Chance-Constrained Efficiency Analysis

Data envelopment analysis (DEA) is extended to the case of stochastic inputs and outputs through the use of chance-constrained programming. The chance-constrained envelope envelops a given set of observations "most of the time." We show that the chance-constrained enveloping process leads to the definition of a conventional (certainty-equivalent) efficiency ratio (a ratio between weighted outputs and weighted inputs). Furthermore, extending the concept of Pareto and Koopmans efficiency to the case of chance-constrained dominance (to be defined), we establish the identity of the following two chance-constrained efficiency concepts: (i) the chance constrained DEA efficiency measure of a particular output-input point is unity, and all chance-constraints are binding; (ii) the point is efficient in the sense Pareto and Koopmans. Finally we discuss the implications of our approach for econometric frontier analysis.IC2 Institut

ROBUSTNESS OF NON-PARAMETRIC MEASUREMENT OF EFFICIENCY AND RISK AVERSION

This paper examines the performance of a risk-adjusted non-parametric approach to measuring efficiency and risk aversion. Prior work is extended to the case where agent behavior is motivated by expected utility maximization. Results indicate the approach significantly outperforms traditional efficiency measurement methods when applied to risk averse agents.Risk and Uncertainty,

Diffusion of Emissions Abating Technology

The environmental Kuznets curve (EKC) has been extensively criticized on econometric and theoretical grounds. Recent econometric results and case studies show that national emissions of important pollutants are monotonic in income but changes in technology can lead over time to reductions in pollution - a lowering of the EKC - and that pollution reducing innovations and standards may be adopted with relatively short time lags in some developing countries. This study combines the recent literature on measuring environmental efficiency and technological change using production frontier methods with the use of the Kalman filter - a time series method for signal extraction - to model the state of abatement technology in a panel of countries over time. The EKC is reformulated as the best practice technology frontier - countries' position relative to the frontier reflects the degree to which they have adopted best practice. The results are used to determine whether countries are converging to best practice over time and how many years it will take each country to achieve current best practice. The model is applied to sulfur dioxide emissions from sixteen mainly developed countries.

Productivity change using growth accounting and frontier-based approaches – Evidence from a Monte Carlo analysis

This study presents some quantitative evidence from a number of simulation experiments on the accuracy of the productivity growth estimates derived from growth accounting (GA) and frontier-based methods (namely Data envelopment Analysis-, Corrected ordinary least squares-, and Stochastic Frontier Analysis-based Malmquist indices) under various conditions. These include the presence of technical inefficiency, measurement error, misspecification of the production function (for the GA and parametric approaches) and increased input and price volatility from one period to the next. The study finds that the frontier-based methods usually outperform GA, but the overall performance varies by experiment. Parametric approaches generally perform best when there is no functional form misspecification, but their accuracy greatly diminishes otherwise. The results also show that the deterministic approaches perform adequately even under conditions of (modest) measurement error and when measurement error becomes larger, the accuracy of all approaches (including stochastic approaches) deteriorates rapidly, to the point that their estimates could be considered unreliable for policy purposes.

Stochastic non-parametric efficiency measurement and yardstick competition in electricity regulation

Stochastic non-parametric efficiency measurement constructs production or cost frontiers that incorporate both inefficiency and stochastic error. This results in a closer envelopment of the mean performance of the companies in the sample and diminishes the effect of extreme outliers. This paper uses the Land, Lovell and Thore (1993) model incorporating information on the covariance structure of inputs and outputs to study efficiency across a panel of 14 electricity distribution companies in the UK during the 1990s. The purpose is to revisit the 1999 distribution price control review carried out by the UK regulator. The regulator’s benchmarking is contrasted with the stochastic nonparametric efficiency results and with other comparative efficiency models offering close envelopment of the data. Some conclusions are offered about the possible regulated price effects in the UK case

SOME GUIDING PRINCIPLES FOR EMPIRICAL PRODUCTION RESEARCH IN AGRICULTURE

Constraints on production economic research are examined in three dimensions: problem focus, methodology, and data availability. Data availability has played a large role in the choice of problem focus and explains some misdirected focus. A proposal is made to address the data availability constraint. The greatest self-imposed constraints are methodological. Production economics has focused on flexible representations of technology at the expense of specificity in preferences. Yet some of the major problems faced by decision makers relate to long-term problems, e.g., the commodity boom and ensuring debt crisis of the 1970s and 1980s where standard short-term profit maximization models are unlikely to capture the essence of decision maker concerns.Production Economics,

SHORT-RUN AND LONG-RUN EFFICIENCIES OF NEW YORK DAIRY FARMS

Short-run and long-run technical and allocative efficiencies were computed for 395 New York dairy farms using data envelopment or nonparametric procedures on 1990 Dairy Farm Business Summary data. The farms were, on average, more allocatively efficient in the short run than in the long run, but were more technically efficient in the long run than in the short run. Stanchion barns were as efficient as milking parlors, and milking more than two times per day did not increase efficiency.Productivity Analysis,

The role of multiplier bounds in fuzzy data envelopment analysis

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The non-Archimedean epsilon ε is commonly considered as a lower bound for the dual input weights and output weights in multiplier data envelopment analysis (DEA) models. The amount of ε can be effectively used to differentiate between strongly and weakly efficient decision making units (DMUs). The problem of weak dominance particularly occurs when the reference set is fully or partially defined in terms of fuzzy numbers. In this paper, we propose a new four-step fuzzy DEA method to re-shape weakly efficient frontiers along with revisiting the efficiency score of DMUs in terms of perturbing the weakly efficient frontier. This approach eliminates the non-zero slacks in fuzzy DEA while keeping the strongly efficient frontiers unaltered. In comparing our proposed algorithm to an existing method in the recent literature we show three important flaws in their approach that our method addresses. Finally, we present a numerical example in banking with a combination of crisp and fuzzy data to illustrate the efficacy and advantages of the proposed approach

Robust optimization in data envelopment analysis: extended theory and applications.

Performance evaluation of decision-making units (DMUs) via the data envelopment analysis (DEA) is confronted with multi-conflicting objectives, complex alternatives and significant uncertainties. Visualizing the risk of uncertainties in the data used in the evaluation process is crucial to understanding the need for cutting edge solution techniques to organizational decisions. A greater management concern is to have techniques and practical models that can evaluate their operations and make decisions that are not only optimal but also consistent with the changing environment. Motivated by the myriad need to mitigate the risk of uncertainties in performance evaluations, this thesis focuses on finding robust and flexible evaluation strategies to the ranking and classification of DMUs. It studies performance measurement with the DEA tool and addresses the uncertainties in data via the robust optimization technique. The thesis develops new models in robust data envelopment analysis with applications to management science, which are pursued in four research thrust. In the first thrust, a robust counterpart optimization with nonnegative decision variables is proposed which is then used to formulate new budget of uncertainty-based robust DEA models. The proposed model is shown to save the computational cost for robust optimization solutions to operations research problems involving only positive decision variables. The second research thrust studies the duality relations of models within the worst-case and best-case approach in the input \u2013 output orientation framework. A key contribution is the design of a classification scheme that utilizes the conservativeness and the risk preference of the decision maker. In the third thrust, a new robust DEA model based on ellipsoidal uncertainty sets is proposed which is further extended to the additive model and compared with imprecise additive models. The final thrust study the modelling techniques including goal programming, robust optimization and data envelopment to a transportation problem where the concern is on the efficiency of the transport network, uncertainties in the demand and supply of goods and a compromising solution to multiple conflicting objectives of the decision maker. Several numerical examples and real-world applications are made to explore and demonstrate the applicability of the developed models and their essence to management decisions. Applications such as the robust evaluation of banking efficiency in Europe and in particular Germany and Italy are made. Considering the proposed models and their applications, efficiency analysis explored in this research will correspond to the practical framework of industrial and organizational decision making and will further advance the course of robust management decisions

Frontier methods for comparing public hospital efficiency

This research examines the impact, if any, of the introduction of casemix funding on public hospitals in Victoria. The results reported here show that in Victoria, during the period under observation, rural hospitals showed a significantly greater preponderance, relative to metropolitan hospitals, to either amalgamate or close down. Since 1 July 1993 public hospitals in Victoria have been compared for efficiency in the delivery of their services. The casemix funding arrangements were installed, among other reasons, to improve efficiency in the delivery of hospital services. Duckett, 1999, p 107 states that under casemix funding 'The hospital therefore becomes more clearly accountable for variation in the efficiency of the services it provides'. Also, 'Generally, case-mix funding is seen as being able to yield efficiency improvements more rapidly than negotiated funding'. Hospital comparisons provide State bodies with information on how to allocate funding between hospitals by means of annual capped budgets. Budgets are capped because funding is restricted to a given number of patients that can be treated in any given year. Thus, casemix funding relies heavily on cost comparisons between hospitals, and the way that hospital output is measured relies on the use of diagnosis related groups (DRGs)