The prevalence of geographic locale mismatch in National Center for Education Statistics’ data

• In Event: Spatial Methods and Geographic Dimensions of Policy Evidence

Abstract

National Center for Education Statistics (NCES) locale codes have been adopted to identify a school’s and its district’s geographic characteristics, enabling researchers to analyze geographic education data and distinguish between city, suburban, town, and rural settings. Although the NCES locale framework provides powerful standardization of geographic concepts in education, district labels rely on majoritarian definitions, which may misrepresent some schools and students. In trying to consolidate such geographic complexities, NCES locales embed particular assumptions about the characteristics of school districts and how educational spaces are organized and experienced.

Accordingly, we ask: to what extent is there a discrepancy between a school’s locale classification and the district within which it is nested? By ascertaining both where and for whom locale assignments mask greater levels of geographic heterogeneity, we contribute to an understanding of how researchers and policymakers should interpret, refine, or supplement spatial measures in education research.

We used administrative data from the NCES’s EDGE program and Common Core of Data (CCD) membership files for the 2024-2025 school year. We conducted a national analysis comparing school-level and district-level NCES locale classifications, leveraging enrollment-weighted student data to discern patterns of alignment and divergence across primary locale and subtypes, states, and student racial groups. The merged dataset was imported into ArcGIS Pro to map the spatial distribution of locale assignment at the national level.

While 83.1% of measured local education agencies (LEAs) are perfectly aligned with their nested schools, 3,037 LEAs exhibited some degree of mismatch. In total, 7,308,060 students (14.8%) were identified as attending a school in a primary locale that differs from their LEA. This nationwide school-to-LEA mismatch shows significant clustering of highly mismatched districts in the Southeast and Mid-Atlantic states, as well as in more rural areas of the Mountain West, Alaska, and Hawaii. Large disparities further exist across racial groups. This includes Black students attending town schools, who are mismatched at a rate nearly twice as high as White and Hispanic students, as well as rural Asian students that experience a 58.6% mismatch rate.

The results demonstrate that the NCES district locale framework may better serve students from specific geographic backgrounds. Our findings suggest that any geographic analysis of student-level data should prioritize the use of the student’s school locale over district-level classifications to effectively inform policy. Researchers focused on geographical education issues that hold a particular salience for certain racial/ethnic groups should take care to recognize the variations existing across NCES locales to ensure robustness and geographic validity.

Authors

• Travis Miller, University of Kansas
  Presenting Author

• Bryan Mann, University of Kansas
  Non-Presenting Co-Author