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.
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