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This paper studies the role of the Community Reinvestment Act (CRA) in the U.S. housing boom-bust cycle. I find that enhanced CRA enforcement in 1998 increased the growth rate of mortgage lending by CRA-regulated banks to CRA-eligible census tracts. I show that during the boom period house price growth was higher in the eligible census tracts because of the shift in mortgage supply of regulated banks. Consequently, these census tracts experienced a worse housing bust. I find that CRA-induced mortgages were awarded to borrowers with lower FICO scores and were more frequently delinquent. [ABSTRACT FROM AUTHOR]

A common policy approach to reducing childhood obesity aims to shape the environment in which children spend most of their time: neighborhoods and schools. This paper uses richly detailed data on the body mass index (BMI) of all New York City public school students in grades K-8 to assess the potential for place-based approaches to reduce child obesity. We document variation in the prevalence of obesity across NYC public schools and census tracts, and then estimate the extent to which this variation can be explained by differences in individual-level predictors (such as race and household income). Both unadjusted and adjusted variability across neighborhoods and schools suggest place-based policies have the potential to meaningfully reduce child obesity, but under most realistic scenarios the improvement would be modest. [ABSTRACT FROM AUTHOR]

Accurate description of the distribution of housing units within sub-County geographies is an important component of small-area population estimation. This paper pilots the use of the Pearl-Reed logistic model to predict housing unit growth in urban Census tracts in Bernalillo County, New Mexico for 2007. The model is based upon 1990 to 2000 growth rates, constrained with respect to a priori estimates of an upper-limit of housing units that could potentially be built within a tract based on its land area. In spite of the simplistic nature of this model, it is found to perform quite well. Further development based on incorporation of additional economic, demographic, and sociologic data would likely improve the model substantially; however, in this study the model out-performed standard trend extrapolation procedures for the study area and displayed error measures comparable to those reported in the literature for extrapolation methods in general. [ABSTRACT FROM AUTHOR]

Abstract: The U.S. EPA periodically evaluates ambient concentrations, human exposures, and health risks for 180 hazardous air pollutants plus diesel particulate matter using modeled estimates from the National-Scale Air Toxics Assessment (NATA). NATA publishes estimates at the spatial resolution of U.S. Census tracts, which are subdivisions of a county. These local scale, model-predicted estimates from NATA are used extensively in community-based assessments; however, evaluation of NATA’s ambient concentrations and human exposure estimates against measurement data has been limited to date. This paper compares modeled annual average benzene results from the 2002 NATA with measured results from the 2004 to 2007 Detroit Exposure and Aerosol Research Study (DEARS) as a case study of the quality of NATA results. NATA model estimates support community-scale characterization and assessment. Benzene is particularly important as it was estimated by the 2002 NATA as the largest single air toxic pollutant in terms of cancer risk in the U.S. We found that the average ambient concentrations of benzene predicted by NATA were within 5 percent, on average, of the 24-h integrated average ambient concentrations measured in DEARS. The NATA human exposure estimates, which include only outdoor sources for benzene, were, on average, approximately half the measured breathing zone concentrations from DEARS. Our analyses support that the factors driving higher DEARS personal benzene concentrations relative to the NATA predicted exposure values are likely due, at least in part, to indoor sources. This work points to further community-scale modeling research to improve characterizations and assessments of human exposures. [Copyright &y& Elsevier]

Abstract: Census data have long indicated that black and Hispanic children in the United States are approximately two to three times more likely than white children to fall below the official poverty line. Yet this well established statistic masks a much higher differential in the incidence of ecological poverty between white and nonwhite children. This paper examines the extent of this racial/ethnic divide through an alternative and new metric of childhood neighborhood poverty. Data from the 1990 and 2000 U.S. Census are used to estimate the percentage of white, black, and Hispanic children residing in high childhood poverty neighborhoods. Our results indicate that black children are up to 14 times more likely to live in a high childhood poverty neighborhood when compared to their white counterparts. In addition, for black children living below the poverty line, the majority will experience the double disadvantage of residing in a poor minority childhood neighborhood as well. Findings for Hispanic children are similar to those for black children, albeit slightly less pronounced. [Copyright &y& Elsevier]

The article examines the impact of economic growth on poverty. In particular, this study examines the impact of economic progress from 1970 until 2005 across populations of black, Hispanic, and white people at the level of the census region. The study examines regional differences in the U.S. regarding industry and economic histories. The study concludes that growth in the gross domestic product (GDP) and declines in unemployment rates reduce poverty. Also discussed is the sensitivity of black poverty rates to economic growth.

The Home Mortgage Disclosure Act (HMDA) mandates the reporting of mortgage loan applications. Nearly all studies of mortgage lending patterns done in recent years rely on the data collected under HMDA. However, not all mortgages are reported under HMDA. Understanding the relationship between HMDA coverage and neighborhood characteristics is particularly important because neighborhood rates of applications and loan originations from HMDA are used by many analysts to measure neighborhood lending activity. If HMDA coverage rates vary systematically with neighborhood characteristics, then studies that use these neighborhood characteristics to explain lending activity will yield biased results. This paper presents the results of an analysis that attempts to estimate the fraction of mortgage activity that is reported under FIMDA, and examines how HMDA coverage rates vary with the racial and income characteristics of the neighborhood. The basic idea for this analysis is simple; identify a group of loans from an independent (non-HMDA) source and count the fraction of those loans that appear in the HMDA data. Our independent source for loans is the loans purchased by Freddie Mac during 1992 and 1993; counts of these loans is compared with counts of loans reported as sold to Freddie Mac in the 1992 and 1993 HMDA datasets. The major finding of the analysis is that the HMDA dataset for 1992 is estimated to contain only around 70% of the total mortgage loans, and the coverage rate only improves to 75% in 1993, despite the increased reporting requirements for 1993. Both the 1992 and 1993 HMDA files exhibit substantial variability in coverage across census tracts and lenders, but the direction of bias is consistent. Measured HMDA coverage rates are higher in lower income census tracts, relative to higher income tracts. [ABSTRACT FROM AUTHOR]

Contextual research on time and place requires a consistent measurement instrument for neighborhood conditions in order to make unbiased inferences about neighborhood change. We develop such a time-invariant measure of neighborhood socio-economic status (NSES) using exploratory and confirmatory factor analyses fit to census data at the tract level from the 1990 and 2000 U.S. Censuses and the 2008-2012 American Community Survey. A single factor model fit the data well at all three time periods, and factor loadings--but not indicator intercepts--could be constrained to equality over time without decrement to fit. After addressing remaining longitudinal measurement bias, we found that NSES increased from 1990 to 2000, and then--consistent with the timing of the "Great Recession"--declined in 2008-2012 to a level approaching that of 1990. Our approach for evaluating and adjusting for time-invariance is not only instructive for studies of NSES but also more generally for longitudinal studies in which the variable of interest is a latent construct. [ABSTRACT FROM AUTHOR]

The American Community Survey (ACS) multiyear estimation program has greatly advanced opportunities for studying change in the demographic and socioeconomic characteristics of U.S. communities. Challenges remain, however, for researchers studying years prior to the full implementation of the ACS or areas smaller than the thresholds for ACS annual estimates (i.e., small counties and census tracts). We evaluate intercensal estimates of the demographic and socioeconomic characteristics of U.S. counties and census tracts produced via linear interpolation between the 2000 census and both the 2010 census and 2005-2009 ACS. Discrepancies between interpolated estimates and reference estimates from the Population Estimates Program, the Small Area Income and Poverty Estimates, and ACS are calculated using several measures of error. Findings are discussed in relation to the potential for measurement error to bias longitudinal estimates of linearly interpolated neighborhood change, and alternative intercensal estimation models are discussed, including those that may better capture non-linear trends in economic conditions over the 21st century. [ABSTRACT FROM AUTHOR]

Father involvement reduces risky youth behavior at the individual level. We examine the association between the scarcity of adult men and youth violence at the Census Tract level across a small midwestern city experiencing decades of economic adversity and high rates of violence. We calculated the ratio of men to women aged 25-64 years and indicators of concentrated disadvantage across residential Census Tracts with 2000 U.S. Decennial Census data and the average monthly assault rates for those aged 10-24 years between June 2006 and December 2008 with data from the local police department. Adult male scarcity and the proportion of individuals aged 25 years or older who had less than a high school degree were the two unique predictors of youth assault rates, together explaining 69% of the variance. Interventions promoting effective social, material, and protective support from fathers and other adult male role models may ameliorate risk for youth violence. [ABSTRACT FROM AUTHOR]

This article considers the ability of large-scale (involving 145 fundamental variables) time-series models, estimated by dynamic factor analysis and Bayesian shrinkage, to forecast real house price growth rates of the four US census regions and the aggregate US economy. Besides the standard Minnesota prior, we also use additional priors that constrain the sum of coefficients of the VAR models. We compare 1- to 24-months-ahead forecasts of the large-scale models over an out-of-sample horizon of 1995:01–2009:03, based on an in-sample of 1968:02–1994:12, relative to a random walk model, a small-scale VAR model comprising just the five real house price growth rates and a medium-scale VAR model containing 36 of the 145 fundamental variables besides the five real house price growth rates. In addition to the forecast comparison exercise across small-, medium- and large-scale models, we also look at the ability of the ‘optimal’ model (i.e. the model that produces the minimum average mean squared forecast error) for a specific region in predictingex antereal house prices (in levels) over the period of 2009:04 till 2012:02. Factor-based models (classical or Bayesian) perform the best for the North East, Mid-West, West census regions and the aggregate US economy and equally well to a small-scale VAR for the South region. The ‘optimal’ factor models also tend to predict the downward trend in the data when we conduct anex anteforecasting exercise. Our results highlight the importance of information content in large number of fundamentals in predicting house prices accurately. [ABSTRACT FROM PUBLISHER]

Background. The purpose of this study was to evaluate whether the divergence in national trends of gonorrhea and syphilis from 1986 to 1989 in the United States was real and if overall trends masked a contemporaneous increase in both diseases in a core group. Methods. We analyzed the following: (1) reported cases of gonorrhea and primary and secondary syphilis in the United States for the years 1981 to 1989, (2) gonorrhea screening results from six states for the years 1985 to 1989, and (3) reported cases of gonorrhea and primary and secondary syphilis by census tract for the years 1986 to 1989 in one city. Results. The incidence of gonorrhea decreased 22% in the United States from 1986 to 1989 while the incidence of primary and secondary syphilis increased 59%. Among Blacks, syphilis incidence increased 100% and gonorrhea incidence decreased 13%; among Whites and Hispanics, the incidence of both diseases decreased. Results from gonorrhea screening among females in six states agree with gonorrhea incidence trends in those areas. Race-specific and census tract analyses of data from a number of metropolitan areas where overall rates diverged did not demonstrate a group in which the incidence of both diseases increased. Conclusions. We conclude that diverging trends of gonorrhea and syphilis from 1986 to 1989 are real and emphasize differences in the epidemiologic characteristics of these two sexually transmitted diseases. [ABSTRACT FROM AUTHOR]

Population projections arc widely used in both the public and private sectors for planning, budgeting, and analysis. For these purposes, projections are often needed for small areas such as census tracts, zip code areas or traffic analysis zones. Population size, growth constraints, shifting boundaries, and data availability create special problems for small-area projections, however, and very little is known about their forecasting performance. In this article we evaluate the accuracy and bias of projections of total population and population by age group for census tracts in three counties in Florida. We use data from 1970 and 1980 and several simple extrapolation techniques to produce projections for 1990; we then compare these projections with 1990 census counts and evaluate the differences. For the total sample, we find mean absolute errors of 17%-20% for the three most accurate techniques for projecting total population and find no indication of overall bias. For individual age groups, mean absolute errors range from 20%-29%. We believe that this analysis provides valuable information for demographers, planners, marketers, and others who make extensive use of small-area projections. [ABSTRACT FROM AUTHOR]