We study judicial in-group bias in Indian criminal courts using a newly collected dataset on over 5 million criminal case records from 2010–2018. After detecting gender and religious identity using a neural-net classifier applied to judge and defendant names, we exploit quasi-random assignment of cases to judges to examine whether defendant outcomes are affected by assignment to a judge with a similar identity. In the aggregate, we estimate tight zero effects of in-group bias based on shared gender, religion, and last name (a proxy for caste). We do find limited in-group bias in some (but not all) settings where identity is salient—in particular, we find a small religious in-group bias during Ramadan, and we find shared-name in-group bias when judge and defendant match on a rare last name.
This paper and accompanying data were previously published by Development Data Lab.
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CITATION
Ash, Elliott, Sam Asher, Aditi Bhowmick, Sandeep Bhupatiraju, Daniel Chen, Tanaya Devi, Christoph Goessmann, Paul Novosad, and Bilal Siddiqi. 2023. In-Group Bias in the Indian Judiciary: Evidence from 5 Million Criminal Cases. Center for Global Development.DISCLAIMER & PERMISSIONS
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