Economic sanctions as a means of pressuring nations to improve human rights protections or end human rights abuses have become an increasingly common practice in recent years. Although the efficacy of sanctions for humanitarian ends remains under heavy scrutiny, the United States Department of State continues to both implement new sanctions and enforce existing policies of this kind. However, there is a notable discrepancy between regimes accused of human rights abuses by non-governmental organizations and those receiving these types of sanctions. This research aims to unveil potential factors that may explain this gap. I theorize that while investment and trade may protect a country from economic sanctions in an effort to keep certain markets open, past or present adherence to communist ideology increases the likelihood of receiving sanctions on the grounds that communism remains a perceived threat. To test my theories, I identify a set of countries that are currently verified by third party organizations as human rights violators. At this point, I conduct multivariate regression analysis to observe the relationship of both economic interest and conflicting ideology and the presence of sanctions citing human rights violations. While I expect to find that while both factors contribute to the presence of sanctions as outlined, I also theorize that economic interest will have a greater influence. This study serves to identify specific influences on sanctions that will enrich future discourse on their implementation.

Although oft-cited, the effects of campaign finance are neither well-understood nor well-evidenced; in particular, the supposed vote-buying motive --- contributions given with the expectation the recipient legislator will change their vote on specific bills --- lacks systematic evidence, and literature is mixed both in findings and in methodological quality. Nevertheless, since 2000 (before which much of the literature of “vote-buying” developed), gross campaign expenditures have exponentially increased; prima facie, this suggests that contributors expect returns to their campaign contributions and that (if their expectations are not systematically irrational) campaign finance does affect political outcomes. Building on the public choice theory of regulation, I theorize agents pay the cost of campaign contribution to produce political pressure on a recipient politician, who must then trade-off between the support of competing interest groups. Thus, it is expected that increased campaign finance contributions from interest groups that favor a bill’s passage are associated with a ceteris paribus increased likelihood of the recipient politician supporting the bill. Moreover, because interest groups are heterogeneous in their ability to generate pressure, the estimated treatment effect should systematically vary by contributor type. Previous literature has generally attempted to find statistical associations between contributions from a select few contributing groups and votes on a handful of selected bills (often selected because they are ex ante expected to give significant results), while also failing to incorporate adequate regression controls. Hence, this paper attempts to reconcile the literature’s differing results by using logistic regression to examine a large dataset of US House members’ legislative votes from 1980-2022 and concurrent campaign finance data. This paper's primary contribution is to extend current methods into a large-n analysis, the statistical strengths and methodological limitations of which are informative for future research into interactive mechanisms in economic theories of regulation.

The most prominent international organizations have emphasized their commitment in aiding the global transition to a carbon neutral world, which is estimated to cost hundreds of trillions of dollars. Despite these global efforts to mitigate climate change, countries, even when faced with similar levels of threat to climate disasters, prioritize the immediate issue differently. This study explores the relationship between economic security and state climate initiatives as a way to explain countries' varying commitment to climate change mitigation. While many scholars focus on the ability democracies have to combat climate change, I build upon and synthesize theories of economic opportunity, welfare programs, and class solidarity to hypothesize that both individual and state economic security impact states' prioritization of climate initiatives. I argue that economic security is a likely influence on the magnitude of effort which states put into climate initiatives, as it reflects both individual ability to safely fulfill essential needs and state ability to adapt capital. To test my hypothesis, I run a multivariate regression analysis to examine whether key indicators of economic security correlate to state carbon emissions per capita, controlling for regime type, institutional trust, political unrest, and median age of the population. I expect to find a negative relationship between economic security and state carbon emissions per capita. These findings would suggest that improving economic security is essential for the development and success of future climate initiatives. Understanding the underlying roadblocks of long term climate mitigation is crucial in guiding climate policy and optimizing climate aid.

Anthropogenic CO₂ emissions are rapidly warming the Earth, pushing it towards climate states without societal precedent. Past warm periods in geologic history can offer important insight into how the Earth responds to increases in atmospheric CO₂. One such period is the early Eocene (EECO; ~50 million years ago), which was characterized by global-mean temperatures ~13 °C higher than during the late 20th century, and is often offered as a potential analog for future unmitigated warming. Atmospheric CO₂ concentrations during the EECO are highly uncertain, but estimates range from 500-1400 ppm. Understanding EECO climate may also help us interpret past changes in biological systems; there is evidence, for example, that significant mammalian reorganization occurred in North America in response to high temperature and precipitation during the EECO. Because we do not have direct observations of the EECO, climate models are useful tools for understanding temperature and precipitation during this period. I use eight climate models from the Deep-Time Model Intercomparison Project to characterize zonal-mean temperature and precipitation under different atmospheric CO₂ concentrations, ranging from present-day CO₂ concentrations to 9x present-day CO₂ concentrations. I analyze temperature and precipitation in the annual-mean and across seasons. In the present-day climate, atmospheric heat transport and its efficiency (“diffusivity”) have been shown to govern the meridional temperature gradient and influence the shape of the hydrological cycle. To better understand the modeled Eocene hydroclimate, I calculate diffusivity across space and seasons. Ultimately, we seek to understand how diffusivity changes under different climatic conditions, and the physical mechanisms driving zonal-mean climate under elevated CO₂ levels. We expect that diffusivity will be relatively invariant with CO₂. This analysis can provide insights for how temperature and precipitation patterns may change under high CO₂ levels in the future.

From 2012 to 2022 violent crime in rural America has either increased or stayed above the national crime rate. At the same time, the amount of firearms in these counties rose dramatically while law enforcement expenditure and poverty reduction stagnated. I theorize that a combination of high poverty rates, increased firearm ownership, and fewer police has contributed to the increase in rural crime over the past decade. As the literature suggests, firearms make it easier to commit violent crimes, fewer police make crime harder to detect, and poverty pushes people into crime due to unstable living conditions. I will evaluate the effect of these three factors on rural crime rates by using FBI Uniform Crime Report, U.S Census Bureau, and RAND Corporation data to perform multivariate regression in order to determine causality. I expect to find a strong positive correlation between these three factors and the rural crime rate. I will use multivariate regression analysis to examine the influence of gun ownership, law enforcement spending and poverty on rural crime rates. By finding the causes of crime in rural communities, I will help identify the areas that local governments will need to address to solve problems of violence.

According to some measures, the United States has the largest economy in the world. Despite its massive gross domestic product, US citizens still face high poverty rates across states and counties. Why are there persistent poverty rates and why do they vary across the nation? To answer this question, I hypothesize a negative relationship between welfare spending and poverty rates. Welfare spending is one of the most direct ways that the government can provide money to people experiencing poverty. When people have their most basic needs provided, through welfare programs, they are significantly more likely to get out of poverty. To explore this relationship I use a multivariate regression, controlling for other factors that can impact poverty rates. Through testing this data, I hope to illustrate the importance of adequately funding welfare programs to reduce poverty across the nation. By increasing welfare spending individuals can get out of poverty and communities can thrive, improving the lives of all citizens.

My research explores why conflict breaks out in some contexts but not others. Specifically, I question whether bilateral security agreements and democracy reduce the likelihood of conflict between states in volatile regions. First, I hypothesize that security measures reduce the likelihood of armed conflict because they prevent or reduce dangerous misperceptions, fear, and insecurity, which international relations and political psychology literature identify as catalysts of conflict. Second, I hypothesize that as the democratic health of states improve, the likelihood that they will engage in conflict decreases. This is because democracies share values and norms, institutional and public opinion restraints, and other entanglements that render conflict too costly to be in either’s interest. To test these hypotheses, I will compare bilateral relations between each state dyad in five historically volatile regions: the Middle East, Eastern Europe, the Balkans, Central Asia, and South Asia between 1990 and 2010. For each dyad, I will measure the number and severity of conflicts, the number and type of bilateral security agreements, and the democratic quality of each country. I will test my hypotheses by running statistical analyses including a multivariate regression, controlling for other confounding variables that may influence the likelihood of conflict. From my quantitative analysis, I expect to find that security agreements and democracy decrease the likelihood of conflict. This research is important because we have observed an increased prioritization of forming security agreements in western diplomatic relations during the 20th century as well as foreign policy guided by an understanding that democratic states are less likely to fight each other, especially during the Bush administration. If the US and West prioritize forming security agreements and promoting the spread of democracy, we should understand whether these truly increase security.

While Tropical Storm Harold struck off the coast of Corpus Cristi, the Texas State Legislature ratified a bill effectively making their state a fossil fuel sanctuary. Scientists agree that the Deep South is one of the most vulnerable regions in the United States to climate change and the rise in climate-caused disasters. Why is it that despite being the most vulnerable region, the Deep South continues to offer pushback against climate mitigation? This research project attempts to understand the psychological components that induce preferences to policy. In the past, researchers have primarily focused on the economic lobbying power of Big Oil and Coal industries within jurisdictions. Utilizing rational choice theory, this project evaluates this puzzle from the perspective of voter preferences in response to economic reliance on the fossil fuel industry. The fossil fuel industry proves vitally important to local communities, providing jobs and resources. Using multivariate regression, I examine the effect of employment in the fossil fuel industry and reliance on nonrenewable energy within 467 different counties across the Deep South and other states. I weigh these results against possible other factors including partisanship, age, race, and vulnerability to observe if economic reliance accurately characterizes the misalignment observed within the Deep South. Based on previous literature and rational choice theory, I hypothesize that in counties economically dependent on the fossil fuel industry there will be less support for climate change legislation. Policy implications of this research include securing resources for communities threatened by a transition to renewable energy sources.

Budding yeast cultures grown in limited sulfate conditions are overtaken by cells with an inverted triplication of the gene SUL1, which encodes for a sulfate transporter. The extra copies of the sulfate transporter provide a selective advantage because these cells outcompete other yeast cells for the limiting resource. To explain the mechanism behind this type of amplification the Brewer and Dunham Labs proposed a model (Origin Dependent Inverted Repeat Amplification or ODIRA), which requires both a DNA replication origin and inverted repeats flanking SUL1. ODIRA starts with a DNA replication error involving replication fork regression that leads to an extrachromosomal DNA intermediate. This intermediate then replicates and recombines into the genome, producing the observed amplification. Because similar triplications are observed in the human genome, including in human disorders, the mechanism of ODIRA offers insights into human genome evolution and disease. While the yeast research is consistent with ODIRA, we still do not know which proteins are responsible for the process. I am testing whether the genes RAD5 and RAD54 — involved in fork regression and strand switching, respectively — are involved in ODIRA. To do so, I am measuring the ODIRA frequency in strains with each gene deleted compared to a wild-type control. If either gene deletion leads to a statistically significant change in ODIRA frequency compared to the wild-type strain, I can conclude this gene is involved in ODIRA. To measure ODIRA frequency, I grow the deletion strains under selection for DNA recombination events and use whole chromosome gel electrophoresis and Southern blotting to detect ODIRA events. Preliminary data analysis suggests that there is a reduction in ODIRA events when either RAD5 or RAD54 is deleted, indicating that these genes are likely needed for ODIRA. These results may provide insight into how inverted triplications may arise.

The Great Recession was one of the most devastating economic downturns experienced in the United States since the Great Depression. However, the rate at which states recovered from the crisis varied drastically across the country, with some states recovering in less than a year while others languished for over five years. While many previous scholars have explored the structural, demographic, fiscal, and other conditions of states leading into the Great Recession and their impact on recovery, this paper explores the impact of a variety of state fiscal policy decisions while actively in the recession and their impact on economic recovery. To conduct this study, I examine the relationship between the change in several state fiscal policy indicators – including tax, expenditure, and budget indicators – from the start of the recession to the trough, and the rate of recovery measured in the number of months for the state to recover fully from the recession. I conduct a multivariate regression analysis to determine the relationship between my selected indicators and the rate of recovery while controlling for various factors that previous scholars have identified as having a potential impact on recovery, including state economic composition, relative federal stimulus, and more. I expect to find a positive relationship between general increases in tax revenue, increases in expenditures on welfare, and a more equal ratio of revenue to spending and a faster recovery (a lower number of months to recovery). The findings of this study will contribute to informing improved strategies for state policymakers as they navigate fiscal policy decisions during future recessions.

The mechanisms underlying the development of neurodegenerative diseases such as Alzheimer's Disease (AD) are not well understood. The characteristic accumulation of pathological protein plaques and tangles has led AD research to focus primarily on abnormal protein function and metabolism contributing to the pathology. Tau is one such protein that forms toxic aggregates in those with AD and various other neurodegenerative diseases. Our lab researches the mechanisms of tau toxicity using the nematode C. elegans. Recent research has implicated improper lipid metabolism as another potential contributor to neurodegeneration. Mutations in a gene known as gba-3, which is critical for lipid metabolism, are risk factors for the development of Parkinson’s Disease (PD), but little is known about the relationship between gba-3 mutations and tau toxicity in AD models. My research project will investigate the role of gba-3 in a C. elegans model of tau toxicity. I have crossed strains with various mutations in the gba-3 gene into two strains with tau toxicity. I will perform motor performance assays, measure the accumulation lipids, quantify the amount of tau, and measure oxidative stress to investigate whether gba-3 mutations modulate tau toxicity. Preliminary results suggest some gba-3 mutations slightly rescue some tau models but not others, but further investigation is needed to validate these findings. Ultimately, this project will provide further insight into the complex mechanisms underlying neurodegenerative diseases such as AD and may guide future development of treatments targeting lipid metabolism.