We will find little evidence that tuition affects student loan borrowing or homeownership for students who did receive Pell Grants

We will demonstrate a strong effect of the tuition charged at public 4-year universities on the student loan borrowing and subsequent homeownership only of students who did not receive any Pell Grant aid

60 month personal loans

Any correlation between the tuition charged at public universities and state-level economic conditions (through the effect of economic conditions on appropriations) raises a concern about the validity of tuition as an instrument. To address this potential source of bias, we split our sample into treatment and control groups, with the treatment group defined as the individuals who attended a public 4-year university before they turned 23. We then compare the outcomes in the treatment group to those in the control group, which consists of all other individuals (except in specifications shows in col. 7 of Table 4, where the control group is all other individuals with at least some postsecondary education before age 23). Treatment group subjects pay the tuition charged at public 4-year universities, so their total borrowing before turning 23 is directly affected by this tuition. In contrast, the control group is not directly affected by the tuition at public 4-year universities (which they did not attend). Our instrument is therefore the interaction between the tuition charged at public 4-year universities and an indicator for membership in the treatment group. This framework therefore allows us to control for any correlations between state-level shocks and tuition rates-either by including tuition rates directly as a control variable or by using state-by-year fixed effects-with the homeownership rate of the control group absorbing unobserved variation in economic conditions. We devote further consideration to the potential endogeneity of tuition in section IV.E.

A further concern might be that changes in tuition reflect other channels not absorbed by the control group, such as changes in school quality, and hence students’ later economic outcomes. However, we can exploit a difference in the source of tuition funds to test for bias along these lines. Specifically, the findings of Belley, Frenette, and Lochner (2014) suggest that the net tuition paid by lower-income students is less strongly linked to the sticker price due to the availability of need-based grants. Estimates of the effect of tuition on these latter students’ subsequent homeownership provides a placebo test for the instrument-students who receive Pell Grants experience the same changes in school and economic environment as their peers without Pell Grants but are not exposed to the same variation in debt. The absence of any negative effect on their homeownership rates suggests that variation in school quality (or other state-level factors specific to the treatment group) are not biasing our main results away from zero. We discuss these results in detail in section IV.E.

Our data allow us to further refine the treatment group into those who did not receive any federal need-based aid in the form of Pell Grants (and whose student loan borrowing therefore varied more closely with the tuition rate) and those who did receive such aid before age 23

We deal with the endogeneity of student loan debt by estimating a first stage in which Xi is modeled using equation (2):

The parameter ?2 captures any partial correlation between tuition rates and homeownership among the control group, absorbing any state-level shocks that affect both tuition and the homeownership rate. Note that in specifications with state-by-year fixed effects ?2 is not identified, as the average tuition rate is collinear with the fixed effects. The parameter ?3 captures the average difference in homeownership rates between the treatment and control groups. We are left identifying ?1, the effect of student loan debt on homeownership, by the widening or shrinking of the gap in homeownership rates between public 4-year school attendees and the general population as tuition rates change, analogous to a difference-in-differences online payday ID estimator.