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Car accidents, smartphone adoption and 3G coverage

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  • Hersh, Jonathan
  • Lang, Bree J.
  • Lang, Matthew

Abstract

This paper examines the relationship between smartphone use by drivers and traffic accidents in California between 2001 and 2013. In order to estimate smartphone use, we first show that widespread adoption of modern smartphones began in 2009 after the release of the iPhone 3G and T-Mobile G1. This information is combined with annual 3G coverage maps that are constructed from cellular tower information in a machine learning framework. In a difference-in-differences framework, we estimate the combined effect of smartphone adoption and 3G coverage along quarter-mile road segments. Controlling for census tract population density, road and year fixed effects, Poisson regression results show that there is a statistically significant increase in the traffic accident rate along a road segment when smartphone use becomes possible. Our preferred specification suggests smartphones caused accident rates to increase by 2.9 percent, resulting in 3500 additional accidents per year in California. Event study results rule out the possibility that our smartphone treatment is capturing a trend in the accident rate. The results are robust to a variety of specifications and consistent with individual-level studies showing that cell phone use leads to lower driving quality. The findings also provide guidance for policies aimed at reducing cell phone related accidents and distracted driving.

Suggested Citation

  • Hersh, Jonathan & Lang, Bree J. & Lang, Matthew, 2022. "Car accidents, smartphone adoption and 3G coverage," Journal of Economic Behavior & Organization, Elsevier, vol. 196(C), pages 278-293.
    • Handle: RePEc:eee:jeborg:v:196:y:2022:i:c:p:278-293
    DOI: 10.1016/j.jebo.2022.01.033
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    References listed on IDEAS

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    1. Matilde Cappelletti & Leonardo M. Giuffrida, 2024. "Targeted Bidders in Government Tenders," CESifo Working Paper Series 11142, CESifo.

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    More about this item

    Keywords

    3G Coverage; Car accidents; Smartphones; Machine learning; Random forest;
    All these keywords.

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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