Decomposition Of Productivity Change Research Articles

Assessment of the Total Factor Productivity Change in the English and Welsh Water Industry: a Färe-Primont Productivity Index Approach

The assessment of productivity change across companies and over time is of great importance for water utilities managers and regulators. In this paper, for the first time, we apply the Fare-Primont productivity index (FPI) to evaluate the productivity change for the 10 water and sewerage companies (WaSCs) and 12 water only companies (WoCs) in England and Wales during the years 2001–2008. The FPI is the only index that without price data allows making comparisons involving many firms and many periods. The results indicate that during the years 2001–2004 productivity improved which was mainly attributed to gains in efficiency whereas technical change remained constant, the exception being the year 2004. During the years 2005–2008 productivity showed a declining trend and any gains in efficiency were lost to the highly negative technical change on productivity growth. From a policy perspective, it has been illustrated the importance of using a reliable index to compute the productivity change of water companies when the performance of companies is used to set water tariffs. Finally, the decomposition of productivity change into several drivers allows water utilities and managers to identify the main factors on which they should act to improve productivity of the company.

Malmquist index analysis of the recent development of the Slovak banking sector from two different angles

Filling the gap that exists in the extant research oriented on productivity and efficiency change of Slovak commercial banks, the paper aims to examine the productivity change and its nature that was effective in the Slovak banking sector in three subsequent sub-periods of its development from 2000 to 2012. In this effort, issues of banking production are interpreted from the standpoint of two competing approaches to banking behaviour, the production approach and the intermediation approach, which permits treating the core essence of banking production from two different angles and comparing results. To this end, the Malmquist index methodology developed originally for group performance comparison is extended and modified to quantify productivity change and decompose it into its sources for the situation when there are multi-year structurally different periods of economic development, between which production technology remains intact and free of structural shift. This methodology is utilized in conjunction with the non-oriented slacks-based model of Tone and with the decomposition of productivity change as proposed by Ray and Desli. Besides the fact that the Slovak banking sector recorded a productivity increase between the first sub-period (2000–2003) and the third sub-period (2009–2012) of its development, it is symptomatic that innovation capacity and espousal of new technologies was tied with large commercial banks and smaller banks were only able to follow up these tendencies. As to the difference between the two approaches, they differ especially in how they view improvement or deterioration of relative technical efficiency of individual banks over the investigated period from 2000 to 2012. This complies with the difference that naturally exists between financial intermediation and provision of banking services.

A new assessment approach of technical efficiency and productivity in European non-life insurance companies

This study intends to improve the recognised literature on insurance sector productivity based on the directional output distance function and Luenberger indicators. In addition to the estimation of efficiency scores and decomposition of productivity change, we have tried to detect the bias attributed to each production factor. We have used a sample of European non-life insurance companies to apply our model. The results show that the average technical efficiency score of European non-life insurance companies is 77.45%. The Luenberger indicators show that decline in productivity is principally owing to a significant drop in technological change rather than the progress in efficiency change. Finally, from the factor bias indicator we developed, we show a considerable improvement in technological change of input and desirable output. However, a strike deterioration is marked on undesirable output bias.

Decomposition of Productivity Change in Korean Banking

Decomposition of Productivity Change in Korean Banking

Identifying sources and trends for productivity growth in a sample of Queensland broad-acre beef enterprises

The research reported in this paper considers the question of the possible sources of productivity change in the broad-acre beef sector in northern Australia over the last decade. Analysis is conducted over the components of total factor productivity growth for a subset of broad-acre beef production enterprises in Queensland. Specifically we consider the contributions of technological progress, scale changes (changes in the ‘size’ of an enterprise), and technical efficiency (how efficiently an enterprise combines their inputs to produce output) changes to total factor productivity growth using an index based on a decomposition of productivity change. The analysis employed a form for the production technology, which allowed for linear technological progress over time, accounted for rainfall and differences in land types and allowed for the testing of a range of sources of efficiency change. Results suggested that productivity growth within the sample was strong between 1999 and 2008 averaging 3.8% per year. The majority of this growth appeared to originate from technological progress (average growth of 2.7% per year) but there is the possibility that sample-leakage effects caused relatively low estimated contributions from technical efficiency growth (averaged 1.2% per year). Participation in a privately operated farm-business auditing program appeared to have a positive influence on enterprise technical efficiency.

Sources of productivity growth on Finnish dairy farms—application of an input distance function

Abstract The objective is to examine sources of productivity change on Finnish dairy farms in the 1990s. The decomposition of productivity change into technical and technical efficiency change is widely recognized but it neglects the scale effect. Generalized decompositions incorporating all three components are calculated for a sample of Finnish dairy farms from 1989–2000. This period is of interest because of the drastic change in agricultural policy when Finland joined the European Union (EU) in 1995. The results indicate that productivity growth was on average low, approximately 0.15% per year. Neutral technical change was identified as the most important source of productivity growth (1.1%). Technical efficiency decreased by almost 0.5% annually. The contribution of the scale effect in productivity change increased towards the end of study period.

Measuring and Decomposing Productivity Change

Measuring productivity change with Malmquist indices has become common practice, because they are easily computed using nonparametric programming techniques and can be readily decomposed into technical and efficiency change. However, this approach is nonstochastic and requires a constant returns to scale assumption to construct the reference technology. We propose estimating productivity change using a stochastic input distance frontier, imposing no restrictions on returns to scale. We derive the analogous decomposition of productivity change and develop a generalized method of moments strategy in which outputs or inputs may be endogenous. We compare two methods in an application to electric utilities.

Estimation and decomposition of productivity change when production is not efficient: a paneldata approach

This paper addresses estimation and decomposition of productivity change, which is mostly identified as technical change under constant (unitary) returns to scale (CRS). If the CRS assumption is not made, productivity change is decomposed into technical change and scale effects.Furthermore, if inefficiency exists, it also contributes to productivity change. Here we decompose productivity change into efficiency change, technical change, and scale effects. Three alternative approaches using parametric production, cost, and profit functions, which differ in terms of behavioral assumptions on the producers and data requirements, are considered.

Total Factor Productivity in the Contiguous United States, 1977-1986

A linear programming technique is used to measure total factor productivity change in the contiguous 48 states during the period 1977 to 1986. The technique allows the decomposition of productivity change into its constituent parts: technological change, changes in pure technical efficiency, and changes in scale economies. This decomposition is used to identify “innovative” states that are pushing out the production frontier as opposed to states that are primarily in the process of ‘catching-up.’

Applying efficiency measurement techniques to the measurement of productivity change

Deterministic frontier analysis (DFA), stochastic frontier analysis (SFA), and data envelopment analysis (DEA) are alternative analytical techniques designed to measure the efficiency of producers. All three techniques were originally developed within a cross-sectional context, in which the objective is to compare the efficiencies of producers. More recently all three techniques have been extended for use in a panel data context. In the latter context it is possible to measure productivity change, and to decompose measured productivity change into its sources, one of which is efficiency change. However when efficiency measurement techniques, particularly SFA, have been applied to panel data, it has infrequently been made clear what the objective of the analysis is: the measurement of efficiency, which may vary through time as well as across producers, or the measurement and decomposition of productivity change. In this paper I explore the use of each technique in a panel data context. I find DFA and DEA to have achieved a more satisfactory reorientation toward productivity measurement than SFA has.