1{2 "validation_errors": [],3 "repairs": [4 "dropped figures.figures_note"5 ],6 "doc_id": "jof_12889",7 "ontology_version": "1.0",8 "prompt_version": "1.0",9 "model": "claude-opus-5-5",10 "effort": "medium",11 "annotated_at": "2026-11-03T23:26:52+00:00",12 "source_sha256": "9c06602bde0ea74e81724e190bbc9b24e7590a8fb5dcae9ebc61b600de962122",13 "parser": "pymupdf-jof-heuristics-1.3",14 "payload_chars": 28017,15 "usage": {16 "input": 9492,17 "output": 2281,18 "cache_read": 3968,19 "cache_write": 020 },21 "cost_usd": 0.084382,22 "request_id": "req_011CggqmMSdP6J2v1avvVgLV",23 "annotation": {24 "genre": "empirical",25 "subfield": "corporate_finance",26 "subfield_secondary": "international_finance",27 "primary_method": "reduced_form_regression",28 "identification": {29 "estimand": "Cross-country conditional associations between firm-level corporate control or ownership concentration and legal origin, development, and institutional characteristics.",30 "strategy": "Firm-level probit, ordered probit, OLS and hierarchical models on a large hand-extended ORBIS sample. Legal origin dummies and institutional proxies are used, with controls for continent, industry, and development. Multiple proxies are used to address measurement error.",31 "key_assumptions": [32 "Legal origin is treated as a reduced-form correlate; no causal identification is claimed.",33 "Control proxies (20% cutoff, Shapley-Shubik) capture actual control."34 ],35 "threats_addressed": [36 "Measurement error in institutional proxies, addressed with multiple proxies.",37 "Firm size and age composition (Holderness critique), addressed via heterogeneity analysis.",38 "Confounding by continent, industry, and development level."39 ],40 "causal_language_strength": 1,41 "uses_causal_claim": false42 },43 "abstract_moves": {44 "question": {45 "present": true,46 "sentence_index": 047 },48 "gap": {49 "present": false,50 "sentence_index": -151 },52 "method": {53 "present": true,54 "sentence_index": 055 },56 "results": {57 "present": true,58 "sentence_index": 159 },60 "magnitude": {61 "present": false,62 "sentence_index": -163 },64 "contribution": {65 "present": true,66 "sentence_index": 767 }68 },69 "introduction": {70 "moves": [71 {72 "paragraph_index": 0,73 "move": "motivation"74 },75 {76 "paragraph_index": 0,77 "move": "literature_gap"78 },79 {80 "paragraph_index": 1,81 "move": "preview_of_approach"82 },83 {84 "paragraph_index": 2,85 "move": "preview_of_results"86 },87 {88 "paragraph_index": 9,89 "move": "related_literature_positioning"90 },91 {92 "paragraph_index": 18,93 "move": "related_literature_positioning"94 },95 {96 "paragraph_index": 30,97 "move": "preview_of_approach"98 },99 {100 "paragraph_index": 30,101 "move": "identification_defense"102 },103 {104 "paragraph_index": 31,105 "move": "preview_of_results"106 },107 {108 "paragraph_index": 56,109 "move": "contribution_statement"110 },111 {112 "paragraph_index": 56,113 "move": "policy_or_broader_implication"114 },115 {116 "paragraph_index": 57,117 "move": "roadmap"118 }119 ],120 "contribution_paragraph_index": 56,121 "contribution_types": [122 "new_data_or_measure",123 "new_evidence"124 ],125 "roadmap_present": true,126 "literature_positioning_location": "inside_introduction"127 },128 "section_roles": [129 {130 "idx": 0,131 "role": "introduction"132 },133 {134 "idx": 1,135 "role": "data"136 },137 {138 "idx": 2,139 "role": "results"140 },141 {142 "idx": 3,143 "role": "results"144 },145 {146 "idx": 4,147 "role": "other"148 },149 {150 "idx": 5,151 "role": "robustness"152 },153 {154 "idx": 6,155 "role": "results"156 },157 {158 "idx": 7,159 "role": "results"160 },161 {162 "idx": 8,163 "role": "conclusion"164 },165 {166 "idx": 9,167 "role": "appendix"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": "mixed",177 "types": [178 "alternative_measure",179 "subsample",180 "alternative_specification",181 "alternative_sample_period"182 ]183 },184 "tables": {185 "convention_notes": "Notes name the estimator (probit ML average marginal effects, ordered probit, OLS) and define dependent variables and their categories in detail. Firm-level estimates are paired with country-level institutional proxies. Standard error treatment is not visible in the excerpts.",186 "units_stated_in_notes": true,187 "standard_errors_description": "unclear"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 "correlates/associated with rather than causes",202 "consistent with / in line with theory",203 "explicit disclaimer that cross-country associations cannot establish causality",204 "suggests / may prevent",205 "first step framing"206 ]207 },208 "claim_evidence": [209 {210 "claim": "Share of controlled firms is highest in French civil-law and lowest in common-law countries, net of continent, industry, and development.",211 "evidence_type": "table",212 "evidence_ref": "Tables II-IV; Figure 6"213 },214 {215 "claim": "Equity blocks exist in over 80% of noncontrolled firms across regions.",216 "evidence_type": "descriptive_statistic",217 "evidence_ref": "Table I; Section II"218 },219 {220 "claim": "The legal origin–control link holds for large, medium, and small firms and for young and old firms.",221 "evidence_type": "table",222 "evidence_ref": "Table V"223 },224 {225 "claim": "The negative income–control correlation appears only among large firms and is zero for small and medium firms.",226 "evidence_type": "table",227 "evidence_ref": "Table V"228 },229 {230 "claim": "Shareholder protection correlates with dispersed ownership, while creditor rights do not.",231 "evidence_type": "table",232 "evidence_ref": "Table VI"233 },234 {235 "claim": "Legal formalism and entry regulation are only weakly related to control.",236 "evidence_type": "table",237 "evidence_ref": "Tables VII-VIII"238 },239 {240 "claim": "Labor regulation and union power correlate strongly with corporate control.",241 "evidence_type": "table",242 "evidence_ref": "Table IX; Figure 8"243 }244 ],245 "structural_summary": "A large descriptive and correlational cross-country study. The introduction opens by noting the lack of consensus on basic correlations. It then previews a new hand-extended dataset and enumerates numbered findings in two blocks: legal origin with heterogeneity, and institutional correlates. Each finding is tied to supporting or challenged prior work, and the opening block closes with a synthesis favoring both legal-origin and political theories, followed by a roadmap. The body moves from data construction and control measures to world-wide descriptive patterns. It then runs legal-origin regressions with sensitivity checks, firm size and age heterogeneity, and institutional correlates. The conclusion restates the three-step findings and lists future research directions.",246 "writing_profile_keywords": [247 "law and finance",248 "descriptive anatomy",249 "new dataset",250 "enumerated findings",251 "cross-country correlations",252 "heterogeneity by firm size",253 "associational language",254 "theory adjudication"255 ],256 "confidence": 0.8,257 "notes": "The introduction paragraph list is fragmented by line breaks, so paragraph indices are approximate. The first contribution (the dataset) is mostly cut from the intro excerpt but is stated in the conclusion. Sections 4–5 are table and figure panel artifacts; section 5 contains the sensitivity analysis. The heterogeneity section is rule-labeled robustness but functions as results."258 },259 "deterministic_checks": {260 "roadmap_regex": true,261 "contribution_regex_in_intro": true,262 "magnitude_in_abstract": false,263 "abstract_n_sentences": 9,264 "intro_n_paragraphs": 64,265 "intro_n_words": 1177,266 "causal_words_in_abstract": 0,267 "hedge_words_in_abstract": 1,268 "stars_mentioned": false,269 "clustered_se_mentioned": false,270 "internet_appendix_mentioned": false,271 "section_roles_rule": {272 "0": "introduction",273 "1": "data",274 "2": "other",275 "3": "other",276 "4": "other",277 "5": "other",278 "6": "robustness",279 "7": "other",280 "8": "conclusion",281 "9": "appendix"282 }283 },284 "schema_valid": true,285 "code_version": "87c73cd-dirty"286}