Competitive, but too small - productivity and entry-exit determinants in European business services

The paper investigates whether scale effects, market structure, and regulation determine the poor productivity performance of the European business services industry. We apply parametric and nonparametric methods to estimate the productivity frontier and subsequently explain the distance of firms to the productivity frontier by market characteristics, entry- and exit dynamics and national regulation. The frontier is assessed using detailed industry data panel for 13 EU countries. Our estimates suggest that most scale advantages are exhausted after reaching a size of 20 employees. This scale inefficiency is persistent over time and points to weak competitive selection. Market and regulation characteristics explain the persistence of X-inefficiency (sub-optimal productivity relative to the industry frontier). More entry and exit are favourable for productivity performance, while higher market concentration works out negatively. Regulatory differences also appear to explain part of the business services' productivity performance. In particular regulation-caused exit and labour reallocation costs have significant and large negative impacts on the process of competitive selection and hence on productivity performance. Overall we find that the most efficient scale in business services is close to 20 employees and that scale inefficiencies show a hump-shape pattern with strong potential scale economies for the smallest firms and diseconomies of scale for the largest firms. The smallest firms operate under competitive conditions, but they are too small to be efficient. And since this conclusion holds for about 95 out of every 100 European business services firms, this factor weighs heavily for the overall productivity performance of this industry

Measuring Performance in Primary Care: Econometric Analysis and DEA

We use data from the Health Service Indicators database to compare different methods of measuring the performance of English Family Health Services Authorities (FHSAs) in providing primary care. A variety of regression and data envelopment analysis methods are compared as summary efficiency measures of individual FHSA performance. The correlation of the rankings of FHSAs across DEA and regression methods, across two years of data and across three different specifications of the technology of primary care are examined. Efficiency scores are highly correlated within variants of the two methods, and across years for a given method. Inter method correlations are smaller and correlations across different specifications of the primary care production process are negligible and sometime negative.primary care, efficiency measurement, DEA, stochastic frontier.

DEA Problems under Geometrical or Probability Uncertainties of Sample Data

This paper discusses the theoretical and practical aspects of new methods for solving DEA problems under real-life geometrical uncertainty and probability uncertainty of sample data. The proposed minimax approach to solve problems with geometrical uncertainty of sample data involves an implementation of linear programming or minimax optimization, whereas the problems with probability uncertainty of sample data are solved through implementing of econometric and new stochastic optimization methods, using the stochastic frontier functions estimation.DEA, Sample data uncertainty, Linear programming, Minimax optimization, Stochastic optimization, Stochastic frontier functions

Efficiency and integration in European banking markets

This paper seeks to contribute to the relatively scarce published research on the relationship between bank efficiency and European integration in the wake of the recent financial crisis. Using Stochastic Frontier Analysis and Data Envelopment Analysis approaches, the study estimates bank efficiency for different panels of European Union countries during the time period 1994-2008. The main conclusions point to the persistence of inefficiencies, which decreased with the implementation of the European Monetary Union (in the time period 2000- 2008) but then increased slightly in the most recent phase (2004-2008), during which the EU had to adapt to the new universe of 27 member-states. On the other hand, there is evidence of a convergence process, although this is very slow and not strong enough to avoid the differences in the country efficiency scores.

Quantifying the effects of modelling choices on hospital efficiency measures: A meta-regression analysis

It has often been argued that the results of efficiency analyses in health care are influenced by the modelling choices made by the researchers involved. In this paper we use meta-regression analysis in an attempt to quantify the degree to which modelling factors influence efficiency estimates. The data set is derived from 253 estimated models reported in 95 empirical analyses of hospital efficiency in the 22-year period from 1987 to 2008. A meta-regression model is used to investigate the degree to which differences in mean efficiency estimates can be explained by factors such as: sample size; dimension (number of variables); parametric versus non-parametric method; returns to scale (RTS) assumptions; functional form; error distributional form; input versus output orientation; cost versus technical efficiency measure; and cross-sectional versus panel data. Sample size, dimension and RTS are found to have statistically significant effects at the 1% level. Sample size has a negative (and diminishing) effect on efficiency; dimension has a positive (and diminishing) effect; while the imposition of constant returns to scale has a negative effect. These results can be used in improving the policy relevance of the empirical results produced by hospital efficiency studies.

Technical inefficiency and public capital in US States: A stochastic frontier approach

This paper estimates a translog stochastic frontier production function in the analysis of all 48 contiguous U.S. states in the period 1970-1983, to attempt to measure and explain changes in technical efficiency. The model allows technical inefficiency to vary over time, and inefficiency effects to be a function of a set of explanatory variables in which the level and composition of public capital plays an important role. Results indicated that U.S. state inefficiency levels were significantly and positively correlated with the ratio of public capital to private capital. The proportion of public capital devoted to highways is negatively correlated with technical inefficiency, suggesting that not only the level but also the composition of public capital influenced state efficiency.Public capital productivity, technical efficiency, stochastic frontier approach

A Benchmarking Analysis of Electricity Distribution Utilities in Switzerland

This paper studies the sensitivity problems of the benchmarking methods used in the regulation practice. Three commonly used methods have been applied to a sample of 52 electricity distribution utilities to estimate their cost efficiency. These methods include stochastic frontier, corrected ordinary least squares and data envelopment analysis. The results indicate that both efficiency scores and ranks are significantly different across various models. Especially considerable differences exist between parametric and non-parametric methods.

Estimation of semiparametric stochastic frontiers under shape constraints with application to pollution generating technologies

A number of studies have explored the semi- and nonparametric estimation of stochastic frontier models by using kernel regression or other nonparametric smoothing techniques. In contrast to popular deterministic nonparametric estimators, these approaches do not allow one to impose any shape constraints (or regularity conditions) on the frontier function. On the other hand, as many of the previous techniques are based on the nonparametric estimation of the frontier function, the convergence rate of frontier estimators can be sensitive to the number of inputs, which is generally known as “the curse of dimensionality” problem. This paper proposes a new semiparametric approach for stochastic frontier estimation that avoids the curse of dimensionality and allows one to impose shape constraints on the frontier function. Our approach is based on the singleindex model and applies both single-index estimation techniques and shape-constrained nonparametric least squares. In addition to production frontier and technical efficiency estimation, we show how the technique can be used to estimate pollution generating technologies. The new approach is illustrated by an empirical application to the environmental adjusted performance evaluation of U.S. coal-fired electric power plants.stochastic frontier analysis (SFA), nonparametric least squares, single-index model, sliced inverse regression, monotone rank correlation estimator, environmental efficiency