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.
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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.
Archibugi, D., & Coco, A. (2004). A new indicator of technological capabilities for developed and developing countries (ArCo). World Development, 32, 629–654.
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.
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.
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.
Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057–1072.
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.
Granger, C. W. J. (1980). Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2, 329–352.
Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2, 111–120.
Green, W. (2003). Econometric analysis (5th ed). Prentic Hall.
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.
Hung, W. C., Lee, L. C., & Tsai, M. H. (2009). An international comparison of relative contribution to academic productivity. Scientometrics, 81(3), 703–718.
Kealey, T. (1996). The economic laws of scientific research. New York: St. Martin’s Press.
King, D. A. (2004). The scientific impact of nations. What different countries get for their research spending. Nature, 430, 311–316.
Marsh, I. (1997). Economic governance in industrialising Asia: Structure, comparisons and impact. Sydney: Australian Graduate School of Management Working Paper, pp. 97–017.
Mathew, J. (1996). High technology industrialisation in East Asia. Journal of Industry Studies, 3(2), 1–67.
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.
Rai, L. P., & Lal, K. (2000). Indicators of the information revolution. Technology in Society, 22, 221–235.
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.
Vinkler, P. (2006). Composite scientometrics indicators for evaluating publications of research institutes. Scientometrics, 68, 629–642.
Vinkler, P. (2008). Correlation between the structure of scientific research, scientometric indicators and GDP in EU and non-EU countries. Scientometrics, 74, 237–254.
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-).
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Lee, LC., Lin, PH., Chuang, YW. et al. Research output and economic productivity: a Granger causality test. Scientometrics 89, 465 (2011). https://doi.org/10.1007/s11192-011-0476-9
- Research output
- Economic productivity
- Time series analysis
- Granger causality