风险投资与政府补贴对新材料企业技术创新的激励效应及差异

doi:10.18402/resci.2020.08.11

摘要/Abstract

摘要：

随着近年资源应用领域逐步向高新技术产业延伸,下游材料端的技术创新突破已成为重构中国资源产业优势的战略着力点。本文利用2008—2017年中国新材料企业面板数据,运用PVAR模型和脉冲响应函数,从技术生命周期视角探究了风险投资与政府补贴对中国新材料企业技术创新的激励效应及其差异性。研究表明：①对于新材料企业,风险投资和政府补贴通过提升企业研发投入进而促进了新材料企业的技术创新。其中,风险投资的激励效应更明显但具有短期性,政府补贴的激励效应则具有滞后性;②对于导入期新材料企业,风险投资对其技术创新的激励效应较小,而政府补贴正好弥补了市场资金配置的不足;对于成长期新材料企业,风险投资对其技术创新的长期激励效应强于政府补贴;对于成熟期新材料企业,政府补贴不能促进其技术创新,风险投资的激励效应则比较显著。因此,为更好地提升中国新材料技术创新水平,政府应引入分类补贴机制,重点向导入期新材料企业倾斜,并引导风险资本投入成长期和成熟期新材料企业。

关键词: 资源高端应用, 新材料企业, 技术创新, 风险投资, 政府补贴, 技术生命周期

Abstract:

In recent years, with the application of resources gradually extended to high-tech industry and strategic emerging industry, the technological innovation of downstream material end has become the strategic focus of reconstructing the advantages of China’s resource industry. Based on the panel data of China’s advanced material enterprises from 2008 to 2017, this study used the panel vector autoregressions (PVAR) model to comprehensively explore the dynamic incentive effects of venture capital and government subsidies on technological innovation of China’s advanced material enterprises, and comparatively analyzed the differences in incentive effects between the two tools from the perspective of technology life cycle. The results show that although both venture capital and government subsidies can improve the technological innovation of advanced material enterprises, the incentive effect of venture capital is short term, while the long-term incentive effect of government subsidies is obvious. For enterprises in the introduction period, the incentive effect of venture capital on their technological innovation is small, and government subsidies can make up for the lack of market capital allocation. For enterprises in the growth period, the incentive effect of venture capital on their technological innovation is stronger than that of government subsidies in the long run. For enterprises in the mature period, the incentive effect of government subsidies is limited, and venture capital can play a leading role in promoting the technological innovation of such enterprises. Therefore, in order to promote the technological innovation of advanced material enterprises, the government should introduce a classified subsidy mechanism, focusing on enterprises in the introduction period, and guiding venture capital investment in enterprises in the growth and mature periods.

Key words: high-end application of resources, advanced material enterprises, technological innovation, venture capital, government subsidy, technology life cycle

王昶, 王恺霖, 宋慧玲. 风险投资与政府补贴对新材料企业技术创新的激励效应及差异[J]. 资源科学, 2020, 42(8): 1566-1579.

WANG Chang, WANG Kailin, SONG Huiling. Incentive effects and differences of venture capital and government subsidies on technological innovation of advanced material enterprises[J]. Resources Science, 2020, 42(8): 1566-1579.

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变量类型	变量名称	变量符号	测量方法
被解释变量	技术创新	lnpat	ln(1+当年新申请专利数)
解释变量	政府补贴	lnsub	ln(1+当年获得的政府补贴)
	风险投资	lnvc	ln(1+风险投资存量)
中介变量	研发投入	lnrd	ln(1+企业研发投入)
控制变量	企业规模	lnsize	ln(1+当年公司总资产)
	资产负债率	lev	总负债/总资产
	资产收益率	roa	净利润/总资产
	股权集中度	hold	第一大股东持股比例/%

	变量	样本	均值	标准差	最小值	最大值
总体样本	lnpat	710	2.325	1.409	0.000	6.548
	lnvc	710	6.497	3.174	0.000	11.090
	lnsub	710	5.140	2.907	0.000	10.311
导入期	lnpat	140	3.094	1.332	0.000	6.548
	lnvc	140	7.277	2.512	0.000	10.716
	lnsub	140	6.322	2.042	0.000	9.851
成长期	lnpat	140	2.199	1.403	0.000	5.380
	lnvc	140	6.607	3.049	0.000	10.661
	lnsub	140	5.326	2.986	0.000	10.311
成熟期	lnpat	430	2.115	1.351	0.000	5.545
	lnvc	430	6.207	3.362	0.000	11.090
	lnsub	430	4.694	3.011	0.000	9.655
中介变量	lnrd	710	6.096	3.514	0.000	11.615
控制变量	lnsize	710	9.574	4.789	0.000	15.047
	lev	710	0.290	0.224	0.000	1.081
	roa	710	0.024	0.040	-0.188	0.285
	hold	710	0.188	0.186	0.000	0.635

变量	LLC		IPS		是否平稳
变量	统计量	P值	统计量	P值	是否平稳
lnvc	-92.3846	0.0000	-34.1357	0.0000	是
lnsub	-20.5261	0.0000	-5.2781	0.0000	是
lnpat	-18.3801	0.0000	-6.0536	0.0000	是
lnsize	-34.0674	0.0000	-9.3688	0.0000	是
lev	-17.8155	0.0000	-3.7711	0.0001	是
roa	-14.3225	0.0000	-4.4254	0.0000	是
hold	-30.3085	0.0000	-3.0371	0.0012	是
lnrd	-31.6318	0.0000	-7.0676	0.0000	是

变量	(1) lnpat	(2) lnpat	(3) lnpat	(4) lnpat	(5) lnpat	(6) lnpat
lnvc	0.136***	0.053***	0.044**
	(0.0153)	(0.0179)	(0.0180)
lnsub				0.226***	0.181***	0.131***
				(0.000)	(0.0379)	(0.0353)
lnsize		0.116***	0.0373*		0.0488*	-0.00765
		(0.0205)	(0.0191)		(0.0269)	(0.0245)
lev		-0.758*	0.639**		-0.950**	0.472
		(0.404)	(0.308)		(0.395)	(0.303)
roa		-2.208	2.174		-2.122	2.182
		(1.474)	(1.423)		(1.459)	(1.413)
hold		1.093**	0.426		0.851*	0.216
		(0.444)	(0.333)		(0.442)	(0.326)
Constant	1.4407***	0.932***	0.598***	1.164***	1.094***	0.744***
	(0.1072)	(0.129)	(0.159)	(0.000)	(0.121)	(0.155)
Observations	710	710	710	710	710	710
R-squared	0.111	0.201	0.312	0.195	0.218	0.319
Ajust_R²	0.012	0.106	0.298	0.106	0.125	0.306
F_value	79.48	31.88	22.52	154.70	35.31	23.30
时间控制	否	否	是	否	否	是

变量	(1)	(2)	变量	(3)	(4)
变量	lnrd	lnrd	变量	lnpat	lnpat
lnvc	0.0667***		lnvc	0.0442**
	（-0.0248）			（-0.0177）
lnsub		0.250***	lnsub		0.150***
		（-0.0523）			（-0.0381）
lnsize	0.664***	0.567***	lnrd	0.134***	0.121***
	（-0.0283）	（-0.0372）		（-0.0283）	（-0.0284）
lev	0.577	0.333	lnsize	0.0272	-0.0197
	（-0.558）	（-0.545）		（-0.0275）	（-0.0311）
roa	-3.690*	-3.567*	lev	-0.836**	-0.990**
	（-2.037）	（-2.012）		（-0.397）	（-0.39）
hold	-0.266	-0.598	roa	-1.712	-1.691
	（-0.614）	（-0.61）		（-1.454）	（-1.443）
Constant	-0.727***	-0.518***	hold	1.128**	0.923**
	（-0.178）	（-0.167）		（-0.437）	（-0.436）
			Constant	1.029***	1.157***
Observations	710	710		（-0.128）	（-0.12）
R-squared	0.757	0.762
			Observations	710	710
			R-squared	0.228	0.24