Replication data for: Good Cop, Bad Cop: Using Civilian Allegations to Predict Police Misconduct
Principal Investigator(s): View help for Principal Investigator(s) Kyle Rozema; Max Schanzenbach
Version: View help for Version V1
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| data | 10/13/2019 05:49:AM | ||
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text/plain | 14.6 KB | 10/13/2019 01:49:AM |
Project Citation:
Rozema, Kyle, and Schanzenbach, Max. Replication data for: Good Cop, Bad Cop: Using Civilian Allegations to Predict Police Misconduct. Nashville, TN: American Economic Association [publisher], 2019. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-13. https://doi.org/10.3886/E114703V1
Project Description
Summary:
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In response to high-profile cases of police misconduct, reformers are calling for greater use of civilian allegations in identifying potential problem officers. This paper applies an Empirical Bayes framework to data on civilian allegations and civil rights litigation in Chicago to assess the predictive value of civilian allegations for serious future misconduct. We find a strong relationship between allegations and future civil rights litigation, especially for the very worst officers. The worst 1 percent of officers, as measured by civilian allegations, generate almost 5 times the number of payouts and over 4 times the total damage payouts in civil rights litigation. These findings suggest that intervention efforts could be fruitfully concentrated among a relatively small group.
Scope of Project
JEL Classification:
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H76 State and Local Government: Other Expenditure Categories
K38 Human Rights Law; Gender Law
K42 Illegal Behavior and the Enforcement of Law
H76 State and Local Government: Other Expenditure Categories
K38 Human Rights Law; Gender Law
K42 Illegal Behavior and the Enforcement of Law
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