Background Adolescent sexual and reproductive health remains a major public health and development issue of global importance. Given that adolescents and young people are heterogenous groups in terms of many charac- teristics, this study expands the literature by comparing the reasons for contraceptive discontinuation between par- enting adolescents (aged 15–19) and parenting young women (aged 20–24) in sub-Saharan Africa (SSA). Methods Data for the study came from Demographic and Health Surveys of 22 SSA countries. The outcome variable was reasons for discontinuation. We performed multilevel binary logistic regression on analytic samples comprising 1485 parenting adolescents and 10,287 parenting young women across the selected SSA countries. Results Findings show that the proportion of respondents who used modern contraceptives was lower among par- enting adolescents (35%) relative to their 20–24-year-old counterparts (43%). Higher percentages of parenting ado- lescents than young women discontinued contraceptives because of reasons such as pregnancy or method failure (i.e., 9.9% and 8.17% accordingly), husband disapproval, access or availability issues, wanting more effective methods, and inconvenience in using methods. The multilevel analysis further highlighted disparities between parenting ado- lescents and parenting young women who discontinued contraceptives. For instance, parenting young women had 30% lower odds of discontinuing contraceptives due to pregnancy or method failure than parenting adolescents. Conclusion The study established disparities in the reasons for contraceptive discontinuation between parenting adolescents and parenting young women, with adolescents demonstrating greater vulnerabilities and higher risks. Considerable attention must be given to parenting adolescents in the efforts to achieve equity goals such as the Sus- tainable Development Goals and universal health coverage in SSA.

We report on the successful completion of a project to upgrade the positional accuracy of every response to the 1990, 2000, and 2010 U.S. decennial censuses. The resulting data set, called Optimized Spatial Census Information Linked Across Time (OSCILAT), resides within the restricted-access data warehouse of the Federal Statistical Research Data Center (FSRDC) system where it is available for use with approval from the U.S. Census Bureau. OSCILAT greatly improves the accuracy and completeness of spatial information for older censuses conducted prior to major quality improvements undertaken by the Bureau. Our work enables more precise spatial and longitudinal analysis of census data and supports exact tabulations of census responses for arbitrary spatial units, including tabulating responses from 1990, 2000, and 2010 within 2020 block boundaries for precise measures of change over time for small geographic areas.

A remarkable and repeatedly replicated rural school attendance and enrollment advantage in the pre-Great Depression period confounds both critical historians and functionalist social scientists, who argue that the processes of urbanization and industrialization was central to the U.S.’s rapid postbellum school system expansion. This expansion, and the rural attendance advantage has typically been attributed to the rise of the high school movement, a decentralized campaign to expand secondary school education that started in New England and rapidly spread to the Midwest. How robust was the rural school participation advantage? Did this advantage translate to into an advantage in conventionally measured educational attainment? I investigate these questions using the 1870 – 1930 full count Census records of children aged 5 to 19, which I link to the full count 1940 Decennial Census, the first to record educational attainment. I document the following four facts. First, at the individual, county-, and state-levels, urbanization and the fraction of the population engaged in manufacturing were associated with higher levels of school attendance at younger ages; the reverse was true at high school ages both before and after 1900. Second, urbanization and industrialization were associated with reduced school attendance among all students aged 5 to 19. Third, I replicate earlier findings that school attendance propensity was decreasing in local population size all else equal for older youth, but, again, the reverse was true at younger ages. Fourth, I compare the reported 1940 educational attainment of rural youth aged 5 to 19 appearing in the 1880, 1900, 1910, and 1920 with urban youth appearing in the same cross-section. Relative to large cities, the age-specific rural attendance advantages I document did not usually translate into elevated educational attainment in the Census cross- sections analyzed. The rapid spread of the high school movement in rural areas and elevated age- specific attendance rates that ensued did not overcome the structural disadvantages associated with growing up in a rural area. In the pre-Great Depression U.S., elevated measures on the age- specific attendance and enrollment statistics used in prior studies of school system expansion may not always have signaled increased educational opportunity.

Cities around the world are piloting projects to evaluate the feasibility and benefits of shared autonomous vehicles (SAVs), as their large-scale implementation and integration into public transit systems have the potential to improve individuals' accessibility and transportation equity. To understand the full potential of SAVs and their likely adoption, it is important to identify how the services can be utilized most effectively and what determines the composition of the ridership. This research aims to explore the usage and adoption of SAVs, fo-cusing on a project called RAPID (Rideshare, Automation, and Payment Integration Demonstration) that was launched in Arlington, Texas. We used real-time trip-level ridership data from the SAV platform, conducted a survey of SAV riders, and developed a study based on ordered logistic regression to estimate the determinants of ridership frequency. Data analysis of real-time ridership data revealed that spatial distribution of activities and service accessibility have crucial roles in forming the current users' travel patterns. The findings from the logistic regression demonstrated that those with higher household incomes are less likely to be frequent riders of RAPID, while those who usually walk, bike, or utilize on-demand ridesharing services are likely to use SAVs often. Users with higher levels of safety perception are also more likely to be frequent users of the service. The findings of this study will provide planners with a better understanding of SAV ridership patterns and will guide decision-makers nationwide in establishing and adopting policies that will be appropriate for future SAV implementation projects.

The past several decades have seen the rise of the Asian “ethnoburb”—communities retaining a disproportionate Asian presence in middle-class and suburban settings. Recent explanations have suggested that ethnoburbs may manifest as a function of “resurgent ethnicity” that indirectly leads to Asian self-segregation. In this study, I examine whether Asian ethnoburbs can also arise as a function of stratification, where White population exodus coincides with Asian population growth. To evaluate this argument, I use census data from 2000 to 2020 to examine the history of White and Asian population change for 1,299 neighborhoods defined as Asian ethnoburbs in 2020. The results suggest, on one hand, that many ethnoburbs experienced White population exit in a fashion consistent with racial turnover. These patterns of White population decline were unexplained by socioeconomic deficits and, in fact, rose in likelihood with socioeconomic status (SES) increases. On the other hand, a near-comparable number of ethnoburbs did not experience White exit in the face of Asian in-migration. However, this tended to be the case when Asians began as a relatively small presence and White households remained the dominant group. These findings suggest that arguments of self-segregation provide a poor explanation for ethnoburb formation. Instead, Asian ethnoburbs appear to emerge as a function of spatial assimilation and ethnic stratification: though Asian households tend to grow most prominently in the Whitest neighborhoods, the prospect of racial turnover looms once Asian households start to comprise a greater share of neighborhood residents.

Construction is one of the most dangerous industries in the United States, with 1,034 fatal occupational injuries among all construction workers and 74,520 nonfatal injuries among private wage-and-salary construction workers in 2020. A majority of fatal occupational injuries and a large proportion of nonfatal injuries result from Construction Focus Four hazards, which include falls to a lower level, struck-by, electrocutions, and caught-in/between injuries. This classification was created in 1994 in response to the impact the top four safety hazards have on construction workers. This Data Bulletin provides information on fatal and nonfatal Focus Four occupational injuries in construction, including by injury type and detailed event/exposure. (In this Data Bulletin, occupational injuries will be referenced as injuries.) Data for fatal injuries from 2011 to 2021 were obtained from the U.S. Bureau of Labor Statistics (BLS) Census of Fatal Occupational Injuries (CFOI), a complete count of fatal injuries and their circumstances. Data for caught/compressed by equipment/objects, which is one of the categories of caught-in/between injuries, were unavailable in 2021 due to the BLS modernized disclosure policy, which impacts all data from 2019 forward, but missing data varies by year and categories examined. For this Data Bulletin only caught in/compressed by equipment/objects in 2021 was impacted. Estimates of nonfatal injuries for private, wage-and-salary construction workers from 2011 to 2020 were obtained from the BLS Survey of Occupational Injuries and Illnesses (SOII), which are based on employer logs. Nonfatal injuries included are those that resulted in days away from work (DAFW). Due to changes in SOII publication frequency to biennially, injury data for 2021 are unavailable at this time, and as a result, charts using SOII data are limited to 2011-2020. Full-time equivalent workers (FTEs) were estimated using the BLS Current Population Survey (CPS), a monthly population survey, downloaded through IPUMS. CPWR calculated fatal injury rates per 100,000 FTEs, while nonfatal rates were calculated per 10,000 FTEs.

A dataset with the numbers of housing units for the 56 largest urban areas in 2020, going back to 1950, is used to examine the changes in their sizes. While all of the areas more than doubled in size, the growth of some was astounding, adding over 99 percent of their 2020 housing units since 1950. The differences in rates of growth produced significant changes to the list of the largest 15 areas, with six new areas added compared only one change in the previous four decades. The areas in the South grew most rapidly. In 1950, the large urban areas in that region accounted for by far the smallest share of all of the housing units in the large urban areas but had the largest share in 2020. In the final decade, nearly half of all growth in housing units for the large urban areas was in the South. The distribution of the sizes of urban areas is often thought to conform to Zipf’s law, postulating that the size and rank are related by a power law with an exponent of one. In 1950 the exponent was slightly greater than one. The exponent dropped by 2020 such that the the smaller urban areas were larger than expected.

To understand rising income inequality in the United States, we use data from the March Current Population Survey (CPS) to decompose income inequality into components attributable to five personal traits: sex, race, education, occupation, and industry of work. By quantifying how income differences across these traits contribute to total inequality, and how those contributions have changed over time, we vet competing hypotheses for the rising gap. In performing this analysis, we correct for data censorship (“Top-coding”) within the CPS by fitting the upper tail of the income distribution and imputing the hidden observations; this represents an extension to previous studies that instead truncate the top several percentiles of income data. Our findings suggest that changes in the returns to education played an important role in driving the observed rise in inequality.

Composites of bibliometric indicators received strong critique in the literature thus far. In this work, we thoroughly reinvestigate a composite proposed by Ioannidis et al. (2020). We rely on the congeneric measurement model (and related models) rooted in classical test theory from the psychometrics literature. We found that one of the proposed indicators clearly violated unidimensionality as a fundamental assumption of the congeneric model. In addition, two more indicators were excluded for redundancy. The newly proposed composite based on only three bibliometric indicators was found to display excellent reliability. Importantly, reliability was on par with reliability for a composite based on five indicators and clearly better than the originally proposed six-indicator composite. We further found rather homogeneous effective weights (i.e., relative contributions of each indicator to composite variance) for simple sum scores, which were pretty close to weights based on an algorithm for equally effective weights. Finally, an algorithm for reliability maximizing effective weights resulted in a composite that correlated perfectly with factor scores based on a congeneric measurement model. Strong measurement invariance of the congeneric model was found across sexes. Loadings and intercepts of the congeneric measurement model, however, were not found to be measurement invariant across scientific fields and academic age groups. Finally, various derived composites were found to correlate positively with academic age which hints at a lack of fairness of the composites. We discuss our findings towards practical applications and a more constructive discussion of bibliometric composites in the scientometric literature.

Causal inference requires knowing causal connections between treatment and outcome variables, and DeepIV, a causal inference framework, is the pioneer work to predict such connections by crossing deep learning with causal inference in applying instrumental variables(IVs) to deep neural network. DeepIV has been proved to be one of the best methods in this field theoretically, but how the framework performs on real-life problems still remains unclear. This paper provides an implementation of DeepIV, and use the framework to predict causal effect from people's educational background on their annual income. DeepIV framework allows us to take advantage of neural network to estimate causal effect by adjusting loss function. To evaluate the performace of DeepIV in solving real-life problems, our experiment is based on real datasets. The result of our experiment shows that DeepIV's ability to predict causal effect on real data is at least as good as those of other casual inference models' whose reliability has been verified in practice. Meanwhile, DeepIV does not have obvious shortcoming in predicting outcomes compared with other supervised learning methods.

Since 1900, the share of unmarried adults aged 18 and older in the U.S. has increased such that by 2020 they represented nearly half the adult population (Marino, 2023). As the U.S. marriage rate has decreased over the past few decades (Schweizer, 2020) and high levels of divorce persist (Reynolds, 2021), the share of adults who experience singlehood, particularly at later ages, has become more prevalent. Additionally, the median age at first marriage has increased for both women and men since the mid-1900s (Payne, 2021), underscoring the utility of examining changing patterns of age variation among unmarried adults. In this family profile, we use data from the 1900 to 1990 U.S. Decennial Censuses and the American Community Survey 1-year estimates from 2000, 2010, and 2020 to examine the age distribution of unmarried adults in the U.S. over a century. We also describe the median ages of all unmarried men and women and unpack the heterogeneity among unmarried adults by examining the median ages of divorced/separated, widowed, and never married adults. It is important to note that some of these changes are simply a function of increased life expectancy, which rose from 47.3 in 1900 to 77.0 in 2020 (Arias & Xu, 2022).

The semiparametric accelerated hazard relational models proposed by Cadena and Denuit (Insurance Math. Econ. 68:1–16, 2016) are built from a reference life table by distorting the age scale. Individuals are made younger or older before performing the computations with the reference life table. In this paper, the mortality of individuals with higher educational attainment is modeled by means of an accelerated hazard relation specification. It is then projected by reference to the general population, using two different approaches. Numerical illustrations are performed on US mortality statistics.

Rental affordability has become a major concern for most Americans since the 2007–2009 recession. Minorities and immigrants, especially in some emerging destination metropolises, have suffered disproportionally from its brunt. Nashville—a prominent southern destination—has not only experienced a considerable gain in diverse immigrant groups, but it has also witnessed skyrocketing housing prices and rents, making them beyond the affordability of even middle-class Americans. Rent burden patterns among racial/ethnic groups, especially the immigrants/foreign-born who are not yet citizens, remain largely unknown. Given Nashville’s suitability as an emerging metropolis reverberating as a nationally representative immigrant gateway, this article explores relationships between rent burden faced by immigrants and other racial/ethnic groups while examining neighborhood-level determinants captured in principal components derived from demographic, socioeconomic, occupation, and built-environment attributes. A newly devised rent burden index—previously applied for metropolises only—is calibrated to census tracts to test its validity in explaining intraurban variabilities. Through multitiered statistical and cartographic approaches, we find that the neighborhoods with higher diversity and a greater presence of foreign-born, noncitizens are the most rent-burdened; so are the tracts with well-off whites and Asians. Newly built neighborhoods associate negatively with the severely burdened category and median gross rent as a percentage of income, thus highlighting the significance of newer-built housing in mitigating rent burden.

We study the optimal design of means-tested transfers and progressive income taxes. In a simple analytical model, we show that adding a transfer to a log-linear tax induces welfare gains almost as large as in the second-best allocation. Transfers allow for more progressive average than marginal tax and transfer rates, achieving redistribution while preserving efficiency. In a rich dynamic model, we quantify the optimal fiscal plan. We use new flexible functions featuring targeted transfers and progressive income taxes, delivering a good empirical fit across the income distribution. Transfers should be larger than currently in the United States and financed with moderate income tax progressivity.

What drives soldiers to risk their life in combat? We show that the legacy of war creates lasting conditions that encourage younger generations to take greater risks when fighting for their country. Using individual-level data from over 4 million British war records, we show that WWI deaths deeply affected local communities and the behaviour of the next generation of soldiers. Servicemen from localities that suffered heavier losses in WWI were more likely to die or to be awarded military honours for bravery in WW2. To explain these findings, we document that WWI deaths promoted civic capital in the inter-war period-as demonstrated by the creation of lasting war memorials, veterans' associations and charities, and increased voter participation. In addition, we show that sons of soldiers killed in WWI were more likely to die in combat, suggesting that both community-level and family-level transmission of values were important in this context.

This paper empirically investigates wage inequality within the group of skilled workers in the recent four decades in the USA using CPS data and finds evidence that the trend of wage growth of the top and bottom 10th percentile of skilled workers significantly diverged starting from 2000. Using a task-based framework of occupation, I find that the changing trend of wage inequality was entirely driven by one category of occupation, namely the nonroutine analytic occupation. Then, I consider in a model task reallocation between two broad task categories, namely the routine and abstract task, induced by an ongoing investment-specific technical change. In my model, the labor in the routine task is replaced by cheaper machines due to investment-specific technical change, then workers that are less productive in the abstract task enter abstract occupations. As a result, the wage inequality in the abstract task widens because of the reallocation of less productive workers from the routine task to the abstract task, that is, the “composition effect.” In addition, since economic agents tend to postpone the investment in machines after the ongoing investment-specific technical change takes place for a while, the expansion path of wage inequality is not linear but features an acceleration of wage dispersion in the middle of the technical change. The quantitative results suggest that the model is able to provide a well-matched timing and magnitude of the nonlinear expansion path in wage inequality that is observed in the data.

Objective: We examine how state spending on children is associated with the size of socioeconomic gaps in maternal childcare time. Background: Persistent socioeconomic divides in the amount and nature of parental time with children have prompted consideration of the factors that mitigate inequalities within the family. At both the national and local levels, the welfare state plays an important role in structuring opportunities for children. Thus, it is important to understand the institutional factors that shape parental behavior. Yet, little research examines how the social safety net is associated with family processes. Method: Using rich data on maternal time with children from the American Time Use Surveys (2003–2016), combined with longitudinal data on public spending in states on major programs affecting children and families, we examine how state spending on children is associated with the size of socioeconomic gaps in maternal childcare time. Results: We found that higher levels of state spending were associated with significant increases in childcare time among low-educated mothers at both the extensive and intensive margin, increasing the likelihood of spending any minutes on primary childcare in a typical day, as well as increasing the number of minutes spent on childcare. In contrast, we observed no variation in the behavior of highly educated mothers as state spending changes. Implications: State-level investments could meaningfully narrow socioeconomic gaps in maternal time with children.

This study investigates whether parents spend different amounts of time in housework, childcare, and employment across birth cohorts. We apply data from the American Time Use Survey (ATUS; 2003–2018) and age-cohort-period models to compare parents' time spent in these activities across three successive birth cohorts: Baby Boomers (1946–1965), Generation X (1966–1980) and Millennials (1981–2000). For housework time, we find no evidence of cohort change for mothers but for fathers, we observe an increase in housework time with each subsequent cohort. For time spent caring for children, we identify a period effect whereby mothers and fathers regardless of which cohort they belong to are spending more time in primary care of children over time. For work time, we find an increase in mothers' contributions across these birth cohorts. But, net of this overall trend, we find Generation X and Millennial mothers are spending less time in employment relative to Baby Boom mothers. Fathers’ employment time, by contrast, has not changed across cohorts or over our measured period. Ultimately, we find gender gaps in childcare, housework and employment across cohorts remain suggesting cohort replacement and period effects are inadequate to close gender gaps in housework, childcare and paid employment time.

Economic inequality in the U.S. is significantly influenced by the integration trajectory of diverse immigrant and racial/ethnic minority groups. It is also increasingly clear that these processes are uniquely gendered. Few studies, however, jointly and systematically consider the complex ways in which race/ethnicity, gender, and nativity intersect to shape minority men’s and women’s economic experiences, and an intersectional understanding of these processes remains underdeveloped. To address this gap, we blend insights from assimilation, stratification, and intersectionality literatures to analyze 2015–2019 American Community Survey data. Specifically, we examine income inequality and group-level mobility among full-time working whites, Blacks, Native Americans, and Asian and Latino subgroups representative of the Southwest—the first U.S. region to reach a majority-minority demographic profile. Sociodemographic and human capital attributes generally reduce group-level income deficits, and we observe a robust pattern of economic mobility among native-born generations. But most groups remain decisively disadvantaged. Persistent income gaps signal multitiered racial/ethnic-gender hierarchies in the Southwest and suggest exclusion of minority men and women. Additionally, race/ethnicity and gender have an uneven impact on the relative position and progress observed among both U.S.- and foreign-born generations. Such findings support an intersectional approach and demonstrate the complex interplay of multiple axes of inequality that together shape contemporary U.S.- and foreign-born men’s and women’s economic experiences and returns.

BACKGROUND: Meeting the needs of a round-the-clock and globalized society has led to an increase in long working hours. This trend has been accompanied by a corresponding rise in sleep disorders and subsequent use of sedating medications. Overtime hours have been associated with adverse health outcomes such as cardiovascular diseases, symptoms of psychological distress, and health behaviors, including risky intake of alcohol and smoking. Hence, the main objectives of this three-paper dissertation were to examine the multi-faceted relationship between working hours, the use of prescription sleep aids, the onset of psychological distress, and the use of health care services. METHODS: The 2010-2019 Medical Expenditure Panel Survey (MEPS) data was utilized. The first paper investigated the relationship between working hours and the use of prescribed sedating medications. Different regression models were employed, ranging from multivariable linear regression, Tobit regression, Heckman regression, and multivariable logistic regression. The second paper utilized a fixed-effect linear model in evaluating the relationship between working hours and the onset of psychological distress. The third paper also employed fixed-effect estimators in modeling the association between working hours, health care utilization, and the use of sedating medications. FINDINGS: Long working hours were associated with increased odds of using sleep aids and medications with sedative properties. Females had a higher likelihood of using sleep aids when compared to males. Also, professional services had the highest likelihood of using sleep medications. Over time hours was associated with the onset of psychological distress, with differences in the risk of onset across gender. Respondents working very long hours had the highest odds of using outpatient medical services. This association between very long hours and the use of outpatient services was significantly more pronounced in respondents using medications with sedating side effects. CONCLUSIONS: Long working hours were associated with an elevated risk of using sedating medications, developing psychological distress, and healthcare utilization. This highlights the probable negative impact of overtime hours on the health status of individuals. Implementing policies that encourage work-life balance and aid interventions that decrease work-related stress may help in mitigating risks associated with long work hours.