Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

93 Pages Posted: 28 Aug 2019 Last revised: 27 Feb 2024

See all articles by Matthew S. Johnson

Matthew S. Johnson

Duke University - Sanford School of Public Policy

David I. Levine

University of California, Berkeley - Haas School of Business

Michael W. Toffel

Harvard Business School

Date Written: October 1, 2023

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 five 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.

JEL Classification: C63, J28, J81, K32, L51

Suggested Citation

Johnson, Matthew and Levine, David Ian and Toffel, Michael W., Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA (October 1, 2023). Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 20-019, Available at SSRN: https://ssrn.com/abstract=3443052 or http://dx.doi.org/10.2139/ssrn.3443052

Matthew Johnson

Duke University - Sanford School of Public Policy ( email )

201 Science Drive
Box 90312
Durham, NC 27708-0239
United States

David Ian Levine

University of California, Berkeley - Haas School of Business ( email )

Berkeley, CA 94720
United States
510-642-1697 (Phone)
510-643-1420 (Fax)

Michael W. Toffel (Contact Author)

Harvard Business School ( email )

Boston, MA 02163
United States
617.384.8043 (Phone)

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