Mexican Americans have a lower prevalence of asthma than White Americans, Black Americans, and Other Hispanics. This is concordant with the Hispanic Paradox, which posits that Hispanics have good health and lower mortality than White Americans despite their relative socioeconomic disadvantages. However, the research is limited in relation to the effects of race on health, independent of ethnicity, among this population. In this study, the author disaggregated Mexican Americans, foreign-born and U.S.-born into two categories, White and Black Mexicans, in order to assess their likelihood of having an asthma diagnosis, compared to White Americans and to each other. This study used harmonized data from the National Health Interview Survey from 2000–2018 with a final analytic sample of N = 1,094,516. The analysis was conducted using binary logistic regression, controlling for acculturation and health behavior-related variables, as well as sociodemographic characteristics. In the results, Black Mexicans had a significant disadvantage in relation to their White counterparts and White Americans. The findings suggest there is an intra-ethnic racial disparity in asthma and the Hispanic paradox is not applicable across racial lines for Mexican Americans. These findings also suggest Black Mexicans’ poor asthma outcomes are the byproduct of various mechanisms of racial inequality.

The Access and Functional Needs population is oftentimes either not ready for a natural emergency, or agencies who provide resources at shelters are not prepared with enough resources for them. Aging and Adult services in Monterey county joined with the Office of Emergency Services are joining forces to make sure that they understand and are ready to meet the needs of this specific population to provide the care needed in case of a natural disaster. The project concentrated in getting accurate community data in order to understand the volume of need for this population. The results showed not only information about overall access and functional needs population, but also information based on the county region.

Artificial intelligence (AI) is a quickly advancing technology that has the potential to displace a great deal of workers. Unlike past automation based technologies, I find that high skilled labor is more impacted by AI than lower skilled labor. In order to analyze the impact that AI will have on the labor market, I utilize a fixed effects model for a historical case of automation's impact on employment and a fitted parameter methodology to analyze the careers most and least exposed to artificial intelligence. My results suggest that an increase in exposure to automation technologies by one percentile leads to a .049% decrease in industry-occupation share of the labor market.

Despite the contraction of many male-dominated occupations, men have made limited progress in entering female-dominated jobs. Using monthly employment histories from the SIPP, we examine whether individual economic conditions—such as a period of unemployment—are associated with men subsequently pursuing female-dominated work. Specifically, we ask whether men are more likely to enter female-dominated jobs after unemployment, compared to men who take a new job directly from employment. We find that unemployment significantly increases the odds of men entering female-dominated work among men who make job transitions. By examining changes in occupational prestige as well as wage differences before and after unemployment, we also find that entering a female-dominated job (compared to other job types) may help men mitigate common scarring effects of unemployment such as wage losses and occupational prestige downgrades. Accordingly, this study reveals a critical occupational route that may allow men to remain upwardly mobile after involuntary unemployment.

Does past migration beget future migration? Do migrants from different backgrounds, origins and ethnicities, and separated by several generations always settle – in a path dependent way – in the same places? Is there a permanent separation between migration-prone and migration-averse areas? This paper examines whether that is the case by looking at the settlement patterns of two very different migration waves, that of Europeans at the end of the 19th and early 20th centuries and that of Latin Americans between the 1960s and the early 21st century. Using Census data aggregated at county level, we track the settlement pattern of migrants and assess the extent to which the first mass migration wave has determined the later settlement pattern of Latin American migrants to the US. The analysis, conducted using ordinary least squares, instrumental variable and panel data estimation techniques, shows that past US migration patterns create a path dependence that has consistently affected the geography of future migration waves. Recent Latin American migrants have flocked, once other factors are controlled for, to the same migration prone US counties where their European predecessors settled, in spite of the very different nature of both migration waves and a time gap of three to five generations.

Purpose: Little is known about the role of structural, performance, and community factors that impact the likelihood of receiving a penalty under the Hospital Readmission Reduction Program. This study examined the association between structural, performance, and community factors and the likelihood of receiving a penalty as well as investigated the likelihood of hospitals serving vulnerable populations of receiving a penalty. Methods: Centers for Medicare and Medicaid Services and United States Census Bureau data were used in this analysis. Ordered logistic regressions in a cross-sectional analysis were employed to estimate the probability of receiving a high or low penalty in the fiscal year 2013 through 2019. Results: On average, medium-sized, major teaching, and safety-net hospitals had the highest proportion of hospitals with a high penalty. After controlling for performance and community factors, structural factor variables such as safety-net status, rural status, and teaching status either were no longer significant or the likelihood magnitude changed. However, after controlling for performance and community factors, the statistical significance of hospital size variables and geographic location persisted across the years. Length of stay and occupancy rate variables were also statistically significant across the 7 years under review. Conclusion: Taken together, structural, performance, and community factors are important in explaining variation in the likelihood of receiving a penalty. There is no evidence that safety-net, rural, and public hospitals are more likely to receive a penalty. The results also suggest that there is room for providers to reduce avoidable readmissions and policymakers to mitigate unintended consequences.

Technological advancements bring changes to our life, altering our behaviors as well as our role in the economy. In this paper, we examine the potential effect of the rise of robotic technology on health. The results of the analysis suggest that higher penetration of industrial robots in the local labor market is positively related to the health of the low-skilled population. A ten percent increase in robots per 1,000 workers is associated with an approximately 10% reduction in the fraction of low-skilled individuals reporting poor health. Further analysis suggests that reallocation of tasks and reduction in unhealthy behavior partly explain this finding.

Income inequality among U.S. families with children has increased over recent decades, coinciding with a period of significant reforms in federal welfare policy. In the most recent reform eras, welfare benefits were significantly restructured and redistributed, which may have important implications for income inequality. Using data from the 1968–2016 March Supplement to the Current Population Survey (N = 1,192,244 families with children) merged with data from the historical Supplemental Poverty Measure, this study investigated how income inequality and, relatedly, the redistributive effects of welfare income and in-kind benefits changed, and whether such changes varied across states with different approaches to welfare policy. Results suggest that cash income from welfare became less effective at reducing income inequality after the 1996 welfare reform, because the share of income coming from cash welfare fell and was also less concentrated among the neediest families. At the same time, tax and in-kind benefits reduced inequality until the Great Recession. Consistent with the “race to the bottom” hypothesis, results suggest that the redistributive effects of welfare income dropped in all states regardless of their approach to welfare policy.

The author considers the mechanisms by which occupational sex composition (the proportion of women and men in an occupation) might be associated with romantic transitions in the United States. Using data from the National Longitudinal Survey of Youth 1979 to 2014, the author estimates the odds of marriage during a period of 35 years as a function of occupational and personal characteristics. Men's odds of marriage are decreased by working in predominately female occupations (75%–100% female) when compared with working in predominately male occupations (0%–25% female) or integrated (26%–74% female) occupations. Also, working in a predominately female occupation increases the odds that men have never married by ages 30 and 40. Women's odds of marriage are unrelated to occupational sex composition. Although the author focuses on marriage, the results are robust to including cohabitation as a competing risk. The author uses data from the National Longitudinal Study of Adolescent to Adult Health 1994 to 2008 to replicate these findings in a more recent cohort with additional control variables. The romantic penalty for men's occupational gender atypicality demonstrates the continued devaluation of female activities and attributes and the resulting rigidity of expectations for men's gendered behavior, which may reinforce occupational segregation.

It has long been recognized that economic inequality may undermine the principle of equal responsiveness that lies at the core of democratic governance. A recent wave of scholarship has highlighted an acute degree of political inequality in contemporary democracies in North America and Europe. In contrast to the view that unequal responsiveness in favor of the affluent is nearly inevitable when income inequality is high, we argue that organized labor can be an effective source of political equality. Focusing on the paradigmatic case of the U.S. House of Representatives, our novel dataset combines income-specific estimates of constituency preferences based on 223,000 survey respondents matched to roll-call votes with a measure of district-level union strength drawn from administrative records. We find that local unions significantly dampen unequal responsiveness to high incomes: a standard deviation increase in union membership increases legislative responsiveness towards the poor by about six to eight percentage points. As a result, in districts with relatively strong unions legislators are about equally responsive to rich and poor Americans. We rule out alternative explanations using flexible controls for policies, institutions, and economic structure, as well as a novel instrumental variable for unionization based on history and geography. We also show that the impact of unions operates via campaign contributions and partisan selection.

Renters represent a multilayered cross section of Salt Lake City and Salt Lake County. Many people living in rented homes belong to groups with limited access to economic opportunities, a reality only partly incident to their life stages. Housing outcomes in the Salt Lake area depend on market forces, population dynamics, housing supply, and local policy. This report provides regional and historical context for local renter characteristics, including educational attainment, occupation, income stability, household size, age, and racial and ethnic identity. The Kem C. Gardner Policy Institute analyzed baseline demographic data on renters as part of its Capital City Demographics contract with Salt Lake City.

Internships have become a ubiquitous component of the college-career transition, yet empirical evidence of the internship market is limited. This study uses data from 1.3 million internship postings collected between 2007-2016 in the United States to (1) identify trends in internship education, experience, and skill requirements over the Great Recession and recovery periods; (2) evaluate how these trends correspond to those observed in the traditional labor market; and (3) assess robustness across labor market sectors. Results indicate that internship education and skill requirements increased substantially throughout the recession and recovery periods, indicative of a longer-term structural shift in employer expectations about internship hiring. Additionally, growth in internship education and skill requirements largely outpaced growth in non-internship education and skill requirements over the same period, suggesting potential substitution of noninterns with interns. Post-recession employers still consider internships to be entry-level positions—yet now expect interns to have skills in hand.

Appendix A: Additional Tables and Figures Appendix B: Additional Robustness Checks Appendix C: Additional Literature and EITC Details Appendix D: Calculating Elasticities Appendix E: Less Parametric Approaches Appendix F: Data Appendix Appendix G: External Validity: Attitude Changes After World War II

Research has increasingly moved toward a consensus that violent crime declines as neighborhoods gentrify, yet some studies find the direction of this relationship varies by type of violent crime. This finding becomes even more important when connected with recent research that finds the structural influences of gang and non-gang homicide are disparate. The current study engages with research in each of these areas by examining the relationship of gentrification with levels of total, gang, and non-gang homicide in Los Angeles Police Department’s (LAPD) Hollenbeck Community Policing Area. We find gentrification was not associated with variation in total or gang homicide, but was positively associated with non-gang homicide.

Background Public health authorities recommend that people practice social distancing, especially if they have symptoms of coronavirus disease (COVID-19), or are older and more at risk of serious illness if they become infected. We test the hypothesis that these groups are following these recommendations and are more likely to undertake social distancing. Methods We conducted an open online survey of 4,676 U.S. adults aged 18 and older between April 4 and April 7, 2020. We model the effects of age and common COVID-19 symptoms in the last two weeks on going out of the home for non-healthcare reasons the day before taking the survey, using a logistic model and the number of close contacts (within 6 feet) that respondents had with non-household members, using a Poisson count model. Our models control for several covariates, including other flu-like symptoms, sex, education, income, whether the respondent worked in February, household size, population density in the respondent’s ZIP code, state fixed effects, and the day of completion of the survey. We also weight our analyses to make the sample representative of the U.S. adult population. Findings About 52 percent of the adult United States population went out of their home the previous day. On average, adults had close contact with 1.9 non-household members. We find that having at least one COVID-19 symptom (fever, dry cough, or shortness of breath) increased the likelihood of going out the previous day and having additional close contacts with non-household members; however, the estimates were not statistically significant. When disaggregating our analysis by COVID-19 symptoms, we find no strong evidence of greater social distancing by people with a fever or cough in the last two weeks, but we do find that those who experienced shortness of breath have fewer close contacts, with an incidence rate ratio (IRR) of 0.49 (95% CI: 0.30–0.78). Having other flulike symptoms reduces the odds of going out by 0.32 (95% CI: 0.18–0.60) and the incidence rate of having close contacts by 42 percent (IRR = 0.58; 95% CI: 0.38–0.88). We find that older people are just as likely to leave their homes as younger people, but people over the age of 50 had less than half the predicted number of close contacts than those who were younger than 30. Our approach has the limitation that the survey sample is self-selected. Our findings may therefore be subject to selection bias that is not adequately controlled for by weighting. In addition, the possibility exists of confounding of the results due to omitted variable bias. 3 Conclusions We provide evidence that older people are having significantly fewer close contacts than younger people, which is in line with the public health authorities’ recommendations. We also find that people experiencing shortness of breath are practicing more intense social distancing. However, we find that those with two other common COVID-19 symptoms, fever and dry cough, are not engaging in greater social distancing, suggesting that increased targeting on relevant symptoms, and messaging, may be required.

Introduction: The public health enterprise has a people problem. An aging workforce coupled with a sustained, strong economy and healthcare sector has made the recruitment and retention of young, educated staff challenging. Approximately one third of public health staff aged 33 years and younger are considering leaving their organization in the next year. Their reasons for leaving, and considerations for staying, are not well characterized within public health. Methods: Data were drawn from the Public Health Workforce Interests and Needs Survey, a nationally representative survey of state and local governmental public health employees across the U.S. In 2017, a total of 43,701 staff responded. Descriptive statistics across age groups were examined, and reasons for leaving were characterized. A latent class model and an intent-to-leave logit model were fit in 2019. Results: Pay and lack of opportunities for advancement were most frequently selected as reasons for considering leaving. Results of a logit model showed that being somewhat or very dissatisfied (versus somewhat or very satisfied) was associated with higher odds of intending to leave (AOR=4.4, p<0.0001), as was pay dissatisfaction (AOR=2.0, p<0.0001). Scoring higher than the agency median on a construct measuring perceived lack of organizational support (AOR=1.8, p<0.0001) and on a scale measuring burnout (AOR=2.6, p<0.0001) was also associated with higher odds of intending to leave. Conclusions: Many factors associated with an increased intent to leave are present among all age groups. However, support is needed for managers as they attempt to develop and implement solutions that seek to retain the younger workforce in particular. Creating paths for promotion, competitive pay practices, organizational support, and engagement are all critical for retention in this group.

Staff, teachers, and students experienced rapid change as school buildings closed in March 2020 because of the spread of the novel coronavirus, COVID-19. As school districts scramble to deliver lessons remotely, particularly as they consider long-term strategies and solutions, it is important to understand variations in the challenges that students are facing across the country. Although school districts may be aware of some of these challenges, such as student disability or English language learner status, other issues may be harder to identify and assess, such as a student’s crowded home conditions, her access to technology for remote learning, and her household’s vulnerability to pandemic-induced economic hardship. In this brief, we use American Community Survey (ACS) data to highlight different types of challenges to remote learning and point to district and educator strategies that might mitigate harm to students as districts navigate long-term school closures. Student needs during a period of remote learning are difficult to measure and do not all directly correlate with other student needs, such as the share of students living in poverty. States and districts need information on where different types of student need are greatest, so that new resources from the CARES Act1 and other aid can be deployed to facilitate remote learning. The identification of districts and regions with similar needs can also facilitate the sharing of best practices for serving a particular need, whether it is reaching linguistically isolated students or providing school meals to families that are vulnerable to COVID-19 job loss.

Dramatic improvements in information technology have the potential to transform healthcare delivery, and a key question is how such changes will affect the healthcare workforce of the future. In this brief, we present the state of knowledge of the effects of health information technology on the workforce. We first lay out the rapidly changing healthcare landscape due to the greater availability and use of information and communication technology (ICT) followed by a description of the evolution of employment, wages, and education across the wide variety of occupations in the healthcare sector since 1980. The healthcare sector has outperformed the rest of the economy and has proven resilient to the multiple downturns over the last four decades, although some groups have done much better than others. Next, we review the literature on the effects of ICT on productivity in terms of patient health outcomes and resource use, as well as the effects on healthcare expenditure. We find that there is evidence of a positive effect of ICT (e.g., especially electronic health records) on clinical productivity, but (i) it takes time for these positive effects to materialize; and (ii) there is much variation in the impact, with many organizations seeing no benefits. Looking at the drivers of adoption, we find that the role of workers is critical, especially physicians' attitudes and skills. Privacy laws, fragmentation, and weak competition are also causes of slow adoption. There is very little quantitative work that investigates directly the impact of new technology on workers' jobs, skills, and wages, but what there is suggests no substantial negative effects. Our own analysis finds no evidence of negative effects looking at aggregate data and hospital-level event studies. These findings are consistent with studies outside of healthcare, which stress the importance of complementary factors (such as management practices and skills) in determining the success of ICT investments. We conclude that management initiatives to increase the skills of workers will be required if the healthcare workforce and society more generally are to substantially benefit from the adoption of these powerful tools.

Research Summary:This article conceptualizes households as a cru-cial pool of labor for small entrepreneurs. The household varied his-torically in its scope (depending on whether bonded workers wereincluded) and work intensity (depending on the authority or coer-cion exercised by household heads). Drawing on data that enumer-ate over 100,000 households in New York City, I examine how theshift from institutions of unfree labor to wage labor affected busi-ness proprietorship between 1790 and 1850. Given the dispropor-tionate importance of unfree household labor to smallentrepreneurs, the contraction of this labor source may offer onegeneral explanation for their decline.Managerial Summary:How does household scope and compositionaffect the ability of an individual to run their own business? Histori-cal archives can provide useful insights into this question. Theytrack long-term declines in family size and the emancipation ofnon-family members—such as apprentices, indentured servants,and slaves—from the authority of household heads. Examining records from early New York City, this study shows that business ownership was strongly linked with the ownership of slaves and the presence of dependent males after the American Revolution.Large households and unfree laborers were especially important for entrepreneurship among individuals with limited wealth. For mod-ern economies, the results suggest that policymakers considerpotential tensions between small business ownership and thedevelopment of free and equitable labor markets.

In this paper we present a formulation of k-anonymity as a mathematical optimization problem. In solving this formulated problem, k-anonymity is achieved while maximizing the utility of the resulting dataset. Our formulation has the advantage of incorporating different weights for attributes in order to achieve customized utility to suit different research purposes. The resulting formulation is a Mixed Integer Linear Program (MILP), which is NP-complete in general. Recognizing the complexity of the problem, we propose two practical algorithms which can provide near-optimal utility. Our experimental evaluation confirms that our algorithms are scalable when used for datasets containing large numbers of records.