1{"text": "{\"genre\":\"empirical\",\"subfield\":\"corporate_finance\",\"subfield_secondary\":\"behavioral_finance\",\"primary_method\":\"reduced_form_regression\",\"identification\":{\"estimand\":\"Cross-city dispersion in financial misconduct rates beyond sampling variation, and the association of city FM rates with measures of local social norms (non-FM misbehavior).\",\"strategy\":\"Negative binomial and Poisson count regressions at the city and city-year level testing excess variance and city fixed effects; cross-sectional regressions of FM on a misbehavior index; controls for regional characteristics, enforcement proxies, and firm characteristics; peer logit regressions.\",\"key_assumptions\":[\"Non-FM misbehavior measures proxy for city-level ethical norms\",\"Detected FM reflects committed FM after controlling for enforcement proxies\",\"Observable controls capture relevant firm and regional heterogeneity\"],\"threats_addressed\":[\"sampling variation / chance clustering\",\"regional enforcement differences (detection and deterrence)\",\"firm heterogeneity in benefits of FM\",\"regional demographic and economic characteristics\",\"reflection problem (acknowledged caveat)\"],\"causal_language_strength\":3,\"uses_causal_claim\":true},\"abstract_moves\":{\"question\":{\"present\":false,\"sentence_index\":-1},\"gap\":{\"present\":false,\"sentence_index\":-1},\"method\":{\"present\":false,\"sentence_index\":-1},\"results\":{\"present\":true,\"sentence_index\":1},\"magnitude\":{\"present\":true,\"sentence_index\":0},\"contribution\":{\"present\":false,\"sentence_index\":-1}},\"introduction\":{\"moves\":[{\"paragraph_index\":0,\"move\":\"motivation\"},{\"paragraph_index\":0,\"move\":\"tension_or_puzzle\"},{\"paragraph_index\":1,\"move\":\"preview_of_approach\"},{\"paragraph_index\":2,\"move\":\"preview_of_results\"},{\"paragraph_index\":4,\"move\":\"preview_of_results\"},{\"paragraph_index\":8,\"move\":\"preview_of_results\"},{\"paragraph_index\":10,\"move\":\"roadmap\"},{\"paragraph_index\":10,\"move\":\"preview_of_approach\"},{\"paragraph_index\":11,\"move\":\"preview_of_approach\"},{\"paragraph_index\":12,\"move\":\"preview_of_results\"},{\"paragraph_index\":13,\"move\":\"preview_of_results\"},{\"paragraph_index\":14,\"move\":\"identification_defense\"},{\"paragraph_index\":14,\"move\":\"mechanism_or_theory_sketch\"},{\"paragraph_index\":15,\"move\":\"related_literature_positioning\"},{\"paragraph_index\":15,\"move\":\"policy_or_broader_implication\"},{\"paragraph_index\":16,\"move\":\"related_literature_positioning\"},{\"paragraph_index\":16,\"move\":\"policy_or_broader_implication\"}],\"contribution_paragraph_index\":15,\"contribution_types\":[\"new_evidence\",\"new_data_or_measure\"],\"roadmap_present\":true,\"literature_positioning_location\":\"inside_introduction\"},\"section_roles\":[{\"idx\":0,\"role\":\"introduction\"},{\"idx\":1,\"role\":\"theory\"},{\"idx\":2,\"role\":\"data\"},{\"idx\":3,\"role\":\"results\"},{\"idx\":4,\"role\":\"results\"},{\"idx\":5,\"role\":\"robustness\"},{\"idx\":6,\"role\":\"conclusion\"},{\"idx\":7,\"role\":\"appendix\"}],\"results_presentation\":{\"economic_significance_discussed\":true,\"statistical_significance_language\":\"mixed\",\"magnitude_statements_in_intro\":true},\"robustness\":{\"location\":\"mixed\",\"types\":[\"alternative_specification\",\"subsample\",\"alternative_measure\",\"alternative_sample_period\"]},\"tables\":{\"convention_notes\":\"Table notes open by naming the estimator (negative binomial, Poisson, panel logit) and defining the dependent variable (area-level count of FM firm-years). Appendix tables repeat the regressions with alternative constructions of the misbehavior index, area-year units, and a large-firm subsample.\",\"units_stated_in_notes\":true,\"standard_errors_description\":\"unclear\"},\"figures\":{\"uncertainty_shown\":\"unclear\"},\"conclusion\":{\"bounded_claims\":true,\"policy_implications\":false,\"open_questions\":true,\"restates_contribution\":true},\"hedging\":{\"level\":\"moderate\",\"typical_devices\":[\"appear to\",\"suggest\",\"cannot definitively rule out\",\"remain agnostic about ultimate causes\",\"explicit caveat on reflection problem\"]},\"claim_evidence\":[{\"claim\":\"FM rates cluster in some cities more than chance predicts, with roughly threefold differences across cities.\",\"evidence_type\":\"table\",\"evidence_ref\":\"Table I, Table II\"},{\"claim\":\"City fixed effects remain significant in panel regressions at the city-year level.\",\"evidence_type\":\"table\",\"evidence_ref\":\"Table III\"},{\"claim\":\"Each of five non-FM misbehavior measures is positively related to city FM rates; the combined index has a t-statistic above four.\",\"evidence_type\":\"table\",\"evidence_ref\":\"Table IV\"},{\"claim\":\"City FM rates and the misbehavior index ranking are strongly correlated (rho of about 0.60).\",\"evidence_type\":\"figure\",\"evidence_ref\":\"Figure 2\"},{\"claim\":\"Including the misbehavior index eliminates significant excess clustering of FM across cities.\",\"evidence_type\":\"table\",\"evidence_ref\":\"Table IV\"},{\"claim\":\"Enforcement proxies and firm characteristics do not explain the city differences in FM.\",\"evidence_type\":\"table\",\"evidence_ref\":\"Table VII, Table VIII\"}],\"structural_summary\":\"Opens with the gap between Becker-style crime models and observed spatial variation in crime, then previews numbered findings and walks through the sections. A stylized cost-benefit model motivates three candidate explanations: norms, enforcement, and firm heterogeneity. The analysis first measures excess clustering of FM with count models, then relates FM to observable regional traits and to an index of non-FM misbehavior. Separate sections rule out enforcement and firm-characteristic explanations. The paper closes by acknowledging that the sources of norms remain unidentified and flags them for future research.\",\"writing_profile_keywords\":[\"social norms\",\"geography\",\"count models\",\"measurement-first\",\"alternative explanations\",\"rare events\",\"culture and finance\"],\"confidence\":0.82,\"notes\":\"The introduction's numbered findings list is split across paragraphs 2-9. Causal language is cautious: norms 'appear' to matter, and the authors explicitly acknowledge the reflection problem and that the sources of norms are unidentified.\"}", "json": {"genre": "empirical", "subfield": "corporate_finance", "subfield_secondary": "behavioral_finance", "primary_method": "reduced_form_regression", "identification": {"estimand": "Cross-city dispersion in financial misconduct rates beyond sampling variation, and the association of city FM rates with measures of local social norms (non-FM misbehavior).", "strategy": "Negative binomial and Poisson count regressions at the city and city-year level testing excess variance and city fixed effects; cross-sectional regressions of FM on a misbehavior index; controls for regional characteristics, enforcement proxies, and firm characteristics; peer logit regressions.", "key_assumptions": ["Non-FM misbehavior measures proxy for city-level ethical norms", "Detected FM reflects committed FM after controlling for enforcement proxies", "Observable controls capture relevant firm and regional heterogeneity"], "threats_addressed": ["sampling variation / chance clustering", "regional enforcement differences (detection and deterrence)", "firm heterogeneity in benefits of FM", "regional demographic and economic characteristics", "reflection problem (acknowledged caveat)"], "causal_language_strength": 3, "uses_causal_claim": true}, "abstract_moves": {"question": {"present": false, "sentence_index": -1}, "gap": {"present": false, "sentence_index": -1}, "method": {"present": false, "sentence_index": -1}, "results": {"present": true, "sentence_index": 1}, "magnitude": {"present": true, "sentence_index": 0}, "contribution": {"present": false, "sentence_index": -1}}, "introduction": {"moves": [{"paragraph_index": 0, "move": "motivation"}, {"paragraph_index": 0, "move": "tension_or_puzzle"}, {"paragraph_index": 1, "move": "preview_of_approach"}, {"paragraph_index": 2, "move": "preview_of_results"}, {"paragraph_index": 4, "move": "preview_of_results"}, {"paragraph_index": 8, "move": "preview_of_results"}, {"paragraph_index": 10, "move": "roadmap"}, {"paragraph_index": 10, "move": "preview_of_approach"}, {"paragraph_index": 11, "move": "preview_of_approach"}, {"paragraph_index": 12, "move": "preview_of_results"}, {"paragraph_index": 13, "move": "preview_of_results"}, {"paragraph_index": 14, "move": "identification_defense"}, {"paragraph_index": 14, "move": "mechanism_or_theory_sketch"}, {"paragraph_index": 15, "move": "related_literature_positioning"}, {"paragraph_index": 15, "move": "policy_or_broader_implication"}, {"paragraph_index": 16, "move": "related_literature_positioning"}, {"paragraph_index": 16, "move": "policy_or_broader_implication"}], "contribution_paragraph_index": 15, "contribution_types": ["new_evidence", "new_data_or_measure"], "roadmap_present": true, "literature_positioning_location": "inside_introduction"}, "section_roles": [{"idx": 0, "role": "introduction"}, {"idx": 1, "role": "theory"}, {"idx": 2, "role": "data"}, {"idx": 3, "role": "results"}, {"idx": 4, "role": "results"}, {"idx": 5, "role": "robustness"}, {"idx": 6, "role": "conclusion"}, {"idx": 7, "role": "appendix"}], "results_presentation": {"economic_significance_discussed": true, "statistical_significance_language": "mixed", "magnitude_statements_in_intro": true}, "robustness": {"location": "mixed", "types": ["alternative_specification", "subsample", "alternative_measure", "alternative_sample_period"]}, "tables": {"convention_notes": "Table notes open by naming the estimator (negative binomial, Poisson, panel logit) and defining the dependent variable (area-level count of FM firm-years). Appendix tables repeat the regressions with alternative constructions of the misbehavior index, area-year units, and a large-firm subsample.", "units_stated_in_notes": true, "standard_errors_description": "unclear"}, "figures": {"uncertainty_shown": "unclear"}, "conclusion": {"bounded_claims": true, "policy_implications": false, "open_questions": true, "restates_contribution": true}, "hedging": {"level": "moderate", "typical_devices": ["appear to", "suggest", "cannot definitively rule out", "remain agnostic about ultimate causes", "explicit caveat on reflection problem"]}, "claim_evidence": [{"claim": "FM rates cluster in some cities more than chance predicts, with roughly threefold differences across cities.", "evidence_type": "table", "evidence_ref": "Table I, Table II"}, {"claim": "City fixed effects remain significant in panel regressions at the city-year level.", "evidence_type": "table", "evidence_ref": "Table III"}, {"claim": "Each of five non-FM misbehavior measures is positively related to city FM rates; the combined index has a t-statistic above four.", "evidence_type": "table", "evidence_ref": "Table IV"}, {"claim": "City FM rates and the misbehavior index ranking are strongly correlated (rho of about 0.60).", "evidence_type": "figure", "evidence_ref": "Figure 2"}, {"claim": "Including the misbehavior index eliminates significant excess clustering of FM across cities.", "evidence_type": "table", "evidence_ref": "Table IV"}, {"claim": "Enforcement proxies and firm characteristics do not explain the city differences in FM.", "evidence_type": "table", "evidence_ref": "Table VII, Table VIII"}], "structural_summary": "Opens with the gap between Becker-style crime models and observed spatial variation in crime, then previews numbered findings and walks through the sections. 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