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Data Sources and Methodology

Source: vignettes/data-sources-methods.Rmd
data-sources-methods.Rmd

Overview

This vignette provides detailed information about the data sources and processing methods used to prepare the data used by the edfinr package. Understanding these details will help you interpret the data appropriately and inform analytical decisions.

Full data processing methods and scripts are available on GitHub via bellwetherorg/edfinr_data_cleaning.

Data Sources

This package provides access to education finance data from:

Data Processing Methods

Data Processing Detail

NCES F-33 Survey Data

Data source: NCES Common Core of Data text files of F-33 data from 2011-12 through 2021-22.

Raw variables selected:

  • Basic information: state, leaid, name, yrdata, V33.
  • Revenue data: totalrev, tlocrev, tstrev, tfedrev.
  • Expenditure data: c11, u11, v91, v92, c24, l12, m12, d11, q11.
  • Current expenditure data: ce1, ce2, and ce3.
  • Detailed expenditure data: z32, z34, v93, v95, v02, k14, e13, z33, v10, e17, v11, v12, e07, v13, v14, e08, v15, v16, e09, v17, v18, v40, v21, v22, v45, v23, v24, v90, v37, v38, e11, v29, v30, v60, v32, v65, ae1, ae2, ae3, ae4, ae5, ae6, ae7, ae8.

Adjustments:

  • 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.

Adjustments:

  • Rename variables to more intuitive names.

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) and median property value (B25077_001).
  • 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).

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.

Joining Data

  • The joining process is implemented in the 07_edfinr_join_and_exclude.R script.
  • Data from the F-33 survey, CCD Directory, ACS (unified, elementary, and secondary), and SAIPE sources are merged using left joins on shared district identifiers (ncesid) and fiscal year.
  • The procedure ensures that each district record is enriched with revenue, expenditure, demographic, and economic data.

Revenue Adjustments

Additional transformations are applied after the join: - Capital expenditures and debt service (C11) are subtrated from state revenues. - Property sales (U11) are subtracted from local revenues. - For Texas local education agencies (LEAs) in school year 2012-13 and earlier, payments to state governments (L12) are subtracted from local revenues. - Payments to other school systems (V91, V92, and Q11) are proportionally subracted from local, state, and federal revenues.

Exclusions

  • Districts with enrollment less than 0 are removed.
  • Districts with total revenue less than 0 are removed.
  • Districts with an invalid LEA type (i.e. where lea_type_id is not one of 1, 2, 3, or 7) are excluded.
  • Districts with invalid LEA/school level type (i.e. where schlev is not one of “01”, “02”, or “03”, except for specified CA exceptions) are excluded.
  • Districts where total revenue per-pupil is greater than $70,000 in school year 2011-12 dollars are excluded.
  • Districts where total revenue per pupil is less than $500 in school year 2011-12 dollars are excluded.
  • Connecticut LEAs consisting of semi-private high schools are removed (NCES IDs “0905371”, “0905372”, and “0905373”).

Data Notes and Cautions

Users should note the following when working with the edfinr datasets:

  • Some variables were originally coded with -1 to indicate missing values; these have been replaced with NA during processing.
  • During data processing, we identified a sharp rise in the number of California districts appearing only from 2019 onward in the data. This reflects the fact that many charter schools became separate LEAs in those years. Beginning in school year 2018–19, a wave of California charter schools switched to independent CALPADS/CBEDS reporting and thus were assigned their own NCES LEA IDs for the first time. Once in the NCES LEA universe, those new charter‐LEAs automatically show up in the F-33 finance survey (with blanks or flags if they report no finance data), and Census’s SAIPE and ACS school‐district products (which mirror NCES LEA boundaries).
  • The joined dataset represents a synthesis of data from multiple sources; discrepancies in source data formats may lead to minor variations.
  • Inflation and adjustment factors (e.g., CPI adjustments) are based on averages and may not perfectly reflect local cost variations.
  • Caution is advised when comparing data across fiscal years due to potential differences in data collection and processing methods.