Does econometric methodology matter to rank universities? An analysis of Italian higher education system

The measurement of efficiency in higher education has gained a growing interest in recent years, especially due to the expansion of the university system. This paper provides a review of the literature on efficiency in higher education institutions by covering empirical articles which applied frontier efficiency measurement techniques from 1997 to 2019. We review the methodological approaches used, both parametric and non-parametric techniques, such as Data Envelopment Analysis, Malmquist index and Stochastic Frontier Analysis. Secondly, we list the applied inputs, input prices, outputs, quality, and environment variables and based on the overview, we discuss the advantages and drawbacks of the different empirical proxy variables used. We address the importance of characterizing students and research funding as raw materials of both the teaching and research services, respectively, and we provide suggestions on how to deal with them empirically. We also discuss the difference between quality and environmental variables, and we give some practical indications to distinguish them in doubtful cases.

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“…Agasisti and Haelermans (2016), Barra and Zotti (2016), Barra, Lagravinese and Zotti (2018), Kempkes and Pohl (2010), Sav (2012a).…”

Section: Both Parametric and Non-parametric Estimatesmentioning

confidence: 99%

Higher Education Efficiency Frontier Analysis: A Review of Variables to Consider

Ferro

D'Elía

2020

ERIES Journal

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“…Agasisti and Bianco (2009) computed efficiency rankings and scores of Italian universities using DEA and Stochastic Frontier analysis (SFA). Barra et al (2018) measured the efficiency of Italian higher education using both DEA and SFA techniques and used the results to provide guidance to university managers and policymakers. De França et al (2010) evaluated the impact of information asymmetry on Brazil organizational efficiency based on an additive DEA approach.…”

Section: Previous Studies On Higher Education Efficiencymentioning

confidence: 99%

Measuring Scientific Productivity in China Using Malmquist Productivity Index

Song

Schubert

Liu

et al. 2019

Journal of Data and Information Science

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Purpose: This paper aims to investigate the scientific productivity of China's science system.Design/methodology/approach: This paper employs the Malmquist productivity index (MPI) based on Data Envelopment Analysis (DEA). Findings:The results reveal that the overall efficiency of Chinese universities increased significantly from 2009 to 2016, which is mainly driven by technological progress. From the perspective of the functions of higher education, research and transfer activities perform better than the teaching activities. Research limitations:As an implication, the indicator selection mechanism, investigation period and the MPI model can be further extended in the future research. Practical implications:The results indicate that Chinese education administrative departments should take actions to guide and promote the teaching activities and formulate reasonable resource allocation regulations to reach the balanced development in Chinese universities.Originality/value: This paper selects 58 Chinese universities and conducts a quantified measurement during the period 2009-2016. Three main functional activities of universities (i.e. teaching, researching, and application) are innovatively categorized into different schemes, and we calculate their performance, respectively.

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“…The efficiency of non-public higher education institutions was not measured in Poland, which is a fundamental limitation of the research conducted so far. Variables considered in previous studies have included the number of academic teachers [Barra, Lagravinese, Zotti, 2018], the value of university fixed assets [Brzezicki, Prędki, 2018], the number of students and graduates [Wolszczak-Derlacz, 2017] and university revenue [Selim, Bursalıoğlu, 2015;Brzezicki, Prędki, 2018].…”

Section: Review Of the Literature On The Dea Method: Efficiency In Edmentioning

confidence: 99%

“…There was a wide range of variables adopted in the second stage of efficiency analysis. Barra et al [2018] considered fees collected from students, the year of establishment of a university, GDP (regional level), and the proportion of women among students, whereas Wolszczak-Derlacz [2017] included in their study the year of establishment of a university, GDP, the share of public funds and fees in the total sum of university funds, and the composition of university departments. All these studies, however, have their limitations because, for example, Wolszczak-Derlacz and Parteka [2011] in the first stage included only the number of graduations in the field of teaching, while Brzezicki and Wolszczak-Derlacz [2015b] in one model adopted the number of graduations and the employer preference index.…”

Section: Review Of the Literature On The Dea Method: Efficiency In Edmentioning

confidence: 99%

The Efficiency of Public and Private Higher Education Institutions in Poland

Brzezicki¹

2020

GNPJE

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Does econometric methodology matter to rank universities? An analysis of Italian higher education system

Cited by 26 publications

References 84 publications

Higher Education Efficiency Frontier Analysis: A Review of Variables to Consider

Higher Education Efficiency Frontier Analysis: A Review of Variables to Consider

Measuring Scientific Productivity in China Using Malmquist Productivity Index

The Efficiency of Public and Private Higher Education Institutions in Poland

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