Support for aggressive stop-and-frisk policy in NYC: does perceived policy effectiveness and perceived disparate treatment explain observed racial and ethnic divides?

Abstract

Prior research finds a fairly consistent racial and ethnic divide between Blacks and Whites, Blacks and Hispanics and Whites and Hispanics on issues of perceived injustice and crime policy preference. This study examines racial and ethnic variation, among a sample of New York City residents, in public views toward an aggressive stop-and-frisk policy. The study first explores whether there are racial and ethnic differences in perceptions that the stop-and-frisk policy is effective at reducing violent crime and gun offenses, and whether there are racial and ethnic differences in perceptions that police treat Blacks and Hispanics differently than Whites. The study then seeks to establish racial and ethnic divides between Blacks and Whites, Blacks and Hispanics, and Hispanics and Whites on support for the aggressive stop-and-frisk policy. Once these racial and ethnic divides in policy support are identified, the study explores whether perceptions of policy effectiveness (most likely held by Whites and Hispanics) and disparate treatment (most likely held by Blacks) account for the racial and ethnic divides in policy support. The study provides a discussion of the findings and their implications for future research and for discussions of racially and ethnically laden policy issues.

Notes

^1. The codebook for the data file contains no reference to overall rate of response. The response rate of 8% was obtained by contacting the Roper Center for Public Opinion Research at the University of Connecticut. The data file was downloaded from the Roper Center website.

^4. The P/E: Composite measure is a summed total of the P/E: Race and Ethnicity Stop and P/E: Disrespect measures. The P/E: Race and Ethnicity stop question asked was: ‘Have you ever felt you were stopped by the police just because of your race or ethnic background?’ For the P/E: Disrespect survey item, respondents were provided the prompt: ‘Some people say the police don't show respect for people, or they use insulting language. Has this ever happened to you, or not?’ This summed measure codes those respondents as 2 who perceived both being stopped because of race and ethnicity and disrespect, codes respondents as 1 where the person perceived either that he or she was stopped because of race/ethnicity or disrespect, but not both, and codes as 0 those respondents who perceived neither a race/ethnicity-based stop or police disrespect. The summative approach was used because it is important to capture the variation between persons who perceived experience with such a stop but did not perceive disrespect and those who perceived both the race/ethnicity-based stop and police disrespect. The summed variable is not a perfect measurement. It does not limit respondent recall of these events to any particular time period, nor does it ensure that any measured perception of race/ethnicity-based stop and perception of disrespect occurred simultaneously.

^5. Respondents to the survey were asked, ‘How much have you heard or read about stop-and-frisk, the police procedure where a police officer detains and searches an individual to determine if the person is carrying a concealed weapon?’

^2. To explore the reasons for the changes in the White, NH1, and the Hispanic1 measures from Model 5 to Model 6, two additional logistic regression analyses were generated. In the first, the two Disparate Treatment measures are excluded, so that the model includes the following variables as predictors of SaF Acceptable: White, NH1, Hispanic1, Other Race/Ethnicity, Violent Crime Reduction, Gun Offense Reduction, P/E Composite, Knowledge, Conservatism, Male, Age, Income, and Education. In the second, the two Effectiveness measures are excluded. The model regressed SaF Acceptable on the following predictor measures: White, NH1, Hispanic1, Other Race/Ethnicity, B/W Police Treat., H/W Police Treat., P/E Composite, Knowledge, Conservatism, Male, Age, Income and Education. Both race and ethnicity measures are statistically significant predictors of policy support in both models. These findings, coupled with the results from Models 5 and 6, imply that the racial and ethnic divides in policy support are accounted for by perceptions of Effectiveness and Disparate Treatment acting together.

^3. Interpretation of the potential processes underlying the change in the Male variable from Model 5 to Model 6 relies on the same two unreported models described in Footnote 1. In the first unreported model, which includes the Effectiveness measures but not the Disparate Treatment measures, the Male variable is not statistically significant. In the second unreported model, which includes the Disparate Treatment measures but not the Effectiveness measures, the Male variable is not a significant predictor of policy support. This suggests that males support the policy because they view it as an effective tactic for crime reduction.

Additional information

Notes on contributors

Kevin Buckler

Kevin G. Buckler is an Associate Professor of Justice Studies at Prairie View A&M University. He received his doctorate in criminal justice from the University of Cincinnati in 2004. His research interests include legal empiricism, media coverage of crime and justice issues, and the racial and ethnic dimensions of perceptions of injustice and crime policy preferences.

George E. Higgins

George E. Higgins a professor in the Department of Justice Administration at the University of Louisville. He received his doctorate in criminology from Indiana University of Pennsylvania in 2001. He currently serves as editor of the Journal of Criminal Justice Education.