Literature DB >> 22081733

Efficiency of European public higher education institutions: a two-stage multicountry approach.

Joanna Wolszczak-Derlacz1, Aleksandra Parteka.   

Abstract

The purpose of this study is to examine efficiency and its determinants in a set of higher education institutions (HEIs) from several European countries by means of non-parametric frontier techniques. Our analysis is based on a sample of 259 public HEIs from 7 European countries across the time period of 2001-2005. We conduct a two-stage DEA analysis (Simar and Wilson in J Economet 136:31-64, 2007), first evaluating DEA scores and then regressing them on potential covariates with the use of a bootstrapped truncated regression. Results indicate a considerable variability of efficiency scores within and between countries. Unit size (economies of scale), number and composition of faculties, sources of funding and gender staff composition are found to be among the crucial determinants of these units' performance. Specifically, we found evidence that a higher share of funds from external sources and a higher number of women among academic staff improve the efficiency of the institution.

Entities:  

Year:  2011        PMID: 22081733      PMCID: PMC3205260          DOI: 10.1007/s11192-011-0484-9

Source DB:  PubMed          Journal:  Scientometrics        ISSN: 0138-9130            Impact factor:   3.238


  3 in total

1.  The determinants of technical efficiency of a large scale HIV prevention project: application of the DEA double bootstrap using panel data from the Indian Avahan.

Authors:  Aurélia Lépine; Anna Vassall; Sudhashree Chandrashekar
Journal:  Cost Eff Resour Alloc       Date:  2015-03-29

Review 2.  Economies of scale and scope in publicly funded biomedical and health research: evidence from the literature.

Authors:  Karla Hernandez-Villafuerte; Jon Sussex; Enora Robin; Sue Guthrie; Steve Wooding
Journal:  Health Res Policy Syst       Date:  2017-02-02

3.  How efficient are German life sciences? Econometric evidence from a latent class stochastic output distance model.

Authors:  Denitsa Angelova; Maya Göser; Stefan Wimmer; Johannes Sauer
Journal:  PLoS One       Date:  2021-03-12       Impact factor: 3.240

  3 in total

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