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Ability, parental valuation of education and the high school dropout decision

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  • Kelly Foley
  • Giovanni Gallipoli

    ()
    (Institute for Fiscal Studies and University of British Columbia)

  • David A. Green

Abstract

We use a large, rich Canadian micro-level dataset to examine the channels through which family socio-economic status and unobservable characteristics affect children's decisions to drop out of high school. First, we document the strength of observable socio-economic factors: our data suggest that teenage boys with two parents who are themselves high school dropouts have a 16 per cent chance of dropping out, compared to a dropout rate of less than 1 per cent for boys whose parents both have a university degree. We examine the channels through which this socio-economic gradient arises using an extended version of the factor model set out in Carneiro, Hansen, and Heckman (2003). Specifically, we consider the impact of cognitive and non-cognitive ability and the value that parents place on education. Our results support three main conclusions. First, cognitive ability at age 15 has a substantial impact on dropping out. The highest ability individuals are predicted never to drop out regardless of parental education or parental valuation of education. In contrast, the lowest ability teenagers have a probability of dropping out of approximately .36 if their parents have a low valuation of education. Second, parental valuation of education has a substantial impact on medium and low ability teenagers. A low ability teenager has a probability of dropping out of approximately .03 if his parents place a high value on education but .36 if their educational valuation is low. These effects are estimated while conditioning on ability at age 15. Thus, under some assumptions, they reflect parental influences during the upper teenage years and are in addition to any impact they might have in the early childhood years leading up to age 15. Third, parental education has no direct effect on dropping out once we control for ability and parental valuation of education. Overall, our results point to the importance of whatever determines ability at age 15 (including, potentially, early childhood interventions) and of parental valuation of education during the teenage years. Our work also provides a small methodological contribution by extending the standard factor based estimator to allow a more non-linear relationship between the factors and a co-variate of interest. We show that allowing for non-linearities has a substantial impact on estimated effects.

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Paper provided by Institute for Fiscal Studies in its series IFS Working Papers with number W09/21.

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Date of creation: Nov 2009
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Handle: RePEc:ifs:ifsewp:09/21

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  1. Lex Borghans & Angela Lee Duckworth & James J. Heckman & Bas ter Weel, 2008. "The Economics and Psychology of Personality Traits," Journal of Human Resources, University of Wisconsin Press, vol. 43(4).
  2. Flavio Cunha & James Heckman & Susanne Schennach, 2010. "Estimating the Technology of Cognitive and Noncognitive Skill Formation," NBER Working Papers 15664, National Bureau of Economic Research, Inc.
  3. Cunha, Flavio & Heckman, James J., 2007. "The Technology of Skill Formation," IZA Discussion Papers 2550, Institute for the Study of Labor (IZA).
  4. James J. Heckman & Jora Stixrud & Sergio Urzua, 2006. "The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior," NBER Working Papers 12006, National Bureau of Economic Research, Inc.
  5. Carneiro, Pedro & Meghir, Costas & Parey, Matthias, 2007. "Maternal Education, Home Environments and the Development of Children and Adolescents," IZA Discussion Papers 3072, Institute for the Study of Labor (IZA).
  6. Carneiro, Pedro & Heckman, James J., 2003. "Human Capital Policy," IZA Discussion Papers 821, Institute for the Study of Labor (IZA).
  7. Flavio Cunha & James J. Heckman, 2008. "Formulating, Identifying and Estimating the Technology of Cognitive and Noncognitive Skill Formation," Journal of Human Resources, University of Wisconsin Press, vol. 43(4).
  8. Giovanni L. Violante & Costas Meghir & Giovanni Gallipoli, 2008. "Equilibrium Effects of Education Policies: a Quantitative Evaluation," 2008 Meeting Papers 868, Society for Economic Dynamics.
  9. Carneiro, Pedro & Hansen, Karsten T. & Heckman, James J., 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College Choice," IZA Discussion Papers 767, Institute for the Study of Labor (IZA).
  10. Heckman, James & Singer, Burton, 1984. "A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data," Econometrica, Econometric Society, vol. 52(2), pages 271-320, March.
  11. Jere R. Behrman & Mark R. Rosenzweig, 2002. "Does Increasing Women's Schooling Raise the Schooling of the Next Generation?," American Economic Review, American Economic Association, vol. 92(1), pages 323-334, March.
  12. Chamberlain, Gary, 1977. "Education, income, and ability revisited," Journal of Econometrics, Elsevier, vol. 5(2), pages 241-257, March.
  13. Michael B Coelli, 2009. "Parental Job Loss, Income Shocks and the Education Enrolment of Youth," Department of Economics - Working Papers Series 1060, The University of Melbourne.
  14. Pedro Carneiro & Karsten T. Hansen & James J. Heckman, 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College," NBER Working Papers 9546, National Bureau of Economic Research, Inc.
  15. Orazio Attanasio & Katja Kaufmann, 2009. "Educational Choices, Subjective Expectations, and Credit Constraints," NBER Working Papers 15087, National Bureau of Economic Research, Inc.
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Cited by:
  1. Bjarne Strøm & Torberg Falch & Päivi Lujala, 2011. "Geographical constraints and educational attainment," Working Paper Series 11811, Department of Economics, Norwegian University of Science and Technology.
  2. Trachter, Nicholas, 2014. "Stepping Stone and Option Value in a Model of Postsecondary Education," Working Paper 14-3, Federal Reserve Bank of Richmond.
  3. Coelli, Michael & Green, David A., 2012. "Leadership effects: school principals and student outcomes," Economics of Education Review, Elsevier, vol. 31(1), pages 92-109.
  4. Louis N. Christofides & Michael Hoy & Joniada Milla & Thanasis Stengos, 2012. "The Implication of Peer and Parental Influences on University Attendance: A Gender Comparison," Working Papers 1201, University of Guelph, Department of Economics and Finance.
  5. Christofides, Louis N. & Hoy, Michael & Milla, Joniada & Stengos, Thanasis, 2012. "Grades, Aspirations and Post-Secondary Education Outcomes," IZA Discussion Papers 6867, Institute for the Study of Labor (IZA).
  6. Hou, Feng & Picot, Garnett, 2013. "Why Immigrant Background Matters for University Participation: A Comparison of Switzerland and Canada," CLSSRN working papers clsrn_admin-2013-50, Vancouver School of Economics, revised 22 Nov 2013.

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