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 effects under endogenous participation and endogenous misreporting. We show that failure to account for...

We study the problem of estimating the average causal effect of treating every member of a population, as opposed to none, using an experiment that treats only some. We consider settings where spillovers have global support and decay slowly with (a generalized notion of) distance. We derive the...

We introduce a general approach for analyzing large-scale text-based data, combining the strengths of neural network language processing and generative statistical modeling to create a factor structure of unstructured data for downstream regressions typically used in social sciences. We generate...

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 effects under endogenous participation and endogenous misreporting. We show that failure to account for...

We provide a general framework for incorporating many types of micro data from summary statistics to full surveys of selected consumers into Berry, Levinsohn, and Pakes (1995)-style estimates of differentiated products demand systems. We extend best practices for BLP estimation in Conlon and...

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...