Ethnic diversity in policing: An application of quantile regression to the New York City Police Department

Abstract

ABSTRACTThis study expanded on the use of Simpson's diversity index (D = 1 - ∑p2) to measure ethnic heterogeneity in policing and other criminal justice-related agencies. The index was applied to the police and civilian workforces of the New York City Police Department (NYPD) by using employment data for January 2015. Concomitantly, quantile regression was used to supplement the Tobit regression and to illustrate its ability to assess the effects of organization-based variables across the conditional distribution of the diversity scores. The quantile regression analysis found that ethnic diversity in the NYPD is affected differently by the type of workforce, the type of police and civilian positions, and by the number of employees in the police and civilian job titles and classifications. Statistically, the quantile regression detected significant relationships at various points of the conditional distribution while the Tobit regression analysis produced mixed results for the three models that were tested.