education-data-source-meps

name	education-data-source-meps
description	MEPS — Urban Institute modeled school-level poverty (% at 100% FPL), from CCD + SAIPE (public schools, 2009-2022, 2-3yr lag). Use when FRPL is unreliable due to CEP. Consistent cross-state measurement. Public schools only.
metadata	{"audience":"any-agent","domain":"data-source","skill-authored":"2026-02-09","skill-last-updated":"2026-02-09"}

MEPS Data Source Reference

Model Estimates of Poverty in Schools (MEPS) — Urban Institute modeled estimates of school-level poverty (% students at or below 100% FPL), derived from CCD and Census SAIPE data (public schools, 2009-2022, 2-3 year lag). Use when analyzing school poverty rates, comparing poverty across states, or when FRPL data is unreliable due to CEP enrollment. Unlike FRPL, MEPS provides consistent cross-state measurement at a standardized 100% FPL threshold. Public schools only.

School-level poverty measure from the Urban Institute that is comparable across states and time, unlike Free/Reduced-Price Lunch (FRPL) data.

CRITICAL: Value Encoding

The Education Data Portal returns MEPS data with integer-encoded categorical and identifier columns. This differs from some external documentation:

Column	Portal Type	Example Value	Notes
fips	Int64	6	State FIPS as integer (California = 6)
ncessch	Int64	10000200277	12-digit NCES school ID as integer
leaid	Int64	100002	7-digit district ID as integer
gleaid	Int64	100013	Geographic LEA ID as integer
year	Int64	2018	Academic year (fall semester)

Missing values: Unlike CCD, MEPS uses native nulls rather than negative coded values (-1, -2, -3). While the codebook lists these codes, actual Portal data contains nulls for missing values.

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

What is MEPS?

MEPS is a modeled estimate of the share of students from households with incomes at or below 100% of the Federal Poverty Level (FPL).

• Purpose: Provide consistent school poverty measurement across all US states
• Key advantage: Comparable across states (unlike FRPL which varies by state policy)
• Data level: School-level (individual schools)
• Coverage: 2009-2022 (actual Portal data range)
• Source: Urban Institute, derived from CCD and SAIPE data
• Primary identifier: ncessch (12-digit NCES school ID)
• Public schools only: Does not cover private schools

Reference File Structure

File	Purpose	When to Read
methodology.md	How MEPS estimates are calculated	Understanding the model, research validation
comparison-to-frpl.md	Detailed FRPL vs MEPS comparison	Deciding which measure to use
data-sources.md	Input data (CCD, SAIPE, ISP)	Understanding data provenance
variable-definitions.md	MEPS variables and codes	Building queries, interpreting results
data-quality.md	Limitations, uncertainty, appropriate uses	Research design, caveats

Decision Trees

Should I use MEPS or FRPL?

What is your research goal?
├─ Compare poverty across states → Use MEPS
│   └─ FRPL varies by state policy, MEPS is standardized
├─ Track poverty over time (post-2010) → Use MEPS
│   └─ CEP adoption makes FRPL inconsistent
├─ Study CEP/universal meals impact → Use both
│   └─ Compare MEPS (true poverty) vs FRPL (program participation)
├─ Match historical research (pre-2010) → Consider FRPL
│   └─ MEPS only available 2006+, but FRPL was more reliable then
├─ Need 185% FPL threshold → Use FRPL with caveats
│   └─ MEPS only measures 100% FPL
└─ Federal funding formulas → Check formula requirements
    └─ Some formulas mandate FRPL; note limitations

Which MEPS variable should I use?

Which estimate type?
├─ Standard analysis → `meps_poverty_pct`
│   └─ Original modeled estimate
├─ High-poverty district adjustment → `meps_mod_poverty_pct`
│   └─ Modified MEPS for districts where model underestimates
├─ Need confidence bounds → `meps_poverty_se`
│   └─ Standard error for uncertainty analysis
└─ Categorical analysis → Derive from `meps_poverty_pct`
    └─ Create quartiles/quintiles as needed

How do I access MEPS data?

Access method?
├─ Mirror download (recommended) → See "Data Access" section below
└─ Join with other data → Use `ncessch` as join key

Quick Reference: MEPS Variables

Data Access: MEPS data is fetched from mirrors (parquet/CSV). See datasets-reference.md for canonical paths, mirrors.yaml for mirror configuration, and fetch-patterns.md for fetch code patterns.

Portal Field Names

The Portal field names differ from some external MEPS documentation:

External Documentation	Portal Field Name
meps / school_poverty	meps_poverty_pct
meps_mod	meps_mod_poverty_pct
meps_se	meps_poverty_se

Variable Reference

All ID and categorical columns use integer encoding in Portal data:

Variable	Description	Type	Range/Notes
ncessch	NCES school ID (12-digit)	Int64	e.g., 10000200277
ncessch_num	NCES school ID (numeric duplicate)	Int64	Same as ncessch
year	School year (fall)	Int64	2009-2022 (actual data range)
fips	State FIPS code	Int64	1-56
leaid	District ID (7-digit)	Int64	e.g., 100002
gleaid	Geographic LEA ID	Int64	e.g., 100013
meps_poverty_pct	Estimated share in poverty (100% FPL)	Float64	0.0-60.5% (actual range)
meps_mod_poverty_pct	Modified MEPS estimate	Float64	0.0-100.0%
meps_poverty_se	Standard error of estimate	Float64	0.5-3.8 (typical range)
meps_poverty_ptl	National percentile (enrollment-weighted)	Int64	1-100
meps_mod_poverty_ptl	Modified percentile (enrollment-weighted)	Int64	1-100

Key Identifiers

ID	Format	Level	Example	Notes
ncessch	Int64 (12-digit)	School	10000200277	Primary join key for school-level joins
leaid	Int64 (7-digit)	District	100002	Use for district-level joins (e.g., with SAIPE)
gleaid	Int64	Geographic LEA	100013	Geographic LEA ID
fips	Int64	State	6	State FIPS code

Missing Data Codes

Code	Meaning	When Used
null	Missing / Not available	All missing values — MEPS uses native nulls, not negative coded values

Important: Unlike CCD and most other Portal sources, MEPS does not use -1, -2, -3 coded values. Use null checks:

# Correct
valid_data = df.filter(pl.col("meps_poverty_pct").is_not_null())

# Wrong (MEPS doesn't use -1, -2, -3 coded values)
# df.filter(pl.col("meps_poverty_pct") >= 0)  # Unnecessary

Data Access

Datasets for MEPS 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.

Dataset	Type	Years	Path	Codebook
School Poverty	Single	2009-2022	meps/schools_meps	meps/codebook_schools_meps

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("meps/codebook_schools_meps")

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

# Filter to valid poverty estimates only (drop nulls)
df = df.filter(pl.col("meps_poverty_pct").is_not_null())

# High-poverty schools (top quartile nationally)
high_poverty = df.filter(pl.col("meps_poverty_ptl") >= 75)

# Use modified MEPS for high-poverty districts
df = df.with_columns(
    pl.when(pl.col("meps_mod_poverty_pct").is_not_null())
    .then(pl.col("meps_mod_poverty_pct"))
    .otherwise(pl.col("meps_poverty_pct"))
    .alias("poverty_pct_best")
)

Common Pitfalls

Pitfall	Issue	Solution
Using negative value filters	Filtering >= 0 to remove missing values; MEPS uses nulls, not -1/-2/-3	Use .is_not_null() instead of >= 0
Confusing MEPS with FRPL thresholds	MEPS measures 100% FPL; FRPL uses 130-185% FPL — rates are not comparable	State clearly which measure and threshold; never mix in same analysis
Using wrong field names	Documentation says meps but actual Portal field is meps_poverty_pct	Always use Portal field names: meps_poverty_pct, meps_mod_poverty_pct, meps_poverty_se
Ignoring standard errors	Treating MEPS as exact counts; they are modeled estimates with uncertainty	Use meps_poverty_se for close comparisons; flag when SE exceeds meaningful difference
Including private schools	MEPS only covers public schools; joining with datasets containing private schools inflates nulls	Filter to public schools before joining
Expecting recent data	MEPS has 2-3 year data lag; latest available may be several years behind	Check actual year range (2009-2022) before planning analysis

Why MEPS Instead of FRPL?

Issue	FRPL Problem	MEPS Solution
CEP schools	All students counted as "free lunch" regardless of income	Uses modeled estimates independent of meal programs
State variation	Different states use different eligibility criteria	Standardized 100% FPL threshold nationwide
Direct certification	Varies by state program participation	Calibrated to Census SAIPE data
Income threshold	130-185% FPL (varies)	Consistent 100% FPL
Time consistency	Policy changes affect comparability over time	Methodology consistent across years

Critical insight: As of 2020, ~60% of schools participate in CEP or other universal meal programs, making FRPL increasingly unreliable as a poverty proxy.

Key Methodological Points

1. Model-based: MEPS uses a linear probability model, not direct counts
2. Calibrated to SAIPE: District totals align with Census poverty estimates
3. School-specific: Reflects enrolled students, not neighborhood demographics
4. 100% FPL threshold: Lower than FRPL (185%) - captures deeper poverty
5. Public schools only: Does not cover private schools

Common Use Cases

Use Case	Recommended Approach
School poverty rankings	Use meps_poverty_pct, note meps_poverty_se for close comparisons
State-level aggregation	Sum weighted by enrollment
Poverty-achievement gaps	Join MEPS with EDFacts assessments on ncessch
Resource allocation analysis	Join MEPS with CCD finance on leaid
CEP impact research	Compare MEPS vs FRPL trends over time
Title I targeting analysis	Use meps_poverty_pct to identify high-poverty schools

Joining MEPS with Other Data

Source	Join Key	Use Case
CCD Directory	ncessch, year	Add school characteristics
CCD Enrollment	ncessch, year	Get enrollment for weighting
CRDC	ncessch, year	Discipline, AP courses + poverty
EDFacts	ncessch, year	Achievement + poverty analysis
SAIPE (district)	leaid, year	Validate against Census estimates

Limitations

• Years available: 2009-2022 (actual Portal data range)
• Public schools only: No private school coverage
• Modeled estimates: Subject to estimation error (use meps_poverty_se)
• 100% FPL only: Does not capture near-poverty (100-185% FPL)
• Not real-time: 2-3 year data lag typical

Related Data Sources

Source	Relationship	When to Use
education-data-source-saipe	District-level poverty (Census)	District-level analysis; MEPS calibration source
education-data-source-ccd	School/district characteristics	Join for enrollment, demographics, finance
education-data-source-crdc	Civil rights/discipline data	Join on ncessch for poverty + discipline analysis
education-data-source-edfacts	State assessment data	Join on ncessch for poverty + achievement analysis
education-data-explorer	Parent discovery skill	Finding available endpoints
education-data-query	Data fetching	Downloading MEPS parquet/CSV files

Topic Index

Topic	Reference File
Linear probability model	./references/methodology.md
SAIPE calibration	./references/methodology.md
Modified MEPS	./references/methodology.md
Validation evidence	./references/methodology.md
CEP impact on FRPL	./references/comparison-to-frpl.md
Direct certification	./references/comparison-to-frpl.md
State policy variation	./references/comparison-to-frpl.md
CCD data inputs	./references/data-sources.md
SAIPE data inputs	./references/data-sources.md
ISP data (MEPS 2.0)	./references/data-sources.md
Variable definitions	./references/variable-definitions.md
Poverty thresholds	./references/variable-definitions.md
Standard errors	./references/data-quality.md
Appropriate uses	./references/data-quality.md
Known limitations	./references/data-quality.md