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Nonparametric estimation of returns to scale in the public sector with an application to the provision of educational services

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  • J Ruggiero

    (University of Dayton)

Abstract

Nonparametric programming models have been developed to measure technical efficiency and scale economies. The programming models used for public sector applications, however, are based on standard private sector production theory. In the public sector environmental variables have a significant impact on the provision of public services. Without controlling for these environmental factors point estimates of efficiency and returns to scale will be biased. This paper extends nonparametric methods to allow measurement of returns to scale in the provision of public services. The method is applied to the provision of educational services in New York State school districts for illustrative purposes.

Suggested Citation

  • J Ruggiero, 2000. "Nonparametric estimation of returns to scale in the public sector with an application to the provision of educational services," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(8), pages 906-912, August.
    • Handle: RePEc:pal:jorsoc:v:51:y:2000:i:8:d:10.1057_palgrave.jors.2600051
    DOI: 10.1057/palgrave.jors.2600051
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    Cited by:

    1. Shawna Grosskopf & Kathy Hayes & Lori Taylor & William L Weber, 2017. "Would weighted-student funding enhance intra-district equity in Texas? A simulation using DEA," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 377-389, April.
    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. Andrew Johnson & John Ruggiero, 2014. "Nonparametric measurement of productivity and efficiency in education," Annals of Operations Research, Springer, vol. 221(1), pages 197-210, October.
    4. Victor V. Podinovski & Finn R. Førsund, 2010. "Differential Characteristics of Efficient Frontiers in Data Envelopment Analysis," Operations Research, INFORMS, vol. 58(6), pages 1743-1754, December.
    5. Huguenin, Jean-Marc, 2015. "Adjusting for the environment in DEA: A comparison of alternative models based on empirical data," Socio-Economic Planning Sciences, Elsevier, vol. 52(C), pages 41-54.
    6. B B M Shao & W S Shu, 2004. "Productivity breakdown of the information and computing technology industries across countries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(1), pages 23-33, January.
    7. Alexandra Medina-Borja & Konstantinos Triantis, 2014. "Modeling social services performance: a four-stage DEA approach to evaluate fundraising efficiency, capacity building, service quality, and effectiveness in the nonprofit sector," Annals of Operations Research, Springer, vol. 221(1), pages 285-307, October.
    8. Paolo Liberati & Raffaele Lagravinese & Giuliano Resce, 2017. "How Does Economic Social And Cultural Status Affect The Efficiency Of Educational Attainments? A Comparative Analysis On Pisa Results," Departmental Working Papers of Economics - University 'Roma Tre' 0217, Department of Economics - University Roma Tre.
    9. José Manuel Cordero & Daniel Santín & Rosa Simancas, 2017. "Assessing European primary school performance through a conditional nonparametric model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 364-376, April.
    10. Victor V. Podinovski & Robert G. Chambers & Kazim Baris Atici & Iryna D. Deineko, 2016. "Marginal Values and Returns to Scale for Nonparametric Production Frontiers," Operations Research, INFORMS, vol. 64(1), pages 236-250, February.
    11. 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.
    12. Kong, Wei-Hsin & Fu, Tsu-Tan, 2012. "Assessing the performance of business colleges in Taiwan using data envelopment analysis and student based value-added performance indicators," Omega, Elsevier, vol. 40(5), pages 541-549.
    13. 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.
    14. Hanson, Torbjørn, 2019. "Estimating output mix effectiveness: An applied scenario approach for the Armed Forces," Omega, Elsevier, vol. 83(C), pages 39-49.
    15. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    16. Hennebel, Veerle & Simper, Richard & Verschelde, Marijn, 2017. "Is there a prison size dilemma? An empirical analysis of output-specific economies of scale," European Journal of Operational Research, Elsevier, vol. 262(1), pages 306-321.
    17. Lagravinese, Raffaele & Liberati, Paolo & Resce, Giuliano, 2020. "The impact of economic, social and cultural conditions on educational attainments," Journal of Policy Modeling, Elsevier, vol. 42(1), pages 112-132.
    18. Atici, Kazim Baris & Podinovski, Victor V., 2012. "Mixed partial elasticities in constant returns-to-scale production technologies," European Journal of Operational Research, Elsevier, vol. 220(1), pages 262-269.
    19. Hanson, Torbjørn, 2016. "Estimating output mix effectiveness: A scenario approach," Memorandum 14/2016, Oslo University, Department of Economics.
    20. Gorman, Michael F. & Ruggiero, John, 2008. "Evaluating US state police performance using data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 113(2), pages 1031-1037, June.

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    Keywords

    data envelopment analysis; education;

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