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Aims/Background This study measures the performance of the health system in 165 countries and its relationship with public financing. Methods We use value efficiency analysis (VEA), a refinement of data envelopment analysis (DEA), to measure the efficiency of the health systems using data on healthy life expectancy and disability adjusted life years as health outcomes. Expenditure on health and education are used as inputs to the health system. Results The group of high income OECD countries shows the largest indexes of efficiency and also the lowest dispersion. In contrast, low income countries also have the most inefficient health systems, which implies that there are more opportunities for improvement. The average efficiency score is 0.96 for high income countries, 0.83 for upper-middle income countries, 0.86 for lower-middle income countries and only 0.76 for low income countries. Only 17 countries have a score equal to 1 and therefore are completely efficient and can be taken as referents. The index of efficiency is found to be positively associated with government expenditure on health as a percentage of total expenditure on health. ConclusionsThe analysis of the results shows that the public share in health expenditure and the weight of health expenditure in public budgets are two factors positively associated with the performance of the health systems. The study also highlights the advantages of using VEA over DEA in the measurement of performance.

Quality of life, Municipalities, Regions, DEA, VEA, Variance components analysis,

Measuring quality of life in municipalities entails two empirical challenges. First, collecting a set of relevant indicators that can be compared across the municipalities in the sample. Secondly, using an appropriate aggregating tool in order to construct a synthetic index. This paper measures quality of life for the largest 237 Spanish municipalities using Value Efficiency Analysis (VEA) to derive comparative scores by combining the information contained in 19 partial indicators. VEA is a refinement of DEA (Data Envelopment Analysis) that imposes some consistency in the weights of the indicators used to construct the aggregate index. The indicators cover aspects related to consumption, social services, housing, transport, environment, labour market, health, culture and leisure, education and security. The results show that the Northern and Central regions in Spain attain the highest levels of quality of life, while the Southern regions report low living conditions. Education is the variable that requires the largest improvement in low performing municipalities, followed by health and culture facilities, pollution and crime. Population density, growth and ageing seem to relate positively to quality of life.

This paper measures quality of life (QoL) in the 393 largest Spanish municipalities in 2011. We follow recent descriptions of QoL dimensions to propose an integrated framework composed of eight dimensions: material living conditions, health, education, environment, economic and physical safety, governance and political voice, social interaction, and personal activities. Using different sources of information we construct 16 indicators, two per each of the QoL dimensions considered. Weight constrained data envelopment analysis (DEA) is then used to estimate a composite indicator of the QoL of each municipality. Robustness is checked by altering the weight ranges introduced within the DEA specification. Results show that the Northern and Central regions in Spain attain the highest levels of QoL, while the Southern and Mediterranean regions report lower scores. These figures are consistent with those obtained by González et al. (Soc Ind Res 82:111–145 2011) for the Spanish municipalities in 2001, although both the sample and the indicators used are different. The analysis also shows that, while it is important to restrict weights in DEA, the specific restrictions used are less important, since all the composite indicators computed are highly correlated. The results also show important differences between per capita gross domestic product and QoL at the provincial level.