中国创新资源结构性错配程度研究
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摘要
创新资源错配乃中国总体创新效率较低的重要原因,其中结构性错配是重要方面。通过构建部门间资源错配程度测算模型,并利用2005-2015年工业数据,研究表明:创新资源的区域结构性错配最为严重,其导致创新效率及产出损失17.4%,而这样的错配主要表现在东北及西北地区资源投入过度,而东南及西南地区却相对不足;其次是所有制结构错配,在国有部门创新资源投入严重过度的情况下,其导致创新效率及产出损失6.56%;最后是高技术产业与非高技术产业间的错配,其导致创新效率及产出损失0.53%,而高技术产业的资源投入依然相对不足,尤其是医疗设备制造业。而进一步分析发现,三类结构性错配都表现出研发物质资本错配比研发人力资本错配严重的局面。
The innovation resources misallocation is an important reason for the low efficiency of China's overall innovation. Structural misallocation is an important aspect. Through constructing a model to calculate resources misallocation degree between departments, using the industrial data from 2005 to 2015, this paper finds that: the innovation resources misallocation degree between regions is most serious, which leads to innovation efficiency loss of 17.4%. Inputs in the northeast and northwest regions is excessivet, while insufficient in the southeast and southwest regions. Misallocation between ownerships is the second, which leads to innovation efficiency loss of 6.56%. Inputs of the state sector is excessive seriously relative to the non-state sector. The last is misallocation between the high and non-high technology industry, which leads to innovation efficiency loss of 0.53%. Inputs in the high technology industry is still relatively insufficient, specially in the medical equipment manufacturing industry. The further analysis finds that the three types of structural misallocation all are mainly reflected in R&D funding compared to R&D labor.
The innovation resources misallocation is an important reason for the low efficiency of China's overall innovation. Structural misallocation is an important aspect. Through constructing a model to calculate resources misallocation degree between departments, using the industrial data from 2005 to 2015, this paper finds that: the innovation resources misallocation degree between regions is most serious, which leads to innovation efficiency loss of 17.4%. Inputs in the northeast and northwest regions is excessivet, while insufficient in the southeast and southwest regions. Misallocation between ownerships is the second, which leads to innovation efficiency loss of 6.56%. Inputs of the state sector is excessive seriously relative to the non-state sector. The last is misallocation between the high and non-high technology industry, which leads to innovation efficiency loss of 0.53%. Inputs in the high technology industry is still relatively insufficient, specially in the medical equipment manufacturing industry. The further analysis finds that the three types of structural misallocation all are mainly reflected in R&D funding compared to R&D labor.
引文
[1] 白俊红,江可申,李婧. 应用随机前沿模型评测中国区域研发创新效率[J]. 管理世界, 2009, (10):51-61.
[2] Hsu P H, Tian X, Xu Y. Financial development and innovation: Cross-country evidence[J]. Journal of Financial Economics, 2014, 112(1): 116-135.
[3] Chowdhury R H, Maung M. Financial market development and the effectiveness of R&D investment: Evidence from developed and emerging countries[J]. Research in International Business and Finance, 2012, 26(2): 258-272.
[4] Kanwar S, Evenson R. On the strength of intellectual property protection that nations provide[J]. Journal of Development Economics, 2009, 90(1): 50-56.
[5] 戴魁早,刘友金. 要素市场扭曲与创新效率——对中国高技术产业发展的经验分析[J]. 经济研究, 2016, (7): 72-86.
[6] 吴延兵. 国有企业双重效率损失研究[J]. 经济研究, 2012, (3): 15-27.
[7] Boeing P. The allocation and effectiveness of China’s R&D subsidies-evidence from listed firms[J]. Research Policy, 2016, 45(9): 1774-1789.
[8] Wei S J, Xie Z, Zhang X. From “made in China” to “innovated in China”: Necessity, prospect, and challenges[J]. Journal of Economic Perspectives, 2017, 31(1): 49-70.
[9] 焦翠红,陈钰芬. R&D资源配置、空间关联与区域全要素生产率提升[J].科学学研究,2018, 36(1):81-92.
[10] 李德山,邓翔. 价格扭曲、资源错配是否抑制了我国创新生产率[J]. 科学学研究, 2018, 36 (4): 654-661.
[11] 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, (7):109-123.
[12] Fujita M, Thisse J F. Does geographical agglomeration foster economic growth? and who gains and loses from it?[J]. The Japanese Economic Review, 2003, 54(2): 121-145.
[13] Hsieh C T, Klenow P J. Misallocation and manufacturing TFP in China and India[J]. Quarterly Journal of Economics, 2009, 124(4): 1403-1448.
[14] Aoki S. A simple accounting framework for the effect of resource misallocation on aggregate productivity[D]. MPRA Paper, 2009, No. 12506.
[15] Brandt L, Tombe T, Zhu X. Factor market distortions across time, space and sectors in China[J]. Review of Economic Dynamics, 2013, 16(1): 39-58.
[16] Syrquin M. Growth and structural change in Latin America since 1960: A comparative analysis[J]. Economic Development and Cultural Change, 1986, 34(3): 433-454.
[17] Hopenhayn H A. Firms, misallocation, and aggregate productivity: A review[J]. Annual Review Economics, 2014, 6(1): 735-770.
[18] 焦翠红,孙海波,董直庆. R&D资源配置效率演化及研发补贴效应——来自制造业的经验证据[J]. 山西财经大学学报, 2017, 39(2):58-71.
[19] Olley G S, Pakes A. The dynamics of productivity in the telecommunications equipment industry[J]. Econometrica, 1996, 64(6): 1263-1297.
[20] Baily M N, Hulten C, Campbell D, et al. Productivity dynamics in manufacturing plants[A]. Brookings Papers on Economic Activity[C]. Microeconomics, 1992, 4: 187-267.
[21] 陈永伟,胡伟民. 价格扭曲,要素错配和效率损失:理论和应用[J]. 经济学(季刊), 2011, 10(4): 1401-1422.
[22] Jefferson G H, Huamao B, Xiaojing G, et al. R&D performance in Chinese industry[J]. Economics of Innovation and New Technology, 2006, 15(4-5): 345-366.
[23] Cheung K Y, Lin P. Spillover effects of FDI on innovation in China: Evidence from the provincial data[J]. Social Science Electronic Publishing, 2004, 15(1):25-44.
[24] Tan Y, Tian X, Zhang C X, et al. Privatization and innova-tion: Evidence from a quasi-natural experiment in China[D]. Kelley School of Business Research Paper, 2015: 15-23.
[25] 周煊,程立茹,王皓. 技术创新水平越高企业财务绩效越好吗?——基于16年中国制药上市公司专利申请数据的实证研究[J]. 金融研究, 2012, (8): 166-179.
① 除了本文介绍的这几种方法外,估计生产率或创新效率的方法还包括常用的DEA方法,然而本文需要估计出投入的产出弹性,DEA方法并不适用。
[2] Hsu P H, Tian X, Xu Y. Financial development and innovation: Cross-country evidence[J]. Journal of Financial Economics, 2014, 112(1): 116-135.
[3] Chowdhury R H, Maung M. Financial market development and the effectiveness of R&D investment: Evidence from developed and emerging countries[J]. Research in International Business and Finance, 2012, 26(2): 258-272.
[4] Kanwar S, Evenson R. On the strength of intellectual property protection that nations provide[J]. Journal of Development Economics, 2009, 90(1): 50-56.
[5] 戴魁早,刘友金. 要素市场扭曲与创新效率——对中国高技术产业发展的经验分析[J]. 经济研究, 2016, (7): 72-86.
[6] 吴延兵. 国有企业双重效率损失研究[J]. 经济研究, 2012, (3): 15-27.
[7] Boeing P. The allocation and effectiveness of China’s R&D subsidies-evidence from listed firms[J]. Research Policy, 2016, 45(9): 1774-1789.
[8] Wei S J, Xie Z, Zhang X. From “made in China” to “innovated in China”: Necessity, prospect, and challenges[J]. Journal of Economic Perspectives, 2017, 31(1): 49-70.
[9] 焦翠红,陈钰芬. R&D资源配置、空间关联与区域全要素生产率提升[J].科学学研究,2018, 36(1):81-92.
[10] 李德山,邓翔. 价格扭曲、资源错配是否抑制了我国创新生产率[J]. 科学学研究, 2018, 36 (4): 654-661.
[11] 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, (7):109-123.
[12] Fujita M, Thisse J F. Does geographical agglomeration foster economic growth? and who gains and loses from it?[J]. The Japanese Economic Review, 2003, 54(2): 121-145.
[13] Hsieh C T, Klenow P J. Misallocation and manufacturing TFP in China and India[J]. Quarterly Journal of Economics, 2009, 124(4): 1403-1448.
[14] Aoki S. A simple accounting framework for the effect of resource misallocation on aggregate productivity[D]. MPRA Paper, 2009, No. 12506.
[15] Brandt L, Tombe T, Zhu X. Factor market distortions across time, space and sectors in China[J]. Review of Economic Dynamics, 2013, 16(1): 39-58.
[16] Syrquin M. Growth and structural change in Latin America since 1960: A comparative analysis[J]. Economic Development and Cultural Change, 1986, 34(3): 433-454.
[17] Hopenhayn H A. Firms, misallocation, and aggregate productivity: A review[J]. Annual Review Economics, 2014, 6(1): 735-770.
[18] 焦翠红,孙海波,董直庆. R&D资源配置效率演化及研发补贴效应——来自制造业的经验证据[J]. 山西财经大学学报, 2017, 39(2):58-71.
[19] Olley G S, Pakes A. The dynamics of productivity in the telecommunications equipment industry[J]. Econometrica, 1996, 64(6): 1263-1297.
[20] Baily M N, Hulten C, Campbell D, et al. Productivity dynamics in manufacturing plants[A]. Brookings Papers on Economic Activity[C]. Microeconomics, 1992, 4: 187-267.
[21] 陈永伟,胡伟民. 价格扭曲,要素错配和效率损失:理论和应用[J]. 经济学(季刊), 2011, 10(4): 1401-1422.
[22] Jefferson G H, Huamao B, Xiaojing G, et al. R&D performance in Chinese industry[J]. Economics of Innovation and New Technology, 2006, 15(4-5): 345-366.
[23] Cheung K Y, Lin P. Spillover effects of FDI on innovation in China: Evidence from the provincial data[J]. Social Science Electronic Publishing, 2004, 15(1):25-44.
[24] Tan Y, Tian X, Zhang C X, et al. Privatization and innova-tion: Evidence from a quasi-natural experiment in China[D]. Kelley School of Business Research Paper, 2015: 15-23.
[25] 周煊,程立茹,王皓. 技术创新水平越高企业财务绩效越好吗?——基于16年中国制药上市公司专利申请数据的实证研究[J]. 金融研究, 2012, (8): 166-179.
① 除了本文介绍的这几种方法外,估计生产率或创新效率的方法还包括常用的DEA方法,然而本文需要估计出投入的产出弹性,DEA方法并不适用。