Compulsory Licensing: Evidence from the Trading with the Enemy Act

Petra Moser; Alessandra Voena (2012) · American Economic Review

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-56")

Petra Moser; Alessandra Voena (2012). American Economic Review. Table 2.

Collection index · Results JSON · QMD · Reproduction code

Source specification

Source coefficient Template on source sample Source N Template source-sample N
0.17316 0.17316 4.5662e+05 4.5662e+05

Source-to-template coefficient difference: 3.43e-12, in original outcome units.

Role Column
Outcome count_usa
Treatment treat
Unit class_id
Time grntyr
Cluster class_id

1 retained control columns. count_for_2

Estimates below divide the outcome by its source-sample standard deviation, 1.14812. 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
456624 7248 336 6912 1 7248
Sample restriction Count
additional rows removed 14496

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: 100 of 7248 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 0.15082 0.031157 0.089747 0.2119 Coefficient estimated
Dynamic TWFE 0.23527 0.046261 0.14458 0.32595 Treated cells estimated
BJS / did2s 0.14823 0.031669 0.086153 0.21031 Treated cells estimated
Sun–Abraham 0.23527 0.046261 0.14458 0.32595 Treated cells estimated
CS-DID (equal event-time) 1.3102 0.94946 -0.55071 3.1711 Equal post-event times estimated
CS-DID (treated cells) 1.3102 0.94946 -0.55071 3.1711 Treated cells estimated
SDID nan nan nan nan Treated cells unsupported
Matrix completion nan nan nan nan Treated cells unsupported
Horizontal ridge nan nan nan nan Treated cells unsupported
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
SDID sdid: this adapter does not implement covariate adjustment; controls retained.
Matrix completion matrix-completion: this adapter does not implement covariate adjustment; controls retained.
Horizontal ridge horizontal-ridge: this adapter does not implement covariate adjustment; controls retained.
Multisynth (cohort-averaged) multisynth-cohort: this adapter does not implement covariate adjustment; controls retained.
Multisynth (individual) multisynth-individual: this adapter does not implement covariate adjustment; controls retained.

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”: “cell-specific rank-reduced column space”, “control_group”: “notyettreated”, “xformla”: “y ~ 1 + x0”, “panel”: true, “effective_panel”: true, “allow_unbalanced_panel”: true, “weights_name”: “weight”, “fix_weights”: null, “base_period”: “universal”, “clustervar”: “cluster”, “aggregate”: “dynamic”, “faster_mode”: true} {}
y__d SDID {} {}
y__d Matrix completion {} {}
y__d Horizontal ridge {} {}
y__d Multisynth (cohort-averaged) {} {}
y__d Multisynth (individual) {} {} 
{
  "did2s_vs_direct_imputation_max_post_difference": 1.6653345369377348e-16,
  "package_aggregate_vs_estimable_cell_weights": null
}