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Correcting for selectivity bias in the estimation of road crash costs

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  • Margaret Giles

Abstract

Police road crash data comprise a non-random sample of the true population of road crashes, the bias being due to the existence of crashes that are not notified to the Police. Heckman viewed similar problems as 'omitted variables' problems in that the exclusion of some observations in a systematic manner (so-called selectivity bias) has inadvertently introduced the need for an additional regressor in least squares procedures. In the case of Police road crash data, selectivity bias arises from factors affecting the notification of crashes to the Police, such as the number of vehicles in the crash and the type and location of the crash. Using Heckman's methodology for correcting for this selectivity bias, Police road crash data for Western Australia are reconciled with total road crash data in the estimation of the property damage costs of road crashes.

Suggested Citation

  • Margaret Giles, 2003. "Correcting for selectivity bias in the estimation of road crash costs," Applied Economics, Taylor & Francis Journals, vol. 35(11), pages 1291-1301.
    • Handle: RePEc:taf:applec:v:35:y:2003:i:11:p:1291-1301
    DOI: 10.1080/0003684032000090717
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    References listed on IDEAS

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    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    2. Goldberger, Arthur S., 1981. "Linear regression after selection," Journal of Econometrics, Elsevier, vol. 15(3), pages 357-366, April.
    3. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
    4. Margaret Giles, 2003. "The Cost of Road Crashes," Journal of Transport Economics and Policy, University of Bath, vol. 37(1), pages 95-110, January.
    5. Patrick Puhani, 2000. "The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
    6. Margaret J. Giles, 2001. "Data for the Study of Road Crashes in Australia," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 34(2), pages 222-230.
    7. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    8. Hausman, Jerry A & Wise, David A, 1977. "Social Experimentation, Truncated Distributions, and Efficient Estimation," Econometrica, Econometric Society, vol. 45(4), pages 919-938, May.
    9. Lee, Lung-fei & Maddala, G S & Trost, R P, 1980. "Asymptotic Covariance Matrices of Two-Stage Probit and Two-Stage Tobit Methods for Simultaneous Equations Models with Selectivity," Econometrica, Econometric Society, vol. 48(2), pages 491-503, March.
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    Cited by:

    1. Mark Clatworthy & Gerald Makepeace & Michael Peel, 2009. "Selection bias and the Big Four premium: New evidence using Heckman and matching models," Accounting and Business Research, Taylor & Francis Journals, vol. 39(2), pages 139-166.

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