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Duke University

Crime, Policing, and Social Status: Identifying Elusive Mechanisms Using New Statistical Approaches

Abstract

dc:description.abstract

<p>Social class is often discussed in crime and social control research but the influence of class in these contexts is not well understood. Stratification studies have identified effects of socioeconomic status on a diverse collection of important outcomes in many facets of society, but the influence of class on criminality and punishment remains largely unidentified. Scholars attempting to connect class position to criminal behavior or risk of arrest and incarceration have either concluded that a robust relationship does not exist, or been confronted with inconsistent or weak evidence. Indeed, despite substantial interest in the influence of social class on criminality and punishment, researchers have been unable to make very many empirical connections between the two. The present study advances understanding about the influence of social class on criminality and punishment, addressing limitations of previous research using new approaches and statistical methods across three studies: (1) a study of the relationship between immigration rates and societal preference for increased police protection and law enforcement spending, (2) a study of heterogeneity in the effect of class on latent categories of self-reported delinquency, and finally, (3) a study of illicit drug use and rates of drug arrest among young adults, and how college attendance may contribute punishment inequality for non-violent drug offenses.</p>

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Fink, Joshua
Advisor dc:contributor.advisor
  • Healy, Kieran

Subjects

dc:subject × 3

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/10161/14522
OAI identifier oai:identifier
oai:dukespace.lib.duke.edu:10161/14522

Chain of custody

source
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Duke University
Base URL
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Last updated
2026-07-24
Source record
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citation

Fink, Joshua. Crime, Policing, and Social Status: Identifying Elusive Mechanisms Using New Statistical Approaches. 2017. https://hdl.handle.net/10161/14522