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Are girls the fairer sex in India? Revisiting intra-household allocation of education expenditure

Author

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  • Mehtabul Azam

    () (The World Bank and IZA.)

  • Geeta Kingdon

    () (Institute of Education, University of London.)

Abstract

This paper revisits the issue of the intra-household allocation of education expenditure with the recently available India Human Development Survey which refers to 2005 and covers both urban and rural areas. In addition to the traditional Engel method, the paper utilizes a Hurdle model to disentangle the decision to enroll (incur any educational expenditure) and the decision of how much to spend on education, conditional on enrolling. Finally the paper also uses household fixed effects to examine whether any gender bias is a within-household phenomenon. The paper finds that the traditional Engel method often fails to pick up gender bias where it exists not only because of the aggregation of data at the household-level but also because of aggregation of the two decisions in which gender can have opposite signs. It is found that pro-male gender bias exists in the primary school age group for several states but that the incidence of gender bias increases with age – it is greater in the middle school age group (10-14 years) and greater still in the secondary school age group (15-19 years). However, gender discrimination in the secondary school age group 15-19 takes place mainly through the decision to enroll boys and not girls, and not through differential expenditure on girls and boys. The results also suggest that the extent of pro-male gender bias in educational expenditure is substantially greater in rural than in urban areas. Finally, our results suggest that an important mechanism through which households spend less on girls than boys is by sending sons to fee-charging private schools and daughters to the fee-free government-funded schools.

Suggested Citation

  • Mehtabul Azam & Geeta Kingdon, 2011. "Are girls the fairer sex in India? Revisiting intra-household allocation of education expenditure," DoQSS Working Papers 11-03, Department of Quantitative Social Science - UCL Institute of Education, University College London.
  • Handle: RePEc:qss:dqsswp:1103
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    References listed on IDEAS

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    Cited by:

    1. Rodríguez, Laura, 2016. "Intrahousehold Inequalities in Child Rights and Well-Being. A Barrier to Progress?," World Development, Elsevier, vol. 83(C), pages 111-134.
    2. Jacob, Arun, 2016. "Gender Bias in Educational Attainment in India : The Role of Dowry Payments," MPRA Paper 76338, University Library of Munich, Germany.
    3. Stephan Klasen & Janneke Pieters, 2015. "What Explains the Stagnation of Female Labor Force Participation in Urban India?," World Bank Economic Review, World Bank Group, vol. 29(3), pages 449-478.
    4. Vinish Shrestha & Rashesh Shrestha, 2017. "Intergenerational Effect of Education Reform Program and Maternal Education on Children's Educational and Labor Outcomes: Evidence from Nepal," Working Papers 2017-03, Towson University, Department of Economics, revised May 2017.
    5. Anukriti, S & Bhalotra, Sonia R. & Tam, Hiu, 2016. "On the Quantity and Quality of Girls: New Evidence on Abortion, Fertility, and Parental Investments," IZA Discussion Papers 10271, Institute for the Study of Labor (IZA).
    6. Kenayathulla, Husaina Banu, 2016. "Gender differences in intra-household educational expenditures in Malaysia," International Journal of Educational Development, Elsevier, vol. 46(C), pages 59-73.
    7. Vinish Shrestha & Rashesh Shrestha, 2017. "Intergenerational effect of education reform program and maternal education on children's educational and labor outcomes: evidence from Nepal," Departmental Working Papers 2017-07, The Australian National University, Arndt-Corden Department of Economics.
    8. Stephen D. O'Connell, 2014. "Political Inclusion and Educational Investment," Working Papers 4, City University of New York Graduate Center, Ph.D. Program in Economics, revised 15 Jul 2015.
    9. repec:eee:injoed:v:56:y:2017:i:c:p:42-51 is not listed on IDEAS
    10. Vinish Shrestha & Rashesh Shrestha, 2017. "Intergenerational effect of education reform: mother's education and children's human capital in Nepal," Working Papers 2017-05, Towson University, Department of Economics, revised Oct 2017.
    11. Amita Majumder & Chayanika Mitra, 2017. "Gender Bias in Education in West Bengal," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 15(1), pages 173-196, March.
    12. Maurice CATIN & Mohamed HAZEM, 2012. "Les Disparités De Taux D’Alphabétisation Selon Les Genres Dans Les Délégations Tunisiennes : Une Approche Par L’Économétrie Spatiale," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 35, pages 177-193.
    13. Lincove, Jane Arnold, 2015. "Improving Identification of Demand-Side Obstacles to Schooling: Findings from Revealed and Stated Preference Models in Two SSA Countries," World Development, Elsevier, vol. 66(C), pages 69-83.
    14. La Mattina, Giulia, 2017. "Civil conflict, domestic violence and intra-household bargaining in post-genocide Rwanda," Journal of Development Economics, Elsevier, vol. 124(C), pages 168-198.
    15. Dercon, Stefan & Singh, Abhijeet, 2013. "From Nutrition to Aspirations and Self-Efficacy: Gender Bias over Time among Children in Four Countries," World Development, Elsevier, vol. 45(C), pages 31-50.
    16. Zimmermann, Laura V, 2012. "Remember When It Rained: The Elusiveness of Gender Discrimination in Indian School Enrollment," IZA Discussion Papers 6833, Institute for the Study of Labor (IZA).
    17. Kelly, Orla & Krishna, Aditi & Bhabha, Jacqueline, 2016. "Private schooling and gender justice: An empirical snapshot from Rajasthan, India's largest state," International Journal of Educational Development, Elsevier, vol. 46(C), pages 175-187.

    More about this item

    Keywords

    Gender bias; educational expenditure; Engel curve; Hurdle model; India;

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing

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