17-daaf-contribution-community-daaf-dot-claude-skills-education-data-source-meps

brycewang-stanford/Auto-Empirical-Research-SkillsEducationAudit pendingSnapshot 96335150ea16

Summary

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SKILL.md

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.

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)

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

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 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

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

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

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

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

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

# 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

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

url = get_codebook_url("meps/codebook_schools_meps")

# 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")
)

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

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

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

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

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	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`

17-daaf-contribution-community-daaf-dot-claude-skills-education-data-source-meps

Summary

SKILL.md

MEPS Data Source Reference

What is MEPS?

Reference File Structure

Decision Trees

Should I use MEPS or FRPL?

Which MEPS variable should I use?

How do I access MEPS data?

Quick Reference: MEPS Variables

Portal Field Names

Variable Reference

Key Identifiers

Missing Data Codes

Data Access

Filtering

Common Pitfalls

Why MEPS Instead of FRPL?

Key Methodological Points

Common Use Cases

Joining MEPS with Other Data

Limitations

Related Data Sources

Topic Index

Related skills