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1{2 "validation_errors": [],3 "doc_id": "jof_12827",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T23:10:35+00:00",9 "source_sha256": "5254c1e1d0d04b63d8df38df3b0e2f854bba8b2ede41cd6dfc2e0d35b0787307",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 20507,12 "usage": {13  "input": 7427,14  "output": 1849,15  "cache_read": 3968,16  "cache_write": 017 },18 "cost_usd": 0.067482,19 "request_id": "req_011CggqbdrpRSgxehFaNq2LC",20 "annotation": {21  "genre": "empirical",22  "subfield": "asset_pricing",23  "subfield_secondary": "market_microstructure",24  "primary_method": "reduced_form_regression",25  "identification": {26   "estimand": "Incremental share of variation in corporate bond yield spread changes, and in their common unexplained component, accounted for by systematic OTC friction proxies",27   "strategy": "Bond-by-bond time-series regressions extending the CDGM structural-variable model with marketwide proxies for inventory, search and bargaining frictions; principal component analysis of residuals to assess the common factor",28   "key_assumptions": [29    "Constructed proxies capture the intensity of systematic inventory, search and bargaining frictions",30    "CDGM structural variables adequately control for fundamental drivers"31   ],32   "threats_addressed": [33    "stale dealer quotes, handled by using transaction prices",34    "crisis-period dominance, handled by excluding the crisis",35    "other potential drivers of yield spread changes",36    "alternative definitions of CDGM variables"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": true,48    "sentence_index": 049   },50   "method": {51    "present": true,52    "sentence_index": 153   },54   "results": {55    "present": true,56    "sentence_index": 157   },58   "magnitude": {59    "present": true,60    "sentence_index": 161   },62   "contribution": {63    "present": true,64    "sentence_index": 465   }66  },67  "introduction": {68   "moves": [69    {70     "paragraph_index": 0,71     "move": "motivation"72    },73    {74     "paragraph_index": 0,75     "move": "tension_or_puzzle"76    },77    {78     "paragraph_index": 0,79     "move": "preview_of_approach"80    },81    {82     "paragraph_index": 0,83     "move": "preview_of_results"84    },85    {86     "paragraph_index": 1,87     "move": "mechanism_or_theory_sketch"88    },89    {90     "paragraph_index": 1,91     "move": "preview_of_results"92    },93    {94     "paragraph_index": 2,95     "move": "mechanism_or_theory_sketch"96    },97    {98     "paragraph_index": 2,99     "move": "preview_of_results"100    },101    {102     "paragraph_index": 3,103     "move": "preview_of_results"104    },105    {106     "paragraph_index": 4,107     "move": "related_literature_positioning"108    },109    {110     "paragraph_index": 5,111     "move": "related_literature_positioning"112    },113    {114     "paragraph_index": 6,115     "move": "roadmap"116    }117   ],118   "contribution_paragraph_index": 0,119   "contribution_types": [120    "new_evidence",121    "new_data_or_measure"122   ],123   "roadmap_present": true,124   "literature_positioning_location": "inside_introduction"125  },126  "section_roles": [127   {128    "idx": 0,129    "role": "introduction"130   },131   {132    "idx": 1,133    "role": "data"134   },135   {136    "idx": 2,137    "role": "empirical_design"138   },139   {140    "idx": 3,141    "role": "data"142   },143   {144    "idx": 4,145    "role": "results"146   },147   {148    "idx": 5,149    "role": "conclusion"150   }151  ],152  "results_presentation": {153   "economic_significance_discussed": true,154   "statistical_significance_language": "unclear",155   "magnitude_statements_in_intro": true156  },157  "robustness": {158   "location": "mixed",159   "types": [160    "alternative_sample_period",161    "alternative_specification",162    "alternative_measure"163   ]164  },165  "tables": {166   "convention_notes": "Notes spell out the regression equation and its variable vectors, the sample filter (bonds with at least 25 monthly observations) and the data source and period. Results come from bond-level regressions summarized across bonds.",167   "units_stated_in_notes": true,168   "standard_errors_description": "unclear"169  },170  "figures": {171   "uncertainty_shown": "no"172  },173  "conclusion": {174   "bounded_claims": true,175   "policy_implications": false,176   "open_questions": false,177   "restates_contribution": true178  },179  "hedging": {180   "level": "moderate",181   "typical_devices": [182    "'consistent with' theory",183    "'suggests'/'indicates'",184    "'associated with'",185    "'support the implications of'"186   ]187  },188  "claim_evidence": [189   {190    "claim": "Systematic inventory, search and bargaining frictions explain 23.4% of the variation in the common component of yield spread changes",191    "evidence_type": "table",192    "evidence_ref": "Table IX"193   },194   {195    "claim": "The CDGM model on transaction prices leaves a large common unexplained factor",196    "evidence_type": "table",197    "evidence_ref": "Tables II, III"198   },199   {200    "claim": "Higher interdealer network connectivity and longer intermediation chains are associated with narrower yield spreads",201    "evidence_type": "table",202    "evidence_ref": "Table VII"203   },204   {205    "claim": "Search frictions capture 6.3% of the common component",206    "evidence_type": "table",207    "evidence_ref": "Table VII"208   },209   {210    "claim": "Results hold when the crisis period is excluded",211    "evidence_type": "table",212    "evidence_ref": "Table X"213   }214  ],215  "structural_summary": "The paper opens with the puzzle of a large unexplained common factor in yield spread changes and introduces OTC friction theory as a candidate explanation. It then previews the friction proxies and results, theory by theory, and positions itself against the liquidity and market-making literatures. The body describes the TRACE data, replicates the CDGM benchmark, and constructs systematic friction measures. It then tests each friction group separately and jointly, followed by robustness checks. A brief conclusion restates the key magnitudes.",216  "writing_profile_keywords": [217   "puzzle-driven",218   "benchmark replication",219   "theory-guided proxies",220   "explained variance",221   "OTC frictions",222   "network measures"223  ],224  "confidence": 0.8,225  "notes": "Sections III and IV are labelled 'other' by the role rule; Section III is mainly measure construction, so it is coded as data. Standard error conventions are not visible in the table notes."226 },227 "deterministic_checks": {228  "roadmap_regex": true,229  "contribution_regex_in_intro": false,230  "magnitude_in_abstract": true,231  "abstract_n_sentences": 7,232  "intro_n_paragraphs": 7,233  "intro_n_words": 1121,234  "causal_words_in_abstract": 0,235  "hedge_words_in_abstract": 0,236  "stars_mentioned": false,237  "clustered_se_mentioned": false,238  "internet_appendix_mentioned": false,239  "section_roles_rule": {240   "0": "introduction",241   "1": "data",242   "2": "theory",243   "3": "other",244   "4": "other",245   "5": "conclusion"246  }247 },248 "schema_valid": true,249 "code_version": "87c73cd-dirty"250}
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