Measurement Of Productive Efficiency Research Articles

Measurement of Dynamic Efficiency in Production : An Application of Data Envelopment Analysis to Japanese Electric Utilities

The purpose of this paper is to measure productive efficiencies when a firm employs quasi-fixed inputs that cannot be instantaneously adjusted to their optimal levels. To this end, data envelopment analysis (DEA) is extended to a dynamic framework so that investment behavior can be modelled with the efficient production frontier. Based on the work of Nemoto and Goto (1999), we show how the efficiencies of quasi-fixed inputs and their adjustment processes are evaluated. An application to Japanese electric utilities over the 1981–1995 period delivers empirically plausible results and proves the usefulness of the procedure.

Peso à maturidade, taxa de maturação e eficiência produtiva em fêmeas da raça Canchim

O objetivo do trabalho foi determinar as relações entre o peso à maturidade e a taxa de maturação com a eficiência produtiva em fêmeas da raça Canchim. O modelo não-linear utilizado para descrever o crescimento em função da idade foi o de Von Bertalanffy. Foram estimados os parâmetros A (peso assintótico) e k (taxa de maturação) da curva de crescimento de 486 fêmeas criadas em regime exclusivo de pastagens na Embrapa Pecuária Sudeste, São Carlos, SP. As relações entre as medidas de eficiência produtiva (número de anos no rebanho, número de bezerros desmamados, peso total de bezerros desmamados, média do peso à desmama e peso de bezerro à desmama por ano no rebanho) e os parâmetros A e k foram analisadas por meio de regressão linear e quadrática. As medidas de eficiência produtiva de vacas da raça Canchim dependem do peso à maturidade e da taxa de maturação. Em geral, as relações entre os parâmetros A e k e as características de eficiência produtiva foram lineares, positivas e da mesma forma quanto à natureza, indicando que existe uma combinação ótima do peso à maturidade e da taxa de maturação em fêmeas da raça Canchim.

The use of parametric and non-parametric frontier methods to measure the productive efficiency in the industrial sector: A comparative study

Parametric frontier models and non-parametric methods have monopolised the recent literature on productive efficiency measurement. Empirical applications have usually dealt with either one or the other group of techniques. This paper applies a range of both types of approaches to an industrial organisation setup. The joint use can improve the accuracy of both, although some methodological difficulties can arise. The robustness of different methods in ranking productive units allows us to make a comparative analysis of them. Empirical results concern productive and market demand structure, returns-to-scale, and productive inefficiency sources. The techniques are illustrated using data from the US electric power industry.

A caveat on the measurement of productive efficiency

The correspondence between used performance measures and enterprise objectives, such as profit maximization and cost minimization, is fundamental for manufacturing companies. This paper identifies, and critically examines, a minimal set of relevant properties that a productivity index needs to satisfy to rightly assess performance development of a decision-making unit. Commonly applied and suggested productivity measurement techniques, such as partial efficiencies, total factor productivity (TFP), index number approaches, integrated partial efficiencies and operational competitiveness ratings, are analyzed in order to assess the alignment with superior objectives. There is a considerable spread in the results of this class of models and the interpretation may prove difficult or misleading. As these apparently less complicated productivity measures increasingly are employed as a component in evaluation of manufacturing efficiency, the question is of high managerial relevance. In particular, the paper points out inconsistencies with properties related to commensurability, monotonicity, and implications of maximizing behavior. Based on this viewpoint, issues such as the consistency with profit maximization will be shown extra interest. The paper also provides a critique of previous work in non-parametric efficiency analysis, where properties have been postulated or based on other arguments. The findings suggest that there is no globally superior measurement technique to be found in this class and that care should be taken when evaluating managerial performance not to penalize rational behavior.

A bargaining model of Farrell inefficiency

An enormous number of empirical papers have estimated technical efficiency, the distance of firms inside a frontier, following the model of Farrell (Farrell, 1957. The measurement of productive efficiency. Journal of the Royal Statistical Society Series A 120 (3), 253–290). We propose a theory that explains the distance these empirical papers seek to measure. The theory is based on the idea that workers can bargain low `effort' (high crew sizes etc.) if they and the firm have some monopoly power. We provide simple theoretical expressions for the empirical measures of technical and allocative efficiency and compare them to those in the statistical literature. We also consider the relation between competition and efficiency.

Measuring technical efficiency

The theoretical consideration of technical efficiency has existed since Koopmans (Koopmans, T.C., 1951. An analysis of production as an efficient combination of activities. In: Koopmans, T.C. (Ed.), Activity Analysis of Production and Allocation. Cowles Commission for Research in Economics, Monograph No. 13, Wiley, New York) defined it as production possibilities for which it is not possible to increase any output without simultaneously increasing any input, ceteris paribus. The non-parametric approach to efficiency calculation known as Data Envelopment Analysis is not based on this definition but rather on the radial index of Farrell (Farrell, M.J., 1957. The measurement of productive efficiency. Journal of the Royal Statistic Society Ser. A 120, 253–281). Even after Farrell efficiency is achieved there may exist additional slack in individual inputs and/or outputs, implying that Farrell's index does not necessarily measure Koopmans inefficiency. Various measures have been proposed to solve this problem. This paper discusses shortcomings of these alternative measures and introduces a new relative measure of technical efficiency. Using simulated data, it is shown that the new measure outperforms existing measures in the majority of cases.

Measurements of reproductive function in stallions treated with trimethoprim-sulfamethoxazole and pyrimethamine

Objective To evaluate the effects of trimethoprim-sulfamethoxazole and pyrimethamine treatment on various measures of reproductive function in healthy pony stallions. Design Randomized complete block study. Animals 12 healthy, mature pony stallions. Procedure Stallions were assigned to treatment and control groups balanced for age and various characteristics of reproductive function. The treated group received trimethoprim-sulfamethoxazole and pyrimethamine for 90 days during summer and fall; the control group was not treated. Semen characteristics, sexual behavior, testicular volume, and sperm production efficiency were evaluated before treatment started and at 30-day intervals until 60 days after treatment ended. Results Effects of treatment were not detected for semen characteristics, testicular volume, sperm production efficiency, libido, erection, and quantitative measures of ejaculatory efficiency. At 30, 60, and 90 days, 4 of 6 treated stallions had unsteadiness upon mounting, clumsy or weak thrusting, failure to flex the back, and thready or inapparent ejaculatory pulses that resulted in dribbling of semen rather than forceful expulsion. Conclusions and Clinical Relevance Although treatment with trimethoprim-sulfamethoxazole and pyrimethamine may not affect semen quality, testicular volume, sperm production efficiency, erection, or libido of healthy stallions, treatment may induce changes in copulatory form and agility and alter the pattern and strength of ejaculation. Stallions that develop neurologic signs during treatment should be used with caution for breeding. (J Am Vet Med Assoc 1999;215:1317–1319)

Farrell-type efficiency under demand and price fluctuations

Nonparametric measurement of productive efficiency is generalized with respect to the impact of market demand and price fluctuations.

A Simple Measure of Restaurant Efficiency

Establishing an appropriate measure of production efficiency is difficult in service industries, especially in restaurants. In manufacturing, such measures are relatively easy to establish: count the number of items produced in a given period of time and compare that number to the rated capacity of the plant or its machinery. To develop a similar measure for restaurants, one must develop analogous measures. The unit of production is one complete turn of the service cycle, from the time the guest is seated to the time the table is reset for the next guest. The time period for measurement can range from a meal period to the entire time the restaurant is operating each day. By factoring the number of seats in the restaurant with the service-cycle time and the hours of operation, the manager can first calculate the restaurant's potential maximum capacity. Then, by counting the number of service cycles in a given period and comparing that to the maximum number of cycles, the manager can establish the ratio of capacity use. That number can be used as a gauge for analyzing the effects of changes in the restaurant's operation, such as, for instance, shifting demand to off-peak hours or shortening the service-cycle time.

An enhanced DEA Russell graph efficiency measure

The measurement of productive efficiency is an issue of great interest. Since Farrell (Farrell, M.J., 1957. Journal of Royal Statistical Society, Series A 120, 253) implemented the first measure of technical efficiency, many researchers have developed new measures or have extended the already existing ones. The beginning of Data Envelopment Analysis (DEA) meant a new way of empirically measuring productive efficiency. Under some specific technologies, Farrell's measure was implemented giving rise to the first DEA models, CCR (Charnes, A., Cooper, W.W., Rhodes, E., 1978. European Journal of Operational Research 2, 429) and BCC (Banker, R.D., Charnes, A., Cooper, W.W., 1984. Management Science, 1078). The fact that these measures only account for radial inefficiency has motivated the development of the so-called Global Efficiency Measures (GEMs) (Cooper, W.W., Pastor, J.T., 1995. Working Paper, Departamento de Estadı́stica e Investigación Operativa, Universidad de Alicante, Alicante, Spain). In this paper we propose a new GEM inspired by the Russell Graph Measure of Technical Efficiency which avoids the computational and interpretative difficulties with this latter measure. Additionally, the new measure satisfies some other desirable properties.

A simple measure of restaurant efficiency

Establishing an appropriate measure of production efficiency is difficult in service industries, especially in restaurants. In manufacturing, such measures are relatively easy to establish: count t...

The Measurement of Dynamic Productive Efficiency

Measuring dynamic productive efficiency from observed input‐output data raises several new issues which are absent in the static production frontier studies. The adjustment cost approach is developed here as an interface between the static and the dynamic frontier studies, and the non‐parametric method of Farrell developed for static efficiency is extended for the specification of a dynamic production frontier.

Estimation of variance of maternal lineage effects at the Langhill dairy herd

AbstractEvidence to support the existence of a maternal lineage variance component for production and food intake traits at the Langhill experimental dairy herd was investigated. Maternal pedigree records of the herd were traced back to the points of cytoplasmic origin using herd book records. Cytoplasmic origin was defined as the earliest maternal ancestor of a cow and used to assign cows to maternal lineages. This was either a grade-up cow or an ancestor traced back to 1920. The tracing resulted in the cows being assigned to 56 maternal lineages, ranging in size from one to 72 cows. A total of 1118 records of 517 cows, all with a first lactation record, were used in the analysis. Traits analysed were daily milk, fat and protein yield, fat %, protein %, food dry-matter intake, net energy of milk production, a measure of milk production efficiency, average condition, and calving condition, all averaged over the first 26 weeks of lactation. The analysis was performed using a residual maximum likelihood animal model with and without a random component for maternal lineage. Possible bias, due to the fact that the sires were a select sample from the population, was also examined. No significant effect was found in the analysis of the full data set that could be assigned to maternal lineage. Fat yield was the only trait to show a variance component approaching a 5% significance level with a magnitude of 4% of phenotypic variance. However, when maternal lineages of at least five cows were considered, a significant 4% maternal lineage component of phenotypic variance was found for fat yield. The power of the analysis to detect a variance component of less than 4% was shown to be poor. No evidence was found for a maternal lineage component of food intake traits or condition score. Treating sire as a fixed effect or regressing data on sire EBV made little difference to the maternal lineage component.

Nonparametric measurement of productive efficiency in the presence of regulation

During the late 1970s and 1980s, volatility in the demand for natural gas in the United States created havoc in the industry's transmission sector. Managers of firms were presented with the task of improving productive efficiency in an uncertain and regulated industry. We measure the relative performance of 20 pipelines, using Data Envelopment Analysis (DEA) to produce a regulation constrained minimum cost frontier. With the results we construct a Fisher Productivity Index (FPI). Results show that despite productive efficiency decline for most years, most firms experienced improvement in technical efficiency and technical progress in most years. Most of the productive efficiency decline was due to scale diseconomies.

Combining ranking scales and selecting variables in the DEA context: The case of industrial branches

Data Envelopment Analysis (DEA) has been introduced by Charnes et al. (Charnes, A., Cooper, W. W. and Rhodes, E., Measuring the efficiency of decision making units. Eur. J. Oper. Res., 1978, 2, 429–444) based on the Farrell measurement of productive efficiency ( J. Royal Statist. Soc., 1957, A120, 253–290). Originally DEA was designed to evaluate the efficiency of decision making units in the public sector (e.g. schools, towns, hospitals and nations) based on their given multiple inputs and outputs which are not measured in unified units (e.g. money). Eventually DEA was used in business and industry (e.g. bank branches). The DEA merely classifies the units into two dichotomic groups, efficient and inefficient. The purpose of our paper is to fully rank the units from the most efficient to the least efficient within the DEA context. For this purpose we use here three recent ranking methods developed within the DEA framework. The multi-ranking approach is utilized for validating the ranks and forming new overall ranking by combining the ranks which statistically fit the DEA classification. In a way this approach bridges between the DEA frontiers approach and the statistical/econometric approach of averages. The motivating example here is the case of ranking Israeli Industrial Branches. In order to determine the appropriate labor variables (number of man hours, average wage, vs total labor cost) we run two versions of the model. In this paper we use various recent scale ranking methods in the DEA (Data Envelopment Analysis) context. Two methods are based on multivariate statistical analysis: canonical correlation analysis (CCA) and discriminant analysis of ratios (DR/DEA), while the third is based on the cross efficiency matrix (CE/DEA) derived from the DEA. This multirank approach is necessary for rank validation of the model. Their consistency and goodness of fit with the DEA are tested by various nonparametric statistical tests. Once we had validated the consistency among the ranking methods, we constructed a new overall rank combining all of them. Actually, given the DEA results, we here provide ranks that complement the DEA for a full ranking scale beyond the mere classification to two dichotomic groups. This new combined ranking method does not replace the DEA, but it adds a post-optimality analysis to the DEA results. In this paper, we combine the ranking approach with stochastic DEA: each approach is in the forefront of DEA. This is an attempt to bridge between the DEA frontier Pareto Optimum approach and the average approach used in econometrics. Furthermore, the quality of this bridge is tested statistically and thus depends on the data. We demonstrate this method for fully ranking the Industrial Branches in Israel. In order to delete unmeaningful input and output variables, and to increase the fitness between the DEA and the ranking, we utilize the canonical correlation analysis to select the meaningful variables. Furthermore, we run the ranking methods on two sets of variables to select the proper combination of variables which best represents labor.

Restructuring and Measurement of Efficiency in Firms in Transition

In this paper we try to explain some restructuring patterns of Czech textile and clothing industries from a microeconomic point of view. We introduce two measures of production efficiency (technical and allocative efficiency) and look at changes in the behavior of enterprises. In 1994, after the chaos during the culmination of transition (1991-1992), firms' profits showed the first signs of dependency on efficiency. This fact can be taken as evidence of the creation of a competitive environment and a signal that long-term restructuring has led to economic patterns characteristic for standard market economics.

Some Remarks on Modified FDH

One of the tools in theoretical and empirical work on the measurement of productive efficiency is the socalled Free Disposal Hull (FDH) method. As a result of its very generous character, many of the observations belonging to an evaluated dataset are labeled efficient by this method. Modified FDH, similar in spirit as Andersen and Petersen's modified Data Envelopment Analysis, may thus be used to discriminate between FDH-efficient units. We aim to show why particularly for FDH this method seems quite apt and may be called for as a valuable complementary tool to standard FDH analysis.

Measuring technical efficiency of output quality in intensive care units.

Presents some examples of the implications derived from imposing the objective of maximizing social welfare, subject to limited resources, on ethical care patients management in respect of quality performance of health services. Conventional knowledge of health economics points out that critically ill patients are responsible for increased use of technological resources and that they receive a high proportion of health care resources. Attempts to answer, from the point of view of microeconomics, the question: how do we measure comparative efficiency in the management of intensive care units? Analyses this question through data from an international empirical study using micro-economic measures of productive efficiency in public services (data envelopment analysis). Results show a 28.8 per cent level of technical inefficiency processing data from 25 intensive care units in the USA.

Production Efficiency Analysis with a Farm-Level Average-Cost Characteristic Curves

Farm program analysis often involves the measurement of production efficiency. This study presents a theoretical model that can be used to measure farm-level long-run and short-run production efficiency. It also provides unbiased estimates of the minimum cost of producing an output within an enterprise. The model is a computational feasible approach that can be applied to agricultural data from primary and secondary sources. l. Introduction Estimates of production costs for farm enterprises'are conventionally estimated from average values of observed data from farm records. The reliability of cost estimates obtained from farm records data usually depends on the specific managerial decisions made by farmers, and thus may not represent the minimum averge production costs for the enterprises. Measures of production efficiency were introduced by Farrell []. Contemporary models are presented by Burely [2] and Fare et. al. [3,4]. This paper presents a theoretical model that can be easily used in the empirical estimates of production efficiency based on standard farm records data. The study provides an estimation of minimum average-cost characteristic curves that can be utilized to measure the overall efficiency of the underlying production technology ; and it also provides unbiased estimates of minimum average production costs which can be used in individual farm decision-makins and written budgeting context.

Choice of metric in the measurement of relative productive efficiency

In this paper different data envelope analysis (DEA) models are compared in terms of their potential to generate a quantitative measure of the degree of inefficiency. The Russell [1] measure in particularly appealing since it is not restricted to simple proportional comparisons. However, its actual value is sensitive to the choice of weights on the different inputs (or outputs) in the objective function. This may be of little significance in a simple binary classification of efficient and inefficient firms. However when evaluating the degree of efficiency, and hence the “distance” of a firm from the efficient envelope, choice of metric becomes critical. A number of alternative metrics are compared, including the “stability index” of Charnes et al. [2] and an alternative approach is suggested using the nature of the linear program itself. The paper concludes with a numerical illustration.