When governments measure economic activity, they count factories, offices, and farms. They track wages, profits, and trade. But there’s a massive blind spot in these calculations: millions of hours of work that women perform every day, yet never appears in any official statistic. This invisible labor-cooking, cleaning, caring for children and elders, fetching water-forms the foundation of every economy, yet remains excluded from GDP calculations and workforce statistics.
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How economic data gets collected
In India, two main systems collect information about who works and what they do. The decennial Census and the National Sample Survey Office surveys gather data on employment and economic activity across the country. These surveys ask households about their members’ work status, occupations, and time spent in various activities.
The National Sample Survey Office classifies people’s economic activity using specific codes. Workers are categorized as employed if they’re self-employed, work for wages, or help in family businesses for pay. But here’s where the problem starts: the surveys use two specific codes-92 and 93-for people “engaged in domestic duties.” Code 92 covers household tasks like cooking and cleaning. Code 93 includes these tasks plus activities like collecting firewood, fetching water, and tending kitchen gardens.
People classified under these codes aren’t counted as workers. They’re labeled as “not in the labor force.” This classification decision has profound consequences, especially for women.
The problem of invisible work
Traditional data collection methods systematically fail to capture much of women’s economic contribution. More than 60 percent of Indian women are classified as engaged in domestic duties, yet this percentage has been rising rather than falling-from 48.8 percent in 1993-94 to 60.9 percent in 2011-12.
This invisibility stems from how economic activity gets defined. The Indian System of National Accounts uses a narrower definition than international standards. Activities like processing primary products for household consumption or collecting free goods don’t count toward GDP, even though the same work would be counted if done for payment.
What counts as work and what doesn’t
Consider a woman who spends hours each day grinding grain, preparing meals, collecting water from a distant well, and gathering firewood. If she were paid to do these exact same tasks for another household, she’d be counted as employed. But because she does them for her own family, she’s economically invisible.
The National Sample Survey data shows only 22 percent of women participate in employment activities, while more than 90 percent engage in unpaid domestic work. The gap between these numbers reveals a massive measurement problem.
How survey methods undercount women’s work
Survey design itself contributes to undercounting. Questions often fail to capture the intermittent, scattered nature of informal work that characterizes much of women’s economic activity. Women frequently view their productive work as simply part of household responsibilities and underreport it. When one household member answers questions about everyone’s work status, women’s contributions get minimized or missed entirely.
The line between “unpaid helper in family enterprise” and “domestic duties” is often arbitrary. Similar activities end up in different categories depending on how survey questions are interpreted, creating inconsistent data about women’s actual economic contributions.
Feminist economists challenge the status quo
For decades, feminist scholars have critiqued how national accounting systems exclude women’s unpaid work. In 1988, economist Marilyn Waring published a groundbreaking analysis showing how the UN System of National Accounts rendered women’s reproductive labor invisible. Her work demonstrated that if a man marries his housekeeper, GDP falls-the same work continues, but it’s no longer counted because it’s unpaid.
The “three Rs” framework
Feminist economists propose a comprehensive approach to addressing unpaid work through recognition, reduction, and redistribution. Recognition means acknowledging unpaid work as real economic activity. Reduction involves decreasing the burden through public services and infrastructure. Redistribution means sharing unpaid work more equally between women and men.
Research reveals that women spend about 297 minutes daily on domestic work in India, compared to just 31 minutes for men. This disparity reflects deep-seated social norms about whose responsibility household work is.
Calls for better measurement
Time-use surveys offer one solution. These detailed studies track how people spend their time across different activities. India’s Time Use Survey revealed that over 90 percent of women participated in unpaid domestic work, providing data that standard employment surveys miss.
The International Labour Organization updated its statistical standards in 2013 to better capture all forms of work, including unpaid activities. However, India’s Periodic Labour Force Survey has avoided adopting these international standards, continuing to use older methodologies that undercount women’s work.
Why better data matters
Invisible work has real consequences. When women’s economic contributions don’t appear in official statistics, policymakers lack the information needed to design effective programs. Infrastructure investments that could reduce unpaid work burdens-like piped water, electricity, childcare facilities-get lower priority. Social protection systems fail to account for the care work that enables others to participate in paid employment.
Feminist economists argue that care work produces public goods in the form of human capabilities that benefit society broadly, justifying public support rather than leaving these costs to be borne disproportionately by women.
Recent amendments to the System of National Accounts have begun encouraging countries to develop extended accounts that reflect unpaid care work’s value. Yet these efforts remain complementary to traditional metrics rather than fundamentally challenging how we measure economic progress.
Moving toward inclusive measurement
Creating more accurate and inclusive economic data requires multiple changes. Survey questionnaires need careful design to capture women’s actual activities without relying on outdated assumptions. Enumerators need better training to recognize diverse forms of work. Statistical definitions should align with international standards that acknowledge a broader range of economic activities.
Most fundamentally, societies need to recognize that unpaid work isn’t a personal choice but often a constraint shaped by inadequate public services, limited employment opportunities, and rigid social norms about gender roles. Women perform domestic work not because they prefer it to paid employment, but because institutions-markets, states, and communities-have failed to create viable alternatives.
The path forward involves both better measurement and substantive policy changes. Improved data collection should be paired with investments in infrastructure, childcare, elder care, and other services that reduce unpaid work burdens. Labor market policies need to create decent employment opportunities for women. Social norms that assign domestic responsibilities primarily to women require ongoing challenge and change.
What do you think? If we counted all of women’s unpaid work in our economic measures, how might that change government priorities and policy decisions? What would truly equal sharing of household responsibilities require in your own community?
References
- https://www.nature.com/articles/s41599-020-0488-2
- https://www.theindiaforum.in/article/reconceptualising-womens-work-national-sample-survey
- https://pmc.ncbi.nlm.nih.gov/articles/PMC11838883/
- https://www.networkideas.org/featured-articles/2025/01/whats-really-happening-with-womens-employment-in-india/
- https://en.wikipedia.org/wiki/Feminist_economics
- https://socio.health/women-in-economy/feminist-economics-challenges-traditional-views/
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