Capturing Spatial Clusters of Activity in the Spanish Mediterranean Axis

Chapter
Part of the Advances in Spatial Science book series (ADVSPATIAL)

Abstract

Concentration of economic activity constitutes a stylized fact of social sciences, giving birth to a very fertile branch of research since the writings of Marshall until today. Many researchers have been working on how to measure and explain concentration patterns of activity, trying to face that challenge and transpose it to a tractable argument in terms of modeling. Cluster analysis is one of the most salient efforts in this direction, with different contributions defining measures of concentration that range from the simplest indexes of inequality of Theil and Gini (Krugman 1992), until the more elaborated measures due to the recent work of Henderson (Henderson 1974, 1988; Henderson and Venables 2009), and Ellison and Glaeser (1997). In general, advances in this literature have focused on refining the construction of concentration indexes for identifying clusters of employment or firms in a certain territory. For example, the pioneer work of Ellison and Glaeser (1997) developed an agglomeration index (EG), together with a co-location one, that has been generalized in the literature as a reference. Its main contribution is that it derives from an explicit theory of firm location behaviour (the random-dartboard approach), controls for differences in the size distribution of establishments among industries, and appears to be robust to the level of spatial aggregation at which industry data are available. Other novel studies in this direction include that of whom evaluates the performance of the EG index but now for different sectors of economic activity, finding that the statistic behaves better for industrial activities than for consumer and business services in measuring concentration levels. Feser and Bergman (2000) that test if the EG index is sensitive to the scale of data employed (at the level of counties, commuting sheds, and zip codes), showing that changes in the spatial scale of data can introduce non-trivial ambiguities in the usual application of the EG index. Because of that, they recommend considerable caution when employing the index in comparative space-time studies about the concentration of industries. Braunerhjelmy and Johansson (2003) employ the EG and Gini locational indexes to evaluate the degree of concentration in 143 industries (at a four-digit level) for Sweden between 1975 and 1993, while Midelfart-Knarvik et al. (2004) use Gini locational index to analyze 36 industrial activities and 5 of services, with both works showing a more disperse pattern for services in comparison with industries. In addition, other locational studies also try to disentangle the forces driving important international flows such as FDI, population, or migrants (see, i.e., Blonigen et al. 2008; Baltagi et al. 2005; Kaushal 2005).

Keywords

Land Price Cluster Area Specialise Employment Neighbouring Municipality High Technology Sector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Alamá A, Artal A, Navarro JM (2011) Industrial location, spatial discrete choice models and the need to account for neighbourhood effects. Ann Reg Sci 47(2):393–418CrossRefGoogle Scholar
  2. Arbia G (2001) The role of spatial effects in the empirical analysis of regional concentration. J Geogr Syst 3:271–281CrossRefGoogle Scholar
  3. Artal-Tur A, Navarro-Azorín JM, Alamá-Sabater L, García-Sánchez A (2012). The role of destination spatial spillovers and technological intensity in the location of manufacturing and services firms, Environment and Planning B: Planning and Design, advance online publication, doi:10.1068/b38024Google Scholar
  4. Baltagi BH, Egger P, Pfaffermayr M (2005) Estimating models of complex FDI: are there third-country effects? J Econ 127(1):260–281Google Scholar
  5. Blonigen BA, Davies RB, Naughton HT, Waddell GR (2008) Spacey parents: spatial autoregressive patterns in inbound FDI. In: Brakman S, Garretsen H (eds) Foreign direct investment and the multinational enterprise. MIT Press, Cambridge, MAGoogle Scholar
  6. Braunerhjelm P, Johansson D (2003) The determinants of spatial concentration: the manufacturing and service sectors in an international perspective. Ind Innov 10(1):41–63CrossRefGoogle Scholar
  7. Brülhart M, Traeger R (2005) An account of geographic concentration patterns in Europe. Reg Sci Urban Econ 35:597–624CrossRefGoogle Scholar
  8. Combes P, Overman HG (2004) The spatial distribution of economic activities in the European Union. In: Henderson JV, Thisse JF (eds) Handbook of regional and urban economics, vol 4. Elsevier, Amsterdam, pp 2845–2909Google Scholar
  9. De Dominicis L, Arbia G, De Groot HLF (2006) Spatial distribution of economic activities in local labour market areas: the case of Italy. In: Paper presented at the ERSA conference 2006, Volos, GreeceGoogle Scholar
  10. Desmet K, Fafchamps M (2006) Employment concentration across U.S. counties. Reg Sci Urban Econ 36:482–509CrossRefGoogle Scholar
  11. Duranton G, Overman H (2005) Testing for localization using micro-geographic data. Rev Econ Stud 72:1077–1106CrossRefGoogle Scholar
  12. Dwass M (1957) Modified randomization tests for non parametric hypotheses. Ann Math Stat 28:181–187CrossRefGoogle Scholar
  13. Ellison G, Glaeser EL (1997) Geographic concentration in US manufacturing industries: a dartboard approach. J Pol Econ 105:889–927CrossRefGoogle Scholar
  14. Feser EJ, Bergman EM (2000) National industry cluster templates: a framework for applied regional cluster analysis. Reg Stud 34:1–19CrossRefGoogle Scholar
  15. Hall R, Ciccone A (1996) Productivity and the density of economic activity. Am Econ Rev 86(1):54–70Google Scholar
  16. Henderson JV (1974) The sizes and types of cities. Am Econ Rev 64:640–660Google Scholar
  17. Henderson JV (1988) Urban development: theory, fact and illusion. Oxford University Press, EnglandGoogle Scholar
  18. Henderson JV, Venables A (2009) The dynamics of city formation. Rev Econ Dyn 12:233–254CrossRefGoogle Scholar
  19. Jacob J (1969) The economy of cities. Random House, New YorkGoogle Scholar
  20. Jensen P, Michel J (2011) Measuring spatial dispersion: exact results on the variance of random spatial distributions. Ann Reg Sci 47:81–110CrossRefGoogle Scholar
  21. Kang H (2010) Detecting agglomeration processes using space–time clustering analyses. Ann Reg Sci 45:291–311CrossRefGoogle Scholar
  22. Kaushal N (2005) New immigrants´ location choices: magnets without welfare. J Lab Econ 23:59–80CrossRefGoogle Scholar
  23. Krugman P (1992) Geography and trade. The MIT Press, Cambridge, MAGoogle Scholar
  24. Kulldorff M (1997) A spatial scan statistic. Commun Stat Theor Method 26:1481–1496CrossRefGoogle Scholar
  25. Kulldorff M, Nagarwalla N (1995) Spatial disease clusters: detection and inference. Stat Med 14:799–810CrossRefGoogle Scholar
  26. Marcon E, Puech F (2010) Measures of the geographic concentration of industries: improving distance-based methods. J Econ Geogr 10:745–762CrossRefGoogle Scholar
  27. Marshall A (1890) Principles of economics. Macmillan, LondonGoogle Scholar
  28. Midelfart-Knarvik KH, Overman HG, Redding SG, Venables AG (2004) The location of European industry. In: Dierx A, Ilzkovitz F, Sekkat K (eds) European integration and the functioning of product markets. E. Edgar Publishing, Massachusetts (USA). Northamton, MA, USGoogle Scholar
  29. Puga D (2010) The magnitude and causes of agglomeration economies. J Reg Sci 50(1):203–219CrossRefGoogle Scholar
  30. Puga D (2011) Learning by working in dense cities. Presentation at 51st European congress of the regional science association international (ERSA), Barcelona, 31 Aug–3 Sept 2011Google Scholar
  31. Ripley B (1976) The second-order analysis of stationary point processes. J Appl Probab 13:255–266CrossRefGoogle Scholar
  32. Ripley B (1977) Modeling spatial patterns. J Roy Stat Soc B 39:172–212Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Dpto de Métodos Cuantitativos e InformáticosUniversidad Politécnica de CartagenaCartagenaEspaña
  2. 2.Dpto. de Análisis EconómicoUniversidad de ZaragozaZaragozaEspaña
  3. 3.Dpto. EconomíaUniversidad Politécnica de CartagenaCartagenaEspaña

Personalised recommendations