To create the school district border dataset, EdBuild used the following data sources.
• School district boundaries: geography for school district borders come from the US Census Bureau, Education Demographic and Geographic Estimates Program (EDGE), Composite School District Boundaries File.
School district area, in square miles, is calculated for each year based on these boundary files. Students per square mile is calculated as the proportion of students enrolled in the district reported in the Common Core of Data (see more below) to the school district area.
• School district revenues: Revenues from federal, state, and local sources come from the Census, Annual Survey of School System Finances (F33).
Per-pupil state and local revenues were caluculated by dividing state and local revenues (adjusted to exclude the monies described below) by fall enrollment counts as reported in the F33 survey.
The following subtractions were made from total state and local revenues for each school district:
1. Because it can contribute to large fluctuations in district revenues from year to year, we exclude revenue for capital from the calculation of state revenues.
2. Similarly, we exclude money generated from the sale of property from local revenues, because it too can contribute to large fluctuations in revenues.
3. In just under 2,000 districts, revenues received by local school districts include monies that are passed through to charter schools that are not a part of the local school district but are instead operated by charter local education agencies (charter LEAs). This artificially inflates the revenues in these local school districts because they include money for students educated outside of the district who are not counted in enrollment totals. To address this, we subtract from state and local revenues a proportional share (based on the percent of each districts’ revenues that come from local, state and federal sources) of the total amount of money sent to outside charter LEAs—an expenditure category included in the F33 survey.
4. In Arkansas, large portions of districts’ revenues that should be considered local are categorized as state revenues. The value of this misattribution for each district is described in the F33 documentation as C24, Census state, NCES local revenue. Before analysis, the value of C24 is subtracted from state revenues and added to local revenues for the state of Arkansas.
5. In Texas, many districts report exorbitantly high per-pupil revenues. This is in part because of the policy and procedures for recapturing and redistributing local revenues raised by property-wealthy districts in the state. In the F33 survey, recapture is reported as expenditure code L12. Because these monies are included in the state revenue for other, receiving districts, we subtract a districts’ L12 expenditures from their local revenues for the state of Texas.
See the F33 Survey Documentation and File Layout for state-specific notes relating to education finance data.
• School district enrollments and racial composition: School district enrollment characteristics come from the US Department of Education, National Center for Education Statistics, Common Core of Data (CCD).
The proportion of students enrolled in a district that are nonwhite was calculated by dividing the number of nonwhite students by the total enrollment within a given school district.
School district FRL rates were computed by dividing the number of FRL-eligible students in a district by the total enrollment within that district. In recent years, FRL rates have become increasingly unreliable as a measure of rates of student disadvantage within given schools and districts because of changes to how the USDA implements the free lunch program. Under Community Eligibility Provision (CEP) of the Healthy, Hunger-Free Kids Act of 2010, schools serving significantly needy student populations—where 40% or more of students are certified to participate in other federal assistance programs like food stamps or Temporary Assistance for Needy Families—may opt to provide free lunches to their whole school community, rather than to individual needy students. In most cases, such schools and districts will report that 100% of their student enrollment is FRL-eligible. This program is positive from the perspective of increasing access to free lunch for children and for reducing administrative burden, by it reduces the accuracy of FRL numbers.
• School district school-age poverty rates: School district-level data on poverty rates among relevant school-age children come from the Census, Small Area Income and Poverty Estimates (SAIPE) .
• School district community indicators: school district-level data on median owner-occupied property value and median household income come from the US Department of Education, National Center for Education Statistics, Education Demographic and Geographic Estimates (EDGE).
• Cost of living index: county-level cost-of-living indices for 2013 and 2016 come from the Council for Community and Economic Research (C2ER).
Some revenue figures presented are cost-adjusted to convert per-pupil revenues into figures that account for variation in the purchasing power of a dollar across different regions. We applied a cost-adjusting conversion by applying county-level cost of living index (COLI) values from C2ER to each district's revenues (each district’s county was identified using National Center for Education Statistics, CCD data). We use the 2013 COLI values to adjust 2012-13 revenues and 2016 COLI values (which are based largely on 2014 data) to adjust 2013-14, 2014-15, 2015-16, 2016-17 and 2017-18 revenues. See our Power in Numbers piece for a discussion of why cost-adjusting is so important in studies of school finance.
Computing enrollmemt and revenue in Vermont
Vermont's state and local revenues and student enrollment have been aggregated to the supervisory union level to be matched with “Small Area Income and Poverty Estimates (SAIPE). In 2015-16, 2016-17, and 2017-18 school district data in Vermont's supervisory unions are the summation of the supervisory union's component school districts.
EdBuild has three different master datasets: geography, general, and finance. Choose the master dataset that is most appropraite for your analysis.