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spb/wp29_uqo

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1{2  "verdict": "accept",3  "issues": [4    {5      "id": "R1",6      "severity": "minor",7      "location": "claims.json C3 / report.md Results",8      "issue": "The OVB-identity check that C3 relies on lives in out/state/checks.md. analysis.py does not compute it and it is not in results.json, so the claim cannot be fully reproduced from the replication entry point.",9      "suggested_fix": "Have analysis.py compute and write the auxiliary slope delta (skill on hours) and the OVB decomposition to results.json, then reference those keys in C3."10    },11    {12      "id": "R2",13      "severity": "minor",14      "location": "results.json naive_estimate / baseline_metric",15      "issue": "The required baseline is fitted on the 1188-row trimmed sample, not on all 1200 input rows. This is disclosed, and the full-sample naive slope is also reported (-0.135), but the baseline_metric (RMSE) exists only for the trimmed sample.",16      "suggested_fix": "State explicitly in the report that both baseline keys use the trimmed sample (optionally also report the full-sample RMSE)."17    },18    {19      "id": "R3",20      "severity": "minor",21      "location": "claims.json C5",22      "issue": "'No evidence of heterogeneity by skill' rests on a single linear hours x skill interaction test. That test cannot detect other forms of heterogeneity or nonlinearity in hours. The caveat covers the nonlinearity-in-hours part only.",23      "suggested_fix": "Narrow the wording to 'no evidence of linear effect modification by skill_index'."24    }25  ],26  "summary": "The deliverables reproduce exactly: a scratch re-run of analysis.py gives byte-identical results.json and table1.csv. Every required key is present, and the code uses only data/firms.csv. The negative-hours exclusion (12 rows) is detected, disclosed and checked in a sensitivity run. The baseline and the adjusted OLS (HC1 SEs, normal CIs) are computed correctly, and the conclusion code matches the CI. The claims stay within the evidence, and the limitations honestly name the untestable selection-on-observables assumption, possible measurement error in skill_index, untested functional form and the simulated nature of the data. Only minor presentation issues remain."27}28
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