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Using Archetypoid Analysis to Classify Institutions and Faculties of Economics

Author

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  • Klaus Wohlrabe
  • Sabine Gralka
Abstract
We use archetypoid analysis as a new tool to categorize institutions and faculties of economics. The approach identifies typical characteristics of extreme (archetypal) values in a multivariate data set. Each entity under investigation is assigned relative shares of the identified archetypoid, which show the affiliation of the entity to the archetypoid. In contrast to its predecessor, the archetypal analysis, archetypoids always represent actual observed units in the data. The approach therefore allows to classify institutions in a rarely used way. While the method has been recognized in the literature, it is the first time that it is used in higher education research and as in our case for institutions and faculties of economics. Our dataset contains seven bibliometric indicators for 298 top-level institutions obtained from the RePEc database. We identify three archetypoids, which are characterized as the top-, the low- and the medium-performer. We discuss the assignment of shares of the identified archetypoids to the institutions in detail. As a sensitivity analysis we show how the classification changes when for four and five archetypoids are considered.

Suggested Citation

  • Klaus Wohlrabe & Sabine Gralka, 2019. "Using Archetypoid Analysis to Classify Institutions and Faculties of Economics," CESifo Working Paper Series 8023, CESifo.
    • Handle: RePEc:ces:ceswps:_8023
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    References listed on IDEAS

    as
    1. Christian Zimmermann, 2013. "Academic Rankings with RePEc," Econometrics, MDPI, vol. 1(3), pages 1-32, December.
    2. Katharina Rath & Klaus Wohlrabe, 2016. "Recent trends in co-authorship in economics: evidence from RePEc," Applied Economics Letters, Taylor & Francis Journals, vol. 23(12), pages 897-902, August.
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    4. Rolf Sternberg & Timo Litzenberger, 2005. "The publication and citation output of German Faculties of Economics and Social Sciences - a comparison of faculties and disciplines based upon SSCI data," Scientometrics, Springer;Akadémiai Kiadó, vol. 65(1), pages 29-53, October.
    5. Hsieh, Chih-Sheng & König, Michael D. & Liu, Xiaodong & Zimmermann, Christian, 2018. "Superstar Economists: Coauthorship Networks and Research Output," IZA Discussion Papers 11916, Institute of Labor Economics (IZA).
    6. Gnewuch, Matthias & Wohlrabe, Klaus, 2018. "Super-efficiency of education institutions: an application to economics departments," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 26, pages 610-623.
    7. Vinué, Guillermo & Epifanio, Irene & Alemany, Sandra, 2015. "Archetypoids: A new approach to define representative archetypal data," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 102-115.
    8. Sabine Gralka & Klaus Wohlrabe & Lutz Bornmann, 2018. "How to Measure Research Efficiency in Higher Education? Research Grants vs. Publication Output," CESifo Working Paper Series 7055, CESifo.
    9. Joanna Wolszczak-Derlacz & Aleksandra Parteka, 2011. "Efficiency of European public higher education institutions: a two-stage multicountry approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 887-917, December.
    10. Ellen Hazelkorn, 2007. "The Impact of League Tables and Ranking Systems on Higher Education Decision Making," Higher Education Management and Policy, OECD Publishing, vol. 19(2), pages 1-24.
    11. Geraint Johnes & Jill Johnes, 2016. "Costs, efficiency, and economies of scale and scope in the English higher education sector," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 32(4), pages 596-614.
    12. Lutz Bornmann & Klaus Wohlrabe, 2019. "Normalisation of citation impact in economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 841-884, August.
    13. Epifanio, Irene, 2016. "Functional archetype and archetypoid analysis," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 24-34.
    14. Bolli, Thomas & Olivares, Maria & Bonaccorsi, Andrea & Daraio, Cinzia & Aracil, Adela Garcia & Lepori, Benedetto, 2016. "The differential effects of competitive funding on the production frontier and the efficiency of universities," Economics of Education Review, Elsevier, vol. 52(C), pages 91-104.
    15. Seiler, Christian & Wohlrabe, Klaus, 2013. "Archetypal scientists," Journal of Informetrics, Elsevier, vol. 7(2), pages 345-356.
    16. Sabine Gralka, 2018. "Persistent inefficiency in the higher education sector: evidence from Germany," Education Economics, Taylor & Francis Journals, vol. 26(4), pages 373-392, July.
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    Cited by:

    1. Sabine Gralka & Klaus Wohlrabe, 2022. "Classifying top economists using archetypoid analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 29(14), pages 1342-1346, August.
    2. Klaus Wohlrabe & Constantin Bürgi, 2021. "What is the benefit from publishing a working paper in a journal in terms of citations? Evidence from economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4701-4714, June.
    3. Alexandra Baumann & Klaus Wohlrabe, 2020. "Where have all the working papers gone? Evidence from four major economics working paper series," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2433-2441, September.
    4. Piotr Śpiewanowski & Oleksandr Talavera, 2021. "Journal rankings and publication strategy," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3227-3242, April.
    5. Denitsa Angelova & Maya Göser & Stefan Wimmer & Johannes Sauer, 2021. "How efficient are German life sciences? Econometric evidence from a latent class stochastic output distance model," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-20, March.

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    More about this item

    Keywords

    archetypoid analysis; classification; RePEc; faculty of economics; economic institutions;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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