Buffalo Hunt: International Trade and the Virtual Extinction of the North American Bison

M. Scott Taylor (2011) · 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-199")

M. Scott Taylor (2011). American Economic Review. Table 2.

Source specification

Source uses market indicators and a common linear trend; the comparison replaces the trend with unrestricted year effects and clusters by market (three clusters).

Source coefficient Template on source sample Source N Template source-sample N
4.3781 10.873 66 66

The source and comparison coefficients use different specifications or aggregation targets, as stated above; they are not an exact-replication comparison.

Role Column
Outcome share
Treatment streat
Unit country
Time year
Cluster country

No additional covariates beyond fixed effects.

Estimates below divide the outcome by its source-sample standard deviation, 42.2346. 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
42 3 2 1 1 3
Sample restriction Count
post adoption untreated rows removed 16
additional rows removed 8

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: 3 of 3 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 0.30025 0.014543 0.23768 0.36282 Coefficient estimated
Dynamic TWFE 0.26613 0.0097778 0.22406 0.3082 Treated cells estimated
BJS / did2s 0.30025 0.009636 0.25879 0.34171 Treated cells estimated
Sun–Abraham 0.26613 0.0097778 0.22406 0.3082 Treated cells estimated
CS-DID (equal event-time) 0.26613 0.0048289 0.25667 0.2756 Equal post-event times estimated
CS-DID (treated cells) 0.26613 0.0048289 0.25667 0.2756 Treated cells estimated
SDID 0.30025 nan nan nan Treated cells estimated
Matrix completion 0.30025 nan nan nan Treated cells estimated
Horizontal ridge 0.11982 nan nan nan Treated cells estimated
Multisynth (cohort-averaged) nan nan nan nan Treated cells failed
Multisynth (individual) nan nan nan nan Treated cells failed

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) RuntimeError: multisynth-cohort failed; see multisynth-cohort.log
Multisynth (individual) RuntimeError: multisynth-individual failed; see multisynth-individual.log

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”, “control_group”: “notyettreated”, “xformla”: “y ~ 1”, “base_period”: “universal”, “anticipation”: 0, “panel”: true, “allow_unbalanced_panel”: false, “clustervar”: “cluster”, “clusters”: 3, “bstrap”: false, “cband”: false, “aggregate”: “dynamic”, “event_aggregate”: “dynamic”, “balance_e”: null, “post_event_min”: 0, “post_event_max”: 8} {“parameter”: null, “value”: null, “status”: “not_applicable”, “fit_scope”: “Group-time DIDs; no synthetic-control pooling parameter”}
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": 1.1102230246251565e-16,
  "package_aggregate_vs_estimable_cell_weights": 0.0
}