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Estimating the Effect of Alcohol on Driver Risk Using Only Fatal Accident Statistics

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  • Steven D. Levitt
  • Jack Porter

Abstract

Measuring the relative likelihood of fatal crash involvement for different types of drivers would seem to require information on both the number of fatal crashes by driver type and the fraction of drivers on the road falling into each category. In this paper, however, we present a methodology for measuring fatal crash likelihood that relies solely on fatal crash data. The key to our identification strategy is the hidden richness inherent to two-car crashes. Crashes involving two drinking drivers are proportional to the square of the number of drinking drivers on the road; crashes with one drinking and one sober driver increase linearly in the number of drinking drivers. Imposing a limited set of assumptions (e.g. independence across crashes, equal mixing on the roads), we are able to estimate both the likelihood of causing a fatal crash and the fraction of drivers of each type on the road. Our estimates suggest that drivers with alcohol in their blood are at least eight times more likely to cause a fatal crash; legally drunk drivers pose a risk at least 15 times greater than sober drivers. Males, young drivers, and drivers with bad past driving records are all more dangerous, but the impact of these other factors is far less than that of alcohol.

Suggested Citation

  • Steven D. Levitt & Jack Porter, 1999. "Estimating the Effect of Alcohol on Driver Risk Using Only Fatal Accident Statistics," NBER Working Papers 6944, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:6944
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    References listed on IDEAS

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    1. Ruhm, Christopher J., 1996. "Alcohol policies and highway vehicle fatalities," Journal of Health Economics, Elsevier, vol. 15(4), pages 435-454, August.
    2. Saffer, Henry & Grossman, Michael, 1987. "Drinking Age Laws and Highway Mortality Rates: Cause and Effect," Economic Inquiry, Western Economic Association International, vol. 25(3), pages 403-417, July.
    3. Peter Asch & David T. Levy, 1987. "Does the minimum drinking age affect traffic fatalities?," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 6(2), pages 180-192.
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    5. Michael Grossman & Frank J. Chaloupka & Henry Saffer & Adit Laixuthai, 1993. "Effects of Alcohol Price Policy on Youth," NBER Working Papers 4385, National Bureau of Economic Research, Inc.
    6. Chaloupka, Frank J & Saffer, Henry & Grossman, Michael, 1993. "Alcohol-Control Policies and Motor-Vehicle Fatalities," The Journal of Legal Studies, University of Chicago Press, vol. 22(1), pages 161-186, January.
    7. Flinn, C. & Heckman, J., 1982. "New methods for analyzing structural models of labor force dynamics," Journal of Econometrics, Elsevier, vol. 18(1), pages 115-168, January.
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    Cited by:

    1. David Figlio & Jens Ludwig, 2012. "Sex, Drugs, and Catholic Schools: Private Schooling and Non-Market Adolescent Behaviors," German Economic Review, Verein für Socialpolitik, vol. 13(4), pages 385-415, November.
    2. Daniel Albalate, 2008. "Lowering blood alcohol content levels to save lives: The European experience," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 27(1), pages 20-39.
    3. Steven D. Levitt & Jack Porter, 2001. "Sample Selection In The Estimation Of Air Bag And Seat Belt Effectiveness," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 603-615, November.
    4. Daniel Albalate, 2007. "Lowering blood alcohol content levels to save lives: A European case study," Working Papers in Economics 173, Universitat de Barcelona. Espai de Recerca en Economia.
    5. Daniel Eisenberg, 2003. "Evaluating the effectiveness of policies related to drunk driving," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 22(2), pages 249-274.
    6. William N. Evans & Thomas S. Dee, 2001. "Behavior Policies and Teen Traffic Safety," American Economic Review, American Economic Association, vol. 91(2), pages 91-96, May.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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