pharmaceutical drugs, medical instruments, transplants, etc )and the number of installed beds (beds ) 7 The technical efficiency frontier is constructed by choosing the number of weighted cases weighted cases, cp. following subsection)as the output variable. Additional to the labour variables described above, the number of installed beds is used as a proxy for capital input b)The erogenous variables concur in both models and are included to control for observable heterogeneity, and to measure their direct effects on inefficiency. First, public hospitals are com- pared to private and non-profit hospitals. Since public subsidies, in particular investments in the hospital's infrastructure, only have an intermediate effect on inefficiency, we use the subsidy sta- tus of the previous year(no. subst-1). A closer look at the non-subsidised hospitals reveals that private hospitals, while forming the minority in the overall sample(15%), are strongly overrepre- sented in this subgroup(76-80%). As a consequence of this observation, we include interactions of subsidy status with ownership type(e.g.(no-subsx private)t-1) to allow for heterogeneous effects The regional dummy(east) differentiates between hospitals located in eastern Germany(including Berlin)and those located in western Germany. Analogously to Zuckerman et al.(1994), the ratio of female patients (female ratio), of patients of at least 75 years of age(plus 75 ratio) and of patients receiving surgeries(surgery ratio) are used to control for further case-mix differences c)The nurse per bed ratio (nurse/bed), which had been shown to decrease efficiency(Farsi and Filippini, 2006), is minimal for private(for-profit)hospitals. The unweighted average length of stay in the final sample turns out to be 3.52 days higher in private than in public institutions in 2000 and declines over time(cf. figure 1). This decline may be due to the expected change payment schemes towards the capitation fee system introduced in 2004 th of Since the health insurance type cannot be observed in the data, it is assumed that privately and statutory ("gesetzlich)insured patients(10% and 85% of the German population in 2003, respectively)are proportionally distributed across all hospital ownership types 2.3 Constructing Case-Mix Weights Demographic and geographic factors and specialisation of hospitals constitute structural differences regarding the severity of illness of the patients and related treatment costs. Most authors add a scalar measure of patient mix, which is based on cost information, such as the Medicare Case-Mix Index(MCI)for US hospitals(Ozcan and Luke, 1992; Rosko, 1999, 2001, 2004) or similar indices for Finland and UK(Linna and Hakkinen, 1997; Linna, 1998; Jacobs, 2001) to their model. The number of beds given in the hospital statistics is the annual average of installed beds for inpatient treatment as opposed to semi-inpatient and post-inpatient treatment) independent of the source of funding. This number does neither include those beds rented out to external physicians nor does it reflect the number of actually used SThe German dataset neither provides information on a patient's DRG nor on costs per patient(pharmaceutical drugs, medical instruments, transplants, etc.) and the number of installed beds (beds).7 The technical efficiency frontier is constructed by choosing the number of weighted cases (weighted cases, cp. following subsection) as the output variable. Additional to the labour variables described above, the number of installed beds is used as a proxy for capital input. b) The exogenous variables concur in both models and are included to control for observable heterogeneity, and to measure their direct effects on inefficiency. First, public hospitals are com￾pared to private and non-profit hospitals. Since public subsidies, in particular investments in the hospital’s infrastructure, only have an intermediate effect on inefficiency, we use the subsidy sta￾tus of the previous year (no subst−1). A closer look at the non-subsidised hospitals reveals that private hospitals, while forming the minority in the overall sample (15%), are strongly overrepre￾sented in this subgroup (76-80%). As a consequence of this observation, we include interactions of subsidy status with ownership type (e.g. (no subs×private)t−1) to allow for heterogeneous effects. The regional dummy (east) differentiates between hospitals located in eastern Germany (including Berlin) and those located in western Germany. Analogously to Zuckerman et al. (1994), the ratio of female patients (female ratio), of patients of at least 75 years of age (plus75 ratio) and of patients receiving surgeries (surgery ratio) are used to control for further case-mix differences. c) The nurse per bed ratio (nurse/bed), which had been shown to decrease efficiency (Farsi and Filippini, 2006), is minimal for private (for-profit) hospitals. The unweighted average length of stay in the final sample turns out to be 3.52 days higher in private than in public institutions in 2000 and declines over time (cf. figure 1). This decline may be due to the expected change in payment schemes towards the capitation fee system introduced in 2004. Figure 1: Unweighted average length of stay by ownership type and year Since the health insurance type cannot be observed in the data, it is assumed that privately and statutory (“gesetzlich”) insured patients (10% and 85% of the German population in 2003, respectively) are proportionally distributed across all hospital ownership types. 2.3 Constructing Case-Mix Weights Demographic and geographic factors and specialisation of hospitals constitute structural differences regarding the severity of illness of the patients and related treatment costs. Most authors add a scalar measure of patient mix, which is based on cost information, such as the Medicare Case-Mix Index (MCI) for US hospitals (Ozcan and Luke, 1992; Rosko, 1999, 2001, 2004) or similar indices for Finland and UK (Linna and H¨akkinen, 1997; Linna, 1998; Jacobs, 2001) to their model.8 7The number of beds given in the hospital statistics is the annual average of installed beds for inpatient treatment (as opposed to semi-inpatient and post-inpatient treatment) independent of the source of funding. This number does neither include those beds rented out to external physicians nor does it reflect the number of actually used beds. 8The German dataset neither provides information on a patient’s DRG nor on costs per patient. 6