American Economic Journal:
Applied Economics
ISSN 1945-7782 (Print) | ISSN 1945-7790 (Online)
Improving Regulatory Effectiveness through Better Targeting: Evidence from OSHA
American Economic Journal: Applied Economics
vol. 15,
no. 4, October 2023
(pp. 30–67)
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 5 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.Citation
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. 2023. "Improving Regulatory Effectiveness through Better Targeting: Evidence from OSHA." American Economic Journal: Applied Economics, 15 (4): 30–67. DOI: 10.1257/app.20200659Additional Materials
JEL Classification
- C63 Computational Techniques; Simulation Modeling
- J28 Safety; Job Satisfaction; Related Public Policy
- J81 Labor Standards: Working Conditions
- K32 Environmental, Energy, Health, and Safety Law
- L51 Economics of Regulation
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