Evaluating the performance of Moroccan social incubators: an SFA analysis of youth platforms under the national initiative for human development

In this paper, a cross-sectional samples data of 115 Malaysian stocks have been employed to compare both Data Envelopment Analysis (DEA) method and Stochastic Frontier Analysis (SFA) method. These approaches are used to provide a review of frontier conceptual measurement, strength and limitation of the parametric and non-parametric models. Stochastic frontier production function of Cobb-Douglas type was utilized for the estimation. The function was estimated using the maximum likelihood estimation technique. Two models in DEA, DEA-CCR and DEA-BCC are applied in this study and the ranking correlation between SFA method and both models DEA are determined by using the Spearman rank method. The result revealed using SFA, the mean technical efficiency of sample consumer product companies is 37.5% and implies that companies operating at means level of technical efficiency could produce 80.1% more output for given level of inputs if they become technically more efficient. From empirical results of the SFA method, we determined that the deviations from the efficient frontiers of production functions are largely attributed to inefficiency effects (technical inefficiency). Finally, the findings also showed that the difference in ranking stocks performance using DEA-CCR, DEA-BCC and SFA methods. The main contribution of the paper is showing the comparative performance based on both model, DEA and SFA method using financial ratio.

The Chinese government made a commitment to achieve a 40–45 % reduction in carbon emissions per unit of gross domestic product (GDP) by 2020 compared with 2005. Most provinces followed the national commitment due to unified task of 40–45 % reduction in carbon emissions. However, different industrial structures, energy consumption structures and natural resources endowment of each province vary the emission abatement costs. Each province should take the carbon dioxide abatement cost into consideration for the carbon dioxide reduction target. Data envelopment analysis (DEA) and linear programming (LP) methods were used to measure the marginal abatement cost in previous studies. In this paper, we built a quadratic parametric directional distance function (DDF) to measure the carbon dioxide marginal abatement cost of Chinese provinces. To overcome the flaw of ignoring random errors in previous research, this paper compared results of stochastic frontier analysis (SFA) method and DEA method. Because DEA method only considers the inefficiency and SFA method can distinguish the random error from inefficiency, the result of the average carbon dioxide marginal abatement cost of each province calculated by SFA was 55 % lower than DEA method. As the random error may be introduced by chosen function form, Spearman test and paired sample T test were used to test the correlation of two methods’ MAC results. The results show that the ranking order MAC results sequence of SFA method and DEA method is highly correlated. But the MAC value of SFA and DEA methods has significant difference. As half of the error comes from the random error, the MAC results calculated by SFA method are more precise than DEA method. So SFA method is more appropriate than DEA in this paper. This result reinforces the feasibility of the Chinese government carbon dioxide emission reduction target. However, this study proved that the carbon dioxide emissions and marginal abatement cost varied from province to province. Furthermore, there was no distinct correlation between carbon dioxide emissions and the marginal abatement cost. On the contrary, the marginal abatement cost was related to the industrial structures, energy consumption structures and natural resources endowment of each province. Therefore, two policy suggestions are proposed as CO2 emission reduction principle: First, central government should establish CO2 emission reduction targets based on MAC and local economic affordability. Second, resource endowments and embodied carbon transfer should be considered.

Tunisia olive production fluctuates yearly because it is highly dependent on annual precipitation, and growers need to enhance productivity and efficiency by introducing irrigation. Investigating how irrigation affects the technical efficiency of olive production may contribute to improvement in productivity. This study employs the Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) methods to estimate non-parametric and parametric frontiers for a sample of Tunisian olive orchards. It identifies factors which determine variations in technical and scale efficiencies among orchards. The DEA results show that average output-oriented technical efficiency under constant returns to scale (CRS) and variable returns to scale (VRS) is 8.9 and 17.8%, respectively. The SFA results show that average technical efficiency of the half-normal model with constant returns to scale is estimated at 81.2%, indicating Tunisian olive growers can raise output by an average of 18.8% by improving technology and using fewer inputs. Average technical efficiency in irrigated orchards under the DEA approach was higher than in irrigated ones while irrigated orchards under the SFA approach was less technically efficient than non-irrigated ones. However, the test results of mean difference indicate that average VRS technical and scale efficiencies in irrigated orchards under the DEA approach were not significantly higher than in non-irrigated ones. On the other hand, technical rather than scale inefficiency is the major source of overall inefficiency in irrigated orchards because room for improvement in technical efficiency was larger than in scale efficiency. These results suggest that Tunisian olive growers should raise output and efficiency by introducing more advanced technologies for improving the performance of irrigation systems.   Key words: Olive orchards, technical efficiency, scale efficiency, irrigation, Tunisia.

BACKGROUND:Given that the need to pay attention to measuring efficiency is considered as one of the main pillars of improving the level of efficiency in hospitals, so this study was carried out aimed to determine the mean technical efficiency (The technical efficiency is bound by zero and one and a score of less than one means that the theatre is inefficient as it could) score in terms of type and activity of the hospital, input-oriented and output-oriented attitude, returns to scale (In economics, returns to scale and economies of scale are related but different concepts that describe what happens as the scale of production increases in the long run, when all input levels including physical capital usage are variable (chosen by the firm). The concept of returns to scale arises in the context of a firm's production function. It explains the behavior of the rate of increase) in hospitals of Iran using data envelopment analysis (DEA) (DEA is a nonparametric method in operations' research and economics for the estimation of production frontiers. It is used to empirically measure productive efficiency of decision-making units) and stochastic frontier analysis (SFA) (SFA is a method of economic modeling. It has its starting point in the stochastic production frontier models simultaneously introduced by Aigner, Lovell and Schmidt[1977] and Meeusen and Van den Broeck[1977] MATERIALS AND METHODS:The present study was carried out with a systematic review of all studies conducted on measuring efficiency of hospitals in Iran from March 21, 2001 to December 21, 2017 using DEA and SFA. Eleven databases were searched using appropriate keywords and 470 articles were found and evaluated using a checklist, and finally, 24 articles were entered into the meta-analysis process. Meta-analysis was performed using random effect model and fixed-effect model, and study heterogeneity was investigated using Q-Cochran test and I2 index. Furthermore, the main reasons of study heterogeneity were identified due to meta-regression.RESULTS:The average technical efficiency score of hospitals using DEA and SFA method was obtained equal to 0.885 and 0.809, respectively. Furthermore, with regard to the DEA method, 0.885, 0.891.0.952 and 0.913 was obtained for input-oriented and output-oriented, general and specialized care hospitals and constant returns respectively. With regard to SFA method, 0.733, 0.664, 0.641, 0.802, was obtained, and the inputs and outputs affect measuring the efficiency.DISCUSSION:In contrast, the DEA method can investigate several input and output simultaneously and is used as an effective and flexible tool in order to measure the efficiency of the hospital. DEA can be easily used for calculating efficiency scores based on the proper selection of input and output indicators. The data envelopment analysis method and different input and output variables have been used in most studies conducted in Iran, and Stochastic Frontier Analysis has been less considered. In the present study, the DEA method in governmental educational hospitals showed a higher efficiency than SFA method in the hospitals under study. But in general, due to lack of optimal efficiency level in the hospital, it is suggested that policymakers determine the hospital efficiency indices in order to evaluate their efficiency from different dimensions.CONCLUSION:The average technical efficiency score of hospitals using DEA and SFA method was obtained equal to 0.885 and 0.809, respectively. Also, the mean technical efficiency score in terms of input-oriented and output-oriented, general and specialized care hospitals and constant returns to scale using the DEA method was obtained equal to 0.885, 0.891.0.952 and 0.913 and using the SFA method, respectively, it was equal to 0.733, 0.664, 0.641, 0.802, and the inputs and outputs affecting measuring the efficiency. There is no significant difference between the mean efficiency score between the two methods, but the data envelopment analysis method is used more. It is suggested that the hospitals efficiency indicators to be determined in order to more accurately evaluate the hospitals efficiency.

Nigeria is trending below the top ginger producing countries of the world in terms of yield per hectare and national output. Technical Inefficiency due to poor combination and utilization of physical resources in the production of ginger is a major factor for low yield/output. Previous studies employed either Stochastic Frontier Analysis (SFA) or Data Envelopment Analysis (DEA) to measure technical efficiency. So, the question arises as whether the technical efficiency estimates from both methods are similar or not. The present study deployed the two common approaches (SFA and DEA) in estimation of technical efficiency and its determinants in order to ascertain the similarity or otherwise of the estimates. Two hundred and five (205) ginger farmers were randomly sampled for primary data collection. Analyses of the data were done through Descriptive Statistics, DEA, Tobit regression and SFA. The results show that means of the efficiency scores from both models namely DEA (0.70) and SFA (0.88) were significantly different at 1% probability level. However, estimates from both method suggest that the ginger farmers were operating at suboptimal level or below frontier. Also, the results of determinants of efficiency from two estimation methods (SFA and DEA-based Tobit regression) were dissimilar. While SFA method shows that household size, level of education and farming experience were responsible for enhancing the efficiency of ginger farms, none of these variables influenced the farmers’ efficiency under Tobit regression. Therefore, it is recommended that selection of efficiency estimation method between SFA and DEA by the researcher should be based on study objective rather than consideration on the premise of alternative opportunity. Also, greater output level or potential output can be achieved, given the current production technology, by educating ginger farmers on how to ensure that production inputs are combined and managed efficiently. Keywords: Comparison, DEA, SFA, Efficiency, Ginger Farms

Background: Full consideration of the performance and efficiency of hospital costs necessitates the application of economic analysis techniques. The aim of this study was to assess the efficiency of hospitals in Kermanshah University of Medical Sciences through Stochastic Frontier Analysis (SFA) method. Methods: The performance of Kermanshah hospitals (n=7) was assessed and analyzed by Stochastic Frontier Analysis (SFA) method during 2005-2011. Inpatient admission was considered as the output variable, while the number of medical doctors, nursing staff other personnel, active beds and outpatient admission were considered as the input variables. Frontier 4.1 software was used to analyze the data. Results: Based on the results of performance evaluation using Cobb-Douglas production function, the mean efficiency score of the hospitals in the SFA method was 0.63. Also, the efficiency capacity in these hospitals could be promoted up to 37 percent. Conclusion: Based on the results of Stochastic Frontier Analysis, downsizing the manpower in hospitals plays a major role in reducing hospital costs and improving their performance. Finally, it is necessary to investigate the effect of factors such as quality of service and patient satisfaction on hospital performance.

The main aim of the study was to evaluate the efficiency of Islamic Azad University branches in East Azerbaijan using stochastic frontier analysis (SFA) and to compare the findings with the results of data envelopment analysis (DEA). In this respect, SFA and DEA techniques were applied to measure production efficiency of 28 branches of East Azerbaijan Islamic Azad University considering the following variables as either output or input: 3 variables as output (including the number of graduated students and students accepted for the further pursuit of their studies, as well as the number of articles, books, and research projects) and 6 variables as input (including the number of university faculty members, educational fields, current students, staff working in different sections of the university, and the total university campus in square meters as well as university costs). Estimation of DEA and obtaining of the units' efficiency indicated that the average efficiency of the universities under investigation were 0.66 and 0.80 in SFA and DEA techniques, respectively. In addition, efficiency distributions in SFA and DEA methods were tremendously different from each other so that in SFA method nearly 40% while in DEA method more than 60% of unit efficiency was between 0.8-1 intervals. The minimum level of estimated efficiency (0.14) was observed for Hadishahr using SFA method, and in DEA approach, the minimum level of efficiency belonged to Khoda Afarin branch (0.065).

IntroductionHospitals are the most expensive health services provider in the world. Therefore, the evaluation of their performance can be used to reduce costs. The aim of this study was to determine the efficiency of the hospitals at the Kurdistan University of Medical Sciences using stochastic frontier analysis (SFA).MethodsThis was a cross-sectional and retrospective study that assessed the performance of Kurdistan teaching hospitals (n = 12) between 2007 and 2013. The Stochastic Frontier Analysis method was used to achieve this aim. The numbers of active beds, nurses, physicians, and other staff members were considered as input variables, while the inpatient admission was considered as the output. The data were analyzed using Frontier 4.1 software.ResultsThe mean technical efficiency of the hospitals we studied was 0.67. The results of the Cobb-Douglas production function showed that the maximum elasticity was related to the active beds and the elasticity of nurses was negative. Also, the return to scale was increasing.ConclusionThe results of this study indicated that the performances of the hospitals were not appropriate in terms of technical efficiency. In addition, there was a capacity enhancement of the output of the hospitals, compared with the most efficient hospitals studied, of about33%. It is suggested that the effect of various factors, such as the quality of health care and the patients’ satisfaction, be considered in the future studies to assess hospitals’ performances.

Introduction: During the last decade, the health sector of many countries in general has been faced significantly with the increase of health care costs and in particular with the growth in hospital costs, that a significant part of it is due to the inefficient use of resources. The present study has been calculated the technical efficiency of the Specialty Ophthalmology Hospital of Tehran University of Medical Sciences by the comprehensive data analysis methods and stochastic frontier analysis. Methods: In this article, the technical efficiency of the Specialty Ophthalmology Hospital of Tehran University of Medical Sciences has been calculated by the years of 2009 to 2011, by the comprehensive data analysis methods and stochastic frontier analysis. For this purpose, the form of input-oriented data envelopment analysis approach was used by assuming the variable Productivity to scale and stochastic frontier analysis method and from the five output, the occupied bed days, outpatient admissions, inpatient admissions, inpatient days and bed occupancy factor, and from the six output, it means active beds, number of doctors, nurses, and other personnel, budget and equipment costs were used for the study. For data analyzing, Deap software, edit 1/2 and Frontier edit 1/4, was used. Result: The results of a comprehensive data analysis method showed: 1) The capacity of improving technical efficiency in the studied sector is 34% (average technical efficiency is 0.663 parts); and 2) some of the sectors are met the excess inputs (factors of production). Technical efficiency using stochastic frontier analysis was equal to 0.937. In fact, the stochastic frontier analysis showed the inefficiency is less than the actual value. Conclusion: Reducing excess capacity factors (factors of production) should be conducted in the form of a comprehensive plan and by considering all regarded aspects, that this reduction plays a major role in the hospital and health sector costs reduction.

Purpose – 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. Design/methodology/approach – 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. Findings – 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). Originality/value – 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.

Background: Assessment of hospitals’ performance in achieving its goals is a basic necessity.Measuring the efficiency of hospitals in order to boost resource productivity in healthcare organizationsis extremely important. The aim of this study was to measure technical efficiency and determiningstatus of resource allocation in some university hospitals, in Tehran, Iran.Methods: This study was conducted in 2012; the research population consisted of all hospitals affiliatedto Iran and Tehran medical sciences universities of. Required data, such as human and capitalresources information and also production variables (hospital outputs) were collected from data centersof studied hospitals. Data were analyzed using data envelopment analysis (DEA) method,Deap2,1 software; and the stochastic frontier analysis (SFA) method, Frontier 4,1 software.Results: According to DEA method, average of technical, management (pure) and scale efficiencyof the studied hospitals during the study period were calculated 0.87, 0.971, and 0.907, respectively.All kinds of efficiency did not follow a fixed trend over the study time and were constantly changing.In the stochastic frontier's production function analysis, the technical efficiency of the studied industryduring the study period was estimated to be 0.389.Conclusion: This study represented hospitals with the highest and lowest efficiency. Referencehospitals (more efficient states) were indicated for the inefficient centers. According to the findings,it was found that in the hospitals that do not operate efficiently, there is a capacity to improve thetechnical efficiency by removing excess inputs without changes in the level of outputs. However, bythe optimal allocation of resources in most studied hospitals, very important economy of scale can beachieved.

This research investigates the efficiency of 100 firms from ten selected manufacturing industries in the Philippines over the period 1995–2004, using the two frontier models. The aim of this research is to evaluate and measure the technical efficiency of selected firms by applying the data envelopment analysis (DEA) and stochastic frontier analysis (SFA) approaches. A total of 1,000 pooled data are analysed using both DEA and SFA methods. New findings reveal that the average technical efficiency scores of DEA and SFA are 57.4% and 82.63, respectively, but no statistically significant correlation found. New results also suggest that older firms tend to be more technically inefficient than younger firms while larger firms tend to be more technically efficient than smaller firms. Significantly, this research has also found that an imposition of higher tariff rates can make firms to be technically inefficient. Overall, this research provides significant evidences on the usefulness of two frontier methods for evaluating manufacturing efficiency as alternative tools of performance measurement for managerial decision making.

The technical and allocative efficiency of broadacre farmers in a southern region of Western Australia is investigated over a three-year period. Applying data envelopment analysis (DEA) and stochastic frontier analysis (SFA) reveals there is some inefficiency in each year, which decreases over time. The distributions of technical efficiency in each year are positively skewed toward higher efficiency levels, indicating a majority of farms produce close to their maximum technical efficiency. DEA and SFA produce similar efficiency rankings of farms yet DEA rankings are more stable. The relationships between farm-specific variables and the DEA and SFA efficiency scores are investigated. There is evidence that farmers benefit from using at least a small amount of tillage, rather than using ‘no-till’ practices. Education levels and farmer age are found to positively influence technical efficiency. Using a DEA profit efficiency model, the duality between the directional distance function and the profit function allows the decomposition of economic efficiency into its technical and allocative components. Greater gains in profitability are possible by improving allocative rather than technical efficiency. Technically efficient farms are not necessarily allocatively efficient. Also, Tobit regression results indicate that the variables associated with variation in technical efficiency are different to those explaining the variation in allocative efficiency.

Transport firms’ inefficiency and managerial optimism: A stochastic frontier analysis

Indonesia's economic growth this decade has good development. Not only growing but also more stable than before the reform era which is visible from the persistence of Indonesia at the level of positive growth during the economic crisis of 2008. Growth was good was followed by a change in the proportion of manufacturing industry in Indonesia which, if seen followed by a decrease in the production of some subsector indices industry. Total factor productivity (TFP) is one measure to look at other factors apart from the impact on production inputs such as technical efficiency and technological growth. In this study, in addition to trying to calculate TFP in some manufacturing industries subsector, in this study also wants to see the value of technical efficiency and the growth of the technology is a component of TFP calculations by the method of Stochastic Frontier Analysis (SFA). The results show that there is growing value of technical efficiency in some industries and most industries experienced relatively low growth of the technology. In the era before and after the crisis most of the industry has increased TFP growth but some industry decreased TFP growth. Keywords: SFA, Technical efficiency, Technological growth, TFP