Zou Wenjie. China’s Pharmaceutical Manufacturing R&D Efficiency and Its Convergence:Based on the DEA-Malmquist Index Approach[J]. Journal of Beijing University of Aeronautics and Astronautics Social Sciences Edition, 2013, 26(6): 69-73.
Citation:
Zou Wenjie. China’s Pharmaceutical Manufacturing R&D Efficiency and Its Convergence:Based on the DEA-Malmquist Index Approach[J]. Journal of Beijing University of Aeronautics and Astronautics Social Sciences Edition, 2013, 26(6): 69-73.
Zou Wenjie. China’s Pharmaceutical Manufacturing R&D Efficiency and Its Convergence:Based on the DEA-Malmquist Index Approach[J]. Journal of Beijing University of Aeronautics and Astronautics Social Sciences Edition, 2013, 26(6): 69-73.
Citation:
Zou Wenjie. China’s Pharmaceutical Manufacturing R&D Efficiency and Its Convergence:Based on the DEA-Malmquist Index Approach[J]. Journal of Beijing University of Aeronautics and Astronautics Social Sciences Edition, 2013, 26(6): 69-73.
This paper uses the DEA-Malmquist Index to measure China's pharmaceutical manufacturing R & D efficiency from 2000 to 2010, and analyze the convergence trend. The results indicate that China's overall pharmaceutical manufacturing R&D efficiency is on an uphill trend, the main driver of growth in R&D efficiency stems from technical efficiency; R&D regional efficiency differs significantly, the main reason being that technological progress and technical efficiency cannot synchronize with growth. Except for the eastern region, central and western regions as well as the national R&D efficiency convergence trend has not yet formed, technology diffusion effect cannot form the main reason for the R&D efficiency unrealized convergence.
Caves D W, Christensen I R, Deceit W E. The economic theory of index numbers and the measurement of input output and productivity[J]. Economic Erica, 1982, 92:73—86.
[8]
Fare R, Gross Kopf S, Norris M, et al. Productivity growth, technical progress, and efficiency change in industrialized countries[J]. American Economic Review, 1994, 84(1):66—81.
[9]
王天营, 沈菊华. 样本数据缺失的灰数补救方法[J]. 统计与决策, 2008(22):7—10.
[10]
Barro R, Martin X. The classical approach to convergence analysis[J]. Journal of Political Economy, 1992, 10(1):223—251.