Using data from the American Time Use Survey, we examine the empirically underexplored ways in which racial and ethnic identity shapes parental time use. Racial/ethnic differences emerge within income groups in terms of trends and trade-offs in time spent with children versus time spent in paid work and other activities. For fathers, trade-offs in paid work and time spent with children are qualitatively similar across income by race/ethnicity. However, our estimates suggest that low-income Hispanic fathers spent approximately 10 minutes less with their children for every hour in time spent in paid work, a substantively starker trade-off than that made by low income non-Hispanic fathers. For mothers, the lowest-income white mothers show the largest reductions in time spent with children for every hour spent in paid work. Increased time in paid work decreases the time spent on other (non-sleep) activities in a qualitatively similar manner for mothers and fathers by racial/ethnic group, across and within income groups.

The wage gap between black and white Americans has narrowed between the 1960s and the 1970s, but its progress has stalled since 1980. This study argues that routine biased technological change (RBTC) contributed to dampening wage gap convergence in 1980-2000, having a differential impact across races and along the wage distribution. Thus, I present new empirical evidence on occupational patterns by race and on determinants of wage disparities along the wage distribution, and rationalize them with an RBTC model in which firms engage in statistical discrimination. I show that, surprisingly, the share of employment in routine intensive occupations has increased for black workers, in contrast with a significant decrease observed for white workers. I decompose the wage gap changes using the Oaxaca-RIF methodology and show that differences in occupational sorting of the workforce increase wage disparities, thwarting wage convergence between races at the bottom of the wage distribution. Together, these new empirical findings and model provide insights to better understand the mechanisms behind racial disparities at the end of the 20th century

The COVID-19 pandemic and the ensuing recession, as well as racial injustices and protest responses throughout 2020, have highlighted that public policies can have very different impacts on populations by race or ethnicity. Tax policies, in particular, are commonly perceived as "race neutral," often because information on race or ethnicity is not solicited in tax collections or explicitly referenced in those policies. But that does not mean tax systems affect people of different races and ethnicities in the same way. We know that Black, Latinx, and Native American households have already been bearing the brunt of COVID-19 cases, deaths, and the economic downturn. Fiscal actions taken by state and local governments going forward can mitigate or amplify these racial inequities, and some places are making targeted efforts to tackle them.

Using CPS data from 2007 to 2012, we examine the contemporaneous effect of the Great Recession on the relative wages of immigrant men. Compared to pre-recession period, immigrants see a modest decline in their relative wages during the recession regardless of model specification. After the recession, immigrants’ relative wages largely recover from the recession-induced decline, but the wage disadvantage does not completely revert back to its pre-recession level. Selective in- and out-migration by immigrants or selection of natives into employment do not seem to drive the results. It appears that, during the recession, immigrants may have traded higher employment with lower wages and employers might have been willing to hire them as a cost-saving measure. The results could have implications for how relative wages of immigrants respond to the ongoing COVID-19 Pandemic Recession.

One of the most common methods for estimating the U.S. unauthorized foreign-born population is the residual method. Over the last decade, residual estimates have typically fallen within a narrow range of 10.5 to 12 million. Yet it remains unclear how sensitive residual estimates are to their underlying assumptions. We examine the extent to which estimates may plausibly vary owing to uncertainties in their underlying assumptions about coverage error, emigration, and mortality. Findings show that most of the range in residual estimates derives from uncertainty about emigration rates among legal permanent residents, naturalized citizens, and humanitarian entrants (LNH); estimates are less sensitive to assumptions about mortality among the LNH foreign-born and coverage error for the unauthorized and LNH populations in U.S. Census Bureau surveys. Nevertheless, uncertainty in all three assumptions contrib utes to a range of estimates, whereby there is a 50% chance that the unauthorized foreign-born population falls between 9.1 and 12.2 million and a 95% chance that it falls between 7.0 and 15.7 million.

Assisting end users to identify desired results from a large dataset is an important problem for multi-criteria decision making. To address this problem, top-k and skyline queries have been widely adopted, but they both have inherent drawbacks, i.e., the user either has to provide a specific utility function or faces many results. The k-regret minimization query is proposed, which integrates the merits of top-k and skyline queries. Due to the NP-hardness of the problem, the k-regret minimization query is time consuming and the greedy framework is widely adopted. However, formal theoretical analysis of the greedy approaches for the quality of the returned results is still lacking. In this paper, we first fill this gap by conducting a nontrivial theoretical analysis of the approximation ratio of the returned results. To speed up query processing, a sampling-based method, STOCPRESGREED, is developed to reduce the evaluation cost. In addition, a theoretical analysis of the required sample size is conducted to bound the quality of the returned results. Finally, comprehensive experiments are conducted on both real and synthetic datasets to demonstrate the efficiency and effectiveness of the proposed methods.

This paper examines the historical evolution of uneven neighborhood food environments in tri-County Detroit, Michigan from 1970 to 2010. It demonstrates how broader economic conditions and business decisions not germane to the region interacted with a landscape marked by economic polarization and racial segregation to create an uneven food environment where Black neighborhoods disproportionately bore the brunt of restrictive food access. It also contextualizes capital-centric accounts of retail decentralization by showing how underlying inequities in the region limited how Detroit’s Black community could respond to store closures in their neighborhoods as well as their ability to relocate closer to suburban stores. This work raises questions about policies that heavily subsidize national and regional supermarket chains to relocate to urban centers as well as policies that solely focus on supply factors rather than the broader inequities remaking the region.

Evidence from the American Time Use Survey 2003–2012 suggests that minority employees, especially men, spend a small but statistically significant amount of time not working at the workplace relative to non-Hispanic whites. The time differences remain significant but decrease by 25 to 50% when accounting for detailed industry and occupation controls. Union status, public- or private-sector attachment, payment method, and educational attainment do not explain the differences, although health status is important among African Americans. The estimates imply that the differences in nonwork at the worksite can explain up to 10% of the adjusted wage gap between minority and non-Hispanic white workers.

Flooding risk results from complex interactions between hydrological hazards (e.g., riverine inundation during periods of heavy rainfall), exposure, vulnerability (e.g., the potential for structural damage or loss of life), and resilience (how well we recover, learn from, and adapt to past floods). Building on recent coupled conceptualizations of these complex interactions, we characterize human–flood interactions (collective memory and risk-enduring attitude) at a more comprehensive scale than has been attempted to date across 50 US metropolitan statistical areas with a sociohydrologic (SH) model calibrated with accessible local data (historical records of annual peak streamflow, flood insurance loss claims, active insurance policy records, and population density). A cluster analysis on calibrated SH model parameter sets for metropolitan areas identified two dominant behaviors: 1) “risk-enduring” cities with lower flooding defenses and longer memory of past flood loss events and 2) “risk-averse” cities with higher flooding defenses and reduced memory of past flooding. These divergent behaviors correlated with differences in local stream flashiness indices (i.e., the frequency and rapidity of daily changes in streamflow), maximum dam heights, and the proportion of White to non-White residents in US metropolitan areas. Risk-averse cities tended to exist within regions characterized by flashier streamflow conditions, larger dams, and larger proportions of White residents. Our research supports the development of SH models in urban metropolitan areas and the design of risk management strategies that consider both demographically heterogeneous populations, changing flood defenses, and temporal changes in community risk perceptions and tolerance.

The present study examines the spatial assimilation patterns of immigrants who arrived as children. The main objective is to predict the likelihood of living in ethnic areas for decimal generation immigrants (1.25, 1.5, and 1.75) among Asian Indians, Japanese, Chinese, Koreans, Filipinos, and Vietnamese. Using 2013–2017 5-Year ACS Estimates and IPUMS, it applies the measure of local spatial clustering (the Local Moran’s I statistic) to identify ethnic areas and the logistic regression model to assess the effects of immigrant generational status, cultural, and socioeconomic assimilation on the probability of living in ethnic areas. The findings show that the 1.25 and 1.5 decimal generation immigrants of Chinese, Filipinos, Japanese, and Koreans demonstrate higher propensities of living in ethnic areas compared to the first generation of each ethnic group, respectively. Meanwhile, their Asian Indians and Vietnamese counterparts show spatial assimilation. Regardless of generational effects, English language ability positively relates to the probability of living in nonethnic areas, whereas economic assimilation indicators reveal mixed results. We found substantial evidence for resurgent ethnicity theory and some support of spatial assimilation model, indicating the ethnic disparity in spatial assimilation patterns among Asian immigrants. Our paper highlights the nonlinear assimilation patterns among Asian decimal generations. Results suggest that, for Asian immigrants in the U.S., age-at-arrival and ethnicity are both significant predictors of residential preference.

We focus on the timing of labor supplied during the day and its interaction with home care responsibilities. Using the American Time Use Survey, we measure the incidence of household care activities between 8 AM and 5 PM (the prime time of the day). Women experience more work interruptions during that time. These work interruptions imply wages that are about 9 percent lower. This result is consistent with occupations offering more flexibility but also a lower wage. We offer suggestive evidence that missing work due to household demands has a larger penalty in occupations with more coordinated work schedules.

This paper studies the labor market effects of both documented and undocumented immigration in a search model featuring nonrandom hiring. As immigrants accept lower wages, they are preferably chosen by firms and therefore have higher job finding rates than natives, consistent with evidence found in US data. Immigration leads to the creation of additional jobs but also raises competition for natives. The dominant effect depends on the fall in wage costs, which is larger for undocumented immigration than it is for legal immigration. The model predicts a dominating job creation effect for the former, reducing natives’ unemployment rate, but not for the latter.

Healthcare affordability affects many in the United States. When patients with chronic health conditions delay or forgo necessary medications due to their inability to pay, their conditions may worsen and require additional healthcare services. This study evaluates the relationship between cost-related medication nonadherence and healthcare costs through regression analyses.

Purpose of Review: The main purpose of this review is to summarize the epidemiology of cardiovascular disease and its risk factors among two of the largest and most diverse immigrant groups in the United States (Hispanics/Latinos and South Asians). Recent Findings: While the migration process generates unique challenges for individuals, there is a wide heterogeneity in the characteristics of immigrant populations, both between and within regions of origin. Hispanic/Latino immigrants to the United States have lower levels of cardiovascular risk factors, prevalence, and mortality, but this assessment is limited by issues related to the “salmon bias.” South Asian immigrants to the United States generally have higher levels of risk factors and higher mortality. In both cases, levels of risk factors and mortality generally increase with time of living in the United States (US). Summary: While immigration acts as a social determinant of health, associations between immigration and cardiovascular disease and its risk factors are complex and vary across subpopulations.

We study the effects of a college on local education attainment using historical college establishment experiments in which runner-up locations were strongly considered to become college sites but ultimately not chosen for as-good-as-random reasons. While losing counties have since had opportunity to establish their own colleges, we show that winners have more years of exposure to a college over their history and are more likely to have a college today. Using this variation, we find that winners have 14 percentage points higher contemporary rates of bachelor's and graduate degree attainment. This educational attainment gap has widened over time.

Historical studies of labor market outcomes frequently suffer from a lack of data on individual income. The occupational income score (OCCSCORE) is often used as an alternative measure of labor market outcomes, particularly in studies of the U.S. prior to 1950. While researchers have acknowledged that this approach introduces measurement error, no effort has been made to quantify its impact on inferences. Using modern Census data, we find that the use of OCCSCORE biases results towards zero and can frequently result in statistically significant coefficients of the wrong sign. We show that a simple adjustment to OCCSCORE can substantially reduce this bias. We illustrate our results using the 1915 Iowa State Census, a rare source of pre-1950 earnings data. Using OCCSCORE in this context yields an attenuated wage gap for blacks and a statistically significant wage gap of the wrong sign for women; our adjusted OCCSCORE eliminates almost all of this bias. We also examine how bias due to the use of OCCSCORE affects estimates of intergenerational mobility using linked data from the 1850-1910 Censuses.

Purpose: Living alone, an indicator of social isolation, has been increasing in the United States; 28% of households in 2019 were one-person households, compared with 13% in 1960. The working-age population is particularly vulnerable to adverse social conditions such as low social support. Although previous research has shown that social isolation and loneliness lead to poorer health and decreased longevity, few studies have focused on the working-age population and heart disease mortality in the United States using longitudinal data. Methods: This study examines social isolation as a risk factor for all-cause and heart disease mortality among U.S. adults aged 18-64 years using the pooled 1998-2014 data from the National Health Interview Survey (NHIS) linked to National Death Index (NDI) (n = 388,973). Cox proportional hazards regression was used to model survival time as a function of social isolation, measured by ''living alone,'' and sociodemographic, behavioral, and health characteristics. Results: In Cox regression models with 17 years of mortality follow-up, the age-adjusted all-cause mortality risk was 45% higher (hazard ratio [HR] = 1.45; 95% confidence interval [CI] = 1.40-1.50) and the heart disease mortality risk was 83% higher (HR = 1.83; 95% CI = 1.67-2.00) among adults aged 18-64 years living alone at the baseline, compared with adults living with others. In the full model, the relative risk associated with social isolation was 16% higher (HR = 1.16; 95% CI = 1.11-1.20) for all-cause mortality and 33% higher (HR = 1.33; 95% CI = 1.21-1.47) for heart disease mortality after controlling for sociodemographic, behavioral-risk, and health status characteristics. Conclusion: In this national study, adults experiencing social isolation had statistically significantly higher relative risks of all-cause and heart disease mortality in the United States than adults living with others.

Within a context dominated by the seemingly paradoxical juxtaposition of gentrification and abandonment in New York City during the early 1980s, Peter Marcuse developed an influential typology of displacement that can be conceptualized as a movement from the most readily observable forms of “last-resident” displacement to increasingly less measurable forms of “exclusionary displacement” and “displacement pressure.” While the typology depends heavily on the explanatory frame of demographic transition and the movement out of space, Marcuse also included the possibility of a contradictory form of “chain displacement” that often occurs in non- and/or pregentrification spaces without demographic change. Using geocoded data from 16 years of eviction records in Dane County Wisconsin, this research not only demonstrates the existence of chain displacement within specific neighborhoods, but also exposes sites of “multiple eviction” that combine with forms of disadvantage and relative demographic “stability” rather than patterns more characteristic of gentrification processes.

Why do some immigrant groups succeed in getting the U.S. government to impose economic sanctions on their former dictators, while others do not? This paper begins by noting that the president is the pivotal player in sanctions policy and that presidents pander to voters in swing states. Therefore, the size of a diaspora's voting block in swing states should determine whether the American government imposes sanctions on their former homeland. Considering dictatorships from 1946 to 2005, this paper finds that a one-percentage-point increase in the size of a country's diaspora in swing states increases the probability of sanctions by 11 percentage points. It then goes on to calculate causal estimates of the effectiveness of economic sanctions on regime change. Using the size of diasporas in swing states as an instrumental variable for the presence of economic sanctions, it finds that sanctions do not have a statistically-significant impact on regime change.

Understanding the impact of policy changes on the distribution of income first requires a good representation of the distribution. There are various ways to do this that range from simple approaches, as the calculus of inequality measures, to more sophisticated approaches, as the kernel estimation of the distribution and, if possible, the observation of its evolution over time. All these methods can be jointly employed to have a clearer view of the concentration of the income, to compare different distributions and to understand the impact of difference policy actions. In this work, we are interested in the analysis of income inequalities in the US between 2010 and 2018. In particular, we concentrate on a local perspective, by studying the inequality recorded in seven Metropolitan Statistical Areas, which have been chosen in order to cover geographically the US territory. It is important to emphasize that evaluating the degree of inequality at a local level, such a city or a metropolitan statistical area, is as important as at the national level. The connection, for instance, between inequality and crime is as strong within urban areas as it is across countries. Moreover, urban inequality seems as likely to generate political uprisings as inequality across large geographic units (Glaeser et al, 2009). The aim of this work is two-fold. On the one hand, we study inequality, measured via some of the most well-known inequality indices. On the other hand, we complete the analysis via the kernel estimate of the distribution and some distribution dynamics analysis. The idea is that to have a completely informative inequality analysis, different perspectives should be considered. The structure of the paper is as follows. In section 2, we present an overview of the main inequality measures. In section 3, we recall two tests to compare distributions. In section 4, we present our empirical analysis on 7 Metropolitan Statistical Areas across the USA. In section 5, we present some conclusions.