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Collecting sex disaggregated agricultural data through surveys

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Presentation for the webinar on Collecting sex-disaggregated agricultural data through surveys that took place on April 21, 2016. Learn more about the webinar here: http://bit.ly/1SkWcSx

PIM Gender team members Cheryl Doss and Caitlin Kieran invited participants to discuss how the "Standards for collecting sex-disaggregated data for gender analysis" drafted by PIM in 2014 have been used to date, with a specific focus on lessons learned by CGIAR centers and external partners.

The webinar was co-organized by the CGIAR Gender and Agriculture Research Network and the CGIAR Research Program on Policies, Institutions, and Markets (PIM).

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Collecting sex disaggregated agricultural data through surveys

  1. 1. Collecting sex-disaggregated agricultural data through surveys On gender and agriculture webinar series April 21, 2016 Cheryl Doss, Yale University Caitlin Kieran, Policies, Institutions, and Markets (PIM)
  2. 2. The presenters Cheryl Doss is a development economist whose research focuses on issues related to agriculture, assets, and gender, with a regional focus on Africa south of the Sahara. As a Senior Lecturer at Yale University, she has taught courses on research methods, the economics of Africa, and agricultural development and food security. She also serves as PIM’s gender advisor. Caitlin Kieran is the Senior Research Assistant for PIM, where she monitors and supports the incorporation of gender analysis within PIM’s research portfolio and researches issues related to gender, agriculture, assets, intrahousehold dynamics, and survey methodology.
  3. 3. Definitions:  Sex-disaggregated data are information that are collected about males and females  Gender-disaggregated data are analytical indicators derived from sex-disaggregated data on social and economic attributes  Intrahousehold data – data with specific information regarding individuals living within the same household  Gender analysis is used to understand the relationships between men and women, their access to resources, their activities, and the constraints they face. It generally requires sex-disaggregated data
  4. 4. Research areas  Baseline or descriptive research  What are the constraints facing farmers?  Where are the opportunities for increased production and livelihoods?  How do farmers respond to living in a risky environment?  What is the impact of projects, programs, and policies? How can projects be designed and monitored to be gender transformative?
  5. 5. Research questions that require gender analysis  Are there productivity differences between male and female farmers? If so, what causes these disparities?  In joint production systems, to what extent does it matter who receives the extension information, purchases the inputs, provides the labor, makes the decisions, and controls the outputs?  Who decides what food is consumed? Who grows food for consumption? Who purchases food for consumption?  Who is most vulnerable to shocks? What are the sources of vulnerability? How do households and individuals cope with risk and vulnerability?  Who works within each node along a value chain? What constraints do they face? How can these constraints be removed?
  6. 6. Key issues to address  Unit of analysis  Who to interview  Asking the “who” questions  Context
  7. 7. What data are needed? What is the appropriate unit of analysis?  Individual  Household  Intrahousehold  Community  Region or nation  Land area  Asset  Resource unit  Formal or informal organization  Value chain Photos credits (left to right, top down): World Agroforestry Centre; Akram Ali/CARE Bangladesh Strengthening the Dairy Value Chain (SDVC) project; S. Mojumder/Drik/CIMMYT; Alternative Futures/CCAFS; Gwendolyn Stansbury/ IFPRI (Flickr)
  8. 8. Individual A farmer or a worker along a value chain. What is the average daily wage for an agricultural worker? Does it differ by gender? Explanation Sample Research Question
  9. 9. Household May consider the household as the production or consumption unit. How much of their income do households spend on food? Explanation Sample Research Question
  10. 10. Intrahousehold Focus is on what happens within the household. This does not treat the household as a single unit, but seeks to understand how multiple individuals within the household interact and affect outcomes. How is food allocated among household members? Explanation Sample Research Question
  11. 11. Community Communities may be the focus of policies or interventions. How do we improve local governance? Explanation Sample Research Question
  12. 12. Region or Nation For cross-country comparisons, including those of trade or policies, national or regional analysis is appropriate. How do trade policies affect agricultural exports? Explanation Sample Research Question
  13. 13. Land area Spatial analyses of how various policies affect land use may use land area as the appropriate unit of analysis. Land parcels may be the unit of analysis for analyses of agricultural production. What proportion of land is irrigated? Explanation Sample Research Question
  14. 14. Asset An asset such as livestock, houses, or savings may be the focus of policy or program interventions. What proportion of cattle are vaccinated? Explanation Sample Research Question
  15. 15. Resource unit A forest, watershed, or lake may be the appropriate unit of analysis for questions about natural resource management. What proportion of forests have active management groups? Explanation Sample Research Question
  16. 16. Formal or informal organization Including farmer cooperatives, extension service providers, credit banks, water user groups, micro- finance groups, self-help groups, etc. What proportion of cooperatives have women in leadership positions? Explanation Sample Research Question
  17. 17. Value chain The value chain, or a node along it, may be the unit of research on the sustainability, inclusivity, or efficiency of value chains. What is the value added at each node along a value chain? Explanation Sample Research Question
  18. 18. Which is the appropriate unit of analysis?  What is the research question?  Are we interested in individuals, households, or resources?  Are we doing baseline research on people? Or testing the impacts of interventions?
  19. 19. Questions on landownership: Individual as unit of analysis - To what extent do men and women own land? 0 10 20 30 40 50 60 70 Percentage of Individuals who Own Land, By Sex Women Men
  20. 20. Landownership Questions: Plot as unit of analysis - What is the form of ownership of plots?
  21. 21. Landownership: Intrahousehold Analysis  How does the distribution of landownership within the household affect outcomes, such as household expenditures or children’s health? Photo credit: Georgina Smith / CIAT. Flickr
  22. 22. Studies of livestock  May want to focus on the men and women who own the livestock: identify the average number of livestock per owner  Or focus on the livestock: how many have received prevention services? Photo credit: ILRI/Susan MacMillan. Flickr
  23. 23. Key issues to address  Unit of analysis  Who to interview  Asking the “who” questions  Context
  24. 24. Who to Interview?  Again, depends on the research question!  Considerations:  Respondent answering for her/himself vs. proxy respondent  Interview multiple respondents within a household? Photo credit: ILRI/Collins (Flickr)
  25. 25. Who provides information: Proxy respondents  For some questions, we typically rely on proxy respondents:  We rely on adults to answer questions about children’s education  Many surveys interview “the most knowledgeable person” – one person in the household – about agricultural production, landownership, or household enterprises  This works best when information is more objective and we would expect fewer information asymmetries
  26. 26. Interview respondents, no proxies  When questions are about preferences, attitudes, or knowledge  Why don’t you use fertilizer, improved seeds, or particular management practices?  WEAI asks women about their roles and responsibilities. Cannot proxy with responses from other household members
  27. 27. But large gray areas? Who to interview?  Gender Asset Gap project finds that men and women report different monetary values when asked the potential sales price for their dwelling  Labor Force Participation rates among household members vary by up to 10% depending on who within the household is asked (Bardasi et al.)  Methodological Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA) finds different levels of asset ownership for men and women, depending on the structure of the survey
  28. 28. Intrahousehold analyses  Some intrahousehold analyses rely on data collected from one household member:  Studies considering the bargaining power of women in the household and the impact on expenditure may rely on data collected from one person  Other studies compare responses among household members. Gender Parity Index of the WEAI compares empowerment indicators of men and women within the same household
  29. 29. What if multiple responses provide different answers?  For analysis, may want one answer.  Thus need to reconcile the differences  Or include both in the analysis – men’s response and women’s response to a question  Or create a variable indicating whether there is consensus
  30. 30. Developing a research plan  Identify key research questions  Appropriate unit of analysis  Who will be interviewed  How responses from different individuals will be combined, as appropriate
  31. 31. Key issues to address  Unit of analysis  Who to interview  Asking the “who” questions  Context
  32. 32. Asking the “who” questions  Key to gender analysis is knowing who is involved in various activities and whether they are men or women  Multiple approaches to soliciting this information, depending on selection of respondent(s):  Ask about men and women, but not specific individuals  Ask respondent(s) about him/herself (the “do you…” questions) o Only facilitates gender analysis if interviewing men and women  Ask questions about specific household member(s) or all household members  Ask respondent who has various rights and responsibilities
  33. 33. Coding responses to the “who” questions  Record sex of individual(s)  Example: o Who provides the labor on this plot? o Response: 1=Men; 2=Women; 3=Men and Women; 4=Children; 5=Men, Women, and Children  Identify individuals by relationship to respondent  Example from Abbreviated Women’s Empowerment in Agriculture Index o Who made the decision to borrow from [SOURCE] most of the time? o Response: (Circle all applicable) 1=Self; 2=Spouse; 3=Other household member; 4=Other non-household member; 96=Not applicable
  34. 34. Coding responses to the “who” questions (continued)  Record ID code (facilitates analysis of additional individual characteristics)  Example from Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) in Malawi o Who in your household kept/decided what to do with earnings from crop sales? o Response: List up to 2 ID codes from network roster  In some contexts, it may be appropriate to leave space for more than 2 ID codes and identify sex of non-household members
  35. 35. Key issues to address  Unit of analysis  Who to interview  Asking the “who” questions  Context
  36. 36. The importance of context  Gender relations are heterogeneous  Adapt all data collection methods to the context (social dynamics, gender roles, other dimensions of identity)  Interview setting o Consider whether it is appropriate for enumerators to be the same or opposite sex of the respondents o Consider whether it is preferable to conduct interviews with men and women separately or together, depending on social norms and topic  Formulation of and training on survey questions and response options o Ensure that questions are culturally sensitive and relevant o Ensure that researchers, enumerators, and respondents share understanding of terms
  37. 37. Tips for learning context  Read existing literature on gender issues before embarking on new study  Collaborate with local researchers  Avoid making assumptions about topics of inquiry  Use mixed methods  Qualitative methods such as participant observation, key informant interviews, focus group discussions, etc. can play important role in developing questionnaires and interpreting findings  Cognitive testing, qualitative method paired with a (quantitative) survey o Purpose: systematically identify and analyze sources of response error in surveys, and use that information to improve the quality and accuracy of survey instruments (Johnson & Diego-Rosell 2015)
  38. 38. Review: key issues to address  Unit of analysis  Appropriate unit of analysis depends on research question  Who to interview  Collect information from men and women  Asking the “who” questions  Collect information about men and women  Context Adapt data collection methods to the context
  39. 39. References and resources  Bardasi, E., Beegle, K., Dillon, A., & Serneels, P. 2010. Do Labor Statistics Depend on How and to Whom the Questions Are Asked: Results from a Survey Experiment in Tanzania. Policy Research working paper; no. WPS 5192. Washington, DC: World Bank.  Johnson, K. B., and P. Diego-Rosell. 2015. Assessing the Cognitive Validity of the Women’s Empowerment in Agriculture Index Instrument in the Haiti Multi-Sectoral Baseline Survey. Survey Practice. 8 (2). ISSN: 2168-0094  Rubin, D. Forthcoming. Qualitative Methods for Gender Research in Agricultural Development. IFPRI Discussion Paper.  Women’s Empowerment in Agriculture (WEAI) Resource Center  The Gender Asset Gap Project: Collecting Sex-Disaggregated Asset Data  The Gender, Agriculture & Assets Project  EnGendering Data Blog  “Good practices” on CGIAR Gender webiste
  40. 40. Gender and economics workshop: Advanced techniques for incorporating gender in research design, data collection, and analysis for economists and other quantitative social scientists  September 14-16, 2016 at IFPRI, Washington, DC  Led by senior economists Dr. Cheryl Doss (Yale University) and Dr. Agnes Quisumbing (IFPRI)  Workshop Objectives:  Participants will acquire and apply latest advances in knowledge of how to incorporate gender into research design, data collection, and analysis in quantitative research studies  Content specifically designed for economists and other quantitative social scientists across CGIAR who are not gender specialists and are seeking to include gender analyses into aspects of their work  More information
  41. 41. Q&A  Have you used the guidelines? What insights can you share from trying to implement them? What challenges have you faced?  What have you learned from collecting sex-disaggregated data that you would not have learned with just household-level data?  How would you use this information to report on CGIAR’s cross-cutting IDO on equity and inclusion achieved and the related sub-IDOs including (1) gender equitable control of productive assets and resources, (2) technologies that reduce women’s labor and energy expenditure developed and disseminated, and (3) improved capacity of women and young people to participate in decision making?  How do we make sure we’re collecting data that allow us to do some comparisons across projects (and answer some larger research questions) without losing the context specificity?

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