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 identi…cation 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 fi…rm size.