Abstract
Data on police stops can be examined to reflect on the relative “opacity” of these encounters and how aggregate patterns on the nature—not just the volume—of reported stops relate to public scrutiny of the police. We hypothesize that public scrutiny on police stops is positively related to the prevalence of opaque stop practices across dimensions of “intrusiveness,” “rationale,” and “setting” derived from agency records. We further argue that this relationship is influenced by neighborhood conditions in the form of concentrated disadvantage, residential instability, and heterogeneity. To develop these ideas, we draw on a publicly available NYPD dataset on police stops to specify a series of fixed and random effects models that describe variation in recorded stop practices across precincts (N = 74) and overtime (T = 7, 2007–2013). We relate these practices to neighborhood conditions derived from the Census and examine their association with rates of SQF complaints to the CCRB. Results show considerable variation in indicators of opacity, particularly across precincts. More importantly, we also find that rates of complaints are higher in precincts that have more vaguely defined, intrusive stops. Results also suggest that concentrated disadvantage is independently and positively related with higher rates of public scrutiny of the police.
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Notes
Stops are recorded using the NYPD UF-250 form. Though these forms represent an important source of data, it is likely that due to incomplete and underreported encounters, SQF records likely undercount the number of police stops that occur each year. Despite these pitfalls, public UF-250 data is routinely used for policy and research as it is not only the “official record” but also because it allows to track police tactics over time and across areas. We mitigate issues in reporting and bias by using multiple measures of police opacity. Data retrieved from: http://www.nyc.gov/html/nypd/html/analysis_and_planning/stop_question_and_frisk_report.shtml
The precinct covering Central Park was omitted from the analysis, because it has no residential population.
An issue with random effects models can be whether the effect of the level of a factor is being drawn from a probability sample of that effect. Meaning, if the effect is “naturally occurring” it would be considered fixed, rather than random. However, repeated measures variables are usually treated as random, and though an argument could be made that some of the independent variables in the equation are traditionally fixed, the Hausman test confirms a random effect model to be the prudent choice.
Bivariate correlations confirm that associations among predictors are below .65 (Pearson’s r), reducing issues of multi-collinearity. Results are available upon request.
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Rengifo, A.F., Fowler, K. Stop, Question, and Complain: Citizen Grievances Against the NYPD and the Opacity of Police Stops Across New York City Precincts, 2007–2013. J Urban Health 93 (Suppl 1), 32–41 (2016). https://doi.org/10.1007/s11524-015-0010-0
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DOI: https://doi.org/10.1007/s11524-015-0010-0