Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach |
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Authors: | Sung Jae Jun Yoonseok Lee Youngki Shin |
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Institution: | 1. Department of Economics and Center for the Study of Auctions, Procurements, and Competition Policy Pennsylvania State University, 619 Kern Bldg., University Park, 16802, PA sjun@psu.edu;2. Department of Economics and Center for Policy Research Syracuse University, 426 Eggers Hall, Syracuse, 13244, NY ylee41@maxwell.syr.edu;3. Economics Discipline Group, University of Technology Sydney, Dr. Chau Chak Wing Building #08.09.092, Ultimo, 2007, NSW, Australia youngki.shin@uts.edu.au |
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Abstract: | We propose the sharp identifiable bounds of the potential outcome distributions using panel data. We allow for the possibility that statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the sharp bounds. Our approach allows for dynamic treatment decisions, where the current treatment decisions may depend on the past treatments or the past observed outcomes. As an empirical illustration, we study the effect of smoking during pregnancy on infant birthweight. We find that for the group of switchers the infant birthweight of a smoking mother is first-order stochastically dominated by that of a nonsmoking mother. |
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Keywords: | Dynamic treatment decisions Panel data Partial identification Stochastic dominance |
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