The US H-1B visa for highly skilled immigrant labor and the accompanying H-4 visa for their dependents lead to structural constraints that exclude dependents from the labor force. This paper investigates the economic consequences of the US immigration policy that decouples work and admission permission for H-4 visa holders. Using a pool of likely H-1B recipients who were recruited through firms’ job offers, we find that—despite labor force restrictions—the vast majority of married H-1B recipients are accompanied by their spouses. This is particularly the case for male H-1B recipients, making wives rather than husbands carry most of the burden associated labor force exclusions. Using a matched sample of married immigrants with work authorization, we estimate labor force participation probabilities and wages for the pool of likely dependent spouses if they were not facing work restrictions. We find that the policy-imposed labor force exclusion of H-4 spouses leads to substantial losses of spouses’ earnings of about (2014) US$28,000 per capita, which—in the aggregate—implies a sizeable productivity loss for the US due to its restrictive US immigration policy.

Immigration policy can have important net fiscal effects that vary by immigrants’ skill level. But mainstream methods to estimate these effects are problematic. Methods based on cash-ow accounting oer precision at the cost of bias; methods based on general equilibrium modeling address bias with limited precision and transparency. A simple adjustment greatly reduces bias in the most influential and precise estimates: conservatively accounting for capital taxes paid by the employers of immigrant labor. The adjustment is required by rms’ prot-maximizing behavior, unconnected to general equilibrium effects. Adjusted estimates of the positive net fiscal impact of average recent U.S. immigrants rise by a factor of 3.2, with a much shallower education gradient. They are positive even for an average recent immigrant with less than high school education, whose presence causes a present-value subsidy of at least $128,000 to all other taxpayers collectively.

Mental illness is more prevalent among African Americans than their Non-Hispanic White counterparts; however, this population is less inclined to receive behavioral treatment. The purpose of this study was to examine the association between perceived social support, substance use, and gender with mental health care services utilization among African Americans. The social ecological model and social support theory grounded this study. The research design was a quantitative cross-sectional analysis of the 2016 Behavioral Risk Factor Surveillance Survey. The sample consisted of 486,3030 African American adults that represented the U.S. population using weighted estimates. The overall logistic regression models for the 3 research questions were significant (p = .000). Controlling for sociodemographic factors, logistic regression analyses indicated that receiving emotional and social support predicted (OR=2.294) use of mental health care services in the last 12 months. Similarly, not using substances in the prior 30 days (OR=1.309) and being female (OR=2.562) predicted use of mental health care services in the last 12 months. The findings from this study may be used to increase awareness among mental health providers to refer African Americans to emotional and social support resources. The findings may lead to positive social change through the development of interventions for those who use substances and for men.

I analyze the impact of the CARES Act unemployment subsidy on US income and inequality in the first month of the COVID-19 crisis using March-April Current Population Survey data. I then use monthly industry unemployment data to extend this panel to July. Next, I estimate the impact of the expiration of the CARES Act subsidy on average income and inequality. Finally, I extend the panel to November to simulate the effects of proposed HEALS and HEROES Acts. I find the CARES Act subsidy was effective at increasing average income above pre-crisis levels and reducing inequality. The expiration of the CARES Act subsidy caused a decrease in average income and increase in inequality relative to pre-crisis levels. I find the proposed HEALS legislation will return inequality to near pre-crisis levels, while the proposed HEROES Act will result in higher income and lower inequality than existed before the crisis.

This paper analyzes the contribution of import competition to the regional divergence among US metropolitan areas over recent decades. I document that the sharp rise in imports of Chinese manufacturing goods had a significant effect on the spatial skill polarization and the divergence of college wage premium among local labor markets. The effects of the China trade shock were systematically different depending on the skill intensity of local services. Among regions with skill-intensive services, a higher exposure to import competition in manufacturing increased the number and wages of collegeeducated workers. The negative effects of the China shock concentrated in exposed regions with a low density of college-educated workers. The heterogeneous effects of import competition explain one third of the spatial skill polarization and one fourth of the divergence in college wage premium. I show that the contribution of the trade shock operates through the reallocation of workers across sectors and regions. Using a novel measure of “labor market exposure to the China shock”, I document that service industries expand when local manufacturers face import competition. High human capital regions exposed to the China shock undergo a faster transition from manufacturing to skill-intensive service industries and attract college-educated workers from other locations.

We document how lifetime utility varies by demographic groups in the US and how these differences have evolved since the start of the 21st century. Using the equivalent variation as our measure of welfare we find that the standard deviation in cross-sectional well being between demographic groups is quite large at around 11 percent, which is comparable to the standard deviation of the logged annual income in prime earning years and more than double the standard deviation of logged consumption. Our metric includes consumption, leisure, mortality risk, and within-group inequality. The results are primarily driven by differences in consumption and life expectancy. Controlling for other demographics, welfare is increasing in educational attainment and is higher for women and those of Asian descent. This qualitative ordering is robust to classifying a broad measure of home production and child care as work and various definitions of real consumption. Finally, we show that changes in mortality rates associated with ‘deaths of despair’ disproportionately lower the welfare of less educated Whites.

Electricity was the catalyst for the second industrial revolution in the early twentieth century. Developing countries are currently making huge investments in this general-purpose technology, with a view to achieving structural change. What can history teach us about its impact on the structure of employment? We use U.S. Census data and an identification strategy based on hydroelectric potential to identify the effects of the geographic expansion of higher-voltage electricity lines. We find that, over the period 1910–1940, electrification increased the share of operatives in the average county by 3.5 percentage points and decreased the share of farmers by 2.9 percentage points. These effects are primarily driven by rural electrification, and they can account for more than half of the aggregate increase in operatives, and more than one quarter of the total decrease in farmers. These results suggest that electrification was a key contributor to U.S. structural transformation.

Background Psychological distress and self-rated health status may create additional complexities in patients already diagnosed with breast cancer. This study aims to assess the association of self-report-based assessment of psychological distress and self-rated health on survival times among women with breast cancer diagnoses. Methods Seventeen-year data from the Integrated Public Use Microdata Series—National Health Interview Survey (IPUMS-NHIS) were pooled and analyzed. Women who were aged 30 to 64 years old, with breast cancer diagnosis were selected (n = 2,819). The outcome variable was time to death. The independent variables were self-reported assessment of psychological distress and self-rated health. Psychological distress was defined using the Kessler-6 scale while self-rated health was measured on a 3-point Likert scale: Poor, Fair, and Good-to-Excellent (referred to as good for brevity). We computed unadjusted and adjusted hazard ratios (HR) using Cox-Proportional Hazard regression models with sociodemographic characteristics and measures of health care access used as potential confounders. Significance was set at alpha = 0.05. Results Women with breast cancer assessed as having psychological distress had 46% (Adjusted HR: 1.46; 95% CI: 1.02–2.09) increased risks of mortality. Also, women who rated their health as poor or fair had a significantly elevated mortality risk (Poor Health: Adjusted HR: 3.05; 95% CI: 2.61–4.69; Fair Health: Adjusted HR: 1.83; 95% CI: 1.43–2.35) as compared to women with good health status. Conclusions Self-reported psychological distress and fair and poor self-rated health are associated with reduced survival times among women with breast cancer diagnoses.

This dissertation consists of three chapters. In the first chapter, I analyze credible intervals for quantiles constructed using Bayesian bootstrap techniques and show that credible intervals constructed using the “continuity-corrected” Bayesian bootstrap (Banks, 1988) have frequentist coverage probability error of only O(n−1). In addition, I show that these “continuity-corrected” Bayesian bootstrap credible intervals achieve the same frequentist coverage probability as the frequentist confidence intervals of Goldman and Kaplan (2017), up to some error term of magnitude O(n−1). Furthermore, I demonstrate that credible intervals constructed using the “continuitycorrected” Bayesian bootstrap have less frequentist coverage probability error than those constructed using the Bayesian bootstrap (Rubin, 1981). In the second chapter, I investigate three strikes laws, which mandate sharply increased sentences for criminals who commit a specific number of felonies. Specifically, I analyze the effect of these laws on violent crime rates using municipal-level data from the FBI. I compare violent crime rates of border municipalities in states with differing treatment statuses using a difference-in-differences specification with a sample matched on pre-treatment outcomes. I find no statistical evidence that three strikes laws reduce violent crime rates. I rule out reductions in violent crime rates greater than 1.3% and reject the hypothesis that three strikes laws reduce violent crime rates at the 5% significance level. Additional analyses and robustness checks support my main findings.vii In the third chapter, I examine medical marijuana laws (MMLs), which legalize the use, possession, and cultivation of marijuana by individuals with qualifying medical conditions. Namely, I employ municipal-level data from the FBI to analyze the effect of MMLs on violent crime rates. I compare municipalities in border regions with different treatments statuses using a difference-in-differences specification with a sample matched on pre-treatment outcomes. I find a lack of evidence for MMLs increasing violent crime rates, but I cannot eliminate the possibility of small-to-medium positive effects. However, I rule out increases in violent crime rates greater than 9.9% and reject the hypothesis that MMLs increase violent crime at the 10% significance level.

In this project, we examine employers’ response to Black immigrants compared to native-born Black Americans. Between July 2017 and December 2018, we applied to publicly advertised positions using fictitious resumes that are manipulated on perceived race and ethnicity (Somali American, African American, and white American). We examine the proportion of resumes that are contacted by employers. We find that male African American applicants are 5 percentage points less likely to be contacted than equivalent white American applicants. Somali American applicants are 11 percentage points less likely to be contacted by employers than equivalent white American applicants and 6 percentage points less likely to be contacted than equivalent African American applicants. For female applicants, the effects followed a similar pattern, but were muted. Signals of language ability, education, and religiosity showed little impact on the proportion contacted by an employer.

We estimate the long-run effects of the 1930s Home Owners Loan Corporation (HOLC) redlining maps on census tract-level measures of socioeconomic status and economic opportunity from the Opportunity Atlas (Chetty et al., 2018). We use two identification strategies to identify the long-run effects of differential access to credit along HOLC boundaries. The first compares cross-boundary differences along actual HOLC boundaries to a comparison group of boundaries that had similar pre-existing differences as the actual boundaries. A second approach uses a statistical model to identify boundaries that were least likely to have been chosen by the HOLC. We find that the maps had large and statistically significant causal effects on a wide variety of outcomes measured at the census tract level for cohorts born in the late 1970s and early 1980s.

As the United States approaches a “minority majority,” such that over 50% of the population will be part of a minority racial or ethnic group, understanding the effects of ethnic diversity becomes increasingly important. The present work examined if areas that have greater ethnic diversity, i.e., context diversity, will show greater support for the Democratic candidate in presidential elections. As a function of cognitive liberalization, areas with a more ethnically diverse population are predicted to evidence greater cognitive flexibility, in line with the United States Democratic party ideals. If this is the case, then areas with greater context diversity may be expected to vote at a higher rate for the Democratic presidential candidate than areas with lesser context diversity. Ethnic diversity was assessed at the county level using the context diversity measures of Black representation, variety, and integration, computed with the American Community Survey 5-year data. Cognitive liberalization was represented by county-level Need for Cognitive Closure from Project Implicit’s public Race IAT dataset. County-level vote for Obama in 2008 and 2012 is publicly available. Metrics for each election year were averaged within a county and only those that had valid and complete metrics for both 2008 and 2012 were retained for analysis (N = 297). Results indicated that, as predicted, places that had ethnic groups more equally represented had lower needs for closure and, in turn, voted in higher proportions for Obama. Places that were more integrated, however, had higher needs for closure, and in turn voted in lower proportions for Obama. The proportion of people in a county who were Black had mixed relationships. It is suggested that proportional equivalence of ethnic groups may not evoke threat and therefore has positive impacts on cognitions, whereas where groups live may have the opposite effect. This thesis suggests that community ethnic diversity can impact both norms, in the form of need for closure, and collective behaviors, in the form of voting. Further, different forms of community diversity may support multiple structures of interaction. Complex environments appear to impact everyday behaviors in equally complex ways.

Despite the general closure of gender disparities in the labor market over the past half century, occupational segregation has been stubbornly persistent. I develop a new model that explains these occupational outcomes through marriage market signaling. Vertically differentiated men have preference over women's unobservable caregiving ability. Heterogenous women choose caregiving occupations to signal their ability to be caregivers. My model generates unique predictions on the influence of marriage market conditions on women's occupational choices. I find empirical support for these predictions using longitudinal data on marriage rates, policy shocks to divorce laws, and shocks to the marriage market sex ratio driven by waves of immigration.

The COVID-19 pandemic saw the percentage of employed Americans enrolled in college hit a new low in 2020, continuing a downward trend that began in 2016. While an uptick toward the end of the year saw enrollment bounce back to pre-pandemic levels, it's unlikely to be sustained as education costs continue to rise and employers continue to demand more from workers.

The authors investigate the training choices made by workers entering the Trade Adjustment Assistance program and their postexit outcomes. This is important as more workers enter these types of programs due to technological change and globalization. Their study shows that workers that choose a training occupation beyond their skill level (skill overshooting) achieve higher earnings ($615 annually) and wage replacement rates (2.0 percentage points) at the cost of lower reemployment rates (−1.9 percentage points) immediately following program exit. An investigation of subsamples shows that skill overshooting is especially beneficial to females and those living in rural areas with earnings gains of $1,443 and $1,080, respectively, without hurting their chances of reemployment.

Environmental justice scholarship argues state power perpetrates environmental inequalities, but less is known about the U.S. Military’s impact on local urban environmental inequalities. To evaluate the role of the military in contributing to environmental health disparities, I draw on the case study of Las Vegas, Nevada, a southwestern city with active military sites. The analysis uses environmental health, demographic, and GIS data from federal and county agencies. Findings from spatial error models support environmental inequality and treadmill of destruction hypotheses by demonstrating that census tracts in closer proximity to military areas have greater estimated cancer risk from air toxics. Census tracts with a higher percent of poor and Latinx residents, independent of their proximity to military areas, have an additional increase in exposure to air pollution. The case study of Las Vegas offers important lessons of environmental injustice on Latinx environmental health vulnerability and military sites in urban areas.

Income inequality in the United States has been growing since the 1980s and is particularly noticeable in large urban areas like the Chicago metro region. While not as high as New York or Los Angeles, the Gini Coefficient for the Chicago metro area (.48) was the same as the United States in 2015 but rising at a faster rate, suggesting it will surpass the US national level in 2020. This chapter examines the Chicago region’s growing income inequality since 1980 using US Census data collected in 1990, 2000, 2010, and 2015, focusing on where people live based on occupation as well as income. When mapped out, the data shows a city and region that is becoming more segregated by occupation and income as it becomes both richer and poorer. A result is a shrinking number of middle-class and mixed neighbourhoods. The resulting patterns of socioeconomic spatial segregation also align with patterns of racial/ethnic segregation attributed to historical housing development and market segmentation, as well as recent efforts to advance Chicago as a global city through tourism and real estate development.

Unauthorized immigration, already a divisive and controversial subject in American society, was reframed as a grave national security threat after the terrorist attacks on Sept. 11, 2001. Yet, despite substantial public, political and policy attention to the issue of undocumented immigration and terrorism, there has been relatively little empirical assessment of the relationship between unauthorized immigration flows and terrorist activity. We attempt to fill this gap by combining newly developed estimates of the unauthorized population, a novel use of sentencing and prosecutorial data to measure terrorism-related activity, and multiple data sources on the criminological, socioeconomic, and demographic context from all 50 states from 1990 to 2014. We then leverage this unique dataset to examine the longitudinal, macro-level relationship between undocumented immigration and various measures of terrorism. Results from fixed effects negative binomial models suggest that increased undocumented immigration over this period is not associated with terrorist attacks, radicalization, or terrorism prosecutions.

The current natural gas and oil boom in North America requires new pipelines, which pose environmental risks from wellheads to their destinations. The environmental justice literature suggests that ethno-racial minorities, populations with low socioeconomic status, and rural communities are disproportionally exposed to risks associated with potentially harmful land uses. Using data from the American Community Survey’s 2015 five-year estimates and data on the route of proposed pipelines compiled by The FracTracker Alliance, this study tests whether the above assumptions are true for proposed FERC-permitted natural gas transmission pipelines in the United States for which planned routes have been made available. The results of logistic regression models provide only limited, and in some cases contradictory, support for these hypotheses. Although an increased share of highly educated residents significantly decreases the likelihood of a pipeline proposal in a census tract, a higher poverty rate also significantly lowers this probability. Likewise, the share of Black and Hispanic residents is significantly and negatively associated with pipeline proposals. However, reliable routing data are needed to test whether this holds true for built pipelines, but these data are considered confidential and thus inaccessible in the United States.

Children require a large amount of time, effort, and resources to raise. Physical help, financial contributions, medical care, and other types of assistance from kin and social network members allow couples to space births closer together while maintaining or increasing child survival. We examine the impact of kin availability on couples’ reproductive success in the early twentieth-century United States with a panel data set of over 3.1 million couples linked between the 1900 and 1910 U.S. censuses. Our results indicate that kin proximity outside the household was positively associated with fertility, child survival, and net reproduction, and suggest that declining kin availability was an important contributing factor to the fertility transition in the United States. We also find important differences between maternal and paternal kin inside the household—including higher fertility among women residing with their mother-in-law than among those residing with their mother—that support hypotheses related to the contrasting motivations and concerns of parents and parents-in-law.