Linking investment spikes and productivity growth

We investigate the relationship between productivity growth and investment spikes using Census Bureau’s plant-level dataset for the U.S. food manufacturing industry. There are differences in productivity growth and investment spike patterns across different sub-industries and food manufacturing industry in general. Our study finds empirical support for the learning-by-doing hypothesis by identifying some cases where the impact of investment spikes on TFP growth presents a U-shaped investment age–productivity growth pattern. However, efficiency and the learning period associated with investment spikes differ among plants across industries. The most pronounced impact of investment age on productivity growth (5.3 % for meat products, 4% for dairy products, and 2.8 % in all food manufacturing plants) occurs during the fifth year of post-investment spike. Thus, in general, the productivity gains tend to be fully realized with a 5-year technology learning period for this industry.     

1978-2007年我国畜牧业全要素生产率及其影响因素研究

本文运用扩展的索洛模型和C-D生产函数测算了1978—2007年我国畜牧业全要素生产率(TFP)及其影响因素。分析结果表明,1978—2007年我国畜牧业TFP的年均增长率为4.71%;从总体上看,我国畜牧业生产处于规模报酬递增阶段,且畜牧业政策、劳动者质量、规模化程度和科技投入量是影响我国畜牧业TFP变动的主要因素。     

广东省高技术产业各行业效率变化的实证研究

采用基于DEA的Malmquist生产率指数法,选取5个投入指标和4个产出指标对1995—2008年广东省高技术产业的4个细分行业进行多投入、多产出的动态效率测度。结果表明:1995—2008年期间广东省高技术产业全要素生产率的年均增长率为12.3%,这是技术进步和技术效率共同推动的结果,其贡献率分别为11.1%和1.1%;电子及通信设备制造业全要素生产率的平均增长率最高,主要由技术进步推动;其次是医疗设备及仪器仪表制造业,由技术进步和规模效率共同推动;然后依次是电子计算机及办公设备制造业和医药制造业,技术进步是两者的主要推动力量。最后,提出促进广东省高技术产业生产率提升的政策建议。     

Spatial analysis of change trend and influencing factors of total factor productivity in China’s regional construction industry

Total factor productivity (TFP) is a measure of long-term economic growth and a comprehensive industry-level productivity measure. There are large gaps in China’s regional construction industry development due to unbalanced regional economy. Based on TFP measurement, this article puts forward a two-hierarchical analysis framework with coefficient of variation, Moran scatterplot and coefficient of convergence to analyse change trend of the construction industry TFP in three major regions in terms of spatial diversity, correlation and convergence. Then, the geographically weighted regression model is utilized to explore the influencing mechanism on the TFP. The results indicate the differences of the regional construction industry TFP are enlarging. There is obvious spatial correlation and heterogeneity in the regional TFP without a relatively stable space pattern. The TFP also exhibits convergence effects among three major regions. The construction industry productivity in all regions is significantly affected by economic environment, industrial organization structure and technological level. Industrial organization structure exerts the various influences on the productivity in different regions.     

Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing

We present the first comprehensive set of firm-level total factor productivity (TFP) estimates for China's manufacturing sector that spans China's entry into the WTO. For our preferred estimate, which adjusts for a number of potential sources of measurement error and bias, the weighted average annual productivity growth for incumbents is 2.85% for a gross output production function and 7.96% for a value added production function over the period 1998-2007. This is among the highest compared to other countries. Productivity growth at the industry level is even higher, reflecting the dynamic force of creative destruction. Over the entire period, net entry accounts for over two thirds of total TFP growth. In contrast to earlier studies looking at total non-agriculture including services, we find that TFP growth dominates input accumulation as a source of output growth.     

中国全要素生产率的顺周期特征与决定因素:1952—2007

笔者通过对1952年～2007年中国经济统计数据的回归分析,采用索罗残差法计算了中国经济的全要素生产率及其增长率,并对我国全要素生产率增长率及其决定因素做了简要分析.结果发现,我国全要素生产率总体上是递增的,但波动剧烈且呈现顺周期的特征.进一步研究显示,劳动储备和技术冲击是中国全要素生产率顺周期的主要原因.     

Financial deregulation and productivity growth in banking sector: empirical evidence from Bangladesh

This article examines the effects of regulatory reform on productivity growth in the Bangladesh banking industry. We use a unique balanced panel dataset comprising bank-level annual data from the early deregulation year (1984) to the most recent available period (2012) from major commercial banks in Bangladesh. Applying the Färe-Primont index, the paper provides estimates of productivity growth and identifies sources of total factor productivity (TFP) change. Empirical results show the sample banks have experienced positive TFP change after the financial deregulation. On average, TFP growth is higher in private banks than their public sector counterparts in the post-reform period. In addition, the decomposition analysis shows technological progress is the main driver of productivity change. Similar results are obtained by using the stochastic frontier analysis (SFA). Thus, empirical results remain robust irrespective of the methodology used. The regression analysis finds a positive technical change in the first stage of the reform program, i.e. during the transition period, as leading banks employ advanced technology to compete with potential new entrants. The result also shows that the banking industry still remains concentrated within the state-owned banks.     

What determines productivity performance of telecommunications services industry? A cross-country analysis

This article investigates the key factors that determine the productivity performance of telecommunications services industry. A simple theoretical model is used to illustrate that the Total Factor Productivity (TFP) growth is attributable to the effects of scale economies, market competition and technical change. We then examine empirically the effect of various factors on the TFP growth in the industry using panel data in 12 Organization for Economic Co-operation and Development (OECD) countries for the period 1983 through 2003. The empirical results are consistent with the theoretical prediction. A new finding in this article is that higher machinery and equipment (M&E) capital intensity and human capital contribute to higher TFP growth in the telecommunications services industry. The decomposition analysis also suggests that technical change induced by changes in M&E capital intensity and human capital are important sources of productivity performance in the industry across the OECD countries, contributing to about 20–50% and 2–7% of TFP growth, respectively. These findings highlight the importance of improving the conditions for M&E capital investment and the quality of human capital, which in turn could facilitate the adoption of new technologies and enhance the productivity in the industry.     

Productivity growth in passenger-bus transportation: A heteroskedastic error component model with unbalanced panel data

This paper considers an econometric approach to measure total factor productivity (TFP) growth and technical change (TC) for 31 publicly-owned passenger-bus companies in India during 1983–1987. A translog variable cost function is used to represent the production technology. Firm heterogeneity is incorporated in the cost function using an error component model with firm-specific variances. TFP growth is decomposed into TC and economies of scale components. The TC component is further decomposed into pure, non-neutral, scale, and quasi-fixed factors/network components. An ownership group-wise comparison reveals that the public undertakings exhibit the highest rate of productivity growth, followed by the units operated by the state and local governments. The main source of TFP growth for the public undertakings and government-operated units is economies of scale, while the main source of falling TFP growth for corporations is technological regress.     

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.     

Of Yeast and Mushrooms: Patterns of Industry-Level Productivity Growth

Abstract. In this paper we analyse labour productivity growth in the United States, four European countries (France, Germany, the Netherlands and United Kingdom), Australia and Canada between 1987 and 2003 from an industry perspective. Rather than analysing broad industry groups, we compare the pattern of growth in all industries through Harberger diagrams. We introduce new summary measures, which indicate the pervasiveness of growth patterns. These indicators show that investment in both information and communication technology (ICT) and non-ICT capital is fairly balanced or 'yeasty', driven by overall macro-economic conditions. However, growth of total factor productivity (TFP) is much more localized or 'mushroom-like'. In particular we find a clear distinction between countries in continental Europe, in which TFP is decelerating after 1995 and becoming more localized, and Anglo-Saxon countries in which TFP growth is accelerating and becoming more broad-based, especially after 2000. The increased breadth of Anglo-Saxon TFP growth is consistent with delayed effects of intangible investments that are complementary to ICT investments.     

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。     

改革开放以来中国乡镇企业全要素生产率增长的时空差异

利用非参数Mamquist指数方法分析了改革开放以来我国乡镇企业的全要素生产率(TFP)增长的时空特征。结果表明:1978年以来,乡镇企业TFP的年均增长率为4.8%,并且呈现出显著的阶段性变化和地区差异特征。从时间上来看,除了整顿提高阶段和调整创新阶段TFP的增长率为负以外,其他阶段TFP均实现了增长,并且表现为要素投入驱动和生产率提升双重驱动的特点,其中要素投入对经济增长的平均贡献占主体;从三大地带来看,乡镇企业TFP从东向西渐次降低;从八大综合经济区来看,北部和东部沿海综合经济区的乡镇企业TFP增长率最高,东北和长江中游综合经济区次之,其他四个综合经济地区相对较低。     

Productivity drivers of infrastructure companies: Network industries utilizing economies of scale in the digital era

What drives the productivity dynamics of infrastructure companies? Using a panel of firms in 14 countries, we study total factor productivity (TFP) enhancers of utility and network services companies. We find that moving TFP closer to the technological frontier drives productivity growth at higher speeds in Asian countries than in European countries. We also find that financial leverage exerts a positive effect on TFP growth for larger infrastructure firms and that more financially developed countries utilize economies of scale through better use of financial resources. Large utility and transportation companies display a higher rate of TFP growth, indicating that a competition policy to encourage M&As would be prudent for the utility/transportation sectors to maximize economies of scale. In contrast, we find diseconomies of scale for energy companies in some countries. Moreover, young network firms improve TFP growth faster than their peers in countries with fewer product market regulations. Therefore, policies should remove entry barriers while facilitating the departure of old and low-productivity firms from network markets. Finally, policymakers should offer well-targeted fiscal incentives for intangible investments to boost TFP because the accumulation of intangible assets such as digital technology promotes more scale economies through network effects.     

Declining output growth volatility: A sectoral decomposition

A decomposition of the U.S. aggregate output growth volatility using two-digit industry-level data shows that more than 60% of the post-1983 reduction in aggregate output growth volatility is attributed to the lowered comovement in total factor productivity (TFP) growth between industries. In contrast, stabilized input and TFP growths within an industry contribute little.     

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。     

资源配置、产业结构与全要素生产率：基于真实经济周期模型的分析*

本文旨在测算、分析中国经济及各产业部门的全要素生产率和年均增长率、产业结构高度及其合理化程度，再结合其他37个国家的相应数据进行国际比较。本文依据真实经济周期模型推导出全要素生产率的测算方程并构建了测算产业结构高度及其合理化程度的两个指标。本文发现，由三大产业部门全要素生产率水平之间的差异和部门间规模巨大的人力资本转移而形成的“结构效益”，能够合理解释中国经济整体全要素生产率年均增长率较高但三大产业部门却处在较低水平甚至负增长这两个看似矛盾的经济事实。本文从数理逻辑和经验研究两个角度验证了“产出增长率效应”和“结构效益”的存在。本文通过国际比较发现，中国经济产业结构高度及其合理化程度大幅落后于多数高收入国家。     

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.     

UNDERSTANDING THE WORLD WOOL MARKET: TRADE,PRODUCTIVITY AND GROWERS’ INCOMES*

Applying recent estimates of total factor productivity (TFP) growth on Australian sheep farms, we analyse the long‐run effects of TFP growth on the world wool market using a detailed model of the world wool market that treats raw wool, wool textiles and wool garments as heterogeneous commodities. The model divides the world wool market into nine regions and eight major industrial sectors capturing the extreme nature of the multistage production system through which wool passes. We find that regional sheep producer's welfare responds by between ‐0.9 per cent and 0.7 per cent assuming conservative TFP growth over the period 1995/96–2003/04. Sensitivity analysis shows the welfare effects to be robust with respect to all model parameters, but sensitive to the size of the TFP growth and the degree of TFP spill‐over. Our realistic representation of the multistage nature of the wool production system shows that wool inputs steadily decline in importance in moving from early‐ to late‐stage processing. This production structure indicates that the productivity of sheep farmers has little effect on the prices of and demand for wool garments. In contrast, trade in wool inputs is very responsive to the productivity of sheep farmers.     

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