Introduction: There are well-known cognitive ability differences between socially-identified racial/ethnic groups in the United States. Ameliorating these differences is considered a top grand challenge for the American social sciences. However, reducing these achievement gaps requires a better understanding of the nature of these group differences and also of the mechanisms by which these differences are intergenerationally transmitted. As a result, it is necessary to understand how cognitive differences relate to admixture among admixed groups. Recent studies show a linear positive relationship between European ancestry and cognitive ability in admixed African-European-Amerindian descent groups. Objectives: This study attempts to determine if the association between admixture and cognitive ability among African, European, and Amerindian descent groups in the USA holds across a large time period. Methods: First, we use the large and nationally representative Adolescent Brain Cognitive Development Study (ABCD) sample to examine the associations between cognitive ability, socially identified-race, genetically-predicted color, and genetic ancestry among Puerto Ricans, and non-Hispanic Whites, Blacks, and American Indians in the 21st century. Second, we use the 1850 to 1930 US censuses to see if we can trace ancestry-associated cognitive differences back to the 19th and early 20th century by taking advantage of early census distinctions by blood and also by using age-heaping based numeracy as a proxy for cognitive ability. Results: In the ABCD sample, we find that European ancestry is positively associated with cognitive ability within race/ethnic groups (_r_s =.05 to.47; _r_weighted-average =.10). In the census data, among African Americans and American Indians but not among Puerto Ricans, we find that greater apparent European admixture is associated with higher numeracy and that this holds when we subset data by age, sex, and literacy-status. Conclusions: In the 19th and early 20th century, European admixture was associated with numeracy among African Americans and Native Americans. To better understand these associations a systematic review of 20th century admixture studies is called for.

This manuscript analyzes the impact of old age benefit policy reforms on government revenue, income sharing, and poverty in Tanzania. This is an instrument for strengthening social protection. In this context, a universal old-age pension might assist Tanzania's Mainland in reducing income inequality in a similar manner to the positive outcome identified among developed countries in the European Union. This study uses a static microsimulation model for Tanzania (TAZMOD v2.4). The simulation model is based on social benefit reforms resulting from changes in the old age benefit policy. The two reforms were proposed. The findings found that in both proposed reforms, there is an increase in Government returns over taxes; Social Security Contribution (SSC) and indirect taxes increase by 5.31 percent from TSH 8,870,422.74 to TSH 9,368,616.08, respectively. Also, the share of the poor population decreased by 0.55%. Regarding the effect on poverty, it decreases by 0.55 percent in the two proposed reforms. The empirical results of the presented quantitative analyses have concrete policy implications: It can be concluded that there is an urgent need to reform the targeting of social pensions as the majority of the elderly and poor continue to be excluded from the benefits. The Government can introduce the old age benefit to people 60 years of age and older; hence, the outcomes are significant, and their space depends unfavorably on whether the profit sum is allocated. The government could improve old age benefits to address the issue of poverty and later improve old-age livelihoods.

In 2021, nearly 2.5 million Latinos in the United States held advanced degrees such as master’s degrees or doctorates. This represented a huge increase over 2000, when 710,000 Latinos held advanced degrees. The shift reflects Latinos’ broader increase in postsecondary enrollment and rising educational attainment. Despite the large increase in the number of Latinos with advanced degrees, they accounted for just 8% of all advanced degree holders in the U.S. in 2021. This was far below their 19% share of the overall U.S. population, according to a Pew Research Center analysis of Census Bureau data.

This study investigates severe weather events that lead to power outages. Despite extensive research on using social media during disasters, little work has focused on combining social media information with power outage data. To address this limitation, we propose a novel and effective approach to enhance the prediction accuracy of weather-related power outages by learning a spatio-temporal multiplex network that integrates information on the impact of inclement weather on the residents extracted from their social media posts with relevant weather, geographic, and grid topology data. In the multiplex network framework, the outage risk is estimated using logistic regression, neural network, support vector machine, random forest, xgboost, and decision tree classifiers where machine learning models are applied separately on individual layers and also jointly on multiplex networks representing layers capturing information related to weather, lightning, and vegetation. Experiments were conducted to predict the risk for weather-related power outages three hours in advance at a county level in five states of the Pacific Northwest region from November 2021 to April 2022. Evidence is provided that using vegetation information improves the quality of all models considered as compared to relying on weather and lighting layers alone. Integrating an additional layer on the impact of inclement weather on the community residents retrieved from public social media posts (Twitter, Reddit) with weather, lightning, and vegetation layers improves the accuracy of outage prediction by 5% – 7%. The results provide evidence that the proposed spatio-temporal multiplex network-based approach offers beneficial insights for predicting power outages three hours ahead at the county level.

The National Health Interview Survey (NHIS) data on self-reported trouble hearing and the use of hearing aids were examined for the 12 recent surveys from 2007 to 2018 for adults from 18 to 85+ years of age. The aggregate dataset for all years included data from 357,714 adult respondents. Sample size for annual data ranged from 22,058 (2008) to 36,798 (2014). The prevalence of self-reported trouble hearing and hearing aid use, both current use and ever-using hearing aids, are reported for males and females for each age decade. Measures of unmet hearing healthcare (HHC) need were derived from estimates of the prevalence of hearing aid use among those with self-reported trouble hearing. Logistic-regression analyses identified variables affecting the odds of having self-reported trouble hearing, of using or rejecting hearing aids, and of having unmet HHC needs. The results largely corroborate and extend the findings of recent analyses of data from the National Health and Nutrition Examination Survey (NHANES) for a similar period (2011–2020). Overall, for males, 18.5% (95% CI [18.2%–18.8%]) had self-reported trouble hearing and 76.6% [76.0%–77.2%] of these individuals had never used hearing aids and, for females 13.1% [12.9%–13.4%] had trouble hearing and 79.5% [78.9%–80.1%] of these individuals had never used hearing aids. Unmet HHC needs are highly prevalent in the United States and have been so for many years.

The first chapter considers the geographic distribution of research and development (R&D). A few cities perform a large share of R&D in the United States. The social return to R&D depends not only on the size of knowledge spillovers but also on the size of profits. To the extent that knowledge transmission is imperfect across space, these two objects vary across space. While prior literature has documented local knowledge spillovers, there is little systematic evidence on geographic variation in private profits from R&D. In this direction, I document a new fact from the market for technology: patent sales from inventor to firm decline steeply with distance, other things equal. My interpretation is that it is hard for inventors to commercialize their ideas in distant markets. Through the lens of a spatial growth model, I then infer that the private returns to R&D are low in remote regions. By contrast, spillovers are relatively flat across space because patent citations decline slowly with distance. Place-based R&D policy subsidizes research not in dense cities, but in remote locations where private returns are low. The optimal policy reallocates research workers across space so as to increase aggregate consumption by about 1% in the long run, with minimal effects on inequality. The second chapter examines spatial inequality through the lens of housing markets. i Why are Americans with and without college degrees are increasingly choosing to live in different cities? I assess the role of housing demand. I first show the demand for housing is nonhomothetic: that is, housing expenditure shares decline with income. High-income households spend relatively little on housing and choose to live in expensive cities, while low-income households devote a high budget share to housing. Differences in cost of living therefore drive spatial sorting between skill groups. Increases in the aggregate skill premium amplify these differences and intensify sorting. To quantify this mechanism, I augment a standard quantitative spatial model with flexible nonhomothetic preferences, disciplining the strength of the housing demand channel using consumption microdata. I find that the rising skill premium caused 23% of the increase in spatial sorting by skill since 1980. The third chapter assesses worker retraining programs. Retraining is a policy tool to support workers displaced by trade, yet there is little in the way of theory or evidence on policy effectiveness. Using administrative data from Germany, a highly open economy with extensive government-subsidized retraining programs, I provide evidence that workers retrain in response to import competition. I then show how retraining changes the gains from trade. I propose a search model in which heterogeneous workers may choose to retrain while unemployed. Retraining changes job-finding rates and labor productivities across sectors. Retraining increases the gains from trade by 7% in the aggregate by speeding labor supply adjustment to trade. Relative to an environment without retraining, some workers gain five times as much while others gain virtually nothing.

Recent crash reports have consistently shown an over-representation of crashes involving non motorized road users relative to their share of the traveled person-miles. Addressing non motorized transportation system users’ safety is crucial due to their vulnerability, as well as to promote human and environmental health benefits. This study employs GIS to investigate various exposure measures for pedestrian and cyclist crashes in Lincoln, Nebraska. The analyses use multimodal traffic volumes predicted from StreetLight Location-Based Services (LBS) data, along with demographic, socioeconomic, land use, and road characteristics data. In terms of predicting crashes, LBS-based traffic variables, such as vehicular, pedestrian, and bike volumes show promising accuracy. Pedestrian and bicycle volumes exhibit a strong correlation with crash rates, whereas vehicular volumes show a relatively weaker correlation. Demographic, socioeconomic, land use, and road characteristics attributes showed limited varying potential as individual predictors of crashes. The integration of these variables with LBS-based traffic data is recommended to enable the accurate identification of crash-prone locations. The paper highlights the importance of LBS data in an equitable transportation planning and decision-making process through the inclusion of older vehicles and non-motorized users.

Objective To examine changes in health insurance coverage when adults age out of dependent coverage at age 26 after the implementation of most Affordable Care Act (ACA) provisions. Our analysis also documented differences by sex, race and ethnicity, and state Medicaid expansion status. Methods We used a regression discontinuity design and the 2014-2019 American Community Survey to estimate coverage changes (uninsured, any private, employer-sponsored coverage, direct purchase, and Medicaid) at age 26. Our main sample consisted of adult citizens aged 22-29 years. Results Uninsurance increased by 2.7 percentage points [95% CI; 1.8 to 3.4] at age 26, which was driven by a significant decline in any private insurance (3.7 percentage point decrease). Young adults experienced a smaller increase in the uninsured rate on turning age 26 in states that expanded in 2014 compared to nonexpansion states (2.2 and 3.2 percentage point increases, respectively), but the difference was not significant (P=0.07). Changes in the uninsured rate at age 26 did not differ significantly by sex or race and ethnicity. Conclusions The 2010 dependent coverage provision led to more coverage options among young adults and in turn the uninsured rate declined among a population historically among the most likely to lack coverage. The 2014 Medicaid and Marketplace expansions reduced the uninsured rate even further among young adults. Despite important progress, our findings for 2014-2019 were similar to previous studies using pre-ACA data suggesting that coverage loss remains a risk when adults age out of dependent coverage at age 26.

Chair McClain, ranking member Porter, and distinguished members of the subcommittee: Thank you for inviting me to testify at this hearing. My name is Mike Konczal, and I'm the Director of Macroeconomic Analysis at the Roosevelt Institute. I've led our organization's research on the economic recoveries from both the Great Recession and the recent recession following the start of the COVID-19 pandemic. Today, I'm going to speak about the most recent recovery, the causes of inflation that have been unique to this recovery, the progress we've made in bringing down inflation so far, and what Congress and the administration can do to further bring down inflation. The Recovery First, it's important to remember that this recovery has been strong. The economy has added more than 12 million jobs over the past two years-a rate of 517,000 jobs per month since President Biden took office. There are now 4 million more jobs than the February 2021 Congressional Budget Office (CBO) projection, with unemployment 1.5 percent lower and labor force participation 0.2 percent higher than what the CBO estimated would have happened without the American Rescue Plan. Measures of labor market dynamism-which had been depressed for decades before the COVID-19 pandemic-have skyrocketed, allowing workers to move up the career ladder and quit their jobs to seek new, better-paying, and more productive ones. We've seen many headlines illustrating this trend, such as a recent one in The Atlantic: "Low-Wage Jobs Are Becoming Middle-Class Jobs" (Lowrey 2023). In contrast to previous recessions, we are also recovering to the pre-COVID macroeconomic trend, with real GDP close to projections of where it would have been without the pandemic. Inflation In addition to the speed of this recovery, however, we've also experienced inflation that has been higher and more sustained than economists, financial markets, and the Federal Reserve anticipated. Because inflation and any interventions around it have real impacts on people and communities, this is a subject that requires serious analysis and responses.

While rising house prices benefit existing homeowners, we document a new channel through which price shocks have intergenerational wealth effects. Using panel data from school zones within a large U.S. school district, we find that higher local house prices lead to improvements in local school quality, thereby increasing child human capital and future incomes. We quantify this housing wealth channel using an overlapping generations model with neighborhood choice, spatial equilibrium, and endogenous school quality. Housing market shocks in the model generate large intra-and intergenerational wealth effects, with the latter accounting for over half of total wealth effects.

Children who experience environmental adversities are at increased risk of both internalizing and externalizing disorders. Epigenetic mechanisms may regulate the influence of environmental adversities on mental health. We examined the hypothesis that salivary DNA-methylation patterns of pace of biological aging (Dunedin pace of biological aging [DunedinPoAm]) and inflammation (DNA-methylation C-reactive protein [DNAm-CRP]) are socially stratified and associated with mental health in 1,183 children (609 female; age: M = 13.6 years) from the Texas Twin Project. Analyses were preregistered. Participants’ DNA-methylation algorithms and psychiatric symptoms differed by socioeconomic contexts and race/ethnicity. Children with more parent-reported internalizing symptoms had higher DunedinPoAm and DNAm-CRP scores, and children with more aggression problems had higher DNAm-CRP. DunedinPoAm partially mediated advantage of White racial identity on internalizing. Likewise, DNAm-CRP partially mediated advantage of higher family socioeconomic contexts and, in a separate model, White racial identity on reduced internalizing symptoms. Children’s epigenetic measures of pace of biological aging and inflammation are associated with social inequalities and mental health.

Banks are one of the key drivers of economic development across communities. Banking deserts—defined by inadequate banking access—limit access to capital, inhibit wealth accumulation, and increase exposure to predatory lending. Banking desert formation could be profit-driven, with lower-income and less densely populated regions more likely to become banking deserts. Discrimination could also play a role here: banks may have less presence in areas with higher minority populations. The authors use a panel, census tract-level data set for the entire state of North Carolina to investigate how these forces impact banking access and banking desert formation. Panel methodologies are incorporated to investigate the extent to which profit and discrimination mechanisms each drive banking access and banking desert formation. Profit and discrimination mechanisms are shown to play roles, highlighting the need for policies that mitigate bank branch losses in underserved neighborhoods.

The dominant norms of mainstream society usually shape the living arrangements of children of immigrants. However, literature has highlighted the role of long-term cultural factors acquired in the country of origin and transmitted by immigrant parents to their children. This study analyses data from the 1910, 1920 and 1930 IPUMS census samples to investigate how the country of origin affects the living arrangements among young children of European second-generation immigrants in the US. The results indicate that, compared to the US white population, children of immigrants tend to stay longer with their parents. However, differences by country of origin emerge, partially reflecting prevailing patterns of transition to adulthood and thus suggesting the potential impact of cultural maintenance on second generations.

Extreme heat under climate change has already begun to threaten health, particularly for mothers and babies in the hottest parts of the world. When exposure occurs in utero, extreme heat can undermine child growth and development, leading to devastating later-life consequences for both health and socio-economic stability. Previous research, however, has often overlooked the role of humidity, which, when paired with extreme heat, can result in deadly heat stress and associated health complications. Understanding the relative effects of heat versus humid heat is important for understanding the magnitude and location of the effects of climate change, and for targeting interventions. I compare the impact of prenatal exposure to heat versus humid heat extremes on child height attainment in three South Asian countries (India, Bangladesh, and Nepal) using fine-scale climate records, trimester-level exposure identification, comprehensive data on 200,000 children from the Demographic and Health Surveys, and a rigorous fixed effects design. I find that extreme humid heat in the third trimester is five times more detrimental to height attainment than heat alone, and that maternal heat exposure in the period preceding pregnancy may have lasting negative impacts on growth trajectories after birth—a critical exposure period that has received little-to-no attention thus far with respect to child health. Specifically, the average child’s height-for-age Z-score declines by 0.002 units for every additional day in the third trimester that wet-bulb globe temperatures exceed 29°C. Combining these effects with new projections of wet-bulb globe temperature, I estimate that by 2050, climate change could increase the mean number of hot-humid days in the third trimester by 56.5% in my study region, pushing more than 930,000 additional children under 5 into stunting even before accounting for future population growth. This estimate shrinks to 315,000 children when I consider future exposure to heat alone, implying that failing to account for humidity may lead to significant underestimates of the true effects of extreme heat on child health. I further find that children without adequate sanitation access and whose mothers lack formal education or belong to systematically marginalized castes are more vulnerable to the adverse health effects of prenatal exposure to humid heat. My results provide new insight into which heat events are most dangerous for early life health and when, in a region where near-annual heat waves already affect millions and are projected to worsen under further climate change.

New hand-collected school administrative data from 1870s Virginia, alongside linked individual US Census records, reveals that temporary school closures had lasting effects on literacy and income in adulthood. Those affected by the closures had lower intergenerational economic mobility, particularly those from low-income backgrounds. The age at which the closures occurred also played a role with younger cohorts more affected by early developmental disruptions and older cohorts more affected by prolonged closures.

A growing body of research considers racial residential segregation to be a form of systemic racism and a fundamental cause of persistent racial disparities in health and mortality. Historical research examining the impact of segregation on health and mortality, however, is limited to a few studies with poor data and inconsistent results. In this study, we examine the association between racial residential segregation and child mortality in the South at the turn of the 20th century. We rely on the new IPUMS 1900 and 1910 complete‐count databases to estimate child mortality in the 5 years before each census and construct segregation measures at the census enumeration district (ED), the lowest level of geography consistently available in the census. We calculate the proportion of households headed by Black individuals in each ED, and the Sequence Index of Segregation (SIS), which is based on the racial sequencing of household heads within each district. We construct models of child mortality for rural and urban areas, controlling for a wide variety of demographic and socioeconomic variables. The results indicate that proportion Black and SIS were strongly and positively associated with the mortality of Black children in most models and in both rural and urban areas. Proportion Black was also positively but more moderately correlated with the mortality of White children, while SIS was not correlated or negatively correlated. These results suggest that racial segregation was a long‐standing fundamental cause of race disparities in health and mortality in the United States.

Particulate matter that is less than 2.5 micrometers in diameter, known as PM2.5, is among the most damaging air pollutants to human health. The peer-reviewed literature shows that exposure to PM2.5 can lead to cardiovascular and respiratory diseases and premature mortality. Ongoing oil and gas (O&G) activity in the Permian Basin region of New Mexico and Texas is a known source of PM2.5 pollution through emissions of precursor pollutants. Against this backdrop, there are clear challenges and opportunities for addressing the public health impacts and associated economic damages of PM2.5 from the Permian Basin O&G sector, including opportunities to better understand the scale and scope of the PM2.5 problem in this region. In this white paper, we undertake a four-phase study of PM2.5 pollution from O&G activity in the Permian Basin. Phase 1 undertakes a location-specific analysis of O&G emissions of PM2.5 precursors (VOC, NOX, SO2, and primary PM2.5), including an investigation of emissions trends over time. Phase 2 investigates spatial and temporal trends in total ambient PM2.5 pollution concentrations in the Permian Basin region to provide context for how the O&G sector has affected pollution levels in the area. Phase 3 uses an attribution analysis to connect O&G precursor emissions to PM2.5 concentrations, allowing us to identify the contribution of the O&G sector to PM2.5 in the Permian Basin and in regions beyond. Finally, Phase 4 estimates the human health impacts and associated dollar-denominated damages of PM2.5 attributable to Permian Basin O&G emissions. Three key findings emerge: 1. Emissions from O&G activity contribute, on average, 27.5% of PM2.5 concentrations in the Permian Basin, over 2011-2017. We find that VOC emissions from O&G activity are the largest source of PM2.5 precursor emissions, primarily from oil well tanks. 2. In 2017, the most recent year of data available, we estimate that, nationwide, 638 premature deaths were associated with PM2.5 from the Permian Basin O&G sector. The majority of these deaths occurred outside of the Permian Basin, indicating that O&G activity is impacting human health in both local and distant communities. 3. Nationwide, the total premature mortality damages of Permian Basin O&G-sourced PM2.5 were $6.57 billion (in 2022 inflation-adjusted dollars) in 2017. We estimate that for each $1 in revenue generated from the sale of oil and gas in the Permian Basin in 2017, $0.11 in damages were created nationwide from premature mortality associated with PM2.5 from O&G activity in the basin. Given concerns about the PM2.5 pollution impacts of O&G activity in the Permian Basin, this work provides a rigorous assessment of the magnitude and scale of the problem, for the first time. Considerations of the human health impacts and economic damages of PM2.5, including the 3 sources and types of emissions of primary concern, are important factors that should influence how the Permian Basin states of New Mexico and Texas approach regulations and policies aimed at mitigating the harms from O&G activity. We offer this white paper as evidence-based admissible information for ongoing air pollution policy discussions in the Permian Basin.

It has been well known that the exponential distribution is the only continuous distribution that has the memoryless property. Here, we observe that, if the exponential distribution dominates an economy as a probability distribution of income acquisition, then the memoryless property imposes equal opportunities on agents in this economy to acquire earnings. Based on this observation, we propose to identify the emergence of an exponential income distribution as a potential necessary condition for guaranteeing the equality of opportunity. Together with other conditions (such as social and economic mobility), it would promote equal opportunities for income acquisition among citizens. Empirically, we employ the latest data available from four representative market-economy countries (the United Kingdom, the United States, Canada, and China) to demonstrate that the exponential distribution is a stylized feature of the income structure of the low- and middle-income class, which occupies the great majority of populations. By contrast, the top income classes in these countries obey the Pareto distribution. To validate the relationship between exponential distribution and equal opportunity, we empirically show how the income structure of the low- and middle-income class in China (from 1978 to 2015) evolved towards an exponential distribution after the market-oriented economic reformation.

The core conceptualization of gentrification is social class ascension. Researchers and the public have often disagreed about how, when, where, and why gentrification occurs. When there is agreement, researchers often add specifications, such as displacement, that further confuse the concept. Reasons for the confusion include non-integrating theoretical dimensions, methods that assume independent effects, and the context of place. The objective of this study is to conceptualize gentrification in a case study city using two key dimensions, socioeconomic and economic processes, in a spatial context. Using principal component analysis to identify the latent constructs that account for change in Pittsburgh, PA, the history of a racialized industrial economy affected the indicator variables associated with social class change. In Pittsburgh, the population is not ascending from working class to middle class, but the middle class is changing in form from physical to intellectual labor. However, localized patterns of change reflect social and racialized agency to transition into a different form of the middle class. Scholars, journalists, and residents identify Pittsburgh as a gentrifying city, but the classic conceptualization of gentrification does not apply. The implications of this study recommend a deeper analysis of the type of social class ascension that occurs in cities. The history of the city and public policies that affect agency to transform from one form of social class to another inform the conceptualization of gentrification more so than socioeconomic and economic dimensions per se.

Output is generated through purposefully initiated physical action. Production needs energy and information, provided by respective factors: hardware ("brawn"), including physical labor and physical capital, and software ("brains"), encompassing human cognitive work and pre-programmed software , in particular artificial intelligence (AI). From first principles, hardware and software are essential and complementary in production, whereas their constituent components are mutually substitutable. This framework generalizes the neoclassical model of production with capital and labor, models with capital-skill complementarity and skill-biased technical change, and unified growth theories embracing also the pre-industrial period. Having laid out the theory, we provide an empirical quantification of hardware and software in the US, 1968-2019. We document a rising share of physical capital in hardware (mechanization) and digital software in software (automation); as a whole software has been growing systematically faster than hardware. Accumulation of digital software was a key contributor to US economic growth.