Why Women’s Economic Contributions Remain Undervalued

Every day, women around the world contribute billions of hours to the economy through work that is rarely measured, poorly documented, and systematically undervalued. From cooking meals and caring for children to farming subsistence crops and fetching water, women perform essential labor that keeps families and communities functioning. Yet when valued at minimum wage, this unpaid work would represent up to 40 percent of GDP in some countries-more than entire sectors like manufacturing or transport. The problem is structural: our economic systems and data collection methods were designed to capture market transactions, not the unpaid labor that makes those transactions possible.

How official statistics ignore women’s economic contributions 🔗

The measurement gap begins with how we define “work” itself. Traditional labor force surveys were designed around formal employment-jobs with regular hours, recorded wages, and clear employer-employee relationships. This framework systematically excludes much of what women do. When a man works on a farm producing crops for sale, that’s counted as employment. When a woman works the same farm producing food for her family’s consumption, that labor often disappears from official statistics.

The bias extends beyond agriculture. Labor force surveys frequently underestimate women’s participation because enumerators are poorly trained to probe for women’s economic activities, especially when women work from home or on family farms. In countries where male household members typically respond to surveys, the problem intensifies. Men may not accurately report-or even fully know-the scope of work their female family members perform.

This methodological blind spot has real consequences. In India, the 2018-19 Periodic Labour Force Survey recorded female labor force participation at just 18.6 percent. Yet when the country conducted its first comprehensive Time Use Survey in 2019, it revealed that 95 percent of women actually worked when unpaid labor was included. The 77-percentage-point gap exists not because women aren’t working, but because our statistical systems weren’t designed to see their work.

The production boundary problem 🔗

Economic statistics rely on something called the “production boundary”-the line that determines what counts as economic activity worthy of measurement. Activities that fall within this boundary get counted toward GDP and employment statistics. Activities outside it become invisible.

The current production boundary includes all market transactions plus some non-market production of goods (like subsistence farming). But it excludes most household services, even when those services are identical to paid work. A professional chef cooking in a restaurant is employed. A woman cooking three meals a day for her family is not counted as working, even though she’s performing the same skilled labor.

This distinction isn’t neutral-it’s gendered. Women perform the vast majority of unpaid domestic and care work. According to UN Women, women globally spend 2.5 times more hours than men on unpaid care work, averaging 4.2 hours daily compared to men’s 1.7 hours. In some developing countries, this gap widens dramatically-Indian women spend more than eight times as much time on unpaid domestic work as men.

Why patriarchal norms distort data accuracy 🔗

The statistical invisibility of women’s work doesn’t happen in a vacuum. It reflects and reinforces patriarchal assumptions about what constitutes “real work” and who does it. These assumptions shape every stage of data collection, from survey design to implementation to analysis.

Consider how surveys are conducted. In many cultures, social norms dictate that men serve as primary respondents for household surveys. When asked about household members’ work status, male respondents often underestimate or fail to report women’s economic activities. They may not consider their wives’ or daughters’ work on family farms or businesses as “real employment” because it doesn’t generate direct cash income. Even women themselves sometimes internalize these biases, failing to identify their own labor as work when asked by surveyors.

The enumeration bias 🔗

Data collection itself carries embedded biases. A study of India’s Time Use Survey found that 36 percent of responses came from proxies rather than the individuals themselves. This creates systematic distortions: women proxies tend to overestimate employment-related activities while underestimating caregiving hours for women. Male proxies show different biases but produce equally skewed results.

The training of enumerators matters enormously. Without specific instruction to probe for women’s multiple economic activities-particularly unpaid work in agriculture, family businesses, and home production-surveyors miss substantial economic contributions. The assumption that “women’s work” means only domestic chores rather than economic production becomes a self-fulfilling prophecy in the data.

Cultural norms about gender roles also determine what gets asked. If a survey doesn’t explicitly inquire about time spent collecting water, gathering firewood, processing food, or caring for livestock, these activities remain uncounted. Yet these tasks are essential economic work, often consuming many hours daily and directly contributing to household survival and well-being.

The consequences of invisibility 🔗

When women’s work remains uncounted, the ripple effects extend far beyond statistics. Policymakers allocating resources can’t address needs they don’t know exist. Economic development programs designed around visible male employment patterns fail to account for how women actually contribute to and participate in the economy.

The invisibility also perpetuates undervaluation. Work that isn’t measured isn’t valued. When unpaid care work would constitute 21.4 percent of GDP in Latin America and the Caribbean if properly valued, yet remains absent from national accounts, it sends a powerful message about whose contributions matter.

New approaches to recognize women’s economic contributions 🔗

The good news is that recognition of these measurement problems has sparked concrete initiatives to correct them. International organizations, national statistical agencies, and researchers have developed new tools and methodologies specifically designed to capture women’s full economic contributions.

Time use surveys as game changers 🔗

Time use surveys represent perhaps the most significant methodological advance in measuring women’s work. Unlike traditional employment surveys that ask whether someone “worked” last week, time use surveys ask respondents to account for how they spent every hour of the previous day. This approach captures both paid and unpaid work, revealing the full scope of economic activity.

The adoption of Sustainable Development Goal 5.4.1-which specifically requires countries to measure “proportion of time spent on unpaid domestic and care work, by sex, age and location”-has accelerated the implementation of time use surveys globally. Countries from India to Tanzania to Argentina now conduct regular time use studies, creating comparable data on how women and men allocate their time across paid work, unpaid work, personal care, and leisure.

These surveys have documented what many women already knew experientially: they work longer total hours than men when paid and unpaid work are combined. The data has proven invaluable for policy design, helping governments understand why interventions like childcare services or improved water infrastructure can dramatically affect women’s labor force participation.

Improved survey methodologies 🔗

Beyond time use surveys, statistical agencies are refining traditional labor force surveys to better capture women’s work. The International Labour Organization has developed “light time-use modules” that can be attached to regular labor force surveys, providing better data on unpaid domestic and care work without the full expense of comprehensive time use studies.

Better enumerator training has also made a difference. When surveyors are specifically instructed to probe for multiple economic activities, to ask about subsistence production, and to record both primary and secondary activities (like tending children while cooking), they capture a more accurate picture of women’s work patterns.

Some countries have also moved toward requiring self-reporting rather than proxy responses wherever possible, reducing the gender biases that occur when male household heads report on women’s activities.

Valuing unpaid work in national accounts 🔗

A growing number of countries are experimenting with “satellite accounts” that estimate the economic value of unpaid household production and care work. These accounts don’t replace GDP but supplement it, showing what the economy would look like if unpaid work were assigned monetary value.

The results are striking. When valued at minimum wage rates, the 16.4 billion hours of unpaid care work performed daily worldwide would represent 9 percent of global GDP-approximately $11 trillion. Women perform three-quarters of this work, contributing an estimated 6.6 percent of global GDP through unpaid labor alone.

These valuations help make visible the massive economic contributions that traditional measures ignore. They’ve proven particularly useful in demonstrating the economic case for investments in care infrastructure, parental leave policies, and other supports that enable women to participate more fully in formal employment.

Policy initiatives that make a difference 🔗

Armed with better data, some governments have implemented policies specifically designed to recognize and support women’s economic contributions. Iceland’s parental leave system, which gives each parent six months of paid leave, has achieved nearly 90 percent take-up among fathers. This both recognizes care work as valuable labor worthy of compensation and redistributes it more equitably between parents.

Several Latin American countries have established national care systems that provide public childcare and eldercare services, explicitly recognizing that care is a social responsibility rather than an individual (female) burden. These systems are grounded in time use data showing exactly how much unpaid care work women perform.

In India, despite persistent challenges, improved data has supported policy discussions about extending maternity benefits, improving rural infrastructure to reduce time spent on water collection, and expanding childcare services for informal sector workers.

Moving forward 🔗

The under-enumeration and undervaluation of women’s work isn’t just a statistical problem-it’s a political and economic one. It reflects power structures that have historically defined men’s activities as economically significant while dismissing women’s contributions as naturally occurring or insignificant.

Correcting this requires sustained effort on multiple fronts: better data collection methodologies, regular time use surveys, training for enumerators, satellite accounts that value unpaid work, and most critically, political will to implement policies based on what the data reveals. When we measure women’s full economic contributions, we can’t continue to ignore them in policy and planning.

The tools exist. The methodologies work. What remains is the commitment to use them consistently and act on what they show us about the true nature of economic activity and who performs it.

What do you think? How might your understanding of economic productivity change if unpaid care work were fully counted in GDP? What policies would you prioritize if you had access to comprehensive data on how women and men allocate their time?

References

1. https://ilostat.ilo.org/topics/unpaid-work/measuring-unpaid-domestic-and-care-work/
2. https://wol.iza.org/articles/female-labor-force-participation-and-development/long
3. https://thethirdeyeportal.in/structure/time-could-be-the-only-measure-for-womens-labour/
4. https://www.unwomen.org/en/articles/faqs/faqs-what-is-unpaid-care-work-and-how-does-it-power-the-economy
5. https://behanbox.com/2025/03/28/what-time-use-surveys-say-and-dont-about-womens-work/
6. https://www.undp.org/latin-america/blog/missing-piece-valuing-womens-unrecognized-contribution-economy