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 reportThe Power of Political Voice: Women’s Political Representation and Crime in India
Lakshmi Iyer; Anandi Mani; Prachi Mishra; Petia Topalova (2012) · American Economic Journal: Applied Economics
report("aeraej-120")Lakshmi Iyer; Anandi Mani; Prachi Mishra; Petia Topalova (2012). American Economic Journal: Applied Economics. Table 3.
Source specification
| Source coefficient | Template on source sample | Source N | Template source-sample N |
|---|---|---|---|
| 0.36511 | 0.36511 | 391 | 391 |
Source-to-template coefficient difference: 1.22e-15, in original outcome units.
| Role | Column |
|---|---|
| Outcome | lpcr_womtot |
| Treatment | postwres |
| Unit | stateid |
| Time | year |
| Cluster | stateid |
No additional covariates beyond fixed effects.
Estimates below divide the outcome by its source-sample standard deviation, 1.1736. 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 |
|---|---|---|---|---|---|
| 357 | 17 | 16 | 1 | 9 | 17 |
No never-treated group is available. The window ends before the latest cohort adopts; that cohort is the untreated comparison group within this window.
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: 17 of 17 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 | 0.33587 | 0.15073 | 0.01634 | 0.6554 | Coefficient | estimated |
| Dynamic TWFE | 0.37285 | 0.11568 | 0.12762 | 0.61808 | Treated cells | estimated |
| BJS / did2s | 0.43355 | 0.1351 | 0.14716 | 0.71994 | Treated cells | estimated |
| Sun–Abraham | 0.34951 | 0.067441 | 0.20654 | 0.49248 | Treated cells | estimated |
| CS-DID (equal event-time) | 0.2052 | 0.1239 | -0.037638 | 0.44804 | Equal post-event times | estimated |
| CS-DID (treated cells) | 0.42864 | 0.12309 | 0.18738 | 0.66989 | Treated cells | estimated |
| SDID | 0.33159 | nan | nan | nan | Treated cells | estimated |
| Matrix completion | 0.45382 | nan | nan | nan | Treated cells | estimated |
| Horizontal ridge | 1.3848 | 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: F(76, 140) = 0.7422; p = 0.9239.** Homoskedastic nested-model F. 180 design columns.
Each line is a cohort deviation. The joint test restricts positive-event-time deviations to zero; event time 0 remains unrestricted.
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”: 17, “bstrap”: false, “cband”: false, “aggregate”: “dynamic”, “event_aggregate”: “dynamic”, “balance_e”: null, “post_event_min”: 0, “post_event_max”: 18} | {“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": 2.220446049250313e-16,
"package_aggregate_vs_estimable_cell_weights": 5.551115123125783e-17,
"saturated_vs_deviations_relative_rss_difference": 2.1331868366544634e-16
}