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Data Needs for Gender Research - IFPRI Gender Methods Seminar

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Presented as part of the IFPRI Gender Methods Seminar Series, hosted by the IFPRI Gender Task Force. Presented by: Cheryl Doss

Presented as part of the IFPRI Gender Methods Seminar Series, hosted by the IFPRI Gender Task Force. Presented by: Cheryl Doss

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  • 1. Data needs for Gender Research Cheryl Doss Consultant, Research Program on Policies, Institutions and Markets, CGIAR Senior Lecturer, Yale University July 8th, 2013 Presentation given as part of IFPRI’s Gender Methods Seminar Series
  • 2. Two sets of questions for gender research for agriculture: 1) How to improve agricultural productivity? And how does gender fit in? 2) How do changes in agricultural production affect women and girls and men and boys? These fit into broader questions about the role of agriculture in development .
  • 3. Data needs 1) Survey data needs to be disaggregated; more data at the individual level 2) Information on how institutions and structures -- markets for inputs, outputs, credit, and labor, and legal systems -- are experienced differently by men and women and how this impacts the well-being of individuals and communities and the processes of agricultural development and economic growth.
  • 4. There are many issues related to data collection for gender. For a detailed discussion of the approaches and variables needed, see Doss, C. (2013). Data Needs for Gender Analysis in Agriculture. IFPRI Discussion Paper 01261. http://www.ifpri.org/sites/default/files/publicati ons/ifpridp01261.pdf
  • 5. This presentation focuses on two issues: • 1) Who to interview • 2) How to handle data from multiple people within a household.
  • 6. Collecting Sex Disaggregated Survey Data Challenge 1: Who should be interviewed? • Does sex disaggregated data mean we have to interview multiple people per household? • What do we mean by data at the individual level?
  • 7. Many possible units of analysis • Individual (farmer, worker, etc) • Household • Intrahousehold (dynamics within household) • Community • Regional/National • Land area or plot • Resource Unit (e.g. a forest or water source) • Institution or Management Unit • Value Chain
  • 8. Who to interview? Many household surveys interview one person – often the household head. If interviewing one person, identify the respondent based on roles and responsibilities, such as the primary farmer. For analysis, compare male headed, female headed, and couple headed. Include measures of household structure.
  • 9. Options for interviewing multiple people in household • Principal couple • One man and one woman • One or two randomly chosen people • Everyone that is relevant for a specific module. Ask each person about their own…
  • 10. What is the goal for interviewing multiple people within a household? • Obtaining a more complete picture of the household. One person doesn’t have all of the information. – Gender differences in roles and responsibilities – Information hidden from spouses • To learn where perceptions within the household differ.
  • 11. Gender Asset Gap Project surveys: • Individual level asset data collected in Ecuador, Ghana, and Karnataka, India • Two respondents • Household inventory and individual questionnaire.
  • 12. Major Assets added by interviewing a second respondent (% added to inventory) Asset Ecuador Ghana India Principal dwelling 0.40 Na na Agricultural parcels 1.36 0.35 0.15 Other real estate 3.91 0.41 0.35 Non-farm businesses 0.52 1.04 0.12
  • 13. Disagreements among couples over who owns the asset. Ecuador Ghana Asset N (assets) % who disagree N (assets) % who disagree Dwelling 450 35.1 510 7.7 Agricultural land 94 30.9 873 3.3 Other real estate 164 20.1 413 7.8 Non-farm business 534 22.3 641 1.6
  • 14. Benefits to interviewing two people: • In this case, few additional household assets were identified by interviewing a second person. • But disagreements over ownership were identified; these may be important for understanding outcomes within household.
  • 15. Challenge 2: How to analyze data with conflicting answers? When you need one consistent answer – such as identifying the owner of a parcel of land. Create a decision rule: 1. Use one answer (from the primary farmer, head, primary income earner, oldest adult) 2. Use the broadest definitions – include everyone as an owner who claims to be an owner. 3. Use other information, such as marital property rules
  • 16. How to analyze data with conflicting answers? • For some issues, the disagreement may be the important issue. • May want to code households where there are major disagreements. • For women’s empowerment, what may be important is her perception.
  • 17. • Gender Disaggregated Survey, Kenya Agricultural Productivity and Agribusiness Project • Interviewed Primary farmer and one other household member
  • 18. Respondents by Sex and Relationship to Head of Household, Kenya Primary Farmer Individual Respondent Relationship to Head Male Female All Male Female All Head 98% 36% 65% 79% 3% 32% Spouse 0% 62% 33% 4% 91% 58% Child 1% 1% 1% 15% 4% 8% Other 0% 1% 1% 2% 2% 2% N=farmers 1,160 1,369 2,529 566 957 1,523
  • 19. Who is the household head? • 35 both respondents claimed headship • 14 both respondents claimed to be the spouse • In 2 households, the two respondents identified different men identified as head
  • 20. Distribution of Responses among household members about land ownership Type of inconsistency Couples Non-couples 1: Primary farmer (PF) does not list a parcel joint w/Individual Respondent (IR); IR lists parcel joint with PF 2: PF lists parcel joint w/ IR, but IR does not list any parcel joint w/PF 3: PF lists self as an owner on all land parcels; IR lists parcel owned individually. N= hhs Female primary farmer Male primary farmer Both males Both females Male primary/ Female ind.resp Female primary/ Male ind.resp None 52% 51% 90% 86% 62% 62% 53% 1 5% 32% 0% 0% 5% 3% 20% 2 32% 2% 0% 0% 0% 2% 11% 3 6% 4% 10% 11% 33% 33% 7% Other 5% 13% 0% 3% 0% 0% 9% N=households 455 860 10 37 39 99 1,500
  • 21. Distribution of Households by Percentage of HH Livestock Owned by Spouse of Primary Farmer. None Some1 Most to All2 More than 100% Conditional mean N=hhs Male Primary Farmer improved cattle 89% 2% 7% 2% 3.79 394 local cattle 87% 6% 5% 2% 4.18 573 Sheep 72% 11% 12% 6% 2.53 206 Goat 60% 21% 12% 8% 4.46 223 chicken (indigenous) 15% 42% 20% 22% 6.27 614 chicken (improved) 67% 0% 6% 28% 24.17 18 Female Primary Farmer improved cattle 22% 7% 54% 18% 5.42 354 local cattle 30% 13% 40% 17% 6.67 349 Sheep 31% 8% 35% 26% 5.40 134 Goat 27% 18% 32% 23% 6.32 159 chicken (indigenous) 74% 11% 6% 9% 8.00 359 chicken (improved) 89% 0% 0% 11% 10.00 9 Primary farmer identified total # of animals owned in the household; Spouse identified own animals. 1 Some includes at least one animal to 95% of household animals 2 Most to all is more than 95% to 100% of household animals.
  • 22. Percentage of Couples who Agree on Responsibilities for Livestock, by Sex of Primary Farmer. Responsible Decide to sell Keep Income Husband Wife Husband Wife Husband Wife Improved Cow 19% 42% 48% 54% 52% 60% Goat 23% 36% 20% 42% 23% 43% Indigenous Chicken 68% 43% 52% 34% 51% 34% Note: The sex of the person responsible is not identified in this table.
  • 23. Conclusions • Whether you interview one or multiple people will depend on the research question. • Interview multiple people if needed for full information or when different perceptions within household will affect outcomes. • Depending on who you interview, you may get very different answers. • Need to consider whose answer you need. • Be transparent in how you choose.
  • 24. References: • The Gender Asset Gap Project: http://genderassetgap.iimb.ernet.in • Cheryl Doss, “The Gender Asset Gap in Agricultural Assets in Kenya.” Draft.

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