Prior analyses of racial bias in the New York City’s Stop-and-Frisk program implicitly assumed that potential bias of police officers did not vary by crime type and that their decision of which type of crime to report as the basis for the stop did not exhibit any bias. In this paper, we first extend the hit rates model to consider crime type heterogeneity in racial bias and police officer decisions of reported crime type. Second, we reevaluate the program while accounting for heterogeneity in bias along crime types and for the sample selection which may arise from conditioning on crime type. We present evidence that differences in biases across crime types are substantial and specification tests support incorporating corrections for selective crime reporting. However, the main findings on racial bias do not differ sharply once accounting for this choice-based selection.
We are grateful to Decio Coviello, Maxwell Pak and Rosina Rodriguez Olivera for their helpful comments and suggestions on this project. We also thank Victor Aguiar and other participants at the Econometrics of Complex Survey Data: Theory and Applications conference for additional comments. NYC Stop-and-Frisk data is publicly available at https://nycopendata.socrata.com as well as at the ICPSR website at the University of Michigan. Lehrer thanks SSHRC for research support. We are responsible for all errors.
Lehrer, S.F. and Lepage, L.-P. (2019), "Does Selective Crime Reporting Influence Our Ability to Detect Racial Discrimination in the Nypd’s Stop-and-Frisk Program?", The Econometrics of Complex Survey Data (Advances in Econometrics, Vol. 39), Emerald Publishing Limited, Bingley, pp. 259-286. https://doi.org/10.1108/S0731-905320190000039015
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