Publications
Publications
- October 2023
- American Economic Journal: Applied Economics
Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA
By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
Abstract
We study how a regulator can best target inspections. Our case study is a U.S. 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 up to $850 million in social value over the decade we examine.
Keywords
Safety Regulations; Regulations; Regulatory Enforcement; Machine Learning Models; Safety; Operations; Service Operations; Production; Forecasting and Prediction; Decisions; United States
Citation
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA." American Economic Journal: Applied Economics 15, no. 4 (October 2023): 30–67. (Profiled in the Regulatory Review.)
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