Applied Spatial Analysis and Policy

, Volume 12, Issue 3, pp 605–629 | Cite as

Path-Dependent Dynamics and Technological Spillovers in the Brazilian Regions

  • Eduardo GonçalvesEmail author
  • Cirlene Maria de Matos
  • Inácio Fernandes de Araújo


This article investigates the influence of path dependence, of spatial spillovers and of production specialization on regional technological specialization. We use patent data and characteristics of industrial activity by Brazilian regions in the period of 2000–2011 to estimate a spatial dynamic panel using the Generalised Method of Moment (GMM) estimator, which deals with unobserved fixed effects and with the endogeneity problem. The results show that the regional production specialization influences technological specialization in Brazilian regions. Furthermore, this article finds that regional technological development is highly path-dependent and characterized by spatial spillovers. The former result means that regional technological development is influenced by its own technological specialization trajectory. The latter shows that the technological specialization of the neighborhood has proved to be a determining factor in local technological specialization. These results may help in the understanding of the development of technological clusters, suggesting that the strategies to reinforce the regional innovation processes should consider the specificities of the regional production pattern.


Technological innovation Technological specialization Production specialization Knowledge spillovers Path dependence 

JEL Classification

O31 R11 R12 



The authors gratefully acknowledge the support of research funding agencies such as the National Council for Scientific and Technological Development (CNPq), the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES) and the Minas Gerais State Research Foundation (FAPEMIG). We are also grateful to INPI team by the patent database.

Compliance with Ethical Standards

Conflict of Interest

Cirlene Maria de Matos declares that she has no conflict of interest. Prof. Eduardo Gonçalves has received research grants from FAPEMIG and CNPq. Inácio Fernandes de Araújo Junior declares that he has no conflict of interest.


  1. Albuquerque, E. M. (2000). Domestic patents and developing countries: arguments for their study and data from Brazil (1980–1995). Research Policy, 29(9), 1047–1060.Google Scholar
  2. Almeida, P., & Kogut, B. (1999). Localization of knowledge and the mobility of engineers in regional networks. Management Science, 45(7), 905–917.Google Scholar
  3. Anselin, L. (1988). Spatial econometrics: Methods and models. Dordrecht: Kluwer Academic.Google Scholar
  4. Anselin, L., Varga, A., & Acs, Z. (1997). Local geographic spillovers between university research and high technology innovations. Journal of Urban Economics, 42(3), 422–448.Google Scholar
  5. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.Google Scholar
  6. Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51.Google Scholar
  7. Arundel, A., & Kabla, I. (1998). What percentage of innovations are patented? Empirical estimates for European firms. Research Policy, 27(2), 127–141.Google Scholar
  8. Audretsch, D. B., & Feldman, M. P. (1996). R&D spillovers and the geography of innovation and production. The American Economic Review, 86(3), 630–640.Google Scholar
  9. Azzoni, C. R. (2001). Economic growth and regional income inequality in Brazil. The Annals of Regional Science, 35(1), 133–152.Google Scholar
  10. Azzoni, C. R. (2005). São Paulo metropolitan area: size, competitiveness and the future. USP/NEREUS, São Paulo, Working Paper 10–2005.Google Scholar
  11. Baum-Snow, N., & Ferreira, F. (2015). Causal inference in urban and regional economics. In G. Duranton, J. V. Henderson, & W. C. Strange (Eds.), Handbook of urban and regional economics. Amsterdam: North-Holland.Google Scholar
  12. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.Google Scholar
  13. Bond, S. R. (2002). Dynamic panel data models: a guide to micro data methods and practice. Portuguese Economic Journal, 1(2), 141–162.Google Scholar
  14. Buzard, K., & Carlino, G. (2013). The geography of research and development activity in the U.S. In F. Giarratani, G. Hewings, & P. McCann (Eds.), Handbook of economic geography and industry studies. London: Edward Elgar.Google Scholar
  15. Camagni, R., & Capello, R. (2013). Regional innovation patterns and the EU regional policy reform: toward smart innovation policies. Growth and Change, 44(2), 355–389.Google Scholar
  16. Capello, R. (2009). Indivisibilities, synergy and proximity: the need for an integrated approach to agglomeration economies. Tijdschrift voor Economische en Sociale Geografie, 100(2), 145–159.Google Scholar
  17. Carlino, G. A., Chatterjee, S., & Hunt, R. M. (2007). Urban density and the rate of invention. Journal of Urban Economics, 61(3), 389–419.Google Scholar
  18. Carrincazeaux, C., Lunga, Y., & Rallet, A. (2001). Proximity and localisation of corporate R&D activities. Research Policy, 30, 777–789.Google Scholar
  19. Ciccone, A., & Hall, R. E. (1996). Productivity and the density of economic activity. American Economic Review, 86, 54–70.Google Scholar
  20. Combes, P. P. (2000). Economic structure and local growth: France, 1984–1993. Journal of Urban Economics, 47(3), 329–355.Google Scholar
  21. Combes, P. P., & Gobillon, L. (2015). The empirics of agglomeration economies. In G. Duranton, J. V. Henderson, & W. C. Strange (Eds.), Handbook of regional and urban economics. Amsterdam: North-Holland.Google Scholar
  22. Combes, P., Overman, H. G., Thisse, J., Henderson, V., Mayer, T., Peri, G., & Puga, D. (2004). The spatial distribution of economic activities in the European Union 1. Economic Geography, 4, 1–63. Scholar
  23. Crescenzi, R., & Rodríguez-Pose, A. (2013). R&D, socio-economic conditions, and regional innovation in the U.S. Growth and Change, 44, 287–320. Scholar
  24. David, P. A. (2005). Path dependence in economic processes: Implications for policy analysis in dynamical systems contexts. In K. Dopfer (Ed.), The evolutionary foundations of economics. Cambridge: University Press.Google Scholar
  25. Debarsy, N., Ertur, C., & Lesage, J. P. (2012). Interpreting dynamic space-time panel data models. Statistical Methodology, 9(1–2), 158–171.Google Scholar
  26. Diniz, C. C. (1994). Polygonized development in Brazil: neither decentralization nor continued polarization. International Journal of Urban and Regional Research, 18(2), 293–314.Google Scholar
  27. Duranton, G., & Puga, D. (2004). Micro-foundations of urban agglomeration economies. In: Henderson, J. V., Thisse, J-F., Handbook of regional and urban economics. Amsterdan: North-Holland, 1. Ed., vol. 4, p. 2063–2117.Google Scholar
  28. Elhorst, J. P. (2010). Applied spatial econometrics: raising the bar. Spatial Economic Analysis, 5(1), 9–28.Google Scholar
  29. Elhorst, J. P. (2014). Spatial econometrics: from cross-sectional data to spatial panels. Berlin: Springer.Google Scholar
  30. Feenstra, R. C. (1998). Integration of trade and disintegration of production in the global economy. The Journal of Economic Perspectives, 12(4), 31–50.Google Scholar
  31. Feldman, M. P. (1993). An examination of the geography of innovation. Industrial and Corporate Change, 2(3), 417–437.Google Scholar
  32. Feldman, M. P., & Florida, R. (1994). The geographic sources of innovation: technological infrastructure and product innovation in the United States. Annals of the Association of American Geographers, 84(2), 210–229.Google Scholar
  33. Feldman, M. P., & Kogler, D. F. (2010). Stylized facts in the geography of innovation. In B. H. Hall & N. Rosenberg (Eds.), Handbook of economics of innovation. Amsterdam: North Holland.Google Scholar
  34. Fitjar, R. D., & Rodríguez-Pose, A. (2017). Nothing is in the air. Growth and Change, 48(1), 22–39.Google Scholar
  35. Flores, M., Villarreal, A., & Flores, S. (2017). Spatial co-location patterns of aerospace industry firms in Mexico. Applied Spatial Analysis and Policy, 10(2), 233–251.Google Scholar
  36. Foray, D. (2015). Smart specialisation: opportunities and challenges for regional innovation policy. Abingdon: Routledge/Regional Studies Association.Google Scholar
  37. Foray, D., David, P., & Hall, B. (2009). Smart specialisation: the concept. Knowledge Economists Policy Brief, 9, 1–5.Google Scholar
  38. Fritsch, M., & Slavtchev, V. (2008). Determinants of the efficiency of regional innovation systems. Regional Studies, 45(7), 905–918.Google Scholar
  39. Garcia, R., Araujo, V., Mascarini, S., Santos, E. G., & Costa, A. (2015). Looking at both sides: how specific characteristics of academic research groups and firms affect the geographical distance of university–industry linkages. Regional Studies, Regional Science, 2(1), 518–534.Google Scholar
  40. Glaeser, E. L., Kallal, H. D., Scheinkman, J. A., & Shleifer, A. (1992). Growth in Cities. Journal of Political Economy 100, 1126–1152.Google Scholar
  41. Gonçalves, E., & Almeida, E. S. (2009). Innovation and spatial knowledge spillovers: evidence from Brazilian patent data. Regional Studies, 43(4), 513–528.Google Scholar
  42. Greene, W. H. (2008). The econometric approach to efficiency analysis. In H. O. Fried, C. A. Lovell, & S. Schmidt (Eds.), The measurement of productive efficiency and productivity growth. Oxford University Press.Google Scholar
  43. Greunz, L. (2003). Geographically and technologically mediated knowledge spillovers between European regions. The Annals of Regional Science, 37(4), 657–680.Google Scholar
  44. Griliches, Z. (1979). Issues in assessing the contribution of research and development to productivity growth. The Bell Journal of Economics, 10(1), 92–16.Google Scholar
  45. Guo, B., & Guo, J. J. (2011). Patterns of technological learning within the knowledge systems of industrial clusters in emerging economies: evidence from China. Technovation, 31(2), 87–104.Google Scholar
  46. Hall, B. H., Mairesse, J., & Mohnen, P. (2010). Measuring the returns to R&D. In B. H. Hall & N. Rosenberg (Eds.), Handbook of economics of innovation. Amsterdam: North Holland.Google Scholar
  47. Hassink, R. (2005). How to unlock regional economies from path dependency? From learning region to learning cluster. European Planning Studies, 13(4), 521–535.Google Scholar
  48. Hu, T. S., Lin, C. Y., & Chang, S. L. (2005). Technology-based regional development strategies and the emergence of technological communities: a case study of HSIP, Taiwan. Technovation, 25(4), 367–380.Google Scholar
  49. Huber, F. (2011). Do clusters really matter for innovation practices in information technology? Questioning the significance of technological knowledge spillovers. Journal of Economic Geography, 12(1), 107–126.Google Scholar
  50. Iammarino, S., & McCann, P. (2006). Structure and evolution of industrial clusters: transactions, technology and knowledge spillovers. Research Policy, 35, 1018–1036.Google Scholar
  51. Jacobs, J. (1969). The economy of cities. New York: Random House.Google Scholar
  52. Jaffe, A., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. The Quarterly Journal of Economics, 108(3), 577–598.Google Scholar
  53. Koo, J. (2005). Agglomeration and spillovers in a simultaneous framework. The Annals of Regional Science, 39, 35–47.Google Scholar
  54. Koo, J. (2007). Determinants of localized technology spillovers: role of regional and industrial attributes. Regional Studies, 41, 995–1011.Google Scholar
  55. Kopczewska, K., Kudła, J., & Walczyk, K. (2017). Strategy of spatial panel estimation: spatial spillovers between taxation and economic growth. Applied Spatial Analysis and Policy, 10(1), 77–102.Google Scholar
  56. Kukenova, M., & Monteiro, J. A. (2008). Spatial dynamic panel model and system GMM: a Monte Carlo investigation. IRENE Institute of Economic Research, University Library of Munich, Germany.Google Scholar
  57. Kuznets, S. (1962). Inventive activity: Problems of definition and measurement. In R. Nelson (Ed.), The rate and direction of inventive activity: economic and social factors. Priceton: Princeton University.Google Scholar
  58. LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics. Chapman & Hall/CRC.Google Scholar
  59. LeSage, J., & Pace, R. K. (2014). The biggest myth in spatial econometrics. Econometrics, 2, 217–249.Google Scholar
  60. Martin, R., & Sunley, P. (1998). Slow convergence? The new endogenous growth theory and regional development. Economic Geography, 74(3), 201–227.Google Scholar
  61. Martin, R., & Sunley, P. (2006). Path dependence and regional economic evolution. Journal of Economic Geography, 6(4), 395–437.Google Scholar
  62. Martine, G., & Diniz, C. C. (1997). Economic and demographic concentration in Brazil: recent inversion of historical patterns. In W. J. Gavin & V. Pravin (Eds.), Urbanization in large developing countries: China, Indonesia, Brazil, and India. Oxford: Oxford University Press.Google Scholar
  63. Mccann, P., & Ortega-Argilés, R. (2015). Smart specialization, regional growth and applications to European Union cohesion policy. Regional Studies, 49(8), 1291–1302.Google Scholar
  64. Meyer-Stamer, J. (1998). Path dependence in regional development: persistence and change in three industrial clusters in Santa Catarina, Brazil. World Development, 26(8), 1495–1511.Google Scholar
  65. Miguelez, E., & Moreno, R. (2013). Do labour mobility and technological collaborations foster geographical knowledge diffusion? The case of European regions. Growth and Change, 44(2), 321–354.Google Scholar
  66. Montenegro, R. L., Gonçalves, E., & Almeida, E. (2011). Dinâmica espacial e temporal da inovação no estado de São Paulo: uma análise das externalidades de diversificação e especialização. Estudos Economicos, 41(4), 743–776.Google Scholar
  67. Montresor, S., & Marzetti, G. V. (2008). Innovation clusters in technological systems: a network analysis of 15 OECD countries for the mid-1990s. Industry and Innovation, 15(3), 321–346.Google Scholar
  68. Moreno, R., Paci, R., & Usai, S. (2005a). Geographical and sectoral clusters of innovation in Europe. The Annals of Regional Science, 39(4), 715–739.Google Scholar
  69. Moreno, R., Paci, R., & Usai, S. (2005b). Spatial spillovers and innovation activity in European regions. Environment and Planning A, 37(10), 1793–1812.Google Scholar
  70. Moreno, R., Paci, R., & Usai, S. (2006). Innovation clusters in the European regions. European Planning Studies, 14(9), 1235–1263.Google Scholar
  71. Nagaoka, S., Motohashi, K., & Goto, A. (2010). Patent statistics as an innovation Indicator. In H. H. Bronwyn & R. Nathan (Eds.), Handbook of the economics of innovation. Amsterdam: North Holland.Google Scholar
  72. Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 49(6), 1417–1426.Google Scholar
  73. North, D. C. (1994). Economic performance through time. The American Economic Review, 84(3), 359–368.Google Scholar
  74. OECD (2011). Organisation for Economic Co-operation and Development. ISIC rev. 3 technology intensity definition : classification of manufacturing industries into categories based on R&D intensities. Directorate for Science, Technology and Industry. Economic Analysis and Statistics Division.Google Scholar
  75. Papke, L. E., & Wooldridge, J. M. (2005). A computational trick for delta-method standard errors. Economics Letters, 86(3), 413–417.Google Scholar
  76. Rodríguez-Pose, A. (2001). Is R&D investment in lagging areas of Europe worthwhile? Theory and empirical evidence. Papers in Regional Science, 80, 275–295.Google Scholar
  77. Rodríguez-Pose, A., & Comptour, F. (2012). Do clusters generate greater innovation and growth? An analysis of European regions. The Professional Geographer, 64(2), 211–231.Google Scholar
  78. Roodman, D. (2006). How to do xtabond2: An introduction to “difference” and “system” GMM in Stata. Working Paper Number 103, Central for Global Development.Google Scholar
  79. Roodman, D. (2009). Practitioners’s corner: a note on the theme of too many instruments. Oxford Bulletin of Economics and Statistics, 71(1), 136–158.Google Scholar
  80. Rosenthal, S. S., & Strange, W. C. (2004). Evidence on the nature and sources of agglomeration economies. In V. Henderson & J. F. Thisse (Eds.), Handbook of regional and urban economics. Amsterdam: North–Holland.Google Scholar
  81. Scott, A. J. (2006). Geography and economy. Oxford: Oxford University Press.Google Scholar
  82. Verspagen, B., Moergastel, T., & Slabbers, M. (1994). MERIT concordance table: IPC - ISIC (rev. 2). MERIT Research Memorandum, Maastricht Economic Research Institute on Innovation and Technology, University of Limburg. Netherlands.Google Scholar
  83. Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51.Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Economics / Territorial and Sectorial Analysis Laboratory (LATES)Federal University of Juiz de ForaJuiz de ForaBrazil
  2. 2.Instituto de Ciências Sociais Aplicadas (ICSA) da Universidade Federal de Alfenas (UNIFAL-MG)VarginhaBrazil

Personalised recommendations