The following post comes to us from Anastasia Zakolyukina of the University of Chicago Booth School of Business.
In the paper, Measuring Intentional Manipulation: A Structural Approach, which was recently made publicly available on SSRN, I suggest a structural model of a manager’s manipulation decision that allows me to estimate his costs of manipulation and to infer the amount of undetected intentional manipulation for each executive in my sample. The model follows the economic approach to crime (Becker, 1968) and incorporates the costs and benefits of manipulation decisions. The model is a dynamic finite-horizon problem in which the risk-averse manager maximizes his terminal wealth. The manager’s total wealth depends on his equity holdings in the firm and his cash wealth. The model yields three predictions. First, according to the wealth effect, managers having greater wealth manipulate less. Second, according to the valuation effect, the current-period bias in net assets increases in the existing bias. Third, the manager’s risk aversion, the linearity of his terminal wealth in reported earnings, and the stochastic evolution of the firm’s intrinsic value produce income smoothing. Furthermore, the structural approach allows partial observability of manipulation decisions in the data; hence, I am able to estimate the probability of detection as well as the loss in the manager’s wealth using the data on detected misstatements (i.e., financial restatements).