The Women’s Empowerment in Agriculture Index – What have we learned?
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This document summarizes efforts to improve the Women's Empowerment in Agriculture Index (WEAI) by discussing various studies and initiatives. It provides an overview of the different versions of the WEAI (original, abbreviated, project-specific, and value chain-specific) and how the index is constructed. It also shares key findings from applying the WEAI in multiple countries, such as workload and access to credit being common constraints. Additionally, it discusses preliminary results from a Philippines pilot that identify workload and group membership as top disempowerment factors. The document demonstrates how the WEAI can inform programming to empower women, using Bangladesh's ANGeL project as an example.
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The Women’s Empowerment in Agriculture Index – What have we learned?
- 2. Working together to create and improve the WEAI
- 3. Efforts to improve data quality and availability for gender analysis • Doss, Cheryl; Kieran, Caitlin; Kilic, Talip. 2017. Measuring ownership, control, and use of assets. Policy Research working paper; no. WPS 8146. World Bank Group. • Seymour, Greg; Malapit, Hazel Jean; Quisumbing, Agnes R. 2017. Measuring time use in development settings. Policy Research working paper; no. WPS 8147. World Bank Group. • Donald, Aletheia Amalia; Koolwal, Gayatri B.; Annan, Jeannie Ruth; Falb, Kathryn; Goldstein, Markus P. 2017. Measuring women's agency. Policy Research working paper; no. WPS 8148. World Bank Group. The Gender Asset Gap Project WEAI Resource Center - http://www.ifpri.org/book-9075/ourwork/program/weai-resource-center
- 4. Where in the world is WEAI? 47 countries and counting
- 5. WEAI Basics Overview and evolution of the Index
- 6. What is the WEAI? • Measures inclusion of women in the agricultural sector • Survey-based index - interviews men and women in the same household • Methodology: – Similar to multi-dimensional poverty indices (Alkire and Foster 2011) and the Foster-Greere- Thorbeck (FGT) indices – Details on index construction in Alkire et al. (2013)
- 7. How is the Index constructed? • An aggregate index in two parts: • Five Domains of Empowerment (5DE) • Gender Parity Index (GPI) • Constructed using interviews of the primary male and primary female adults in the same household
- 8. Why so many WEAIs? Different strokes for different folks! Original WEAI Abbreviated WEAI (A-WEAI) Project WEAI (Pro-WEAI) WEAI for Value Chains (WEAI4VC)
- 9. Pro-WEAI • Project-level WEAI under development in Phase 2 of the Gender, Agriculture & Assets Project (GAAP2) • Supported by the Bill & Melinda Gates Foundation, USAID, and A4NH • A-WEAI as starting point – Adds intervention-specific modules/questions – Comparable to other projects/activities working on similar interventions Core set of pro-WEAI empowerment modules • Quantitative survey • Qualitative protocols Standardized add-ons depending on project needs: • Nutrition and health • Livestock-enhanced +
- 10. WEAI4VC • Expands empowerment measure to cover multiple stages, different types of actors in the value chain • Pro-WEAI quantitative and qualitative protocols as starting point • Expands production module to livelihoods, including entrepreneurship and wage work Bangladesh WEAI4VC Pilot • Supported by USAID • Assess empowerment and gender parity of women as producers, entrepreneurs, wage workers across entire agricultural value chain • Pilot survey on 1200 households in FTF ZOI (400/group) Philippines WEAI4VC Pilot • Supported by MCC • Assess empowerment and gender parity of women across 4 priority value chains (abaca, coconut, seaweed, swine) • Pilot survey with 1600 households in 4 provinces (Sorsogon, Cebu, Bohol, Leyte)
- 12. What have we learned? Cross-country trends
- 13. Cross-country baseline findings: credit, workload and group membership are constraints across countries 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 DisempowermentIndex(1-5DE) Leisure Workload Speaking in public Group member Control over use of income Access to and decisions on credit Purchase, sale, or transfer of assets Ownership of assets Autonomy in production Input in productive decisions Source: Malapit et al. (2014)
- 14. 0 10 20 30 40 50 60 70 80 90 Change in % of primary female decision-makers with adequacy in Access to and control over credit Source: USAID/BFS MEL Team INDICATOR INCREASED OR DECREASED BETWEEN BASELINE AND INTERIM
- 15. Source: USAID/BFS MEL Team 0 10 20 30 40 50 60 70 80 90 Change in % of primary female decision-makers with adequacy in Workload INDICATOR INCREASED OR DECREASED BETWEEN BASELINE AND INTERIM
- 16. What have we learned? Dimensions of empowerment and maternal and child nutrition
- 17. What dimensions of empowerment matter for maternal and child nutrition? • Data from 6 countries: Bangladesh, Cambodia, Ghana, Nepal (Suaahara), Mozambique, Tanzania • Bangladesh is nationally-representative of rural areas • The rest representative of project areas and/or the ZOI • Estimate relationship between nutrition outcomes and women’s empowerment using quantitative (regression) analysis • The analysis also looked at differential effects on the nutrition of girls compared to boys • Associations only, NOT causality! • Accounts for individual (age, education), household (household size, wealth quintile) and community characteristics Agnes Quisumbing, Kathryn Sproule, Elena Martinez, Hazel Malapit (2017)
- 18. 0.04** 0.05* -0.06** -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 HHS WDDS BMI HAZ WHZ WAZ EBF CDDS Standarddeviation Bangladesh Women’s 5DE score and nutritional outcomes -0.05** 0.10*** 0.05* -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 HHS WDDS BMI HAZ WHZ WAZ EBF CDDS Nepal 0.48*** -0.5 -0.3 -0.1 0.1 0.3 0.5 HHS WDDS BMI HAZ WHZ WAZ EBF CDDS Cambodia -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 HHS WDDS BMI HAZ WHZ WAZ EBF CDDS Standarddeviation Ghana -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 HHS WDDS CDDS Mozambique -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 HHS WDDS BMI Tanzania Notes: Preliminary findings from A4NH report by Quisumbing et al (2017), “Gender and women’s empowerment in nutrition-sensitive agriculture: New evidence and implications for programming”. Charts report effect sizes, defined as the number of sample standard deviations in the household, maternal, and child nutrition variables that are associated with a 1.0-SD change in the empowerment measure. Stars indicate statistical significance at the 10% (*), 5% (**) and 1% (***) levels.
- 19. -0.05*** -0.04* 0.09* -0.11* -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 HHS WDDS BMI HAZ WHZ WAZ EBF CDDS Standarddeviation Bangladesh -0.09*** -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 HHS WDDS BMI HAZ WHZ WAZ EBF CDDS Nepal -0.28* -0.5 -0.3 -0.1 0.1 0.3 0.5 HHS WDDS BMI HAZ WHZ WAZ EBF CDDS Cambodia Intrahousehold inequality score and nutritional outcomes -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 HHS WDDS BMI HAZ WHZ WAZ EBF CDDS Standarddeviation Ghana -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 HHS WDDS CDDS Mozambique -0.18* -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 HHS WDDS* BMI Tanzania Notes: Preliminary findings from A4NH report by Quisumbing et al (2017), “Gender and women’s empowerment in nutrition-sensitive agriculture: New evidence and implications for programming”. Charts report effect sizes, defined as the number of sample standard deviations in the household, maternal, and child nutrition variables that are associated with a 1.0-SD change in the empowerment measure. Stars indicate statistical significance at the 10% (*), 5% (**) and 1% (***) levels.
- 20. Nepal - women’s nutritional outcomes -0.10*** -0.05** -0.07*** 0.06*** 0.10*** -0.06*** -0.04* 0.07*** -0.06** -0.06*** -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 Ag decisions Autonomy in production Ag assets owned Ag assets w/ rights Credit decisions Income decisions Group membership Speaking in public Hours worked Leisure Standarddeviation WDDS BMI Notes: Preliminary findings from A4NH report by Quisumbing et al (2017), “Gender and women’s empowerment in nutrition-sensitive agriculture: New evidence and implications for programming”. Charts report effect sizes, defined as the number of sample standard deviations in the household, maternal, and child nutrition variables that are associated with a 1.0-SD change in the empowerment measure. Stars indicate statistical significance at the 10% (*), 5% (**) and 1% (***) levels.
- 21. Lessons learned • Overall empowerment appears to be more important in the Asian countries (especially Bangladesh and Nepal) in our sample compared to the African ones • Greater equality within households is almost always associated with positive nutritional outcomes, indicating importance of a household working together to generate good nutrition for the family • Tradeoffs exist between agriculture-nutrition pathways and women’s empowerment
- 22. Lessons learned • The WEAI can be used to identify policy and programming priorities by disaggregating the contribution of each indicator to women’s disempowerment • Our results suggest that interventions targeting top contributors to disempowerment that could potentially improve a range of nutritional outcomes could be very cost-effective, BUT we need to be mindful of tradeoffs • Given results are based on associations, not impact evaluations, gender- and nutrition-sensitive agricultural programs that address the top contributors to women’s disempowerment would need to be rigorously evaluated both in terms of impact and cost-effectiveness to guide future programming
- 23. What have we learned? Preliminary findings from Philippines WEAI4VC pilot
- 24. Workload and group membership contribute most to disempowerment 0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 Women Men Women Men Women Men Women Men AbacaCoconutSeaweedSwine Disempowerment score (1-5DE) Input in productive decisions Autonomy in production Ownership of assets Purchase, sale, or transfer of assets Access to and decisions on credit Control over use of income Group member Workload
- 25. Gender gaps in average achievements by sub-indicator Legend: Colors represent whether gaps favor FEMALES, MALES, or NEITHER. Rankings 1, 2, 3 indicate the sub-indicators with the largest achievement gaps between women and men within each value chain. Value chain Sub-indicators Abaca Coconut Seaweed Swine Input in productive decisions Input in decisions about VC activities Participation in decisions about VC activities Autonomy in production 3 Access to information about agricultural activities Access to information important for VC activities Autonomy in working conditions Autonomy in wage work Ownership of assets Rights over assets 2 Access to and decisions on credit 1 Access to financial account 1 1 Control over use of income Control over use of agricultural income Control over use of non-agricultural income 1 Control over household purchases Input in decisions about income from VC activities Input in decisions about consumption of output Autonomy in income 2 2 Group membership Workload 3 1 Mutual respect among household members 2 Attitudes about domestic violence from husband Attitudes about domestic violence from employer Access to community programs 3 Access to extension services Livelihoods Resources Income Leadership Time Intrahousehold relationships Access to information & extension
- 26. Gender gaps in average achievements by sub-indicator Legend: Colors represent whether gaps favor FEMALES, MALES, or NEITHER. Rankings 1, 2, 3 indicate the sub-indicators with the largest achievement gaps between women and men within each value chain. Value chain Sub-indicators Abaca Coconut Seaweed Swine Input in productive decisions Input in decisions about VC activities Participation in decisions about VC activities Autonomy in production 3 Access to information about agricultural activities Access to information important for VC activities Autonomy in working conditions Autonomy in wage work Ownership of assets Rights over assets 3 Access to and decisions on credit 1 Access to financial account 1 1 Control over use of income Control over use of agricultural income Control over use of non-agricultural income 1 Control over household purchases Input in decisions about income from VC activities Input in decisions about consumption of output Autonomy in income 2 2 Group membership Workload 3 1 Mutual respect among household members 2 Attitudes about domestic violence from husband Attitudes about domestic violence from employer Access to community programs 3 Access to extension services Livelihoods Resources Income Leadership Time Intrahousehold relationships Access to information & extension
- 27. Gender gaps in average achievements by sub-indicator Legend: Colors represent whether gaps favor FEMALES, MALES, or NEITHER. Rankings 1, 2, 3 indicate the sub-indicators with the largest achievement gaps between women and men within each value chain. Value chain Sub-indicators Abaca Coconut Seaweed Swine Input in productive decisions Input in decisions about VC activities Participation in decisions about VC activities Autonomy in production 3 Access to information about agricultural activities Access to information important for VC activities Autonomy in working conditions Autonomy in wage work Ownership of assets Rights over assets 2 Access to and decisions on credit 1 Access to financial account 1 1 Control over use of income Control over use of agricultural income Control over use of non-agricultural income 1 Control over household purchases Input in decisions about income from VC activities Input in decisions about consumption of output Autonomy in income 2 2 Group membership Workload 3 1 Mutual respect among household members 2 Attitudes about domestic violence from husband Attitudes about domestic violence from employer Access to community programs 3 Access to extension services Livelihoods Resources Income Leadership Time Intrahousehold relationships Access to information & extension
- 28. Philippines WEAI4VC Pilot: Preliminary findings • (Original) WEAI scores are relatively high • Some consistent findings across value chains • Top constraints: Workload and group membership • Very low achievements in autonomy in wage work and working conditions • Some sub-indicators favor men, some favor women • Implications for program design • Explore ways to reduce time burdens • Groups may not be an effective delivery platform for interventions • To reduce gender gaps, specific interventions targeted to men or women • Points to what constraints to pay attention to, but now how to overcome them – Need qualitative work to dig deeper (stay tuned!)
- 29. What have we learned? Using the WEAI to inform programming
- 30. Example: The Agriculture, Nutrition, and Gender Linkages (ANGeL) project in Bangladesh • Bangladesh had the lowest women’s empowerment scores out of 19 Feed the Future Countries at baseline (2012) • The Ministry of Agriculture worked with IFPRI to design, implement, and evaluate a pilot program to see what worked best to empower women • Agricultural extension directed to men and women farmers (Reach) • Behavior change communication to improve nutrition knowledge (Benefit) • Gender sensitization of men and communities to support women in their productive and reproductive roles (Empower) • The project is now being piloted; endline results will be available next year (and we will know which approach works best to improve food security).
- 31. What’s next for WEAI?
- 32. WEAI in other organizations • Implemented in 47 countries – all types of WEAIs • Exploring integration of WEAI into national surveys • Ongoing discussions with FAO, WB, BMGF
- 33. What’s next? • Additional analyses: How is empowerment related to everything else that we care about? • How do we understand women’s empowerment in the context of households, families, and communities? • How can empowerment questions and modules best be incorporated into national surveys?
- 34. WEAI Resource Center http://www.ifpri.org/book-9075/ourwork/program/weai-resource-center Pro-WEAI updates http://gaap.ifpri.info/ Questions? Contact: Hazel Malapit | h.malapit@cgiar.org Photo credit: USAID/Riccardo Gangale
Editor's Notes
- Country count as of Oct 2, 2017
- Might be good to mention as part of the partnership with BMGF
- The WEAI was developed by IFPRI, USAID, and OPHI in 2012 to measure the greater inclusion of women in the agricultural sector as a result of US Government’s Feed the Future (FTF) Initiative It is a survey-based index constructed using interviews of the primary male and primary female adults in the same household Key aspect of index construction: similar to family of multi-dimensional poverty indices (Alkire and Foster 2011, J of Public Econ) and the Foster-Greere-Thorbeck (FGT) indices Details on index construction in Alkire et al. (2013), World Development
- Population-based household surveys Shorter, resource-efficient tool Diversity of interventions
- Under development. Stands for “Project WEAI”. Intended to be applicable to various types of agriculture and food security projects, depending upon their focus (i.e. nutrition, livestock, etc.). Uses the A-WEAI as a starting point and adds specialized project-relevant modules, designed and tested by the WEAI team. Indicators still to be validated; cut-offs and weights to be adjusted. What are we doing in GAAP2? Develop methods and tools for pro-WEAI: develop and pilot pro-WEAI, to consist of a refined, tested, minimal set of core indicators of women’s empowerment in agriculture, plus add-on modules adaptable to the needs of specific projects. Identify and recommend evidence-based strategies for empowering women through agricultural development projects, based on assessments of 12-14 participating projects that have used and adapted these strategies, plus comparative analysis and synthesis. Build a cadre of development professionals: implementers, monitoring and evaluation specialists, donors and researchers—who understand and use measures of women’s empowerment to design, implement and assess programs.
- Shows how WEAI has evolved through the different versions
- Track empowerment of and identify the constraints facing the female agricultural entrepreneurs and wage earners Identify opportunities for empowerment in different value chains Mixed methods: In both sites, the qualitative research will take place between Aug-Oct, to: (1) Validate the quantitative surveys, (2) Explore men’s and women’s views on empowerment across the value chain, (3) Investigate barriers to entry and growth in value chains of different commodities Bangladesh -- Conducted as part of IFPRI’s Policy Research and Strategy Support Program in USAID’s Zone of Influence Philippines -- Conducted by the Office of Population Studies (OPS) of the University of San Carlos on the coconut and seaweed value chains, in Bohol and Leyte
- WHAT HAVE WE LEARNED (IFPRI): Very brief intro to WEAI (five domains/10 indicators; 5DE and GPI) Evolution of the WEAI/rationale for developing new WEAIs (also brief) Snapshot of WEAI results (changes from baseline to interim) Any regional trends? High level findings based on interim findings? What type of analysis has been conducted using WEAI? (Bangladesh? Ethiopia? Ghana?) How has the WEAI been used to inform programming? (BD? ETH? Other examples from projects?) Any other significant learnings?
- -Breaking down the 5DE score into its component indicators provides additional insight as to which indicators contribute substantially more or less to women’s empowerment. For example, compare Ghana and Kenya, whose 5DE scores are similar but whose composition of disempowerment differs. The contribution of production and resources to disempowerment is greater in Ghana, whereas lack of time and leadership opportunities are more disempowering in Kenya. In contrast, Zambia and Malawi have quite similar patterns of disempowerment, but Zambian women are slightly less empowered, primarily due to their greater constraint in workload. Examining the highest and lowest 5DE scores, women in Bangladesh, Liberia, and Tajikistan are more than three times as disempowered as women in Rwanda (excluding Cambodia which appears an outlier). -Looking at scores by region, Asia has the greatest range in scores, followed by East Africa due to Kenya’s notably lower achievement. Both southern and West Africa exhibit the greatest similarity in score, although they have fewer countries of comparison. In the majority of countries, limited ownership of assets and lack of leisure time contribute least to women’s disempowerment. Conversely, access to and decisions on credit emerges as a major constraint in most countries, with low levels of group membership and heavy workloads also significant contributors to women’s disempowerment. However, in general, there is no simple pattern to women’s disempowerment, in terms of either the depth of disempowerment or the relative contribution of each indicator.
- Endpoints of the bars represent baseline and midline % Green means the indicator increased during the period, Red means it decreased during the period Use this information to see whether you are on the right track What is behind this change? Good news or bad news? It depends! Programming affect it? Any shocks during period? Any structural shifts that occurred in the economy?
- Lots of green, but is it good news or bad news? What types of activities are they no longer spending time on and why? If increasing productivity, good! If lost livelihood, unemployed, bad!
- What's different in the A-WEAI (all target countries under GFSS will collect this) Project-level WEAI (demand from partners for intervention-specific tool; who are partners? External Advisory Committee--want to show it's a multi-stakeholder effort, demand-driven; how will pro-WEAI add value? Gates to integrate into all development projects; USAID will be working on the same) Pro-WEAI: What's different in the pro-WEAI? (maybe your table with WEAI, A-WEAI, and pro-WEAI indicators) Timeline for release of Pro-WEAI? WEAI (gender data, in general) in national surveys (maybe a peek into discussions with World Bank, BMGF, FAO, etc. on gender data) Anything else? Results?
- Key point: Associational, not causal, analysis
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- In these graphs we see the results, showing the significance of the association between the 5 dimensions of empowerment and nutritional outcomes at household, maternal and child level as measured by specific indicators (as shown on the horizontal axis). Results in these graphs and following use effect sizes to assess the relative effectiveness (or associations) between alternative women’s empowerment outcomes on various indicators of household, maternal, and child nutrition. Larger bars indicate a greater association between empowerment and the nutritional outcome. Key points: There is not a clear relationship between women’s empowerment and nutritional outcomes; however, context emerges as important in as it relates to the significance of women’s empowerment scores and nutritional outcomes. For instance, overall 5DE scores are much more important as they relate to nutritional outcomes in the Asian countries (especially Bangladesh and Nepal) in our sample compared to the African ones. Other more technical info – 5DE definition Weighted average of achievements in the 10 indicators if the female respondent is disempowered, = 1 if she is empowered. Censored empowerment scores used. The effect size is defined as the number of sample standard deviations in the household, maternal, and child nutrition variables that are associated with a 1.0-SD change in the empowerment variable.
- Intrahousehold inequality score - Difference in the male and female empowerment scores, = 0 if the female respondent is empowered. Again, intrahousehold inequality scores are much more important as they relate to nutritional outcomes in the Asian countries (especially Bangladesh) in our sample compared to the African ones Key point: Where significant, greater equality within households is almost always associated with positive nutritional outcomes. This suggests that nutritional programs that also aim at improving intrahousehold inequality could have greater impacts than those that do not. The finding that greater gender equality within households is associated with better nutritional outcomes indicates the importance of a household working together to generate good nutrition for the family.
- There are many associations between women’s empowerment and women’s nutritional outcomes, both positive and negative. Hours worked has a negative effect on women’s BMI (expend more calories) while satisfaction with leisure has a positive effect on women’s dietary diversity (consume X food group more)
- Satisfaction with leisure has positive effect on children’s dietary diversity Group membership has negative association with exclusive breastfeeding, which may be indicative of competing demands on women’s time
- Highlight ag assets owned and ag assets with rights both have negative effect on HAZ scores Number of hours worked per day has negative effect for HAZ and WAZ scores In summary, finding ways to decrease women’s workload emerges as a potential entry point for interventions to improve nutrition outcomes for women and children in Nepal while a consistent negative association with group membership further illustrates the need to better understand the competing demands on women’s time and other resources.
- Tradeoffs In Nepal, control over assets is associated with lower hunger at the HH level but also poorer outcomes for women (WDDS, BMI) and children (HAZ) Control over income matters for improving women’s diets, but if intensifying participation in agriculture increases workload, then both maternal and child nutrition could be at risk
- WEAI application to policy/programming: Previous analyses using the WEAI identified the top two or three contributors to disempowerment and recommended that programs be designed to support empowerment in these specific areas. The present analysis finds that looking at the top two or three contributors to women’s disempowerment provides little, if not potentially misguided, direction for improving nutritional outcomes. Focusing on the top two contributors to disempowerment would be misleading because different empowerment indicators matter for different nutritional outcomes and the results are largely country specific. The model with all 10 indicators provides a much fuller picture of which indicators matter for which nutritional outcomes in a given context. It also suggests prime areas for policy and program work whenever overlap exists between a top contributor to disempowerment and a strong association between an indicator and positive nutritional outcomes.
- What's different in the A-WEAI (all target countries under GFSS will collect this) Project-level WEAI (demand from partners for intervention-specific tool; who are partners? External Advisory Committee--want to show it's a multi-stakeholder effort, demand-driven; how will pro-WEAI add value? Gates to integrate into all development projects; USAID will be working on the same) Pro-WEAI: What's different in the pro-WEAI? (maybe your table with WEAI, A-WEAI, and pro-WEAI indicators) Timeline for release of Pro-WEAI? WEAI (gender data, in general) in national surveys (maybe a peek into discussions with World Bank, BMGF, FAO, etc. on gender data) Anything else? Results?
- WEAI indicators only, focus on agricultural production, excludes new WEAI4VC indicators
- Includes all indicators, original WEAI plus proposed WEAI4VC indicators Credit, financial account, autonomy in income favor women Workload, Rights over assets favor men
- Includes all indicators, original WEAI plus proposed WEAI4VC indicators Credit, financial account, autonomy in income favor women Workload, Rights over assets favor men
- Includes all indicators, original WEAI plus proposed WEAI4VC indicators Credit, financial account, autonomy in income favor women Workload, Rights over assets favor men
- Includes all indicators, original WEAI plus proposed WEAI4VC indicators Credit, financial account, autonomy in income favor women Workload, Rights over assets favor men
- Includes all indicators, original WEAI plus proposed WEAI4VC indicators Credit, financial account, autonomy in income favor women Workload, Rights over assets favor men
- Stay tuned! WEAI4VC is still under construction What does it mean for programming?
- What's different in the A-WEAI (all target countries under GFSS will collect this) Project-level WEAI (demand from partners for intervention-specific tool; who are partners? External Advisory Committee--want to show it's a multi-stakeholder effort, demand-driven; how will pro-WEAI add value? Gates to integrate into all development projects; USAID will be working on the same) Pro-WEAI: What's different in the pro-WEAI? (maybe your table with WEAI, A-WEAI, and pro-WEAI indicators) Timeline for release of Pro-WEAI? WEAI (gender data, in general) in national surveys (maybe a peek into discussions with World Bank, BMGF, FAO, etc. on gender data) Anything else? Results?
- What's different in the A-WEAI (all target countries under GFSS will collect this) Project-level WEAI (demand from partners for intervention-specific tool; who are partners? External Advisory Committee--want to show it's a multi-stakeholder effort, demand-driven; how will pro-WEAI add value? Gates to integrate into all development projects; USAID will be working on the same) Pro-WEAI: What's different in the pro-WEAI? (maybe your table with WEAI, A-WEAI, and pro-WEAI indicators) Timeline for release of Pro-WEAI? WEAI (gender data, in general) in national surveys (maybe a peek into discussions with World Bank, BMGF, FAO, etc. on gender data) Anything else? Results?
- New google count of “Women’s empowerment in agriculture index” gave 65,300, and on page 10 and 14 they were still all “our” WEAI [FR: Maybe discuss how much more we can learn about the WEAI when it is integrated into national surveys—link to Greg’s paper here] Can talk about the list (national surveys, who else is adopting etc.) or can use an updated map (unless you want to have the map in the beginning)