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Modeling income dynamics for public policy design: An application to income contingent student loans

  • Higgins, Tim
  • Sinning, Mathias

This paper studies the importance of dynamic earnings modeling for the design of income contingent student loans (ICLs). ICLs have been shown to be theoretically optimal in terms of efficiency in the presence of risk aversion, adverse selection and moral hazard, and have attractive equity properties. Recognition of their benefits has led to their adoption for tertiary education tuition fees in countries including Australia, New Zealand, and the UK. Since the design of ICLs relies on the prediction of the underlying costs, we explore the extent to which the complexity of earnings modeling affects the estimation of loan subsidies. The use of Australian data allows us to compare our simulated debt repayments to actual repayments under the Australian Higher Education Contribution Scheme (HECS). Our findings reveal that the complexity of earnings modeling has considerable implications for the calculation of loan subsidies.

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Article provided by Elsevier in its journal Economics of Education Review.

Volume (Year): 37 (2013)
Issue (Month): C ()
Pages: 273-285

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Handle: RePEc:eee:ecoedu:v:37:y:2013:i:c:p:273-285
Contact details of provider: Web page: http://www.elsevier.com/locate/econedurev

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  1. G Migali, 2011. "Funding Higher Education and Wage Uncertainty: Income Contingent Loan versus Mortgage Loan," Working Papers 609506, Lancaster University Management School, Economics Department.
  2. Robert J. Gary-Bobo & Alain Trannoy, 2015. "Optimal student loans and graduate tax under moral hazard and adverse selection," RAND Journal of Economics, RAND Corporation, vol. 46(3), pages 546-576, 09.
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  5. Michael Baker & Gary Solon, 2003. "Earnings Dynamics and Inequality among Canadian Men, 1976-1992: Evidence from Longitudinal Income Tax Records," Journal of Labor Economics, University of Chicago Press, vol. 21(2), pages 267-288, April.
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  7. Baker, Michael, 1997. "Growth-Rate Heterogeneity and the Covariance Structure of Life-Cycle Earnings," Journal of Labor Economics, University of Chicago Press, vol. 15(2), pages 338-75, April.
  8. Bruce Chapman & Kiatanantha Lounkaew, 2009. "Income Contingent Student Loans for Thailand: Alternatives Compared," CEPR Discussion Papers 595, Centre for Economic Policy Research, Research School of Economics, Australian National University.
  9. Fatih Guvenen, 2007. "An Empirical Investigation of Labor Income Processes," NBER Working Papers 13394, National Bureau of Economic Research, Inc.
  10. Haider, Steven J, 2001. "Earnings Instability and Earnings Inequality of Males in the United States: 1967-1991," Journal of Labor Economics, University of Chicago Press, vol. 19(4), pages 799-836, October.
  11. Darragh Flannery & Cathal O’Donoghue, 2011. "The Life-cycle Impact of Alternative Higher Education Finance Systems in Ireland," The Economic and Social Review, Economic and Social Studies, vol. 42(3), pages 237–270.
  12. Costas Meghir & Luigi Pistaferri, 2004. "Income Variance Dynamics and Heterogeneity," Econometrica, Econometric Society, vol. 72(1), pages 1-32, 01.
  13. Martin Browning & Mette Ejrnæs & Javier Alvarez, 2010. "Modelling Income Processes with Lots of Heterogeneity," Review of Economic Studies, Oxford University Press, vol. 77(4), pages 1353-1381.
  14. Adriaan S. Kalwij & Rob Alessie, 2007. "Permanent and transitory wages of British men, 1975-2001: year, age and cohort effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1063-1093.
  15. Dickens, Richard, 2000. "The Evolution of Individual Male Earnings in Great Britain: 1975-95," Economic Journal, Royal Economic Society, vol. 110(460), pages 27-49, January.
  16. Lorenzo Cappellari, 2004. "The Dynamics and Inequality of Italian Men’s Earnings: Long-term Changes or Transitory Fluctuations?," Journal of Human Resources, University of Wisconsin Press, vol. 39(2).
  17. MaCurdy, Thomas E., 1982. "The use of time series processes to model the error structure of earnings in a longitudinal data analysis," Journal of Econometrics, Elsevier, vol. 18(1), pages 83-114, January.
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