Carolina Caetano, Brantly Callaway
arXiv 21 Jun 2024 · Econometrics · 11 citations (OpenAlex)
arXiv:2406.15288 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we study difference-in-differences identification and estimation strategies when the parallel trends assumption holds after conditioning on covariates. We consider empirically relevant settings where the covariates can be time-varying, time-invariant, or both. We uncover a number of weaknesses of commonly used two-way fixed effects (TWFE) regressions in this context, even in applications with only two time periods. In addition to some weaknesses due to estimating linear regression models that are similar to cases with cross-sectional data, we also point out a collection of additional issues that we refer to as hidden linearity bias that arise because the transformations used to eliminate the unit fixed effect also transform the covariates (e.g., taking first differences can result in the estimating equation only including the change in covariates over time, not their level, and also drop time-invariant covariates altogether). We provide simple diagnostics for assessing how susceptible a TWFE regression is to hidden linearity bias based on reformulating the TWFE regression as a weighting estimator. Finally, we propose simple alternative estimation strategies that can circumvent these issues.
appendix boundary found by appendix_command · 89% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cheng, Cheng, Hoekstra, Mark (2013) Does strengthening self-defense law deter crime or escalate violence? Evidence from expansions to Castle Doctrine | 1.000 | 15 | 3 | 100% |
| 2 | Callaway, Brantly, Sant'Anna, Pedro HC (2021) Difference-in-differences with multiple time periods self | 1.000 | 14 | 5 | 100% |
| 3 | (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 1.000 | 10 | 3 | 100% |
| 4 | Goodman-Bacon, Andrew (2021) Difference-in-differences with variation in treatment timing | 1.000 | 8 | 4 | 100% |
| 5 | Sant’Anna, Pedro H. C., Zhao, Jun (2020) Doubly robust difference-in-differences estimators | 1.000 | 6 | 4 | 100% |
| 6 | Aronow, Peter M, Samii, Cyrus (2016) Does regression produce representative estimates of causal effects? | 0.928 | 4 | 3 | 100% |
| 7 | Borusyak, Kirill, Jaravel, Xavier, Spiess, Jann (2024) Revisiting event-study designs: Robust and efficient estimation | 0.928 | 4 | 3 | 100% |
| 8 | Gardner, John, Thakral, Neil, Tô, Linh T, Yap, Luther (2023) Two-stage differences in differences | 0.928 | 4 | 3 | 100% |
| 9 | Blandhol, Christine, Bonney, John, Mogstad, Magne, Torgovitsky, Alex… (2022) When is TSLS actually late? | 0.874 | 6 | 2 | 100% |
| 10 | Chattopadhyay, Ambarish, Zubizarreta, José R (2023) On the implied weights of linear regression for causal inference | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 50 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.