Technical change and total factor productivity growth for Swedish manufacturing and service industries

This article presents alternative specifications of the production functions of a large panel of Swedish firms for the period 1992 to 2000. The period can be characterized as a transition when long-run productivity growth in the Swedish economy improved from being among the weakest to one of the strongest within the Organization for Economic Co-operation and Development (OECD). In order to present a detailed exploration of this dramatic change, the time trend and general index models are applied to estimate Total Factor Productivity (TFP) growth, rate of technical change and returns to scale. The models are extended to allow for firm specific as well as time-varying technical change. The parametric TFP measures are also compared with the nonparametric Solow residual, and several hypotheses are tested to explain the growth patterns in the Swedish economy. It is found that the improved growth rate, initially starting in large exporting manufacturing firms, after a deep economic crisis at the beginning of the 1990s, spilled over to the rest of the economy, both manufacturing and services.     

Estimation of TFP growth: a semiparametric smooth coefficient approach

This article uses a semiparametric smooth coefficient model (SPSCM) to estimate TFP growth and its components (scale and technical change). The SPSCM is derived from a nonparametric specification of the production technology represented by an input distance function (IDF), using a growth formulation. The functional coefficients of the SPSCM come naturally from the model and are fully flexible in the sense that no functional form of the underlying production technology is used to derive them. Another advantage of the SPSCM is that it can estimate bias (input and scale) in technical change in a fully flexible manner. We also used a translog IDF framework to estimate TFP growth components. A panel of U.S. electricity generating plants for the period 1986?C1998 is used for this purpose. Comparing estimated TFP growth results from both parametric and semiparametric models against the Divisia TFP growth, we conclude that the SPSCM performs the best in tracking the temporal behavior of TFP growth.     

产业集聚、企业规模与全要素生产率增长——基于长三角制造业行业面板数据的分析

本文首先从理论上分析了产业集聚、企业规模对TFP增长的影响;其次,运用非参数的Malmquist生产率指数方法,测算了1998—2007年我国长三角地区两省一市(江苏、浙江、上海)20个制造业行业的全要素生产率增长及其技术效率和技术进步的变化;最后,用面板数据的计量回归模型对长三角两省一市制造业行业全要素生产率的影响因素进行了实证检验。得出以下结论:长三角地区两省一市制造业行业全要素生产率的增长主要是由技术进步带来的,而技术效率变化指数表现出负增长;产业集聚和企业规模对上海、江苏和浙江制造业行业TFP增长的作用显著,且产业集聚的作用大于企业规模的作用。     

中国服务业分行业生产率变迁及异质性考察

本文采用全国两次经济普查后的修订数据,引入"技术不会遗忘"假定,运用序列DEA-Malmquist生产率指数法测算了1990—2010年中国服务业细分行业的全要素生产率(TFP)、技术进步、纯技术效率与规模效率增长率。结果表明,中国服务业总体及细分行业的TFP均处于上升通道,在上世纪90年代,服务业TFP增长的主导因素是技术效率改进,进入21世纪后,则主要是技术进步提高,且技术效率改进已开始由以纯技术效率为主转向以规模效率为主,但服务业发展的粗放型特征仍然明显。同时,中国服务业TFP增长表现出了较大的行业异质性;与工业(制造业)行业对比,服务业TFP增长是滞后的。2010年与1991年相比,TFP及技术效率增长的行业间异质程度有所下降,而技术进步却有进一步拉大的迹象。可能的内在机制是现代信息技术对不同服务业企业资源配置的异质影响,以及中国服务业体制改革的渐进式道路。     

我国制造业技术进步、技术效率及区域差异——基于DEA方法的实证研究

利用Malmquist生产率指数方法,对我国29个省的制造业在1999--2005年间的全要素生产率（TFP）的变化进行了测算,把TFP的增长构成分解为技术进步和生产效率变化两个成分,并对其区域差异进行了分析,最后对省际制造业TFP进行了趋同分析。结果显示：我国制造业TFP的增长主要是由技术进步推动的,当技术进步促进TFP提升时,总会受到生产效率下降对TFP增长的抑制影响;区域间技术进步及技术效率存在较大差异,省级制造业TFP存在条件β收敛。     

我国工业行业全要素生产率变动分析

基于2006—2009年我国工业行业的面板数据,采用非参数Malmquist指数分析法,对我国工业的全要素生产率变动情况进行了实证研究,并将其分解为技术进步、纯技术效率和规模效率。结果表明:2006—2009年我国工业全要素生产率年均增长率仅为4.3%,技术进步年均增长率为1.6%,技术效率年均增长率为2.7%,技术进步已失去其推动全要素生产率增长的主导性优势地位;我国工业发展需注重同时提高技术效率和技术进步水平,否则全要素生产率增长会受到限制。     

FDI和产业集聚对TFP增长影响的实证检验——基于上海制造业行业的数据

运用非参数的Malmquist生产率指数方法,测算了1998—2007年上海20个制造业行业的全要素生产率指数及其技术效率和技术进步的变化指数;用基于面板数据的计量回归模型对上海市制造业行业全要素生产率的影响因素进行了实证检验。得出以下结论:上海制造业行业全要素生产率的增长主要是由技术进步带来的,技术效率变化指数表现出负增长;外商直接投资对上海制造业企业没有明显的外溢效应,产业集聚对上海制造业行业的全要素生产率增长有显著的促进作用,出口贸易具有显著的阻碍作用,国有产权比重具有显著的反向作用。     

FACTOR DETERMINANTS OF TOTAL FACTOR PRODUCTIVITY GROWTH FOR THE JAPANESE MANUFACTURING INDUSTRY

This study uses industrial panel data for Japanese manufacturing to estimate the sources of productivity growth by simultaneously considering embodied technical progress, spillover effects, and openness, after controlling for returns to scale, imperfect competition, and capacity utilization. Estimation results show the existence of considerable embodied technical progress and interindustry externalities of capital investments positively affecting productivity growth. Furthermore, embodied technical progress causes research and development (R&D) capital to affect productivity growth insignificantly, suggesting that the impact of R&D is realized only after being embodied into other capitals. From sector‐wise estimations, we notice differences in factors affecting productivity growth between the durable and nondurable manufacturing sectors. (JEL D24, O30)     

Singapore's Manufacturing Sector's TFP Growth: A Decomposition Analysis

Singapore has been criticized recently for experiencing insignificant total factor productivity (TFP) growth. This paper examines whether this criticism is valid in the context of the manufacturing sector of Singapore. Using new data and the stochastic production frontier approach, TFP growth is decomposed into technological progress and changes in technical efficiency. While the results could not reject the hypothesis that Singapore's output growth is mostly input-driven, they show that, despite technological progress, technical inefficiency is the cause for the low and declining TFP growth in the manufacturing sector.J. Comp. Econom., December 2000, 28(4), pp. 828–839. The University of Queensland; Research School of Pacific and Asian Studies, The Australian National University.     

Capital utilization,economies of scale and technical change in the growth of total factor productivity: An Explanation of South Korean manufacturing Growth

This paper presents a method of interpreting the growth of total factor productivity, directly linking the productivity growth to key parameters of a specific cost function. It shows that the productivity index can be decomposed into effects due to (a) technical change, (b) non-constant returns to scale, and (c) change in capital utilization. The decomposition framework was applied to data on South Korean manufacturing. During the 1961–1980 period in South Korean manufacturing, the total factor productivity was found to have grown at 3% per annum, scale economies contributed about 38% of the growth of TFP, the technical change 45%, and the change in the capital utilization rate 17%. The results of this study support of the view that for growing, less-developed economies, the growth in capital utilization rate is a source of growth in total productivity that is too significant to be ignored.     

金融发展与全要素生产率增长* ——基于中国省际面板数据的实证分析

本文以2000-2012年中国省际平衡面板数据为样本,从金融规模发展和金融效率发展两个维度衡量金融发展水平,实证检验金融发展与全要素生产率增长的联系以及中间渠道。研究结果发现,2000-2012年中国全要素生产率出现正增长且主要是由技术进步推动的;无论是从金融规模发展角度还是从金融效率发展角度去分析,都证实了金融发展显著地促进了全要素生产率的增长,金融效率发展的全要素生产率增长效应显著地高于金融发展规模的全要素生产率增长效应,且金融发展促进全要素生产率增长的中间渠道是技术进步效应而非技术效率增长效应;通过引入衡量金融发展缺陷程度指标（民间金融发展规模）,证实了中国金融发展体系效率低下。民间金融发展具有TFP增长效应,且民间金融发展TFP增长效应要显著地大于正规金融发展的TFP增长效应。     

What determines the productivity growth? Evidence from Chinese renewable energy firms by using Bayesian stochastic frontier approach

This paper examines the determinants of the total factor productivity (TFP) growth of Chinese renewable energy firms from 2011 to 2016, using a Bayesian stochastic frontier approach. Employing the “Bauer–Kumbhakar” decomposition method, the TFP growth is decomposed into the technology part and the market part. The empirical results reveal that the TFP improvement of Chinese renewable energy firms is mainly due to technical progress, followed by technical efficiency change. With regard to the market part, the misallocation of production factors has hindered the TFP growth. Our findings also indicate that only for large firms, the TFP growth can benefit from the scale economy effect. Compared with non‐state‐owned firms, state‐owned firms suffer much lower allocative efficiency.     

Public information as a deterrent to environmental infractions

This paper examines the effect of inflation on total factor productivity growth (TFP), using time-series data for every two digit Greek manufacturing industries. In order to do the above a translog flexible cost function is estimated and used to decompose TFP growth into scale economies, inflation and technical change. The advantage of estimating a very general and flexible cost function is that it allows us, for the first time, to examine empirically a large number of significant relationships between TFP growth and economies of scale, inflation and technical change. The main conclusion drawn from this analysis is that inflation reduces TFP growth in a way, which is sizeable. Furthermore, using standard causality test the direction of causality between inflation and TFP growth was tested at the manufacturing level.     

人力资本与中国区域农业全要素生产率增长——基于DEA视角的实证分析

文章在考虑人力资本要素和技术非效率的前提下,使用非参数的DEA曼奎斯特生产率指数方法,对1988-2006年中国区域农业全要素生产率增长进行估计和测算,通过将其分解为技术进步、纯技术效率变化和规模效率变化三部分,来寻找农业TFP增长的源泉.实证表明,1988-2006年中国农业TFP增长较为显著,大致可以分为四个阶段,其对农业增长的贡献基本上是顺周期的,各个省区的TFP增长差异性则较为明显.从其内部构成来看,TFP增长主要由技术进步贡献,技术效率改善的作用则很有限.另外,是否考虑人力资本要素对农业增长的作用,会对TFP的估计产生较大影响.文章最终认为,应努力通过制度创新来消除农业技术扩散的各种制度性障碍,实现农业技术进步和技术效率增进共同推动农业TFP增长.     

GAUGING THE SOURCES OF GROWTH OF HIGH‐TECH AND LOW‐TECH INDUSTRIES: THE CASE OF KOREAN MANUFACTURING*

Literature argues that the source of growth for high‐tech industries emanates from technological progress, while that for low‐tech industries comes from technical efficiency improvement. Also, some empirical studies have shown that technological progress is often accompanied by deterioration in technical efficiency. The focus in this study is to discuss a methodology and test the above two hypotheses with regard to Korean manufacturing data from 1970 to 1997. The study found that the 2.5 per cent average annual rate of technological progress during this period was the major contributor to total factor productivity (TFP) growth in Korean manufacturing whereas technical efficiency grew by a modest 1.1 per cent per annum. The analysis also showed that technological progress was responsible for TFP growth in both high‐tech and low‐tech industries and that both technological progress and technical efficiency improvement co‐existed in the case of Korean manufacturing industries.     

Long-run determinant of Korean economic growth: empirical evidence from manufacturing

This paper challenges the recent contrarian view that the phenomenal growth of East Asian NICs is fuelled mainly by the accumulation of production inputs not by TFP growth. To appraise this view, we investigated and re-evaluated South Korean manufacturing growth (1973(Q1)–1993(Q4)). Using Johansen's cointegrating analysis, our results show that South Korean manufacturing appears to have increasing returns to scale in production technology. The ‘learning by doing’ effect defined by Lucas (1988) is empirically supported. This effect appears to be observed as a long-run determinant of South Korean manufacturing growth. Consequently, South Korean manufacturing growth can be described by an endogenous economic growth model, such as the Lucas (1988) model, contradicting the contrarian view.     

Why is TFP growth sectorally concentrated?

Research shows that total factor productivity (TFP) growth is weak in European countries. This is inter alia attributed to the fact that substantial TFP growth is limited to a few industries. Because TFP growth is typically understood as technological progress, it is concluded that technology diffusion between sectors in Europe is hampered. We use EU KLEMS data sets to decompose sectoral TFP for nine European countries by means of a Malmquist approach in order to identify potential sources besides technical progress. Applying Harberger diagrams, we describe the sectoral distribution of TFP growth, efficiency gains and losses, economies of scale and technological progress. The analysis reveals that technological progress is quite evenly distributed across sectors in most European countries. The wide scattering of TFP growth is explained by deviating efficiency developments and the unused economies of scale. We conclude that the technology transfer between sectors in most European countries seems to work. Therefore, Europe in general does not need a new technology policy, but a further integration of the markets and a reduction of national market entry barriers. This requires further unification of pan-European standards in fields like trade and crafts codes or consumer protection policies.     

中国钢铁行业全要素生产效率实证分析

基于动态DEA的非参数Malmquist指数法,分析了1992～2007年中国28个省市钢铁行业全要素生产率以及七大经济区钢铁行业全要素生产率变动情况,并将全要素生产率的变动分解为技术进步、纯技术效率、规模效率三个指标,分析了这三个指标对中国钢铁行业全要素生产率的影响。结果显示,中国钢铁行业TFP的增长主要是由技术进步推动的,当技术进步促进TFP提升时,总会受到技术效率相对下降对TFP增长的抑制;七大经济区钢铁行业全要素生产率存在明显级差,低效经济区学习和追赶高效经济区的效应也应该发挥更大作用;钢铁产业各经济区产能应实现在高效率地区的相对集中,获取行业的规模经济;各经济区都应该注重钢铁行业管理水平的提高,突破钢铁行业能效提高的技术效率瓶颈。     

Returns to scale: concept,estimation and analysis of Japan's turbulent 1964–88 economy

Abstract There is policy interest in factoring productivity growth into technical progress and returns to scale components. Our approach uses exact index number methods to reduce the parameters that must be estimated, and allows us to exploit the cross‐sectional dimension of plant‐level panel data. We show that the same equation can also be used to estimate ‘Harberger’ scale economies and technical progress indicators that require fewer assumptions. Estimates of the elasticity of scale for Japanese establishments in three major industries over 1964–88 are presented. Our study spans the high growth era of the 1960s, two oil shocks, and other exogenous shocks.     

Technical progress,urban unemployment,outputs, and welfare under variable returns to scale

This paper analyzes the implications of technical progress for a small Harris–Todaro (H–T) economy under variable returns to scale (VRS). It is shown that under VRS, technical progress occurring either in the agriculture or the manufacturing may be immiserizing; the effects of technical progress on sectoral outputs, factor prices, urban unemployment, and welfare crucially depend on the signs and the relative magnitudes of sectoral elasticities of returns to scale and the employment effect; the Corden–Findlay type of ultrabiased output effect of technical progress in the constant returns to scale (CRS) H–T model carries over to the case of VRS, but with much more stringent conditions than the CRS case.