Collecting sex-disaggregated data is one of the most common approaches that researchers use to integrate gender in their agriculture-nutrition-health research. However, researchers new to this method may be unsure about why it’s important and what it entails. We asked two gender researchers from the CGIAR Research Program on Policies, Institutions, and Markets (PIM), Cheryl Doss, an economist at Yale University, and Caitlin Kieran, Senior Research Assistant on gender for PIM at the International Food Policy Research Institute, to answer some common questions researchers have about sex-disaggregated data collection and interpretation.
Photo by Neil Palmer (CIAT). Source: Flickr (CIAT in pictures - the best of)
Question #1: Let’s start from the beginning. What exactly do we mean by sex-disaggregated data?
Sex-disaggregated data are data that are collected and analysed separately on males and females. This typically involves asking the “who” questions in an agricultural household survey: who provides labor, who makes the decisions, who owns and controls the land and other resources. Or it may involve asking men and women about their individual roles and responsibilities.
When talking about sex-disaggregated data, we are not referring to comparisons of male- and female-headed households. This type of data is already collected as part of common practice. However, limiting analyses to this kind of comparison is problematic because it confounds gender and household structure. Male- and female-headed households are not comparable in most cases due to the way in which they are defined. “Male-headed” households generally include all households in which women are married to men while “female-headed” households are usually those households lacking adult men. Female-headed households are often more labor and resource constrained than male-headed households, but these disparities cannot necessarily be attributed to the sex of the household head. Unless a survey asks questions about individuals within a household, we’ll miss important data on women living in male-headed households – the majority of the world’s women.
Question #2: What is the biggest misunderstanding about the purpose of collecting sex-disaggregated data in agriculture research?
Some people mistakenly believe that the goal of collecting sex-disaggregated data and conducting gender analysis is simply to understand the situation for women. But, both men and women are involved in agricultural production, so it’s necessary to understand both of their roles and responsibilities and how these may change in the context of new policies, markets, and technologies. The purpose of collecting sex-disaggregated data is to provide a more complete understanding of agricultural production and rural livelihoods in order to develop better policies and programs.
Question #3: Many people think that collecting sex-disaggregated data is too costly and too burdensome for respondents. Is that always the case?
Many people think that, in order to collect sex-disaggregated data through a household survey, you have to interview a man and a woman in the same household. This would obviously increase the costs of conducting the survey, particularly if you ask men and women the same full set of questions. While this type of data can be very useful for conducting intrahousehold analyses and for investigating research questions on individual preferences, beliefs, or perceptions, there are less burdensome ways to collect sex-disaggregated data.
Sex-disaggregated data are not necessarily collected from women and men within the same household, but they are collected about women and men. In household surveys, depending on the question, you may be able to achieve this by interviewing one household member and asking the “who” questions. Or you might split the sample, so that in half of the households you interview men and in the other half, you interview women. A final approach would be to ask different questions to men and women within the same household so that information is not duplicated.
One persuasive argument is that simply adding one or two questions to a survey module can produce a wealth of information for minimal costs. For example, instead of simply asking, “Does the household own a cow?” you can follow it with “Who in the households owns a cow?”
In addition, connecting the responses with household member IDs facilitates analyses based not only on sex but also on age, marital status, education, religion, and any other information included in the household roster. If the “who” questions are simply coded as male and female or head and spouse, they cannot be matched with these other characteristics that help us understand the household and the individuals within it.
This post is part of a blog, the Gender-Nutrition Idea Exchange, maintained by the CRP on Agriculture for Nutrition and Health. To add your comments below, please register with Disqus or log-in using your Facebook, Twitter, or Google accounts. You must be signed-in or registered in order to leave a comment.