通过更好的目标来提高监管效率:来自OSHA的证据

IF 5.5 1区 经济学 Q1 ECONOMICS
Matthew S Johnson, David I Levine, Michael W Toffel
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引用次数: 0

摘要

我们研究监管机构如何才能最好地瞄准检查。我们的案例研究是美国职业安全与健康管理局(OSHA)的一个项目,该项目随机分配了一些检查。在接下来的5年里,每次检查平均减少了2.4%(9%)的严重伤害。使用新的机器学习方法,我们发现OSHA可以通过将检查目标锁定在预期避免伤害最高的工作场所来避免多达两倍的伤害,并且通过瞄准最高预期伤害水平来避免几乎同样多的伤害。在我们研究的十年中,这两种方法都将产生高达8.5亿美元的社会价值。(jl c63, j28, j81, k32, 51)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Regulatory Effectiveness through Better Targeting: Evidence from OSHA
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 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)
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来源期刊
CiteScore
9.10
自引率
1.60%
发文量
63
期刊介绍: American Economic Journal: Applied Economics publishes papers covering a range of topics in applied economics, with a focus on empirical microeconomic issues. In particular, we welcome papers on labor economics, development microeconomics, health, education, demography, empirical corporate finance, empirical studies of trade, and empirical behavioral economics.
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