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1{2 "validation_errors": [],3 "doc_id": "jof_12780",4 "ontology_version": "1.0",5 "prompt_version": "1.0",6 "model": "claude-opus-5-5",7 "effort": "medium",8 "annotated_at": "2026-11-03T23:09:09+00:00",9 "source_sha256": "94cf0a1c3495d43fd153a6803395d6164711488734c800bacbf0aa6d3461144e",10 "parser": "pymupdf-jof-heuristics-1.3",11 "payload_chars": 26100,12 "usage": {13  "input": 8773,14  "output": 2352,15  "cache_read": 3968,16  "cache_write": 017 },18 "cost_usd": 0.082926,19 "request_id": "req_011CggqUyzLMpPtf6uLRAVKr",20 "annotation": {21  "genre": "mixed_theory_empirical",22  "subfield": "asset_pricing",23  "subfield_secondary": "international_finance",24  "primary_method": "reduced_form_regression",25  "identification": {26   "estimand": "Cross-sectional risk premium (portfolio alpha) of firms in low versus high shipping-cost industries, and the sign of the price of foreign productivity shock risk",27   "strategy": "Portfolio sorts on industry shipping costs and weight-to-value, with five-factor Fama-French alphas; conditional sorts on size and profitability; betas on Chinese import growth; checks of earnings-announcement returns and analyst forecast errors; a calibrated Melitz-type GE model",28   "key_assumptions": [29    "Shipping costs capture structural, slow-moving barriers to trade that determine exposure to globalization",30    "Five-factor alphas reflect compensation for priced risk rather than mispricing",31    "Chinese import growth proxies for foreign productivity shocks"32   ],33   "threats_addressed": [34    "loadings on known risk factors",35    "mispricing or a wedge between ex post realized and ex ante expected returns (tested with earnings announcements and analyst forecasts)",36    "sample specificity (European stocks, subperiods)",37    "weighting scheme (value-weighted portfolios)"38   ],39   "causal_language_strength": 3,40   "uses_causal_claim": true41  },42  "abstract_moves": {43   "question": {44    "present": true,45    "sentence_index": 046   },47   "gap": {48    "present": false,49    "sentence_index": -150   },51   "method": {52    "present": true,53    "sentence_index": 154   },55   "results": {56    "present": true,57    "sentence_index": 258   },59   "magnitude": {60    "present": true,61    "sentence_index": 262   },63   "contribution": {64    "present": true,65    "sentence_index": 566   }67  },68  "introduction": {69   "moves": [70    {71     "paragraph_index": 0,72     "move": "motivation"73    },74    {75     "paragraph_index": 1,76     "move": "preview_of_approach"77    },78    {79     "paragraph_index": 1,80     "move": "mechanism_or_theory_sketch"81    },82    {83     "paragraph_index": 2,84     "move": "preview_of_approach"85    },86    {87     "paragraph_index": 3,88     "move": "preview_of_results"89    },90    {91     "paragraph_index": 3,92     "move": "robustness_mention"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": "mechanism_or_theory_sketch"105    },106    {107     "paragraph_index": 6,108     "move": "preview_of_results"109    },110    {111     "paragraph_index": 7,112     "move": "mechanism_or_theory_sketch"113    },114    {115     "paragraph_index": 8,116     "move": "related_literature_positioning"117    },118    {119     "paragraph_index": 8,120     "move": "contribution_statement"121    },122    {123     "paragraph_index": 9,124     "move": "contribution_statement"125    },126    {127     "paragraph_index": 10,128     "move": "related_literature_positioning"129    },130    {131     "paragraph_index": 11,132     "move": "related_literature_positioning"133    },134    {135     "paragraph_index": 12,136     "move": "related_literature_positioning"137    },138    {139     "paragraph_index": 13,140     "move": "roadmap"141    }142   ],143   "contribution_paragraph_index": 9,144   "contribution_types": [145    "new_evidence",146    "new_theory_or_model"147   ],148   "roadmap_present": true,149   "literature_positioning_location": "inside_introduction"150  },151  "section_roles": [152   {153    "idx": 0,154    "role": "introduction"155   },156   {157    "idx": 1,158    "role": "results"159   },160   {161    "idx": 2,162    "role": "theory"163   },164   {165    "idx": 3,166    "role": "conclusion"167   }168  ],169  "results_presentation": {170   "economic_significance_discussed": true,171   "statistical_significance_language": "unclear",172   "magnitude_statements_in_intro": true173  },174  "robustness": {175   "location": "within_results",176   "types": [177    "subsample",178    "alternative_measure",179    "alternative_sample_period",180    "alternative_specification",181    "sensitivity_to_parameters"182   ]183  },184  "tables": {185   "convention_notes": "Table notes define how shipping costs and weight-to-value are constructed, give the sample (exchanges, SIC range, period), and state that monthly alphas are multiplied by 12 to annualize. Panels split the shipping-cost and weight-to-value sorts. The calibration tables give parameter sources and report simulated moments with Newey-West standard errors.",186   "units_stated_in_notes": true,187   "standard_errors_description": "mixed"188  },189  "figures": {190   "uncertainty_shown": "unclear"191  },192  "conclusion": {193   "bounded_claims": true,194   "policy_implications": false,195   "open_questions": true,196   "restates_contribution": true197  },198  "hedging": {199   "level": "moderate",200   "typical_devices": [201    "suggesting that",202    "can be consistent with",203    "we hope to stimulate",204    "we leave to future research",205    "may help rationalize"206   ]207  },208  "claim_evidence": [209   {210    "claim": "Shipping costs vary substantially across industries, persist over time, and correlate negatively with trade flows",211    "evidence_type": "table",212    "evidence_ref": "Tables I-III"213   },214   {215    "claim": "The long high-SC, short low-SC portfolio earns about -7% per year, and five-factor alphas are large",216    "evidence_type": "table",217    "evidence_ref": "Tables IV-V"218   },219   {220    "claim": "The premium also holds in European stocks",221    "evidence_type": "table",222    "evidence_ref": "Table VI"223   },224   {225    "claim": "No evidence of mispricing from earnings-announcement returns or analyst forecast errors",226    "evidence_type": "table",227    "evidence_ref": "Tables VII-VIII"228   },229   {230    "claim": "The premium is concentrated among small, less profitable firms, which load negatively on Chinese import growth",231    "evidence_type": "table",232    "evidence_ref": "Tables IX-X"233   },234   {235    "claim": "The premium is stronger in industries with high trade elasticity and strong business-stealing effects, as the model predicts",236    "evidence_type": "table",237    "evidence_ref": "Tables XI-XII"238   },239   {240    "claim": "With perfect risk-sharing the model fails; with limited risk-sharing consumption falls after foreign shocks",241    "evidence_type": "figure",242    "evidence_ref": "Figures 1-3, Table XIV"243   }244  ],245  "structural_summary": "The introduction moves from the mixed domestic effects of globalization to an asset-pricing lens. It previews the shipping-cost measure, the portfolio results and robustness, and the displacement interpretation, which it frames as a puzzle. It then sketches the model, previews the calibration outcome, positions the paper in four literatures, and closes with a roadmap. Section I builds the measure and estimates the premium, with robustness and mispricing checks folded into the results. Section II develops and calibrates a Melitz-type model and tests its cross-sectional predictions. A short conclusion restates the findings and leaves alternative frictions to future work.",246  "writing_profile_keywords": [247   "portfolio sorts",248   "risk premium",249   "puzzle framing",250   "trade model with asset prices",251   "calibration",252   "limited risk-sharing",253   "shipping costs proxy"254  ],255  "confidence": 0.8,256  "notes": "The empirical part relies on portfolio sorts with factor alphas; reduced_form_regression is the closest method category. The excerpts do not show how significance is reported in the main tables."257 },258 "deterministic_checks": {259  "roadmap_regex": true,260  "contribution_regex_in_intro": true,261  "magnitude_in_abstract": true,262  "abstract_n_sentences": 6,263  "intro_n_paragraphs": 14,264  "intro_n_words": 1887,265  "causal_words_in_abstract": 0,266  "hedge_words_in_abstract": 1,267  "stars_mentioned": false,268  "clustered_se_mentioned": false,269  "internet_appendix_mentioned": false,270  "section_roles_rule": {271   "0": "introduction",272   "1": "other",273   "2": "theory",274   "3": "conclusion"275  }276 },277 "schema_valid": true,278 "code_version": "87c73cd-dirty"279}
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