|
|
|
| Government Innovation Preference, Innovation Factor Flow and Manufacturing Upgrading |
| Song Xiaoling1,2,Li Jinye1 |
| (1.School of Economics and Management，Xinjiang University，Urumqi 830046，China；2.School of Economics，Xinjiang University of Finance&Economics，Urumqi 830012，China) |
|
|
|
|
Abstract China's economy has changed from high-speed growth to high-quality development. The high-quality development of manufacturing industry has become an important support to promote the national high-quality economic development，and it is an important part of modern economic system construction. Meanwhile，in the current stage，China's manufacturing industry is at the middle and low end of the value chain，and high-end manufacturing is underdeveloped. Therefore，there is an urgent need to transform the industrial development from the traditional tangible factors of production driven model to innovation driven model. Innovation factors are important strategic resources for implementing innovation-driven strategy and promoting industrial upgrading. At the same time，with the implementation of the innovation driven development strategy and change the achievements appraisal，local governments also strengthen their support for industrial innovation activities through financial investment in science and technology to attract innovative elements. Under this background，can the innovative factors flow promote manufacturing upgrade? What effect does government innovation preference play in the process of manufacturing upgrading driven by innovation factor flow? It is of great practical significance to guide the flow of innovation factors and promote the upgrading of manufacturing industry by clarifying these problems .#br#This paper systematically reveals the influence mechanism of innovation factor flow on manufacturing upgrading and the nonlinear adjustment mechanism of government innovation preference. Firstly，innovation factor flow has a positive promoting effect on the advanced and rationalization of manufacturing industry. Innovation factor flow has the spatial knowledge spillover effect and the spatial resource allocation effect,both of which can accelerate the spread of new knowledge，new technology and new mode in different geographical space units，improve the efficiency of resource allocation and then promote the advanced and rationalization of manufacturing industry. Secondly，on the aspect of the nonlinear adjustment mechanism of government innovation preference on manufacturing upgrading, government innovation preference can enhance the promotion effect of innovation factor flow on manufacturing industry upgrading. However，there is a range of government innovation subsidy intensity for enterprise innovation，for it is not conducive to enterprises' innovation activities if the intensity is too low or too high.#br#In terms of empirical research，based on the panel data of 30 provinces in China from 2004 to 2019，this paper uses the spatial Durbin model，mediating effect model and threshold regression analysis methods to systematically examine the impact of innovation personnel flow,innovation capital flow on manufacturing upgrading and the moderating effect of government innovation preference. The results show that firstly，both innovation personnel flow and innovation capital flow have significant positive effects on the advanced manufacturing and rationalization of manufacturing industry. Secondly，the path test shows that innovation personnel flow and innovation capital flow indirectly promote the advanced manufacturing and rationalization of manufacturing industry through improving innovation capacity. Thirdly，government innovation preference has a significant positive moderating effect on the process of innovation capital flow driving the advanced manufacturing and rationalization of manufacturing industry，while for innovation personnel flow, government innovation preference can promote the advanced manufacturing，but has no significant promoting effect on the rationalization of manufacturing industry.Forthly，in the process of manufacturing upgrading driven by the flow of two types of innovation factors，the moderating effect of government innovation preference is nonlinear. #br#Based on the above conclusion，in order to accelerate manufacturing upgrading，this article puts forward the following policy recommendations. The first is to promote the rational flow of innovation factors.The second is to strengthen the industrial connection with the geographical proximity or the same economic level of the region and plan industrial spatial layout rationally. The third is to improve industrial innovation capacity continuously. The fourth is to attach importance and give full play to the positive regulating role of government science and technology expenditure in the process of innovation factor flow promoting manufacturing upgrading, and it’s vital to set a reasonable range of innovation subsidy.#br#
|
|
Received: 19 November 2021
|
|
|
|
|
|
| [1] 张营营,高煜.创新要素流动能否促进地区制造业结构优化——理论解析与实证检验[J].现代财经(天津财经大学学报),2019,39(6):98-113.[2] ALMEIDA P,KOGUT E.Localization of knowledge and the mobility of engineers in regional networks[J].Management Science,1999,45(7):905-917.[3] HOPENHAYN H A.Firms,misallocation and aggregate productivity:a review[J].Annual Review of Economics,2014,13(6):735-770.[4] 郑新立．自主创新是实现产业升级的中心环节[J].求是,2010(2):21-24.[5] 付宏,毛蕴诗,宋来胜.创新对产业结构高级化影响的实证研究——基于2000—2011年的省际面板数据[J].中国工业经济,2013,30(9):56-68.[6] 蔡玉蓉,汪慧玲.创新投入对产业结构升级的影响机制研究——基于分位数回归的分析[J].经济问题探索,2018,39(1):138-146.[7] 魏新颖,王宏伟,徐海龙.创新投入、创新环境与高技术产业绩效[J].中国科技论坛,2019,35(11):30-37.[8] 李政,杨思莹.创新投入、产业结构与经济增长[J].求是学刊,2015,42(4):61-67.[9] 周学政.区域创新要素聚集的理论基础及政策选择[J].科学管理研究,2013,31(2):43-46.[10] 卓乘风,邓峰.创新要素流动与区域创新绩效——空间视角下政府调节作用的非线性检验[J].科学学与科学技术管理,2017,38(7):15-26.[11] GREUNZ L.Industrial structure and innovation-evidence from European regions [J].Journal of Evolutionary Economics,2004,14(5): 563-592.[12] BECKER W，DIETZ J.R&D cooperation and innovation activities of firms evidence for the German manufacturing industry[J].Research Policy,2004,33(2):209-223.[13] LEE C Y.The differential effects of public R&D support on firm R&D:theory and evidence from multi-country data[J].Technovation,2011,31(5):256-269.[14] GUO D，GUO Y，JIANG K.Government-subsidized R&D and firm innovation: evidence from China[J].Research Policy,2016,45(6):1129-1144[15] 张宽,黄凌云.政府创新偏好与区域创新能力:如愿以偿还是事与愿违[J].财政研究,2020,41(4):66-82.[16] BUSOM I. An empirical evaluation of the effects of R&D subsidies[J]. Economics of Innovation & New Technology,2000,9(2):111-148.[17] FELDMAN M P，KELLEY M R. The exante assessment of knowledge spillovers：government R&D policy,economic incentives and private firm behavior[J]. Research Policy,2006,35(10):1509-1521.[18] 卞元超,吴利华,白俊红.财政科技支出竞争是否促进了区域创新绩效提升——基于研发要素流动的视角[J].财政研究,2020,41(1):45-58.[19] DARON A,UFUK A,HARUN A,et al.Innovation,reallocation and growth[J].American Economic Review,2018,108(11):3450-3491.[20] 黎文靖,郑曼妮.实质性创新还是策略性创新——宏观产业政策对微观企业创新的影响[J].经济研究,2016,51(4):60-73.[21] GORG H，STROBL E. The effect of R&D subsidies on private R&D[J]. Economica,2007，74(294):215-234.[22] 毛其淋,许家云.政府补贴对企业新产品创新的影响——基于补贴强度“适度区间”的视角[J].中国工业经济,2015,32(6):94-107.[23] 高康,原毅军.生产性服务业空间集聚如何推动制造业升级[J].经济评论,2020,41(4):20-36.[24] 宛群超,袁凌.创新要素流动与高技术产业创新能力[J].科研管理,2021,42(12):80-87.[25] 白俊红,王钺,蒋伏心,等.研发要素流动、空间知识溢出与经济增长[J].经济研究,2017,52(7):109-123.[26] 高素英,钦彦祥,张烨.创新投入影响产业结构优化升级路径分析——基于本地效应与多元空间溢出效应[J].科技进步与对策,2017,34(19):60-67.[27] 白东北,张营营,王珏.产业集聚与中国企业出口:基于创新要素流动视角[J].国际贸易问题,2021,47(2):63-79.[28] 郭兵,罗守贵.地方政府财政科技资助是否激励了企业的科技创新——来自上海企业数据的经验研究[J].上海经济研究,2015,34(4):70-78,86.[29] 车德欣,吴传清,任晓怡,等.财政科技支出如何影响企业技术创新——异质性特征、宏微观机制与政府激励结构破解[J].中国软科学,2020,35(3):171-182.[30] 宋砚秋,齐永欣,高婷,等.政府创新补贴、企业创新活力与创新绩效[J].经济学家,2021,33(6):111-120.[31] 周芬,李希璠.政府R&D资助对企业创新产出的影响研究——基于中国31个省级行政区划单位面板数据的实证分析[J].南京大学学报(哲学·人文科学·社会科学),2020,57(3):65-72.[32] ANSELIN L,GETIS A.Spatial statistical analysis and geographic information systems[J]. The Annals of Regional Science,1992,26(1):19-33.[33] 汪克亮,赵斌,丁黎黎,等.财政分权、政府创新偏好与雾霾污染[J].中国人口·资源与环境,2021,31(5):97-108.[34] HANSEN B E.Threshold effects in non-dynamic panels:estimation,testing and inference [J].Journal of Econometrics,1999,93(2):345-368. |
|
|
|
