During Hispanic Heritage Month, the Pew Research Center, a Washington-based research center, published a series of figures that highlight the importance and growth of the Hispanic community in the United States, referencing the 10-year period between 2010 and 2020.

In the United States, most married couples are homogamous, meaning they share similar characteristics such as educational level or race/ethnicity. This profile is an update of previous family profiles on homogamy in the U.S.: FP-18-18, FP-15-16, FP-15-15, and FP-15-14. It investigates educational, racial/ethnic, age, and nativity homogamy among newlyweds (married less than one year) versus those married at least one year, using the 2019 American Community Survey.

Methane superemitters emit non-methane copollu-tants that are harmful to human health. Yet, no prior studies have assessed disparities in exposure to methane superemitters with respect to race/ethnicity, socioeconomic status, and civic engagement. To do so, we obtained the location, category (e.g., landfill, refinery), and emission rate of California methane superemitters from Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) flights conducted between 2016 and 2018. We identified block groups within 2 km of superemitters (exposed) and 5−10 km away (unexposed) using dasymetric mapping and assigned level of exposure among block groups within 2 km (measured via number of superemitter categories and total methane emissions). Analyses included 483 superemitters. The majority were dairy/manure (n = 213) and oil/gas production sites (n = 127). Results from fully adjusted logistic mixed models indicate environmental injustice in methane superemitter locations. For example, for every 10% increase in non-Hispanic Black residents, the odds of exposure increased by 10% (95% confidence interval (CI): 1.04, 1.17). We observed similar disparities for Hispanics and Native Americans but not with indicators of socioeconomic status. Among block groups located within 2 km, increasing proportions of non-White populations and lower voter turnout were associated with higher superemitter emission intensity. Previously unrecognized racial/ethnic disparities in exposure to California methane superemitters should be considered in policies to tackle methane emissions.

This study investigates the impacts of anticancer drug parity laws on mortality rates in the United States using a difference-in-differences approach. Using data from 2004 to 2017 Compressed Mortality Files, we show that the anticancer drug parity laws reduce the mortality rate for head/neck malignant cancers but have no impact on malignant cancers of other types. We also rule out an insurance expansion channel that may influence the relationship between anticancer drug parity laws and malignant cancer mortality. Our results are robust to various specifications and falsification tests. Our findings imply that providing equal access to oral anticancer drugs is an effective tool for the prevention of premature mortality.

Why renters choose to move likely plays a role in whether the outcomes of their mobility will be positive (e.g. labor market efficiency) or negative (e.g. lower student achievement). If we can determine why renters move, lawmakers can design housing policies to foster positive mobility and reduce negative mobility. The purpose of this paper is to build on prior models of renter mobility to develop a conceptual model that can be mapped to the various effects of mobility. Within the model, I identify reasons for (im)mobility that may be receptive to housing policy intervention. As an illustration of the model, I use a difference-in-difference-in-difference (DDD) model and data from the 1981–2014 March supplements of the Current Population Survey (IPUMS-CPS) to show how various landlord-tenant policies impact the frequency of renter mobility. I use the conceptual model to hypothesize the nature of the relationship between public policy and mobility. Results indicate policies regulating late fees and self-help remedies play a role in reducing renter mobility. While all policies tested here do not have a significant effect on renter mobility, the impact of some policies highlights the need for targeted, evidence-driven interventions.

While the household in which a child grows up is considered a critical environment that influences nutrition outcomes, there is little research examining the influence of household composition and structure on complementary feeding practices. This study examined the influence of household structure and composition on complementary feeding practices, using the Ethiopian Demographic and Health Survey (EDHS), 2000 to 2016. The composition variables were calculated from the attributes of household members (alters) and the structure variables from their kinship status. A multilevel mixed-effects regression model, specifying survey rounds as the random effect, was used to examine the association between household structure/composition and the Minimum Meal Frequency (MMF) and Minimum Dietary Diversity (MDD). The average Marginal Effects (MEs) were calculated to facilitate practical interpretation. Children of caregivers with a higher number of alters (degree), unique number of kinship category (effect size), closely related (constraint), and mixed-age alters (age diversity) seemed to increase the probability of meeting the MDD. Degree and effective size decreased the probability of meeting MMF, while constraint increased it. Overall, this study revealed some associations between household structure and composition and complementary feeding practices. Hence, complementary feeding interventions could be adapted to account for the household structure and composition variations.

Background: The psychological effects from the COVID-19 pandemic and response are poorly understood. Objective: To understand the effects of the pandemic and response on anxiety and health utility in a nationally representative sample of US adults. Design: A de-identified, cross-sectional survey was administered at the end of April 2020. Probability weights were assigned using estimates from the 2018 American Community Survey and Integrated Public Use Microdata Series Estimates. Participants: US adults 18–85 years of age with landline, texting-enabled cellphone, or internet access. Intervention: Seven split-half survey blocks of 30 questions, assessing demographics, COVID-19-related health attitudes, and standardized measures of generalized self-efficacy, anxiety, depression, personality, and generic health utility. Main Measures: State/Trait anxiety scores, EQ-5D-3L Visual Analog Scale (VAS) score, and demographic predictors of these scores. Key Results: Among 4855 respondents, 56.7% checked COVID-19-related news several times daily, and 84.4% at least once daily. Only 65.7% desired SARS-CoV-2 vaccination for themselves, and 70.1% for their child. Mean state anxiety (S-anxiety) score was significantly higher than mean trait anxiety (T-anxiety) score (44.9, 95%CI 43.5–46.3 vs. 41.6, 95%CI 38.7–44.5; p = 0.03), with both scores significantly higher than previously published norms. In an adjusted regression model, less frequent news viewing was associated with significantly lower S-anxiety score. Mean EQ-5D-3L VAS score for the population was significantly lower vs. established US normative data (71.4 CI 67.4–75.5, std. error 2 vs. societal mean 80, std. error 0.1; p < 0.001). EQ-5D-3L VAS score was bimodal (highest with hourly and no viewing) and significantly reduced with less media viewership in an adjusted model. Conclusions: Among a nationally representative sample, there were higher S-anxiety and lower EQ-5D-3L VAS scores compared to non-pandemic normative data, indicative of a potential detrimental acute effect of the pandemic. More frequent daily media viewership was significantly associated with higher S-anxiety but also predictive of higher health utility, as measured by EQ-5D-3L VAS scores.

Knowledge accumulation and its spillovers are important determinants of the regional economic growth process in the U.S. As such, this paper relies on a regional knowledge production function to examine the heterogeneous determinants of knowledge creation across 5 U.S. manufacturing sectors and 853 metropolitan counties. Using a Tobit model with State fixed effects, our results indicate that local intra-sectoral and inter-sectoral R&D investments by the private sector as well as university R&D play a key role in knowledge creation across all sectors under study. We also find that the role of short-distance vs. long-distance interregional spillovers on knowledge creation varies greatly across sectors. These key features improve the design of future local and national innovation policies.

More than one-third of US children live in families with three or more children. The contemporary impact of larger family size on children’s family resources remains an under-explored point of inequity. Larger family size is not only more common among Black and Hispanic children, but Black and Hispanic children in larger families (Black children, especially so) face higher poverty risks relative to White children in larger families. This analysis uses children’s number of siblings and children’s race and ethnicity to chart the intersectional aspects of disparity in the risk and incidence of poverty and the anti-poverty effects of large federal cash supports, the Earned Income Tax Credit and Child Tax Credit. It draws upon 2014–2017 Current Population Survey data and the NBER TAXSIM calculator to apply 2018 tax law, inclusive of the Tax Cuts and Jobs Act of 2017. It reveals well-documented disparities in poverty rates and benefit access and receipt experienced by children of color are further exacerbated by policy structures that discriminate against an under-acknowledged aspect of children’s family life: their family size. Racial bias in policy design that sees tax credit access mechanisms and earnings and benefit structures disproportionately exclude that Black and Hispanic children also disproportionately exclude Black and Hispanic children by their family size. Without reforms that tackle both inequities, policy action that closes the poverty gap between larger and smaller families will see the racial gap in child poverty remain.

Background: Theories of partner selection emphasize the principal role of available partners in the relationship market. As education is a common socioeconomic attribute through which individuals choose a mate, macrostructure theory highlights the importance of the asymmetric change in educational attainment seen in Europe. As women increasingly participate in tertiary education, this restructuring might result in the decline of traditional hypergamous unions. Objective: We aim to verify previous results confirming the decline of educational hypergamy and the rise of hypogamy, which has been found to be related to women’s growing educational advantage. We also wish to provide a current picture of this process in Europe by looking at the youngest cohort available during the analysis. Methods: We pooled nine waves of the European Social Survey and examined trends in seven cohorts. Apart from simple percentage differences in education, we reconstructed the indices of female educational advantage (F-index) and the prevalence of hypergamy (H-index), with provided correlation statistics and fitted linear trend lines. Results: Our results corroborated the findings of previous multi-country analyses. In nearly all selected countries, women were more present in higher education than men, resulting in a uniform increase in the female educational advantage. Parallel to this, hypergamy declined through the cohorts, which correlated with women’s emerging educational lead. Contribution: Our results verify the findings from previous years, using a newer dataset and detailed cohort perspective, and confirm the decline of hypergamy as women gain advantage in education, with only four countries out of 27 showing some remnants of a male educational lead.

California’s diverse population provides a natural laboratory for understanding how diseases and conditions interact within different racial/ethnic groups. This report seeks to illustrate the differential effects of the COVID-19 pandemic in the state’s “majority-minority” population and to discuss the resulting implications for public health. Laboratory-confirmed COVID-19 cases in California (disaggregated by race/ethnicity into mutually exclusive groups) were integrated with their respective population values to create case rates per 100,000 population, categorized by age group and race/ethnicity. The case rates within each non-White population, in almost every age group, were higher than the White Non-Hispanic population, ranging from one-and-a-half to nearly six times as high. Public health prevention measures such as sheltering-at-home rely on standard assumptions and models. The disparity in case rates found here suggests that alternative narratives such as the epidemiology of diversity may inform additional policies or measures.

We present a novel theory that immigrants facilitate innovation and entrepreneurship by being willing and able to invest in new skills. Immigrants whose human capital is not immediately transferable to the host country face lower opportunity costs of investing in new skills or methods and will be more exible in their human capital investments than observationally equivalent natives. Areas with large numbers of immigrants may therefore lead to more entrepreneurship and innovation, even among natives. We provide empirical evidence from the United States that is consistent with the theory’s predictions.

We build a new longitudinal dataset of job tasks and technologies by transforming the U.S. Dictionary of Occupational Titles (DOT, 1939 - 1991) and four books documenting occupational use of tools and technologies in the 1940s, into a database akin to, and comparable with its digital successor, the O*NET (1998 - today). After creating a single occupational classification stretching between 1939 and 2019, we connect all DOT waves and the decennial O*NET databases into a single dataset, and we connect these with the U.S. Decennial Census data at the level of 585 occupational groups. We use the new dataset to study how technology changed the gender pay gap in the United States since the 1940s. We find that computerization had two counteracting effects on the pay gap - it simultaneously reduced it by attracting more women into better-paying occupations, and increased it through higher returns to computer use among men. The first effect closed the pay gap by 3.3 pp, but the second increased it by 5.8 pp, leading to a net widening of the pay gap.

Modeling and simulating urban expansion is required for assessing and predicting the consequences of the current urban growth patterns. Given the dynamic and convoluted nature of the urban expansion process and the necessity of handling continuous and categorical variables, non-normal distributed data, and non-linear relationships, urban expansion modeling is challenging. It is also critically important to find an appropriate method for modeling and simulating urban expansion in order to meticulously identify spatiotemporal variables and predicting the direction of land use/land cover (LULC) changes. To handle these issues effectively and enhance the quality of urban expansion prediction, the capabilities of machine learning methods are explored in this dissertation. Machine learning methods are relatively unknown in urban expansion modeling and have not been evaluated thoroughly in the current literature. The machine learning methods allow the exploration of a variety of data sampling strategies, predictor variables, and model configurations to enhance the accuracy and predictability of urban expansion modeling. The models are calibrated using spatiotemporal data of 2001-2016 and are applied to simulate future urban developments for two urbanized counties—Guilford and Mecklenburg in NC, USA. The accuracy and reliability of the models are evaluated by apposite evaluation metrics. Distance to highways is recognized as the most important predictor variable in both study areas, however, the importance of the predictor variables varies in different geographic contexts and with different methods. A comparative study on machine learning methods demonstrated that the random forest (RF) model is a fast, high-performance, and accurate model with low uncertainty; therefore, it can be effectively utilized to evaluate a wide range of urban development scenarios and support decision-making to accomplish the goal of implementing environmentally sustainable development. Sustainable urban growth management in addition to sophisticated and elaborative

We examine how the racial patchwork of federal and state minimum wage changes between 1990 and 2019 has affected racial wage gaps, with specific attention to effects on labor market frictions. Black workers on average are less likely to live in high-wage states that have raised their wage floors. The effect of state minimum wages on the national racial wage gap is thus not self-evident. Using five different causal specifications, including the “bunching” estimator of Cengiz et al. (2019), and data from the CPS and the QWI, we find that minimum wage changes since 1990 did reduce the 2019 racial wage gaps, by 12 percent among all workers and 60 percent among less-educated workers. The reductions are greater among black women and among black prime age workers. The gains for black workers are concentrated well above the new minimum wage, beyond the usual spillover estimates. Earnings of all race/ethnic/gender groups grew, with larger effects among black workers. We do not find disemployment effects for any group. Surprisingly, racial differences in initial wages do not explain the reduction in the racial wage gap. Rather, minimum wages expand job opportunities for black workers more than for white workers. We present a model in which minimum wages assist the job search of workers who do not own automobiles and who live farther from jobs. Our causal results using the ACS show that minimum wages increase commuting via automobile among black workers, supporting our model. Minimum wages also reduce racial gaps in separations and hires, further suggesting the policies especially enhance job opportunities for black workers.

Rationale and objective: Research has documented a robust mental health advantage among Asian and Latino immigrants to the United States relative to the native-born. The current investigation extended this line of research, asking whether Middle Eastern immigrants to the United States enjoy a similar mental health advantage. Methods: Drawing on pooled cross-sections from the 2007–2018 National Health Interview Surveys, we used OLS regression to examine psychological distress in Middle Eastern immigrants relative to both native-born Whites and immigrants from other global regions. We used statistical interactions to assess whether gender and period differences are contingent on region of birth. Results: Findings reveal that the average level of psychological distress is higher among Middle Eastern immigrants than among both U.S.-born Whites and immigrants from other regions. Despite changing circumstances of migration for Middle Easterners and implementation in the United States of anti-immigrant policies, we see no evidence that distress increased more among immigrants compared to native-born Whites. Results point to greater psychological distress among Middle Eastern women than their native-born White counterparts and women from other immigrant groups, as well as Middle Eastern men. In contrast, psychological distress levels for Middle Eastern and native-born White men were indistinguishable, suggesting that the Middle Eastern mental health disadvantage in the United States is borne solely by women. Conclusions: Results show that the mental health advantage enjoyed by some immigrant groups does not extend to Middle Eastern women, contradicting the healthy migrant model and challenging the assumption of a uniform mental health advantage across immigrant groups.

This dissertation includes three essays in economic history and applied microeconomics. In the first chapter, I investigate the intergenerational effects of a negative shock in wealth for African Americans by examining the failure of the Freedman's Bank. I find that children of depositors were more likely to be literate than children of non-depositors after the bank failure. The positive literacy effect is explained by an increase in schooling and literacy for the depositors' children prior to the bank failure. I find that the bank was able to promote education for the depositors' children through its connection with a Christian educational organization, the American Missionary Association. While children from families who lost a higher proportion of wealth were less likely to attend school after the bank failure, the human capital gains which occurred prior to the bank failure outweigh and outlast the adverse effect of wealth loss. In the second chapter, I test whether the failure of the Freedman’s Bank contributed to the mistrust and underutilization of financial institutions by African Americans today using present day survey data. I find that African Americans are less likely to be banked if they reside in a county with higher exposure to knowledge of the bank failure. In addition, for unbanked households, those who reside in a county with higher exposure to knowledge of the bank failure are more likely to report “mistrust” in bank as the primary reason to be unbanked. The results suggest that the collapse of the Freedman’s Bank can partly explain persistent gaps in the utilization of financial services by African Americans. Finally, in the third chapter, I explore whether low-income individuals with a guaranteed income had a higher likelihood of occupation turnover using from a randomized experiment that occurred in the 1970s, the Manitoba Basic Annual Income Experiment. I find that guaranteed income treatment increased the probability of an occupation switch, where most of the individuals who switched were above the age of 35. From survey results, I find that occupation turnovers resulted in non-pecuniary gains for all occupation switchers. Overall, these results suggest that when relieved of financial pressure, a subset of low-income individuals were more likely to switch occupations. Those in the treatment group accrued more non-pecuniary gain simply because they switched occupations more often.

Objectives: To investigate the heterogeneous effects of education on sleep duration for Black and White adults and how the education effects changed between 2004 and 2018. Methods: A total of 251,994 adult participants in the 2004 to 2018 National Health Interview Survey were included in pooled cross-sectional data analyses. Separately for Black and White men and women, we calculated prevalence ratio and average marginal probability of short sleep (<7 hours) for each education level over the study period based on weighted logistic regression models. Results: Opposite educational gradients in short sleep were observed between Black and White adults. Greater educational attainment was associated with lower likelihood of short sleep among White adults but higher likelihood of short sleep among Black adults. Such heterogeneous educational gradients were robust after accounting for a set of socioeconomic, family, and health factors and persisted between 2004 and 2018. Conclusions: The health implications of education are not uniform in the US population, and heterogeneous education effects on sleep duration persisted over the past decade. More scholarly attention is needed to identify challenges and barriers that may be unique for race, sex, and education subpopulations to maintain healthy sleep.

When a public policy makes it less costly to take maternity leave, take-up rates are likely to increase as a direct consequence of the lower costs, but if enough people take leave, leave-taking may increase further as norms adjust. This paper provides evidence of these indirect impacts by considering how leave-taking by foreign-born mothers in the U.S. respond to maternity leave policies in their home countries. Because immigrants in the U.S. are not exposed to home country policies but do bring with them norms from their home countries, the impacts of additional weeks of paid leave in home countries on leave-taking behaviors of immigrants can be seen as evidence of a role played by norms. Exploiting variation in the timing of emigration in conjunction with the timing of changes in home country leave policies, we show that even in models controlling for country of origin fixed effects, changes in home country policies yield changes in leave-taking among immigrants in the United States. Interestingly, more recent immigrant arrivers are more affected by current day norms in their home countries than the norms associated with the policies in place when they migrated, a finding potentially explained by the increased role of social media in the transmission of norms.

Background Despite declining HIV infection rates, persistent racial and ethnic disparities remain. Appropriate calculations of diagnosis rates by HIV transmission category, race and ethnicity, and geography are needed to monitor progress towards reducing systematic disparities in health outcomes. We estimated the number of heterosexually active adults (HAAs) by sex and state to calculate appropriate HIV diagnosis rates and disparity measures within subnational regions. Methods The analysis included all HIV diagnoses attributed to heterosexual transmission in 2018 in the United States, in 50 states and the District of Columbia. Logistic regression models estimated the probability of past-year heterosexual activity among adults in three national health surveys, by sex, age group, race and ethnicity, education category, and marital status. Model-based probabilities were applied to estimated counts of HAAs by state, which were synthesized through meta-analysis. HIV diagnoses were overlaid to calculate racial- and ethnic-specific rates, rate differences (RDs), and rate ratios (RRs) among HAAs by sex and state. Results Nationally, HAA women have a two-fold higher HIV diagnosis rate than HAA men (rate per 100,000 HAAs, women: 6.57; men: 3.09). Compared to White non-Hispanic HAAs, Black HAAs have a 20-fold higher HIV diagnosis rate (RR, men: 21.28, women: 19.55; RD, men: 15.40, women: 31.78) and Hispanic HAAs have a 4-fold higher HIV diagnosis rate (RR, men: 4.68, RD, women: 4.15; RD, men: 2.79, RD, women: 5.39). Disparities were ubiquitous across regions, with >75% of states in each region having Black-to-White RR ≥10. Conclusion The racial and ethnic disparities across regions suggests a system-wide failure particularly with respect to preventing HIV among Black and Hispanic women. Pervasive disparities emphasize the role for coordinated federal responses such as the current Ending the HIV Epidemic (EHE) initiative.