Where are the women? Filling the gap in sex-disaggregated data in agriculture

El Iza Mohamedou December 07, 2020

The numbers — or lack of them — don’t lie.

Less than 12 countries are recording progress on Sustainable Development Goal (SDG) 5.a.1, which tracks by sex the percentage of people with ownership or secure rights over agricultural land out of the total agricultural population. Similarly, the Food and Agriculture Organization (FAO)’s Gender and Land Rights Database shows huge data gaps for important indicators such as the share of female holders and the distribution of agricultural land area owned by women. These specific examples show what we know in general: in our effort to measure global progress towards sustainable agriculture, women are not being counted.

This data gap impedes our ability to make agricultural policies that address gender-based discrimination or to carry out gender-sensitive budgeting, investment, and planning. It prevents us from understanding rural women’s access to and management of resources (earnings, harvest, land, etc.), the diversity of their income sources, how they use their time, and how much decision-making power they have.

And with more than two-thirds of the population in low-income countries working in agriculture, the failure to collect data that captures women’s voices and reveals gender-differentiated nuances in agriculture means missing out on an enormous piece of the labor and productivity picture and ultimately, on the full reality of life and progress in these countries.

Using specific instruments to fill data gaps

One way to fill these gaps is through the instruments we use to collect the data. To start, agricultural and rural surveys need to cover gender issues. The 50×2030 Initiative survey tools are designed to capture gender-relevant information with differing degrees of gender-disaggregated information.

They look at land tenure, agricultural production costs, agricultural income, labor (e.g., who works on the holding), gender differentials in decision-making and management (e.g., who is the plot holder and livestock manager and who has control over crop and livestock products sale earnings), productivity, and education.

This information will be used to disaggregate SDG indicators 2.3.1, 2.3.2, 2.4.1, and 5.a.1 by sex and to understand how the drivers of agricultural productivity and income relate to gender. The 50×2030 survey program also aims to improve the measurement of new indicators such as those relating to women’s empowerment — building on our cooperation with IFPRI and Emory University on the Women’s Empowerment Metric for National Survey Systems (WEMNS) project, among others.

How we collect the data matters

Of equal concern is how we collect data. Many common practices are problematic: asking individuals to report on things such as asset ownership and rights and time allocation of activities through proxy respondents; collecting data in non-private settings; or having a gender mismatch between the enumerator and respondent. Although these business-as usual-choices are made with the aim of reducing survey time and costs, they often lead to misreporting and are not conducive to capturing the specificities of women’s experiences.

For example, in Malawi, the World Bank’s Living Standards Measurement Study program (LSMS) through the LSMS+ project found clear gender implications for land ownership and rights depending on which interview practices they used. In common practice, only the “most knowledgeable” household member is interviewed, instead of all adults in a household being interviewed directly and individually. As a result, this common practice resulted in a higher incidence of men reported to exclusively own or exclusively have rights to economic benefits while a lower incidence of women were reported to jointly own or jointly have rights to economic benefits.

As the COVID-19 pandemic pushes more countries to collect data via mobile phones, we also risk missing out on respondents without access to a mobile phone. The Global System for Mobile Communications estimates that over 300 million fewer women than men access the internet on a mobile phone, and women are 8% less likely than men to own a mobile phone.

©Benedicte Kurzen/NOOR for FAO / FAO

What we know and where we go from here

It’s not that we know nothing about women in agriculture. Thanks to data and research from our partner agencies — the World Bank, FAO, and IFAD — we know that women:

  • Operate smaller farms on average than men and that gender norms often dictate that they cultivate lower-value subsistence crops than men.
  • Control less land than men, and that the land they control is often of poorer quality with an insecure tenure.
  • Own less livestock and often do not control the income from them.
  • Are less likely than men to use modern inputs such as improved seeds, fertilizers, pest control measures, and mechanical equipment.
  • Have a greater overall workload that includes a heavy burden of low-productivity activities such as fetching water and firewood. And when they are employed, women are likely to be in part-time, seasonal, and low-paying jobs, and receive lower wages for the same work.

Even though we have gained these insights, there is still much to do. There are geographical gaps that cause us to know less about women in some regions than in others. We also lack an in-depth understanding of the drivers of inequality, which requires more policy-oriented investigation.

While we have made significant progress in the methods and tools to collect gender-disaggregated agricultural data, global uptake of these methods still needs work. Further, it will be impossible to trigger the policies to address these gaps systematically unless capacity development in data use is improved, an issue the International Fund for Agricultural Development will address under 50×2030.

What happens, though, once we become deliberate about collecting data on women in agriculture? First, ministries of agriculture and others involved in gender equality will have the evidence to inform dialogue and policy around disparities. They would also have a common set of data to work from, increasing chances of high-level coordination. Second, this data would allow for the creation of gender-smart policies and programs that target women. These include programs and legislation that aim to increase women’s access to land, livestock, education, financial services, extensions, technology and rural employment. These shifts could boost women’s productivity and generate gains in terms of agricultural production, food security, economic growth and social welfare.

But more than anything, we normalize the regular collection of sex-disaggregated data for the agricultural sector, moving to a scenario in which over-taxed NSOs see documenting women as a top priority as they make hard choices between what data to collect.

The road to that objective is long and paved with sustainability — meaning partner countries provide the resources themselves to collect sex-disaggregated data. In our program, the 50 countries we will work with must lead on planning and agree to the financial and technical takeover of data collection after program support ends. Indeed, it is a prerequisite for participation.

This way, we move from acknowledging this glaring gap in agricultural data, to finding the most technically sound and cost-effective ways of filling them, to incorporating data on all aspects of women’s work and lives into data collection for the sector. Once visible and heard, they will never again be overlooked.

El Iza Mohamedou is the Program Manager for the 50×2030 initiative.

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