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The purpose of this paper is to investigate the input- and output-specific technical inefficiency of Vietnamese prawn-rice rotational crops (PRRC) and to identify the impacts of the socio-economic characteristics of farmers and farms on these technical inefficiencies.

This study first used a Russell-type (input-output) directional distance function to estimate the input- and output-specific technical inefficiency. Second, it applied a bootstrap truncated regression to analyze the factors influencing these technical inefficiencies. Data were gathered through a survey among 94 farmers, from Ben Tre and Kien Giang provinces, the two popular PRRC areas in the Mekong Delta.

Results show that Vietnamese PRRC farmers could reduce the water surface area by 3%, the use of seedlings by 15%, labor by 16%, fertilizers by 26%, and the use of others by 24%, while simultaneously increasing the revenue of farming system by 57% relative to the variable returns to scale (VRS) frontier. Farmers with more years of experience are generally better in managing the use of seedlings and in improvement of revenue. Farmers in Kien Giang province are more efficient in achieving revenue of the PRRC farming system than farmers in Ben Tre province.

Outcomes of this study are useful to identify strategies in minimizing the use of inputs while simultaneously maximizing PRRC production.

This paper relates to the comparison of two mostly different ecological zones, being the dominant production areas of PRRC, in which, Kien Giang represents the western part, while Ben Tre is in the eastern part of the Vietnam’s Mekong Delta. The findings not only expand the current understanding but also suggest various meaningful research questions regarding the development of Vietnamese PRRC under the impacts of climate change. The study also contributes to the literature on examining the input- and output-specific technical inefficiencies and influencing factors.

The paper investigates relative efficiency of the banking industry in Bahrain by employing a panel of 31 banks for the years 1998 and 2000. We employ non‐parametric (Data Envelopment Analysis) to examine five efficiency measures, namely, cost, allocative, technical, pure technical and scale efficiency scores. We also investigate the conventional accounting measures of performance, and correlate them with five measures of efficiency to investigate whether higher accounting performance impact the bank cost efficiency. Our results show that, on the average, the banking industry in Bahrain is profitable with average ROE and ROA being 10.36% 1.622% in 1998 while 13.49% and 2.097% in 2000 respectively. The average allocative efficiency (inefficiency) is about 73% (37%), whereas the average technical efficiency (inefficiency) is about 56% (78%). This indicates that the dominant source of inefficiency in Bahrain banks is due to technical inefficiency rather than allocative inefficiency, which is mainly attributed to diseconomies in scale. Overall, average scale efficiency (inefficiency) is about 79% (26%), and average pure technical efficiency (inefficiency) is about 71% (41%), suggesting that the major source of the total technical inefficiency for Bahrain banks is pure technical inefficiency (input related) and not scale inefficiency (output related). The results also indicate that all banks have improved their efficiency levels and experienced some gains in productivity. Finally, regression analysis is used to investigate the determinants of the overall efficiency scores. We find that larger and profitable banks are more likely to operate at a higher level of efficiency. Also, another finding reveals that market power plays an important role in cost and technical efficiencies. Notably, banks with greater contribution from shareholders tend to be more technical efficient

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

The purpose of this paper is to measure technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from actual inefficiency using comprehensive household-level panel data.

This paper estimates technical efficiency based on the true random-effects stochastic production frontier estimator with a Mundlak adjustment. By utilising comprehensive panel data with 4,694 observations from 39 districts of four major maize-producing regions in Ethiopia, the author measures technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from technical inefficiency. By using competing stochastic production frontier estimators, the author provides insights into the influence of farm heterogeneity on measuring farm efficiency and the subsequent impact on the ranking of farmers based on their efficiency scores.

The study results indicate that ignoring unobservable farmer heterogeneity leads to a downwards bias of technical efficiency estimates with a consequent effect on the ranking of farmers based on their efficiency scores. The mean technical efficiency score implied that about a 34% increase in maize productivity can be achieved with the current input use and technology in Ethiopia. The key determinants of the technical inefficiency of maize farmers are the age, gender and formal education level of the household head, household size, income, livestock ownership, and participation in off-farm activities.

While the findings of this study are critical for informing policy on improving agricultural production and productivity, a few important things are worth considering in terms of the generalisability of the findings. First, the study relied on secondary data, so only a snapshot of environmental factors was accounted for in the empirical estimations. Second, there could be other sources of unmeasured potential sources of heterogeneity caused by persistent technical inefficiency and endogeneity of inputs. Third, the study is limited to one country. Therefore, future research should extend the analysis to ensure the generalisability of the empirical findings regarding the extent to which unmeasured potential sources of heterogeneity caused by persistent technical inefficiency, endogeneity of inputs and other unobservable country-specific features – such as geographical differences.

This paper contributes to the literature on agricultural productivity and efficiency by providing new evidence on the influence of unobservable heterogeneity in a farm efficiency analysis. While agricultural production is characterised by heterogeneous production conditions, the influence of unobservable farm heterogeneity has generally been ignored in technical efficiency estimations, particularly in the context of smallholder farming. The value of this paper comes from disentailing producer-specific random heterogeneity from the actual inefficiency.

This paper proposes the use of a gauge function as a measure of technical efficiency. The measure of technical inefficiency from a gauge function is desirable as the estimation of a gauge function is not subject to the endogeneity problem under the behavioral assumption of profit maximization in the competitive market.

The authors address three important properties of a gauge function, i.e. linear homogeneity, monotonicity and convexity in inputs and outputs, and show how such properties are utilized in its estimation. Then, the authors apply the estimation of a gauge function to US Blacksmiths in 1850 and 1880 to show that a failure to satisfy such properties may lead to an incorrect inference on the technical efficiency.

The authors find that the Blacksmiths in the 1850s were technically more efficient than the ones in the 1880s, indicating technical regress in Blacksmithing when the properties are satisfied.

This paper introduces a measure of technical inefficiency from a gauge function and shows how to estimate the gauge function parametrically for the measure. The authors show McFadden's gauge function and its properties, which differ from the properties of other distance functions. The authors emphasize linear homogeneity as well as monotonicity and convexity in inputs and outputs, which must be satisfied in the estimation of a gauge function.

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.

This paper aims to evaluate the technical efficiency of the health-care systems in 21 selected middle-income countries during the period (2000–2017) and determine the source of inefficiency whether it is transient (short run) or persistent (long run).

The study uses the stochastic frontier analysis technique through employing the generalized true random effects model which overcomes the drawbacks of the previously introduced stochastic frontier models and allows for the separation between unobserved heterogeneity, persistent inefficiency and transient inefficiency.

Persistent efficiency is lower than the transient efficiency; hence, there are more efficiency gains that can be made by the selected countries by adopting long-term policies that aim at reforming the structure of the health-care system in the less efficient countries such as South Africa and Russia. The most efficient countries are Vietnam, Mexico and China which adopted a social health insurance that covers almost the whole population with the aim of increasing access to health-care services. Also, decentralization in health-care has assisted in adopting health-care policies that are suitable for both the rural and urban areas based on their specific conditions and health-care needs. A key success in the implementation of the adopted long-term policies by those countries is the continuous monitoring and evaluation of their outcomes and comparing them with the predefined targets and conducting any necessary modifications to ensure their movement in the right path to achieve their goals.

Although several studies have evaluated the technical efficiency both across and within countries using non-parametric (data envelopment analysis) and parametric (stochastic frontier analysis) approaches, to the best of the authors’ knowledge, this is the first attempt to evaluate the technical efficiency of selected middle-income countries during the period (2000–2017) using the generalized true random effects stochastic frontier analysis model.

This paper aims to analyze farm-level technical inefficiency of rice farming in Assam, India, using a multiple-output generalized stochastic frontier framework.

Primary data for this study were collected in 2009-2010 from 310 farm-households in four non-contiguous districts of Dhubri, Morigaon, Dibrugarh and Cachar that are located in different agro-climatic regions of Assam. Based on a Cobb–Douglas production function for multiple rice varieties, the paper simultaneously estimates the generalized stochastic production frontier and examines effects of exogenous factors on farm-level technical inefficiency.

Results of this study show that the average technical inefficiency of farms is 8.5 per cent in the sample. Further, inefficiency is lower in the frequently flood prone areas, and availability of government support helps reduce such inefficiency as well. However, technical efficiency is higher for the Muslim farm-households, and it decreases with greater land fragmentation. The study also finds that the use of primitive technology like bullock reduces technical efficiency of rice farming.

This paper is based on a novel data set that has specially been collected to examine productivity and efficiency of rice cultivation in the flood plains of Assam that has not been studied before. Further, to the best of the authors’ knowledge, this paper is the first one to model rice production as a multiple-output stochastic production frontier and analyze technical efficiency of rice production accordingly.

The purpose of this paper is to explore the issues of efficiency, economies of scale and technical change in Southern European banking.

The flexible Fourier functional form of the stochastic cost frontier approach is used to calculate inefficiencies for a large sample of Southern European banks between 1999 and 2004. Economies of scale estimates are calculated by estimating firm‐specific cost elasticities.

The findings suggest that the largest sized banks are generally the least efficient banks and the smallest sized banks are the most efficient. The strongest economies of scale are displayed by Spanish banks, while the weakest economies of scale are reported by Greek banks. The impact of technical change in reducing bank costs (generally about 3 and 4 per cent per annum) does not appear to differ according to bank size.

This study does not distinguish private banks from public or mutual banks and does not account for the effect of risk on the employment of bank capital.

The value of this paper is to provide new and recent evidence on the very important issue of efficiency and technical change in Southern European markets.

The purpose of this paper is to provide an outline of the major contributions in the literature on the determination of the least distance in data envelopment analysis (DEA). The focus herein is primarily on methodological developments. Specifically, attention is mainly paid to modeling aspects, computational features, the satisfaction of properties and duality. Finally, some promising avenues of future research on this topic are stated.

DEA is a methodology based on mathematical programming for the assessment of relative efficiency of a set of decision-making units (DMUs) that use several inputs to produce several outputs. DEA is classified in the literature as a non-parametric method because it does not assume a particular functional form for the underlying production function and presents, in this sense, some outstanding properties: the efficiency of firms may be evaluated independently on the market prices of the inputs used and outputs produced; it may be easily used with multiple inputs and outputs; a single score of efficiency for each assessed organization is obtained; this technique ranks organizations based on relative efficiency; and finally, it yields benchmarking information. DEA models provide both benchmarking information and efficiency scores for each of the evaluated units when it is applied to a dataset of observations and variables (inputs and outputs). Without a doubt, this benchmarking information gives DEA a distinct advantage over other efficiency methodologies, such as stochastic frontier analysis (SFA). Technical inefficiency is typically measured in DEA as the distance between the observed unit and a “benchmarking” target on the estimated piece-wise linear efficient frontier. The choice of this target is critical for assessing the potential performance of each DMU in the sample, as well as for providing information on how to increase its performance. However, traditional DEA models yield targets that are determined by the “furthest” efficient projection to the evaluated DMU. The projected point on the efficient frontier obtained as such may not be a representative projection for the judged unit, and consequently, some authors in the literature have suggested determining closest targets instead. The general argument behind this idea is that closer targets suggest directions of enhancement for the inputs and outputs of the inefficient units that may lead them to the efficiency with less effort. Indeed, authors like Aparicio et al. (2007) have shown, in an application on airlines, that it is possible to find substantial differences between the targets provided by applying the criterion used by the traditional DEA models, and those obtained when the criterion of closeness is utilized for determining projection points on the efficient frontier. The determination of closest targets is connected to the calculation of the least distance from the evaluated unit to the efficient frontier of the reference technology. In fact, the former is usually computed through solving mathematical programming models associated with minimizing some type of distance (e.g. Euclidean). In this particular respect, the main contribution in the literature is the paper by Briec (1998) on Hölder distance functions, where formally technical inefficiency to the “weakly” efficient frontier is defined through mathematical distances.

All the interesting features of the determination of closest targets from a benchmarking point of view have generated, in recent times, the increasing interest of researchers in the calculation of the least distance to evaluate technical inefficiency (Aparicio et al., 2014a). So, in this paper, we present a general classification of published contributions, mainly from a methodological perspective, and additionally, we indicate avenues for further research on this topic. The approaches that we cite in this paper differ in the way that the idea of similarity is made operative. Similarity is, in this sense, implemented as the closeness between the values of the inputs and/or outputs of the assessed units and those of the obtained projections on the frontier of the reference production possibility set. Similarity may be measured through multiple distances and efficiency measures. In turn, the aim is to globally minimize DEA model slacks to determine the closest efficient targets. However, as we will show later in the text, minimizing a mathematical distance in DEA is not an easy task, as it is equivalent to minimizing the distance to the complement of a polyhedral set, which is not a convex set. This complexity will justify the existence of different alternatives for solving these types of models.

As we are aware, this is the first survey in this topic.