The Stanford Education Data Archive (SEDA) is part of the Educational Opportunity Project at Stanford University (https:\\edopportunity.org), an initiative aimed at harnessing data to help scholars, policymakers, educators, and parents learn how to improve educational opportunities for all children. SEDA includes a range of detailed data on educational conditions, contexts, and outcomes in schools, school districts, counties, commuting zones, and metropolitan statistical areas across the United States. Available measures differ by aggregation; see Sections I.A. and I.B. for a complete list of files and data. By making the data files available to the public, we hope that anyone who is interested can obtain detailed information about U.S. schools, communities, and student success. We hope that researchers will use these data to generate evidence about what policies and contexts are most effective at increasing educational opportunity, and that such evidence will inform educational policy and practices. The construction of SEDA has been supported by grants from the Institute of Education Sciences, the Spencer Foundation, the William T. Grant Foundation, the Bill and Melinda Gates Foundation, the Overdeck Family Foundation, and by a visiting scholar fellowship from the Russell Sage Foundation. Some of the data used in constructing the SEDA files were provided by the National Center for Education Statistics (NCES). The findings and opinions expressed in the research and reported here are those of the authors alone; they do not represent the views of the U.S. Department of Education, NCES, or any of the aforementioned funding agencies.

We apply a mixed-payer economy model to study the effects of changes in the generosity of children's public health insurance programs – measured by Medicaid and Children's Health Insurance Program income thresholds – on substance use disorder (SUD) treatment provider behavior. Using government data on specialty SUD treatment providers over the period 1997–2011 combined with a two-way fixed-effects model and local event study, we show that increases in the generosity of children's public health insurance induce providers to participate in some, but not all, public markets. Our effects appear to be driven by non-profit and government providers. Non-profit providers also appear to increase treatment quantity slightly in response to coverage expansions.

Post-war neighbourhoods across the USA have declined in socioeconomic status over the past few decades. Over this same time period, the relative status of many of these neighbourhoods has dipped below that of older neighbourhoods. With the characteristics of post-war housing being arguably undesirable by current standards, extant literature claims the functional obsolescence of post-war housing is contributing to low and declining neighbourhood socioeconomic status. What remains unclear is whether the effect observed is due to housing age – post-war housing is vulnerable to physical depreciation given its age – or if there is a true post-war vintage effect influencing neighbourhood socioeconomic status beyond what age alone would predict. Using a panel model spanning 1990 to 2010, three main findings emerge. First, the presence of greater shares of post-war housing in neighbourhoods is associated with a small but significant decrease in neighbourhood status. Second, this effect varies across and within urban and suburban neighbourhoods. Third, there exists substantial heterogeneity in the effect across metropolitan areas that differ by housing supply growth and price. Together, these results imply that policymakers should consider the negative effects of functional obsolescence on top of the ills associated with ageing homes within certain spatial contexts.

Espionage poses a threat to national security and the private property rights of Americans. The government should address the threat of espionage in a manner whereby the benefits of government actions taken to reduce it outweigh the costs of those actions. To aid in that goal, this policy analysis presents the first combined database of all identified spies who targeted both the U.S. government and private organizations on U.S. soil. This analysis identifies 1,485 spies on American soil who, from 1990 through the end of 2019, conducted state or commercial espionage. Of those, 890 were foreign-born, 583 were native-born Americans, and 12 had unknown origins. The scale and scope of espionage have major implications for immigration policy, as a disproportionate number of the identified spies were foreign-born. Native-born Americans accounted for 39.3 percent of all spies, foreign-born spies accounted for 59.9 percent, and spies of unknown origins accounted for 0.8 percent. Spies who were born in China, Mexico, Iran, Taiwan, and Russia account for 34.7 percent of all spies. The chance that a native-born American committed espionage or an espionage-related crime and was identified was about 1 in 13.1 million per year from 1990 to 2019. The annual chance that a foreign-born person in the United States committed an espionage-related crime and was discovered doing so was about 1 in 2.2 million during that time. The government was the victim in 83.3 percent of espionage cases, firms were the victims of commercial espionage in 16.3 percent of the cases, and hospitals and universities were the victims of espionage in 0.1 percent and 0.3 percent of the cases, respectively. The federal government should continue to exclude foreign-born individuals from entering the United States if they pose a threat to the national security and private property rights of Americans through espionage. A cost-benefit analysis finds that the hazards posed by foreign-born spies are not large enough to warrant broad and costly actions such as a ban on travel and immigration from China, but they do warrant the continued exclusion of potential spies under current laws.

Hours growth is negatively related to future equity returns. At the firm-level, a 1 percent increase in hours predicts a 0.6 percent decrease in future equity return. At the portfolio-level, the quintile low-minus-high spread yields a 7-percent annual risk premium. A production-based asset pricing model rationalizes this negative relation with adjustment cost on hours and adjustment cost shock. A positive adjustment cost shock lowers adjustment cost in the economy and redistributes output from consumption to investment. Firms adjusting hours take advantage of the positive adjustment cost shock and pay out more consumption when marginal utility is high. Therefore, these firms are less risky, explaining low equity returns in equilibrium. Structural estimation matches real quantity moments and equity return predictability. Consistent with the model, the adjustment cost shock recovered from the data is an aggregate shock in the business cycles and a systematic risk in the crosssections, affecting a firm’s cash flow and equity return via its hours choice.

Taking advantage of geographic (and time) variation in the proportion of routine occupations in the US, we study the impact of this variation on the wage rate of workers by educational group. Using individual data and a Bartik-type IV strategy, we show that not only non-college-educated workers but also, in the same proportion, workers with fewer than four years of college are negatively impacted by this routine-biased technological change. The latter skill group currently represents 30% of the US population. We show that only 10% to 20% of the impact on both educational groups is related to occupational and industrial downgrading (the composition eect) and that most of the wage impact occurs within industries and occupations, including manual service occupations. This is consistent with the displacement eect described in the theoretical literature on task-biased technological change and automation.

In 2020, the City Colleges of Chicago (CCC) began working with the Urban Institute to develop a comprehensive picture of adult learners in Chicago and to investigate various opportunities and gaps that exist in the city for distinct subpopulations of adult learners. CCC is the largest community college system in Illinois, with 4,000 faculty and staff serving 68,000 students annually at seven colleges and five satellite sites. The system strives to serve as a critical avenue for upward economic mobility. CCC engaged in this work after internal data analysis indicated that the system could do a better job of attracting and serving adult learners. Through the development of this profile of adult learners in Chicago and subsequent interviews with key stakeholders in Chicago’s adult-learner ecosystem, the Urban Institute provided information to CCC leadership to help inform their strategy for attracting and serving adult learners moving forward. By releasing this data profile publicly, we aim to inform other Chicago-based organizations in their efforts to serve adult learners, as well as to provide other communities outside of Chicago a framework for understanding their adult learner population. This data profile provides a picture of adult learners in Chicago by identifying and describing key subgroups of learners. First, we review available literature to define adult learners and key subgroups. We then present a picture of adult learners in Chicago using data from the American Community Survey, or ACS. Additional information is drawn from available literature and websites.

Background: Racial and ethnic as well as economic disparities in access to care among persons with asthma and COPD have been described, but long-term access trends are unclear. Research Question: Have health coverage and access to care and medications among adults with airways disease improved, and have disparities narrowed? Study Design and Methods: Using the 1997 through 2018 National Health Interview Survey, we examined time trends in health coverage and the affordability of medical care and prescription drugs for adults with asthma and COPD, overall and by income and by race and ethnicity. We performed multivariate linear probability regressions comparing coverage and access in 2018 with that in 1997. Results: Our sample included 76,843 adults with asthma and 30,548 adults with COPD. Among adults with asthma, lack of insurance rose in the first decade of the twenty-first century, peaking with the Great Recession, but fell after implementation of the Affordable Care Act (ACA). From 1997 through 2018, the uninsured rate among adults with asthma decreased from 19.4% to 9.6% (adjusted 9.27 percentage points; 95% CI, 7.1%-11.5%). However, the proportions delaying or foregoing medical care because of cost or going without medications did not improve. Racial and ethnic as well as economic disparities present in 1997 persisted over the study period. Trends and disparities among those with COPD were similar, although the proportion going without needed medications worsened, rising by an adjusted 7.8 percentage points. Interpretation: Coverage losses among persons with airways disease in the first decade of the twenty-first century were reversed by the ACA, but neither care affordability nor disparities improved. Further reform is needed to close these gaps.

Objective: This article examines whether the availability of Head Start during the Great Recession mitigated the impact of this crisis on poverty rates among families with young children. Background: The first 2 decades of the 21st century have witnessed two major economic crises: the Great Recession and the COVID-19 pandemic. Poverty rates among families with young children grew substantially during the Great Recession. Families with young children are also more vulnerable to instability during the COVID-19 pandemic because job losses have been steeper and childcare availability has been significantly curtailed. Programs such as Head Start that support at-risk families may mitigate such negative consequences. Method: This study used data from the American Community Survey from 2006 through 2016 and state-level data on Head Start availability from Program Information Reports. Growth curve modeling was used to examine how the availability of Head Start predicted poverty growth during the Great Recession and the speed of recovery post-recession. Results: States with higher rates of Head Start enrollment had a smaller increase in family poverty during the Great Recession and a more stable recovery than states with lower Head Start enrollment. Conclusions: These findings suggest that greater access to Head Start programs prevented many families from falling into poverty and helped others exit poverty during the Great Recession. Implications: The findings provide clear, evidence-based policy recommendations. Increased federal funding for Head Start is needed to support families during a COVID-19 recession. States should supplement these allocations to expand Head Start enrollment for all eligible families.

Objectives: To investigate the heterogeneous effects of education on sleep duration for Black and White adults and how the education effects changed between 2004 and 2018. Methods: A total of 251,994 adult participants in the 2004 to 2018 National Health Interview Survey were included in pooled cross-sectional data analyses. Separately for Black and White men and women, we calculated prevalence ratio and average marginal probability of short sleep (<7 hours) for each education level over the study period based on weighted logistic regression models. Results: Opposite educational gradients in short sleep were observed between Black and White adults. Greater educational attainment was associated with lower likelihood of short sleep among White adults but higher likelihood of short sleep among Black adults. Such heterogeneous educational gradients were robust after accounting for a set of socioeconomic, family, and health factors and persisted between 2004 and 2018. Conclusions: The health implications of education are not uniform in the US population, and heterogeneous education effects on sleep duration persisted over the past decade. More scholarly attention is needed to identify challenges and barriers that may be unique for race, sex, and education subpopulations to maintain healthy sleep.

Introduction: This study compared tobacco use and cessation for African Americans (AA), Asians/Pacific Islanders (API), Hispanics/Latinos (H/L), American Indian/Alaskan Natives (AI/AN), and non-Hispanic Whites (NHW) in the United States (US) to California (CA), the state with the longest continually funded tobacco control program. The purpose of this study was to identify tobacco use disparities across racial/ethnic groups across time. Methods: Cigarette use prevalence (uptake and current use), consumption (mean number of cigarettes smoked per day [CPD]), and quit ratios were calculated across survey years and trends were examined within each race/ethnic group and comparing between CA and the US utilizing the 1992-2019 Tobacco Use Supplements to the Current Population Survey. Results: Prevalence decreased for all race/ethnic groups. Current use among CA NHW showed significant decline compared to US counterparts, while US H/L showed greater decline than CA counterparts. CPD decreased by approximately 30% across race/ethnic groups, with CA groups having lower numbers. The greatest decrease occurred among AA in CA (average 10.3 CPD (95% CI: 10.3,12.6) in 1992/93 to 3 CPD (95% CI: 2.4,3.7) in 2018/19). Quit ratios increased from 1992/93 to 2018/19 for CA H/L 52.4% (95% CI: 49.8,53.0) to 59.3 (95% CI: 55.8, 62.5), and CA NHWs 61.5% (95% CI: 60.7, 61.9) to 63.8% (95% CI:63.9, 66.9). Conclusions. Although overall prevalence decreased over time for each racial/ethnic group, declines in CA outpaced the US only for NHWs. Reductions in CPD were encouraging but the quit ratio points to the need to increase tobacco control efforts toward cessation.

This study examines the extent and reasons for differences in occupational distributions by race and ethnicity in the U.S. labor market from 2007 to 2018. Using IPUMS data, the study found that racial differences in occupational distributions were lower than ethnic disparities in occupational distributions. Racial disparity in occupational distributions increased slightly, while the ethnic disparity in occupational distributions decreased from 2007 to 2018. In addition, racial and ethnic disparities in occupational distributions were found to be not only due to observed socio-demographic variables of workers but also due to other unexplained factors. The effect of unexplained variables had more pronounced effects on the racial differences in occupational distributions than on the ethnic differences in occupational distributions.

This paper disaggregates the GTAP U.S. labor data into 20 worker types, by gender, education, and broad occupation category, to understand how different workers in the U.S. are exposed to hypothetical changes in tariff rates. First, a methodology is provided to disaggregate the U.S. labor input in GTAP into twenty types. A database of wage bill shares and mean wages by GTAP sector are calculated using 2017 data from the Current Population Survey (CPS) Annual Social and Economic Supplements (ASEC). The wage bill share matrix can be used as a tool to split wage payments in GTAP to each labor type. Second, the paper provides illustrative policy simulations on heavy manufacturing, agriculture, and services trade, demonstrating differential impacts on worker types for each of the scenarios considered.

The recent increase in interior immigration enforcement has reduced the number of low-skilled workers in the U.S. In my first chapter I study how this decrease in labor supply affects citizens’ self-employment. I examine the impact of four immigration enforcement policies; each implemented with a staggered roll-out across the U.S. and different levels of adoption. I find that increased immigration enforcement had a negative effect on male and female citizens’ self-employment. This is evidence that undocumented immigrants have a level of complementarity to self-employed citizens. The reduction of citizens’ self-employment is concentrated among high school graduate natives. The lower levels of self-employment are not accompanied by an increase in the wage and salary sector, suggesting that there is no switching within sectors happening. The industries that are more affected are construction and wholesale self-employment. However, self-employment among Hispanic citizens’ had the opposite effect. To enable comparison with previous studies, I estimate the effects of the immigration enforcement programs on the employment of citizens. I find that E-Verify mandates have a negative effect not accounted for in previous studies. The second chapter studies the effects of establishing an unemployment insurance (UI) program on workers’ unemployment duration in a developing market economy. Mexico City was the first city in Mexico to provide formal government-funded unemployment benefits. A job search model with duration dependence predicts that workers stay longer unemployed but increase their search intensity when they approach UI benefits exhaustion. I exploit the UI program’s temporal and geographical variation in a Differences-in-Differences framework and estimate an unemployment duration model, where the reemployment probability varies between major Mexican cities across time with the introduction of the UI program after controlling for individual characteristics, time and location fixed effects. I find no evidence of an effect on unemployment duration with the introduction of UI in Mexico City. Even workers with low education levels do not have a higher probability of staying unemployed with the introduction of UI. There are two potential explanations for these null effects: 1) UI benefits levels are low enough that staying on unemployment is an unattractive option, as the replacement rate after two months of unemployment was only 10% in Mexico, compared with 60% among OECD countries 60% (2007) and 2) the program effectively enforces the requirement that the unemployed search for work, which leads them to find work quickly. Finally, in my third chapter, we examine the gender wealth gap, including pension wealth and statutory pension rights. The empirical basis of this examination is the Socio-Economic Panel (SOEP), which is one of the few datasets where information on wealth as well as on pension entitlements is collected at the individual level. Pension wealth data are available for 2012 only. Individual-level wealth data allows us to analyze the gender wealth gap between women and men across all households. Due to the longitudinal character of the underlying data on employment trajectories and family-related events, we examine how pension entitlements are affected by childbirth, marriage, divorce, widowhood, and other factors.

The authors use a combination of national microdata from the Integrated Public Use Microdata Series (IPUMS) and regional population and health microdata, spatialized to the household level, and use historical GIS ( SIGH) to track the transmission of influenza infection among public school children on the northern peninsula of Michigan during the 1918 pandemic. Microdata are non-aggregated data from aextreme degree of precision. The authors describe three important advantages of using historical microdata in the context of HGIS the contextualization of data in space and time in correspondence with the period, the avoidance of ecological error and the ability to navigate freely between the micro and macro scales. They show the potential of studying historical pandemics using historical microdata by conducting a spatiotemporal analysis of this infectious respiratory disease in three schools from April to June 1918. In this paper we utilize a combination of national microdata from the Integrated Public Use Microdata Series (IPUMS) with local population and health microdata, spatialized to thehousehold level, and employ an historical GIS (HGIS) to follow infectious disease transmission between public school children in Michigan’s Upper Peninsula during the 1918 influenza pandemic. Microdata are data at the finest, non-aggregated level of precision. We illustrate three important advantages of using historical microdata within an HGIS framework contextualization of data within their period-accurate space—time, avoidance of the ecological fallacy, and the ability to move freely between micro and macro scales. We demonstrate the potential for studying historic pandemics using historical microdata by doing a spatiotemporal analysis following infectious respiratory disease through three schools from April to June 1918.

We investigate women’s likelihood of withdrawing from paid labor to care for children and help them with schoolwork as a result of COVID and school closures. Were women more likely to shift out of paid labor in states where property-division rules would better protect the financial interests of stay-at-home parents? Such higher protection is offered in states with community property regimes or with homemaking provisions, the alternative being equitable-division and no homemaking provisions. We use monthly data from the U.S. Current Population Survey and compare the labor force participation of women with children in grades K-6 between 2019 and 2020, before and after COVID started. We find an association between marital property laws offering women more financial protection and women’s labor supply response to COVID-19, especially among non-immigrants.

Drawing on the traditions of environmental justice, intersectionality, and social determinants of health, and using data from the EPA's NATA 2014 estimates of cancer risk from air toxics, we demonstrate a novel quantitative approach to evaluate intersectional environmental health risks to communities: Eco-Intersectional Multilevel (EIM) modeling. Results from previous case studies were found to generalize to national-level patterns, with multiply marginalized tracts with a high percent of Black and Latinx residents, high percent female-headed households, lower educational attainment, and metro location experiencing the highest risk. Overall, environmental health inequalities in cancer risk from air toxics are: (1) experienced intersectionally at the community-level, (2) significant in magnitude, and (3) socially patterned across numerous intersecting axes of marginalization, including axes rarely evaluated such as gendered family structure. EIM provides an innovative approach that will enable explicit consideration of structural/institutional social processes in the social production of intersectional and geospatial inequalities.

The second decade of the twentieth century is viewed as the pivotal period for ghetto formation in the United States. This decade witnessed the onset of the Great Migration and it was during this time that modern ghettos, massive agglomerations of tens of thousands of Blacks and virtually only Blacks, became visible. Despite the importance of this period for ghetto formation and the subsequent segregation experienced by Blacks, our understanding of the dynamics of segregation during this period is incomplete. We utilize recently released fine-grained census data to present a more precise and complete picture of segregation in American cities in the second decade of the twentieth century and in doing so make several contributions to the historical literature on residential segregation. First, we document how segregation varied across the full range of American cities, including Southern and smaller cities overlooked in most historical accounts. Second, we assess how the influx of Southern Black migrants into Northern cities was related to increasing segregation. Third, we ascertain the role of Blacks’ socioeconomic status in determining proximity to Whites. Fourth, we examine racial zoning’s impact on segregation. Finally, we consider how the presence of immigrants in a city was related to the residential segregation experienced by Blacks. This research thus adds to the literature on residential segregation by providing more reliable estimates of the degree of residential segregation experienced by Blacks at the beginning of the Great Migration as well as exploring other factors associated with varying levels of segregation at that time.

This study uses demographic methods to describe ethnoracial and educational inequality in the cumulative risk of homicide death and life lost to violence in the United States. If age-specific homicide rates were to continue at 2018–2019 levels, more than 1 in 19 Black males without a high school diploma would die by homicide. In contrast, 1 in 152 White males without a high school diploma and 1 in 233 Black males with a bachelor’s degree would be violently killed. Among Black males without a high school diploma, homicide led to a decrease in life expectancy at ages 15–19 of more than two years. The impact of U.S. violence on the life expectancy of socially marginalized people exceeds the life-expectancy impact on the full U.S. population of all causes of death except heart disease and cancer.