Spanish unemployment: Normative versus analytical regionalisation procedures
In applied regional analysis, statistical information is usually published at different territorial levels with the aim of providing information of interest for different potential users. When using this information, there are two different choices: first, to use normative regions (towns, provinces, etc.), or, second, to design analytical regions directly related with the analysed phenomena. In this paper, provincial time series of unemployment rates in Spain are used in order to compare the results obtained by applying two analytical regionalisation models (a two stages procedure based on cluster analysis and a procedure based on mathematical programming) with the normative regions available at two different scales: NUTS II and NUTS I. The results have shown that more homogeneous regions were designed when applying both analytical regionalisation tools. Two other obtained interesting results are related with the fact that analytical regions were also more stable along time and with the effects of scale in the regionalisation process.
|Date of creation:||2004|
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- Enrique López-Bazo & Tomás del Barrio & Manuel Artis, 2002.
"The regional distribution of Spanish unemployment: A spatial analysis,"
Papers in Regional Science,
Springer;Regional Science Association International, vol. 81(3), pages 365-389.
- Lopez-Bazo, Enrique & Del Barrio, Tomas & Artis, Manuel, 2002. "The regional distribution of spanish unemployment. A spatial analysis," ERSA conference papers ersa02p020, European Regional Science Association.
- Juan Carlos Duque & Raúl Ramos, 2004. "Design of homogenous territorial units: a methodological proposal," ERSA conference papers ersa04p6, European Regional Science Association.
- Juan Carlos Duque & Raul Ramos Lobo & Jordi Surinach Caralt, 2004. "Design of Homogeneous Territorial Units: A Methodological Proposal," Working Papers in Economics 115, Universitat de Barcelona. Espai de Recerca en Economia.
- Gordon, A. D., 1996. "A survey of constrained classification," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 17-29, January. Full references (including those not matched with items on IDEAS)
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