Geographic Inequalities in Employment Indicators Among Men and Women in India: An Analysis of 720 Districts, 2019–2021

India is poised to supply nearly a quarter of the global incremental workforce in the coming decades, yet subnational employment statistics remain lacking. Using data on 108,785 females aged 15–49 years and 101,839 males aged 15–54 years from the fifth National Family Health Survey (2019–2021) of India, this study provides estimates of key employment indicators across 720 districts of India, disaggregated by type of occupation, gender and place of residence. We implemented a four-level Bayesian random intercept model using Markov Chain Monte Carlo (MCMC) methodology to estimate the proportion of workers, main workers (year-round cash-based employment), marginal workers (seasonal/occasional cash-based employment) and occupational categories. Nationally, among the reproductive age population, one-third of females and four-fifths of males are employed. There is a predominance of irregular, short-term and unpaid work among females, and substantial geographic differences, with a north–south divide in workforce participation. Female work participation is lower and more variable across districts compared to male work participation, both within and between districts. Cluster-level factors account for the largest share of this variation, followed by state-level factors. These geographic patterns point to the need for district-level hyperlocal policies addressing the specific barriers, such as limited access to childcare, skill mismatch, poor transport connectivity, and entrenched gender norms in districts with low female participation and high employment volatility, for stable and remunerative employment.

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Journal
Journal of Development Policy and Practice
Published
2026-01-09
DOI
https://doi.org/10.1177/24551333251406765
Primary Topic
Gender, Labor, and Family Dynamics
Type
article
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article

Geographic Inequalities in Employment Indicators Among Men and Women in India: An Analysis of 720 Districts, 2019–2021

R Kim, Ekaterina Zhelenkova, Avnish Singh Pal, Suraj Maiti et al.
Journal of Development Policy and Practice
Gender, Labor, and Family Dynamics
article

Geographic Inequalities in Employment Indicators Among Men and Women in India: An Analysis of 720 Districts, 2019–2021

R Kim, Ekaterina Zhelenkova, Avnish Singh Pal, Suraj Maiti, S.V. Subramanian
article en

Abstract

India is poised to supply nearly a quarter of the global incremental workforce in the coming decades, yet subnational employment statistics remain lacking. Using data on 108,785 females aged 15–49 years and 101,839 males aged 15–54 years from the fifth National Family Health Survey (2019–2021) of India, this study provides estimates of key employment indicators across 720 districts of India, disaggregated by type of occupation, gender and place of residence. We implemented a four-level Bayesian random intercept model using Markov Chain Monte Carlo (MCMC) methodology to estimate the proportion of workers, main workers (year-round cash-based employment), marginal workers (seasonal/occasional cash-based employment) and occupational categories. Nationally, among the reproductive age population, one-third of females and four-fifths of males are employed. There is a predominance of irregular, short-term and unpaid work among females, and substantial geographic differences, with a north–south divide in workforce participation. Female work participation is lower and more variable across districts compared to male work participation, both within and between districts. Cluster-level factors account for the largest share of this variation, followed by state-level factors. These geographic patterns point to the need for district-level hyperlocal policies addressing the specific barriers, such as limited access to childcare, skill mismatch, poor transport connectivity, and entrenched gender norms in districts with low female participation and high employment volatility, for stable and remunerative employment.

Journal of Development Policy and Practice
University of Vienna (AT), Harvard University (US), Korea University (KR), Vienna Institute of Demography (AT), Indian Institute of Technology Tirupati (IN), Virginia Tech (US)
No poverty
Openalex Percentile: Top 95%
Gender, Labor, and Family Dynamics
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