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We consider a linear panel event-study design in which unobserved confounds may be related both to the outcome and to the policy variable of interest. We provide sufficient conditions to identify the causal effect of the policy by exploiting covariates related to the policy only through the confounds. Our model implies a set of moment equations that are linear in parameters. The effect of the policy can be estimated by 2SLS, and causal inference is valid even when endogeneity leads to pre-event trends (“pre-trends”) in the outcome. Alternative approaches perform poorly in our simulations. (JEL C23, C26)
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Simon Freyaldenhoven
Federal Reserve Bank of Philadelphia
Christian Hansen
Yale University
Jesse M. Shapiro
Harvard University Press
American Economic Review
University of Chicago
Brown University
Federal Reserve Bank of Philadelphia
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Freyaldenhoven et al. (Thu,) studied this question.
synapsesocial.com/papers/69d83e7ba6384138a5d17c8b — DOI: https://doi.org/10.1257/aer.20180609