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1{2 "validation_errors": [],3 "doc_id": "jof_12467",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T22:56:21+00:00",9 "source_sha256": "4093c4ee9f55187820d8b17add241413334bf8a03bcc902064bce6113f166283",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 21989,12 "usage": {13  "input": 7754,14  "output": 2111,15  "cache_read": 3968,16  "cache_write": 017 },18 "cost_usd": 0.07403,19 "request_id": "req_011CggpWUprHSiSyMixMJ3cu",20 "annotation": {21  "genre": "empirical",22  "subfield": "financial_intermediation_banking",23  "subfield_secondary": "macro_finance (monetary policy transmission)",24  "primary_method": "reduced_form_regression",25  "identification": {26   "estimand": "Effect of the short-term policy rate (federal funds rate) on the ex ante risk rating of newly issued bank loans, and how that effect varies with bank capitalization",27   "strategy": "Loan-level panel regressions of internal loan risk ratings on the federal funds rate interacted with bank capital, with loan, bank, regional and macro controls. Endogeneity is addressed by using new loans, Taylor rule residuals, regional business-cycle asynchrony, and small-bank and state exclusions.",28   "key_assumptions": [29    "Policy rate is plausibly exogenous to individual bank lending risk at loan origination",30    "Internal risk ratings comparably measure ex ante loan risk across banks",31    "Controls capture demand-side and economic-condition confounders"32   ],33   "threats_addressed": [34    "Reverse causality of monetary policy responding to bank risk-taking",35    "Changes in economic conditions",36    "Securitization and loan sales",37    "Loans under preexisting commitment",38    "Large-bank influence on policy",39    "Liquidity versus capital as the relevant bank characteristic"40   ],41   "causal_language_strength": 3,42   "uses_causal_claim": true43  },44  "abstract_moves": {45   "question": {46    "present": true,47    "sentence_index": 048   },49   "gap": {50    "present": false,51    "sentence_index": -152   },53   "method": {54    "present": true,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": 068   }69  },70  "introduction": {71   "moves": [72    {73     "paragraph_index": 0,74     "move": "motivation"75    },76    {77     "paragraph_index": 0,78     "move": "policy_or_broader_implication"79    },80    {81     "paragraph_index": 0,82     "move": "mechanism_or_theory_sketch"83    },84    {85     "paragraph_index": 1,86     "move": "mechanism_or_theory_sketch"87    },88    {89     "paragraph_index": 2,90     "move": "tension_or_puzzle"91    },92    {93     "paragraph_index": 3,94     "move": "preview_of_approach"95    },96    {97     "paragraph_index": 3,98     "move": "preview_of_results"99    },100    {101     "paragraph_index": 3,102     "move": "identification_defense"103    },104    {105     "paragraph_index": 4,106     "move": "related_literature_positioning"107    },108    {109     "paragraph_index": 4,110     "move": "contribution_statement"111    },112    {113     "paragraph_index": 5,114     "move": "related_literature_positioning"115    }116   ],117   "contribution_paragraph_index": 4,118   "contribution_types": [119    "new_evidence",120    "new_data_or_measure"121   ],122   "roadmap_present": false,123   "literature_positioning_location": "inside_introduction"124  },125  "section_roles": [126   {127    "idx": 0,128    "role": "introduction"129   },130   {131    "idx": 1,132    "role": "empirical_design"133   },134   {135    "idx": 2,136    "role": "data"137   },138   {139    "idx": 3,140    "role": "results"141   },142   {143    "idx": 4,144    "role": "conclusion"145   },146   {147    "idx": 5,148    "role": "appendix"149   }150  ],151  "results_presentation": {152   "economic_significance_discussed": true,153   "statistical_significance_language": "unclear",154   "magnitude_statements_in_intro": true155  },156  "robustness": {157   "location": "within_results",158   "types": [159    "subsample",160    "alternative_measure",161    "alternative_specification",162    "other"163   ]164  },165  "tables": {166   "convention_notes": "Table notes state the sample period (1997Q2–2011Q4), define the dependent variable as the STBL internal risk rating on a 1–5 scale, list the data sources for the controls, and describe sample restrictions column by column.",167   "units_stated_in_notes": true,168   "standard_errors_description": "unclear"169  },170  "figures": {171   "uncertainty_shown": "unclear"172  },173  "conclusion": {174   "bounded_claims": true,175   "policy_implications": false,176   "open_questions": true,177   "restates_contribution": true178  },179  "hedging": {180   "level": "moderate",181   "typical_devices": [182    "'associated with' wording alongside causal claims",183    "'consistent with' a theoretical channel",184    "scope qualifier limiting results to the new-loan margin",185    "noting difficulty of quantifying the channel versus establishing it"186   ]187  },188  "claim_evidence": [189   {190    "claim": "Loan risk ratings rise when the federal funds rate falls; a one-SD rate decrease raises ratings by about 0.11",191    "evidence_type": "table",192    "evidence_ref": "Table IV"193   },194   {195    "claim": "The negative rate–risk relationship is weaker for poorly capitalized banks, consistent with risk-shifting",196    "evidence_type": "table",197    "evidence_ref": "Table IV"198   },199   {200    "claim": "Average loan risk rating co-moves negatively with the target federal funds rate over time",201    "evidence_type": "figure",202    "evidence_ref": "Figure 1"203   },204   {205    "claim": "Results are robust to Taylor rule residuals and to controls for economic conditions",206    "evidence_type": "table",207    "evidence_ref": "Table VIII"208   },209   {210    "claim": "Theory gives ambiguous predictions depending on maturity mismatch and leverage",211    "evidence_type": "equation_or_proof",212    "evidence_ref": "Appendix A"213   },214   {215    "claim": "Capital, not liquidity, drives the heterogeneity",216    "evidence_type": "table",217    "evidence_ref": "Table VI"218   }219  ],220  "structural_summary": "The introduction motivates the topic with post-crisis policy debate and lays out competing theoretical channels (portfolio, risk-shifting, search for yield), concluding that the sign is an empirical question. It previews confidential loan-level data, main results with magnitudes, and an endogeneity defense, then positions the paper against prior risk measures and Jimenez et al. The body follows a short methodology section, a data section, and a long results section whose subsections are mostly robustness checks. The conclusion restates magnitudes and contrasts the findings with Spain, and a simple theoretical model sits in an appendix.",221  "writing_profile_keywords": [222   "loan-level confidential data",223   "interaction with bank capital",224   "risk-taking channel",225   "competing theoretical predictions",226   "extensive robustness subsections",227   "policy-relevant motivation"228  ],229  "confidence": 0.8,230  "notes": "Standard-error and significance conventions are not visible in the table-note excerpts. The abstract mentions regional business-cycle synchrony and financial distress, which correspond to the Table X analyses."231 },232 "deterministic_checks": {233  "roadmap_regex": false,234  "contribution_regex_in_intro": false,235  "magnitude_in_abstract": false,236  "abstract_n_sentences": 5,237  "intro_n_paragraphs": 6,238  "intro_n_words": 1076,239  "causal_words_in_abstract": 0,240  "hedge_words_in_abstract": 0,241  "stars_mentioned": false,242  "clustered_se_mentioned": false,243  "internet_appendix_mentioned": false,244  "section_roles_rule": {245   "0": "introduction",246   "1": "empirical_design",247   "2": "data",248   "3": "results",249   "4": "conclusion",250   "5": "appendix"251  }252 },253 "schema_valid": true,254 "code_version": "feb6c9e-dirty"255}
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