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

Peter Hull

No 35974, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: I use AI-assisted replication to assess the empirical importance of negative weights in two-way fixed effects (TWFE) estimation, analyzing 1,430 staggered-adoption specifications from 116 economics papers. TWFE estimates are highly correlated with modern difference-in-differences estimates, with sign reversals in 12.0–14.9% of comparisons and greater precision for TWFE. Hierarchical models accounting for estimation noise imply estimand correlations of 91–97% and a 0.7–2.4% probability of a statistically significant TWFE estimate opposing the modern estimand’s sign. These findings suggest negative weights may be a less serious problem in practice and illustrate how AI can support systematic, large-scale empirical assessments of new econometric methods.

JEL-codes: C21 C23 C83 (search for similar items in EconPapers)
Date: 2026-10
Note: LS PE TWP
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