Measuring Judicial Sentiment: Methods and Application to US Circuit Courts

Elliott Ash, Daniel L. Chen and Sergio Galletta
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Daniel L. Chen: IAST - Institute for Advanced Study in Toulouse, TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS - Centre National de la Recherche Scientifique

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Abstract: This paper provides a general method for analysing the sentiments expressed in the language of judicial rulings. We apply natural language processing tools to the text of US appellate court opinions to extrapolate judges' sentiments (positive/good vs. negative/bad) towards a number of target social groups. We explore descriptively how these sentiments vary over time and across types of judges. In addition, we provide a method for using random assignment of judges in an instrumental variables framework to estimate causal effects of judges' sentiments. In an empirical application, we show that more positive sentiment influences future judges by increasing the likelihood of reversal but also increasing the number of forward citations.

Date: 2022
New Economics Papers: this item is included in nep-big and nep-law
Note: View the original document on HAL open archive server: https://hal.science/hal-03597819v1
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Published in Economica, 2022, 89 (354), pp.362-376. ⟨10.1111/ecca.12397⟩

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Journal Article: Measuring Judicial Sentiment: Methods and Application to US Circuit Courts (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03597819

DOI: 10.1111/ecca.12397

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