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Improvements on the energy efficiency of the induction motors bear on not only these motors but also on the whole industry as a result of preference of these types of motors. In recent projects, energy efficiency of the induction motors is approaching to 90 per cent. The first necessary condition of the efficiency improvements is an accurate estimation of energy efficiency. This study aims to estimate the energy efficiency of induction motors by using three innovative estimation methods.

Data for 307 motors were taken from three different companies and their torque, power, power factor and speed data were used. Three hybrid models were created by estimating the error of three autoregressive (AR)-based efficiency estimation models with the back-propagation artificial neural network (ANN) structure. In these proposed hybrid models, the AR models were supported with artificial neural networks to obtain a minimum estimation error. These three hybrid models were called as AR1-ANN, AR4-ANN and residual-ANN.

Without hybridization of AR models by back-propagation ANNs, the best estimation result was obtained by residual model. On the other hand, for the proposed hybrid models, the best estimation was obtained by AR1-ANN, followed by AR4-ANN and finally the residual-ANN according to ME values.

Proposed AR-ANN hybrid models relieve of longtime experiments for the energy efficiency measurement of induction motors. Furthermore, these AR-ANN models give more accurate results than the available methods in the literature. Engineering value of this research is three different issues in finding energy efficiency. The first one is minimizing of the test cost, the second one is no requirement the test equipment and the third one is not interrupting the motor. Every company that needs motors can use these estimation methods due to the advantages.

Novel three AR-ANN hybrid models for energy efficiency estimation were studied. These novel methods give better response than the other methods which were used for estimation of induction motors in the literature.

The purpose of this paper is to examine the relationship between competition and efficiency in the Ghanaian banking industry.

Data on 26 banks from 2004 to 2011 is used to estimate technical and cost-efficiency scores by the data envelopment analysis while the Boone indicator is employed to proxy for competition. Controlling for bank size, lending, income diversification, tangibility, leverage and profitability, ordinary least squares, instrumental variables and fixed effects estimations are used to estimate the panel regression model. The authors also apply the growth convergence theory to examine the existence of efficiency convergence.

The results points to improvements in cost efficiency (CE) and competition within the banking industry. From the empirical estimations, the findings suggest that competition exerts a positive influence on CE. The authors also find evidence of convergence in both technical and CE.

The study recommends that efforts at improving competitiveness of the banking industry will translate into lower interest rate spread through improved CE. This will ultimately improve access to bank credit and impact positively on economic growth. Future studies could also examine productivity changes and scale economies in the banking industry.

To the authors best knowledge, this is the first study to apply the Boone (2001) indicator in assessing the competitiveness of the Ghanaian banking industry. This is also the first study to examine efficiency convergence within the banking industry in Ghana.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

Examines the applicability of an optional rule for determining ideal product volumes and mixes under various technical and market conditions. Explains an alternative, similar method of estimation for use where data are inadequate. Considers difficulties and areas where the method is of dubious value.

The purpose of this paper is to estimate the degree of technical efficiency, determinants of technical inefficiencies and driving forces behind the production growth for a panel data set collected during the 1998/1999 and 2004/2006 Kharif cropping season, from 452 small-scale rice farming households in the Giridih and Purulia districts of Eastern India.

The estimations of technical efficiency utilize stochastic frontier production function with a sub-model of inefficiency effects at both aggregated farm level and disaggregated plot level where traditional varieties (TVs) and high-yielding varieties (HYVs) are differentiated. The output growth decomposition analysis identifies the main contributor to the total rice production growth.

The results indicate that the sampled farms are operated at moderate levels of technical efficiency. The production of HYV rice is associated with higher technical efficiency compared to TV rice. Farming experience, education attainment, landholding size, the share of non-agricultural income and the share of land in the lower terraces account for the differences in technical inefficiencies across the sampled farms. The decomposition analysis suggests that as technical efficiency decreased, technical change is the main source of production growth during the survey period.

The small sample size applied in the analysis will result in an insufficient representativeness of the study area.

This paper fills the literature gap as estimations of technical efficiency that account for subtle differences in adopted rice varieties are still rare in India.

A brief review of earlier studies on the productivity scenario of Indian industry shows that most of the studies analysed are confined to either parametric approach or growth accounting approach of measuring productivity. At the same time, the few studies based on the non-parametric [namely, Malmquist productivity index (MPI)] overlook the returns to scale conditions as well as the bias involved in the estimation of distance functions. Given this backdrop, this study aims to provide a robust measure of productivity, which considers the returns to scale assumptions and correct for the bias involved in the estimation of productivity.

This study empirically tests for the returns to scale that exists in the chemical and chemical products industry in India. The test result suggests that Ray and Desli (1997) approach of MPI is the appropriate one for the present context. Initially, the conventional Ray and Desli (1997) estimation and decomposition of MPI for the period 2001 to 2017 is being used. Subsequently, to correct for the bias in the estimation of efficiency scores used for the estimation of MPI, the bootstrapping algorithm of Simar and Wilson (2007) has been extended into the context of MPI estimation.

The results from the conventional Malmquist productivity estimates testifies to an improvement of total factor productivity (TFP) in seven out of 16 years under consideration. On the contrary, TFP growth is recorded only in the four years throughout the period after the bias correction. A greater discrepancy between the two measures has been found in the case of scale change factor component of MPI.

The technical change (TC) component positively influences TFP, whereas scale change factor (SCF) deteriorates the TFP condition of this industry. It will be appropriate for these firms to identify and operate under an optimal scale of operation, along with reaping the benefits of technological change. From a methodological perspective, researchers should consider the potential bias that arise in estimation of TFP and use a larger sample whenever possible.

This paper brings in a new perspective to the existing literature on industrial productivity. As against earlier studies, this study empirically tests the returns to scale of the sector under consideration and uses the most appropriate approach to measure productivity. The effect of sampling bias on TFP and its components is analysed.

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.

At the center of apple production province in China, how to increase apple per unit yield and to improve apple production efficiency in Shaanxi become big issues. The objectives of this paper are to investigate the technical efficiency (TE) and cost efficiency (CE) of apple orchard and to analyse determinants which affect apple production efficiency. The paper aims to discuss these issues.

This paper conducts a two‐stage DEA to achieve the objectives. In the first‐stage, a cost minimization model is applied to measure apple production efficiency. Then, a Tobit model is provided to estimate regression coefficients which are used to adjust the efficiency scores in the first‐stage.

The results indicate that TE and CE are rather low in Shaanxi. The research also explores that the inefficient production of apple farmers is caused mainly by two aspects. One is the inefficient apple orchards operation of farmers. The other is the disadvantageous environmental conditions which heavily affect apple growth and fruit bearing situation.

Most of the research are based on national statistical data, and only measure the TE. This paper primarily focuses on the estimation of efficiency scores with the data involved in both quantity and price information of traditional inputs and outputs based on 255 apple farmers in Shaanxi. Specifically, This paper also consider farmer's opportunity cost to which little literature refers. The results not only reveal the efficient determinants of apple production, but also provide suggestions for agricultural policy makers.

This study aims to analyze the efficiency of banks under board gender diversity and to examine the determinants of bank efficiency.

Data for analysis were sourced from annual reports of 21 banks for the period from 2009 to 2017. A two-step framework was used: first, an examination of efficiency scores with and without board gender diversity computed using data envelopment analysis; and second, a regression of board gender diversity as a determinant of bank efficiency using panel estimation on an unbalanced panel data.

The results reveal that gender diversity promotes bank efficiency up to a maximum of two female directors on a nine-member board of directors, suggesting a threshold effect on bank efficiency. Board size improves bank efficiency. Board independence is negatively related to bank efficiency. Also, powerful chief executive officers are detrimental for bank efficiency. Finally, the authors find that ownership structure, bank size, bank age and loan-to-deposit ratio are important factors affecting bank efficiency.

All bank-year observations with no female representation on the board were excluded. As such, this paper is limited to 21 banks. Future research should look at a larger data set and account for dynamic endogeneity.

The paper contributes to bank governance structure, namely, gender composition of boards, and provides an insight for regulators and shareholders to estimate the role of men and women on boards.

The novel feature of the efficiency model used is that it incorporates board gender diversity as an additional input variable, in line with the preposition of proponent of resource dependency theory.

Major structural changes have affected the French banking industry during the second half of the 1980s, what suggests that the French banks were operating with a significant level of inefficiencies before this period. The purpose of this study is to present estimates of X‐Efficiencies and Scale‐Efficiencies in French banks for the 1988–1992 period which followed this wave of changes. The data are annual accounting data for corporate, mutual and savings banks. The sample contains 375 depository banks. By using the “distribution free” method of efficiency estimation, our estimations show that average X‐efficiencies of the French banks are in the range of 70% to 90%. Our results confirm also the existence of scale economies in French banking industry. Scale efficiency estimates show clearly that French banks could reduce average costs by about 15% on average by increasing size in order to reach the efficient size. Note that this result is also in conformity with the hypothesis that some excess capacity could exist in French banking industry.