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| Formato: | Recurso digital |
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Zenodo
2026
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| Materias: | |
| Acceso en línea: | https://doi.org/10.5281/zenodo.20228819 |
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- <p>Hospital charges in the United States bear little relation to costs, negotiated prices, or Medicare reimbursement --- yet no formal model explains the structure of these markups. We develop a log-linear pricing model in which the charge-to-payment ratio (CPR), the ratio of a hospital's listed charge to its Medicare payment, depends on market concentration, private payer share, and regulatory transparency. The model generates testable monotonicity conditions: CPR weakly increases in each channel. We calibrate against 145,879 hospital-DRG pairs across 2,906 hospitals drawn from CMS Medicare Inpatient Provider Utilization and Payment data. The median CPR is 5.63 (95% CI: [5.61, 5.64]), ranging from 1.23 in Maryland --- which operates an all-payer rate-setting system --- to 11.23 in Nevada. Beyond calibration, we derive two overidentifying predictions that the model could fail. First, the model predicts that hospitals with higher volume (a market-power proxy) should exhibit higher CPR; if volume and CPR were uncorrelated or negatively correlated, the opacity-market-power mechanism would be falsified. The observed Spearman correlation is 0.36 [0.32, 0.39] --- the prediction survives. Second, the model predicts that higher-complexity DRGs (greater information asymmetry) should carry higher CPR; kidney transplants (DRG 652) at 12.66x should exceed major joint replacements (DRG 470) at 5.87x. This ordering holds --- the prediction survives. These overidentifying tests transform the model from a curve-fitting exercise into a framework that made specific bets and won them. The contribution is organizational: a common parametric structure unifying four previously disconnected regularities --- the Maryland outlier, Nevada extreme, volume-CPR correlation, and DRG heterogeneity --- into a framework whose falsifiable predictions survived contact with the data.</p> <p>---</p>