The Good, the Bad, and the Regulator: An Experimental Test of Two Conditional Audit Schemes

Abstract

Conditional audit rules are designed to achieve regulatory compliance with fewer inspections than required by random auditing. A regulator places individuals into audit pools that differ in probability of audit or severity of fine and specifies transition rules between pools. Future pool assignment is conditional on current audit results. We conduct an experiment to compare two specific schemes—Harrington's Past‐Compliance Targeting and Friesen's Optimal Targeting—against random auditing. We find a production possibility frontier between compliance and minimizing inspections. Optimal targeting generates the lowest inspection rates as predicted, but random auditing the highest compliance. Past‐compliance targeting is intermediate.