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.     

Productivity growth in British manufacturing: spatial variation in the role of scale economies,technological growth and industrial structure

This paper investigates the role of scale economies, technological growth and industrial structure in creating spatial variation in manufacturing labour and Total Factor (TFP) productivity in Britain. Separate estimates of a translog specification are presented for British manufacturing firms located in defined areas of the country over the period 1994–1998. The results show that TFP change due to scale economies and technological growth has been of much less important in influencing the output growth of manufacturing firms than input growth or industrial structure. Regarding the components of TFP, technological growth has been the dominant force at play. The analysis of average labour productivity identifies shifts to other factors of production and industrial structure as being the main determinants of change, scale economies appear to have had a marginal role. The results identify spatial patterns indicating that more favourable locational effects arise for firms in areas adjacent to large urban centres, rather than for those located within cities, on the extreme periphery of the urban hinterland, or in rural areas and smaller towns.     

Productivity Trends in Australian and New Zealand Manufacturing

The purpose of this article is to study relative trends in total factor productivity (TFP) between the Australian and New Zealand manufacturing sectors from 1986 to 1996. Since 1984 both economies have undergone major structural changes with varying degrees of speed and intensity. We use the Malmquist index to measure TFP growth and decompose it into an efficiency change and a technical change component. This decomposition provides extra insight on assessing relative productivity trends during a period of economic reform. The results indicate the Australian manufacturing sector exhibits better rates of individual factor productivity performance while multifactor productivity is estimated to be higher in New Zealand manufacturing. TFP growth in New Zealand is driven by technical rather than efficiency change. In fact, the New Zealand manufacturing average rate of efficiency change is estimated to be negative over the sample period.     

Parametric Approaches to Productivity Measurement: A Comparison among Alternative Models

This paper deals with modeling total factor productivity (TFP) growth in a flexible manner using panel data. Several competing parametric models are used to explore whether there are any similarities in the estimates of TFP growth and technical change among these models. Using a primal approach, we decompose TFP growth into different components. The models are then used to measure productivity and technical change in the Swedish cement industry. In general, the results are found to be model dependent and often conflicting, although much less so for returns to scale and overall productivity growth.
JEL classification: O 30; C 33     

Human capital,employment protection and growth in Europe

Using data for a large sample of manufacturing and service sectors in 14 EU countries, this paper shows that the value added and TFP growth rate differential between high and low human capital intensive industries is greater in countries with low than countries with high levels of employment protection legislation. We also find that such negative effect of EPL is slightly stronger for countries near the technology frontier, in the manufacturing sector and after the 1990s. We interpret these results suggesting that technology adoption depends on the skill level of the workforce and on the capacity of firms to adjust employment as technology changes: therefore, firing costs have a stronger impact in sectors where technical change is more skill-biased and technology adoption more important.     

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.     

Be productive or face decline. On the sources and determinants of output growth in Italian manufacturing firms

This article investigates the sources and determinants of output growth of Italian manufacturing firms. Applying stochastic frontier techniques, we decompose output growth into factor accumulation and TFP growth for the period 1998–2003. TFP growth is further decomposed into technological change, efficiency change and scale effects. Two key results emerge from the analysis. After confirming that both input accumulation and TFP growth are important in explaining output growth, we show that efficiency change (technological catch-up) is the most significant component of TFP growth in explaining output growth distribution. Furthermore, using a specific model of the asymmetric error component, we find that R&D spillovers, banking efficiency and public infrastructures have statistically significant and economically relevant effects on technological catch-up.     

Government subsidy and aggregate productivity dynamics: Evidence from China

Based on data for Chinese manufacturing firms from 1999 to 2007, this study explores the dynamic evolution of aggregate total factor productivity (TFP) from the perspective of firmsʼ entry and exit. It also quantifies how government subsidies influence the aggregate productivity growth. By decomposing aggregate productivity growth into components, including technological progress, reallocation, entry, and exit effects, we found that aggregate TFP growth in Chinese manufacturingfollows an upward trend during the sample period. This tendency originates from the contribution of technological progress, reallocation, and exit effects. Moreover, the effects of these four components on aggregate TFP growth of different industries, regions, and ownership types are different. Furthermore, technological progress, reallocation, and exit effects are important pathways for government subsidies to promote aggregate TFP growth in Chinese manufacturing.     

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。     

Exports,technical progress and productivity growth in a transition economy: a non-parametric approach for China

Theories suggesting either static or dynamic productivity gains derived from exports often assume the prior existence of a competitive market. In the presence of market imperfection and distortion, however, the competition and resource reallocation effects of exports on productive efficiency may be greatly reduced; and there may actually be disincentives for innovation. This paper analyses the impact of exports on aggregate productivity growth in a transition economy using a panel of Chinese manufacturing industries over the period 1990–1997. TFP growth is estimated by employing a non-parametric approach and is decomposed into technical progress and efficiency change. No evidence has been found suggesting significant productivity gains at the industry level resulting from exports. Findings of the current study suggest that, for exports to generate significant positive effect on TFP growth, a well-developed domestic market and a neutral, outward-oriented policy are necessary.     

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。     

The dynamics of market structure and firm-level adjustment to India's pro-market economic liberalizing reforms, 1988–2006: A Time Varying Panel Smooth Transition Regression (TV-PSTR) approach

This paper for the first time employs the Time Varying Panel Smooth Transition Regression (TV-PSTR) approach to model the dynamic adjustments of firms and the evolution of industrial structure in the bigger setting of decades against the backdrop of India's dramatic liberalizing reform starting from 1991. Using Indian manufacturing firm data, it finds that the transition of market structure and productivity after liberalization did follow a smooth transition process. Instead of the previously assumed instantaneous ‘big-bang’ shift just after reforms, it actually took years for the Indian manufacturing industries start to react to the reforms, and the transitional impact of reforms took approximately four to eight years to complete. There is strong evidence of increased competition after the transition, with shrinked returns to scale (RTS) in most industries except for leather and chemical industries. The results on total factor productivity (TFP) are mixed: most import-competing industries, which suffer most from the shrinking of market size experienced no change or decreasing TFP growth; whereas the export-oriented industry, as the industry which benefits most from economy of scale, enjoyed a huge TFP growth following the reforms.     

企业演化:中国工业生产率增长的重要途径

本文从企业动态演化的视角,对"十五"前后我国工业全要素生产率变动来源进行考察。通过采用企业微观数据,结合BHC方法①和偏离份额方法,分析技术进步和资源重新配置在工业生产率变动中的作用,为转轨时期工业生产率的变迁提供了新的解释。本文发现,在考察期内,工业企业的生产率水平和增速都表现出很强的异质性:存活企业生产率水平最高,进入企业生产率增速最快,退出企业无论是水平还是速度都处于最后。对于工业生产率的增长,存活企业的技术进步贡献占近一半,而企业演化导致的资源重新配置贡献占另外一半,说明企业演化带来的资源重新配置是中国工业生产率增长的重要途径,揭示出以建立优胜劣汰竞争机制的制度改革及新上投资项目对现阶段经济增长质量的意义。从长远看,随着我国在转型过程中制度潜力的趋弱以及产业发展趋于成熟,增强存活企业的自主创新能力,加快技术进步步伐,则是实现我国工业可持续发展的重要保障。     

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。     

Productivity and technical change: the case of Taiwan

This paper analyses productivity growth in 16 of Taiwan's manufacturing industries during the period 1978–1992. The non-parametric Data Envelopment Analysis approach is used to compute Malmquist productivity indexes. These are decomposed into efficiency change and technical change. The latter is further decomposed into an output bias, an input bias and a magnitude component. In addition, the direction of input bias is identified. Empirical results indicate that the sector's TFP increased at a rate of 2.89% per annum, which could be ascribed to a technical progress (2.56%) and an efficiency improvement (0.33%).     

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.     

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。     

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.     

ChinHow R&D Investments Influence TFP Growth: Evidence from China’s Large and Medium- Sized Industrial Enterprises

This paper investigates the potential channels through which R&D may influence TFP growth using industry-level panel data of China’s large and medium-sized industrial enterprises over the period of 2000–2007. Comparing with existing literature, we provide a closer look of the relationship between R&D and TFP growth by decomposing TFP growth into efficiency change and technical change components using Malmquist productivity index and distinguishing between upstream R&D spillovers and downstream R&D spillovers. We find TFP grow slightly during 2000–2007, and R&D investment indeed serves as an engine of productivity growth just as endogenous growth theories argued, which is largely because R&D accelerates technical progress even it also results in enlarging technical inefficiency. However, we find a robust negative effect of downstream R&D spillovers on TFP growth, the effects of upstream is positive but not statistically significant. In addition, we do not find the positive effects of human capital on TFP as endogenous growth theories indicated, but find human capital severs as “assimilation device” for R&D spillovers both in promoting TFP growth and increasing technical efficiency even the effects on technical progress is adverse.     

Firm-Level TFP Growth in the Chinese Automobile Industry

China has been the world’s largest automobile producer since 2009,but it still lags behind other countries in terms of productivity. Based on theNational Bureau of Statistics of China (NBSC) firm-level data and the improvedapproach proposed by Ackerberg et al. (2015), this paper investigates thecontribution of total factor productivity (TFP) growth to the Chinese automobileindustry and evaluates the impact of firm entry and exit on TFP growth. Theempirical results show that the TFP of the Chinese automobile industry grows at10.7% per year. Joint venture and foreign-owned firms have a significantly higherTFP growth rate than others. Large-scale firms have a higher TFP growth rate thando small-scale firms, but the latter have caught up after 2004. Moreover, the entryof new firms and exit of old firms significantly improve the aggregate TFP growthrate.