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Replication Data for: Front-door Difference-in-Differences Estimators

By Adam Glynn and Konstantin Kashin · as credited by the source

Published 2017-04-01 · American Journal of Political Science (AJPS) Dataverse

Deposited on Harvard Dataverse doi:10.7910/DVN/XZFHCP licence: see repository 9 files
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About this dataset

We develop front-door difference-in-differences estimators as an extension of front-door estimators. Under one-sided noncompliance, an exclusion restriction, and assumptions analogous to parallel trends assumptions, this extension allows estimation when the front-door criterion does not hold. Even if the assumptions are relaxed, we show that the front-door and front-door difference-in-differences estimators may be combined to form bounds. Finally, we show that under one-sided noncompliance, these techniques do not require the use of control units. We illustrate these points with an application …

Collection: American Journal of Political Science (AJPS) Dataverse

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Cite

@dataset{glynn2017,
  title     = {Replication Data for: Front-door Difference-in-Differences Estimators},
  author    = {Glynn, Adam and Kashin, Konstantin},
  year      = {2017},
  publisher = {Harvard Dataverse},
  doi       = {10.7910/DVN/XZFHCP},
  url       = {https://doi.org/10.7910/DVN/XZFHCP}
}

Research that uses it

No published research on E2ER uses this dataset yet.

Description

Data model
Discipline
Social sciences
Method family
Empirical (quantitative)Replication
Design
Difference-in-differences
Research stage
Data acquisition
Contributors
Adam Glynn (Data curation); Konstantin Kashin (Data curation)
Usage
not used in published research yet
Source
Harvard Dataverse · American Journal of Political Science (AJPS) Dataverse
Record
dataset:dataverse/10-7910-dvn-xzfhcp · JSON

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