中国科技服务业的异质性研究
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摘要
中国各地区的科技服务业发展差异性较为明显。对中国科技服务业发展的异质性进行分析,首先计算各省份、各区域的基尼系数进行区域间对比和区域内贡献量分析,其次运用因子分析法测度各省份科技服务业发展的投入产出效率,最后利用面板模型对影响各地区科技服务业发展的因素进行回归。研究结果表明:华北与华东地区的基尼系数较其他地区的大,北京和上海的科技服务业发展水平较高;科技服务业的投入产出效率为北京、上海、天津较高而江苏、山东较低,整体发展水平为广东、北京、江苏位于前三;地区生产总值和技术成交额与基尼系数之间成反比例关系,而科技服务业从业人员与基尼系数之间则成正相关关系,各省份之间的情况略有差异。
There are obvious differences in the development of science and technology service industry in different regions of China. This paper analyzes the heterogeneity of the development of science and technology service industry in China. Firstly, the Gini coefficient of each province and region is calculated for interregional comparison and intra-regional contribution analysis. Secondly, the factor analysis method is used to measure the input-output efficiency of the development of science and technology service industry in each province. Finally, the factors affecting the development of science and technology service industry in each region are regressed by the panel model. The results show that:the Gini coefficient of North China and East China is larger than that of other regions, and the development level of science and technology service industry in Beijing and Shanghai is higher than that of other regions; the input-output efficiency of science and technology service industry is Beijing, Shanghai, Tianjin is higher than Jiangsu, Shandong is low, the overall level of development is Guangdong, Beijing, Jiangsu is in the top three; The Gini coefficient is inversely proportional to the regional GDP and technology turnover, while the Gini coefficient is positively correlated with the technology service workers, and the situation among the provinces is slightly different.
There are obvious differences in the development of science and technology service industry in different regions of China. This paper analyzes the heterogeneity of the development of science and technology service industry in China. Firstly, the Gini coefficient of each province and region is calculated for interregional comparison and intra-regional contribution analysis. Secondly, the factor analysis method is used to measure the input-output efficiency of the development of science and technology service industry in each province. Finally, the factors affecting the development of science and technology service industry in each region are regressed by the panel model. The results show that:the Gini coefficient of North China and East China is larger than that of other regions, and the development level of science and technology service industry in Beijing and Shanghai is higher than that of other regions; the input-output efficiency of science and technology service industry is Beijing, Shanghai, Tianjin is higher than Jiangsu, Shandong is low, the overall level of development is Guangdong, Beijing, Jiangsu is in the top three; The Gini coefficient is inversely proportional to the regional GDP and technology turnover, while the Gini coefficient is positively correlated with the technology service workers, and the situation among the provinces is slightly different.
引文
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[7]张琴,赵丙奇,郑旭.科技服务业集聚与制造业升级:机理与实证检验[J].管理世界,2015(11):178-179.
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[11]张清正,魏文栋,孙瑜康.中国科技服务业区域非均衡发展及影响因素研究[J].科技管理研究,2016,36(1):89-94.
[12]李晓龙,冉光和,郑威.科技服务业空间集聚与企业创新效率提升: 来自中国高技术产业的经验证据[J].研究与发展管理,2017,29(4):1-10.
[13]谢泗薪,侯蒙.经济新常态下科技服务业的产业集聚结构模式与立体攻略[J].科技管理研究,2017,37(2):171-175.
[14]廖晓东,邱丹逸,林映华.基于区位熵的中国科技服务业空间集聚测度理论与对策研究[J].科技管理研究,2018,38(2):171-178.
[15]韩鲁南,关峻,邢李志,等.国内外科技服务业行业统计分类对比研究[J].科技进步与对策,2013,30(9):48-53.
[16]冯华,王智毓.我国科技服务业与经济增长关系的实证研究[J].软科学,2018,32(2):6-10.
[17]KRUGMAN P. Increasing returns and economic geography[J].Journal of Political Economy, 1991,99(3):483-499.
[2]赵晓伟.科技服务业发展问题研究: 以江苏省为例[D].南京: 江苏大学,2009.
[3]TAYLOR A J, BEAVERSTOCK J V, COOK G, et al. Financial services clustering and its significance for London[EB/OL].(2003-02-01)[2018-06-20].http://ethicalfinancehub.org/wpcontent/uploads/20.
[4]PALIVOS T, WANG P. Spatial agglomeration and endogenous growth[J].Regional Science and UrbanEconomics, 1996, 26(6):645-669.
[5]FINGLETON B. Theoretical economic geography and spatial econometrics: dynamic perspectives [J].Journal of Economic Geography, 2001, 1(2):201-225.
[6]张振刚,李云健,陈志明.科技服务业对区域创新能力提升的影响: 基于珠三角地区的实证研究[J].中国科技论坛,2013(12):45-51.
[7]张琴,赵丙奇,郑旭.科技服务业集聚与制造业升级:机理与实证检验[J].管理世界,2015(11):178-179.
[8]张清正.中国科技服务业空间演化及影响因素研究[J].科技进步与对策,2015,32(10):36-39.
[9]张清正.中国科技服务业集聚的空间分析及影响因素研究[J].软科学,2015,29(8):1-4, 24.
[10]张清正,李国平.中国科技服务业集聚发展及影响因素研究[J].中国软科学,2015(7):75-93.
[11]张清正,魏文栋,孙瑜康.中国科技服务业区域非均衡发展及影响因素研究[J].科技管理研究,2016,36(1):89-94.
[12]李晓龙,冉光和,郑威.科技服务业空间集聚与企业创新效率提升: 来自中国高技术产业的经验证据[J].研究与发展管理,2017,29(4):1-10.
[13]谢泗薪,侯蒙.经济新常态下科技服务业的产业集聚结构模式与立体攻略[J].科技管理研究,2017,37(2):171-175.
[14]廖晓东,邱丹逸,林映华.基于区位熵的中国科技服务业空间集聚测度理论与对策研究[J].科技管理研究,2018,38(2):171-178.
[15]韩鲁南,关峻,邢李志,等.国内外科技服务业行业统计分类对比研究[J].科技进步与对策,2013,30(9):48-53.
[16]冯华,王智毓.我国科技服务业与经济增长关系的实证研究[J].软科学,2018,32(2):6-10.
[17]KRUGMAN P. Increasing returns and economic geography[J].Journal of Political Economy, 1991,99(3):483-499.