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variables – such as the receipt of scholarships/grants, whether the borrower participated in work study and whether the borrower had other employment – but found none of them to be significant. Meyer, however, determined that the probability of default declined with increases in the cost of attendance, controlling for type of institution. He further discovered that the likelihood of default increased substantially for borrowers who received more than $1,000 from non-loan aid sources. He noted a small decrease in the chances of defaulting as the expected family contribution of borrowers increased.

Several of the studies have also included loan-related variables. Four of the analyses determined that there was not a statistically significant relationship between the amount of loans borrowed and default behavior (Knapp & Seaks, 1990; Volkwein & Szelest, 1995; Volkwein et. al., 1995; Woo, 2002). Meyer, however, found that larger amounts of total debt at 4-year schools increased the probability of default by one percentage point. And Dynarski determined that the probability of default rose with increases in the size of borrowers’ monthly loan payments. Furthermore, Woo detected a small increase in the likelihood of default associated with an increase in the number of loans a borrower has. Meyer also examined the types of federal loans that borrowers received and showed that borrowers with only subsidized Stafford loans had the highest probability of default. In his study, he further demonstrated that borrowers who utilized deferments had a somewhat smaller chance of defaulting.

Compared to past studies, the present study will evaluate a far greater number and variety of variables. It will more thoroughly examine how variables that describe the performance of borrowers in college relate to the probability of default. It will look at a large number of performance characteristics, ranging from variables that measure success in college, like the number of course hours passed, to factors that describe the pattern of the borrower’s attendance at TAMU, such as how many semesters the borrower attended and how many times the borrower withdrew from school. Furthermore, the study will expand the number and type of variables that depict the borrower’s experience in high school, such as the number of course hours the student took in various subject areas. In addition, this analysis will consider a much larger number of variables that portray the financial aid process, including financial need, expected family contribution and aid amounts.


The study sample includes 12,776 Texas A&M University undergraduate borrowers who went into repayment between October 1, 1996 and September 30, 1999 on a Stafford or SLS loan guaranteed by Texas Guaranteed (TG). The sample is made up of three cohorts or groups of borrowers, each representing a federal fiscal year. (The federal fiscal year spans from October 1 through September 30 of the following calendar year.) The first cohort includes student borrowers who entered repayment in Fiscal Year (FY) 1997 or, rather, between October 1, 1996 and September 30, 1997. The second cohort represents borrowers who entered repayment during FY 1998. And borrowers who entered repayment during FY 1999 comprise the third cohort.

Though the initial study design called for analyzing graduate and professional students as well, early work on the study indicated that this was not practical. The sample size for graduate students (2,802 borrowers, with 67 defaulters) would make it more difficult than with the undergraduate population to detect a large number of significant and strong relationships between the study factors and default behavior. Additionally, from a practical standpoint, the extremely


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