Trends in Education-specific Differences in Disability-Free Life Expectancy in Spain, 2008-2017

Background: There is mounting evidence for a recent increase of social disparities in chronicdisease prevalence and mortality. However, little is known about how these trends are reflected incombined measures of morbidity, disability and mortality.Method: We use two nationally representative surveys of the Spanish population for the years2008 to 2017 and standard measures of expected duration of disability and illness to assess timetrends and social disparities in mortality, morbidity and expected years lived in disability (DFLE)and with chronic illness (chrDFLE). We provide empirical evidence of shifting trends for thesemeasures. We then decompose these changes into contributions associated with disability, chronicillness and mortality. Finally, we estimate the size of education differentials in DFLE and chrDFLEand evaluate the magnitude and direction of changes of these differentials over time.Results: While the disability based indicator suggests a decrease of expected years withoutdisability for both men and women (expansion of morbidity), the morbidity based indicator showsan increase in time spend free of chronic disease for women but a slight decrease for men. Thedecrease in time spent without disability was observed for all education groups but is particularlymarked for those with low education.Conclusion: We find evidence of an expansion of morbidity in Spain between 2008 and 2017.The bulk of this development is related to increases in time spent with functional limitations overthis period. These patterns occur in conjuncture with growing social disparities in time spend withchronic illness or disability.

Future population and human capital in heterogeneous India

Within the next decade India is expected to surpass China as the world’s most populous country due to still higher fertility and a younger population. Around 2025 each country will be home to around 1.5 billion people. India is demographically very heterogeneous with some rural illiterate populations still having more than four children on average while educated urban women have fewer than 1.5 children and with great differences between states. We show that the population outlook greatly depends on the degree to which this heterogeneity is explicitly incorporated into the population projection model used. The conventional projection model, considering only the age and sex structures of the population at the national level, results in a lower projected population than the same model applied at the level of states because over time the high-fertility states gain more weight, thus applying the higher rates to more people. The opposite outcome results from an explicit consideration of education differentials because over time the proportion of more educated women with lower fertility increases, thus leading to lower predicted growth than in the conventional model. To comprehensively address this issue, we develop a five-dimensional model of India’s population by state, rural/urban place of residence, age, sex, and level of education and show the impacts of different degrees of aggregation. We also provide human capital scenarios for all Indian states that suggest that India will rapidly catch up with other more developed countries in Asia if the recent pace of education expansion is maintained.

Education and Labor Market Discrimination

Using a model of statistical discrimination and educational sorting, we explain why blacks get more education than whites of similar cognitive ability, and we explore how the Armed Forces Qualification Test (AFQT), wages, and education are related. The model suggests that one should control for both AFQT and education when comparing the earnings of blacks and whites, in which case a substantial black-white wage differential emerges. We reject the hypothesis that differences in school quality between blacks and whites explain the wage and education differentials. Our findings support the view that some of the black-white wage differential reflects the operation of the labor market. (JEL I21, J15, J24, J31, J71)