National Neighborhood Data Archive (NaNDA): School District Characteristics and School Counts by Census Tract, ZIP Code Tabulation Area, and School District, 2000-2018 (ICPSR 38569)

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These datasets highlight the concentration of K-12 public, charter, magnet, and private schools in the United States from 2000 through 2018 at the census tract, ZIP code tabulation area (ZCTA), and school district levels. District level enrollment, staffing, and fiscal data are also included. The collection aims to contribute to the discussion about the causes and the consequences of variations in educational context across geographies.

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School Counts

To create school counts, the research team obtained the following school-level data on all public schools in the United States:

• Latitude and longitude of school
• Lowest and highest grade offered
• Whether or not the school is a charter school
• Whether or not the school is a magnet school

The research team used the charter school and magnet school flags to create counts of traditional public, public charter, and public magnet schools within each geographic coverage area. Note that charter schools are a subset of all public schools. For example, in a location containing two traditional public schools and one charter school, the variable PUBLIC_SCHOOL would be three, not two. Note also that a school may be considered both a charter school and a magnet school. As a result, the total number of charter and magnet schools may exceed the total number of public schools in a given geographic area.

For private schools, the research team obtained addresses and lowest/highest grades from the National Center for Education Statistics (NCES) Private School Survey (PSS). Since the survey is collected every two years, data from each two-year academic year range was used to represent both years. For example, data for 2005 and 2006 comes from the survey collected for the 2005-2006 academic year.

Each school's highest and lowest grade offered was used to classify the school as an elementary, middle, and/or high school. The algorithm for determining how to classify a school was as follows:

• Elementary school = lowest grade < 5
• Middle school = lowest grade < 8 + highest grade > 6
• High school = highest grade > 9

The research team then used these classifications to create counts of all public, traditional public, public charter, public magnet, and all private elementary, middle, and high schools. Note that one school may count toward more than one classification. For example, a public school offering grades K-8 would count as both a public elementary school and a public middle school. A location containing one charter school offering grades K-8 would have a value of 1 in the variables PUBLIC_CHARTER, PUBLIC_CHARTER_ELEM, and PUBLIC_CHARTER_MIDDLE.

Missing/zero values were handled differently for public and private schools due to differences in data sources. If no public schools were found in a location, the research team represented the count as zero because the Common Core of Data (CCD) is comprehensive and response is mandatory. The PSS is recommended but not mandatory. If no private schools were found in a location, it may have meant there were none, or that school administrators did not respond. Because of this uncertainty, the research team represented zero values as missing data.

Enrollment and Staffing Data

For the district level data, the research team obtained measures on total enrollment, enrollment by race and ethnicity, number of students receiving free/reduced price lunch, number of English language learners, and full time equivalent (FTE) number of teachers, librarians, guidance counselors, and school counselors. Each district's student-teacher ratio and proportion of students eligible for free/reduced price lunch were then calculated.

Missingness in enrollment data can be attributed to missingness within the Common Core data itself. For example, some districts do not report teacher or librarian FTE or English language learners. Data on school counselors is available for 2016-2018 only. Missing values in ratios are caused by a small number of districts with zero enrollment.

Fiscal Data

The research team obtained district level data on certain categories of school revenue, expenditures, and assets from the CCD fiscal files. They selected categories to align with general topics of interest in the study of school finance (e.g., general formula assistance), to highlight potential points of interest to the local community (e.g., sinking fund, property taxes), and to relate to other variables within the dataset (e.g., funding for school lunch programs and bilingual education). Ratios on per-pupil revenue, per-school payments to charter schools, and per-school payments to private schools were also calculated.

Zero-Area Districts

Data were removed from school districts that appeared as points in the NCES shapefiles in order to eliminate non-traditional districts (e.g., districts associated with a residential care or treatment program).

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Universe View help for Universe

School districts, census tracts, and ZIP code tabulation areas in the United States, including U.S. island territories.

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2022-10-10

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