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 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.

JEL Classification: I18, L51, J38, J8

Suggested Citation: Suggested Citation

Johnson, Matthew and Levine, David Ian and Toffel, Michael W., Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA (April 19, 2022). Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 20-019, Available at SSRN: https://ssrn.com/abstract=3443052 or http://dx.doi.org/10.2139/ssrn.3443052