Good practice for collecting sex-disaggregated agriculture data

Jemimah Njuki

It is now widely recognized that gender equality is critical for increasing agricultural productivity, reducing poverty and improving food and nutrition security. Transforming agriculture, will require addressing current gender inequalities.

Despite their significant contribution to the agricultural sector worldwide, women, on average, have access to fewer resources than do men. The FAO suggests that equalizing access to agricultural resources could increase yields by 20-30% and reduce the world’s hungry by 12-17%; these estimates, however, are based on the very limited data that are currently available.

The availability of contextual gender data is critical in understanding what needs to be done and how programs should be designed to impact on women and on reducing inequalities. More often than not however, sex disaggregated data is often not available, which constrains the design of gender responsive programs. The unavailability of data also limits the ability of researchers, policy makers and program managers to measure the extent to which programs or policies are effective in addressing gender inequalities and to even determine what amount of resources should go into gender and women’s empowerment programs leading to the common adage of ‘what doesn’t get measured, doesn’t get done’.

A good gender analysis aims to illuminate “differences in the needs, roles, statuses, priorities, capacities, constraints and opportunities of women and men”.

Researchers Cheryl Doss and Caitlin Kieran of the CGIAR Program on Policies, Institutions and Markets, in an article on “Standards for collecting sex-disaggregated data for gender analysis: A guideline for CGIAR researchers” outline common mistakes often made in the collection of sex data. These include; studying only women, comparing male and female headed households, making assumptions on who needs to be interview e.g interviewing only male heads of households. They argue rightly, that such kind of data sheds minimal light on the relationship between men and women, a key component of understanding gender relations. A robust gender analysis should aim to illuminate “differences in the needs, roles, statuses, priorities, capacities, constraints and opportunities of women and men.”

Doss and Kieran give some guidelines for collecting good sex disaggregated data and for conducting a robust gender analysis. These include but are not limited to;

1. Make sure that both women’s and men’s perspectives are represented and identified. Regardless of who is interviewed, it is important to always note who the respondent is, with some basic identifying information, including sex, age, and marital status.
2. Collect information about both men and women. Ask questions about specific individuals or groups and identify them by sex.
3. Collect information from men and women. This does not necessarily require interviewing men and women in the same household. Studies that fail to include male and female respondents will be subject to biases; the extent of the bias will depend on the knowledge and perceptions of the respondent(s).
4. Understand the context. Those collecting and analyzing the data need to understand gender roles and social dynamics and this knowledge must also guide the settings for interviews or focus groups.
5. Avoid comparing male and female headed households as this is not gender analysis. Differences between these diverse household types cannot necessarily be attributed to the sex of the household head.
6. Budget for the additional costs of collecting sex-disaggregated data and include gender expertise from early on.

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