daaf-contribution-community/education-data-source-meps

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 |