This document discusses data needs for gender research in agriculture. It addresses who to interview in households to collect sex-disaggregated data and how to handle conflicting answers from multiple respondents. Interviewing just the household head may miss gender differences, but interviewing everyone can be complex. Options include interviewing the principal couple, one man and one woman, or people relevant to specific modules. While additional assets are identified, disagreements over ownership are also important. The document examines strategies for resolving conflicting responses.

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

15. 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.

16. 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

17. 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.

18. • Gender Disaggregated Survey, Kenya
Agricultural Productivity and Agribusiness
Project
• Interviewed Primary farmer and one other
household member

19. 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

20. 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

21. 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

22. 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.

23. 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.

24. 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.

25. References:
• The Gender Asset Gap Project:
http://genderassetgap.iimb.ernet.in
• Cheryl Doss, “The Gender Asset Gap in
Agricultural Assets in Kenya.” Draft.