Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

93 Pages Posted: 28 Aug 2019 Last revised: 29 Aug 2019

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: August 23, 2019

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 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 (August 23, 2019). 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)

Register to save articles to
your library

Register

Paper statistics

Downloads
17
Abstract Views
160
PlumX Metrics