The Invisible Workforce: Under Enumeration and Valuation of Women’s Work

When we talk about the workforce, we often picture factories, offices, and fields. But there is a vast segment of workers – mostly women – whose labour never makes it into official statistics. Whether it is tending to livestock, processing food at home, helping in a family farm, or running a small home-based enterprise, the contributions of millions of women remain invisible. This invisibility is not accidental. It is the result of deep-rooted social biases, flawed data collection methods, and a narrow definition of what counts as “work.” Understanding under enumeration and undervaluation of women’s work is essential to addressing gender inequality in the labour market.

What do under enumeration and undervaluation mean? 🔗

Under enumeration refers to the systematic undercounting of women’s economic activities in official data – the census, labour force surveys, and national accounts. When a woman who spends hours every day working on the family farm, making handicrafts for sale, or rearing poultry is recorded simply as a “housewife” in the census, her productive work is erased from the record.

Undervaluation, on the other hand, refers to the failure to recognise the economic worth of the work women do, both paid and unpaid. Even when women’s activities are recorded, they are often categorised as supplementary or marginal, never commanding the same respect or remuneration as comparable work done by men.

Together, these twin problems create a cycle. Because women’s work is not counted, it is not valued. And because it is not valued, there is little incentive to count it properly.

How big is the problem? A look at the numbers 🔗

The scale of this invisibility is staggering. Recent labour statistics show that while eight in ten men participate in the labour force, only about four in ten women do. India’s female labour force participation rate has been persistently lower than in many countries at similar levels of income and development.

Out of an estimated 625-million-strong labour force, only slightly more than 215 million are women, and the gender imbalance is even sharper in urban areas, where fewer than 50 million women are in the labour force. But do these numbers reflect reality, or do they reflect a data collection system that routinely misses what women do?

Consider this: estimates suggest that over 90 percent of working women are engaged in the informal sector and are not captured in official statistics. These include domestic servants, small traders, artisans, and unpaid family farm workers. Although these jobs are supposed to be recorded in the census, undercounting remains likely.

Why does under enumeration happen? 🔗

Under enumeration is not just about numbers falling through cracks. It is a systemic issue driven by how data is collected, who collects it, and who provides the information.

The proxy respondent problem 🔗

One of the most significant causes of under enumeration is the reliance on proxy respondents. In the Indian census, any member of the household – usually the head of the household – responds to questions about the work status of all members. In most cases, this respondent is a man. And men often do not recognise, or choose not to report, the economic activities of women in the household.

Research on respondent biases in household surveys confirms that proxy respondents introduce measurement error through asymmetric information – they simply may not know or accurately recall the activities of other household members. When the male head of a family reports that his wife “does not work,” he may genuinely not perceive her activities – sorting grain, tending animals, or stitching garments for sale – as economic work.

A revealing field study by IDinsight found that when women were being surveyed, men present in the room would often interrupt, suggest answers, or repeatedly say things like “she doesn’t know anything,” causing women to retreat and become less willing to share their own experiences. In such an environment, women’s actual economic contributions rarely get recorded accurately.

Narrow definitions of “work” 🔗

Official statistical frameworks often define work in ways that systematically exclude much of what women do. In Indian and international labour statistics, activities that produce goods for self-use and sale are considered work – so growing vegetables for the household counts, and selling them counts too. But for services, only activities producing an output that can be sold are counted as work. This means that cooking those vegetables, feeding children, or caring for the elderly – activities overwhelmingly performed by women – fall outside the boundary of “productive work.”

This distinction has enormous consequences. Of the roughly 300 million women outside the labour force, the majority are classified as attending to childcare and home-making responsibilities. They are working – but the system refuses to see it.

Enumerator bias and gender stereotypes 🔗

The ILO has noted that gender bias can be imported into survey data through instrument design, interviewer behaviour, the interview context such as self-censorship in the presence of other household members, and insufficient restrictions on proxy reporting. When enumerators themselves carry gender stereotypes – assuming that women in rural households are not “workers” – they are less likely to probe further or ask follow-up questions that might uncover women’s economic roles.

The United Nations guidelines on census data recognise this and emphasise that gender-based stereotypes can introduce serious biases in census data, and considerable effort is needed in preparatory stages to minimise such biases. They recommend hiring and training female enumerators and ensuring that training materials specifically address gender bias issues.

The question framing effect 🔗

How a question is asked can dramatically change the answer. A study published in The India Forum experimented with different ways of asking employment questions. When women were asked a single question about their employment status in the previous week, the female employment rate was about 44 percent. But when the survey used detailed, activity-specific questions about the week, the estimate jumped to 56 percent – a 12 percentage point increase.

Notably, there was no such difference for men, whose employment rates remained between 82 and 87 percent regardless of how the question was framed. This shows that standard survey methods are not gender-neutral – they are designed in ways that fail to capture the fragmented, multiple, and often informal work patterns typical of women’s employment.

The undervaluation of unpaid care work 🔗

Even setting aside the problem of undercounting, there is the equally serious issue of undervaluation. The bulk of women’s labour falls into the category of unpaid domestic and care work – and this work is treated as economically worthless by conventional accounting.

According to the latest Time Use Survey (2024), women spend 289 minutes a day on unpaid domestic work, compared to just 88 minutes for men. Females also spend 137 minutes daily on caregiving activities – looking after children and the elderly – compared to 75 minutes for males. This unpaid burden only increases with age, particularly during the child-rearing years.

The economic value of this invisible work is substantial. A government policy brief estimated that caregiving alone contributes between 15 and 17 percent of GDP, yet this value is largely created within households and routinely disregarded. When calculated using weighted average wage methods, the economic value of women’s unpaid care work is approximately ₹34.5 lakh crore, or about 17 percent of GDP.

Despite this, public sector spending on care infrastructure – including pre-primary education, childcare centres, and maternity and disability benefits – stands at less than one percent of GDP. The gap between the value women produce and the investment made to support their work is vast.

Why does undervaluation persist? 🔗

Cultural norms and the “homemaker” identity 🔗

A dominant social and cultural norm treats married women’s time as naturally devoted to household duties. This “homemaker norm” means that even when women are educated and able to take up employment outside the home, the disproportionate burden of the care economy discourages them from doing so. Women who work within the household – cooking, cleaning, rearing children, managing family health – are classified as non-workers by statistical systems, reinforcing the cultural belief that their labour has no economic value.

Exclusion from national accounts 🔗

The System of National Accounts (SNA), the international framework used to measure economic output, has historically excluded unpaid domestic services from GDP calculations. Although the SNA has included household production of goods in GDP calculations since 1993, unpaid care work in the form of services remains excluded. Some countries have developed satellite accounts to provide a fuller picture, but these remain supplementary rather than mainstream. Without inclusion in core economic metrics, unpaid care work remains a policy blind spot.

The impact on women’s participation 🔗

Undervaluation has direct consequences for women’s economic participation. Research shows that an additional hour of daily caregiving reduces a woman’s probability of labour market participation by 20 percentage points, with no comparable effect on men. The OECD has found that in countries where women spend an average of five hours on unpaid work, only about 50 percent of working-age women are economically active, and a two-hour reduction in unpaid work leads to a 10 percentage point rise in female participation.

What can be done? 🔗

Addressing under enumeration and undervaluation requires action on multiple fronts.

Better survey design is a starting point. Research has shown that incorporating detailed, activity-specific questions and recovery prompts – asking respondents if they missed reporting any income-generating or household-supporting activities – significantly improves the accuracy of women’s employment data. Surveys should also aim to speak directly to women rather than relying on proxy respondents.

Training enumerators on gender sensitivity is equally critical. Census guidelines recommend recruiting both men and women as field staff and ensuring that training manuals specifically address gender bias issues. When enumerators are trained to probe deeper – asking about activities like livestock rearing, food processing, and home-based production – more of women’s work comes to light.

Expanding the definition of work in official statistics is a long-standing demand of feminist economists. The resolution adopted at the International Conference of Labour Statisticians in 2013 – classifying unpaid work as work – is a starting point, and the “4Rs” framework of recognising, reducing, redistributing, and representing unpaid care work should guide policy.

Investing in care infrastructure can reduce the unpaid burden on women and free up their time for paid employment. Affordable and accessible childcare, elder care facilities, and community service centres are not just welfare measures – they are economic investments. The Observer Research Foundation has argued that investing in the care economy is a crucial strategy for stimulating women’s employment and preventing occupational downgrading among women in the workforce.

Conducting regular time use surveys is essential for making women’s work visible. India’s first large-scale Time Use Survey in 2019, followed by a second round in 2024, has already provided invaluable data. Periodic surveys with not more than a three-to-five year gap are recommended to track progress and inform policy.

The bigger picture 🔗

Under enumeration and undervaluation are not merely statistical problems. They reflect and reinforce deeper structures of gender inequality. When women’s work does not appear in national data, it does not appear in policy discussions. When it does not feature in policy, budgets do not allocate resources for it. And when resources are absent, women remain trapped in a cycle of invisible, uncompensated labour.

Breaking this cycle requires not just better data, but a fundamental shift in how society defines and values work. The woman who wakes up before dawn to milk cattle, prepare meals, tend to children, and then work in the fields is not a “non-worker.” She is, in many ways, the backbone of the household economy. Official statistics just haven’t caught up to this reality yet.

What do you think? If unpaid domestic and care work were formally recognised in GDP calculations, how would it change the way governments design welfare policies for women? And in your own experience, have you seen examples where women’s economic contributions go unnoticed simply because they happen within the household rather than in a formal workplace?

References

1. https://www.dataforindia.com/women-and-work/
2. https://www.sciencedirect.com/science/article/abs/pii/S0304387823001542
3. https://www.idinsight.org/article/tackling-gender-inequality-through-improved-data-quality/
4. https://ilostat.ilo.org/blog/breaking-the-bias-for-better-gender-data/
5. https://unstats.un.org/wiki/spaces/genderstatmanual/pages/85788122/Introduction+Population+and+housing+census
6. https://www.theindiaforum.in/public-policy/what-do-we-miss-womens-employment-survey-data
7. https://www.orfonline.org/expert-speak/underlining-the-work-that-women-do-findings-from-time-use-survey-2024
8. https://www.oxfamindia.org/sites/default/files/2020-01/India%20supplement.pdf