The Importance of the Geographic Level of Analysis in the Assessment of the Quality of Life: The Case of Spain

There is a growing literature on the assessment of quality of life conditions in geographically and/or politically divided regions. Sometimes these territories are countries within a specified supranational structure, such as the European Union, for instance, and sometimes they are regions within countries. There is also some research that focuses on the municipal level of analysis, measuring the quality of life in cities. In the end what the researcher obtains is, at best, an average of the living conditions in the specified territory. However, if results are intended to have policy implications, attention should be paid to the variance in living conditions within regions. In this paper we attempt to quantify the relative importance of three different geographic levels of analysis in assessing the quality of life of the Spanish population. The geo-political division in Spain consists firstly of regions called Comunidades Autónomas, which are then divided into provinces which in turn are divided into municipalities. We are interested in evaluating the extent to which the quality of life conditions of an average person living in a given municipality are explained by the province and region in which the municipality is located. To do so, we first construct a composite indicator of quality of life (QoL) for the 643 largest municipalities of Spain using 19 variables which are weighted using Value Efficiency Analysis (VEA). VEA is a refinement of Data Envelopment Analysis (DEA) that imposes some consistency on 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. We then make a variance decomposition of the VEA scores to assess the importance of the three levels of geo-political administration. The results show that the municipal level is the most important of these, accounting for 52% of the variance in QoL. Regions explain 38% while provinces only account for a moderate 10%. Therefore, political action at the regional and municipal level would seem to have a larger impact on QoL indicators.