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Rent-Seeking and Criminal Politicians: Evidence from Mining Booms

The Review of Economics and Statistics 2023 105(1), 20-39 open access
Abstract 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.

Rural Roads and Local Economic Development

American Economic Review 2020 110(3), 797-823 open access
Nearly one billion people worldwide live in rural areas without access to national paved road networks. We estimate the impacts of India’s $40 billion national rural road construction program using a fuzzy regression discontinuity design and comprehensive household and firm census microdata. Four years after road construction, the main effect of new feeder roads is to facilitate the movement of workers out of agriculture. However, there are no major changes in agricultural outcomes, income, or assets. Employment in village firms expands only slightly. Even with better market connections, remote areas may continue to lack economic opportunities. (JEL J43, O13, O18, R23, R42)

In-Group Bias in the Indian Judiciary: Evidence from 5 Million Criminal Cases

The Review of Economics and Statistics 2025
Abstract We study judicial in-group bias in Indian criminal courts using newly collected data on over 5 million criminal case records from 2010–2018. After classifying gender and religious identity with a neural network, we exploit quasi-random assignment of cases to judges to determine whether judges favor defendants with similar identities to themselves. In the aggregate, we estimate tight zero effects of in-group bias based on shared gender or religion, including in settings where identity may be especially salient, such as when the victim and defendant have discordant identities. Proxying caste similarity with shared last names, we find a degree of in-group bias, but only among people with rare names; its aggregate impact remains small.