Scientometrics

, 89:465 | Cite as

Research output and economic productivity: a Granger causality test

  • Ling-Chu Lee
  • Pin-Hua Lin
  • Yun-Wen Chuang
  • Yi-Yang Lee
Article
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Abstract

The correlation between GDP and research publications is an important issue in scientometrics. This article provides further empirical evidence connecting revealed comparative advantage in national research with effects on economic productivity. Using quantitative time series analysis, this study attempts to determine the nature of causal relationships between research output and economic productivity. One empirical result is that there is mutual causality between research and economic growth in Asia, whereas in Western countries the causality is much less clear. The results may be of use to underdeveloped nations deciding how to direct their academic investment and industry policy.

Keywords

Research output Economic productivity Time series analysis Granger causality 

Notes

Acknowledgments

We thank the help from Mr. Chu-Tzu-Liang. We also gratefully acknowledge financial support from the National Science Council in Taiwan (NSC 99-2410-H-301-003-).

References

  1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.), Proceedings of the second international symposium on information theory, pp. 267–281.Google Scholar
  2. Archibugi, D., & Coco, A. (2004). A new indicator of technological capabilities for developed and developing countries (ArCo). World Development, 32, 629–654.CrossRefGoogle Scholar
  3. Chuang, Y. W., Lee, L. C., Hung, W. C., & Lin, P. H. (2010). Forgoing into the innovation lead—A comparative analysis of scientific capacity. International Journal of Innovation Management, 14(3), 511–529.CrossRefGoogle Scholar
  4. Desai, M., Fukuda-Parr, S., Johansson, C., & Sagasti, F. (2002). Measuring the technology achievement of nations and capacity to participate in the network age. Journal of Human Development, 3(1), 95–122.CrossRefGoogle Scholar
  5. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427–431.MathSciNetMATHCrossRefGoogle Scholar
  6. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057–1072.MathSciNetMATHCrossRefGoogle Scholar
  7. Dodgson, M. (2000). Policies for science, technology, and innovation in Asian newly industrializing economies. In L. Kim & R. Nelson (Eds.), Technology, learning, and innovation: Experiences of newly industrializing economies. New York: Cambridge University Press.Google Scholar
  8. Granger, C. W. J. (1980). Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2, 329–352.MathSciNetCrossRefGoogle Scholar
  9. Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2, 111–120.MATHCrossRefGoogle Scholar
  10. Green, W. (2003). Econometric analysis (5th ed). Prentic Hall.Google Scholar
  11. Hobday, M. (2000). East versus Southeast Asian Innovation System: Comparing OEM—And TNCled growth in electronics. In L. Kim & R. Nelson (Eds.), Technology, learning, and innovation: Experiences of newly industrializing economies. New York: Cambridge University Press.Google Scholar
  12. Hung, W. C., Lee, L. C., & Tsai, M. H. (2009). An international comparison of relative contribution to academic productivity. Scientometrics, 81(3), 703–718.CrossRefGoogle Scholar
  13. Kealey, T. (1996). The economic laws of scientific research. New York: St. Martin’s Press.Google Scholar
  14. King, D. A. (2004). The scientific impact of nations. What different countries get for their research spending. Nature, 430, 311–316.CrossRefGoogle Scholar
  15. Marsh, I. (1997). Economic governance in industrialising Asia: Structure, comparisons and impact. Sydney: Australian Graduate School of Management Working Paper, pp. 97–017.Google Scholar
  16. Mathew, J. (1996). High technology industrialisation in East Asia. Journal of Industry Studies, 3(2), 1–67.CrossRefGoogle Scholar
  17. Price, D. S. (1978). Toward a model for science indicators. In Y. Elkana, G. J. Lederber, R. K. Merton, A. Thackray, & H. Zuckerman (Eds.), Toward a metric of science: the advent of science indicators (pp. 69–95). New York: John Wiley & Sons.Google Scholar
  18. Rai, L. P., & Lal, K. (2000). Indicators of the information revolution. Technology in Society, 22, 221–235.CrossRefGoogle Scholar
  19. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.MathSciNetMATHCrossRefGoogle Scholar
  20. Vinkler, P. (2006). Composite scientometrics indicators for evaluating publications of research institutes. Scientometrics, 68, 629–642.CrossRefGoogle Scholar
  21. Vinkler, P. (2008). Correlation between the structure of scientific research, scientometric indicators and GDP in EU and non-EU countries. Scientometrics, 74, 237–254.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2011

Authors and Affiliations

  • Ling-Chu Lee
    • 1
  • Pin-Hua Lin
    • 1
    • 3
  • Yun-Wen Chuang
    • 1
  • Yi-Yang Lee
    • 2
  1. 1.Science and Technology Policy Research and Information Centre (STPI)TaipeiTaiwan
  2. 2.Taiwan Institute of Economic Research (TIER)TaipeiTaiwan
  3. 3.Graduate Institute of Industrial EconomicsNational Central UniversityJung-li CityTaiwan

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