Overcoming the benchmark problem in estimating bias in traffic enforcement: the use of automatic traffic enforcement cameras

Roni Factor*, Gal Kaplan-Harel, Rivka Turgeman, Simon Perry

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Objectives: The existence of bias in law enforcement can be difficult to verify or disprove, in part because of the difficulty of finding a benchmark—an objective estimate of actual offenses committed by the studied population—that can be compared with police enforcement. In the current study, we propose and test a method for examining bias in enforcement of speeding offenses. Method: Using all speeding tickets issued in Israel in 2013–2015, we compare speeding tickets generated by stationary automatic traffic cameras, which provide an objective estimate of speed offenses, with speeding tickets issued manually by police officers, based on drivers’ ethnicity with further distribution by gender and age. Results: Initial findings indicate that, overall, speeding tickets issued by police officers in Israel are not biased based on drivers’ ethnicity. Conclusions: This study highlights the importance of distinguishing between overrepresentation and bias in law enforcement, which sometimes seem to be blurred in the literature.

Original languageAmerican English
Pages (from-to)217-237
Number of pages21
JournalJournal of Experimental Criminology
Volume17
Issue number2
DOIs
StatePublished - Jun 2021

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature B.V.

Keywords

  • Automatic traffic cameras
  • Enforcement bias
  • Ethnic and racial minorities
  • Policing
  • Road policing
  • Speeding offenses
  • Traffic violations

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