IDEAS home Printed from https://ideas.repec.org/a/taf/applec/45y2013i9p1109-1120.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2011.613795
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2011.613795?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
    2. Colegrave, Andrew D. & Giles, Margaret J., 2008. "School cost functions: A meta-regression analysis," Economics of Education Review, Elsevier, vol. 27(6), pages 688-696, December.
    3. World Bank, 2008. "The Road Not Traveled : Education Reform in the Middle East and North Africa," World Bank Publications - Books, The World Bank Group, number 6303.
    4. Banker, Rajiv D. & Thrall, R. M., 1992. "Estimation of returns to scale using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 62(1), pages 74-84, October.
    5. Essid, Hédi & Ouellette, Pierre & Vigeant, Stéphane, 2010. "Measuring efficiency of Tunisian schools in the presence of quasi-fixed inputs: A bootstrap data envelopment analysis approach," Economics of Education Review, Elsevier, vol. 29(4), pages 589-596, August.
    6. repec:bla:scandj:v:87:y:1985:i:4:p:594-604 is not listed on IDEAS
    7. Foreman-Peck, James & Foreman-Peck, Lorraine, 2006. "Should schools be smaller? The size-performance relationship for Welsh schools," Economics of Education Review, Elsevier, vol. 25(2), pages 157-171, April.
    8. Kneip, Alois & Park, Byeong U. & Simar, Léopold, 1998. "A Note On The Convergence Of Nonparametric Dea Estimators For Production Efficiency Scores," Econometric Theory, Cambridge University Press, vol. 14(6), pages 783-793, December.
    9. Banker, Rajiv D., 1984. "Estimating most productive scale size using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 17(1), pages 35-44, July.
    10. Kuziemko, Ilyana, 2006. "Using shocks to school enrollment to estimate the effect of school size on student achievement," Economics of Education Review, Elsevier, vol. 25(1), pages 63-75, February.
    11. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    12. Chabotar, Kent John, 1989. "Measuring the costs of magnet schools," Economics of Education Review, Elsevier, vol. 8(2), pages 169-183, April.
    13. Simar, Leopold & Wilson, Paul W., 2002. "Non-parametric tests of returns to scale," European Journal of Operational Research, Elsevier, vol. 139(1), pages 115-132, May.
    14. Stiefel, Leanna & Schwartz, Amy Ellen & Iatarola, Patrice & Chellman, Colin C., 2009. "Mission matters: The cost of small high schools revisited," Economics of Education Review, Elsevier, vol. 28(5), pages 585-599, October.
    15. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    16. Rajiv D. Banker & Richard C. Morey, 1986. "Efficiency Analysis for Exogenously Fixed Inputs and Outputs," Operations Research, INFORMS, vol. 34(4), pages 513-521, August.
    17. Fare, Rolf & Grosskopf, Shawna, 1994. "Estimation of returns to scale using data envelopment analysis: A comment," European Journal of Operational Research, Elsevier, vol. 79(2), pages 379-382, December.
    18. Monk, David H., 1987. "Secondary school size and curriculum comprehensiveness," Economics of Education Review, Elsevier, vol. 6(2), pages 137-150, April.
    19. Park, B.U. & Jeong, S.-O. & Simar, L., 2010. "Asymptotic distribution of conical-hull estimators of directional edges," LIDAM Reprints ISBA 2010025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kristof De Witte & Laura López-Torres, 2017. "Efficiency in education: a review of literature and a way forward," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 339-363, April.
    2. Élisé Wendlassida Miningou & Medjy Pierre‐Louis & Jean‐Marc Bernard, 2022. "Improving learning outcomes in francophone Africa: More resources or improved efficiency?," African Development Review, African Development Bank, vol. 34(1), pages 127-141, March.
    3. 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.
    4. 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.
    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), Democritus University of Thrace (DUTH), Kavala Campus, Greece, 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    2. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    3. Michali, Maria & Emrouznejad, Ali & Dehnokhalaji, Akram & Clegg, Ben, 2023. "Subsampling bootstrap in network DEA," European Journal of Operational Research, Elsevier, vol. 305(2), pages 766-780.
    4. Sebastian Kohl & Jan Schoenfelder & Andreas Fügener & Jens O. Brunner, 2019. "The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals," Health Care Management Science, Springer, vol. 22(2), pages 245-286, June.
    5. Aggelopoulos, Eleftherios & Georgopoulos, Antonios, 2017. "Bank branch efficiency under environmental change: A bootstrap DEA on monthly profit and loss accounting statements of Greek retail branches," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1170-1188.
    6. Calogero Guccio & Giacomo Pignataro & Ilde Rizzo, 2014. "Evaluating the efficiency of public procurement contracts for cultural heritage conservation works in Italy," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 38(1), pages 43-70, February.
    7. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    8. Ohene-Asare, Kwaku & Turkson, Charles & Afful-Dadzie, Anthony, 2017. "Multinational operation, ownership and efficiency differences in the international oil industry," Energy Economics, Elsevier, vol. 68(C), pages 303-312.
    9. Massimo Finocchiaro Castro & Calogero Guccio & Ilde Rizzo, 2014. "An assessment of the waste effects of corruption on infrastructure provision," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 21(4), pages 813-843, August.
    10. Bernardino Benito & José Solana & María-Rocío Moreno, 2014. "Explaining efficiency in municipal services providers," Journal of Productivity Analysis, Springer, vol. 42(3), pages 225-239, December.
    11. Singh, Shweta & Murthi, B.P.S. & Steffes, Erin, 2013. "Developing a measure of risk adjusted revenue (RAR) in credit cards market: Implications for customer relationship management," European Journal of Operational Research, Elsevier, vol. 224(2), pages 425-434.
    12. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499.
    13. Avkiran, Necmi K., 2001. "Investigating technical and scale efficiencies of Australian Universities through data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 35(1), pages 57-80, March.
    14. Zhu, Joe, 2000. "Further discussion on linear production functions and DEA," European Journal of Operational Research, Elsevier, vol. 127(3), pages 611-618, December.
    15. Peyrache, Antonio & Rose, Christiern & Sicilia, Gabriela, 2020. "Variable selection in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 282(2), pages 644-659.
    16. Nguyen, Bao Hoang & Simar, Léopold & Zelenyuk, Valentin, 2022. "Data sharpening for improving central limit theorem approximations for data envelopment analysis–type efficiency estimators," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1469-1480.
    17. Forsund, Finn R. & Sarafoglou, Nikias, 2005. "The tale of two research communities: The diffusion of research on productive efficiency," International Journal of Production Economics, Elsevier, vol. 98(1), pages 17-40, October.
    18. Mehdiloo, Mahmood & Podinovski, Victor V., 2019. "Selective strong and weak disposability in efficiency analysis," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1154-1169.
    19. Ole Bent Olesen & Niels Christian Petersen & Victor V. Podinovski, 2022. "Scale characteristics of variable returns-to-scale production technologies with ratio inputs and outputs," Annals of Operations Research, Springer, vol. 318(1), pages 383-423, November.
    20. Essid, Hédi & Ouellette, Pierre & Vigeant, Stéphane, 2010. "Measuring efficiency of Tunisian schools in the presence of quasi-fixed inputs: A bootstrap data envelopment analysis approach," Economics of Education Review, Elsevier, vol. 29(4), pages 589-596, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:applec:45:y:2013:i:9:p:1109-1120. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.