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.     

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.     

An Index Number Method for Estimating Scale Economies and Technical Processes Using Time-series of Cross-section Data: Sources of Total Factor Productivity Growth for Japanese Manufacturing, 1964–1988

Sample multicollinearity often makes it difficult to estimate returns to scale. We present an index number method to overcome potential multicollinearity problems when the production function is homogeneous of degree k . We apply our method to estimate empirically the effects of returns to scale and technical progress on growth in total factor productivity (TFP) using establishment data for Japanese manufacturing industries. We find that, while significant scale economies exist in many manufacturing industries, the TFP growth in the last twenty-five years is attributable primarily to technical progress. This finding also validates the current practice of assuming constant-returns-to-scale production functions in macroeconometric modelling.
JEL Classification Numbers: C43, D24, 030.     

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.     

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.     

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.     

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     

AN ATTEMPT TO EXPLAIN DIFFERENCES IN ECONOMIC GROWTH: A STOCHASTIC FRONTIER APPROACH

Total factor productivity (TFP), factor accumulation, and growth are analysed for a panel of 40 countries in 2001–11. TFP growth and technical inefficiency are estimated using a stochastic frontier model. Environmental variables are found to have an important role in explaining differences in inefficiency across countries. Over 2001–11, the general improvement in technical efficiency of countries is almost outweighed by technological regress. Results indicate that differences in factor accumulation between OECD and emerging economies are more important than differences in TFP change to explain differences in economic growth. Results also indicate negative and significant random shocks for the OECD countries.     

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.     

Does productivity growth lower inflation in Korea?

We investigate Granger causality between productivity growth and inflation in Korea using quarterly data for the period 1985Q1–2002Q4. Our results indicate unidirectional Granger causality from productivity growth to inflation. In light of such causality, we estimate the effect of productivity and other variables on productivity. According to our regression results, a 1% increase in labour and Total Factor Productivity (TFP) reduces Consumer Price Index (CPI) inflation by 0.07–0.08% and 0.37–0.44%, respectively. Our results also suggest that the productivity-inflation nexus became stronger in Korea since the Asian financial crisis, and that this was largely due to structural reform and technological progress.     

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.     

IS THERE A CASE FOR LOW INFLATION‐INDUCED PRODUCTIVITY GROWTH IN SELECTED ASIAN ECONOMIES?

The authors examine the evidence on the relationship between inflation and productivity growth for nine Asian economies using causality analysis in a multivariate model with money supply as a possible effective monetary policy tool. The inflation-productivity growth relationship is found to be non-uniform, as the evidence of uni-directional, bi-directional, and no causality between the two variables is varied and significant for some countries and insignificant for others. An attempt is made to explain the inflation-productivity nexus for these countries and to discuss implications for anti-inflationary policies such as inflation targeting. ( JEL E31, O47, C32)     

Cost structure,technological change,and productivity growth in the greek banking sector

This paper first empirically investigates the cost structure of the Greek banking sector. Secondly, it provides measures of economies (diseconomies) of scale and quantifies technical change and its sources. Finally, this paper measures total factor productivity growth and identifies its sources. Bank production is presented with two different approaches (the intermediation and the production approach) which are used to specify a translog cost function. The two different translog cost models are estimated through the full information maximum likelihood method of estimation on pooled time series and cross sectional data. The results obtained are not significantly affected by model specification. Both models indicate significant economies of scale and negative annual rates of growth in technical change and in total factor productivity.     

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。     

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.     

中国农业全要素生产率的变动趋势及区域差异——基于1978～2008年曼奎斯特指数分析

使用1978～2008年省域面板数据,运用非参数曼奎斯特(Malmquist)生产率指数模型测算了中国农业全要素生产率的变动指数,并分析了全要素生产率(TFP)的时空差异。结果表明:全国、三大地区及绝大多数省份农业TFP的增长主要属于技术进步型的增长;全国及三大地区农业TFP增长呈现出明显的波动特征;农业TFP增长呈现出显著的区域间和区域内省际间的不平衡;农业技术效率的下滑制约全国及三大地区农业TFP的增长,而农业技术效率下滑是纯技术效率损失和规模效率下降共同作用的结果。     

中国制造业空间集聚对全要素生产率的影响机理研究——基于双门限回归模型的实证分析

文章根据中国2000-2008年20个制造行业的面板数据,建立了以产业空间集聚水平为门限变量的双门限回归模型,以考察产业空间集聚对全要素生产率的影响机理。研究发现,产业空间集聚主要促进体现型技术进步,对技术效率的影响较弱;产业空间集聚的影响存在门限效应,其影响力随集聚水平的提高而逐步减弱,集聚规模存在最佳边界;产业空间集聚对全要素生产率及技术进步的影响在不同门限区间的行业技术梯度特征较明显,资源性和中低技术行业的集聚水平较低但集聚效应较高。     

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。     

Revisiting the threshold effect of remittances on total factor productivity growth in South Asia: a study of Bangladesh and India

Both Bangladesh and India are among the top recipient of remittances in absolute terms. However, in relative terms – remittances as a per cent of GDP – the two countries stand at 6.1% and 2.8%, respectively, well below the levels of the top 10 recipients. In this article, we explore the effect of remittances on the total factor productivity (TFP) growth considering Bangladesh and India, as reference countries over the periods 1980–2012 and 1977–2012, respectively. We examine the presence of a long-run association between remittances and TFP using a number of tests. The results indicate that remittances have threshold effects on TFP growth in both countries. Despite the two countries receiving substantial amount of remittances, we note that Bangladesh has a U-shaped relationship whereas India has an inverted U-shaped relationship with TFP growth. For Bangladesh, a minimum threshold of remittances (% GDP) is 5.3% and for India, a tipping point of remittances (% GDP) is at 1.8%. The causality tests confirm a bidirectional effect, which implies that remittances and TFP growth are mutually reinforcing. Interestingly, while the two economies have similar remittances impact in regards to causality, the study highlights two different tipping points of remittances.     

The impact of regulation on productivity growth: An application to the transmission sector of the interstate natural gas industry

We estimate a regulated cost function to examine the contributions of technical change, scale economies, and regulatory bias to productivity growth in an eleven-year, twenty-firm panel of interstate natural gas pipeline companies. We derive the unregulated cost function from the regulated cost function to produce estimates of movements in the unconstrained production technology. We find that the regulation constraint is binding in most years and that scale economies explain 38 percent more growth in the unconstrained production space than they do in the regulation-constrained production space.We are grateful to Gary Biglaiser, David Guilkey, C. A. Knox Lovell, and Helen Tauchen for their suggestions, and to Karen Bauer, Philip Budzik, Ronald Colter, and Mary Streitwieser for their assistance in compiling the data sample. We are also grateful to the participants at the Georgia Productivity Workshop and two anonymous referees for their comments and suggestions.