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Measuring China's innovative capacity: a stochastic frontier exercise

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  • Chiara Franco
  • Riccardo Leoncini

Abstract

We adopt a stochastic frontier analysis of innovative activity to disentangle countries’ patenting capacity from patenting efficiency. We analyse the determinants of innovative capacity of a set of 26 OECD countries plus China, over the period 1992--2007, to show if and how China's technological activity is growing faster than commonly held as compared to the most innovative countries of the world. Our results highlight that both internal and external elements jointly contribute to enhance countries’ innovative capacity and efficiency. In particular, while government-funded R&D is more important for innovative capacity, privately funded R&D as well as foreign direct investments (FDIs) affects technical efficiency (TE). Moreover, as for the whole set of countries, FDIs seem to exert a resource-seeking role (as they negatively affect TE), this does not happen for China, where FDIs exert a positive effect. Results are robust to the use of alternative measures of innovative inputs (such as higher education expenditure in R&D and R&D personnel, but also FDI flows rather than stocks). Finally, human capital measures are generally not very effective in enhancing patenting efficiency, apart from tertiary education.

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  • Chiara Franco & Riccardo Leoncini, 2013. "Measuring China's innovative capacity: a stochastic frontier exercise," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 22(2), pages 199-217, March.
    • Handle: RePEc:taf:ecinnt:v:22:y:2013:i:2:p:199-217
    DOI: 10.1080/10438599.2012.744174
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    References listed on IDEAS

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    1. Gabriele, Alberto & Khan, Ali Haider, 2008. "Enhancing technological progress in a market-socialist context:China's national innovation system at the crossroads," MPRA Paper 10695, University Library of Munich, Germany.
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    Cited by:

    1. Kyriakos Drivas & Claire Economidou & Efthymios G. Tsionas, 2018. "Production of output and ideas: efficiency and growth patterns in the United States," Regional Studies, Taylor & Francis Journals, vol. 52(1), pages 105-118, January.
    2. Shuai Liu & Xiao-Yu Xu & Kai Zhao & Li-Ming Xiao & Qi Li, 2021. "Understanding the Complexity of Regional Innovation Capacity Dynamics in China: From the Perspective of Hidden Markov Model," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    3. Puertas, Rosa & Marti, Luisa & Guaita-Martinez, José M., 2020. "Innovation, lifestyle, policy and socioeconomic factors: An analysis of European quality of life," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    4. Aoife Hanley & Wan-Hsin Liu & Andrea Vaona, 2015. "Credit depth, government intervention and innovation in China: evidence from the provincial data," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 5(1), pages 73-98, June.
    5. Si Zhang & Shasha Zhao & Ioannis Bournakis & Robert Pearce & Marina Papanastassiou, 2018. "Subsidiary roles as determinants of subsidiary technology sourcing: empirical evidence from China," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 35(2), pages 623-648, August.

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