This paper fills a gap in the literature by presenting new evidence of the negative impact of robotization on the change in high-skill employment in US labor markets between 2000 and 2014. I find that the adoption of one robot per thousand workers reduced the change in the high-skill employment-to-population ratio by 0.18 to 0.24 percentage points, which implies that each robot prevented the creation of 4 high-skill jobs. Though these findings are surprising with regard to the literature, which globally argues that technological change favors high-skill employment, they give new support to a range of theoretical studies. I show that these results can be explained by a model of tasks commonly used in the literature. In such models robotization generates reallocations of tasks between low-skill and high-skill workers, which can negatively impact the employment of high-skill workers. I calibrate the model and find that robotization reduced the magnitude of the effects that increase high-skill employment by almost half during this period.

This paper presents a case study on the IPUMS NHIS database, which provides data from censuses and surveys on the health of the U.S. population, including data related to COVID-19. By addressing gaps in previous studies, we propose a machine learning approach to train predictive models for identifying and measuring factors that affect the severity of COVID-19 symptoms. Our experiments focus on four groups of factors: demographic, socioeconomic , health condition, and related to COVID-19 vaccination. By analysing the sensitivity of the variables used to train the models and the VEC (variable effect characteristics) analysis on the variable values, we identify and measure importance of various factors that influence the severity of COVID-19 symptoms.

This study examines the earnings differential in each year from 2000-2020 to determine if the gender pay gap has changed in recent years. We estimate average weekly wages for full-time, year-round workers for men and women to determine women's wages relative to men's wages for each year. Our results show that although average earnings for women relative to men have increased since 2000 when adjusting for individual worker characteristics, we find little progress in shrinking the gender wage gap. This result is even more discouraging since women's education rates have increased at a significantly higher rate during this time.

Ensuring the social equity of planning measures in social systems requires an understanding of human dynamics, particularly how individual relationships, activities, and interactions intersect with individual needs. Spatial microsimulation models (SMSMs) support planning for human security goals by representing human dynamics through realistic, georeferenced synthetic populations, that a) provide a complete representation of social systems while b) also protecting individual privacy. In this paper, we present UrbanPop, an open and reproducible SMSM framework for analysis of human dynamics with high spatial, temporal, and demographic resolution. UrbanPop creates synthetic populations of demographically detailed worker and student agents, positioning them first at probable nighttime locations (home), then moving them to probable daytime locations (work/school). Summary aggregations of these populations match the granular detail available at the census block group level in the American Community Survey Summary File (SF), providing realistic approximations of the actual population. UrbanPop users can select particular demographic traits important in their application, resulting in a highly tailored agent population. We first lay out UrbanPop's baseline methodology, including population synthesis, activity modeling, and diagnostics, then demonstrate these capabilities by developing case studies of shifting population distributions and high-risk populations in Knox County, TN during the global COVID-19 pandemic.

Transformation of the Earth's social and ecological systems is occurring at a rate and magnitude unparalleled in human experience. Data science is a revolutionary new way to understand human-environment relationships at the heart of pressing challenges like climate change and sustainable development. However, data science faces serious shortcomings when it comes to human-environment research. There are challenges with social and environmental data, the methods that manipulate and analyze the information, and the theory underlying the data science itself; as well as significant legal, ethical and policy concerns. This timely book offers a comprehensive, balanced, and accessible account of the promise and problems of this work in terms of data, methods, theory, and policy. It demonstrates the need for data scientists to work with human-environment scholars to tackle pressing real-world problems, making it ideal for researchers and graduate students in Earth and environmental science, data science and the environmental social sciences.

A national assessment of compliance and related solutions is a necessary next step in increasing access to safe drinking water across the United States. This report represents a first step in outlining what an assessment of that scale can and should look like in the next decade. It identifies and describes four key phases such an assessment would need to undertake; existing and emerging efforts to build on; as well as data, analytical and policy challenges and gaps such an effort would face.

Universal Health Coverage (UHC) is an urgent global priority outlined in the Sustainable Development Goals (SDGs) to ensure the accessibility of health services for all people without causing financial hardship. If current progress continues to 2030, 37% to 61% of the global population will not be covered by essential health services.1 Therefore, we need to accelerate the increase of service coverage to achieve the UHC target by 2030. There are three specific aims of this dissertation; 1) To identify both individual and contextual factors that are consistently associated with utilization of nine essential maternal and child health services (i.e., ANC, facility-based delivery, modern contraceptive use, immunizations, and childhood illnesses), across survey years and household geolocations, using five national representative cross-sectional surveys in Nigeria; 2) To estimate grid-level coverage of selected essential MCH services in Nigeria using generalized additive models (GAMs) and Gradient Boosting (GB) 3) To estimate required costs and avoidable child deaths by increasing selected essential health service coverage in each community, and to identify the priority sub-national areas. This dissertation emphasizes the importance of multi-dimensional priority setting in achieving Universal Health Coverage in Nigeria. By identifying the factors influencing health service utilization, assessing regional disparities, estimating required costs, and quantifying potential impacts, policymakers can make evidence-based decisions to maximize the efficiency and effectiveness of healthcare interventions. The findings and recommendations of this research contribute to the broader global agenda of achieving UHC and improving health outcomes for all populations, particularly in low- and middle-income countries.

Purpose Housing quality is associated with health and well-being; however, little research examines these factors by location. In this policy brief, we examine rates of two key housing quality indicators (having incomplete plumbing and incomplete kitchen facilities) by rural-urban location, as well as at the intersections of racial/ethnic identity and disability status.

Urban housing markets are characterized by racial sorting, but equilibrium prices respond to marginal buyers and thus may mask underlying preferences for segregation. Large migration shocks can make visible these otherwise infra-marginal preferences. We study the effects of Hurricane Katrina-induced displacement on housing markets in receiving neighborhoods in Texas, where 1 in 5 New Orleanians relocated. Using an event study design, we find that the relocation of 100 additional Katrina survivors to a receiving ZIP code is associated with a 2.2% decline in relative house prices five years after the storm. This effect is driven by responses to movers from predominantly Black origin blocks. We argue that our findings are best explained by a preference for segregation on the part of incumbent White residents. In this case, racial stratification in the effect of a disaster is followed by racial stratification in economic responses.

During the twentieth century, state health authorities in California recommended sterilization for over 20,000 individuals held in state institutions. Asian immigrants occupied a marginalized position in racial, gender, and class hierarchies in California at the height of its eugenic sterilization program. Scholars have documented the disproportionate sterilization of other racialized groups, but little research exists connecting the racist, gendered implementation of Asian immigration restriction to the racism and sexism inherent in eugenics. This study examines patterns of coercive sterilization in Asian immigrants in California, hypothesizing higher institutionalization and sterilization rates among Asian-born compared with other foreign-and US-born individuals. We used complete count census microdata from 1910 to 1940 and digitized sterilization recommendation forms from 1920 to 1945 to model relative institutionalization and sterilization rates of Asian-born, other foreign-born, and US-born populations, stratified by gender. Other foreign-born men and women had the highest institutionalization rates in all four census years. Sterilization rates were higher for Asian-born women compared with US-born [Incidence Rate Ratio (IRR) = 2.00 (95% CI: 1.61, 2.48)] and other foreign-born women (p < 0.001) across the entire study period. Sterilization rates for Asian-born men were not significantly

Educational Spillover Effects of New English Learners in a New Destination State The number of English learners enrolled in public schools has grown substantially in the United States over the past two decades. The growth is especially large in states in the South and Midwest that have not been traditional destinations for recent immigrants. In this study, we examine the effects of an influx of new English learners on students in receiving schools in Delaware, which is one of the so-called "new destination" states. We find significant positive spillover effects in the short term of new English learners on the test scores of the other students in the receiving schools. The positive effects are mainly concentrated among current and former English learners.

This paper investigates how ancillary geographic data – in particular, information on land or assessor parcels – might be used to improve estimates for small area populations and population characteristics. It seeks to determine whether parcel land use codes can be used to reliably replicate population and housing distributions within small (subcounty) areas and whether other parcel attributes – in addition to land use – exhibit any explanatory power in replicating population and housing characteristics within these same places. The basis for this paper was Professor Barbara Buttenfield’s service on the Census Scientific Advisory Committee, in which her working group explored the utility of administrative source data as an alternative or complement to federal survey data. This analysis highlights some of the benefits and complications of the incorporation of parcel data into geodemographic estimation, and the findings demonstrate that such a use is problematic but encouraging.

Since the 1980s, US cities with a growing share of high-skill workers have experienced higher growth in wages and rent than cities with a declining share of high-skill workers. I document novel empirical facts about this "Great Divergence" showing that high-skill, high-rent cities also experience a reduction in long-run unemployment rates. Since wage and unemployment rates are jointly determined , incorporating geographic variation in unemployment rates is quintessential in understanding the welfare implication of this divergence. This paper develops a spatial equilibrium model with frictional labor markets that give rise to unemployment, featuring workers of different skill levels that share a housing market. I calibrate the model to the US economy between 2005 and 2019 and find that the worker population is inefficiently small in high-wage, high-rent locations. The share of high-skill workers in these locations is inefficiently high. This misallocation is caused by the distortion resulting from the inseparability between the labor market and housing market location. Comparing the model to its competitive counterpart without unemployment shows that search frictions moderate the divergence, allowing an additional channel to balance the spatial equilibrium, leading to smaller utility differences between high-and low-skill workers. Policies that encourage low-skill workers to relocate to high-wage locations improve aggregate welfare.

Previous studies have documented the rural-urban disparities of the opioid crisis, but rarely consider the heterogeneities within rural and urban areas, nor the changing dynamics of the rural-urban boundaries. Taking advantage of a continuous measure of rurality, this study separates counties into "becoming-more-rural" and "becoming-more-urban" groups according to the change of rurality between 2000 and 2010, and compares the determinants of the development of prescription opioid-, heroin-, and synthetic opioid-related opioid mortality in the two groups of counties. The analysis uses longitudinal data of 2010-2018 from a variety of national datasets such as the condential Multiple-Cause of Death data, the U.S. Opioid Dispensing Rate Maps, and the American Community Survey. The results show that while increasing rurality does not predict increasing prescription opioid-and synthetic opioid-involved mortality, the growth of heroin-involved mortality could be a severe issue for becoming-more-rural counties that have already observed heroin overdose deaths. The demographic groups that have higher risk of opioid overdoses may be different in different areas, for example increasing males and people engaged in manual labor occupations are associated with increasing prescription opioid-involved mortality for becoming-more-rural counties but in becoming-more-urban areas, heroin-involved mortality correlates to a higher percentage of Hispanics. Concentrations of socioeconomically disadvantaged populations like veterans and people in poverty increase opioid overdose deaths particularly for becoming-more-urban counties, while healthcare services shown more benecial to becoming-more-rural areas. The ndings suggest that the programs and regulations to reduce opioid-involved mortality should consider the changing nature of counties in the degree of rurality.

Introduction: Policymakers have suggested and implemented work requirements for safety-net programs such as the Supplemental Nutrition Assistance Program. If these work requirements impact program participation, they may lead to greater food insecurity. This paper investigates the effects of implementing the work requirement for the Supplemental Nutrition Assistance Program on emergency food assistance usage. Methods: Data were used from a cohort of food pantries in Alabama, Florida, and Mississippi, which imposed the Supplemental Nutrition Assistance Program work requirement in 2016. Event study models were run in 2022, leveraging geographic variation in exposure to the work requirement to measure changes in the number of households served by the food pantries. Results: The 2016 introduction of the Supplemental Nutrition Assistance Program work requirement increased the number of households served by food pantries. The impact is concentrated among urban food pantries. On average, an urban agency exposed to the work requirement served 34% more households in the 8 months after the work requirement than an agency with no exposure. Conclusions: Individuals who lose Supplemental Nutrition Assistance Program eligibility owing to the work requirement remain in need of assistance and seek other sources of food. Supplemental Nutrition Assistance Program work requirements thus increase the burden on emergency food assistance programs. Work requirements for other programs may also lead to increased emergency food assistance use.

Agricultural production and household food security are hypothesized to play a critical role connecting climate change to downstream effects on women's health, especially in communities dependent on rainfed agriculture. Seasonal variability in agriculture strains food and income resources and makes it a challenging time for households to manage a pregnancy or afford a new child. Yet, there are few direct assessments of the role locally varying agricultural quality plays on women's health, especially reproductive health. In this paper we build on and integrate ideas from past studies focused on climate change and growing season quality in low-income countries with those on reproductive health to examine how variation in local seasonal agricultural quality relates to childbearing goals and family planning use in three countries in sub-Saharan Africa: Burkina Faso, Kenya, and Uganda. We use rich, spatially referenced data from the Performance Monitoring for Action (PMA) individual surveys with detailed information on childbearing preferences and family planning decisions. Building on recent advances in remote monitoring of seasonal agriculture, we construct multiple vegetation measures capturing different dimensions of growing season conditions across varying time frames. Results for the Kenya sample indicate that if the recent growing season is better a woman is more likely to want a child in the future. In Uganda, when the growing season conditions are better, women prefer to shorten the time until their next birth and are also more likely to discontinue using family planning. Additional analyses reveal the importance of education and birth spacing in moderating these findings. Overall, our findings suggest that, in some settings, women strategically respond to growing season conditions by adjusting fertility aspirations or family planning use. This study also highlights the importance of operationalizing agriculture in nuanced ways that align with women's lives to better understand how women are impacted by and respond to seasonal climate conditions.

Tabular data is arguably one of the most commonly used data structures in various practical domains, including finance, healthcare and e-commerce. The inherent heterogeneity allows tabular data to store rich information. However, based on a recently published tabular benchmark, we can see deep neural networks still fall behind tree-based models on tabular datasets. In this paper, we propose Trompt--which stands for Tabular Prompt--a novel architecture inspired by prompt learning of language models. The essence of prompt learning is to adjust a large pre-trained model through a set of prompts outside the model without directly modifying the model. Based on this idea, Trompt separates the learning strategy of tabular data into two parts. The first part, analogous to pre-trained models, focus on learning the intrinsic information of a table. The second part, analogous to prompts, focus on learning the variations among samples. Trompt is evaluated with the benchmark mentioned above. The experimental results demonstrate that Trompt outperforms state-of-the-art deep neural networks and is comparable to tree-based models.

The divorce rate in the U.S. has declined in recent decades. In 1990, 19 people per 1,000 currently married individuals divorced compared to 13 per 1,000 in 2021. The overall trend in the divorce rate masks substantial variation by age. The divorce rate for younger people has been on the decline since the 1990s (Kennedy and Ruggles, 2014) whereas the divorce rate among those 50 and older has more than doubled since 1990 (Brown and Lin, 2012). This family profile updates FP-21-16 and charts the divorce rates by age groups in 1990 and 2021 using U.S. Census data and the 2021 American Community Survey provided by IPUMS-USA

On June 25, 2013, the Supreme Court ended enforcement of Section 5 of the 1965 Voting Rights Act in Shelby County v. Holder. As a result, over 3500 municipalities were released from the preclearance requirement to seek federal approval prior to enacting changes to elections. Despite the Court’s majority opinion that Section 5 was no longer needed, practices like enforcing strict voter ID requirements and last-minute polling place changes increased dramatically after Shelby County. However, one underexamined election change is changing municipal boundaries through annexations. Municipal annexations can weaken minority political representation in municipal elections if minority population shares decrease after annexation. Using difference-in-differences models, I analyze annexations for over 15,000 municipalities from 2007–2020 across all forty U.S. states with annexable land. I find no evidence that municipalities previously covered by Section 5 increased annexation activity or that they conducted more annexations that dilute Black and minority resident shares after Shelby County. Patterns of annexations pre-Shelby County suggest that the null finding can be explained by the limited effectiveness of Section 5 in preventing minority dilution through annexations when it was in place. This study underscores how municipal boundaries can be manipulated to perpetuate inequality and the limitations of federal legislation in preventing this practice.

Supporting American Latino institutions across the United States is vital to preserving and promoting American Latino life, art, history, and culture.1 As the largest and second-fastest-growing ethnic minority in the United States, American Latinos influence music, literature, art, and traditions for generations of families and communities. American Latino museums play an integral role in preserving this diverse culture by providing opportunities for people to work, enjoy, create, share, and learn about American Latino experiences. Recognizing this important role, in 2020 Congress enacted legislation to establish a new American Latino History and Culture (ALHC) program at the Institute of Museum and Library Services (IMLS). The ALHC program was authorized as part of the National Museum of the American Latino Act ( Public Law 116-260), after 15 years of bipartisan efforts by American Latino leaders. The ALHC program will use grantmaking to build the capacity of American Latino museums and educational organizations to promote the study and appreciation of American Latino life, art, history, culture, and their impact on US society. Accordingly, it aims to strengthen, sustain, and grow the field by nurturing museum professionals, expanding institutional capacity, and increasing access to American Latino museums and archival collections at Hispanic-Serving Institutions (HSIs) nationwide. From September 2022 to December 2023 IMLS contracted with the nonprofit Urban Institute to conduct field research and planning activities to inform development of the ALHC program. For this work, Urban engaged a large, multidisciplinary team of predominantly American Latino researchers, consulted with two museum professionals who were former chairs of the Latino Network of the American Alliance of Museums, and integrated broad input from American Latino museum collaborators, potential applicants, and funders. Urban’s team focused on five objectives: inform the design of the ALHC program to reflect cultural literacy about American Latino institutions and the communities they serve; identify the potential universe of relevant cultural institutions and organizations; develop recommended priorities for IMLS in implementing the ALHC program to build applicant capacity and assets and address needs; clarify how IMLS can work with similar federal programs to support, expand, and sustain the ALHC program’s growth; and support program performance measurement and evaluation. These objectives evolved into research questions that frame the content of this report ( see technical appendix B for objectives, questions, and data sources). Our work included secondary and primary mixed-methods research. Secondary research activities included an extensive review of English, Spanish, and Portuguese literature and an environmental scan to map the American Latino museum field. Primary research included two national, community listening sessions, an online survey fielded in English and Spanish, and multiple interviews in English and Spanish. Research participants included potential applicants to the ALHC program (also referred to as “stakeholders”), collaborators in the American Latino museum field ( such as associations and organizations that might partner with future applicants), and governmental and private funders in the American Latino museum space. Collectively, Urban’s field research involved the following:  a review of more than 250 pieces of literature in English, Spanish, and Portuguese  an environmental scan of 30 directories and databases to map the American Latino museum field  two community listening sessions, one in English and one in Spanish, with 49 total attendees  an online survey in English and Spanish that received 74 responses from potential ALHC program applicants  interviews in English and Spanish with 40 stakeholders, collaborators, and funders See technical appendix C for more details about Urban’s research methodology. In this report, we summarize findings from these activities (additional details are available in technical appendix D). Findings are organized into four sections: understanding the American Latino museum field, assessing funding experiences of American Latino museums, growing the capacity of American Latino museums, and implementing the American Latino History and Culture program. In these sections, we present field research results in response to guiding research questions, shown as headers. Throughout, we provide relevant citations to the literature, quotations from participants, and descriptive statistics from the survey, and we note whether findings reflect perspectives of ALHC program stakeholders (potential applicants), collaborators, and/or funders. We also integrate recommendations to IMLS throughout the report to inform the design of the ALHC program based on the findings. Several themes repeatedly emerge, elevating the need for the ALHC program to support diversity, inclusion, community, equity, collaboration, authenticity, transparency, accessibility, sustainability, and impact for American Latino museums across the United States.