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Friday, April 24th

Analyzing the Regulations and Statistics for Opportunity Zones

Justin Grigg

The Tax Cuts and Jobs Act of 2017 (TCJA) was the most significant tax reform since the Tax Reform Act of 1986. TCJA ushered in tax law changes including reforms to stimulate the economy. One such TCJA reform was the enactment of tax incentives called opportunity zones. Opportunity zones were designed to encourage economic development and job creation in “distressed communities,” which are defined by the Internal Revenue Service. There are different views on the effectiveness of the opportunity zones legislation. Some believe the tax incentives are too generous, while others believe the regulations are too lax. Analyzing the success of opportunity zones is very difficult, since qualified opportunity zones, which encompass distressed communities, were not announced until April of 2018. This paper seeks to analyze existing opportunity zone regulations and statistics to explore ways for improving the regulatory guidance. More focused regulatory guidance may allow distressed communities to realize the benefits of economic development and job creation as intended, rather than just tax benefits to investors. Opportunity zones are a brilliant idea, but there needs to be more regulatory guidance to instill greater benefits for distressed communities.

Artificial Intelligence and the Trucking Industry: How Many Jobs Are at Stake?


Tyrrell Keim

Faculty Mentor: Louis Pantuosco, Ph.D.

As of 2018, the trucking industry in America employed over 3 million drivers and brought in almost $800 billion dollars. The industry’s labor cost is a third of this amount, with the average driver making about $22 an hour. How much of this cost could be minimized with the use of artificial intelligence in order to make the industry more profitable? Andrew Yang, an early candidate for the Democratic nomination for the U.S. Presidency, was the first political candidate in recent memory to speak of the potential pitfalls and job loss that automation could potentially bring to an industry, specifically for truckers. Could automation truly put a lot of workers in this industry out of a job? If so, when can we expect these changes to occur and at what rate? Furthermore, is it possible for these truckers to find employment in a new industry in order to keep their jobs and continue to provide for themselves and their families? This paper will discuss the potential overall impact that artificial intelligence could have on the trucking industry and its employees in the near to intermediate future.

Establishing an Optimal Withdrawal Rate and Portfolio Allocation for FIRE Investors

Helena Morrow, Winthrop University

There is a relatively new movement among young investors called Financial Independence Retire Early (FIRE). A significant portion of FIRE investors are in their mid- to upper thirties. While this movement of being financially independent and retiring early has become more popular, little research has been done on the sustainability of their financial assets over the course of their lives. One of the first studies to look at an optimal portfolio withdrawal rate, was done by Bengen (1994). Bengen looked at individuals who retired around age 65, and determined that if these individuals withdrew four percent of their portfolios, adjusted for inflation and appropriate asset allocation, their portfolios would last throughout retirement. More recently, a study by Finke, Pfau and Blanchett (2013) shows that the historical four-percent withdrawal rate is not optimal for today’s low interest rate environment. They determined that a more ideal rate would be closer to three percent. While both studies provided guidance on how much money someone should spend each year in retirement, they only looked at individuals who retired after age 65. So, what is a sustainable withdrawal rate for those who attain financial independence and retire at an early age? Furthermore, individuals who retire before age 65 do not have access to Medicare, which means they will have higher healthcare costs from health insurance premiums. The purpose of this study is to determine an appropriate withdrawal rate and portfolio allocation for individuals who retire in their late 30s or early 40

Impact of Student Loan Debt On Low-Income Black Students


Arrion Rogers, Winthrop University

Faculty Mentor: Anthony Hill, Ph.D.

African American students from low-income communities who are often plagued with generational poverty have limited options for paying for higher education. Student loans are often the only viable option for this population. These students are less likely to have access to external resources, which furthers their hardship. Loans are an essential tool as a means of receiving higher education. Ultimately, students are forced to choose between borrowing themselves into debt, delaying obtaining higher education, or dropping out due to the financial burden. The literature identifies the consequential effects of student loan debt on African American students On a larger scale, student loan debt hinders economic growth and obstructs future investments, including purchasing a home. Other consequences of student loan debt include impacts on credit, further debt, and loan defaults. The impact of student loan debt needs to be addressed on the micro, mezzo, and macro levels to prevent generations of African American children from falling into debt or delaying their educations. This presentation takes a multifaceted approach to addressing the impact of student loan debt on African American students from resource-limited communities.

Implementation of a Data-Driven Solution for Student Loans: Utilizing Data Mining Algorithms Approach

James Kachamila, Winthrop University

As of 2020, student loans debt hit $1.6 trillion, with private loan debt volume reaching over $125 billion. Student loans have grown to become the second largest category of household debt in the U.S. It has also become the largest financial burden in terms of debt for graduates, with nearly 44 million individuals holding outstanding student loans. The private loan industry accounts for about 8% of the market. The private sector, even with a stronger underwriting process, still has a relatively high default rate with about 1 in 10 individuals defaulting on their loans. Credit-risk assessments conducted by these private lending institutions are heavily reliant on variables such as debt-to-income ratio, credit history, FICO scores and co-signer availability. This paper explores a data mining algorithmic approach with the utilization of “untraditional” variables to determine an individual’s credit risk in regard to student loans. Using a neural network model with data from the U.S Department of Education, the aim is to extrapolate a reliable predictive model affecting student loan repayment. The goal is also to understand the business viability and business integration value of an automated credit-risk assessment tool that in theory should reduce default risk and increase efficiency by eliminating one of lending institutions’ major areas of overhead: underwriting costs.

Student Loan Debt: The Pursuit for a Brighter Future

Christopher Simpson

Faculty Mentor: Ginger Williams, Ph.D.

This research paper examines the issues surrounding the student loan debt crisis in America from an educational, economic, and political point of view. Also, the research helps gauge the impact of student loan debt at the national level. The issues of high importance surrounding student loan debt impact the lives of every American, whether they have a post-secondary education or not. Also, these issues continue to impact a large majority of college graduates further, even if they did not require student loans in order to finish college. The rising costs of tuition and fees are the most prevalent driving force behind the growing student loan debt crisis in America. Therefore, new students naturally assume more debt with each passing year. The repeating cycle of higher tuition and fees, along with more Americans than ever seeking higher education, perpetuates the issue of student loan debt further. Most all Americans seek to improve their economic influence or range through some manner of education, but the issues around student loan debt place college students in an immediate hardship upon graduation, if they graduate at all. This paper focuses on finding better solutions to the issues surrounding the American student debt crisis. Experts suggest some courses of action that students can use to improve their economic footprints surrounding student loan debt. The research seeks to provide concrete solutions to help the ever growing student loan debt crisis in America.