Stochastic Frontier Analysis Model Research Articles

Cost Efficiency Analysis of Local Commercial Banks in Taiwan

This study uses a stochastic frontier analysis (SFA) model to evaluate the relative cost efficiency of commercial banks in Taiwan in the wake of liberalization. The model is estimated using data made available through Taiwan's Banking Bureau and Financial Supervisory Commission. With data from before and after the 2008 financial crisis, our central result is that banks affiliated with bank holding companies (BHC) have become increasingly more efficient. While unaffiliated banks have generally increased their efficiency, they have not kept pace with respect to efficiency gains. Secondary findings are that, independent of ownership structure, both balance sheet and income statement composition affect efficiency. In particular, bank efficiency increases with growth in loans and capital prices. We also find evidence that labor productivity is a key explanatory factor behind efficiency differences across banks. As a result, a policy recommendation is made that banks invest in maximizing worker productivity. Finally, in addition to the insights gained from this study we contribute to the general literature on both bank efficiency and the use of the SFA model by applying it to new data in a new setting.

Measuring banking efficiency in Vietnam: parametric and non-parametric methods

The article aims to evaluate the business efficiency of commercial banks in Vietnam using both parametric and non-parametric approaches. In this study, the Stochastic Frontier Analysis (SFA), which belongs to a parametric method, and Data Envelopment Analysis (DEA), a non-parametric approach, are applied to a sample of 30 joint stock commercial banks in Vietnam in the period of 2011–2015. Applying Tobit regression model, the impact of bank size, bank age, and the ownership feature on the efficiency of bank service industry in Vietnam is also investigated. The analysis results show that in general, the Vietnamese banking efficiency is improving during the selected period regardless of techniques used. However, there is small level of similarity in efficiency rankings identified from the SFA and DEA models. In terms of efficiency determinants, the results show that all three variables of size, age, and state ownership have a positive impact on bank efficiency.

Energy Efficiency of the Baltic Sea Countries: An Application of Stochastic Frontier Analysis

Using the stochastic frontier analysis (SFA) model, this research measures total-factor energy efficiency (TFEE) and disaggregate input efficiency for 10 countries across the Baltic Sea from 2004 to 2014. Real capital, labor, energy use, and carbon dioxide (CO2) are input variables, real gross domestic product (GDP) is the output variable, and renewable energy consumption and urban population are the environmental variables. The results provide not only the TFEE scores, in which statistical noise is considered, but also the determinants of inefficiency, which show the following. (i) Norway, Sweden, Finland, and Latvia perform better with respect to energy efficiency than other countries in the Baltic Sea Region. (ii) Interestingly, the average energy use efficiency scores from 2004 to 2014 in the 10 Baltic countries exhibit a gradual upward trend except for 2009. (iii) For the inefficiency estimates, higher renewable energy consumption and urban population correspond to higher TFEE scores.

EFFICIENCY ANALYSIS OF INDONESIAN TAX OFFICES

This research studies the technical efficiency of Tax Offices (KPP) in Indonesia by applying stochastic frontier analysis model. Observation was in the form of data aggregation at the level of province to all Tax Offices in Indonesia, besides those within the scopes of the Large Taxpayer Regional Office and Jakarta Special Regional Offices for approximately seven years (from 2010 to 2016). Results of the analysis show different rates of technical efficiency in inter-provinces taxation. Analysis on the efficiency determinants reveals a number of internal factors contributing to the technical efficiency of taxation, like the audit effort and external factors, such as community education level. Increase on these two can improve the efficiency and productivity of tax offices.

Assessing the Productivity Consequences of Agri-Environmental Practices When Adoption Is Endogenous

We address the general problem of selection bias, endemic to analyzing the effects of any policy where adoption is voluntary, with empirical application to environmental policies for agriculture. Many voluntary practices for mitigating the environmental impacts of agriculture provide external benefits while lowering productivity. Policy analysis of the productivity consequences is complicated by the fact that decision-makers can choose their own policy levers, an action that ruins any notion of random assignment. We introduce an identification strategy to correct this kind of endogeneity, combining classic methods from stochastic frontier analysis and selection models. Applying it to micro-level data from Finnish grain farms, we find that more efficient producers are more likely to enroll in subsidized practices. And, because these practices tend to reduce yield, frontier analysis without the endogeneity correction greatly understates productivity losses. In other words, naively basing the frontier estimator on the subset of less productive farms leads to downward bias in the resulting frontier estimates. In fact, average inefficiency more than doubles after the correction in this case. An outlier investigation also suggests that the lowest decile of farms are responsible for most of the selection bias in the uncorrected model.

A Demand Forecasting Method Based on Stochastic Frontier Analysis and Model Average: An Application in Air Travel Demand Forecasting

Demand forecasting is often difficult due to the unobservability of the applicable historical demand series. In this study, the authors propose a demand forecasting method based on stochastic frontier analysis (SFA) models and a model average technique. First, considering model uncertainty, a set of alternative SFA models with various combinations of explanatory variables and distribution assumptions are constructed to estimate demands. Second, an average estimate from the estimated demand values is obtained using a model average technique. Finally, future demand forecasts are achieved, with the average estimates used as historical observations. An empirical application of air travel demand forecasting is implemented. The results of a forecasting performance comparison show that in addition to its ability to estimate demand, the proposed method outperforms other common methods in terms of forecasting passenger traffic.

Quality and resource efficiency in hospital service provision: A geoadditive stochastic frontier analysis of stroke quality of care in Germany.

We specify a Bayesian, geoadditive Stochastic Frontier Analysis (SFA) model to assess hospital performance along the dimensions of resources and quality of stroke care in German hospitals. With 1,100 annual observations and data from 2006 to 2013 and risk-adjusted patient volume as output, we introduce a production function that captures quality, resource inputs, hospital inefficiency determinants and spatial patterns of inefficiencies. With high relevance for hospital management and health system regulators, we identify performance improvement mechanisms by considering marginal effects for the average hospital. Specialization and certification can substantially reduce mortality. Regional and hospital-level concentration can improve quality and resource efficiency. Finally, our results demonstrate a trade-off between quality improvement and resource reduction and substantial regional variation in efficiency.

Operational Efficiency of Chinese Provincial Electricity Grid Enterprises: An Evaluation Employing a Three-Stage Data Envelopment Analysis (DEA) Model

With the implementation of new round electricity system reform in China, the provincial electricity grid enterprises (EGEs) of China should focus on improving their operational efficiency to adapt to the increasingly fierce market competition and satisfy the requirements of the electricity industry reform. Therefore, it is essential to conduct operational efficiency evaluation on provincial EGEs. While considering the influences of exterior environmental variables on the operational efficiency of provincial EGEs, a three-stage data envelopment analysis (DEA) methodology is first utilized to accurately assess the real operational efficiency of provincial EGEs excluding the exterior environmental values and statistical noise. The three-stage DEA model takes the amount of employees, the fixed assets investment, the 110 kV and below distribution line length, and the 110 kV and below transformer capacity as input variables and the electricity sales amount, the amount of consumers, and the line loss rate as output variables. The regression results of the stochastic frontier analysis model indicate that the operational efficiencies of provincial EGEs are significantly affected by exterior environmental variables. Results of the three-stage DEA model imply that the exterior environmental values and statistical noise result in the overestimation of operational efficiency of provincial EGEs, and the exclusion of exterior environmental values and statistical noise has provincial-EGE-specific influences. Furthermore, 26 provincial EGEs are divided into four categories to better understand the differences of operational efficiencies before and after the exclusion of exterior environmental values and statistical noise.

Farm size and production efficiency in Chinese agriculture: output and profit

PurposeThe purpose of this paper is to analyze the relationship between farm size and agricultural production efficiency from the aspects of output and profit in order to find an optimal farm size that achieves both output and profit efficiency in agricultural production in China.Design/methodology/approachThis study uses the 2012 China Family Panel Studies survey data and employs the stochastic frontier analysis (SFA) models to investigate empirically the relationship between farm size and agricultural production efficiency.FindingsThe study finds that there is an inverted-U curve relationship between farm size and output efficiency and a U-shaped curve relationship between farm size and profit efficiency in agricultural production in China. Based on the empirical results, the study estimates that the appropriate farm size is around 10–40 mu and the optimal farm size is around 20–40 mu both in terms of output efficiency and profit efficiency in Chinese agricultural production under the current agricultural technology and land management system.Practical implicationsThe findings of this study suggest that appropriate land consolidation will bring more benefits to farmer households and agricultural production efficiency. There are some policy implications. First, governments should give long term and more stable land using rights to farmers through extending the period of land contract and verifying land using rights. Second, governments should encourage transfers of land using rights and promote land consolidation. But the implementation of this policy should consider regional differences and not be used for blindly pursuing increasing land size. Third, land consolidation should be accompanied with the development of specialized agricultural services.Originality/valueThe paper makes two major contributions to the literature. First, the authors use the SFA model to investigate the relationship between land size and agricultural production efficiency. Second, the authors establish two SFA models – the stochastic frontier output analysis model and the stochastic frontier profit analysis model – to estimate the optimal land size to achieve both output and profit efficiency of agricultural production in China.

Inter-state analysis of energy efficiency- a stochastic frontier approach to the Indian paper industry

PurposeThis paper aims to measure the state-level energy efficiency in Indian paper industry and simultaneously explain inter-state variation in efficiency by inefficiency effect model. Three variables, labor productivity, capital intensity and structure of paper industry, are included in inefficiency effect model to assess the likely impact on energy efficiency.Design/methodology/approachSub-vector input distance function is derived through neo-classical production function which provides measures to estimate energy efficiency. Assuming a translog production function specification, energy efficiency is estimated by using Battese and Coelli (1995) stochastic frontier analysis (SFA). The authors also estimated four other SFA models, and energy efficiency from all the models is compared for robustness checking.FindingsThe results show the existence of a vast potential to improve energy efficiency. Inefficiency effect model reported a positive impact of structure of the industry and capital intensity on energy efficiency performance, while labor productivity does not have any significant impact on energy efficiency. There exists considerable energy efficiency variation among states. Uttarakhand, Punjab and Orissa are the best performing states while Rajasthan, Jharkhand and Goa have worst energy efficiency performance based on average efficiency. The ranks assigned to states according to inefficiency effects model are found contrary to the simple measure of energy efficiency, i.e. energy intensity. Thus, energy intensity may not always be a good proxy for underlying energy efficiency and need to be compared with a comprehensive possible measure.Originality/valueTo the best of the authors’ knowledge, this is the first study which measures energy efficiency of Indian paper industry through stochastic frontier model using region-level data. Instead of relying on traditional energy efficiency indicators (energy-output ratio), total-factor energy efficiency approach is used to conduct the empirical exercise. Deviations from the frontier because of factors beyond the scope of producers are also incorporated into analysis to portray the magnitude of random factors in influencing the efficiency performance.

Measuring bank efficiency and market power in the household and corporate credit markets considering credit risks

The aim of this paper is to estimate the efficiency of Hungarian banks with several models and to calculate the Lerner index for both the household and the corporate credit market. We apply stochastic frontier analysis (SFA) and data envelopment analysis (DEA) models to estimate the efficiency and calculate profit and cost efficiency with and without taking credit losses into consideration. In terms of cost efficiency, banks are nearly homogeneous and improved their efficiency after the crisis. Banks, however, are extremely heterogeneous in terms of profit efficiency. During the crisis, a gradual improvement could be observed across the sector after the initial downturn. Since the operating conditions of the household and the corporate credit markets are different, we estimated the intensity of competition separately for both the markets. While the Lerner index showed strong market power in the household credit market, the corporate credit market was characterised by intense competition. Regarding efficiency, various models often resulted in different conclusions, especially in the case of cost efficiency. Therefore we recommend that the regulatory decision-making process should always consider the results of several models. Moreover, the Lerner indices demonstrate that it might be important to use disaggregated models when modelling the features of credit markets.

A comparative study of production efficiency in coastal region and non-coastal region in Mainland China: An application of metafrontier model

ABSTRACTThis paper examines production efficiency of coastal region and non-coastal region in Mainland China between 2000 and 2010 using one-step stochastic frontier analysis and two-step metafrontier model. Unlike previous studies, this study not only discusses production efficiencies but also considers differences in production patterns. The empirical results indicate that coastal region has better production performance in metafrontier technology efficiency (MTE) and technology gap ratio (TGR). In addition, this study finds that the gap of MTE and TGR between coastal region and non-coastal region is increasing for the past years.

Factors contributing to technical efficiency in a mixed fishery: Implications in buyback programs

Understanding the factors that define technical efficiency in a fishing fleet is a requirement for the implementation of management strategies, such as buybacks. The goal of this study was to evaluate the efficiency of a semi-industrial fleet operating in the Bank of Campeche, Mexico. The reduction in the catches of main target species necessitates the implementation of management alternatives to reduce the current fishing pressure. A stochastic frontier analysis model was used to evaluate the efficiency and the factors that influence efficiency of four fleet segments defined by fishing gear. Five variables were included: days at sea, crew size, engine power, month and year. Data was retrieved from logbooks that had recorded details of a total of 5497 fishing trips that had taken place during the seasons of 2005, 2012 and 2014. Interviews were also conducted in the Yucatan coast to explore the skippers’ perspectives on the factors that influence fleet efficiency. The results were different between fleet segments, both in estimated efficiencies as significant variables. The difficulty of establishing global targets of fishing effort reduction, through buybacks, for a mixed fleet, with different gears, different target species and different performance is discussed.

Examining the efficiency of biomass energy: Evidence from the Chinese recycling industry

Developing biomass energy could help to solve problems which limit the sustainable development of human society. These problems include environmental degradation and resource depletion. Using the stochastic frontier analysis model, we conducted an empirical analysis of technical efficiency for the recycling industry and the factors that impact it in 20 provinces in China. We found that technical efficiency is at a moderate level. There are large gaps in efficiency among the eastern, central and western regions. Technical efficiency is highest in the eastern provinces. Ownership structure and technical efficiency exhibit a significant inverted "U" shaped correlation. While urbanization does not affect technical efficiency, economic, industrial and education levels do have a positive impact on it. Based on the results of our study we have proposed a number of policies to promote the development of the recycling industry.

Disaggregate energy efficiency of regions in Taiwan

PurposeThe purpose of this paper is to explore Taiwan’s regional energy efficiency trend and complement the work of the total-factor energy efficiency (TFEE) index proposed by Hu and Wang (2006). It further extends panel data stochastic frontier analysis (SFA) modeling for estimating disaggregate energy efficiency.Design/methodology/approachThis paper applies the panel data stochastic production frontier to estimate the TFEE scores for 20 administrative regions in Taiwan over the period 2004-2015. The SFA models include five inputs (employed population, amount of productive electricity power consumed, amount of electricity consumed for household and non-household electric lighting, amount of gasoline sales, and amount of diesel sales) and one output (total real income in the base year of 2011).FindingsThis research concludes with three main findings: the inefficient administrative regions of Taiwan include mostly large industrial parks and the petrochemical industry cluster; the top five administrative regions with inefficient diesel use are mostly metropolitan areas that the concern of air pollution caused by diesel system arouses the awareness to use less diesel fuel; and the average TFEE score on household and non-household electric lighting is higher than the usage efficiency of productive electricity power, gasoline, and diesel, but there is still room for efficiency improvement.Originality/valueMost administrative regions in Taiwan are not efficient in almost all kinds of energy use. The results show that the efficiencies of using productive electricity power, gasoline, and diesel need to be improved a lot more.

Efficiency Analysis with Different Models: The Case of Container Ports

This paper estimates and compares the technical efficiency of the container ports using data envelopment analysis (DEA) and stochastic frontier analysis (SFA) models and check the role of the characteristics of infrastructure on container port efficiency. The comparisons are based on cross-sectional data for 26 world’s major container ports in 2015 using an input variables related to the characteristics of port infrastructure, such as: the total quay length (meter), the maximum alongside depth (meter), the total terminal area (square meter) and the storage capacity thousand Twenty-foot equivalent unit (thousand TEU/year). The result represents the mean DEA-SFA efficiency differential. DEA-BCC model results indicate that 16 container ports have more than 0.5 score efficiency while with the SFA models all container ports achieved score efficiency more than 0.5. The results show that SFA models improve the score technical efficiency of the container ports. The most efficient container ports in 2015 are Shanghai and Shenzhen achieved the high value technical efficiency with an undetectable difference.

Performance Evaluation Research of Ecological Civilization Policy Based on Stochastic Frontier Analysis and Artificial Neural Networks Model

DOI: 10.7176/JLPG/77-139 Abstract Ecological civilization is a new form of human civilization that solves the contradiction between development and environment. Because the ecological civilization policy system has is complex and nonlinear, this paper combines artificial neural network technology with the Stochastic Frontier Analysis(SFA) method and proposes the hybrid SFA-Artificial Neural Network (SFANN) model to estimate the impact of ecological civilization policy on economic development. The model uses regional eco-efficiency evaluated by SFA as one of the inputs for the neural network; the neural network integrates the total input and output results to provide a quantitative estimate of the ecological civilization policy in different provincial regions from 2003-2016. By training and examining the evaluation results with the SFANN model, this paper tries to predict the impact of 13th Five-Year Total Emission Reduction Policy on economic development. The empirical research results and suggestions can improve the ecological civilization policy performance by negative feedback mechanisms. Keywords: Eco-efficiency; Ecological Civilization Policy; Performance Evaluation; Stochastic Frontier Analysis; Artificial Neural Network

DETERMINING THE EFFICIENCY OF INVESTMENTS AND EVOLVING TEMPORAL-SPATIAL TRENDS IN ENVIRONMENTAL GOVERNANCE USING THE STOCHASTIC FRONTIER ANALYSIS (SFA) MODEL

Environmental pollution has become a major global problem. However, despite increased environmental investment, studies have not clarified whether environmental pollution has been effectively controlled. On the basis of 1998-2014 panel data on three types of industrial waste from 31 mainland China provinces, a stochastic frontier analysis (SFA) model was used to estimate the input efficiency of environmental governance in China, explore the overall characteristics of input efficiency, and explore the evolving trends in terms of the degree of match between environmental input efficiency and input efficiency in various provinces. The results indicate that (1) the input and output of the environmental governance of industrial pollution are positively correlated. The population, industrialization level, and area of a region have a negative impact on the efficiency of investment in environmental governance, whereas gross domestic product, education level, urbanization, and foreign direct investment have a positive impact on it. (2) Generally, the trend in the input efficiency of China s environmental governance is downward. The average value of the central provinces is the only one that is greater than the national average, and the efficiency of environmental governance in each province exhibits a downward trend. Convergence analysis indicates that the efficiency of environmental investment in various Chinese provinces is moving toward differentiation and that the provinces within each region have developed to a balanced level, although a large gap exists between their equilibrium levels. (3) The degree of matching between investment efficiency is not very high, and only six provinces in central and east China exhibit the characteristics of high input-high efficiency. On the basis of these conclusions, we propose four key policy recommendations.

Profit-orientation and efficiency in microfinance industry: an application of stochastic frontier approach

ABSTRACTThe aim of this study is to ascertain the effect of the current wave of commercialization of microfinance institutions (MFIs) on their financial and social efficiency. We hypothesize that during the period of analysis, while the for-profit institutions achieve higher levels of financial efficiency, the non-profit MFIs achieve better social efficiency levels. In order to realize the underlined objective of this study, we use a sample of 162 MFIs gathered from Microfinance Information eXchange (MIX) market database, for the period 2007–2013 from 30 countries located in four regions Africa, MENA, Asia and Latin America. The analysis is based on a two-stage model. The first stage consists on classifying MFIs into two groups according to their main orientations (for-profit and non-profit). We design afterward two models to obtain both social and financial estimates for each particular type of MFIs, using the stochastic frontier analysis model. In the second stage, a regression analysis is carried out to determine which variables have an effect on financial and social efficiency.

The relative efficiency of Iranian\u2019s rural traffic police: a three-stage DEA model

BackgroundRoad traffic Injuries (RTIs) as a health problem imposes governments to implement different interventions. Target achievement in this issue required effective and efficient measures. Efficiency evaluation of traffic police as one of the responsible administrators is necessary for resource management. Therefore, this study conducted to measure Iran’s rural traffic police efficiency.MethodsThis was an ecological study. To obtain pure efficiency score, three-stage DEA model was conducted with seven inputs and three output variables. At the first stage, crude efficiency score was measured with BCC-O model. Next, to extract the effects of socioeconomic, demographic, traffic count and road infrastructure as the environmental variables and statistical noise, the Stochastic Frontier Analysis (SFA) model was applied and the output values were modified according to similar environment and statistical noise conditions. Then, the pure efficiency score was measured using modified outputs and BCC-O model.ResultsIn total, the efficiency score of 198 police stations from 24 provinces of 31 provinces were measured. The annual means (standard deviation) of damage, injury and fatal accidents were 247.7 (258.4), 184.9 (176.9), and 28.7 (19.5), respectively. Input averages were 5.9 (3.0) patrol teams, 0.5% (0.2) manpower proportions, 7.5 (2.9) patrol cars, 0.5 (1.3) motorcycles, 77,279.1 (46,794.7) penalties, 90.9 (2.8) cultural and educational activity score, 0.7 (2.4) speed cameras. The SFA model showed non-significant differences between police station performances and the most differences attributed to the environmental and random error. One-way main road, by road, traffic count and the number of household owning motorcycle had significant positive relations with inefficiency score. The length of freeway/highway and literacy rate variables had negative relations, significantly. Pure efficiency score was with mean of 0.95 and SD of 0.09.ConclusionsIran’s traffic police has potential opportunity to reduce RTIs. Adjusting police performance with environmental conditions is necessary. Capability of DEA method in setting quantitative targets for every station induces motivation for managers to reduce RTIs. Repetition of this study is recommended, annually.