In most countries, universities accumulate a significant number of public financial resources (Pruvot et al. 2015). Higher education experiences expansion and budget constraints (Agasisti et al. 2021). The COVID-19 crisis worsened the financial situation (e.g. Bolton and Hubble 2021), and the major way of maximizing university performance is to guarantee the efficiency of resources utilization. The approach based on efficiency suggests that a university is considered as a production unit that transforms the set of available resources into a set of outputs, primarily teaching and research.

The number of studies on efficiency in higher education is growing. This growth is related to the interest of the government and other public stakeholders in the topic. For example, efficiency is part of the EU priorities for higher education and research (Estermann and Kupriyanova, 2019). If we look at all articles in Scopus supported by the largest funds (EU, USA, UK, China) that are related to higher education (there are 833 such articles in 2017-2021), “efficiency” is at the top of popularity within the keywords.

Although efficiency is part of popular policy issues, the methodological part of the evaluation is worth discussing. Despite the developed econometric approaches, it is very difficult to strike a balance between the subtlety of methodical settings and practical clarity, which is often a limitation on the contribution of efficiency studies in higher education research. The workshop will provide a space for discussion of the opportunities and challenges that research on efficiency may bring in higher education. It will be organized as brief presentations of four studies on efficiency in higher education, followed by discussions and reflections. The studies are following:

1) The first study investigates how the efficiency of universities is affected by the external environment, specifically the characteristics of the territory in which they operate. The theoretical framework combines two perspectives: 1) the resource dependence theory; 2) institutional isomorphisms. To test this theoretical framework, we use data on Russian universities and a non-parametric conditional order-m efficiency estimator with two categories of exogenous (contextual) variables.

2) The second study analyses how different internal governance practices influence their efficiency and performance. The dimensions investigated here are related to the autonomy level of universities’ departments, centralization of decision-making, the structure of the salary fund, and performance contracts for faculty. To study the relationship between these practices and efficiency, we use a conditional DEA approach. The empirical analysis is performed separately for teaching-oriented and research-oriented universities.

3) The third paper analyzes the efficiency of universities and the stratification of the higher education system. Firstly, we evaluate the efficiency of universities using non-parametric robust DEA and analyze its dynamics through calculating the Malmquist productivity index. Then we calculate sigma- and beta-convergence measures in order to study the changes in stratification of universities in terms of their efficiency. We also provide potential explanations of the observed productivity changes and convergence in terms of different public policies conducted in the Russian higher education sector during the considered period (2013-2020).

4) This fourth study proposes an alternative way of existing state funding allocation in Russia and compares outputs produced under different approaches. An alternative mechanism is based on efficiency principles when the amount of money is determined with accordance input-output ratio. Following Wang (2019) and Korhonen & Syrjänen (2004), we simulate a two-stage DEA model. The first stage of the model is devoted to performance assessment. At the second stage – resource allocation – multi-objective linear programming (MOLP) is used to maximize the overall outputs. We compare different scenarios of allocation and conclude on the possibility of state funding reallocation and enhancing the aggregate outputs.

It must be acknowledged that HEIs have some features that make it difficult to assess their efficiency in the traditional framework of economic theory. Specifically, they are non-profit organizations, and many resources they use cannot be easily converted into monetary equivalent. To address these problems, specific measurement techniques have been developed in the academic literature, ranging from linear regression (Johnes and Taylor, 1990) to modern methods that involve building complex statistical and mathematical models. These approaches are mainly based on Stochastic Frontier Analysis (SFA) (Aigner et al., 1977; Meeusen and van Den Broeck, 1977) and Data Envelopment Analysis (DEA) (Charnes et al., 1978). In this workshop, we will discuss a methodological approach based on DEA-related tools, also given that they are the most widely used for assessing organizational performance in education (De Witte and López-Torres, 2017). DEA is based on Farrell's concept of efficiency (Farrell, 1957), which involves evaluating efficiency relative to the frontier drawn from the “best” observed practices. This method is a convenient tool for assessing the efficiency of universities: (i) it does not require assumptions about the functional relationship between the parameters of resources and performance and (ii) it allows evaluating the efficiency in the case of several resources used by the organization and the production of several outputs (as teaching and research in the case of HEIs). Despite the advantages noted above, the traditional model based on Data Envelopment Analysis suffers some limitations: the sensitivity of the efficiency assessment to the presence of outliers in the sample, the determinism of the model and the impossibility of statistical inference on efficiency scores, and the lack of methods for considering how endogenous variables influence efficiency scores.

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