Claudia Marangon


Last Name

Marangon

First Name

Claudia

ORCID

0000-0003-3925-2697

Organisational unit

09627 - Ash, Elliott / Ash, Elliott

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2024 - 20253
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Publications 1 - 3 of 3
  • Identity and Narratives: Three Essays in Political Economy
    Item type: Doctoral Thesis
    Marangon, Claudia (2025)
  • Judging Disparities: Recidivism Risk, Image Motives, and In-Group Bias on Wisconsin Criminal Courts
    Item type: Working Paper
    Ash, Elliott; Marangon, Claudia (2024)
    Center for Law & Economics Working Paper Series
    This paper studies racial in-group disparities in Wisconsin, which has one of the highest Black-to-White incarceration rates among all U.S. states. The analysis is motivated by a model in which a judge may want to incarcerate more due to three factors: (1) taste-based preferences about the defendant’s group identity; (2) higher recidivism risk where the defendant is more likely to commit future crimes; and (3) image motives stemming from the defendant being in the same group as the judge. Further, a judge may have better information on recidivism risk due to two factors: (4) becoming more experienced, and (5) sharing the same group as the defendant. We take these ideas to new data on 1 million cases from Wisconsin criminal courts, 2005-2017. Looking at racial disparities between majority (White) and minority (Black) judges and defen dants, we find no evidence for anti-out-group bias (1). Using a recidivism risk score that we construct using machine learning tools to predict reoffense, we find evidence that judges do tend to incarcerate defendants with a higher recidivism risk (2). Consistent with judge experience leading to better information on defendant recidivism risk (4), we find that more experienced judges are more responsive in jailing defendants with a high recidivism risk score. Consistent with image motives (3), we find that when the minority group is responsible for most crimes, minority-group judges are harsher on their in-group. Finally, consistent with judges having better information on recidivism risk for same-group defendants (5), we find that judges are more responsive to the recidivism risk score for defendants from the same group when that group makes up a relatively small share of defendants.
  • WCLD: Curated Large Dataset of Criminal Cases from Wisconsin Circuit Courts
    Item type: Conference Paper
    Ash, Elliott; Goel, Naman; Li, Nianyun; et al. (2024)
    Advances in Neural Information Processing Systems 36
    Machine learning based decision-support tools in criminal justice systems are subjects of intense discussions and academic research. There are important open questions about the utility and fairness of such tools. Academic researchers often rely on a few small datasets that are not sufficient to empirically study various real-world aspects of these questions. In this paper, we contribute WCLD, a curated large dataset of 1.5 million criminal cases from circuit courts in the U.S. state of Wisconsin. We used reliable public data from 1970 to 2020 to curate attributes like prior criminal counts and recidivism outcomes. The dataset contains large number of samples from five racial groups, in addition to information like sex and age (at judgment and first offense). Other attributes in this dataset include neighborhood characteristics obtained from census data, detailed types of offense, charge severity, case decisions, sentence lengths, year of filing etc. We also provide pseudo-identifiers for judge, county and zipcode. The dataset will not only enable researchers to more rigorously study algorithmic fairness in the context of criminal justice, but also relate algorithmic challenges with various systemic issues. We also discuss in detail the process of constructing the dataset and provide a datasheet. The WCLD dataset is available at https://clezdata.github.io/wcld/.
Publications 1 - 3 of 3