In-Group Bias in the Indian Judiciary: Evidence from 5 Million Criminal Cases
Elliott Ash,
Sam Asher (samuel.asher@gmail.com),
Aditi Bhowmick (bhowmick@devdatalab.org),
Sandeep Bhupatiraju (sbhupatiraju@worldbank.org),
Daniel Chen (daniel.chen@iast.fr),
Tanaya Devi (tdevi@g.harvard.edu),
Christoph Goessmann (christoph.goessmann@gess.ethz.ch),
Paul Novosad (paul.novosad@dartmouth.edu) 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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