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SKILL.md
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
File
Purpose
When to Read
lehd-methodology.md
How LEHD produces tabulations, data matching process
Participating states, coverage rates, data partners
Understanding limitations
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
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
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
ID
Format
Level
Example
Notes
unitid
Integer
Institution
100751
IPEDS Unit ID (University of Alabama)
opeid
Integer
Institution
105100
Portal stores as integer (Census uses 8-digit zero-padded string)
fips
Integer
State
48
State of institution (Texas)
cipcode
2-digit integer
Field of study
11
Computer Science; Portal uses integers, not "11.01"
Key Filters (Portal Integer Encoding)
Parameter
Description
Example
degree_level
Degree type integer
5 (Bachelor's)
pseo_cohort
Graduation cohort
"2016-2020" or "2019-2021" (string format, full year range)
Item doesn't apply to this entity (Portal convention)
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.
Dataset
Type
Path
Codebook
Earnings and Flows
Yearly (2001-2021)
pseo/colleges_pseo_{year}
pseo/codebook_colleges_pseo
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:
Actual data file (what you observe in the parquet/CSV) -- this IS the truth
Live codebook (.xls in mirror) -- authoritative documentation, may lag
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)
PSEO Explorer: Interactive visualization tool at https://lehd.ces.census.gov/data/pseo_explorer.html
Census bulk download: CSV/XLS files at https://lehd.ces.census.gov/data/pseo/
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
Pitfall
Issue
Solution
Using Census string codes
Portal uses integers (e.g., 5 for Bachelor's), not Census strings ("05")
Always check encoding; see variable-definitions.md
Ignoring suppression
Cells with <30 graduates are suppressed; missing data looks like no program exists
Check total_grads_count to confirm cell exists; null earnings may mean suppression
Cross-institution comparison without controlling degree/CIP
Institutions offer different program mixes; aggregate comparison is misleading
Always filter to same degree_level and cipcode when comparing institutions
Treating PSEO as comprehensive
Only ~29% of graduates covered; participating states differ systematically
Acknowledge selection bias; do not generalize to all U.S. graduates
Ignoring labor attachment
Workers need 3+ quarters above minimum wage threshold to appear in earnings data
Some graduates are employed but excluded; note this limitation
Treating Portal opeid as string
Portal stores opeid as integer (e.g., 105100), not Census's 8-digit zero-padded string ("00105100")
Use integer comparison in Portal data; only Census API uses string format
Mixing cohort spans
Bachelor's uses 3-year cohorts; all others use 5-year
Filter by degree_level first, then verify cohort format matches
Assuming inflation comparability
All earnings are in 2022 CPI-U dollars
No manual inflation adjustment needed; values are already real dollars
PSEO vs Other Data Sources
Feature
PSEO
College Scorecard
State Systems
Coverage
Graduates only
All enrollees
Graduates only
Geographic scope
National (cross-state)
National
In-state only
Sample
All graduates from partners
Federal aid recipients
All graduates
Earnings detail
25th/50th/75th percentile
Median only
Varies
Industry data
Yes (NAICS sector)
No
Varies
Geographic flows
Yes (Census Division)
No
No
Privacy method
Differential privacy
Traditional suppression
Varies
Common Use Cases
Use Case
Data Needed
Key Considerations
Compare programs within institution
Earnings by CIPCODE
Check cell counts for suppression
Compare institutions for same program
Earnings by INSTITUTION
Ensure same degree level and CIP
Analyze brain drain/retention
Flows by division + in-state
Only 9 Census Divisions
Career pathway analysis
Flows by NAICS sector
2-digit NAICS only
ROI by degree level
Earnings across DEGREE_LEVEL
Different cohort spans
Important Limitations
Experimental status: Not official Census statistics; methodology may change
Partial coverage: Only ~29% of graduates from participating institutions
Selection bias: Participating states/institutions may differ systematically
Employment coverage: Excludes self-employed, independent contractors, military, some federal
Labor attachment requirement: Workers must have 3+ quarters of earnings above minimum wage threshold
Suppression: Cells with fewer than 30 graduates are suppressed
Earnings inflation-adjusted: All earnings in 2022 dollars (CPI-U)
Related Data Sources
Source
Relationship
When to Use
education-data-source-scorecard
Alternative earnings source (median only, all enrollees)
When PSEO coverage is insufficient or need non-graduate outcomes
