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1995 | Buch

Theory of DEA Models Kapitel lesen Erstes Kapitel lesen

Dynamics of Data Envelopment Analysis

Theory of Systems Efficiency

verfasst von: Jati K. Sengupta

Verlag: Springer Netherlands

Enthalten in: Professional Book Archive

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Über dieses Buch

Data envelopment analysis develops a set of nonparametric and semiparametric techniques for measuring economic efficiency among firms and nonprofit organizations. Over the past decade this technique has found most widespread applications in public sector organizations. However these applications have been mostly static. This monograph extends this static framework of efficiency analysis in several new directions. These include but are not limited to the following: (1) a dynamic view of the production and cost frontier, where capital inputs are treated differently from the current inputs, (2) a direct role of the technological progress and regress, which is so often stressed in total factor productivity discussion in modem growth theory in economics, (3) stochastic efficiency in a dynamic setting, where reliability improvement competes with technical efficiency, (4) flexible manufacturing systems, where flexibility of the production process and the economies of scope play an important role in efficiency analysis and (5) the role of economic factors such as externalities and input interdependences. Efficiency is viewed here in the framework of a general systems theory model. Such a view is intended to broaden the scope of applications of this promising new technique of data envelopment analysis. The monograph stresses the various applied aspects of the dynamic theory, so that it can be empirically implemented in different situations. As far as possible abstract mathematical treatments are avoided and emphasis placed on the statistical examples and empirical illustrations.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Theory of DEA Models
Abstract
Data envelopment analysis (DEA) developed originally as a set of techniques for measuring the relative efficiency of a set of decisionmaking units (DMUs), when the price data for inputs and outputs are either unavailable or unknown. These techniques are nonparametric in the sense that they are entirely based on the observed input-output data. The statistical aspects of the data set are almost ignored by the traditional DEA models and in this sense they are far from nonparametric. Over the last two decades the DEA models have been widely applied in management science and operations research literature and the theoretical formulations of DEA have been generalized in several directions as follows:
(i)
Various types of DEA models have been formulated which clarify the concepts of technical and allocative efficiency and their link with the concept of Pareto efficiency in economic theory,
 
(ii)
Log-linear and nonlinear formulations have extended the linear DEA models, and generalized the concepts of increasing, decreasing or constant returns to scale as applied to multiple-output and multiple-input cases, and
 
(iii)
Sources of inefficiency identified through the DEA models have been incorporated in regression models in various ways.
 
Jati K. Sengupta
Chapter 2. Dynamics of Efficiency
Abstract
Dynamisation of efficiency in data envelopment analysis may be viewed in several forms, depending on the interpretation of efficiency. Four different interpretations have been discussed in the current literature. One is the economic theory of technical efficiency and allocative (price) efficiency, where the former characterizes a production frontier and the latter the degree of correctness in adaptation of factor proportions to the ratio of factor prices. When the factor prices are observed as competitive market prices, the allocatively efficient firms follow an expansion path which is optimal in each period. This optimal expansion path may be called the expansion frontier. A second view is to consider the production function underlying the DEA models with dynamic inputs such as capital, technology and capacity variables, each of which has significant impact on output for several periods in the future. Efficiency in this framework is characterized by the capacity frontier or the technology frontier. The third form of efficiency defines an adjustment frontier as in model (3.l0c) of Chapter One, where the coefficients of the production frontier are modified due to various time lags of adjustment of inputs. This type of model allows a two-stage interpretation of efficiency, with a short and a long run view of the state space model.
Jati K. Sengupta
Chapter 3. Technical Change and Efficiency
Abstract
Technical change plays a central role in dynamic economics. Economic theory considers technology as the main driving force behind economic growth of capitalist economies. From an economic viewpoint three aspects of technical change have been discussed as important in the current economic literature. Technological progress embodied in labor and capital inputs, or disembodied in a time-varying form has focused on the effects of technology in improving factor productivity over time. Optimal rules of switching from one technique to another so that the economies of scale can be exploited over time have been widely investigated in modern theories of economic growth. At the microeconomic level the specification of technological diffusion and the way it affects the dynamic production process have provided an active field of modern research. One major implication of this research has been to modify the production frontier parameters, so that the impact of technological substitution and updating can be incorporated. Finally, the multi-stage aspects of the production process and the need for flexibility rather than fine tuning have been evidenced in the modern technology-intensive industries. The whole new field of flexible manufacturing systems, which is discussed in some detail in Chapter Five deals with these multi-stage aspects of the production process.
Jati K. Sengupta
Chapter 4. Stochastic Efficiency
Abstract
Efficiency measurement in data envelopment analysis (DEA) has mostly used deterministic models, where the input-output data D = (X,Y) are assumed to be known. Here the input and output matrices (X,Y) are deterministic. If a particular decision-making unit (DMU) e.g., DMUk is found to be efficient by a certain type of DEA model, one could aggregate these efficient units into a number N1, where N2 = N - N1 would then be the total number of inefficient units in the total industry comprising N units. The proportion p = N1/N of efficient units provides in this framework a natural measure of efficiency in the whole industry. When one considers time series data Dt =(Xt,Yt), two additional dimensions are introduced. One is due to the wider choice of DEA formulations e.g., one may specify a DEA model for each t and then observe how pt = N1t/Nt changes over time. Alternatively, one may take a cumulative volume of input and output Dc =(Xc,Yc) over a certain period and then apply a DEA model based on the data set Dc to measure efficiency of a DMUk. A second problem is due to the nonstationary nature of input-output data particularly for growing firms or DMUs. In such a case the steady state (t -→ ∞) version of the DEA model may not be valid.
Jati K. Sengupta
Chapter 5. Theory of Systems Efficiency
Abstract
Over the last decade the nonparametric method of data envelopment analysis (DEA) has found extensive applications in efficiency measurement in public sector enterprises. Recent generalizations of DEA models in operations research and econometrics have emphasized several new aspects of the concept of efficiency employed in DEA models, e.g., vector efficiency, dynamic efficiency and stochastic efficiency. However the broader concept of systems efficiency, which comprises DEA efficiency as a component has not been so far explored in the DEA framework. Even in the static input-output systems where most DEA models have been applied, the concept of efficiency has ignored or under emphasized several key factors which may cause systems inefficiency. For example if some of the data set are fuzzy or imprecise, the DEA efficiency concept no longer holds. A second example is provided by the flexible manufacturing systems (FMS), where the input and output mixes can change frequently. Finally, changes in technology frequently affect the input and output compositions over time. Hence a system which is efficient in the DEA sense at the static level may not be so over time in a dynamic sense.
Jati K. Sengupta
Chapter 6. Entropy, Efficiency and the Index Numbers
Abstract
Measuring productive efficiency of a set of decision making units (DMUs) basically involves the comparison of two sets of distribution of input-output data: one is the observed set and the other the optimal set. The technique of data envelopment analysis (DEA) defines the optimal set in terms of a Pareto criterion of efficiency and uses the positive distance between the two sets as a measure of inefficiency. The DEA approach however fails to incorporate any statistical aspect of the data distribution, e.g., skewness, asymmetry or heteroscedasticity. Farrell (1957) as the precursor of the DEA approach noted in his empirical analysis of the agricultural farms that the statistical distribution of the efficiency measure is highly skewed and also outlier sensitive. Since entropy provides a measure of diversity of the data distribution, it is useful to explore its application in comparing different output distributions.
Jati K. Sengupta
Chapter 7. Economic Theory and DEA
Abstract
Recently the nonparametric techniques of efficiency measurement known as data envelopment analysis (DEA) have been frequently applied in economic studies of productivity. The DEA models compare the performance of a decision-making unit (DMU) or a firm with a standard measure. The standard measure, constructed in various ways through suitable linear programming (LP) models yields potential or frontier output. The difference between the firm’s actual output and its potential output enables one to arrive at a measure of the firm’s technical efficiency. Recent applications of DEA models include the comparisons of the British prison system by Ganley and Cubbin (1992), the information theory applications by Sengupta (1993) and the various public and private sector applications by Fried et al. (1993).
Jati K. Sengupta
Chapter 8. Frontiers of Efficiency Research
Abstract
Efficiency Analysis in general systems theory is an active field of modern research. It covers not only the input-output systems, but other systems in control theory and engineering. Data envelopment analysis (DEA) deals only with a small component of input-output systems. It uses a specific concept of Pareto efficiency and applies it mainly to public sector decision-making units (DMUs) which do not operate under the private market framework governed by market prices and profit maximization.
Jati K. Sengupta
Backmatter
Metadaten
Titel
Dynamics of Data Envelopment Analysis
verfasst von
Jati K. Sengupta
Copyright-Jahr
1995
Verlag
Springer Netherlands
Electronic ISBN
978-94-015-8506-4
Print ISBN
978-90-481-4582-9
DOI
https://doi.org/10.1007/978-94-015-8506-4