Redistricting Rural Students: A Simulation of Reorganizing Pennsylvania Students to Understand Racial, Ethnic, and Economic Segregation

• In Event: Setting Boundaries: Exploring Policy Implications of Rural Locale Classifications and School District Borders

Abstract

Objective
This study holds children constant into their 2020 residencies but randomly reassigns children under age 18 to randomly simulated school district shapes to show the effect that present school district boundaries have on segregation of rural communities based on race and class. Researchers employ this strategy to examine the nature of gerrymandered legislative districts, though less attention has been paid to other political geographies like school districts. Furthermore, segregation is often closely examined in urban communities, but less so between rural geographies (Tieken, 2017).
Methods & Data
The units of this study are Pennsylvania children, which are measured through the 2020 US Census where individuals age 18 and older are subtracted from the total population to estimate the number of children for each Census block group neighborhood. Present school district boundaries are those reported by the National Center for Education Statistics (EDGE) for the 2021-2022 school year. Race is measured by Black, American Indian, Asian/Pacific Islander, Alaskan/Hawaiian, Two or More Races, and Other categories. For Ethnicity, I categorize individuals into Hispanic and Not Hispanic categories.
To form randomized counterfactual simulations of Pennsylvania school districts, I graph block group neighborhoods and randomly reassign them into district shapes along a Markov Chain sequence one million times (Deford et al., 2021).

Analysis and Results
To compare the present maps to the randomized counterfactuals, I show the difference and percent difference between levels of Nonwhite-white, Black-white, Hispanic-Not Hispanic dissimilarity and isolation scores. Table 1 shows these differences for all Pennsylvanian children by first illustrating the present level of segregation, the segregation that occurs when drawing an equivalent number of Pennsylvania districts, and then when drawing a severely reduced number of school districts to reflect consolidation.

Randomized maps typically fracture city districts and consolidate rural areas, thus, I expect a majority of rural children to reside within larger-area school districts within the counterfactual maps. In the final report, I will include estimations of the size and distance of these simulated school districts.
Furthermore, I rely on Sharp and Lee’s (2014) typology that labels the proportional characteristics of school districts. Most of Pennsylvania’s schools are either 90%+ white or 50-90% white. Randomizing Pennsylvania school districts severely lowers the number of districts with these characteristics; thus, I expect most rural students to reside within more diverse and less racially concentrated districts.
Significance
Rural school district boundaries are often the target of policymakers seeking to incorporate better resource distribution and reduce the harmful impacts of segregation. This paper illustrates how counterfactual map ensembles could change the nature of these conditions. However, it does not promote using consolidation or redistricting to amend harmful inequities. Instead, the findings in this paper suggest that policymakers end the overreliance on school district boundaries, particularly for rural students, as meaningful financial and policy vehicles to service education to communities.

Author

• Joshua S. Almes, Pennsylvania State University