Identifying and Testing Models of Managerial Compensations

We develop a pure moral hazard model, and a closely related hybrid one, where there are both hidden actions and hidden information, to derive the restrictions from optimal contract theory that characterize set identification. In pure moral hazard models, the expected utility of managers is equalized across states, whereas in a hybrid model the optimal contract equates the expected utility of truth telling with the expected utility of lying. These restrictions are testable. Our identification analysis establishes sharp and tight bounds on the identified set. Our tests and estimators are based on these bounds. We apply semiparametric methods to test the models, estimate the structural parameters, and quantify the effects of hidden actions versus hidden information. The pure moral hazard model is rejected on a large panel data set measuring the compensation of chief executive officers and the financial and accounting returns of the publicly traded firms they manage. We do not, however, reject the restrictions of the hybrid model, and our structural estimates for that model show the degree of private information varies considerably across sectors and over firm size.