datamirror: coefficient-preserving synthetic data for restricted microdata

Reproducibility has become a condition of publication, but studies on restricted microdata remain its standing exception. The code can leave the secure environment; the data cannot. Synthetic data is the natural substitute, yet existing generators preserve distributions, not the published estimates. Re-run a published regression on their output and a different number comes back. This talk introduces datamirror, a Stata command that adds a coefficient-preserving layer to distributional synthesis. Inside the enclave the researcher checkpoints a chosen set of regressions, and the extract step writes a small bundle of aggregates with no individual records. From that bundle alone, on any machine, anyone can rebuild the synthetic data, and the checkpointed regressions return their published coefficients, exactly for linear estimators and within sampling noise for the rest. No synthetic-data tool does this. The same bundle is both a replication package and a synthetic copy to work on outside the enclave. Across four American Economic Association replication packages, datamirror reproduces 377 of the 378 checkpointed regressions that yield an evaluable coefficient. The talk includes a live demonstration of the checkpoint, extract, and rebuild workflow.