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1{2 "validation_errors": [],3 "doc_id": "jof_12704",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T23:03:25+00:00",9 "source_sha256": "1b50702d7ad6b1fce3a2f80dea73ad83ed1304ce5fd912c1f8f01d409f1c241d",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 25609,12 "usage": {13  "input": 8535,14  "output": 2236,15  "cache_read": 3968,16  "cache_write": 017 },18 "cost_usd": 0.079654,19 "request_id": "req_011Cggq3fD3bwhUtKUdbbWbB",20 "annotation": {21  "genre": "empirical",22  "subfield": "corporate_finance",23  "subfield_secondary": "behavioral_finance",24  "primary_method": "reduced_form_regression",25  "identification": {26   "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).",27   "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.",28   "key_assumptions": [29    "Non-FM misbehavior measures proxy for city-level ethical norms",30    "Detected FM reflects committed FM after controlling for enforcement proxies",31    "Observable controls capture relevant firm and regional heterogeneity"32   ],33   "threats_addressed": [34    "sampling variation / chance clustering",35    "regional enforcement differences (detection and deterrence)",36    "firm heterogeneity in benefits of FM",37    "regional demographic and economic characteristics",38    "reflection problem (acknowledged caveat)"39   ],40   "causal_language_strength": 3,41   "uses_causal_claim": true42  },43  "abstract_moves": {44   "question": {45    "present": false,46    "sentence_index": -147   },48   "gap": {49    "present": false,50    "sentence_index": -151   },52   "method": {53    "present": false,54    "sentence_index": -155   },56   "results": {57    "present": true,58    "sentence_index": 159   },60   "magnitude": {61    "present": true,62    "sentence_index": 063   },64   "contribution": {65    "present": false,66    "sentence_index": -167   }68  },69  "introduction": {70   "moves": [71    {72     "paragraph_index": 0,73     "move": "motivation"74    },75    {76     "paragraph_index": 0,77     "move": "tension_or_puzzle"78    },79    {80     "paragraph_index": 1,81     "move": "preview_of_approach"82    },83    {84     "paragraph_index": 2,85     "move": "preview_of_results"86    },87    {88     "paragraph_index": 4,89     "move": "preview_of_results"90    },91    {92     "paragraph_index": 8,93     "move": "preview_of_results"94    },95    {96     "paragraph_index": 10,97     "move": "roadmap"98    },99    {100     "paragraph_index": 10,101     "move": "preview_of_approach"102    },103    {104     "paragraph_index": 11,105     "move": "preview_of_approach"106    },107    {108     "paragraph_index": 12,109     "move": "preview_of_results"110    },111    {112     "paragraph_index": 13,113     "move": "preview_of_results"114    },115    {116     "paragraph_index": 14,117     "move": "identification_defense"118    },119    {120     "paragraph_index": 14,121     "move": "mechanism_or_theory_sketch"122    },123    {124     "paragraph_index": 15,125     "move": "related_literature_positioning"126    },127    {128     "paragraph_index": 15,129     "move": "policy_or_broader_implication"130    },131    {132     "paragraph_index": 16,133     "move": "related_literature_positioning"134    },135    {136     "paragraph_index": 16,137     "move": "policy_or_broader_implication"138    }139   ],140   "contribution_paragraph_index": 15,141   "contribution_types": [142    "new_evidence",143    "new_data_or_measure"144   ],145   "roadmap_present": true,146   "literature_positioning_location": "inside_introduction"147  },148  "section_roles": [149   {150    "idx": 0,151    "role": "introduction"152   },153   {154    "idx": 1,155    "role": "theory"156   },157   {158    "idx": 2,159    "role": "data"160   },161   {162    "idx": 3,163    "role": "results"164   },165   {166    "idx": 4,167    "role": "results"168   },169   {170    "idx": 5,171    "role": "robustness"172   },173   {174    "idx": 6,175    "role": "conclusion"176   },177   {178    "idx": 7,179    "role": "appendix"180   }181  ],182  "results_presentation": {183   "economic_significance_discussed": true,184   "statistical_significance_language": "mixed",185   "magnitude_statements_in_intro": true186  },187  "robustness": {188   "location": "mixed",189   "types": [190    "alternative_specification",191    "subsample",192    "alternative_measure",193    "alternative_sample_period"194   ]195  },196  "tables": {197   "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.",198   "units_stated_in_notes": true,199   "standard_errors_description": "unclear"200  },201  "figures": {202   "uncertainty_shown": "unclear"203  },204  "conclusion": {205   "bounded_claims": true,206   "policy_implications": false,207   "open_questions": true,208   "restates_contribution": true209  },210  "hedging": {211   "level": "moderate",212   "typical_devices": [213    "appear to",214    "suggest",215    "cannot definitively rule out",216    "remain agnostic about ultimate causes",217    "explicit caveat on reflection problem"218   ]219  },220  "claim_evidence": [221   {222    "claim": "FM rates cluster in some cities more than chance predicts, with roughly threefold differences across cities.",223    "evidence_type": "table",224    "evidence_ref": "Table I, Table II"225   },226   {227    "claim": "City fixed effects remain significant in panel regressions at the city-year level.",228    "evidence_type": "table",229    "evidence_ref": "Table III"230   },231   {232    "claim": "Each of five non-FM misbehavior measures is positively related to city FM rates; the combined index has a t-statistic above four.",233    "evidence_type": "table",234    "evidence_ref": "Table IV"235   },236   {237    "claim": "City FM rates and the misbehavior index ranking are strongly correlated (rho of about 0.60).",238    "evidence_type": "figure",239    "evidence_ref": "Figure 2"240   },241   {242    "claim": "Including the misbehavior index eliminates significant excess clustering of FM across cities.",243    "evidence_type": "table",244    "evidence_ref": "Table IV"245   },246   {247    "claim": "Enforcement proxies and firm characteristics do not explain the city differences in FM.",248    "evidence_type": "table",249    "evidence_ref": "Table VII, Table VIII"250   }251  ],252  "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.",253  "writing_profile_keywords": [254   "social norms",255   "geography",256   "count models",257   "measurement-first",258   "alternative explanations",259   "rare events",260   "culture and finance"261  ],262  "confidence": 0.82,263  "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."264 },265 "deterministic_checks": {266  "roadmap_regex": false,267  "contribution_regex_in_intro": true,268  "magnitude_in_abstract": false,269  "abstract_n_sentences": 4,270  "intro_n_paragraphs": 17,271  "intro_n_words": 1216,272  "causal_words_in_abstract": 0,273  "hedge_words_in_abstract": 1,274  "stars_mentioned": false,275  "clustered_se_mentioned": false,276  "internet_appendix_mentioned": false,277  "section_roles_rule": {278   "0": "introduction",279   "1": "theory",280   "2": "data",281   "3": "other",282   "4": "other",283   "5": "robustness",284   "6": "conclusion",285   "7": "appendix"286  }287 },288 "schema_valid": true,289 "code_version": "feb6c9e-dirty"290}
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