: Kodi ndi malo akulu bwanji amene munalima (SOYA) munyengo yolima yapitayi? − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h3_unhta (required) h3b: Kodi ndi akulu bwanji[mulingo] − RECORD IN UNIT Question relevant when: true () HECTARES Ma Hekitala ACRES Ma ekala SQMETERS Ma sikweya mitazi FOOTBALL PITCHES Ma galaundi a mpira - > h1_filterx > farming > h4_begina1 Group relevant when: ${h4_soya} =1 and ${h3a} >200000.0 h3a_prompt (required) h3_prompt: Mukutsimikiza kuti munalima Soyabeans pa malo a [h3a][h3_unhta]? − INTERVIWER: If the Answer is NO, Please go back to H3a and Reconcile with respondent the correct amount of land Question relevant when: true () 1 Inde 0 Ayi - > h1_filterx > farming > h4_begina2 Group relevant when: ${h4_soya} =1 h3a_ver (required) h3_ver: Kodi [h3a][h3_unhta], ndi malo akulilapo, ochepelapo kapena ofanana kuyelikeza ndi omwe munalima ulendo womaliza womwe tinabwera? Question relevant when: true () 1 More 2 Less 3 About the same 99 Don’t know - 66 N/A. not applicable - > h1_filterx > farming > h4_begina3 Group relevant when: ${h4_soya} =1 h_soytype (required) Munagwiritsa ntchito mtundu wanji wa SOYA? Question relevant when: true () 1 Serenade 2 Tikolore 3 Makwacha -99 Sakudziwa USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 258 Field Question Answer other Other h_soytype_other Specify other. Question relevant when: selected(${h_soytype}, 'other') - > h1_filterx > farming > Kakololedwe ka Soya Group relevant when: ${h4_soya} =1 h4aa (required) h4aa: Kodi khomo lanu linapeza ma kilogalamu angati a (SOYA) munyengo yolima yapitayi, chonde phatikizilani zokolora zonse za (SOYAYU) pamodzi. − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h6aa (required) h6a: Ndimakilogalamu angati a (SOYA) amene munasunga kuti khomo lanu lizigwiritsa ntchito? − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h4ba (required) h4ba: Kodi ndi makilogalamu angati a (SOYA) omwe khomo lanu linagulitsa? Chonde phatikizilani zokolora zonse za (mbeuyi) zomwe munagulitsa pamodzi. − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () - > h1_filterx > farming > Sales of Soyabeans Group relevant when: ${h4ba} >0 h5a (required) h5: Zonse pamodzi, munalandila ndalama zingati mutagulitsa (SOYAYU) munyengo yolima yapitayi? − RECORD IN MK . Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h6ba (required) h6b: Kodi zinakutengelani nthawi yaitali bwanji kuti mugulitse zokolora za SOYAYU? (masiku) Question relevant when: true () - > h1_filterx > farming > njira zodzalira Soya Group relevant when: ${h4_soya} =1 h7aa (required) h7ac: Kodi munathira feteleza ku SOYA Question relevant when: true () 1 Inde USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 259 Field Question Answer 0 Ayi - 99 Sakudziwa h7a8a (required) h7a8c: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati zogulila feteleza omwe munathila pa SOYA] − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7a9a (required) h7a9c: Kodi munagwiritsa ntchito feteleza wochulka bwanji pa [KG] − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7ba (required) h7ba: Kodi munathila manyowa ku SOYA Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7b8a (required) h7b8a: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati zogulila manyowa omwe munathila pa SOYAYU − RECORD IN MK .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7b9a (required) h7b9a: Kodi munagwiritsa ntchito manyowa wochuluka bwanji pa [KG] − RECORD IN KGs Question relevant when: true () h7ca (required) h7cc: Kodi munagwiritsa ntchito mbewu ya njere/mbande podzala SOYA? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7c8a (required) h7c8c: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati zogulila SOYA [MK] − Write "-77" if Don't Know or Refused to answer. Question relevant when: true () - > h1_filterx > farming > njira zodzalira Soya > beginh7a USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 260 Field Question Answer h7c9a (required) h7c9c: Kodi munagwiritsa ntchito Mbewu yochuluka bwanji ya SOYA − RECORD IN KGs or SEEDINGS Question relevant when: true () h7c9a2 (required) lembani mulingo wa mbewu ya SOYA Question relevant when: true () 1 ma KG 2 Mbewu - 99 Sakudziwa h7da (required) h7da: Kodi munagwilitsapo tchito waganyu pobzala, kupalila, kapena pokolola mbewu ya SOYAyi Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7d8a (required) h7d8a: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati pa waganyu pobzala, kupalila, kapena pokolola mbewu ya [Soya] − RECORD IN MK.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7d9a (required) h7d9a: Kodi munagwiritsa ntchito ndalama zingati polipira a ganyu wopalira, kukolora mbewu ya SOYAyi? − RECORD IN HOURS.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7ea (required) h7ea: Kodi munagwilitsapo ntchito zida/zipangizo zobweleka pa mbewu ya [Soya] Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7e8a (required) h7e8a: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati pazida/zipangizo zobweleka ku Mbewuyi − RECORD IN MK Question relevant when: true () - > h1_filterx > farming > Zokolora za Mtedza Group relevant when: ${h4_grcham} =1 h3b (required) h3b: Kodi ndi malo akulu bwanji amene munalima (MTEDZA) munyengo yolima yapitayi? USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 261 Field Question Answer − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h3_unhtb (required) h3_unhtb: Kodi ndi akulu bwanji − RECORD IN UNIT Question relevant when: true () HECTARES Ma Hekitala ACRES Ma ekala SQMETERS Ma sikweya mitazi FOOTBALL PITCHES Ma galaundi a mpira - > h1_filterx > farming > h4_beginb1 Group relevant when: ${h4_grcham} =1 and ${h3b} >200000.0 h3b_prompt (required) h3b_prompt: Mukutsimikiza kuti munalima Mtedza pa malo a [h3b][h3_unhtb]? − INTERVIWER: If the Answer is NO, Please go back to H3b and Reconcile with respondent the correct amount of land Question relevant when: true () 1 Inde 0 Ayi - > h1_filterx > farming > h4_beginb2 Group relevant when: ${h4_grcham} =1 h3b_ver (required) h3b_ver: Kodi [h3b][h3_unhtb], ndi malo akulilapo, ochepelapo kapena ofanana kuyelikeza ndi omwe munalima ulendo womaliza womwe tinabwera? Question relevant when: true () 1 More 2 Less 3 About the same 99 Don’t know - 66 N/A. not applicable - > h1_filterx > farming > h4_beginb3 Group relevant when: ${h4_grcham} =1 h_gntype (required) h_gntype: munalima mbeu ya MTEDZA yamtundu wanji? Question relevant when: true () 1 CG7 2 Chalimbana 3 Chalimbana 2000 -99 Sakudziwa other Other h_gntype_other Specify other. Question relevant when: selected(${h_gntype}, 'other') h4ab (required) h4ab: Kodi khomo lanu linapeza ma kilogalamu angati a (MTEDZA ) munyengo yolima yapitayi, chonde phatikizilani zokolora zonse pamodzi. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 262 Field Question Answer − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h6ab (required) h6ab: Ndimakilogalamu angati a (MTEDZA ) amene munasunga kuti khomo lanu lizigwiritsa ntchito? − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h4bb (required) h4bb: Kodi ndi makilogalamu angati a (MTEDZA ) omwe khomo lanu linagulista? Chonde phatikizilani zokolora zonse pamodzi − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h5b (required) h5b: Zonse pamodzi, munalandila ndalama zingati mutagulitsa (MTEDZA ) munyengo yolima yapitayi? − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h6bb (required) h6bb: Kodi zinakutengelani nthawi yaitali bwanji kuti mugulitse zokolora za MTEDZA zi? (masiku) Question relevant when: true () - > h1_filterx > farming > Njira zolimira Mtedza Group relevant when: ${h4_grcham} =1 h7ab (required) h7ab: Kodi munathira feteleza ku mbeu ya MTEDZA Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7a8b (required) h7a8c: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati zogulila feteleza omwe munathila pa mbewuyi? − RECORD IN MK Question relevant when: true () h7a9b (required) h7a9c: Kodi munagwiritsa ntchito feteleza wochulka bwanji pa [MTEDZA ] − RECORD IN KGs Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 263 Field Question Answer h7bb (required) h7bb: Kodi munathila manyowa ku MTEDZA wu? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7b8b (required) h7b8b: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati zogulila manyowa omwe munathila pa MTEDZA wu? − RECORD IN MK.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7b9b (required) h7b9b: Kodi munagwiritsa ntchito manyowa wochulka bwanji pa MTEDZA ? − RECORD IN KGs Question relevant when: true () h7cb (required) h7cb: Kodi munachita kudzala Mbewu ya MTEDZA yi? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7c8b (required) h7c8c: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati zogulila Mbewuyi − RECORD IN MK.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () - > h1_filterx > farming > Njira zolimira Mtedza > begin_h7b h7c9b (required) h7c9b: Kodi munagwiritsa ntchito Mbewu yochuluka bwanji pa MTEDZA ? − RECORD IN KGs or SEEDINGS Question relevant when: true () h7c9b2 (required) Lembani mulingo wa mbewu Question relevant when: true () 1 ma KG 2 Mbewu - 99 Sakudziwa h7db (required) h7da: Kodi munagwilitsapo tchito waganyu pobzala, kupalila, kapena pokolola MTEDZA Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 264 Field Question Answer h7d8b (required) h7d8a: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati pa waganyu pobzala, kupalila, kapena pokolola mbewu ya MTEDZA yi? − RECORD IN MK.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7d9b (required) h7d9a: Kodi ndi maola angati omwe munagwiritsa ntchito pa waganyu pobzala, kupalila, kapena pokolola mbewu ya [MTEDZA ] − RECORD IN HOURS.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7eb (required) h7ea: Kodi munagwilitsapo tchito zida/zipangizo zobweleka pa mbewuyi? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7e8b (required) h7e8a: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati pazida/zipangizo zobweleka ku MTEDZA ? − RECORD IN MK.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () - > h1_filterx > farming > Zokolora za MBATATA ya kholowa ya olenji mkati Group relevant when: ${h4_sweetpot} =1 h3f (required) h3: Kodi ndi malo akulu bwanji amene munalima (MBATATA ya kholowa ya olenji mkati) munyengo yolima yapitayi? − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h3_unhtf (required) h3b: Kodi ndi akulu bwanji − RECORD IN UNIT Question relevant when: true () HECTARES Ma Hekitala ACRES Ma ekala SQMETERS Ma sikweya mitazi FOOTBALL PITCHES Ma galaundi a mpira - > h1_filterx > farming > h4_beginf1 Group relevant when: ${h4_sweetpot} =1 and ${h3b} >200000.0 USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 265 Field Question Answer h3f_prompt (required) h3f_prompt: Mukutsimikiza kuti munalima MBATATA ya kholowa ya olenji mkati pa malo a [h3f][h3_unhtf]? − INTERVIWER: If the Answer is NO, Please go back to H3f and Reconcile with respondent the correct amount of land Question relevant when: true () 1 Inde 0 Ayi - > h1_filterx > farming > h4_beginf2 Group relevant when: ${h4_sweetpot} =1 h3f_ver (required) h3f_ver: Kodi [h3f][h3_unhtf], ndi malo akulilapo, ochepelapo kapena ofanana kuyelikeza ndi omwe munalima ulendo womaliza womwe tinabwera? Question relevant when: true () 1 More 2 Less 3 About the same 99 Don’t know - 66 N/A. not applicable - > h1_filterx > farming > h4_beginf3 Group relevant when: ${h4_sweetpot} =1 h4af (required) h4a: Kodi khomo lanu linapeza ma kilogalamu angati a (MBATATA ya kholowa ya olenji mkati) munyengo yolima yapitayi, chonde phatikizilani zokolora zonse pamodzi. − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h4bf (required) h4b: Kodi ndi makilogalamu angati a (MBATATA ya kholowa ya olenji mkati) omwe khomo lanu linagulista? Chonde phatikizilani zokolora zonse pamodzi − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h5f (required) h5: Zonse pamodzi, munalandila ndalama zingati mutagulitsa (MBATATA ya kholowa ya olenji mkati) munyengo yolima yapitayi? − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h6af (required) h6a: Ndimakilogalamu angati a (MBATATA ya kholowa ya olenji mkati) USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 266 Field Question Answer amene munasunga kuti khomo lanu lizigwiritsa ntchito? − .Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h6bf (required) h6b: Kodi zinakutengelani nthawi yaitali bwanji kuti mugulitse zokolora za MBATATA ya kholowa ya olenji mkatizi? (masiku) Question relevant when: true () - > h1_filterx > farming > Njira zolimira MBATATA ya kholowa ya olenji mkati Group relevant when: ${h4_sweetpot} =1 h7af (required) h7a: Kodi munathira feteleza ku mbeu ya MBATATA ya kholowa ya olenji mkati Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7a8f (required) h7a8c: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati zogulila feteleza omwe munathila pa MBATATA ya kholowa ya olenji mkati − RECORD IN MK.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7a9f (required) h7a9c: Kodi munagwiritsa ntchito feteleza wochulka bwanji pambewuyi? − RECORD IN KGs.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7bf (required) h7ba: Kodi munathila manyowa ku mbeu y MBATATA ya kholowa ya olenji mkati yi Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7b8f (required) h7b8a: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati zogulila manyowa omwe munathila pambewuyi? − RECORD IN MK.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7b9f (required) h7b9a: Kodi munagwiritsa ntchito manyowa wochulka bwanji pambewuyi USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 267 Field Question Answer ya MBATATA ya kholowa ya olenji mkati? − RECORD IN KGs.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7cf (required) h7cc: Kodi munagwiritsa ntchito mbewu podzala MBATATA ya kholowa ya olenji mkati? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7c8f (required) h7c8c: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati zogulila Mbewuyi? − RECORD IN MK.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () - > h1_filterx > farming > Njira zolimira MBATATA ya kholowa ya olenji mkati > h7f h7c9f (required) h7c9c: Kodi munagwiritsa ntchito Mbewu yochuluka bwanji pa mbewuyi? − RECORD IN KGs or SEEDINGS.Write "-77" if Don't Know or Refused to answer. Question relevant when: true () h7c9b2 (required) Lembani mulingo wa mbewu ya MBATATA ya kholowa ya olenji mkati Question relevant when: true () 1 ma KG 2 Mbewu - 99 Sakudziwa h7df (required) h7da: Kodi munagwilitsapo tchito waganyu pobzala, kupalila, kapena pokolola MBATATA ya kholowa ya olenji mkati? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7d8f (required) h7d8a: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati pa waganyu pobzala, kupalila, kapena pokolola mbewuyi − RECORD IN MK Question relevant when: true () h7d9f (required) h7d9a: Kodi ndi maola angati omwe munagwiritsa ntchito pa waganyu pobzala, kupalila, kapena pokolola mbewu yi? − RECORD IN HOURS USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 268 Field Question Answer Question relevant when: true () h7ef (required) h7ea: Kodi munagwilitsapo tchito zida/zipangizo zobweleka pa mbewu ya MBATATA ya kholowa ya olenji mkatiyi? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h7e8f (required) h7e8a: Kodi zonse pamodzi munagwiritsa ntchito ndalama zingati pazida/zipangizo zobweleka ku Mbewuyi? − RECORD IN MK Question relevant when: true () h27 (required) h27: Kodi pakhomo pano alipo amene amatenga nawo gawo mu makalabu/magulu a ulimi? Question relevant when: true () 1 Inde 0 Ayi h28 (required) h28: Kodi pakhomo pano ndani amatenga nawo mbali? Question relevant when: true () 1 Mzimayi wamkulu kapena mkazi wawo 2 Mzibambo wamkulu kapena mwamuna wawo 3 Mzibambo wina wapakhomopo 4 Mzimayi wina wapakhomopo n04 (required) n04: kodi mwagwiritsako ntchito foni yammanja pochita ma bizinesi mumiyezi 12 yapitayi (monga kuyang'ana mitengo ya mbewu) Question relevant when: true () 1 Inde 0 Ayi h30 (required) h30: Kodi zokolora zanu zaposachedwapa munagulitsa kuti ndipo motani? Question relevant when: true () 1 Wogula amabwera pakhomo 2 Amapita ku msika 3 Kudzera kunyumba zosungitsa katundu 4 Kudzera kumakina a internet - 77 Zina - 99 Sakudziwa - 66 N/A h30a (required) H30a: Mumiyezi khumi ndi iwiri yapitayi, munatayako gawo lina la zokolola 1 Inde 0 Ayi USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 269 Field Question Answer munthawi yosankha chifukwa chakuwonongeka (such as aflatoxin, mold, other) Question relevant when: true () - 99 Sakudziwa h30b (required) H30b: Ndi mbewu yanji imeneyi? Question relevant when: true () 1 Groundnuts 2 Soya 3 Sweet potatoes 4 Maize other Other h30b_other Specify other. Question relevant when: selected(${h30b}, 'other') h30c (required) H30c: Mumiyezi khumi ndi iwiri yapitayi, mwakumanako ndi vuto lo kanidwa kugulisa zokolola zanu kumsika kapena kosungira katundu chifukwa cha kukayikiridwa kuti ndizowonongeka? Question relevant when: true () 1 Inde 0 Ayi - 99 Sakudziwa h30d (required) H30d. Inali mbewu yanji? Question relevant when: true () 1 Groundnuts 2 Soya 3 Sweet potatoes 4 Maize other Other h30d_other Specify other. Question relevant when: selected(${h30d}, 'other') h31 (required) h31: Miyezi 12 yapitayi, mwasinthako njira zamalimidwe anu? Question relevant when: true () 1 Inde 0 Ayi h32 (required) h32: Ngati inde, ndi njira ziti zatsopano za malimidwe zomwe mwayamba kutsatira? Question relevant when: true () 1 kugwiritsa ntchito madzi wochepa 2 Kugwiritsa ntchito Feteleza 3 Nyongolotsi zamunthaka 4 Mbeu zosiyanasiyana 5 Kuphatikiza mbeu 6 Kudzala mbewu mwakasinthasintha 7 Kuthirira USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 270 Field Question Answer 8 Mbeu zamakono 9 Kugwiritsa ntchito manyowa 10 Malo abwino osungila -77 Zina -99 Sakudziwa other Other h32_other Specify other. Question relevant when: selected(${h32}, 'other') h33 (required) h33: Kodi mtundu wambeu zimene mumabzyala wasitha muzaka zochepa zapitazi? Question relevant when: true () 1 Inde 0 Ayi h34 (required) h34: Ndi chifukwa chiyani mbeu zimene banja lanu limalima zasintha? Question relevant when: true () 1 Bungwe lomwe silaboma linapatsa mbeu yatsopano/anatiuza kuti tisinthe mbeu 2 Mtundu wina wake wa mbeu unatchipa ndipo unali wasavuta kupeza 3 Tinapeza malo owonjezera 4 Tinapeza ngongole ndipo tinakwanitsa kugula mbeu 5 Tinaphunzitsidwa za ubwino wosintha mbeu mmunda 6 Timafuna kuonjezera mbeu yatsopano 7 Kagwedwe ka mvula kanasintha - 99 Sakudziwa o25 o25: INTERVIEWER: Kodi pali wothandizira kuyankha mafunso ? − Do not ask 1 Inde 0 Ayi - > a21_begin Group relevant when: ${o25} =1 a20 (required) a21 Kodi dzina la labanja la amene wathandizira kuyankha mafunso ndi ndani? Question relevant when: true () USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 271 Field Question Answer a21_begin (required) a22 Mayina a amene wathandizira kuyankha mafunsowo ndi ndani? Question relevant when: true () a22 (required) a20 Ofunsa: Onani kuti kodi wothandizira kuyankha mafunsoyo ndi wamkazi kapena wamwamuna? − (observe) Question relevant when: true () 0 wamwamuna 1 Wamkazi a23 (required) a23 Wothandiza kuyankha mafunsoyo ali ndi udindo wanji pa banjapo? Question relevant when: true () 1 Mutu wabanja 2 mkazi/mwamuna wa mutu wakhomolo 3 wamkulu wina wakhomopo a24 a24 kodi munthu ameneyu anathandiza kuyankha gawo liti? A Background B Household members roster C Education D Well-being E Household features F Assets G Credit/loans H Farming I Food security J Environment K Health L Household decision-making M Participation and governance O GPS, Recontact, Notes o O. Recontact Information o02 (required) o02: Zikomo chifukwa chanthawi yanu pothandiza kafukufukuyu. Kuti tionenesese kuti zinthu zili bwino, mutha kundipatsa nambala yanu ya foni yomwe ingazathe kugwiritsidwa ntchito polumikizana nanu patakhala kuti tikufuna kumvesesa pa zinthu zina mu ma sabata angapo akubwerawa? Question relevant when: true () 1 Inde 0 Ayi o04 o04: Nambala ya foni yomwe tingathe kulumikizana ndi khomo ino USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 272 Field Question Answer − Include 9 digits only with no zero in front and no dashes or spaces Question relevant when: ${o02} =1 o06 o06: Nambala ya foni ina ya pakhomo pano − Include 9 digits only with no zero in front and no dashes or spaces Question relevant when: ${o02} =1 o22 o22: Location Notes: Please provide descriptive location of household. (example: near primary school, off main road, etc) Question relevant when: ${o02} =1 o24 o24: Interview Notes: (Please make notes on responsiveness of household, difficulties encountered during interview, etc.) Question relevant when: ${o02} =1 comments GENERAL COMMENTS ON THE INTERVIEW − Only Relevant comments are encouraged o18 o18: GPS Reading − Wait until accuract is less than 10 meters if possible USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 273 ANNEX C. FOCUS GROUP DISCUSSION GUIDE Malawi CDCS Endline Evaluation 2018 BOOKING: 1. Ask for 5 males and 5 females, representing average, normal people in the community. 2. All participants must be living there at least 2 years PREPARATION: Draw a matrix that looks like this: Over the past TWO years (since 2016) have any of these become worse, remained the same or become better in your community? Worse Same Better Food Availability & Quality Kupezeka kwachakudya ndi koliteyake? Health services availability and quality Kupezeka kwa chithandizo chazaumoyo ndi koliteyake? Education Local Government capacity Upangili wa boma (kwa a DC) Environment Poverty Uphawi NOTE TAKER: At the top of your notes, write the district and village name and make a list with numbers assigned to each participant and indication of whether each is male or female. Do not record names. For example: District: _________________ Village: _____________________ Length of interview: __________ Audio file number: __________ USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 274 Participants: 1 2 3 4 5 6 7 8 9 10 INFORMED CONSENT Thank you very much for coming today. I am working with Invest in Knowledge and Social Impact. We are conducting a study to assess the impact of the USAID/Malawi Country Development Cooperation Strategy. USAID has been doing some programs in this area, and the results of this study may help inform them on whether their approach is worthwhile or if they need improvements. You have been invited to participate in this group discussion because you may be able to provide information about changes in this community in the past two years (since 2016). Sikomo kwejinji ligongo lyakwisa lero, une ngukamula masengo ni wa Invest in knowledge ni wa Social Impact. tukutenda kaungunya pkusaka kulora mugakwendela msengo gachitukuko ga USAID/Malawi Country Development Strategies. Yakuichisya ya kaungunyaju ikomboleche kamuchisya kukwesya ma pologaramu gagachipelechedwa mMalawi muno kusogoloku.Awilanjidwe kuti ajigale nawo gawo pa yakukambilana ya palikugayi/guluyi ligongo mpaka akomboleche kupeleka utenga wakusana ni kusinda kwa indu mdela jawo jino mu chaka chipitechi. If you agree to participate in this study, we would like to ask some general questions about changes you've noticed in this community in the past two years. We are seeking your honest opinions and observations from everyone in the group. This interview will take about 1 hour and 30 minutes. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 275 Naga akwitika kujigala nawo mbali mukaungunyaju, tukusaka tuwusyeko mausyo gangapo gakusana ni kusinda kwakuweni mudela jawo jino muchaka chipitechi. tukuwenda nganisyo syawo syakulola ni yiwaiweni kutyochela kwa wane waliwose wa likuga alino. kungulukaku kujigale pafupifupi ola jimo ni manisi makumi gatatu (1 hour 30 munites) Your participation is completely voluntary. You can choose not to participate now, or at any time between now and the end of the discussion you can leave. There is no penalty or problem if you choose not to participate. Should you feel uncomfortable with any question, you may refuse to answer it. Kujigala nawo gawo kuli kwaulele. mpaka asagule kuti akajigala nawo mbali apano, kapena ndawi jine jili jose kutandila apano mpaka kumapeto kwayakambilanayi. pangali chilango kapena vuto jampakana jagwile ali awele kuti nganajikala nawo gawo, naga ngakugopoka ni liusyo line, mpaka akomboleche kukana kwanga liwusyo lyelelo. There are no known risks of participating in this activity other than losing an hour and a half of productive time. Although we will not really talk about sensitive topics, in order to make you feel free to speak freely, I encourage everyone who chooses to participate to keep the conversation confidential out of respect for your neighbors here just in case. But know that when we analyze the information you share, your name or position in this community will never be referenced, so your answers will be anonymous to outsiders. Pangali yakogoya ine iliyose yakwisa ligongo lyakwanga mausyoga kupatula kuti chajase ola jimo ndi mbindi makumi gatatu (1hour 30 munites) jakawele ali mkamula masengo gawo. amtamose kuti ngitukamba ya ngani syakupanikanya, nguwenda jwine juli jose juchajigale nawo mbali payakukambilanayi kuti asunje chinsisi pakusunga ulemu wajawo wituli nawo panopano. gamba kwa manyisya kuti pakuwanganya gichatume, lina ni udindo wawo wa mmusi muno ngisiikolanjidwa, mwantiyoyo yakwanga yawo ichwa ya chinsisi kwa wandu wakusa. Ngisalipidwa kuti ajigale nawo mbali, nambosoni pangali phindu jwakwisa kupatula ichatusalire ikamuchisye USAID kwausya pasogolo masengo gakwe mMlalawi. tukupeleka yakumwayi mpela litala limo lyakutogolera pa ndawi jawo jawiche akuno You will not be paid to participate, and there are no direct benefits to you other than knowing your information may help USAID improve its services in Malawi. We are providing these modest refreshments as a way to thank you for your time to come here. Simudzalipidwa kuti mutenge nawo mbali, ndipo palibe maphindu obwera kwa inu kupatula zomwe mutiuze zomwe zithandize USAID kupititsa patsogolo ntchito zake Mmalawi. Tikupereka zakumwazi ngati njira yokuthokozani pa nthawi yanu yomwe mwabwerera kuno. USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 276 I also request your permission to record our conversation so that I can remember what was said. Ndikupemphanso chilorezo chanu kuti tigwiritse ntchito choteperachi kuti ndikakhonze kukumbukira zomwe zinanenedwa. If you have any questions or concerns now or in the future, you may contact James Mkandawire at 0999-412-756 james.mkandawire@investinknowledge.org. Ngati mungakhale ndi mafunso kapena zina zofuna kudziwa, mukhonza kuyankhula ndi James Mkandawire pa 0999-412-756 james.mkandawire@investinknowledge.org. In case you have any compliant with regards to your rights as a study participant you can contact the Social Impact Institutional Review Board: irb@socialimpact.com +1-703-465-1884. Ngati mungakhale ndi madandaulo monga wotenga nawo gawo mu kafukufukuyu mukhoza kuyankhula ndi a Social Impact Institutional Review Board: +1-703-465-1884. Or you can contact the Research in the Social Sciences and Humanities in Malawi committee of the National Commission for Sciences and Technology on the following address: Kapena mukhonza kuyankhula ndi a Research in the Social Sciences and Humanities in Malawi committee of the National Commission for Sciences and Technology pa address iyi: NCST 1st Floor Lingadzi House, Robert Mugabe Crescent Private Bag B303 Lilongwe 3 Malawi. Email: directorgeneral@ncst.mw Phone: +265 1 771 550 Do you have any questions? Muli ndi mafunso aliwonse? Do you agree to participate in the study? Yes No (if any say no, allow them to leave before proceeding) Do you agree to let me record our conversation? Yes No (if any say no, allow them to leave, or do not record if large consensus to not record) Mukuvomereza kutenga nawo gawo mu zokambiranazi? Yes No (ngati ena anena kuti ayi aloreni amuke/ kapena kusajambule ngati avomerezana kuti zokambidwazo zisajambulidwe) USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 277 INTRODUCTIONS: First let’s get to know each other… [facilitate group greetings, including name, number of years living in community, and any leadership positions held] Poyamba tiyeni tidziwane… [facilitate group greetings, including name, number of years living in community, and any leadership positions held] INSTRUCTIONS: For this discussion, I would like to learn about any changes that have occurred in this community over the past 2 years. I am going to ask about certain topics, and for each one, please tell me whether you feel the situation has gotten worse, gotten better, or stayed almost the same in the past 2 years. I expect that some of you may not fully agree on each thing, and that is OK. I look forward to hearing you discuss with each other about your views. These questions ask for your own perceptions, and there are no right or wrong answers. Each person’s perspective is valuable to us, so please feel free to speak your mind. Muzokambirana zathuzi, ndikufuna tiphunzire za zinthu zomwe zasintha mu dera lanu lino mu zaka ziwiri zapitazi. Ndifunsako zokhudzana ndi mitu ya zinthu zina, ndipo pa chimodzi chimodzi cha zimenezi, mundiwuze ngati mukuona kuti zinthuzo zayipa kwambiri, zilibwinoko kapena ngati zili chimodzimodzi mu zaka ziwiri zapitazi. Ndikuyembekezera kuti ena a inu simungagwirizane kwa thunthu pa china chili chonse, izo ndizololedwa. Ndikuyembekezera kumva inu mukukambirana ndi anzanuwo za maganizo anu. Mafunso awa akufuna kumva maganizo anu, ndipo palibe mayankho olondola kapena olakwa. Maganizo a munthu wina aliyense ndi wofunika kwa ife. Kotero mukhale omasuka kulankhula maganizo anu. Helpful probes to promote general discussion for each question: • Do you agree with this view? Mukugwirizana nawo maganizo amenewa? • Why do you think so? Ndichifukwa chiyani mukuganiza choncho? • Can you give examples for why you think so? Mungapereke zitsanzo?zifukwa zome zikupangitsa inu kuti muziganiza choncho? • Is there any part that has improved in past 2 years? Kodi pali mbali ina iliyonse imene yatukukako/yapita patsogolo muzaka ziwiri zapitazi? Ending question for each section should ask: • Is there consensus on which category I should use? Kodi pali mgwirizano pa magawo amene aperekedwa? Mark the number of votes for each topic on the flipchart. 1. Let’s start by talking about FOOD SECURITY, (availability of sufficient food and quality of food in this community). In the past 2 years, do you think food security has gotten worse, better, or is the same? Why? Tiyeni tiyambe ndi kupezeka kwa chakudya ( kupezeka kwa chakudya chokwanira komanso chakudya chabwino mdera lino). Mzaka ziwiri zapitazi, mukuganiza kuti kapezekedwe ka chakudya katsiaka koyipa kwambiri, kalibwinoko kapena kali chimodzimodzi ngati kale? Chifukwa Chiyani? USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 278 Possible ways to prompt discussion: a. What about Quantity of food/ Quality of food/ Hunger? b. Must ask: How are crop yields (Maize, Groundnuts, Soya) in the past 2 years compared to before that? Why? Kodi zokolola za (Chimanga, Mtedza, Soya) zinali bwanji mu zaka ziwiri zapitazi kuyerekeza ndi zaka zambuyomo? Chifukwa chiyani? c. Do people need to purchase food from market besides what you produce? Did it increase/decrease in last 2 years? Kupatula zakudya zimene inuyo mumalima, kodi mumayeneranso kugula zakudya zinakuchokera kumsika? Kodi kugulaku, kwaonjezereka kapena kwacheperapo muzaka ziwiri zapitazi? d. Do people produce enough to sell in the market? Did it increase/decrease in last 2 years? Kodi munalima zakudya zochukluka zoti nkugulitsa kumsika? 2. What about access to QUALITY HEALTH SERVICES and HEALTH INFORMATION in general? Has it gotten worse, better, or stayed the same over the past 2 years? Then, Probe for the following: Nanga kumbali ya chithandizo chabwino cha zaumoyo ? nanga kapezedwe ka mauthenga a zaumoyo kayipa kwambiri, kalibwinoko, kapena kali chimodzimodzi mu zaka ziwiri zapitazi poyerekeza ndi zaka zapitazi? a. Are people able to get to the hospital or health center when needed? Kodi anthu amatha kupita kuchipatala chachikulu kapena chaching’ono pakafunika kutero? b. To what extent are local health facilities able to meet the needs of this community (to see doctor/nurse, midwife, medicines etc.,)? What about health workers? Nkufikira pati pamene zipatala zakudera kuno zimakwanitsa zosowa za anthu amdera lino (madotolo/anamwino, azamba, mankhwala ndi zina ). Nanga ogwira ntchito yazaumoyo? c. To what extent are you satisfied with quality of health services? Waiting times? Nkufikira pati pamene inu mumakhutitsidwa ndi chithandizo chabwino chokhuzana ndi zaumoyo? d. How common is it for couples to get voluntary counseling and testing for HIV? Are people able to get HIV care? Is this service (easily) available now? e. Are people learning better food nutrition practices? Where are they getting this information? Ndipafupipafupi bwanji kunoko pamene athu amene ali pabanja amakatenga uphungu wokhudzana ndi HIV ndikuyezetsa magazi limodzi? Kodi anthu amalandira thandizo lokhudzana ndi HIV? Kodi chithandizo chimenechi chikupezeka mosavuta panopa? Nanga mauthenga amenewa amawapeza kuchokera kuti? 3. Tell me about access to quality EDUCATION in this community in general? Has it gotten worse, better, or is the same over the past 2 years? Tsopano mundiuzeko zokhudzana ndi maphunziro abwino a mdera lino? Mu USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 279 zaka ziwiri zapitazi kodi ayipa kwambiri, akhalako bwino, kapena sanasinthe? a. To what extent are people in this community satisfied with primary and secondary schools their children go to? Why? (probe whether its due to availability, costs, quality etc.,) Nkufikira pati pamene anthu anthu amdera lino amakhutisidwa ndi maphunziro a sukulu za pulayimale ndi sekondale komwe ana awo amapitako? Chifukwa chiyani? ( funsani ngati izi zili chifukwa cha kapezekedwe ka sukulu, ndalama zolipira, maphuzitsidwe ndi zina) b. Do people view educational needs for girl and boy children differently? Why? Kodi anthu amaona zofunika pamaphunziro aatsikana ndi anyamata misiyana? Chifukwa chiyani? c. Any changes in school drop outs for GIRLS and BOYS at primary / secondary levels? Why? Kodi atsikana osiya sukulu ku pulayimale ndi sekondale achuluka kapena achepa kapena chili chimodzimodzi? Chifukwa chiyani? d. What about ability to read, for boys, girls at primary levels? Of men, women in the community? Any changes? Why? Nanga pa nkhani zokhudza kuwerenga kwa anyamata ndi atsikana akupulayimale? Azibambo ndi azimayi amdera lino? Pali kusintha kulikonse? Chifukwa chiyani? 4. How is the CAPACITY OF DISTRICT GOVERNMENT now to provide services to this community (for example, District Councilor, District Commissioner, and government provision of public services)? Are they better able to meet your needs, less able, or is it the same in the past 2 years? Kodi boma lanu/DC [tchulani] lathandiza bwanji popereka zofunikira kudera lino? Kodi akukutumikiraniko bwino, mochepera kapena zili chimodzimodzi poyerekeza ndi zaka ziwiri zapitazi? a. Must ask: What do you know about the role of the District Councilors? Are they fulfilling their duties? Kodi inuyo mumadziwapo chiyani pantchito zimene zaofesi ya kwa DC? Kodi iwowa, nthito yomwe amayenera kugwira akuyikwanilitsa? b. Do you know who to reach and how to reach in district offices to get help from local government to improve your community? And, complain about the problems? Kodi mukudziwa yemwe mungamufikire ndi momwe mungamufikire ku ma ofesi a bwanankubwa [DC] kuti mukapeze chithandizo chokweza dera lanu? Ndi kukadandaula za mavuto? c. Are district government representatives responsive to requests for assistance or complaints? Kodi akuboma amathandiza pa zopempha kapena madandaulo? d. Do you think they have enough manpower and financial resources to meet your request? USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 280 Kodi mukuganiza kuti ali ndi ogwira ntchito okwanira komanso zipangizo zokwanira kuti akwaniritse zopempha zanu? 5. Thinking about your surrounding ENVIRONMENT, such as forests, fish and lakes, and even the soil you farm on, to what extent have there been efforts in this community to protect those resources for future generations? Why? Kuganizira za chilengedwe monga nkhalango, nsomba, Nyanja, nthaka yomwe mumalimapoyi, nkuthekera kotani kumene kwachitika pokhuza ndondomeko yoteteza zachilengedwechi poganizira mibado yakutsogoloku mdera lanu lino? a. What is your perception of environmental resource availability here? (more/less than 2 years ago?) b. Are you concerned about the availability of resources? 6. What about POVERTY in this community now? Do you feel the level of poverty has gotten worse, gotten better, or stayed almost the same in past 2 years? Nanga ku nkhani Yaumphawi mdera lino pakadali pano? Mukuona kuti nkhani zaumphawi zayipa kwambiri, zakhalako bwino, kapena zili chimodzimodzi muzaka ziwiri zapitazi? Possible ways to prompt discussion: a. Do you think your community has done better than neighboring communities? Why? Kodi mukuganiza kuti muzaka ziwiri zapitazi dera lanu lino lapangako bwino kuposa madera ena oyandikirana nawo. Chifukwa chiyani? b. What are the main jobs people in this community have? (e.g. farming, other types of labor?). Has this changed? Kodi ndi ntchito zanji zikuluzikulu zimene zimagwilidwa ndi anthu akudera lino. (Mwachitsanzo, zaulimi, zina zaulebala) c. Is it common for children to work instead of going to school? Has this changed in the past two years? How/why? Kodi ana ena apanyumba pano azaka zakubadwa zochepera 14 amafunika azigwira ntchito? Amagwira ntchito pafupipafupi bwanji? Kodi ntchitoyi ndi ya sizoni (monga nthawi yokolora, yopalira) kapena ndi yanthawi zones? Kodi nthawi imeneyi, anawa amatha kujomba kusukulu? Additional Questions 1. Are you aware of any projects supported by USAID in this community? Which ones? Kodi mukudziwako ma polojekiti ena aliwonse othandizidwa ndi USAID mdera lino? Ntchulani mapolojekitiwa? 2. What type of interaction do you or other community members have with those implementing these USAID projects? Need examples. Kodi pali ubale wanji pakati pa inu kapena madera ena ndi anthu omwe amayendetsa ma polojekiti amenewa a USAID? Pakhale zitsanzo? USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 281 3. Can you tell me about how the interaction has been? Has it been positive, negative, or neutral? Why (need examples)? Mungandiuzeko momwe ubalewu wakhalira? Kuti unali wabwino, woyipa kapena pakatikati? Ndikufuna zitsanzo? 4. Are you aware of any ways aid projects are working together in your community? Kodi mukudziwapo zina zilizose zomwe mapolojekiti ochita kuthandizidwa mmene amagwilira ntchito mdera lanu lino? 5. Have there been any challenges from your perspective related to these projects ongoing in this community? Kodi pakhalako mavuto kumbali yanu pokhudzana ndi ma pulojekiti omwe akuchitika mdera lino 6. What suggestions do you have to improve quality of life in your community? Muli ndi maganizo otani opititsira patsogolo umoyo wathanzi mu dera lanu lino? USAID.GOV MALAWI CDCS IMPACT EVALUATION ENDLINE REPORT | 282 ANNEX D. QUALITATIVE CODEBOOK Codebook for CDCS endline FGDs 1. General a. Barrier to improvement (co-code liberally) b. Facilitator to improvement (co-code liberally) c. Gender difference d. Good quote e. Unclear 2. Food Security/Agriculture a. Rainfall/Drought b. Farming practices c. Improved seeds d. Crop markets e. Groundnuts f. Soya/soybeans g. Sweet potatoes 3. Health services availability and quality a. Willingness/ability to seek health care b. Availability of medicine c. Quality of medical care d. Corruption/mistreatment by health workers e. Contraception f. HIV/AIDS g. Maternal/child health 4. Education a. Willingness/ability to attend school b. Literacy c. Quality of education d. Corruption/mistreatment by school staff e. Gender differences 5. Local Government capacity a. Government role in development b. Satisfaction with government’s work c. Community involvement/lack of in government processes 6. Environment a. Awareness of climate change b. Effort to help environment 7. Poverty a. Employment 8. Development activities a. Mentions USAID b. Government role c. Mentions NGO/support project d. Awareness of integration in development projects e. Community interaction with development projects