高技术产业技术创新效率关键影响因素分析——基于DEA-Malmquist和BMA方法的实证研究

摘要/Abstract

摘要： 高技术产业技术创新是适应和引领经济发展新常态的重要驱动。本研究运用DEA-Malmquist指数法测度中国大陆29个省市2011—2016年的技术创新效率，然后引入贝叶斯模型平均(BMA)方法，对22个可能影响技术创新效率的潜在因素进行识别和检验。结果表明：几年间中国高技术产业的技术创新效率平稳中略有上升，技术创新在增效和研发之间摇摆，不能兼顾，技术进步水平微降是阻碍各省市技术创新效率提高的主因；创新氛围、对外开放度、产业结构、经济发展水平、研发税收、所有制结构、政府支持是技术创新效率的关键影响因素；此外，自主创新倾向、企业与高校及科研院所合作水平对技术效率有关键影响，也应予以重点关注。

关键词: 技术创新效率, 关键影响因素, 贝叶斯模型平均, DEA-Malmquist, 高技术产业

Abstract: Technological innovation in high-tech industries is an important driver for adapting and leading the new normal of economic development. In view of the low efficiency of technological innovation in Chinese high-tech industries, the government will inevitably solve the problem by increasing the number and intensity of innovation strategies and policies. Therefore, identifying the key factors affecting the efficiency of technological innovation in high-tech industries is of great significance. It is crucial to the effective formulation of strategies, the full release of policy dividend, and the sound and rapid development of industry.
However, although the existing literature has conducted in-depth research on the influencing factors of technological innovation efficiency in high-tech industries, there is still room for improvement. On one hand, scholars usually select one or a few influencing factors subjectively based on existing literature. It is easy to cause mission and omission of factors. On the other hand, most studies apply the traditional single equation regression model to examine the influence of selected factors. Because of inherent uncertainty problems existing in model, the empirical results will vary considerably depending on different influencing factors chosen and different model settings, resulting in reduction in the guiding value of research conclusions. Bayesian Model Averaging (BMA) is a method that is suitable for large information sets, and thus can be used to screen explanatory variables and solve the uncertainty problem effectively. Based on above all, this paper adopts the method, trying to make up for the lack of the existing literature.
First, this paper uses the DEA-Malmquist method to measure the technological innovation efficiency of 29 provincial high-tech industries in China from 2011 to 2016. The results show that EFFCH has risen in the fluctuation and TECH has decreased slightly in the fluctuation. When EFFCH rises from the previous year, TECH decreases from the previous year, and vice versa. In other words, there is no good balance between the improvement of technological efficiency and the promotion of technological progress. As a result of the unbalanced innovation status, the overall technological innovation efficiency of Chinese high-tech industries has increased slightly in the six years, not achieving significant improvement. From the results of the provincial efficiency estimation, it can be seen that the level of technological progress of most provinces has declined slightly, but due to the improvement of technological efficiency, the efficiency of technological innovation has improved. Therefore, the slight decline in the level of technological progress is the main reason hindering the efficiency of technological innovation.
Then, based on a comprehensive review of relevant empirical literature in recent years, this paper selected 22 potential factors that may affect the efficiency of technological innovation from the perspective of direct and indirect participants in technological innovation, regional internal innovation environment and external international environment. To find out the precise influence of potential factors, taking the Malmquist index, EFFCH and TECH as explanatory variables respectively while establishing regression equation.
After estimation using the Bayesian Model Averaging method, it can be concluded that: (1) The key influencing factors in the direct participants in technological innovation include innovation atmosphere, the tendency of independent innovation and ownership structure. The innovation atmosphere is the key negative influencing factor of Malmquist index, also has negative impact on EFFCH and TECH as well. The tendency of independent is the key negative influencing factor of EFFCH, also has negative impact on Malmquist index and positive impact on TECH. The ownership structure is the key factor affecting the three indices. It has negative impact on EFFCH and positive impact on Malmquist index and TECH. (2) The key influencing factors in the indirect participants in technological innovation include the level of cooperation between enterprises and universities and scientific research institutes, government support and R&D tax. The level of cooperation between enterprises and universities and scientific research institutes is the key positive influencing factor of EFFCH, also has negative impact on Malmquist index and positive impact on TECH. Government support is the key positive influencing factor of Malmquist index and EFFCH, also has the negative impact on TECH. R&D tax is the key positive influencing factor of Malmquist index and TECH, also has the positive impact on EFFCH. (3) The key influencing factors in regional internal innovation environment include the level of economic development and industrial structure. The level of economic development is the key positive influencing factor of Malmquist index, also has negative impact on EFFCH and positive impact on TECH. Industrial structure is the key positive influencing factor of Malmquist index and EFFCH, also has positive impact on TECH. (4) The key influencing factor in external international environment is the degree of openness. It is the key positive influencing factor of Malmquist index and TECH, also has positive impact on EFFCH. (5) Ranking the key influencing factors of the three indices according to posterior probability. The results show that: the key influencing factors of Malmquist index are innovation atmosphere, the degree of openness, industrial structure, the level of economic development, R&D tax, ownership structure and government support; the key influencing factors of EFFCH are ownership structure, government support, the tendency of independent innovation, the level of cooperation between enterprises and universities and scientific research institutes and industrial structure; the key influencing factors of TECH are ownership structure, the degree of openness and R&D tax. When formulating relevant strategies and policies to promote technological innovation in high-tech industries, the government should not simply treat all the key factors equally, nor pay full attention to the important factors while ignoring the minor ones. It is well advised to focus separately on the key influencing factors according to their ranking. Since the level of technological progress is the main reason hindering the efficiency of technological innovation, the key influencing factors of TECH should be given priority control. In particular, since each key factor has different influence directions and degrees on the three indexes, the different influence should be considered comprehensively when formulating targeted policies. Otherwise, it is easy to get half the results with double the effort.

Key words: technological innovation efficiency, key influencing factors, Bayesian Model Averaging, DEA-Malmquist, high-tech industries

范德成谷晓梅. 高技术产业技术创新效率关键影响因素分析——基于DEA-Malmquist和BMA方法的实证研究[J]. 科研管理, 2022, 43(1): 70-78.

Fan Decheng, Gu Xiaomei. An analysis of the key influencing factors of technological innovation efficiency in high-tech industries: An empirical study based on the DEA-Malmquist and Bayesian Model Averaging approach[J]. Science Research Management, 2022, 43(1): 70-78.

参考文献

[1] 杨青峰. 高技术产业地区研发创新效率的决定因素—基于随机前沿模型的实证分析[J].管理评论，2013，25(6):47-58.
[2] 孙早，徐远华. 信息基础设施建设能提高中国高技术产业的创新效率吗?—基于2002-2013年高技术17个细分行业面板数据的经验分析[J].南开经济研究，2018(2):72-92.
[3] 支燕，白雪洁. 我国高技术产业创新绩效提升路径研究——自主创新还是技术外取?[J].南开经济研究，2012(5):51-64.
[4] 高晓光. 中国高技术产业创新效率影响因素的空间异质效应—基于地理加权回归模型的实证研究[J].世界地理研究，2016，25 (4):122-131.
[5] 洪银兴. 科技创新体系的完善与协同发展探讨[J]. 经济学动态，2016( 2) :4-9.
[6] 俞立平，王作功，胡林瑶. 高技术产业创新速度的影响机制研究[J].科学学研究，2018，36(5):913-921.
[7] 董钰，孙赫. 知识产权保护对产业创新影响的定量分析—以高技术产业为例[J].世界经济研究，2012(4)：11-15，87.
[8] Beaudry C, Breschi S. Are firms in clusters really more innnvative?[J].Economics of Innovation and New Technology, 2003，12 (4) :325-342.
[9] 杨浩昌，李廉水，刘军. 高技术产业聚集对技术创新的影响及区域比较[J].科学学研究，2016，34(2):212-219.
[10] Guan J. C., Chen K. H. Measuring the innovation production process: a cross-region empirical study of China’s high-tech innovations[J]. Technovation, 2010，30(5-6):348-358.
[11] 刘伟. 考虑环境因素的高新技术产业技术创新效率分析—基于2000-2007年和2008-2014年两个时段的比较[J].科研管理，2016，37 (11) : 18 -25.
[12] 肖仁桥，陈忠卫，钱丽. 异质性技术视角下中国高技术制造业创新效率研究[J].管理科学，2018，31(1):48-68.
[13] Femandez C ，Ley E，Steel M. Model uncertainty in cross-country growth regressions[J].Journal of Applied Econometrics，2001，16(5): 563-576.
[14] Wright J. H. Forecasting US inflation by Bayesian model averaging[J]. Journal of Forecasting，2009，28(2):131-144.
[15] Chun L.，Maheu J.H. .Forecasting realized volatility: a Bayesian model averaging approach[J]. Journal of Applied Econometrics，2009，24(5):709-733.
[16] Culka M. Applying Bayesian model averaging for uncertainty estimation of input data in energy modelling[J]. Energy, Sustainability and Society，2014，4(1):1-17.
[17] 王亮，刘金全. 中国经济增长的决定因素分析—基于贝叶斯模型平均(BMA)方法的实证研究[J].统计与信息论坛，2010，25(9):3-7.
[18] 卢二坡，张超. 基于贝叶斯模型平均(BMA)方法的中国房地产价格影响因素分析[J].长安大学学报(社会科学版)，2016，18(4):68-76.
[19] 陈浪南，王升泉，陈文博. 中国城乡居民收入差距决定因素的实证研究—基于贝叶斯模型平均(BMA)法[J].经济学报，2016，3(3):61-78.
[20] 柯忠义. 创业板上市公司经济绩效及影响因素—基于贝叶斯模型平均法(BMA)的实证研究[J].数量经济技术经济研究，2017(1):146-161.
[21] 张超，郑长娟. 中国生产性服务业发展的影响因素分析—基于贝叶斯模型平均(BMA)方法的实证研究[J].山东财经大学学报，2018，30(2):43-51,59.
[22] 陈伟，牛霖琳. 基于贝叶斯模型平均(BMA)方法的中国通货膨胀的建模及预测[J]. 金融研究，2013(11):15-27.
[23] 许培源，张华. 福建省技术创新能力的影响因素—基于贝叶斯模型平均方法的研究[J].东南学术，2014(1):124-133.
[24] 唐建荣，石文. 基于BMA的企业技术创新驱动因素分析[J].财经科学，2015(11):67-77.
[25] Fare R，Grosskopf S，Norris M，et al. Productivity growth, technical progress and efficiency change in industrialized countries[J].American Economic Review，1994，84(1):66-83.
[26] Zeugner S. Bayesian Model Averaging with BMS，2011.
[27] Moral-Benito E. Determinants of economic growth: A bayesian panel data approach[J].The Review of Economics and Statistics，2012， 94 (2):566–579.
[28] George E. I.，Mcculloch R. E. .Approaches for Bayesian variable selection[J]. Statistica Sinica，1997，7(2):339-373.
[29] Madigan D，York J，Allard D. Bayesian Graphical Models for Discrete Data[J]. International Statistical Review，1999，63(2):215-232.
[30] 刘伟. 中国高新技术产业研发创新效率测算—基于三阶段DEA模型[J].数理统计与管理，2015,34(1):17-28.
[31] 叶丹，黄庆华. 区域创新环境对高技术产业创新效率的影响研究—基于DEA -Malmquist方法[J].宏观经济研究，2017(8):132-140.
[32] 吴传清，黄磊，文传浩. 长江经济带技术创新效率及其影响因素研究[J].中国软科学，2017(5)：160-170.
[33] Doloreux D，Parto S. Regional innovation systems: Current discourse and unresolved issues[J]. Technology in Society，2005，27(2):133-153.
[34] 中国科技发展战略研究小组等. 中国区域创新能力评价报告2016[M]. 北京：科学技术文献出版社，2016:205-212.
[35] 陈建丽，孟令杰，姜彩楼. 两阶段视角下高技术产业技术创新效率及影响因素研究[J].数学的实践与认识，2014，44(4):63-74.
[36] 夏海力，李卿，吴松强. 装备制造业技术创新效率及其影响因素研究—以苏州为例[J].科技进步与对策，2016,33(6):65-70.
[37] 俞立平. 技术创新金融研究[M]. 北京：经济科学出版社，2013:73-89.
[38] 朱有为，徐康宁. 中国高技术产业研发效率的实证研究[J].中国工业经济，2006(11):38-45.
[39] 张美云，宋宇. 中国与高收入国家(地区)技术创新能力的影响因素比较[J].宏观经济研究，2017(9):91-97.
[40] 范德成，李盛楠. 考虑空间效应的高技术产业技术创新效率研究[J].科学学研究，2018，36(5):901-912.
[41] 徐建中，曲小瑜. 装备制造业环境技术创新效率及其影响因素研究—基于DEA-MaIalmquist和Tobit的实证分析[J].运筹与管理，2015，24(1):246-254.
[42] 刘伟. 中国高技术产业的技术创新影响因素:基于面板数据模型的实证检验[J].数学的实践与认识，2010，40(22):62-70.
[43] 薛庆根. 高技术产业创新、空间依赖与研发投入渠道—基于空间面板数据的估计[J].管理世界，2014(12):182-183.
[44] Goto A，Suzuki K. R&D capital, rate of return on R&D investment and spillover of R&D in Japanese manufacturing industries[J]. Review of Economics and Statistics，1989，(4): 555-564.
[45] Griliches Z. Patents statistics as economic indicators: A survey[J]. Journal of Economic Literature，1990，28(4): 1661-1707.
[46] Coe S，Helpman M. International R&D spillovers [J]. European Economic (Review)，1995，39(5):859-887.
[47] Griliches Z.，Lichtenbegr F. Inter-industry Technology Flows and Productivity Growth: A Reexamination[J].Review of Economics Statistics, 1984，66(2):324-329.
[48] 李向东，李南，白俊红，等. 高技术产业研发创新效率分析[J].中国软科学，2011(2):52-61.
[49] Liang F，Paulo R，Molina G，et al. Mixtures of g priors for Bayesian variable selection[J]. Journal of the American Statistical Association，2008，103(481):410-423.
[50] Kass R. E.，Raftery A. E. . Bayes factors[J]. Journal of the American Statistical Association ，1995，90(430):773-795.
[51] Sala-i-Martin X. I just ran two million regressions[J]. Arnerican Economic Review，1997，87(2):178-183.
[52] 冯伟，徐康宁，邵军. 基于本土市场规模的产业创新机制及实证研究[J].中国软科学，2014(1):55-67.
[53] 李政，陆寅宏.国有企业真的缺乏创新能力吗：基于上市公司所有权性质与创新绩效的实证分析与比较[J].经济理论与经济管理，2014(2):27-38.
[54] 张玉，陈凯华，乔为国. 中国大中型企业研发效率测度与财政激励政策影响[J].数量经济技术经济研究，2017(5)：38-54.
[55] 王燕，王志强，刘伯凡. 生产性服务业发展、研发集聚与高技术产业研发效率—基于随机前沿模型的实证研究[J].软科学，2018，32(3):1-4,15.
[56] 时省，洪进，赵定涛. 知识密集型服务业对高技术产业两阶段创新效率的影响研究[J].中国科技论坛，2013(1):43-49.
[57] Helpman E. . Trade, FDI, and the organization of firms[J]. Journal of Economic Literature，2006，XLIV:589-630.