Dearden, L; (2017) Evaluating and designing student loan systems: an overview of empirical approaches. In: Asian and Australasian Society of Labour Economics (AASLE) Inaugural Conference 2017 Proceedings. AASLE: Australian National University, Canberra.

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Abstract

To understand and design student loan systems, it is important to have appropriate earnings and/or income projections for current and future graduates. In this paper, we use Current Population Survey (CPS) data from the US to demonstrate empirical approaches that can be used to simulate lifetime income and earnings profiles that are needed to both understand and design effective and sustainable student loan systems. The crucial element in getting this analysis right, is having reliable simulations of the whole distribution of current and/or future graduate earnings or income. We show that the traditional way of using unconditional quantile regression (UQR) is not always correct for evaluating student loans and conditional quantile regression (CQR) with appropriate age smoothing is a more reliable approach. We show that this approach can be used even in countries where income is censored and recorded in income groups. We then show a simple way of introducing dynamics into income projections even when individuals only have access to very short panel data. This involves using copula functions drawing on earlier work by Dearden et. al (2009) and Bonhomme and Robin (2009). Having reliable dynamic estimates turns out to be important in assessing not only the taxpayer costs of designing an ICL (as has already been shown in Dearden et. al (2009) and Higgins and Sinning (2013)) but for correctly assessing the extent of loan repayment hardship for individuals.

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