The Good, the Bad, and the Regulator: An Experimental Test of Two Conditional Audit Schemes
通过实验比较两种条件审计方案（Harrington的过去合规定向和Friesen的最优定向）与随机审计，发现合规与最小化检查之间存在权衡，最优定向检查率最低但随机审计合规率最高。
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.