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This paper uses factor models to identify and estimate distributions of counterfactuals. We extend LISREL frameworks to a dynamic treatment effect setting, extending matching to account for unobserved conditioning variables. Using these models, we can identify all pairwise and joint treatment effects. We apply these methods to a model of schooling and determine the intrinsic uncertainty facing agents at the time they make their decisions about enrollment in school. Reducing uncertainty in returns raises college enrollment. We go beyond the ?Veil of Ignorance? in evaluating educational policies and determine who benefits and loses from commonly proposed educational reforms.

This paper summarizes our recent research on evaluating the distributional consequences of social programs. This research advances the economic policy evaluation literature beyond estimating assorted mean impacts to estimate distributions of outcomes generated by different policies and determine how those policies shift persons across the distributions of potential outcomes produced by them. Our approach enables analysts to evaluate the distributional effects of social programs without invoking the ?Veil of Ignorance? assumption often used in the literature in applied welfare economics. Our methods determine which persons are affected by a given policy, where they come from in the ex-ante outcome distribution and what their gains are. We apply our methods to analyze two proposed policy reforms in American education. These reforms benefit the middle class and not the poor.

Abstract
In real-world marketplaces, one may encounter an alternative that is inferior to another one in the assortment. While the presence of such seemingly irrelevant inferior alternatives should ostensibly have no influence on consumers' decisions, an extensive literature using stylized lab experiments has found that, surprisingly, their presence matters. Specifically, the dominance effect suggests that the presence of an inferior alternative shifts consumer's preferences toward the alternative made to be superior. However, null results in some recent lab studies and a lack of real-world evidence call into question whether, when, and how the effect exists. In this work, we find clear evidence that dominance matters in the wild. We also identify an important moderator for the dominance effect—preference uncertainty—and test it in both a real-world marketplace for digital freelance services and a lab experiment. Further, we find evidence for additional moderators that help explain how the effect works, including the count of dominated alternatives and the magnitude of dominance, consistent with a perceptual mechanism. This work is the first to use consequential field data to shed light on when and why the dominance effects occur, with implications for marketers, choice architects, user interface designers, and policymakers.