Download Free Sensitivity And Integration Of Efficiency Estimates From Input Distance Functions And Stochastic Production Frontiers Book in PDF and EPUB Free Download. You can read online Sensitivity And Integration Of Efficiency Estimates From Input Distance Functions And Stochastic Production Frontiers and write the review.

A Practitioner's Guide to Stochastic Frontier Analysis Using Stata provides practitioners in academia and industry with a step-by-step guide on how to conduct efficiency analysis using the stochastic frontier approach. The authors explain in detail how to estimate production, cost, and profit efficiency and introduce the basic theory of each model in an accessible way, using empirical examples that demonstrate the interpretation and application of models. This book also provides computer code, allowing users to apply the models in their own work, and incorporates the most recent stochastic frontier models developed in academic literature. Such recent developments include models of heteroscedasticity and exogenous determinants of inefficiency, scaling models, panel models with time-varying inefficiency, growth models, and panel models that separate firm effects and persistent and transient inefficiency. Immensely helpful to applied researchers, this book bridges the chasm between theory and practice, expanding the range of applications in which production frontier analysis may be implemented.
In order to measure the technical efficiency of any observed input-output bundle one needs to know the maximum quantity of output that can be produced from the relevant input bundle. One possibility is to explicitly specify a production function. The value of this function at the input level under consideration denotes the maximum producible output quantity. It is common to estimate the parameters of the specified function empirically from a sample of input-output data. Because the least squares procedure permits observed points to lie above the fitted line, in a stochastic frontier model one includes a composite error. The composite error is a sum of a one-sided disturbance term representing shortfalls of the actually produced output from the frontier due to inefficiency and two sided disturbance term representing upward or downward shifts in the frontier itself due to the random factors. The econometric procedure requires relation of a particular functional form.The distance function representation of a production technology, proposed by Shephard (1953, 1970), provides a multi output primal alternative, which requires no aggregation, no prices and no behavioural assumption.
This book covers recent advances in efficiency evaluations, most notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) methods. It introduces the underlying theories, shows how to make the relevant calculations and discusses applications. The aim is to make the reader aware of the pros and cons of the different methods and to show how to use these methods in both standard and non-standard cases. Several software packages have been developed to solve some of the most common DEA and SFA models. This book relies on R, a free, open source software environment for statistical computing and graphics. This enables the reader to solve not only standard problems, but also many other problem variants. Using R, one can focus on understanding the context and developing a good model. One is not restricted to predefined model variants and to a one-size-fits-all approach. To facilitate the use of R, the authors have developed an R package called Benchmarking, which implements the main methods within both DEA and SFA. The book uses mathematical formulations of models and assumptions, but it de-emphasizes the formal proofs - in part by placing them in appendices -- or by referring to the original sources. Moreover, the book emphasizes the usage of the theories and the interpretations of the mathematical formulations. It includes a series of small examples, graphical illustrations, simple extensions and questions to think about. Also, it combines the formal models with less formal economic and organizational thinking. Last but not least it discusses some larger applications with significant practical impacts, including the design of benchmarking-based regulations of energy companies in different European countries, and the development of merger control programs for competition authorities.
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.
Efficiency Analysis details the important econometric area of efficiency estimation, both past approaches as well as new methodology. There are two main camps in efficiency analysis: that which estimates maximal output and attributes all departures from this as inefficiency, known as Data Envelopment Analysis (DEA), and that which allows for both unobserved variation in output due to shocks and measurement error as well as inefficiency, known as Stochastic Frontier Analysis (SFA). This volume focuses exclusively on SFA. The econometric study of efficiency analysis typically begins by constructing a convoluted error term that is composed on noise, shocks, measurement error, and a one-sided shock called inefficiency. Early in the development of these methods, attention focused on the proposal of distributional assumptions which yielded a likelihood function whereby the parameters of the distributional components of the convoluted error could be recovered. The field evolved to the study of individual specific efficiency scores and the extension of these methods to panel data. Recently, attention has focused on relaxing the stringent distributional assumptions that are commonly imposed, relaxing the functional form assumptions commonly placed on the underlying technology, or some combination of both. All told exciting and seminal breakthroughs have occurred in this literature, and reviews of these methods are needed to effectively detail the state of the art. The generality of SFA is such that the study of efficiency has gone beyond simple application of frontier methods to study firms and appears across a diverse set of applied milieus. This review should appeal to those outside of the efficiency literature seeking to learn about new methods which might assist them in uncovering phenomena in their applied area of interest.
The purpose of this dissertation is to apply stochastic frontier analysis methodology to the study of efficiency and productivity in industry and in education. It is comprised of three separate studies employing stochastic frontier analysis. First, stochastic frontier analysis is used to study technical efficiency in Indian industry and its impact on India's recent economic growth. The Indian economy has sustained a consistently high rate of growth since initiating economic reforms in the early 1990's. This encouraging growth episode has the rest of the world watching their economic progress closely. This study examines the role of industrial productivity and efficiency in the post reform Indian economy by using the stochastic frontier production function model over the period 1998 through 2004. We find that industry in India has moved closer to the production frontier and the gap between the states with the highest and lowest technical efficiency in industry has narrowed across time. Most of the growth in industrial output is attributed to total factor productivity. As Indian industry approaches the production frontier, growth will necessarily require further technological innovation and/or increasing input resources. In the second study, efficiency of public education in Illinois is estimated. Public education in the United States has received a great deal of attention from both constituents and policy makers alike over the past thirty years. Identifying less efficient school districts and examining the sources of inefficiency has important policy implications. School districts might improve efficiency by managing educational resources differently. In this study, we estimate technical efficiency for all three types of school districts in the state of Illinois K-12 public education system. Technical efficiency in the Illinois school system averaged 90% for unit school districts, 85% for elementary school districts, and 82% for high school districts. Possible factors associated with inefficiency in Illinois school districts are also investigated. The percentage of student enrollment that qualifies as low income and the size of the school district are positively related to inefficiency. School districts that have a larger percentage of teachers with advanced degrees are more efficient. Having a lower ratio of students per administrator in a school district increases technical efficiency. The final study analyzes efficiency among schools within a particular school district. Efficiency of public high schools in the City of Chicago School District is estimated. Technical efficiency of Chicago public high schools averaged 72%. We also investigate possible factors associated with inefficiency. The percentage of student enrollment that is chronically truant, mobility rate, and percentage of nonwhite students are all positively related to inefficiency. Schools with higher parental involvement are more efficient. Larger high schools display greater technical efficiency than smaller high schools.