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A class of spatial econometric methods in the empirical analysis of clusters of firms in the space

  • Giuseppe ArbiaEmail author
  • Giuseppe Espa
  • Danny Quah
Chapter
Part of the Studies in Empirical Economics book series (STUDEMP)

In this paper we aim at identifying stylized facts in order to suggest adequate models for the co-agglomeration of industries in space. We describe a class of spatial statistical methods for the empirical analysis of spatial clusters. The main innovation of the paper consists in considering clustering for bivariate (rather than univariate) distributions. This allows uncovering co-agglomeration and repulsion phe nomena between the different sectors. Furthermore we present empirical evidence on the pair-wise intra-sectoral spatial distribution of patents in Italy in 1990s. We identify some distinctive joint patterns of location between different sectors and we propose some possible economic interpretations.

Keywords

Agglomeration Bivariate K-functions Co-agglomeration Spatial clusters Spatial econometrics 

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References

  1. Acs Audretsch DB, Feldman MP (1994) R&D spillovers and recipient firm size. Rev Econ Stat 76(2):336–340CrossRefGoogle Scholar
  2. Arbia G (1989) Spatial data configuration in regional economic and related problems. Kluwer, DordrechtGoogle Scholar
  3. Arbia G (2001) Modelling the geography of economic activities in a continuous space. Pap Reg Sci 80: 411–424CrossRefGoogle Scholar
  4. Arbia G (2006) Spatial econometrics: with applications to regional convergence. Springer, BerlinGoogle Scholar
  5. Arbia G, Espa G (1996) Statistica economica territoriale. Cedam, PaduaGoogle Scholar
  6. Arbia G, Copetti M, Diggle PJ (2008) Modelling individual behaviour of firms in the study of spatial concentration. In: Fratesi U, Senn L (eds) Growth in interconnected territories: innovation dynamics, local factors and agents, Springer, Berlin (forthcoming)Google Scholar
  7. Arrow KJ (1962) The economic implications of learning by doing. Rev Econ Stud 155–173Google Scholar
  8. Audretsch DB, Feldman MP (1996) R&D Spillovers and the geography of innovation and production. Am Econ Rev 86(3):630–640Google Scholar
  9. Bairoch P (1988) Cities and economic development: from the dawn of history to the present. Chicago Press, ChicagoGoogle Scholar
  10. Barnard GA (1963) Contribution to the discussion of Professor Bartlett's paper. J R Stat Soc B25:294Google Scholar
  11. Besag J (1977) Contribution to the discussion of Dr. Ripley's paper. J R Stat Soc B 39:193–195Google Scholar
  12. Besag J, Diggle PJ (1977) Simple Monte Carlo test for spatial pattern. Appl Stat 26:327–333CrossRefGoogle Scholar
  13. Breschi S, Lissoni F (2001) Knowledge spillovers and local innovation systems: a critical survey. Ind Corp Change 10(4):975–1005CrossRefGoogle Scholar
  14. Breschi S, Lissoni F (2006) Cross-firm inventors and social networks: localised knowledge spillovers revisited. Ann Econ StatGoogle Scholar
  15. Ciccone A, Hall RR (1996) Productivity and the density of economic activity. Am Econ Rev 86(1):54–70Google Scholar
  16. Cliff AD, Ord JK (1981) Spatial processes: models and applications. Pion, LondonGoogle Scholar
  17. Devereux MP, Griffith R, Simpson H (2004) The geographic distribution of production activity in the UK. Reg Sci Urban Econ 34:533–564CrossRefGoogle Scholar
  18. Diggle PJ (1983) Statistical analysis of spatial point patterns. Academic, New YorkGoogle Scholar
  19. Diggle PJ (1993) Point process modelling in environmental epidemiology. In: Barnett V, Turkman KF (eds) Statistics for the environment. Wiley, ChichesterGoogle Scholar
  20. Diggle PJ, Chetwynd AG (1991) Second-order analysis of spatial clustering. Biometrics 47:1155–1163CrossRefGoogle Scholar
  21. Diggle PJ, Chetwynd AG, Haggkvist R, Morris S (1995) Second-order analysis of space—time clustering. Stat Methods Med Res 4:124–136CrossRefGoogle Scholar
  22. Dixon PM (2002) Ripley's K function. In: El-Shaarawi AH, Piegorsch WW (eds) Encyclopedia of environmetrics, vol 3. Wiley, Chichester, pp 1796–1803Google Scholar
  23. Driffield N (2006) On the search for spillovers from Foreign Direct Investment (FDI) with spatial dependency. Regional Studies 40, pp 107–119CrossRefGoogle Scholar
  24. Duranton G, Overman HG (2005) Testing for localisation using micro-geographic data. Rev Econ Stud 72:1077–1106CrossRefGoogle Scholar
  25. Ellison G, Glaeser EL (1997) Geographic concentration in U.S. manufacturing industries: A dartboard approach. J Pol Econ 105(5):889–927CrossRefGoogle Scholar
  26. Fan J, Gijbels I (1996) Local polynomial modelling and its applications. Chapman and Hall, LondonGoogle Scholar
  27. Fujita M, Krugman P, Venables A (1999) The spatial economy: cities, regions, and international trade. MIT Press, CambridgeGoogle Scholar
  28. Gatrell AC, Bailey TC, Diggle PJ, Rowlingson BS (1996) Spatial point pattern analysis and its application in geographical epidemiology. Trans Inst Br Geogr 21:256–274CrossRefGoogle Scholar
  29. Glaeser EL, Kallal HD, Scheinkman JA, Schleifer A (1992) Growth in cities. J Pol Econ 100(6):1126–1152CrossRefGoogle Scholar
  30. Goreaud F, Pélissier R (1999) On explicit formulas of edge effect correction for Ripley's K-function. J Veg Sci 10:433–438CrossRefGoogle Scholar
  31. Goreaud F, Pélissier R (2003) Avoiding misinterpretation of biotic interactions with intertype K 12(t)-function: population independence vs. random labelling hypotheses. J Veg Sci 14:681–692CrossRefGoogle Scholar
  32. Griliches Z (1979) Issues in assessing the contribution of research and development to productivity growth. Bell J Econ 10:92–116CrossRefGoogle Scholar
  33. Haining RP (2003) Spatial data analysis: theory and practice. Cambridge University Press, CambridgeGoogle Scholar
  34. Hastie T, Tibshirani R (1990) Generalized additive models. Chapman and Hall, LondonGoogle Scholar
  35. Henderson JV (2003) Marshall's scale economies. J Urban Econ 53(1):1–28 JanuaryCrossRefGoogle Scholar
  36. Henderson VA, Kunkoro M (1995) Turner. Industrial development of cities. J Pol Econ 103:1067–1090CrossRefGoogle Scholar
  37. Ioannides YM, Overman HG (2004) Spatial evolution of the US urban system. J Econ Stud 4:131–156Google Scholar
  38. Jacobs J (1969) The economy of cities. Random House, New YorkGoogle Scholar
  39. Jacobs J (1984) Cities and the wealth of nations: principles of economic life. Vintage, New YorkGoogle Scholar
  40. Jaffe AB (1989) Real effects of academic research. Am Econ Rev 79(5):957–970Google Scholar
  41. Jaffe AB, TrajtenbergM (1996) Flowsofknowledge from universities and federal laboratories: modeling the flow of patent citations over time and across institutional and geographic boundaries. In: Proceedings of national academy of science vol 93, pp 12671–12677Google Scholar
  42. Jaffe AB, Trajtenberg M (2002) Patents, Citations and innovations: a window on the knowledge economy. MIT Press, CambridgeGoogle Scholar
  43. Jaffe AB, TrajtenbergM,HendersonR(1993) Geographic localization ofknowledge spilloversasevidenced in patent citations. Q J Econ 108(3):577–598CrossRefGoogle Scholar
  44. Jaffe AB, Trajtenberg M, Fogarty M (2000) The meaning of patent citations: report of the NBER/Case western reserve survey of patentees. NBER working paper No. 7631Google Scholar
  45. Krugman P (1991) Geography and trade. MIT Press, CambridgeGoogle Scholar
  46. Kulldorff M (1998) Statistical methods for spatial epidemiology: test for randomness. In: Gatrell A, Löytönen M (eds) GIS and health. Taylor #x0026; Francis, London, pp 49–62Google Scholar
  47. Lotwick HW, Silverman BW (1982) Methods for analysing spatial processes of several types of points. J R Stat Soc B44:406–413Google Scholar
  48. Lucas RE (1988) On the mechanics of economic development. J Monet Econ 22(1):3–42CrossRefGoogle Scholar
  49. Marcon E, Puech F (2003a) Evaluating geographic concentration of industries using distance-based meth ods. J Econ Geogr 3(4):409–428 Marcon E, Puech F (2003b) Generalizing Ripley's K function to inhomeogeneous populations. mimeoGoogle Scholar
  50. Marshall A (1890) Principles of economics. Macmillan, LondonGoogle Scholar
  51. Martens SN, Breshears DD, Meyer CW, Barnes FJ (1997) Scales of above-ground competition in semi arid woodland detected from spatial patterns. J Veg Sci 8:655–664CrossRefGoogle Scholar
  52. Maurel F, Sedillot B (1999) A measure for geographical concentration of French Manufacturing industries. Reg Sci Urban Econ 29(5):575–604CrossRefGoogle Scholar
  53. Porter ME (1990) The competitive advantage of nations. Free Press, New YorkGoogle Scholar
  54. Quah D, Simpson H (2003) Spatial cluster empirics. mimeoGoogle Scholar
  55. Rauch JE (1993) Productivity gains from geographic concentration of human capital: evidence from the cities. J Urban Econ 43(3):380–400CrossRefGoogle Scholar
  56. Ripley BD (1976) The second-order analysis of stationary point processes. J Appl Probab 13:255–266CrossRefGoogle Scholar
  57. Ripley BD (1977) Modelling Spatial Patterns (with discussion). J R Stat Soc B39:172–212Google Scholar
  58. Ripley BD (1979) Test of ‘randomness’ for spatial point pattern. J R Stat Soc B41:368–374Google Scholar
  59. Ripley BD (1981) Spatial Statistics, WileyGoogle Scholar
  60. Romer PM (1986) Increasing returns and long-run growth. J Pol Econ 94:1002–1037CrossRefGoogle Scholar
  61. Yule GU, Kendall MG (1950) An introduction to the theory of statistics. Griffin, LondonGoogle Scholar

Copyright information

© Physica-Verlag Heidelberg 2009

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of TrentoTrentoItaly
  2. 2.Economics DepartmentLondon School of EconomicsLondonUK

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