Abstract
An increasing effort has been put into dealing with the question of time-series analysis regarding institutional efficiency, including in the area of higher education. Universities are important institutions for economies and societies and are expected to provide excellence as well as efficiency in their processes and outputs. This is reflected in the context of an increased global competitive environment by more refined international university rankings. Combining the two areas, this paper points towards a methodological challenge in comparing different ranking datasets for their use in a data envelopment analysis (DEA) Malmquist index time-series efficiency analysis, namely, index-based data compared to additive data. The problem is discussed in a theoretical framework and complemented with an empirical application: calculations for 70 European universities with budget and staff input data and different ranking output data for the timeframe of 2011–2016 show that there is no evidence for a specific index data problem. Important implications regarding university management and higher education policies are outlined. Efficiency improvements among the analyzed universities are significant but also unevenly distributed and not easy to obtain for individual institutions.
Highlights
University institutions play an important role in economic development, innovation, and internationalization, e.g., through their objectives of research, teaching, and third mission, and for societies at large
This paper addresses the research question of if university ranking data is applicable for longitudinal efficiency analysis endeavors
Reasons for the increasing use of this efficiency analysis technique in higher education research are the fact that no a priori knowledge about a production function is required, only real-life data is used, and a multitude of inputs can be combined with a multitude of outputs, which is very typical for universities as “multi-product-organisations” [68,69,70,71,72,73]
Summary
University institutions play an important role in economic development, innovation, and internationalization, e.g., through their objectives of research, teaching, and third mission, and for societies at large. Steering resources within university systems, as done by higher education politicians regarding public budgets, by university managers within the institutions themselves as well as by stakeholders, such as corporations as research partners, and students as study program participants, is an important task within the economic and management domain. Rankings have evolved regarding their principal setup, incorporating criticism addressing indicators, institutional inclusion, and data quality, including the discourse on journal publication and the individual researcher level [5,6,7,8] This went hand in hand with an increased influence on policies and resource decisions in higher education [9,10,11,12]. Regarding the analysis and use of ranking data as well as for higher education efficiency analysis in general, increasing emphasis is put on the question of dynamic time-series developments. The underlying technique for efficiency measurement is the DEA introduced by Charnes, Cooper, and Rhodes [24] in
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