Impact of Gender Equality Policies on Workforce Participation Rates in India: A Time-Series Analysis Using a Composite Policy Index Approach

Abstract

This study examines the impact of gender equality policies on workforce participation rates in India using annual time-series data from the World Development Indicators. A composite Gender Equality Policy Index (GEPI) is constructed using principal component analysis based on female education, financial inclusion, and labor market parity indicators. The study employs an Autoregressive Distributed Lag (ARDL) framework to capture both short-run dynamics and long-run equilibrium relationships between gender equality policies and workforce participation. The results reveal that gender equality policies significantly increase workforce participation in both the short and long run, with stronger effects observed in the long run. Macroeconomic factors such as GDP per capita and urbanization positively influence workforce participation, while fertility rates and unemployment exert negative effects. The error correction mechanism confirms stable convergence toward long-run equilibrium following short-run shocks. Nonlinear analysis indicates diminishing marginal returns of gender equality policies, suggesting that early-stage policy interventions yield stronger impacts compared to later stages of institutional development. Robustness checks using FMOLS and DOLS estimators confirm the stability of the findings. The study concludes that gender equality policies play a crucial role in enhancing labor market participation in India, although their effectiveness depends on complementary economic and demographic conditions. The findings contribute to the literature by providing dynamic macro-level evidence on the gender equality–labor participation nexus in a major developing economy.

Received: 2026-04-12 | Revised: 2026-06-02 | Accepted: 2026-06-23 | Published: 2026-06-30

Declarations

Ethics and Guidelines: Not applicable.

Consent to participate: Not applicable.

Consent to publish: The authors have provided consent to publish.

Competing interests: The authors declare no competing interests.

Data availability statement: Data will be made available on reasonable request from the corresponding author.

Funding: This research received no external funding.

Clinical Trial Number: Not Applicable.

Declaration of using generative AI: During the preparation of this work the author(s) used ChatGPT in order to correct the grammatical errors. After using this tool/service, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the published article.

Author Contributions: Conceptualization, H.A.R.; methodology, H.A.R.; formal analysis, H.A.R.; writing—original draft preparation, H.A.R.; writing—review and editing, H.A.R. All authors have read and agreed to the published version of the manuscript.