The Current Population Survey is the gold-standard data source for studying who turns out to vote in elections. However, it suffers from potentially nonignorable unit and item nonresponse. Fortunately, after elections, the total number of voters is known from administrative sources and can be used to adjust for potential nonresponse bias. We present a model-based approach to utilize this known voter turnout rate, as well as other population marginal distributions on demographic variables, in multiple imputation for unit and item nonresponse. In doing so, we ensure that the imputations produce design-based estimates that are plausible given the known margins. We introduce and utilize a hybrid missingness model comprising a pattern mixture model for unit nonresponse and selection models for item nonresponse. Using simulation studies, we illustrate repeated sampling performance of the model under different assumptions about the missingness mechansisms. We apply the model to examine voter turnout by subgroups using the 2018 Current Population Survey for North Carolina. As a sensitivity analysis, we examine how results change when we allow for over-reporting, i.e., individuals self-reporting that they voted when in fact they did not.

This paper proposes the application of an existing measure in a new context. Specifically, we adapt a measure originally proposed to characterize the degree of educational assortative mating to measure relative intergenerational mobility along the educational trait and inequality of opportunity. The new inequality measure is more suitable for controlling for the variations in the trait distributions of individuals and their parents than the commonly used intergenerational persistence coefficient. This point is illustrated by our empirical analysis of US census data from the period between 1960 and 2010: we show that controlling for the variations in the trait distributions adequately is vital in assessing the part of intergenerational mobility which is not caused by the educational expansion. Failing to do so can potentially reverse the relative priority of various policies aiming at reducing the “heritability” of (lack of) high school degrees and (lack of) tertiary education diplomas.

This paper examines how a decrease in capital costs affects factor income distribution through its varied impact across firms. Using a directed search model with convex vacancy posting costs, I explore how firms manage vacancy expenses by either raising wages to improve hiring rates or increasing automation to reduce labor needs. The model shows that more productive firms tend to automate more, and as capital prices fall, automation rises, leading to a lower labor share, a higher wage premium for non-routine workers, and increased residual wage dispersion. Quantitatively, the model suggests that the aggregate decrease in labor share is balanced by an increase in capital share, resulting in a net seven percentage point decline. Additionally, unemployment risk creates inefficiencies, which can be mitigated through progressive taxation and capital subsidies, enhancing the welfare of the new generation.

Introduction A growing literature documents the associations between discrimination and health. Emerging evidence suggests that among Hispanic/Latinx immigrants, discrimination leads to the deterioration of health outcomes over time. While sleep has been proposed as an important mediator of the relationship between discrimination and health, few studies have explicitly investigated this pathway, particularly among Hispanic/Latinx populations. Objective To investigate the relationships between racial/ethnic discrimination, sleep, and physical and mental health among Hispanic/Latinx immigrants in the USA. Data and Methods Using data from a parent study of first-generation Hispanic/Latinx immigrants in the southeastern USA, we conducted sequential mediation analyses using the bootstrapping method to investigate whether self-reported sleep duration, sleep quality, and fatigue mediate the relationship(s) between self-reported discrimination, as measured by the discrimination subscale of the Riverside Acculturative Stress Inventory, and self-reported physical and mental health. Results Nocturnal awakenings, fatigue, and sleep quality were statistically significant sequential mediators of the relationship between discrimination and physical health (b = −.001, SE = .001, CI [−.0027, −.0001]); fatigue alone also mediated this relationship (b = −.01, SE = .01, CI [−.0279, −.0003]). Nocturnal awakenings, fatigue, and sleep quality were also significant sequential mediators of the relationship between discrimination and mental health (b = −.001, SE = .001, CI [−.0031, −.0001]). Conclusion Sleep and fatigue play an important role in linking discrimination and health among first-generation Hispanic/Latinx immigrants. The development and implementation of interventions that focus on reducing fatigue among this population could mitigate the effects of unfair treatmen]); fatigue alone also mediated this relationship (b = −.01, SE = .01, CI [−.0279, −.0003]). Nocturnal awakenings, fatigue, and sleep quality were also significant sequential mediators of the relationship between discrimination and mental health (b = −.001, SE = .001, CI [−.0031, −.0001]). Conclusion Sleep and fatigue play an important role in linking discrimination and health among first-generation Hispanic/Latinx immigrants. The development and implementation of interventions that focus on reducing fatigue among this population could mitigate the effects of unfair treatment on health outcomes.t on health outcomes.

The sixth cycle of the octennial California Regional Housing Needs Assessment indicates that, over the next eight years, the City of Los Angeles needs to plan for the creation of nearly 260,000 homes for families earning 30 percent to 80 percent of area median income and for the creation of 197,000 units for families earning more than that amount. Meeting this goal would require the production of more than 100,000 homes per year over the next eight years, with more than 50 percent of them available at affordable rents. The magnitude of the region’s housing needs has led many policymakers and other stakeholders to call for an all-of-the-above approach to expanding the housing supply that includes increasing the production of both publicly funded affordable housing and market-rate housing, incentivizing increased density for infill housing projects, doubling down on such innovations as modular housing, and increasing support for the preservation of the existing affordable housing stock. In this report, we attempt to inform such an approach by focusing on one channel that could be an important part of the overall approach: the adaptive reuse (AR) of underutilized commercial real estate (CRE) as multiunit housing. The objectives of this report were to (1) generate evidence on the potential capacity of AR to bolster the supply of housing in the region, (2) assess how recent trends in prices and utilization rates of CRE affect the financial feasibility of AR, (3) explore how the geographic distribution of underutilized CRE coincides with social and environmental goals related to the siting of housing, and (4) assess how the distinct aspects of AR projects and relevant policy might affect the feasibility of this approach in terms of meeting regional housing goals. This research was conducted by the RAND Center for Housing and Homelessness in Los Angeles (CHHLA), part of the Community Health and Environmental Policy Program within RAND’s Social and Economic Well-Being division. The CHHLA is focused on providing policymakers and stakeholders with timely research and analysis to address the dual crises of housing affordability and homelessness in the Los Angeles region and beyond. For more information, visit www.rand.org/chhla.

In this paper we explain how the Data Basis platform helps decisively solve the data access problem for different types of users. We describe its core products: a powerful search engine, a freely accessible data lake featuring a unified schema and hundreds of interoperable tables, and APIs in various programming languages. We exemplify the platform's utility with discussions of three datasets on labor markets, elections, and local public finances in Brazil. The project is extraordinarily cost-effective: dividing a measure of yearly benefits generated by a conservative estimate of yearly costs to run the organization yields a lower bound social return of 74. We conclude by laying out a roadmap to guide the organization's future steps.

Many parents are single parents; that is, raising a child but not living with the child's other biological parent or a romantic partner. Being a single parent, though, does not mean that parents are living alone with their children. They may be living with other family members or roommates. In this Family Profile, we focus on resident mothers and fathers (i.e., living with at least one minor child) who are single (not married or cohabiting). We distinguish between "single, solo parents-single parents who are raising their minor children alone (with no spouse, cohabiting partner, parent, sibling, adult child, or roommate in their home)-and "single, nonsolo parents"-single parents who are living with another individual over the age of 18. Using data from the 2021 ASEC Current Population Survey from IPUMS-CPS, we identify the prevalence of solo parenthood and examine demographic characteristics including parental gender, marital history, educational attainment, and race and ethnicity (FP-17-17; FP-18-06; FP-21-26). For brevity, throughout this profile resident single, nonsolo parents will be referred to as "nonsolo" and resident single, solo parents will be referred to as "solo."

A few cities perform a high share of research and development (R&D) in the United States. If R&D generates local knowledge spillovers, then the social returns to R&D vary across cities and the geographic distribution of researchers may be inefficient. Equally important, but less well understood, is whether the private returns to R&D vary systematically across space. 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, the social returns 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 increases patenting by 2.8% and aggregate consumption by 0.8% in the long run, with minimal effects on inequality across regions or workers.

Introduction to U.S. Census Data Participants will be introduced to the various U.S. Bureau of Census surveys and how to access the available demographic data from the surveys using the data platform, census.data.gov. Additionally, participants will learn how to use the library GIS tool, Social Explorer, for demographic mapping and IPUMS to retrieve public use microsample data samples.

Despite similar educational aspirations, black students persist in higher education at much lower rates than white undergraduates. This paper advances a theoretical explanation for the racial gap in persistence by examining whether the differential attrition in college reflects contrasting incentives for educational persistence. To account for the highly unequal hurdles faced by black men and women in college and in the labor market, we propose a method that addresses race-gender-specific opportunity structures in both institutions simultaneously. This approach is based on forward-looking estimates of outcomes where students draw information from their race-gender reference group ahead of them. The model estimates the earnings payoffs of persistence separately for each race-gender group at three consecutive educational decision nodes: at high school graduation, college entry, and after one year in college. We subsequently apply one version of this model to data from the American Community Surveys (2001-2017), calculating the absolute and relative incentives for educational persistence across racial groups. In addition to large dollar earnings differentials, the analyses reveal striking racial gaps of the relative incentives to stay enrolled: "incentive inequality." This incentive race gap is largest at the earliest stages of the higher education career—high school graduation and college entry—where the black undergraduate dropout rate is highest. Our findings have substantive and methodological implications for situations where returns to investments are unequal across groups affected by discrimination.

Exploiting the staggered passage of labour protection laws in the United States, we find that higher labour adjustment costs increased the likelihood of observing zero leverage firms by 22%. This effect is significantly larger in states with stronger unionization, in industries with higher volatility and concentration, and in firms with higher labour intensity. Both within-firm changes in debt policies and higher propensity of newer firms to be debt-free are important in explaining these patterns. Overall, our work contributes to the literature on the relation between financial and labour markets by highlighting the role of labour laws in explaining the zero-leverage puzzle.

BACKGROUND AND OBJECTIVES: Rhetoric and policies aimed at restricting immigration to the United States, such as those proposed during the Trump administration, may lead to reduced enrollment in Medicaid for children of immigrants, even those who were legally eligible. This study assessed how children's health insurance coverage changed before versus during the Trump administration by parental immigration status. METHODS Using American Community Survey data, we compared changes in rates of uninsurance and Medicaid enrollment for children in the United States before (2015 to 2016) versus during (2017 to 2019) the Trump administration. Children were categorized by parental immigration status: citizen children with US-born parents, citizen children with naturalized parents, children from mixed-status families, or noncitizen children. RESULTS The study population included 2 963 787 children between 2015 and 2019, representing approximately 64 million children annually. Throughout our study period, uninsurance rates for children from mixed-status families and noncitizen children were higher than citizen children with United States-born parents. Beginning in 2017, there were significant increases in uninsurance among children from mixed-status families (0.48 percentage points [PP], 95% confidence interval [CI]: 0.06 to 0.91) that increased to 1.48 PP (95% CI: 0.98 to 1.99) by 2019 when compared with concurrent trends among citizen children with US-born parents. Changes were accompanied by significant decreases in Medicaid enrollment by 2019 (−0.89 PP, 95% CI: −1.62 to −0.16). CONCLUSIONS There were substantial disparities in uninsurance rates by parental immigration status. Compared with citizen children with US-born parents, uninsurance rates among children from mixed-status families significantly increased between 2017 and 2019, with the magnitude of disparity widening over time.

Immigration economists often disagree about whether comparably skilled immigrants and natives are perfect substitutes in the United States and other developed countries, leading these scholars to different assessments of the labor market impacts of immigration and policy recommendations. This article attempts to provide theoretical bases for understanding the immigrant-native substitution and to introduce machine learning techniques to resolve the empirical debate. Using the male subsample from the US Census and American Community Survey, it shows that the difference in covariate selection explains substantial disagreements in estimating immigrant-native substitution. Given the difficulties in providing compelling theoretical justifications for covariates selected, this article proposes estimating via the Lasso-type (least absolute shrinkage and selection operator) estimators. My Lasso-based estimation rejects perfect substitution, but it also implies easier substitution than that preferred by Ottaviano and Peri, suggesting more direct immigrant-native competition. By extending the sample to women, I find similar immigrant-native substitution across gender. Therefore, this article casts doubt on previous immigration impact assessments. Indeed, my simulation suggests considerable precision gains concerning the immigration's wage impacts on immigrants themselves. Furthermore, this article identifies immigrant segregation as a critical source of the national-level imperfect substitution, which decreases within progressively smaller regions and almost disappears in the same city. By introducing the Lasso-type estimators into migration studies, this article makes solid progress toward evaluating and understanding imperfect immigrant-native substitution and its socioeconomic consequences.

Purpose: It is well documented that the interaction between many social factors can affect clinical outcomes. However, the independent effects of economics on outcomes following surgery are not well understood. The goal of this study is to investigate the role socioeconomic status has on postoperative outcomes in a cerebellopontine angle (CPA) tumor resection population. Materials and methods: Over 6 years (07 June 2013 to 24 April 2019), 277 consecutive CPA tumor cases were reviewed at a single, multihospital academic medical center. Patient characteristics obtained included median household income, Charlson Comorbidity Index (CCI), race, BMI, tobacco use, amongst 23 others. Outcomes studied included readmission, ED evaluation, unplanned return to surgery (during and after index admission), return to surgery after index admission, and mortality within 90 days, in addition to reoperation and mortality throughout the entire follow-up period. Univariate analysis was conducted amongst the entire population with significance set at a p value <0.05. The population was divided into quartiles based on median household income and univariate analysis conducted between the lowest (Q1) and highest (Q4) socioeconomic quartiles, with significance set at a p value <0.05. Stepwise regression was conducted to determine the correlations amongst study variables and identify confounding factors. Results: Regression analysis of 273 patients did not find household income to be associated with any of the long-term outcomes assessed. Similarly, a Q1 vs Q4 comparison did not yield significantly different odds of outcomes assessed. Conclusion: Although not statistically significant, the odds ratios suggest socioeconomic status may have a clinically significant effect on postsurgical outcomes. Further studies in larger, matched populations are necessary to validate these findings.

The Status of Women in Kansas was prepared for the United Women’s Empowerment (United WE) by the Center for Science, Technology & Economic Policy at the Institute for Policy & Social Research, the University of Kansas. This is an update of the 2016 study, The Status of Women in Kansas and the BiState Region, prepared for The Women’s Foundation by the Institute for Policy & Social Research, the University of Kansas. The views expressed in this document are those of the authors and do not necessarily reflect the views of United WE or the University of Kansas.

We study how decades-long exposure to individuals of a given foreign descent shapes natives’ attitudes and behavior toward that group. Using individualized donations data from large charitable organizations, we show that long-term exposure to a given foreign ancestry leads to more generous behavior specifically toward that group’s ancestral country. To shed light on mechanisms, we focus on attitudes and behavior toward Arab-Muslims, combining several existing large-scale surveys, cross-county data on implicit prejudice, and a newly-collected national survey. We show that greater long-term exposure: (i) decreases both explicit and implicit prejudice against Arab-Muslims, (ii) reduces support for policies and political candidates hostile toward Arab-Muslims, (iii) leads to more personal contact with Arab-Muslim individuals, and (iv) increases knowledge of Arab-Muslims and Islam in general.

Home pregnancy tests give early fertility information and help women make timely family-planning decisions. This paper studies how the introduction of home pregnancy tests in the US in 1977 impacted fertility, early prenatal care, and later-life outcomes. Using county-level drugstore accessibility to approximate test availability, I document significant trend breaks in fertility rates after 1977 among women who had access to drugstores. The effects are the strongest for those aged 15-29 and concentrated among those with access to abortion services. In the long run, women exposed to home pregnancy tests were more likely to delay childbirth, participate in the labor force, and never marry; these women were also less likely to divorce.

The COVID-19 pandemic prompted social distancing, workplace closures, and restrictions on mobility and trade that had cascading effects on economic activity, food prices, and employment in low- and middle-income countries. Using longitudinal data from Bangladesh, Kenya, and Nigeria covering a period from October 2020 to April 2021, the paper assesses whether knowledge of a person infected with COVID-19 is associated with food insecurity, job loss and business closures, and coping strategies to smooth consumption. The likelihood of households to experience food insecurity at the extensive and intensive margins increased among those who knew an infected person in Bangladesh and Kenya.

This paper analyzes the impact of supply-side abortion restrictions on aggregate abortion and birth rates in the United States. Specifically, I exploit state and time variation in the implementation of the first targeted regulation of abortion provider (TRAP) law in a state to identify the effects of the laws. I find that TRAP laws are associated with a reduction in the abortion rate of approximately 5% the year the first law is implemented, and an average reduction of 11-14% in subsequent years. There is also evidence that TRAP laws increased birth rates by 2-3%, which accounts for approximately 80-100% of the observed decline in abortion rates.

Background: Certificate-of-need (CON) laws in place in most US states require healthcare providers to prove to a state board that their proposed services are necessary in order to be allowed to open or expand. While CON laws most commonly target hospital and nursing home beds, many states require CONs for other types of healthcare providers and services. As of 2020, 23 states retain CON laws specifically for substance use treatment, requiring providers to prove their “economic necessity” before opening or expanding. In contrast to the extensive academic literature on how hospital and nursing home CON laws affect costs and access, substance use CON laws are essentially unstudied. Methods: Using 2002–19 data on substance use treatment facilities from the Substance Abuse and Mental Health Services Administration’s National Survey of Substance Abuse Treatment Services, we measure the effect of CON laws on access to substance use treatment. Using fixed-effects analysis of states enacting and repealing substance use CON laws, we measure how CON laws affect the number of substance use treament facilities and beds per capita in a state. Results: We find that CON laws have no statistically significant effect on the number of facilities, beds, or clients and no significant effect on the acceptance of Medicare. However, they reduce the acceptance of private insurance by a statistically significant 6.0%. Conclusions: Policy makers may wish to reconsider whether substance use CON laws are promoting their goals.