22q11.2 deletion syndrome (22q11DS) and 22q duplication syndrome present a wide range of medical challenges. The health and well-being of pediatric patients with 22q11DS may be influenced by socioeconomic factors, which can significantly shape their healthcare experiences, access to services, and overall quality of life. The objective of this study is to identify what factors are correlated with no-show clinic visits for children with 22q11DS.

Sparsely populated regions (SPRs) have specific features like remoteness and low population densities but also specific identities constructed by their inhabitants based on their relationship with their environment and the consequences to their lifestyles. Although theoretical frameworks have been developed for SPRs, two challenges remain when it comes to applying them to actual places. The first one is identifying them on the map. What would the demographic threshold of “sparse” be? How do we quantify the isolation? The second one is evaluating how SPR features reverberate in the lifestyle and self-image of their inhabitants. What are their views about themselves and their geographical situation? Are they linked? This article attempts to elaborate on both dimensions. It uses the state of Arizona as a test area and proposes an approach that combines quantitative methods and geographic information systems to determine which part of Arizona can be considered an SPR and a qualitative analysis to analyze how this population sees and conceptualizes its lifestyle, as well as how they relate to more densely populated areas, especially on the issue of isolation relative to place attachment and place identity. As a result, this article will offer a better grasp of SPRs in the United States and suggest new trends to be investigated in other geographical contexts.

Background: Race/ethnic disparities in preventable diabetes-specific hospital care may exist among adults with diabetes who have Medicaid coverage. Objective: To examine race/ethnic disparities in utilization of preventable hospital care by adult Medicaid enrollees with diabetes across nine states over time. Design: Using serial cross-sectional state discharge records for emergency department (ED) visits and inpatient (IP) hospitalizations from the Healthcare Cost and Utilization Project, we quantified race/ethnicity-specific, state-year preventable diabetes-specific hospital utilization. Participants: Non-Hispanic Black, non-Hispanic White, and Hispanic adult Medicaid enrollees aged 18–64 with a diabetes diagnosis (excluding gestational or secondary diabetes) who were discharged from hospital care in Arizona, Iowa, Kentucky, Florida, Maryland, New Jersey, New York, North Carolina, and Utah for the years 2008, 2011, 2014, and 2017. Main Measures: Non-Hispanic Black-over-White and Hispanic-over-White rate ratios constructed using age- standardized state-year, race/ethnicity-specific ED, and IP diabetes-specific utilization rates. Key Results: The ratio of Black-over-White ED utilization rates for preventable diabetes-specific hospital care increased across the 9 states in our sample from 1.4 (CI 95, 1.31–1.50) in 2008 to 1.73 (CI 95, 1.68–1.78) in 2017. The cross-year-state average non-Hispanic Black-over-White IP rate ratio was 1.46 (CI 95, 1.42–1.50), reflecting increases in some states and decreases in others. The across-state-year average Hispanic-over-White rate ratio for ED utilization was 0.67 (CI 95, 0.63–0.71). The across-state-year average Hispanic-over-White IP hospitalization rate ratio was 0.72 (CI 95, 0.69–0.75). Conclusions: Hospital utilization by non-Hispanic Black Medicaid enrollees with diabetes was consistently greater and often increased relative to utilization by White enrollees within state programs between 2008 and 2017. Hispanic enrollee hospital utilization was either lower or indistinguishable relative to White enrollee hospital utilization in most states, but Hispanic utilization increased faster than White utilization in some states. Among broader patterns, there is heterogeneity in the magnitude of race/ethnic disparities in hospital utilization trends across states.

Using newly digitized U.S. city-level data on hospitals, we explore how pandemics alter preferences for healthcare. We find that cities with higher levels of mortality during the Great Influenza of 1918-1919 subsequently expanded hospital capacity by more than cities experiencing less influenza mortality: cities in the top half of the mortality distribution increased their count of hospitals by 8-10 percent in the years after the pandemic. This effect persisted to 1960 and was driven by increases in non-governmental hospitals. Growth responded most in richer cities, exacerbating existing inequalities in access to healthcare. We do not find evidence that government-run hospitals or other types of city-level spending related to healthcare responded to pandemic intensity, suggesting that large health shocks do not necessarily lead to increased public provision of health services.

The adoption of health information and communication technology (HICT) has surged over the past two decades. We survey the medical and economic literature on HICT adoption and its impact on clinical outcomes, productivity, and the health care workforce. We find that HICT improves clinical outcomes and lowers health care costs; however, (a) the effects are modest so far, (b) it takes time for these effects to materialize, and (c) there is much variation in the impact. More evidence on the causal effects of HICT on productivity is needed to improve our analytical understanding and to guide further adoption. There is little econometric work directly investigating the impact of HICT on labor market outcomes, but the existing literature suggests that there are no substantial negative effects on employment and earnings. Overall, although health care is in many ways exceptional, we are struck by the similarities of our conclusions to the wider findings on the relationship between productivity and information and communication technologies, which stress the importance of complementary factors (e.g., management and skills) in determining the impact of these new technologies.

This dissertation comprises three distinct papers investigating different types of individuallevel entrepreneurial behaviors and their determinants. Table A-1 gives an overview of the most important characteristics of each study. All studies have distinct research questions and contributions and draw on methodological approaches and theory, which are most pertinent to explore the hypothesized relationships.

Introduction: This study focuses on the demographic and socioeconomic changes occurring within the Puerto Rican population of the New York metropolitan area between 1970 and 2020. In 2020, there were about 1.19 million Puerto Rican-origin people living in the New York City metro area. Methods: This report uses the American Community Survey PUMS (Public Use Microdata Series) data for all years released by the Census Bureau and reorganized for public use by the Minnesota Population Center, University of Minnesota, IPUMSusa, (https://usa.ipums.org/usa/index.shtml). See Public Use Microdata Series Steven Ruggles, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota, 2020. Census tract data depicted in maps were derived from Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 15.0 [dataset]. Minneapolis, MN: IPUMS. Discussion: The Puerto Rican population of New York City peaked in 1970 at close to 900,000 and gradually declined thereafter to about 666,000 in 2020. However, during the 1970s a process of suburbanization began in earnest as many second and third generation Puerto Ricans moved to urban areas of New Jersey, suburban counties in New York to the north of the City, Fairfield County Connecticut (mainly Bridgeport), and to Long Island. In 1970 only 17% of all Puerto Ricans living in the New York metropolitan area lived outside of the City. This increased to 44% in 2020. In 2020 nearly 21% of all Puerto Ricans in the region lived in New Jersey; 14% in the northern New York counties and Fairfield County, in Connecticut; and 9% on Long Island. Nativity, or place of birth, was an important factor in the changing socioeconomic transformations within the Puerto Rican community of the region. One important change was in educational atainment. In 1970 only 0.6% of the adult Puerto Rican population (25 years of age and older) had graduated college with a B.A. degree or higher. This steadily increased to nearly 18% in 2020. However, among U.S.-born Puerto Ricans 22% completed at least a university degree compared with only 11% of Puerto Rican adults born on the island. Puerto Rican women 25 years of age and older had consistently higher college graduation rates than Puerto Rican men. By 2020 nearly 21% of all Puerto Rican women had completed a B. A. degree or higher, compared with about 16% of Puerto Rican men.

Labor protection policies in the 1950s and 1960s helped many low-and middle-wage white workers in the United States achieve the American Dream. This coincided with historically low levels of inequality across income deciles. After the Civil Rights Act of 1964, many of the policies that had previously helped build the white middle class reversed, especially in states with a larger Black population. Calibrating a labor search model to match unemployment benefits, bargaining power, and minimum wages before and after the Civil Rights Act, I find declining labor protections explain 60 percent of the rise in 90/10 income inequality since the 1960s.

We investigate how income tax reductions affect work hours. Our empirical strategy relies on the fact that, in states where taxpayers can deduct federal tax payments from state taxable income, federal tax changes are dampened. We study 2003 tax reforms (JGTRRA) that dramatically reduced federal tax rates on dividends and capital gains, and moderately reduced rates on ordinary income. Difference-in-Difference analysis indicates that work hours decreased most among high income and wealthy taxpayers who were most directly affected by the tax reductions. The decrease in hours was larger for residents of states in which the effective tax reductions were larger. Conversely, we find possible evidence that larger ordinary income tax rate reductions in the 1980s, accompanied by effective tax increases on capital gains, had the opposite effect and induced an increase in work hours. These results suggest that the effect of tax reductions may depend on the type of income targeted.

We employ a structural model of location choice to estimate household sorting across the U.S. in response to hurricane risk. Using spatially detailed projections of future hurricane energy, we simulate regional population shifts and welfare impacts of hurricane risk-induced migration in 2100. We find heterogeneous responses to hurricane risk for households that vary by number of children, age, educational attainment, and prior exposure to hurricane risk. Under future hurricane risk, although changes are small, we find declines in regional population shares along the hurricane-prone coasts and negative overall welfare impact. However, ignoring the spatial heterogeneity of hurricanes underestimates these impacts.

OBJECTIVE: To analyze national and state-specific trends in diabetes-related hospital admissions and determine whether disparities in rates of admission exist between demographic groups and geographically dispersed states. RESEARCH DESIGN AND METHODS: We conducted serial cross-sectional analyses of the National Inpatient Sample (2008, 2011, 2014, and 2016) and State Inpatient Databases for Arizona, Florida, Kentucky, Iowa, Maryland, Nebraska, New Jersey, New York, North Carolina, Utah, and Vermont for 2008, 2011, 2014, and 2016/2017 among adult patients with type 1 and type 2 diabetes-related ICD codes (ICD-9 [250.XX] or ICD-10 [E10.XXX, E11.XXX, and E13.XXX]. We measured hospitalization rates for people with diabetes (all-cause hospitalizations) and for admissions with a primary diagnosis of diabetes or diabetes-related complications (diabetes-specific hospitalizations) per 10,000 people per year. RESULTS: Nationally, all-cause and diabetes-specific hospitalizations declined by 3.1% (95% CI -5.5, -0.7) and 19.1% (95% CI -21.6, -16.6), respectively, over 2008 to 2016. The analysis of individual states showed that diabetes-specific admissions in individuals ≥65 years old declined during this time (16.3-48.8% decrease) but increased among patients 18-29 years old (10.5-81.5% increase) and that rural diabetes-specific admissions decreased in just over half of the included states (15.2-69.2% decrease). There were no differences in changes in admission rates among different racial/ethnic groups. CONCLUSIONS: Overall, rates of diabetes-related hospitalizations decreased over 2008 to 2016/2017, but there were large state-level differences across subgroups of patients. The rise in diabetes hospitalizations among young adults is a cause for concern. These state- and subpopulation-level differences highlight the need for state-level policies and interventions to address disparities in diabetes health care use.

We document a robust negative relationship between mean annual hours in an occupation and the dispersion of annual hours within that occupation. We study a unified model of occupational choice and labor supply that features heterogeneity across occupations in the return to working additional hours and show that it can match the key features of the data both qualitatively and quantitatively. Occupational choice in our model is shaped both by selection on comparative advantage and selection on tastes for leisure. Our quantitative work finds that the dominant source of differences in hours across occupations is selection on tastes for leisure. Abstract We document a robust negative relationship between mean annual hours in an occupation and the dispersion of annual hours within that occupation. We study a unified model of occupational choice and labor supply that features heterogeneity across occupations in the return to working additional hours and show that it can match the key features of the data both qualitatively and quantitatively. Occupational choice in our model is shaped both by selection on comparative advantage and selection on tastes for leisure. Our quantitative work finds that the dominant source of differences in hours across occupations is selection on tastes for leisure.

The COVID-19 epidemic resulted in a dramatic contraction in employment in the U.S., but the effects of this contraction have been unevenly distributed. We examine differences in employment among foreign- and native-born workers by race/ethnicity during the course of the epidemic. We test individual fixed-effects models based on data from the monthly CPS panel from January 2020 to December 2021 adjusting for seasonality. Immigrant men and women experienced greater declines in employment than non-immigrants of the same race/ethnicity when both compared to native-born Whites, but their disadvantage were limited to the initial months of the epidemic. Ethnoracial and immigrant status disparities were substantially reduced by the fall of 2020, except for Hispanic immigrant men and women, who still experienced substantial employment gaps with their native-born White counterparts. Differences in family characteristics account for Hispanic immigrant women’s lower employment rates during the epidemic but do not appear to account for differences between Black and Asian women and native-born Whites. Observed disparities in employment by race/ethnicity and immigrant status cannot be fully explained by differences in education, the concentration of minority and immigrant workers in industries and occupations that suffered steeper employment declines, or regional differences in the intensity of the epidemic.

The COVID-19 pandemic and its disproportionate impact on Black and other non-White communities illustrated existing economic and health disparities, not only nationally, but also in Washtenaw County. Mirroring national trends, Black people in Washtenaw County had higher rates of COVID-19 infection and were more likely to die from COVID-19 than White people due to economic and health disparities that increased their risk for severe disease.1 Prior to the pandemic, Washtenaw County and health care organizations within the county had begun to take steps to mitigate unequal outcomes by race and income on education, employment, and health by intentionally creating policies and targeting resources to address those inequities. In the wake of the public health crisis and the negative economic impacts it created, the federal government allocated money in the American Rescue Plan Act (ARPA) to provide local governments money to address the public health and economic impacts of the pandemic on families and businesses, maintain services amidst declines in revenue due to the crisis, and create a strong, equitable recovery by making investments that support long-term growth and opportunity. These funds provide Washtenaw County with a unique chance to bolster current efforts to eliminate gaps in opportunity. This memo outlines the programs funded through the initial allocations of ARPA funding and discusses core themes county officials should bear in mind in determining how to spend the second installment of federal funds. In particular, county officials should: 1) ensure programs funded in their initial allocation are adequate to achieve program success; 2) be cautious in funding programs requiring a permanent funding source; and 3) prioritize spending on one-time investments that are likely to yield long-term impacts.

A growing literature has pointed out disparities in teleworking among different racial and ethnic (hereafter racial) workers. This study estimated racial disparities in teleworking due to the COVID-19 pandemic and the extent to which these disparities were mediated by four-year college education and occupation in the United States. The data source for this study was the Current Population Survey, May 2020 through July 2021. The results showed that in the reduced model, the odds for Black and Hispanic workers to telework were 35% and 55% lower, respectively, and for Asian workers 44% higher than for White workers, controlling for covariates. When four-year college education and occupation were included as mediator variables in the model, the odds for Black and Hispanic workers to telework were reduced to 7% and 16%, respectively. Overall, disparities in four-year college education and occupation explained 83% and 78% of the variation in the odds of teleworking for Black and Hispanic workers, respectively. Between the mediators, occupation explained more than 60% of the total effect. The results of this study could not rule out the possibility of racial discrimination in teleworking. Ultimately, reducing racial disparities in four-year college education and in different occupations might be a long-term solution for reducing racial disparities in teleworking.

Importance: The health care sector lost millions of workers during the COVID-19 pandemic and job recovery has been slow, particularly in long-term care. Objective: To identify which health care workers were at highest risk of exiting the health care workforce during the COVID-19 pandemic. Design, Setting, and Participants: This was an observational cross-sectional study conducted among individuals employed full-time in health care jobs from 2019 to 2021 in the US. Using the data from the Current Population Survey (CPS), we compared turnover rates before the pandemic (preperiod, January 2019-March 2020; 71 843 observations from CPS) with the first 9 months (postperiod 1, April 2020-December 2020; 38 556 observations) and latter 8 months of the pandemic (postperiod 2, January 2021-October 2021; 44 389 observations). Main Outcomes and Measures: Health care workforce exits (also referred to as turnover) defined as a health care worker's response to the CPS as being unemployed or out of the labor force in a month subsequent to a month when they reported being actively employed in the health care workforce. The probability of exiting the health care workforce was estimated using a logistic regression model controlling for health care occupation, health care setting, being female, having a child younger than 5 years old in the household, race and ethnicity, age and age squared, citizenship status, being married, having less than a bachelor’s degree, living in a metropolitan area, identifier for those reporting employment status at the first peak of COVID-19, and select interaction terms with time periods (postperiods 1 and 2). Data analyses were conducted from March 1, 2021, to January 31, 2022. Results: The study population comprised 125 717 unique health care workers with a mean (SD) age of 42.3 (12.1) years; 96 802 (77.0%) were women; 84 733 (67.4%) were White individuals. Estimated health care turnover rates peaked in postperiod 1, but largely recovered by postperiod 2, except for among long-term care workers and physicians. We found a 4-fold difference in turnover rates between physicians and health aides or assistants. Rates were also higher for health workers with young children (<5 years), for both sexes and highest among women. By race and ethnicity, persistently higher turnover rates were found among American Indian/Alaska Native/Pacific Islander workers; White workers had persistently lower rates; and Black and Latino workers experienced the slowest job recovery rates. Conclusions and Relevance: The findings of this observational cross-sectional study suggest that although much of the health care workforce is on track to recover to prepandemic turnover rates, these rates have been persistently high and slow to recover among long-term care workers, health aides and assistants, workers of minoritized racial and ethnic groups, and women with young children. Given the high demand for long-term care workers, targeted attention is needed to recruit job-seeking health care workers and to retain those currently in these jobs to lessen turnover.

In the United States, four million African Americans migrated from the South to the North between 1940 and 1970. How did it impact aggregate US output and the welfare of African Americans and others? To answer this question, I quantify a dynamic general equilibrium model of the spatial economy in which cohorts of African Americans and others migrate across states. I compare the baseline equilibrium matched with US data from 1940 to 2010 with the counterfactual equilibria in which African Americans or others cannot relocate across the North and the South between 1940 and 1970. The mobility of African Americans and others increased aggregate output by 0.7 and 0.3 percent, respectively. Although African Americans accounted for about 10 percent of the US population, their relocation impacted the aggregate economy more than the relocation of the other 90 percent did. The mobility of African Americans induced a large increase in the welfare of African Americans in the South, a small decrease in the welfare of African Americans in the North, and little change in the welfare of others.

The economic characteristics of one’s childhood neighborhood have been found to deter mine long-term well-being. Policies enacted dur ing child hood may change neighborhood trajectories and thus impact long-term outcomes for children. We use individual-level data from the Wisconsin Longitudinal Study to examine the enduring consequences of childhood exposure to local-area New Deal emergency employment work-relief activ ity. Our out comes include ado les cent cog ni tion, edu ca tional attain­ment, mid­life income, health behav­iors, late-life cog­ni­tion, and mor­tal­ity. We find that children (ages 0–3) living in neighborhoods with moderate work-relief activity in 1940 had higher adolescent IQ scores, had higher class rank, and were more likely to obtain at least a bach­e­lor’s degree. We find endur­ing ben­e­fits for mid­life income and late-life cognition for males who grew up in areas with a moderate amount of work relief. We find mixed results for males who grew up in the most dis­ad­van­taged areas with the highest levels of work-relief activity. These children had similar educational outcomes as those in the most advantaged districts with the lowest work-relief activity but had higher adult smok­ing rates. Our find­ings pro­vide some of the first evi­dence of the long-term consequences of New Deal policies on children’s long-term life course outcomes.

This article examines the effects of brain drain caused by the migration of knowledge workers from India to the United States of America (USA) during the pre- and post-global financial crisis periods, 2005–2007 and 2011–2013. Data from the Integrated Public Use Microdata Series for the year 2018 are used to compare the different population groups in the USA and to estimate the hourly wages of Indian and White workers using Mincer regression and Oaxaca decomposition. Following Khadria’s (2001–2010) analytical propositions, three premium characteristics of Indian-origin workers to the USA consisting of: (1) an age-premium, (2) a wage-premium and (3) a vintage-premium are empirically examined. The findings uphold the theoretical construct and suggest that the migration of Indian knowledge workers to the USA results in brain drain to India and brain gain for the USA.

There has been a large body of work exploring the discriminatory nature of the home mortgage risk maps produced by the Home Owners' Loan Corporation in the late 1930s. However, little attention has been paid to the question of whether these maps are still descriptive of racial residential boundaries in their cities 80 years after their creation. To address this gap, Markov Chain Monte Carlo, previously unutilized in the relevant literature, is employed to randomly generate many plausible alternative mortgage security maps. Then, the racial evenness of the HOLC maps and the generated maps is compared using Shannon's entropy. These findings indicate that the HOLC maps are significantly descriptive of the precise racial residential boundaries prevalent across eleven US cities in 2010. The methodology used here is highly modular and reproducible, allowing for future work measuring different outcome statistics, locations, and time periods.