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1{2 "validation_errors": [],3 "doc_id": "jof_12684",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T23:02:56+00:00",9 "source_sha256": "cdfb00be0202a3841db323402cb687839d878ed4b168d0dd48c95355614d93f0",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 18935,12 "usage": {13  "input": 6159,14  "output": 2065,15  "cache_read": 3968,16  "cache_write": 017 },18 "cost_usd": 0.06673,19 "request_id": "req_011Cggq1dsNFCpXhUZj8U3gC",20 "annotation": {21  "genre": "mixed_theory_empirical",22  "subfield": "asset_pricing",23  "subfield_secondary": "macro_finance (production-based asset pricing, production networks)",24  "primary_method": "theoretical_model_analytical",25  "identification": {26   "estimand": "Risk prices of innovations in network sparsity and concentration in the cross-section of stock returns; return spreads of network-beta-sorted portfolios",27   "strategy": "General equilibrium multisector model derives the network factors in closed form; factors are computed from input-output/Compustat data; stocks are sorted on factor betas; Fama-MacBeth estimation of risk prices; time-series regressions of dividend growth on the factors",28   "key_assumptions": [29    "Cobb-Douglas production with an exogenous, stochastically evolving network",30    "Perfect competition and a representative household",31    "Concentration and sparsity are sufficient statistics for aggregate risk",32    "Factor betas estimated from returns proxy for true exposures"33   ],34   "threats_addressed": [35    "Spreads explained by CAPM or Fama-French three-factor model",36    "Sensitivity to the choice of test assets (seven sets)"37   ],38   "causal_language_strength": 2,39   "uses_causal_claim": false40  },41  "abstract_moves": {42   "question": {43    "present": true,44    "sentence_index": 045   },46   "gap": {47    "present": false,48    "sentence_index": -149   },50   "method": {51    "present": true,52    "sentence_index": 353   },54   "results": {55    "present": true,56    "sentence_index": 457   },58   "magnitude": {59    "present": true,60    "sentence_index": 461   },62   "contribution": {63    "present": true,64    "sentence_index": 265   }66  },67  "introduction": {68   "moves": [69    {70     "paragraph_index": 0,71     "move": "motivation"72    },73    {74     "paragraph_index": 0,75     "move": "contribution_statement"76    },77    {78     "paragraph_index": 1,79     "move": "mechanism_or_theory_sketch"80    },81    {82     "paragraph_index": 2,83     "move": "mechanism_or_theory_sketch"84    },85    {86     "paragraph_index": 3,87     "move": "preview_of_approach"88    },89    {90     "paragraph_index": 3,91     "move": "preview_of_results"92    },93    {94     "paragraph_index": 3,95     "move": "roadmap"96    },97    {98     "paragraph_index": 4,99     "move": "literature_gap"100    },101    {102     "paragraph_index": 4,103     "move": "related_literature_positioning"104    },105    {106     "paragraph_index": 5,107     "move": "related_literature_positioning"108    },109    {110     "paragraph_index": 6,111     "move": "related_literature_positioning"112    },113    {114     "paragraph_index": 7,115     "move": "related_literature_positioning"116    }117   ],118   "contribution_paragraph_index": 0,119   "contribution_types": [120    "new_theory_or_model",121    "new_data_or_measure",122    "new_evidence"123   ],124   "roadmap_present": true,125   "literature_positioning_location": "inside_introduction"126  },127  "section_roles": [128   {129    "idx": 0,130    "role": "introduction"131   },132   {133    "idx": 1,134    "role": "theory"135   },136   {137    "idx": 2,138    "role": "results"139   },140   {141    "idx": 3,142    "role": "conclusion"143   }144  ],145  "results_presentation": {146   "economic_significance_discussed": true,147   "statistical_significance_language": "unclear",148   "magnitude_statements_in_intro": true149  },150  "robustness": {151   "location": "within_results",152   "types": [153    "alternative_specification",154    "other"155   ]156  },157  "tables": {158   "convention_notes": "Notes state what is reported (statistics, excess returns, alphas, risk prices, coefficients with t-statistics, R2) and describe the panel and column structure. Market capitalization units (inflation-adjusted billions of 2013 dollars) are given, and test-asset sets are enumerated by column.",159   "units_stated_in_notes": true,160   "standard_errors_description": "unclear"161  },162  "figures": {163   "uncertainty_shown": "no"164  },165  "conclusion": {166   "bounded_claims": true,167   "policy_implications": false,168   "open_questions": true,169   "restates_contribution": true170  },171  "hedging": {172   "level": "low",173   "typical_devices": [174    "'consistent with the model predictions'",175    "'supportive evidence'",176    "'can be interpreted as'",177    "'to the best of my knowledge'"178   ]179  },180  "claim_evidence": [181   {182    "claim": "Sparsity-beta sorted portfolios earn a high-minus-low spread of about 4.6% per year",183    "evidence_type": "table",184    "evidence_ref": "Table II"185   },186   {187    "claim": "Concentration-beta sorted portfolios earn a spread of about -3.2% per year, not explained by CAPM or FF3",188    "evidence_type": "table",189    "evidence_ref": "Table II"190   },191   {192    "claim": "Sparsity and concentration are priced across multiple sets of test assets",193    "evidence_type": "table",194    "evidence_ref": "Table III"195   },196   {197    "claim": "Sparsity innovations relate to higher and concentration innovations to lower aggregate dividend growth",198    "evidence_type": "table",199    "evidence_ref": "Table IV"200   },201   {202    "claim": "Concentration and sparsity are sufficient statistics for aggregate risk and determine consumption dynamics",203    "evidence_type": "equation_or_proof",204    "evidence_ref": "Section I.C-D"205   },206   {207    "claim": "Distinct networks can share the same factor values, illustrating how the factors differ",208    "evidence_type": "figure",209    "evidence_ref": "Figures 2-4"210   }211  ],212  "structural_summary": "The paper opens with a concrete motivation (technological change reshaping input-output links) and states a first-mover contribution. It then sketches the economic mechanisms behind each factor and previews the empirical tests and magnitudes, followed by a related-literature subsection inside the introduction. A long closed-form theory section derives the two network factors and illustrates them with stylized network figures. A compact evidence section tests the signed risk-price predictions through beta-sorted portfolios, Fama-MacBeth estimation and macro regressions. The conclusion restates the findings and lists extensions: endogenous network formation and credit frictions.",213  "writing_profile_keywords": [214   "production-based asset pricing",215   "theory-first then test",216   "closed-form model",217   "first-person singular",218   "signed prediction testing",219   "portfolio sorts",220   "Fama-MacBeth"221  ],222  "confidence": 0.8,223  "notes": "Single-author model-plus-test paper. The theory section dominates by equation count (23 of 24). Robustness appears as a subsection within the evidence section; its details are not visible. Standard error type is not stated in the excerpts."224 },225 "deterministic_checks": {226  "roadmap_regex": true,227  "contribution_regex_in_intro": true,228  "magnitude_in_abstract": true,229  "abstract_n_sentences": 5,230  "intro_n_paragraphs": 8,231  "intro_n_words": 1245,232  "causal_words_in_abstract": 0,233  "hedge_words_in_abstract": 1,234  "stars_mentioned": false,235  "clustered_se_mentioned": false,236  "internet_appendix_mentioned": false,237  "section_roles_rule": {238   "0": "introduction",239   "1": "theory",240   "2": "results",241   "3": "conclusion"242  }243 },244 "schema_valid": true,245 "code_version": "feb6c9e-dirty"246}
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