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This study investigates the gender wage gap in the Indian labour market by analysing a range of human capital components, including education, work experience, cognitive and technical skills and occupational choices, using data from the India Human Development Survey 2011–2012. We employ the recentred influence functions (RIF) regression and Firpo, Fortin and Lemieux (FFL) decomposition techniques. The results from the RIF regression analysis underscore the significant role of education, work experience, skills and occupation in wage inequality. While education and English language proficiency lead to widening wage inequality for both men and women, the effects are much larger for the latter. Next, the FFL decomposition unveils a positive gender wage gap, indicating potential favourable returns for women’s qualifications and skills, yet it exposes a concerning wage structure effect suggesting women are often employed in lower-paying jobs. These results suggest that enhancing education and skills among women can be important tools in reducing gender wage inequality. JEL codes: E24, J24, J16
Azad et al. (Mon,) studied this question.