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RESEARCH METHODS

The purpose of this data product is to examine school district revenues based on racial and socioeconomic characteristics at the national and state level.

DATA SOURCES

• School district revenues from state and local sources: revenues from state and local sources for the 2015-16 school year come from the Census, Annual Survey of School System Finances (F33).

• Cost of living index: county-level cost-of-living index for 2016 comes from the Council for Community and Economic Research (C2ER).

• School district enrollments and racial composition: school district enrollment characteristics from the 2015-16 school year come from the US Department of Education, National Center for Education Statistics, Common Core of Data (CCD).

• District-level, school-age poverty rates: school district-level data on poverty rates among relevant school-age children in 2016 come from the Census, Small Area Income and Poverty Estimates (SAIPE).

• School district median owner-occupied property value and median household income for the 2015-16 school year come from the US Department of Education, National Center for Education Statistics, Education Demographic and Geographic Estimates (EDGE).

• Native American reservations: American Indian Areas/Alaska Native Areas/Hawaiian Home Lands Boundary File from the Census Bureau’s MAF/TIGER geographic database.

METHODS

Figures in the report and website come from the data sources described above. Further details about these figures are presented below.

Percent nonwhite calculations. 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.

Revenue calculations. All of the 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 2016 county-level revenues (each district’s county was identified using National Center for Education Statistics, CCD data).

Per-pupil state and local revenues were calculated by dividing state and local revenues (adjusted to exclude the monies described below) by fall enrollment counts as reported in the F33 survey. Per-pupil state and local revenues for school districts displayed in the map on the website and in the report’s tables and text are from the 2015-16 school year.

Prior to computing per-pupil revenue amounts, 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.

School district exclusions. Our analysis includes all school districts in the country that meet our standard requirements for a finance-based analysis:

• Excludes districts that are of types 5 (vocational or special education), 6 (nonoperating) or 7 (educational service agency) in the F33 data

• If F33 school type is missing, excludes districts that are of types 4 (regional education service agency), 5 (state agency), 6 (federal agency), 7 (charter agency) or 8 (other education agency) based on Common Core of Data

• Excludes districts with missing or zero total enrollments

• Excludes districts that have missing or zero operational schools

• Excludes districts that have missing revenues

• Excludes districts that have very low revenues (<$500)

• Excludes districts that have very high revenues (>$100,000)

• Excludes districts from the US territories

We additionally exclude school districts that intersect with Native American Reservations because federal dollars are a much larger proportion of revenue for Bureau of Indian Affairs (BIA) schools and the federal dollars are not always intended to supplement funds from BIA.

ANALYSIS

Each school district was categorized by 1) the proportion of nonwhite students enrolled in the district and 2) the poverty rate for student-age children estimated to live within the district. Using these rates, we created six categories for analysis:

Racially concentrated nonwhite school districts
Proportion of students that are nonwhite > 75%

Racially concentrated white school districts
Proportion of students that are white > 75%

Racially concentrated nonwhite, low-poverty school districts
Proportion of students that are nonwhite > 75%
Student poverty rate ≤ 20%

Racially concentrated nonwhite, high-poverty school districts
Proportion of students that are nonwhite > 75%
Student poverty rate > 20%

Racially concentrated white, low-poverty school districts
Proportion of students that are white > 75%
Student poverty rate ≤ 20%

Racially concentrated white, high-poverty school districts
Proportion of students that are white > 75%
Student poverty rate > 20%

National analysis. In the national analysis, we summarized cost-adjusted state and local revenue within each of the six categories listed above. We then conducted the following analyses:

1. Compare the average, cost-adjusted total revenue between:

a. Racially concentrated nonwhite school districts
b. Racially concentrated white school districts

2. Compare the average, cost-adjusted total revenue between:

a. Racially concentrated nonwhite, high-poverty school districts
b. Racially concentrated white, low-poverty school districts

3. Compare the average, cost-adjusted total revenue between:

a. Racially concentrated nonwhite, high-poverty school districts
b. Racially concentrated white, high-poverty school districts

State analysis. Not every state has a school district in each of the six categories. We performed these analyses at the state level for each state that meets the following requirements:

To be included in the state-level analysis, a state must have at least five school districts in each category for analysis OR at least 2% of its total enrollment in each category for analysis. For example, racially isolated white, high-poverty districts in Massachusetts include only three districts and less than 1% of the state’s enrollment. Massachusetts, therefore, is not included in the analysis which compares racially concentrated white, high-poverty school districts to racially concentrated nonwhite, high-poverty school districts.

Other variables included in analysis

Enrollment variables:

• Districts—number of districts in the category
• Students—number of students enrolled in the districts included in the category
• Percent districts:

• National—proportion of all school districts in the country that are included in the category
• State—proportion of all school districts in the state that are included in the category

• Average enrollment: average number of students enrolled in the districts included in the category
• Percent enrollment:

• National—proportion of all students in the country that are enrolled in the districts included in the category
• State—proportion of all students in the state that are enrolled in the districts included in the category

• Average poverty rate—average poverty rate of the districts included in the category

Revenue variables:

• State and local revenue, per pupil, coli—average total revenue, per pupil for the districts included in the category, cost-adjusted

• Local revenue, per pupil, coli—average local revenue, per pupil for the districts included in the category, cost-adjusted

• State revenue, per pupil, coli—average state revenue, per pupil for the districts included in the category, cost-adjusted

• State and local revenue, coli—sum of total revenue for all districts included in the category, cost-adjusted

• Local revenue, coli—sum of local revenue for all districts included in the category, cost-adjusted

• State revenue, coli—sum of state revenue for all districts included in the category, cost-adjusted

• Average MHI—average median household income for the districts included in the category

• Average MPV—average median property value for the districts included in the category

Notes on Unit of Analysis
In this report, we compare the average revenue of school districts that have high concentrations of white and nonwhite students. This contrasts with average revenue of students living in those areas (i.e., the analysis is not weighted for district size, all districts are treated as equal). The chosen unit of analysis is the school district in order to approach the question of how geography and school district borders affect school resources. Since every state provides funding through school districts, which act as independent administrative units, the pattern of resource distribution among those units is important. This distribution indicates whether a state funding formula is working. Providing additional weight to larger districts may under or overstate systemic bias in the provision of funding to smaller districts and is not in line with what we are attempting to discern. While we’ve been consistent with this focus and methodology across publications, it is especially relevant to this report because of the historic and widespread housing segregation experienced by certain communities of color. If school-district boundaries are specifically acting as partitions of wealth and resources, we are interested in identifying that mechanism of systemic inequity.