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A hierarchical Bayes approach to adjust for selection bias in before–after analyses of vision zero policies

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Listed:
  • Jonathan Auerbach

    (Columbia University)

  • Christopher Eshleman

    (The Port Authority of New York & New Jersey)

  • Rob Trangucci

    (University of Michigan)

Abstract

American cities devote significant resources to the implementation of traffic safety countermeasures that prevent pedestrian fatalities. However, the before–after comparisons typically used to evaluate the success of these countermeasures often suffer from selection bias. This paper motivates the tendency for selection bias to overestimate the benefits of traffic safety policy, using New York City’s Vision Zero strategy as an example. The NASS General Estimates System, Fatality Analysis Reporting System and other databases are combined into a Bayesian hierarchical model to calculate a more realistic before–after comparison. The results confirm the before–after analysis of New York City’s Vision Zero policy did in fact overestimate the effect of the policy, and a more realistic estimate is roughly two-thirds the size.

Suggested Citation

  • Jonathan Auerbach & Christopher Eshleman & Rob Trangucci, 2021. "A hierarchical Bayes approach to adjust for selection bias in before–after analyses of vision zero policies," Computational Statistics, Springer, vol. 36(3), pages 1577-1604, September.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:3:d:10.1007_s00180-021-01070-x
    DOI: 10.1007/s00180-021-01070-x
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    References listed on IDEAS

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    1. Roya Amjadi & Wendy Martinez, 2021. "The 2016 Data Challenge of the American Statistical Association," Computational Statistics, Springer, vol. 36(3), pages 1553-1560, September.
    2. Friedman, Milton, 1992. "Do Old Fallacies Ever Die?," Journal of Economic Literature, American Economic Association, vol. 30(4), pages 2129-2132, December.
    3. repec:cup:cbooks:9780521885881 is not listed on IDEAS
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    1. Roya Amjadi & Wendy Martinez, 2021. "The 2016 Data Challenge of the American Statistical Association," Computational Statistics, Springer, vol. 36(3), pages 1553-1560, September.

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