For decades, Asian Americans have been characterized as a “model minority” due to perceived success in educational attainment and labor market outcomes. However, there are concerns that Asians remain underrepresented in top level positions in the workplace. This study presents new evidence on the extent of this underrepresentation between US-born Asian American and White men and examines mechanisms driving racial leadership gaps. Results indicate that Asian men are significantly less likely to work in management and executive positions compared to White men with similar qualifications. However, Asian men are not underrepresented in other high-paying, non-leadership occupations, suggesting this is a phenomenon unique to leadership occupations. Furthermore, these gaps are only present among East Asian and Southeast Asian men, while South Asian men do not differ from White men in the likelihood of working in leadership occupations. I examine several mechanisms and find no evidence that gaps are driven by racial differences in preferences for leadership positions, selection into self-employment, intergenerational transmission effects, immigration recency, or ethnic attrition.

Purpose: This study aimed to examine physical activity (PA) as a protective measure of health outcomes among sexual minority (SM) and heterosexual adults. Methods: Using data from the 2018 National Health Interview Survey, differences in key health outcomes between three groups (heterosexual, SM, and “other”) and the mediating effects of PA were explored. Chi-square tests were used to examine group differences. Multinomial logistic regression models were fit to examine associations between PA and health outcomes (body mass index [BMI], psychological distress, health status, and use of alcohol, cigarettes, and e-cigarettes). Results: Compared with heterosexual respondents, “other” adults had higher levels of BMI above 24.9 and were less active; SM respondents had elevated levels of alcohol use, smoking, binge drinking, and psychological distress, and reported worse overall health status. Unadjusted models showed that PA served as a protective factor for BMI above 30 and health status for all groups. PA was also protective for BMI below or above 18.5–24.9, smoking, psychological distress, and health status for both heterosexual and SM groups. Alternatively, PA behaved as a risk factor for alcohol use and binge drinking for all groups. Adjusted modeling removed PA as a risk factor for light alcohol use and binge drinking for SM respondents and added a protective factor for e-cigarette use for heterosexual respondents. Conclusion: This study adds to current literature on health disparities affecting SM populations and the health benefits associated with PA. Further research is needed to explore tailored PA intervention for SM populations and those who mark “other,” “something else,” “don't know,” or refuse to answer questions on sexual orientation.

Labor force participation and average hours of work both fell sharply at the beginning of the COVID-19 pandemic. Neither had fully recovered by the end of 2022. The drop in participation between December 2019 and December 2022 implies a loss of 3 million people from the labor force; the decline in average hours over the same period translates to the equivalent of 2.6 million fewer workers. Demographic and other trend factors that predated the pandemic explain most of the participation shortfall. Taken together, COVID-19-related health effects and the persistent (though shrinking) effects of the fear of contracting COVID-19 more than explain the rest. In contrast, pre-pandemic factors account for little of the shortfall in hours. COVID-19-related health effects account for perhaps 40 percent of that decline, but we are unable to explain the majority of the hours shortfall. We speculate that the lower level of hours in the post-pandemic period may reflect a shift in the desired balance between work and other aspects of workers' lives.

The circular cartogram, also known as the famous DorlingMap, is widely used to visualize geographical statistics by representing geographical regions as circles. However, all existing approaches for circular cartograms are only designed for static data. While applying these approaches for time-varying data, the circle locations in each circular cartogram are recomputed separately and will result in low efficiency and low visual stability between sequential circle cartograms. To generate visually stable circular cartograms for time-varying data efficiently, we propose a novel approach by improving the elastic beam algorithm with a hierarchical optimization strategy. First, the time-varying data at different time points are grouped using a hierarchical clustering method based on their similarity, and a hierarchy is then built for their corresponding circular cartograms. Second, we generate intermediate circle locations level by level for clusters of circular cartograms according to the built hierarchy with an improved elastic beam algorithm iteratively. The elastic beam algorithm is improved in its proximity graph construction and force computation by considering that the algorithm will be applied to displace circles in a cluster of circular cartograms. The iterative process stops until we obtain satisfactory circular cartograms for each time point. The evaluation results indicate that the proposed approach can achieve a higher quality (184.85%↑ and 265.69%↑) on visual stability, and a higher efficiency (58.54%↑ and 73.96%↑) with almost the same quality on overlap avoidance and relation maintenance by comparing to the existing approaches. Project website: https://github.com/TrentonWei/DorlingMap .

Previous research finds that the greater geographic mobility of foreign than native-born workers following economic shocks helps to facilitate local labor market adjustment to shifting regional economic conditions. We examine the role that immigration may have played in enabling U.S. commuting zones to respond to manufacturing job loss caused by import competition from China. Although population headcounts of the foreign-born fell by more than those of the native-born in regions exposed to the China trade shock, the overall contribution of immigration to labor market adjustment in this episode was small. Because most U.S. immigrants arrived in the country after manufacturing regions were already mature, few took up jobs in industries that would later see increased import penetration from China. The foreign-born share of the working-age population in regions with high trade exposure was only three-fifths that in regions with low exposure. Immigration thus appears more likely to aid adjustment to cyclical shocks, in which job loss occurs in regions that had recent booms in hiring, rather than facilitating adjustment to secular regional decline, in which hiring booms occurred in the more distant past.

Infant mortality remains high and uneven in much of sub-Saharan Africa. Even low-cost, highly effective therapies can only save lives in proportion to how successfully they can be targeted to those children who, absent the treatment, would have died. This places great value on maximizing the accuracy of any targeting or means-testing algorithm. Yet, the interventions that countries deploy in hopes of reducing mortality are often targeted based on simple models of wealth or income or a few additional variables. Examining 22 countries in sub-Saharan Africa, we illustrate the use of flexible (machine learning) risk models employing up to 25 generally available pre-birth variables from the Demographic and Health Surveys. Using these models, we construct risk scores such that the 10 percent of the population at highest risk account for 15-30 percent of infant mortality, depending on the country. Successful targeting in these models turned on several variables other than wealth, while models that employ only wealth data perform little or no better than chance. Consequently, employing such data and models to predict high-risk births in the countries studied flexibly could substantially improve the targeting and thus the life-saving potential of existing interventions.

The US consistently lags nearly all developed nations in percentage of population covered by basic health insurance. To address this, ‘RomneyCare’ was implemented in Massachusetts (state level) in 2006, and later on ObamaCare’s similar major provisions came into effect in 2014 at the federal level. ObamaCare came under scrutiny and, therefore, properly quantifying its effects is paramount. In this paper a new way of doing so is proposed, by looking at health coverage outcomes in Massachusetts as part of an ‘always treated’ control group. Quantifying ObamaCare’s impacts by comparing against a control group, while using American Community Survey data, can only be done through this method due to constraints in data availability. While this method has been used long before, Kim and Lee’s (2019) article formalized this difference-in-differences in reverse (DDR) approach, thereby making it possible to attempt to fill a gap in this literature. Moreover, the implementation of this identification strategy with readily available public use individual data should strengthen the case for the use of this method for ex-post policy analyses. The quasi-experimental setting existent in neighboring states Massachusetts and Rhode Island allowed for the conclusion that this health policy did indeed increase the odds of an individual being insured by 88,5%, after controlling for several demographic and socioeconomic variables. Furthermore, for the case of a representative individual who is at risk of being uninsured, the probability of insurance went up by about 22%.

For many families of children with intellectual disability and autism (ID/ASD), private health insurance and public programs, such as Medicaid and the Children’s Health Insurance Program (CHIP), are critical sources of support. The purpose of this study was to examine the impact of health insurance coverage on workforce engagement of parents of children with ID/ASD. The study utilized 2014–2018 pooled National Health Interview Survey data to construct national estimates and test the effects of health insurance status on parent workforce outcomes. Primary findings indicate significantly higher odds of workforce absence (OR ¼ 2.83, p, .0038) and unemployment (OR ¼ 8.91, p, .0038) among parents with children with ID/ASD using public health insurance, compared to the reference group. Additionally, parents of children with ID/ASD who were uninsured were found to experience significantly higher unemployment (OR ¼ 4.86, p, .0038) than the reference group. Findings have policy and research implications related to workforce engagement for parents, including issues impacting health insurance coverage, specifically related to Medicaid and CHIP.

In 2022, 40% of adults were single—not currently in a cohabiting or marital union (Juteau, 2022). Singlehood is partly dependent on the timing of entry into unions, but also the dissolution of these unions across the life course. Hence, some singles are never married, and others are previously married. We use data from the 2022 Current Population Survey (CPS) in this Family Profile to assess singlehood from young to middle adulthood, differentiating four age groups: 18-29, 30-39, 40-49, and 50-59. We present the age variation in singlehood according to gender, race and ethnicity, nativity, and educational attainment. Singlehood is defined as not currently being married or cohabiting.

Public schools in the United States saw unprecedented reductions to in-person instruction during the 2020–2021 school year. Using the Elementary School Operating Status database, the American Community Survey, and the Current Population Survey, we show remote instruction was associated with reduced employment among mothers compared with fathers and women without children. The gender gap in employment between mothers and fathers grew as much as 5 percentage points in areas with remote instruction. Compared to women without children, mothers’ employment fell by as much as 2 percentage points under remote schooling. Employment disparities among mothers deepened by race, educational attainment, and marital status. We show employment disparities endured through spring 2021, even as many school districts returned to in-person instruction.

Purpose Several small studies have associated exposure to elevated average temperature with specific vision problems. However, no large-scale studies have examined the relationship between vision impairment and average area temperature in the general population. We conducted a cross-sectional analysis of a large nationally representative sample of older adults to further explore this relationship. Methods Secondary analysis of the American Community Survey (ACS). The survey was conducted through mail, telephone and in-person interviews. Data from six consecutive years of the cross-sectional survey were analysed (2012–2017). The subsample analysed included community-dwelling and institutionalized older adults aged 65 and older in the coterminous US who lived in the same state in which they were born (n = 1,707,333). The question on severe vision impairment was “Is this person blind or does he/she have serious difficulty seeing even when wearing glasses?”. Average annual temperature data from the National Oceanic and Atmospheric Administration was combined into a 100-year average and mapped to corresponding US Census Bureau’s public use microdata areas from the ACS. Results Higher average temperature is consistently associated with increased odds of severe vision impairment across all cohorts (i.e. age, sex, race, income, and educational attainment cohorts) with the exception of Hispanic older adults. Compared to those who lived in counties with average temperature of < 50 °F (< 10 °C) , the odds of severe vision impairment were 44% higher in counties with average temperature of 60 °F (15.5 °C) or above (OR 1.44; 95% CI 1.42–1.46). Conclusion If the association is found to be causal, the predicted rise in global temperatures could impact the number of older Americans affected by severe vision impairment and the associated health and economic burden.

In self-reported data from the US National Health Interview Survey (NHIS), a greater likelihood of vocal improvement in older adults has been associated with treatment and certain sociodemographic characteristics.1 However, NHIS data do not include comprehensive health care utilization, and self-report of specialty care can be inaccurate with increasing age.2 Using a novel national data linkage3 of self-reported data from the NHIS 2012−Voice, Speech, and Language Supplement4 and health care utilization data from Medicare for individual patients, we created the first national population-based data source to concurrently evaluate clinical care utilization and patient reported voice outcomes. This linkage includes adults 65 years and older, the population subset with the highest voice disorder prevalence. This study aimed to identify sociodemographic and clinical factors associated with self-reported voice improvement. Based on previous literature,1 we hypothesized that factors associated with voice improvement in NHIS data would include younger age, White race, urban residence, higher level of education, good overall health, specialty care, and greater Medicare visit volume.

This paper seeks to investigate the causal effects of changes in state minimum wage on the likelihood of an individual being beneath the poverty threshold. This paper contributes to existing research by exploring the impact of minimum wage at a state level on poverty through a narrower scope. By comparing the outcomes of all industries with industries that have the largest ratios of minimum wage workers, we are able to see the impact in both a broad and targeted context. Our investigation into the relationship between the probability of being below the poverty threshold and key regressors — real state minimum wage, state fixed effects, year fixed effects, and industry fixed effects — employs causal methods, specifically a fixed effects regression model and logistic model. We leveraged data from the American Community Survey and Federal Reserve Economic Data from 2012 through 2019, and through our analysis, the results indicate that real minimum wage does not have a large nor significant impact on the probability of an individual being below the poverty threshold across the industries included in the dataset. The findings indicate that the minimum wage is ineffective as the only policy tool to combat poverty.

This study examines the effect of internet job search (IJS) on job-finding rates among unemployed job seekers during the rapid expansion of the internet from the mid-1990s to the early 2010s. To address endogenous selection into IJS, I use an instrumental variables (IV) strategy exploiting the rise of IJS within occupations over time, which varied across occupations depending on pre-internet exposure to computers at work. The analysis sample includes unemployed workers from the December 1998, August 2000, September 2001, October 2003, and July 2011 Current Population Survey (CPS) Computer and Internet Use Supplements and the September 1992 Basic Monthly CPS, longitudinally matched with their employment outcomes from the subsequent monthly CPS files. The IV estimates indicate that IJS increased the 15-month job-finding rate by 12.9 percentage points (25.1% relative to the mean). Results from placebo exercises and various specification checks support a causal interpretation of the estimated effects. Additionally, the effectiveness of IJS remained stable over time throughout the analysis period.

Technical change that extends market scale can generate winner-take-all dynamics, with large income growth among top earners. I test this “superstar model” in the entertainer labor market, where the historic rollout of television creates a natural experiment in scale-related technological change. The resulting inequality changes are consistent with superstar theory: the launch of a local TV station skews the entertainer wage distribution sharply to the right, with the biggest impact at the very top of the distribution, while negatively impacting workers below the star level. The findings provide evidence of superstar effects and distinguish such effects from popular alternative models.

A common finding in the literature on heterogeneity in entrepreneurial entry is that workers of small firms relative to large firms are more likely to become entrepreneurs (i.e., the “small firm effect”). Researchers disagree, however, on how to interpret this relationship, and in which contexts. A traditional argument is that work experience in small firms facilitates the acquisition of knowledge relevant to entrepreneurship, which, in turn, facilitates transitions into entrepreneurship. An alternative explanation argues that, at least in technical domains, the small firm effect derives from the potential for large firms to better integrate ideas internally (i.e., intrapreneurship). We inform this debate by leveraging a unique, nationally representative survey that provides rich details about the emergence of entrepreneurial ideas and their potential implementation inside vs. outside of firms. Consistent with the former explanation of the small firm effect, for the average worker, we find strong evidence that small firms (relative to large firms) are better at providing an environment that facilitates transitions to entrepreneurship. However, we also find that technical workers are more likely to attempt to implement ideas in larger firms relative to non-tech workers. Our results thus imply that while the traditional mechanism drives the small firm effect for non-tech workers, both mechanisms contribute to the effect for the tech workers, thus helping to reconcile competing explanations in prior studies. In addition to unpacking the drivers of the small firm effect, our study has broader implications for the recent discussion on the drivers of entrepreneurial dynamism.

Housing discrimination has been an ethical, social, and economic blight on the American society. Among the negative outcomes of this practice are higher crime rates, lower educational attainment, and concentrated poverty. Beyond the moral injustice of this practice, housing discrimination adversely affects the socio-economic mobility of those victimized and this extends across generations. The research on the intersection of race and Latino identity demonstrates the complexity of the issue and suggests that an examination such as done in this study can add to the current knowledge. The purpose of this study was to determine what, if any, association race has on housing stratification for Latinos in NYS. It analyzed data based on the patterns of household race identifications among Latinos living in Public Use Microdata Area (PUMA). The operating hypothesis was that the same patterns of housing stratification that exist among the non-Latino populations will be found among the Latino population. The study used 2019, five-year (2015 – 2019, America Community Survey (ACS) data The independent variable in this analysis was ethno-racial identity. The dependent variable was housing stratification measured using the index of dissimilarity. Among all the Latino groups, White Latinos reflected the lowest dissimilarity score. Other Latinos scored 31.51 on the index of dissimilarity and, most notably, the highest score on the index was found among those that identified as Black Latinos at 32.75. All differences were found to be significant at p < 0.001.

The percentage of all U.S. adults who have never been married or have yet to marry has doubled from 15% in 1960 to 31% in 2020 (Marino, 2023). More recently, the national homeownership rate declined from about 67% in 2006 to 64% in 2019 (Mateyka & Mazur, 2021). Homeownership rates for never married adults declined from 2005 to 2013, and single adults had lower shares of homeownership than currently and previously married adults (Mundra & Uwaifo Oyelere, 2019). Further, homeownership among never married adults tended to vary by gender, race, and education level (Mundra & Uwaifo Oyelere, 2019). Home prices have risen nearly 30% since 2019 (National Association of Realtors, 2022), underscoring the salience of assessing the potential impact of this rapid increase on the growing never married population. This family profile examines the percentages of homeowners across marital status using the 2021 American Community Survey 1-year estimates, and also illustrates differentials in homeownership among never married adults (aged 18 and older) by gender, race and ethnicity, and educational attainment.

Understanding peculiar and anomalous behavior of machine learning models for specific data subgroups is a fundamental building block of model performance and fairness evaluation. The analysis of these data subgroups can provide useful insights into model inner working and highlight its potentially discriminatory behavior. Current approaches to subgroup exploration ignore the presence of hierarchies in the data, and can only be applied to discretized attributes. The discretization process required for continuous attributes may significantly affect the identification of relevant subgroups. We propose a hierarchical subgroup exploration technique to identify anomalous subgroup behavior at multiple granularity levels, along with a technique for the hierarchical discretization of data attributes. The hierarchical discretization produces, for each continuous attribute, a hierarchy of intervals. The subsequent hierarchical exploration can exploit data hierarchies, selecting for each attribute the optimal granularity to identify subgroups that are both anomalous, and with enough elements to be statistically and practically significant. Compared to non-hierarchical approaches, we show that our hierarchical approach is more powerful in identifying anomalous subgroups and more stable with respect to discretization and exploration parameters.

“Disinterested intellectual curiosity is the lifeblood of real civilization. [. . . ] There is nothing that more divides civilized from semi-savage man than to be conscious of our forefathers as they really were, and bit by bit to reconstruct the mosaic of the longforgotten past. To weigh the stars, or to make ships sail in the air or below the sea, is not a more astonishing and ennobling performance on the part of the human race in these latter days, than to know the course of events that had long been forgotten, and the true nature of men and women who were here before us.” Trevelyan (1942)