The Index Number Problem with DEA: Insights from European University Efficiency Data

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.

Assessing and Classification of Academic Efficiency in Engineering Teaching Programs

“…When comparing with the research (Johnes, 2006), where they analyze 130 universities in the UK using six inputs and three outputs, the minimum efficiency score was estimated at around 60%. Similarly, Klumpp (2018) in the research of 17 European universities identified a minimum efficiency score of 61.60%; The minimum threshold of 60% for the efficiency score increases in our research when institutions are grouped by similarity factors. Kuah and Wong (2011) evaluated universities' efficiency through a DEA model.…”

Measuring the efficiency of university departments: an empirical study using data envelopment analysis and cluster analysis

“…In the literature, two types of efficiency evaluations at the university level have been extensively discussed (for example, Abbott & Doucouliagos, 2003;Kao & Hung, 2008;Sellers-Rubio, Mas-Ruiz, & Casado-Díaz, 2010;Agasisti & Wolszczak-Derlacz, 2015). The first type compares the efficiency of different universities; for example, in the last five years, such studies have included Lee and Worthington (2016), Sagarra et al (2017), Visbal-Cadavid et al (2017, Guironnet and Peypoch (2018), Klumpp (2018), Quiroga-Martinez et al (2018, Yang et al (2018), Hou et al (2019, Moreno-Gómez et al (2019), Koçak et al (2019), Shamohammadi and Oh (2019), Dumitrescu et al (2020), Łącka and Brzezicki (2020), Moncayo-Martínez et al (2020), Salas-Velasco (2020), Tran et al (2020), andZhang et al (2020). The second type of efficiency evaluation compares the efficiency of different departments within a university, such as in the studies of Sinuany-Stern et al (1994), Gander (1995), Kao and Hung (2008), Barra and Zotti (2016), Ding et al (2020), andGhasemi et al (2020).…”

DEA models as a tool for evaluating and measuring the efficiency of public universities

“…On the input side, he used the financial resources of the college, and on the output side, he used the number of students, the number of PhD students, and the number of indexed publications. Klumpp (2018) compared the efficiency of 70 European universities between 2011 and 2016. He also used DEA analysis for both constant returns to scale (CCR model) and variable returns to scale (BCC model) for the evaluation.…”