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In recent decades, austerity measures have been widely adopted in public healthcare systems, so as to cope with financial constraints. This paper assesses the impact of a specific austerity policy implemented in Italy since 2007, with the purpose of reducing the budget deficit of regional governments originated by an excessive healthcare expenditure, the so called Recovery Plans (Piani di rientro). We exploit this context for a counterfactual analysis of the effects of Recovery Plans on the technical efficiency of hospitals operating in the regions where the Plans have been implemented, using as a control group similar hospitals in the regions where the policies were not enacted. The empirical analysis is based on a unique sample of administrative data relative to a large panel of hospitals in the period 2003–2010, and it employs, as identification strategy, the exogenous introduction of the austerity policy in some regions. We find that the policy had a detrimental effect on the efficiency of the hospitals operating in the regions subjected to the policy. The results show that the efficiency loss grows over time, suggesting the existence of negative cumulative effects of the austerity policy. • Counterfactual analysis of the impact of austerity on efficiency of Italian hospitals. • Use of a balanced panel of 547 hospitals for the period 2003–2010. • The austerity policy had a detrimental effect on the efficiency of hospitals. • The efficiency loss grows over time, that is austerity had cumulative effects. [ABSTRACT FROM AUTHOR]

Building on the idea that different hospitals may operate with different technologies, the objective of this paper is a spatial characterization of the production function of hospital services through the identification of different spatial regimes. We introduce an original methodology to identify spatially constrained regimes, namely spatially constrained portions of territory in which the production units are maximally homogeneous in functional terms. The empirical algorithm can be described as a k -means cluster-wise regression procedure in which the units are belonging to a proximity graph and where distance is assessed in regressive terms. The analysis is implemented, first, on simulated data and, then, on output and input data of Italian hospitals for the year 2010. Our results, besides their methodological value, allow to shed light on the working of the hospital sector in Italy. The heterogeneity of the identified technological regimes can be associated to spatial heterogeneity of relevant aspects, like demand, internal organization, clinical and managerial governance, etc., and consequential policy implications can be, therefore, gathered. • A methodology to identify spatially constrained regimes, with production units maximally homogeneous in functional terms. • A production function is estimated for a large sample (681) Italian hospitals. • Spatial heterogeneity of relevant aspects associated to the heterogeneity of the spatial regimes, for policy implications. [ABSTRACT FROM AUTHOR]

Building on the idea that different hospitals may operate with different technologies, the objective of this paper is a spatial characterization of the production function of hospital services through the identification of different spatial regimes. We introduce an original methodology to identify spatially constrained regimes, namely spatially constrained portions of territory in which the production units are maximally homogeneous in functional terms. The empirical algorithm can be described as a k -means cluster-wise regression procedure in which the units are belonging to a proximity graph and where distance is assessed in regressive terms. The analysis is implemented, first, on simulated data and, then, on output and input data of Italian hospitals for the year 2010. Our results, besides their methodological value, allow to shed light on the working of the hospital sector in Italy. The heterogeneity of the identified technological regimes can be associated to spatial heterogeneity of relevant aspects, like demand, internal organization, clinical and managerial governance, etc., and consequential policy implications can be, therefore, gathered. • A methodology to identify spatially constrained regimes, with production units maximally homogeneous in functional terms. • A production function is estimated for a large sample (681) Italian hospitals. • Spatial heterogeneity of relevant aspects associated to the heterogeneity of the spatial regimes, for policy implications. [ABSTRACT FROM AUTHOR]