The issue of immigration continues to hover at the forefront of society. Whether in politics, society, or faith groups, the topic provides a challenge to every strata of American society. Immigrants from Spanish-speaking nations are pouring into the United States, and with them come new tests straining at the fabric of this country. Many immigrants come with strong cultural moorings and minimal, if any, education. This lack of education is trapping immigrants into an economic malaise that inhibits their ability to advance in American society. This project examined how Wilson College created a department within their academic structure where Spanish-speaking only individuals can pursue educational opportunities. The project included the participation of Wilson College administration, faculty, and staff, newly enrolled students, pastors and ministers, professional translators, and many other bilingual individuals to bring it to fruition. Results saw the genesis of a program that ultimately will be offering degrees in time that are completely in the Spanish language, 100 percent online, and with affordable tuition. The empowering aspects of the Holy Spirit coupled with education create a new dynamic in a people whose cultural value system has minimized education. Based upon responses in the surveys completed by the first group of students that completed the initial phase of implementation, their confidence has increased after successfully completing the first phase of classes. Many have embraced the process and overcome preconceived notions about education that were placed on them by their Hispanic culture.

We propose a mechanism for labor-market polarization based on the nonhomotheticity of demand that we call the income-driven channel. Our mechanism builds on a novel empirical fact: expenditure elasticities and production intensities in low- and high-skill occupations are positively correlated across sectors. Thus, as income grows, demand shifts towards expenditureelastic sectors, and the relative demand for low- and high-skill occupations increases, causing labor-market polarization. A calibrated general-equilibrium model suggests this mechanism accounts for 90% and 35% of the increase in the wage-bill share of low- and high-skill occupations observed in the US during 1980-2016, and for 64%and 28% of the rise in the employment shares of low- and high-skill occupations. This mechanism is similarly important for the polarization of labor markets in Western Europe during 1980-2016, as well as in the US during earlier decades and, possibly, the near future.

This study examines issues of Small Area Estimation (SAE) that are raised by reliance on the American Community Survey (ACS), which reports tract-level data based on much smaller samples than the decennial census long-form that it replaced. We demonstrate the problem using a 100% transcription of microdata from the 1940 census. By drawing many samples from two major cities, we confirm a known pattern: random samples yield unbiased point estimates of means or proportions, but estimates based on smaller samples have larger average errors in measurement and greater risk of large error. Sampling variability also inflates estimates of measures of variation across areas (reflecting segregation or spatial inequality). This variation is at the heart of much contemporary spatial analysis (Sampson 2012). We then evaluate possible solutions. For point estimates, we examine three Bayesian models, all of which reduce sampling variation, and we encourage use of such models to correct ACS small area estimates. However, the corrected estimates cannot be used to calculate estimates of variation, because smoothing toward local or grand means artificially reduces variation. We note that there are potential Bayesian approaches to this problem, and we demonstrate an efficacious alternative that uses the original sample data.

Skyline and ranking queries are two popular, alternative ways of discovering interesting data in large datasets. Skyline queries are simple to specify, as they just return the set of all non-dominated tuples, thereby providing an overall view of potentially interesting results. However, they are not equipped with any means to accommodate user preferences or to control the cardinality of the result set. Ranking queries adopt, instead, a specific scoring function to rank tuples, and can easily control the output size. While specifying a scoring function allows one to give different importance to different attributes by means of, e.g., weight parameters, choosing the “right” weights to use is known to be a hard problem. In this article, we embrace the skyline approach by introducing an original framework able to capture user preferences by means of constraints on the weights used in a scoring function, which is typically much easier than specifying precise weight values. To this end, we introduce the novel concept of F-dominance, i.e., dominance with respect to a family of scoring functions F: a tuple t is said to F-dominate tuple s when t is always better than or equal to s according to all the functions in F. Based on F-dominance, we present two flexible skyline (F-skyline) operators, both returning a subset of the skyline: nd, characterizing the set of non-F-dominated tuples; po, referring to the tuples that are also potentially optimal, i.e., best according to some function in F. While nd and po coincide and reduce to the traditional skyline when F is the family of all monotone scoring functions, their behaviors differ when subsets thereof are considered. We discuss the formal properties of these new operators, show how to implement them efficiently, and evaluate them on both synthetic and real datasets.

The Hispanic health paradox (HHP) is an epidemiological paradox that describes the observation that the Hispanic population in the United States tends to have better health outcomes than would be expected, given the socioeconomic profile of this population. As a clear correlation between socioeconomic status (SES) and health outcomes has been established, one would expect the Hispanic population to have relatively poorer outcomes than the non-Hispanic (NH) white population and comparable outcomes to the NH black population. Among other health outcomes, the HHP has been observed with respect to cardiovascular disease (CVD), as analyses by the American Heart Association and Centers for Disease Control have shown that the Hispanic population has a lower prevalence of CVD than the NH white and NH black populations. However, some smaller studies challenge the existence of the HHP. Additionally, the impact of CVD risk factors (such as diabetes) and level of acculturation to the United States on the HHP is understudied. By utilizing data from the 2018 National Health Interview Survey, this thesis will support the existing literature that shows that the HHP is found with respect to CVD. I will also argue that the HHP with respect to CVD is present among populations with diabetes but to a lesser extent than within the general population. This suggests that the prevalence of CVD among Hispanic individuals in the general population may be artificially low due to lack of health care utilization. Finally, I will argue that the HHP is present among both U.S.-born and foreign-born populations, indicating that the high proportion of immigrants in the Hispanic population and “healthy immigrant selection” are not enough to account for the paradox.

This paper is an application of a new Shapley income decomposition methodology, in which we isolate two subjective factors in income differences - race and gender - that contribute to income inequality within the population of blacks and whites in the United States over the period 2005-2017. We show that the purely racial contribution to income inequality as defined by the Gini index varies from 1% to 4% depending on the geographical administrative divisions used. Race tends to contribute more to inequality in the Western and Southern part of the country. Whatever the division, the share of income inequality associated with gender exceeds greatly that of race. While gender income inequality falls over time, income inequality associated with race tends to increase.

This paper studies the contribution of both labor and non-labor income in the growth in income inequality in the United States and large European economies. The paper first shows that the capital to labor income ratio disproportionately increased among high-earnings individuals, further contributing to the growth in overall income inequality. That said, the magnitude of this effect is modest, and the predominant driver of the growth in income inequality in recent decades is the growth in labor earnings inequality. Far more important than the distinction between total income and labor income, is the way in which educational factors account for the growth in US labor and capital income inequality. Growing income gaps among different education groups as well as composition effects linked to a growing fraction of highly educated workers have been driving these effects, with a noticeable role of occupational and locational factors for women. Findings for large European economies indicate that inequality has been growing fast in Germany, Italy, and the United Kingdom, though not in France. Capital income and education don't play as much as a role in these countries as in the United States.

A growing body of evidence shows that America’s racial geography shapes access to basic financial services (e.g. banking), highlighting a mechanism connecting segregation to economic vulnerability: spatially organized institutional marginalization.s While the practices and policies of “mainstream” commercial banks are central to this dynamic, the costs they impose on the communities they serve have been understudied. This study leverages survey data from a stratified random sample of 1344 banks across the United States to investigate variation in the costs and fees of entry-level checking accounts at commercial banks. Our evidence shows banks charge more to open and maintain checking accounts in neighborhoods and cities with larger Black and Latinx populations even after controlling for demographic and socioeconomic characteristics as well as market competition. The higher costs of banking imposed on Black and Latinx communities are further compounded by parallel disparities in income. These findings reveal the unequal costs of banking in segregated America.

Living apart together (LAT) relationships are an emerging relationship form typically defined as unmarried couples in a committed, long-term relationship who choose to live apart (maintain separate residences) rather than cohabit or marry (Connidis, et al., 2017). Other researchers have classified married couples who live separately as LATs, as well (Binstock & Thornton, 2004). This profile examines the LAT status of newly married individuals who report having entered a marriage in the last year. We define newlywed LATs as those not living with their spouse for reasons other than marital discord. Using data from IPUMS-USA, we present their characteristics by times married, age, and educational attainment.

We analyze the supply-side disruptions associated with Covid-19 across firms and workers. To do so, we exploit differences in the ability of workers across industries to work remotely using data from the American Time Use Survey (ATUS). We find that sectors in which a higher fraction of the workforce is not able to work remotely experienced significantly greater declines in employment, significantly more reductions in expected revenue growth, worse stock market performance, and higher expected likelihood of default. In terms of individual employment outcomes, lower-paid workers, especially female workers with young children, were significantly more affected by these disruptions. Last, we combine these ex-ante heterogeneous industry exposures with daily financial market data to create a stock return portfolio that most closely replicate the supply-side disruptions resulting from the pandemic

The Social Security earnings test reform in 2000 eliminated the earnings test for all individuals between the normal retirement age (NRA) and age sixty-nine. The earnings test has long been accused of imposing a disincentive on the labor supply among older workers. In this article, we argue that the policy change may also have affected the labor supply among people who were just below the NRA around 2000. We utilize approaches embedded with a difference-in-differences design to estimate the anticipation effect on the labor supply of the slightly younger group (aged sixty-two to sixty-four) using an even younger cohort as the control group. Our preferred estimates indicate that the cohort aged sixty-two to sixty-four had worked 5.4 percent more weeks per year and 5.1 percent more hours per week after the earnings test reform in 2000.

COVID-19 pandemic did not only lead to a broad concern regarding public health but also cause an unusual economic recession worldwide. In this paper, I examine the short-term labor consequences of COVID-19 and evaluate the gender gap variations in the United States. In this study, I use panel data from the U.S. Current Population Survey to examine changes in females' and males' work hours from January 2018 through September 2020. With the Difference-indifference framework, I find that mothers reduce their time spent on work significantly more than fathers, which is especially true for parents with primary school-age (6-12) kids who are in intense demand of accompany and homeschooling. Despite this, mothers have been suffering from a higher possibility to be laid off after the outbreak of coronavirus compared to fathers. Evidence from this article also carry out a signal that fathers have begun to take more responsibilities regarding childcare and reduce their hours spent on working to a certain extent. It is of great importance for policymakers and employers to pay close attention to the variation in gender gap with the impact of current recession and take actions according to the dynamic situations.

The report shines a light on the achievements and potential of people with disabilities as workers, the barriers and challenges they face, the widespread benefits of an inclusive workplace, and the policies necessary to make Vermont a state where everyone can work productively. • Vermonters with disabilities are able and willing to work. But only half of the 46,000 of working age are employed. • Barriers and challenges persist, including the lack of universally designed office systems, limited education options, and inaccessible transportation. • A truly inclusive workforce would benefit all Vermonters—not just people with disabilities, but their families and communities, other workers and employers—as well as the state economy. • To fully integrate people with disabilities into the state’s workforce, they need to be able to meet their basic needs; have access to training and higher education; and all workplaces, both in the public and private sectors, need to be accessible to people with disabilities.

This appendix documents the technical steps supporting the Access to Opportunity through Equitable Transportation report and the “The Unequal Commute: Examining Inequities in Four Metro Areas’ Transportation Systems” feature. In the report, we examine transportation equity and inclusion in different types of metropolitan regions and explore how these regions might track and improve transportation equity over time. We draw on case studies of four Metropolitan Statistical Areas (MSAs) facing different barriers to providing equitable transportation: the Seattle, Washington, MSA, a West Coast region that faces exponential growth and housing affordability issues; the Lansing, Michigan, MSA, a smaller Midwestern metro with a state capital and university; the Baltimore, Maryland, MSA, an East Coast metro with fiscal challenges and a declining population; and the Nashville, Tennessee, MSA, a sprawling southern metro with population growth but low population density in many areas. In the feature, we display results from a quantitative analysis that examines access to opportunity through transportation in these four case study cities. In this document, we detail the data sources and methodology used in our analyses.

Shared housing is a symptom of a housing affordability problem. Sharing, co-residence, and doubling-up all describe non-nuclear living arrangements and household adaptations to housing market conditions. Employing three conventional measures of housing affordability, I find that decreases in the affordability of housing increase individuals’ odds of sharing by 2%. My findings confirm that housing affordability is relevant in individuals’ decision to share. At the same time, my research sheds doubt on the reliability of conventional measures of affordability as the effects on sharing are marginal.

Linear instrumental variables models are widely used in empirical work, but often associated with low estimator precision. This paper proposes an estimator that is robust to outliers and shows that the estimator is minimax optimal in a class of estimators that includes the limited maximum likelihood estimator (LIML). Intuitively, this optimal robust estimator combines LIML with Winsorization of the structural residuals and the Winsorization leads to improved precision under thick-tailed error distributions. Consistency and asymptotic normality of the estimator are established under many instruments asymptotics and a consistent variance estimator which allows for asymptotically valid inference is provided.

This paper extends previous measurements of the children’s income-health gradient through 2015. We first replicate findings about the steepness of the gradient from 1986 to 2005. We then find that since 2005, the health gradient between children in low-income families and high-income families has flattened, but it still steepens with age. We also show that overall children’s health has improved since 2005.

Prior meta-regression models (MRMs) of wetland values pool value estimates associated with diverse commodity types—for example recreation, flood control, nutrient cycling, habitat provision, nonuse value, and carbon sequestration. Neither theory nor economic intuition justify the inclusion of such dissimilar commodities within a single meta-analytic value function, leading to validity concerns. This article seeks to advance methods for commodity and welfare consistent MRMs, applied to a particular category of wetland values. We develop a wetland value MRM restricted to a specific wetland type (coastal marshes), general location (US and Canada), commodity type (habitat provision and services), and valuation approach (stated preference methods). Results indicate that willingness to pay per household for marsh habitat changes is responsive to scope, spatial scale, market extent, the type of habitat change, household characteristics, and other factors suggested by theory and intuition. Results supersede those of prior wetland value MRMs in terms of statistical performance, estimation of anticipated value surface patterns, and capacity to support conceptually valid benefit transfers. Comparison with an otherwise identical but less commodity consistent MRM demonstrates that commodity consistency leads to improved statistical and benefit transfer performance.

Background While optimal cardiovascular risk factor (CRF) profile is associated with lower mortality, morbidity, and healthcare expenditures among individuals with atherosclerotic cardiovascular disease (ASCVD), less is known regarding its impact on financial hardship from medical bills. Therefore, we assessed whether an optimal CRF profile is associated with a lower burden of financial hardship from medical bills and a reduction in cost-related barriers to health. Methods We used a nationally representative sample of adults between 18 and 64 years from the National Health Interview Survey between 2013 and 2017. We assessed ASCVD status and the number of risk factors to categorize the study population into 4 mutually exclusive categories: ASCVD (irrespective of CRF profile) and non-ASCVD with poor, average, and optimal CRF profile. Adjusted logistic regression model was used to determine the association of ASCVD/CRF profile with financial hardship from medical bills and cost-related barriers to health (cost-related medication non-adherence (CRN), foregone/delayed care, and high financial distress). Results We included 119,388 non-elderly adults, representing 189 million individuals annually across the United States. Non-ASCVD/optimal CRF profile individuals had a lower prevalence of financial hardship and an inability paying medical bills when compared with individuals with ASCVD (24% vs 45% and 6% vs 19%, respectively). Among individuals without ASCVD and an optimal CRF profile, the prevalence of each cost-related barrier to health was <50% compared with individuals with ASCVD. Poor/low income and uninsured individuals within non-ASCVD/average CRF profile strata had a lower prevalence of financial hardship and an inability paying medical bills when compared with middle/high income and insured individuals with ASCVD. Non-ASCVD individuals with optimal CRF profile had the lowest odds of all barriers to health. Conclusion Optimal CRF profile is associated with a lower prevalence of financial hardship from medical bills and cost-related barriers to health despite lower income and lack of insurance.

This correlational, explanatory, cross-sectional study explains the influence of neighborhoods’ structural determinants on the rate of violent crimes in New York City’s communities. Guided by the theoretical foundation of social disorganization theory, the variables in this study included the economically disadvantaged, racial/ethnic heterogeneity, residential instability/mobility, and the level of educational attainment. The statistical analysis in this study included correlational matrix and simultaneous multiple regression model (ordinary least squares). The study consisted of 59 New York City community districts (encompassing the City’s population of 8,622,698 residents) and included the violent crime rates for 2017. The findings in this study indicated that the level of the community’s economically disadvantaged and residential instability/mobility does influence the rate of violent crimes in New York City communities. Conversely, racial/ethnic heterogeneity and the level of educational attainment did not influence the rate of violent crime in New York City communities. The findings suggest that more resources should be directed to address poverty within communities with high rates of violent crime