The Impact of Privatisation on the Efficiency of Train Operation in Britain

Twenty-five train operating companies (TOCs) were created between 1994-1997, as part of the restructuring process of the railway industry in Great Britain. The TOCs operate monopoly franchises for the provision of passenger rail services over certain routes - some of which continue to receive government subsidies. This paper investigates how the efficiency of these train operating companies evolved prior to the October 2000 Hatfield crash (which caused significant disruption to the network) using data envelopment analysis and stochastic frontier analysis. Our data allows us to look at the relative efficiency and productivity through the privatisation, to control the efficiency scores for environmental data and to correlate these results with safety and quality indicators. The analysis sheds some light on the successes and failures of the UK’s most controversial privatisation to date.Railways, Comparative Efficiency, Data Envelopment Analysis, Stochastic Frontier Analyisis, Malmquist Productivity Index, Train Operating Companies, Privatisation

A Monte Carlo Study of Old and New Frontier Methods for Efficiency Measurement

This study presents the results of an extensive Monte Carlo experiment to compare different methods of efficiency analysis. In addition to traditional parametric-stochastic and nonparametric-deterministic methods recently developed robust nonparametric-stochastic methods are considered. The experimental design comprises a wide variety of situations with different returns-to-scale regimes, substitution elasticities and outlying observations. As the results show, the new robust nonparametric-stochastic methods should not be used without cross-checking by other methods like stochastic frontier analysis or data envelopment analysis. These latter methods appear quite robust in the experiments.Monte Carlo experiment, efficiency measurement, nonparametric stochastic methods

The shape of aggregate production functions: evidence from estimates of the World Technology Frontier

The article provides multifaceted evidence on the shape of the aggregate country-level production function, derived from the World Technology Frontier, estimated on the basis of annual data on inputs and output in 19 highly developed OECD countries in the period 1970–2004. A comparison of its estimates based on Data Envelopment Analysis and Bayesian Stochastic Frontier Analysis uncovers a number of significant discrepancies between the nonparametric estimates of the frontier and the Cobb–Douglas and translog production functions in terms of implied efficiency levels, partial elasticities, and returns-to-scale properties. Furthermore, the two latter characteristics as well as elasticities of substitution are found to differ markedly across countries and time, providing strong evidence against the constant-returns-to-scale (CRS) Cobb–Douglas specification, frequently used in related literature. We also find notable departures from perfect substitutability between unskilled and skilled labor, consistent with the hypotheses of skill-biased technical change and capital–skill complementarity. In the Appendix, as a corollary from our results, we have also conducted a series of development accounting and growth accounting exercises.world technology frontier, aggregate production function, Data Envelopment Analysis, Stochastic Frontier Analysis, partial elasticity, returns to scale, substitutability

PRINCIPAL COMPONENT ANALYSIS TO RANKING TECHNICAL EFFICIENCIES THROUGH STOCHASTIC FRONTIER ANALYSIS AND DEA

The Stochastic Frontier Analysis permits evaluating the Technical Efficiency scores for one output variable to obtain the corresponding Technical Efficiency of n Decision-Making Units (DMU). The objective of this work is a comparison between a Stochastic Frontier Analysis, with same input and different output variables, and the Data Envelopment Analysis. You get k Technical Efficiency TE(yi) which are unified by a Principal Component Analysis and compared with the results of a DEA on the same data

Local Government Efficiency: Evidence from the Czech Municipalities

We measure cost efficiency of 202 Czech municipalities of extended scope in period 2003-2008. The study is the first application of overall efficiency measurement of the local governments in the new EU member states, and the second in post-communist countries. We measure government efficiency through established quantitative and qualitative indicators of the provision of education, cultural facilities, infrastructure and other local services. First, we employ non-parametric approach of the data envelopment analysis and adjust the efficiency scores by bootstrapping. Second, we employ the stochastic frontier analysis and control for effects of various demographic, economic, and political variables. We compare scores under our preferred specification, i.e. pseudo-translog time-variant stochastic-frontier analysis with determinants, with alternative scores. The determinants that robustly increase inefficiency are population size, distance to the regional center, share of university-educated citizens, capital expenditures, subsidies per capita, and the share of self-generated revenues. Concerning political variables, increase in party concentration and the voters' involvement increases efficiency, and local council with a lower share of left-wing representatives also tend to be more efficient. We interpret determinants both as indicators of slack, non-discretionary inputs, and unobservable outputs. The analysis is conducted also for the period 1994-1996, where political variables appear to influence inefficiency in a structurally different way. From comparison of the two periods, we obtain that small municipalities improve efficiency significantly more that large municipalities.Public spending efficiency, Data Envelopment Analysis, Stochastic Frontier Analysis, local governments

Metafrontier Functions for the Study of Inter-regional Productivity Differences

The paper uses the concept of metafrontier functions to study regional differences in production technologies. The paper has three components. The first deals with the analytical framework necessary for the definition of metafrontier functions. The second component studies the properties of the metafrontier estimated using nonparametric data envelopment analysis (DEA). The third component focuses on the estimation of metafrontiers within the parametric framework of stochastic frontier analysis (SFA). The empirical application of the models uses cross-country agricultural sector data. The DEA and SFA metafrontiers are presented and discussed.

Stochastic non-smooth envelopment of data : semi-parametric frontier estimation subject to shape constraints

The field of productive efficiency analysis is currently divided between two main paradigms: the deterministic, nonparametric Data Envelopment Analysis (DEA) and the parametric Stochastic Frontier Analysis (SFA). This paper examines an encompassing semiparametric frontier model that combines the DEA-type nonparametric frontier, which satisfies monotonicity and concavity, with the SFA-style stochastic homoskedastic composite error term. To estimate this model, a new two-stage method is proposed, referred to as Stochastic Non-smooth Envelopment of Data (StoNED). The first stage of the StoNED method applies convex nonparametric least squares (CNLS) to estimate the shape of the frontier without any assumptions about its functional form or smoothness. In the second stage, the conditional expectations of inefficiency are estimated based on the CNLS residuals, using the method of moments or pseudolikelihood techniques. Although in a cross-sectional setting distinguishing inefficiency from noise in general requires distributional assumptions, we also show how these can be relaxed in our approach if panel data are available. Performance of the StoNED method is examined using Monte Carlo simulations.v2012o

Stochastic Efficiency Analysis with a Reliability Consideration

Stochastic Data Envelopment Analysis (DEA) models have been introduced in the literature to assess the performance of operating entities with random input and output data. A stochastic DEA model with a reliability constraint is proposed in this study that maximizes the lower bound of an entity\u27s efficiency score with some pre-selected probability. We define the concept of stochastic efficiency and develop a solution procedure. The economic interpretations of the stochastic efficiency index are presented when the inputs and outputs of each entity follow a multivariate joint normal distribution

The cost efficiency of German banks: a comparison of SFA and DEA

We investigate the consistency of efficiency scores derived with two competing frontier methods in the financial economics literature: Stochastic Frontier and Data Envelopment Analysis. We sample 34,192 observations for all German universal banks and analyze whether efficiency measures yield consistent results according to five criteria between 1993 and 2004: levels, rankings, identification of extreme performers, stability over time and correlation to standard accounting-based measures of performance. We find that non-parametric methods are particularly sensitive to measurement error and outliers. Furthermore, our results show that accounting for systematic differences among commercial, cooperative and savings banks is important to avoid misinterpretation about the status of efficiency of the total banking sector. Finally, despite ongoing fundamental changes in Europe?s largest banking system, efficiency rank stability is very high in the short run. However, we also find that annually estimated efficiency scores are markedly less stable over a period of twelve years, in particular for parametric methods. Thus, the implicit assumption of serial independence of bank production in most methods has an important influence on obtained efficiency rankings. --Cost Efficiency,Banks,Stochastic Frontier Approach,Data Envelopment Analysis

A Monte Carlo Study of Old and New Frontier Methods for Efficiency Measurement

This study presents the results of an extensive Monte Carlo experiment to compare different methods of efficiency analysis. In addition to traditional parametric-stochastic and nonparametric-deterministic methods recently developed robust nonparametric-stochastic methods are considered. The experimental design comprises a wide variety of situations with different returns-to-scale regimes, substitution elasticities and outlying observations. As the results show, the new robust nonparametric-stochastic methods should not be used without cross-checking by other methods like stochastic frontier analysis or data envelopment analysis. These latter methods appear quite robust in the experiments