Keywords
Industrial localization coefficients
Industrial conglomerates
Geographic information systems
Spatial correlation (or spatial autocorrelation)
Employment matrix
How to Cite
Abstract
This article presents a temporal and spatial analysis of the location and concentration of industrial employment within Costa Rica’s Greater Metropolitan Area (GAM). To this end, the last five Population and Housing Censuses (1963, 1973, 1984, 2000, and 2011) are used, and a series of tools from spatial statistics, geographic information systems, and urban and regional economics are applied to analyze the dynamics of the labor market associated with industry. The results show that over the past 25 years, there has been a process of relocation of industrial activities within the GAM, as some cantons have lost relative importance in the generation of industrial employment, while others have improved their position in this regard. The applied spatial statistical models reveal strong concentrations of industrial employment in seven cantons of the GAM and clusters in two cantons. The study introduces a novel topic in labor market analysis: the origin-destination matrix of industrial employment, which shows that there is significant worker mobility within the cantons of the GAM, helping to understand the internal dynamics of the industrial sector and employment within that sector.
References
Anselin, L. (1995). Local Indicators of Spatial Association-LISA. Journal of Geographical Analysis, vol. 27, nº 2, p. 93-115. [8011-Texto...0-20130301 | PDF]
Anselin, L., Florax, R.J.G.M. & Rey, S. (eds). (2004). Advances in Spatial Econometrics, Methodology, Tools and Applications. Berlín: Springer. [8011-Texto...0-20130301 | PDF]
Anselin, L. & Florax, R.J.G.M. (eds). (1995). New Directions in Spatial Econometrics. Berlin: Springer. [8011-Texto...0-20130301 | PDF]
Anselin, L. (1992). Space Stat tutorial. A workbook for using SpaceStat in the analysis of spatial data. Technical Report S-92-1. National Center for Geographic Information and Analysis, University of California. Santa Bárbara, CA. [8011-Texto...0-20130301 | PDF]
Anselin, L. (1988). Spatial Econometrics: Methods and Models. Boston, MA: Kluwer Academic. [8011-Texto...0-20130301 | PDF]
Castells, M. (1999). La era de la información. Economía, sociedad y cultura. La sociedad red. Editorial Siglo XXI, Vol. 1. México, D.F. [8011-Texto...0-20130301 | PDF]
Cliff, A. & Ord, J. (1981). Spatial processes, models and applications. Londres: Pion. [8011-Texto...0-20130301 | PDF]
Cliff, A. & Ord, J. (1973). Spatial autocorrelation. Londres: Pion. [8011-Texto...0-20130301 | PDF]
Cliff, A. & Ord, J. (1972). Testing for spatial autocorrelation among regression residuals. Geographical Analysis, vol. 4, p. 267-284. [8011-Texto...0-20130301 | PDF]
Geary, R. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, vol. 5, p. 115-145. [8011-Texto...0-20130301 | PDF]
Getis, A., Mur, J., Zoller, H.G. (eds). (2004). Spatial econometrics and spatial statistics. Nueva York: Palgrave Macmillan. [8011-Texto...0-20130301 | PDF]
Lesage, J.P. & Pace, R.K. (eds). (2004). Spatial and spatio-temporal econometrics. Ámsterdam: Elsevier. [8011-Texto...0-20130301 | PDF]
Moran, P. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society B, vol. 10, p. 243-251. [8011-Texto...0-20130301 | PDF]
Tobler, W. (1979). Cellular Geography. Philosophy in Geography (ed. Gale, S., Olsson, G).
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Copyright (c) 2012 Rafael Arias Ramírez, Leonardo Sánchez Hernández