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

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Abstract

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.

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Notes

  1. The use of the DEA methodology to estimate a composite index of quality of life traces back to the early work of Hashimoto and Ishikawa (1993) who assessed the quality of life in Japan’s prefectures.

  2. There are also other methods, not included in the Handbook, such as the multidimensional distance (DP2) proposed by Pena (1977) and the hedonic price methods proposed by Rosen (1979) and Roback (1982), although the latter falls outside the social indicators approach. The papers that have dealt with the measurement of QoL in regional samples of Spanish municipalities have relied on the DP2 distance measure (Sánchez and Rodríguez 2003; Zarzosa 2005).

  3. The DEA approach tries to reduce inputs to the minimum possible because they imply a cost in production. It also tries to increase outputs to the maximum because they have a positive value for the productive firm. In our setting, city drawbacks imply a cost associated with living in the municipality and should be reduced to a minimum, while advantages imply a benefit for citizens and should be increased to the frontier maximum. Thus, the parallelism is clear, as is the applicability of DEA to our research setting.

  4. We describe the dual DEA programs instead of the more usual primal specifications because we will use the weights of drawbacks and advantages in these dual programs to perform the VEA analysis. The primal specification would, of course, generate exactly the same results.

  5. We used the software LINGO to solve the DEA and VEA programs of this research. While many packages are pre-programmed to solve DEA, we are not aware of any that can solve VEA. However, any mathematical programming software can be used to solve (4).

  6. In the computation of this index, the INE uses scores that go from 0 (unemployed) to 3 (entrepreneur).

  7. To compute this index, La Caixa takes into account the population, number of phones, automobiles, trucks and vans, banking offices and retail activities. In order to make this index comparable across municipalities we divided it by the population and multiplied by 10,000.

  8. To make the numbers comparable we divided the total number of facilities by the population and multiply by 10,000.

  9. For the computation of the index, INE uses scores that go from 1 (illiterate) to 10 (Ph.D.).

  10. This index, elaborated by the INE, ranges from 0 to 100 and takes into account characteristics of the buildings such as their age, tumbledown status, hygienic conditions, running water, accessibility, heating, etc.

  11. The raw data distinguishes between these two destinations. Our variable is the arithmetic average of both. We should also indicate that the INE does not compute an index associated with these variables. Instead the report includes the percentage of people on seven intervals that go from "less than 10 min" to "more than 90 min". We took scores in the mean of the intervals (90 for the last interval) and weighted each score by the percentage of population within the interval. The weighted sum can be interpreted as the average time employed to get to the school or work and is the variable used in this paper.

  12. In the other dimensions it scores about average, although far below the best performers.

  13. Boadilla del Monte is a municipality in Madrid that excels in many dimensions (education, socio-economic condition, housing, pollution). On the other hand, its citizens must incur costly hours driving to schools or jobs and the level of facilities (health, cultural, etc.) is relatively low.

  14. OCU stands for Organización de Consumidores y Usuarios and is the largest consumers association in Spain.

  15. Security was the main concern of citizens with an average weight of 18%, followed by the labour market (15%), housing (13%) and health services (12%).

  16. Therefore it is the only one that can be used as MPS. Barcelona, Madrid and Valencia could not be considered as the MPS because the VEA program would not have a feasible solution as these cities are not on the DEA frontier.

  17. Other good candidates to be the MPS were Vitoria, Getxo and San Sebastian. However, we were not able to find the same independent support of other studies as we did with Pamplona. We repeated the VEA analysis with these municipalities as MPS and found no important differences.

  18. In the DEA program Boadilla del Monte was assigned a zero-weight to communications and commuting times. Although it still is a good place to live it is no longer a reference (frontier) under the VEA formulation.

  19. Figures 2 and 3 show the weighted average of QoL in the municipalities included in the sample for each province and region respectively. The weights are the ratio of the population of a municipality to the sum of the population of all the municipalities of that province or region included in the sample.

  20. In an unbalanced design (as is our case) many different estimators of the variance components can be used (Searle 1971: Chap. 10). All of these would collapse to the Analysis of Variance estimator in a balanced design. Searle et al. (1992) manifest a strong preference for the REML estimator in unbalanced designs. We checked the results obtained with other estimators (ANOVA type 1 and 3, Minimum Variance Quadratic Unbiased Estimator types 0 and 1, and Maximum Likelihood estimator) and the results are nearly identical.

  21. Of course, many services that are provided at a local level are financed at the regional level. However, if the regional authorities assure a similar level in the provision of these services to the population in different municipalities or equal access to the services, the effect would be captured by the regional component in the decomposition of the variance. Therefore, it is fair to interpret the municipal component in the variance decomposition as the impact of variables that are most influenced by local government decisions.

  22. Caja España also provides on its webpage a municipal database, but most of the information is taken from the INE statistics.

  23. Only one municipality with a population over 10,000 was excluded because data on commuting times and universitary studies were not reported in the INE database. This municipality is La Vall d'Uixo (Castellón).

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Acknowledgments

we gratefully acknowledge the helpful comments received from an anonymous referee. Financial support for this research was provided by the Spanish Ministerio de Educación y Ciencia (Plan Nacional de I+D+I: SEJ2007-67001/ECON) with FEDER funding.

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Authors and Affiliations

  1. Business Department, University of Oviedo, Av. Cristo s/n, 33071, Oviedo, Spain

    Eduardo González & Juan Ventura

  2. Accounting Department, University of Oviedo, Av. Cristo s/n, 33071, Oviedo, Spain

    Ana Cárcaba

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  1. Eduardo González
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Correspondence to Eduardo González.

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González, E., Cárcaba, A. & Ventura, J. The Importance of the Geographic Level of Analysis in the Assessment of the Quality of Life: The Case of Spain. Soc Indic Res 102, 209–228 (2011). https://doi.org/10.1007/s11205-010-9674-8

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