Shared-Donor Inference for Fixed-Set Heterogeneity in Synthetic Difference-in-Differences

Takahiro Hoshino and Makoto Nakakita

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Abstract: Empirical studies often estimate several synthetic-control or synthetic Difference-in-Differences effects from a common donor pool and then summarize their heterogeneity. Because the same donors enter several comparisons, the first-stage errors are jointly distributed and affect different targets differently. We characterize three regimes for linear fixed-set summaries: nonconcentration, root-rate donor exposure, and donornegligibility. We also show that removing shared-donor uncertainty from a quadratic summary requires the shared-donor covariance to vanish in every direction entering that summary. We take a valid joint first-order experiment for the effect vector as the input to the second stage. It yields simultaneous intervals for a prespecified linear family, a fixedgrid projection band, a centered effect-vector confidence region, image sets for total and explained heterogeneity and their ratio, and a sampling-center homogeneity test. For the hard-simplex SDID estimator, an operator-normalized numerical directional bootstrap re-estimates the donor and time weights in every draw. We use an exact critical-cone derivative to check the numerical approximation. In the Medicaid expansion application, omitting off-diagonal covariance understates uncertainty for the equal-jurisdiction mean but overstates it for a centered projection on the baseline uninsured rate. Persistent counterfactual mismatch is reported separately through deterministic sensitivity regions.

Date: 2026-07, Revised 2026-09
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