This dissertation consists of three essays related to environmental justice. The first essay examines the impact of decarbonization of the US electric grid on air quality and assesses how the health benefits of better air quality will be distributed among people of different ages and races. This work was done for the contiguous US at the county level. These benefits are estimated through three regulatory-grade models: Integrated Planning Model (IPM), Community Multiscale Air Quality Modeling System (CMAQ), and Environmental Benefits Mapping and Analysis Program (BenMAP). Air quality improvements and health gains (premature deaths avoided) are reported for the years 2030, 2040, and 2050. Most of the PM2.5 and O3 reductions are concentrated in the Eastern US. Black communities experience the largest improvement in air quality compared to all other races. For health benefits, I find that Whites have the largest benefits in terms of absolute numbers, but when appropriate race-specific mortality incidence rates are used and population-weighted race-age decomposition is conducted, Blacks have 20% larger gains compared to Whites in age group 25-74. Moreover, when premature deaths averted are converted to life years, I find that disparity in health benefits between age groups is sharply reduced, shifting 2.86 percentage points of the total gains from Whites to Blacks. Age-race decomposition analysis for decarbonization of US electric grid thus suggests improvement in environmental justice. The finding from this paper can help policymakers understand how health disparities are reduced with respect to age and race due to decarbonization. In the second essay I examine how improved air quality due to the decarbonization of the US power sector can reduce asthma exacerbation among children disaggregated by poverty status, race, and geography. These benefits are estimated through three regulatory-grade models: Integrated Planning Model (IPM), Community Multiscale Air Quality Modeling System (CMAQ),

A series of recent, high-profile controversies have brought renewed attention to Black Hebrew Israelism (BHI), the belief that modern-day American blacks are descended from the ancient Israelites, while-according to some, but not all, Black Hebrew Israelites-modern-day Jews are not. BHI has been associated with both violence and antisemitism. But relatively little is known about its prevalence, or about how predictive BHI views are of antisemitism and support for violence. This report provides details on the prevalence and correlates of Black Hebrew Israelism, based on an original survey of 1,075 black Americans and 555 nonblack Americans. It finds: • Roughly 26% of the black population, and 14% of the nonblack population, plausibly profess to believe that modern American blacks are descended from the ancient Israelites, the key belief of Black Hebrew Israelism. • Roughly 9% of blacks and 3% of nonblacks credibly profess these beliefs and identify as "Hebrew Israelites." • Profession of these beliefs and identification as a Hebrew Israelite is associated with warmer feelings toward Jews. But it is also associated with a greater willingness to agree with antisemitic beliefs, such as the claim that Jews have greater loyalty to Israel, that boycotting Jewish businesses over Israel's actions is justified, and that Jews were involved in the slave trade. • Profession of BHI beliefs/identification is suggestively associated with greater support for political violence, but it is associated with greater support for interpersonal violence only among nonblack BHI believers. These findings suggest that Black Hebrew Israelism-at least in some of its more radical manifestations-may be one factor contributing to rising extremism and antisemitism.

In this dissertation, I conduct three studies examining how evolving gender norms, economic factors, and other structural forces shaped marriage dynamics among different-sex couples in the United States between the 1990s and the 2010s, each involving analysis of data from the U.S. Current Population Survey. In the first study, I examine the rising proportion of sole-breadwinner married couples in the U.S., analyzing changes in the predictors of such arrangements to better understand the structural forces that might push couples toward a single-earner setup even as individuals increasingly express egalitarian gender ideals. I find that male-sole-breadwinner arrangements have become particularly more likely among couples with young children or multiple children; that men who work in professional or managerial occupations or regularly work 50 or more hours per week have been increasingly likely to serve as sole breadwinners; and that sole-breadwinner couples, whether the sole earner is female or male, have become increasingly economically disadvantaged over time.

Since the War on Poverty in the 1960s, the U.S. social safety net has shifted away from direct cash assistance for the lowest-income households and toward tax-based transfers targeted at working families with children. Previous research has assessed this shift by evaluating its effect on the national poverty rate. Doing so, however, overlooks how it may also have led to increased inequality among low-income households. We apply a decomposition framework to measure how changes in taxes/transfers and composition have affected trends in inequality below the poverty line from 1967 to 2019. Income inequality among the poorest households has been volatile since the 1960s, and changes to the American welfare state played a decisive role in expanding or reducing inequality below the poverty line. Unlike in previous decades, after the mid-1990s, the policies that most reduced poverty were also those that most increased inequality among the poor. These findings challenge standard theories regarding the effectiveness of income transfers in reducing poverty by revealing that recent state-led antipoverty efforts have placed the near poor and the deeply poor on divergent paths.

This research aims to assess the influence of Craigslist’s presence and adoption on fraud arrests within metropolitan statistical areas (MSAs) where it was introduced compared to areas where it was not available. Utilizing the consumer vulnerability framework (Hill & Sharma, 2020), the study used diverse data sources, including Craigslist entry data, the Uniform Crime Reporting (UCR) dataset, and the US Census Bureau Current Population Survey (CPS) data from 1995-2006. Employing differences-in-differences (DID) models, this study's primary findings indicate a reduction in fraud arrests, ranging from 11% to 23% following the introduction of Craigslist. This might appear counterintuitive considering online platforms are sometimes fraud hotspots. However, explanations range from Craigslist’s peer-to-peer transaction format, the existence of a digital trail, platform and community-generated scam education, and an inherent self-policing mechanism where suspicious ads are flagged, reviewed, and removed. While minor frauds may persist and potentially go unreported, Craigslist’s enduring popularity (Oravec, 2014) subjects listings to vast public scrutiny, making large-scale frauds challenging. In collaboration with U.S. law enforcement, Craigslist has introduced safety measures such as posting limitations that deter unsafe activities (Freese, 2011). On the Craigslist website, there is a section that talks about how to avoid scams on the platform (Craigslist, 2023a). Potential extrinsic factors influencing fraud arrests are numerous. Craigslist’s marketplace vitality might present genuine income avenues, reducing fraud incentives. As users become adept at recognizing scams, successful frauds could decline. Additionally, as online platforms become integral in regional economies, law enforcement could foster refined online fraud identification and prosecution tools generating a deterrent effect. Practical implications are discussed and suggestions for future research are provided.

The development of Internet technology enables an analysis on the whole population rather than a certain number of samples, and leads to increasing requirement for privacy protection. Local differential privacy (LDP) is an effective standard of privacy measurement ; however, its large variance of mean estimation causes challenges in application. To address this problem, this paper presents a new LDP approach, an improved Christofides mechanism. It compared four statistical survey methods for conducting surveys on sensitive topics-modified Warner, Simmons, Christofides, and the improved Christofides mechanism. Specifically, Warner, Simmons and Christofides mechanisms have been modified to draw a sample from the population without replacement, to decrease variance. Furthermore, by drawing cards without replacement based on modified Christofides mechanism, we introduce a new mechanism called the improved Christofides mechanism, which is found to have the smallest variance under certain assumption when using LDP as a measurement of privacy leakage. The assumption is do satisfied usually in the real world. Actually, we decrease the variance to 28.7% of modified Christofides mechanism's variance in our experiment based on the HCOVANY dataset-a real world dataset of IPUMS USA. This means our method gets a more accurate estimate by using LDP as a measurement of privacy leakage. This is the first time the improved Christofides mechanism is proposed for LDP framework based on comparative analysis of four mechanisms using LDP as the same measurement of privacy leakage .

In Fighting Invisibility, Monica Mong Trieu argues that we must consider the role of physical and symbolic space to fully understand the nuances of Asian American racialization. By doing this, we face questions such as, historically, who has represented Asian America? Who gets to represent Asian America? This book shifts the primary focus to Midwest Asian America to disrupt—and expand beyond—the existing privileged narratives in United States and Asian American history. Drawing from in-depth interviews, census data, and cultural productions from Asian Americans in Ohio, Wisconsin, Nebraska, Minnesota, Illinois, Iowa, Indiana, and Michigan, this interdisciplinary research examines how post-1950s Midwest Asian Americans navigate identity and belonging, racism, educational settings, resources within co-ethnic communities, and pan-ethnic cultural community. Their experiences and life narratives are heavily framed by three pervasive themes of spatially defined isolation, invisibility, and racialized visibility. Fighting Invisibility makes an important contribution to racialization literature, while also highlighting the necessity to further expand the scope of Asian American history-telling and knowledge production.

Previous research shows that racial identification patterns among stateside Puerto Ricans deviate from the Black-White binary that has historically characterized the U.S. colour line. Thus, scholars and policymakers continue to debate the most accurate approach for racially classifying the stateside Puerto Rican – and broader Latinx – population. I address this debate by examining the process of racial identification among Puerto Ricans living in New York City and Orlando, Florida. Data from 58 in-depth interviews and the U.S. Census show that Puerto Ricans merge racial and ethnic conceptualizations when constructing racial identities. This is why most Puerto Ricans report their race as Hispanic/Latina/o. Place-based differences in racial identification also emerge, particularly when respondents are presented with separate race and Hispanic origin questions. I argue that the timing of Puerto Rican migration and the racial histories and demography of each place lead Puerto Ricans to develop different understandings of race.

Employing Differential Privacy (DP), the state-of-the-art privacy standard, to answer aggregate database queries poses new challenges for users to understand the trends and anomalies observed in the query results: Is the unexpected answer due to the data itself, or is it due to the extra noise that must be added to preserve DP? We propose to demonstrate DPXPlain, the first system for explaining group-by aggregate query answers with DP. DPXPlain allows users to compare values of two groups and receive a validity check, and further provides an explanation table with an interactive visualization, containing the approximately 'top-k' explanation predicates along with their relative influences and ranks in the form of confidence intervals, while guaranteeing DP in all steps.

Unions play a crucial role in determining wages and employment outcomes. However, union bargaining power may also have important effects on non-pecuniary working conditions. We study the effects of right-to-work laws, which removed agency shop protection and weakened union powers on long hours and non-standard work schedules that may adversely affect workers’ health and safety. We exploit variation in the timing of enactment across US states and compare workers in bordering counties across adopting states and states that did not adopt the laws yet. Using the stacked approach to difference-in-differences estimates proposed by Cengiz et al. (2019), we find evidence that right-to-work laws increased the share of workers working long hours by 6%, while there is little evidence of an impact on hourly wages. The effects on long hours are larger in more unionized sectors (i.e. construction, manufacturing, and transportation). While the likelihood of working non-standard hours increases for particular sectors (education and public administration), there is no evidence of a significant increase in the overall sample.

This paper estimates the returns to human capital accumulation during the first era of mega-firms in the United States by linking employees at General Electric-a canonical enterprise associated with the "visible hand" of managerial hierarchies-to the 1940 census. I find large returns to higher education through seniority in the hierarchy, span of control, earnings, and selection into management training, using the proximity of land-grant colleges and historical universities to birth states for identification. The findings highlight the human capital determinants of the managerial revolution at a prominent firm, driven by earlier public investments in the US education system.

Ensuring the social equity of planning measures in social systems requires an understanding of human dynamics, particularly how individual relationships, activities, and interactions intersect with individual needs. Spatial microsimulation models (SMSMs) support planning for human security goals by representing human dynamics through realistic, georeferenced synthetic populations, that a) provide a complete representation of social systems while b) also protecting individual privacy. In this paper, we present UrbanPop, an open and reproducible SMSM framework for analysis of human dynamics with high spatial, temporal, and demographic resolution. UrbanPop creates synthetic populations of demographically detailed worker and student agents, positioning them first at probable nighttime locations (home), then moving them to probable daytime locations (work/school). Summary aggregations of these populations match the granular detail available at the census block group level in the American Community Survey Summary File (SF), providing realistic approximations of the actual population. UrbanPop users can select particular demographic traits important in their application, resulting in a highly tailored agent population. We first lay out UrbanPop's baseline methodology, including population synthesis, activity modeling, and diagnostics, then demonstrate these capabilities by developing case studies of shifting population distributions and high-risk populations in Knox County, TN during the global COVID-19 pandemic.

This study estimates the prevalence of prescription opioid use (POU) in the United States (US) in 2019–2020, both in the general population and specifically among adults with pain. It also identifies key geographic, demographic, and socioeconomic correlates of POU. Data were from the nationally-representative National Health Interview Survey 2019 and 2020 (N = 52,617). We estimated POU prevalence in the prior 12 months among all adults (18+), adults with chronic pain (CP), and adults with high-impact chronic pain (HICP). Modified Poisson regression models estimated POU patterns across covariates. We found POU prevalence of 11.9% (95% CI 11.5, 12.3) in the general population, 29.3% (95% CI 28.2, 30.4) among those with CP, and 41.2% (95% CI 39.2, 43.2) among those with HICP. Findings from fully-adjusted models include the following: In the general population, POU prevalence declined about 9% from 2019 to 2020 (PR = 0.91, 95% CI 0.85, 0.96). POU varied substantially across US geographic regions: It was significantly more common in the Midwest, West, and especially the South, where adults had 40% higher POU (PR = 1.40, 95% CI 1.26, 1.55) than in the Northeast. In contrast, there were no differences by rural/urban residence. In terms of individual characteristics, POU was lowest among immigrants and among the uninsured, and was highest among adults who were food insecure and/or not employed. These findings suggest that prescription opioid use remains high among American adults, especially those with pain. Geographic patterns suggest systemic differences in therapeutic regimes across regions but not rurality, while patterns across social characteristics highlight the complex, opposing effects of limited access to care and socioeconomic precarity. Against the backdrop of continuing debates about benefits and risks of opioid analgesics, this study identifies and invites further research about geographic regions and social groups with particularly high or low prescription opioid use.

We show that Covid-19 illnesses and related work absences persistently reduce labor supply. Using an event study, we estimate that workers with week-long Covid-19 absences are 7 percentage points less likely to be in the labor force one year later compared to otherwise-similar workers who do not miss a week of work for health reasons. Our estimates suggest Covid-19 absences have reduced the U.S. labor force by approximately 500,000 people (0.2 percent of adults) and imply an average labor supply loss per Covid-19 absence equivalent to $9,000 in forgone earnings, about 90 percent of which reflects losses beyond the initial absence week.

Based on observational studies, conventional wisdom suggests that citizenship carries economic benefits. We leverage a randomized experiment from New York where low-income registrants who wanted to become citizens entered a lottery to receive fee vouchers to naturalize. Voucher recipients were about 36 p.p. more likely to naturalize. Yet, we find no discernible effects of access to citizenship on several economic outcomes, including income, credit scores, access to credit, financial distress, and employment. Leveraging a multi-dimensional immigrant integration index, we similarly find no measurable effects on non-economic integration. However, we do find that citizenship reduces fears of deportation. Explaining our divergence from past studies, our results also reveal evidence of positive selection into citizenship, suggesting that observational studies of citizenship are susceptible to selection bias.

This dissertation examines various aspects of the macroeconomics of labor markets. Chapter 1 develops a frictional labor market model that incorporates worker vacating, i.e., workers exiting the labor market hence vacating their positions. It provides novel insights into the business cycle theory of unemployment: Procyclical employment-to-nonparticipation quits contribute to vacancy fluctuations, accounting for about one-third of unemployment fluctuations. It also sheds new light on the possibility of a "soft landing" during the "Great Resignation": While creating a new job as investment activity is responsive to the interest rate, reposting a vacated position is not. Chapter 2, joint with Moritz Kuhn and Iourii Manovskii, studies the spatial differences across local labor markets. Guided by the novel facts on the geography of vacancies and job filling, we develop a spatial version of a Diamond-Mortensen-Pissarides model with endogenous separations and on-the-job search that quantitatively accounts for all the documented empirical regularities. The model also quantitatively rationalizes why the job-separation rate is more important in accounting for spatial differences in unemployment while the job-finding rate is more important in accounting for business cycle fluctuations in unemployment. Chapter 3, joint with Jincheng (Eric) Huang, investigates how wealth affects the allocation of workers and jobs. Using NLSY79 and O*NET, we document that wealth-poor workers are more mismatched with their jobs. We develop a model featuring worker and firm heterogeneity, search frictions, and incomplete markets, where a lack of wealth induces workers to trade off wages for finding a job faster due to precautionary motives. This phenomenon, referred to as "precautionary mismatch," leads to substantial within-type earnings and productivity gaps between the wealth-rich and the wealth-poor, especially among high-skilled workers. Chapter 4, joint with Hanming Fang, proposes and empirically implements a framework to infer from the repeated cross-sectional earnings data the experience effect, the cohort effect, and the time effect, under an identifying assumption that the growth of the experience effect stops at the end of one's working career. Applying this framework to data from China and the United States, we find that China has experienced a much larger inter-cohort productivity growth and higher increase in the rental price to human capital but lower returns to experience, compared to the U.S. We use the inferred components to revisit several applications in macroeconomics and labor economics.

Country-level sanitation access is monitored globally by the Joint Monitoring Programme (JMP). However, recent reports on sanitation access in high-income countries indicate that the JMP data may underestimate the prevalence of unsafely managed sanitation in these settings. This study explains the surveys that collect household-level wastewater management data in the U.S. and analyzes the accuracy and reliability of these data sets. From 1940 to 1990, sewage disposal data were collected comprehensively through the U.S. Decennial Census. These data are currently collected through the American Housing Survey (AHS) which appears to greatly underestimate the usage of onsite wastewater treatment systems (OWTS). In addition to these surveys , we highlight current efforts to introduce a sewage disposal question to the American Community Survey (ACS), localized efforts to collect wastewater data, and the Point-in-Time count of people experiencing homelessness. Using estimates of OWTS usage in new housing, this study provides the first defensible national estimate of OWTS usage since 1990. We estimate that 25.03% of U.S. households use OWTS which exceeds the AHS estimate (15.7%) by over 12 million households. This study discusses the potential for better wastewater data collection to inform future wastewater policy and improve the quality of life for U.S. residents.

For western colonists in the early American backcountry, disputes often ended in bloodshed and death. Making the Frontier Man examines early life and the origins of lawless behavior in Pennsylvania, Virginia, Kentucky, and Ohio from 1750 to 1815. It provides a key to understanding why the trans-Appalachian West was prone to violent struggles, especially between white men. Traumatic experiences of the Revolution and the Forty Years War legitimized killing as a means of self-defense—of property, reputation, and rights—transferring power from the county courts to the ordinary citizen. Backcountry men waged war against American Indians in state-sponsored militias as they worked to establish farms and seize property in the West. And white neighbors declared war on each other, often taking extreme measures to resolve petty disputes that ended with infamous family feuds. Making the Frontier Man focuses on these experiences of western expansion and how they influenced American culture and society, specifically the nature of western manhood, which radically transformed in the North American environment. In search of independence and improvement, the new American man was also destitute, frustrated by the economic and political power of his elite counterparts, and undermined by failure. He was aggressive, misogynistic, racist, and violent, and looked to reclaim his dominance and masculinity by any means necessary.

Infant mortality remains high and uneven in much of sub-Saharan Africa. Even low-cost, highly effective therapies can only save lives in proportion to how successfully they can be targeted to those children who, absent the treatment, would have died. This places great value on maximizing the accuracy of any targeting or means-testing algorithm. Yet, the interventions that countries deploy in hopes of reducing mortality are often targeted based on simple models of wealth or income or a few additional variables. Examining 22 countries in sub-Saharan Africa, we illustrate the use of flexible (machine learning) risk models employing up to 25 generally available pre-birth variables from the Demographic and Health Surveys. Using these models, we construct risk scores such that the 10 percent of the population at highest risk account for 15-30 percent of infant mortality, depending on the country. Successful targeting in these models turned on several variables other than wealth, while models that employ only wealth data perform little or no better than chance. Consequently, employing such data and models to predict high-risk births in the countries studied flexibly could substantially improve the targeting and thus the life-saving potential of existing interventions.

Job quality is important for worker well-being (Congdon et al. 2020). However, all occupations are not equal in quality, and quality employment is distributed unevenly by race and gender. This study uses occupational crowding methodology to better understand how this occurs and who is impacted. We conducted a review of major definitions of job quality across multiple fields to develop an organizing framework of the main elements of job quality. The main categories we identified were pay, hours, scheduling, benefits, job security, working conditions, on-the-job training, advancement, and worker voice. We then identified these job quality indicators in 108 occupations and developed a combined job-quality score. The average job-quality score was 5.8 out of a total possible score of 11 across the occupations,the most frequent total scores were 7 or 8, and few occupations were very below average.