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Payday lenders, check cashers, and other “alternative” financial services (AFS) have garnered attention from policymakers and advocates for the poor because they are more expensive than traditional banking—constituting what some call a “Ghetto Tax.” This is the first study to explore neighborhood-level AFS geography on the national scale. Leveraging a dataset comprising the universe of AFS in 2015, I show that not only are there substantial differences in AFS presence between white and non-white neighborhoods, but that these disparities are largest in the most segregated metropolitan areas. This finding supports theories that racial segregation creates easily identifiable markets for institutions to avoid, target, and exploit. I further show that while AFS presence declines with neighborhood income, the gap between black and white neighborhoods is widest among high-income neighborhoods, reflecting the unique vulnerability of even affluent blacks to institutional marginalization. This work documents how the overlapping geographies of racial isolation and AFS prevalence shape the very cost of money for different racial groups, illustrating the importance of institutions transmitting the effects of racial isolation.

This article leverages a unique data set, recently developed regression methods, and qualitative interviews to investigate the multiple ways real estate agents produce housing inequality. We find that the clustering of agents in and around certain neighborhoods correlates positively with house prices. Our results also show a significant relationship between agent concentration and racial segregation. Our qualitative data reveal how agents engage in steering and upselling. The findings enhance our understanding of mechanisms in the housing market, and provide more empirical clarity on the role real estate agents play in asset and place inequality.

“Superstorm Sandy” brought unprecedented storm surge to New York City neighborhoods and like previous severe weather events exacerbated underlying inequalities in part because socially marginalized populations were concentrated in environmentally exposed areas. This study makes three primary contributions to the literature on vulnerability. First, results show how the intersection of social factors (i.e., race, poverty, and age) relates to exposure to flooding. Second, disruption to the city’s transit infrastructure, which was most detrimental for Asians and Latinos, extended the consequences of the storm well beyond flooded areas. And third, data from New York City’s 311 system show there was variation in distress across neighborhoods of different racial makeup and that flooded neighborhoods remained distressed months after the storm. Together, these findings show that economic and racial factors overlap with flood risk to create communities with both social and environmental vulnerabilities.

The hypothesis of neighborhood stigma predicts that individuals who reside in areas known for high crime, poverty, disorder, and/or racial isolation embody the negative characteristics attributed to their communities and experience suspicion and mistrust in their interactions with strangers. This article provides an experimental test of whether neighborhood stigma affects individuals in one domain of social life: economic transactions. To evaluate the neighborhood stigma hypothesis, this study adopts an audit design in a locally organized, online classified market, using advertisements for used iPhones and randomly manipulating the neighborhood of the seller. The primary outcome under study is the number of responses generated by sellers from disadvantaged relative to advantaged neighborhoods. Advertisements from disadvantaged neighborhoods received significantly fewer responses than advertisements from advantaged neighborhoods. Results provide robust evidence that individuals from disadvantaged neighborhoods bear a stigma that influences their prospects in economic exchanges. The stigma is greater for advertisements originating from disadvantaged neighborhoods where the majority of residents are black. This evidence reveals that residence in a disadvantaged neighborhood not only affects individuals through mechanisms involving economic resources, institutional quality, and social networks but also affects residents through the perceptions of others. Significance Although previously theorized, virtually no rigorous empirical evidence has demonstrated an impact of neighborhood stigma on individual outcomes. To test for the effects of neighborhood stigma on economic transactions, an experimental audit of an online classified market was conducted in 2013–2014. In this market, advertisements were placed for used iPhones in which the neighborhood of the seller was randomly manipulated. Advertisements identifying the seller as a resident of a disadvantaged neighborhood received significantly fewer responses than advertisements identifying the seller as a resident of an advantaged neighborhood. The results provide strong evidence for an effect of neighborhood stigma on economic transactions, suggesting that individuals carry the stigma of their neighborhood with them as they take part in economic exchanges.

A growing body of evidence shows that America’s racial geography shapes access to basic financial services (e.g. banking), highlighting a mechanism connecting segregation to economic vulnerability: spatially organized institutional marginalization.s While the practices and policies of “mainstream” commercial banks are central to this dynamic, the costs they impose on the communities they serve have been understudied. This study leverages survey data from a stratified random sample of 1344 banks across the United States to investigate variation in the costs and fees of entry-level checking accounts at commercial banks. Our evidence shows banks charge more to open and maintain checking accounts in neighborhoods and cities with larger Black and Latinx populations even after controlling for demographic and socioeconomic characteristics as well as market competition. The higher costs of banking imposed on Black and Latinx communities are further compounded by parallel disparities in income. These findings reveal the unequal costs of banking in segregated America.

The racially unequal impacts of flooding on local housing prices and community recovery are well established, but an intermediary stage of the housing process that has significant consequences for where people live – the mortgage market – has received less attention. We consider how flood events contribute to racial and spatial inequalities in mortgage access by linking new national data on coastal and river flood disasters in the United States to detailed national data on mortgage applications between 2009 and 2019. Specifically, we examine how flood disasters change lenders’ willingness to issue loans, and how these associations differ by applicant race and ethnicity. Flood disasters are followed by a reduced probability of approval by about 2% for all applicants. For white borrowers, this “flood penalty” on mortgage approvals weakens as the share of white residents increases in a neighborhood, but for Asian, Black, Latinx borrowers, this penalty remains regardless of neighborhood racial composition. Together, these findings suggest that lenders’ responses to flooding exacerbate existing racial disparities in access to mortgage credit, with implications for communities’ post-disaster recovery and families’ ability to accumulate wealth.

My dissertation explores the geography and consequences of the Great Recession by analyzing a dataset of the 14 million foreclosures filed between 2006 and 2012. While foreclosures have important implications for the study of inequality between individuals, I am interested in understanding these dynamics through an analysis of place. My dissertation has three main substantive chapters. I begin by leveraging variation over time within neighborhoods and metropolitan areas to show significant and dramatic differences in foreclosure rates across neighborhoods of different racial and ethnic makeups. The raw differences between majority white and black or Latino neighborhoods cannot be accounted for by the inclusion of a wide array of control variables. Next, I explore the relationship between foreclosures and the expansion of fringe financial services (i.e. check cashers). Results show that racially isolated neighborhoods in New York City, often where subprime lending was common, were uniquely vulnerable during the foreclosure crisis and were communities where check cashing outlets sprouted, highlighting a mechanism for the reproduction of inequality over time. Finally, I evaluate the relationship between foreclosures and changes in home purchase decisions made by households of differing racial and ethnic makeup. Perhaps surprisingly, I find that the number of white households buying homes within a census tract was positively correlated with foreclosure volume and that this relationship was strongest during the peak of the Great Recession. However, I also present evidence that the net impact of foreclosures combined with home purchases may not have had an integrative effect, likely because of racial disparities in foreclosures.

The immune response to COVID-19 infection is variable. How COVID-19 influences clinical outcomes in hospitalized patients needs to be understood through readily obtainable biological materials, such as blood. We hypothesized that a high-density analysis of host (and pathogen) blood RNA in hospitalized patients with SARS-CoV-2 would provide mechanistic insights into the heterogeneity of response amongst COVID-19 patients when combined with advanced multidimensional bioinformatics for RNA. We enrolled 36 hospitalized COVID-19 patients (11 died) and 15 controls, collecting 74 blood PAXgene RNA tubes at multiple timepoints, one early and in 23 patients after treatment with various therapies. Total RNAseq was performed at high-density, with >160 million paired-end, 150 base pair reads per sample, representing the most sequenced bases per sample for any publicly deposited blood PAXgene tube study. There are 770 genes significantly altered in the blood of COVID-19 patients associated with antiviral defense, mitotic cell cycle, type I interferon signaling, and severe viral infections. Immune genes activated include those associated with neutrophil mechanisms, secretory granules, and neutrophil extracellular traps (NETs), along with decreased gene expression in lymphocytes and clonal expansion of the acquired immune response. Therapies such as convalescent serum and dexamethasone reduced many of the blood expression signatures of COVID-19. Severely ill or deceased patients are marked by various secondary infections, unique gene patterns, dysregulated innate response, and peripheral organ damage not otherwise found in the cohort. High-density transcriptomic data offers shared gene expression signatures, providing unique insights into the immune system and individualized signatures of patients that could be used to understand the patient’s clinical condition. Whole blood transcriptomics provides patient-level insights for immune activation, immune repertoire, and secondary infections that can further guide precision treatment.

The SARS-CoV-2 pandemic, starting in 2019, has challenged the speed at which labs perform science, ranging from discoveries of the viral composition to handling health outcomes in humans. The small ~30kb single-stranded RNA genome of Coronaviruses makes them adept at cross species spread and drift, increasing their probability to cause pandemics. However, this small genome also allows for a robust understanding of all proteins coded by the virus. We employed protein modeling, molecular dynamic simulations, evolutionary mapping, and 3D printing to gain a full proteome and dynamicome understanding of SARS-CoV-2. The Viral Integrated Structural Evolution Dynamic Database (VIStEDD) has been established (prokoplab.com/vistedd), opening future discoveries and educational usage. In this paper, we highlight VIStEDD usage for nsp6, Nucleocapsid (N), and Spike (S) surface glycoprotein. For both nsp6 and N we reveal highly conserved surface amino acids that likely drive protein-protein interactions. In characterizing viral S protein, we have developed a quantitative dynamics cross correlation matrix insight into interaction with the ACE2/SLC6A19 dimer complex. From this quantitative matrix, we elucidated 47 potential functional missense variants from population genomic databases within ACE2/SLC6A19/TMPRSS2, warranting genomic enrichment analyses in SARS-CoV-2 patients. Moreover, these variants have ultralow frequency, but can exist as hemizygous in males for ACE2, which falls on the X-chromosome. Two noncoding variants (rs4646118 and rs143185769) found in ~9% of African descent individuals for ACE2 may regulate expression and be related to increased susceptibility of African Americans to SARS-CoV-2. This powerful database of SARS-CoV-2 can aid in research progress in the ongoing pandemic.The SARS-CoV-2 pandemic, starting in 2019, has challenged the speed at which labs perform science, ranging from discoveries of the viral composition to handling health outcomes in humans. The small ~30kb single-stranded RNA genome of Coronaviruses makes them adept at cross species spread and drift, increasing their probability to cause pandemics. However, this small genome also allows for a robust understanding of all proteins coded by the virus. We employed protein modeling, molecular dynamic simulations, evolutionary mapping, and 3D printing to gain a full proteome and dynamicome understanding of SARS-CoV-2. The Viral Integrated Structural Evolution Dynamic Database (VIStEDD) has been established (prokoplab.com/vistedd), opening future discoveries and educational usage. In this paper, we highlight VIStEDD usage for nsp6, Nucleocapsid (N), and Spike (S) surface glycoprotein. For both nsp6 and N we reveal highly conserved surface amino acids that likely drive protein-protein interactions. In characterizing viral S protein, we have developed a quantitative dynamics cross correlation matrix insight into interaction with the ACE2/SLC6A19 dimer complex. From this quantitative matrix, we elucidated 47 potential functional missense variants from population genomic databases within ACE2/SLC6A19/TMPRSS2, warranting genomic enrichment analyses in SARS-CoV-2 patients. Moreover, these variants have ultralow frequency, but can exist as hemizygous in males for ACE2, which falls on the X-chromosome. Two noncoding variants (rs4646118 and rs143185769) found in ~9% of African descent individuals for ACE2 may regulate expression and be related to increased susceptibility of African Americans to SARS-CoV-2. This powerful database of SARS-CoV-2 can aid in research progress in the ongoing pandemic.