Before revision:  Available tools to account for such a problem typically require the researcher to observe the receipt of program access for every individual. However, in many experiments, this is not the case as data is not collected on where any individual received access. In this paper, I develop a framework to show how data on only each individual's treatment assignment status, program participation decision and outcome can be exploited to learn about the average effects of program access .
After revision:  Available tools to account for such a problem typically require the researcher to observe the receipt of program access across individuals. I propose a new nonparametric multiple treatment selection model that provides a general setup to define a range of parameters evaluating the effects of program access .