Policing The Drunk Driving Problem: A Longitudinal Examination of DUI Enforcement and Alcohol Related Crashes in the U.S. (1985–2015)

Abstract

This project examines the relationship between police enforcement of driving under the influence (DUI) and fatal alcohol related crashes. This article merged data from several sources to fit several 3-level growth curve models that assess the relationship between DUI arrests and fatal alcohol related crashes in U.S. counties from 1985 to 2015. The findings indicate that increases in DUI arrests are related to decreased fatal alcohol related crashes during the period. However, the two are not linearly related and the relationship varies across states. The non-linearity indicates there is a point of diminished returns where increased arrests are no longer related to reductions in fatalities. These findings suggest that policy makers should explore alternative methods of reducing crashes to supplement enforcement efforts such as addressing problems of alcoholism and traffic safety.

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Notes

  1. 1.

    Specifically, prior to 1994, any originating agency that reported less than 6 months of data were excluded, and those that reported six to 11 months were increased and weighted by 12/months reported (FBI, 2014). To increase the quality of data, a new imputation procedure was implemented in 1994 to provide more accurate estimates of arrests. Specifically, while agencies reporting three to 11 months were increased by a weight of 12/months reported, and agencies reporting zero to 2 months were set as missing and imputed (FBI, 2014).

  2. 2.

    The percentage of reporting agencies with missing information was 4% for DUI arrests and violent crime, and 24% for self-report DUI. The missing value analysis indicates that the missing data are missing at random (MAR), and thus there is no systematic error in the missing values and the missingness is ignorable (see Rubin, 1987). As such, these data are appropriate for multiple imputation. The imputation algorithm was also constrained to return a value within the range of the original data for each of the 10 imputations. The sensitivity analysis indicates the missing data have been properly imputed and the imputed mean values do not significantly vary from the original data.

  3. 3.

    Prior to imputation both DUI arrests and part I offenses were logarithmically transformed so they would meet the assumption of normality for predictions. After imputation the logarithmic measures were exponentiated and returned to their original form before the values for each reporting agency were aggregated to the county level. The predictors used for the imputation algorithm mirrored those used for the post 1993 data including state, arrest rates, and geographic stratum. Specifically, dichotomous variables controlled for state with Texas as the referent, also year was controlled with dichotomous measures with 1985 as the reference category, arrest rates were calculated per 100,000 population, and counties and cities by population range.

  4. 4.

    While some have used measures such as police force size, police per capita, and police expenditures to measure certainty of punishment, a meta-analysis by Pratt and Colleagues (Pratt et al., 2006) suggests that the arrest ratio (arrests/offenses known) has a stronger effect size in relation to crime than these measures. However, some argue the arrest ratio introduces an artefactual statistical relationship where none is present originally (Jacob & Rich, 1980), and that it creates tautological results when assessing trends in crime and arrest rates (Chilton, 1982). Thus, scholars purport that the frequency of arrests should be utilized instead of the arrest ratio (Decker & Kohfeld, 1985; Sampson & Cohen, 1988). In fact, Decker and Kohfeld (1985) contend that criminals will not be knowledgeable about complex ratios and probabilities of arrest and are more likely to know about the number of arrests taking place in their neighborhoods which is more likely to affect their perceptions of the certainty of arrest. Moreover, although the public only has an ambiguous knowledge about the probability of arrest for serious index crimes, police interventions for driving violations are much more visible to the public and lead to increased perceptions of apprehension (Sampson & Cohen, 1988). The use of the arrest ratio is also particularly problematic when assessing DUI offenses due to high correlation between arrests and offenses known because DUI offenses generally come to the attention of the police through their own observations, rather than reports from citizen reports like other offenses (Snortum et al., 1990). Thus, a ratio calculated by dividing arrests by offenses known would be severely biased toward one with very little variance.

  5. 5.

    Random effects across states were estimated for the following measures: DUI Arrests, DUI arrests2, crashes involving a driver with a prior DUI conviction, non-alcohol related crashes, violent crime, bachelor’s degree, White, Black, Hispanic, sex ratio, poverty, Beale Code, age 18–24, and 25–34.

  6. 6.

    The assumptions of multilevel modeling were all assessed and have been met. Multicollinearity was assessed with variance inflation factors (VIF), and no factors were collinear (VIF < 2). Correlations matrices were inspected at level 2 and 3 to check for highly correlated variables, and standard errors were monitored throughout to check for cross-level collinearity. Finally, residual scatterplots and Q-Q plots indicate that assumptions of linearity and homogeneity of the variance have been met.

  7. 7.

    While the variance explained by these models may appear odd, it is important to consider it in light of the ICC. As such, these models explain about 92 % of the forty-three and 31 % of the explainable variance at level two and three, respectively. Furthermore, the interaction of unmeasured variables through random effects is also likely playing a role in these explained variances (see Snijders & Bosker, 2012). Any unresolved serial autocorrelation would also exist at level one, rather than levels two and three which examine heterogeneity across place, which may explain the lower explained variance over time at level one.

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This work was supported by the U.S. Department of Justice [grant number 2016-R2-CX-0027].

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Stringer, R.J. Policing The Drunk Driving Problem: A Longitudinal Examination of DUI Enforcement and Alcohol Related Crashes in the U.S. (1985–2015). Am J Crim Just 44, 474–498 (2019). https://doi.org/10.1007/s12103-018-9464-4

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Keywords

  • Policing
  • Drunk driving
  • DUI
  • Fatal crashes
  • Deterrence