Specification of Inputs and Outputs Used in Higher Education

A crucial decision for researchers dealing with efficiency measurement in HE has been the specification of the most appropriate measures of inputs and outputs. A substantial amount of research has been undertaken with regard to the effect of input and output specification on efficiency scores, much of it in the context of DEA. DEA,116 despite its comparative advantage over alternative methods (statistical techniques), cannot provide in its basic form the significance of a set of inputs or outputs, significance tests for comparing different models, or for drawing a parallel between efficiency scores of individual groups or DMUs.

According to Johnes and Johnes, (2004), in the context of HE, the conclusions in the results range from rankings being reasonably stable regardless of input and/or output specification (Tomkins and Green, 1988; Abbot and Doucouliagos, 2003; Johnes, 2003) to results being prone to specification errors (Johnes and Johnes, 1992; Ahn and Seiford, 1993). Based on existing studies, there are considerable problems with defining and measuring the inputs and outputs of the HE production process, since, apart from the specification problem, there is a second issue regarding the importance of each of the inputs and outputs in the DEA model.

Some further concerns arise, in the process of separating inputs fully self-controlled by each university and environmental factors that may differentiate or affect the efficiency outcome. Analysts try to cover this angle by either including all inputs, whether controllable or not, in the efficiency analysis (Grosskopf, 1996); Cubbin and Tzanidakis, 1998) or by adopting a two-stage procedure, in which DEA results are derived using a sub-set of controllable inputs, and then the efficiencies from this stage are analysed at a second stage in relation to the non-controllable inputs. In practice, the

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first approach occurs more frequently in the HE sector, despite the overestimation of the results. However, both approaches have been identified having several shortcomings, since misspecification errors in the second stage, or serial correlation in DEA estimates, are some of the potential limitations, making standard methods of inference invalid, (Simar and Wilson, 2004). However, the capacity to assess the performance of HEIs and systems is an even more complicated process, due to the fact that inputs and outputs in the production process are difficult to define and quantify (NRC, 2012).

Traditionally, similar to previous studies, (Worthington, 2001; Abbott and Doucouliagos, 2003; Worthington and Lee, 2008; Abbot and Doucouliagos, 2009; Glass et al., 2009); Worthington and Higgs, 2011), the input-output framework, for organising and measuring the multiple inputs and outputs in HE117 follows a production approach to modelling university behaviour; that is, universities combine raw materials (such as students), energy (utilities), materials (e.g. paper, pens, computers if not capitalised), labour (academic staff, academic-related staff, and/or other staff), and non- labour factors (physical and financial capital) of production and produce outputs in the form of two main outputs of teaching and research (research output, research income, and research students) (Glass et al., 2002).

From a more rigorous perspective, the practical burdens of measuring labour inputs differs in the HE sector since, even if HE is largely a non-market activity, its workforce emerges from a competitive market in which faculty and other employees have a range of different options. In most cases the quantity of labour can be approached by the number of hours or full-time-equivalent workers. However, the main limitation here is the assumption that all workers have the same skills and so inherently are paid equivalent wages. Indeed, this is an unstable hypothesis and remains true only in situations in which changes and variations in the skill level of the workforce are known to be negligible (NRC, 2012). As a labour proxy in the literature, it is common to use academic and non-academic staff (Avkiran, 2001; Abbot and Doucoulianos 2003; Agasisti and Salerno, 2007) enrolments of undergraduate/postgraduate students, (Agasisti and Perez-Esparrells, 2010; Abbou-Warda, 2011), FTE of undergraduate/- postgraduate students (Arcelus and Coleman, 1997), and FTE of total number of teaching and non-teaching staff and student’s own time and effort118 such as credit hours operating (actual hours offered by each department) (Agha et al. 2011). Note here that, in research-led institutions, the time and cost of faculty and administrative personnel must be divided between research and instruction.

Turning to capital inputs, an intriguing feature is their durable nature and, as such, they generate a stream or flow of services over an extended period. Therefore, the

117 _{See appendix 11 chapter 3 for an input-output list}

118 _{Significant concerns have been expressed regarding this type of measure since student effort should be treated as both an input}

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contribution of capital can be better approached as a measure of service or rental flow, i.e. the cost of using once for one period of time and not by their price for the acquisition. These rental rates are equivalent to a wage rate and can be used in the same way to aggregate across different types of capital service and as a measure of capital income in aggregating the various inputs to production (NRC, 2012). Common proxies are expenditures on library, computing and other learning resources, subsidies, facilities required for teaching (Abbot and Doucoulianos, 2002), governance, administration and staff development, funding for research, total number of places available in teaching rooms, libraries and laboratories space, equipment, and IT, highly-qualified human resources, and library budgets.

A further classification of inputs that is deemed to be definite is between instructional and non-instructional inputs. The first class of input involves regular faculty, adjunct faculty, and graduate student instructors, while non-instructional and indirect costs encompass any administration, athletics, entertainment, student amenities, services, hospital operation, R&D, student housing and transportation, etc. (NRC, 2012). Turning to the output specifications in HE, these tend to be organised into four different categories: instructional outputs, institutional environment outputs, research outputs, and public service outputs (Breneman, 2001). The most frequently occurring outputs are number of graduates (teaching output), and research output (i.e. income received for research purposes, funding council grants plus income from research grants and contracts). Alternative choices for research output may be research books, book chapters, and journal articles (Abbot and Doucoulianos, 2009), medical-and non- medical research funding (Abbot and Doucoulianos, 2003), student contact hours, number of publications, contribution to publications, and citations (as research output). Tertiary education qualifies graduates for jobs or additional training, intensifying their competence and analytical capacities. In this sense, they acquire advance qualifications that boost their professional education with concurrent direct income effects, increased social mobility, and health as well as other indirect effects. Additional metrics to be mentioned as suitable measures are the success rates of undergraduate students, number of doctoral dissertations, number of students enrolled on PhD courses, foreign students enrolled as a percentage of all students, and revenues from financed activities, etc. Finally, the amount of external resources attracted to research activities (grants, consultancies, etc.), promotions (number of promotions attained by the academic staff of each department, public service activities (number of workshops, conferences, training courses and other activities by the teaching staff of each department), could be vital proxies for output measures. As mentioned previously, apart from the two traditional outputs of teaching and research, universities have developed a third output that reflects their involvement with wider society; thus, in the next section an introduction to third-stream activities in HE is presented.

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