1{2 "validation_errors": [],3 "doc_id": "jof_12432",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T22:55:27+00:00",9 "source_sha256": "ca0ebf3d253fbf0194f7d3a3f16b844b78695593229c84582679f39f735f66cf",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 22627,12 "usage": {13 "input": 7888,14 "output": 2408,15 "cache_read": 3968,16 "cache_write": 017 },18 "cost_usd": 0.080506,19 "request_id": "req_011CggpSEWGEgMr5GvRdYip3",20 "annotation": {21 "genre": "empirical",22 "subfield": "corporate_finance",23 "subfield_secondary": "behavioral_finance",24 "primary_method": "reduced_form_regression",25 "identification": {26 "estimand": "Effect of the intensity of a CEO's early-life exposure to fatal natural disasters (none vs. medium vs. extreme fatality) on corporate risk-taking policies and outcomes.",27 "strategy": "Panel OLS and linear probability models using early-life disaster exposure inferred from birth county and year. Specifications include firm, year, CEO birth-year and CEO birth-state fixed effects and control for the county's historical disaster incidence. Supplemented by a difference-in-differences comparison with neighboring unaffected counties, a placebo with random reassignment of birth county, and an analysis of exogenous CEO turnovers.",28 "key_assumptions": [29 "Disaster exposure during the formative years is exogenous to later corporate outcomes, conditional on fixed effects and historical county risk.",30 "Birth county proxies for where the CEO lived during childhood.",31 "Disaster experience does not affect selection into becoming a CEO, nor CEO-firm matching in ways that drive results."32 ],33 "threats_addressed": [34 "cohort effects",35 "growing up in a high-risk county vs. actually experiencing a disaster",36 "measurement error in childhood residence",37 "omitted variables",38 "CEO-firm matching",39 "spurious correlation (placebo)"40 ],41 "causal_language_strength": 3,42 "uses_causal_claim": true43 },44 "abstract_moves": {45 "question": {46 "present": true,47 "sentence_index": 148 },49 "gap": {50 "present": true,51 "sentence_index": 052 },53 "method": {54 "present": false,55 "sentence_index": -156 },57 "results": {58 "present": true,59 "sentence_index": 260 },61 "magnitude": {62 "present": false,63 "sentence_index": -164 },65 "contribution": {66 "present": true,67 "sentence_index": 168 }69 },70 "introduction": {71 "moves": [72 {73 "paragraph_index": 0,74 "move": "motivation"75 },76 {77 "paragraph_index": 5,78 "move": "related_literature_positioning"79 },80 {81 "paragraph_index": 5,82 "move": "literature_gap"83 },84 {85 "paragraph_index": 6,86 "move": "preview_of_approach"87 },88 {89 "paragraph_index": 7,90 "move": "preview_of_results"91 },92 {93 "paragraph_index": 8,94 "move": "preview_of_results"95 },96 {97 "paragraph_index": 9,98 "move": "mechanism_or_theory_sketch"99 },100 {101 "paragraph_index": 10,102 "move": "identification_defense"103 },104 {105 "paragraph_index": 10,106 "move": "robustness_mention"107 },108 {109 "paragraph_index": 11,110 "move": "contribution_statement"111 },112 {113 "paragraph_index": 12,114 "move": "policy_or_broader_implication"115 },116 {117 "paragraph_index": 13,118 "move": "roadmap"119 }120 ],121 "contribution_paragraph_index": 11,122 "contribution_types": [123 "new_evidence",124 "new_identification"125 ],126 "roadmap_present": true,127 "literature_positioning_location": "both"128 },129 "section_roles": [130 {131 "idx": 0,132 "role": "introduction"133 },134 {135 "idx": 1,136 "role": "literature"137 },138 {139 "idx": 2,140 "role": "data"141 },142 {143 "idx": 3,144 "role": "results"145 },146 {147 "idx": 4,148 "role": "robustness"149 },150 {151 "idx": 5,152 "role": "conclusion"153 },154 {155 "idx": 6,156 "role": "appendix"157 }158 ],159 "results_presentation": {160 "economic_significance_discussed": true,161 "statistical_significance_language": "unclear",162 "magnitude_statements_in_intro": true163 },164 "robustness": {165 "location": "main_text_section",166 "types": [167 "alternative_measure",168 "placebo",169 "alternative_specification",170 "other"171 ]172 },173 "tables": {174 "convention_notes": "Notes state the estimator (OLS or LPM), the dependent variable by column, and the fixed-effect set (firm, year, CEO birth year, CEO birth state). Descriptive tables come first, followed by policy-by-policy regression tables, then robustness tables (placebo, DiD-style treated vs. control, exogenous turnovers). The excerpts do not show how standard errors are computed.",175 "units_stated_in_notes": true,176 "standard_errors_description": "unclear"177 },178 "figures": {179 "uncertainty_shown": "no_figures"180 },181 "conclusion": {182 "bounded_claims": false,183 "policy_implications": false,184 "open_questions": true,185 "restates_contribution": true186 },187 "hedging": {188 "level": "moderate",189 "typical_devices": [190 "'results support the view/hypothesis'",191 "'appear to be more cautious'",192 "'suggests that our results may have broader implications'",193 "'all else equal' framing"194 ]195 },196 "claim_evidence": [197 {198 "claim": "Inverse U-shaped relation between the intensity of CEO fatal disaster exposure and firm leverage and cash policy.",199 "evidence_type": "table",200 "evidence_ref": "Table IV"201 },202 {203 "claim": "Leverage differences reflect active financing choices: moderate-disaster CEOs fill financing deficits with debt.",204 "evidence_type": "table",205 "evidence_ref": "Table V"206 },207 {208 "claim": "Moderate-disaster CEOs face higher interest costs and loan spreads, lower ratings and more bankruptcy; extreme-disaster CEOs show the reverse.",209 "evidence_type": "table",210 "evidence_ref": "Tables VI, VII"211 },212 {213 "claim": "Acquisition frequency, payment method, diversification and announcement returns vary nonmonotonically with disaster intensity.",214 "evidence_type": "table",215 "evidence_ref": "Tables VIII, IX"216 },217 {218 "claim": "Moderate-disaster CEOs run firms with higher total and idiosyncratic volatility.",219 "evidence_type": "table",220 "evidence_ref": "Table X"221 },222 {223 "claim": "CEOs' childhood disaster exposure resembles that of the general U.S. population, suggesting no selection into CEO role.",224 "evidence_type": "table",225 "evidence_ref": "Table II"226 },227 {228 "claim": "Results survive random reassignment placebo, treated vs. neighboring control counties, and exogenous turnovers.",229 "evidence_type": "table",230 "evidence_ref": "Tables XI, XII, XIII"231 }232 ],233 "structural_summary": "The paper opens with an epigraph and a literature-based observation that prior work assumes a monotonic link between experience and managerial risk-taking, then poses a dosage (intensity) hypothesis. The introduction previews the measure, policy-by-policy results with magnitudes, and the interpretation (desensitization vs. caution). It then gives an identification defense (fixed effects, historical-risk control, DiD), contribution claims, broader implications and a roadmap. A short motivation section grounds the hypothesis in the literature, followed by data construction, main results across financing, credit, acquisitions and volatility, and a dedicated robustness section. The conclusion restates the findings and points to investor-behavior research.",234 "writing_profile_keywords": [235 "managerial style",236 "early-life experience",237 "nonmonotonic effect",238 "natural disasters",239 "fixed effects",240 "dosage metaphor",241 "epigraph opening",242 "policy-by-policy results"243 ],244 "confidence": 0.8,245 "notes": "The Motivation section (I) functions as literature-based hypothesis development; it is coded as literature. The excerpts do not show standard errors or how significance is reported. The introduction's first five paragraph entries are fragments of the epigraph, so the substantive intro text begins at index 5."246 },247 "deterministic_checks": {248 "roadmap_regex": true,249 "contribution_regex_in_intro": false,250 "magnitude_in_abstract": false,251 "abstract_n_sentences": 5,252 "intro_n_paragraphs": 14,253 "intro_n_words": 1227,254 "causal_words_in_abstract": 0,255 "hedge_words_in_abstract": 0,256 "stars_mentioned": false,257 "clustered_se_mentioned": false,258 "internet_appendix_mentioned": false,259 "section_roles_rule": {260 "0": "introduction",261 "1": "other",262 "2": "data",263 "3": "results",264 "4": "robustness",265 "5": "conclusion",266 "6": "appendix"267 }268 },269 "schema_valid": true,270 "code_version": "feb6c9e-dirty"271}