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The consistency of stochastic frontier analysis (SFA) and data envelopment analysis (DEA) cost efficiency measures using a sample of 650 commercial and domestic banks in the United States is investigated based on cluster analysis while accounting for the yearly variation in banks.

Due to the importance of efficiency measures for policy and managerial decision-making, the cost efficiency measures of SFA and DEA estimators are examined according to four criteria: levels, rankings, stability over time and stability over clustering groups. In this paper, we present two clustering methods, Gap Statistic and Dindex, that involve SFA and DEA cost efficiency measures. The clustering approach creates homogeneous groups of banks offering a similar mix of efficiency levels. Hence, each evaluated bank knows the cluster to which it belongs. Furthermore, this paper provides nonparametric statistical tests of SFA and DEA cost efficiency measures estimated with and without a clustering approach.

The results suggest that the clustering approach plays a considerable role in the rankings of US banks. Furthermore, the average SFA and DEA cost efficiency measures over time of the homogeneous US banks are substantially higher than those of the heterogeneous US banks.

This research is the first to provide comparative efficiency measures needed for desirable policy conclusions of heterogeneous and homogeneous US banks.

The paper aims to enrich previous findings for an emerging banking industry such as Vietnam, reporting the difference between the parametric and nonparametric methods when measuring cost efficiency. The purpose of the study is to assess the consistency in issuing policies to improve the cost efficiency of Vietnamese commercial banks.

The cost efficiency of banks is assessed through the data envelopment analysis (DEA) and the stochastic frontier analysis (SFA). Next, five tests are conducted in succession to analyze the differences in cost efficiency measured by these two methods, including the distribution, the rankings, the identification of the best and worst banks, the time consistency and the determinants of efficiency frontier. The data are collected from the annual financial statements of Vietnamese banks during 2005–2017.

The results show that the cost efficiency obtained under the SFA models is more consistent than under the DEA models. However, the DEA-based efficiency scores are more similar in ranking order and stability over time. The inconsistency in efficiency characteristics under two different methods reminds policy makers and bank administrators to compare and select the appropriate efficiency frontier measure for each stage and specific economic conditions.

This paper shows the need to control for heterogeneity over banking groups and time as well as for random noise and outliers when measuring the cost efficiency.

The purpose of this paper is to estimate and compare the efficiency of several water utilities using three frontier techniques. Moreover, this study estimates the impact of several qualities of service variables on water utilities’ performance.

The paper utilizes three frontier techniques such as data envelopment analysis (DEA), stochastic frontier analysis (SFA) and stochastic non-parametric envelopment of data (StoNED) to estimate efficiency scores.

Efficiency scores for each methodological approach were different being on average, 0.745, 0.857 and 0.933 for SFA, DEA and StoNED methods, respectively. Moreover, it was evidenced that water leakage had a statistically significant impact on water utilities’ costs.

The choice of an adequate and robust method for benchmarking the efficiency of water utilities is very relevant for water regulators because it affects decision making process such as water tariffs and design incentives to improve the performance and quality of service of water utilities.

This paper evaluates and compares the performance of a sample of water utilities using three different frontier methods. It has been revealed that the choice of the efficiency assessment method matters. Unlike SFA and DEA, a lower variability was shown in the efficiency scores obtained from the StoNED method.

The purpose of this paper is to evaluate the importance of financial liquidity and solvency on US commercial and domestic banks’ cost efficiency while accounting for internal and external factors.

The Stochastic Frontier Analysis and Data Envelopment Analysis estimators are used to estimate the cost efficiency of 11,044  US commercial and domestic banks from 2005 to 2017. Using Tobit regression model, the importance of financial liquidity and solvency on cost efficiency is examined.

The results provide evidence that the financial liquidity and solvency negatively impact the cost efficiency of US commercial and domestic banks. Overall, US commercial and domestic banks were inefficient during the financial crisis in comparison to the tranquil period. The importance of financial solvency on the cost efficiency was not statistically significant, while the financial liquidity negatively collapsed because of contagion. Finally, the results provide evidence that the amount of total assets matters in the improvement of the cost efficiency.

This paper estimates and identifies the 2007-2009 financial crisis with liquidity, solvency or both financial factors.

By utilizing the two most commonly used approaches to generate “best practice frontier” to estimate efficiency of observed units, the purpose of this research paper is to estimate technical efficiency for total population of 200 Slovenian municipalities for the 2011 fiscal year.

Stochastic frontier analysis (SFA) and data envelopment analysis (DEA) methods are used to estimate technical efficiency levels. Namely, the majority of studies have utilized these two “traditional” approaches. Since the advantages of one method often represent the disadvantages of the other method, the two methods have been selected to compare the results obtained on the technical efficiency levels.

The results suggest that mean technical inefficiency should be approximately 22-25 percent (SFA method), whereas DEA method suggests the inefficiency in the range 12-18 percent. The DEA approach also suggests that the paper has many more technically efficient units compared to the SFA estimates. Nevertheless, the SFA assessment has revealed that, although on average the inefficiency should be larger compared to the DEA assessment, more than one-third of municipalities should exhibit relatively low levels of inefficiency (less than 5 percent).

This study utilizes both parametric as well as non-parametric approaches to assess the technical efficiency, which is not very common in the empirical literature. Besides, it focusses on the local government efficiency in a post-socialist country.

Since there is no agreement on the consistency of their estimates, the purpose of this paper is to investigate whether parametric stochastic frontier analysis (SFA) and nonparametric data envelopment analysis (DEA) generate consistent bank efficiency assessments.

The authors utilize four alternative efficiency computation models: two DEA technical efficiency models based on constant and variable returns to scale, and two SFA cost efficiency models employing Translog and Fourier functional specifications. An unbalanced panel of 59 Indian banks over 1990‐2007 is employed as a model, developing country, banking market.

The Translog and Fourier specifications in SFA and the constant and variable returns to scale assumptions in DEA are found to rank and identify “best‐practice” and “worst‐practice” approximately in the same order. The association between DEA efficiency estimates and non‐frontier standard performance measures, however, is mixed and inconclusive. Unlike DEA scores, SFA efficiency assessments were found to be consistent with cost and profit ratios and hence are “believable”.

For regulators and bankers alike, the authors' findings highlight the importance of investigating the consistency of efficiency scores across various research methods. They should ensure that frontier‐based efficiency assessments are not simply “artificial constructs” of models' assumptions/specifications.

This paper extends the existing literature by checking jointly the statistical consistency of both DEA technical efficiency scores and SFA cost efficiency scores. The prior studies focus either on technical efficiency or cost efficiency, but not both. Moreover, as far as the authors are aware, this is the first cross‐methodological validation study to focus on bank efficiency in the context of a developing country banking market.

The purpose of this paper is to propose a performance evaluation framework using an integrated approach of stochastic frontier analysis (SFA) and technique of order preference with similarity to ideal solution (TOPSIS) called efficiency ranking method using SFA and TOPSIS (ERM-ST) specifically in the banking sector where service excellence is of prime importance for business growth.

The proposed approach ERM-ST measures the performance of a DMU in the SFA framework by considering multiple outputs and multiple inputs. It is a non-parametric tool which does not need any prior model assumptions which enhances its applicability in real-life business scenarios. Moreover, the efficiency score obtained using the proposed model ERM-ST lies between 0 and 1, unlike in case of super efficiency data envelopment analysis (DEA) which may go well above 1.

The proposed framework is evaluated for its applicability using two various data sets and is further used to evaluate the performance of a group of 26 public sector banks in India. The results obtained by the proposed method ERM-ST are compared with those obtained by super efficiency DEA using Friedman’s test.

The proposed approach ERM-ST is developed to evaluate the performance of a service unit with multiple outputs and multiple inputs in the SFA framework.

This paper aims to examine alternative methods for recording and treating costs in studies of bank efficiency.

This study used stochastic frontier analysis (SFA) models with core costs and total costs to estimate the cost efficiency of banks in two different economies, Vietnam where the banking system is under-developed (and thus is dominated by traditional banking activities) and New Zealand where the banking system is well-developed (and thus non-traditional banking activities play an important role).

The authors found that models using total cost tend to underestimate the banks’ cost efficiency. This underestimation relates to the extent of modern activities in a banking system: it is larger in an advanced banking system (i.e. New Zealand) and smaller in a less-developed banking system (i.e. Vietnam).

Research is limited to two countries, and it would be useful to apply the same technique to other data sets.

The paper suggests a new approach to cost SFA studies in banking.

The paper provides a much more searching analysis of costs in banking than has generally been seen in previous research.

The paper aims to evaluate the technical efficiency of African Microfinance Institutions (MFIs) and examine if there is performance difference by ownership type.

The paper applies stochastic frontier analysis (SFA) assuming that the translog production functions to estimate the technical efficiency of 134 Microfinance Institutions operating in 36 African countries. The parametric SFA is preferred over the non-parametric, as it captures the random and inefficiency effects. Though the suitable approach is SFA, for the purpose of consistency and robustness of the results, the alternative data envelopment analysis (DEA) approach is also run and the results are compared with those derived from SFA.

In our analysis we have found that African MFIs are technically inefficient. The average technical efficiency for the sample institutions is 0.489, which is quite low and suggests that on average, African MFIs are achieving only 48.9 per cent of the maximum achievable output. Our results also revealed the presence of significant technical inefficiencies with considerable differences in inefficiency among the MFIs. Further, we found statistically significant difference in the efficiency performance among the different ownership types of MFIs. More importantly, the NGO and non-bank financial institutions are relatively more efficient, while the cooperatives/credit unions are the least efficient.

The study contributes to the continuing debate on the effect of ownership type on performance of institutions. Moreover, it indicates the importance of using certain approaches and complementing them with other alternatives for a better insight.

The study found that the least efficient type of MFIs are the cooperatives/credit unions. This might be related to the nature of these institutions where the members are owners and borrowers. This might affect efficiency negatively, although it may somehow address the agency problem.

This paper provides an evidence on efficiency performance of African MFIs, taking a large data set and applying SFA. DEA was also used to complement the SFA results. It provides useful empirical evidence and perspective on this important issue for policy makers and analysts.

The study estimates total factor productivity growth (TFPG) and its components of the 4-digit manufacturing industries of chemical and chemical products in India from 1998–1999 to 2017–2018, pre-economic crises period (from 1998–1999 to 2007–2008) and post-economic crises period (from 2008–2009 to 2017–2018) using frontier approaches, that is, data envelope analysis DEA and stochastic frontier approach (SFA). The components of TFPG are technological progress (TP), technical efficiency change (TEC) and economic scale change (SC). It is found that the growth rates of total factor productivity (TFP) in most of the 4-digit industries of chemical and chemical products in India increased during the post-economic crises period (from 2008–2009 to 2017–2018) and the increase in TFPG of them during that period is mainly accounted for by the increase in TP of the same during that period. The TEC of almost all the industries remains the same, however, declined during the post-economic crises (from 2008–2009 to 2017–2018) and SC of them remains very low or even negative during the aforementioned study periods.