基于溢出源视角的R&D溢出对生产率影响的实证研究
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摘要
内生增长理论认为,R&D溢出是克服规模报酬递减和维持经济增长的一大引擎。大量文献运用各国不同层面的时间、截面或面板数据就R&D溢出对生产率的影响进行了经验实证,然而已有研究对R&D溢出内在机理的分析缺乏系统性,经验研究大多以发达国家为样本,以发展中国家为样本的经验实证还没有得到足够的重视。本文基于溢出源视角,利用中国企业、行业和地区数据为样本,从行业内、行业间和空间三个来源,系统研究了R&D溢出对我国不同层面生产率的影响方式和深度。
     在理论方面,本文梳理了大量文献,结合所选研究视角,归纳出R&D溢出影响生产率的两条路径:R&D溢出一方面直接作用于生产率,另一方面通过影响自身R&D投入进而间接影响生产率。本文在Barro Sala-I-Martin(2004)的质量阶段模型基础上,考虑R&D溢出作用,从直接路径和间接路径刻画R&D溢出对生产率的影响机理。通过对R&D溢出模型稳态解的比较静态分析发现,R&D溢出是影响生产率的关键变量;R&D投入决定要素模型显示,R&D溢出是影响R&D投入的重要变量,R&D溢出通过降低R&D成本促进了R&D投入的增加。
     在实证分析层面,首先基于机理分析框架,检验了我国制造业行业间、服务业行业间及制造业与服务业之间的R&D溢出对生产率的影响。与现有研究通常忽略R&D溢出异质性不同的是,本文考虑了R&D溢出异质性,测度行业间R&D溢出时区分了物化溢出和纯知识溢出,同时为避免现有研究中存在解释变量内生性依然使用OLS估计的不足,本文使用FMOLS准确估算了中国行业间的R&D溢出效应。研究发现,我国存在行业间R&D溢出,但行业间R&D溢出具有非对称性,即我国存在制造业行业间R&D溢出、服务业行业间R&D溢出以及制造业对服务业的行业间R&D溢出,不存在服务业对制造业的行业间R&D溢出。行业间R&D溢出对R&D投入的影响分析显示,制造业行业间R&D溢出与制造业R&D投入之间、服务业行业间R&D溢出与服务业R&D投入之间存在互补效应。
     其次,从生产成本视角,利用中国制造业14,227家企业微观数据,进一步考察了行业内和行业间R&D溢出对生产率的影响,这是本文区别于现有研究的新视角。为避免已有研究因提前设定函数形式可能产生估计有误的不足,我们采用超越对数成本函数进行了较准确估计。研究发现,行业内和行业间R&D溢出消减了生产成本并积极促进了R&D投入的增加,对生产率产生了积极的直接和间接效应。
     再次,基于开放经济条件,通过国际贸易这一媒介,就国际行业内和国际行业间R&D溢出对生产率的影响进行了检验。有别于现有研究中测度国际R&D资本选取相对数据的不足,我们使用各国行业层面R&D资本的绝对数据,采用动态面板准确估算了国际行业内、国际行业间R&D溢出的生产率促进效应。我们发现,我国获取的国际R&D溢出主要来源于行业间,国际行业内R&D溢出对生产率的影响不显著。R&D溢出对R&D投入的影响实证分析显示,国际行业间R&D溢出与R&D投入之间存在互补效应,而国际行业内R&D溢出与R&D投入之间存在替代效应。国际行业间R&D溢出对生产率产生了积极的直接和间接影响,而国际行业内R&D溢出对生产率产生了负面的直接和间接影响。
     本文随后引入空间因素、修正MRW模型,构建了空间面板模型和空间联立方程,避免了已有研究使用截面数据和遗漏空间变量导致的估计有偏的缺陷,准确测度了R&D的空间溢出效应,这是本文对现有研究的新拓展。我们发现,相邻地区的R&D资本对本地生产率产生了积极的空间溢出效应,然而R&D空间溢出效应主要来源于企业为主体的R&D资本。跨国公司在华R&D投资的空间联立方程估计结果显示:各地的生产率受到邻省区跨国R&D积极的空间溢出作用。地区总R&D空间溢出、跨国R&D空间溢出积极促进了地区企业R&D投入。R&D空间溢出对生产率产生了积极的直接和间接效应。
     最后,本文根据R&D溢出效应受吸收能力影响的假设建立了动态面板门槛检验模型,使用开放度、市场化程度、R&D投入、人力资本、行业增长等多变量测算了综合吸收能力,避免了已有研究大多使用人力资本等单一变量测度吸收能力的局限。研究发现,不同来源的R&D溢出模型中,各要素的影响作用迥异;R&D溢出影响要素存在门槛效应,而不同来源的R&D溢出影响要素门槛值具有一定的差距。上述结论为行业或地区选择适宜的技术提供了定量依据。
Endogenous growth theory regards R&D spillovers as an engine to overcome diminishing returns to scale and maintain economic growth. A large number of literature carried out empirical research on R&D spillover effects on productivity using data of different levels from different countries such as time series data, cross-section data or panel data. However, systematicness was missing in pre-existing research on intrinsic mechanism of R&D spillovers, empirical study mostly focused on developed economies, empirical research on developing countries as the sample has not attracted enough attention. This article carries out systematical study on the depth and mechanism of R&D spillovers using the data from Chinese enterprise, industries and regions, specifically through the channels of intra-industry, inter-industy and space based on the perspective of spillover source.
     In theory, this article sorts out the extensive literature combined with selected research perspective, and we sum up two ways to R&D spillover effects on productivity:on the one hand, R&D spillovers have effects on productivity directly, on the other hand, R&D spillovers have effects on productivity through effects on factor inputs such as R&D. Based on the model of quality stage of Barro Sala-I-Martin (2004),the article describes mechanism on the R&D spillover effects on productivity from direct and indirect paths,considering the R&D spillovers.Through the comparative static analysis of R&D spillover steady state solution,we finds that R&D spillovers is the key variable to productivity; Determinant model of R&D effort shows that R&D spillovers is the important variable to affect R&D effort and that R&D spillovers promote R&D effort by reducing the cost of R&D.
     In the analysis of experience, firstly, the article examines not only manufacturing inter-industry, service inter-industry but also the inter-industry spillovers between manufacturing and service sectors in China based on the framework of mechanism analysis. Different from existing research which neglected considering heterogeneity of R&D spillovers, this article distinguishes between rent spillovers and pure knowledge spillovers.Unlike existing research with disadvatage of bias on using OLS to estimate to explain the endogenous variables, we accurately estimate inter-industry R&D spillover effects by FMOLS. The empirical results show that there exists inter-industry R&D spillovers in China, which is asymmetrical,that is to say there exists manufacturing inter-industry R&D spillover effects, service sector inter-industry R&D spillover effects and manufacturing to service sector interindustry R&D spillover effects, not existing interindustry R&D spillover effects on manufacturing from service sector. The analysis on inter-industry R&D spillovers impacting R&D effort shows that there exists the complementary effect between the inter-industry R&D spillovers in manufacturing and R&D effort in manufacturing, so does the service industry.
     Secondly, this article further investigates R&D spillover effects on productivity using micro data of14,227enterprises of Chinese manufacturing industry from the perspective of production cost, which is a new perspective different from previous study. To avoid the disadvantage caused by setting functional form in advance, we use the logarithm function model to accurately estimate them. The results show that intra-industry and inter-industry R&D spillovers reduce the cost of production and actively promote the R&D effort increased and have positive direct and indirect effects on productivity.
     Then, this article examines the effect of international inter-industry and intra-industry R&D spillovers on productivity based on the media of international trade in the open economic conditions. Different from the previous study with disadvantage of selecting relative data to measure indicators, we accurately estimate international intra-industry and inter-industry R&D spillover effect on productivity promotion through absolute data. We finds that our access to international R&D spillovers mainly comes from inter-industry R&D spillovers, the international intra-industry spillovers don't have significant effects on TFP,TC and EC. The analysis on R&D spillovers impacting R&D effort shows that there exists the complementary effects between international interindustry R&D spillovers and R&D effort, there exists the substitution effects between international intraindustry R&D spillovers and R&D effort. International interindustry R&D spillovers have a positive direct and indirect effects on productivity. However, international intraindustry R&D spillovers have a negative direct and indirect effects on productivity.
     Further, considering spatial factor and modifying MRW model, this article constructs the spatial panel model and spatial simultaneous equations and estimates accurately the R&D spatial effects, which avoids partial defects because of using cross-section data and the omission of spatial variables, which is a new expansion to previous study. Results of spatial panel model show that R&D of spatial adjacent areas of region has positive spillover effects on economy growth, but spillover effects mainly come from R&D spillovers of enterprises. The results from Spatial simultaneous equations between productivity and R&D from Multinational companies in China show that R&D spillovers from Multinational companies by neighboring areas has positive spillover effects on productivity. Regional R&D spatial spillover, R&D spatial spillovers of Multinational companies actively promote own R&D effort. What's more, R&D spatial spillovers have a positive direct and indirect spillover effects on productivity.
     Finally, according to the assumption that the absorption capacity has effects on R&D spillover, this article establishes dynamic threshold panel data model and firstly examines the threshold value of different R&D spillovers. Using some variables such as openness, market-oriented, R&D effort, industry growth,the article measures the integrated absorption capacity unlike most existing literature using a single variable such as human capital to measure. We finds that different elements play different role in R&D spillovers in model of different spillover sources and that the threshold in different spillover sources has certain disparity. The above conclusion provides a quantitative basis with choice of appropriate technology.
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