Food insecurity is found at large throughout the world, yet many are not aware of the impact it has in their own neighborhoods. Many populations are affected; however, the severity has not been extensively studied or determined within the university student population. The purpose of this study is to investigate the relationship of food insecurity between university students. This study explores three objectives: 1) identify the prevalence of food insecurity in University of Wisconsin-Stout (UW-Stout) students pre-COVID-19 (2017) and during COVID-19 (2022), 2) determine the relationship between food insecurity prevalence and race/ethnicity, specifically in White or students of color, and 3) find the correlation between GPA status and food security among UW-Stout students. Two samples of UW-Stout students were collected in 2017 (n = 253) and 2022 (n = 244) from an online Qualtrics survey. Pearson Correlation and independent t-tests were performed to elucidate the information on food insecurity. The results found no significant findings of food insecurity prevalence between 2017 and 2022, race/ethnicity, and GPA status. Regardless of no significant results, there is a high prevalence of food insecurity present at UW-Stout. Future research is warranted in university students to continue to reveal the relationships between food insecurity and identifiable factors. Therefore, future research should focus on university students including a potential to assess pre-, during, and post-COVID-19 information, assess larger and more diverse populations, and specify GPA or GPA ranges that may be related to food insecurity.

More millennials now own their own home than rent one, leaving Gen Z the only group where most have a landlord, according to RentCafe data. Millennials – also known as Gen Y – were born between 1981 and 1996. They come after Gen X and before Gen Z, or those born between 1996 and 2012. According to RentCafe, which analyzed data from IPUMS of the 110 largest metropolitan areas in the US, the number of millennial homeowners rose to 18.2 million in the past decade. Just over half the cohort, or 51.5%, now own a property, although 17.2 million still rent.

In the wake of the 2008 Great Recession, a section of the Dodd–Frank Act (DFA) ostensibly provided greater protection to low-income borrowers by capping bank charges on small dollar mortgages (defined here as less than $100,000) and requiring more bank oversight by outside auditors. Previous research has shown that both actions had the perverse consequence of reducing the availability of mortgage credit to low-income borrowers. Building on these findings, we investigate the following question: Did the decline in access to credit lead to a decrease in demand for homes in high poverty census tracts, and a corresponding decline in property values in these areas? Using an OLS regression model of home values and census tract data in Forsyth County, North Carolina from 2007 to 2020, we calculate that nominal home values dropped by over 40% in the county’s lowest income census tracts after the institution of the DFA, relative to the rest of the county. Our paper offers evidence that DFA has harmed those that the Act was intended to help.

Redlining refers to discriminatory lending practices based on the demographic composition of neighborhoods. The term is often attributed to boundaries drawn on maps by the Home Owners’ Loan Corporation (HOLC) in the 1930s to represent the perceived credit risk of neighborhoods. Combined with other discriminatory actions, redlining restricted access to mortgage financing for racial minorities, and areas subject to historic redlining practices still exhibit worse outcomes on various socio-economic dimensions. This study examines contemporary differences in residential housing values along historic redlined boundaries. Boundary fixed effects models are constructed using contemporary property sale data for Seattle, WA and Richmond, VA from 2000 to 2018. Results indicate that properties inside a redlined boundary continue to sell at significantly discounted prices compared to houses across the redlined border. Further investigation, using historic data from the 1930s and 1940s, finds that there was also a large and significant historic difference in housing values across the redlined boundaries at that time, including before the advent of HOLC maps. This suggests that contemporary differences in housing values are likely not a direct effect of HOLC maps but rather depict the lingering effect of broader redlining and discriminatory practices that existed before the advent of these maps.

A prominent argument holds that the chief purpose of municipal civil service reform in the United States was to dislodge the overrepresentation of recent immigrants in city government. Using new data on all municipal employees from 1850 to 1940 and employing three research designs, we detect no evidence that the share of local government jobs held by foreign-born whites decreased following the introduction of reforms. Instead, we show that foreign-born whites-Irish immigrants in particular-experienced substantial gains in local government employment, concentrated in blue-collar occupations in small-and medium-sized municipalities. Our results call for a revisionist interpretation of Progressive Era reforms by questioning generalizations drawn from the experience of the largest cities in the United States. For most municipalities, instead, civil service reform in fact opened avenues to representation for members of foreign-born constituencies who had previously been locked out of government jobs.

Governments, civic society, businesses, and citizens all strive to make cities more livable. However, evidence about what aspects of cities actually contribute to the subjective well-being of their residents is incomplete. This paper examines the links between life satisfaction and indicators of the “quality” of U.S. metropolitan areas such as leisure/cultural opportunities, crime, climate, transportation, racial/ethnic diversity, incomes, cost of living, income inequality, the environment, healthcare, population growth, and political affiliation and polarization. Using mixed-effects regression and controlling for individual demographics, data on 9,498 respondents in 161 U.S. metropolitan statistical areas (MSAs) suggest that MSA characteristics have little relationship with life satisfaction. The only consistently significant characteristics are the natural log of median MSA per capita income, which is negatively associated with life satisfaction, and climate quality, which is positively associated with it. The association between the percentage of the population voting Republican and life satisfaction is negative but only borderline significant. Further, principal components analysis shows that MSAs with characteristics similar to California's Central Valley or the Texas/Mexico border are actually associated with higher life satisfaction. The finding that subjective well-being tends to be higher in places with better climates is well-supported by prior literature; past research also helps explain why poorer places may be happier, since people tend to be happier when their income compares favorably to their peer group's.

RentCafe looked at renter and owner households data across 260 US metro areas, and used the IPUMS Current Population Survey to determine how the home owning/renting habits of different generations have changed between 2017 and 2022. Their generations were broadly defined by the Pew Research Center. According to one estimate, Millennials make up 22 percent, Baby Boomers make up 21 percent, Gen Z make up 20 percent, Gen X make up 19 percent, seniors make up five percent and children make up 13 percent of the total population.

This dissertation is a collection of three studies on topics in economic history and labor economics, in the United States. In the first chapter, I investigate the relationship between temporary paternal absence and long-term labor market outcomes. Specifically, I exploit the natural experiment of fathers’ WWII conscription, combined with longitudinal Census and Social Security data, to construct a quasi-exogenous – and temporary – measure of paternal absence. I document that, consistent with existing hypotheses on female “role model” effects, wives of draftees were 51% more likely to enter covered employment and that their daughters were 17% more likely to be in the labor force themselves when surveyed as adults. However, I find a stark substitution pattern between mothers’ and children’s wartime labor, and I contend that the latter better predicts higher future labor force attachment and income for women who were teenagers during WWII. I argue that paternal absence may not have necessarily been detrimental in this setting, in that it might have empowered daughters to be working girls first, and working women later. 4 In the second chapter, I study the long-term effects of marriage restrictions on children. I exploit the staggered implementation of U.S. State laws which – starting in 1936 – required a negative syphilis test as a prerequisite for obtaining a marriage license. I find that these regulations increased the “cost of marriage”, and resulted in a 53% drop in marriage licenses and a 10% rise in births out of wedlock among non-Whites. I show that my results are primarily driven by less educated women and first time mothers - 45% of which remain unmarried several decades after delivery. Finally, I provide suggestive evidence for the consequences of single motherhood by comparing long-term outcomes for cohorts born in years straddling the passage of the legislation in each State. In the third chapter I explore the effects of military service on women during WWII. Using original Census transcripts combined with marriage and military records, I show how these women’s paths dramatically differed from the reactionary gender-conforming expectations of the 1950s in terms of labor force participation and their marital and fertility choices. I address issues of selection into military service by using propensity score matching and instrumental variable methods and argue for mechanisms through which the GI Bill and service itself might have empowered women, and Black women especially, allowing them to take on non-traditional gender roles.

This dissertation consists of three chapters focusing on incentives and choices in the context of the economics of education and health economics. The first two chapters explore potential explanations underlying a puzzle in health economics: why do individuals appear to remain inadequately insured for their health? Furthermore, Chapters I and II explore the possible role of salient information about health, delivered to individuals in various forms, on health plan choices. Chapter III focuses on the socioeconomic and racial sorting effects of a policy that expanded school choice options for students attending low performing public schools. In Chapter I “Who Avoids Health Information? Experimental Evidence on Health Insurance Choice,” I explore the phenomenon of avoidance of information related to mortality, illness, and salient health events. Because information avoidance cannot be classified using existing observational data, I design an experiment to separate a group of “avoiders” and “non-avoiders” based on their willingness-to-pay to avoid uncomfortable or tedious information related to health. This approach enables me to document several observable traits about this newly defined group of “avoiders.” I simulate a potential future health shock for all respondents and I observe that insurance preferences of avoiders and non-avoiders appear to be different. I rule out the role of different information exposure in helping explain this modest, yet significant difference in insurance preferences. In Chapter II “Private Health Insurance Patterns Following Spousal Health Shocks,” I explore how individuals select private health plans after a spouse has experienced a hospital admission. I document a 7.4pp increase in private plan coverage, specifically for individuals who did not initially hold a private health plan when they entered the longitudinal Health and Retirement Study. Across all individuals, both the previously uninsured and those who held insurance, I observe an increased $82 annual premium associated with private plans, suggesting individuals switched towards more generous coverage as a result of the spousal hospital admission. While I cannot rule out other possible explanations, such as lowered transaction costs, these results are consistent with individuals responding to salient health information by selecting into higher levels of insurance coverage. Finally, in Chapter III “School Choice and Student Mobility from Low-Performing Schools: Evidence from the California Open Enrollment Act,” Hayley Abourezk-Pinkstone and I answer the question of how minority and low-income students respond to a policy incentive that allows them to transition to better-performing public schools of their choice. Using variation in the policy roll-out across different schools in different years, we find that minority and low-income students respond to the policy by exiting low-performing schools and substituting towards higher-performing schools. The effect is likely driven by a combination of both a nudge to shift schools (from a letter mailed home) and the actual policy that allowed students to switch schools

Within coastal communities, sea level rise (SLR) will result in widespread intermittent flooding and long-term inundation. Inundation effects will be evident, but isolation that arises from the loss of accessibility to critical services due to inundation of transportation networks may be less obvious. We examine who is most at risk of isolation due to SLR, which can inform community adaptation plans and help ensure that existing social vulnerabilities are not exacerbated. Combining socio-demographic data with an isolation metric, we identify social and economic disparities in risk of isolation under different SLR scenarios (1-10 ft) for the coastal U.S. We show that Black and Hispanic populations face a disproportionate risk of isolation at intermediate levels of SLR (4 ft and greater). Further, census tracts with higher rates of renters and older adults consistently face higher risk of isolation. These insights point to significant inequity in the burdens associated with SLR. Risk of isolation is expected to disproportionately affect racial minority populations in the U.S. as sea level rise increases. Communities with more renters, older adults, and lower-income populations will also be impacted.

We compared the representation of the four largest Latino subpopulation groups in the health workforce with that group's representation in the US workforce, using 2016-20 data. Mexican Americans were the most underrepresented subpopulation in professions requiring advanced degrees. All groups were overrepresented in occupations requiring less than a bachelor's degree. Among recent health professions graduates, overall Latino representation has been increasing over time.

In the wake of the Great Recession, Georgetown University researchers projected that 58 percent of Tennessee jobs would require education beyond high school by 2020 (Carnevale et al., 2013). This inspired the “Drive to 55” in Tennessee—the goal to have at least 55 percent of the working-age population hold a postsecondary certificate or degree by 2025. The state invested considerable resources in providing new pathways and encouraging completion of a variety of credentials and degree programs, largely focused on two-year college credentials. Much has changed in the decade since the Drive to 55 was initiated. According to the American Community Survey, 58% of Tennesseans 25 and older had at least some college-level education in 2019, up from 53% in 2010 (Figure 1). The Lumina Foundation estimates that 47% of working-age Tennesseans now have a workforce-relevant postsecondary credential or industry-recognized certificate.1 Technology has also marched ahead over the last ten years, and automation threatens many jobs that do not require a college education (Haar & Scott, 2016; Hershbein & Kahn, 2018).

Millennial homeowners outnumbered millennial renters for the first time last year, despite creeping interest rates and a tight real estate market. Driving the news: Nearly 52% of the first generation to grow up in the Internet age ​​— people born from 1981 to 1996 — were homeowners in 2022, making the largest ownership gains of any generation in the last five years, according to a new RentCafe report.

The COVID-19 pandemic led to a large increase in the practice of working from home, with over one-third of US-employed individuals involved in remote work (for coronavirus reasons) in May 2020. This study examines the effects of having a creative occupation on remote work between May 2020 and September 2021. Results show that indi-viduals with Creative Economy occupations were more likely than oth-ers to work from home for COVID-19 reasons, which is consistent with creative workers having more job flexibility and a greater ability to adapt to economic shocks. Although members of the creative core (e.g., pro-grammers, artists, and scientists) and creative professionals (e.g., finan-cial analysts) were both able to pivot to remote work at the beginning of the pandemic, heterogeneity in the effects on working from home in later months suggests that individuals in the creative core were more apt than creative professionals to return to pre-pandemic work arrangements.

Rurality is often viewed as presenting challenges to community sustainability, well-being, and equity. To address the unique challenges of rural areas, policies and practices have been designed specifically for application in places designated as “rural”. Yet what is “rural”? Some recent measures of rurality have gone beyond a dichotomous rural-urban divide conceptualization of rural communities. However, most measures still emphasize proximity to metropolitan areas and population density as the primary components of rurality. Few studies consider the critical role that services and amenities play in the life of a community. We suggest a new measure based on the concept of sustainable development that integrates measures of environmental, social, and economic resources. We present the Community Assets and Relative Rurality index for census block-groups in the coterminous United States and illustrate how this measure is consistent with existing measures of rurality yet offers additional insight into issues of sustainable rural development.

Children born into poverty in rural America achieve higher average income levels as adults than their urban peers. As economic opportunity tends to be more abundant in cities, this “rural advantage” in income mobility seems paradoxical. This article resolves this puzzle by applying multilevel analysis to new spatial measures of rurality and place-level data on intergenerational income mobility. We show that the high level of rural income mobility is principally driven by boys of rural-origin, who are more likely than their urban peers to grow up in communities with a predominance of two-parent households. The rural advantage is most pronounced among Whites and Hispanics, as well as those who were raised in the middle of the country. However, these dynamics are more nuanced for girls. In fact, girls from lower-income rural households exhibit a disadvantage in their personal income attainment, partly due to the persistence of traditional gender norms. These findings underscore the importance of communities with strong household and community supports in facilitating later-life income mobility, particularly for boys. They also challenge the emerging consensus that attributes the rural income mobility advantage to migration from poorer rural areas to wealthier towns and cities.

We illustrate the point with an empirical analysis of assortative mating in the US, namely, that the outcome of comparing two distant groups can be sensitive to whether comparing the groups directly, or indirectly via a series of counterfactual decompositions involving the groups’ comparisons to some intermediate groups. We argue that the latter approach is typically more fit for its purpose.

Studying urban dynamics is essential given the ever-increasing changes in urban areas with all its ensuing consequences, whether negative or positive. It is of paramount importance to take into account the temporal dimension of urban dynamics when studying its patterns and processes. Nevertheless, the majority of studies overlook this consideration and take cross-sectional research approaches. Moreover, a large body of literature in urban dynamics is dedicated to the explanatory analysis and causal inference only, neglecting the importance of predictive analysis. Addressing these two main gaps, this research explores urban dynamics through both causal inference and predictive modeling using longitudinal research designs. Urban dynamics are studied from two aspects in this work; transportation/land-use interactions, and economic growth. In the first article, the impact of built environment on commuting duration is assessed in 2000 and 2015 in Mecklenburg County, NC using spatial panel data models. Results show that the built environment has a statistically significant impact on commuting duration. However, it is important to note that the practical magnitude of the impact is small. In the second and third articles, the business performance of businesses are forecasted for non-business services and business services respectively in Mecklenburg County, NC, using recurrent neural networks long short-term memory deep learning method. After building and training the sequential model, its predictive performance is assessed using out-of-sample evaluation.

This dissertation studies three markets with regulated prices. I focus on how these regulations shape the behavior of firms along non-price dimensions. Chapter 1 studies the effects of community rating regulations in the US individual health insurance exchange market. In this market, the Affordable Care Act established community rating areas made up of groups of counties in which insurers must offer plans at uniform prices, but insurers do not have to enter all counties in a rating area. Allowing partial entry creates tradeoffs in rating area design: larger areas may support more competition, but heterogeneous areas may promote partial entry as firms choose to not enter high cost areas. To evaluate these trade-offs, I develop a model of insurer entry and pricing decisions and investigate how insurers respond to rating area design. I find that banning partial entry increases overall entry, average prices, and consumer welfare. I quantify the trade-offs of increasing rating area size and find returns to size concentrated when marginal costs are similar across counties in a rating area. Regulators must balance promoting competition with pooling high and low cost consumers in rating area design. 4 Chapter 2 (which is joint work with Molly Schnell) studies how regulated prices can change the supply-side responses to health insurance expansions in the setting of retail clinics. Retail clinics are on-demand health care clinics located in retail settings. Exploiting county-level changes in insurance coverage following the Affordable Care Act and 1,721 retail clinic entries and exits, we find that local increases in insurance coverage do not lead to growth in the concentration of clinics on average using two-way fixed effects and instrumental variable designs. However, this null effect masks important heterogeneity by insurance type: growth in private insurance leads to large growth in clinic entry, whereas clinic penetration is dampened by increases in Medicaid coverage. Consistent with a model in which firms face demand from both markets with administered and marketbased pricing, we find that the positive (negative) supply-side effects of private insurance (Medicaid coverage) are concentrated in states with low provider reimbursements under Medicaid. We further show that similar location patterns are observed among other types of health care clinics, including urgent care centers. While it has long been accepted that reductions in the prices paid by consumers following insurance expansions should lead the supply side to expand to meet increased demand (Arrow 1963), our results demonstrate that whether health insurance expansions cause the supply side to expand or contract further depends on how the prices received by providers are affected. Chapter 3 (which is joint work with Nicole Holz) studies how landlords behave when they remain in the rental market when rent control policies are enacted. Rent control policies seek to ensure affordable and stable housing for current tenants; however, they also increase the incentive for landlords to evict tenants since rents re-set when tenants leave in a vacancy decontrol system. Evictions may reduce both the anti-displacement 5 and rent reduction effects of rent control. We exploit variation across ZIP codes in policy exposure to the 1994 rent control referendum in San Francisco to study the effects of rent control on eviction behavior. We find that for every 1,000 newly rent controlled units in a ZIP code, there were 20 additional eviction notices filed in that ZIP code and an additional 7 wrongful eviction claims. These effects were concentrated in low income ZIP codes and were larger in years when average rent prices rose faster than the allowed rent increases for controlled units.

Selection bias is a massive problem in infectious disease epidemiology that can result in needless morbidity and mortality. This bias is both subtle and ubiquitous, occurring even in randomized clinical trials. For example, medical researchers cannot randomize responses to treatment intermediate to the outcome of interest, and epidemiologists cannot force patients to report sensitive demographic information. In order to do inference in these complex scenarios, we need new classes of models that capture the scientific process of interest while accounting for how the data were observed. In this thesis, I develop theory and practice for Bayesian model expansion to mitigate and adjust for selection bias in the analysis of observational and experimental data arising in the areas of missing data, causal inference, and survey research. In the second chapter, I propose a novel method to infer stratified incidence in disease surveillance data with partially-observed stratum information. Public health researchers often compare the risk of disease among demographic subgroups in order to design interventions. Missingness in demographic covariates like race/ethnicity or age group complicates this endeavor; dropping cases with missing covariates can lead to endogenous selection bias. Instead, I develop a locally-identifiable joint model for the missingness process and the disease process that allows for the missingness process to be not missing at random. The model is identified by marrying spatial information in the disease data with spatial Census data. I investigate the finite-sample properties of the model via a simulation study and apply my model to COVID-19 case data in Southeastern Michigan. I show that the burden of COVID-19 from March to July of 2020 for non-Whites relative to that of Whites is understated when cases that are missing race/ethnicity information are omitted. In the third chapter, I develop a method to point-identify vaccine efficacy (VE) against post-infection outcomes such as severe illness and death. Policy makers need to quantify post-infection outcome VE so as to design effective vaccination strategies, but these causal estimands are typically nonidentifiable. I propose a method to identify these estimands under measurement error on infection and post-infection outcomes by taking advantage of the structure of vaccine efficacy trials; these trials are typically run across different health systems and collect pretreatment covariates related to an individual’s susceptibility to infection. I show that my method not only yields identifiability of the causal estimand but also identifies the infection measurement error parameters. I then investigate the Type I error and power of my method via a simulation study. In the final chapter, I propose a new Bayesian generative semiparametric model for characterizing the cumulative spatial exposure to an environmental health hazard that is not well-represented by a single point in space, like a system of wastewater canals. The model couples a dose-response model with a log-Gaussian Cox process integrated against a distance kernel with an unknown length-scale. I show that this model is well-defined, and that a simple integral approximation adequately controls the computational error. Before applying the model to survey data from Mexico, I quantify the finite-sample properties and the computational tractability of the discretization scheme in a simulation study.