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This work focuses on measuring and explaining producer performance. The authors view performance as a function of the state of technology and economic efficiency, with the former defining a frontier relation between inputs and outputs; the former incorporating waste and misallocation relative to this frontier. They show that insights can be gained by allowing for the possibility of a divergence between the economic objective and actual performance, and by associating this inefficiency with causal variables subject to managerial or policy influence. Derived from a series of lectures held on techniques and applications of the three approaches to the construction of production frontiers and measure of efficiency, this work will be an essential reference to scholars of a variety of disciplines who are involved with quantitative methods or policy.
This paper provides estimation method to measure technical efficiency of production units and the speed of adjustment of output, both varying with time, from a dynamic stochastic production frontier that incorporates the sluggish adjustment of inputs. Using a panel dataset on private manufacturing establishments in Egypt, I find that the speed of adjustment of output is lower than unity in every period and slowly increases over time. When compared to the results from the static model, the dynamic model is found to produce higher estimates of technical efficiency on average, captures more variation in the time pattern of technical efficiency, and provides a different ranking of production units.
This book employs different parametric and non-parametric panel data models which have been used in history of developed panel data efficiency measurement literature. It assesses the differences of models based on characteristics and efficiency scores measurement using a systematic sensitivity analysis of the results. On the whole twelve parametric and four nonparametric models were studied. Parametric models are classified in four groups in terms of the assumptions made on the temporal behavior of inefficiency. A common issue among all the parametric models is that inefficiency is individual producer-specific. This is consistent with the notion of measuring the efficiency of decision-making units. Non-parametric models are divided into partial and full frontier models. A main contribution of this volume is that it helps to understand differences between parametric and non-parametric models. On empirical part of the volume, technical efficiency of two agricultural strategic crops (cotton and sugar beet) in different provinces of the Iran are analyzed. Using different models, the most efficient and inefficient provinces in cotton and sugar beet production of Iran are recognized.
Cross-country comparisons of social indicators controlling for income and/or social spending have been widely used to measure and explain "social efficiency" analogously to "technical efficiency" in production. The author argues that these methods are clouded in ambiguities about what exactly is being measured. Standard methods of measuring technical efficiency require assumptions that seem unlikely to hold for social indicators. In the context of a simple parametric model of life expectancy, conditions are identified under which there will be a systematic pattern of bias in estimates of efficient health spending.
This article proposes a procedure to incorporate cross-sectional information in the estimation of technical efficiency indexes obtained from panel data. A conventional index of technical efficiency is estimated in a first stage using panel data on inputs and outputs. The individual effects from the first stage are then adjusted using cross-sectional information, obtaining a corrected technical efficiency index. The model is applied to a panel of eighty-two Spanish dairy farms, where only cross-sectional information about input quality is available. An analysis of variance is performed between some variables and both the corrected and the uncorrected indexes, finding that the conclusions derived from the two analyses are different.
Provides a comprehensive approach to productivity and efficiency analysis using economic and econometric theory.
Softcover version of the second edition Hardcover. Incorporates a new author, Dr. Chris O'Donnell, who brings considerable expertise to the project in the area of performance measurement. Numerous topics are being added and more applications using real data, as well as exercises at the end of the chapters. Data sets, computer codes and software will be available for download from the web to accompany the volume.
Modern textbook presentations of production economics typically treat producers as successful optimizers. Conventional econometric practice has generally followed this paradigm, and least squares based regression techniques have been used to estimate production, cost, profit and other functions. In such a framework deviations from maximum output, from minimum cost and cost minimizing input demands, and from maximum profit and profit maximizing output supplies and input demands, are attributed exclusively to random statistical noise. However casual empiricism and the business press both make persuasive cases for the argument that, although producers may indeed attempt to optimize, they do not always succeed. This book develops econometric techniques for the estimation of production, cost and profit frontiers, and for the estimation of the technical and economic efficiency with which producers approach these frontiers. Since these frontiers envelop rather than intersect the data, and since the authors continue to maintain the traditional econometric belief in the presence of external forces contributing to random statistical noise, the work is titled Stochastic Frontier Analysis.