Individuals of high socioeconomic power and wealth often face disproportionately lenient consequences for committing crimes in comparison to the baseline population, or the “average” person.  Whether through legal strategies or monetary payments in lieu of jail time, the lack of accountability that powerful individuals face perpetuates disparities in the justice system between the treatment of the wealthy versus poor in the face of crime. By combining aggregate criminal justice data for DUI/DWI offenses in the United States with relevant case studies involving high-status individuals, this thesis examines how socioeconomic status influences the extent to which people are held accountable for misconduct. 

These datasets will be drawn from publicly available material in the Bureau of Justice Statistics, Uniform Crime Reporting program, and National Archive of Criminal Justice Data. To control for cross-state comparability, this study will use data beginning from 2005, when all states standardized at 0.08 BAC. The aggregate baseline DUI/DWI penalties for the US population provides a basis for a comparative analysis across case studies, which will be pulled from eJournals, including the WSJ, Forbes, and Barron’s. Analysis of this data will reveal the degree of correlation between socioeconomic status and the extent to which proper prosecution is faced. The results contribute to the broader discussion of how socioeconomic status serves as a means for wealthy, high status individuals to navigate around legal penalties, disrupting the foundation of equality and public trust in their own government institutions.

Corporate philanthropy has become a prominent feature of modern business practices, with major corporations investing billions of dollars annually in charitable initiatives and social programs. Concurrently, heightened public skepticism has raised concerns that corporate charity efforts may mask contradictions between stated social commitments and business behavior. This thesis examines whether practices of corporate philanthropy align with declared social values or act as performative contradictions to hide diverging corporate action. Elite firms frequently promote commitments to sustainability, equity, and community development through charitable giving and public marketing, while simultaneously engaging in lobbying, political contributions, and regulatory advocacy designed to advance business interests. These discrepancies raise an important question: does corporate philanthropy reflect consistency or divergence between declared social values and corporate strategic behavior?

This study conducts a comparative case analysis of five major United States corporations: Berkshire Hathaway, Amazon, Microsoft, ExxonMobil, and JPMorgan Chase. The foundation of this thesis draws on publicly available materials, including corporate mission statements, sustainability and philanthropy reports, financial disclosures, lobbying records, political contribution data, and regulatory filings. These sources are used to evaluate how firms articulate their social commitments and how they behave in relevant political and market arenas. Through systematic document analysis and process tracing, their philanthropy initiatives are assessed alongside relevant corporate actions to determine whether patterns of consistency or contradiction emerge. The central hypothesis is that corporate philanthropy is often utilized strategically, particularly in industries facing regulatory scrutiny or public criticism, helping firms maintain legitimacy while pursuing business strategies that counteract their charity values.

Elite actors play a central role in shaping political and regulatory outcomes in the United States. Prior research demonstrates that financial institutions maintain close ties to government agencies, particularly through career movement between regulatory bodies and private firms. Former regulators are often recruited into finance, creating durable institutional linkages between the financial sector and the state. While this literature documents the existence of these ties, it does not systematically compare levels of state connectedness across different elite sectors.

This study examines whether financial elites are more institutionally connected to the state than non-financial elites in the United States. To address this question, a dataset of elite individuals is constructed using publicly available rankings and leadership listings. Individuals are classified into three groups: financial elites, hybrid elites with cross-sector careers, and non-financial elites. For each individual, a state connection score is developed based solely on documented institutional ties to government bodies, including direct public sector employment and formal advisory appointments. These roles are weighted and adjusted for duration to generate a standardized measure of institutional connectedness.

I hypothesize that financial elites will exhibit higher average state connection scores than non-financial elites. By identifying systematic differences across sectors, this study offers clearer empirical insight into how elite influence is structured within the public sector.  It contributes to broader debates about elite power and the relationship between economic influence and the state in contemporary American politics.

This thesis examines which U.S. economic sectors have generated the most billionaire wealth since the onset of the dot-com bubble in 1997. Using data from the Forbes billionaire lists linked to North American Industry Classification System (NAICS) codes, the study classifies individual fortunes into major economic sectors such as finance, technology, healthcare, etc. To account for inflation, the study will include individuals from the Forbes 400 lists whose net worths are valued in billions in today’s dollars. By aggregating billionaire wealth by sector and year, this analysis traces changes in the sectoral composition of extreme wealth over time and visualizes these trends. By documenting how billionaire wealth creation has evolved across industries over the past several decades, this research contributes to the broader literature on wealth concentration, sectoral dynamics, and long-run economic change.

This thesis investigates whether elite corporate networks shape access to land and state-supported resources in the U.S. data center industry. Data centers rely heavily on zoning approvals, infrastructure, and public incentives, yet little research has examined whether firms embedded in elite networks gain advantages in securing these resources. Drawing on theories of embeddedness, corporate interlocks, and political economy, this study constructs a novel firm-level dataset combining data center ownership, public subsidy records, and corporate board networks. Using network analysis and regression models, it tests whether more network-embedded firms own more data centers and are more likely to receive public incentives, controlling for firm size and industry characteristics. This research extends classic theories of corporate power to the physical infrastructure of the digital economy.

Sustainable fashion has arisen to combat the problems of pollution and climate change, which are results of the consumption of fast fashion. Transformable clothing, a niche segment of sustainable fashion, consolidates multiple functions and aesthetics within a single garment of clothing. It offers a potential pathway to reduce overconsumption, yet fast fashion continues to dominate the market despite positive consumer views. This study investigates the barriers and drivers to adopting transformable clothing and why positive attitudes about sustainability do not translate directly into purchasing behavior, examining lifestyle choices for a correlation. The hypotheses of this study are guided by the Theory of Planned Behavior, examining how aesthetics, usability, and perceived ease of use influence purchase intention and willingness to pay. The research follows a mixed-method design, using a structured survey with visual stimuli of contrasting transformable garment prototypes and randomized framings to describe the business behind the garments’ creation. This approach tests whether the prototypes and framings produce different levels of interest and perceived value. Interviews further contextualize decision drivers across lifestyle segments, including travel frequency, nightlife participation, and storage constraints. In hopes of guiding designers and brands on messaging for transformable items, this study will identify high-potential consumer segments through positioning transformability as having functional and sustainable value.

In my honor thesis, I will be studying unethical business practices directed toward emerging creators and how these practices shape their financial stability, mental health, and long-term brand value. As digital platforms grow and entertainment markets become more crowded, smaller public figures consisting of social media influencers, streamers, and niche creators often face significant power imbalances when working with agencies. With limited bargaining power and a strong need for exposure, they become easy targets for obfuscating contracts, manipulative publicity, and pressuring management tactics.

Since small creators now operate as personal brands, the ethics of how they are managed has become both a business and personal issue. This study compares harmful practices across entertainment sectors and examines how audiences respond when unethical management becomes publicly known rather than ethical management. Ethical management is defined as “choosing to do what’s right, not what’s easy, and consistently upholding those values, especially when faced with difficult situations where there is not always a clear ‘right’ path forward.” (Akuman). On the contrary, unethical management is the complete opposite of that, prioritizing short-term gains, control, or publicity at the expense of choosing what is right. I wanted to take this dive in order to inform future emerging creators who will create their own brand and  have their own audience to uphold, the dangers these unethical practices and abuses have on themselves in order to protect both the creator and their community.

Youth cheerleading centers that serve athletes ages 5 to 18 require a large financial investment and often face a high risk of failure. Many people assume that bigger sports facilities are more successful because they can serve more athletes and generate more revenue. However, large facilities also come with higher startup costs and greater financial risk. This research examines how the size of a youth cheer facility affects its economic sustainability. Understanding these financial challenges is important for entrepreneurs who must decide how much to invest when starting a new facility.

This study compares small startup gyms, medium sized independent facilities, and large regional sports centers by analyzing their startup costs, operating expenses, time needed to become profitable, and long term business survival. The research uses industry reports, financial data, and case studies of existing youth sports facilities to evaluate how different investment levels impact business success.

The study hypothesizes that although larger facilities may earn more overall revenue, smaller and medium sized cheer centers are more likely to reach profitability faster and remain financially stable over time due to lower startup costs and reduced financial risk. The findings of this research can help future business owners, investors, and community planners better understand which types of youth sports facilities are the most economically sustainable.

Most people who live in the United States probably have noticed that their local pharmacy has been replaced by a CVS or other big chain store. The once friendly community store has been replaced by a larger corporation that will often change prices without any notice. Across the United States, the monopolization of pharmacies has quietly reshaped how people access their medications and healthcare services. While most customers will see an average run of the mill retail chain pharmacy, few realize how a handful of corporations have come to dominate nearly every aspect of the prescription drug market. 

Achieving national climate goals depends not only on large-scale energy reforms, but also on widespread household adoption of renewable technologies such as solar panels and electric vehicles. Despite financial incentives often used to encourage adoption, participation remains uneven. Existing research shows that financial and economic factors matter, yet they do not fully explain why some households adopt renewable technologies while others do not. In environmentally progressive communities such as Amherst, Massachusetts, psychological and social influences may play an especially important role, but these local behavioral dynamics are not well understood.

This study aims to examine how behavioral factors shape renewable energy adoption decisions among households in Amherst. Specifically, it will investigate the role of perceived social norms, environmental attitudes, confidence in managing the adoption process, trust in institutions, and awareness of financial incentives in shaping both adoption and future intentions.

Using a survey-based research design, data will be collected from adult Amherst residents to measure renewable energy adoption, behavioral perceptions, and demographic characteristics. Statistical analysis will be performed to identify which factors most strongly predict adoption decisions and how behavioral influences compare to financial considerations.

The findings of this research contribute to a better understanding of how psychological and social factors shape clean energy decisions at the community level. By pinpointing crucial drivers and barriers, this thesis can inform more effective local outreach strategies and policy design, helping accelerate renewable energy adoption and supporting broader climate goals.

Emerging at the forefront of the global energy transition, the Indian government is

implementing renewable energy policies to address the country’s energy crisis, climate

goals and economic well-being. PM KUSUM, an Indian scheme, is part of the

governmental push to increase the use of solar energy in agriculture. Under the policy,

farmers receive financial subsidies and institutional support to install solar-powered

irrigation pumps and decentralized solar panels. However, since its launch in 2019,

there has been limited literature examining the scheme's effectiveness and the factors

influencing its success. With over 5 billion USD allocated to enforcing the policy, it

becomes critical for policymakers to determine state-level characteristics that affect the

scheme’s impact and improve its outcomes.

This research aims to evaluate the effectiveness of PM-KUSUM through empirical

analysis. Two regression models were developed to assess the variation of the

scheme’s impact across Indian states. The first model employs a multiple linear

regression framework to study the relationship between state-wide deployment count of

solar technology under the scheme, and key characteristics such as per-capita income,

urbanization rate, education rate, and installed grid-interactive renewable power. The

second model is a panel data regression that accounts for within-state, over-time

effects. We expect to find that the chosen socio-economic, political, and demographic

predictors influence the deployment count at a statistically significant level.

The purpose of our thesis is to examine the trajectory of the artificial intelligence industry by using AI to research itself. We wished to examine the future of AI in business. This is a difficult question to answer because AI is always changing and there are many conflicting opinions. We felt the best approach to understanding the AI industry was to research Nvidia because its chips are central to the training and development of every major AI model today. They are also an investor in various AI software. Therefore, the AIs will be analyzing the same topic that is directly related to all their futures yet far removed enough that they answer without blatant bias. 

We plan to use four relevant large language models: OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini, and xAI’s Grok. We will ask them all the same research question: can Nvidia maintain its position atop the AI chip industry? From there, we will have a guided discussion that leads the AI into answering the question, while taking note of their problem solving and research tendencies.

We suspect that all of the selected Large Language Models will have a cautious, but positive outlook on Nvidia’s future, evidenced primarily by their selection of sources.

Artificial Intelligence is already making strides in changing research techniques. Especially by college students, who have been the quickest to embrace AI. In researching this, we want to see what biases these technologies create in research and gain a better understanding of the AI industry.