Participation in social programs is often misreported in survey data, complicating the estimation of the effects of those programs. In this paper we propose a model to estimate treatment effect under endogenous participation and endogenous misreporting. We show that failure to account for endogenous misreporting can result in the estimates of the treatment effect having opposite sign from the true effect. We present an expression for the asymptotic bias of both OLS and IV estimators and discuss the conditions under which sign reversal may occur. We provide a method of eliminating this bias when researchers have access to information related to both participation and misreporting. We establish the consistency and asymptotic normality of our estimator and present its small sample performance through Monte Carlo simulations. Joint with Pierre Nguimkeuy and Augustine Denteh.