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Research on Innovation Efficiency and Technology Gap in China Economic Development

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  • Jian-Wen Fang

    (School of Business of Soochow University, 50 Donghuan Road, Suzhou 215021, P. R. China)

  • Yung-ho Chiu

    (Department of Economics, Soochow University, No. 56, Sec. 1, Kuei-yang, Taipei 100, Taiwan)

Abstract

In this paper, we use the meta-frontier network DEA approach to evaluate the innovation efficiency of 30 provinces in China from 2009 to 2011. These provinces have been classified into two groups based on their levels of economic development. The first group comprises provinces in the Eastern region, while the second group comprises provinces in the Central and Western regions. First, we use the meta-frontier network DEA method to estimate the technology gaps of innovation efficiency between different operating types. Second, the quadrant analysis method explores the reasons for efficiency losses. Finally, we take the fixed effect model to examine whether industry–university–research cooperation influences technology efficiency. The empirical results indicate (i) the Eastern region has significantly higher innovation efficiency than the Central and Western regions. (ii) Some Eastern provinces have a high technology level, yet their resource allocation capabilities still need to be improved. (iii) Industry–university–research cooperation is an effective way to improve innovation performance.

Suggested Citation

  • Jian-Wen Fang & Yung-ho Chiu, 2017. "Research on Innovation Efficiency and Technology Gap in China Economic Development," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(02), pages 1-22, April.
  • Handle: RePEc:wsi:apjorx:v:34:y:2017:i:02:n:s0217595917500051
    DOI: 10.1142/S0217595917500051
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    1. Yujiro Hayami, 1969. "Sources of Agricultural Productivity Gap Among Selected Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 51(3), pages 564-575.
    2. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    3. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    6. Wang, Eric C. & Huang, Weichiao, 2007. "Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach," Research Policy, Elsevier, vol. 36(2), pages 260-273, March.
    7. Nasierowski, W. & Arcelus, F. J., 2003. "On the efficiency of national innovation systems," Socio-Economic Planning Sciences, Elsevier, vol. 37(3), pages 215-234, September.
    8. Hayami, Yujiro & Ruttan, Vernon W, 1970. "Agricultural Productivity Differences Among Countries," American Economic Review, American Economic Association, vol. 60(5), pages 895-911, December.
    9. Wade D. Cook & Joe Zhu, 2007. "Data Irregularities And Structural Complexities In Dea," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 1-11, Springer.
    10. Zhong, Wei & Yuan, Wei & Li, Susan X. & Huang, Zhimin, 2011. "The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data," Omega, Elsevier, vol. 39(4), pages 447-455, August.
    11. Loet Leydesdorff & Henry Etzkowitz, 1998. "The Triple Helix as a model for innovation studies," Science and Public Policy, Oxford University Press, vol. 25(3), pages 195-203, June.
    12. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    13. Li, Xibao, 2009. "China's regional innovation capacity in transition: An empirical approach," Research Policy, Elsevier, vol. 38(2), pages 338-357, March.
    14. Zhang, Anming & Zhang, Yimin & Zhao, Ronald, 2003. "A study of the R&D efficiency and productivity of Chinese firms," Journal of Comparative Economics, Elsevier, vol. 31(3), pages 444-464, September.
    15. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
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    5. Wenzhong Ye & Yaping Hu & Lingming Chen, 2021. "Urban Innovation Efficiency Improvement in the Guangdong–Hong Kong–Macao Greater Bay Area from the Perspective of Innovation Chains," Land, MDPI, vol. 10(11), pages 1-19, October.

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