IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v71y2015icp31-45.html
   My bibliography  Save this article

Investigating the nature and impact of reporting bias in road crash data

Author

Listed:
  • Abay, Kibrom A.

Abstract

This paper investigates the nature, and impact of the reporting bias associated with the police-reported crash data on inferences made using this data. In doing so, we merge a detailed emergency room data and police-reported crash data for a specific region in Denmark. To disentangle potentially common observable and unobservable factors that affect drivers’ injury severity risk and their crash reporting behavior, we formulate a bivariate ordered-response probit model of injury severity risk and crash reporting propensity. To empirically identify the reporting bias in this joint model, we exploit an exogenous police reform that particularly affects some specific municipalities of the region under consideration. The empirical analysis reveals substantial reporting bias in the commonly used police-reported road crash data. This non-random sample selection associated with the police-reported crash data leads to biased estimates on the effect of some of the explanatory variables in injury severity analysis. For instance, estimates based on the police-reported crash data substantially underestimate the effectiveness of seat belt use in reducing drivers’ injury severity risk.

Suggested Citation

  • Abay, Kibrom A., 2015. "Investigating the nature and impact of reporting bias in road crash data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 71(C), pages 31-45.
    • Handle: RePEc:eee:transa:v:71:y:2015:i:c:p:31-45
    DOI: 10.1016/j.tra.2014.11.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856414002687
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2014.11.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    2. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
    3. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    4. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
    5. Abay, Kibrom A. & Paleti, Rajesh & Bhat, Chandra R., 2013. "The joint analysis of injury severity of drivers in two-vehicle crashes accommodating seat belt use endogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 74-89.
    6. 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.
    7. Cosslett, Stephen R, 1981. "Maximum Likelihood Estimator for Choice-Based Samples," Econometrica, Econometric Society, vol. 49(5), pages 1289-1316, September.
    8. Bierlaire, M. & Bolduc, D. & McFadden, D., 2008. "The estimation of generalized extreme value models from choice-based samples," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 381-394, May.
    9. Abay, Kibrom A., 2013. "Examining pedestrian-injury severity using alternative disaggregate models," Research in Transportation Economics, Elsevier, vol. 43(1), pages 123-136.
    10. Manski, Charles F & Lerman, Steven R, 1977. "The Estimation of Choice Probabilities from Choice Based Samples," Econometrica, Econometric Society, vol. 45(8), pages 1977-1988, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. José Mateos-Granados & Luis Miguel Martín-delosReyes & Mario Rivera-Izquierdo & Eladio Jiménez-Mejías & Virginia Martínez-Ruiz & Pablo Lardelli-Claret, 2021. "Sex Differences in the Amount and Patterns of Car-Driving Exposure in Spain, 2014 to 2017: An Application of a Quasi-Induced Exposure Approach," IJERPH, MDPI, vol. 18(24), pages 1-9, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Esmeralda Ramalho, 2004. "Covariate Measurement Error in Endogenous Stratified Samples," Economics Working Papers 2_2004, University of Évora, Department of Economics (Portugal).
    2. Esmerelda A. Ramalho & Richard Smith, 2003. "Discrete choice non-response," CeMMAP working papers 07/03, Institute for Fiscal Studies.
    3. Norman E. Breslow, 2003. "Are Statistical Contributions to Medicine Undervalued?," Biometrics, The International Biometric Society, vol. 59(1), pages 1-8, March.
    4. Ramalho Esmeralda A., 2010. "Covariate Measurement Error: Bias Reduction under Response-Based Sampling," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-34, September.
    5. Hindsley, Paul & Landry, Craig E. & Gentner, Brad, 2011. "Addressing onsite sampling in recreation site choice models," Journal of Environmental Economics and Management, Elsevier, vol. 62(1), pages 95-110, July.
    6. Abay, Kibrom A. & Berhane, Guush & Taffesse, Alemayehu Seyoum & Koru, Bethlehem & Abay, Kibrewossen, 2016. "Understanding farmers’ technology adoption decisions: Input complementarity and heterogeneity:," ESSP working papers 82, International Food Policy Research Institute (IFPRI).
    7. Jeongsik “Jay” Lee, 2010. "Heterogeneity, Brokerage, and Innovative Performance: Endogenous Formation of Collaborative Inventor Networks," Organization Science, INFORMS, vol. 21(4), pages 804-822, August.
    8. Esmeralda A. Ramalho & Richard J. Smith, 2013. "Discrete Choice Non-Response," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(1), pages 343-364.
    9. Newman, Jeffrey P. & Ferguson, Mark E. & Garrow, Laurie A., 2013. "Estimating GEV models with censored data," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 170-184.
    10. Abhirup Chakrabarti & Will Mitchell, 2013. "The Persistent Effect of Geographic Distance in Acquisition Target Selection," Organization Science, INFORMS, vol. 24(6), pages 1805-1826, December.
    11. Andrew Daly, 2014. "Forecasting choice," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 25, pages 627-646, Edward Elgar Publishing.
    12. Nevo, Aviv, 2003. "Using Weights to Adjust for Sample Selection When Auxiliary Information Is Available," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 43-52, January.
    13. Abay, Kibrom A., 2013. "Examining pedestrian-injury severity using alternative disaggregate models," Research in Transportation Economics, Elsevier, vol. 43(1), pages 123-136.
    14. Griffith, Rachel & Crawford, Gregory & Iaria, Alessandro, 2016. "Preference Estimation with Unobserved Choice Set Heterogeneity using Sufficient Sets," CEPR Discussion Papers 11675, C.E.P.R. Discussion Papers.
    15. Ipek Sener & Naveen Eluru & Chandra Bhat, 2009. "An analysis of bicycle route choice preferences in Texas, US," Transportation, Springer, vol. 36(5), pages 511-539, September.
    16. Biswas, Mehek & Bhat, Chandra R. & Ghosh, Sulagna & Pinjari, Abdul Rawoof, 2024. "Choice models with stochastic variables and random coefficients," Journal of choice modelling, Elsevier, vol. 51(C).
    17. Ramalho, Esmeralda A., 2002. "Regression models for choice-based samples with misclassification in the response variable," Journal of Econometrics, Elsevier, vol. 106(1), pages 171-201, January.
    18. James J. Heckman, 2005. "Micro Data, Heterogeneity and the Evaluation of Public Policy Part 2," The American Economist, Sage Publications, vol. 49(1), pages 16-44, March.
    19. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    20. Nathan H. Miller, 2008. "Competition When Consumers Value Firm Scope," EAG Discussions Papers 200807, Department of Justice, Antitrust Division.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:71:y:2015:i:c:p:31-45. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.