Predictably Unpredictable Inspections

Ashvin Gandhi (), Andrew Olenski () and Maggie Shi ()
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Ashvin Gandhi: University of California, Los Angeles - Anderson School of Management
Andrew Olenski: Lehigh University
Maggie Shi: University of Chicago - Harris School of Public Policy

No 2026-43, Working Papers from Becker Friedman Institute for Research In Economics

Abstract: Inspections are a common tool for acquiring information and incentivizing compliance. Although inspections are typically unannounced, their timing often follows a predictable schedule. We study how this predictability shapes firm effort and patient outcomes in U.S. nursing homes, leveraging detailed administrative data on staffing, care, and health outcomes. Nursing homes "slack" in the low-risk period following an inspection and ramp up effort as their next inspection approaches.Patient survival mirrors this pattern, suggesting that these fluctuations in effort have meaningful consequences for the quality of patient care. We embed these estimates in a dynamic model capturing how inspection regimes incentivize effort and provide information about quality. Our estimates indicate that moving to unpredictable inspections could induce as much additional effort as increasing the frequency of inspections by 12%, while only minimally reducing their informational value

JEL-codes: I18 L51 (search for similar items in EconPapers)
Pages: 97 pages
Date: 2026
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