Taxation and International Migration of Superstars: Evidence from the European Football Market

Henrik Jacobsen Kleven; Camille Landais; Emmanuel Saez (2013) · 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-14", tranche="tranche100")

Henrik Jacobsen Kleven; Camille Landais; Emmanuel Saez (2013). American Economic Review. Appendix Figure A7: Greece two-group pre/post DiD.

Source specification

The source pools birth-cohort observations into treatment and pre/post groups. The point specification is unchanged; comparison intervals are heteroskedasticity-robust (observation clusters), whereas the source reports IID errors.

Source coefficient Template on source sample Source N Template source-sample N
0.10501 0.10501 20 20

Source-to-template coefficient difference: 2.36e-16, in original outcome units.

Role Column
Outcome playabr
Treatment DD
Unit treatment
Time period
Cluster observation_id

No additional covariates beyond fixed effects.

Estimates below divide the outcome by its source-sample standard deviation, 0.0669387. 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
20 2 1 1 1 20

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: 2 of 2 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 1.5687 0.55797 0.40089 2.7366 Coefficient estimated
Dynamic TWFE 1.5687 0.55797 0.40089 2.7366 Treated cells estimated
BJS / did2s 1.5687 0.49907 0.52418 2.6133 Treated cells estimated
Sun–Abraham 1.5687 0.55797 0.40089 2.7366 Treated cells estimated
CS-DID (equal event-time) 1.5687 0.49907 0.59059 2.5469 Equal post-event times estimated
CS-DID (treated cells) 1.5687 0.49907 0.59059 2.5469 Treated cells estimated
SDID 1.5687 nan nan nan Treated cells estimated
Matrix completion 1.643 nan nan nan Treated cells estimated
Horizontal ridge 2.1631 nan nan nan Treated cells estimated
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
Multisynth (cohort-averaged) multisynth-cohort: requires one observation per unit-period; no observations collapsed.
Multisynth (individual) multisynth-individual: requires one observation per unit-period; no observations collapsed.

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”: false, “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} {“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 Multisynth (cohort-averaged) {} {}
y__d Multisynth (individual) {} {}
{
  "did2s_vs_direct_imputation_max_post_difference": 0.0,
  "package_aggregate_vs_estimable_cell_weights": 0.0
}