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Racial/Ethnic Residential Segregation, Poor Self-rated Health, and the Moderating Role of Immigration

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Previous research has demonstrated a relationship between black residential segregation and poor health outcomes. However, this association is less clear for the segregation of other racial/ethnic minority groups in the United States, such as Latinos and Asians. We argue that immigration may moderate this relationship, and that this could help explain these disparate results. We test this using multilevel statistical models of individual-level health data nested within Census tracts in a study of the Houston area using the 2009–2014 Kinder Houston Area Survey, the 2010 U.S. Census, and the 2006–2010 American Community Survey. We find that black and Latino residential segregation is associated with greater poor health reporting, though not for Asian segregation. Further, we find that immigration moderates this relationship for Latino segregation, such that where tract-level immigration is low, Latino segregation is positively related to poor health, but that this slope becomes flatter as immigration increases.

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  1. This time frame also happens to coincide with a period of recession and recovery in the U.S., which could have health implications. In order to examine whether or not this particular time frame may impact the outcome, we also ran all of the same models with a set of dummy variables for the year in which the individual was surveyed. However, none of these coefficients were significant. For the sake of parsimony, we excluded these variables in the reported tables (results available upon request).

  2. It would have been ideal to use the 2005–2009 version of the ACS data as it pre-dates all years of the survey used, which is from 2009 to 2014. However, the 2005–2009 version of the ACS uses the 2000 Census tract boundaries, which is not compatible with the 2009–2014 version of the KHAS survey data, which is pegged to the 2010 boundaries. It would be possible to normalize the data to the 2000 boundaries, but this process involves a lot of assumptions and would introduce unnecessary error. Moreover, the full population counts from the 2010 Census are superior to the ACS, which comes from a sample survey, and thus has higher margins of error, especially at a small unit of analysis like the Census tract.

  3. We include all of these measures together in a single model, as opposed to adding them one-by-one as they are not particularly correlated in a manner that is problematic for the analysis. We provide the full correlation matrix in Table 2 for the area-level measures, as many of these measures are aimed at capturing related concepts. However, as is evident from the table, the area-level measures, while clearly related, are not highly correlated in a manner that might be problematic for the following analysis with the exception of the group specific measures, which is why they are included as a secondary analysis in addition to the main models on immigrant clustering. In this case, Latino clustering and the percent of the foreign-born population that is from Latin America have a correlation of 0.86, and Asian clustering and the percent of the foreign-born population that is from Asia have a correlation of 0.81. We also tested for multicollinearity using the variance inflation factor and it was not found to be a problem (mean VIF = 2.45).

  4. We also ran all of the same models with cross-level interactions between the individual-level race/ethnicity variables and the area-level segregation variables in order to test whether or not the effect of segregation depends on the minority status of the individual as well. None of these interactions terms were significant, and therefore, we do not report these findings in the table. It appears as though the relationship with segregation is an area-level ecological effect that is related to individual-level self-rated health regardless of whether or not the individual living within that area identifies as a racial/ethnic minority themselves, which is fitting with the theorized mechanisms on segregation and health discussed above (Williams & Collins, 2001). Similarly, we also considered stratifying the models based on the three broad racial/ethnic categories used here. However, since we use a small areal unit of analysis (the Census tract), and with a high degree of segregation, this approach left many Census tracts within the city with either no respondents or too few respondents in order to conduct multilevel analysis.

  5. One standard deviation below the mean would actually be a negative number (− 34.87) in the case of immigrant clustering. Since this is a non-sensical value in the case of immigrant clustering, we use 0.

  6. As noted above, we also tried this same set of models using the percent of the population that is from Asia in order to interact it with the Asian clustering score. However, this measure was not significant, the interaction was not significant, and its inclusion did not alter the relationship between Asian clustering and the outcome. Therefore, we do not report these results in the table (results available upon request).


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This work was conducted with support from the Joseph Sidney Werlin Sociology Faculty Award to promote Latin American-US Cultural Understanding at the University of Houston.

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Correspondence to Kathryn Freeman Anderson.

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Anderson, K.F., Simburger, D. Racial/Ethnic Residential Segregation, Poor Self-rated Health, and the Moderating Role of Immigration. Race Soc Probl 14, 131–149 (2022).

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