1{2 "validation_errors": [],3 "doc_id": "jof_12605",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T23:01:19+00:00",9 "source_sha256": "a1f3d730db3f5614e201ad683ab34b0e3ced3f71d95752824074fc4325537d28",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 18145,12 "usage": {13 "input": 6023,14 "output": 2133,15 "cache_read": 3968,16 "cache_write": 017 },18 "cost_usd": 0.067546,19 "request_id": "req_011CggptREu5Xmii87CayiFh",20 "annotation": {21 "genre": "empirical",22 "subfield": "household_finance",23 "subfield_secondary": "asset_pricing",24 "primary_method": "reduced_form_regression",25 "identification": {26 "estimand": "Conditional association between survey-measured financial literacy and household portfolio returns and dynamics: risk-taking, inertia and rebalancing direction.",27 "strategy": "OLS, probit and IV regressions on matched administrative and survey panel data with demographic and wealth controls. IV is used only to decompose active versus passive changes in the risky share, instrumented with the zero-rebalancing passive change; it is not used to instrument literacy.",28 "key_assumptions": [29 "Controls for risk, wealth and demographics capture confounders correlated with literacy",30 "No reverse causality from returns to literacy acquisition",31 "Validity of the zero-rebalancing passive change as instrument for the observed passive change"32 ],33 "threats_addressed": [34 "reverse causality (returns inducing literacy acquisition)",35 "omitted wealth and demographic confounders",36 "differences in portfolio risk",37 "crisis period and delegated management (robustness table)"38 ],39 "causal_language_strength": 2,40 "uses_causal_claim": false41 },42 "abstract_moves": {43 "question": {44 "present": false,45 "sentence_index": -146 },47 "gap": {48 "present": false,49 "sentence_index": -150 },51 "method": {52 "present": true,53 "sentence_index": 054 },55 "results": {56 "present": true,57 "sentence_index": 258 },59 "magnitude": {60 "present": true,61 "sentence_index": 162 },63 "contribution": {64 "present": false,65 "sentence_index": -166 }67 },68 "introduction": {69 "moves": [70 {71 "paragraph_index": 0,72 "move": "motivation"73 },74 {75 "paragraph_index": 1,76 "move": "literature_gap"77 },78 {79 "paragraph_index": 2,80 "move": "preview_of_approach"81 },82 {83 "paragraph_index": 2,84 "move": "contribution_statement"85 },86 {87 "paragraph_index": 3,88 "move": "preview_of_results"89 },90 {91 "paragraph_index": 3,92 "move": "mechanism_or_theory_sketch"93 },94 {95 "paragraph_index": 4,96 "move": "preview_of_results"97 },98 {99 "paragraph_index": 4,100 "move": "tension_or_puzzle"101 },102 {103 "paragraph_index": 5,104 "move": "preview_of_results"105 },106 {107 "paragraph_index": 6,108 "move": "preview_of_results"109 },110 {111 "paragraph_index": 6,112 "move": "contribution_statement"113 },114 {115 "paragraph_index": 6,116 "move": "related_literature_positioning"117 }118 ],119 "contribution_paragraph_index": 2,120 "contribution_types": [121 "new_evidence",122 "new_data_or_measure"123 ],124 "roadmap_present": false,125 "literature_positioning_location": "inside_introduction"126 },127 "section_roles": [128 {129 "idx": 0,130 "role": "introduction"131 },132 {133 "idx": 1,134 "role": "data"135 },136 {137 "idx": 2,138 "role": "data"139 },140 {141 "idx": 3,142 "role": "results"143 },144 {145 "idx": 4,146 "role": "results"147 },148 {149 "idx": 5,150 "role": "conclusion"151 },152 {153 "idx": 6,154 "role": "appendix"155 }156 ],157 "results_presentation": {158 "economic_significance_discussed": true,159 "statistical_significance_language": "stars_and_tstats",160 "magnitude_statements_in_intro": true161 },162 "robustness": {163 "location": "mixed",164 "types": [165 "subsample",166 "alternative_specification",167 "other"168 ]169 },170 "tables": {171 "convention_notes": "OLS, probit (marginal effects) and IV tables with robust t-statistics in parentheses and conventional 10/5/1% stars. Variable definitions are deferred to the appendix, and returns are stated in percentage points.",172 "units_stated_in_notes": true,173 "standard_errors_description": "robust"174 },175 "figures": {176 "uncertainty_shown": "mixed"177 },178 "conclusion": {179 "bounded_claims": true,180 "policy_implications": true,181 "open_questions": false,182 "restates_contribution": true183 },184 "hedging": {185 "level": "moderate",186 "typical_devices": [187 "associational verbs ('is associated with', 'relates to')",188 "'suggestive' qualifiers on descriptive figures",189 "explicit acknowledgment of lacking exogenous variation in literacy",190 "reliance on prior IV literature to support causal reading"191 ]192 },193 "claim_evidence": [194 {195 "claim": "Controlling for risk, the most literate households earn about 0.4% higher annual returns than the least literate.",196 "evidence_type": "table",197 "evidence_ref": "Table III; Figure 2"198 },199 {200 "claim": "Literate households raise their risky share more when expected excess returns of risky funds are high.",201 "evidence_type": "table",202 "evidence_ref": "Table V; Figures 3-4"203 },204 {205 "claim": "Passive changes account for a larger share of risky-share changes for low-literacy households (64% vs 30%).",206 "evidence_type": "table",207 "evidence_ref": "Table VI; Figure 5"208 },209 {210 "claim": "More literate households rebalance in a contrarian direction, keeping risk exposure stable.",211 "evidence_type": "table",212 "evidence_ref": "Table VII"213 },214 {215 "claim": "The literacy-returns relationship survives crisis-period and delegation controls.",216 "evidence_type": "table",217 "evidence_ref": "Table IV"218 },219 {220 "claim": "Literate households do not experience more extreme pre-survey returns, easing reverse causality concerns.",221 "evidence_type": "none_stated",222 "evidence_ref": "Internet Appendix (mentioned in conclusion)"223 }224 ],225 "structural_summary": "Opens with established heterogeneity in household returns and literacy, then frames mechanisms as poorly understood because suitable data are scarce. Introduces a novel matched administrative-survey dataset and previews three dimensions of portfolio dynamics (risk timing, inertia, rebalancing direction), each with a quantitative finding and links to prior claims. It closes the introduction with a link to returns and literature positioning. Body sections cover data, construction of the literacy measure, returns, and portfolio choices organized by the three dimensions. The conclusion concedes the lack of exogenous variation and offers mitigating evidence and policy relevance.",226 "writing_profile_keywords": [227 "matched administrative-survey data",228 "mechanism decomposition",229 "household portfolio dynamics",230 "associational with causal caveats",231 "three-result preview structure",232 "financial literacy"233 ],234 "confidence": 0.85,235 "notes": "Single-author slip ('my') appears in a multi-author intro. IV regressions instrument the passive change, not literacy. No roadmap paragraph is visible in the intro excerpt. 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