Profit efficiency analysis under limited information. With an application to German farm types.

Efficiency; Information;

Quality of Service, Efficiency and Scale in Network Industries: An analysis of European electricity distribution

Quality of service is of major economic significance in natural monopoly infrastructure industries but is generally not reflected in efficiency analysis. In this paper we present an efficiency analysis of electricity distribution networks using a sample of about 500 electricity distribution utilities from seven European countries. We apply the stochastic frontier analysis (SFA) method on multi-output translog input distance function models to estimate cost and scale efficiency with and without incorporating quality of service. We show that introducing the quality dimension into the analysis affects estimated efficiency significantly. In contrast to previous research, smaller utilities seem to indicate lower technical efficiency when incorporating quality. We also show that incorporating quality of service does not alter scale economy measures. Quality of service should be an integrated part of efficiency analysis and incentive regulation regimes, as well as in the economic review of market concentration in regulated natural monopolies

Measuring and Explaining Technical Efficiency of Dairy Farms: A Case Study of Smallholder Farms in East Africa

Replaced with revised version of paper 11/18/10.Dairy farms, efficiency scores, Data Envelopment Analysis, fractional regression, returns to scale, Livestock Production/Industries,

Analyzing the accuracy of variable returns to scale data envelopment analysis models

The data envelopment analysis (DEA) model is extensively used to estimate efficiency, but no study has determined the DEA model that delivers the most precise estimates. To address this issue, we advance the Monte Carlo simulation-based data generation process proposed by Kohl and Brunner (2020). The developed process generates an artificial dataset using the Translog production function (instead of the commonly used Cobb Douglas) to construct well-behaved scenarios under variable returns to scale (VRS). Using different VRS DEA models, we compute DEA efficiency scores with artificially generated decision-making units (DMUs). We employ five performance indicators followed by a benchmark value and ranking as well as statistical hypothesis tests to evaluate the quality of the efficiency estimates. The procedure allows us to determine which parameters negatively or positively influence the quality of the DEA estimates. It also enables us to identify which DEA model performs the most efficiently over a wide range of scenarios. In contrast to the widely applied BCC (Banker-Charnes-Cooper) model, we find that the Assurance Region (AR) and Slacks-Based Measurement (SBM) DEA models perform better. Thus, we endorse the use of AR and SBM models for DEA applications under the VRS regime

Estimating Technical Efficiency of Australian Dairy Farms Using Alternative Frontier Methodologies

In this paper we estimate and examine technical efficiency for a cross-section of Australian dairy farms using various frontier methodologies; Bayesian and Classical stochastic frontiers, and Data Envelopment Analysis. Our results indicate technical inefficiency is present in the sample data. We also identify statistical differences between the point estimates of technical efficiency generated by the various methodologies. However, the rank of farm level technical efficiency is statistically invariant to the estimation technique employed. Finally, when we compare confidence/credible intervals of technical efficiency we find significant overlap for many of the farms’ intervals for all frontier methods employed. Our results indicate that the choice of estimation methodology may matter, but the explanatory power of all frontier methods is significantly weaker when we examine interval estimate of technical efficiency