Using micro data for India from 1983 to 2005, this paper finds that the tertiary (college)-secondary (high school) wage premium has been increasing in India over the past decade and that this increase differs across age groups. The increase in wage premiums has been driven mostly by younger age groups while older age groups have not experienced any significant increase. Using a demand and supply model with imperfect substitution across age groups (developed in Card and Lemieux, 2001), this paper demonstrates that workers are not perfect substitutes across age groups. The paper finds that the increase in the wage premium has come mostly from demand shifts in favor of workers with a tertiary education. More importantly, the growth rate of demand for tertiary educated workers relative to secondary educated workers was fairly stable in the 1980s and the 1990s. However, the relative supply played an important role not only in determining the extent of increase in wage premium, but also its timing. The increase in relative supply of tertiary workers during 1983-1993 negated the demand shift; as a result, the wage premium did not increase much. But during 1993-1999, the growth rate of the relative supply of tertiary workers decelerated, while relative supply became virtually stagnant during 1999-2004. Both these periods saw an increase in the wage premium as the countervailing supply shift was weak.
We examine educational transmission between fathers (mothers) and daughters in India for daughters born during 1962–1991. We find that educational persistence, as measured by the regression coefficient of father’s (mother’s) education as a predictor of daughter’s education, has declined. However, the correlation between educational attainment of daughters and fathers (mothers), another commonly used measure of persistence, suggests only a marginal decline. Further we decompose the intergenerational correlation. We also find that the probability of a daughter attaining senior secondary or above education (top end of educational distribution) is not only positively associated with father’s (mother’s) education levels but the gaps in those probabilities have not declined over time. Similarly, there is no convergence over time in the probability of a daughter attaining senior secondary or above education with the same level of father’s (mother’s) education for daughters belonging to Higher Hindu Castes and disadvantaged groups such as Other Backward Castes or Scheduled Castes/Tribes. Although conditional on having same educated fathers, sons are more likely to achieve senior secondary or above education in each cohort compared to daughters, the gap in those probabilities has declined over time.
Although, the male labor force participation rate is comparable in China and India, female labor force participation rate remains very low in India. In this paper, we examine the factors responsible for the difference in female labor force participation rate between the two countries by carrying out decomposition exercise at three points of time covering two decades. We find that the differences in female labor force participation rate are not explained by the differences in characteristics across the two countries in each of the three year studied. The differences in returns to these characteristics explain most of the differences in participation rate.
We address the relationship between number of children and investment in child quality, known as quantity–quality (Q–Q) trade-off, for India. Using a number of investment and outcome measures, we find that the OLS estimates suggest the presence of Q–Q trade-offs in nine out of ten measures considered. Using the gender of the first-born child as an instrument, the trade-offs in all measures disappear. Given the concerns about the exogeneity of the instrument, we apply Oster (2016) bounds to assess the sensitivity of OLS estimates to omitted variables. We find robust trade-off estimates in three measures currently enrolled in school, years of schooling and height-for-age. The results are more robust when observing trade-offs in rural areas. Trade-offs appear in ever enrolled in school, private school attendance, expenditure on education and private coaching in addition to the trade-offs in the three measures for all India sample.