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We make use of newly available data that include roughly 5 million linked household and population records from 1850 to 2015 to document long-term trends in intergenerational social mobility in the United States. Intergenerational mobility declined substantially over the past 150 y, but more slowly than previously thought. Intergenerational occupational rank–rank correlations increased from less than 0.17 to as high as 0.32, but most of this change occurred to Americans born before 1900. After controlling for the relatively high mobility of persons from farm origins, we find that intergenerational social mobility has been remarkably stable. In contrast with relative stability in rank-based measures of mobility, absolute mobility for the nonfarm population—the fraction of offspring whose occupational ranks are higher than those of their parents—increased for birth cohorts born prior to 1900 and has fallen for those born after 1940.

Public health professionals have long played a vital—albeit underappreciated—role in shaping, not simply using, US Census data, so as to provide the factual evidence required for good governance and health equity. Since its advent in 1790, the US Census has constituted a key political instrument, given the novel mandate of the US Constitution to allocate political representation via a national decennial census. US Census approaches to categorizing and enumerating people and places have profound implications for every branch and level of government and the resources and representation accorded across and within US states. Using a health equity lens to consider how public health has featured in each generation’s political battles waged over and with census data, this essay considers three illustrations of public health’s engagement with the enduring ramifications of three foundational elements of the US Census: its treatment of slavery, Indigenous populations, and the politics of place. This history underscores how public health has major stakes in the values and vision for governance that produces and uses census data.

The article challenges the notion that below-replacement fertility and its local variation in China are primarily attributable to the government's birth planning policy. Data from the 2000 census and provincial statistical yearbooks are used to compare fertility in Jiangsu and Zhejiang, two of the most developed provinces in China, to examine the relationship between socioeconomic development and low fertility. The article demonstrates that although low fertility in China was achieved under the government's restrictive one-child policy, structural changes brought about by socioeconomic development and ideational shifts accompanying the new wave of globalization played a key role in China's fertility reduction.

This article examines the recent rise in the sex ratio at birth in Vietnam and relates its emergence to kinship systems and ethnic composition using 2009 census micro-data. Presentation of the main socioeconomic and ethnic differentials in birth masculinity is followed by a review of the three intermediate factors leading to increases in the sex ratio at birth: prenatal technology, declining fertility, and gender bias. An indirect measurement of fertility behavior is used to demonstrate the close association between levels of the sex ratio at birth and the intensity of son preference. Data on household composition indicate that Vietnam is characterized by the co-existence of kinship patterns typical of East and Southeast Asia. Son preference in Vietnam is found to be related to the prevalence of more traditional patrilineal systems. The article concludes by considering the implications of the cultural dimensions of prenatal sex selection for policy responses and for the likely future change in the sex ratio at birth.

Racial distinctions in the United States have long been characterized as uniquely rigid and governed by strict rules of descent, particularly along the black-white boundary. This is often contrasted with countries, such as Brazil, that recognize \"mixed\" or intermediate racial categories and allow for more fluidity or ambiguity in racial classification. Recently released longitudinal data from the IPUMS Linked Representative Samples, and the brief inclusion of a \"mulatto\" category in the U.S. Census, allow us to subject this generally accepted wisdom to empirical test for the 1870-1920 period. We find substantial fluidity in black-mulatto classification between censuses—including notable \"downward\" racial mobility. Using person fixed-effects models, we also find evidence that among Southern men, the likelihood of being classified as mulatto was related to intercensal changes in occupational status. These findings have implications for studies of race and inequality in the United States, cross-national research on racial classification schemes in the Americas, and for how demographers collect and interpret racial data.

The upswing in economic inequality that has affected a number of advanced industrial societies in the late 20th century has been particularly conspicuous in the United States. The authors explore its causes using data on the distribution of family income in 3,098 U.S. counties in 1970, 1980, 1990, and 2000. The authors build a model of within-county income inequality that assumes that distribution processes involving labor market and sociodemographic variables operate primarily at the county level and those involving the political and institutional context operate primarily at the state level. Multilevel methods are used to distinguish county cross-sectional, state cross-sectional, and longitudinal effects on inequality. The authors find that, when features of the state-level institutional and political context are associated with inequality, these effects are larger longitudinally than cross-sectionally. A range of other factors, including economic development, labor force changes, shifts in the racial/ethnic and gender composition of the labor force, educational expansion, and urbanization are found to have comparatively large effects, both longitudinally and cross-sectionally.

Stephen Fienberg was the ultimate public statistician. One of his most significant social contributions was making a convincing case that bad science sends innocent people to prison. He used statistics to assess the foundations of legal evidence - fingerprint analysis, hair matching, polygraphs - and found them wanting. His work helped to limit the use of shoddy techniques and forced reviews of past cases.

Between 1960 and 2000, fertility fell sharply in Brazil, but this transition was unevenly distributed in space and time. Using Bayesian spatial statistical methods and microdata from five censuses, we develop and apply a procedure for fitting logistic curves to the fertility transitions in more than 500 small regions of Brazil over this 40-year period. Doing so enables us to map the main features of the Brazilian fertility transition in considerable detail. We detect early declines in some regions of the country and document large differences between early and late transitions in regard to both the initial level of fertility and the speed of the transition. We also use our results to test hypotheses regarding changes in the level of development at the onset of the ferility transition and identify a temporary stall in the Brazilian transition that occurred in the late 1990s. A web site with project details is at http://schmert.net/BayesLogistic.

Background Geographic variables play an important role in the study of epidemics. The role of one such variable, population density, in the spread of influenza is controversial. Prior studies have tested for such a role using arbitrary thresholds for population density above or below which places are hypothesized to have higher or lower mortality. The results of such studies are mixed. The objective of this study is to estimate, rather than assume, a threshold level of population density that separates low-density regions from high-density regions on the basis of population loss during an influenza pandemic. We study the case of the influenza pandemic of 1918–19 in India, where over 15 million people died in the short span of less than one year. Methods Using data from six censuses for 199 districts of India (n=1194), the country with the largest number of deaths from the influenza of 1918–19, we use a sample-splitting method embedded within a population growth model that explicitly quantifies population loss from the pandemic to estimate a threshold level of population density that separates low-density districts from high-density districts. Results The results demonstrate a threshold level of population density of 175 people per square mile. A concurrent finding is that districts on the low side of the threshold experienced rates of population loss (3.72%) that were lower than districts on the high side of the threshold (4.69%). Conclusions This paper introduces a useful analytic tool to the health geographic literature. It illustrates an application of the tool to demonstrate that it can be useful for pandemic awareness and preparedness efforts. Specifically, it estimates a level of population density above which policies to socially distance, redistribute or quarantine populations are likely to be more effective than they are for areas with population densities that lie below the threshold.

In 2006, the U.S. celebrates the 100th birthday of the census tract. These geographic units, born out of concerns for urban well-being, were first proposed in 1906 to provide a \"convenient and scientific city map system\" for the City of New York. They were employed for the first time in the U.S. census in 1910 in eight cities, via a joint effort involving the U.S. Census Bureau and state and local health departments. Initially termed \"sanitary areas\" because of their relevance to planning for public health and health services, census tracts are now widely used by all sectors of government and by myriad disciplines in the health, social, and geographic sciences for research as well as policy development, implementation, and evaluation. In this article, I describe the census tract's underappreciated origins, give examples of its current use in analyzing and addressing social disparities in health and health care, and discuss its continued significance and implications for population health and the public data required for informed democratic governance.