Demography最新文献

Causal life course research examining consequences of early-life exposures has largely relied on associations between early-life environments and later-life outcomes using exogenous environmental shocks. Nonetheless, even with (quasi-)randomized early-life exposures, these associations may reflect not only causation ("scarring") but also selection (i.e., which members are included in data assessing later life). Investigating this selection and its impacts on estimated effects of early-life conditions has, however, often been ignored because of a lack of pre-exposure data. This study proposes an approach for assessing and correcting selection, separately from scarring, using genetic measurements. Because genetic measurements are determined at the time of conception, any associations with early-life exposures should be interpreted as selection. Using data from the UK Biobank, we find that in utero exposure to a higher area-level infant mortality rate is associated with genetic predispositions correlated with better educational attainment and health. These findings point to the direction and magnitude of selection from this exposure. Corrections for this selection in examinations of effects of exposure on later educational attainment suggest underestimates of 26-74%; effects on other life course outcomes also vary across selection correction methods.

U.S. women's age at first birth has increased substantially. Yet, little research has considered how this changing behavior may have affected the motherhood pay penalty, or the wage decrease with a child's arrival, experienced by the current generation. Using Rounds 1-19 of the National Longitudinal Survey of Youth 1997 (NLSY97), in this research note we examine shifts in hourly pay with childbirth for a cohort of women who became mothers mostly in the 2000s and 2010s. Results from fixed-effects models indicate that the motherhood pay penalty for NLSY97 women who had their first child before their late 20s is generally similar to that of previous cohorts. Those who became mothers near or after age 30, however, encounter a parenthood premium, as men do. The growing proportion of women delaying motherhood, coupled with the rising heterogeneity in motherhood wage outcomes by childbearing timing, contributes to a comparatively small motherhood penalty for this recent cohort. The pay advantage of "late mothers" cannot be explained by factors such as their labor market locations, number of children, stage of childrearing, marital status, or ethnoracial composition. Instead, the hourly gain stems from such mothers' tendency to reduce working hours more than other mothers without experiencing a commensurate decrease in total pay. Unlike the fatherhood premium, the premium for late mothers does not lead to a real boost in income.

Estimation and prediction of subnational mortality rates for small areas are essential planning tools for studying health inequalities. Standard methods do not perform well when data are noisy, a typical behavior of subnational datasets. Thus, reliable estimates are difficult to obtain. I present a Bayesian hierarchical model framework for prediction of mortality rates at a small or subnational level. By combining ideas from demography and epidemiology, the classical mortality modeling framework is extended to include an additional spatial component capturing regional heterogeneity. Information is pooled across neighboring regions and smoothed over time and age. To make predictions more robust and address the issue of model selection, a Bayesian version of stacking is considered using leave-future-out validation. I apply this method to forecast mortality rates for 96 regions in Bavaria, Germany, disaggregated by age and sex. Uncertainty surrounding the forecasts is provided in terms of prediction intervals. Using posterior predictive checks, I show that the models capture the essential features and are suitable to forecast the data at hand. On held-out data, my predictions outperform those of standard models lacking a regional component.

A central premise of the first demographic transition theory is that demographic change would occur more slowly in rural than urban areas. Few studies, however, have investigated whether rural areas remain holdouts during the second demographic transition. To address this gap, this study (1) examines trends among rural and urban families in Canada and the United States over a 30-year period and (2) determines whether compositional differences in demographic, socioeconomic, and religious factors explain current differences between rural and urban families. We find that rural Canadian women continue to have, on average, 0.6 more children than urban women. However, rural families do not trail behind urban families on any other indicator of family change. In fact, rural women in both countries are now significantly more likely to cohabit and roughly 10 percentage points more likely to have children outside of marriage than urban women. These differences are largely explained by lower levels of education and income among rural American women and fewer immigrants in rural Canada. Examining family change through a rural-urban lens fills important empirical gaps and yields novel insights into current debates on the fundamental causes of ongoing family change in high-income countries.

This study evaluates the extent to which metropolitan racial segregation occurs between neighborhoods-from tract to tract-and within neighborhoods-from block to block-and is framed theoretically by Putnam's (2007) "hunkering down" hypothesis. Analyses are based on complete-count block, tract, and metropolitan data from the last four U.S. decennial censuses. We document recent patterns of block-to-block segregation between Whites and racial and ethnic minorities (Blacks, Asians, and Hispanics) and between different minority pairs. For example, roughly 40% of all metro Black-White segregation is due to segregation from block to block within neighborhoods. Among Asians, the between-neighborhood component of metropolitan segregation has increased over time but was largely compensated by declines in the within-neighborhood (or block) component. Metropolitan fixed-effects models show that trends and racial and ethnic differences in segregation-overall and within and between neighborhoods-are broadly observed across metro areas but are most evident in the largest, oldest, and most highly segregated metro areas. The results are robust to alternative estimates that adjust for differential privacy, metropolitan reclassification, and neighborhood boundary changes. Analyses of neighborhood change in Atlanta, Georgia, further reinforce the generality of our multiscale approach.

Fertility is a life course process that is strongly shaped by geographic and sociodemographic subgroup contexts. In the United States, scholars face a choice: they can situate fertility in a life course perspective using panel data, which is typically representative only at the national level; or they can attend to subnational contexts using rate schedules, which do not include information on life course statuses. The method and data source we introduce here, Census-Held Linked Administrative Records for Fertility Estimation (CLAR-FE), permits both. It derives fertility histories and rate schedules from U.S. Census Bureau-held data for the nation and by state, racial and ethnic subgroups, and the important life course status of parity. We generate three types of rates for 2000-2020 at the national and state levels by race and ethnicity: age-specific rates and both unconditional and conditional parity- and age-specific rates. Where possible, we compare these rates with those produced by the National Center for Health Statistics. Our new rate schedules illuminate state and racial and ethnic differences in transitions to parenthood, providing evidence of the important subgroup heterogeneity that characterizes the United States. CLAR-FE covers nearly the entire U.S. population and is available to researchers on approved projects through the Census Bureau's Federal Statistical Research Data Centers.

Originally developed for estimating healthy life expectancy, the traditional Sullivan method continues to be a popular tool for obtaining point-in-time estimates of the population impacts of a wide range of health and social conditions. However, except in rare data-intensive cases, the method is subject to stringent stationarity assumptions, which often do not align with real-world conditions and restrict its resulting estimates and applications. In this research note, we present an expansion of the original method to apply within a population projection framework. The Sullivan method projection framework produces estimates that offer new insights about future trends in population health and social arrangements under various demographic and epidemiologic scenarios, such as the percentage of life years that population members can expect to spend with a condition of interest in a time interval under different assumptions. We demonstrate the utility of this framework using the case of long COVID, illustrating both its operation and potential to reveal insights about emergent population health challenges under various theoretically informed scenarios. The traditional Sullivan method provides a summary measure of the present, while its incorporation into a projection framework enables preparation for a variety of potential futures.

We propose a novel decomposition approach that breaks down the levels and trends of lifespan inequality as the sum of cause-of-death contributions. The suggested method shows whether the levels and changes in lifespan inequality are attributable to the levels and changes in (1) the extent of inequality in the cause-specific age-at-death distribution (the "Inequality" component), (2) the total share of deaths attributable to each cause (the "Proportion" component), or (3) the cause-specific mean age at death (the "Mean" component). This so-called Inequality-Proportion-Mean (or IPM) method is applied to 10 low-mortality countries in Europe. Our findings suggest that the most prevalent causes of death (in our setting, "circulatory system" and "neoplasms") do not necessarily contribute the most to overall levels of lifespan inequality. In fact, "perinatal and congenital" causes are the strongest drivers of lifespan inequality declines. The contribution of the IPM components to changes in lifespan inequality varies considerably across causes, genders, and countries. Among the three components, the Mean one explains the least lifespan inequality dynamics, suggesting that shifts in cause-specific mean ages at death alone contributed little to changes in lifespan inequality.

In the wake of the COVID-19 pandemic, the International Organization for Migration has postulated that international migrant stocks fell short of their pre-pandemic projections by nearly 2 million as a result of travel restrictions. However, this decline is not testable with migration data from traditional sources. Key migration stakeholders have called for using data from alternative sources, including social media, to fill these gaps. Building on previous work using social media data to analyze migration responses to external shocks, we test the hypothesis that COVID-related travel restrictions reduced migrant stock relative to expected migration without such restrictions using estimates of migrants drawn from Facebook's advertising platform and dynamic panel models. We focus on four key origin countries in North and West Africa (Côte d'Ivoire, Algeria, Morocco, and Senegal) and on their 23 key destination countries. Between February and June 2020, we estimate that a destination country implementing a month-long total entry ban on arrivals from Côte d'Ivoire, Algeria, Morocco, or Senegal might have expected a 3.39% reduction in migrant stock from the restricted country compared with the counterfactual in which no travel restrictions were implemented. However, when broader societal disruptions of the pandemic are accounted for, we estimate that countries implementing travel restrictions might paradoxically have expected an increase in migrant stock. In this context, travel restrictions do not appear to have effectively curbed migration and could have resulted in outcomes opposite their intended effects.

Migration scholars have long regarded the trajectory of the third generation as a critical test of assimilation; however, scholarship to date has been limited and largely focused on socioeconomic attainment. In this article, we rely on a large dataset of adolescent respondents in England, Germany, and the Netherlands to compare the second and third generations in terms of their social networks and cultural identities. The third generation shows stronger ties to the native fourth-plus generation alongside weaker ties to coethnics. We document comparable, albeit more moderate, dynamics of assimilation over generations in regard to national and ethnic identification, along with substantial variation by country of destination and ethnic origin group. Our results point to a dominant trend of assimilation at the third generation and suggest future challenges to provide a more durable assessment of postwar migration waves two generations after settlement.