Education Data Source Pseo

PSEO Data Source Reference

Postsecondary Employment Outcomes (PSEO) — Census Bureau experimental statistics linking college graduates to employment outcomes via UI wage records (LEHD program). Covers earnings (25th/50th/75th percentile, measured 1, 5, and 10 years post-graduation) and employment flows by institution, degree level, and CIP field. Use when comparing graduate earnings across programs or institutions, analyzing industry entry patterns, or studying geographic migration of graduates. Coverage limited to ~29% of graduates from ~31 participating states.

Postsecondary Employment Outcomes (PSEO) is an experimental data product from the U.S. Census Bureau that links college graduate records to national employment data, providing earnings and employment outcomes by institution, degree level, and field of study.

CRITICAL: Value Encoding

This document describes Education Data Portal integer encodings, which differ from Census API string codes. The Portal converts categorical variables to integers for consistency.

Context	Baccalaureate	Associates	Masters	Census Division Pacific
Portal (integers)	5	3	7	9
Census API (strings)	05	03	07	9

Key differences: Degree level uses simple integers (1-10), not string codes like "1C", "05". CIP codes are 2-digit integers (11 for Computer Science), not strings like "11.01".

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

What is PSEO?

• Producer: U.S. Census Bureau, LEHD program (Longitudinal Employer-Household Dynamics)
• Coverage: ~29% of all U.S. college graduates from 31 states + D.C. + Western Governors University
• Content: Links university transcript data with national UI wage records to track graduate employment outcomes
• Two data types: Graduate Earnings (percentile earnings) and Employment Flows (industry/geography)
• Frequency: Updated periodically; cohorts span 3-year (Bachelor's) or 5-year (all others) windows
• Primary identifiers: unitid (IPEDS Unit ID, integer), opeid (integer in Portal data)
• Privacy method: Differential privacy mechanisms protect individual data
• Available through: Education Data Portal mirrors (restructured from Census Bureau LEHD format with integer encodings and lowercase variable names)

Reference File Structure

Decision Trees

What type of outcome am I researching?

Graduate outcomes research?
├─ Earnings by program/institution
│   ├─ Median earnings → `p50_earnings` column, filter by `years_after_grad`
│   ├─ Earnings distribution → `p25_earnings`/`p50_earnings`/`p75_earnings`
│   └─ See ./references/earnings-data.md
├─ Where graduates work (geography)
│   ├─ Census Division of employment → `census_division` column
│   ├─ In-state vs out-of-state → `employed_instate_grads_count`
│   └─ See ./references/geographic-flows.md
├─ What industries graduates enter
│   ├─ NAICS sector employment → `industry` column (String)
│   └─ See ./references/industry-flows.md
└─ How many graduates are employed
    ├─ Employment counts → `employed_grads_count_f`
    ├─ Non-employed/marginal → `jobless_m_emp_grads_count`
    └─ See ./references/variable-definitions.md

What degree level am I researching?

Degree level?
├─ Certificate (<1 year) → degree_level=1
├─ Certificate (1-2 years) → degree_level=2
├─ Certificate (2-4 years) → degree_level=4
├─ Associate's → degree_level=3
├─ Bachelor's → degree_level=5 (default, 3-year cohorts)
├─ Post-Bacc Certificate → degree_level=6
├─ Master's → degree_level=7 (2-digit CIP only)
├─ Post-Masters Certificate → degree_level=8
├─ Doctoral-Research → degree_level=9 (2-digit CIP only)
└─ Doctoral-Professional Practice → degree_level=10

Note: Portal uses integers 1-10. Census Bureau source data uses string codes like "05", "1C" -- these do not appear in Portal data.

Is my institution/state covered?

Checking data availability?
├─ Which states participate → ./references/state-coverage.md
├─ Which institutions have data → Check PSEO Explorer or mirror data
├─ Coverage rate for state → ./references/state-coverage.md
└─ Why data might be missing
    ├─ Institution not partnered
    ├─ Cell suppressed (count < 30)
    └─ Insufficient labor force attachment

Quick Reference: PSEO Variables

Earnings Variables

Flows Variables

Note: Portal uses restructured schema with years_after_grad column instead of Census API's Y1_*/Y5_*/Y10_* naming. The industry column is String type because some NAICS sectors span ranges (e.g., "31-33" for Manufacturing, "44-45" for Retail Trade).

Key Identifiers

Key Filters (Portal Integer Encoding)

Cohort Definitions

Missing Data Codes

Note: PSEO data has no null values in the parquet files. All missing/suppressed data uses integer codes (-1, -3). Filter with pl.col("p50_earnings") > 0 to get valid earnings, not .is_not_null(). PSEO uses differential privacy rather than traditional suppression. Cells with fewer than 30 graduates are suppressed entirely (coded as -3). Earnings values coded -1 may indicate insufficient labor force attachment.

Data Access

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

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

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.

Fetching PSEO Data

import polars as pl

# PSEO is a yearly dataset -- fetch individual years
df = fetch_yearly_from_mirrors(
    path_template="pseo/colleges_pseo_{year}",
    years=[2018, 2019, 2020],
)

# Or fetch a single year
df = fetch_from_mirrors("pseo/colleges_pseo_2020")

Filtering

# Filter by institution
df.filter(pl.col("unitid") == 100751)  # University of Alabama

# Filter by field of study
df.filter(pl.col("cipcode") == 11)  # Computer Science

# Filter by cohort (note: full year range format)
df.filter(pl.col("pseo_cohort") == "2019-2021")

# Earnings rows only (exclude missing/suppressed)
df.filter(pl.col("p50_earnings") > 0)

# Filter by industry (String column, not integer)
df.filter(pl.col("industry") == "54")  # Professional Services

Additional Access Methods (Census Bureau Source)

1. PSEO Explorer: Interactive visualization tool at https://lehd.ces.census.gov/data/pseo_explorer.html
2. Census bulk download: CSV/XLS files at https://lehd.ces.census.gov/data/pseo/
3. Census API: https://api.census.gov/data/timeseries/pseo/earnings and .../flows (uses different variable naming and string codes; not used in this system)

Common Pitfalls

PSEO vs Other Data Sources

Common Use Cases

Important Limitations

1. Experimental status: Not official Census statistics; methodology may change
2. Partial coverage: Only ~29% of graduates from participating institutions
3. Selection bias: Participating states/institutions may differ systematically
4. Employment coverage: Excludes self-employed, independent contractors, military, some federal
5. Labor attachment requirement: Workers must have 3+ quarters of earnings above minimum wage threshold
6. Suppression: Cells with fewer than 30 graduates are suppressed
7. Earnings inflation-adjusted: All earnings in 2022 dollars (CPI-U)

Related Data Sources

Topic Index