Paper:
Interaction Between Population Aging and Technological Innovation: A Chinese Case Study
Xiang Li* and Xindong Zhao**,
*School of Economics and Finance, Huaqiao University
No. 269, Chenghua North Road, Fengze District, Quanzhou, Fujian 362021, China
**Institute of Quantitative Economics, Huaqiao University
No. 668, Jimei Avenue, Jimei District, Xiamen, Fujian 361021, China
Corresponding author
Based on the method of unidirectional causality measure, this paper analyzes the long-term and short-term dynamic effects and causality between China’s population aging and technological innovation. According to the empirical results, first, the aging of the population will eventually have a continuous long-term impact, although it has little effect on the technology innovation in the short term. Second, when compared with the old-age dependency ratio, the child-raising ratio has a remarkable unidirectional causal effect on the technological innovation in the short term. Third, when compared with the old-age dependency ratio, the total dependency ratio has a stronger impact on the scientific and technological innovation ability, which is a long-term effect. The finding indicates that the elderly population and the children’s population have a continuous impact on China’s scientific and technological innovation, that is, the increase in social support burden affects the technological innovation for a long time.
- [1] Z. W. Zhai, J. J. Chen, and L. Li, “Future Trends of China’s Population and Aging: 2015-2100,” Population Research, Vol.41, No.4, pp. 60-71, 2017 (in Chinese).
- [2] D. Ikeda and M. Saito, “The effects of demographic changes on the real interest rate in Japan,” Japan and the World Economy, Vol.32, pp. 37-48, 2014.
- [3] D. M. Yao, S. X. Li, and S. S. Lin, “Does Aging Affect Scientific and Technological Innovation? A Literature Analysis Based on Age Structure and Innovation Capability,” Management Review, Vol.27, No.8, pp. 56-67, 2015 (in Chinese).
- [4] W. L. Hu, “On the Relationship between Population Aging and Technological Progress,” The J. of Quantitative and Technical Economics, No.11, pp. 27-34, 1991 (in Chinese).
- [5] B. Mahlberg, I. Freund, and A. Prskawetz, “Ageing, productivity and wages in Austria: sector level evidence,” Empirica, Vol.40, No.4, pp. 561-584, 2013.
- [6] F. Lancia and G. Prarolo, “A politico-economic model of aging, technology adoption and growth,” J. of Population Economics, Vol.25, No.3, pp. 989-1018, 2012.
- [7] J. Berk and D. N. Weil, “Old teachers, old ideas, and the effect of population aging on economic growth,” Research in Economics, Vol.69, No.4, pp. 661-670, 2015.
- [8] P. Ilmakunnas and T. Miyakoshi, “What are the drivers of TFP in the Aging Economy? Aging labor and ICT capital,” J. of Comparative Economics, Vol.41, No.1, pp. 201-211, 2013.
- [9] Y. L. Xiong, J. G. Xie, and B. C. Xu, “Demographic Structure,Capital Accumulation and China’s Total Factor Productivity,” J. of Yunnan University of Finance and Economics, No.1, pp. 38-49, 2016.
- [10] D. M. Yao, J. Ning, and S. Y. Wei, “How Does the Population Aging Affect Technology Innovation?,” The J. of World Economy, Vol.40, No.4, pp. 105-128, 2017 (in Chinese).
- [11] J. B. Ang and J. B. Madsen, “Imitation versus innovation in an aging society: international evidence since 1870,” J. of Population Economics, Vol.28, No.2, pp. 299-327, 2015.
- [12] Y. H. Sun, J. G. Xie, and Y. L. Xiong, “Ageing Labor Force, Education and Regional Total Factor Productivity,” China Economic Studies, Vol.3, No.3, pp. 3-16, 2017 (in Chinese).
- [13] J. X. Wang and S. J. Wang, “Aging Population, Technological Innovation and Economic Growth,” J. of Xi’an Jiaotong University (Social Sciences), Vol.37, No.6, pp. 7-38, 2017 (in Chinese).
- [14] W. Wang and Z. M. Jiang, “The Influence of Population Aging on Technological Innovation: Based on Innovation Evaluation Using Dynamic Factor Analysis and Dynamic GMM Model,” J. of Shanghai University of Finance and Economics, Vol.19, No.6, pp. 4-17, 2017 (in Chinese).
- [15] X. Yang and L. Hou, “A Study on the Macro and Micro Effects of Population Aging to Economic and Social Society in China,” Population J., No.4, pp. 46-53, 2011 (in Chinese).
- [16] Y. Hosoya, “The decomposition and measurement of the interdependency between second order stationary processes,” Probability Theory and Related Fields, Vol.88, No.4, pp. 429-444, 1991.
- [17] F. Yao and Y. Hosoya, “Inference on one-way effect and evidence in Japanese macroeconomic data,” J. of Econometrics, Vol.98, No.2, pp. 225-255, 2000.
- [18] C. W. J. Granger, “Investigating Causal Relations by Econometric Models and Cross-spectral Methods,” Econometrica, Vol.37, No.3, pp. 424-438, 1969.
- [19] M. Osterwald-Lenum, “A Note with Quantiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistics,” Oxford Bulletin of Economics & Statistics, Vol.54, No.3, pp. 461-472, 1992.
- [20] J. R. M. Hosking, “The multivariate portmanteau statistics,” J. of the American Statistical Association, Vol.75, No.371, pp. 602-608, 1980.
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