  The empirical literature on program evaluation limits its scope almost
exclusively to models where treatment effects are homogenous for
observationally identical individuals. This paper considers a treatment effect
model in which treatment effects may be heterogeneous, even among
observationally identical individuals. Specifically, extending the classical
instrumental variables (IV) model with an endogenous binary treatment and a
binary instrument, we allow the heteroskedasticity of the error disturbance to
also depend upon the treatment variable so that treatment has both mean and
variance effects on the outcome. In this endogenous heteroskedasticity IV
(EHIV) model with heterogeneous individual treatment effects, the standard IV
estimator can be inconsistent and lead to incorrect inference. After showing
identification of the mean and variance treatment effects in a nonparametric
version of the EHIV model, we provide closed-form estimators for the linear
EHIV for the mean and variance treatment effects and the individual treatment
effects (ITE). Asymptotic properties of the estimators are provided. A Monte
Carlo simulation investigates the performance of the proposed approach, and an
empirical application regarding the effects of fertility on female labor supply
is considered.
