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Small is not that beautiful after all: measuring the scale efficiency of Tunisian high schools using a DEA-bootstrap method

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  • H餩 Essid
  • Pierre Ouellette
  • St鰨ane Vigeant

Abstract

Allocation of resources to schools in a centrally managed state system, as the Tunisian one, should depend on the performance of the individual institutions. The optimal size is of crucial importance in this context and we need accurate measurement for sound policies. This article discusses and implements a nonparametric statistical test procedure for organization scale efficiency. This procedure allows us to test whether the observed scale efficiency is optimal or not, using a smooth bootstrap methodology for efficiency measures estimated using Data Envelopment Analysis (DEA) methods. Because school principals do not control for the size of their institution, i.e. the capital available at decision time, the scale efficiency measures are defined so as to include quasi-fixed inputs. The results show that scale efficiency measures are subject to sampling variation. We also found that the schools that are scale efficient are usually mid-sized and large schools, when size is measured by the number of students. This contradicts the largely shared view among decision makers that small schools were optimal.

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  • H餩 Essid & Pierre Ouellette & St鰨ane Vigeant, 2013. "Small is not that beautiful after all: measuring the scale efficiency of Tunisian high schools using a DEA-bootstrap method," Applied Economics, Taylor & Francis Journals, vol. 45(9), pages 1109-1120, March.
  • Handle: RePEc:taf:applec:45:y:2013:i:9:p:1109-1120
    DOI: 10.1080/00036846.2011.613795
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    2. Ramzi, Sourour & Afonso, António & Ayadi, Mohamed, 2016. "Assessment of efficiency in basic and secondary education in Tunisia: A regional analysis," International Journal of Educational Development, Elsevier, vol. 51(C), pages 62-76.
    3. 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.
    4. Élisé Wendlassida Miningou & Jean-Marc Bernard & Medjy Pierre-Louis, 2019. "Improving learning outcomes in Francophone Africa: More resources or improved efficiency?," Cahiers de recherche 19-01, Departement d'Economique de l'École de gestion à l'Université de Sherbrooke.
    5. Cordero, José Manuel & Santín, Daniel & Sicilia, Gabriela, 2015. "Testing the accuracy of DEA estimates under endogeneity through a Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 244(2), pages 511-518.
    6. Dimitrios Sotiriadis & Georgios Menexes & Constantinos Tsamadias, 2018. "Investigating the Efficiency of Senior Secondary Schools: Evidence from Schools in the Greek region of Central Macedonia," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 11(2), pages 36-43, September.
    7. 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).
    8. Son Nghiem & Ha Trong Nguyen & Luke B. Connelly, 2016. "The Efficiency of Australian Schools: A Nationwide Analysis Using Gains in Test Scores of Students as Outputs," Economic Papers, The Economic Society of Australia, vol. 35(3), pages 256-268, September.
    9. Nghiem, Son & Nguyen, Ha & Connelly, Luke, 2014. "The Efficiency of Australian Schools: Evidence from the NAPLAN Data 2009-2011," MPRA Paper 56231, University Library of Munich, Germany.
    10. Brennan, Shae & Haelermans, Carla & Ruggiero, John, 2014. "Nonparametric estimation of education productivity incorporating nondiscretionary inputs with an application to Dutch schools," European Journal of Operational Research, Elsevier, vol. 234(3), pages 809-818.
    11. Yahia, F.B. & Essid, H., 2019. "Determinants of Tunisian Schools’ Efficiency: A DEA-Tobit Approach," Journal of Applied Management and Investments, Department of Business Administration and Corporate Security, International Humanitarian University, vol. 8(1), pages 44-56, February.
    12. José Manuel Cordero & Cristina Polo & Daniel Santín & Gabriela Sicilia, 2016. "Monte-Carlo Comparison of Conditional Nonparametric Methods and Traditional Approaches to Include Exogenous Variables," Pacific Economic Review, Wiley Blackwell, vol. 21(4), pages 483-497, October.
    13. Cordero, José Manuel & Santín, Daniel & Sicilia, Gabriela, 2013. "Dealing with the Endogeneity Problem in Data Envelopment Analysis," MPRA Paper 47475, University Library of Munich, Germany.

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