Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA
88 Pages Posted: 28 Aug 2019 Last revised: 22 Apr 2022
Date Written: April 19, 2022
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. We use new machine learning methods to estimate the effects of alternative targeting rules. OSHA could have averted 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 nearly $1 billion in social value over the decade we examine.
JEL Classification: I18, L51, J38, J8
Suggested Citation: Suggested Citation