Sex-Disaggregated Data in Agriculture

Sex-disaggregated data is any data that is collected and analysed separately for males, females, and other gendered people. In the domain of agricultural research, sex-disaggregated data is vital to understanding the complex gender relations that underpin access to nutrition, agricultural productivity, capital resources such as land and participation in markets and trade. In India, almost 75% of full time workers on farms are women (Hindustan Times), and yet their contribution is invisibilised in the data that is collected in surveys on farming. Here, we bring to you 6 reads on sex-disaggregated data in agriculture, which illustrate that data is not just a technical issue. Rather, what is not measured fails to materialise in policies and government interventions, thereby not only making it harder to empower and protect women in agriculture but also to transition to more equitable, sustainable and productive food systems.

The Need for Sex-Disaggregated Data

Where are the women? Filling the gap in sex-disaggregated data in agriculture. 2020. El Iza Mohamedou.

https://data2x.org/where-are-the-women-filling-the-gap-in-sex-disaggregated-data-in-agriculture/

This article illuminates the urgent need to count women in our global effort to progress towards sustainable agriculture. The gender data gap in agriculture, the author argues, indicates a failure to capture the gender-differentiated nuances in agriculture, and therefore a failure to capture a large piece of the agricultural labour and productivity puzzle. To fill this data gap, the author recommends using specific instruments (such as surveys that capture gender-relevant information) as well as taking a closer look at how we collect data. Reflection is needed on problematic data collection practices such as using proxy respondents, collecting data in non-private settings, gender mismatch between the enumerator and respondent and collecting data via mobile phones in geographies where women have limited access to technology.

Can better data change the fate of India’s invisible female farmers? 2020. Sunaina Kumar.

https://www.devex.com/news/can-better-data-change-the-fate-of-india-s-invisible-female-farmers-96664

In India, despite women carrying out nearly 75% of all farm-related work, their labour is grossly underreported. The article sheds light on this paradox, arguing that women farmers are invisibilised in data systems because land operations are the criteria by which farmers are identified. Since only a small minority of women own land in the country, their farm labour goes unrecognised. Additionally, the complexity of agriculture in India implies that along with the feminization of agriculture, there is also simultaneously de-feminization taking place in pockets, due to reasons such as mechanisation and/or more women pushed into household work. Data is needed to capture the diversity of such trends and some state governments are taking the first step, by building sex-disaggregated databases that are decoupled from land ownership, so as to give a more comprehensive view of India’s agricultural labour force.

Methods To Collect Disaggregated Data

Standards for Collecting Sex-disaggregated Data for Gender Analysis: A Guide for CGIAR Researchers. 2013. Cheryl Doss and Caitlin Kieran.

https://pim.cgiar.org/files/2012/05/Standards-for-Collecting-Sex-Disaggregated-Data-for-Gender-Analysis.pdf

This guidelines document, prepared by CGIAR researchers, spells out some simple and achievable steps for collecting relevant sex-disaggregated data. The authors implore researchers to avoid the common and grave error of assuming that gender analysis is to “only study women”, but rather, to focus on understanding the relationship between men and women. The comprehensive guidelines help researchers identify research questions and the unit of analysis (household, individual, community etc.), and also how to choose respondents. Interestingly, the guidelines also mention that beyond identifying the right respondents it is also imperative to ask “who” questions (for eg: ‘Who in the household engages in this task?”). “Who” questions unlock the key to gender analysis in agriculture – knowing the sex of the person behind the task or decision. Finally, the guidelines conclude by instructing researchers to adapt their questions and data-collection methods to the socio-cultural context within which their respondents exist.

Sex-disaggregated data in agriculture and sustainable resource management New approaches for data collection and analysis. 2019. FAO.

https://www.fao.org/3/i8930en/i8930en.pdf

This guidance document examines existing gender data gaps in agriculture, sources of data that have emerged recently to address these gaps, as well as analysis and indicators that can be developed. Specifically, the document emphasises the need to address gender data gaps in agriculture through nationally-representative household and/ or agricultural surveys that rely on individual-level data collection, that can then be used to inform regional and national policies. The document also delves into how management of resources – through climate-smart agriculture, conserving biodiversity and ecosystem resources, and efficient use of working time – has varying implications for men’s and women’s livelihoods in agriculture, and how surveys can be enhanced further to examine sex-disaggregated outcomes across these areas.

Thinking Critically about Gendered Data in Agriculture

Data Needs for Gender Analysis in Agriculture. 2013. Cheryl Doss.

http://cdm15738.contentdm.oclc.org/utils/getfile/collection/p15738coll2/id/127482/filename/127693.pdf

This paper offers insight into how to improve data collection efforts to ensure that women farmers are interviewed and that their voices are heard. Researchers need to clarify who should be interviewed, how to structure the interview, and how to identify which people are involved in various activities, as owners, managers, workers, and decisionmakers. The paper suggests that much more of the microlevel data needs to be sex-disaggregated, which will require that the data be collected at the level of the individual, rather than just at the household or farm level, or that data are collected both on the agricultural holdings and on the holder. Secondly, data is needed for researchers to analyse how institutions and structures such as markets, are experienced differently by men and women and how this has an impact on the well-being of individuals and communities and the processes of agricultural development and economic growth. Explicitly incorporating gender analysis into discussions of agricultural productivity should also expand the definitions of agricultural production to include a greater level of processing and preparation, much of which is done by women. Incorporating the full range of agricultural production, from farm to table, would provide better insights into some of the constraints both male and female farmers face.

What can we really learn from sex-disaggregated data? 2021. Bjorn Van Campenhout, Els Lecoutere and David Spielman.

https://pim.cgiar.org/2021/12/14/what-can-we-really-learn-from-sex-disaggregated-data/

A reflective piece written by researchers at IFPRI, this article critically examines the implications of widely differing responses between men and women in sex-disaggregated studies. The researchers conduct an experiment to understand the cause for this discrepancy in sex-disaggregated data – findings from this experiment show that information asymmetries occur due to a variety of reasons from measurement error to spouses hiding certain decisions from each other or even partners overstating/understating their labour contribution as per social norms. While this may lead us to the conclusion that sex-disaggregated responses to the same questions may amount to little more than mere differences in perspectives, the researchers argue that instead of becoming disillusioned with sex-disaggregated data, we must endeavour to not take survey data at face value but to always account for how data is collected as well as the cultural context within which that data is collected. We should do more to understand the nature of the discord between the responses of men and women rather than dismissing such data as unusable.

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