Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

85 Pages Posted: 28 Aug 2019 Last revised: 4 Mar 2020

See all articles by Matthew S. Johnson

Matthew S. Johnson

Duke University - Sanford School of Public Policy

David I. Levine

University of California, Berkeley - Economic Analysis & Policy Group

Michael W. Toffel

Harvard Business School

Date Written: February 26, 2020

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 averted 2.4 serious injuries (9%) over the next five years. We use new machine learning methods to estimate the effects of counterfactual targeting rules. OSHA could have averted over twice as many injuries by targeting the highest expected averted injuries and nearly as many by targeting the highest expected level of injuries. Either approach would have generated over $1 billion in social value over the decade we examine.

JEL Classification: I18, L51, J38, J8

Suggested Citation

Johnson, Matthew and Levine, David Ian and Toffel, Michael W., Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA (February 26, 2020). 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 - Economic Analysis & Policy Group ( 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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