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Quadruple Helix R&D Growth Models: A Panel Cointegration Analysis Applied to a Sample of OECD Countries

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Abstract

The Quadruple Helix theory (QH), [Carayannis and Campbell (Knowledge creation, diffusion and use in innovation networks and knowledge clusters. A comparative system approach across the USA, Europe and Asia (pp. ix–xxvi). 2006, knowledge creation, diffusion and use in innovation networks and knowledge clusters: A comparative System Approach across the USA, Europe and Asia 2009a, International Journal of Technology Management 46(3):201–234, 2009b), Arnkil et al. (Work Research Center, 2010), MacGregor et al. (Journal of the Knowledge Economy, 1(3):173–190, 2010)] seeks to explain the new reality of the so-called innovation economies and the interplay between innovation and economic growth. Following this approach, the economic structure of a country relies on four helices: on one hand the university and technology infrastructures, on the other hand on firms, government and the civil society where differentiated productive units that are complementary and interact with each other are responsible for growth by generating a permanent stream of innovation. This theory was only subject to theoretic modelling quite recently with Afonso et al. (Journal of Business Economics and Management 13(5):849–865, 2012), Monteiro (Economie de l’innovation, dépenses publiques productives et croissance économique: une étude empirique pour l’évaluation du rôle des infrastructures technologiques dans les pays de l’OECD. Economies et finances, 2013) and Afonso et al. (Metroeconomica 65(4):671–689, 2014) who contributed to fill the gap by modelling the QH concept on the basis of (two) research and development (R&D) growth models. Additionally, Monteiro (Economie de l’innovation, dépenses publiques productives et croissance économique: une étude empirique pour l’évaluation du rôle des infrastructures technologiques dans les pays de l’OECD. Economies et finances, 2013) presents the first attempt to empirically test the main predictions of the first QH-R&D growth model by Afonso et al. (Journal of Business Economics and Management 13(5):849–865, 2012) with the help of non-stationary panel techniques, applied to a sample of 24 Organisation for Economic Co-operation and Development countries, over the period 1980–2008.

We extend the scope of the empirical study by Monteiro (Economie de l’innovation, dépenses publiques productives et croissance économique: une étude empirique pour l’évaluation du rôle des infrastructures technologiques dans les pays de l’OECD. Economies et finances, 2013) in several ways: (a) we address the issue of heterogeneity of the regressors; (b) we regress long-run as well as short-run equations, the latter accounting for transitional dynamics, seeking to (c) confirm cointegration relationships, (d) test for weak-endogeneity and (e) test for causality in the short run; finally, (f) special emphasis is given to the role played by political-institutional and social capital variables. As in Monteiro (Economie de l’innovation, dépenses publiques productives et croissance économique: une étude empirique pour l’évaluation du rôle des infrastructures technologiques dans les pays de l’OECD. Economies et finances, 2013), our results also confirm the main theoretical model predictions, namely the role played by governments and by public expenditures in a growing innovation economy.

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References

  • Acemoglu, D. (2009). Introduction to modern economic growth. Princeton: Princeton University Press.

    Google Scholar 

  • Afonso, O., Monteiro, S., & Thompson, M. (2012). A growth model for the Quadruple Helix. Journal of Business Economics and Management, 13(5), 849–865.

    Article  Google Scholar 

  • Afonso, O., Monteiro, S., & Thompson, M. (2014). Innovation economy, productive public expenditure and economic growth. Metroeconomica, 65(4), 671–689.

    Article  Google Scholar 

  • Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employement equations. Review of Economic Studies, 58(2), 277–297 Avril.

    Article  Google Scholar 

  • Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68, 29–52.

    Article  Google Scholar 

  • Arnkil, R., Jarvensivu, A., Koski, P., & Piirainen, T. (2010). Exploring the Quadruple Helix. Work Research Center, University of Tampere .Available at: www.cliqproject.eu/en/activities/research/quadruple_helix_research/?id=127

  • Azariadis, C. (1996). The economics of poverty traps part one: Complete markets. Journal of Economic Growth, 449–486.

    Google Scholar 

  • Baltagi, B. H. (2001). Econometric analysis of panel data. New York: John Wiley and Sons.

    Google Scholar 

  • Baltagi, B. H., & Kao, C. (2000). Nonstationary panels, cointegration in panels and dynamic panels: a survey. Center for Policy Research Working Papers 16, Maxwell School, Syracuse University.

    Google Scholar 

  • Barro, R. J. (1990). Government spending in a simple model of endogenous growth. Journal of Political Economy, 98(5), S103–S126.

    Article  Google Scholar 

  • Barro, R. J. (1991). Economic growth in a cross section of countries. Quarterly Journal of Economics, 106(2), 407–443.

    Article  Google Scholar 

  • Barro, R. J., & Sala-i-Martin, X. (1991). Convergence across states and regions. Brookings Papers on Economic Activity, 1, 107–182.

    Article  Google Scholar 

  • Barro, R., & Lee, J. W. (2010). A new data set of educational attainment in the world, 1950–2010. NBER Working Paper 15902. Cambridge, MA: NBER. Working Papers.

    Google Scholar 

  • Baumol, W. (1986). Productivity growth, convergence and welfare: what the long-run data show. American Economic Review, 76, 1072–1085.

    Google Scholar 

  • Breitung, J., & Pesaran, M. (2005). Unit roots and cointegration in panels, CESifo Working Paper Series 1565. Munich: CESifo. Working Papers.

    Google Scholar 

  • Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.

    Article  Google Scholar 

  • Bond, S., Anke, H., & Temple, J. (2001). GMM estimation of empirical growth models. CEPR Discussion Paper 3048.

    Google Scholar 

  • Carayannis, E. G., & Campbell, D. F. (2006). Introduction and chapters summaries. In E. C. Carayannis & J. Campbell D. F. (Eds.), Knowledge creation, diffusion and use in innovation networks and knowledge clusters. A comparative system approach across the United States, Europe and Asia (pp. ix–xxvi). Westport: Praeger.

    Google Scholar 

  • Carayannis, E. G., & Campbell, D. F. (2009a). Knowledge creation, diffusion and use in innovation networks and knowledge clusters: A comparative system approach accross the United States, Europe and Asia. Westport: Praeger.

    Google Scholar 

  • Carayannis, E. G., & Campbell, D. F. (2009b). Mode 3 and Quadruple Helix Toward a 21st century fractal innovation ecosystem. International Journal of Technology Management, 46(3), 201–234.

    Article  Google Scholar 

  • Castellacci, F., & Natera, J. M. (2011). A new panel dataset for cross-country analyses of national systems, growth and development (CANA), MPRA Paper 28376. Available at http://mpra.ub.uni-muenchen.de/28376/

  • Cingranelli, D., & Richards, D. (2008). The Cingranelli-Richards (CIRI) Human Rights Data Project Coding Manual Version 2008.3.13.

    Google Scholar 

  • Coe, D. T., & Helpman, E. (1995). International R&D spillovers. European economic Review, 39(5), 859–887.

    Article  Google Scholar 

  • Costantini, M., & Lupi, C. (2013). A Simple Panel-CADF Test for Unit Roots. Oxford Bulletin of Economics and Statistics, 75, 276–296.

    Article  Google Scholar 

  • Demetrescu, M., Hassler, U., & Tarcolea, A. (2006). Combining significance of correlated statistics with application to panel data. Oxford Bulletin of Economics and Statistics, 68(5), 647–663.

    Article  Google Scholar 

  • Durlauf, S., & Quah, D. T. (1999). Chapter 4: “The new empirics of economic growth,” handbook of macroeconomics. In: J. B. Taylor, & M. Woodford (Eds.), Handbook of macroeconomics (1st ed., Vol. 1, pp. 235–308) Elsevier.

    Google Scholar 

  • Durlauf, S., Johnson, P. A., & Temple, J. (2004). Growth econometrics. In P. Aghion & S. Durlauf (Eds.), Handbook of economic growth (Forthcoming). Amsterdam/North Holland: Madison SSRI.

    Google Scholar 

  • Durlauf, S. N., West, K. D., & Brock, W. A. (2007). Model uncertainty and policy evaluation: Some theory and empirics, Journal of Econometrics, Elsevier, 136 (2), 629–664.

    Google Scholar 

  • Engle, R., & Yoo, S. (1987). Forecasting and testing in cointegrated systems. Journal of Econometrics, 35, 143–159.

    Article  Google Scholar 

  • Etzkowitz, H., & Klofsten, M. (2005). The innovating region: toward a theory of knowledge-based regional development. R&D Management, 35(3), 243–255.

    Article  Google Scholar 

  • Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: From national systems and “Mode 2” to a Triple Helix of university-industry-government relations. Research Policy, 29(22), 109–123.

    Article  Google Scholar 

  • Evans, G., Honkapohja, S., & Romer, P. (1998). Growth cycles. American Economic Review, 88(3), 495–515.

    Google Scholar 

  • Freeman, C. (1987). Technology and economic performance: lessons from Japan. London: Pinter Publishers.

    Google Scholar 

  • Freeman, C. (1999). Technical change and economic growth: The case of “Catch-up”. In M. M. G. Fase, W. Kanning, & D. A. Walker (Eds.), Economics, welfare Policy and the history of economic thought : Essays in honour of Arnold Heertje. Cheltenham: Elgar.

    Google Scholar 

  • Hansen, B. (1995). Rethinking the univariate approach to unit root testing: Using covariates to increase power. Econometric Theory, 11(05), 1148–1171.

    Article  Google Scholar 

  • Hanck, C. (2013). An intersection test for panel unit roots. Econometric Reviews, 32(2), 183–203.

    Article  Google Scholar 

  • Hartung, J. (1999). A note on combining dependent tests of significance. Biometrical Journal, 41(7), 849–855.

    Article  Google Scholar 

  • Henderson, D., & Parmeter, C. (2015). Applied nonparametric econometrics. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Knack, S., & Keefer, P. (1997). Does social capital have an economic payoff? A cross country investigation. Quarterly Journal of Economics, 112(4), 1251–1288.

    Article  Google Scholar 

  • Kao, C., & Chiang, M. H. (2000). On the estimation and inference of a cointegrated regression in panel data. Advances in Econometrics, 15, 179–222.

    Article  Google Scholar 

  • Khan, M. R., & Al-Ansari, M. (2005). Sustainable innovation as a corporate strategy. available at http://www.triz-journal.com/archives/2005/01/02.pdf

  • Kormendi, R. C., & Meguire, P. G. (1985). Macroeconomic determinants of growth: Cross-country evidence. Journal of Monetary Economics, Elsevier, 16 (2), 141–163.

    Google Scholar 

  • Leydesdorff, L., & Meyer, M. (2006, December). Triple Helix indicators of knowledge-based innovation systems: Introduction to the special issue. Research Policy, Elsevier, 35(10), 1441–1449.

    Google Scholar 

  • Li, Q., & Racine, J. (2007). Nonparametric econometrics: Theory and practice. Princeton: Princeton University Press.

    Google Scholar 

  • Liljemark, T. (2001). Comparing innovation systems: A framework and application to China transitional context. Research Policy, 30, 1091–1114.

    Article  Google Scholar 

  • Lundvall, B.-Å. (1988). Innovation as an interactive process: From user-producer interaction to the national system of innovation. In G. Dosi et al. (Eds.), Technical change and economic theory (pp. 349–369). London: Pinter.

    Google Scholar 

  • Lundvall, B.-Å. (1992). National systems of innovation: towards a theory of innovation and interactive learning. London: Pinter.

    Google Scholar 

  • MacGregor, S. P., Marques-Gou, P., & Simon-Villar, A. (2010). Gauging readiness for the Quadruple Helix: A study of 16 European organizations. Journal of the Knowledge Economy, 1(3), 173–190.

    Article  Google Scholar 

  • Mankiw, N. G., Romer, D., & Weil, D. (1992). A contribution to the empirics of economic growth. Quarterly Journal of Economics, 107(2), 407–437.

    Article  Google Scholar 

  • Marris, R. (1984). Comparing the incomes of nations: A critique of the international comparison project. Journal of Economic Literature, 22(1), 40–57.

    Google Scholar 

  • Monteiro, S. (2013). Economie de l’innovation, dépenses publiques productives et croissance économique: une étude empirique pour l’évaluation du rôle des infrastructures technologiques dans les pays de l’OECD. Economies et finances. Université Nice Sophia Antipolis, 2013. Français.

    Google Scholar 

  • Moral-Benito, E. (2012, May). Determinants of economic growth: A Bayesian panel data approach. The Review of Economics and Statistics, 94(2), 566–579.

    Article  Google Scholar 

  • Nelson, R., & Winter, S. (1982). An evolutionary theory of economic change. Cambridge: Belknap.

    Google Scholar 

  • Nelson, R., & Rosenberg, N. (1993). Technical innovation and national systems. In R. R. Nelson (Ed.), National innovation Systems: A comparative study. New York: Oxford University Press.

    Google Scholar 

  • Nelson, R., & Romer, P. (1996). Science, economic growth and public policy. In B. Smith & Bartfield (Eds.), Technology, R&D and the economy (pp. 49–74). Washington, DC: Brookings Institution Press.

    Google Scholar 

  • OECD. (2014). OECD science, technology and industry outlook 2014, OECD Publishing Paris. http://dx.doi.org/10.1787/sti_outlook-2014-en

  • Pelikan, P. (1988). Can the imperfect innovation systems of capitalism be outperformed? In G. Dosi, C. Freeman, R. Nelson, G. Silverberg, & L. Soete (Eds.), Technical change and economic theory (pp. 370–398). London/New York: Pinter Publisher.

    Google Scholar 

  • Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61, 653–670.

    Article  Google Scholar 

  • Pedroni, P. (2000). Fully Modified OLS for heterogenous cointegrated panels. Advances in Econometrics, 15, 93–132.

    Article  Google Scholar 

  • Pedroni, P. (2001). Purchasing power parity tests in cointegrated panels. Review of Economics and Statistics, 83, 727–731.

    Article  Google Scholar 

  • Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory, 20, 597–625.

    Article  Google Scholar 

  • Perotti, R. (1996). Growth, income distribution, and democracy: What the data say. Journal of Economic Growth, 1(2), 149–187.

    Article  Google Scholar 

  • Pesaran, M. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics, 0435. Cambridge: CWP. Working Papers.

    Google Scholar 

  • Quah, D. (1993). Empirical cross-section dynamics in economic growth. European Economic Review, 37, 426–434.

    Article  Google Scholar 

  • Quah, D. (1996). Twin Peaks: Growth and convergence in models of distribution dynamics. Economic Journal, 106, 1045–1055.

    Google Scholar 

  • Quah, D. (2014, November). Convergence determines governance—within and without, In K. Dervis and H. Kharas (Eds.), Growth, convergence, and income distribution. Brookings Institution: Brookings.

    Google Scholar 

  • Racine, J. (2008). Nonparametric econometrics: A primer. In W. Greene (Eds.), Foundations and trends in econometrics (Vol. 3: No 1, pp. 1–88). Delft: Publishers Inc.

    Google Scholar 

  • Rockey, J.,& Temple, J. (2015). Growth econometrics for agnostics and true believers. CEPR Discussion Papers 10590, C.E.P.R: London. Discussion Papers.

    Google Scholar 

  • Rodrik, D. (2003). In search of prosperity: Analytic narratives on economic growth. Princeton: Princeton University Press.

    Book  Google Scholar 

  • Sachs, J., McArthur, J. W., Schmidt-Traub, G., Kruk, M., Bahadur, C., Faye, M., & McCord, G. (2004). Ending Africa’s poverty trap. Brookings Papers on Economic Activity, 1, 117–240.

    Article  Google Scholar 

  • Sala-i-Martin, X., Doppelhofer, G., & Miller, R. (2004). Determinants of long-term growth: A bayesian averaging of classical estimates (BACE) approach. American Economic Review, 94(4), 813–835.

    Article  Google Scholar 

  • Sheather, S., & Jones, M. (1998). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society: Series B: Methodological, 53(3), 683–690.

    Google Scholar 

  • Temple, J. (2000). Growth regressions and what the textbooks don’t tell you. Bulletin of Economic Research, 53(3), 181–205.

    Article  Google Scholar 

  • Thompson, M. (2008). Complementarities and costly investment in a growth model. Journal of Economics, 94(3), 231–240.

    Article  Google Scholar 

  • Zak, P. J., & Knack, S. (2001). Trust and Growth. Economic Journal, 111(470), 295–321.

    Google Scholar 

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Authors and Affiliations

  1. P-BIO Portugal’s Biotechnology Industry Organization, Biocant Park, Cantanhede, Portugal

    Sara Paulina De Oliveira Monteiro

  2. European Banking Authority, London, UK

    Sara Paulina De Oliveira Monteiro

  3. Elixir-Europe.Org, Cambridgeshire, UK

    Sara Paulina De Oliveira Monteiro

  4. Católica Porto Business School, Porto, Portugal

    Sara Paulina De Oliveira Monteiro

  5. Faculdade de Economia da Universidade de Coimbra, GEMF, CeBER, Av. Dias da Silva, 165, 3004-512, Coimbra, Portugal

    Maria Adelaide Pedrosa da Silva Duarte

Authors
  1. Sara Paulina De Oliveira Monteiro
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  2. Maria Adelaide Pedrosa da Silva Duarte
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Editors and Affiliations

  1. P-BIO Portugal’s Biotechnology Industry Organization, Coimbra, Portugal

    Sara Paulina De Oliveira Monteiro

  2. Department of Information Systems and Technology Management, School of Business George Washington University, Washington, District of Columbia, USA

    Elias G. Carayannis

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De Oliveira Monteiro, S.P., da Silva Duarte, M.A.P. (2017). Quadruple Helix R&D Growth Models: A Panel Cointegration Analysis Applied to a Sample of OECD Countries. In: De Oliveira Monteiro, S., Carayannis, E. (eds) The Quadruple Innovation Helix Nexus. Palgrave Studies in Democracy, Innovation, and Entrepreneurship for Growth. Palgrave Macmillan, New York. https://doi.org/10.1057/978-1-137-55577-9_3

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