Giving Mom a Break: The Impact of Higher EITC Payments on Maternal Health

William N. Evans; Craig L. Garthwaite (2014) · American Economic Journal: Economic Policy

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-57")

William N. Evans; Craig L. Garthwaite (2014). American Economic Journal: Economic Policy. Table 3: simple DiD, labor-force participation.

Source specification

The selected source outcome is labor-force participation from Table 3’s linear specification, not the nonlinear mental-health headline. The comparison retains state clustering and adds calendar-year effects.

Source coefficient Template on source sample Source N Template source-sample N
0.023168 0.023183 82907 82907

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

Role Column
Outcome inlf
Treatment dd_treatment
Unit twoplus_kids
Time year
Cluster fips

No additional covariates beyond fixed effects.

Estimates below divide the outcome by its source-sample standard deviation, 0.451005. 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
82907 2 1 1 1 51

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 0.051402 0.017611 0.01603 0.086775 Coefficient estimated
Dynamic TWFE 0.047925 0.025346 -0.0029839 0.098833 Treated cells estimated
BJS / did2s 0.051517 0.017418 0.016531 0.086502 Treated cells estimated
Sun–Abraham 0.047925 0.025346 -0.0029839 0.098833 Treated cells estimated
CS-DID (equal event-time) 0.045985 0.025064 -0.0031398 0.095111 Equal post-event times estimated
CS-DID (treated cells) 0.047925 0.025104 -0.0012779 0.097127 Treated cells estimated
SDID 0.05154 nan nan nan Treated cells estimated
Matrix completion 0.05154 nan nan nan Treated cells estimated
Horizontal ridge 0.078419 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": 2.2898349882893854e-16,
  "package_aggregate_vs_estimable_cell_weights": 0.0
}