Better data on women’s economic lives for better policy

This blog is written by Alba Bautista, Program Officer, Data2X and Gayatri Koolwal, Fellow, Data2X. It is the twelfth in a new blog series on women, work and economic empowerment.  

THIES, SENEGAL – AUGUST 19: Women working in a communal garden growing vegetables as an income generating activity. The women use the income generated to have some financial independence, help with sharing the family expenditures, and not rely on their husband when spending money on their health and education for their children. This income also helps them when deciding on family planning options. August 19, 2014 in Thies, Senegal. (Photo by Jonathan Torgovnik/Reportage by Getty Images).

Picture a woman living in a rural African community. Her home is on a small farm, where crops are grown mostly for her family’s consumption. Her husband plants the seeds and maintains the land, and she takes part in planting and harvesting, while also preparing food and caring for her small children. If it is a particularly good season, she helps sell surplus harvest in the local market; but this year there was a drought and the farm only produced enough to feed her family.

Would you consider this woman to be employed as a farmer? Or is she a housewife? Or both? How would you begin to ask her how she spends her time? These questions demonstrate how challenging it can be to accurately measure and value women’s work.

Around the world, women spend many hours each day inside and outside the home doing both paid and unpaid work activities. This is particularly true for rural women, who often face the double responsibility of producing and preparing food for their households and communities. However, though rural women comprise an average of 43 percent of agricultural employment, we know little about the amount of time these women spend on paid and unpaid activities and the nature of their work.

Partnering for Better Gender Data

Better data on all of women’s work – paid and unpaid – helps us understand women’s work burdens and design more effective economic and social policies such as employment, entrepreneurship training, and child care programmes. But how do we ensure that we are collecting this “better data,” especially in complex rural settings? This question underpins the Women’s Work and Employment (WW+E) partnership between Data2X, a gender data technical and advocacy platform, the Food and Agriculture Organization of the United Nations (FAO), the International Labour Organization (ILO), and the World Bank (WBG).

New Definitions and Data Challenges: Blurred lines between Paid and Unpaid Work

In 2014, the WW+E partnership launched efforts to improve the way countries measure women’s overall workload. The partnership followed recent important developments in the world of statistics. Recognising the limitations of existing statistics, the international community of labour statisticians, under the leadership of the ILO, adopted new global standards in 2013. For the first time, all paid and unpaid activities are recognised as productive “work,” and “employment” is only work performed for pay or profit. Before, a woman like our farmer above would only have been counted as either employed or inactive. Now, both her work as a subsistence farmer and at home during the drought will be counted.

Ultimately, having a more complete picture of women’s multiple roles and economic contributions can fuel better policymaking. However, for this to be possible, a concerted effort is needed by partners at global, regional, and country levels to adopt and implement the new definitions as well as to ensure an informed transition between the old and new numbers. The WW+E partnership was designed to address this need and, in the process, to increase synergies and harmonisation between the different partner agencies’ survey instruments.

The partnership focuses on distinguishing between the work that goes into growing crops on farms for a household’s own use from crops that are sold in the market. From 2014-2016, WW+E partners conducted in-country field studies and tested several survey questions. The guidance produced from this process will be an important support for countries trying to meet measurement challenges.

Important lessons have been learned. All agencies found that respondents often encountered difficulties in interpreting concepts like “work” and “for profit” in distinguishing work across paid and unpaid activities, as well as in reporting activities over different timeframes. ILO survey field tests in 10 countries show that over half of the women who work in a family business do not recognise their work as such and thus do not report it unless specifically asked about “helping” in a family business. Follow-up questions are needed to ensure that women who help in the family business or farm do not simply report their work as “household work.” Some field tests also highlighted the need to recognize preconceived or cultural norms regarding women’s and men’s roles and division of labor when soliciting responses. For example, in Vietnam, women answered “no” to engaging in construction or renovation work of their own houses, even if they did help their husbands in this productive activity, because they considered this “men’s work”. The timing and other practicalities of survey implementation also matter; the World Bank’s field study in Ghana, for example, found that women’s employment rates are more sensitive to the timing/season of data collection.

Next Steps

The WW+E partnership has created an important platform for collaboration across organisations. For the next phase of the partnership, the ILO and the World Bank plan to implement a joint survey in Sri Lanka. Findings from this study will be used to produce guidance on how to apply the new standards on employment and own-use production work consistently across different types of household and labor force surveys. The study will also shed light on how to monitor gains in women’s paid work across surveys.


Learn more about the Data2X Women’s Work and Employment partnership.

Watch the Better Data, Better Decisions video.




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