中国高技术产业创新空间分布及其影响因素——基于面板数据的空间计量分析

doi:10.18306/dlkxjz.2016.10.005

摘要/Abstract

摘要：

本文基于2003-2012年省域面板数据,采用地统计方法分析中国高技术产业创新的空间分布,并运用空间计量模型对其影响因素进行探讨。研究发现：中国高技术产业创新存在明显的空间差异,其中东部地区的创新水平明显高于中部和西部地区;2003-2012年间,高技术产业创新整体空间差异表现出先扩大后缩小的趋势,空间分布呈现出先集聚后扩散的变化特征;相邻省域间的创新水平存在明显溢出现象,研发资金投入、研发人员投入、高校研发水平、企业规模和市场开放度均对高技术产业创新起到促进作用,且研发人员投入和市场开放度还能进一步促进相邻地区的创新发展,而企业规模则对相邻地区的创新存在负面影响。因此,加大对中西部地区研发资金和研发人员投入,重视高校在创新发展中的作用,破除区域壁垒促进技术流动以及加大市场开放度,都有利于提高整体创新水平和缩小创新空间差异。

关键词: 高技术产业, 创新, 空间分布, 空间计量模型, 影响因素, 中国

Abstract:

Using panel data of Chinese provinces for the period 2003-2012, this article investigates the spatial distribution of high-tech industry innovation by applying the global difference index and kernel density estimation, and analyzes its influencing factors with spatial econometric models. The results show that: There are striking regional disparities in high-tech industry innovation, and the innovation level in the eastern area is significantly higher than the central and western areas. During the period from 2003 to 2012, regional disparity of high-tech industry innovation showed a narrowing tendency after the initial expansion, and the spatial distribution of regional innovation showed a trend from agglomeration to spreading. A clear spillover of innovation between adjacent provinces is verified by this study. Research and development (R & D) capital, R & D personnel, university research, size of high-tech firms, and openness of the market are all positively related to high-tech industry innovation. R & D personnel inputs and openness of the market can benefit neighboring regions as well, but the size of firm has a negative influence on regions with close geographic proximity. We propose that strengthening the inputs of R & D capitals and R & D personnel in western and central China, emphasizing the role that university plays in the development of innovation, breaking regional barriers for facilitating technology flow, and promoting the openness of the market are beneficial for increasing the overall innovation level and reducing regional differences.

Key words: high-tech industry, innovation, spatial distribution, spatial econometric model, influencing factor, China

郭泉恩, 孙斌栋. 中国高技术产业创新空间分布及其影响因素——基于面板数据的空间计量分析[J]. 地理科学进展, 2016, 35(10): 1218-1227.

Quanen GUO, Bindong SUN. Spatial distribution and influencing factors of high-tech industry innovation in China:Based on spatial econometric analysis of panel data[J]. PROGRESS IN GEOGRAPHY, 2016, 35(10): 1218-1227.

图/表 8

表1

图1

图2

表2

表3

表4

表5

表6

参考文献 37

[1]	程叶青, 王哲野, 马靖. 2014. 中国区域创新的时空动态分析[J]. 地理学报, 69(12): 1779-1789. doi: 10.11821/dlxb201412004
	[Cheng Y Q, Wang Z Y, Ma J.2014. Analyzing the space-time dynamics of innovation in China[J]. Acta Geographica Sinica, 69(12): 1779-1789.] doi: 10.11821/dlxb201412004
[2]	方创琳, 马海涛, 王振波, 等. 2014. 中国创新型城市建设的综合评估与空间格局分异[J]. 地理学报, 69(4): 459-473. doi: 10.11821/dlxb201404003
	[Fang C L, Ma H T, Wang Z B, et al.2014. Comprehensive assessment and spatial heterogeneity of the construction of innovative cities in China[J]. Acta Geographica Sinica, 69(4): 459-473.] doi: 10.11821/dlxb201404003
[3]	方大春, 张凡, 芮明杰. 2016. 我国高新技术产业创新效率及其影响因素实证研究: 基于面板数据随机前沿模型[J]. 科技管理研究, 36(7): 66-70, 75. doi: 10.3969/j.issn.1000-7695.2016.07.012
	[Fang D C, Zhang F, Rui M J.2016. Empirical study on the high-tech industry innovation efficiency and its influencing factors: Based on the panel data analysis of stochastic frontier model[J]. Science and Technology Management Research, 36(7): 66-70, 75.] doi: 10.3969/j.issn.1000-7695.2016.07.012
[4]	封伟毅, 李建华, 赵树宽. 2012. 技术创新对高技术产业竞争力的影响: 基于中国1995-2010年数据的实证分析[J]. 中国软科学, (9): 154-164. doi: 10.3969/j.issn.1002-9753.2012.09.016
	[Feng W Y, Li J H, Zhao S K.2012. Impact of technological innovation on competitiveness of high-tech industry: An empirical analysis on 1995-2010 data[J]. China Soft Science, (9): 154-164.] doi: 10.3969/j.issn.1002-9753.2012.09.016
[5]	郭国峰, 温军伟, 孙保营. 2007. 技术创新能力的影响因素分析: 基于中部六省面板数据的实证研究[J]. 数量经济技术经济研究, 24(9): 134-143. doi: 10.3969/j.issn.1000-3894.2007.09.013
	[Guo G F, Wen J W, Sun B Y.2007. The affected factor analysis of the ability of technology innovation for the six provinces of the central China[J]. The Journal of Quantitative & Technical Economics, 24(9): 134-143.] doi: 10.3969/j.issn.1000-3894.2007.09.013
[6]	何键芳, 张虹鸥, 叶玉瑶, 等. 2013. 广东省区域创新产出的空间相关性研究[J]. 经济地理, 33(2): 117-121, 140.
	[He J F, Zhang H O, Ye Y Y, et al.2013. Research on spatial autocorrelation of innovation output in Guangdong Province[J]. Economic Geography, 33(2): 117-121, 140.]
[7]	侯润秀, 官建成. 2006. 外商直接投资对我国区域创新能力的影响[J]. 中国软科学, (5): 104-111.
	[Hou R X, Guan J C.2006. The impact of FDI on China regional innovation capacity[J]. China Soft Science, (5): 104-111.]
[8]	蒋天颖. 2014. 浙江省区域创新产出空间分异特征及成因[J]. 地理研究, 33(10): 1825-1836. doi: 10.11821/dlyj201410004
	[Jiang T Y.2014. Spatial differentiation and its influencing factors of regional innovation output in Zhejiang Province[J]. Geographical Research, 33(10): 1825-1836.] doi: 10.11821/dlyj201410004
[9]	李国平, 王春杨. 2012. 我国省域创新产出的空间特征和时空演化: 基于探索性空间数据分析的实证[J]. 地理研究, 31(1): 95-106. doi: 10.11821/yj2012010010
	[Li G P, Wang C Y.2012. Spatial characteristics and dynamic changes of provincial innovation output in China: An investigation using the ESDA[J]. Geographical Research, 31(1): 95-106.] doi: 10.11821/yj2012010010
[10]	李文亮, 许正中. 2015. 考虑空间效应的高技术企业创新溢出效应研究[J]. 软科学, 29(4): 1-4. doi: 10.13956/j.ss.1001-8409.2015.04.01
	[Li W L, Xu Z Z.2015. Research on spillover effect of high-tech innovation based on spatial effect[J]. Soft Science, 29(4): 1-4.] doi: 10.13956/j.ss.1001-8409.2015.04.01
[11]	刘和东. 2012. 开放经济下区域创新溢出效应的空间计量分析[J]. 南京农业大学学报: 社会科学版, 12(3): 102-109, 123. doi: 10.3969/j.issn.1671-7465.2012.03.015
	[Liu H D.2012. Analysis of spatial econometrics on the spillovers of regional innovation under the open economy[J]. Journal of Nanjing Agricultural University: Social Sciences Edition, 12(3): 102-109, 123.] doi: 10.3969/j.issn.1671-7465.2012.03.015
[12]	吕拉昌, 李勇. 2010. 基于城市创新职能的中国创新城市空间体系[J]. 地理学报, 65(2): 177-190.
	[Lv L C, Li Y.2010. A research on Chinese renovation urban system based on urban renovation function[J]. Acta Geographica Sinica, 65(2): 177-190.]
[13]	牛方曲, 刘卫东. 2012. 中国区域科技创新资源分布及其与经济发展水平协同测度[J]. 地理科学进展, 31(2): 149-155. doi: 10.11820/dlkxjz.2012.02.003
	[Niu F Q, Liu W D.2012. Relationships between scientific & technological resources and regional economic development in China[J]. Progress in Geography, 31(2): 149-155.] doi: 10.11820/dlkxjz.2012.02.003
[14]	逄淑媛, 陈德智. 2009. 专利与研发经费的相关性研究: 基于全球研发顶尖公司10年面板数据的研究[J]. 科学学研究, 27(10): 1500-1505.
	[Pang S Y, Chen D Z.2009. A study on the relationship between R & D inputs and patents: Panel data analysis of top global companies by R & D investment[J]. Studies in Science of Science, 27(10): 1500-1505.]
[15]	丘东, 王维才, 谢宗小. 2016. R & D投入对地区创新绩效的影响: 企业R & D投入的中介效应[J]. 科技进步与对策, 33(8): 41-48.
	[Qiu D, Wang W C, Xie Z X.2016. The influence of R & D input impact on the region's innovation performance: A mediating perspective[J]. Science & Technology Progress and Policy, 33(8): 41-48.]
[16]	谭俊涛, 张平宇, 李静. 2014. 2001-2010年黑龙江省城市创新能力格局与发展过程[J]. 地理科学进展, 33(4): 508-516. doi: 10.11820/dlkxjz.2014.04.008
	[Tan J T, Zhang P Y, Li J.2014. Structure and development of urban innovation capability in Heilongjiang Province during 2001-2010[J]. Progress in Geography, 33(4): 508-516.] doi: 10.11820/dlkxjz.2014.04.008
[17]	王春杨, 张超. 2014. 中国地级区域创新产出的时空模式研究: 基于ESDA的实证[J]. 地理科学, 34(12): 1438-1444.
	[Wang C Y, Zhang C.2014. Spatial-temporal pattern of prefecture-level innovation outputs in China: An investigation using the ESDA[J]. Scientia Geographica Sinica, 34(12): 1438-1444.]
[18]	王洋, 方创琳, 盛长元. 2013. 扬州市住宅价格的空间分异与模式演变[J]. 地理学报, 68(8): 1082-1096.
	[Wang Y, Fang C L, Sheng C Y.2013. Spatial differentiation and model evolution of housing prices in Yangzhou[J]. Acta Geographica Sinica, 68(8): 1082-1096.]
[19]	王子敏. 2012. 基于空间溢出视角的城乡消费差距问题研究[J]. 农业技术经济, (2): 88-98.
	[Wang Z M.2012. Jiyu kongjian yichu shijiao de chengxiang xiaofei chaju wenti yanjiu[J]. Journal of Agrotechnical Economics, (2): 88-98.]
[20]	肖仁桥, 钱丽, 陈忠卫. 2012. 中国高技术产业创新效率及其影响因素研究[J]. 管理科学, 25(5): 85-98.
	[Xiao R Q, Qian L, Chen Z W.2012. Research on the innovation efficiency and its affecting factors in China's high-tech industries[J]. Journal of Management Science, 25(5): 85-98.]
[21]	张惠璇, 刘青, 李贵才. 2016. 广东省城市创新联系的空间格局演变及优化策略[J]. 地理科学进展, 35(8): 952-962. doi: 10.18306/dlkxjz.2016.08.004
	[Zhang H X, Liu Q, Li G C.2016. Spatial structure change and optimization strategies of innovation linkage among the cities in Guangdong Province[J]. Progress in Geography, 35(8): 952-962.] doi: 10.18306/dlkxjz.2016.08.004
[22]	赵炎, 郑向杰. 2013. 网络嵌入性与地域根植性对联盟企业创新绩效的影响: 对中国高科技上市公司的实证分析[J]. 科研管理, 34(11): 9-17.
	[Zhao Y, Zheng X J.2013. Impact of network embeddedness and geographic embeddedness on the innovation performance of the alliance enterprises: An empirical research on the high-tech listed companies in China[J]. Science Research Management, 34(11): 9-17.]
[23]	周桂荣, 徐作君. 2007. 区域产业创新体系构建: 天津滨海新区与深圳、浦东之比较[J]. 中国科技论坛,(2): 77-80, 93. doi: 10.3969/j.issn.1002-6711.2007.02.017
	[Zhou G R, Xu Z J.2007. Quyu chanye chuangxin tixi goujian: Tianjin Binhai Xinqu yu Shenzhen, Pudong zhi bijiao[J]. Forum on Science and Technology in China,(2): 77-80, 93.] doi: 10.3969/j.issn.1002-6711.2007.02.017
[24]	Acs Z J, Anselin L, Varga A.2002. Patents and innovation counts as measures of regional production of new knowledge[J]. Research Policy, 31(7): 1069-1085. doi: 10.1016/S0048-7333(01)00184-6
[25]	Bruche G.2009. A new geography of innovation: China and India rising[J]. Transnational Corporations Review, 1(4): 24-27.
[26]	Buzard K, Carlino G A.2009. The geography of research and development activity in the U.S.[R]. Philadelphia, PA: Social Science Research Network (SSRN).
[27]	Carlino G A, Hunt R M, Carr J K, et al.2012. The agglomeration of R & D labs[R]. Philadelphia, PA: Social Science Research Network (SSRN).
[28]	Carrincazeaux C, Lung Y, Rallet A.2001. Proximity and localisation of corporate R & D activities[J]. Research Policy, 30(5): 777-789. doi: 10.1016/S0048-7333(00)00121-9
[29]	Charlot S, Duranton G.2004. Communication externalities in cities[J]. Journal of Urban Economics, 56(3): 581-613.
[30]	Cowan R, Zinovyeva N.2013. University effects on regional innovation[J]. Research Policy, 42(3): 788-800. doi: 10.1016/j.respol.2012.10.001
[31]	Elhorst J P.2010. Applied spatial econometrics: Raising the bar[J]. Spatial Economic Analysis, 5(1): 9-28. doi: 10.1080/17421770903541772
[32]	Elhorst J P.2014. Spatial panel data models[M]//Elhorst J P. Spatial econometrics: From cross-sectional data to spatial panels. Berlin & Heidelberg, Germany: Springer: 63-68.
[33]	Feldman M P, Audretsch D B.1999. Innovation in cities: Science-based diversity, specialization, and localized competition[J]. European Economic Review, 43(2): 409-429.
[34]	Griliches Z.1986. Productivity, R & D, and basic research at the firm level in the 1970's[J]. The American Economic Review, 76(1): 141-154.
[35]	Jaffe A B.1989. Real effects of academic research[J]. The American Economic Review, 79(5): 957-970. doi: 10.2307/3502129
[36]	Kemeny T.2011. Are international technology gaps growing or shrinking in the age of globalization[J]. Journal of Economic Geography, 11(1): 1-35. doi: 10.1093/jeg/lbp062
[37]	Silverman B W.1986. Introduction[M]//Silverman B W. Density estimation for statistics and data analysis. New York: Chapman & Hall: 2-10.

	专利申请数/项				年增长率/%
	2003年		2008年	2012年	2003-2008年	2008-2012年	2003-2012年
中国	8270	39656	127806		36.82	33.99	35.56
东部地区	7045	35615	105753		38.28	31.27	35.12
辽宁	75	452	1772		43.22	40.71	42.10
河北	31	265	627		53.60	24.02	39.67
北京	896	2856	9972		26.09	36.70	30.70
天津	461	1217	3441		21.43	29.67	25.03
山东	589	2058	6970		28.43	35.66	31.59
江苏	676	2776	16999		32.65	57.31	43.09
上海	835	1639	6174		14.44	39.32	24.89
浙江	353	2467	10237		47.53	42.73	45.38
福建	243	604	3444		19.97	54.53	34.26
广东	2831	21185	45449		49.56	21.03	36.13
海南	—	8	329		—	153.24	—
广西	55	88	339		9.86	40.10	22.39
中部地区	442	2033	12484		35.69	57.42	44.95
黑龙江	66	185	736		22.89	41.23	30.73
吉林	79	147	542		13.22	38.57	23.86
内蒙古	2	—	39		—	—	39.10
山西	45	70	376		9.24	52.24	26.60
河南	85	581	1812		46.88	32.89	40.49
湖北	42	444	2626		60.26	55.945	58.33
安徽	49	185	3182		30.44	103.65	59.00
湖南	29	270	2306		56.24	70.95	62.62
江西	45	151	865		27.40	54.71	38.88
西部地区	783	2008	9569		20.73	47.75	32.07
重庆	72	222	1153		25.26	50.96	36.09
四川	260	854	5054		26.85	55.97	39.05
贵州	55	298	966		40.21	34.18	37.50
云南	140	79	409		-10.81	50.84	12.65
陕西	226	473	1606		15.92	35.74	24.34
甘肃	10	38	249		30.60	59.99	42.93
青海	4	1	3		-24.21	31.61	-3.15
宁夏	16	29	125		12.63	44.09	25.66
新疆	—	14	4		—	-26.89	—

年份	Moran's I值	P值	Z值
2003年	0.633^***	0.001	5.396
2004年	0.205^**	0.030	2.196
2005年	0.250^**	0.025	2.415
2006年	0.178^**	0.047	2.113
2007年	0.160^**	0.036	2.015
2008年	0.409^**	0.012	3.556
2009年	0.408^***	0.008	3.246
2010年	0.425^***	0.007	3.521
2011年	0.494^***	0.003	3.941
2012年	0.495^***	0.004	4.116

变量	混合OLS模型	空间固定效应模型	时间固定效应模型	双向固定效应模型
研发资金投入	0.696^***(0.000)	0.571^***(0.000)	0.656^***(0.000)	0.564^***(0.000)
研发人员投入	10.671^***(0.000)	8.049^***(0.003)	8.352^***(0.000)	7.091^***(0.008)
高校研发水平	-0.440^**(0.012)	1.427^***(0.002)	-0.302^*(0.074)	1.372^***(0.004)
企业规模	-0.057(0.432)	0.241^**(0.031)	0.057(0.441)	0.268^**(0.017)
市场开放度	0.035(0.148)	0.143^***(0.000)	0.004(0.870)	0.127^***(0.000)
常数	-0.031(0.285)
σ²	0.128	0.064	0.080	0.059
R²	0.805	0.665	0.800	0.550
调整R²	0.802	0.66	0.797	0.544
自然对数似然函数值	-61.020	-10.560	-44.328	1.234
拉格朗日乘数滞后检验	8.683^***(0.003)	8.830^***(0.003)	1.993(0.158)	4.011^**(0.045)
拉格朗日乘数误差检验	16.210^***(0.000)	7.430^***(0.006)	10.914^***(0.001)	3.245^*(0.072)
稳健拉格朗日乘数滞后检验	1.494(0.222)	2.927^*(0.087)	0.020(0.889)	1.221(0.269)
稳健拉格朗日乘数误差检验	9.021^***(0.003)	1.527(0.217)	8.941^***(0.003)	0.455(0.500)

LR	检验值	P值
空间固定效应	91.125^***	0.000
时间固定效应	23.589^***	0.001

变量	双固定效应模型	随机效应模型
W×创新水平	0.357^***(0.000)	0.342^***(0.000)
研发资金投入	0.481^***(0.000)	0.512^***(0.000)
研发人员投入	6.183^***(0.009)	7.729^***(0.000)
高校研发水平	1.415^***(0.001)	-0.239(0.288)
企业规模	0.410^***(0.000)	0.173^**(0.024)
市场开放度	0.032(0.291)	0.003(0.903)
W×研发资金投入	-0.494^***(-0.009)	-0.402^**(0.023)
W×研发人员投入	8.858^***(0.029)	10.172^***(0.007)
W×高校研发水平	0.533(0.553)	-0.951^*(0.062)
W×企业规模	-0.572^***(0.000)	-0.663^***(0.000)
W×市场开放度	0.467^***(0.000)	0.379^***(0.000)
横截面元素的权重phi		0.484^***(0.000)
豪斯曼检验		34.472^***
σ²	0.046	0.049
R²	0.911	0.874
调整R²	0.656	0.810
自然对数似然函数值	52.068	-0.735
空间滞后的怀特检验	122.849^***(0.000)	117.422^***(0.000)
空间误差的怀特检验	86.172^***(0.000)	73.139^***(0.000)