Publication | Open Access
An Analysis of the New York City Police Department's “Stop-and-Frisk” Policy in the Context of Claims of Racial Bias
852
Citations
39
References
2007
Year
EthnicityCommunity PolicingRace LawCrime AnalysisTraffic EnforcementLawEducationCriminal LawDiscrimination LawRacial BiasRacial DisparitiesTraffic InjuryRaceAfrican American StudiesEthnic GroupPublic HealthCrime PreventionStatisticsPolice DepartmentsPublic PolicyCrime ForecastingRacial JusticeDisparate ImpactMultilevel ModelingCriminal JusticeSociologyDemographyJusticeHierarchical Multilevel Models
Recent studies confirm that police disproportionately stop racial and ethnic minorities, though some argue stops reflect crime rates or neighborhood factors, and most research focuses on traffic stops rather than pedestrian stops. This article analyzes 125,000 pedestrian stops by the NYPD over 15 months to examine racial disparities. The authors disaggregate stops by precinct, control for prior race‑specific arrest rates, and use hierarchical multilevel models to adjust for precinct‑level variability. They find that African and Hispanic individuals were stopped more frequently than whites, even after controlling for precinct variability and crime participation estimates.
Recent studies by police departments and researchers confirm that police stop persons of racial and ethnic minority groups more often than whites relative to their proportions in the population. However, it has been argued that stop rates more accurately reflect rates of crimes committed by each ethnic group, or that stop rates reflect elevated rates in specific social areas, such as neighborhoods or precincts. Most of the research on stop rates and police–citizen interactions has focused on traffic stops, and analyses of pedestrian stops are rare. In this article we analyze data from 125,000 pedestrian stops by the New York Police Department over a 15-month period. We disaggregate stops by police precinct and compare stop rates by racial and ethnic group, controlling for previous race-specific arrest rates. We use hierarchical multilevel models to adjust for precinct-level variability, thus directly addressing the question of geographic heterogeneity that arises in the analysis of pedestrian stops. We find that persons of African and Hispanic descent were stopped more frequently than whites, even after controlling for precinct variability and race-specific estimates of crime participation.
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