Data Envelopment Analysis Results Research Articles

A two-stage performance assessment of utility-scale wind farms in Texas using data envelopment analysis and Tobit models

Wind power becomes one of the most promising energy sources in the electricity generation sector in Texas over the past decade by declining levelized cost of wind energy. However, recent studies show that the wind farms in Texas are relatively less productive. Hence, this study aims to find out reasons of inefficiencies by constructing a two-stage performance assessment of wind farms in Texas. In the first stage of analysis, comprehensive input- and output-oriented Data Envelopment Analysis (DEA) models are applied to evaluate productive efficiencies of the 95 large utility-scale wind farms by using pre-determined three input and two output variables. The sensitivity analysis is provided for the robustness of the DEA models with different combinations of input and output variables of the original model. The slack analysis and projection data are obtained for inefficient wind farms to find out optimal input-output variables. Tobit regression models are conducted for the second stage of the analysis to investigate the reasons of inefficiencies. DEA results indicate that half of the wind farms were operated efficiently in Texas during 2016. 13 wind farms were performed at the most productive scale size, ten wind farms should reduce their operational size to improve production efficiency, and 72 wind farms have the notable potential to increase their production efficiency by expanding operational sizes with modern wind turbine technologies. The sensitivity analysis shows the importance of each input-output variables. Tobit regression models indicate that elevation of the site, rotor diameter, hub height, and brand of the turbine have significant contributions to the relative efficiency scores of the wind farms, and the age of turbine has a negative impact on the productive efficiency of the wind farms.

Are football managers as efficient as coaches? Performance analysis with ex ante and ex post inputs in the Premier league

ABSTRACTThere is a controversy on sport performance literature about what type of inputs might explain more deeply the performance of sports clubs (inputs specification controversy). By one side, several papers have analysed sports teams’ performance using the match-related statistics or wages as inputs, well-known as ex post inputs. By other side, some authors have criticized the use of these ex post inputs, and recommend the use of ex ante inputs, as the market value of the players. We have analysed the performance of football teams estimating technical efficiency with three different inputs specification. The methodologies employed were data envelopment analysis (DEA) and a bootstrapped DEA. Our sample is composed by English Premier League football clubs, during three seasons (2012/13–2014/15). The DEA results indicate that the correlation between the three models is positive and significant. The DEA-bootstrapped results help to restate the robustness of the estimations and endorsed the inputs choices. The correlations of the estimations with market value and match-related statistics are the most striking (90% and 94%, DEA and bootstrapped DEA), which indicate that the existent discussion related to the use of match-related statistics as input is unjustified, because it does not affect significantly the efficiency estimations.

Expansion of environmental impact assessment for eco-efficiency evaluation of China's economic sectors: An economic input-output based frontier approach

Due to the increasing environmental burdens caused by dramatic economic expansion, eco-efficiency indicating how efficient the economic activity is with respect to its environmental impacts has become a topic of considerable interest in China. In this context, Economic Input-output Life Cycle Assessment (EIO-LCA) and Data Envelopment Analysis (DEA) are combined to assess the environmental impacts and eco-efficiency of China's 26 economic sectors. The EIO-LCA results indicate that Electricity Production and Supply sector is the largest net exporter in energy usage, CO2 emission and exhaust emission categories, while Construction sector is the largest net importer for five impact categories except for water withdrawal. Moreover, Construction sector is found to be the destination of the largest sector-to-sector environmental impact flows for the five impact categories and make the most contributions to the total environmental impacts. Another key finding is that Agriculture sector is both the largest net exporter and the greatest contributor for water withdrawal category. DEA results indicate that seven sectors are eco-efficient while over 70% of China's economic sectors are inefficient and require significant improvements. The average target improvements range between 23.30% and 35.06% depending on the impact category. Further sensitivity analysis reveals that the average sensitivity ratios vary from 7.7% to 15.7% among the six impact categories, which are found to be negatively correlated with their improvement potentials. Finally, several policy recommendations are made to mitigate environmental impacts of China's economic sectors and improve their eco-efficiency levels.

NEGATIVE VALUES IN DEA: EXAMINATION OF THE RELATIONSHIP BETWEEN ECONOMIC GROWTH RATES AND AIRLINE PERFORMANCES

The main goal of this study is to determine whether there is a relationship between the economic growth rates and the performances of airline companies. From this point of view, Data Envelopment Analysis (DEA) is applied to star alliance member airlines for a period of 2012-2014. Firstly, the technical, pure technical and scale efficiency scores were calculated. Then, airlines are divided into four regional groups as America, Europe, Asia and Africa in order to determine the most efficient region. Further, countries economic growth rates which the airline is located are analyzed to support DEA results. Finally, Asia group is found as the most efficient airline group. http://dx.doi.org/10.17130/ijmeb.2018239939

Measuring efficiencies of Bangladeshi and Indonesian microfinance institutions

PurposeThe purpose of this paper is to measure the technical efficiency and growth trajectory of Bangladeshi and Indonesian microfinance institutions (MFIs). The motivation for this study was derived from crucial roles that these institutions play in the socio-economic transformation of any nations, especially Bangladesh and Indonesia which are at least prominent in the Asian context in this regard. Rather than “proving” impact, research endeavors have shifted to focusing on “improving” the impact of MFIs, because the ability to improve their impact as socio-economic transformation platform may be hinged on their efficiency over time.Design/methodology/approachData were obtained from MIX market database covering a five-year period from 2007 to 2011 for 20 Bangladeshi and 11 Indonesian MFIs. The data obtained were subjected to both efficiency and trajectory analysis using data envelopment analysis (DEA) based on Malmquist productivity index, independent t-test, and latent growth curve modeling (LGCM).FindingsOverall, DEA results indicate that both Bangladeshi and Indonesian MFIs are approximately efficient under constant returns to scale, variable returns to scale, and scale. There has been an improvement in the management practices of Bangladeshi MFIs, while Indonesian MFIs have increased in optimum size. Independent t-test result shows that Bangladeshi MFIs are significantly efficient in terms of performance and firm’s size compared to Indonesian MFIs, but there is no significant difference in their efficiencies with regard to technology. The intercept and the slope of the regression weight in the estimated model using LGCM are not significantly different.Research limitations/implicationsThis study measures technical efficiency and growth trajectory of Bangladeshi and Indonesian MFIs over a five-year period. However, future studies could explore this in greater depth by incorporating more data.Practical implicationsThe research findings have great implications for the Bangladeshi and Indonesian MFIs. Since this study is among the first of its kind, the researchers have paved ways for further investigation in this area. Moreover, the study encourages the Bangladeshi and Indonesian MFIs to be more concerned of their efficiencies.Originality/valueThis study measures technical efficiency and growth trajectory of the Bangladeshi and Indonesian (MFIs). These have never been examined together in this way before.

Türkiye deki Bankaların Karşılaştırmalı Etkinlik Analizi - The Comparative Efficiency Analysis Of Banks in Turkey

The aim of this present study is investigation of Turkish banking sector efficiency. The research is conducted on private, public and foreign capital deposit banks. Under the assumptions of BCC (variable return to scale) and CCR (constant return to scale), Data Envelopment Analysis results are evaluated between the years of 2008 to 2016. Deposit, interest expense and numbers of employee as an input and loan and interest income as an output were used in the analyses. According to results of DEA, Deutsche Bank and Arap Turk Bankasi reached fully efficient point more than other banks under the assumption of CCR. Furthermore, under the assumption of BCC, 2016 lambda values are found and inputs and outputs are recalculated for chosen banks in order to make them efficient.

Technical and total factor energy efficiency of European regions: A two-stage approach

This paper considers energy as a basic resource which together with capital and labor contributes to output creation. It aims to explore whether European Union (EU) regions use these production factors in an efficient way as well as to assess the impact of various environmental variables on technical and energy efficiency. To that end, a two-stage analysis was conducted for the period 2005–2013. The first stage included an estimation of pure technical and total factor energy efficiency (TFEE) scores using the data envelopment analysis (DEA) methodology, while the second one included an exploration of the factors affecting efficiency scores obtained in the first stage by using random-effects panel Tobit regression.The results of DEA show that regional differences in technical and energy efficiency are considerable, whereby most of EU regions failed to utilize efficiently all of their resources. More developed EU regions are more technically and energy efficient. Moreover, most of EU regions experienced deterioration in TFEE in the recession years. The results of Tobit regression analysis, showing that there is a difference in the determinants of technical and energy efficiency, point out that human capital and innovation are particularly important for improving the region's efficiency or ecological performance.

A Two-Stage Performance Assessment of Utility-Scale Wind Farms in Texas Using Data Envelopment Analysis and Tobit Models

Wind power becomes one of the most promising energy sources in the electricity generation sector in Texas over the past decade by declining levelized cost of wind energy. However, recent studies show that the wind farms in Texas are relatively less productive. Hence, this study aims to find out reasons of inefficiencies by constructing a two-stage performance assessment of wind farms in Texas. In the first stage of analysis, comprehensive input- and output-oriented Data Envelopment Analysis (DEA) models are applied to evaluate productive efficiencies of the 95 large utility-scale wind farms by using pre-determined three input and two output variables. The sensitivity analysis is provided for the robustness of the DEA models with different combinations of input and output variables of the original model. The slack analysis and projection data are obtained for inefficient wind farms to find out optimal input-output variables. Tobit regression models are conducted for the second stage of the analysis to investigate the reasons of inefficiencies. DEA results indicate that half of the wind farms were operated efficiently in Texas during 2016. 13 wind farms were performed at the most productive scale size, ten wind farms should reduce their operational size to improve production efficiency, and 72 wind farms have the notable potential to increase their production efficiency by expanding operational sizes with modern wind turbine technologies. The sensitivity analysis shows the importance of each input-output variables. Tobit regression models indicate that elevation of the site, rotor diameter, hub height, and brand of the turbine have significant contributions to the relative efficiency scores of the wind farms, and the age of turbine has a negative impact on the productive efficiency of the wind farms.arms, and the age of turbine has a negative impact on the productive efficiency of the wind farms.

Implications of Industrialized Building System on Labor Demand and Cost

High dependence on foreign workers in the construction industry has been long known to be one of the contributing factors of labor demand issue. To address this problem, the implementation of new technology innovations, such as the Industrialized Building System (IBS) is suggested. The purpose of this research paper is to identify the implication of IBS on the labor requirement of the construction industry. In this paper, the authors used the survey-questionnaire method. The research involved data from the surveys completed by the contractors registered with the CIDB. Data Envelopment Analysis (DEA) has been applied, and the reliability of the DEA result has also been proved. The result obtained from the Data Envelopment Analysis (DEA), indicates that IBS contractors are still required to hire an unskilled worker to undergo the new technology transition. However, when the adoption rate of IBS gets higher, the issue of substantial influx and dependency on a foreign worker in the construction industry can be resolved gradually. Although the investment cost is high during the initial stage, IBS can help to reduce the construction cost in the long-term.

Investigating the effectiveness of safety costs on productivity and quality enhancement by means of a quantitative approach

Senior managers face a number of challenges in safety area, e.g., to what extent it should be invested in safety programs. This study aims at determining the effectiveness of safety cost factors in productivity, quality, and safety performance in steel industry. The data related to five steel companies were gathered from documents of the companies. These data pertained to five safety cost indicators and eight indices relating to safety performance, productivity, and quality. Data envelopment analysis (DEA) approach was used to compute the influence of safety costs and to analyze the obtained results. The results of DEA demonstrated that safety cost indicators would positively affect the performance of decision-making units (DMUs) in this study. Furthermore, the results regarding sensitivity analysis indicates that Capita Total Safety Costs (CTSC) plays a considerable role in the enhancement of productivity and quality in this particular case study. To the best of the researchers' knowledge, this is the first study that evaluates the influence of safety cost indicators on productivity, quality, and safety performance through a quantitative method in steel industry. Therefore, it can assist managers and stockholders invest purposefully in safety and health programs to reduce interruptions and increase productivity.

Advanced scoring method of eco-efficiency in European cities

This paper analyzes a set of selected German and French cities' performance in terms of the relative behavior of their eco-efficiencies, computed as the ratio of their gross domestic product (GDP) over their CO2 emissions. For this analysis, eco-efficiency scores of the selected cities are computed using the data envelopment analysis (DEA) technique, taking the eco-efficiencies as outputs, and the inputs being the energy consumption, the population density, the labor productivity, the resource productivity, and the patents per inhabitant. Once DEA results are analyzed, the Malmquist productivity indexes (MPI) are used to assess the time evolution of the technical efficiency, technological efficiency, and productivity of the cities over the window periods 2000 to 2005 and 2005 to 2008. Some of the main conclusions are that (1) most of the analyzed cities seem to have suboptimal scales, being one of the causes of their inefficiency; (2) there is evidence that high GDP over CO2 emissions does not imply high eco-efficiency scores, meaning that DEA like approaches are useful to complement more simplistic ranking procedures, pointing out potential inefficiencies at the input levels; (3) efficiencies performed worse during the period 2000-2005 than during the period 2005-2008, suggesting the possibility of corrective actions taken during or at the end of the first period but impacting only on the second period, probably due to an increasing environmental awareness of policymakers and governors; and (4) MPI analysis shows a positive technological evolution of all cities, according to the general technological evolution of the reference cities, reflecting a generalized convergence of most cities to their technological frontier and therefore an evolution in the right direction.

Assessment of the productive efficiency of large wind farms in the United States: An application of two-stage data envelopment analysis

Wind power is one of the most promising renewable energy sources that has gained enormous attention, especially in the electricity generation sector over the past decade in the United States. In this study Data Envelopment Analysis (DEA) is implemented to quantitatively evaluate the relative efficiencies of the 236 large utility-scale wind farms. Input- and output-oriented CCR (Charnes, Cooper, and Rhodes) and BCC (Banker, Charnes, and Cooper) models are applied to pre-determined three input and three output variables. The sensitivity analysis is conducted for the robustness of DEA by introducing seven new models with the various combinations of input and output variables of the original model. Tobit regression models are developed for the second stage of the analysis to investigate the effects of specifications of the wind turbine technologies. DEA results indicate that two-thirds of the wind farms are operated efficiently. On average, 70% of the wind farms have a considerable potential for further improvement in operational productivity by expanding these wind farm projects, 24% of them should reduce their operational size to increase their productivity level, and 6% of them are operating wind power at the most productive scale size. Nonparametric statistical tests show that the most efficient wind farms are located in Oklahoma because of the relatively high wind speed resources. Tobit regression model indicates the selection of the brand of the wind turbine has a significant contribution to the productive efficiency of the wind farms. The results of this study shed some light on the current efficiency assessments of the 236 large utility-scale wind farms in the United States and the future of wind energy for both energy practitioners and policy makers.

Assessment of the Productive Efficiency of Large Wind Farms in the United States: An Application of Two-Stage Data Envelopment Analysis

Wind power is one of the most promising renewable energy sources that has gained enormous attention, especially in the electricity generation sector over the past decade in the United States. In this study Data Envelopment Analysis (DEA) is implemented to quantitatively evaluate the relative efficiencies of the 236 large utility-scale wind farms. Input- and output-oriented CCR (Charnes, Cooper, and Rhodes) and BCC (Banker, Charnes, and Cooper) models are applied to pre-determined three input and three output variables. The sensitivity analysis is conducted for the robustness of DEA by introducing seven new models with the various combinations of input and output variables of the original model. Tobit regression models are developed for the second stage of the analysis to investigate the effects of specifications of the wind turbine technologies. DEA results indicate that two-thirds of the wind farms are operated efficiently. On average, 70% of the wind farms have a considerable potential for further improvement in operational productivity by expanding these wind farm projects, 24% of them should reduce their operational size to increase their productivity level, and 6% of them are operating wind power at the most productive scale size. Nonparametric statistical tests show that the most efficient wind farms are located in Oklahoma because of the relatively high wind speed resources. Tobit regression model indicates the selection of the brand of the wind turbine has a significant contribution to the productive efficiency of the wind farms. The results of this study shed some light on the current efficiency assessments of the 236 large utility-scale wind farms in the United States and the future of wind energy for both energy practitioners and policy makers.

The efficiency of microfinance institutions with problem loans: A directional distance function approach

This article examines the question of whether the inclusion of problem loans leads to any variation in the technical efficiency of microfinance institutions (MFIs). This question has become pertinent as MFIs, which are well known for their excellent asset quality, have been vulnerable to a delinquency crisis worldwide. Traditionally, the efficiency of MFIs has been measured through non-parametric data envelopment analysis (DEA) or parametric stochastic frontier analysis. As both methods are not flexible enough to cover undesirable outputs, we have instead used the method of directional distance function (DDF) that accounts for the joint production of both desirable and undesirable outputs. Using data from 64 large MFIs, this study reveals corroborative evidence that, with the inclusion of at-risk portfolios as undesirable outputs in the efficiency analysis, the scores and rankings of sample MFIs differ significantly from the results of conventional DEA after the use of DDF. MFIs whose numbers of at-risk portfolios are comparatively high have exhibited lower efficiency scores and vice versa. It is therefore critical that MFIs also include problem loans in their efficiency assessment. This would help MFIs get a more accurate picture of their performance as compared to their peers.

The Effect of Electricity Prices on the Productive Efficiency of 39 State's Wind Power

Wind power is the largest renewable energy source which produces a negligible amount of greenhouse gas (GHG) emissions, has gained enormous attention in the electricity generation sector over the past decade in the United States. In this study, a Data Envelopment Analysis (DEA) is implemented to quantitatively evaluate the relative efficiencies of the 39 states’ wind power productivity for the electricity generation. Eight output-oriented CCR (Charnes, Cooper, and Rhodes) models are developed with different combinations of pre-determined four input and five output variables to investigate the effect of electricity prices on the productive efficiency and to test the robustness of the DEA models. The DEA results indicate that two-thirds of the states operate wind power efficiently. Although the high retail price of electricity has a significant contribution to the productive efficiency of the six states, it does not affect the relative efficiency scores of the nineteen states. The location and the size of operation are not advantages or disadvantages for operating wind power at the most productive scale.

Financial performance measurement with the use of financial ratios: case of Mongolian companies

The aim of this paper is to examine the efficiency of Mongolian 100 public companies listed on Mongolian Stock Exchange (MSE) which are divided into 6 major sectors. This study conducts the performance of companies in terms of profitability by using three different output variables i.e., revenue, pretax profit and ROA (Return on Assets). In the beginning of this research, nine variables which are connected with profitability are chosen as output variables, while 24 variables expressing growth, financial structure, solvency, and turnover together with some fundamental financial data are chosen as input variables. 10 variables out of 24 input variables, which determine the financial structure, solvency, and profitability, are chosen as input variables based on the calculation of stepwise regression analysis. Stepwise regression, multi co-linearity analysis are made by SPSS and DEA (Data envelopment analysis) is evaluated by benchmarking package in R excel statistical program covering the period of 2012-2015. This paper uses the input-oriented version of DEA based on financial ratios and some crucial components of a financial statement. The results of DEA show that food and grocery sector was the most efficient, and mining sector was at the second place by its efficiency, while agriculture and service sector were the worse than other sectors.

Financial performance measurement with the use of financial ratios: case of Mongolian companies

The aim of this paper is to examine the efficiency of Mongolian 100 public companies listed on Mongolian Stock Exchange (MSE) which are divided into 6 major sectors. This study conducts the performance of companies in terms of profitability by using three different output variables i.e., revenue, pretax profit and ROA (Return on Assets). In the beginning of this research, nine variables which are connected with profitability are chosen as output variables, while 24 variables expressing growth, financial structure, solvency, and turnover together with some fundamental financial data are chosen as input variables. 10 variables out of 24 input variables, which determine the financial structure, solvency, and profitability, are chosen as input variables based on the calculation of stepwise regression analysis. Stepwise regression, multi co-linearity analysis are made by SPSS and DEA (Data envelopment analysis) is evaluated by benchmarking package in R excel statistical program covering the period of 2012-2015. This paper uses the input-oriented version of DEA based on financial ratios and some crucial components of a financial statement. The results of DEA show that food and grocery sector was the most efficient, and mining sector was at the second place by its efficiency, while agriculture and service sector were the worse than other sectors.

The Productive Efficiency Assessment of Wind Power Generation in the United States

Wind power is the largest renewable energy source which produces a negligible amount of GHG emissions, have gained enormous attention, especially in the electricity generation sector over the past decade in the United States. In this study, a Data Envelopment Analysis (DEA) is developed to quantitatively evaluate the relative efficiencies of the 34 state’s wind power productivity for the electricity generation. An output-oriented CCR (Charnes, Cooper, and Rhodes (1978)) and BCC (Banker, Charnes, and Cooper (1984)) models are applied to pre-determined two input and three output variables. The sensitivity analysis is conducted to test the robustness of the DEA models. The DEA results indicate that more than two-thirds of the states operate wind power efficiently even though there are only five states that reach the maximum efficiency score. Findings of this study shed some light on the current efficiency assessments of the states and the future of wind energy for both energy practitioners and policy makers.

Towards an equitable healthcare in China: evaluating the productive efficiency of community health centers in Jiangsu Province

BackgroundWhile the demand for the health service keeps escalating at the grass root or rural areas of China, a substantial portion of healthcare resources remains stagnant in the more developed cities and this has entrenched health inequity in many parts of China. At its conception, the Deepening Health Care Reform in 2012 China was intended to flush out these discrepancies and promote a more equitable and efficient distribution of health resources. Nearly half a decade of this reform, there are uncertainties as to whether the attainment of the objectives of the reform is in sight.MethodsWe divided Jiangsu Province into 3 zones according to the level of economic and social development i.e. developed, developing, and undeveloped areas. Using a hybrid of Panel data analysis and an augmented Data Envelopment Analysis (DEA), we model human resources, capital inputs of Community Health Centers to comprehensively determine the technical and scale efficiency of community health resources in 3 zones in Jiangsu Province.ResultsWe sampled data and analysed efficiency and productivity growth of 75 Community Health Centers in 13 cities of Jiangsu Province from 2011 to 2015, which shows that a significant productive growth among Community Health Centers between 2011 and 2015. Mirroring the behavior of Community Health Centers, technological progress was the underlying force for the growth and the deterioration in efficiency change was found. This can be credited partly to the Deepening Health Care Reform measures aimed at improving technology availability in health centers in sub-urban areas. The regional summary of the DEA result shows that the stage of economic development and the efficiency performance of hospital did not necessarily go hand in hand among the 3 zones of Jiangsu.ConclusionsThe government of China in general and Jiangsu province in particular could improve the efficiency of health resources allocation by improving the community health service system, rationalizing the allocation of health personnel, optimizing the allocation of material resources and enhancing the level of health of financial resources allocation.

A two-stage data envelopment analysis model for efficiency assessments of 39 state’s wind power in the United States

The average global surface temperature increased by 0.85°C since 1850 because of irrepressible increase of the concentration of greenhouse gases (GHG). Electricity generation is the primary source of GHG emissions in the United States. Hence, renewable energy sources, which produce a negligible amount of GHG emissions, have gained enormous attention, especially in the electricity generation sector over the past decade. Wind power is the second largest renewable energy source to generate electricity in the United States. Therefore, in this study, a two-stage Data Envelopment Analysis (DEA) is developed to quantitatively evaluate the relative efficiencies of the 39 state’s wind power performances for the electricity generation. Both input- and output-oriented CCR (Charnes, Cooper, and Rhodes (1978)) and BCC (Banker, Charnes, and Cooper (1984)) models are applied to pre-determined four input and six output variables. The sensitivity analysis is conducted to test the robustness of the DEA models. Tobit regression models are conducted by using the DEA results for the second stage analysis. The DEA results indicate that more than half of the states operate wind power efficiently. Tobit regression indicates that early installed wind power was more expensive and less productive relative the currently installed wind power. Findings of this study shed some light on the current efficiency assessments of the states and the future of wind energy for both energy practitioners and policy makers.