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Improving Regulatory Effectiveness through Better Targeting: Evidence from OSHA

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

Abstract

We study how a regulator can best allocate its limited inspection resources. We direct our analysis to a US Occupational Safety and Health Administration (OSHA) inspection program that targeted dangerous establishments and allocated some inspections via random assignment. We find that inspections reduced serious injuries by an average of 9% over the following five years. We use new machine learning methods to estimate the effects of counterfactual targeting rules OSHA could have deployed. OSHA could have averted over twice as many injuries if its inspections had targeted the establishments where we predict inspections would avert the most injuries. The agency could have averted nearly as many additional injuries by targeting the establishments predicted to have the most injuries. Both of these targeting regimes would have generated over $1 billion in social value over the decade we examine. Our results demonstrate the promise, and limitations, of using machine learning to improve resource allocation. JEL Classifications: I18; L51; J38; J8

Suggested Citation

  • Johnson, Matthew S & Levine, David I & Toffel, Michael W, 2019. "Improving Regulatory Effectiveness through Better Targeting: Evidence from OSHA," Institute for Research on Labor and Employment, Working Paper Series qt1gq7z4j3, Institute of Industrial Relations, UC Berkeley.
    • Handle: RePEc:cdl:indrel:qt1gq7z4j3
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    References listed on IDEAS

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    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 LLC, 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. Liu, Qing & Lu, Ruosi & Teng Sun, Stephen & Zhang, Meng, 2024. "Unintended workplace safety consequences of minimum wages," Journal of Public Economics, Elsevier, vol. 239(C).
    3. 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.
    4. Kirill Ponomarev & Vira Semenova, 2024. "On the Lower Confidence Band for the Optimal Welfare," Papers 2410.07443, arXiv.org, revised Oct 2024.
    5. Raghunandan, Aneesh & Ruchti, Thomas, 2024. "The impact of information frictions within regulators: evidence from workplace safety violations," LSE Research Online Documents on Economics 122404, London School of Economics and Political Science, LSE Library.
    6. Drescher, Katharina & Janzen, Benedikt, 2025. "When weather wounds workers: The impact of temperature on workplace accidents," Journal of Public Economics, Elsevier, vol. 241(C).
    7. 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.
    8. Augusto Cerqua & Marco Letta & Gabriele Pinto, 2024. "On the (Mis)Use of Machine Learning with Panel Data," Papers 2411.09218, arXiv.org, revised May 2025.
    9. Juan Carlos Perdomo, 2023. "The Relative Value of Prediction in Algorithmic Decision Making," Papers 2312.08511, arXiv.org, revised May 2024.

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    More about this item

    Keywords

    Social and Behavioral Sciences; Public Policy;

    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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