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中文题名:

 人力资本错配、研发创新与企业全要素生产率    

姓名:

 徐家威    

学号:

 2020106028    

保密级别:

 公开    

论文语种:

 chi    

学科代码:

 120202    

学科名称:

 管理学 - 工商管理 - 企业管理(含:财务管理、市场营销、人力资源管理)    

学生类型:

 硕士    

学位:

 管理学硕士    

学校:

 南京农业大学    

院系:

 经济管理学院    

专业:

 企业管理    

研究方向:

 现代企业管理    

第一导师姓名:

 陈超    

第一导师单位:

 南京农业大学    

完成日期:

 2023-05-20    

答辩日期:

 2023-05-28    

外文题名:

 Human Capital Misallocation, R&D Innovation and Total Factor Productivity of Companies    

中文关键词:

 人力资本错配 ; 创新投入 ; 创新产出 ; 企业全要素生产率    

外文关键词:

 Human capital misallocation ; Innovation input ; Innovation output ; Total factor productivity of firms    

中文摘要:

计算机设备制造业是我国实体经济的支柱产业,也是数字经济发展的重要依托行业。然而,目前我国计算机设备制造业存在着高研发投入与低生产率的问题,人力资本的存量不断增长却存在着明显的错配现象。企业全要素生产率不仅决定了整个行业的生产率情况,还是一个国家实现经济持续发展的重要源泉。因此,寻求计算机设备制造业企业全要素生产率的发展方式变革对于实现我国计算机设备制造业高质量发展具有十分重要的意义。

人力资本和研发创新都是影响企业全要素生产率的重要因素,为进一步了解企业全要素生产率的发展情况,本文通过对人力资本错配、研发创新、企业全要素生产率相关研究文献进行了梳理和总结,再通过对相关理论的整理和现状的分析,提出了相关的研究假说,使用2011-2020年计算机设备制造业上市公司的面板数据来进行分析,从人力资本错配的角度去解释高创新投入和低生产率的谜团。本文主要通过基准回归分析、异质性检验、稳健性检验和中介效应机制分析得出了以下几个结论:(1)人力资本错配对企业全要素生产率的增长产生了明显的抑制作用。(2)从区域发展程度上分析,人力资本错配对企业全要素生产率的抑制程度在东部地区最为严重和显著,但在中部地区和西部地区却并不存在显著的抑制作用。从企业规模大小上分析,人力资本错配对中小企业全要素生产率的阻碍作用要比大企业来得更加严重。此外,稳健性检验的结果依然显著,表明结论是可靠的。(3)分别以研发创新投入和研发创新产出为中介变量来进行机制的分析,结果表明创新投入并没有起到中介作用,人力资本错配是通过抑制企业的创新产出从而阻碍了企业全要素生产率的提升。

最后,基于上述结论,为促进计算机设备制造业企业全要素生产率的提高,本文提出如下对策建议:(1)完善就业相关法规,减少人力资本配置不当现象。一方面,政府和企业需要通力合作,改良人才引进政策,另一方面,企业自身要完善人才队伍建设,加强与高校的产学研结合,吸引人才进入的同时要留下人才,减少员工的流动性,提高人力资本配置效率。(2)国家在制定地区政策时要尽力避免“偏爱”,在实施政策时要一视同仁,打破区域壁垒,建立资源共享平台,让不同地区的企业享有均等的发展机会。另外,应当深化金融供给侧结构性改革,针对不同规模企业的资金需要提供不同的产品,缓解中小企业因资金问题导致的引才失败,从而建立更好的人才队伍。(3)激发企业主动创新的意愿和活力,加强科技成果转化机制建立,完善科研成果保护政策,同时要明确研发投入产生的成本和风险,完善研发投入使用渠道,通过搭建政府、研究机构与企业的联动创新平台,避免要素错配引发的效率损失,让创新为企业带来更大的价值。

外文摘要:

Computer equipment manufacturing industry is the pillar industry of China's real economy and an important relying industry for the development of digital economy. However, there is a problem of high R&D investment and low productivity in China's computer equipment manufacturing industry, and the stock of human capital keeps growing but there is an obvious misallocation phenomenon. Total factor productivity of enterprises not only determines the productivity situation of the whole industry, but also is an important source for a country to achieve sustainable economic development. Therefore, it is of great importance to seek changes in the development of total factor productivity of computer equipment manufacturing enterprises to achieve high-quality development of China's computer equipment manufacturing industry.

Both human capital and R&D innovation are important factors affecting enterprise total factor productivity. To further understand the development of enterprise total factor productivity, this paper composes and summarizes the research literature related to human capital misallocation, R&D innovation, and enterprise total factor productivity, and then puts forward relevant research hypotheses through the collation of relevant theories and analysis of the current situation, using 2011-2020 panel data of listed companies in computer equipment manufacturing industry to conduct the analysis, hoping to explain the mystery of high innovation input and low productivity from the perspective of human capital misallocation. This paper mainly draws the following conclusions through benchmark regression analysis, heterogeneity test, robustness test and mediating effect mechanism analysis: (1) The human capital misallocation has a significant inhibiting effect on the growth of total factor productivity of enterprises. (2) In terms of the degree of regional development, the inhibitory effect of human capital misallocation on the total factor productivity of firms is most severe and significant in the eastern region, but not in the central and western regions. In terms of firm size, the human capital misallocation is a more serious impediment to the total factor productivity of the small and medium-sized enterprises than the large firms. Moreover, the results of the robustness test are still significant, indicating that the findings are reliable. (3) The analysis of the mechanism using R&D innovation input and R&D innovation output as mediating variables shows that innovation input does not play a mediating role and that the human capital misallocation hinders the increase in total factor productivity by suppressing the innovation output of firms.

Finally, based on the above conclusions, in order to promote the improvement of total factor productivity of computer equipment manufacturing enterprises, this paper proposes the following countermeasures: (1) improve employment-related regulations and reduce the phenomenon of human capital misallocation. On the one hand, the government and enterprises need to cooperate to improve the policy of talent introduction, on the other hand, enterprises themselves should improve the construction of talent team, strengthen the combination of industry, academia and research with universities, attract talents to enter while keeping them, reduce the mobility of employees, and improve the efficiency of human capital allocation. (2) The state should try to avoid "favoritism" when formulating regional policies, and treat them equally when implementing policies, break down regional barriers, and establish a platform for resource sharing so that enterprises in different regions can enjoy equal development opportunities. In addition, we should deepen the structural reform on the supply side of finance and provide different products to meet the capital needs of enterprises of different scales, so as to alleviate the failure of SMEs in attracting talents due to capital problems and build a better talent pool. (3) Stimulate the willingness and vitality of enterprises to take the initiative to innovate, strengthen the establishment of the mechanism for the transformation of scientific and technological achievements, improve the policy for the protection of scientific research achievements, and at the same time, clarify the costs and risks arising from R&D investment, improve the channels for the use of R&D investment, and avoid the loss of efficiency caused by factor mismatch by building a linkage innovation platform between the government, research institutions and enterprises, so that innovation can bring greater value to enterprises.

参考文献:

[1] 安同良,周绍东,皮建才.R&D补贴对中国企业自主创新的激励效应[J].经济研究,2009,44(10):87-98.

[2] 陈林,夏俊.高校扩招对创新效率的政策效应——基于准实验与双重差分模型的计量检验[J].中国人口科学,2015(05):45-57.

[3] 陈言,李欣泽.行业人力资本、资源错配与产出损失[J].山东大学学报(哲学社会科学版),2018(04):146-155.

[4] 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(04):250-271.

[5] 程晨.技术创新溢出与企业全要素生产率——基于上市公司的实证研究[J].经济科学,2017(06):72-86.

[6] 程郁,陈雪.创新驱动的经济增长——高新区全要素生产率增长的分解[J].中国软科学,2013(11):26-39.

[7] 方文中,罗守贵.自主研发与技术引进对全要素生产率的影响——来自上海高新技术企业的实证[J].研究与发展管理,2016,28(01):1-9.

[8] 冯根福,郑明波,温军,张存炳.究竟哪些因素决定了中国企业的技术创新——基于九大中文经济学权威期刊和A股上市公司数据的再实证[J].中国工业经济,2021(01):17-35.

[9] 高帆.我国经济转型中的创新之谜[J].探索与争鸣,2017(04):109-115.

[10] 葛晶,李勇.行政垄断视角下人力资本错配的成因及其解释[J].中南财经政法大学学报,2019(05):43-52.

[11] 龚关,胡关亮.中国制造业资源配置效率与全要素生产率[J].经济研究,2013,48(04):4-15.

[12] 龚晓莉,胡汉辉.基于DEA的产业效率分析——通信设备、计算机及其他电子设备制造业的实证分析[J].工业技术经济,2009,28(11):68-72.

[13] 郭彦彦,吴福象.专利权行政保护、关键技术创新与企业全要素生产率增长[J].经济经纬,2021,38(05):101-110.

[14] 胡亚茹,陈丹丹.中国高技术产业的全要素生产率增长率分解——兼对“结构红利假说”再检验[J].中国工业经济,2019(02):136-154.

[15] 黄漫宇,王孝行.数字经济、资源错配与企业全要素生产率[J].宏观经济研究,2022(12):43-53.

[16] 黄群慧,余泳泽,张松林.互联网发展与制造业生产率提升:内在机制与中国经验[J].中国工业经济,2019(08):5-23.

[17] 纪雯雯,赖德胜.人力资本、配置效率及全要素生产率变化[J].经济与管理研究,2015,36(06):45-55.

[18] 纪雯雯,赖德胜.人力资本配置与中国创新绩效[J].经济学动态,2018(11):19-31.

[19] 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(01):81-92.

[20] 靳来群.地区间资源错配程度分析(1992-2015)[J].北京社会科学,2018(01):57-66.

[21] 靳来群.所有制歧视所致金融资源错配程度分析[J].经济学动态,2015(06):36-44.

[22] 鞠晓生,卢荻,虞义华.融资约束、营运资本管理与企业创新可持续性[J].经济研究,2013,48(01):4-16.

[23] 赖敏.土地要素错配阻碍了中国产业结构升级吗?——基于中国230个地级市的经验证据[J].产业经济研究,2019(02):39-49.

[24] 李健,张满林,张兰.高校扩招对中国全要素生产率增长的政策效应——基于双重差分方法的实证分析[J].宏观质量研究,2018,6(03):105-118.

[25] 李静,楠玉,刘霞辉.中国经济稳增长难题:人力资本错配及其解决途径[J].经济研究,2017,52(03):18-31.

[26] 李静,司深深.人才错配下的消费增长——公共部门人才膨胀何以影响消费支出[J].当代经济科学,2020,42(01):49-59.

[27] 李勇,葛晶,李桥鸽.国有产权、人力资本错配和全要素生产率损失[J].中国经济问题,2021(01):35-51.

[28] 李勇,马芬芬.人力资本错配如何扭曲了产业结构升级[J].经济经纬,2021,38(02):82-90.

[29] 李子彪,孙可远,刘爽.人力资本特征如何影响企业创新绩效?——基于创新合作的调节[J].科技管理研究,2020,40(06):22-31.

[30] 刘伟.基于Bootstrap-Malmquist指数的高新技术产业技术创新效率分析[J].经济学动态,2013(03):42-52.

[31] 鲁晓东,连玉君.中国工业企业全要素生产率估计:1999—2007[J].经济学(季刊),2012,11(02):541-558.

[32] 罗雨泽,罗来军,陈衍泰.高新技术产业TFP由何而定?——基于微观数据的实证分析[J].管理世界,2016(02):8-18.

[33] 吕承超,王志阁.要素资源错配对企业创新的作用机制及实证检验——基于制造业上市公司的经验分析[J].系统工程理论与实践,2019,39(05):1137-1153.

[34] 吕祥伟.制造业集聚与企业绿色全要素生产率:理论分析与经验证据[J].统计与决策,2022,38(23):126-131.

[35] 牛泽东,张倩肖,王文.中国装备制造业全要素生产率增长的分解:1998-2009——基于省际面板数据的研究[J].上海经济研究,2012,24(03):56-73.

[36] 裴政,罗守贵.人力资本要素与企业创新绩效——基于上海科技企业的实证研究[J].研究与发展管理,2020,32(04):136-148.

[37] 蒲艳萍,顾冉.劳动力工资扭曲如何影响企业创新[J].中国工业经济,2019(07):137-154.

[38] 任灿灿,郭泽光,田智文.研发费用加计扣除与企业全要素生产率[J].华东经济管理,2021,35(05):119-128.

[39] 邵敏,包群.政府补贴与企业生产率——基于我国工业企业的经验分析[J].中国工业经济,2012(07):70-82.

[40] 邵挺.金融错配、所有制结构与资本回报率:来自1999~2007年我国工业企业的研究[J].金融研究,2010(09):51-68.

[41] 邵宜航,步晓宁,张天华.资源配置扭曲与中国工业全要素生产率——基于工业企业数据库再测算[J].中国工业经济,2013(12):39-51.

[42] 盛明泉,蒋世战.高管股权激励、技术创新与企业全要素生产率——基于制造业企业的实证分析[J].贵州财经大学学报,2019(02):70-76.

[43] 宋清华,林永康.杠杆率会影响全要素生产率吗——基于企业和地区异质性的视角[J].山西财经大学学报,2021,43(03):112-126.

[44] 苏科,周超.人力资本、科技创新与绿色全要素生产率——基于长江经济带城市数据分析[J].经济问题,2021(05):71-79.

[45] 孙婧.人力资本与FDI技术溢出的门槛效应研究[J].商业时代,2013(15):92-93.

[46] 孙雪,宋宇,赵培雅.智能化技术应用是否改善了人力资本要素错配[J/OL].科学学研究:1-22.

[47] 汤二子,刘海洋,孔祥贞,孙振.中国制造业企业研发投入与效果的经验研究[J].经济与管理,2012,26(08):57-61.

[48] 陶长琪,徐冬梅.非金融企业杠杆偏离对企业效率的影响[J].当代财经,2020(10):111-123.

[49] 汪浩瀚,徐建军,吕博.空间视角下要素市场扭曲与高技术产业TFP增长——基于电子及通信设备制造业的实证检验[J].经济地理,2019,39(09):129-137.

[50] 王红建,王靖茹,吴鼎纹.并购活跃度、全要素生产率与资源错配——来自制造业上市公司的经验证据[J/OL].南开管理评论:1-31.

[51] 王启超,王兵,彭睿.人才配置与全要素生产率——兼论中国实体经济高质量增长[J].财经研究,2020,46(01):64-78.

[52] 王卫,綦良群.要素错配、技术进步偏向与全要素生产率增长——基于装备制造业细分行业的随机前沿模型分析[J].山西财经大学学报,2018,40(12):60-75.

[53] 王欣,曹慧平.金融错配对中国制造业全要素生产率影响研究[J].财贸研究,2019,30(09):43-53.

[54] 王亚飞,柏颖,廖甍.人力资本错配与人力资本贡献率:系统测度与实证关联[J].系统管理学报,2022,31(03):509-521.

[55] 温忠麟,叶宝娟.中介效应分析:方法和模型发展[J].心理科学进展,2014,22(05):731-745.

[56] 温忠麟.张雷,侯杰泰,刘红云.中介效应检验程序及其应用[J].心理学报,2004(05):614-620.

[57] 吴延兵.R&D存量、知识函数与生产效率[J].经济学(季刊),2006(03):1129-1156.

[58] 夏良科.人力资本与R&D如何影响全要素生产率——基于中国大中型工业企业的经验分析[J].数量经济技术经济研究,2010,27(04):78-94.

[59] 谢冬水.土地资源错配与城市创新能力——基于中国城市面板数据的经验研究[J].经济学报,2020,7(02):86-112.

[60] 熊正德,魏唯.金融错配对企业创新投资的影响——来自中国数字创意上市公司的经验证据[J].湖南大学学报(社会科学版),2023,37(01):50-57.

[61] 徐盈之,蔡海亚,严春蕾.要素市场扭曲与我国雾霾污染防治[J].中国地质大学学报(社会科学版),2019,19(01):22-33.

[62] 叶初升,李承璋,罗连发.资源错配的多层次识别、分解与比较——畅通国民经济循环的分析视角[J].社会科学战线,2022(03):42-55.

[63] 余东华,孙婷,张鑫宇.要素价格扭曲如何影响制造业国际竞争力[J].中国工业经济,2018(02):63-81.

[64] 袁宝龙,李琛.环境规制政策下创新驱动中国工业绿色全要素生产率研究[J].产业经济研究,2018(05):101-113.

[65] 张杰,周晓艳,李勇.要素市场扭曲抑制了中国企业R&D?[J].经济研究,2011,46(08):78-91.

[66] 张莉,朱光顺,李世刚,李夏洋.市场环境、重点产业政策与企业生产率差异[J].管理世界,2019,35(03):114-126.

[67] 张少辉,余泳泽.土地出让、资源错配与全要素生产率[J].财经研究,2019,45(02):73-85.

[68] 张同斌.研发投入的非对称效应、技术收敛与生产率增长悖论——以中国高技术产业为例[J].经济管理,2014,36(01):131-141.

[69] 张璇,李子健,李春涛.银行业竞争、融资约束与企业创新——中国工业企业的经验证据[J].金融研究,2019(10):98-116.

[70] 中国经济增长前沿课题组,张平,刘霞辉,袁富华,王宏淼,陆明涛,张磊.中国经济增长的低效率冲击与减速治理[J].经济研究,2014,49(12):4-17.

[71] 周艳菊,邹飞,王宗润.盈利能力、技术创新能力与资本结构—基于高新技术企业的实证分析[J].科研管理,2014,35(01):48-57.

[72] 朱杰堂,焦冉晴,谢伟丽.数字普惠金融如何影响绿色全要素生产率——理论分析与经验证据[J].金融监管研究,2022(03):54-70.

[73] 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.

[74] 邹薇,任娟娟.信贷错配、非金融企业结构性去杠杆与全要素生产率[J].湘潭大学学报(哲学社会科学版),2020,44(04):92-97.

[75] 郭妙琳. 中国人力资本错配及其对全要素生产率的影响[D].广州大学,2020.

[76] 孙立强. 中国电子通信制造业全要素生产率分析[D].辽宁大学,2018.

[77] 王红月. 我国计算机、通信和其他电子设备制造业升级研究[D].西北师范大学,2018.

[78] 肖千慧. 人力资本投资、人力资本存量与企业绩效的关系实证研究[D].天津师范大学,2015.

[79] 张晓雨. 企业人力资本对产业升级的影响研究[D].山东财经大学,2022.

[80] 赵琪.人力资本错配对企业创新的影响研究[D].华东师范大学,2020.

[81] Kalemli-Ozcan S, Sørensen B E. 5. Misallocation, Property Rights, and Access to Finance: Evidence from within and across Africa[M]. University of Chicago Press, 2016.

[82] Acharya R C, Keller W. Technology transfer through imports[J]. Canadian Journal of Economics, 2009, 42(4): 1411-1448.

[83] Bagheri M, Mitchelmore S, Bamiatzi V, et al. Internationalization orientation in SMEs: The mediating role of technological innovation[J]. Journal of International Management, 2019, 25(1): 121-139.

[84] Baron R M, Kenny D A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations[J]. Journal of personality and social psychology, 1986, 51(6): 1173-1182.

[85] 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.

[86] Chang X, Fu K, Low A, et al. Non-executive employee stock options and corporate innovation[J]. Journal of financial economics, 2015, 115(1): 168-188.

[87] Ciccone A, Papaioannou E. Human capital, the structure of production, and growth[J]. The review of economics and statistics, 2009,91(1):66-82.

[88] F. A. Hayek. The Use of Knowledge in Society[J]. The American Economic Review, 1945, 35(4): 519-530.

[89] Friesenbichler K, Peneder M. Innovation, competition and productivity[J]. Economics of Transition, 2016, 24(3):535-580.

[90] Galunic D C, Anderson E. From security to mobility: Generalized investments in human capital and agent commitment[J]. Organization Science, 2000, 11(1): 1-20.

[91] Griffith R, Redding S, Reenen J V. Mapping the two faces of R&D: Productivity growth in a panel of OECD industries[J]. Review of economics and statistics, 2004, 86(4): 883-895.

[92] Griliches Z. Productivity,R&D and basic research at the firm level in the 1970s[J]. American Economic Review, 1986, 76(1): 141-154.

[93] Hsieh C T, Klenow P J. Misallocation and manufacturing TFP in China and India[J]. The Quarterly journal of economics, 2009, 124(4): 1403-1448.

[94] Jan Čadil, Ludmila Petkovová, Dagmar Blatná. Human Capital, Economic Structure and Growth[J]. Procedia Economics and Finance,2014,12:85-92.

[95] Jones C I, Romer P M. The new Kaldor facts: ideas, institutions, population, and human capital[J]. American Economic Journal: Macroeconomics, 2010, 2(1): 224-245.

[96] Kim T, Maskus K E, Oh K Y. Effects Of Patents On Productivity Growth In Korean Manufacturing: A Panel Data Analysis[J]. Pacific Economic Review, 2009, 14(2):137-154.

[97] Legros D, Galia F. Are innovation and R&D the only sources of firms’ knowledge that increase productivity? An empirical investigation of French manufacturing firms[J]. Journal of Productivity Analysis, 2012, 38(2): 167-181.

[98] Ljungwall C, Tingvall P G. Is China different? A meta-analysis of the growth-enhancing effect from R&D spending in China[J]. China Economic Review, 2015, 36: 272-278.

[99] March J G. Exploration and exploitation in organizational learning[J]. Organization science, 1991, 2(1): 71-87.

[100] Melitz M J. The impact of trade on intra‐industry reallocations and aggregate industry productivity[J]. Econometrica, 2003, 71(6): 1695-1725.

[101] Midrigan V, Xu D Y. Finance and misallocation: Evidence from plant-level data[J]. American economic review, 2014, 104(2): 422-458.

[102] Mohnen P, Hall B H. Innovation and productivity: An update[J]. Eurasian Business Review, 2013, 3(1): 47-65.

[103] Mulligan C B, Sala-i-Martin X. A Labor Income-Based Measure of the Value of Human Capital: An Application to the States of the United States[J]. Japan and the World Economy, 1997, 9(2): 159-191.

[104] Murphy K M, Shleifer A, Vishny R W. The allocation of talent: Implications for growth[J]. The quarterly journal of economics, 1991, 106(2): 503-530.

[105] Philipp K, The Relationship Between Technology, Innovation and Firm Performance: Empirical Evidence From E-business in Europe [J].Journal of Accounting and Economics,2008,9(16):33-36.

[106] Psacharopoulos, G., H. Patrinos. Returns to Investment in Education: A Decennial Review of the Global Literature[J]. Education Economics, 2018, 26(5):445-458.

[107] Raul Ramos, Jordi Surinach, Manuel Artís. Regional Economic Growth and Human Capital: The Role of Over-education[J]. Regional Studies,2012,46(10):1389-1400.

[108] Restuccia D, Rogerson R. Policy distortions and aggregate productivity with heterogeneous establishments[J]. Review of Economic dynamics, 2008, 11(4): 707-720.

[109] Sterlacchini A, Venturini F. R&D and productivity in high-tech manufacturing: a comparison between Italy and Spain[J]. Industry and Innovation, 2014, 21(5): 359-379.

[110] Tang J, Le C D. Multidimensional innovation and productivity[J]. Economics of Innovation and New Technology, 2007, 16(7): 501-516.

[111] Taveira J G , Goncalves E , Freguglia R D S . The missing link between innovation and performance in Brazilian firms: a panel data approach[J]. Applied Economics, 2019, 51(31-33):3632-3649.

[112] Vollrath D. The efficiency of human capital allocations in developing countries[J]. Journal of Development Economics, 2014, 108: 106-118.

[113] Yian C. Misallocation of human capital and productivity: evidence from China[J]. Economic research-Ekonomska istraživanja, 2019, 32(1): 3342-3359.

[114] Hsieh C T, Hurst E, Jones C I, et al. The allocation of talent and us economic growth[R]. National Bureau of Economic Research, Inc, 2013.

中图分类号:

 F27    

开放日期:

 2023-06-13    

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