Paul Novosad

We study residential segregation and access to public services across 1.5 million urban and rural neighborhoods in India. Muslim and Scheduled Caste segregation in India is high by global standards, and only slightly lower than Black-White segregation in the U.S. Within cities, public facilities and infrastructure are systematically less available in Muslim and Scheduled Caste neighborhoods. Nearly all regressive allocation is across neighborhoods within cities-at the most informal and least studied form of government. These inequalities are not visible in the aggregate data typically used for research and policy.

We study residential segregation and access to public services across 1.5 million urban and rural neighborhoods in India. Muslim and Scheduled Caste segregation in India is high by global standards, and only slightly lower than Black-White segregation in the U.S. Within cities, public facilities and infrastructure are systematically less available in Muslim and Scheduled Caste neighborhoods. Nearly all regressive allocation is across neighborhoods within cities—at the most informal and least studied form of government. These inequalities are not visible in the aggregate data typically used for research and policy.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

We study how natural resource rents affect the selection and behavior of holders of public office. Using global price shocks to thirty-one minerals and nationwide geological and political data from India, we show that local mineral rent shocks cause the election of politicians charged with serious crimes. We also find a moral hazard effect: politicians commit more crimes and accumulate greater wealth when mineral prices rise during their terms in office. These politicians have direct influence over mining operations but no access to fiscal windfalls from mining; we thus isolate the direct political impacts of mining sector operations.

We study judicial in-group bias in Indian criminal courts, collecting data onover 80 million legal case records from 2010–2018. We exploit quasi-random assignment of judges and changes in judge cohorts to examine whether defendant outcomes are affected by being assigned to a judge with a similar religious or gender identity. We estimate tight zero effects of in-group bias. The upper end of our 95% confidence interval rejects effect sizes that are one-fifth of those in most of the prior literature.

India’s national COVID death totals remain undetermined. Using an independent nationally representative survey of 0.14 million (M) adults, we compared COVID mortality during the 2020 and 2021 viral waves to expected all-cause mortality. COVID constituted 29% (95% confidence interval, 28 to 31%) of deaths from June 2020 to July 2021, corresponding to 3.2 M (3.1 to 3.4) deaths, of which 2.7 M (2.6 to 2.9) occurred in April to July 2021 (when COVID doubled all-cause mortality). A subsurvey of 57,000 adults showed similar temporal increases in mortality, with COVID and non-COVID deaths peaking similarly. Two government data sources found that, when compared to prepandemic periods, all-cause mortality was 27% (23 to 32%) higher in 0.2 M health facilities and 26% (21 to 31%) higher in civil registration deaths in 10 states; both increases occurred mostly in 2021. The analyses find that India’s cumulative COVID deaths by September 2021 were six to seven times higher than reported officially.

Objectives To estimate age-specific and sex-specific mortality risk among all SARS-CoV-2 infections in four settings in India, a major lower-middle-income country and to compare age trends in mortality with similar estimates in high-income countries. Design Cross-sectional study. Setting India, multiple regions representing combined population >150 million. Participants Aggregate infection counts were drawn from four large population-representative prevalence/seroprevalence surveys. Data on corresponding number of deaths were drawn from official government reports of confirmed SARS-CoV-2 deaths. Primary and secondary outcome measures The primary outcome was age-specific and sex-specific infection fatality rate (IFR), estimated as the number of confirmed deaths per infection. The secondary outcome was the slope of the IFR-by-age function, representing increased risk associated with age. Results Among males aged 50–89, measured IFR was 0.12% in Karnataka (95% CI 0.09% to 0.15%), 0.42% in Tamil Nadu (95% CI 0.39% to 0.45%), 0.53% in Mumbai (95% CI 0.52% to 0.54%) and an imprecise 5.64% (95% CI 0% to 11.16%) among migrants returning to Bihar. Estimated IFR was approximately twice as high for males as for females, heterogeneous across contexts and rose less dramatically at older ages compared with similar studies in high-income countries. Conclusions Estimated age-specific IFRs during the first wave varied substantially across India. While estimated IFRs in Mumbai, Karnataka and Tamil Nadu were considerably lower than comparable estimates from high-income countries, adjustment for under-reporting based on crude estimates of excess mortality puts them almost exactly equal with higher-income country benchmarks. In a marginalised migrant population, estimated IFRs were much higher than in other contexts around the world. Estimated IFRs suggest that the elderly in India are at an advantage relative to peers in high-income countries. Our findings suggest that the standard estimation approach may substantially underestimate IFR in low-income settings due to under-reporting of COVID-19 deaths, and that COVID-19 IFRs may be similar in low-income and high-income settings.

The SHRUG is an open data platform describing multidimensional socioeconomic development across 600,000 villages and towns in India. This paper presents three illustrative analyses only possible with high-resolution data. First, it confirms that nighttime lights are highly significant proxies for population, employment, per capita consumption, and electrification at very local levels. However, elasticities between night-lights and these variables are far lower in time series than in cross section, and vary widely across context and level of aggregation. Next, this study shows that the distribution of manufacturing employment across villages follows a power law: the majority of rural Indians have considerably less access to manufacturing employment than is suggested by aggregate data. Third, a poverty mapping exercise explores local heterogeneity in living standards and estimates the potential targeting improvement from allocating programs at the village—rather than at the district—level. The SHRUG can serve as a model for open high-resolution data in developing countries.

There are very few estimates of the age-specific infection fatality rate (IFR) of SARS-CoV-2 in low- and middle-income countries. India reports the second highest number of SARS-CoV-2 infections in the world. We estimate age-specific IFR using data from seroprevalence surveys in Mumbai (population 12 million) and Karnataka (population 61 million), and a random sample of economically distressed migrants in Bihar with mortality followup. Among men aged 50–89, IFR is 0.12% in Karnataka (95% C.I. 0.09%–0.15%), 0.53% in Mumbai (0.52%–0.54%), and 5.64% among migrants in Bihar (0–11.16%). IFR in India is approximately twice as high for men as for women, is heterogeneous across contexts, and rises much less at older ages than in comparable studies from high income countries.

Background: India has the second-highest number of SARS-CoV-2 cases in the world. Age-specific infection fatality rates (IFR) are critical parameters for public health policy, but little is known about IFR in low- and middle-income countries. This paper estimates age- and sex-specific IFR of SARS-CoV2 in four regions in India, with a combined population exceeding 150 million. Methods: We estimated infection levels from large, population-representative prevalence/seroprevalence surveys. We drew data on the corresponding number of deaths from official reports and estimated IFR as the number of deaths per infection. We accounted for test inaccuracies, survey design, and potential biases with a battery of alternative models. We calculated the slope of the IFR-by-age function, representing increased risk associated with age. Findings: Among males aged 50–89, measured IFR was 0·037% in Tamil Nadu (95% CI: 0·035%, 0·040%), 0·12% in Karnataka (0·09%, 0·15%), 0·53% in Mumbai (0·52%, 0·54%), and an imprecise 5·64% (0, 11·16%) among migrants in Bihar. Estimated IFR was approximately twice as high for males as for females, heterogeneous across contexts, and rose less dramatically at older ages compared to similar studies in high-income countries (HIC). Interpretation: Estimated age-specific IFRs in India are generally lower than in HIC, but vary widely across the country. Low estimated IFR and geographic variation within India may partly be due to under-testing of suspected COVID-19 deaths, though reporting errors are unlikely to explain cross-country disparities. The elderly in India are at an advantage relative to peers in HIC. Low elderly IFR may partly be due to survivorship bias in a country with lower life expectancy. Funding: Emergent Ventures Grant #466. Declaration of Interests: Authors declare no competing interests. Ethics Approval Statement: This study was exempted from ethical approval because it involved no human subjects. All data were secondary. Ethical approvals and protocols for collecting primary seroprevalence data have been published in separate papers.

Objective To model how known COVID-19 comorbidities affect mortality rates and the age distribution of mortality in a large lower-middle-income country (India), and to identify which health conditions drive differences with high-income countries. Design Modelling study. Setting England and India. Participants Individual data were obtained from the fourth round of the District Level Household Survey and Annual Health Survey in India, and aggregate data were obtained from the Health Survey for England and the Global Burden of Disease, Risk Factors and Injuries Studies. Main outcome measures The primary outcome was the modelled age-specific mortality in each country due to each COVID-19 mortality risk factor (diabetes, hypertension, obesity and respiratory illness, among others). The change in overall mortality and in the share of deaths under age 60 from the combination of risk factors was estimated in each country. Results Relative to England, Indians have higher rates of diabetes (10.6% vs 8.5%) and chronic respiratory disease (4.8% vs 2.5%), and lower rates of obesity (4.4% vs 27.9%), chronic heart disease (4.4% vs 5.9%) and cancer (0.3% vs 2.8%). Population COVID-19 mortality in India, relative to England, is most increased by uncontrolled diabetes (+5.67%) and chronic respiratory disease (+1.88%), and most reduced by obesity (−5.47%), cancer (−3.65%) and chronic heart disease (−1.20%). Comorbidities were associated with a 6.26% lower risk of mortality in India compared with England. Demographics and population health explain a third of the difference in share of deaths under age 60 between the two countries. Conclusions Known COVID-19 health risk factors are not expected to have a large effect on mortality or its age distribution in India relative to England. The high share of COVID-19 deaths from people under age 60 in low- and middle-income countries (LMICs) remains unexplained. Understanding the mortality risk associated with health conditions prevalent in LMICs, such as malnutrition and HIV/AIDS, is essential for understanding differential mortality.