Relative Efficiency of Universities Using Data Envelopment Analysis: Theory and Implications for the Arab Countries

Introduction

Education is important for any society (Johnes et al., 2017). Hanushek and Woessmann (2008, 2010, 2012), provide evidence that education and economic growth are positively related. According to Johnes et al. (2017), an university (as a higher education institution) can be seen as a multi-product organization, producing a set of outputs from diverse inputs. Given the important role of universities in achieving sustainable development, The Times Higher Education launched in April 2019 the first version of university rankings around the world based on their role in achieving the 17 sustainable development goals (SDGs) of the United Nations (UN). This report was called The University Impact Rankings 2019 (www.timeshighereducation.com).

Measuring the performance of universities is a complex task. Statisticians, economists, and accountants developed different approaches to measure performance efficiency. One of these approaches is partial productivity indicators; i.e., output to input ratios, which do not include all outputs and inputs. Partial productivity measures were used widely because they are simple to be calculated, however, they need to be interpreted with caution (Cooper et al., 2007). Moreover, these measures are always only partial, in that they do not account for the relationships and trade-off between different inputs and outputs. Consequently, this can be considered as a significant drawback in their application to any government services delivery, which typically involves multiple inputs and outputs.

Another approach was developed to cure the drawbacks of partial productivity in their application to show performance indicators of any service provider. This approach combines all outputs and inputs into a comprehensive measure of overall productivity; i.e., the data envelopment analysis (DEA). Data envelopment analysis is a non-parametric linear programming technique that identifies the apparent best providers of services by their ability to produce the highest levels of services with given set of inputs, or to produce given services with the least amount of inputs (Johnes & Yu, 2008). Other service providers receive an efficiency score that is determined by their performance relative to that of the best performers. The technique can also determine whether the main source of inefficiency is due to operations or the managerial capabilities and effort of service provider. One of the main advantages of DEA is that it can readily incorporate multiple inputs and outputs, and calculate technical efficiency. It only requires information on output and input quantities, not prices (Steering Committee, 1997; Mikušová, 2017). This makes it particularly suitable for analyzing the efficiency of government service providers and reveals the performance variations of the organization's processes, especially those providing human services where it is difficult or impossible to assign prices to many of the outputs. Moreover, possible sources of inefficiency can be determined as well as the efficiency level.

A major problem encounters services providers, particularly universities, is how to identify ways to improve their operations performance; i.e., whether there is an excess resources to provide their mix of services provided to customers, or whether there is a shortfall in their services to meet customers’ requirements (Emrouznejad & Cabanda, 2014). In other words, universities are seeking ways with their available resources to maximize their outputs, or to minimize their resources without changing the volume of production and quality of resources provided to customers. Therefore, a main question arises about how to improve the efficiency of universities.