This paper describes a novel database and an associated suicide event prediction model that surmount longstanding barriers in suicide risk factor research. The database comingles person-level records from the National Violent Death Reporting System (NVDRS) and the American Community Survey (ACS) to establish a case-control study sample that includes all identified suicide cases, while faithfully reflecting general population sociodemographics, in sixteen USA states during the years 2005 2011. It supports a statistical model of individual suicide risk that accommodates person-level factors and the moderation of these factors by their community rates. Named the United States Multi-Level Suicide Data Set (US-MSDS), the database was developed outside the RDC laboratory using publicly available ACS microdata, and reconstructed inside the laboratory using restricted access ACS microdata. Analyses of the latter version yielded findings that largely amplified but also extended those obtained from analyses of the former. This experience shows that the analytic precision achievable using restricted access ACS data can play an important role in conducting social research, although it also indicates that publicly available ACS data have considerable value in conducting preliminary analyses and preparing to use an RDC laboratory. The database development strategy may interest scientists investigating sociodemographic risk factors for other types of low-frequency mortality.
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Mortality in a Multi-State Cohort of Former State Prisoners, 2010-2015
February 2022
Working Paper Number:
CES-22-06
Previous studies report that individuals who have been imprisoned have higher mortality rates than their demographic counterparts in the general population, particularly non-Hispanic white former prisoners. Most of these studies have been based on a single state's prison system, and the extent to which their findings can be generalized has not been established. In this study we explore the role that race/Hispanic origin, other demographic characteristics, and custodial/ criminal history factors have on post-release mortality, including on the timing of deaths. We also assess whether conditional release to community supervision or reimprisonment may explain the higher post-release mortality found among non-Hispanic whites. In the second part of the analysis, we estimate standardized mortality ratios (SMRs) by sex, age group, and race/Hispanic origin using as reference the U.S. general population. The data come from state prison releases from the Bureau of Justice Statistics' (BJS) National Corrections Reporting Program (NCRP). The NCRP records were linked to the Census Numident to identify deaths occurring within five years from prison release. We also linked NCRP records to previous decennial censuses and survey responses to obtain self-reported race and Hispanic origin if available. We found that non-Hispanic white former prisoners were more likely to die within five years after prison release and more likely to die in the initial weeks after release compared to racial minorities and Hispanics. Reimprisonment, age at release, and a history of multiple prison terms had a similar influence on the odds of dying across all race/Hispanic origin groups. Other factors, such as the type of release and the duration of the last term in prison, were associated with higher risks of mortality for some groups but not for others.
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Leaving Home: Modeling the Effect of Civic and Economic Structure on Individual Migration Patterns
June 2002
Working Paper Number:
CES-02-16
This research analyzes the effect of community structure upon individuals' probabilities of moving between 1985 and 1990. Using the full Census sample long form microdata for 1990, we re-allocate adult persons in 1990 to their 1985 county of residence. Then, using origin county macro-structural variables (derived from the Economic Census microdata) and individual characteristics (from Decennial Census microdata), we develop a two level hierarchical linear model. In level 1, we construct a logistic equation modeling individual probabilities of moving. In level 2, we model the contextual effects of origin community structure on these models. These contextual effects fall into two categories: 1) economic conditions that comprise the usual aggregate 'push' factors and 2) civic community factors that act to retain people in their community. Results specify the relationship between community context and individual migration patterns, and demonstrate effects of local economic structure and local civic structure on these individual probabilities. Most notably, we find that civic attributes of communities are associated with a propensity to stay in place, net of community economic factors and individual characteristics.
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Who are the people in my neighborhood? The 'contextual fallacy' of measuring individual context with census geographies
February 2018
Working Paper Number:
CES-18-11
Scholars deploy census-based measures of neighborhood context throughout the social sciences and epidemiology. Decades of research confirm that variation in how individuals are aggregated into geographic units to create variables that control for social, economic or political contexts can dramatically alter analyses. While most researchers are aware of the problem, they have lacked the tools to determine its magnitude in the literature and in their own projects. By using confidential access to the complete 2010 U.S. Decennial Census, we are able to construct'for all persons in the US'individual-specific contexts, which we group according to the Census-assigned block, block group, and tract. We compare these individual-specific measures to the published statistics at each scale, and we then determine the magnitude of variation in context for an individual with respect to the published measures using a simple statistic, the standard deviation of individual context (SDIC). For three key measures (percent Black, percent Hispanic, and Entropy'a measure of ethno-racial diversity), we find that block-level Census statistics frequently do not capture the actual context of individuals within them. More problematic, we uncover systematic spatial patterns in the contextual variables at all three scales. Finally, we show that within-unit variation is greater in some parts of the country than in others. We publish county-level estimates of the SDIC statistics that enable scholars to assess whether mis-specification in context variables is likely to alter analytic findings when measured at any of the three common Census units.
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Using Small-Area Estimation (SAE) to Estimate Prevalence of Child Health Outcomes at the Census Regional-, State-, and County-Levels
November 2022
Working Paper Number:
CES-22-48
In this study, we implement small-area estimation to assess the prevalence of child health outcomes at the county, state, and regional levels, using national survey data.
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The Mortality Risk of Raising Grandchildren in the United States
February 2026
Working Paper Number:
CES-26-13
In the United States, grandparents who live with and provide primary care to their grandchildren have emerged as a particularly vulnerable group since the 1990s. Using confidential data from the U.S. Census Bureau and Social Security Administration, this study linked individuals aged 50 years or older from the 2000 census long-form sample to their death records from 2000'2019 (weighted n = 64,027,000) and examined the longitudinal association between coresident grandparenting status and mortality for non-Hispanic Whites, non-Hispanic Blacks, Hispanics, and Asians. We found consistently higher rates of mortality for White coresident grandparents and lower rates for Asian coresident grandparents, regardless of the duration of primary caregiving, compared to their peers without coresident grandchildren. We also found increased risks of mortality among Hispanic long-term primary caregivers but reduced risks among Black short-term primary caregivers, compared to their peers without coresident grandchildren.
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Shift or replenishment? Reassessing the prospect of stable Spanish bilingualism across contexts of ethnic change
June 2023
Working Paper Number:
CES-23-28
Much of the existing literature on Latinos' use of Spanish claims that a general pattern of intergenerational decline in the use of Spanish will produce an overall shift away from Spanish use in the U.S. (Rumbaut, Massey, and Bean 2006; Veltman 1983b, 1990). In contrast, recent works emphasize the importance of the social and linguistic context in reinforcing the use of Spanish as well as (pan)ethnic identities among U.S.-born Latinos (Linton 2004; Linton and Jim'nez 2009; Stevens 1992). This literature suggests conditions under which Spanish-English bilingualism might become stable at the level of metropolitan areas; however, such conditions depend on how immigration shapes the context of language use for native-born Latinos. Given the declining levels of immigration from Latin America, will bilingualism subside in the U.S., or have certain communities created conditions in which bilingualism can be stable? Using geocoded data from restricted access versions of the Survey of Income and Program Participation (SIPP) and the American Community Survey (ACS), we model the probability of Spanish-English bilingualism among second- and third-generation Latinos using multilevel models with contextual measures of immigration and language use at both the neighborhood and metropolitan levels. We find evidence that U.S.-born Latinos are heavily influenced by the prevalence of Spanish use among U.S. born Latinos at both the metropolitan and neighborhood levels. Further, the proportion of foreign-born Latinos has little effect on the native born, after controlling for Spanish use among U.S,-born Latinos. These results are a first step in understanding the link between ethnic or panethnic contexts and language practices, and also in producing a better characterization of stable bilingualism that can be tested quantitatively.
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Evaluating Race and Hispanic Origin Responses of Medicaid Participants Using Census Data
April 2015
Working Paper Number:
carra-2015-01
Health and health care disparities associated with race or Hispanic origin are complex and continue to challenge researchers and policy makers. With the intention of improving the measurement and monitoring of these disparities, provisions of the Patient Protection and Affordable Care Act (ACA) of 2010 require states to collect, report and analyze data on demographic characteristics of applicants and participants in Medicaid and other federally supported programs. By linking Medicaid records to 2010 Census, American Community Survey, and Census 2000, this new large-scale study examines and documents the extent to which pre-ACA Medicaid administrative records match self-reported race and Hispanic origin in Census data. Linked records allow comparisons between individuals with matching and non-matching race and Hispanic origin data across several demographic, socioeconomic and neighborhood characteristics, such as age, gender, language proficiency, education and Census tract variables. Identification of the groups most likely to have non-matching and missing race and Hispanic origin data in Medicaid relative to Census data can inform strategies to improve the quality of demographic data collected from Medicaid populations.
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SYNTHETIC DATA FOR SMALL AREA ESTIMATION IN THE AMERICAN COMMUNITY SURVEY
April 2013
Working Paper Number:
CES-13-19
Small area estimates provide a critical source of information used to study local populations. Statistical agencies regularly collect data from small areas but are prevented from releasing detailed geographical identifiers in public-use data sets due to disclosure concerns. Alternative data dissemination methods used in practice include releasing summary/aggregate tables, suppressing detailed geographic information in public-use data sets, and accessing restricted data via Research Data Centers. This research examines an alternative method for disseminating microdata that contains more geographical details than are currently being released in public-use data files. Specifically, the method replaces the observed survey values with imputed, or synthetic, values simulated from a hierarchical Bayesian model. Confidentiality protection is enhanced because no actual values are released. The method is demonstrated using restricted data from the 2005-2009 American Community Survey. The analytic validity of the synthetic data is assessed by comparing small area estimates obtained from the synthetic data with those obtained from the observed data.
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The Case of the Missing Ethnicity: Indians without Tribes in the 21st Century
June 2011
Working Paper Number:
CES-11-17
Among American Indians and Alaska Natives, most aspects of ethnicity are tightly associated with the person's tribal origins. Language, history, foods, land, and traditions differ among the hundreds of tribes indigenous to the United States. Why did almost one million of them fail to respond to the tribal affiliation part of the Census 2000 race question? We investigate four hypotheses about why one-third of multiracial American Indians and one-sixth of single-race American Indians did not report a tribe: (1) survey item non-response which undermines all fillin- the-blank questions, (2) a non-salient tribal identity, (3) a genealogy-based affiliation, and (4) mestizo identity which does not require a tribe. We use multivariate logistic regression models and high-density restricted-use Census 2000 data. We find support for the first two hypotheses and note that the predictors and results differ substantially for single race versus multiple race American Indians.
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Dynamics of Race: Joining, Leaving, and Staying in the American Indian/Alaska Native Race Category between 2000 and 2010
August 2014
Working Paper Number:
carra-2014-10
Each census for decades has seen the American Indian and Alaska Native population increase substantially more than expected. Changes in racial reporting seem to play an important role in the observed net increases, though research has been hampered by data limitations. We address previously unanswerable questions about race response change among American Indian and Alaska Natives (hereafter 'American Indians') using uniquely-suited (but not nationally representative) linked data from the 2000 and 2010 decennial censuses (N = 3.1 million) and the 2006-2010 American Community Survey (N = 188,131). To what extent do people change responses to include or exclude American Indian? How are people who change responses similar to or different from those who do not? How are people who join a group similar to or different from those who leave it? We find considerable race response change by people in our data, especially by multiple-race and/or Hispanic American Indians. This turnover is hidden in cross-sectional comparisons because people joining the group are similar in number and characteristics to those who leave the group. People in our data who changed their race response to add or drop American Indian differ from those who kept the same race response in 2000 and 2010 and from those who moved between a single-race and multiple-race American Indian response. Those who consistently reported American Indian (including those who added or dropped another race response) were relatively likely to report a tribe, live in an American Indian area, report American Indian ancestry, and live in the West. There are significant differences between those who joined and those who left a specific American Indian response group, but poor model fit indicates general similarity between joiners and leavers. Response changes should be considered when conceptualizing and operationalizing 'the American Indian and Alaska Native population.'
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