Does the identity of journalists shape the news, and through it, readers’ behavior? I study this question in the context of reporting on violence against women in Italy. Using the universe of murders with female victims from 2006 to 2022 matched to newspaper articles, I construct validated indexes of two reporting styles: victim-centered narratives, which contextualize the crime and point to support resources, and perpetrator-centered narratives, which focus on motives such as jealousy or a fit of rage. I show that female journalists use more victim-centered and fewer perpetrator-centered narratives, exploiting the conditionally random assignment of reporters to crimes within newsrooms to adjust for selection in the stories and topics covered. Combining reporter assignment with provincial readership in a staggered difference-in-differences design, I show that greater exposure to female reporters after a murder raises calls to the national helpline for violence against women. An online experiment identifies the mechanism: victim-centered narratives shift norms around help-seeking, while perpetrator-centered narratives raise engagement among men but alienate female readers. Reporter identity is thus a consequential supply-side determinant of both news content and reader behavior.

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 defendants, 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.

This paper studies the visual representation of crime in U.S. news and whether policies aimed at protecting the identity of arrested individuals affect crime reporting. We assemble 3.46 million crime articles published by 1,848 outlets between 2015 and 2024 and measure their images with a face-level pipeline that detects mugshots, infers race, and links the same individual across articles. Benchmarking against FBI arrest records, we show that Black arrestees are over-represented in mugshot coverage relative to their share of arrests, and the gap is concentrated in high-volume, low-severity offenses such as DUI and drug crimes, where publishing any given booking photograph is discretionary. We then exploit staggered state restrictions on mugshot disclosure in a difference-in-differences design. The bans reduce mugshot publication by about a third and compress the over-representation of Black arrestees where it was largest. Next, we investigate whether crime reporting also adjusts to the removal of mugshots. Using the same design, we show that outlets replace booking photographs with other images, cover fewer arrests and more investigations, and also write about crime differently. Finally, we provide preliminary evidence that reducing mugshot use affects crime perceptions by reducing the salience of crime.

This paper investigates how social proximity between judges and defendants influences sentencing outcomes in Wisconsin criminal courts. Moving beyond race and gender, we use name similarity as a novel measure of shared social background. We construct FastText embeddings of defendants appearing in more than 1 million cases (2005–2021) using defendants from the same street as context, and compute cosine similarity between their name vectors and those of their assigned judges. Leveraging the random assignment of judges to cases within courts, we find that higher judge-defendant name similarity reduces the probability of incarceration and sentence length, suggesting that social background similarity plays a meaningful role in explaining heterogeneity in sentencing outcomes. To investigate whether these differences are driven by taste-based or statistical discrimination, we propose a model of judicial decision-making that incorporates taste-based preferences and information about the defendants’ risk of re-offending. Assuming that the precision of information signals increases with both judicial experience and similarity to the defendant, the model predicts that judges will be more responsive to defendants’ recidivism risk in these scenarios. We test this hypothesis using a machine learning-predicted measure of defendants’ recidivism risk, and we show that more experienced judges and those judging defendants with higher name similarity respond more to recidivism risk signals.

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 .