However, Medicare patients do not cover all available treatments such that the Mci might be biased In this paper, severity of illness weights are constructed using the average length of stay(los) of each inpatient diagnosis in Germany. A mean los by year and main diagnosis m = l,...,M (ICD-10 Version 2.0, three digits) over all N= 2, 290 German hospitals is calculated: los N2s,(daysmi/casesmi ). The mean length of stay over all diagnoses and all hospitals is denoted by losG. The weight Tm Josm is bigger(smaller)than one if the treatment of diagnosis m takes more(less) time than the overall average los. These weights rely on the idea that length of stay is a good proxy for resource use. However, weights of rehabilitation care diagnoses may b upward biased compared to their costs, while weights for severe cases with high mortality rates may be biased downwards. Comparing the variable cases with the number of cases of each diagnosis multiplied by Tm( denoted by weighted cases), between -7, 065 and 6, 250 cases(-60% and 140 are added due t 3 Result 3.1 Cross Sectional Analysi Cross section estimation results for the three years 2001 to 2003 are reported in Table 2(cost efficiency) and Table 3(technical efficiency ). The hypothesis that there is no inefficiency(which means ou=0)can be rejected for each year under study. Over the three years, the signs of most coefficient estimates coincide in both models. Although standard errors and point estimates differ between the years and the models, both tables show similar and consistent results. The estimated effect of input prices on the cost frontier and of the inputs on the technical frontier are presented in the first part of Tables 2 and 3. The variation across time within each model is small and all but two coefficients are significantly different from zero at a one per cent level. They also show the expected positive effects on the respective dependent variables. The coefficient estimates of the exogenous factors in the second part of Tables 2 and 3, are read as effects on inefficiency. First and most importantly, they reveal that both private and non-profit hospitals are less efficient than public hospitals in Germany. This finding confirms the results of international hospital efficiency studies. Although in germany the health insurance coverage of treatments is highly regulated and hospitals cannot negotiate prices, we find differences in the hospitals efficiency. Studies analysing profits and debts of german hospitals show that public hospitals face a much higher risk of insolvency and closure(Augurzky et al., 2004). One explanation of this paradox is the regulatory regime. The former system of cost reimbursement including per diem payments offers profit maximising hospitals an incentive to boost revenues by increasing the lengths of stay. This conclusion is derived from the descriptive statistics in Table 1, from Figure 1 and from the rank correlation matrix in the Appendix (Table A-2). The pairwise correlation matrix reveals that efficiency rankings are negatively correlated with length of stay by at least 43% at the 1% significance level across all models. Thus, a further decrease of the average lengths of stay in private hospitals due to the introduction of capitation fees in 2004 may reduce the differences in efficiency across the ownership types in the long run. This question will be left to further research when more data points are available and the transformation process has proceeded. Finally, one may argue that public authorities mainly privatised unprofitable hospitals in order to rehabilitate their finances. However, the calculated efficiency scores of the 43 privatised hospitals are, on average, only slightly below those of all other hospitals. The result that private hospitals are less efficient than public hospitals does not imply that hospitals which have been privatised are less or more efficient than if they had not been privatised gThe time index t is suppressed for ease of illustration.However, Medicare patients do not cover all available treatments such that the MCI might be biased. In this paper, severity of illness weights are constructed using the average length of stay (los) of each inpatient diagnosis in Germany. A mean los by year9 and main diagnosis m = 1, . . . , M (ICD-10 Version 2.0, three digits) over all N = 2, 290 German hospitals is calculated: losm = 1 N PN i=1(daysmi/casesmi). The mean length of stay over all diagnoses and all hospitals is denoted by losG. The weight πm = losm losG is bigger (smaller) than one if the treatment of diagnosis m takes more (less) time than the overall average los. These weights rely on the idea that length of stay is a good proxy for resource use. However, weights of rehabilitation care diagnoses may be upward biased compared to their costs, while weights for severe cases with high mortality rates may be biased downwards. Comparing the variable cases with the number of cases of each diagnosis multiplied by πm (denoted by weighted cases), between −7, 065 and 6, 250 cases (−60% and 140%) are added due to weighting. 3 Results 3.1 Cross Sectional Analysis Cross section estimation results for the three years 2001 to 2003 are reported in Table 2 (cost efficiency) and Table 3 (technical efficiency). The hypothesis that there is no inefficiency (which means σu = 0) can be rejected for each year under study. Over the three years, the signs of most coefficient estimates coincide in both models. Although standard errors and point estimates differ between the years and the models, both tables show similar and consistent results. The estimated effect of input prices on the cost frontier and of the inputs on the technical frontier are presented in the first part of Tables 2 and 3. The variation across time within each model is small and all but two coefficients are significantly different from zero at a one per cent level. They also show the expected positive effects on the respective dependent variables. The coefficient estimates of the exogenous factors in the second part of Tables 2 and 3, are read as effects on inefficiency. First and most importantly, they reveal that both private and non-profit hospitals are less efficient than public hospitals in Germany. This finding confirms the results of international hospital efficiency studies. Although in Germany the health insurance coverage of treatments is highly regulated and hospitals cannot negotiate prices, we find differences in the hospitals’ efficiency. Studies analysing profits and debts of German hospitals show that public hospitals face a much higher risk of insolvency and closure (Augurzky et al., 2004). One explanation of this paradox is the regulatory regime. The former system of cost reimbursement including per diem payments offers profit maximising hospitals an incentive to boost revenues by increasing the lengths of stay. This conclusion is derived from the descriptive statistics in Table 1, from Figure 1 and from the rank correlation matrix in the Appendix (Table A-2). The pairwise correlation matrix reveals that efficiency rankings are negatively correlated with length of stay by at least 43% at the 1% significance level across all models. Thus, a further decrease of the average lengths of stay in private hospitals due to the introduction of capitation fees in 2004 may reduce the differences in efficiency across the ownership types in the long run. This question will be left to further research when more data points are available and the transformation process has proceeded. Finally, one may argue that public authorities mainly privatised unprofitable hospitals in order to rehabilitate their finances. However, the calculated efficiency scores of the 43 privatised hospitals are, on average, only slightly below those of all other hospitals. The result that private hospitals are less efficient than public hospitals does not imply that hospitals which have been privatised are less or more efficient than if they had not been privatised. 9The time index t is suppressed for ease of illustration. 7