Improving Regulatory Effectiveness through Better Targeting: Evidence from OSHA

• 成果类型: Article
• 署名作者: Johnson, Matthew S.; Levine, David I.; Toffel, Michael W.
• 署名单位: Duke University; University of California System; University of California Berkeley; Harvard University
• 刊物名称: AMERICAN ECONOMIC JOURNAL-APPLIED ECONOMICS
• ISSN/ISSBN: 1945-7782
• DOI: 10.1257/app.20200659
• 发表日期: 2023
• 页码: 30-67
• 关键词: inspections; ENFORCEMENT; injury; IMPACT; safety; audit

摘要

We study how a regulator can best target inspections. Our case study is a US Occupational Safety and Health Administration (OSHA) pro-gram 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. (JEL C63, J28, J81, K32, L51)