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Dependent variables are log-transformed and execution costs are given in basis points relative to a quote benchmark. Significance is marked with stars. Structural estimates report sandwich standard errors in parentheses. Transition-matrix tables compare empirical and model-implied persistence.",170 "units_stated_in_notes": true,171 "standard_errors_description": "mixed"172 },173 "figures": {174 "uncertainty_shown": "mixed"175 },176 "conclusion": {177 "bounded_claims": true,178 "policy_implications": true,179 "open_questions": false,180 "restates_contribution": true181 },182 "hedging": {183 "level": "moderate",184 "typical_devices": [185 "conditional welfare statements (unless nontrade value captured efficiently)",186 "'can decrease welfare'",187 "acknowledging model is stylized",188 "'suggest' for empirical patterns"189 ]190 },191 "claim_evidence": [192 {193 "claim": "About one-third of insurers trade with a single dealer while some insurers have large dealer networks",194 "evidence_type": "figure",195 "evidence_ref": "Figure 3; Table III"196 },197 {198 "claim": "Execution costs fall with network size up to roughly 20 dealers and rise beyond that",199 "evidence_type": "table",200 "evidence_ref": "Table V"201 },202 {203 "claim": "Insurers' network size is persistent from year to year",204 "evidence_type": "table",205 "evidence_ref": "Table IV"206 },207 {208 "claim": "The estimated model quantitatively fits the network size distribution and the price–network relation",209 "evidence_type": "figure",210 "evidence_ref": "Figures 5-6; Table VI"211 },212 {213 "claim": "Reducing repeat trading or unbundling nontrade services can raise costs or reduce welfare for some insurers",214 "evidence_type": "figure",215 "evidence_ref": "Figure 7; Section IV.E"216 },217 {218 "claim": "Trading activity follows a power law",219 "evidence_type": "figure",220 "evidence_ref": "Figure 2"221 }222 ],223 "structural_summary": "The introduction motivates insurers' need for liquidity in OTC bond markets, then previews regulatory-data stylized facts. It sketches the one-to-many search model and identifies a gap: existing models predict either network size or prices, not both. It previews counterfactual policy results and closes with literature positioning and a roadmap. The body moves through data, three empirical stylized-fact subsections, model setup and predictions, and structural estimation with identification and fit, ending with policy counterfactuals. A short conclusion restates the facts and the policy relevance.",224 "writing_profile_keywords": [225 "stylized facts then structural model",226 "OTC search and bargaining",227 "regulatory transaction data",228 "counterfactual policy analysis",229 "power law distributions",230 "relationship trading"231 ],232 "confidence": 0.8,233 "notes": "Section IV combines estimation and policy counterfactuals; it is assigned the results role here even though the rule-based role was theory. 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