Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Participation in social programs is often misreported in survey data, complicating the estimation of treatment effects. We propose a model to estimate treatment effects under endogenous participation and endogenous misreporting. 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 for eliminating this bias when researchers have access to information regarding participation and misreporting. We establish the consistency and asymptotic normality of our proposed estimator and assess its small sample performance through Monte Carlo simulations. An empirical example illustrates the proposed method.