创新技术进步要素偏向视角下区域异质性研究——基于SFA和广东数据的实证分析
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摘要
关于创新技术进步要素偏向视角下的区域发展异质性,少有研究关注。本文运用随机前沿超越对数生产函数模型和2006-2014年广东省市际面板数据做实证分析,测算出各要素偏向指数,进而根据测算结果将广东省划分成三个区域并对各区就创新活动和经济发展两方面异质性作对比研究。研究发现:(1)广东省创新技术进步整体偏向资本,偏向指数大都介于0-0.5且呈"N"型;(2)全省"创新断层"明显,一区创新技术进步整体偏向创新型人力资本,偏向指数在0.5左右且呈"U"型;二区创新技术进步为希克斯中性;三区创新技术进步偏向资本,偏向指数波动很大且呈"M"型。广东要加快实施创新驱动发展核心战略,把握创新技术进步要素偏向、构建适合各区创新和经济发展特质的创新体系至关重要。
There is little research on the heterogeneity of regional development from the perspective of factor-biased of innovation technology progress. Using the stochastic frontier trans-logarithmic production function and the intercity panel data of Guangdong province in 2006-2014, this paper conducts an empirical analysis and calculate the factor-biased index of innovation technology progress. And then according to the index, Guangdong province is divided into three regions. Furthermore, it does a research on regional heterogeneity between innovation and economic development about the three regions. The results show that:(1) The innovation technology progress of Guangdong preferred to capital, and the factor-biased index which is typing N most range between 0 and 0.5.(2) The innovation technology progress of the first region preferred to creative human capital, and the factor-biased index which is typing U is all around 0.5; the factor bias of innovation technology progress in the second region is Hicks neutral; the innovation technology progress of the third region preferred to capital, and the factor-biased index which is typing M is very unstable. So, in order to accelerately implement the core strategy of innovation driven development, it is very important for Guangdong to grasp the factor-biased of the innovation technology progress and build an innovation system which is suitable for the innovation and economic development of the three regions.
There is little research on the heterogeneity of regional development from the perspective of factor-biased of innovation technology progress. Using the stochastic frontier trans-logarithmic production function and the intercity panel data of Guangdong province in 2006-2014, this paper conducts an empirical analysis and calculate the factor-biased index of innovation technology progress. And then according to the index, Guangdong province is divided into three regions. Furthermore, it does a research on regional heterogeneity between innovation and economic development about the three regions. The results show that:(1) The innovation technology progress of Guangdong preferred to capital, and the factor-biased index which is typing N most range between 0 and 0.5.(2) The innovation technology progress of the first region preferred to creative human capital, and the factor-biased index which is typing U is all around 0.5; the factor bias of innovation technology progress in the second region is Hicks neutral; the innovation technology progress of the third region preferred to capital, and the factor-biased index which is typing M is very unstable. So, in order to accelerately implement the core strategy of innovation driven development, it is very important for Guangdong to grasp the factor-biased of the innovation technology progress and build an innovation system which is suitable for the innovation and economic development of the three regions.
引文
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[19]白俊红,江可申,李婧.中国地区研发创新的相对效率与全要素生产率增长分解[J].数量经济技术经济研究,2009,(3):139-151.
[20] Griliches Z. R&D and the productivity slowdown[J]. American Economic Review, 1980, 70(2):343-348.
[21] Coelli T. A Guide to frontier version 4.1:A computer program for stochastic frontier production and cost function estimation[R]. University of New England Centre for Efficiency and Productivity Analysis(CEPA), Working Paper, 1996,(7):1-27.
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[2] Hicks J R. The Theory of Wages[M]. London:Macmillan, 1963.
[3] Griliches Z. Productivity, R&D and basic research at the firm level in the 1970s[J]. American Economic Review, 1986,(3):141-154.
[4] David P A, Klundert T. Biased efficiency growth and capital-labor substitution in the US:1899-1960[J]. American Economic Review, 1965,(55):357-394.
[5] Klump R, McAdam P, Willman A. Factor substitution and factor-augmenting technical progress in the united states:A normalized supply-side system approach[J]. Review of Economics and statistics,2007,(89):183-192.
[6] Klump R, McAdam P, Willman A. Unwrapping some euro area growth puzzles:Factor substitution,productivity and unemployment[J]. Journal of Macroeconomics, 2008,(30):645-666.
[7] Sato R, Morita T. Quantity or quality:The impact of labor-saving innovation on US and Japanese growth rates:1960-2004[J]. Japanese Econimic Review, 2009,(4):407-434.
[8]戴天仕,徐现祥.中国的技术进步方向[J].世界经济,2010,(11):54-70.
[9]陆雪琴,章上峰.技术进步偏向定义及其测度[J].数量经济技术经济研究,2013,(8):20-34.
[10]蒋晶晶,冯邦彦.广东省要素投入与全要素生产率的实证分析[J].广东商学院学报,2011,(1):76-82.
[11]李太龙,朱曼,王志斌.长三角地区技术进步偏向的测算与分析[J].浙江理工大学学报(社会科学版),2015, 34(10):363-370.
[12]钟世川.技术进步偏向与中国工业行业全要素生产率增长[J].经济学家,2014,(7):46-54.
[13]杨振兵.中国制造业创新技术进步要素偏向及其影响因素研究[J].统计研究,2016,(1):26-34.
[14]刘伟.中国高新技术产业研发创新效率测算一基于三阶段DEA模型[J].数理统计与管理,2015, 34(1):17-28.
[15] Battese E, Coelli T. Frontier production functions technical efficiency and panel data:With application to paddy farmers in India[J]. Journal of Productivity Analysis, 1992,(3):153-169.
[16]舒伯利.C·昆伯卡,C·A·诺克斯·拉维尔.随机边界分析[M].上海:复旦大学出版社,2007.
[17] Diamond P A. Disembodied technical change in a two-sector model[J]. Review of Economic Studies,1965,(32):161-168.
[18]朱芳芳.不同类型专利对经济增长影响的实证研究[J].数理统计与管理,2017, 36(5):879-890.
[19]白俊红,江可申,李婧.中国地区研发创新的相对效率与全要素生产率增长分解[J].数量经济技术经济研究,2009,(3):139-151.
[20] Griliches Z. R&D and the productivity slowdown[J]. American Economic Review, 1980, 70(2):343-348.
[21] Coelli T. A Guide to frontier version 4.1:A computer program for stochastic frontier production and cost function estimation[R]. University of New England Centre for Efficiency and Productivity Analysis(CEPA), Working Paper, 1996,(7):1-27.
[22]陈美章.专利制度在我国科技进步和经济发展中的作用[J].知识产权,1998,(4):7-14.