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 reportAwarding Price, Contract Performance, and Bids Screening: Evidence from Procurement Auctions
Francesco Decarolis (2014) · American Economic Journal: Applied Economics
report("aeraej-59")Francesco Decarolis (2014). American Economic Journal: Applied Economics. Table 2.
Source specification
| Source coefficient | Template on source sample | Source N | Template source-sample N |
|---|---|---|---|
| 13.095 | 13.095 | 1262 | 1262 |
Source-to-template coefficient difference: 1.6e-14, in original outcome units.
| Role | Column |
|---|---|
| Outcome | discount |
| Treatment | fpsb_auction |
| Unit | authority_code |
| Time | year |
| Cluster | auth_anno |
No additional covariates beyond fixed effects.
Estimates below divide the outcome by its source-sample standard deviation, 7.89132. 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 |
|---|---|---|---|---|---|
| 1262 | 15 | 1 | 14 | 1 | 101 |
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: 15 of 15 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 | 1.6594 | 0.20394 | 1.2548 | 2.064 | Coefficient | estimated |
| Dynamic TWFE | 1.2787 | 0.061689 | 1.1563 | 1.4011 | Treated cells | estimated |
| BJS / did2s | 1.6481 | 0.16754 | 1.3157 | 1.9805 | Treated cells | estimated |
| Sun–Abraham | 1.2787 | 0.061689 | 1.1563 | 1.4011 | Treated cells | estimated |
| CS-DID (equal event-time) | 1.4026 | 0.090345 | 1.2256 | 1.5797 | Equal post-event times | estimated |
| CS-DID (treated cells) | 1.2792 | 0.14857 | 0.98798 | 1.5704 | Treated cells | estimated |
| SDID | 1.4652 | nan | nan | nan | Treated cells | estimated |
| Matrix completion | 1.493 | nan | nan | nan | Treated cells | estimated |
| Horizontal ridge | 1.5559 | 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": 4.440892098500626e-16,
"package_aggregate_vs_estimable_cell_weights": 0.0
}