Profit Efficiency

The ability of Nigerian agriculture to perform its role in the development of the economy has been on the decline in the last three decades. Therefore, the 2030 Agenda's historic commitment to rid the world of the twin scourges of poverty and hunger cannot become a reality if nothing is done about improving resource productivity so as to reduce poverty. This article examined the effect of resource productivity of maize-based farmers on poverty reduction in SouthWest , Nigeria. Multi-stage sampling procedure was used to select 180 respondents and data were collected with the aid of a structured questionnaire. The stochastic frontier production function, poverty index and probit regression model were used to analyze data from the study. Results showed that age, farming experience, cooperative membership, credit, extension visits, farm distance, and land ownership were significant determinants of efficiency of maize-based farmers. The poverty incidence, poverty gap and poverty severity were 42%, 50% and 11.2% respectively. Results further showed that efficiency and other variables were significant determinants of poverty among respondents in the study area. Policies that facilitate increased production of maize, increased level of education, increased cooperative membership, and access to credit are essential to help reducing poverty among maize-based farmers and among the rural poor in general.

The dairy farmers in Iran are faced with milk price distortion due to the market imperfection. To measure an unbiased farm-specific efficiency, prices should be adjusted in an imperfect market. To examine this issue, a shadow-price profit frontier was applied to a sample of 860 Iranian small intensive dairy farms surveyed in 2005-06 in order to calculate profit efficiency of individual dairy farmers. This adjusted measure was then compared with that of unadjusted measure that assumes undistorted market. A multiple general linear model (GLM) technique was applied to the data to examine the multiple effects of pure-bred animals, and the used farm capacity on profit efficiency indices. The mean value of adjusted profit efficiency was 0.40, significantly different from the latter measure, i.e. 0.72, revealing overstating efficiency by ignoring imperfect structure of market. The difference between the figures is attributed to an index of market efficiency that was estimated of 46% in ave...

The study employed a stochastic frontier profit function to measure profit efficiency among rice farmers in North Central, Nigeria. A multi-stage sampling procedure was employed in selecting 218 rice farming household heads in six Local Government Areas of North Central, Nigeria. The results showed that rice production is highly profitable with an estimated net farm income and Gross margin of N67,808.31 and N 70,273.56 per hectare and the return on investment of N1.26 was realized as profit for every one naira invested in production of rice in North Central Nigeria. The gross ratio of 0.46 and the operating ratio of 0.44 are indicators that rice production was profitable in the study area. Furthermore, the result of tranlog stochastic function revealed that rice farmers in North Central were not all fully profit efficient and thus an average farmer in study area could potentially increase profit level if resources are more efficiently utilized. However, the results show that fertili...

Using data envelopment analysis techniques, we evaluate the relative cost and profit efficiencies of a panel of six Singapore-listed banks during the period 1992-96. Average profit efficiency (83%) was found to be significantly lower than average cost efficiency (95%). The mean profit efficiency is, however, higher than the averages of banks in the US (64%) and Spain (72%). In a follow-up regression study using the modified efficiency scores of Anderson and Peterson (Management Science, 39 (1993) 1261-1264), we find that percentage changes in the prices of the bank shares reflect percentage changes in profit rather than cost efficiencies (correlation coefficients of 0.82 versus 0.32). This opens up a new window for understanding share price fluctuations and is to be expected as shareholders desire dividends which are paid out of profits and not income.

Using data envelopment analysis techniques, we evaluate the relative cost and profit efficiencies of a panel of six Singapore-listed banks during the period 1992-96. Average profit efficiency (83%) was found to be significantly lower than average cost efficiency (95%). The mean profit efficiency is, however, higher than the averages of banks in the US (64%) and Spain (72%). In a follow-up regression study using the modified efficiency scores of Anderson and Peterson (Management Science, 39 (1993) 1261-1264), we find that percentage changes in the prices of the bank shares reflect percentage changes in profit rather than cost efficiencies (correlation coefficients of 0.82 versus 0.32). This opens up a new window for understanding share price fluctuations and is to be expected as shareholders desire dividends which are paid out of profits and not income.

Measuring profit efficiency is a challenging task, and many different approaches have been suggested. This paper synthesizes existing approaches and develops a general Farrelltype approach of the profit efficiency measurement. Our derivations unveil new and useful relationships between existing measures and the proposed new Farrell-type measures. In addition, this helps us establish a generalized and unifying framework for studying efficiency behavior of firms, where the profit efficiency measure satisfies desirable properties and contains the conventional Farrell measures of technical efficiency and allocative efficiency as multiplicative components. A new component of the decomposition of profit efficiency, which we call the revenue-efficient allocative efficiency measure, is introduced to identify the necessary changes of input scale and input mix in addition to output changes. The proposed new approach also encompasses and is coherent with profit-maximizing behavior as a benchmark case.

As the most popular measures of technical efficiency, Farrell measures have not been incorporated explicitly in the decomposition of profit efficiency. By synthesizing existing measures of profit efficiency, the paper “A Unifying Framework for Farrell Profit Efficiency Measurement” establishes a general oriented model that includes many existing profit efficiency measures as special cases. Furthermore, the oriented measures generated from this model have consistent meaningful interpretation. This is also perhaps the first time in the literature that the Farrell technical efficiency and allocative efficiency are included as multiplicative components of profit efficiency. A new component is introduced to help decision makers distinguish input changes from output changes in improving profit efficiency.

Rice is staple food in Liberia and therefore self-sufficiency in rice production remains a major concern. However, Liberia is experiencing insufficiency in rice production due to high production cost and low national average output of 1.2 t/ha, which is significantly low as compared to other West African Countries. This has resulted in the increased importation of rice in the country consequently taking significant portion of the nation’s foreign exchange. The best and most effective way to improve productivity and profitability is through more efficient utilization of scare resources. This study was conducted to analyze profit efficiency among smallholder rice farmers in Nimba and Bong Counties, Central Liberia. The results indicated that smallholder rice farmers were not operating at full profit efficiency level. Profit efficiency level varies among the farmers ranging from 13% to 93%, with mean efficiency level of 67%. To operate on the full profit efficiency levels, on average, ...

In this article, we analyze the impact of land fragmentation, farm size, land ownership and crop diversity on farm profit and efficiency of 90 groundwater irrigated farms in the hard rock areas of South India. As we hypothesize that these variables may impact both, farm profit and efficiency in alternative ways, we develop four different stochastic frontier and inefficiency effect models by shifting some of these variables from the inefficiency model into the profit function. The underlining reason is to know the impact of different combination of these structural variables on farm profit and efficiency. Our analysis shows that there exist high levels of inefficiency among the sample farms. Among the variables influencing efficiency, the most pronounced effects are observed with land fragmentation, land ownership and crop diversity. Land fragmentation is positively and significantly associated with inefficiency, whereas land ownership and crop diversity is negatively and significantly associated with inefficiency. In addition land fragmentation can also have a significant negative effect on farm profit. We further find that smaller farms appear to have lower inefficiencies than larger farms due to the more efficient use of inputs by the former category. Importantly, when a farmer owns a failed well, this also contributes to the inefficiency, since well failure increases cost of irrigation. Further the average profit efficiencies are higher for unfragmented farms, large farms, owner operated farms and farms with a diversified cropping pattern as compared with their counterparts. Knowledge on the factors influencing farm profit and efficiency is crucial for policy makers and extension agents for improving efficiency levels of the groundwater irrigated farms especially in water scarce regions of the country.

The dominance of the banking sector shows that the banking industry plays an important role in driving the economy in Indonesia. As part of the national banking industry, Regional Development Banks (RDB) are required to contribute to the economy through optimal performance. One of the important indicators in measuring company performance, including in the banking industry, is the level of efficiency. Efficiency in the banking industry can be observed from a micro and macro perspective, which leads to the ability of banks to survive in conditions of increasingly fierce competition in technology-based products and services, as well as the ability of banks to allocate financial resources to increase investment activities that can stimulate the economy. Therefore, this study examined the level of cost and profit efficiency of the RDB industry in Indonesia for the 2011–2020 period, as well as the internal and external variables that affect RDB inefficiency using Stochastic Frontier Analy...

Many banks are competing to achieve bank cost efficiency for business continuity amidst the competition in the banking sector. So banks need to review cost-efficiency regularly. The purpose of this study was to analyze determinants of bank cost efficiency. Variable use bank size, capital adequacy, return on asset, group of the bank, credit risk, economic growth, and inflation on bank cost efficiency in Indonesia.  Determination of bank input and output variables using a bank intermediation approach. This study used panel data on 38 banks in Indonesia for the period 2012-2018. This paper used the Stochastic Frontier Analysis (SFA) analysis method. The results of this study reveal four things. First, bank size has a negative effect bank cost efficiency. Second, credit risk has a positive effect on bank cost efficiency. Third, capital adequacy has a positive effect on bank efficiency. Fourth, the small core capital bank group has a negative effect on bank cost efficiency. Bank external...

The reason of this study is to examine the technical efficiency of the Malaysian domestic banks listed on the Kuala Lumpur Stock Exchange (KLSE) market over the period of 2005-2010. A parametric approach, Stochastic Frontier Approach (SFA) is used in this analysis. The findings show that Malaysian domestic banks have exhibited an average overall efficiency of 55 percent, implying sample banks have wasted on average 45 percent of their inputs. Among the banks, MAYBANK is highly efficient with score 0.969 and AFFIN Bank is lowest efficient with score 0.228. The results also find that the level of efficiency has increased during the period of study and Translog Production Function is preferable than Cobb-Douglas Production Function.

The efficiency literature, both using parametric and non-parametric methods, has been focusing mainly on cost efficiency analysis rather than on profit efficiency. In for-profit organisations, however, the measurement of profit efficiency and its decomposition into technical and allocative efficiency is particularly relevant. In this paper a newly developed method is used to measure profit efficiency and to identify the sources of any shortfall in profitability (technical and/or allocative inefficiency). The method is applied to a set of Portuguese bank branches first assuming long run and then a short run profit maximisation objective. In the long run most of the scope for profit improvement of bank branches is by becoming more allocatively efficient. In the short run most of profit gain can be realised through higher technical efficiency.

I use the stochastic frontier methodology to estimate a cost and a profit frontier functions. The Fourier-flexible form is used in this paper because of its flexibility. Results show that, although foreign banks are more cost efficient than domestic banks, domestic banks are more profit efficient than foreign banks, in Haiti. The paper reveals also that, although treasury bills constitute an alternative source of profit for banks in Haiti, a growth of interest rate on treasury bills increases profit efficiency in current period whereas it decreases profit efficiency one period after this growth. The main implication of this paper is that foreign banks are not always more efficient than domestic banks in developing countries, and even in a country with low income level.

In this paper we analyze the impact of the current financial crisis on the determination of technical efficiency in a sample of Italian small banks, highlighting the interaction of the crisis with different regulatory regimes existing for cooperative banks (CB’s) and other banks. We find that the crisis has a negative impact on efficiency, more so for CB's. This is to be expected, as the CB's principle of external mutuality and their branching regulations are likely to locate them in less performing areas. In accordance with this prior, the differential impact of the crisis attenuates or vanishes when we include in the production set some indicators of local environment (GDP per capita). Correspondingly, we find novel evidence in favour of the “bad luck” hypothesis suggested by Berger and De Young (Journal of Banking and Finance, 1997).

This paper examines convergence of cost and profit efficiency levels in the Latin American and Caribbean banking systems, using dynamic data panel models. The test results indicate suggesting that both beta-convergence and sigma-convergence in cost and profit efficiency have occurred among the 16 Latin American banking sectors. In contrast to these results, when we estimate the Phillips and Sul ( ) tests for overall group convergence, heterogeneous convergence patterns are obtained depending on type of efficiency and the time window examined. Regarding cost efficiency, convergence is rejected over the whole sample period. Regarding profit efficiency, convergence is detected only when considering the largest time spans. The initial beta and sigma convergence results are further reinforce when we look at club convergence within the Phillip and Sul context. These results indicate club convergence both in cost and profit efficiency. However, despite some weak common trends across group, club formation is sensitive to the time span examined. Taken together, the various pieces of evidence support the view that convergence in the region seems to occurring with heterogeneity across countries and with different paces with regard to type of efficiency.

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This study intends to test, analyze, and verify the influence of bank size, capital adequacy, liquidity, credit risk, and market power on commercial banks profitability. Quantitative research methods applied in this study are explanatory method, which aims to analyze the influence of independent variables on dependent variable and descriptive method to describe the object studied. The study also applies the Stochastic Frontier Analysis (SFA) approach to estimate the technical efficiency of commercial banks. The results show that bank size, capital adequacy (CAR), liquidity (LDR), credit risk (NPL) and market power significantly affect the profitability of commercial banks in Indonesia in the period of 2010-2016. The result of yearly financial report of each bank is caused by the fact that: 1). some banks are in the process of mergers; 2). the allowance for impairment losses on financial assets and non-financial assets increased primarily with banks in the merger process; 3). banks have credits in default status and under special surveillance with an increasing amount of credits from year to year.

Objective-The banking sector plays an important role in the Indonesian economy. The sustainability of the Indonesian banking sector will depend on the ability of every banking institution to maintain their competitiveness. Banking competitiveness is reflected in the level of efficiency of the banking system itself. Methodology/Technique-The purpose of this research is to assess the efficiency of 21 banks on the IDX between 2008-2017 using Stochastic Frontier Analysis. Findings-The findings of this research show a maximum efficiency score of 0.69 and the bank's average score among the research sample with the input and output allocation which can generate profits is 0.69-0.43 = 0.26. Overall, the banking sector in the Indonesian capital market between 2008-2017 recorded an efficiency score of 0.43. With this score, the banking system in the Indonesian capital market is still considered to be inefficient (0.43 <0.5). The results also demonstrate that the distribution of efficiency scores polarized in two poles. In the first pole, there were 11 banks that had a relatively good efficiency score (> = 0,5) and in the second pole, there were 10 banks with low efficiency scores (less than 0.5). Novelty-From the results, it can be concluded that several output variables, such as total loans (Y1) and securities (Y3), and input variables such as prices of labor (W2) and inflation (Z), have a significant effect on banking profits. Meanwhile, input variables such as the price of fund variables or the total funds (W1) and the price of physical capital were reflected in the depreciation of fixed assets (W3), and the output variables of income and interest (Y2) had an insignificant effect on bank profits.

In this paper, our research question that could analyze how efficiency in Swedish financial enterprises has changed since the banking crisis in 1993. We estimate the time-invariant and time-variant efficiencies of Swedish financial enterprises with four different estimators. These estimators are the Pooled Model (Aigner et al., 1977), the fixed effects model (Schmidt & Sickles, 1984), the random effects model (Battese & Coelli, 1995) and the TRUE fixed effects model (Greene, 2005) efficiency estimators. We predict cost function by employing panel stochastic frontier approach. These allow us to construct cost efficiency.

DEA (Data Envelopment Analysis) is the optimization method of mathematical programming to measure the relative efficiencies of decision making units (DMUs). Due to its wide applicability, the DEA has been studied extensively for the last 30 years to solve decision making problems. Since, there are a lot of selection decisions in manufacturing, DEA as an appropriate tool to be necessary-especially for engineers-to improve learning for decision making. In this paper, the DEA method is applied in decision making process through DEA Excel-Solver software and the required processes are explained step by step to help academics and practitioners to get appropriate results in making decision.

Compared with previous crises few banks failed as a result of the U.S. financial crisis of 2007-2009. We investigate the role played by managerial efficiency in the non-systemic bank failures during the crisis. During previous waves of bank failures, cost-inefficient banks and banks with relatively less capital or low-quality assets were more likely to fail. Using data from 2001 to 2010, we show that profit inefficiency—our proxy for managerial inefficiency— is a robust predictor of bank failures while cost inefficiency is unrelated to them. In addition, capital adequacy lost importance in predicting non-systemic bank failures during the crisis while loan quality remained a strong predictor. Our results suggest that profit efficiency can be an important managerial indicator in monitoring banks.

This paper is, to my knowledge, the first to estimate a stochastic frontier model in which foreign bank ownership interacts with both the frontier and the inefficiency effects. Few studies in this field have focused on countries with a low income level, such as some African and Caribbean nations. This paper considers the case of Haiti, which is an excellent example of a developing economy with a low income level. The results reveal that foreign banks significantly impact both the frontier and the inefficiency effects. Although they enjoy technological progress, compared with domestic banks, foreign banks are less profit efficient.

There are plenty of studies in the literature of Data Envelopment Analysis (DEA) to handle real data, but a few are about those data which are restricted to the set of integer numbers, such as the numbers of employees, vehicles, publications, goods and so on. Rounding the results of DEA models based on the robust DEA axioms (observation, convexity and free disposability) can be introduced as a simple approach to remove such gaps; however, while the integer DEA axioms are considered, linear integer DEA models do not mathematically represent the Integer Production Possibility Set (IPPS). Therefore, the benchmarking may not be appropriate. In this paper, the gap is clearly elucidated, a proposed model to remove the gap is introduced, and the validity of the method is mathematically proved. A real-life application on university efficiency is examined to depict the differences of several integer DEA models in benchmarking decision making units (DMUs).

Sending children to kindergarten at the early age can be considered as very important responsibilities for parent nowadays. In this study, it is supposed that a parent wants to get decision which kindergarten from 12 kindergartens of Selangor, Malaysia, should be selected to enroll their child. For each kindergarten 6 factors are selected inclusive 4 inputs such as expected number of students, cost of operating in a month, time in travelling and tuition fee per student, and 2 outputs such as number of teachers and area of school. The study shows how a recent robust Data Envelopment Analysis (DEA) model, Kourosh and Arash Model (KAM) is applied to rank all kindergartens significantly, and suggests the best selection to the parent. Microsoft Excel Solver 2013 is used to solve KAM and represent its outcomes.

Arash Method (AM) was proposed to measure the instabilities of technically efficient Decision Making Units (DMUs) where very small errors are introduced in the input values. In this study, the instabilities of technically efficient DMUs are measured when very small errors are defined in the output values. The method is able to measure the revenue efficiency and has the advantages of AM in comparison with Slacked Based Measure model (SBM). Indeed, the model not only has all SBM properties, but also similar to AM it is appropriately able to arrange both technically efficient and inefficient DMUs.

Usually in many applications some of inputs or outputs data may characteristically be integer values such as the number of students, hospitals and vehicles. However, the traditional Data Envelopment Analysis (DEA) models would project a Decision Making Unit (DMU) onto targets that generally do not respect such type of integrality constraints. There are some methods in DEA to assess the performance of DMUs inclusive integer-valued data. This study surveys the previous Mixed Integer Linear Programming (MILP) and illustrates the flaw of them with some counter examples. The study improves the previous MILP models and characterizes its capabilities with a numerical example. The simulations have been also performed with Lingo11/win64 software.

Arash Method (AM) is a new technique in Data Envelopment Analysis (DEA), which estimates the performance of Decision Making Units (DMUs) with flexible linear programming based on Additive DEA model (ADD). It is simultaneously able to discriminate technically efficient DMUs and/or inefficient ones without using statistical techniques, super-efficiency methods or requiring additional information in the case of weight restrictions. It simultaneously benchmarks both inefficient and technically efficient DMUs. AM is also able to measure the cost-efficiency of DMUs when cost information is available. It can be extended as a non-linear programming to have all the properties of linear AM and all capabilities of the Slack Based Measure (SBM) model. A practical definition based on AM score not only can find the best technically efficient DMUs, where small errors are introduced in their input values even if data are accurate, but it also provides an assurance that "small" errors in the measurement of input quantities did not result in "large" errors in the calculation of the efficiency index, which prompted introducing the axioms of continuity. This study clearly discusses about the capabilities of AM in comparison with most of previous DEA models with some numerical examples.

Business operations can be more efficient by measuring the performance evaluation and estimating the production frontier as well as benchmarking and ranking Decision Making Units (DMUs). Data Envelopment Analysis (DEA) is a non-parametric technique to calculate these assessments. This paper introduces a new robust technique for approximating the S-shape empirically frontier while simultaneously benchmarking and ranking DMUs. Firstly, the main foundations of the new technique are illustrated with clear examples followed by depicting its properties and capabilities. A numerical example is also illustrated to show the advantages of the method for measuring the performance evaluation of DMUs. It is discussed how small errors in inputs and outputs may change the efficiency scores of DMUs significantly as well as benchmark and rank technically efficient and inefficient DMUs at the same time. The results represent whether a DMU has a good efficient combination of its data or how a DMU can regulate its inputs and outputs to improve its efficiency in response to small errors in the data. The simulations are performed with Microsoft Excel Solver as it required simple linear programming.

Islamic Republic of Iran has 8 international airports. This paper reports the performance evaluation of these airports in the end of forth development plan of Iran in 2009 by applying Kourosh and Arash Model (KAM) in Data Envelopment Analysis (DEA). The area of airport, apron, terminal and runway are considered as inputs and the number of flights, the number of passengers and cargo are three selected outputs for each airport. Several scenarios are considered to rank and benchmark these airports without concerning about the number of airports. In other words, the scenarios are when inputs/outputs are controllable, when some of them are non-controllable and when the number of flights and passengers are restricted to the set of integer numbers and so on while the number of inputs and outputs are approximately the same as the number of airports. The results of these scenarios not only show the robustness of KAM to assess the performance evaluation of Decision Making Units (DMUs), but they also suggest the best international airports of Iran in 2009 as well as rank and benchmark them for each scenario.

This article illustrates how Kourosh and Arash Model (KAM) is able to measure profit-efficiency of Decision Making Units (DMUs) in Data Envelopment Analysis (DEA). The advantages of KAM in comparison with the current profit efficiency DEA models proposed by Cooper et al. (2007) and Coelli et al. (2005) are also illustrated with a clear example.

Arash Method (AM) was proposed to measure the instabilities of technically efficient Decision Making Units (DMUs) where very small errors are introduced in the input values. In this study, the instabilities of technically efficient DMUs are measured when very small errors are defined in the output values. The method is able to measure the revenue efficiency and has the advantages of AM in comparison with Slacked Based Measure model (SBM). Indeed, the model not only has all SBM properties, but also similar to AM it is appropriately able to arrange both technically efficient and inefficient DMUs.

Arash Method (AM) is a new technique in Data Envelopment Analysis (DEA), which estimates the performance of Decision Making Units (DMUs) with flexible linear programming based on Additive DEA model (ADD). It is simultaneously able to discriminate technically efficient DMUs and/or inefficient ones without using statistical techniques, super-efficiency methods or requiring additional information in the case of weight restrictions. It simultaneously benchmarks both inefficient and technically efficient DMUs. AM is also able to measure the cost-efficiency of DMUs when cost information is available. It can be extended as a non-linear programming to have all the properties of linear AM and all capabilities of the Slack Based Measure (SBM) model. A practical definition based on AM score not only can find the best technically efficient DMUs, where small errors are introduced in their input values even if data are accurate, but it also provides an assurance that “small” errors in the measurement of input quantities did not result in “large” errors in the calculation of the efficiency index, which prompted introducing the axioms of continuity. This study clearly discusses about the capabilities of AM in comparison with most of previous DEA models with some numerical examples.

Usually in many applications some of inputs or outputs data may characteristically be integer values such as the number of students, hospitals and vehicles. However, the traditional data envelopment analysis (DEA) models would project a decision making unit (DMU) onto targets that generally do not respect such type of integrality constraints. There are some methods in DEA to assess the performance of those DMUs with integer-valued data. This study surveys the previous Mixed Integer linear programming (MILP) and illustrates the flaw of them with some counter examples. The study improves the previous MILP models and characterizes its capabilities with a numerical example. The simulations have been also performed with Lingo11 win64 software.

Arash Method (AM) was recently proposed to distinguish between efficient decision making units (DMUs) and technical efficient ones where there are no any prices, weights or other assumptions between inputs and outputs of DMUs. This paper illustrates that AM is also able to evaluate the cost-efficiency of DMUs. Indeed, this paper represents that there is no require using cost-efficiency model with possessing AM with a proposition and some examples.

The uncontrollable data such as environmental variables are a kind of data which cannot be controlled by managers or users, but they may affect in assessing the performance evaluation of decision making units (DMU). In this paper, the Arash Method (AM) is improved to assess the efficiency scores of DMUs which have some uncontrollable data. Moreover, the paper illustrates how it would be possible to examine the effects of uncontrollable data to find the efficient DMUs among the technical efficient ones and also arrange both inefficient and technical efficient DMUs at the same time. A numerical example is also considered to depict the validity and capabilities of the proposed extension Arash Method.

Airport efficiency with data envelopment analysis (DEA) is one of the common research studies in the last decade. DEA is a nonparametric method in operations research which has been provided some significant tools to estimate the performance evaluation, efficiency and productivity of homogenous decision making units (DMUs) such as firms, factories and organizations. Although, the conventional DEA models (radial or non-radial) have been usually applied in airport efficiency studies, those models are not almost always able to distinguish between efficient DMUs and technical efficient ones. Recently, Arash Method (AM) was proposed in DEA to improve the previous DEA techniques and remove their shortcomings to arrange both inefficient and technical efficient DMUs together. In this paper, the importance of using AM in DEA to assess the efficiency of airports is illustrated. The method is examined with a numerical example of 17 airports with four inputs (apron, number of baggage belts, check-incounter and boarding gates) and three outputs (passenger movements, aircraft operations and cargo). The results characterize how the neglect in differences between efficiency and technical efficiency definitions can introduce a weak performer as a strong one which does the jobs right.

We study the efficiency of Community Development Loan Funds (CDLFs) in the U.S.A. between 2002 and 2005. We find that the largest CDLFs tend to be most efficient and that efficiency decreases with age and the proportion of minority representation on the board, while board size does not affect efficiency.