Close the Gender Gap: Women in Workforce · PLFS 2025
Data insights from the Periodic Labour Force Survey
The demand-supply mismatch for economic opportunities is sharpest in urban areas. Women are as educated as men, yet formal job growth is not pulling them in. Closing this gap requires ecosystem-level solutions, not just skilling pipelines, but changes in hiring, childcare, and mobility.
Two key pathways to improve FLFPR for young women are:
The blue-collar workforce in large enterprises stood at nearly 6 million in 2024-25, and has been growing rapidly, at 17% CAGR since 2022-23 (BRSR filings of listed NSE firms). Strategic partnerships between the State Governments and the private sector have improved women's entry into entry-level formal roles through awareness and aspiration-building and the dismantling of demand-side and ecosystem barriers.
The declining share of unpaid helpers in rural areas is the one positive signal in this data, but the pace is too slow.
* LIS includes: trade, transport, hospitality, domestic work · KIS includes: finance, IT, education, health, public administration
Women's concentration in self-employment and casual labour is not a reflection of preference. It is the shape of a labour market that has not been designed to accommodate them.
The self-employment gap is large because a significant share of women work as unpaid helpers, and those who do run their own enterprises operate at a small scale with weak market linkages. Most government schemes/interventions are focused only on improving access to credit. That's not enough. Closing this gap requires holistic interventions, with access to markets, networks and digital payments.
Women participate less, earn less, and work in more precarious conditions — across every sector, every age group, every level of education.
The next set of insights (7 to 9) focuses on gaps in regular wage, self-employment and casual labour.
Household domestic services tops the count at 4.43M — but it is diffuse and practically impossible to regulate. Manufacturing is different: it has addresses.
Unlike domestic work or education, manufacturing has factory inspectors, export compliance audits, and global buyers — three pressure points that don't exist elsewhere.
These are export-facing sectors. The brands buying from Indian factories already have supplier codes of conduct. Contract verification could be built into audit requirements today.
Wearing apparel alone — 559K women, no contract. Textiles: 373K. Food products: 368K. The concentration is not spread thin; it sits in a handful of accountable supply chains.
The absence of a written contract is the most concrete and measurable form of labour market exclusion. It is also the most tractable: a contract is binary, it either exists or it does not.
Employers: 1%. Own-account workers: 55%. Unpaid helpers: 44%.
Of 62.2M own-account women, 12.4M are in manufacturing. Of 50.1M unpaid helpers, only 2.4M do manufacturing work — for no pay. Same sector, very different outcomes.
Own-account: apparel (6.19M), textiles (1.76M), food (455K) — 67% of their manufacturing total. Helpers: textiles (636K), apparel (502K), food (254K) — 58% of theirs. Same work, same sub-sectors. One side works on own account and earns. The other does not.
There are no legal definitions or provisions for unpaid household enterprise helpers in the new labour codes. That's 114 million women without access to safety nets. The labour ministry needs to take concrete steps to transition unpaid helpers into own-account workers or regular wage workers.
The total is 31 million women. Dot size shows scale. The concentration in just two sectors becomes unmistakable when laid out.
The second-largest casual employer of women. Daily-wage work on sites with no maternity cover, no injury compensation, and no route into formal employment.
Seasonal, uncontracted, paid below minimum wage in most states. This is where the majority are, and the hardest to reach.
Casual labour is where India's gender and rural crises intersect. The 20.9 million women in agricultural casual labour are the least protected and most invisible in policy discourse. Collective organising, legal literacy, and wage transparency can meaningfully improve conditions for this group.
No contracts in manufacturing. Agricultural casual labour dominating rural women's work. Self-employed helpers earning nothing. The where and the how-many are established.
The next insights (10 to 12) identify priority sub-sectors across manufacturing, knowledge-intensive services, and labour-intensive services, where growth trajectories and current female exclusion make the case for targeted intervention by 2030.
Apparel leads at 28% — largely because it is labour-intensive and low-wage. Basic metals, Auto, and Fabricated metals sit below 10%. Each bar shows the share of women in the current workforce.
CAGR since 2022 is shown above each bar. Auto and Chemicals are growing at 20% annually. Basic metals and Rubber at 16%. Women's share in all four: under 16%. The mismatch is structural, not incidental.
Each sub-sector qualifies on at least one of three criteria: high growth, an existing and improving share of women workers, or conditions more conducive to women's employment than the manufacturing average.
Formal job growth in manufacturing is concentrated in a handful of high-density sub-sectors. Clusters co-locate demand, supply, and infrastructure, which increases potential impact and reduces the cost of intervention. The effectiveness of any programme, however, depends on two things: the state's willingness to work with nonprofits, and the baseline infrastructure already in place for women workers.
Women make up 53% of Education and 56% of Human health. In computer programming, 29%. In financial services, 21%. Across the rest of the group, the numbers are similar or lower, but the workforces are too small to present the same scale of opportunity.
Financial services is growing at 13% annually and is projected to reach 8 million workers by 2030, the largest projected workforce in this group after Education. Computer programming's workforce is projected at 5 million but growing slowly at 3% CAGR. Education and Health are growing steadily and will absorb large numbers of workers regardless.
Education (19.6M by 2030) and Human health (7.6M) are welcoming to women, the task is ensuring entry into higher-value roles within them. Financial services (8M) and computer programming (5M) have the scale but not the representation. These are the intervention points.
In knowledge-intensive services, the opportunity is clearest where workforce scale and growth intersect with low women's representation. Financial services and computer programming fit that description. In Education and Health, where women are already present, the priority shifts to role quality and career progression.
Retail trade has 10 million workers but only 18% are women. Personal services sits at the other end: 64% women but a workforce of just 1 million today. Warehousing and accommodation are small today but growing fast.
Accommodation leads at 22% CAGR, warehousing at 12%, food and beverage at 13%. These are accessible sectors that do not require advanced credentials. Retail grows at 6% but its sheer size makes it the largest absolute opportunity.
Retail trade is projected to add 3.44M jobs by 2030 with only 18% women today. Accommodation is growing at 22% annually. Personal services already has 64% women — the care economy is where women are concentrated, and formalising it is the most direct way to convert their work into counted, paid employment.
The care economy — personal services, domestic work, health support — already employs large numbers of women but mostly informally. Formalising this work is the single largest lever to convert existing women's labour into protected, paid employment.