中国R&D资本存量测算：1952~2014年

2008年SNA修订了R&D支出的核算方法,R&D支出不再视为中间消耗,而是作为固定资本形成处理。本文依据2008年SNA和我国R&D统计调查数据等,科学测算了1952年以来每年R&D活动所形成的资产价值,同时对R&D资产进口和出口进行了适当调整。然后,在合理选取和估计R&D资产折旧率、R&D价格指数和基年R&D资本存量的基础上,根据永续盘存法对我国1952~2014年R&D资本存量进行了测算。结果显示,我国R&D投资与R&D内部经费支出的年平均比例为95.5%,我国R&D资本存量总体上呈快速增长趋势,2014年达到42244亿元,且R&D资本存量与GDP之比呈“N”形趋势,经历了先上升后下降再上升的过程。     

多视角下R&D资本化测算方法比较与应用

R&D资本化基本测度方法主要有三种：Goldsmith方法、Griliches方法和BEA方法,在不同的经济理念下,三种测度方法既各有特色,又有共同特征。在Goldsmith方法设定理念中,当期R&D资本存量由当期不变价R&D投资额与扣除折旧后的上一期R&D资本存量两部分构成,且当期不变价R&D投资额全部转化为当期资本存量;在Griliches方法设定理念中,当期R&D资本存量由上一期不变价R&D投资额与扣除折旧后的上一期R&D资本存量两部分构成,且上一期不变价R&D投资额全部转化为当期资本存量;而在BEA方法设定理念中,当期R&D资本存量由当期不变价R&D投资额与扣除折旧后的上一期R&D资本存量两部分构成,且当期不变价R&D投资额的一半发生折旧。在具体测算过程中,三种测度方法均要考虑R&D资本存量增长率、R&D资本折旧率、R&D支出价格指数等关键参数的设定。同时,基于三种测度方法比较研究,将其应用于中国R&D资本化估算实践。     

中国工业行业R&D投入的产出效率与影响因素

运用知识生产函数模型,以中国工业行业（1993~2008）为研究对象,在将R&D资本存量分为本国本行业R&D、本国其它行业R&D、外国本行业R&D、外国其它行业R&D资本存量的基础上,首先运用随机前沿生产函数方法对中国R&D投入的产出效率进行了实证研究,随后综合考虑了人力资本、贸易开放度、技术距离、行业竞争程度、物质资本存量、行业技术水平、产权结构等因素对R&D产出效率的影响。结论显示,除外国其它行业R&D资本对R&D投入的产出效率影响不显著外,其它三种R&D资本存量均有显著的正的影响,且人力资本、贸易开放度、物质资本存量等因素均显著的促进了R&D产出效率的提升,技术距离则对R&D产出效率的提升有抑制作用,行业竞争程度对R&D产出效率影响不显著,高技术行业R&D产出效率要比非高技术行业低,国有产权比重较高行业R&D产出效率与国有产权比重较低行业相比并无显著差异。     

中国研发资本测算及其经济增长贡献

本文在国民账户体系（SNA,2008）框架下对中国1952—2014年研发（R&D）资本进行科学测算,在此基础上测度了研发对经济增长的贡献,并利用扩展的索洛模型及隐性变量法进一步分析了考虑研发资本投入的真实全要素生产率水平。研究表明：（1）1952年以来,中国当期新增R&D资产绝对额及相对额均呈快速增长趋势,但与美国相比,水平仍较低;（2）R&D资本投入有效促进了经济增长,其对经济增长贡献达21.95%;（3）过去中国的R&D资本中硬技术比重较高,规则、标准方面的软技术含量相对较低,即技术进步更多地为体现型技术进步;（4）考虑R&D资本投入后的真实全要素生产率水平总体呈先上升后下降的趋势,其经济增长贡献也呈相同趋势,且在近年来对经济增长产生了负向影响。     

家族企业代际传承模式与长期投资决策研究——基于家族化方式的调节效应

以2008—2018年沪深上市家族企业为研究对象，实证检验了家族企业代际传承模式和家族化方式对长期投资决策的影响。研究发现：与职业经理人传承模式相比，子承父业传承模式家族企业的投资规模下降幅度较大，但主要降低的是资本支出，R&D支出在长期投资中的占比下降幅度较小；并且与间接创办型家族企业相比，直接创办型家族企业有利于促进子承父业家族企业长期投资规模的下降，但是却抑制了该类家族企业R&D投入占比的下降。     

重点行业资本配置扭曲对中国全要素生产率的影响

本文基于线性规划，建立重点行业间的资本要素优化模型，通过估算各重点行业的资本存量、劳动力人力资本及其产出弹性，得到各行业优化后的资本配置量和总产出，最后运用基于Fre-Primont指数的DEA方法测算各行业资本配置优化前后全要素生产率的变化。研究发现：重点行业间资本投入过度与资本投入不足现象并存，其中房地产业、交通运输仓储和邮政业以及水利环境和公共设施管理业资本要素投入过度，批发和零售业、制造业以及建筑业资本投入不足；矫正各行业资本要素配置扭曲后行业总产出和全要素生产率明显增大；矫正资本要素配置扭曲可以通过提高剩余混合效率进而提高全要素生产率效率，最终实现提高全要素生产率。     

基于十大分类的中国资本存量重估：1978~2016年

研究目标：根据最新的统计数据测算我国的资本存量。研究方法：根据我国1978~2016年的相关数据,利用永续盘存法进行测算。研究发现：本文的测算的资本存量要略小于现有文献的结果;我国的资本产出比正处于快速上升阶段。研究创新：使用最新统计数据将资本构成进一步细化为十个大类,分别考虑了每类资本的初始资本存量、折旧率、资本形成额和价格指数,计算得到1978~2016年各类资本存量;在应用几何折旧法时,将折旧率拆解为固定折旧率与可变折旧率两部分,其中可变折旧率与经济增长率正相关,更加贴近实际情况。研究价值：在对已有研究进行深入分析的基础上,采用更为精细的方法和更贴近现实的假设,应用最新的数据,重新估算中国资本存量,得出的研究结果相比过去的研究更加可靠。研究结果对今后的基础研究具有重要借鉴意义。     

高管任期、R&D支出与企业投资效率——来自中国A股资本市场的经验证据

选取2001—2013年我国沪深A股上市公司数据,对高管任期、R&D支出与企业投资效率三者之间的关系进行实证检验,结果表明：R&D投入随着高管任期的延长而增加;高管任期越长,企业投资效率越高;R&D支出越多,企业投资效率越高;高管任期的延长增强了R&D支出对企业非效率投资的抑制作用;上市公司广泛存在着程度不同的非效率投资现象。     

环境规制与异质性企业技术创新 ——基于工业行业上市公司的研究

本文收集整理2003—2015年工业行业上市公司数据,构建多种环境规制和技术创新指标,研究环境规制对异质性企业技术创新的影响及其背后的作用机制。研究发现,环境规制与企业技术创新之间呈现出显著的U型曲线关系;进一步的异质性分析发现,环境规制会因为企业所在地区、所处行业和产权性质的不同,对企业的技术创新水平产生不同的影响;机制分析发现,环境规制可以通过促进R&D经费内部支出和R&D人员全时当量的机制,提高企业的技术创新水平     

农村金融发展、资本存量提升与农村经济增长

研究目标：农村金融发展、资本存量提升对农村经济增长的影响。研究方法：采用永续盘存法测算农村资本存量，以1980~2014年小样本数据构建VEC模型，再进一步用Bootstrap Chow检验来研究制度变量的影响。研究发现：农户储蓄存款、资本存量能显著促进农村经济的增长，但提高农村金融机构存贷比对经济贡献不大；农村资本存量发挥正效应要经历较长时滞；农村金融促进经济增长的力度受制于国家宏观政策。研究创新：从技术层面对现有文献的金融指标进行精练，重新估测农村资本存量，特别注重小样本约束下的VEC建模技术，并在Chow检验时采用Bootstrap仿真方法来确定临界值。研究价值：从技术上深化了VEC模型适用性探讨，增强了有关农村金融实证结论的有效性。     

中国装备制造业全要素生产率增长的波动与异质性

研究目标：中国装备制造业及其细分行业TFP增长率的测定与分解。研究方法：构建超越对数形式的随机前沿生产函数模型,测算中国装备制造业及其细分行业TFP增长率,并对其进行分解。研究发现：中国装备制造业TFP增长率不但在区域和省份之间存在差异,而且存在明显的内部行业异质性;技术进步和配置效率变化分别是提高和阻碍装备制造业TFP增长率的主要原因和障碍;除技术进步均为正值外,技术效率变化、规模效率变化和配置效率变化在装备制造业各行业中异质性相当明显;装备制造业及其细分行业仍未从真正意义上实现由粗放型向集约型增长方式的转变。研究创新：中国装备制造业细分行业TFP增长率及其异质性。研究价值：为装备制造业转型与升级提供经验证据。     

Reprint of "Performance evaluation of China's high-tech innovation process :Analysis based on the innovation value chain"

The Chinese high-tech industry has developed greatly since the beginning of China's “National High-tech R&D (863) Program” and “China Torch Program”. This paper introduces a conceptual model extended from the innovation value chain model to simultaneously estimate the R&D and commercialization efficiencies for the high-tech industries of 29 provincial-level regions in China. To match reality, a network DEA incorporating both shared inputs and additional intermediate inputs is constructed to open the “black box” view of decision making units used in single-stage DEA. This study is the first attempt to link the R&D and commercialization with a solid theoretical foundation and feasible mathematical methods. The empirical findings show that most of the 29 regions have low efficiency in the commercialization sub-process compared to the R&D sub-process, although there are regional differences in China's high-tech industry. Pearson correlation shows that the R&D sub-process is not closely correlated to the commercialization sub-process in terms of efficiency. Our analysis can provide information for the formulation of policies to achieve high innovation efficiency.     

Interindustry R&D Spillovers for Electrical and Electronic Products: The Canadian Case

This paper estimates the effects of interindustry R&D spillovers on the cost and production structure for 10 Canadian manufacturing industries. Because of their high-tech nature and productivity performance, spillovers from electrical and electronic products are distinguished from other spillover sources. Generally, spillovers from electrical and electronic products generate cost reductions and render production processes for Canadian manufacturing industries more capital intensive (i.e. either more physical or R&D capital intensive). Social rates of return for R&D capital are calculated for all 10 industries. The social rates are 5–11 times greater than are the private rates. Indeed, the social rates are high for all the industries. This implies that the electrical and electronic products industry is an important interindustry spillover source but, like other industries, a major spillover-using industry.     

Efficiency evaluation of China's high-tech industry with a multi-activity network data envelopment analysis approach

This paper employs a Russell multi-activity network DEA model and divides the overall innovation process into the upstream Research and Development (R&D) process and the downstream commercialization process to appraise the innovation performance of China's high-tech industries from 2009 to 2013. This model can deal with the problems of intermediates, shared inputs and slack-based measure in a unified framework, and the result can provide policy makers with process-specific information on how to improve the innovation performance of China's high-tech industries. The main findings are presented as follows. First, the overall efficiency of China's high-tech industries still remains at a low level, which has its roots mainly in commercialization inefficiencies other than R&D inefficiencies. Second, for most provinces, their R&D efficiencies do not match up with their commercialization efficiencies. Finally, the innovative activities of China's high-tech industries should be driven by the market demand -oriented for the improvement of innovation efficiency.     

Performance evaluation of China's high-tech innovation process: Analysis based on the innovation value chain

The Chinese high-tech industry has developed greatly since the beginning of China's “National High-tech R&D (863) Program” and “China Torch Program”. This paper introduces a conceptual model extended from the innovation value chain model to simultaneously estimate the R&D and commercialization efficiencies for the high-tech industries of 29 provincial-level regions in China. To match reality, a network DEA incorporating both shared inputs and additional intermediate inputs is constructed to open the “black box” view of decision making units used in single-stage DEA. This study is the first attempt to link the R&D and commercialization with a solid theoretical foundation and feasible mathematical methods. The empirical findings show that most of the 29 regions have low efficiency in the commercialization sub-process compared to the R&D sub-process, although there are regional differences in China's high-tech industry. Pearson correlation shows that the R&D sub-process is not closely correlated to the commercialization sub-process in terms of efficiency. Our analysis can provide information for the formulation of policies to achieve high innovation efficiency.     

Intangible capital in the pharmaceutical and chemical industry

We investigate whether measures of intangible capital based on advertising and R&D can explain variation in Tobin's Q ratio for the pharmaceutical and chemical industry. The study is motivated by prior literature studying this relation in other industries, recent literature investigating intangible capital in this industry, and the larger controversy about whether stock valuations have been high due to irrational investors or large investment in intangible capital. We find that our measures of intangible capital are statistically significant determinants of Q and explain 20% of the variation in our sample. When age and industry are incorporated into the model our explanatory power reaches 25%.     

How to optimize the allocation of research resources? An empirical study based on output and substitution elasticities of universities in Chinese provincial level

Although significant progress has been made in China's basic research in recent years, there remains a wide gap between research in China and that from developed countries. How to optimize the allocative efficiency of research resources is of great importance for increasing research output. In this paper, using the fixed effect stochastic frontier model based on the translog production function, we estimate output and substitution elasticities of research and development (R&D) inputs at universities in China's provincial level during 2009–2016. We find that the R&D technical efficiency of China's universities, after a rapid growth, has tended to become relatively stable. Improvements of internationalization degree and exogenous R&D capabilities are conducive to promoting R&D technical efficiency, whereas expenditures from government grants inhibit the promotion of R&D technical efficiency; the effects of R&D capital deepening and internet penetration are not evident. The output elasticity of R&D capital is much higher than that of R&D personnel, suggesting that R&D capital is the main driving force of research output. The substitution elasticity between R&D capital and personnel has experienced a change from substitution to complementary since 2014. To realize sustained growth of research output, we should increase R&D input with positive output elasticity or reduce R&D input with negative output elasticity, making the necessary trade-offs according to the substitution relationship between the two R&D inputs.     

Interactive strategies in supply chains–a double-edged portfolio approach to small-and medium-sized subcontractors’ position analyses

China is concerned to improve the technical capability of its industry. It has chosen Science and Technology Industry Parks (STIPs) as the model for incubating its R&D capability and driving its hi-tech policy. Against this background, the authors examine two main issues. First, we review assessments of university science parks in the UK and a wider context extracted from the literature before examining specifically China's R&D intensity and hi-tech policy. Second, we examine the performance of hi-tech companies situated on STIPs and those located outside STIPs, comparing their success in commercializing technology. We pay particular attention to the role of entrepreneurship in this activity by those engaged in it. Our findings are based on secondary quantitative data and qualitative data collected by means of interviews and focus groups in the Beijing and Shanghai areas in March 2004. From our research it is clear that China lags behind OECD countries in its R&D capability and the technology transfer rate is low, hampering China's hi-tech potential, although China is achieving some success in hi-tech exports, notably of ICT goods. Furthermore, innovation capability, locational factors such as being located in a regional industry cluster (in or outside STIPs), guanxi and networking opportunities, entrepreneurial skills, including international business experience and access to more financial sources and capital for developing the business, are essential for commercializing technology effectively in China. The role of entrepreneurship is evident. However, it remains still underdeveloped in China's STIPs.     

改革开放以来中国出口增长推动力的阶段性演进及地区分布差异

研究目标：研究中国出口增长推动力的阶段性演进及地区分布差异。研究方法：基于中国1978~2014年的省级面板数据,采用超越对数形式的随机前沿模型进行分析。研究发现：在经济转型期,市场化改革与对外开放的发展是中国出口增长的首要推动力;在“入世”准备期,中国出口增长由制度因素、物质要素投入协同推动;在“入世”增长期,物质要素投入成为中国出口增长的首要来源;在全球引擎期,物质要素投入协同贸易潜力主导中国出口贸易的发展;地区间出口贸易差距主要源于贸易潜力、资本与制度因素的三重差异。研究创新：对改革开放以来至2008年金融危机以后中国出口增长推动力的阶段性演进进行系统研究。研究价值：针对中国贸易可持续发展面临的主要挑战,提供政策建议。     

R&D,Interindustry and International Technology Spillovers and the Total Factor Productivity Growth of Manufacturing Industries in Canada, 1974-1989

The paper presents new econometric evidence on the relationship between total factor productivity growth and the R&D expenditures of Canadian manufacturing industries in the presence of interindustry and international spillovers of technology. In contrast to studies that presume that international spillovers are incorporated in imports of intermediate and/or capital equipment goods, the present paper assumes that the principal channel of transmission of new technology is foreign direct investment. Three original proxies for international spillovers use information on patenting, the size and the origin of foreign ownership in the host country and the R&D expenditures in the country of origin. The results suggest that the nexus between industry's own R&D expenditures and the TFP growth is significant and positive, especially for the process-related R&D. Domestic interindustry spillovers of new technology have a larger effect on TFP than industry's own R&D expenditures. All three proxies for international technology spillovers are associated positively and significantly with TFP growth. However, international spillovers contribute to TFP growth less than domestic interindustry spillovers and less than own process-related R&D.