54 Pages Posted: 27 Oct 2010 Last revised: 2 Jul 2013
Date Written: July 1, 2013
The paper studies corporate fraud and detection, using an empirical framework that models the strategic interdependence between fraud and detection and accounts for the possibility that some fraud is undetected (incomplete detection). The framework first models fraud as a discrete choice and then models the magnitude of fraud. The results find that failure to account for incomplete detection could lead to downward biases in estimating the effects of various factors on the likelihood of fraud. Furthermore, the empirical model in the paper has a number of potential applications.
Keywords: Fraud, detection, corporate misreporting, monitoring, detection controlled estimation, incomplete detection, partial observability, probit models, Tobit models, simultaneous equation models
JEL Classification: G30, C34, C35, K22, K42
Suggested Citation: Suggested Citation
By Kevin Murphy