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Research on Regional Disparity and Influencing Factors of TFP Growth in China High-Tech Zones |
Sun Hongjun1, 2, 3, Wang Shengguang2, 3 |
1. School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China; 2. Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China; 3. China's High-Tech Zones Research Center, Beijing 100190, China |
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Abstract This paper estimates the Total Factor Productivity (TFP) of 88 national high-tech zones from 2012 to 2018, and empirically investigates the regional gap, dynamic evolution of distribution and influencing factors.The results show that:①The overall, intra-regional and inter-regional gap of TFP growth in national high-tech zones continues to expand, and the contribution of intra-regional is significantly greater than that of inter-regional and super-variable density.②The TFP index of high-tech zones in the whole, Eastern, Central, Western and Northeastern regions has been significantly increased, and the phenomenon of multi-pole differentiation is disappearing, but the regional gap is gradually increasing.③From the perspective of internal components, technological progress gap is the main reason for TFP growth gap.④The gap of economic development level has a significant negative impact on the TFP growth gap of the overall, Eastern, Central, Western and Northeastern national high-tech zones.The gap of capital accumulation has a significant positive impact on the TFP growth gap of the overall, Central and Western national high-tech zones.The gap of industrial structure has a significant positive impact on the TFP growth gap of the overall, Central and Western national high-tech zones.The gap between technology and finance and internationalization has a significant positive impact on the TFP growth gap of the overall, Eastern, Central, and Northeastern national high-tech zones.
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Received: 24 September 2019
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[1]王频, 刘会武, 孙红军, 等.国家高新区高质量发展的思考与建议——兼评广西域内国家高新区高质量发展态势[J].科技中国, 2019 (4):31-36.
[2]陈明华, 张晓萌, 仲崇阳, 等.长江经济带全要素生产率增长的地区差异及影响因素[J].经济社会体制比较, 2018 (2):162-172.
[3]蔡昉.中国经济增长如何转向全要素生产率驱动型[J].中国社会科学, 2013 (1):56-71.
[4]程郁, 陈雪.创新驱动的经济增长——高新区全要素生产率增长的分解[J].中国软科学, 2013 (11):26-39.
[5]AIGNER D J, LOVELL C A K, SCHMIDT P.Formulation and estimation of stochastic frontier production models[J].Journal of econometrics, 1977 (6):21-37.
[6]MEEUSEN W, BROECK J.Efficiency estimation from cobb-douglas production functions with composed error[J].Journal of development economics, 1981 (9):43-64.
[7]KUMBHAKAR S C.Production frontiers, panel data and time-varying technical inefficiency[J].Journal of econometries, 1990, 46 (12):201-211.
[8]刘建国.区域经济效率与全要素生产率的影响因素及其机制研究[J].经济地理, 2014, 34 (7):7-11.
[9]朱承亮.中国地区经济差距的演变轨迹与来源分解[J].数量经济技术经济研究, 2014, 31 (6):36-54.
[10]陈明华, 刘华军, 孙亚男.中国五大城市群金融发展的空间差异及分布动态:2003—2013年[J].数量经济技术经济研究, 2016, 33 (7):130-144.
[11]杨汝岱.中国制造业企业全要素生产率研究[J].经济研究, 2015, 50 (2):61-74.
[12]刘华军, 李超, 彭莹.中国绿色全要素生产率的地区差距及区域协同提升研究[J].中国人口科学, 2018 (4):30-41+126.
[13]李敬, 陈澍, 万广华, 等.中国区域经济增长的空间关联及其解释——基于网络分析方法[J].经济研究, 2014, 49 (11):4-16.
[14]KUMBHAKAR S C, LOVELL C A K.Stochastic frontier analysis[M].Cambridge:Cambridge University Press, 2000.
[15]张红凤, 吕杰.食品安全风险的地区差距及其分布动态演进——基于Dagum基尼系数分解与非参数估计的实证研究[J].公共管理学报, 2019, 16 (1):77-88+172-173.
[16]杨明海, 卢晓杨, 孙亚男.三大经济支撑带创新能力地区差距及分布动态演进——利用Dagum基尼系数和非参数估计方法的实证研究[J].科技进步与对策, 2017, 34 (7):34-42.
[17]SCOTT J P.Social network analysis[M].4th ed.London:Sage Publications, 2017.
[18]王兵, 吴延瑞, 颜鹏飞.中国区域环境效率与环境全要素生产率增长[J].经济研究, 2010, 45 (5):95-109.
[19]汪锋, 解晋.中国分省绿色全要素生产率增长率研究[J].中国人口科学, 2015 (2):53-62+127.
[20]BATTESE G E, COELLI T J.Frontier production functions, technical efficiency and panel data:with application to paddy farmers in india[J].The journal of productivity analysis, 1992 (3):153-169.
[21]GRILICHES Z.R&D and productivity slowdown[J].American economic review, 1980 (70):343-348. |
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