The EdBuild COVID-19 database joins the New York Times database of county-level counts of coronavirus cases with EdBuild's master dataset of school districts in order to identify school districts in heavily impacted areas throughout the country.

Data Sources

To create the county-level coronavirus, school district dataset, EdBuild used the following data sources:

Coronavirus (covid-19) County-level data: Data on coronavirus cases and deaths in the U.S. come from The New York Times, Github repository. We will continuously update our data and maps as the New York Times releases newer data.

County estimated population data: County-level population data for relevant school-age children and total population come from the Census, Small Area Income and Poverty Estimates (SAIPE)for 2018.

School district estimated population data: School district-level population data for relevant school-age children and total population come from the Census, Small Area Income and Poverty Estimates (SAIPE)for 2018.

Methods

Processing County population estimates data:

• Total Population was calculated by dividing the poverty estimate for all ages by the estimated percent of individuals of all ages living in poverty.

• Student Population (5-17) was calculated by dividing the poverty estimate for individuals between the ages of 5 and 17 in families by the percent of individuals between the ages of 5 and 17 in families.

Processing school district population estimates data:

• Students (5-17) as a Percent of Total Population was calculated by dividing the estimated population between the ages of 5 and 17 by the estimated total population for each school district.

Processing coronavirus data:

• County incidence rates (cases and deaths per 100,000 people) were calculated as the number of cases in a county divided by the total population of a county in 2018.

• Heavily impacted areas were identified as those counties with greater than or equal to 100 cases per 100,000 people and greater than or equal to 10 cases.

• Counties reported as “Unknown” and counties whose FIPS code is reported as “Null” were excluded from our analysis with the following exceptions:

o New York City, NY—Five counties (New York, Queens, Bronx, Kings, and Queens) are assigned to a single area.

o Kansas City, MO—Four counties (Clay, Jackson, Cass, and Platte) are aggregated with the Kansas City, MO NYT data and assigned to a single area.

• See the nytimes github repository for more details on methodology and definitions.

County level coronavirus and population data were joined to our 2017 master school district dataset and our 2017 school district shapefile.

PURPOSE

The purpose of this data product is to present school-age poverty rates, free and reduced-price lunch (FRL) rates, percent of students who are nonwhite, median owner-occupied property value, median household income, local revenues, and state revenues for each US school district with geography, for the school years 2012-13 through 2016-17.

DATA
Sources

• School district geography: maps of school district geographies were created using the 2014, 2016, and 2018 shapefiles from the US Department of Education, National Center for Education Statistics, Education Demographic and Geographic Estimates (EDGE).

• District-level, school-age poverty rates: school district-level data on poverty rates among relevant school-age children in 2013, 2014, 2015, 2016 and 2017 come from the Census, Small Area Income and Poverty Estimates (SAIPE).

• School district revenues from state and local sources: revenues from state and local sources for the 2012-13, 2013-14, 2014-15, 2015-16 and 2016-17 school years come from the Census, Annual Survey of School System Finances (F33).

• Cost of living index: county-level cost-of-living indices for 2013 and 2016 come from the Council for Community and Economic Research (C2ER).

• School district FRL rates and percent nonwhite: school district enrollment characteristics from the 2012-13, 2013-14, 2014-15, 2015-16 and 2016-17 school years come from the US Department of Education, National Center for Education Statistics, Common Core of Data (CCD).

• School district median owner-occupied property value and median household income for the 2012-13, 2013-14, 2014-15, 2015-16 and 2016-17 school years come from the US Department of Education, National Center for Education Statistics, Education Demographic and Geographic Estimates (EDGE).

METHODS
Geography-based exclusions

Only unified, elementary, and secondary school districts with geography—those included in the National Center for Education Statistics, EDGE shapefiles—are included in this product. Districts that did not match up with a geography were excluded.

School district shapefiles are released every other year. The 2014 shapefile was used to map 2012-13 and 2013-14 school district data; the 2016 shapefile was used to map 2014-15 and 2015-16 school district data; the 2018 shapefile was used to map the 2016-17 school district data.

Processing school district finance data

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 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 and 2016-17 revenues. See our Power in Numbers piece for a discussion of why cost-adjusting is so important in studies of school finance.

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.
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.

Computing district FRL rates

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 prospective of increasing access to free lunch for children, and for reducing administrative burden, but it reduces the accuracy of FRL numbers.

Computing district percent nonwhite

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.

Computing enrollment 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 and 2016-17, school district data in Vermont's supervisory unions are the summation of the supervisory union's component school districts.