The low rate of female labor force participation (FLFP) in India is puzzling given the rapid fertility transition, increases in women’s educational attainment, and substantial economic growth over the recent decades. FLFP has been consistently low (20%) in urban areas (Klasen and Pieters 2013), and has declined in rural areas, from 52% to 38% during 1987-2011 (Afridi, Dinkelman and Mahajan 2017). This is in sharp contrast with increases in FLFP globally (World Development Report, 2012), and in other developing countries (World Bank, 2012) during the same period.
While there have been attempts to understand this puzzle, research has focused on specific constraints on FLFP in a reduced-form framework. However, it is plausible that multiple factors together hinder FLFP when we envision decisions being made over the life cycle. This project seeks to holistically evaluate multiple constraints on FLFP within a single macroeconomic framework in order to inform policy better.
A structural model that explains the observed changes in FLFP will be built and tested, using data from the Time Use Survey (1998) and National Sample Survey (NSS), on urban married women. The model will focus on women’s labor force participation, educational attainment, and other characteristics, and time spent in the
labor market, home production, and leisure. This approach would enable understanding of the comparative impacts of multiple constraints on FLFP – on both the demand (for example, through changes in wages received by men and women, and available job opportunities) and supply side (for example, preferences, social norms, and skills). Further, the impact of various policy measures on the female labor supply response can be simulated. For instance, the simulation exercise could help predict the effect of marketisation of home production, relaxation of norms around women’s participation in labor market activities, increase in wages, growth in manufacturing jobs, and so on.
A related puzzle in India is that, unlike the international experience, increasing female education has not been accompanied by a commensurate rise in FLFP. Since a pro-male bias typically exists in developing countries like India, parents have implicit preference towards investing in boys' education. This can endow boys with higher quality of schooling and skills, making them a better fit for jobs. While parents also invest in girls’ education, the reasons may be completely different, say, to make
them a good match in the marriage market, where acquiring a certain level of education, rather than the quality of schooling or skills, is more important. Due to lower skills, the market returns for women may be lower relative to men. If women, as rational decision-makers, observe that the return from the time spent at home on
kids or home production is sufficiently higher than their market wage, they would prefer staying at home, which in turn would reduce FLFP. If this hypothesis is held up by the data, it would point to considerable inefficiencies in educational investments, and underline the need for policy measures that strengthen skill training
for women.
