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1{2 "validation_errors": [],3 "repairs": [4  "dropped hedging.typical_devices_note"5 ],6 "doc_id": "jof_12828",7 "ontology_version": "1.0",8 "prompt_version": "1.0",9 "model": "claude-opus-5-5",10 "effort": "medium",11 "annotated_at": "2026-11-03T23:26:32+00:00",12 "source_sha256": "ca4b28a8c13c90780b51e3debe66b974f4b0e5f5a3690fe847cb7efa23fa6701",13 "parser": "pymupdf-jof-heuristics-1.3",14 "payload_chars": 22026,15 "usage": {16  "input": 7557,17  "output": 2199,18  "cache_read": 3968,19  "cache_write": 020 },21 "cost_usd": 0.075002,22 "request_id": "req_011CggqZshU4Ub4QVJn4becS",23 "annotation": {24  "genre": "mixed_theory_empirical",25  "subfield": "household_finance",26  "subfield_secondary": "behavioral_finance",27  "primary_method": "calibration_simulation",28  "identification": {29   "estimand": "How savings and dissavings behavior relates to survival optimism (subjective minus actuarial survival probability), alongside model-implied effects of subjective versus actuarial mortality beliefs on life-cycle saving.",30   "strategy": "Survey elicitation of beliefs; a canonical life-cycle model calibrated to the elicited beliefs and compared with an actuarial benchmark, with fit checked by SMM; ordered logit regressions with controls; Altonji-Elder-Taber/Oster coefficient bounding; a salience-based belief measure presumed orthogonal to confounders.",31   "key_assumptions": [32    "Elicited survey beliefs reflect true subjective survival probabilities",33    "Controls capture sophistication and other confounders",34    "Weight placed on salient risk factors is orthogonal to health status and general optimism",35    "Selection on observables is informative about selection on unobservables (Oster bounds)"36   ],37   "threats_addressed": [38    "omitted variable bias from sophistication or financial literacy",39    "confounding by income, education and demographics",40    "general optimism or health status",41    "risk preferences"42   ],43   "causal_language_strength": 3,44   "uses_causal_claim": true45  },46  "abstract_moves": {47   "question": {48    "present": true,49    "sentence_index": 050   },51   "gap": {52    "present": false,53    "sentence_index": -154   },55   "method": {56    "present": true,57    "sentence_index": 258   },59   "results": {60    "present": true,61    "sentence_index": 162   },63   "magnitude": {64    "present": true,65    "sentence_index": 366   },67   "contribution": {68    "present": false,69    "sentence_index": -170   }71  },72  "introduction": {73   "moves": [74    {75     "paragraph_index": 0,76     "move": "motivation"77    },78    {79     "paragraph_index": 1,80     "move": "motivation"81    },82    {83     "paragraph_index": 1,84     "move": "preview_of_approach"85    },86    {87     "paragraph_index": 1,88     "move": "preview_of_results"89    },90    {91     "paragraph_index": 1,92     "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_approach"105    },106    {107     "paragraph_index": 5,108     "move": "preview_of_results"109    },110    {111     "paragraph_index": 6,112     "move": "preview_of_results"113    },114    {115     "paragraph_index": 7,116     "move": "identification_defense"117    },118    {119     "paragraph_index": 8,120     "move": "related_literature_positioning"121    },122    {123     "paragraph_index": 9,124     "move": "related_literature_positioning"125    },126    {127     "paragraph_index": 9,128     "move": "contribution_statement"129    },130    {131     "paragraph_index": 10,132     "move": "roadmap"133    }134   ],135   "contribution_paragraph_index": 9,136   "contribution_types": [137    "new_evidence",138    "new_data_or_measure",139    "resolution_of_puzzle"140   ],141   "roadmap_present": true,142   "literature_positioning_location": "inside_introduction"143  },144  "section_roles": [145   {146    "idx": 0,147    "role": "introduction"148   },149   {150    "idx": 1,151    "role": "data"152   },153   {154    "idx": 2,155    "role": "theory"156   },157   {158    "idx": 3,159    "role": "results"160   },161   {162    "idx": 4,163    "role": "results"164   },165   {166    "idx": 5,167    "role": "conclusion"168   }169  ],170  "results_presentation": {171   "economic_significance_discussed": true,172   "statistical_significance_language": "unclear",173   "magnitude_statements_in_intro": true174  },175  "robustness": {176   "location": "within_results",177   "types": [178    "alternative_specification",179    "other"180   ]181  },182  "tables": {183   "convention_notes": "Notes define key variables, e.g., survival optimism as subjective minus SSA actuarial survival probability. Regression tables report ordered logit log-odds. Model tables list calibration parameters and their survey sources.",184   "units_stated_in_notes": true,185   "standard_errors_description": "unclear"186  },187  "figures": {188   "uncertainty_shown": "unclear"189  },190  "conclusion": {191   "bounded_claims": true,192   "policy_implications": true,193   "open_questions": true,194   "restates_contribution": true195  },196  "hedging": {197   "level": "moderate",198   "typical_devices": [199    "explicit caution about causal interpretation of reduced-form regressions",200    "'suggests' / 'gets closer to understanding'",201    "'presumably orthogonal'",202    "'associated with' wording for regression results"203   ]204  },205  "claim_evidence": [206   {207    "claim": "Young respondents underestimate survival and older respondents overestimate it relative to actuarial data.",208    "evidence_type": "figure",209    "evidence_ref": "Figure 1"210   },211   {212    "claim": "Salience of cohort-specific causes of death shapes mortality belief distortions.",213    "evidence_type": "figure",214    "evidence_ref": "Table II; Figures 2-3"215   },216   {217    "claim": "With subjective beliefs, the young undersave by about 26% and retirees draw down assets about 27% slower than under actuarial calibration.",218    "evidence_type": "table",219    "evidence_ref": "Table IV; Figure 5"220   },221   {222    "claim": "Calibrating to subjective beliefs substantially improves the model's fit to consumption and net worth data.",223    "evidence_type": "figure",224    "evidence_ref": "Figure 6"225   },226   {227    "claim": "Survival optimism correlates with savings rates after controlling for literacy, risk tolerance and socioeconomic factors.",228    "evidence_type": "table",229    "evidence_ref": "Table V; Figure 7"230   },231   {232    "claim": "Survival beliefs relate to retirement dissavings, with an association comparable to that of bequest motives.",233    "evidence_type": "table",234    "evidence_ref": "Table VI"235   }236  ],237  "structural_summary": "The paper opens with behavioral motivation about overweighting rare, salient events. It then documents a new survey of mortality beliefs and their salience-based formation. Next, it presents a canonical life-cycle model calibrated to the elicited beliefs and compares its results with an actuarial benchmark, including SMM fit to data and comparison with alternative mechanisms. Reduced-form ordered logit regressions on the survey then validate the model's predictions, with an explicit omitted-variable defense. The conclusion restates the findings, discusses policy relevance and points to general-equilibrium and insurance questions for future work.",238  "writing_profile_keywords": [239   "new survey",240   "subjective expectations",241   "life-cycle calibration",242   "salience",243   "household finance puzzles",244   "model-plus-regression validation",245   "cautious causality"246  ],247  "confidence": 0.8,248  "notes": "Section IV is labeled as validating model predictions; it is assigned the results role. Standard error type is not visible in the excerpts."249 },250 "deterministic_checks": {251  "roadmap_regex": true,252  "contribution_regex_in_intro": true,253  "magnitude_in_abstract": true,254  "abstract_n_sentences": 6,255  "intro_n_paragraphs": 11,256  "intro_n_words": 1279,257  "causal_words_in_abstract": 1,258  "hedge_words_in_abstract": 0,259  "stars_mentioned": false,260  "clustered_se_mentioned": false,261  "internet_appendix_mentioned": true,262  "section_roles_rule": {263   "0": "introduction",264   "1": "other",265   "2": "theory",266   "3": "theory",267   "4": "other",268   "5": "conclusion"269  }270 },271 "schema_valid": true,272 "code_version": "87c73cd-dirty"273}
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