I use AI-assisted replication to assess the empirical importance of negative weights in two-way fixed effects (TWFE) estimation, drawing on 1,346 staggered-adoption specifications from 108 economics papers. TWFE estimates are highly correlated with estimates from modern difference-in-differences methods, with observed sign reversals in 10.8-14.5% of comparisons and greater precision for TWFE. Hierarchical models that account for estimation noise imply a 91-98% correlation between estimands and a 0.6-2.1% probability of obtaining a statistically significant TWFE estimate with the opposite sign from the modern estimand. These findings suggest negative weights may be a less serious problem in practice, and illustrate how AI can support large-scale empirical assessments of new econometric methods.
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