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Improving Regulatory Effectiveness through Better Targeting: Evidence from OSHA

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  • Matthew S. Johnson
  • David I. Levine
  • Michael W. Toffel

Abstract

We study how a regulator can best target inspections. Our case study is a US Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection led to 2.4 (9 percent) fewer serious injuries over the next 5 years. Using new machine learning methods, we find that OSHA could have averted as much as twice as many injuries by targeting inspections to workplaces with the highest expected averted injuries and nearly as many by targeting the highest expected level of injuries. Either approach would have generated up to $850 million in social value over the decade we examine.

Suggested Citation

  • Matthew S. Johnson & David I. Levine & Michael W. Toffel, 2023. "Improving Regulatory Effectiveness through Better Targeting: Evidence from OSHA," American Economic Journal: Applied Economics, American Economic Association, vol. 15(4), pages 30-67, October.
    • Handle: RePEc:aea:aejapp:v:15:y:2023:i:4:p:30-67
    DOI: 10.1257/app.20200659
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    References listed on IDEAS

    as
    1. Ling Li & Perry Singleton, 2019. "The Effect of Workplace Inspections on Worker Safety," ILR Review, Cornell University, ILR School, vol. 72(3), pages 718-748, May.
    2. Matias D. Cattaneo & Michael Jansson & Xinwei Ma, 2018. "Manipulation testing based on density discontinuity," Stata Journal, StataCorp LP, vol. 18(1), pages 234-261, March.
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    Cited by:

    1. Christensen, Peter & Francisco, Paul & Myers, Erica & Shao, Hansen & Souza, Mateus, 2024. "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, Elsevier, vol. 234(C).
    2. Andr's Gonz'lez Lira & Ahmed Mushfiq Mobarak, 2018. "Slippery Fish: Enforcing Regulation when Agents Learn and Adapt," Cowles Foundation Discussion Papers 2143R, Cowles Foundation for Research in Economics, Yale University, revised Mar 2021.
    3. Augusto Cerqua & Marco Letta & Gabriele Pinto, 2024. "On the (Mis)Use of Machine Learning with Panel Data," Papers 2411.09218, arXiv.org.
    4. Kirill Ponomarev & Vira Semenova, 2024. "On the Lower Confidence Band for the Optimal Welfare," Papers 2410.07443, arXiv.org, revised Oct 2024.
    5. Juan Carlos Perdomo, 2023. "The Relative Value of Prediction in Algorithmic Decision Making," Papers 2312.08511, arXiv.org, revised May 2024.
    6. Anja Bondebjerg & Trine Filges & Jan Hyld Pejtersen & Malene Wallach Kildemoes & Hermann Burr & Peter Hasle & Emile Tompa & Elizabeth Bengtsen, 2023. "Occupational health and safety regulatory interventions to improve the work environment: An evidence and gap map of effectiveness studies," Campbell Systematic Reviews, John Wiley & Sons, vol. 19(4), December.

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    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • J28 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Safety; Job Satisfaction; Related Public Policy
    • J81 - Labor and Demographic Economics - - Labor Standards - - - Working Conditions
    • K32 - Law and Economics - - Other Substantive Areas of Law - - - Energy, Environmental, Health, and Safety Law
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation

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