Abay, C., Miran, B., & Günden, C. (2004). An Analysis of Input Use Efficiency in Tobacco Production with Respect to Sustainability: The Case Study of Turkey. Journal of Sustainable Agriculture, 24(3), 123–143.
Abstract: The purpose of this paper is to analyze the efficiency of input use in tobacco production in Turkey with respect to sustainability. In the construction of cross sectional data, the provinces in the Aegean, Northwestern, Eastern-Southeastern Anatolian and Black Sea regions were considered. Face to face interviews were carried out with 300 farmers from the provinces which together produce at least 75% of regional tobacco output. Efficiency measures of tobacco farms in each region were calculated by Data Envelopment Analysis (DEA). Efficiency measures obtained from constant return to scale DEA were then decomposed into pure technical efficiency and scale efficiency. Total tobacco production (in kg) was used as the output indicator, and land (ha), labor (hours), tractor use (hours), nitrogen (kg), phosphorus (kg) and pesticide (kg) were considered as the main inputs. Econometric models were developed in order to determine the factors that affect the efficiency of regional tobacco production. The average technical efficiency score for all regions was found to be 0.456, implying that the same level of production per plots can be obtained even if the inputs used for tobacco production are decreased by 54.4%. Although none of the regions is completely efficient in tobacco production, the Eastern-Southeastern Region, which obtained the highest pure technical efficiency and scale efficiency scores, turned out to be relatively more successful in input use. Considering all regions together, the inefficiency did not seem to result from non-optimal production, but instead from the failure to produce a given level of output with the minimum amount of inputs possible. The results also indicated a strong positive relationship between the efficiency of input use and the sustainability of agriculture.
|
Abbott, M. (2006). The productivity and efficiency of the Australian electricity supply industry. Energy Economics, 28(4), 444–454.
Abstract: Australia’s electricity supply industry has been through a period of reform over the last 10 years. The purpose of this paper is to analyse the changes that have occurred to the Australian electricity supply industry over the past 30 years, in order to evaluate to what degree these reforms have improved the productivity and efficiency performance of the industry.
|
Abbott, M., & Doucouliagos, C. (2003). The efficiency of Australian universities: a data envelopment analysis. Economics of Education Review, 22(1), 89–97.
Abstract: With participation in higher education amongst young people rising, governments around the world have been faced with increasing pressure on their finances, giving rise to the need to operate universities with a higher degree of efficiency. In this paper, non-parametric techniques are used to estimate technical and scale efficiency of individual Australian universities. Various measures of output and inputs are used. The results show that regardless of the output-input mix, Australian universities as a whole recorded high levels of efficiency relative to each other.
|
Adang, E. M. M., & Borm, G. F. (2007). Is there an association between economic performance and public satisfaction in health care? European Journal of Health Economics, 8(3), 279–285.
Abstract: Earlier studies on the association between health systems’ economic performance and public satisfaction were based on between-countries comparisons. This approach can be challenged as it ignores the fact that subjective measures like ‘satisfaction’ might be relative. Cohort analysis is a way of dealing with this issue as it focuses on within-countries comparisons. The association between change in satisfaction with health care systems and change in economic performance, determined by an output-orientated constant returns to scale DEA Malmquist model over the period 1995 to 2000/2002 using OECD data, is explored. The results show that a health care systems’ economic performance is not associated with public satisfaction.
|
Adler, N., & Berechman, J. (2001). Measuring airport quality from the airlines’ viewpoint: An application of data envelopment analysis. Transport Policy, 8(3), 171–181.
Abstract: The main objective of this paper is to develop a model to determine the relative efficiency and quality of airports. This factor seems to have a strong effect on the airlines? choice of hubs. Previous studies of airport quality have used subjective passenger data whereas in this study airport quality is defined from the airlines? viewpoint. Accordingly, we have solicited airlines’ evaluations of a number of European and non-European airports by means of a detailed questionnaire. Statistical analysis of the median score has shown that these evaluations vary considerably relative to quality factors and airports. The key methodology used in this study to determine the relative quality level of the airports is Data Envelopment Analysis (DEA), which has been adapted through the use of principle component analysis. Of the set of West-European airports analyzed, Geneva, Milan and Munich received uniformly high, relative efficiency scores. In contrast, Charles de Gaulle, Athens and Manchester consistently appear low in the rankings.
|
Adler, N., & Golany, B. (2001). Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to Western Europe. European Journal of Operational Research, 132(2), 260–273.
Abstract: US experience shows that deregulation of the airline industry leads to the formation of hub-and-spoke (HS) airline networks. Viewing potential HS networks as decision-making units, this study uses data envelopment analysis (DEA) to select the most efficient networks configurations from the many that are possible in the deregulated European Union airline market. To overcome the difficulties that DEA encounters when there is an excessive number of inputs or outputs, principal component analysis (PCA) is employed to aggregate certain, clustered data, while ensuring very similar results to those achieved under the original DEA model. The DEA-PCA formulation is then illustrated with real-world data gathered from the West European air transportation industry.
|
Adler, N., & Golany, B. (2002). Including principal component weights to improve discrimination in data envelopment analysis. Journal of the Operational Research Society, 53(9), 985–991.
Abstract: This research further develops the combined use of principal component analysis (PCA) and data envelopment analysis (DEA). The aim is to reduce the curse of dimensionality that occurs in DEA when there is an excessive number of inputs and outputs in relation to the number of decision-making units. Three separate PCA–DEA formulations are developed in the paper utilising the results of PCA to develop objective, assurance region type constraints on the DEA weights. The first model applies PCA to grouped data representing similar themes, such as quality or environmental measures. The second model, if needed, applies PCA to all inputs and separately to all outputs, thus further strengthening the discrimination power of DEA. The third formulation searches for a single set of global weights with which to fully rank all observations. In summary, it is clear that the use of principal components can noticeably improve the strength of DEA models.
|
Adler, N., & Raveh, A. (2008). Presenting DEA graphically. Omega, 36(5), 715–729.
Abstract: This paper introduces a methodology that permits presentation of the results of data envelopment analysis (DEA) graphically. A specialized form of multi-dimensional scaling, Co-Plot, enables presentation of the DEA results in a two-dimensional space, hence in a clear, understandable manner. When plotting ratios rather than original data, DEA efficient units can be visualized clearly, as well as their connections to specific variables and/or ratios. Furthermore, Co-Plot can be used in an exploratory data analysis to identify outliers, whose data require additional scrutiny, and potentially inconsequential variables that could be aggregated or removed from the analysis with little effect on the subsequent DEA results. The Co-Plot diagram of ratios presents super-efficient observations on an outer ring or sector of the plot and all reasonably efficient units on a slightly inner ring/sector, surrounding the remaining inefficient decision-making units. First, the well-known 35 Chinese Cities dataset is provided as an illustration. Second, a simulation study tests the applicability of Co-Plot to present the results of DEA.
|
Adler, N., & Yazhemsky, E. (2010). Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction. European Journal of Operational Research, 202(1), 273–284.
Abstract: Within the data envelopment analysis context, problems of discrimination between efficient and inefficient decision-making units often arise, particularly if there are a relatively large number of variables with respect to observations. This paper applies Monte Carlo simulation to generalize and compare two discrimination improving methods; principal component analysis applied to data envelopment analysis (PCA-DEA) and variable reduction based on partial covariance (VR). Performance criteria are based on the percentage of observations incorrectly classified; efficient decision-making units mistakenly defined as inefficient and inefficient units defined as efficient. A trade-off was observed with both methods improving discrimination by reducing the probability of the latter error at the expense of a small increase in the probability of the former error. A comparison of the methodologies demonstrates that PCA-DEA provides a more powerful tool than VR with consistently more accurate results. PCA-DEA is applied to all basic DEA models and guidelines for its application are presented in order to minimize misclassification and prove particularly useful when analyzing relatively small datasets, removing the need for additional preference information.
|
Adler, N., Friedman, L., & Sinuany-Stern, Z. (2002). Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research, 140(2), 249–265.
Abstract: Within data envelopment analysis (DEA) is a sub-group of papers in which many researchers have sought to improve the differential capabilities of DEA and to fully rank both efficient, as well as inefficient, decision-making units. The ranking methods have been divided in this paper into six, somewhat overlapping, areas. The first area involves the evaluation of a cross-efficiency matrix, in which the units are self and peer evaluated. The second idea, generally known as the super-efficiency method, ranks through the exclusion of the unit being scored from the dual linear program and an analysis of the change in the Pareto Frontier. The third grouping is based on benchmarking, in which a unit is highly ranked if it is chosen as a useful target for many other units. The fourth group utilizes multivariate statistical techniques, which are generally applied after the DEA dichotomic classification. The fifth research area ranks inefficient units through proportional measures of inefficiency. The last approach requires the collection of additional, preferential information from relevant decision-makers and combines multiple-criteria decision methodologies with the DEA approach. However, whilst each technique is useful in a specialist area, no one methodology can be prescribed here as the complete solution to the question of ranking.
|
