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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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    Cited by:

    1. 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).
    2. Ying Chen & Mingyi Li & Jiaming Mao & Jingyi Zhou, 2025. "Consumption Stimulus with Digital Coupons," Papers 2507.01365, arXiv.org.
    3. Kirill Ponomarev & Vira Semenova, 2024. "On the Lower Confidence Band for the Optimal Welfare in Policy Learning," Papers 2410.07443, arXiv.org, revised Sep 2025.
    4. Zhang, Jiaqi & Elliott, Robert J.R. & Zhang, Bing & Liu, Mengdi, 2025. "Public environmental complaints and regulatory intensity," Journal of Environmental Economics and Management, Elsevier, vol. 134(C).
    5. Zequn Jin & Gaoqian Xu & Xi Zheng & Yahong Zhou, 2025. "Policy Learning under Unobserved Confounding: A Robust and Efficient Approach," Papers 2507.20550, arXiv.org.
    6. Sarah Dolfin & Nan Maxwell & Ankita Patnaik, "undated". "WHD Compliance Strategies: Directions for Future Research," Mathematica Policy Research Reports b7a5ca876e0b448f9b9c0850e, Mathematica Policy Research.
    7. Aneesh Raghunandan & Thomas G. Ruchti, 2024. "The Impact of Information Frictions Within Regulators: Evidence from Workplace Safety Violations," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 62(3), pages 1067-1120, June.
    8. 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).
    9. Guy Tchuente, 2026. "Scale and Capacity Limits in Decentralized FDA Food-Safety Enforcement," Papers 2602.12392, arXiv.org.
    10. 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.
    11. 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.
    12. Athey, Susan & Keleher, Niall & Spiess, Jann, 2025. "Machine learning who to nudge: Causal vs predictive targeting in a field experiment on student financial aid renewal," Journal of Econometrics, Elsevier, vol. 249(PC).
    13. Juan Carlos Perdomo, 2023. "The Relative Value of Prediction in Algorithmic Decision Making," Papers 2312.08511, arXiv.org, revised May 2024.
    14. Drescher, Katharina & Janzen, Benedikt, 2025. "When weather wounds workers: The impact of temperature on workplace accidents," Journal of Public Economics, Elsevier, vol. 241(C).
    15. Chen, Ying & Mao, Jiaming & Wang, Yue, 2025. "The revenue and welfare implications of digital coupon stimulus programs," China Economic Review, Elsevier, vol. 91(C).
    16. 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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    Keywords

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    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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