Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA
研究美国职业安全与健康管理局（OSHA）如何通过优化检查靶向策略来减少工伤，发现若按预期伤害减少量或预期伤害水平最高来靶向，可多避免一倍伤害并创造近10亿美元社会价值。
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.