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Credit Constraints in Higher Education in a Context of Unobserved Heterogeneity

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Listed:
  • Villena, Mauricio
  • Sanchez, Rafael
  • Rojas, Eugenio

Abstract

This article tests the existence of credit constraints on higher education access by estimating actual marginal returns in the context of unobserved heterogeneity. We estimate higher education returns for those who attend to it and compare them with those of individuals who are at the margin of attending to it. Following the Carneiro and Heckman (2002) reasoning, if the returns of the latter group are larger than those of the former one we could be in presence of unobservable barriers to higher education access, such as credit constraints. We use a rich administrative database composed from three sources: data of enrollment and graduation from the Chilean higher education system, test scores and labor market outcomes from the Chilean Unemployment Insurance database. Our results suggest that, given the existing financial aid scheme, the returns for those individuals that are at the margin of attending to higher education are lower than for those who decided to attend to it. This is, no evidence of credit constraints is found for the Chilean Higher Education system. However, when conditioning on family income, we find that for the richer households some evidence of credit constraints is found.

Suggested Citation

  • Villena, Mauricio & Sanchez, Rafael & Rojas, Eugenio, 2012. "Credit Constraints in Higher Education in a Context of Unobserved Heterogeneity," MPRA Paper 62095, University Library of Munich, Germany, revised 08 Jul 2014.
  • Handle: RePEc:pra:mprapa:62095
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    1. Thomas J. Kane, 2007. "Evaluating the Impact of the D.C. Tuition Assistance Grant Program," Journal of Human Resources, University of Wisconsin Press, vol. 42(3).
    2. Willis, Robert J & Rosen, Sherwin, 1979. "Education and Self-Selection," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 7-36, October.
    3. Pedro Carneiro & James J. Heckman & Edward Vytlacil, 2010. "Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin," Econometrica, Econometric Society, vol. 78(1), pages 377-394, January.
    4. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    5. Lance J. Lochner & Alexander Monge-Naranjo, 2011. "The Nature of Credit Constraints and Human Capital," American Economic Review, American Economic Association, vol. 101(6), pages 2487-2529, October.
    6. Katja Maria Kaufmann, 2014. "Understanding the income gradient in college attendance in Mexico: The role of heterogeneity in expected returns," Quantitative Economics, Econometric Society, vol. 5(3), pages 583-630, November.
    7. David Card, 1994. "Earnings, Schooling, and Ability Revisited," Working Papers 710, Princeton University, Department of Economics, Industrial Relations Section..
    8. Pedro Carneiro & James J. Heckman, 2002. "The Evidence on Credit Constraints in Post--secondary Schooling," Economic Journal, Royal Economic Society, vol. 112(482), pages 705-734, October.
    9. Pedro Carneiro & James J. Heckman & Edward J. Vytlacil, 2011. "Estimating Marginal Returns to Education," American Economic Review, American Economic Association, vol. 101(6), pages 2754-2781, October.
    10. Edward Vytlacil & James J. Heckman, 2001. "Policy-Relevant Treatment Effects," American Economic Review, American Economic Association, vol. 91(2), pages 107-111, May.
    11. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    12. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    13. Kane, Thomas J & Rouse, Cecilia Elena, 1995. "Labor-Market Returns to Two- and Four-Year College," American Economic Review, American Economic Association, vol. 85(3), pages 600-614, June.
    14. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    15. repec:fth:prinin:331 is not listed on IDEAS
    16. Thomas J. Kane, 1996. "College Cost, Borrowing Constraints and the Timing of College Entry," Eastern Economic Journal, Eastern Economic Association, vol. 22(2), pages 181-194, Spring.
    17. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-1160, September.
    18. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    19. Stephen V. Cameron & James J. Heckman, 2001. "The Dynamics of Educational Attainment for Black, Hispanic, and White Males," Journal of Political Economy, University of Chicago Press, vol. 109(3), pages 455-499, June.
    20. Stephen V. Cameron & James J. Heckman, 1998. "Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts of American Males," Journal of Political Economy, University of Chicago Press, vol. 106(2), pages 262-333, April.
    21. Stephen V. Cameron & Christopher Taber, 2004. "Estimation of Educational Borrowing Constraints Using Returns to Schooling," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 132-182, February.
    22. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    23. David Card, 1994. "Earnings, Schooling, and Ability Revisited," Working Papers 710, Princeton University, Department of Economics, Industrial Relations Section..
    24. Christopher R. Taber, 2001. "The Rising College Premium in the Eighties: Return to College or Return to Unobserved Ability?," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 68(3), pages 665-691.
    25. Stephen V. Cameron & James J. Heckman, 1998. "Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts," NBER Working Papers 6385, National Bureau of Economic Research, Inc.
    26. Tomás Rau & Eugenio Rojas & Sergio Urzúa, 2013. "Loans for Higher Education: Does the Dream Come True?," NBER Working Papers 19138, National Bureau of Economic Research, Inc.
    27. Loreto Reyes & Jorge Rodríguez & Sergio S. Urzúa, 2013. "Heterogeneous Economic Returns to Postsecondary Degrees: Evidence from Chile," NBER Working Papers 18817, National Bureau of Economic Research, Inc.
    28. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
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    More about this item

    Keywords

    Higher Education; Credit Constraints; Structural Models; Marginal Treatment Effect;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • I2 - Health, Education, and Welfare - - Education
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • I25 - Health, Education, and Welfare - - Education - - - Education and Economic Development
    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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