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Assessing the evolution of school performance and value-added: trends over four years

Author

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  • M. Portela
  • A. Camanho
  • A. Keshvari
Abstract
This paper explores the changes in value added (VA) of a sample of schools for cohorts of students finishing secondary education between 2005 and 2008. VA estimates are based on distance measures obtained from DEA models. These measures are computed for each pupil in each school, and evaluate the distance between the school frontier in a given year and a pooled frontier comprising all schools analysed. The school VA is then computed by aggregating the VA scores for the cohort of pupils attending that school in a given year. The ratio between VA estimates for two consecutive cohorts, that attended the school in different years, is taken as the index of VA change. However, the evolution of school performance over time should consider not only the movements of the school frontier, but should also take into account other effects, such as the proximity of the students to the best-practices, represented by the school frontier, observed over time. For that purpose we developed an enhanced Malmquist index to evaluate the evolution of school performance over time. One of the components of the Malmquist index proposed measures VA change, and the other measures the ability of all school students to move closer to their own school best practices over time. The approach developed is applied to a sample of Portuguese secondary schools. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • M. Portela & A. Camanho & A. Keshvari, 2013. "Assessing the evolution of school performance and value-added: trends over four years," Journal of Productivity Analysis, Springer, vol. 39(1), pages 1-14, February.
    • Handle: RePEc:kap:jproda:v:39:y:2013:i:1:p:1-14
    DOI: 10.1007/s11123-012-0263-5
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    2. 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.
    3. Aparicio, Juan & López-Torres, Laura & Santín, Daniel, 2018. "Economic crisis and public education. A productivity analysis using a Hicks-Moorsteen index," Economic Modelling, Elsevier, vol. 71(C), pages 34-44.
    4. Paola Azar & Gabriela Sicilia, 2021. "An assessment of pupil and school performance in public primary education in Uruguay," Documentos de Trabajo (working papers) 21-22, Instituto de Economía - IECON.
    5. Alexander Arévalo S & Víctor Giménez G & Diego Prior J, 2022. "Análisis de eficiencia en educación: una aplicación del método StoNED," Revista Desarrollo y Sociedad, Universidad de los Andes,Facultad de Economía, CEDE, vol. 92(2), pages 45-91, October.
    6. Aparicio, Juan & Ortiz, Lidia & Santín, Daniel, 2021. "Comparing group performance over time through the Luenberger productivity indicator: An application to school ownership in European countries," European Journal of Operational Research, Elsevier, vol. 294(2), pages 651-672.
    7. Blackburn, Vincent & Brennan, Shae & Ruggiero, John, 2014. "Measuring efficiency in Australian Schools: A preliminary analysis," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 4-9.
    8. Aparicio, Juan & Crespo-Cebada, Eva & Pedraja-Chaparro, Francisco & Santín, Daniel, 2017. "Comparing school ownership performance using a pseudo-panel database: A Malmquist-type index approach," European Journal of Operational Research, Elsevier, vol. 256(2), pages 533-542.
    9. Liu, Xiaohong & Yang, Jiangjiang & Xu, Chengzhen & Li, Xingchen & Zhu, Qingyuan, 2023. "Environmental regulation efficiency analysis by considering regional heterogeneity," Resources Policy, Elsevier, vol. 83(C).
    10. Tommaso Agasisti, 2014. "How does schools’ efficiency look like across Europe? An empirical analysis of Germany, Spain, France, Italy and UK using OECD PISA2012 data," Working papers 9, Società Italiana di Economia Pubblica.
    11. Azar, Paola & Sicilia, Gabriela, 2023. "Assessing potential improvement in education: Unravelling pupils' and schools’ inefficiencies using a multi-level metafrontier approach," Economic Modelling, Elsevier, vol. 118(C).
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    More about this item

    Keywords

    Data envelopment analysis; Malmquist index; Secondary education; School value-added; C61; C67; I21;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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