The Journal of Technology Transfer

, Volume 38, Issue 5, pp 537–564 | Cite as

The cliometrics of academic chairs. Scientific knowledge and economic growth: the evidence across the Italian Regions 1900–1959

  • Cristiano Antonelli
  • Nicola Crepax
  • Claudio Fassio
Article

Abstract

The paper elaborates and tests two hypotheses. First, that knowledge is not a homogeneous activity, but rather a bundle of highly differentiated disciplines that have different characteristics, both in terms of generation and exploitation, that bear a differentiated impact on economic growth. Advances in scientific knowledge that can be converted into technological knowledge with high levels of fungibility, appropriability, cumulability and complementarity have a higher chance to affect economic growth. Second, that academic chairs are a reliable indicator of the amount and types of knowledge being generated by the academic system. Hence the analysis of the evolution of the academic chairs of an academic system is a promising area of investigation. In this paper the exploration of the evolution of the size and the disciplinary composition of the stock of academic chairs in five Italian macro-regions in the years 1900–1959 provides an opportunity to understand the contribution of scientific knowledge to economic growth in each regional system. The econometric analysis confirms that advances in engineering and chemistry, as proxied by the number of chairs, had much a stronger effect on the regional economic growth than advances in other scientific fields. These results have important implications for research policy, as they highlight the differences in the economic effects of academic disciplines, and for the economics of science, as they support the hypothesis that academic chairs can be used as reliable indicators of on-going research activities in the different types of scientific knowledge.

Keywords

Academic chairs Types of knowledge Knowledge fungibility Knowledge exploitation Knowledge externalities of knowledge types 

References

  1. Adams, J. D. (1990). Fundamental stocks of knowledge and productivity growth. Journal of Political Economy, 98(4), 673–702.CrossRefGoogle Scholar
  2. Amatori, F. (2011). Entrepreneurial typologies in the history of industrial Italy: Reconsiderations. Business History Review, 85 (Spring 2011), pp. 151–180.Google Scholar
  3. Amatori, F., & Colli, A. (2003). Impresa e Industria in Italia dall’Unità a Oggi. Padova: Marsilio.Google Scholar
  4. Anselin, L., Varga, A., & Acs, Z. (1997). Local geographic spillovers between University research and technology innovations. Journal of Urban Economics, 42, 422–448.CrossRefGoogle Scholar
  5. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.CrossRefGoogle Scholar
  6. Arrow, K. J. (1969). Classificatory notes on the production and transmission of technical knowledge. American Economic Review, 59, 29–35.Google Scholar
  7. Baffigi, A. (2011). Italian National Accounts. Economic History working papers, Bank of Italy, n. 18.Google Scholar
  8. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data Models, Journal of Econometrics, 87, 115–144.Google Scholar
  9. Chandler, A. D. (1962). Strategy and structure: Chapters in the history of the industrial enterprise. Cambridge: The MIT Press.Google Scholar
  10. Chandler, A. D. (1977). The visible hand: The managerial revolution in American business. Cambridge: The Belknap Press of Harvard University Press.Google Scholar
  11. Chandler, A. D. (1990). Scale and scope: The dynamics of industrial capitalism. Cambridge: The Belknap Press of Harvard University Press.Google Scholar
  12. Chandler, A. D. (2002). Organization capabilities and the history of the industrial enterprise. Journal of Economic Perspectives, 6, 79–100.CrossRefGoogle Scholar
  13. Chandler, A. D., Hagstrom, P., & Solvell, O. (Eds.). (1998). The dynamic firm: The role of technology strategy organization and regions. Oxford: Oxford University Press.Google Scholar
  14. Chesbrough, H. (2003). Open innovation. The new imperative for creating and profiting from technology. Boston: Harvard Business School Press.Google Scholar
  15. Cohen, W. M., & Levinthal, D. A. (1989). Innovation and learning: The two faces of R&D. The Economic Journal, 99, 569–596.CrossRefGoogle Scholar
  16. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152.CrossRefGoogle Scholar
  17. Daniele, V., & Malanima, P. (2007). Il prodotto delle regioni e il divario Nord-Sud in Italia (1861–2004). Rivista di Politica Economica, 97(2), 267–316.Google Scholar
  18. Daniele, V., & Malanima, P. (2011). Il Divario Nord-Sud in Italia 1861–2011. Catanzaro: Rubettino.Google Scholar
  19. Daniele, V., & Malanima, P. (2012). The changing occupational structure of Italy 18612001. A National and Regional Perspective, Mimeo.Google Scholar
  20. De Stefani, A. (1925). L’azione dello stato Italiano per le opere pubbliche (1862–1924). Roma: Libreria dello Stato.Google Scholar
  21. D’Este, P., & Iammarino, S. (2010). The spatial profile of university-business research partnerships. Papers in Regional Science, 89, 335–350.CrossRefGoogle Scholar
  22. Fenoaltea, S. (2003). Notes on the rate of industrial growth in Italy, 1861–1913. Journal of Economic History, 63, 695–735.Google Scholar
  23. Fenoaltea, S. (2005). The growth of the Italian economy, 1861–1913: Preliminary second-generation estimates. European Review of Economic History, 9, 273–312.CrossRefGoogle Scholar
  24. Geuna, A. (1999). The economics of knowledge production. Cheltenham: Edward Elgar.Google Scholar
  25. Geuna, A., Salter, A., & Steinmueller, W. E. (Eds.). (2003). Science and innovation. Rethinking the rationales for funding and governance. Cheltenham: Edward Elgar.Google Scholar
  26. Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115, 53–74.CrossRefGoogle Scholar
  27. Jaffe, A. (1989). Real effects of academic research. American Economic Review, 79, 957–970.Google Scholar
  28. Kao, C. (1999). Spurious regression and residual-based tests for co integration in panel data. Journal of Econometrics, 90, 1–44.CrossRefGoogle Scholar
  29. Kao, C., & Chen, B. (1995). On the estimation and inference for cointegration in panel data when the cross-section and time-series dimensions are comparable. Manuscript, Center for Policy Research, Syracuse University.Google Scholar
  30. Kao, C. & Chiang, M. H. (2000). On the estimation and inference of a cointegrated regression in panel data, Advances in Econometrics, 15, 179–222.Google Scholar
  31. Lawton Smith, H. (2006). Universities innovation and the economy. New York: Routledge.CrossRefGoogle Scholar
  32. Levin, A., Lin, C.-F., & Chu, C.-S. J. (2002). Unit root test in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24.CrossRefGoogle Scholar
  33. Maddison, A. (1991). A revised estimate of Italian economic growth, 1861–1989. Banca Nazionale del Lavoro Quaterly Review, 177, 225–241.Google Scholar
  34. Malanima, P., & Zamagni, V. (2010). 150 years of the Italian economy, 1861–2010. Journal of Modern Italian Studies, 15, 1–20.CrossRefGoogle Scholar
  35. Mansfield, E., & Lee, J. Y. (1996). The modern university: Contributor to industrial innovation and recipient of industrial R&D support. Research Policy, 25, 1047–1058.CrossRefGoogle Scholar
  36. Meisenzahl, R. R., & Mokyr, J. (2012). The rate and direction of invention in British industrial revolution: Incentives and institutions. In J. Lerner & S. Stern (Eds.), The rate and direction of inventive activity revisited. Chicago: The University of Chicago Press for the National Bureau of Economic Research.Google Scholar
  37. Montanaro, P. (2002), Lo stock di capitale pubblico: una stima per regione e per tipologia di bene. Mimeo.Google Scholar
  38. Nelson, R. R. (Ed.). (1993). National innovation system: A comparative analysis. Oxford: Oxford University Press.Google Scholar
  39. Paci, R., & Saba, A. (1998). The empirics of regional economic growth in Italy 1951–93. Rivista Internazionale di Scienze Economiche e Commerciali, 45, 657–675.Google Scholar
  40. Breitung J., & Pesaran, M. H. (2008). Unit roots and cointegration in panels. In: L. Matyas, & P. Sevestre (Eds.), The econometrics of panel data: Fundamentals and recent developments in theory and practice. Kluwer Academic Publishers.Google Scholar
  41. Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors, Oxford Bulletin of Economics and Statistics, 61, 653–670.Google Scholar
  42. Pedroni, P. (2001). Purchasing power parity tests in cointegrated panels. Review of Economics and Statistics, 83, 727–731.Google Scholar
  43. Picci, L. (2002). Le opere pubbliche dall’Unità d’Italia: l’informazione statistica. Rivista di Politica Economica, 92, 29–82.Google Scholar
  44. Schumpeter, J. A. (1912, 1934). The theory of economic development. Harvard University Press, Cambridge.Google Scholar
  45. Schumpeter, J. A. (1942). Capitalism, socialism and democracy. New York: Harper and Brothers.Google Scholar
  46. Stock, J. H., & Watson, M. W. (1993). A simple estimator of cointegrating vectors in higher-order integrated systems. Econometrica, 61, 783–820.CrossRefGoogle Scholar
  47. Von Tunzelman, G. N. (2000). Technology generation, technology use and economic growth. European Review of Economic History, 3, 121–146.CrossRefGoogle Scholar
  48. Williamson, J. (2011), Industrial catching-up in the poor periphery 1870–1975, NBER Working Paper, n. 16809, February.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Cristiano Antonelli
    • 1
  • Nicola Crepax
    • 2
    • 3
    • 4
  • Claudio Fassio
    • 1
  1. 1.Dipartimento di EconomiaUniversità di Torino & Collegio Carlo Alberto, BRICKTurinItaly
  2. 2.Compagnia San PaoloTurinItaly
  3. 3.Università BocconiMilanItaly
  4. 4.Università del Piemonte OrientaleNovaraItaly

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