  This paper shows how to use a randomized saturation experimental design to
identify and estimate causal effects in the presence of spillovers--one
person's treatment may affect another's outcome--and one-sided
non-compliance--subjects can only be offered treatment, not compelled to take
it up. Two distinct causal effects are of interest in this setting: direct
effects quantify how a person's own treatment changes her outcome, while
indirect effects quantify how her peers' treatments change her outcome. We
consider the case in which spillovers occur within known groups, and take-up
decisions are invariant to peers' realized offers. In this setting we point
identify the effects of treatment-on-the-treated, both direct and indirect, in
a flexible random coefficients model that allows for heterogeneous treatment
effects and endogenous selection into treatment. We go on to propose a feasible
estimator that is consistent and asymptotically normal as the number and size
of groups increases. We apply our estimator to data from a large-scale job
placement services experiment, and find negative indirect treatment effects on
the likelihood of employment for those willing to take up the program. These
negative spillovers are offset by positive direct treatment effects from own
take-up.
