Hospital readmission rates are increasingly being used as signals of hospital performance and a basis for hospital reimbursement. However for some interventions their interpretation may be complicated by differential patient survival rates after the initial intervention. If patient characteristics are not perfectly observable and hospitals differ in their mortality rates, then hospitals with low mortality rates are likely to have a larger share of un-observably sicker patients at risk of a

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... ission. Their performance on readmissions with respect to other hospitals will then be underestimated. We therefore examine hospitals' performance on readmission rates relaxing the assumption of independence between the data generating process for mortality and readmissions implicitly adopted in the vast majority of empirical applications. We use administrative data on emergency admissions for fractured hip in 290,000 patients aged 65 and over from 2003-2008 in England. We find strong evidence of sample selection bias in the identification of hospitals' performance on 28 days emergency readmissions when the residual correlation between mortality and readmissions is ignored. We use a bivariate sample selection model to allow for the selection process and the dichotomous nature of the outcome variables. Our study suggests that when, as in this example, the residual correlation is different from zero, inference from traditional models of hospital performance on readmissions might be invalid, and we offer a more appropriate method of inferring performance. The results have important implications for performance assessment and financial penalties related to readmissions.