Race, Ethnicity, and Policing: New and Essential Readings

By Stephen K. Rice; Michael D. White Roberts | Go to book overview

Introduction to Part II

Michael D. White

This section introduces the reader to the most common methods used to study issues related to race/ethnicity, bias, and policing. There are two overriding themes from the chapters in this section. First, there is no single best method to be used in the study of race/ethnicity, bias, and policing. The chapters by Ridgeway and MacDonald, and Paulhamus, Kane, and Piquero persuasively make this point, and they call for researchers to both understand the limits of their methods and use a range of approaches to compensate for those limitations. Second, while there may be no “magic bullet” when it comes to methodology, there are very clearly some techniques and approaches that outperform others. Several chapters take to task traditional benchmark measures such as population estimates via census data, arrest and crime data, and hit rates—the percentage of post-stop searches that produce contraband. Chapters by Brunson and Nobles offer viable alternatives to these traditional measures. Professor Brunson argues for an increased reliance on qualitative methodologies, in a way calling for a return to the observation-and narrative-based roots of police research. Nobles offers “cutting-edge” alternative techniques that center on the collection and analysis of police data through geographic information systems (GIS), best typified by the CompStat model. Each of the chapters is summarized briefly below.

In perhaps the most comprehensive review to date, Ridgeway and MacDonald's chapter serves as a must-read primer on methods for assessing racially biased policing. They note that the central issue in documenting the existence of racially biased policing is the identification of an appropriate benchmark, a task that is far more complicated than conventional wisdom suggests. Ridgeway and MacDonald present a detailed, cogent discussion of the entire range of external benchmarks, internal benchmarks, and post-stop outcomes that have been used in research on race/ ethnicity, bias, and policing. Though the authors acknowledge that each measure has limitations and there “is no unifying method,” they persuasively demonstrate that some measures are more useful than others. With regard to external benchmarking, Ridgeway and MacDonald note that the critical task is to identify the population atrisk of being stopped; most external measures—population data (via the census), licensed driver data, traffic accident data, researcher observation methods, and arrest/ crime suspect data—fall short in accomplishing this task. They highlight the use of instrumental variables analysis, which relies on naturally occurring randomization,

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