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

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  • Peter Hull

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.

Suggested Citation

  • Peter Hull, 2026. "How Different are Different Differences-in-Differences? A Large-Scale Meta-Analysis," NBER Working Papers 35974, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:35974
    Note: LS PE TWP
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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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