On the (Mis) Use of the Fixed Effects Estimator

Data that span multiple units and time periods allow controlling for time‐invariant heterogeneity correlated with the covariates. While researchers can do this in different ways, the fixed effects estimator—also known as the within estimator, and equivalent to the least squares dummy variable approach—has become the default choice. But when time‐invariant attributes are not invariant to time—that is, when they are not invariant to the length of the panel—the fixed effects estimator can be considerably biased as researchers incorporate additional time periods. We show that, in finite samples, first‐differencing and novel rolling estimators can offer researchers a practical alternative to the fixed effects estimator in this case. These estimators are simple to implement and can significantly reduce bias relative to the fixed effects estimator under certain data‐generating processes. Most importantly, researchers should always provide results from multiple estimators. We illustrate this with simulations and four replications.