IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/7761.html
   My bibliography  Save this paper

Borrowing Constraints and the Returns to Schooling

Author

Listed:
  • Stephen Cameron
  • Christopher Taber

Abstract

To a large degree, the expansion of student aid programs to potential college students over the past 25 years in the United States has been based on the presumption that borrowing constraints present an obstacle to obtaining a college education. Economists and sociologists studying schooling choices have found empirical support for college subsidies in the well-documented, large positive correlation between family income and schooling attainment. This correlation has been widely interpreted as evidence of credit constraints. Recently, however, Cameron, and Heckman (1998, 2000), Keane and Wolpin (1999), and Shea (1999) have questioned whether borrowing constraints plays any role on college choices. Over the last 30 years, a separate literature in economics has aimed at estimating measured returns to schooling purged of various biases. One potential source of bias arises when students have differential access to sources of credit for educational investments. The connection between credit access and returns to schooling-first articulated by Becker (1972)- has been recently explored by Lang (1993) and Card (1995a, 2000). Lang and Card term this bias discount rate bias,' and argue it can help explain anomalously high instrumental variables estimates of returns to schooling documented by a multitude of empirical researchers. This argument implicitly suggests borrowing constraints are important for schooling decisions. Our paper attempts to integrate and reconcile these two literatures. Building on the seminal work of Willis and Rosen (1979), we develop a framework that allows us to study schooling determinants and returns together. Identification of the effect of borrowing constraints arises from the fact that foregone earnings-the indirect costs of school-and the direct costs of schooling affect borrowing constrained persons differently from unconstrained individuals. We apply this idea using least-squares, instrumental variables regression, and a structural economic model to measure the extent of borrowing constraints on schooling choices. Because returns to schooling and quantity of schooling are jointly determined, the structural approach allows us to explore the importance of credit market constraints on schooling choices once the influences of ability and relative wages are parceled out. This type of experiment cannot be done in standard models of schooling-attainment. None of these methods produces evidence of borrowing constraints.

Suggested Citation

  • Stephen Cameron & Christopher Taber, 2000. "Borrowing Constraints and the Returns to Schooling," NBER Working Papers 7761, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:7761
    Note: CH PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w7761.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. James Heckman & Edward Vytlacil, 1998. "Instrumental Variables Methods for the Correlated Random Coefficient Model: Estimating the Average Rate of Return to Schooling When the Return is Correlated with Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 33(4), pages 974-987.
    2. Shea, John, 2000. "Does parents' money matter?," Journal of Public Economics, Elsevier, vol. 77(2), pages 155-184, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guillaume Allègre & Thomas Melonio & Xavier Timbeau, 2012. "Dépenses publiques d'éducation et inégalités. Une perspective de cycle de vie," Revue économique, Presses de Sciences-Po, vol. 63(6), pages 1055-1079.
    2. Gordon Dahl, 2010. "Early teen marriage and future poverty," Demography, Springer;Population Association of America (PAA), vol. 47(3), pages 689-718, August.
    3. Alan B. Krueger, 2002. "Inequality, Too Much of a Good Thing," Working Papers 845, Princeton University, Department of Economics, Industrial Relations Section..
    4. Erik Plug & Wim Vijverberg, 2003. "Schooling, Family Background, and Adoption: Is It Nature or Is It Nurture?," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 611-641, June.
    5. Joseph G. Altonji & Rosa L. Matzkin, 2001. "Panel Data Estimators for Nonseparable Models with Endogenous Regressors," NBER Technical Working Papers 0267, National Bureau of Economic Research, Inc.
    6. Fatoke Dato, Mafaizath A., 2015. "Impact of income shock on children’s schooling and labor in a West African country," MPRA Paper 64317, University Library of Munich, Germany.
    7. Ralph Stinebrickner & Todd R. Stinebrickner, 2003. "Working during School and Academic Performance," Journal of Labor Economics, University of Chicago Press, vol. 21(2), pages 449-472, April.
    8. Aslund, Olof & Fredriksson, Peter, 2005. "Ethnic Enclaves and Welfare Cultures: Quasi-Experimental Evidence," IZA Discussion Papers 1536, Institute of Labor Economics (IZA).
    9. Lídia Farré & Roger Klein & Francis Vella, 2013. "A parametric control function approach to estimating the returns to schooling in the absence of exclusion restrictions: an application to the NLSY," Empirical Economics, Springer, vol. 44(1), pages 111-133, February.
    10. Robert Dur & Coen Teulings, 2003. "Are Education Subsides an Efficient Redistributive Device?," CEE Discussion Papers 0030, Centre for the Economics of Education, LSE.
    11. Aakvik, Arild & Salvanes, Kjell G. & Vaage, Kjell, 2003. "Measuring Heterogeneity in the Returns to Education in Norway Using Educational Reforms," IZA Discussion Papers 815, Institute of Labor Economics (IZA).
    12. Chanda, Areendam, 2008. "The rise in returns to education and the decline in household savings," Journal of Economic Dynamics and Control, Elsevier, vol. 32(2), pages 436-469, February.
    13. Irineu Evangelista de Carvalho Filho, 2012. "Household Income as a Determinant of Child Labor and School Enrollment in Brazil: Evidence from a Social Security Reform," Economic Development and Cultural Change, University of Chicago Press, vol. 60(2), pages 399-435.
    14. Christian Belzil, 2008. "Testing the Specification of the Mincer Wage Equation," Annals of Economics and Statistics, GENES, issue 91-92, pages 427-451.
    15. Daniele Checchi & Luca Flabbi, 2013. "Intergenerational Mobility and Schooling Decisions in Germany and Italy: The Impact of Secondary School Tracks," Rivista di Politica Economica, SIPI Spa, issue 3, pages 7-57, July-Sept.
    16. Bingley, Paul & Corak, Miles & Westergård-Nielsen, Niels C., 2011. "The Intergenerational Transmission of Employers in Canada and Denmark," IZA Discussion Papers 5593, Institute of Labor Economics (IZA).
    17. repec:dau:papers:123456789/11242 is not listed on IDEAS
    18. Liwen Chen & Bobby Chung & Guanghua Wang, 2022. "Stay-at-Home Peer Mothers and Gender Norms: Short-run Effects on Educational Outcomes," Working Papers 2022-039, Human Capital and Economic Opportunity Working Group.
    19. Haoming Liu, 2014. "The quality–quantity trade-off: evidence from the relaxation of China’s one-child policy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 27(2), pages 565-602, April.
    20. Xavier d'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2013. "Nonlinear difference-in-differences in repeated cross sections with continuous treatments," CeMMAP working papers CWP40/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Sandra E. Black & Paul J. Devereux & Katrine V. L�ken & Kjell G. Salvanes, 2014. "Care or Cash? The Effect of Child Care Subsidies on Student Performance," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 824-837, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:7761. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.