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Buckeyes count the cost of student lending

Speed read
  • Ohio Supercomputer Center uncovers the impact of loan forgiveness programs 
  • Student loan repayment plans could affect postsecondary education, debt, and life choices
  • Oakley cluster reduces compute time from 10 years to 6 months

In 2017, 44.2 million Americans held student loan debt, totaling more than $1.4 trillion, according to the US Federal Reserve. To count the full cost, researchers looked to high-performance computers in Ohio.

Over the last decade, student loan debt increased by 26 percent per year on average, the fastest growing form of debt in the US.

The drastic growth in student loan debt can be attributed to at least two causes. The first is that the cost of postsecondary schooling has increased rapidly. According to Trends in College Pricing 2015, CPI-deflated average tuition and fees rose by about 3 percent per year on average between 2004 and 2014.

The estimation takes around 10 years if I’m running just one single computer. The Oakley cluster . . . saves me a lot of time. ~ Hongyu Chen

The second explanation for the student loan 'explosion' is the increase in postsecondary enrollment. Between 2000 and 2010, total undergraduate enrollment rose 37 percent, resulting in more borrowing by college students.”

To reduce the weight of student loan debt on borrowers, the US Congress passed the College Cost Reduction and Access Act in 2007. This major change in loan repayment plans can relieve debt for many Americans sooner than expected.

This legislation created two programs: One that would forgive student loans after 10 years of service in the public sector, and another that would allow borrowers at a certain income to make reduced payments. After 20 years, the outstanding loan balance would be forgiven.

To evaluate the wide-reaching effects student loan policy change could have on borrowers’ lives, researchers from the Department of Economics at The Ohio State University ran thousands of situational simulations using the Ohio Supercomputer Center.

Working under the guidance of Lucia Dunn, professor of economics at Ohio State, graduate assistant Hongyu Chen used data from the National Longitudinal Survey of Youth to model individuals’ education, career, borrowing, and spending decisions based on whether or not they were part of a loan forgiveness program.

<strong>Annie, get your . . . diploma? </strong> Researchers used the Oakley supercomputer to analyze loan repayment scenarios on 2,500 student borrowers. Findings reveal a greater willingness to further an education can result from loan forgiveness programs. Courtesy Ohio Supercomputer Center.

Chen wrote a program that ran simulations of over 151 parameters on a population of approximately 2,500 individuals between the ages of 18 to 65 during different periods of their lives.

“The estimation takes around 10 years if I’m running just one single computer,” Chen says. “That’s why I have to use the cluster of computers, like the supercomputer, that use multiple cores at the same time, so that saves me a lot of time.”

Still, the model took Chen around six months to complete on OSC’s Oakley Cluster using a program he wrote specifically for this project.

Chen found that the population affected by the change in student loan repayment plans increased the average total years of postsecondary education by 9 percent.

Individuals were also more likely to take on more debt, holding 12 percent more before age 30 under the new plan, when knowing their loans would be forgiven. Chen says this data could be useful in informing federal government policies.

With the cost of tuition rising at most higher education institutions and enrollment increasing, the numbers of borrowers and the amounts borrowed is expected to keep climbing. 

“(Student loans) are very expensive for the government. So, this directly provides the evidence (for determining) the best policies for the government to improve college enrollment or social welfare,” Chen says. “It has very important direct policy implications.” 

Read the original article from the Ohio Supercomputer Center here.

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