How Different are Different Differences-in-Differences? A Large-Scale Meta-Analysis

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1. Close Review Not relevant

   When Can We Use Two-Way Fixed Effects (TWFE)? A Comparison of TWFE and Novel Dynamic Difference-in-Differences Estimators

   Tobias Rüttenauer, O. Aksoy · 2026 · Sociological methodology · Not summarized yet

   This comparison uses Monte Carlo simulations to explain when TWFE and five dynamic DiD estimators differ, whereas the paper compares estimates across replicated empirical specifications.

   Abstract

   The conventional two-way fixed effects (TWFE) estimator has come under increasing scrutiny in settings with staggered treatment adoption. Recent research shows that TWFE can be biased when treatment effects vary across time or groups, prompting the development of several alternative dynamic difference-in-differences (DiD) estimators. Yet for applied researchers, it often remains unclear when TWFE is biased, what the newer estimators solve, and what limitations they all have. The authors provide an accessible overview of this literature and compare conventional TWFE with five dynamic DiD estimators using Monte Carlo simulations. The simulations consider realistic scenarios with time- and grou…

2. Close Groundwork for this paper Cited by this paper Not relevant

   Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects

   C. de Chaisemartin, Xavier D'Haultfœuille · 2018 · The American Economic Review · cited 4,281 times · Not summarized yet

   This paper shows that heterogeneous treatment effects can give TWFE negative weights and proposes an alternative estimator, motivating the paper’s empirical assessment of negative weights.

   Abstract

   Linear regressions with period and group fixed effects are widely used to estimate treatment effects. We show that they estimate weighted sums of the average treatment effects (ATE ) in each group and period, with weights that may be negative. Due to the negative weights, the linear regression coefficient may for instance be negative while all the ATEs are positive. We propose another estimator that solves this issue. In the two applications we revisit, it is significantly different from the linear regression estimator. (JEL C21, C23, D72, J31, J51, L82)

3. Close Broader context Cited by this paper Not relevant

   Two-way fixed effects, the two-way mundlak regression, and difference-in-differences estimators

   Jeffrey M. Wooldridge · 2025 · Empirical Economics · cited 573 times · Not summarized yet

   This paper establishes equivalences between TWFE, Mundlak, and pooled-OLS formulations, while the paper measures agreement between TWFE and modern DiD estimates.

   Abstract

   I derive a result on the equivalence between the two-way fixed effects (TWFE) estimator and an estimator obtained from a pooled ordinary least squares regression that includes unit-specific time averages and time-period-specific cross-sectional averages—the two-way Mundlak (TWM) regression. The equivalence between TWFE and TWM implies that various estimators used for intervention analysis can be computed using pooled OLS that controls for time-constant treatment cohort indicators, time-period indicators, covariates, and interactions among them—allowing for considerable treatment effect heterogeneity. An extended version of TWFE (ETWFE) is equivalent to the POLS approach. I show that an imput…

4. Close Another approach to the problem Cited by this paper Not relevant

   Two-stage differences in differences

   J. Gardner · 2022 · cited 231 times · Not summarized yet

   This paper proposes a two-stage estimator for staggered adoption with heterogeneous effects, while the paper evaluates how TWFE compares with modern estimators in applied studies.

   Abstract

   A recent literature has shown that when adoption of a treatment is staggered and average treatment effects vary across groups and over time, difference-in-differences regression does not identify an easily interpretable measure of the typical effect of the treatment. In this paper, I extend this literature in two ways. First, I provide some simple underlying intuition for why difference-in-differences regression does not identify a group × period average treatment effect. Second, I propose an alternative two-stage estimation framework, motivated by this intuition. In this framework, group and period effects are identified in a first stage from the sample of untreated observations, and average treatmen…

5. Close Review Cited by this paper Not relevant

   What’s trending in difference-in-differences? A synthesis of the recent econometrics literature

   J. Roth, Pedro H. C. Sant'Anna, A. Bilinski et al. · 2022 · Journal of Econometrics · cited 1,782 times · Not summarized yet

   This synthesis organizes recent DiD advances around treatment timing, parallel trends, and inference, while the paper empirically assesses TWFE against modern estimators.

   Abstract

   This paper synthesizes recent advances in the econometrics of difference-in-differences (DiD) and provides concrete recommendations for practitioners. We begin by articulating a simple set of ``canonical'' assumptions under which the econometrics of DiD are well-understood. We then argue that recent advances in DiD methods can be broadly classified as relaxing some components of the canonical DiD setup, with a focus on $(i)$ multiple periods and variation in treatment timing, $(ii)$ potential violations of parallel trends, or $(iii)$ alternative frameworks for inference. Our discussion highlights the different ways that the DiD literature has advanced beyond the canonical model, and helps to…

6. Close Broader context Cited by this paper Not relevant

   Interpreting event-studies from recent difference-in-differences methods

   Jon Roth · 2024 · Japanese Economic Review · cited 33 times · Not summarized yet

   This note examines how recent DiD methods produce event-study plots that differ from TWFE plots, rather than comparing estimates across replicated applications.

   Abstract

   This note discusses the interpretation of event-study plots produced by recent difference-in-differences methods. I show that even when specialized to the case of non-staggered treatment timing, the default plots produced by software for several of the most popular recent methods do not match those of traditional two-way fixed effects (TWFE) event-studies. The plots produced by the new methods may show a kink or jump at the time of treatment even when the TWFE event-study shows a straight line. This difference stems from the fact that the new methods construct the pre-treatment coefficients asymmetrically from the post-treatment coefficients. As a result, visual heuristics for evaluating vio…

7. Close Same question Cited by this paper Not relevant

   Causal Panel Analysis under Parallel Trends: Lessons from a Large Reanalysis Study

   Albert Chiu, Xingchen Lan, Ziyi Liu et al. · 2023 · American Political Science Review · cited 48 times · Not summarized yet

   This reanalysis studies the reliability of TWFE results in applied panel studies using HTE-robust estimators, while the paper compares TWFE with modern DiD estimates across replicated specifications.

   Abstract

   Two-way fixed effects (TWFE) models are widely used in political science to establish causality, but recent methodological discussions highlight their limitations under heterogeneous treatment effects (HTE) and violations of the parallel trends (PT) assumption. This growing literature has introduced numerous new estimators and procedures, causing confusion among researchers about the reliability of existing results and best practices. To address these concerns, we replicated and reanalyzed 49 studies from leading journals that employ TWFE models for causal inference using observational panel data with binary treatments. Using six HTE-robust estimators, diagnostic tests, and sensitivity analy…

8. Close Broader context Cited by this paper Used in depth by this paper Not relevant

   Negative Weights are No Concern in Design-Based Specifications

   K. Borusyak, P. Hull · 2024 · Social Science Research Network · cited 11 times · Not summarized yet

   This paper argues negative weights are not problematic in design-based specifications, whereas the paper measures their empirical consequences in staggered-adoption TWFE designs.

   Abstract

   Recent work shows that popular partially-linear regression specifications can put negative weights on some treatment effects, potentially producing incorrectly-signed estimands. We show this is not an issue in design-based specifications, in which low-dimensional controls span the conditional expectation of the treatment. Specifically, the estimands of such specifications are convex averages of causal effects with ex-ante weights that average the potentially negative ex-post weights across possible treatment realizations. This result extends to design-based instrumental variable estimands under a first-stage monotonicity condition and applies to formula treatments and instruments such as shi…

Prior work it builds on

1. Related Groundwork for this paper Used in depth by this paper Not relevant

   Difference-in-Differences with multiple time periods

   Brantly Callaway, Pedro H. C. Sant'Anna · 2018 · Journal of Econometrics · cited 7,276 times · Not summarized yet

   This paper develops DiD identification and estimation for multiple periods and treatment timing variation, the setting in which the paper assesses TWFE performance.

   Abstract

   Difference-in-Differences (DID) is one of the most important and popular designs for evaluating causal effects of policy changes. In its standard format, there are two time periods and two groups: in the first period no one is treated, and in the second period a "treatment group" becomes treated, whereas a "control group" remains untreated. However, many em- pirical applications of the DID design have more than two periods and variation in treatment timing. In this article, we consider identification and estimation of treatment effect parameters using DID with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the "parallel trends assumption" holds potentially only…

2. Related Another approach to the problem Not relevant

   Finitely Heterogeneous Treatment Effect in Event-study

   Myung-Gil Shin · 2022 · Review of Economics and Statistics · cited 1 time · Not summarized yet

   This paper proposes type-specific DiD estimates under latent treatment-effect heterogeneity and relaxed parallel trends, rather than assessing TWFE’s empirical agreement with modern estimators.

   Abstract

   A key assumption of the differences-in-differences designs is that the average evolution of untreated potential outcomes is the same across different treatment cohorts: parallel trend assumption. In this paper, we relax the parallel trend assumption by assuming a latent type variable and developing a type-specific parallel trend. With a finite support assumption on the latent type and long pretreatment time periods, an extremum classifier consistently estimates the type assignment. Based on the classification, we propose a type-specific DiD estimator for type-specific ATT. By estimating the type-specific ATT, we study heterogeneity in treatment effect, in addition to heterogeneity in baselin…

3. Related Review Not relevant

   Difference-in-Differences Designs: A Practitioner's Guide

   A. Baker, Brantly Callaway, Scott Cunningham et al. · 2025 · cited 47 times · Not summarized yet

   This guide organizes DiD designs and estimators, including weights and staggered treatments, while the paper quantifies TWFE’s performance in applied staggered-adoption studies.

   Abstract

   Difference-in-differences (DiD) is arguably the most popular quasi-experimental research design. Its canonical form, with two groups and two periods, is well-understood. However, empirical practices can be ad hoc when researchers go beyond that simple case. This article provides an organizing framework for discussing different types of DiD designs and their associated DiD estimators. It discusses covariates, weights, handling multiple periods, and staggered treatments. The organizational framework, however, applies to other extensions of DiD methods as well.

4. Related Groundwork for this paper Not relevant

   Difference-in-differences with variation in treatment timing

   A. Goodman-Bacon · 2018 · Journal of Econometrics · cited 7,120 times · Not summarized yet

   This paper decomposes staggered-timing TWFE into weighted two-group, two-period DiD estimates and identifies conditions for causal interpretation, informing the paper’s negative-weight analysis.

   Abstract

   Abstract The canonical difference-in-differences (DD) estimator contains two time periods, ”pre” and ”post”, and two groups, ”treatment” and ”control”. Most DD applications, however, exploit variation across groups of units that receive treatment at different times. This paper shows that the two-way fixed effects estimator equals a weighted average of all possible two-group/two-period DD estimators in the data. A causal interpretation of two-way fixed effects DD estimates requires both a parallel trends assumption and treatment effects that are constant over time. I show how to decompose the difference between two specifications, and provide a new analysis of models that include time-varying…

5. Related Broader context Not relevant

   Identification of and Correction for Publication Bias

   Maximilian Kasy, Isaiah Andrews · 2017 · The American Economic Review · cited 373 times · Not summarized yet

   This paper develops corrections for selective publication using replication and meta-study data, while the paper uses large-scale replication to evaluate differences among DiD estimators.

   Abstract

   Some empirical results are more likely to be published than others. Selective publication leads to biased estimates and distorted inference. We propose two approaches for identifying the conditional probability of publication as a function of a study’s results, the first based on systematic replication studies and the second on meta-studies. For known conditional publication probabilities, we propose bias-corrected estimators and confidence sets. We apply our methods to recent replication studies in experimental economics and psychology, and to a meta-study on the effect of the minimum wage. When replication and meta-study data are available, we find similar results from both.(JEL C13, C90, …