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Exploring the Gender Wage Gap in Indian Agriculture - An Interaction-Based Panel Regression Approach

  • Publication | 2026

This study investigates the gender wage disparities in Indian agriculture utilising a pseudo-panel dataset and an interaction-based panel regression framework. The study seeks to examine the association of gender on agricultural wages over the years, especially in response to the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), which was enacted in 2005. The study examines whether MGNREGA, which mandates equal wages for male and female workers in public works, is associated with differential gender wage dynamics in the agriculture by shifting wage expectations and enhancing women's bargaining power. The analysis uses a random effects model with a clustered panel-level standard errors to overcome the problem of serial correlation and heteroscedasticity in the datasets. The findings indicate persistent gender-based wage disparities across various agricultural activities, where male labourers are consistently paid higher wages than their female counterparts. At the same time, the implementation of MGNREGA revealed a significantly positive change observed on rural agricultural wages, with consistent post-policy wage gains reported across most of the farm activities. However, the interaction-based panel regression estimates do not consistently show statistically significant differential gender wage trends during the post-MGNREGA period, suggesting that while overall wages increased, gender disparities remained largely unchanged. Based on these findings, the study discusses policy implications associated with the task-specific nature of gender wage disparities in Indian agriculture. While not directly estimated, the persistence of gender wage gaps highlights the importance of strengthening wage transparency and monitoring mechanisms in informal labour markets, as well as promoting skill development initiatives to enhance women's participation in higher-paying agricultural activities.

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