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On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data
Name
FEmatch-twoway.pdf
Description
Accepted version
Size
355.73 KB
Format
Adobe PDF
Checksum (MD5)
e1efacc9bfd5bde4ff6d5fbcd038baf1
Author(s)
Imai, Kosuke
Kim, In Song
Date Issued
2021
Journal
Political Analysis
Publisher
Cambridge University Press (CUP)
Citation
Imai, K., & Kim, I. (2021). On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data. Political Analysis, 29(3), 405-415.
Version
Author's final manuscript
Abstract
The two-way linear fixed effects regression (2FE) has become a default method for estimating causal effects from panel data. Many applied researchers use the 2FE estimator to adjust for unobserved unit-specific and time-specific confounders at the same time. Unfortunately, we demonstrate that the ability of the 2FE model to simultaneously adjust for these two types of unobserved confounders critically relies upon the assumption of linear additive effects. Another common justification for the use of the 2FE estimator is based on its equivalence to the difference-in-differences estimator under the simplest setting with two groups and two time periods. We show that this equivalence does not hold under more general settings commonly encountered in applied research. Instead, we prove that the multi-period difference-in-differences estimator is equivalent to the weighted 2FE estimator with some observations having negative weights. These analytical results imply that in contrast to the popular belief, the 2FE estimator does not represent a design-based, nonparametric estimation strategy for causal inference. Instead, its validity fundamentally rests on the modeling assumptions.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
10.1017/PAN.2020.33
