Technological progress and economic growth: evidence from Poland

Gurgul, Henryk and Lach, Łukasz (2012): Technological progress and economic growth: evidence from Poland. Published in: Ekonometria. Zastosowania Metod Ilościowych , Vol. 34, (2012): pp. 354-386.

Abstract

In this paper the results of testing the causal interdependence between technological progress and GDP in Poland are presented. The results obtained for quarterly data from the period Q1 2000 – Q4 2009 indicate causality running from technological progress to GDP in Poland. In addition, causality from number of patents to employment and from employment to R&D outlays is found, which indicates causality from patents to R&D expenditure. The robustness of these results is also approved. The empirical findings of this paper imply some policy recommendations. Polish government and private firms should definitely increase investment in developing new technologies.

• Item Type: MPRA Paper
• Language: English
• Keywords: Patents, R&D sector, economic growth, Granger causality
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O31 - Innovation and Invention: Processes and Incentives; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O34 - Intellectual Property and Intellectual Capital; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O40 - General

References

[1] Agiakoglu, C. and Newbold, P. (1992). ‘Empirical Evidence on Dickey–Fuller Type Tests’, Journal of Time Series Analysis, 13, pp. 471–83.

[2] Andrews, D.W.K. and Buchinsky, M. (2000). ‘A Three–Step Method for Choosing the Number of Bootstrap Repetitions’, Econometrica, 68, pp. 23–52.

[3] Arrow, K. (1962). ‘The Economic Implications of Learning by Doing’, Review of Economic studies 29, pp. 155–73.

[4] Baek, E. and Brock, W. (1992). ‘A general test for Granger causality: Bivariate model’ Technical Report, Iowa State University and University of Wisconsin, Madison.

[5] Bean, A. (1995). ‘Why some R&D organizations are more productive than others’, Research and Technology Management, 38, pp. 25–9.

[6] Bernstein, J. and Nadiri, M. (1988). ‘Inter–industry R&D spillovers, rates of return and production in high–tech industries’, American Economic Review, Papers and Proceedings, 78, pp. 429–34.

[7] Branstetter, L.G. (2001). ‘Are Knowledge Spillovers International or Intranational in Scope? Microeconometric Evidence from the U.S. and Japan’, Journal of International Economics 53, pp. 53–79.

[8] Brock, W. (1991). ‘Causality, chaos, explanation and prediction in economics and finance’, in Casti, J. and Karlqvist, A. (Eds.), Beyond Belief: Randomness, Prediction and Explanation in Science. CRC Press, Boca Raton, Fla., pp. 230–79.

[9] Cohen, W.M. and Levinthal, D.A. (1989). ‘Innovation and Learning: The Two Faces of R&D’, The Economic Journal, 99, pp. 569–96.

[10] Diks, C.G.H. and DeGoede, J. (2001). ‘A general nonparametric bootstrap test for Granger causality’, in Broer, H.W., Krauskopf, W. And Vegter, G. (Eds.), Global analysis of dynamical systems. Institute of Physics Publishing, Bristol, United Kingdom, pp. 391–403.

[11] Diks, C.G.H. and Panchenko, V. (2006). ‘A new statistic and practical guidelines for nonparametric Granger causality testing’, Journal of Economic Dynamics and Control, 30, pp. 1647–69.

[12] Eurostat, (2008). ‘Patents and R&D personnel’, Statistics in Focus, 107/2008.

[13] Fagerberg, J. (1988). ‘International Competitiveness’, The Economic Journal, 98, pp. 355–74.

[14] Fagerberg, J. (2000). ‘Technological progress, structural change and productivity growth: a comparative study’, Structural Change and Economic Dynamics, 11, pp. 341–93.

[15] Falk, M. (2006). ‘What drives business research and development (R&D) intensity across Organisation for Economic Cooperation and Development (OECD) countries’, Applied Economics, 38, pp. 533–47.

[16] Firth, L. and Mellor, D. (2000). ‘Learning and new growth theories’, Research Policy, 29, pp. 1157–63.

[17] Freeman, C. and Soete, L. (1997). The Economics of Industrial Innovation, MIT Press, Cambridge, Massachusetts.

[18] Goel, R.K., Payne, J.E. and Ram, R. (2008). ‘R&D expenditures and U.S. economic growth: A disaggregated approach’, Journal of Policy Modeling, 30, pp. 237–50.

[19] Granger, C.W.J. (1969). ‘Investigating causal relations by econometric models and cross spectral methods’, Econometrica, 37, pp. 424–38.

[20] Granger, C.W.J. (1988). ‘Some recent developments in the concept of causality’, Journal of Econometrics, 39, pp. 199–211.

[21] Granger, C.W.J. and Newbold, P. (1974). ‘Spurious regression in econometrics’, Journal of Econometrics, 2, pp. 111–20

[22] Granger, C.W.J., Huang, B. and Yang, C. (2000). ‘A bivariate causality between stock prices and exchange rates: evidence from recent Asian Flu’, The Quarterly Review of Economics and Finance, 40, pp. 337–54.

[23] Griliches, Z. (1990). ‘Patent Statistics as Economic Indicators: A Survey’, Journal of Economic Literature, 28, pp. 1661–707.

[24] Griliches, Z. and Regev, H. (1995). ‘Firm productivity in Israeli industry 1979–1988’, Journal of Econometrics, 65, pp. 175–203.

[25] Griliches, Z. (1996). ‘R&D and Productivity: Econometric Results and Measurement Issues’, in Stoneman, P. (Ed), Handbook of the Economics of Innovation and Technological Change, Blackwell, Oxford, pp. 52–89.

[26] Gurgul, H. and Lach, Ł. (2010). ‘The causal link between Polish stock market and key macroeconomic aggregates’, Betriebswirtschaftliche Forschung und Praxis, 4, pp. 367–83.

[27] Hacker, R.S. and Hatemi–J, A. (2006). ‘Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application’, Applied Economics, 38, pp. 1489–500.

[28] Hall, B. and Mairesse, J. (1995). ‘Exploring the relationship between R&D and productivity in French manufacturing firms’, Journal of Econometrics, 65, pp. 263–93.

[29] Helpman, E. and Coe, D.T. (1995). ‘International R&D Spillovers’, European Economic Review, 39, pp. 859–87.

[30] Horowitz, J.L. (1995). ‘Bootstrap methods in econometrics: Theory and numerical performance’, in Kreps, D.M. and Wallis, K.F. (Eds.), Advances in Economics and Econometrics: Theory and Applications. Cambridge University Press, Cambridge, pp. 188–232.

[31] Jalles, J.T. (2010). ‘How to measure innovation? New evidence of the technology–growth linkage’, Research in Economics, 64, pp. 81–96.

[32] Johansen, S. (1995). Likelihood–based Inference in Cointegrated Vector Autoregressive Models, Oxford University Press, Oxford.

[33] Krugman, P. (1990). Rethinking International Trade. MIT Press, Cambridge, Massachusetts.

[34] Lichtenberg, F. and Siegel, D. (1991). ‘The impact of R&D investment on productivity: new evidence using R&D–LRD data’, Economic Inquiry, 29, pp. 203–29.

[35] Lipsey, R.G. and Carlaw, K. (2001). ‘What does total factor productivity measure?’ (available at http://www.csls.ca/ipm/1/lipsey-e.pdf).

[36] Lucas, R. (1988). ’On the Mechanics of Economic Development’, Journal of Monetary Economics, 22, pp. 3–42.

[37] Lütkepohl, H. (1993). Introduction to Multiple Time Series Analysis, second ed., Springer–Verlag, New York.

[38] Madden, G. and Savage, S. (2000). ‘R&D spillovers, information technology and telecommunications, and productivity in Asia and the OECD’, Information Economics and Policy, 12, pp. 367–92.

[39] Mansfield, E. (1991). ‘Academic Research and Industrial Innovations’, Research Policy, 20, pp. 1–12.

[40] Mulas–Granados, C. and Sanz, I. (2008). ‘The dispersion of technology and income in Europe: Evolution and mutual relationship across regions’, Research Policy, 37, pp. 836–48.

[41] OECD (2000). ‘Science, technology and industry outlook 2000 – Chapter II: Science, technology and innovation policy in OECD countries – A review of recent developments’, OECD, Paris.

[42] Paulsen, J. (1984). ‘Order determination of multivariate autoregressive time series with unit roots’, Journal of Time Series Analysis, 5, pp. 115–27.

[43] Pavitt, K. (1998). ‘The inevitable limits of EU R&D funding’, Research Policy, 27, pp. 559–68.

[44] Peterson, J. and Sharp, M. (1998). Technology Policy in the European Union, Macmillan Press, London.

[45] Phillips, P.C.B. (1986). ‘Understanding the spurious regression in econometrics’, Journal of Econometrics, 33, pp. 311–40.

[46] Raa, T. and Wolff, E. (2000). ‘Engines of growth in the US economy’, Structural Change and Economic Dynamics, 11, pp. 473–89.

[47] Romer, P. (1986). ‘Increasing Returns and Long–Run Growth’, Journal of Political Economy, 94, pp. 1002–37.

[48] Romer, P. (1990). ‘Endogenous technological change’, Journal of Political Economy, 98, pp. 71–102.

[49] Scherer, F.M. (1993). ‘Lagging productivity growth measurement, technology and shock effects’, Empirica, 20, pp. 5–24.

[50] Schumpeter, J. (1911). Theorie der wirtschaftlichen Entwicklung; Neuausgabe hrgg. von Jochen Röpke und Olaf Stiller, Berlin 2006.

[51] Solow, R. (1957). ‘Technical change and the aggregate production function’, Review of Economics and Statistics, 34, pp. 312–20.

[52] Terleckyj, N.E. (1974). Effect of R&D on the Productivity Growth of Industries. National Planning Association, Washington D.C.

[53] Terleckyj, N.E., (1980). ‘Direct and indirect effects of industrial R&D on the productivity growth of industries’, in Kendrik, J.N. and Vaccara, B.N. (Eds), New Developments in Productivity Measurement and Analysis. University Chicago Press, Chicago.

[54] Toda, H.Y. and Yamamoto, T. (1995). ‘Statistical inference in vector autoregressions with possibly integrated processes’, Journal of Econometrics, 66, pp. 225–50.

[55] Tsipouri, L.J. (2004). ‘Innovations for European competitiveness and cohesion: opportunities and difficulties of co–evolution’, Science and Public Policy, 31, pp. 465–74.

[56] Zif, J. and McCarthy, D. (1997). ‘R&D cycle: The influence of product and process R&D on short–term ROI’, IEEE Transactions on Engineering Management, 44, pp. 114–23.