Education Data Source Ccd

CCD Data Source Reference

Common Core of Data (CCD) — the federal complete-universe database of all U.S. public K-12 schools and districts (~100,000 schools, ~18,000 districts), collecting enrollment, staffing, finance, and directory data annually (1986-present). Use when analyzing public school enrollment by grade/race/sex, district finances, school staffing, or directory attributes. Public schools and districts only; excludes private schools and postsecondary. Note significant variable encoding and race/ethnicity definition changes over time.

The CCD is the Department of Education's comprehensive, annual, national database of all public elementary and secondary schools and school districts in the United States. It is the only federal dataset that provides a complete universe census (not a sample) of U.S. public K-12 education.

CRITICAL: Value Encoding

The Education Data Portal uses integer codes for categorical variables that differ from NCES's original string codes. Always verify codes against codebooks.

Context	school_type	charter	urban_centric_locale
Portal (integers)	1 (Regular)	0 (No) / 1 (Yes)	11 (City-Large)
NCES original	1-Regular school	Yes / No	11-City: Large

Note: charter and magnet use 0/1 encoding, NOT 1=Yes / 2=No as some NCES documentation shows.

See ./references/variable-definitions.md for complete encoding tables.

What is CCD?

• Primary K-12 database: DOE's authoritative source for public elementary/secondary education statistics
• Universe survey: Covers ALL public schools and districts, not a sample
• Annual collection: Data submitted by State Education Agencies (SEAs) each year
• Six major components: Directory, Membership, Staffing, Finance (state and district), Dropout/Completers
• Coverage: ~100,000 public schools and ~18,000 school districts nationwide
• Historical depth: Data available from 1986 to present (varies by component)
• Collector: National Center for Education Statistics (NCES) via EDFacts
• Available through: Education Data Portal mirrors (5 of 6 survey components; see Data Access section for details)

Reference File Structure

Decision Trees

What CCD component do I need?

What information do you need?
├─ School/district names, addresses, contacts → Directory
│   └─ See ./references/survey-components.md#directory
├─ Student enrollment counts → Membership
│   ├─ By grade → Membership (grade disaggregation)
│   ├─ By race/ethnicity → Membership (race disaggregation)
│   ├─ By sex → Membership (sex disaggregation)
│   └─ See ./references/survey-components.md#membership
├─ Staff/teacher counts → Staffing
│   └─ See ./references/survey-components.md#staffing
├─ Revenue and expenditure → Finance
│   ├─ State-level totals → National Public Education Financial Survey
│   ├─ District-level detail → School District Finance Survey (F-33)
│   └─ See ./references/survey-components.md#finance
├─ Graduation/dropout rates → Dropout and Completers
│   └─ See ./references/survey-components.md#dropout-completers
└─ School type, charter status, locale → Directory
    └─ See ./references/survey-components.md#directory

Is this a data quality issue?

Unexpected data values?
├─ Negative numbers (-1, -2, -3, -9) → Missing data codes
│   └─ See ./references/variable-definitions.md#missing-data-codes
├─ Very different from prior year → Check for definition changes
│   └─ See ./references/historical-changes.md
├─ State appears as outlier → Check state-specific reporting
│   └─ See ./references/data-quality.md#state-variations
├─ Large number of zeros → Check suppression rules
│   └─ See ./references/data-quality.md#suppression
└─ Locale codes don't match → Pre/post 2006 locale system change
    └─ See ./references/historical-changes.md#locale-codes

Can I compare across time?

Building a time series?
├─ Race/ethnicity categories → Major change in 2010
│   └─ See ./references/historical-changes.md#race-ethnicity
├─ Locale codes → Completely revised in 2006
│   └─ See ./references/historical-changes.md#locale-codes
├─ School/district IDs → Check for ID changes
│   └─ See ./references/variable-definitions.md#identifiers
├─ Free/reduced lunch → CEP and direct certification changes
│   └─ See ./references/data-quality.md#frpl
└─ Finance data → Definition changes and inflation
    └─ See ./references/historical-changes.md#finance

Quick Reference: CCD Components

Note: Not all components listed above are available through the Portal mirrors. See the Data Access section for which datasets are mirrored.

Key Identifiers

ID Type Warning: ncessch and leaid may be String or Int64 depending on the dataset. In the Schools Directory, ncessch is String (preserving leading zeros); in enrollment data, ncessch is Int64. In the Districts Directory, leaid is Int64; in Finance data, leaid is String. Always check the actual dtype and cast as needed when joining across datasets.

Missing Data Codes

The Portal uses both null and negative integer codes to represent missing/special values. The specific pattern varies by dataset:

Check actual data. Some datasets use null where others use -1 for effectively the same condition. Always check the observed values in the data before applying a blanket missing-value filter.

School Types (school_type)

LEA Types (agency_type)

Grade -1 Encoding

In CCD enrollment data:

• grade = -1 means Pre-Kindergarten, NOT missing data
• grade = 99 means Total across all grades

Do NOT filter grade >= 0 — this removes all Pre-K students!

# WRONG - removes Pre-K students!
df = df.filter(pl.col("grade") >= 0)

# CORRECT
pre_k = df.filter(pl.col("grade") == -1)  # Pre-K only
k12 = df.filter(pl.col("grade").is_between(0, 12))  # K-12
total = df.filter(pl.col("grade") == 99)  # All grades

Portal Column Name Mapping

Variable Name Mapping: The Portal column urban_centric_locale contains locale codes. Some documentation may refer to this as simply locale. Use urban_centric_locale when filtering or selecting columns in Portal data.

Dataset-to-Component Mapping

Data Collection Flow

Schools → Local Education Agencies (LEAs)
                ↓
    State Education Agencies (SEAs)
                ↓
        EDFacts Submission System
                ↓
    NCES Quality Review & Editing
                ↓
        CCD Public Data Files

Timeline: Data for school year 20XX-YY typically submitted spring 20YY, released fall 20YY (preliminary) to spring 20YY+1 (provisional/final).

Data Access

Datasets for CCD are available via the mirror system. See datasets-reference.md for canonical paths, mirrors.yaml for mirror configuration, and fetch-patterns.md for fetch code patterns.

Key datasets (5 datasets; see datasets-reference.md for the authoritative list):

Not in Portal mirrors: The following CCD components are documented in this skill for reference but are not available through the Education Data Portal mirrors:

• Dropout/Completers — completion and dropout data by demographics
• State Finance (NPEFS) — state-level education revenue and expenditure

For these components, access NCES directly at https://nces.ed.gov/ccd/.

Codebooks are .xls files co-located with data in all mirrors. Use get_codebook_url() from fetch-patterns.md to construct download URLs:

url = get_codebook_url("ccd/codebook_schools_ccd_directory")

Truth Hierarchy: When interpreting variable values, apply this priority:

1. Actual data file (what you observe in the parquet/CSV) -- this IS the truth
2. Live codebook (.xls in mirror) -- authoritative documentation, may lag
3. This skill documentation -- convenient summary, may drift from codebook

If this documentation contradicts the codebook, trust the codebook. If the codebook contradicts observed data, trust the data and investigate.

Filtering

All filtering is done locally with Polars after download:

import polars as pl

# Filter by state (California)
df = df.filter(pl.col("fips") == 6)

# Filter by year
df = df.filter(pl.col("year").is_in([2020, 2021, 2022]))

# Get totals only (enrollment)
df = df.filter(pl.col("grade") == 99)

# Get specific grades (K-12)
df = df.filter(pl.col("grade").is_between(0, 12))

Finance Data Notes

• Finance data lag: The latest available year in the mirror is 2020 (empirically verified). Finance data typically lags 2+ years behind current school year.
• Finance dataset has 163 columns -- by far the most complex CCD dataset
• Some finance columns use _total suffix (e.g., exp_current_instruction_total)
• leaid is String type in Finance data (unlike the Districts Directory where it is Int64)

Common Pitfalls

Coverage Notes

What CCD Includes

• All public schools (traditional, charter, magnet, alternative)
• All public school districts and LEAs
• Bureau of Indian Education (BIE) schools
• Department of Defense Education Activity (DoDEA) schools
• State-operated schools (deaf, blind, correctional)

What CCD Excludes

• Private schools (use Private School Universe Survey - PSS)
• Homeschool students
• Postsecondary institutions (use IPEDS)
• Detailed student-level data (CCD is aggregate only)

Related Data Sources

Topic Index