Using misstatement data, we find that the distribution of detected fraud features a heavy tail. We propose a theoretical mechanism that explains such a relatively high frequency of extreme frauds. In our dynamic model, a manager manipulates earnings for personal gain. A monitor of uncertain quality can detect fraud and punish the manager. As the monitor fails to detect fraud, the manager's posterior belief about the monitor's effectiveness decreases. Over time, the manager's learning leads to a slippery slope, in which the size of frauds grows steeply, and to a power law for detected fraud. Empirical analyses corroborate the slippery slope and the learning channel. As a policy implication, we establish that a higher detection intensity can increase fraud by enabling the manager to identify an ineffective monitor more quickly. Further, nondetection of frauds below a materiality threshold, paired with a sufficiently steep punishment scheme, can prevent large frauds.

中文翻译：

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欺诈幂律

使用错报数据，我们发现检测到的欺诈行为的分布具有重尾特征。我们提出了一种理论机制来解释这种相对较高频率的极端欺诈行为。在我们的动态模型中，经理为了个人利益而操纵收入。质量不确定的监控器可以发现欺诈行为并惩罚经理。由于监控器无法检测到欺诈行为，管理者对监控器有效性的后验信念就会降低。随着时间的推移，经理的学习会导致滑坡，欺诈规模急剧增长，并导致检测到的欺诈遵循幂律。实证分析证实了滑坡和学习渠道。作为政策含义，我们确定较高的检测强度可以使管理人员更快地识别无效的监控器，从而增加欺诈行为。此外，未发现低于实质性阈值的欺诈行为，再加上足够严厉的惩罚计划，可以防止大规模欺诈行为。