Staggered Adoption and Two-Way Fixed Effects Bias

Two-way fixed effects (TWFE) regression is the workhorse tool for difference-in-differences: it controls for unit fixed effects and time fixed effects, then reads the treatment effect off a single coefficient. That works cleanly when every treated unit switches on at the same date. It breaks down when adoption is staggered, some states pass a law in 2015, others in 2018, others never, because the regression starts using early adopters as an implicit control group for late adopters, and vice versa.

The problem is that "already-treated" units are not a valid comparison group if the treatment effect changes over time (grows, fades, or varies by cohort). The regression's single coefficient turns out to be a weighted average of many two-group, two-period comparisons, and some of those comparisons carry negative weights, meaning a genuinely positive effect in the data can flip the sign of the reported estimate.

Where it shows up

A researcher studying a policy that rolled out state by state over a decade runs one TWFE regression expecting a clean "average treatment effect." The Goodman-Bacon decomposition of that same coefficient reveals it is actually built from comparisons between early- and late-adopting states, using each as the other's control at different points, with weights driven by group size and treatment timing rather than by anything a researcher would choose on purpose.

What to do instead

Modern estimators, Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille, explicitly build the comparison group only from not-yet-treated or never-treated units, and let the treatment effect vary by adoption cohort and time since treatment. They report a cleaner, interpretable average effect at the cost of needing more data structure (cohort indicators, staggered event-time dummies) than a single TWFE regression. The practical fix is not a regression tweak but a different estimator family, one that never lets an already-treated unit serve as another unit's control.