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Determinants of Parental Choice for Public – Private Schooling of Children:A Study of Rawalpindi-Islamabad

Author

Listed:
  • Manzoor, Hamad
  • Rasul, Saira
  • Ahsan, Henna
  • Safdar, Aisha

Abstract

This study aimed at analysing the factor of parental choice for public-private schools in the area of Rawalpindi-Islamabad, Pakistan. For this purpose 150 parents, having at least one child attending school, were approached. Dependent variables in the present study are number of child in private school, number of child in public school, number of girls in private school and number of boys in private school. Independent variables are parent’s income, parent’s education, private school fee, quality of education, confidence building, infrastructure and values. Negative Binomial Regression was used and it was found that quality of education and mother’s education are significant factors in sending girls to private school. Parent’s income and private school fee are positive significant factors in sending children to private schools and negative significant factor in sending children to public schools. The constructs like quality of education, confidence building, infrastructure and values are found to be insignificant.

Suggested Citation

  • Manzoor, Hamad & Rasul, Saira & Ahsan, Henna & Safdar, Aisha, 2017. "Determinants of Parental Choice for Public – Private Schooling of Children:A Study of Rawalpindi-Islamabad," MPRA Paper 96469, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:96469
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    File URL: https://mpra.ub.uni-muenchen.de/96469/1/MPRA_paper_96469.pdf
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    References listed on IDEAS

    as
    1. Bast, Joseph L. & Walberg, Herbert J., 2004. "Can parents choose the best schools for their children?," Economics of Education Review, Elsevier, vol. 23(4), pages 431-440, August.
    2. Caroline M. Hoxby, 2002. "Would School Choice Change the Teaching Profession?," Journal of Human Resources, University of Wisconsin Press, vol. 37(4), pages 846-891.
    3. Rainer Winkelmann, 2008. "Econometric Analysis of Count Data," Springer Books, Springer, edition 0, number 978-3-540-78389-3, January.
    4. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
    5. Mikiko Nishimura & Takashi Yamano, 2008. "School Choice between Public and Private Primary Schools under the Free," GRIPS Discussion Papers 08-02, National Graduate Institute for Policy Studies.
    6. Raschke, Christian & Greene, William H., 2010. "Corrigendum to "Functional forms for the negative binomial model for count data" [Economics Letters 99 (2008) 585-590]," Economics Letters, Elsevier, vol. 107(2), pages 313-313, May.
    7. Jean-Pierre Vidal & Michael Bräuninger, 2000. "Private versus public financing of education and endogenous growth," Journal of Population Economics, Springer;European Society for Population Economics, vol. 13(3), pages 387-401.
    8. A. Colin Cameron & Pravin K. Trivedi, 1986. "Econometric models based on count data. Comparisons and applications of some estimators and tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(1), pages 29-53, January.
    9. McCormick, Robert & Moore, Cora & Yandle, Bruce, 1994. "Private and Public Choices in Public Education: An Investigation of Trustee Effects," Public Choice, Springer, vol. 78(3-4), pages 219-230, March.
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    JEL classification:

    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid

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