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

Elliott Ash, Sam Asher (), Aditi Bhowmick (), Sandeep Bhupatiraju (), Daniel Chen (), Tanaya Devi (), Christoph Goessmann (), Paul Novosad () and Bilal Siddiqi
Additional contact information
Aditi Bhowmick: Development Data Lab
Sandeep Bhupatiraju: World Bank
Daniel Chen: Toulouse
Tanaya Devi: Harvard
Christoph Goessmann: ETH Zurich
Paul Novosad: Dartmouth College

No 637, Working Papers from Center for Global Development

Abstract: 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.

JEL-codes: J15 J16 K4 O12 (search for similar items in EconPapers)
Pages: 60 pages
Date: 2023-03-17
New Economics Papers: this item is included in nep-law
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Persistent link: https://EconPapers.repec.org/RePEc:cgd:wpaper:637

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