The 10th United Nations Global Forum on Gender Statistics, organised by the United Nations Statistics Division and hosted by Georgia’s National Statistics Office in October 2025, marked 30 years of the Beijing Platform for Action. The emphasis was unambiguous: gender statistics are not a technical add-on but central to evidence-based policy and to tracking progress on the Sustainable Development Goals.
That focus has implications for how countries design labour-market data systems. Employment statistics, as currently constructed, struggle to answer basic questions about gender inequality. They measure participation but explain little about constraint, choice, or the conditions under which women enter—or exit—the labour market.
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What employment data capture—and what they miss
India illustrates the problem clearly. The Periodic Labour Force Survey (PLFS) shows female labour force participation rising from 25.3% in 2017–18 to 45.2% in 2023–24. On paper, this looks like a structural improvement. A closer look complicates the picture. Nearly two-thirds of working women—67.4%—are self-employed.
The headline increase raises questions that standard labour surveys are poorly equipped to answer. Is self-employment a preference or a fallback? Are women opting for flexibility, or being pushed into low-paying, insecure work by constraints unrelated to the labour market itself? Participation rates alone cannot settle this.
Gender statisticians have long warned against reading such data at face value. Adriana Mata Greenwood has argued that official statistics remain partial, often reinforcing distorted views of an economy and perpetuating gender inequality through policy blind spots. Employment data record outcomes, not the social and institutional forces shaping them.
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Preferences shaped by constraint
The 20th International Conference of Labour Statisticians (2018) recognised this gap by noting that “preferences” for non-permanent or part-time work often reflect circumstances rather than choice. Women’s disproportionate engagement in part-time, low-skilled, or informal jobs is closely linked to unpaid domestic and care work.
Yet labour surveys rarely integrate employment with education trajectories, caregiving responsibilities, or household structure. Without that linkage, they risk misclassifying constrained behaviour as voluntary preference.
Gender statistics: Why time use data matter
This is where Time Use Surveys (TUS) become indispensable. India’s national TUS, conducted by the Ministry of Statistics and Programme Implementation in 2024, showed that women aged 15–59 spend an average of 5 hours a day on unpaid domestic work and 2.3 hours on unpaid caregiving. These figures do more than fill a data gap; they explain labour-market outcomes that employment surveys treat as anomalies.
Time use data offer a fuller measure of work by capturing paid, unpaid, and simultaneous activities. They are particularly valuable for self-employed workers, home-based workers, and those in irregular or part-time employment—categories where women are overrepresented. By measuring how time is actually allocated, TUS provide better estimates of working hours than conventional labour surveys.
They also force harder questions. Does part-time or irregular work lead to upward mobility, or does it lock women into low-productivity segments of the economy? Are these arrangements transitional, or structurally embedded through social norms that normalise unpaid work for women?
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Lessons from international practice
Several countries have used time use data to redesign policy. Uruguay’s time use surveys informed the National Integrated Care System, the Care Act, and the National Care Plan (2016–2020), explicitly recognising unpaid care work within social protection. Mexico and Ecuador have drawn on similar data to assess the gender impact of conditional cash transfer programmes. Japan and South Korea have used time use evidence to support work–family balance policies.
In Africa, Kenya has used time use data to strengthen care economy interventions and public care infrastructure. Insights on firewood collection and household energy use fed into Kenya’s National E-Cooking Strategy 2024, linking health, employment, and gender equity. These examples underline a common point: time use data translate into policy only when treated as core economic information, not supplementary statistics.
India officially acknowledges the relevance of time use surveys for planning and policy on women and children. But recognition has yet to translate into systematic integration with labour force surveys or regular data collection cycles.
From measurement to policy design
Time use surveys also capture multiple and simultaneous activities, a feature especially relevant for women. Time stress—working, caregiving, and managing households concurrently—helps explain detachment from regular employment more convincingly than skill deficits or lack of motivation.
For policy, this matters. Without understanding the time tax on women, interventions risk misfiring. Employment incentives, skilling programmes, or formalisation drives will have limited impact if care responsibilities remain invisible in data and policy design.
Gender mainstreaming in labour statistics therefore requires more than disaggregated participation rates. It demands probing questions on employment status alongside information on caregiving, marital status, presence of children, location, and access to services. Regular time use surveys are not optional extras; they are essential to improving labour force estimates, understanding gender inequality, and designing credible social protection systems.
Better data will not, by itself, solve labour-market inequities. But without it, policy will continue to treat women’s constrained choices as preferences—and mistake statistical improvement for substantive change.
Dr Ellina Samantroy is Faculty Co-ordinator, Centre for Gender and Labour, VV Giri National Labour Institute, Noida.