Loren Henderson

Black Americans continue to experience higher rates of poverty, hyper-residential segregation, and exposure to neighborhood violence compared to their white counterparts1,2. Among Black women, 25% live in poverty.

This study employs multi-level and mixed-methods approaches to examine how structural violence affects the health of low-income, single Black mothers. We use multilevel regression models to examine how feeling “trapped” in racially segregated neighborhoods with high levels of violence on the South Side of Chicago affects mothers’ (N = 69) reports of posttraumatic stress disorder and depressive symptoms. The relationship between feeling “trapped” and variations in expression of mRNA for the glucocorticoid receptor gene NR3C1 using microarray assays was also examined. The regression models revealed that feeling “trapped” significantly predicted increased mental distress in the form of PTSD, depressive symptoms, and glucocorticoid receptor gene regulation. The mothers’ voices revealed a nuanced understanding about how a lack of financial resources to move out of the neighborhood creates feelings of being “trapped” in dangerous situations.

The effects of race, class, and residential segregation on housing values continue to be a major focus of sociological research. Nevertheless, there has yet to be a study that places these factors in the context of the great recession of 2008 and 2009. Accordingly, the purpose of this work is to assess the extent to which the great recession affected housing values for African Americans and whites relative to the joint effects of race, class, and residential segregation. The following research questions are addressed: (1) How do segregation and socioeconomic status (SES) affect racial differences in housing values? (2) What were the levels of racial disparity in housing values before, during, and after the great recession? and (3) Were the housing values of higher status African Americans insulated from the negative impact of segregation and the great recession compared with their lower status counterparts? Using the Integrated Public Use Micro-data Series, the 2010 metropolitan area dissimilarity and population density scores, and hierarchical linear modeling, the findings revealed that the great recession exacerbated racial differences in housing values most in the higher SES categories. Higher status African Americans were more disadvantaged relative to comparable whites than lower status African Americans compared with similar whites in terms of housing values. The article concludes with a discussion of the implications of the findings.

Using data from the 2011 National Academy of Sciences (NAS) rankings of U.S. research universities, this paper examines whether gender diversity is related to the rankings of academic programs in science, technology, engineering, and math (STEM) disciplines at research universities. The NAS data set consists of several indicators relating to research productivity, student support and outcomes, and program diversity from nearly 4,000 STEM doctoral programs at U.S. research universities. The results show that, for the net of factors such as publication rates, grants, scholarly awards, program size, region, and whether the institution is public or private, gender diversity among faculty at research universities is positively associated with departmental rankings in STEM disciplines. The paper discusses the implications of these findings for diversity in higher education.

Using a “mass incarceration” framework and county-level national data, this paper examines the relationship between incarceration, ex-offender reentry locations, and HIV rates in counties with different racial compositions. A series of “race-of-county” stratified regression models estimate HIV prevalence rates with incarceration and ex-offender reentry locations when taking into consideration residential segregation (that is, Black isolation and White isolation), region, high school graduation rates, sex ratios, unemployment rates, median income, healthcare professional shortages, percentage of residents without insurance, population density, and income inequality. As predicted, HIV rates are higher in counties with high incarceration rates or with ex-offender reentry facilities. A race-of-county stratified analysis, however, reveals nuanced patterns: In White counties and the highest-percentage Black counties, HIV rates increase as incarceration rates increase. In integrated counties, they do not. In the highest-percentage Black counties, the presence of reentry locations is associated with higher rates of HIV, but this is not true in White and integrated counties. In integrated counties, higher levels of Black isolation are associated with high HIV rates. In counties of all racial compositions, higher levels of White isolation are associated with lower rates of HIV. Implications of these results are discussed. Key words: HIV, mass incarceration, residential segregation, racial disparities in HIV.

This research seeks to understand the degree to which credit scores of new business startups are influenced by racial or gender discrimination. It examines the degree to which access to business credit lines is influenced by racial and gender-related factors that go beyond would-be borrowers’ credit scores. Using credit data from new startups, the analysis finds that, when controlling for firm and human capital characteristics, Black-owned startups receive lower than expected business credit scores. Whites are more favorably treated in credit score determination than are African Americans with the same firm characteristics and owner characteristics. Moreover, Whites are more favorably treated when it comes to access to credit lines than are African Americans, Latinos, and Asians with the same firm characteristics, owner characteristics, and credit scores. Men are more favorably treated when it comes to access to credit lines than are women. A Blinder-Oaxaca decomposition suggests that credit lines for Black-owned businesses would more than double, Latino-owned businesses’ lines of credit would nearly triple, Asian-owned businesses’ lines of credit would more than triple, and those where the primary owners are women would be more than twice as large if their business lines of credit were determined in the same way as those for businesses owned primarily by Whites and by men. The implications of these results are discussed.