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An extended Korteweg–Sorensen Bayesian random-effects model, estimated by MCMC, then quantifies skill. Alternative explanations are probed with LP-type subsamples, public-LP controls, return-distribution quartiles and first-time or follow-on fund samples.",28 "key_assumptions": [29 "Under the null of no skill, LPs draw returns from a common distribution; the bootstrap restrictions mimic actual investment opportunities.",30 "GP abilities are not competed away by fundraising (no strong Berk–Green effect), so GP quality is persistent.",31 "Within an LP type, risk preferences are similar.",32 "The parametric latent-skill structure and its priors are adequate."33 ],34 "threats_addressed": [35 "Differences in risk tolerance across LPs",36 "Political pressure on public pension funds",37 "Differential access to high-quality GPs (first-time funds, reinvestment)",38 "Chance variation in returns"39 ],40 "causal_language_strength": 3,41 "uses_causal_claim": true42 },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": 255 },56 "results": {57 "present": true,58 "sentence_index": 159 },60 "magnitude": {61 "present": true,62 "sentence_index": 263 },64 "contribution": {65 "present": false,66 "sentence_index": -167 }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": "motivation"82 },83 {84 "paragraph_index": 1,85 "move": "preview_of_approach"86 },87 {88 "paragraph_index": 2,89 "move": "preview_of_results"90 },91 {92 "paragraph_index": 3,93 "move": "identification_defense"94 },95 {96 "paragraph_index": 4,97 "move": "identification_defense"98 },99 {100 "paragraph_index": 5,101 "move": "related_literature_positioning"102 },103 {104 "paragraph_index": 5,105 "move": "contribution_statement"106 }107 ],108 "contribution_paragraph_index": 5,109 "contribution_types": [110 "new_evidence",111 "new_method"112 ],113 "roadmap_present": false,114 "literature_positioning_location": "inside_introduction"115 },116 "section_roles": [117 {118 "idx": 0,119 "role": "introduction"120 },121 {122 "idx": 1,123 "role": "data"124 },125 {126 "idx": 2,127 "role": "results"128 },129 {130 "idx": 3,131 "role": "results"132 },133 {134 "idx": 4,135 "role": "robustness"136 },137 {138 "idx": 5,139 "role": "conclusion"140 },141 {142 "idx": 6,143 "role": "appendix"144 }145 ],146 "results_presentation": {147 "economic_significance_discussed": true,148 "statistical_significance_language": "unclear",149 "magnitude_statements_in_intro": true150 },151 "robustness": {152 "location": "mixed",153 "types": [154 "alternative_specification",155 "subsample",156 "alternative_sample_period",157 "other"158 ]159 },160 "tables": {161 "convention_notes": "Tables run from summary statistics to bootstrap comparisons of actual versus simulated distributions, then to Bayesian posterior estimates by period and LP type. Notes describe sample restrictions, panels and model variants (odd versus even columns for Model 1/2), and cross-reference variable definitions in Table V.",162 "units_stated_in_notes": false,163 "standard_errors_description": "unclear"164 },165 "figures": {166 "uncertainty_shown": "unclear"167 },168 "conclusion": {169 "bounded_claims": true,170 "policy_implications": false,171 "open_questions": false,172 "restates_contribution": true173 },174 "hedging": {175 "level": "moderate",176 "typical_devices": [177 "suggest/suggesting",178 "potentially reflects",179 "it does not appear that",180 "unlikely to be the main factor",181 "explicit limitations subsection"182 ]183 },184 "claim_evidence": [185 {186 "claim": "Dispersion of LP persistence in top-half funds exceeds what bootstrapped no-skill samples would produce.",187 "evidence_type": "table",188 "evidence_ref": "Table II, Table III, Figure 1"189 },190 {191 "claim": "A one-SD increase in LP skill raises annual IRR by roughly 1-2 pp, and by more in venture funds.",192 "evidence_type": "table",193 "evidence_ref": "Table V, Figure 2"194 },195 {196 "claim": "The skill effect is stronger in earlier vintages and declines over the sample period.",197 "evidence_type": "table",198 "evidence_ref": "Table V panels B-D"199 },200 {201 "claim": "Risk preferences do not explain the results: variation persists within LP types, and high-skill LPs outperform across the return distribution.",202 "evidence_type": "table",203 "evidence_ref": "Table VII, Table VIII, Table X"204 },205 {206 "claim": "Public pension funds do not earn systematically different returns, which argues against political pressure as the driver.",207 "evidence_type": "table",208 "evidence_ref": "Table IX"209 },210 {211 "claim": "Skill estimates hold in first-time-fund and reinvestment samples, addressing the access explanation.",212 "evidence_type": "table",213 "evidence_ref": "Table XI, Table XII"214 }215 ],216 "structural_summary": "The paper motivates the question with the economic importance of institutional investors and notes a gap in measuring their skill, focusing on private equity fund selection. After describing a merged LP-investment dataset, it runs model-free bootstrap tests of persistence and return dispersion. It then quantifies skill with an extended Bayesian latent-skill model estimated by MCMC, with a variance decomposition and individual skill estimates. A separate section weighs alternative interpretations (risk tolerance, political pressure, access) and limitations. The conclusion restates findings with hedged interpretation, and an appendix details the MCMC algorithm and priors.",217 "writing_profile_keywords": [218 "investor skill",219 "bootstrap null distribution",220 "Bayesian MCMC",221 "persistence",222 "alternative explanations",223 "private equity LPs",224 "variance decomposition"225 ],226 "confidence": 0.8,227 "notes": "Primary method is coded 'other' because the design combines bootstrap tests with a Bayesian hierarchical random-effects model (extended Korteweg–Sorensen); structural_estimation is a defensible alternative. Section II is labelled 'theory' by the rule but contains empirical model-free tests, so it is coded as results. No explicit roadmap is visible in the excerpt."228 },229 "deterministic_checks": {230 "roadmap_regex": false,231 "contribution_regex_in_intro": false,232 "magnitude_in_abstract": false,233 "abstract_n_sentences": 5,234 "intro_n_paragraphs": 6,235 "intro_n_words": 933,236 "causal_words_in_abstract": 1,237 "hedge_words_in_abstract": 0,238 "stars_mentioned": false,239 "clustered_se_mentioned": false,240 "internet_appendix_mentioned": false,241 "section_roles_rule": {242 "0": "introduction",243 "1": "data",244 "2": "theory",245 "3": "results",246 "4": "other",247 "5": "conclusion",248 "6": "appendix"249 }250 },251 "schema_valid": true,252 "code_version": "87c73cd-dirty"253}