Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

Matthew S. Johnson; David I. Levine; Michael W. Toffel

Working Paper | HBS Working Paper Series | 2020

Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

by Matthew S. Johnson, David I. Levine and Michael W. Toffel

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.

Keywords: Government Administration; Working Conditions; Safety; Quality; Production; Analysis; Resource Allocation; Manufacturing Industry; United States;

Language: English

Format: Print

85 pages

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Citation:

Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA." Harvard Business School Working Paper, No. 20-019, August 2019. (Revised February 2020.)

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Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA

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