Research

Working Papers

Algorithmic Policing 

2022 Conference on Empirical Legal Studies Theodore Eisenberg Prize winner

Predictive policing algorithms are increasingly used by law enforcement agencies in the United States. These algorithms use past crime data to generate predictive policing boxes, specifically the highest crime risk areas where law enforcement is instructed to patrol every shift. I collect a novel dataset on predictive policing box locations, crime incidents, and arrests from a large urban jurisdiction where predictive policing is used. Using institutional features of the predictive policing policy, I isolate quasi-experimental variation to examine the causal impacts of police presence induced by predictive policing algorithms. I find that algorithm-induced police presence decreases serious property and violent crime. At the same time, I also find disproportionate racial impacts on arrests for serious violent crimes as well as arrests in traffic incidents. These results highlight that using predictive policing to target neighborhoods can generate a tradeoff between crime prevention and equity.

Predictive Power at What Cost? Economic and Racial Justice of Data-Driven Algorithms 

This paper studies how algorithms use variables to maximize predictive power at the cost of group equity. Group inequity arises if variables enlarge disparities in risk scores across groups. I develop a framework to examine a recidivism risk assessment tool using risk scores and novel pretrial defendant case data from 2013-2016 in Broward County, Florida. I find that defendants' neighborhood data only negligibly improve predictive power but substantially widen disparities in defendant risk scores and false positive rates across race and economic status. Higher risk scores may lead to longer pretrial incarceration and downstream consequences by impacting labor market outcomes. These findings underscore that machine learning objectives tuned to maximize predictive power can be in conflict with racial and economic justice.

When Does Crime Respond to Punishment?: Evidence from Drug-Free School Zones  (joint with Robert Gonzalez and Sarah Komisarow) 

Economic theory suggests that crime should respond to punishment severity. However, empirical evidence on this link is ambiguous. We propose one explanation for this discrepancy: Punishments deter crime but only when the probability of detection is moderate. Using increases in punishment severity in drug-free school zones along with changes in the probability of detection resulting from a community crime-monitoring program, we demonstrate that drug-related crime drops in blocks just within the drug-free school zones, where punishments are more severe, but only if the monitoring intensity--and hence the probability of detection--is at intermediate levels.

Selected Works in Progress

The Evolution of Racial Differences in the Economic Well-being of Women: 1940-2020 (joint with Patrick Bayer and Kerwin Charles)

The Impacts of a Rule to Eliminate Racial Bias in Jury Selection