My mother was born behind the Iron Curtain in Prague, Czechoslovakia. The communist government stole her family’s belongings and forced them to work demanding physical labor jobs. Wishing for a better life, her family illegally fled across the border and immigrated to America in 1968. The communist government eventually fell in 1989 due to the Velvet Revolution, an anti-communist protest fueled by an economic downturn. In June 1990, Czechoslovakia’s first democratic elections established a new government that transformed the economy by moving company ownership from the state to private entities. While learning about this historical event at the core of my family story, I was drawn in by the dynamic relationship between economic trends and political stability: an economic depression caused an upheaval of the governmental system, ultimately leading to a new political system and administrative economic policy transition. This sparked a question: how are political stability and economic trends correlated beyond this seemingly insignificant revolution, and how could this relationship be quantified?

I began my journey into research in 10th grade (2020). Covid-19 was the style du jour, masks were all the rage, and I was enrolling in a class at my school called Science Research. This class was meant to guide students, helping them reach out to experienced mentors in academia and industry. Initially, I was tasked with finding a broad area of study, for example, cancer, in which I could later specify and find a niche that fit me precisely. Even at this early stage, however, I was torn. I had always loved mathematics, but I also adored biology and any topics which had to do with the human body, and I was struggling with finding a topic that neatly tied the two together. It was on a random winter day during the weekend, when I went to a diner with my father for breakfast … There, they had a TV set playing the news, where the latest projected Covid-19 numbers were being displayed. It was at that point my father suggested to me that I should research disease models. There was heavy mathematics used in the creation and analysis of these models, while a strong understanding of diseases and disease epidemiology was required to accurately understand and implement disease models. After some basic investigation, I was smitten … For the rest of the year, I became engrossed in epidemiology, learning about compartmental and agent-based models, disease routes of infection and interventions, and quite excitingly, following the most up-to-date research on Covid-19. When the summer rolled around, I eagerly sent emails to research groups working with disease models. A group at NYU Langone’s Translational Research Department wrote back, and I eagerly began interning at their lab. They worked more on HIV than Covid-19, however, so for the first summer, I investigated their HIV model, EMOD, and learned how to interface with it. I also began learning about HIV itself and applied that knowledge to understanding EMOD. My mentor had also mentioned to me that EMOD, their model, was unable to allocate money to different disease treatment programs. Including this capability was their next goal, and so I also began studying optimization algorithms, specifically those used for more complex problems.

Like so many others, COVID-19 has had a major impact on my life. My home state, New York was caught unprepared for the pandemic, at great loss. The ensuing response from government and private entities was scattered at best, and at times dangerous. The immense impact COVID has had on my family, community and country inspired me to research it. As the shock of a pandemic began to wear off, and states began reopening, conjectures about the differences between parties and states were widely articulated in the media and public discourse, with little data to support it. As a result, I decided to analyze the differences in lockdowns and reopening’s in each state, hoping to provide data and evidence for these assumptions. To do so, I knew that no study could be perfect. Comparing 50 states lockdowns, often created using arbitrary benchmarks, would undoubtedly be difficult. I categorized the severity of each state’s lockdown for a period of over 18 months. As I collected data, I read about what became of Wisconsin’s lockdown, where state Republican lawmakers caused the overturning of the Democratic Governor’s restrictions. I then decided to expand my study to analyze not just the differences between states’ governor’s political parties, but the differences between their legislative control in comparison to the governor.

Starting from a young age, I had seen people in my life suffer as a result of domestic violence as well as seen and experienced firsthand the stress associated with financial instability. In 2020, the first case COVID-19 was reported in America, thus sparking the news to be flooded with stories of the toll this pandemic was taking on our well-being and the economy. Due to my understanding of the connection between relationship and economic instability from personal experiences, I became concerned about how the economic crisis brought on by the COVID-19 pandemic could affect marital relationships … For many individuals, being in debt and living paycheck to paycheck or based on future paychecks is their reality (Cecchetti et al., 2011). In 2020, the total credit card debt in the United States was $756 billion (“Credit Card Debt”, 2020). Additionally, household debt totaled $14.6 trillion at the end of 2020 (Cox, 2021). Historically, financial hardship has been exacerbated by extenuating circumstances like disease. An example of this is the SARS epidemic in 2002-2004 (Beutels et al., 2009). A similar economic crisis occurred in 2020 as a result of the COVID-19 pandemic. Unique non-economic circumstances result in economic crises by prohibiting travel, closing business, increasing unemployment, etc. (Borio, 2020). For example, during the pandemic, unemployment rates hit an estimated 16% (“Frequently asked questions”, 2020). Also in 2020, there were 764,282 bankruptcy filings in the United States and 473,349 more in the first four months of 2021 (“New Bankruptcy Filings”, 2021). These factors may lead to economic stress (Elder & Caspi, 1988) …

Economics was a subject I first discovered in high school a class that all students were required to take which inexplicitly became my favorite class of the year. I was enthralled by the idea of using mathematics to model the behavior of individuals and firms across the country. Until I met my mentor Professor Cheng-Zhong Qin, that is how I saw economics: a collection of simple and elegant theories that described all of economic activity. In truth, however, the complexities of the modern economy far outpace the mainstream theories economists use to describe it, most of which were developed before the mid-20th century. The great struggle of the next generation of economic researchers is not to develop new theories, but to adapt existing theory to the 21st century. Only this process can reconcile the original goal of economicsto be a model of the global economy�with the realities of our world … Cross-holdings exist when one business holds a non-voting stake in a direct competitor. Because these stakes are non-voting, they are allowed under current antitrust regulations; since sales are unaffected, they also fail to affect the HHI. However, recent economic literature has established that cross-holdings have a substantial impact on competition. In fact, in extreme scenarios it is possible for a multi-firm industry with cross-holdings to operate indistinguishably from a pure monopoly … Our project has two parts: first, we use conditions of Cournot Equilibrium (a special case of oligopoly) to derive a generalized form of the HHI, which we call the Generalized Herfindahl-Hirschman Index (GHHI) …

It was a routine Thanksgiving dinner at our home on Long Island. Amidst the light banter, I overheard a troubling conversation between two sets of grandparents. One was describing the upcoming trips and recreational activities they were planning with their peers from both Florida and New York, where they had residences. The other couple was struggling to determine if the man-hours they were dedicating to their small retail business was cost effective as their income was slowing to almost a halt. The inequity in their lifestyles was glaringly apparent. I wondered how there could be such disparity between two similarly situated families that emanated from similar communities and cultures, and that had earned similar wages over the many years of hard work. Clearly, one was financially prepared for a vibrant, long-lasting retirement, while the other was facing unsettled financial uncertainty in retirement. Had one family prepared better than the other?