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Modeling Income Dynamics for Public Policy Design: An Application to Income Contingent Student Loans

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  • Higgins, Tim

    (Australian National University)

  • Sinning, Mathias

    (Australian National University)

Abstract

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.

Suggested Citation

  • Higgins, Tim & Sinning, Mathias, 2013. "Modeling Income Dynamics for Public Policy Design: An Application to Income Contingent Student Loans," IZA Discussion Papers 7556, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp7556
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    Cited by:

    1. Dearden, Lorraine & Nascimento, Paulo Meyer, 2019. "Modelling alternative student loan schemes for Brazil," Economics of Education Review, Elsevier, vol. 71(C), pages 83-94.
    2. Dearden, Lorraine & Nagase, Nobuko, 2017. "Getting student loans right in Japan: problems and possible solutions," Discussion Paper Series 668, Institute of Economic Research, Hitotsubashi University.
    3. Wenhua Di & Kelly D. Edmiston, 2017. "Student Loan Relief Programs: Implications for Borrowers and the Federal Government," The ANNALS of the American Academy of Political and Social Science, , vol. 671(1), pages 224-248, May.
    4. Cathal O'Donoghue & Gijs Dekkers, 2018. "Increasing the Impact of Dynamic Microsimulation Modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 61-96.
    5. Britton, Jack & van der Erve, Laura & Higgins, Tim, 2019. "Income contingent student loan design: Lessons from around the world," Economics of Education Review, Elsevier, vol. 71(C), pages 65-82.
    6. Roger Wilkins, 2021. "Economic Wellbeing," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 54(4), pages 469-481, December.
    7. Cai, Yu & Chapman, Bruce & Wang, Qing, 2019. "Repayment burdens of mortgage-style student loans in China and steps toward income-contingent loans," Economics of Education Review, Elsevier, vol. 71(C), pages 95-108.
    8. Diana Bílková, 2015. "Financial Position of Czech Employees at the Beginning of the 3rd Millennium according to Educational Attainment," Prague Economic Papers, Prague University of Economics and Business, vol. 2015(3), pages 307-331.
    9. Chapman, Bruce & Lounkaew, Kiatanantha, 2013. "Introduction to the special issue on Economic Research for Education Policy," Economics of Education Review, Elsevier, vol. 37(C), pages 200-203.
    10. Dearden, Lorraine, 2019. "Evaluating and designing student loan systems: An overview of empirical approaches," Economics of Education Review, Elsevier, vol. 71(C), pages 49-64.
    11. Armstrong, Shiro & Dearden, Lorraine & Kobayashi, Masayuki & Nagase, Nobuko, 2019. "Student loans in Japan: Current problems and possible solutions," Economics of Education Review, Elsevier, vol. 71(C), pages 120-134.

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    More about this item

    Keywords

    income contingent loans; educational finance; dynamic stochastic modeling; panel data;
    All these keywords.

    JEL classification:

    • H81 - Public Economics - - Miscellaneous Issues - - - Governmental Loans; Loan Guarantees; Credits; Grants; Bailouts
    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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