Research on geographic differences in health focuses largely on children less than five years; little is known about adolescents—and even less regarding younger adolescents—a vulnerable group at a critical stage of the life course. Africa’s rapid population growth and urbanization rates, coupled with stagnant rates of undernutrition, further indicate the need for country-specific data on rural-urban health disparities to inform development policies. This study examined rural-urban disparities in body mass index-for-age-and-sex (BAZ) and height-for-age-and-sex z-scores (HAZ) among younger adolescents in Tanzania. Participants were randomly selected adolescents aged 10–14 years (N = 1,125) residing in Kilosa (rural) and Moshi (urban) districts of Tanzania. Individual and household-level data were collected using surveys and anthropometric data was collected on all adolescents. Age, sex, household living conditions, and assets were self-reported. BAZ and HAZ were calculated using the WHO reference guide. The prevalence of undernutrition was 10.9% among rural and 5.1% among urban adolescents (p<0.001). Similarly, stunting prevalence was greater in rural (64.5%) than urban (3.1%) adolescents (p<0.001). After adjusting for covariates, rural residence was significantly and inversely associated with BAZ (B = -0.29, 95% CI: -0.52, -0.70, p = 0.01), as well as with HAZ (B = -1.79, 95% CI: -2.03, -1.54, p<0.001). Self-identified males had lower BAZ (B = -0.23, 95% CI: -0.34, -0.11, p<0.001) and HAZ (B = -0.22, 95% CI: -0.35, -0.09, p = 0.001) than self-identified female adolescents. Rural-urban disparities in nutritional status were significant and gendered. Findings confirm place of residence as a key determinant of BAZ and HAZ among younger adolescents in Tanzania. Targeted gender-sensitive interventions are needed to limit growth faltering and improve health outcomes in rural settings.

We estimate effects of the COVID-19 pandemic on self-reported school enrollment using a sample of 16-to-18-year-old youth from the January 2010 to the December 2020 Current Population Survey (CPS). The pandemic reduced the likelihood of students reporting that they were enrolled in high school by about 1.8 percentage points in April 2020 vs. in the same month in prior years, although enrollment rebounded back to typical levels by October 2020. Adverse effects on school enrollment were magnified for older vs. younger students, males vs. females, and among adolescents without a college-educated household member vs. adolescents from more educated households. Greater school responsiveness to the pandemic and high school graduation exit exams appear to have protected students from disengaging from school.

This paper revisits capital-skill complementarity and inequality, as in Krusell, Ohanian, Rios-Rull and Violante (KORV, 2000). Using their methodology, we study how well the KORV model accounts for more recent data, including the large changes in labor’s share of income that were not present in KORV. We study both labor share of gross income (as in KORV), and income net of depreciation. We also use non-farm business sector output as an alternative measure of production to real GDP. We find strong evidence for continued capital-skill complementarity in the most recent data, and that the model continues to closely account for the skill premium. The model captures the average level of labor share, though it overpredicts its level by 2-4 percentage points at the end of the period.

Economic portrait of U.S. Afro-Latinx community in the twenty-first century.

We use a novel identification strategy to investigate whether regional universities make their local economies more resilient to adverse economic shocks. Our strategy is based on state governments assigning normal schools (to train teachers) and insane asylums to counties between 1830 and 1930. Normal schools later became much larger regional universities while asylum properties mostly continue as small state-owned psychiatric health facilities. Because site selection criteria were similar for these two types of institutions, comparing counties assigned a normal school versus an insane asylum identifies the effect of a regional university. We find that having a regional university roughly offset the negative effects of exposure to manufacturing declines, and we attribute a significant share of this resilience to the resilience of regional public university spending

The Resource Conservation and Recovery Act (RCRA) is a cornerstone of environmental policy in the United States (US). It regulates the generation, use, transportation, and eventual disposal of hazardous chemicals. Focusing on the 2,389 treatment, storage, and disposal facilities (TSDFs) in the contiguous US, we frame a difference-in-differences and triple differences quasi-experiment that exploits the temporal and spatial variation in contamination and cleanup events. Hedonic property value regressions are estimated using a sample of over 9.6 million single-family home transactions from 2000-2018. The discovery of contamination and subsequent investigation is associated with up to a 5% depreciation in the value of homes within 750 meters of a TSDF, but the evidence is mixed. In contrast, we find robust, causal evidence that the completion of cleanup leads to an average 6-7% increase in the value of homes within 750 meters. This implies that a total increase in housing stock value of $323 million can be attributed to the 195 TSDFs that have been remediated since the inception of the RCRA cleanup program. The completion of cleanup at a TSDF is estimated to yield an average lower bound, ex post benefit of about $8,400 per household.

We apply deep learning to daytime satellite imagery to predict changes in income and population at high spatial resolution in US data. For grid cells with lateral dimensions of 1.2km and 2.4km (where the average US county has dimension of 55.6km), our model predictions achieve R2 values of 0.85 to 0.91 in levels, which far exceed the accuracy of existing models, and 0.32 to 0.46 in decadal changes, which have no counterpart in the literature and are 3-4 times larger than for commonly used nighttime lights. Our network has wide application for analyzing localized shocks.

Evidence shows technology automates middle-wage occupations’ routine tasks. I argue technology only partially automates these, simplifying them so that they can be performed by less-skilled workers. Thus, post-automation costs include technology and low-wage workers to use it. The minimum wage raises these costs, lowering the profitability of automation and slowing the adoption of routine-replacing technologies. I test this claim using new cross-state variation in the minimum wage (induced by state price differences) and new cross-industry variation in the importance of low-skilled labor for technology (measuring using the Current Population Survey Computer Use Supplement and the Dictionary of Occupational Titles). Because low-skilled workers are needed alongside technology, I show that a low minimum wage increases the automation of routine jobs.

In California, the question of whether and under what conditions labor standards requirements should be included in housing bills typically hinges on the issue of how much it would add to the cost of the project.1 However, one important aspect of cost has so far not been considered: the cost to the public safety net resulting from low-road employment practices common in residential construction. Our analysis calculates the cost of utilization of the five major means-tested safety net programs by California construction workers and their families. We find almost half of families of construction workers in California are enrolled in a safety net program at an annual cost of over $3 billion. By comparison, just over a third of all California workers have a family member enrolled in one or more safety net program.

Residential segregation by income and race is a salient feature of most US cities. An important determinant of residential location choice is access to desirable urban amenities via affordable travel modes. The first chapter of the dissertation studies residential and travel mode choices of commuters in US cities to estimate the heteregenous demand for access to neighborhoods offering faster commutes and to characterize what that means for how the gains from mass transit improvements are distributed among rich and poor commuters. I show that cities where transit improvements would be most effective at generating new transit ridership and overall welfare gains are ones where the gains accrue more to higher income commuters. Within cities, who gentrify transit-accessible neighborhoods and ride mass transit depends on the type (e.g. bus versus rail) and location of the transit improvements. The second chapter of this dissertation models household choices of where to live and how to travel in a stylized city with a competitive housing market. I characterize when and where marginal improvements in transit access reduce residential segregation by income instead of exacerbating it, and I show that an urban planners trying to maximize transit ridership is often incentivized to expand the transit network where it increases income segregation. Residential segregation has important implications for inequality. The third chapter of the dissertation studies how racially segregated housing markets have historically exacerbated racial inequality in US cities. The Great Migration of black families from the rural South to northern cities in the 1930s saw a growing number of segregated city blocks transition racially. Over a single decade, while rental prices soared on city blocks that transitioned from all white to majority black and pioneering black families paid large premiums to buy homes on majority white blocks, such homes quickly lost value on blocks that transitioned from majority white to majority black. These findings suggest that segregated housing markets eroded much of the gains for black families moving out of ghettos.

There are a number of reasons young adults live with their parents, including mental health, finances (Sandberg-Thoma et al., 2015), family connectivity, characteristics of the parental home, and physical health of parents (South & Lei, 2015). Patterns of parental co-residence vary by age, gender, and school enrollment. Using the Current Population Survey (CPS), we produce an update on young adults’ parental co-residence by age, gender, and school enrollment from 2007 through 2021. This update provides a glimpse into the residential patterns of young adults at time points associated with the beginning of and nearly one year into the COVID-19 pandemic (shown by the gray shading in the figures). We define parental co-residence as living with one’s own parent(s) or a partner/spouse’s parent(s). This family profile updates previous profiles on parental co-residence using recent data

Decision tree (DT) algorithms have been applied for classification and change detection in various geospatial studies and more recently, for urban expansion and land use/land cover (LULC) change modeling. However, these studies have not elaborated on specification of DT algorithms regarding data sampling, predictor variables, model configuration, and model evaluation. The focus of this study is to explore several balanced and unbalanced sampling methods, various predictor variables, different configurations of stopping rules, and reliable evaluation metrics to enhance the performance of classification and regression tree (CART), one of the most efficacious DT algorithms, for urban expansion modeling. The implementation of the model in the Triangle Region, North Carolina (NC) State, over the period of 2001 to 2011 demonstrates a striking performance with the training accuracy of 97%, the testing accuracy of 94%, and the Kappa value of 0.80. This performance was achieved using a training dataset containing all changed land cells and three times of that randomly selected from unchanged land cells and regulating the minimum number of records in a leaf node equal to 1, the minimum number of records in a parent node equal to 2, and the value of 10,000 for the maximum number of splits. The CART DT algorithm indicates that proximity to built areas, proximity to highways, current LULC type, elevation, and distance to water bodies are the most significant predictor variables for the urban expansion prediction in the study area.

COVID-19 has affected daily life in unprecedented ways. Using a longitudinal dataset linking biometric and survey data from several cohorts of young adults before and during the pandemic (N=685), we document large disruptions to physical activity, sleep, time use, and mental health. At the onset of the pandemic, average steps decline from 9,400 to 4,600 steps per day, sleep increases by about 25-30 minutes per night, time spent socializing declines by over half to less than 30 minutes, and screen time more than doubles to over 5 hours per day. The proportion of participants at risk of clinical depression increases to 65%, over twice the rate in the same population prior to the pandemic. Our analyses suggest that disruption to physical activity is a leading risk factor for depression during the pandemic. However, restoration of those habits–either naturally or through policy intervention–has limited impact on restoring mental well-being.

This article studies the impact of immigration on wages, internal migration, and welfare. Using U.S. Census data, I estimate a spatial equilibrium model where labour differs by skill level, gender, and nativity. Workers are heterogeneous in city preferences. Cities vary in productivity levels, housing prices, and amenities. I use the estimated model to assess the distributional consequences of several immigration policies. The results show that a skill selective immigration policy leads to welfare gains for low skill workers, but welfare losses for high skill workers. The negative impacts are more substantial among the incumbent high skill immigrants. Internal migration mitigates the initial negative impacts, particularity in cities where housing supplies are inelastic. However, the negative wage impacts on some workers intensify. This is because an out-migration of workers of a given type may raise the local wages for workers of that type, while reducing the local wages of workers with complementary characteristics. Overall, there are substantial variations in the welfare effects of immigration across and within cities. Further, I use the model to assess the welfare effects of the border wall between Mexico and the U.S. The results show that the potential benefits are significantly smaller than the proposed cost of construction.

We study the origins of entrepreneurship (culture) in the United States. For the analysis we make use of a quasi-natural experiment – the gold rush in the second part of the 19th century. We argue that the presence of gold attracted individuals with entrepreneurial personality traits. Due to a genetic founder effect and the formation of an entrepreneurship culture, we expect gold rush counties to have higher entrepreneurship rates. The analysis shows that gold rush counties indeed have higher entrepreneurship rates from 1910, when records began, until the present as well as a higher prevalence of entrepreneurial traits in the populace.

We study the implications of incarceration for the earnings and employment of different groups, characterized by their race, gender, and education. Our hidden Markov model distinguishes between first-time and repeat incarceration, along with other persistent and transitory nonemployment and earnings risks, and accounts for nonresponse bias. We estimate the model using the National Longitudinal Survey of Youth 1979 (NLSY79), one of the few panel datasets that includes incarcerated individuals. The consequences of incarceration are enormous: First-time incarceration reduces expected lifetime earnings by 39% (59%) and employment by 8 (13) years for black (white) men with a high school degree. Conversely, nonemployment and adverse earnings shocks increase expected years in jail. Among less-educated men, differences in incarceration and nonemployment can explain a significant portion of the black-white gap in lifetime earnings-44% of the gap for high school graduates and 52% of the gap for high school dropouts.

Background: Research indicates that transition-age youth with disabilities face several obstacles with regard to finding employment. However, research on the extent to which barriers and facilitators differ across disability types and contexts is lacking. Objective: The primary purpose of this qualitative study was to understand employment related challenges encountered by a cross-section of transition-age youth with disabilities across multiple settings. In addition, the study also examined transition-aged youth’s knowledge and use of rights under the Americans with Disabilities Act (ADA). Methods: We adopted a focus group strategy to understand the barriers faced by transition-aged youth with disabilities; five focus groups were conducted at five community-based locations in three states (Maryland, Delaware, and Virginia) in Federal Region 3 (i.e., Mid-Atlantic). Participants ranged in age from 16 to 24 (53.5% male; 44.2% White). Findings: Findings indicated that youth with disabilities faced several barriers in the form of stigma, lack of workplace supports and accommodations, their disability condition, and anxiety. In addition, a very small proportion of the sample were aware about the ADA and their rights under Title I. Conclusions: Findings highlight the need to develop programs that equip transition-aged youth with disabilities with the necessary skills as they prepare to enter the work force. In addition, efforts should be targeted at addressing the barriers identified in the study, such as stigma, as well as at increase students’ knowledge of the ADA by embedding information within secondary and postsecondary academic curricula.

Hearing loss (HL) can negatively impact patient-provider communication and limit access to health promotion information, which may lead to decreased preventive care utilization. Using data from the 2015 and 2018 National Health Interview Survey, we examined the association between perceived HL with and without hearing aid use with self-reported age-appropriate uptake of breast and colon cancer screening, and influenza and pneumococcal vaccination. In models adjusted for sociodemographic characteristics, access to care, and health status, people with HL had lower odds of receiving breast cancer screening (odds ratio [OR] = 0.83, 95% confidence interval [CI] = 0.72-0.96) and higher odds of receiving pneumococcal vaccination (OR = 1.11, 95% CI = 1.00-1.24) relative to those without HL. There were no differences in their colon cancer or influenza vaccination uptake. Compared with those without HL, people with HL who used hearing aids had increased odds of colon cancer screening and influenza and pneumococcal vaccination, while people with HL who did not use hearing aids were less likely to report cancer screening. Overall, Americans with untreated HL were less likely to report completing cancer screening. Hearing aid use may modify the association between HL and preventive care uptake. Screening for HL in primary care settings and communication trainings for providers may help reduce cancer screening disparities.

The COVID-19 pandemic has brought about health, employment, and educational crises in California and in the nation. State and federal policy measures—including stimulus payments, increased unemployment insurance (UI) payments, increased food assistance benefits, and extended eligibility periods for some safety net programs—averted a sharp increase in poverty in the early months of the pandemic. Nonetheless, we know low-wage workers take longer to recover from recessions and may have an even longer-term need for the social safety net than they did after other recent downturns. California’s social safety net bolsters low incomes, and many safety net programs also have individual goals, including providing a safety net for children, supporting nutrition and health, and incentivizing work. When economic crises occur, how well do—and can—these programs respond to increased demand? Are there gaps in the safety net? In this report we look at evidence from the Great Recession and draw from stakeholder convenings that we held in January 2021. Our analysis focuses on the responsiveness of Earned Income Tax Credit (EITC), CalWORKs cash assistance, and several nutrition programs: CalFresh, WIC, and school meals.  We find that benefits and participation increased after the Great Recession. This is true even of CalWORKs—California’s Temporary Assistance for Needy Families (TANF) program—despite national evidence that cash assistance through TANF did not expand to meet increased need.  We find robust evidence of elevated need for safety net benefits during the last economic recovery. Likely reasons for this prolonged need include laid-off workers turning to unemployment insurance first, families relying initially on informal resources such as food banks or family and friends, and a slower economic recovery for low-income workers compared to high-income workers.  We also find evidence of gaps in the availability of expanded support. Key groups facing serious limitations in the social safety net include working-age adults who do not live with dependents and undocumented immigrants and their family members.  California’s largest social safety net programs share several features that create particular challenges during economic downturns. Stakeholders point to gaps in program eligibility, incomplete take-up of programs, and an insufficiently systemic approach. Although much of the safety net is funded and regulated by federal policymakers, state decisionmakers can take steps to support the safety net and foster an equitable recovery:  Filling gaps in the federal recessionary response, such as those that affect adults without dependents or mixed-status families. CONTENTS SUMMARY

In the late 1930s, the Federal Housing Administration (FHA) used a process called redlining to section off districts in an attempt to signal the level of risk to lenders. Consequently, lenders felt justified in turning away non-white borrowers, effectively leading to segregated communities. This practice carried on through the mid-1960s, after which some communities underwent socio-demographic transformations due to gentrification, urban renewal, and deindustrialization, amongst other things. This paper analyzes the current demographic trends in Boston, Detroit, and Los Angeles to determine to what extent the resulting segregation from redlining practices has persisted. Redlining and census data from 1930 and 2020 were obtained and analyzed in order to compare the racial demographics over time. White vs. non-white population in redline districts in 1930 versus 2020 were compared in each of these cities. Whereas percent change between white majority and non-white minority provides a demographic trend over time, a local spatial autocorrelation (LISA) analysis helped to identify dense areas of minority population. Lastly, the index dissimilarity, interaction, and isolation were used to better expose persistent levels of segregation. This research provides one example of how historical GIS analysis can be done using diverse datasets to show spatio-temporal patterns of importance to ongoing issues of social justice and inequality.