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Exposure to School and Residential Diversity: A New Test of Perpetuation Theory

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Abstract

Perpetuation theorists have recently hypothesized intergenerational consequences associated with participation in racially/ethnically diverse environments; however, few studies have considered possible intergenerational implications when offering empirical tests of perpetuation theory. By linking longitudinal administrative, geographic, and census data from students who attended Jefferson County Public Schools—one of the most racially/ethnically desegregated school districts in the USA—we specifically test whether early exposure to racial/ethnic diversity in neighborhoods and in high schools is associated with students living in more racially/ethnically diverse neighborhoods later in life. Using multilevel models, we find that early exposure to diversity in both neighborhoods and schools is significantly related to neighborhood diversity in both early and mid-adulthood and for both black and white students. However, the strength of these relationships diminished over time.

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Notes

  1. We acknowledge that it would be beneficial and perhaps ideal to examine the influence of attending a racially/ethnically diverse elementary or middle school on residential diversity later in life. However, we use exposure to diversity in high school as our measure of school diversity early in life due to the inherent difficulties and attrition that are naturally associated with tracking students over a greater amount of time. Examining students’ high school experiences yielded the most appropriate sample for addressing our research questions.

  2. In contrast, other studies have utilized zip codes from census data as their basic unit of analysis (Ainsworth 2002; Goldsmith 2010), which encompass much larger geographic areas. When using data aggregated at the zip code level, one runs the risk of assuming that a geographic area defined by the zip code is racially/ethnically diverse when various racial/ethnic groups may actually be isolated in certain geographic areas within the zip code. Within such contexts, a measure of racial/ethnic diversity is less meaningful because it fails to describe the extent to which residents are exposed to neighbors who are racially/ethnically different from themselves.

  3. This measure is commonly referred to by many names, such as the racial/ethnic fragmentation (Alesina and La Ferrara 2000), ethnolinguistic fragmentation (Easterly and Levine 1997), diversity (Osgood and Chambers 2000), or Herfindahl index (Hipp 2007).

  4. One of the drawbacks of the index of racial/ethnic heterogeneity is that one cannot discern which specific racial/ethnic groups are contributing to diversity in students’ neighborhoods; however, it is one of the few measures that account for multi-group diversity, which is a useful consideration, particularly when accounting for the demographic shifts in the USA 13 years after the students in our sample graduated from high school.

  5. The district administrative records also included the number of years each student had been enrolled in the district, whether or not the student attended their zoned high school, the number of years each student remained in the school district for their high school experience, whether or not the student lived in their same high school neighborhood (or BG) either 5 or 13 years after graduation, and the student’s guardianship status. We controlled for these variables in our initial analyses; however, they were not important predictors and thus were removed for parsimony.

  6. The data provided by the district included only measures of whether or not a student participated in free or reduced-price lunch, as opposed to students who qualified for the program. As such, our measure of student poverty is underestimated at the high school level. To adjust for this under-representation, we analyzed observations from every year the student was enrolled in JCPS. If a student ever participated in the free- or reduced-price-lunch program, we coded them as participants in our study.

  7. Similar to the student poverty background variable, students were coded as participants if they had ever participated in free or reduced-price lunch while attending a school within the district in order to account for the underreporting of free and reduced-price lunch participation at the high school level.

  8. Effect sizes are determined by multiplying the coefficient by its standard deviation and then dividing the product by the standard deviation of the dependent variable.

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  1. Department of Sociology, Brigham Young University, 2008 JFSB, Provo, UT, 84663, USA

    Kristie J. Rowley & Shelby M. McNeill

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  1. Kristie J. Rowley
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  2. Shelby M. McNeill
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Correspondence to Kristie J. Rowley.

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Rowley, K.J., McNeill, S.M. Exposure to School and Residential Diversity: A New Test of Perpetuation Theory. Race Soc Probl 9, 234–253 (2017). https://doi.org/10.1007/s12552-017-9205-1

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