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. All numerical source targets condition on narrow1_prediction_NS, an XGBoost prediction absent from the deposit. The supplied R script reconstructs audit timing only, not outcomes or the final dataset. Rebuilding the documented week-long ML preparation is outside this template run; no timing-only object is passed off as an estimated effect. This working assessment is not fully vetted and may change. See the report below for its recorded replication or assessment status. Provisional eligibility log

A Machine Learning Approach to Analyze and Support Anticorruption Policy

Elliott Ash; Sergio Galletta; Tommaso Giommoni (2025) · American Economic Journal: Economic Policy

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-144", tranche="tranche150")

Elliott Ash; Sergio Galletta; Tommaso Giommoni (2025). American Economic Journal: Economic Policy. .

Collection index · Results JSON · QMD · Reproduction code

Template assessment

All numerical source targets condition on narrow1_prediction_NS, an XGBoost prediction absent from the deposit. The supplied R script reconstructs audit timing only, not outcomes or the final dataset. Rebuilding the documented week-long ML preparation is outside this template run; no timing-only object is passed off as an estimated effect.

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