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

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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.

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Notes

  1. The data were provided by the Transport Department of the Israel Police. The police data link traffic tickets data with information from the National Civil Registration on drivers’ religion, age and gender, both for the automated speed cameras and the manual laser speedometer.

  2. The model can be also interpreted as follows: given that a driver was caught speeding and received a ticket, is the probability that he/she is non-Jewish similar regardless of the source of the report (manual or automatic) while controlling for the other variables in the model.

  3. Additional analyses without clustered robust standard errors yielded similar results.

  4. The predictions for the non-Jewish drivers in total were estimated from Model 2 (with controls but without interactions). The predictions for the other variables were estimated from Model 8 with the interactions. However, the predictions for non-Jews in total from Model 8 yielded similar effects—23.4% (23.12–23.67%) and 18.2% (17.78–18.68%) for automatic and manual enforcement, respectively.

  5. Data on the distribution of drivers in the population by road type and daytime/nighttime hours do not exist.

  6. It is interesting to note that, without the control variables, the estimated proportion (from Model 1 in Table 2) of tickets issued by manual enforcement that went to non-Jewish drivers (33%) was significantly higher (\( {\chi}_1^2=726.91 \); p < 0.001) than the percentage of tickets generated automatically that went to this group (27%). See the left side of Fig. 2 in the Appendix.

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Authors and Affiliations

  1. Institute of Criminology, Faculty of Law, The Hebrew University of Jerusalem, Jerusalem, Israel

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

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  1. Roni Factor
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  2. Gal Kaplan-Harel
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  3. Rivka Turgeman
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  4. Simon Perry
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Correspondence to Roni Factor.

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Appendix

Appendix

Fig. 2
figure 2

Predicted rate of non-Jewish drivers ticketed through automatic and manual enforcement, from logistic regressions (Models 1 and 2). Note: The dotted line represents the percentage of non-Jews in the driver population in Israel

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Factor, R., Kaplan-Harel, G., Turgeman, R. et al. Overcoming the benchmark problem in estimating bias in traffic enforcement: the use of automatic traffic enforcement cameras. J Exp Criminol 17, 217–237 (2021). https://doi.org/10.1007/s11292-020-09414-1

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