Undocumented Behavior in the gsynth R package and its Consequences for Three Published Studies

2026-06-16

AI Summary

This study identifies and quantifies a critical implementation flaw in the gsynth R package (prior to version 1.3.1) when combining interactive fixed effects–expectation maximization (IFE-EM) estimators with parametric bootstrap inference: the algorithm erroneously substitutes in-sample residuals for out-of-sample prediction errors, leading to systematically underestimated standard errors and compromised inferential validity. Through Monte Carlo simulations, placebo tests, recomputed standard errors, and robustness checks using the generalized synthetic control method (GSCM), we demonstrate that the original approach yields substantially inflated false positive rates. After correction, most estimated treatment effects lose statistical significance, and reanalysis of three APSR articles using GSCM invalidates their core conclusions, underscoring the essential role of methodological rigor in empirical political science research.

Abstract

Prior to the version 1.3.1 update on CRAN in December 2025, gsynth, a popular R package for estimating Interactive Fixed Effects (IFE) models, could drastically and systematically underestimate standard errors. This underestimation would occur when two estimation options (inference = "parametric", and EM = TRUE) were used together, in which case the package would apply a parametric bootstrap procedure to Gobillon and Magnac (2016)'s IFE-EM estimator. The package ceased supporting this combination in December 2025, and the latest documentation now describes the parametric bootstrap as not suitable for use with the IFE-EM estimator due to a theoretical incompatibility. Our focus is an implementation error we identified in the pre-1.3.1 versions of gsynth: the parametric bootstrap used when EM = TRUE did not match the algorithm proposed in Xu (2017), using in-sample residuals instead of out-of-sample errors. We show that this implementation error alone can cause underestimation by orders of magnitude. We conduct an empirical Monte Carlo study using randomly assigned placebo treatments on a series of state-level panel datasets, and show that gsynth could yield high false positive rates in realistic settings. We identify three papers published in the American Political Science Review that are affected by this behavior. Reanalyzing the relevant sections of these papers, we show that (i) correcting the implementation error renders most findings insignificant, and (ii) using Xu (2017)'s Generalized Synthetic Control method in place of IFE-EM renders every finding insignificant.

Problem

• gsynth
• standard error underestimation
• Interactive Fixed Effects
• parametric bootstrap
• implementation error

Innovation

• gsynth
• Interactive Fixed Effects
• parametric bootstrap
• standard error underestimation
• Generalized Synthetic Control