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Evaluating and designing student loan systems: An overview of empirical approaches

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  • Dearden, Lorraine

Abstract

To understand and design student loan systems, realistic earnings and/or income projections for current and future graduates are crucial. In this paper, Current Population Survey (CPS) data from the US is used to demonstrate empirical approaches that can be exploited to simulate lifetime income and earnings profiles for graduates which are needed to understand and design effective and sustainable student loan systems. The crucial element in getting this analysis correct is having reliable simulations of the whole distribution of future graduate earnings and income. Typically, in this literature, the repayment burdens (RBs) of student loans are calculated at different quantiles of the graduate income or earnings distribution. Often, unconditional quantile regression (UQR) is used to calculate age–earnings profiles for different quantiles of the income or earnings distribution. The paper shows that this approach has limitations when evaluating student loans and that simple raw quantile estimation by age with some age smoothing is preferable. This approach can also be used when income is censored and recorded in income bands as occurs with relevant data in some countries. The paper shows a simple way of incorporating dynamics utilizing these age–earnings profiles by quantile even when only very short panel data is available. This involves using copula functions. Having reliable dynamic estimates turns out to be important in assessing not only the taxpayer costs of designing an income-contingent loan (ICL) but also for correctly assessing the extent of loan repayment hardship for individuals.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecoedu:v:71:y:2019:i:c:p:49-64
    DOI: 10.1016/j.econedurev.2018.11.003
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    References listed on IDEAS

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    1. Mark B. Stewart, 1983. "On Least Squares Estimation when the Dependent Variable is Grouped," Review of Economic Studies, Oxford University Press, vol. 50(4), pages 737-753.
    2. 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.
    3. Looney, Adam & Yannelis, Constantine, 2019. "How useful are default rates? Borrowers with large balances and student loan repayment," Economics of Education Review, Elsevier, vol. 71(C), pages 135-145.
    4. Stéphane Bonhomme & Jean-Marc Robin, 2009. "Assessing the Equalizing Force of Mobility Using Short Panels: France, 1990-2000," Review of Economic Studies, Oxford University Press, vol. 76(1), pages 63-92.
    5. Higgins, Tim & Sinning, Mathias, 2013. "Modeling income dynamics for public policy design: An application to income contingent student loans," Economics of Education Review, Elsevier, vol. 37(C), pages 273-285.
    6. Barr, Nicholas & Chapman, Bruce & Dearden, Lorraine & Dynarski, Susan, 2019. "The US college loans system: Lessons from Australia and England," Economics of Education Review, Elsevier, vol. 71(C), pages 32-48.
    7. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    8. 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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    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. Barr, Nicholas & Chapman, Bruce & Dearden, Lorraine & Dynarski, Susan, 2019. "The US college loans system: Lessons from Australia and England," Economics of Education Review, Elsevier, vol. 71(C), pages 32-48.

    More about this item

    Keywords

    Student loans; Student loan design; Repayment burdens; Income-contingent loans; Copula functions; Dynamic lifetime earnings and income simulations;

    JEL classification:

    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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