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International efficiency evaluation of education and impacts of bullying: a value inversion–data envelopment analysis approach

Author

Listed:
  • Kouhei Kikuchi

    (Hokkai-Gakuen University)

  • Soushi Suzuki

    (Hokkai-Gakuen University)

  • Peter Nijkamp

    (Open University of the Netherlands)

Abstract

Education plays a vital role in the development of any country or region, making it imperative to address obstacles that hinder educational quality such as school bullying. This is an under-researched topic in the social sciences. Bullying is a form of social mistreatment that may have detrimental effects, because students who experience frequent bullying tend to perform poorer compared to their peers who do not report such incidents. Given this evidence, it is crucial to assess the educational performance of countries by considering the overall well-being, including mental well-being, of students. In this context, data envelopment analysis (DEA) is a valuable method for evaluating the performance of decision-making units in education. In our paper, we discuss various approaches to apply DEA when dealing with "undesirable" outputs like bullying. Common techniques involve transforming undesirable outputs using reciprocal transformations and employing a "bad-output" model. However, these methods have several drawbacks, such as altering the nature of the selected output items, loss of linearity, reduced robustness of the efficiency frontier, and limited versatility. To address these concerns, our paper proposes and tests a value inversion–DEA model that can consistently transform "undesirable" data into a reverse measurement scale, while preserving linearity. We demonstrate the high versatility of this model across different types of DEA models with undesirable outputs. Furthermore, we apply this proposed method to assess the educational efficiency of OECD countries, focusing on bullying as an undesirable output. Our findings show that significant improvements in performance are possible in many countries by addressing school bullying.

Suggested Citation

  • Kouhei Kikuchi & Soushi Suzuki & Peter Nijkamp, 2024. "International efficiency evaluation of education and impacts of bullying: a value inversion–data envelopment analysis approach," Asia-Pacific Journal of Regional Science, Springer, vol. 8(1), pages 137-164, March.
  • Handle: RePEc:spr:apjors:v:8:y:2024:i:1:d:10.1007_s41685-023-00320-8
    DOI: 10.1007/s41685-023-00320-8
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    References listed on IDEAS

    as
    1. Gisela Rusteholz & Mauro Mediavilla & Luis Pires, 2021. "Impact of bullying on academic performance. A case study for the Community of Madrid," Working Papers 2021/01, Institut d'Economia de Barcelona (IEB).
    2. Aparicio, Juan & Cordero, Jose M. & Ortiz, Lidia, 2019. "Measuring efficiency in education: The influence of imprecision and variability in data on DEA estimates," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    3. Halkos, George & Petrou, Kleoniki Natalia, 2019. "Treating undesirable outputs in DEA: A critical review," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 97-104.
    4. Soushi Suzuki & Karima Kourtit & Peter Nijkamp, 2017. "The robustness of performance rankings of Asia-Pacific super cities," Asia-Pacific Journal of Regional Science, Springer, vol. 1(1), pages 219-242, April.
    5. Delprato, Marcos & Antequera, Germán, 2021. "School efficiency in low and middle income countries: An analysis based on PISA for development learning survey," International Journal of Educational Development, Elsevier, vol. 80(C).
    6. Liu, Wenbin & Zhou, Zhongbao & Ma, Chaoqun & Liu, Debin & Shen, Wanfang, 2015. "Two-stage DEA models with undesirable input-intermediate-outputs," Omega, Elsevier, vol. 56(C), pages 74-87.
    7. Huan Xu & Fangtao Liu, 2017. "Measuring the Efficiency of Education and Technology via DEA approach: Implications on National Development," Social Sciences, MDPI, vol. 6(4), pages 1-13, November.
    8. Juan Aparicio & Jose M. Cordero & Lidia Ortiz, 2021. "Efficiency Analysis with Educational Data: How to Deal with Plausible Values from International Large-Scale Assessments," Mathematics, MDPI, vol. 9(13), pages 1-16, July.
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    More about this item

    Keywords

    Data envelopment analysis (DEA); Undesirable outputs; Value inversion; Educational efficiency; Bullying;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid

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