长三角地区城市创新投入要素的经济溢出效应及趋同格局

doi:10.18306/dlkxjz.2021.01.014

摘要/Abstract

摘要：

近年来,城市创新投入要素在长三角一体化进程中的作用不断增加。论文基于2010—2017年长三角地区各城市空间面板数据,利用空间计量模型与马尔可夫链等方法,测度创新投入要素的经济溢出效应并揭示长三角一体化背景下创新外溢对区域经济趋同格局的影响。结果表明：① 长三角地区城市创新投入和创新产出均对城市自身经济增长表现出显著的直接效应,创新投入的溢出效应显著且大于其直接效应,而创新产出的溢出效应不显著;② 受创新投入要素影响,长三角地区经济增长存在“俱乐部趋同”现象,但毗邻不同创新投入类型的城市对本地经济增长呈现出“高促进、低抑制”的态势;③ 长三角一体化经济格局呈现由中心区高水平趋同向外围低水平趋同转变的特征,且具有明显的空间转移惰性,创新投入外溢作用将加剧区域经济增长的“马太效应”;④ 经济增长类型转移的城市集中于省域边界,在局部溢出作用下表现出复杂的转移机理。基于创新局部外溢促进经济俱乐部趋同的分析结果,对长三角区域一体化发展提出针对性建议。

关键词: 创新空间溢出, 经济增长格局, 空间“X”马尔可夫链, 俱乐部趋同, 长三角地区

Abstract:

In recent years, urban innovation input feature has played an increasingly role in the economic integration process of the Yangtze River Delta. This study used the spatial econometric model and Markov chain to measure the economic spillover effects of innovation input feature based on the spatial panel data of cities in the Yangtze River Delta from 2010 to 2017, and revealed the impact of innovation spillovers on the pattern of regional economic convergence under the economic integration of the Yangtze River Delta. The main conclusions are as follows: 1) The innovation features and economic growth of the Yangtze River Delta show strong spatial agglomeration characteristics, the trend of Moran's I value of innovation input feature and economic growth are similar, and the Moran's I value of innovation output feature shows an unstable trend in time series. Both innovation input and output features have positive effects on economic growth. 2) From the global view, the innovation input feature has a significant spillover effect on regional economic growth, and the spillover effect of innovation input feature is greater than the direct effect, suggesting that innovation investment will affect the pattern of economic integration development of the Yangtze River Delta; while the spatial spillover effect of the innovation output feature is not significant. 3) From the local view, cities adjacent to higher levels of innovation input are positive for economic growth by local positive spillover, and cities adjacent to lower levels of innovation input are negative for economic growth by local negative spillover. Under the spillover effects of innovation input feature, the economic growth of the Yangtze River Delta region has formed a "club convergence" phenomenon, and the spatial pattern of the regional economic structure is changing from a high level convergence in the center of the Yangtze River Delta to a low level convergence in the periphery. 4) The cities with economic growth type transferred are mainly concentrated at the provincial boundaries, showing complex transfer mechanisms under the effect of local spillovers. The developed regions and the developing regions tend to be stable and have obvious inertia of spatial transfer, and the spillover effect of innovation will exacerbate the Matthew effect of economic growth in the Yangtze River Delta. Based on the results of the analysis that innovation spillovers promote club convergence of economic growth, this study proposed specific recommendations for the development of regional integration in the Yangtze River Delta.

Key words: innovation spatial spillover, economic growth patterns, spatial X Markov chain, club convergence, Yangtze River Delta

孙彪, 杨山. 长三角地区城市创新投入要素的经济溢出效应及趋同格局[J]. 地理科学进展, 2021, 40(1): 147-160.

SUN Biao, YANG Shan. Economic spillover effects and convergence patterns of urban innovation input features in the Yangtze River Delta region[J]. PROGRESS IN GEOGRAPHY, 2021, 40(1): 147-160.

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年份		单变量Moran's I值		双变量Moran's I值
年份	创新投入	创新产出	经济增长	经济增长与创新投入	经济增长与创新产出
2010	0.5260^***	0.4381^***	0.4938^***	0.4990^***	0.4927^***
2011	0.5793^***	0.4938^***	0.5355^***	0.5439^***	0.5303^***
2012	0.5591^***	0.4784^***	0.5308^***	0.5284^***	0.5222^***
2013	0.5307^***	0.4662^***	0.5104^***	0.5075^***	0.5179^***
2014	0.5510^***	0.5687^***	0.5177^***	0.5184^***	0.5623^***
2015	0.5814^***	0.5644^***	0.5815^***	0.5713^***	0.5696^***
2016	0.5937^***	0.5223^***	0.6073^***	0.5899^***	0.5459^***
2017	0.5748^***	0.5096^***	0.5772^***	0.5675^***	0.5206^***

变量	OLS	变量	SPDM
lnPRD	0.2161^***	lnPRD	0.1154^***
lnPPA	0.0395^**	lnPPA	0.0357^**
lnPFAI	0.3344^***	lnPFAI	0.2575^***
lnPFDI	-0.0463^***	lnPFDI	0.0136
lnPHC	0.4425^***	lnPHC	0.4699^***
Adjust R²	0.8632	W×lnPRD	0.0854^*
LM-spatial lag	8.6276^***	W×lnPPA	-0.0143
Robust LM-spatial lag	0.5217	W×lnPFDI	-0.1395^**
LM-spatial error	30.9958^***	W×lnPFAI	-0.0951^***
Robust LM-spatial error	22.8899^***	W×lnPHC	0.0027
Loglikelyhood	358.4237	W×lnPGDP	0.4430^***
		Adjust R²	0.8876
		Wald-spatial lag	23.3299^***
		LR-spatial lag	26.2797^***
		Wald-spatial error	29.9622^***
		LR-spatial error	36.4688^***
		Hausman	930.5488^***
		Loglikelyhood	406.8503

变量	lnPGDP
IV	lnPRD_{time_lag1}	lnPRD_{space_lag}	lnPRD_{time_lag1_space_lag}
lnPRD	0.2678^***	0.2066^***	0.2236^**
控制变量	是	是	是
城市固定效应	是	是	是
年份固定效应	是	是	是
R²	0.9851	0.9853	0.9857

变量	lnPRD	lnPPA	lnPFAI	lnPFDI	lnPHC
直接效应	0.1320^***	0.0359^**	0.2520^***	0.0029	0.4948^***
溢出效应	0.2328^***	0.0027	-0.0456	-0.1503^***	0.3633
总效应	0.3648^***	0.0386	0.2064^**	-0.1474^***	0.8580^**

t时期	t+1时期
t时期	低水平	较低水平	较高水平	高水平	转移数量
低水平	0.9167	0.0833	0	0	48
较低水平	0.0182	0.9273	0.0545	0	110
较高水平	0	0.1111	0.8095	0.0794	63
高水平	0	0.0152	0.0758	0.9091	66