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The relationship between mental health and social disadvantage in low and middle income countries is poorly understood. Our study contributes the first populationlevel analysis of mental health disparities in India, where the two marginalized groups that we study constitute a population larger than that of the United States. Applying two complementary empirical strategies to data on 10,125 adults interviewed by the World Health Organisation's Survey of Global Ageing and Adult Health (WHO-SAGE), we document and standardize gaps in self-reported mental health between the dominant social group (higher caste Hindus) and two marginalized social groups (Scheduled Castes and Muslims). We find that differences in socioeconomic status cannot fully explain the large disparities in mental health that we document, especially for Muslims. Our results highlight the need for research to understand the causes and consequences of mental health disparities in India, and for policies to move beyond redistribution and address discrimination against Scheduled Castes and Muslims.

India is one of the most rigidly stratified societies in the world, yet little is known about life expectancy disparities in the country. We provide direct estimates of social differences in life expectancy in India using survey data spanning two decades. We show that individuals from the Scheduled Castes and Scheduled Tribes have drastically and persistently lower life expectancies than high-caste individuals (between 4.2-4.4 years for women and 6.1-7.0 years for men in [2013][2014][2015][2016]. While Muslims had a modest life expectancy disadvantage compared to high castes in 1997-2000, this disadvantage has grown substantially over the past 20 years. Mortality disparities between marginalized and privileged social groups are present across the entire life-course and are increasingly driven by older-age mortality. Our findings reveal a pressing need for far greater attention to the health of marginalized populations in India.

The population health impacts of the COVID-19 pandemic are less well understood in low and middle-income countries, where mortality surveillance before the pandemic was patchy. Interpreting the limited all-cause mortality data available in India is challenging. We use existing data on all-cause mortality from civil registration systems of twelve Indian states comprising around 60% of the national population to understand the scale and timing of excess deaths in India during the COVID-19 pandemic. We carefully characterize the reasons why registration is incomplete and estimate the extent of coverage in the data. Comparing the pandemic period to 2019, we estimate excess mortality in twelve Indian states, and extrapolate our estimates to the rest of India. We explore sensitivity of the estimates to various assumptions. For the 12 states with available all-cause mortality data, we document an increase of 28% in deaths during April 2020–May 2021 relative to expectations from 2019. This level of increase in mortality, if it applies nationally, would imply 2.8–2.9 million excess deaths. More limited data from June 2021 increases national estimates of excess deaths during April 2020–June 2021 to 3.8 million. With more optimistic or pessimistic assumptions, excess deaths during this period could credibly lie between 2.8 million and 5.2 million. The scale of estimated excess deaths is broadly consistent with expectations based on seroprevalence and COVID-19 fatality rates observed internationally. Moreover, the timing of excess deaths and recorded COVID-19 deaths is similar–they rise and fall at the same time. The surveillance of pandemic mortality in India has been extremely poor, with 8–10 times as many excess deaths as officially recorded COVID-19 deaths. India is among the countries most severely impacted by the pandemic. Our approach highlights the utility of all-cause mortality data, as well as the significant challenges in interpreting it.

Coal power generation is expanding rapidly in India and other developing countries. In addition to consequences for climate change, present-day health externalities may also substantially increase the social cost of coal. Health consequences of air pollution have proven important in studies of developed countries, but, despite clear importance, similarly well-identified estimates are less available for developing countries, and no estimates exist for the important case of coal in India. We exploit panel data on Indian households, matched to local changes in exposure to coal plants. Increased exposure to coal plants is associated with worse respiratory health. Consistent with a causal mechanism, the effect is specific: no effect is seen on diarrhea or fever, and no effect on respiratory health is seen of new non-coal plants. Our result is not due to endogenous avoidance behavior, or to differential trends in determinants of respiratory health, either before the period studied or simultaneously.