Data Envelopment Analysis a Technique for Measuring Efficiency

This paper uses both the stochastic and nonstochastic production function approach to measure technical efficiency in public education in Utah.The stochastic specification estimates technical efficiency, assuming half normal and exponential distributions.The nonstochastic specification uses two-stage DEA to separate the effects of fixed inputs on the measure of technical efficiency.The empirical analysis shows substantial variation in efficiency among school districts.While these measures are insensitive to the specific distributional assumptions about the one-sided component of the error term in stochastic specification, they are sensitive to the treatment of fixed socioeconomic inputs in the two-stage DEA.

Purpose – Data envelopment analysis (DEA) is a non-parametric technique of computing efficiencies of decision-making units using similar set of inputs to give similar set of outputs. The objective is to pick out inefficient units from a data set of similar units and thus analyse their performance amongst their peer group. Stock markets can be considered to be an economy’s barometer. Thus, evaluation of efficiency effectiveness of the companies operating at stock exchange is a valuable exercise. Further, if the inefficient units can be given a benchmark for improvement, they can increase their market value. The purpose of this paper is to evaluate the efficiencies of the Oil, Gas and Power (OGP) sector of India for the companies that form a part of the CNX Energy Index and CNX 500 Index of the National Stock Exchange of India. Design/methodology/approach – A group of 24 units has been included in the study. DEA was applied for ranking the units as per their efficiency levels by computing their technical, pure technical and scale efficiencies (SE). It was observed that only nine units are efficient and the remaining 15 were inefficient. It was observed that ONGC is the most efficient unit and CESC Ltd is the least efficient unit in this group. Also in this group there are ten units that show inefficiency due to their scales of operations. Further, benchmarking for the inefficient units has also been done in terms of inputs/outputs and the targets are suggested. It was observed that some of the Public Sector Companies like NTPC are using more inputs compared to the other units from the same group for achieving the same efficiency. Findings – The present study attempted a limited objective of establishing the technical, pure technical and scale inefficiencies of the companies operating in OGP sector in India and listed on National Stock Exchange with the help of the non-parametric technique of DEA and suggesting how they can strive to improve their performance. It is observed that 37.5 per cent are technically efficient as well as scale efficient, whereas 62.5 per cent are pure technically efficient. There are 42 per cent companies representing approximately half of the output and more than half of the input that have scale inefficiencies characterized by their PTE less than SE. Out of the efficient companies, ONGC appears to be the best whereas Essar Oil has a comparatively lower rank. Out of the inefficient companies, the worst performer is CESC Ltd. However, inspite of being the worst performer, this unit does not have the worst benchmarking targets. The units like Sterlite technologies and KSK energy ventures need to improve their profit by almost 1,000 per cent. These kind of targets are very difficult to attain. Hence these units need to improve their scale of operation. The managers of these units must take up this issue seriously and take measures to improve their productivity. The study also attempted benchmarking where various inefficient units have been suggested targets they need to scale to improve their efficiency. If addressed, they can have micro as well as macro benefits. Research limitations/implications – In the present paper, the analysis is restricted only to the OGP sector of Indian economy. The study can be further extended to various other sectors of Indian economy such as agriculture, telecommunications etc. This would help in the holistic analysis of the economy. The flag bearer efficient units would set up a benchmark for the improvement to the inefficient units that would help improve the developing economy of India. Originality/value – An increase in productivity is the most crucial management objective for any industry. Assessing the performance of companies listed and traded in stock market is imperative for investors and financial managers. Researchers have widely studied the performance evaluation of listed companies. Establishing efficiency of stock markets as a whole as well as of the constituent companies has been subject of wide research, but to the understanding no study has been done on evaluating the efficiencies of the OGP sector of India. In the present study the authors have concentrated on companies, out of the universe of energy companies operating in India, which form part of the CNX Energy Index and CNX 500 Index of the National Stock Exchange of India. The reason is that they represent the Indian energy market pretty well.

Waqf is a voluntary charity that cannot be disposed of and the ownership cannot be transferred once it is declared as waqf assets. Waqf institutions play an important role in helping the development of Muslims ummah through wealth distribution. State Islamic Religious Councils (SIRCs) in Malaysia are the sole trustee that manage and develop waqf assets. Based on selected input and output, the intermediary approach assumes that cash waqf received as output while total expenditure of SIRCs as input. Under this approach SIRCs act as intermediary between waqif (giver) and beneficiaries. Thus, this paper attempts to analyze the efficiency of waqf institutions in Malaysia by using Data Envelopment Analysis (DEA) method under output-orientation using Variable Return to Scale (VRS) assumptions. Four SIRCs were selected as decision making units (DMU) for the period of 2011 to 2015. The result indicates that changes in average technical efficiency for every year is contributed by both pure technical and scale. However, inefficiency of Malaysian waqf institutions is mostly contributed by pure technical efficiency aspects rather than scale. 2012 showed the highest average technical efficiency with 73.9% as most of the institutions operated in optimum level of input to produce output. Thus, the result suggests that both technical and scale efficiency should be improved to achieve the most efficient and productive level of performance in order to fulfill objectives of the institutions as an intermediary between waqif and beneficiaries.

This study focuses on evaluating the technical and scale efficiencies of smallholder pineapple farmers in Ghana’s Central Region. We surveyed 320 participants selected using random sampling and applied an input-oriented Data Envelopment Analysis (DEA) method to gauge their technical, pure, and scale efficiencies. Our findings indicate that the mean technical efficiency among these farmers is 0.505, with individual scores ranging from 0.079 to 1.000. Notably, 90.82% of the farmers are operating below maximum efficiency levels, suggesting a potential input reduction of up to 49.5% while maintaining current production levels. Relaxing the assumption of constant returns under Variable Returns to Scale (VRS) conditions reveals a notable improvement in technical efficiency, with 10.82% more farmers achieving optimal efficiency levels. Furthermore, our analysis highlights scale inefficiencies, with 67.26% of farmers operating below optimal scale levels. By increasing production by 22.8%, these scale-inefficient farmers could enhance their efficiency and productivity within existing technological frameworks. These findings underscore the importance of collaborative efforts among policymakers, practitioners, and stakeholders within the agricultural value chain to implement interventions such as improving access to technology and innovation for smallholder farmers and making necessary investments in farmer education and training programs to enhance both technical and scale efficiencies in Ghana’s pineapple sector. Such initiatives can drive sustainable growth, improve farmers’ livelihoods, and bolster the sector’s overall competitiveness.

Over the past two decades, the hub & spoke system has significantly changed the behavior of strategic investment decisions in the port sector. Transshipment has significantly changed the composition and throughput traffic for the maritime industry, as a result, capacity has largely increased. This work aims to measure and compare the terminal economic performance based on two criteria: technical efficiency and scale efficiency for 33 container terminals in the Mediterranean basin. We have opted for the DEA (Data Envelopment Analysis) method for the measurement of the economic performance and we have used cross-sectional data for the benchmark in one point of time. DEA doesn't require any specification of the functional form. DEA as a non-parametric programming approach helps in determining the efficiency of a DMU (Decision Making Unit). The findings of this work show that 58% represents the Constant Return to Scale Technical efficiency, this result is broken down into 69% for pure technical efficiency and 83% for scale efficiency, therefore, the findings show that terminals are suffering from a shortfall in terms of overall technical efficiency. Most terminals are operating on decreasing scales. Strategic decisions in terms of investment are of major interest to the management of the container terminals sector.

The purpose of this study was to measure Greek hospital performance using different input-output combinations, and to identify the factors that influence their efficiency thus providing policy makers with valuable input for the decision-making process. Using a unique dataset, we estimated the productive efficiency of each hospital through a bootstrapped data envelopment analysis (DEA) approach. In a second stage, we explored, using a bootstrapped truncated regression, the impact of environmental factors on hospitals' technical and scale efficiency. Our results reveal that over 80% of the examined hospitals appear to have a technical efficiency lower than 0.8, while the majority appear to be scale efficient. Moreover, efficiency performance differed with inclusion of medical examinations as an additional variable. On the other hand, bed occupancy ratio appeared to affect both technical and scale efficiency in a rather interesting way, while the adoption of advanced medical equipment and the type of hospital improves scale and technical efficiency, correspondingly. The findings of this study on Greek hospitals' performance are not encouraging. Furthermore, our results raise questions regarding the number of hospitals that should operate, and which type of hospital is more efficient. Finally, the results indicate the role of medical equipment in performance, confirming its misallocation in healthcare expenditure.

This study aimed to measure the efficiency scores of 75 dairy buffalo farms in the province of Nueva Ecija, Central Luzon, Philippines, using an input-oriented, variable-return-to-scale Data Envelopment Analysis (DEA) model. The farmer-informants or decision-making units (DMUs) were categorized as smallholders, family modules, and semi-commercial in operations. Personal interviews using structured questionnaires were done to gather various information on the socio-economic and management practices of the DMUs. Output in the form of volume and value of milk produced and inputs such as quantities and costs of biologics, feeds, forage, and labor were also collected and evaluated among individual DMUs. The efficiency scores were computed using PIM-DEA software, which identified fully efficient DMUs lying on the frontier line (scores of 1.0) and those enveloped by it (inefficient DMUs with scores of less than 1.0). The overall mean Technical Efficiency (TE), Allocative Efficiency (AE), and Economic Efficiency (EE) scores among the DMUs were 0.80, 0.81, and 0.65, respectively. Most of the inefficient DMUs were in the smallholder category. In sum, smallholder DMUs classified under low and moderate TE clusters should reduce their inputs by 53.31% and 40.01%, respectively, to become fully efficient. Likewise, higher lambda values among efficient peer DMUs indicate the best practice frontiers that the inefficient peer DMUs can benchmark with. Extension and advisory services can help promote the best management practices of the frontiers to improve the TE, AE, and EE of the inefficient DMUs.

This study employed the input-oriented DEA approach with variable returns to scale assumption to assess the technical, scale, allocative and economic efficiencies of 124 randomly selected tomato farmers under irrigation in the Upper East Region using data of the 2017/2018 production season. The mean technical efficiency and scale efficiency were 97.1 and 97% respectively with many farmers experiencing increasing returns to scale. The mean allocative and economic efficiencies were 42.1 and 41.5% respectively. Farmer’s age, tomato land size, fuel quantity, fertilizer quantity and chemical quantity (herbicide and pesticide) were all significant determinants of technical efficiency scores whereas extension visit, tomato land size and chemical quantity significantly influenced both allocative and economic efficiencies. It is recommended that agro-inputs and fuel usage for tomato farmers under irrigation in the dry season be increased to improve technical efficiency. It is also recommended that extension education to farmers on effective inputs allocation and cost minimization strategies be intensified. Key words: Allocative, economic and technical efficiency, data envelopment analysis, Tobit regression, tomatoes, Upper East Region of Ghana &nbsp

Leibenstein has suggested that losses from Xor technical inefficiency may be far more important than losses from allocative inefficiency., Several recent studies have attempted to measure the magnitude of these former losses, while Timmer and Miiller2 have noted that invocation of the term "technical efficiency" may imply an admission of the analyst's incomplete understanding of the production process. Improved specification of the model may relate interfirm productivity variability to input variability, rather than to the somewhat enigmatic technical efficiency. This improved specification may be extremely helpful to policymakers in developing nations, where the list of proposed programs often includes many that aim at improving management. For example, in the area of agricultural development there are frequent proposals related to extension services, farmer training centers, model farmer programs, best-farmer awards, field days, mass-media programs, and the like. The specific content of such programs may vary considerably: teaching specific farm skills, teaching general skills such as literacy and arithmetic, exhorting farmers to work harder, stimulating demand for cash goods, and attempting to develop a generally more modern, change-oriented outlook. If policymakers know why some farmers are better managers (i.e., why there are technical efficiency differentials), they might have firmer grounds for

The research aims to analyze the technical efficiency of the chocolate industry, which has outstanding performance in producing chocolate products in East Java, Indonesia. The research sample includes all small and large-scale chocolate industries in East Java, with 42 Decision Making Units (DMUs). Efficiency research uses Data Envelopment Analysis (DEA), which is usually used in agricultural production research. However, in this research, DEA was used to analyze the efficiency of the chocolate industry making this concept as an empirical novelty. The analysis results show that there are industries operating at the Constant Returns to Scale (CRS) level (0.971), with a percentage of 66.67%, consisting of 28 DMUs, and the Variable Returns to Scale (VRS) level (0.992), with a percentage of 85.71%, which is represented by 36 DMUs. The Technical Efficiency (TE) is categorized as full technical efficiency at 85.72%, with a high category at 14.28%, and no DMUs fall under the moderate and low categories. This indicates that the allocation of inputs in each DMU is significantly different. Six DMUs require improvement in the distribution of industrial capital and raw materials input to achieve full efficiency category by making decisions based on the DMU's recommendations as a reference (benchmarks). This article concludes that industrial efficiency is a priority for the establishment of a business to achieve its goals easily. When the industry can control the quality and quantity of its products, it can be highly beneficial. The policy implications required for this case research to maintain and improve the efficiency of the chocolate industry in East Java, Indonesia, are for industries to prioritize joint management, increase the scale of operations, increase production frequency, expand innovative chocolate processing technology, prioritize product quality, and be able to penetrate export markets.

In the wake of global competition with greater economic and regional integration, a country's economic growth is largely determined by industrial efficiency. Operating and staying within market under cut throat competition appears as an unprecedented challenge for exporting and non-exporting firms. A data of 403 firms from World Bank Enterprise Survey 2013 is used for estimation of technical and scale efficiencies between and within exporting and non-exporting firms. Data Envelopment Analysis (DEA) is applied for constant returns to scale (CRS) and variable returns to scale (VRS) efficiency scores. It has been found that the average technical efficiency scores for non-exporting firms is slightly greater than the exporting firms. But majority of the firm's technical efficiency remained the same for both type of firms. Industries with highest technical efficiency comprise of chemicals, food, garments and non-metals. Efficiency scores of some firms depict interesting results and found efficient in either of exporting and non-exporting firms. The efficiency analysis can help to identify potential changes which industry may require for its consistent growth in current surge of global competition and nationalization.

This research aimed to measure technical efficiency in light of constant and variable of return to scale, the scale efficiency, resources level that achieving economic efficiency, surplus and deficit for broiler projects using the data envelope analysis (DEA) program as well as measuring the super efficiency using the Super Efficiency Measurement (EMS). The research relied on the primary data obtained from the questionnaire, which included 36 projects in Baghdad and 30 projects in Al-Qadisiyah governorate, representing 15.5% and 16.75% of the total projects in the two governorates, respectively. The research was divided into two stages; the first one included measuring technical and scale efficiencies, while in the second stage, the projects that achieved full-scale efficiency were separated from the inefficient sample projects using DEA to measure the efficiency of using productive resources. The results showed that broiler projects in Baghdad governorate achieved technical efficiency in according to constant and variable of return to scale and scale efficiency amounted to an average of (83.30%, 92.02%, and 95.40%), respectively, while in Al-Qadisiyah governorate it reached (96.74%, 83.15%, 92.45%). ) respectively. The research concluded that there is a surplus in the use of resources, which indicates that breeders are far from achieving economic efficiency. Accordingly, the research recommends the need for optimal use of the quantities of inputs based on the prices of these inputs, as well as following a production policy aimed at increasing economic efficiency.

This study uses a sample of 715 banks from 95 countries and two-stage data envelopment analysis (DEA) to provide international evidence on the impact of regulations and supervision approaches on banks’ efficiency. We first use DEA to estimate technical and scale efficiency. We then use Tobit regression to investigate the impact of several regulations related to capital adequacy, private monitoring, banks’ activities, deposit insurance schemes, disciplinary power of the authorities, and entry into banking on banks’ technical efficiency. We estimate several specifications while controlling for bank-specific attributes and country-level characteristics accounting for macroeconomic conditions, financial development, market structure, overall institutional development, and access to banking services. In several cases, the results provide evidence in favour of all three pillars of Basel II that promote the adoption of strict capital adequacy standards, the development of powerful supervisory agencies, and the creation of market disciplining mechanisms. However, only the latter one is significant in all of our specifications. While the remaining regulations do not appear to have a robust impact on efficiency, several other country-specific characteristics are significantly related to efficiency.

Data envelopment analysis (DEA), introduced in Charnes, Cooper, and Rhodes (1978), provides a new approach to the estimation of relative efficiencies of decision making units (DMUs). As described by Banker (1980b) and Banker, Charnes, and Cooper (1984), DEA also encompasses estimation of production frontiers making minimal assumptions—such as convexity—about the production possibility set. DEA may be employed to estimate technical and scale efficiencies as in Banker, Charnes, and Cooper, rates of substitution between inputs as in Banker, Charnes, and Cooper and Charnes, Cooper, and Rhodes (1978), and returns to scale and most productive scale sizes as in Banker (1984) and Banker, Charnes, and Cooper (1984). These estimates of different production characteristics pertain to the efficient production surface, unlike the commonly employed regression techniques which estimate the average production correspondence. In this chapter, we report on the results of a simulation study in which DEA was employed to estimate the production frontier from input and output data randomly generated from a known technology.

ABSTRACTThe purpose of this paper is to measure the relative performance of forest management units, and to analyze the impact of the external non-discretionary (ND) factors on these units’ technical efficiency. Toward this end, data envelopment analysis (DEA) technique in variable returns to scale environment with both discretionary and ND factors has been used. The required data are collected from 24 Iranian forest management units (as decision-making units (DMUs)) and included four inputs and two outputs. The ND factors are first ignored and it is shown that most of the forest management units are operating at high efficiency levels. Then the variable “area” is considered as the exogenously fixed (or the external ND) input in a modified ND model because it is outside of the forest manager’s control. The results indicate that the number of efficient units and the average technical efficiency score are reduced to 12 (approximately 50%) and 0.85, respectively. The forest management units should therefore increase their average efficiency score by 0.15 to move onto the new efficient frontier which is made by applying this strict criterion. As a consequence, it is recommended to apply the ND models for controlling the exogenously fixed factors and carrying out a correct and accurate evaluation, because the traditional DEA approaches may overestimate the efficiency of DMUs.