Data Sources and Methodology

NCES F-33 Survey Data

Data source: NCES Common Core of Data text files of F-33 data from 2011-12 through 2022-23.

A note on year conventions used throughout the package: year is the fiscal year in which the school year ends, so year == 2023 is FY2023, covering SY2022-23. F-33 reports on state-defined fiscal years, which in most states run July through June. The enrollment denominator for all per-pupil measures is F-33 item V33, the district’s fall membership count (enroll).

The crosswalk below maps every F-33-derived column in the full dataset to the F-33 survey item(s) it is drawn from. It is generated from the package’s data dictionary, so it cannot drift from what list_variables() reports; single codes are 1:1 mappings and formula strings describe simple combinations.

list_variables("full") |>
  filter(source == "NCES F-33 Survey", !is.na(f33_item)) |>
  select(name, f33_item, description) |>
  knitr::kable()

In addition to the items above, the cleaning scripts read F-33 variables U11, C24, L12, M12, and D11 to construct the revenue adjustments described under “Revenue Adjustments” below. C11 (state revenue for capital outlay anddebt service) both feeds those adjustments and ships directly asrev_state_cap_debt, so it appears in the crosswalk. Variables with an NA f33_item in list_variables() are either drawn from non-F-33 sources or are edfinr-adjusted measures.

Adjustments applied during cleaning:

• Rename variables.
• Convert district names to title case.
• Ensure enrollment is a numeric variable.
• Replace -1 and -2 codes with NA values.

CCD Directory Data

Data source: NCES CCD Directory data obtained via the educationdata package.

Raw variables selected:

• Core district identifiers and location: state, ncesid, county, dist_name, state_leaid.
• Institutional details: lea_type, lea_type_id, urbanicity, congressional_dist.
• Two related CCD-derived classifications, cbsa (core based statistical area) and schlev (LEA school level), arrive on the F-33 files rather than through the directory pull.

Adjustments:

• Rename variables to more intuitive names.
• Directory attributes come from the directory vintage for the same school year as the fiscal year they describe. (Releases before 0.2.0 joined the following school year’s vintage; see “Changes from 0.1.x” below.)

SAIPE Poverty Estimates

Data source: Census Bureau SAIPE Estimates.

Raw variables selected:

• Basic geographic and demographic fields: State Postal Code, State FIPS Code, District ID, Name
• Population estimates: Estimated Total Population, Estimated Population ages 5-17, and the estimated number of relevant children ages 5 to 17 living in poverty

Adjustments:

• Convert population fields to numeric
• Construct a combined NCES district identifier by concatenating state FIPS and District ID

ACS 5-Year Estimates

Data source: American Community Survey 5-Year Estimates accessed via the tidycensus package.

Raw variables selected:

• Economic indicators: Median household income (B19013_001), mean household income (aggregate household income divided by households), median property value (B25077_001), and the Gini index of income inequality (B19083_001).
• Household and labor characteristics: owner-occupied share of occupied housing (B25003), share of households receiving SNAP (B22003), and the unemployment rate among the civilian labor force (B23025).
• Educational attainment: Total population 25 years or older (B15003_001) and subsets of that population holding bachelor’s degrees (B15003_022), master’s degrees (B15003_023), professional degrees (B15003_024), and doctoral degrees (B15003_025).
• Data are pulled for different geographic breakdowns (unified, elementary, and secondary school districts).

Each fiscal year is joined to the ACS 5-year release ending in the same calendar year: FY2023 carries the 2019-2023 5-year estimates, FY2012 the 2008-2012 estimates. The 5-year window smooths across years, and its endpoint (December) extends a few months past the June close of the fiscal year.

Adjustments:

• Reshape data from long to wide format.
• Rename “GEOID” to a standard ncesid and ensure proper formatting of district identifiers.
• Convert estimates to numeric as needed.

CPI

Data source: U.S. Bureau of Labor Statistics, specifically the Consumer Price Index for All Urban Consumers (CPI-U).

Raw variables selected:

• CPI time series data (specific variable names as provided in the raw file).

Adjustments:

• Calculate an averaged CPI value using the second half of one year and the first half of the following year to align with the academic calendar, with the 2011-12 school year as the baseline year.
• Clean and reformat CPI data for consistency across processing scripts.

CWIFT (Comparable Wage Index for Teachers)

Data source: NCES EDGE Comparable Wage Index for Teachers (CWIFT), LEA-level releases.

Raw variables selected:

• District identifier (LEAID), the CWIFT estimate (LEA_CWIFTEST), and its standard error (LEA_CWIFTSE).

Adjustments and coverage:

• Each CWIFT<yyyy> release maps to edfinr fiscal year yyyy.
• FY2012-FY2014 have no CWIFT release and are returned as NA.
• FY2020 (no NCES release, owing to withheld ACS 2020 estimates) is interpolated as the mean of the FY2019 and FY2021 values for LEAs present in both years; the interpolated standard error is an approximation, not an NCES-published value.
• FY2023 is carried forward from FY2022 (no CWIFT2023 release as of the 2026-07-20 check).
• cwift_imputed flags interpolated or carried-forward values and cwift_impute_method records how each value was produced.

Census Gazetteer Files (District Land Area)

Data source: U.S. Census Bureau Gazetteer Files, school district vintages.

Raw variables selected:

• District identifier (GEOID) and land area (ALAND_SQMI, land only, excludes water area).

Adjustments and coverage:

• GEOID is renamed to ncesid and ALAND_SQMI to land_area_sq_mi; each Gazetteer vintage is joined to the edfinr fiscal year covering the same school year.
• s_per_sq_mi (enroll / land_area_sq_mi) is derived after the join, and is NA, never Inf, where land area is zero or unavailable.
• LEAs without a Census boundary – charters, education service agencies, and state-operated agencies – have no Gazetteer match, so both fields are NA for those districts in every year.
• Vermont’s FY2016-FY2021 Act 46 district consolidation left many post-consolidation LEAs without a matching Gazetteer boundary for those years; match rates there run roughly 7-12%, versus 97%+ elsewhere and in other Vermont years. Restrict Vermont trend analyses that depend on land_area_sq_mi or s_per_sq_mi to FY2012-FY2015 and FY2022 onward.