Working Paper: NBER ID: w8497
Authors: John Geweke; Gautam Gowrisankaran; Robert J. Town
Abstract: This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random admission leads to spurious inference about hospital quality. This study controls for hospital selection using a model in which distance between the patient's residence and alternative hospitals are key exogenous variables. Bayesian inference in this model is feasible using a Markov chain Monte Carlo posterior simulator, and attaches posterior probabilities to quality comparisons between individual hospitals and groups of hospitals. The study uses data on 74,848 Medicare patients admitted to 114 hospitals in Los Angeles County from 1989 through 1992 with a diagnosis of pneumonia. It finds the smallest and largest hospitals to be of high quality and public hospitals to be of low quality. There is strong evidence of dependence between the unobserved severity of illness and the assignment of patients to hospitals. Consequently a conventional probit model leads to inferences about quality markedly different than those in this study's selection model.
Keywords: hospital quality; Bayesian inference; nonrandom selection; mortality rates; econometric methods
JEL Codes: C11; C34; I11; I12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
Cause | Effect |
---|---|
Hospital Quality (I11) | Mortality Rates (I12) |
Hospital Size (I11) | Hospital Quality (I11) |
Public Hospitals (I19) | Hospital Quality (I11) |
Unobserved Severity of Illness (I12) | Hospital Assignment (I11) |
Hospital Assignment (I11) | Hospital Quality (I11) |
Distance from Hospital (I11) | Hospital Choice (I11) |