Scorecard Data Source Reference

College Scorecard — the primary institutional-level source for post-enrollment labor market outcomes, linking NSLDS financial aid records to IRS/Treasury earnings data. Use when comparing institutions on post-graduation earnings, loan repayment, or student debt, or when actual tax-record-based earnings are required rather than survey estimates. Covers six sub-datasets accessed via Portal mirrors. Critical limitation: tracks only Title IV federal aid recipients, not all students.

Federal data on post-college outcomes including earnings, debt, and repayment for students who received Title IV financial aid. Links education records to IRS tax data for actual earnings, making it the primary source for post-college labor market outcomes.

CRITICAL: Value Encoding and Missing Data

The Education Data Portal uses integer encodings for all categorical variables and lowercase, restructured variable names that differ from the original Scorecard column names. Suppression encoding differs by dataset:

• Earnings/counts:

  -3

  integer code is the primary suppression indicator
• Yes/No flags (institutional characteristics):

  null

  for missing,

  0

  /

  1

  for valid
• Rates (repayment, default):

  null

  for missing
• The original Scorecard string

  "PrivacySuppressed"

  does NOT appear in Portal data

Context	pred_degree_awarded_ipeds	HBCU / tribal flags	religious_affiliation
Portal (integer)	0 - 4	0 / 1	Integer codes 22-200
Original Scorecard	String labels	String labels	String labels

See

./references/variable-definitions.md

for complete encoding tables.

What is College Scorecard?

• Publisher: U.S. Department of Education
• Primary value: Post-college labor market outcomes (earnings) and debt/repayment metrics
• Data sources: NSLDS (loans/aid), IRS/Treasury (earnings), IPEDS (institutional characteristics)
• Coverage: Title IV federal aid recipients only — not all students
• Unique feature: Links education to IRS tax records for actual earnings data
• Access: Education Data Portal mirrors (parquet/CSV); see

  datasets-reference.md

  for paths,

  mirrors.yaml

  for mirror config,

  fetch-patterns.md

  for fetch code
• Primary identifier:

  unitid

  (IPEDS institution ID)

Reference File Structure

earnings-data.md

debt-repayment.md

completion-rates.md

population-coverage.md

variable-definitions.md

data-quality.md

field-of-study.md

Decision Trees

What outcome am I researching?

Outcome type?
├─ Post-college earnings
│   ├─ Institution-level → ./references/earnings-data.md
│   └─ By field of study → ./references/field-of-study.md
├─ Student debt levels
│   ├─ Cumulative borrowing → ./references/debt-repayment.md
│   └─ Debt by field → ./references/field-of-study.md
├─ Loan repayment/default
│   └─ Repayment rates → ./references/debt-repayment.md
├─ Completion rates
│   └─ Scorecard completion → ./references/completion-rates.md
└─ Understanding limitations
    ├─ Who is included → ./references/population-coverage.md
    └─ Data quality issues → ./references/data-quality.md

How do I interpret this data?

Interpretation question?
├─ Why are earnings suppressed?
│   └─ Privacy thresholds → ./references/data-quality.md
├─ What does "6-year earnings" mean?
│   └─ Cohort timing → ./references/earnings-data.md
├─ Why don't Scorecard rates match IPEDS?
│   └─ Different cohorts → ./references/completion-rates.md
├─ What loans are included in debt?
│   └─ Federal only → ./references/debt-repayment.md
└─ How representative is this data?
    └─ Title IV coverage → ./references/population-coverage.md

Building a query?

Query construction?
├─ Variable names and codes → ./references/variable-definitions.md
├─ Suppression flags to handle → ./references/data-quality.md
├─ Understanding cohort years → ./references/earnings-data.md
└─ Field-level queries → ./references/field-of-study.md

Quick Reference: Scorecard Variables

Portal Data Structure (CRITICAL)

The Portal uses LONG format with time horizon as a column, NOT the WIDE format from original Scorecard bulk download files. Portal column names are all lowercase and differ significantly from original Scorecard names.

MD_EARN_WNE_P6

earnings_med

years_after_entry == 6

MD_EARN_WNE_P10

earnings_med

years_after_entry == 10

COUNT_WNE_P6

count_working

years_after_entry == 6

MN_EARN_WNE_P6

earnings_mean

years_after_entry == 6

CONTROL

INSTNM

inst_characteristics

CDR3

default_rate

repayment_fsa

years_since_entering_repay

RPY_3YR_RT

repay_rate

repayment_nslds

years_since_entering_repay == 3

Earnings Dataset Columns (Actual Portal Names)

Source dataset:

scorecard/colleges_scorecard_earnings

(203,066 rows x 33 columns)

unitid

UNITID

opeid

OPEID

year

years_after_entry

cohort_year

earnings_med

MD_EARN_WNE_P*

earnings_mean

MN_EARN_WNE_P*

earnings_sd

SD_EARN_WNE_P*

earnings_pct10

PCT10_EARN_WNE_P*

earnings_pct25

PCT25_EARN_WNE_P*

earnings_pct75

PCT75_EARN_WNE_P*

earnings_pct90

PCT90_EARN_WNE_P*

count_working

COUNT_WNE_P*

count_not_working

COUNT_NWNE_P*

earnings_greater_than_25k_pct

GT_25K_P*

earnings_lowinc_mean

MN_EARN_WNE_INC1_P*

earnings_midinc_mean

MN_EARN_WNE_INC2_P*

earnings_highinc_mean

MN_EARN_WNE_INC3_P*

earnings_dep_mean

earnings_dep_lowinc_mean

earnings_ind_mean

earnings_female_mean

earnings_male_mean

count_working_*

Key Identifiers

unitid

110635

opeid

"00100200"

opeid6

1002

Data Timing

years_after_entry

years_since_entering_repay

years_since_entering_repay

"After entry" means after first enrollment, not after graduation.

Categorical Value Encodings (Institutional Characteristics Dataset)

pred_degree_awarded_ipeds

religious_affiliation

Missing Data Codes

-3

null

import polars as pl

# Filter for valid earnings (handle -3 suppression code)
valid = df.filter(
    (pl.col("earnings_med").is_not_null()) &
    (pl.col("earnings_med") != -3)
)

# Filter for 6-year earnings specifically
six_yr_valid = valid.filter(pl.col("years_after_entry") == 6)

Data Access

Datasets for Scorecard are available via the mirror system. See

datasets-reference.md

mirrors.yaml

fetch-patterns.md

Codebooks are

.xls

get_codebook_url()

fetch-patterns.md

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.

All Scorecard Datasets (6 total)

scorecard/colleges_scorecard_earnings

scorecard/codebook_colleges_scorecard_earnings

scorecard/colleges_scorecard_repayment_fsa

scorecard/codebook_colleges_scorecard_default

scorecard/colleges_scorecard_inst_characteristics

scorecard/codebook_colleges_scorecard_institutional-characteristics

scorecard/colleges_scorecard_repayment_nslds

scorecard/codebook_colleges_scorecard_repayment

scorecard/colleges_scorecard_student_body_nslds

scorecard/codebook_colleges_scorecard_student-characteristics_aid-applicants

scorecard/colleges_scorecard_student_body_treasury

scorecard/codebook_colleges_scorecard_student-characteristics_home-neighborhood

Scorecard naming note: Data file paths differ significantly from codebook paths. Notable mismatches: data

repayment_fsa

vs codebook

default

; data

inst_characteristics

vs codebook

institutional-characteristics

; data

repayment_nslds

vs codebook

repayment

; data

student_body_nslds

vs codebook

student-characteristics_aid-applicants

; data

student_body_treasury

vs codebook

student-characteristics_home-neighborhood

. Always use the exact paths shown above.

Fetching Data

import polars as pl
from fetch_utils import fetch_from_mirrors  # See fetch-patterns.md

# Fetch earnings data
earnings = fetch_from_mirrors("scorecard/colleges_scorecard_earnings")

# Filter by time horizon (LONG format — filter, don't use wide column names)
six_yr = earnings.filter(pl.col("years_after_entry") == 6)

# Filter for valid earnings (exclude -3 suppression code)
valid = six_yr.filter(
    (pl.col("earnings_med").is_not_null()) &
    (pl.col("earnings_med") != -3)
)

# Institution names/control are NOT in the earnings dataset.
# Join to inst_characteristics or IPEDS directory:
inst = fetch_from_mirrors("scorecard/colleges_scorecard_inst_characteristics",
                          years=[2020])
valid = valid.join(
    inst.select("unitid", "inst_name", "pred_degree_awarded_ipeds"),
    on="unitid", how="left"
)

Common Pitfalls

MD_EARN_WNE_P10

years_after_entry

-3

is_not_null() & != -3

MD_EARN_WNE_P6

earnings_med

Critical Limitation: Title IV Recipients Only

The single most important caveat for all Scorecard analysis:

Scorecard tracks ONLY students who received federal financial aid (Title IV):

• Pell Grants
• Federal student loans (Direct, Perkins, PLUS)
• Federal work-study

Coverage varies dramatically by institution type:

Data systematically overrepresents lower-income students who are more likely to need federal aid.

What Scorecard Data Does NOT Include

Comparison: Scorecard vs IPEDS

Related Data Sources

education-data-source-ipeds

unitid

education-data-source-pseo

education-data-source-fsa

education-data-explorer

education-data-query

Topic Index

./references/earnings-data.md

./references/earnings-data.md

./references/earnings-data.md

./references/data-quality.md

./references/debt-repayment.md

./references/debt-repayment.md

./references/debt-repayment.md

./references/debt-repayment.md

./references/completion-rates.md

./references/completion-rates.md

./references/population-coverage.md

./references/population-coverage.md

./references/population-coverage.md

./references/variable-definitions.md

./references/variable-definitions.md

./references/data-quality.md

./references/data-quality.md

./references/field-of-study.md

./references/field-of-study.md