United States Census Bureau

Background Uncertainties exist about the prevalence and comorbidity of substance use disorders and independent mood and anxiety disorders. Objective To present nationally representative data on the prevalence and comorbidity ofDSM-IValcohol and drug use disorders and independent mood and anxiety disorders (including only those that are not substance induced and that are not due to a general medical condition). Design Face-to-face survey. Setting The United States. Participants Household and group quarters' residents. Main Outcome Measures Prevalence and associations of substance use disorders and independent mood and anxiety disorders. Results The prevalences of 12-monthDSM-IVindependent mood and anxiety disorders in the US population were 9.21% (95% confidence interval [CI], 8.78%-9.64%) and 11.08% (95% CI, 10.43%-11.73%), respectively. The rate of substance use disorders was 9.35% (95% CI, 8.86%-9.84%). Only a few individuals with mood or anxiety disorders were classified as having only substance-induced disorders. Associations between most substance use disorders and independent mood and anxiety disorders were positive and significant (P<.05). Conclusions Substance use disorders and mood and anxiety disorders that develop independently of intoxication and withdrawal are among the most prevalent psychiatric disorders in the United States. Associations between most substance use disorders and independent mood and anxiety disorders were overwhelmingly positive and significant, suggesting that treatment for a comorbid mood or anxiety disorder should not be withheld from individuals with substance use disorders.

The view that small businesses create the most jobs remains appealing to policymakers and small business advocates. Using data from the Census Bureau's Business Dynamics Statistics and Longitudinal Business Database, we explore the many issues at the core of this ongoing debate. We find that the relationship between firm size and employment growth is sensitive to these issues. However, our main finding is that once we control for firm age, there is no systematic relationship between firm size and growth. Our findings highlight the important role of business start-ups and young businesses in U.S. job creation.

The literature on urban sprawl confuses causes, consequences, and conditions. This article presents a conceptual definition of sprawl based on eight distinct dimensions of land use patterns: density, continuity, concentration, clustering, centrality, nuclearity mixed uses, and proximity. Sprawl is defined as a condition of land use that is represented by low values on one or more of these dimensions. Each dimension is operationally defined and tested in 13 urbanized areas. Results for six dimensions are reported for each area, and an initial comparison of the extent of sprawl in the 13 areas is provided. The test confirms the utility of the approach and suggests that a clearer conceptual and operational definition can facilitate research on the causes and consequences of sprawl.

An adaptation of the James-Stein estimator is applied to sample estimates of income for small places (i.e., population less than 1,000) from the 1970 Census of Population and Housing. The adaptation incorporates linear regression in the context of unequal variances. Evidence is presented that the resulting estimates have smaller average error than either the sample estimates or an alternate procedure of using county averages. The new estimates for these small places now form the basis for the Census Bureau's updated estimates of per capita income for the General Revenue Sharing Program.

Andrew B. Bernard, J. Bradford Jensen, Robert Z. Lawrence, Exporters, Jobs, and Wages in U.S. Manufacturing: 1976-1987, Brookings Papers on Economic Activity. Microeconomics, Vol. 1995 (1995), pp. 67-119

An optimal pace of business dynamics—encompassing the processes of entry, exit, expansion, and contraction—would balance the benefits of productivity and economic growth against the costs to firms and workers associated with reallocation of productive resources. It is difficult to prescribe what the optimal pace should be, but evidence accumulating from multiple datasets and methodologies suggests that the rate of business startups and the pace of employment dynamism in the US economy has fallen over recent decades and that this downward trend accelerated after 2000. A critical factor in accounting for the decline in business dynamics is a lower rate of business startups and the related decreasing role of dynamic young businesses in the economy. For example, the share of US employment accounted for by young firms has declined by almost 30 percent over the last 30 years. These trends suggest that incentives for entrepreneurs to start new firms in the United States have diminished over time. We do not identify all the factors underlying these trends in this paper but offer some clues based on the empirical patterns for specific sectors and geographic regions.

Using questions expressly added to the Consumer Expenditure Survey, we estimate the change in consumption expenditures caused by the 2001 federal income tax rebates and test the permanent income hypothesis. We exploit the unique, randomized timing of rebate receipt across households. Households spent 20 to 40 percent of their rebates on nondurable goods during the three-month period in which their rebates arrived, and roughly two-thirds of their rebates cumulatively during this period and the subsequent three-month period. The implied effects on aggregate consumption demand are substantial. Consistent with liquidity constraints, responses are larger for households with low liquid wealth or low income.

We measure the change in household spending caused by receipt of the economic stimulus payments of 2008, using questions added to the Consumer Expenditure Survey and variation from the randomized timing of disbursement. Households spent 12–30 percent (depending on specification) of their payments on nondurable goods during the three-month period of payment receipt, and a significant amount more on durable goods, primarily vehicles, bringing the total response to 50–90 percent of the payments. The responses are substantial and significant for older, lower-income, and home-owning households. Spending does not vary significantly with the method of disbursement (check versus electronic transfer). (JEL D12, D14, E21, E62)

This paper documents how plant-level wages, occupational mix, workforce education, and productivity vary with the adoption and use of new factory automation technologies such as programmable controllers, computer-automated design, and numerically controlled machines. Our cross-sectional results show that plants that use a large number of new technologies employ more educated workers, employ relatively more managers, professionals, and precision-craft workers, and pay higher wages. However, our longitudinal analysis shows little correlation between skill upgrading and the adoption of new technologies. It appears that plants that adopt new factory automation technologies have more skilled workforces both pre- and postadoption.

BACKGROUND: Population-based cancer registry data from the Surveillance, Epidemiology, and End Results (SEER) Program at the National Cancer Institute (NCI) are mainly based on medical records and administrative information. Individual-level socioeconomic data are not routinely reported by cancer registries in the United States because they are not available in patient hospital records. The U.S. representative National Longitudinal Mortality Study (NLMS) data provide self-reported, detailed demographic and socioeconomic data from the Social and Economic Supplement to the Census Bureau's Current Population Survey (CPS). In 1999, the NCI initiated the SEER-NLMS study, linking the population-based SEER cancer registry data to NLMS data. The SEER-NLMS data provide a new unique research resource that is valuable for health disparity research on cancer burden. We describe the design, methods, and limitations of this data set. We also present findings on cancer-related health disparities according to individual-level socioeconomic status (SES) and demographic characteristics for all cancers combined and for cancers of the lung, breast, prostate, cervix, and melanoma. METHODS: Records of cancer patients diagnosed in 1973-2001 when residing 1 of 11 SEER registries were linked with 26 NLMS cohorts. The total number of SEER matched cancer patients that were also members of an NLMS cohort was 26,844. Of these 26,844 matched patients, 11,464 were included in the incidence analyses and 15,357 in the late-stage diagnosis analyses. Matched patients (used in the incidence analyses) and unmatched patients were compared by age group, sex, race, ethnicity, residence area, year of diagnosis, and cancer anatomic site. Cohort-based age-adjusted cancer incidence rates were computed. The impact of socioeconomic status on cancer incidence and stage of diagnosis was evaluated. RESULTS: Men and women with less than a high school education had elevated lung cancer rate ratios of 3.01 and 2.02, respectively, relative to their college educated counterparts. Those with family annual incomes less than $12,500 had incidence rates that were more than 1.7 times the lung cancer incidence rate of those with incomes $50,000 or higher. Lower income was also associated with a statistically significantly increased risk of distant-stage breast cancer among women and distant-stage prostate cancer among men. CONCLUSIONS: Socioeconomic patterns in incidence varied for specific cancers, while such patterns for stage were generally consistent across cancers, with late-stage diagnoses being associated with lower SES. These findings illustrate the potential for analyzing disparities in cancer outcomes according to a variety of individual-level socioeconomic, demographic, and health care characteristics, as well as by area measures available in the linked database.

Abstract We study the sources of racial disparities in income using anonymized longitudinal data covering nearly the entire U.S. population from 1989 to 2015. We document three results. First, black Americans and American Indians have much lower rates of upward mobility and higher rates of downward mobility than whites, leading to persistent disparities across generations. Conditional on parent income, the black-white income gap is driven by differences in wages and employment rates between black and white men; there are no such differences between black and white women. Hispanic Americans have rates of intergenerational mobility more similar to whites than blacks, leading the Hispanic-white income gap to shrink across generations. Second, differences in parental marital status, education, and wealth explain little of the black-white income gap conditional on parent income. Third, the black-white gap persists even among boys who grow up in the same neighborhood. Controlling for parental income, black boys have lower incomes in adulthood than white boys in 99% of Census tracts. The few areas with small black-white gaps tend to be low-poverty neighborhoods with low levels of racial bias among whites and high rates of father presence among blacks. Black males who move to such neighborhoods earlier in childhood have significantly better outcomes. However, less than 5% of black children grow up in such areas. Our findings suggest that reducing the black-white income gap will require efforts whose impacts cross neighborhood and class lines and increase upward mobility specifically for black men.

This paper examines unintended effects of air quality regulation, using plant data for 1963–92. A key regulatory tool since 1978 is the annual designation of county air quality attainment status. Nonattainment status triggers specific equipment requirements, with the severity and enforcement of regulations rising with plant size. The differential in regulation favors attainment areas, reducing births for polluting industries in nonattainment areas by 26–45 percent. Industries and sectors with bigger plants are affected the most, shifting industrial structure toward less regulated single‐plant firms. Large preregulation plants do benefit from grand‐fathering provisions, but both grandfathering and shifts to small‐scale new plants contribute to environmental degradation.

Several multiple imputation techniques are described for simple random samples with ignorable nonresponse on a scalar outcome variable. The methods are compared using both analytic and Monte Carlo results concerning coverages of the resulting intervals for the population mean. Using m = 2 imputations per missing value gives accurate coverages in common cases and is clearly superior to single imputation (m = 1) in all cases. The performances of the methods for various m can be predicted well by linear interpolation in 1/(m — 1) between the results for m = 2 and m = ∞. As a rough guide, to assure coverages of interval estimates within 2% of the nominal level when using the preferred methods, the number of imputations per missing value should increase from 2 to 3 as the nonresponse rate increases from 10% to 60%.

The patterns of comorbidity among prevalent mental disorders in adults lead them to load on "externalizing," "distress," and "fears" factors. These factors are themselves robustly correlated, but little attention has been paid to this fact. As a first step in studying the implications of these interfactor correlations, we conducted confirmatory factor analyses on diagnoses of 11 prevalent Diagnostic and Statistical Manual of Mental Disorders (4th ed.) mental disorders in a nationally representative sample. A model specifying correlated externalizing, distress, and fears factors fit well, but an alternative model was tested in which a "general" bifactor was added to capture what these disorders share in common. There was a modest but significant improvement in fit for the bifactor model relative to the 3-factor oblique model, with all disorders loading strongly on the bifactor. Tests of external validity revealed that the fears, distress, and externalizing factors were differentially associated with measures of functioning and potential risk factors. Nonetheless, the general bifactor accounted for significant independent variance in future psychopathology, functioning, and other criteria over and above the fears, distress, and externalizing factors. These findings support the hypothesis that these prevalent forms of psychopathology have both important common and unique features. Future studies should determine whether this is because they share elements of their etiology and neurobiological mechanisms. If so, the existence of common features across diverse forms of prevalent psychopathology could have important implications for understanding the nature, etiology, and outcomes of psychopathology.

A set of new indices for interpreting change in life expectancies, as well as a technique for explaining change in life expectancies by change in mortality at each age group are presented in the paper. The indices, as well as the new technique for explaining the differences in life expectancies, have been tested and examples using United States life tables are presented. The technique for explaining life expectancy differentials can be used for analyzing change in mortality or mortality differentials by sex, ethnicity, region, or any other subpopulations. The technique can be applied to life expectancies at birth or temporary life expectancies between any desirable ages.

Recent research using establishment-and firm-level data has raised a variety of conceptual and measurement questions regarding our understanding of aggregate productivity growth. 1 Several key related findings are of interest.First, there is large-scale, ongoing reallocation of outputs and inputs across individual producers.Second, the pace of this reallocation varies over time (both secularly and cyclically) and across sectors.Third, much of this reallocation reflects within-rather than betweensector reallocation.Fourth, there are large differentials in the levels and the rates of growth of productivity across establishments within the same sector.The rapid pace of output and input reallocation along with differences in productivity levels and growth rates are the necessary ingredients for the pace of reallocation to play an important role in aggregate (i.e., industry) productivity growth.However, our review of the existing studies Lucia Foster is an economist at the Bureau of the Census.

X-12-ARIMA is the Census Bureau's new seasonal-adjustment program. It provides four types of enhancements to X-ll-ARIMA—(1) alternative seasonal, trading-day, and holiday effect adjustment capabilities that include adjustments for effects estimated with user-defined regressors; additional seasonal and trend filter options; and an alternative seasonal-trend-irregular decomposition; (2) new diagnostics of the quality and stability of the adjustments achieved under the options selected; (3) extensive time series modeling and model-selection capabilities for linear regression models with ARIMA errors, with optional robust estimation of coefficients; (4) a new user interface with features to facilitate batch processing large numbers of series.

The U.S. retail trade sector underwent a massive restructuring and reallocation of activity in the 1990s with accompanying technological advances. Using a data set of establishments in that sector, we quantify and explore the relationship between this restructuring and reallocation and labor productivity dynamics. We find that virtually all of the labor productivity growth in the retail trade sector is accounted for by more productive entering establishments displacing much less productive exiting establishments. The productivity gap between low-productivity exiting single-unit establishments and entering high-productivity establishments from large, national chains plays a disproportionate role in these dynamics.

An examination of survey pretesting reveals a paradox. On the one hand, pretesting is the only way to evaluate in advance whether a questionnaire causes problems for interviewers or respondents. Consequently, both elementary textbooks and experienced researchers declare pretesting indispensable. On the other hand, most textbooks offer minimal, if any, guidance about pretesting methods, and published survey reports usually provide no information about whether questionnaires were pretested and, if so, how, and with what results. Moreover, until recently there was relatively little methodological research on pretesting. Thus pretesting’s universally acknowledged importance has been honored more in the breach than in the practice, and not a great deal is known about many aspects of pretesting, including the extent to which pretests serve their intended purpose and lead to improved questionnaires. Pretesting dates to the founding of the modern sample survey in the mid-1930s or shortly thereafter. The earliest references in scholarly journals are from 1940, by which time pretests apparently were well established. In that year Katz reported, “The American Institute of Public Opinion [i.e., Gallup] and Fortune [i.e., Roper] pretest their questions to avoid phrasings which will be unintelligible to the public and to avoid issues unknown to the man on the street” (Katz 1940, p. 279).

We construct a publicly available atlas of children's outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population.For each tract, we estimate children's earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender.These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up.We find that children's outcomes vary sharply across nearby areas: for children of parents at the 25th percentile of the income distribution, the standard deviation of mean household income at age 35 is $5,000 across tracts within counties.We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications.First, the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes.Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child's own Census tract, characteristics of tracts that are one mile away have little predictive power for a child's outcomes.Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time.Second, we show that the observational estimates are highly predictive of neighborhoods' causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers' outcomes.We then identify high-opportunity neighborhoods that are affordable to lowincome families, providing an input into the design of affordable housing policies.Our measures of children's long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets.