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.

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.