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

Elliott Ash¹; Sam Asher²; Aditi Bhowmick³; Sandeep Bhupatiraju⁴; Daniel Chen⁵; Tanaya Devi⁶; Christoph Goessmann¹; Paul Novosad⁷; Bilal Siddiqi⁸

¹ ETH Zurich · ² Imperial College London · ³ Development Data Lab · ⁴ World Bank · ⁵ Toulouse School of Economics and World Bank · ⁶ Harvard · ⁷ Dartmouth College · ⁸ IDinsight

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.

DOI

10.1162/rest_a_01569

Pages

1-45

Language

en

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