Applications of data envelopment analysis in education

Non-parametric methods for efficiency evaluation were designed to analyse industries comprising multi-input multi-output producers and lacking data on market prices. Education is a typical example. In this chapter, we review applications of DEA in secondary and tertiary education, focusing on the opportunities that this offers for benchmarking at institutional level. At secondary level, we investigate also the disaggregation of efficiency measures into pupil-level and school-level effects. For higher education, while many analyses concern overall institutional efficiency, we examine also studies that take a more disaggregated approach, centred either around the performance of specific functional areas or that of individual employees.

Decomposing school and school-type efficiency

This paper puts forward a Data Envelopment Analysis (DEA) approach to decomposing a pupil's under-attainment at school. Under-attainment is attributed to the pupil, the school and the type of funding regime under which the school operates. A pupil-level analysis is used firstly on a within school and secondly on a between school basis, grouping schools by type such as state-funded, independent and so on. Overall measures of each pupil's efficiency are thus disentangled into pupil, school and school-type efficiencies. This approach provides schools with a set of efficiency measures, each one conveying different information. © 2001 Elsevier Science B.V.

Ranking efficient decision-making units in data envelopment analysis using fuzzy concept

This paper introduces a new mathematical method for improving the discrimination power of data envelopment analysis and to completely rank the efficient decision-making units (DMUs). Fuzzy concept is utilised. For this purpose, first all DMUs are evaluated with the CCR model. Thereafter, the resulted weights for each output are considered as fuzzy sets and are then converted to fuzzy numbers. The introduced model is a multi-objective linear model, endpoints of which are the highest and lowest of the weighted values. An added advantage of the model is its ability to handle the infeasibility situation sometimes faced by previously introduced models.

Profit, productivity and price performance changes in the water and sewerage industry:an empirical application for England and Wales

This paper aims to analyse the impact of regulation in the financial performance of the Water and Sewerage companies (WaSCs) in England and Wales over the period 1991–2008. In doing so, a panel index approach is applied across WaSCs over time to decompose unit-specific index number-based profitability growth as a function of the profitability, productivity and price performance growth achieved by benchmark firms, and the catch up to the benchmark firm achieved by less productive firms. The results indicated that after 2000 there is a steady decline in average price performance, while productivity improves resulting in a relatively stable economic profitability. It is suggested that the English and Welsh water regulator is now more focused on passing productivity benefits to consumers, and maintaining stable profitability than it was in earlier regulatory periods. This technique is of great interest for regulators to evaluate the effectiveness of regulation and companies to identify the determinants of profit change and improve future performance, even if sample sizes are limited.

Evaluation of research in efficiency and productivity:a survey and analysis of the first 30 years of scholarly literature in DEA

Since the original Data Envelopment Analysis (DEA) study by Charnes et al. [Measuring the efficiency of decision-making units. European Journal of Operational Research 1978;2(6):429–44], there has been rapid and continuous growth in the field. As a result, a considerable amount of published research has appeared, with a significant portion focused on DEA applications of efficiency and productivity in both public and private sector activities. While several bibliographic collections have been reported, a comprehensive listing and analysis of DEA research covering its first 30 years of history is not available. This paper thus presents an extensive, if not nearly complete, listing of DEA research covering theoretical developments as well as “real-world” applications from inception to the year 2007. A listing of the most utilized/relevant journals, a keyword analysis, and selected statistics are presented.

Costs and efficiency of higher education institutions in England:a DEA analysis

As student numbers in higher education in the UK have expanded during recent years, it has become increasingly important to understand its cost structure. This study applies Data Envelopment Analysis (DEA) to higher education institutions in England to assess their cost structure, efficiency and productivity. The paper complements an earlier study that used parametric methods to analyse the same panel data. Interestingly, DEA provides estimates of subject-specific unit costs that are in the same ballpark as those provided by the parametric methods. The paper then extends the previous analysis and finds that further student number increases of the order of 20–27% are feasible through exploiting operating and scale efficiency gains and also adjusting student mix. Finally the paper uses a Malmquist index approach to assess productivity change in the UK higher education. The results reveal that for a majority of institutions productivity has actually decreased during the study period.

A combined neural network and DEA for measuring efficiency of large scale datasets

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper proposes a neural network back-propagation Data Envelopment Analysis to address this problem for the very large scale datasets now emerging in practice. Neural network requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to five large datasets and compared with the results obtained by conventional DEA.