Increasing Organ Donor Registration as a Means to Increase Transplantation: An Experiment with Actual Organ Donor Registrations

Judd B. Kessler; Alvin E. Roth (2025) · American Economic Journal: Economic Policy

Published

October 10, 2026

import sys
from pathlib import Path

ROOT = next(
    p for p in [Path.cwd(), *Path.cwd().parents]
    if (p / "scripts/tranche50_report.py").exists()
)
sys.path.insert(0, str(ROOT / "scripts"))
from tranche50_report import report
report("aeraej-87")

Judd B. Kessler; Alvin E. Roth (2025). American Economic Journal: Economic Policy. Table 3.

Source specification

Source coefficient Template on source sample Source N Template source-sample N
-0.019438 -0.019438 820 820

Source-to-template coefficient difference: 8.33e-14, in original outcome units.

Role Column
Outcome PercentDonor
Treatment posttreat
Unit state_num
Time yq
Cluster state_num

No additional covariates beyond fixed effects.

Estimates below divide the outcome by its source-sample standard deviation, 0.131225. Static TWFE is the treatment coefficient. Dynamic TWFE, BJS and Sun–Abraham average supported post-treatment cells by observation count. CS-DID additionally reports an equal-event-time average.

Analysis sample and treatment timing

Observations Units Treated Control units Cohorts Clusters
818 38 1 37 1 38
Sample restriction Count
additional rows removed 516

Adoption is the first observed treated period. Initially treated units and units without an observed minus-one baseline are excluded. Post-adoption untreated rows are excluded for switching policies. The plot shows at most 100 seeded-sample units; estimation uses the full eligible sample. Empty cells remain missing.

y__d: 38 of 38 units shown (seed 20261008). All eligible units enter estimation. Purple: untreated; yellow: treated; white: unobserved.

Estimates

Estimator Estimate (SD) SE 95% lower 95% upper Target Status
Static TWFE -0.14813 0.037257 -0.22362 -0.072636 Coefficient estimated
Dynamic TWFE -0.12259 0.037546 -0.19867 -0.046516 Treated cells estimated
BJS / did2s -0.14813 0.036119 -0.22131 -0.074947 Treated cells estimated
Sun–Abraham -0.12259 0.037546 -0.19867 -0.046516 Treated cells estimated
CS-DID (equal event-time) -0.17714 0.067443 -0.30933 -0.044958 Equal post-event times estimated
CS-DID (treated cells) -0.17714 0.067443 -0.30933 -0.044958 Treated cells estimated
SDID -0.17386 nan nan nan Treated cells estimated
Matrix completion -0.15111 nan nan nan Treated cells estimated
Horizontal ridge nan nan nan nan Treated cells unidentified
Multisynth (cohort-averaged) nan nan nan nan Treated cells unsupported
Multisynth (individual) nan nan nan nan Treated cells unsupported

Intervals use the configured clusters and the estimator’s available inference. Hollow points have no computed interval; no new refit-bootstrap batch is run.

Estimator Reason
Horizontal ridge ValueError: ridge cohort 6, period 9: no donor/pre-period overlap for observed treated outcomes
Multisynth (cohort-averaged) multisynth-cohort: requires a complete finite outcome matrix after unit-period aggregation; 38 x 28 grid has 246 missing cells, which are not zero-filled.
Multisynth (individual) multisynth-individual: requires a complete finite outcome matrix after unit-period aggregation; 38 x 28 grid has 246 missing cells, which are not zero-filled.

Event studies

Cohort homogeneity

The imported TestingInEventStudies deviations design tests positive-event-time cohort deviations jointly. This is the homoskedastic nested-model F-test; the estimator confidence intervals above use clustered inference.

**y__d: homogeneity test not identified.** Only one treated cohort remains on the common supported sample.

Fitting settings and checks

Specification Estimator Settings Pooling
y__d CS-DID {“est_method”: “dr”, “covariate_basis”: “intercept”, “control_group”: “notyettreated”, “xformla”: “y ~ 1”, “panel”: true, “effective_panel”: false, “allow_unbalanced_panel”: true, “weights_name”: “weight”, “fix_weights”: null, “base_period”: “universal”, “clustervar”: “cluster”, “aggregate”: “dynamic”, “faster_mode”: true} {}
y__d SDID {} {“parameter”: null, “value”: null, “status”: “not_applicable”, “fit_scope”: “Separate adoption-cohort fits”, “within_cohort_target”: “Mean treated outcome history”, “cross_cohort_partial_pooling”: false, “shared_structure”: null, “note”: “Regularization penalties are not partial-pooling parameters.”}
y__d Matrix completion {“lambda_fraction”: 0.25, “max_iterations”: 1000, “tolerance”: 1e-12} {“parameter”: null, “value”: null, “status”: “not_applicable”, “fit_scope”: “Joint untreated-cell fit”, “within_cohort_target”: null, “cross_cohort_partial_pooling”: false, “shared_structure”: “Low-rank matrix and unit/time effects”, “note”: “Regularization penalties are not partial-pooling parameters.”}
y__d Horizontal ridge {“penalty”: 1.0} {}
y__d Multisynth (cohort-averaged) {} {}
y__d Multisynth (individual) {} {}
{
  "did2s_vs_direct_imputation_max_post_difference": 0.0,
  "package_aggregate_vs_estimable_cell_weights": 0.0
}