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A Directional Distance Approach Applied To Higher Education: An Analysis Of Teaching-Related Output Efficiency

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  • Cristian BARRA
  • Roberto ZOTTI

Abstract

This paper applies a data envelopment analysis (DEA) method to assess technical efficiency of both private and public universities in Italy. A directional distance function approach has been applied in order to handle both desirable (i.e. number of graduates) and undesirable (i.e. number of dropouts) outputs. The findings based on a panel from academic year 2003/2004 to 2007/2008 reveal the presence of interesting geographical (both by macro areas and regions) and ownership (private, public) effects. Several quality and quantity proxies have also been used in order to check whether the estimates depend on the output specification. Finally, the possible evidence of variation in the universities performances by subject of study has been taken into account in order to check whether the results are still consistent comparing universities within subject rather than across subjects.

Suggested Citation

  • Cristian BARRA & Roberto ZOTTI, 2016. "A Directional Distance Approach Applied To Higher Education: An Analysis Of Teaching-Related Output Efficiency," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 87(2), pages 145-173, December.
  • Handle: RePEc:bla:annpce:v:87:y:2016:i:2:p:145-173
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    File URL: http://onlinelibrary.wiley.com/doi/10.1111/apce.2016.87.issue-2/issuetoc
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    Cited by:

    1. Vanesa D’Elia & Gustavo Ferro, 2019. "Empirical Efficiency Measurement in Higher Education: An Overview," CEMA Working Papers: Serie Documentos de Trabajo. 708, Universidad del CEMA.
    2. Ben Yahia, Fatma & Essid, Hédi & Rebai, Sonia, 2018. "Do dropout and environmental factors matter? A directional distance function assessment of tunisian education efficiency," International Journal of Educational Development, Elsevier, vol. 60(C), pages 120-127.
    3. Rebai, Sonia & Ben Yahia, Fatma & Essid, Hédi, 2020. "A graphically based machine learning approach to predict secondary schools performance in Tunisia," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).

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