Objective: To study the impact of Medicaid funding structures before and after the implementation of the Affordable Care Act (ACA) on health care access for Latinos in New York (Medicaid expansion), Florida (Medicaid non-expansion), and Puerto Rico (Medicaid block grant). Data Sources: Pooled state-level data for New York, Florida, and Puerto Rico from the 2011–2019 Behavioral Risk Factor Surveillance System and data from the 2011–2019 American Community Survey and Puerto Rico Community Survey. Study Design: Cross-sectional study using probit with predicted margins to separately compare four health care access measures among Latinos in New York, Florida, and Puerto Rico (having health insurance coverage, having a personal doctor, delayed care due to cost, and having a routine checkup). We also used difference-in-differences to measure the probability percent change of having any health insurance and any public health insurance before (2011–2013) and after (2014–2019) the ACA implementation among citizen Latinos in low-income households. Data Collection: The sample consisted of Latinos aged 18–64 residing in New York, Florida, and Puerto Rico from 2011 to 2019. Principal Findings: Latinos in Florida had the lowest probability of having health care access across all four measures and all time periods compared with those in New York and Puerto Rico. While Latinos in Puerto Rico had greater overall health care access compared with Latinos in both states, health care access in Puerto Rico did not change over time. Among citizen Latinos in low-income households, New York had the greatest post-ACA probability of having any health insurance and any public health insurance, with a growing disparity with Puerto Rico (9.7% any [1.6 SE], 5.2% public [1.8 SE]). Conclusions: Limited Medicaid eligibility (non-expansion of Florida's Medicaid program) and capped Medicaid funds (Puerto Rico's Medicaid block grant) contributed to reduced health care access over time, particularly for citizen Latinos in low-income households.

Researchers often need to work with categorical income data. The typical nonparametric (including midpoint) and parametric estimation methods used to estimate summary statistics both have advantages, but they carry assumptions that cause them to deviate in important ways from real-world income distributions. The method introduced here, random empirical distribution imputation (REDI), imputes discrete observations using binned income data, while also calculating summary statistics. REDI achieves this through random cold-deck imputation from a real-world reference data set (demonstrated here using the Current Population Survey Annual Social and Economic Supplement). This method can be used to reconcile bins between data sets or across years and handle top incomes. REDI has other advantages for computing values of an income distribution that is nonparametric, bin consistent, area and variance preserving, continuous, and computationally fast. The author provides proof of concept using two years of the American Community Survey. The method is available as the redi command for Stata.

Despite the long history of racial hostility, African Americans after 1990 began moving from the city of Detroit to the surrounding suburbs in large numbers. After World War II, metropolitan Detroit ranked with Chicago, Cleveland, and Milwaukee for having the highest levels of racial residential segregation in the United States. Detroit’s suburbs apparently led the country in their strident opposition to integration. Today, segregation scores are moderate to low for Detroit’s entire suburban ring and for the larger suburbs. Suburban public schools are not highly segregated by race. This essay describes how this change has occurred and seeks to explain why there is a trend toward residential integration in the nation’s quintessential American Apartheid metropolis.

We study the extent of automation angst and its role for policy preferences, labor market choices and real donation decisions using a customized survey in Germany and the US. We first document that a majority perceives automation as a major threat to overall employment and as a cause of rising inequality, whereas less than a third is concerned about their own labor-market prospects. We find evidence that automation angst is strongly associated with people's trust in governments and general political beliefs, especially in the US. At the same time, automation angst is associated with preferences for more policy interventions and also relates to stated and actual behavior. Using randomized survey experiments, we find that scientific information about zero net employment effects of automation, on average, reduce related concerns. Yet, treatment responses are multidimensional and depend on prior beliefs about the future or work. This translates into heterogeneous and sometimes even opposing effects on policy preferences and individual behavior.

Considerable interest has been placed on the subject of what to do with the sizable undocumented population currently residing in the United States. Having a full understanding of the size of this population and the impact immigration policy has on them is of critical importance to policy makers. Data limitations in nationally-representative surveys have limited the analyzes of the effects of immigration policy in the academic literature. As such, this dissertation consists of three essays contributing to the literature on the impact of immigration policy and on identifying unobserved populations. In my first essay, I examine the labor market response of undocumented youth that participated in the Deferred Action for Childhood Arrivals (DACA) program which provides them temporary deportation relief and work authorization. I use data from the U.S. Citizenship and Immigration Services to construct a probabilistic measure for unobserved DACA participation. Using the American Community Survey (ACS), I estimate a twosample model of the effect of participating in the DACA program. I also estimate spillover effects of DACA on eligible but non-participating undocumented youth. I find that DACA significantly improved labor market and education outcomes of DACA recipients, with magnitude of the treatment-on-the-treated effects at least twice as large as the intent-to-treat estimates obtained from using only the observed eligibility indicator typically used in literature. Evidence of a negative spillover effect on eligible non-participants is documented with a decrease in labor force participation and school attendance. My second essay considers nonsampling error due to item nonresponse in the estimates of the size and legal composition of the foreign-born population produced using the ACS. The standard practice to address item nonresponse is to impute values under the assumption that nonresponse is conditionally random. I form credible interval estimates that make no assumptions about the values of missing data by considering all uncertainty due to item nonresponse. Without this assumption, the size of the foreign-born population in the US falls somewhere between 40.4 and 59.4 million as of 2019 compared to the Census estimate of 44.9 million. Bounding estimates of the size of the undocumented population fall between 7.3 and 23.3 million compared to the widely accepted estimate of 11 million undocumented immigrants. In my third essay, I return to analyzing the effects of the DACA program. I examine misclassification bias arising from item-nonresponse in the estimated intent-to-treat effects of the DACA program. Assigning DACA eligibility is based on the responses to specific demographic questions, any of which the individual may not respond to. If the assumption that nonresponse to these questions are conditionally missing fails, this can lead to traditional misclassification bias and attenuate the results when using imputed values. Adjusting for potential misclassification bias by removing non-respondents leads to estimates of the intent-to-treat effects of DACA on labor market outcomes that are 22% to 77% higher than when including non-respondents, depending on the outcome of interest.

Social scientists have frequently attempted to assess the relative contribution of age, period, and cohort variables to the overall trend in an outcome. We develop an R package APCI (and Stata command apci) to implement the age-period-cohort-interaction (APC-I) model for estimating and testing age, period, and cohort patterns in various types of outcomes for pooled cross-sectional data and multi-cohort panel data. Package APCI also provides a set of functions for visualizing the data and modeling results. We demonstrate the usage of package APCI with empirical data from the Current Population Survey. We show that package APCI provides useful visualization and analytical tools for understanding age, period, and cohort trends in various types of outcomes.

The COVID-19 crisis elevated the importance of private vehicles. The pandemic drove riders off public transit and spawned additional car-based activities such as drive-through testing and vaccinations and curbside pick-ups. Yet millions of low-income and non-white households do not own vehicles. This chapter draws on a unique credit panel dataset to examine automobile debt and delinquency in California. In particular, we examine whether automobile debt patterns during the pandemic differed from those during and coming out of the Great Recession (December 2007–June 2009). We also analyze the response to the COVID-19 recession across neighborhoods by income and race. Similar to the situation during the Great Recession, we find that the number of automobile loans per borrower declined. While the automobile debt burden (the ratio between total automobile debt and aggregate income) also declined, it fell far less during the pandemic than during the Great Recession. Moreover, automobile loan delinquencies spiked during the Great Recession but instead continued to drop during the pandemic. Finally, the COVID-19 crisis affected consumers differently by both race and income. Automobile debt burden rose in low-income, Latino/a, and Black neighborhoods, a pattern that preceded but continued unabated during the pandemic. The findings suggest that COVID-19 relief may have helped some families manage their automobile-related expenditures. However, other factors, such as increasing automobile prices, likely contributed to growing debt burdens, a potential source of financial distress.

Official poverty estimates for the United States are presented annually and are reported with a considerable lag. The COVID-19 pandemic highlighted the problematic nature of this lag, prompting researchers to develop methods to provide more timely estimates of poverty that could be used to understand economic need, and the impact of the government’s response, in closer to real time. One monthly poverty measure, introduced in Parolin, Curran, Matsudaira, Waldfogel, and Wimer (2022; hereafter PCMWW), provides monthly estimates of poverty based on a family unit’s likely monthly income. A separate measure, introduced in Han, Meyer, and Sullivan (2020; hereafter HMS), produces monthly updates of annual poverty estimates based on a survey question asking individuals about their families’ total cash income over the past 12 months.

The spatialization of violent crime is explored in two large case studies, Chicago and Sydney, using spatial econometric methods and macro-sociological variables derived from Social Disorganization Theory. Social Disorganization Theory (SDT) is introduced in terms of its formulation in response to highly specific conditions arising in Chicago, as well as its adoption of methodological and theoretical developments from existing traditions. This specificity belies its breadth of application and enduring presence in criminology. With “Social Disorganization Theory” hosting a wealth of highly nuanced academic dialogue conducted under its banner, current incarnations of SDT appear as branches on an evolutionary tree. This research addresses the theoretical roots of that tree, from which two primary benefits are derived. The first is that the resulting focus on macro-structural variables permits large-scale urban studies to be conducted with existing datasets. The second is that this effectively isolates the spatial analysis from specific theoretical developments and generalizes the results. The difference from the classical formulation of SDT is that an “augmented” set of five variables is used as independent variables: disadvantage, population heterogeneity, residential mobility, family disruption and urbanization. Criminal violence forms the dependent variable. All variables are observed to exhibit spatial autocorrelation. In response, Spatial Durbin Models are selected for each case study. This selection is supported by diagnostics, with some qualification noted. Initial results suggest a basis for further exploration. In Sydney, this leads to a fully mediated model with family disruption as the mediator. In the case of Chicago, the strong landscape of segregation leads to a model which accommodates for the resulting structural instability. This introduces a model which provides separate treatment to highly homogeneous areas. Results indicate mixed support for SDT. With the exception of heterogeneity and – to a lesser extent – urbanization, variables broadly align with expectations derived from SDT in the initial Sydney and Chicago studies. However, these observations are muted by other outcomes. Firstly, the spatial complexity portrayed in the results is not formally conveyed by SDT. Using the argument that methodology both enables and constrains theory development, this is to be expected. Social Disorganization Theory is credited with founding the ecological tradition in criminology. However, concepts of criminogenic place which have evolved from it are typically intertwined with co-location models of space. This is regarded as a limitation in which spatial autocorrelation is treated as a nuisance, rather than being theoretically embraced. That gap is highlighted by spatially rich results. The second threat to SDT in results from the Sydney case study is that a more parsimonious model is derived from family disruption alone. Furthermore, when disruption is employed as a mediator, full mediation is observed. The final response to SDT is that it does not accommodate structural instability as indicated in Chicago. The results of this exploratory study offer insights into the spatial richness of violence in urban areas from an ecological perspective. This complexity poses a challenge to SDT. The thesis closes by discussing this challenge and includes an outline of proposed future work.

In light of growing difficulties for schools to attract teachers in science, technology, engineering, and mathematics (STEM) fields and the continued discussions surrounding the unionization of education, this paper examines the effect of collective bargaining (CB) laws on the salary of teachers with a STEM degree. To identify the effect, we examine the salary differentials between otherwise similar STEM teachers located in the same labor market but exposed to differential CB rights. Our results show that the bargaining laws increase female STEM teachers' salaries by 7 percentage points. In exploring a potential mechanism, we demonstrate that female teachers in mandatory CB states accrue more years of experience.

Since 1980, US wage growth has been fastest in large cities. Empirically, we show that most of this urban-biased growth reflects wage growth at large Business Services firms, which are also the most intensive users of ICT capital in the US economy. We provide an explicit economic mechanism whereby ICT is more complementary with labor at larger firms. Quantitatively , we find that with such a complementarity, the observed decline in ICT prices alone can account for most of the urban-biased growth, since Business Services firms in big cities tend to be large.

Does a universal basic income (UBI) affect voter turnout? This article argues that the introduction of an unconditional cash payment-where citizens receive money independent of employment status, age, or indigence-can have a turnout-enhancing effect. I evaluate the argument using the introduction of the Permanent Fund Dividend in Alaska. Differences-indifferences estimates covering November general elections from 1978 to 2000 provide compelling evidence that the Alaskan UBI has a significant positive effect on turnout. The results further suggest that the turnout increase was not a one-off effect but persists over a period of almost 20 years. Thus, a UBI has the potential to positively affect turnout among an entire electorate, adding to the discussion around potential welfare reforms in western democracies.

OBJECTIVES: The COVID-19 pandemic has profoundly affected the lives of people globally, widening long-standing inequities. We examined the COVID-19 pandemic's impact on employment conditions by race/ethnicity, gender, and educational attainment and the association between such conditions and well-being in older adults in the United States. METHODS: Using data from the Health and Retirement Study respondents interviewed between May 2020 and May 2021 when they were ≥55 years of age, we examined intersectional patterns in COVID-19-related changes in employment conditions among 4,107 participants working for pay at the start of the pandemic. We also examined the compounding nature of changes in employment conditions and their association with financial hardship, food insecurity, and poor self-rated health. RESULTS: Relative to non-Hispanic White men with greater than high school education (>HS), Black and Latinx men and women were more likely to experience job loss irrespective of education; among those who did not experience job loss, men with ≤HS reporting Black, Latinx, or "other" race were >90% less likely to transition to remote work. Participants who experienced job loss with decreased income or continued in-person employment with decreased income/shift changes had greater prevalence of financial hardship, food insecurity, and poor/fair self-rated health than others. DISCUSSION: The impact of COVID-19 on employment conditions is inequitably patterned and is associated with financial hardship, food insecurity, and adverse health in older adults. Policies to improve employment quality and expand social insurance programs among this group are needed to reduce growing inequities in well-being later in life.

This article examines the ways in which culture plays a role in the gender division of household labor. To explore this issue, the study uses data on early-arrival first- and second-generation immigrants living in the United States who have a married/unmarried partner present in the household. Because all of these individuals have grown up under the same laws, institutions, and economic conditions that prevail in the US, the differences between them in the gender division of housework may be attributed to cultural differences in their countries of ancestry. The study finds that the stronger the culture of gender equality in the country of ancestry, the greater the equality in immigrants’ current division of housework. This result is maintained when considering both housework and childcare as household labor. This work is extended by examining how culture operates and is transmitted, and whether culture may influence the work–life balance.

Despite extensive research on the changes in partnership formation patterns in the United States over the past few decades, we know relatively little about how Asians are approaching marriage and cohabitation in early adulthood. Using the 2014–2018 American Community Survey, we examine whether Asians are delaying marriage and whether their postponement of marriage is offset by a rise in cohabitation. When doing so, we pay close attention to variations by ethnicity and immigrant generational statuWhaley, K. (2020). An Economic Study of US Housing and Education Policy. https://tigerprints.clemson.edu/all_dissertationss. We find that there is a substantial generational decline in marriage among Asians, accompanied by a relatively small increase in cohabitation. Thus, it is likely that 1.5-generation and U.S.-born Asians are waiting longer to enter coresidential partnerships than Asians who immigrated to the U.S. after age 12 and U.S.-born whites. Also, there are ethnic variations: cohabitation is rare among Indians whereas it is more common for Japanese and Filipino/as. The distinct patterns of 1.5-generation and U.S.-born Asians suggest that they are selectively acculturating. Overall, our findings demonstrate that Asians should not be treated as a monolithic group when studying their demographic and social patterns.

Background: In patients with atherosclerotic cardiovascular disease (ASCVD), barriers related to transportation may impair access to care, with potential implications for prognosis. Although few studies have explored transportation barriers among patients with ASCVD, the correlates of delayed care due to transportation barriers have not been examined in this population. We aimed to examine this in U.S. patients with ASCVD using nationally representative data. Methods: Using data from the 2009-2018 National Health Interview Survey, we estimated the self-reported prevalence of delayed medical care due to transportation barriers among adults with ASCVD, overall and by sociodemographic characteristics. Logistic regression was used to examine the association between various sociodemographic characteristics and delayed care due to transportation barriers. Results: Among adults with ASCVD, 4.5% (95% CI; 4.2, 4.8) or ∼876,000 annually reported delayed care due to transportation barriers. Income (low-income: odds ratio [OR] 4.43, 95% CI [3.04, 6.46]; lowest-income: OR 6.35, 95% CI [4.36, 9.23]) and Medicaid insurance (OR 4.53; 95% CI [3.27, 6.29]) were strongly associated with delayed care due to transportation barriers. Additionally, younger individuals, women, non-Hispanic Black adults, and those from the U.S. South or Midwest, had higher odds of reporting delayed care due to transportation barriers. Conclusions: Approximately 5% of adults with ASCVD experience delayed care due to transportation barriers. Vulnerable groups include young adults, women, low-income people, and those with public/no insurance. Future studies should analyze the feasibility and potential benefits of interventions such as use of telehealth, mobile clinics, and provision of transportation among patients with ASCVD in the U.S.

Introduction: Pasifika (Native Hawaiian and Pacific Islander) people living in the United States experience health, economic, and social inequities, and a disproportionate burden of COVID-19 cases and deaths. This study examines employment among Pasifika living in the 10 US states with the largest Pasifika populations during the COVID-19 pandemic. Methods: We use the Current Population Survey to examine racial differences in employment status, paid work from home (PWFH), and industry telework friendliness. We use data from the Washington Office of Fiscal Management and the Washington State (WA) Employment Security Department to examine county-level unemployment claims. Results: Nationally, Pasifika did not self-report unemployment significantly more than Black, Latino, Asian, and American Indian/Alaska Native respondents, but in WA counties with high Pasifika concentrations, unemployment insurance claim rates were higher compared with all other racial groups, particularly Whites and Asians. Surprisingly, Pasifika had more PWFH opportunities, but worked in less telework-friendly industries nationally. Discussion: This study demonstrates the complexity of employment among Pasifika during the COVID-19 pandemic. The findings correspond with national reports of racialized communities impacted by unemployment, including Pasifika. Marginally significant differences in unemployment nationally may be due to Pasifika working largely in essential industries requiring workplace attendance. Health Equity Implications: Although overlooked or overshadowed by size, our findings highlight the need for continued advocacy to support data disaggregation and Pasifika data sovereignty. This can be achieved through collaborations between researchers as well as local and community organizations to address data needs of Pasi-fika communities.

Growing ethnoracial diversity is a defining characteristic of the America’s urban and suburban populations. Here we shift attention to growing but underappreciated diversity in rural areas and use U.S. Census data from 1980 to 2020 to investigate trends in rural diversity. We compare three rural Americas: metropolitan expansion counties (i.e., growth at the metro fringe), emergence counties (recently reclassified small metropolitan areas), and left behind counties (consistently rural places). We estimate exposure to diversity across ethnoracial groups and produce counterfactual estimates that highlight the unique contributions of each group’s growth and decline to overall diversity. Although diversity is increasing across county types, the demographic profiles of expansion and emergence counties remain similar to left behind counties. White and minority exposure to diversity is increasing overtime, with disparities in exposure having converged overtime. Finally, rural diversity also is increasing due to both growing multiracial populations and White population decline.

The effect of negative shifts in public opinion on the economic lives of minorities is unknown. We study the role of racial bias in the U.S. labor market by investigating sudden changes in public opinion about Asians following the anti-Chinese rhetoric that emerged with the COVID-19 pandemic, and associated changes in employment status and earnings. Using Current Population Survey (CPS) data from January 2019 to May 2021, we find that, unlike other minorities, Asians who worked in occupations or industries with a higher likelihood of face-to-face interactions before the pandemic were more likely to become unemployed afterwards. Consistent with a role for public opinion affecting labor market outcomes, we find that the effects are larger in magnitude in strongly Republican states, where anti-Asian rhetoric might have had more influence. Additionally, we show that, while widespread along the political spectrum, negative shifts in views of Asians were much stronger among those who voted for President Trump in 2016 and those who report watching Fox News channel.

People with disabilities are at increased risk of food insecurity (FI). In the United States, the Current Population Survey is used to provide official FI estimates. Other federal surveys that include disability and FI measures as well as additional health, housing, nutrition, and functioning measures offer opportunities to examine determinants of FI more fully for people with disabilities. We estimate FI prevalence for people with disabilities across four surveys, finding that all sources identify disability as a risk factor for FI, with some variation in estimates. We identify policy-specific areas each source could inform and suggest avenues for future research.