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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. [ABSTRACT FROM AUTHOR]

This review covers several of the core methodological and empirical developments surrounding stochastic frontier models that incorporate various new forms of dependence. Such models apply naturally to panels where cross-sectional observations on firm productivity correlate over time, but also in situations where various components of the error structure correlate between each other and with input variables. Ignoring such dependence patterns is known to lead to severe biases in the estimates of production functions and to incorrect inference.

Numerous studies have focused on estimating technical inefficiency in electricity distribution firms. However, most of these studies did not distinguish between persistent and transient technical inefficiency. Furthermore, almost none of the studies estimated the cost of input misallocation arising from non-optimal use of inputs. One reason is that the cost function (input distance function) typically used in the literature does not allow for the separation of technical inefficiency and allocative inefficiency. In this study, we estimate both the persistent and transient components of technical inefficiency and input misallocation of Norwegian electricity distribution firms, using panel data from 2000 to 2016. Our modeling and estimation strategy is to use a system approach, consisting of the production function and the first-order conditions of cost minimization. Input misallocation for each pair of inputs is modeled via the first-order conditions of cost minimization. We also estimate the costs of each component of technical inefficiency and input misallocation by deriving the cost function for a multi-output separable production technology. Our modeling and estimation strategy handles endogeneity of inputs. Finally, we allow for inclusion of determinants of persistent and transient technical inefficiency. Our results show that the costs of input misallocation of Norwegian electricity distribution firms are non-negligible. [ABSTRACT FROM AUTHOR]

We study an airport cost function with a four-random components stochastic frontier model that split the inefficiency in two parts: (1) persistent (long-run) and (2) temporary (short-run). We analyze as determinant of both inefficiency types several exogenous factors such as the airport ownership and LCCs share. We apply the model to the Italian airport system during the period 2010–15. We find that (1) average total cost inefficiency is 14%, and that a it is mainly due to temporary inefficiency, (2) that work load units have the largest cost–output elasticity (+0.31%), (3) that price of airport services has the largest cost-input elasticity (+0.50%) while labor has a +0.41% cost elasticity and capital only a +0.09% cost elasticity. There is also evidence of economies of scale. Moreover we find that LCCs reduce both types of inefficiency and that private airports are more cost efficient than public ones. These findings have interesting managerial (e.g., strictly monitoring service costs, expand LCCs flights) and policy (e.g., provide incentive to reduce temporary inefficiency, push airport privatization) implications. [ABSTRACT FROM AUTHOR]

This paper investigates energy use efficiency at the province level in China using the stochastic frontier panel data model approach. The stochastic frontier model is a parametric model which allows for the modeling of the relationship between energy use and its determinants using different control variables. The main control variables in this paper are energy policy and environmental and regulatory variables. This paper uses province level data from all provinces in China for the period 2010–2017. Three different models are estimated accounting for the panel nature of the data; province-specific heterogeneity and province-specific energy inefficiency effects are separated. The models differ because of their underlying assumptions, but they also complement each other. The paper also explains the degree of inefficiency in energy use by its possible determinants, including those related to the public energy policy and environmental regulations. This research supplements existing research from the perspective of energy policy and regional heterogeneity. The paper identifies potential areas for improving energy efficiency in the western and northeastern regions of China. Its findings provide new empirical evidence for estimating and evaluating China’s energy efficiency and a transition to cleaner energy sources and production.

Non-parametric Data Envelopment Analysis (DEA) and parametric Stochastic Frontier Analysis (SFA) have become very popular in the analysis of productive efficiency. This study undertakes a comparison of the SFA and the constant returns to scale (CRS) and variable returns to scale (VRS) output-oriented DEA models, based on a sample of 240 farms in Bangladesh. However, the aim of this paper is not only to compare estimates of technical efficiency obtained from the two approaches, but also to examine the determinants of inefficiency. The detection and measurement of technical inefficiency and its determinants can be used as a basis of policy recommendations. The results from both the approaches indicated that there is a potential for increasing farm production through efficiency improvement and that farmers' age and educational level have a significant effect on efficiency. [ABSTRACT FROM AUTHOR]

Abstract: The purposes of this research were to measure the technical efficiency of sample vegetable farms and subsequently to explore determinants of technical inefficiency in the Samsun province of Turkey. Stochastic Frontier Analysis was used to measure technical efficiency. Farm managers from 75 randomly selected farms were interviewed for farm level data in the 2002–2003 production periods. Research results revealed that the average output of vegetable farms in Samsun could increase by 18% under prevailing technology. The technical efficiency of the sample vegetable farms ranged from 0.56 to 0.95 (0.82 average). The variables of schooling, experience, credit use, participation by women and information score negatively affected technical inefficiency. However, age, family size, off-farm income and farm size showed a positive relationship with inefficiency. Therefore, this study proposes strategies such as providing better extension services and farmer training programs, integrating women into the training and extension programs, raising the educational level of farmers, and providing farmers with greater access to credit, to enhance technical efficiency. [Copyright &y& Elsevier]

This paper investigates energy use efficiency at the province level in China using the stochastic frontier panel data model approach. The stochastic frontier model is a parametric model which allows for the modeling of the relationship between energy use and its determinants using different control variables. The main control variables in this paper are energy policy and environmental and regulatory variables. This paper uses province level data from all provinces in China for the period 2010–2017. Three different models are estimated accounting for the panel nature of the data; province-specific heterogeneity and province-specific energy inefficiency effects are separated. The models differ because of their underlying assumptions, but they also complement each other. The paper also explains the degree of inefficiency in energy use by its possible determinants, including those related to the public energy policy and environmental regulations. This research supplements existing research from the perspective of energy policy and regional heterogeneity. The paper identifies potential areas for improving energy efficiency in the western and northeastern regions of China. Its findings provide new empirical evidence for estimating and evaluating China's energy efficiency and a transition to cleaner energy sources and production. [ABSTRACT FROM AUTHOR]

Wheat and wheat products are an important part of the daily diet of Uzbek people, and thus, are under strategic consideration in terms of food security in Uzbekistan since the beginning of independence. In this study, wheat production during agricultural transition is reviewed and determinants of technical efficiency of wheat-cultivating farms in the Samarkand region are analyzed. Analyses are accomplished in two steps. In the initial step, technical efficiency of wheat farms is estimated using data envelopment analyses (DEA) and determenants of inefficiencies are analyzed by the Tobit model in the second step. Data for this study were collected from 124 randomly sampled private farms engaged in wheat production in the Samarkand region. The mean value of technical efficiency scores of wheat-growing farmers were found to be 0.79 and 0.82 under constant return to scale (CRS) and variable return to scale (VRS) assumptions. Empirical results suggest that there is a considerable scope for increasing production through reallocation of existing resources or that private farmers can reduce their input costs by 21 and 18 percent while holding the same production levels. The age of farmers, farmers' education on agriculture, soil fertility, and the quality of seeds were found as the main determinants of technical efficiency in the study area. [ABSTRACT FROM AUTHOR]