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Misclassification in Difference-in-differences Models

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2
Author
Augustine Denteh Désiré Kédagni
Category
Quantitative
Date Posted
2022/08/04
Date Retrieved
2022/08/04
Date Revised
Empty
Date Written
2022/08/04
Description
The difference-in-differences (DID) design is one of the most popular methods used in empirical economics research. However there is almost no work examining what the DID method identifies in the presence of a misclassified treatment variable. This paper studies the identification of treatment effects in DID designs when the treatment is misclassified. Misclassification arises in various ways including when the timing of a policy intervention is ambiguous or when researchers need to infer treatment from auxiliary data. We show that the DID estimand is biased and recovers a weighted average of the average treatment effects on the treated (ATT) in two subpopulations - the correctly classified and misclassified groups. In some cases the DID estimand may yield the wrong sign and is otherwise attenuated. We provide bounds on the ATT when the researcher has access to information on the extent of misclassification in the data. We demonstrate our theoretical results using simulations and provi
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JEL Classifications
C14 C31 C35 C36
Keywords
Difference-in-differences average treatment effect on the treated misclassification
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33
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URL
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4181736
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