Original collectionUnder development

Under development — library extension. This report is part of the expanded-library work, not the original collection. Source preparation, methods and results are subject to revision.

Provisional eligibility assessment — under development. Deep DiD follow-up: D and T are explicitly defined and used to form treated/control before/after bunching cells (lines 228 onward). This is genuine DiD structure for distributional statistics; raw earnings are not an interchangeable outcome. Preserve first-stage bunching construction and uncertainty. This working assessment is not fully vetted and may change. See the report below for its recorded replication or assessment status. Provisional eligibility log

Taxes Today, Benefits Tomorrow

Thomas Le Barbanchon (2025) · American Economic Journal: Applied Economics

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-127", tranche="tranche150")

Thomas Le Barbanchon (2025). American Economic Journal: Applied Economics. Figure 3 / Section II.

Collection index · Results JSON · QMD · Reproduction code

Template assessment

The headline estimates tax-benefit responses using bunching at unemployment-insurance earnings thresholds. Appendix E splits claimants by eligibility and policy period, then estimates changes in density bunching and elasticities with bunch_count; it does not fit an outcome-on-binary-adoption panel regression. Mapping those density and elasticity targets to the current ATT template requires a different estimator, not an invented individual-outcome DiD.

No comparison point or homogeneity-test decision is assigned to this paper.