  In this study, we identify and relax the assumption of data "poolability" in
difference-in-differences (DID) estimation. Poolability, or the combination of
observations from treated and control units into one dataset, is often not
possible due to data privacy concerns. For instance, administrative health data
stored in secure facilities is often not combinable across jurisdictions. We
propose an innovative approach to estimate DID with unpoolable data: UN--DID.
Our method incorporates adjustments for additional covariates, multiple groups,
and staggered adoption. Without covariates, UN--DID and conventional DID give
identical estimates of the average treatment effect on the treated (ATT). With
covariates, we show mathematically and through simulations that UN--DID and
conventional DID provide different, but equally informative, estimates of the
ATT. An empirical example further underscores the utility of our methodology.
The UN--DID method paves the way for more comprehensive analyses of policy
impacts, even under data poolability constraints.
