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

Abstract

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

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.

Figures/Tables 8

Tab.1

Fig.1

Fig.2

Tab.2

Tab.3

Tab.4

Tab.5

Tab.6

References 37

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	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)

Spatial distribution and influencing factors of high-tech industry innovation in China:Based on spatial econometric analysis of panel data

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变量	直接效应	间接效应
研发资金投入	0.444^***(3.930)	-0.476(-1.593)
研发人员投入	7.504^***(3.120)	16.379^***(2.715)
高校研发水平	1.555^***(3.055)	1.616(1.126)
企业规模	0.363^***(3.476)	-0.633^***(-2.835)
市场开放度	0.079^***(2.712)	0.700^***(7.447)

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