Fraud Power Laws
研究发现已发现欺诈的规模分布呈厚尾特征，即极端大欺诈事件频发。通过动态模型揭示管理者学习过程导致欺诈规模陡增的“滑坡效应”，并发现提高检测强度反而可能助长欺诈。
ABSTRACT 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.