pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
PSEO — Census data linking graduates to employment via LEHD wage records. Earnings percentiles at 1/5/10 years post-graduation by institution, degree, CIP. Use for graduate earnings analysis. Coverage: ~29% of graduates from ~31 states.
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill education-data-source-pseo --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/education-data-source-pseoContext preview
The summary Claude sees to decide when to auto-load this skill.
PSEO — Census data linking graduates to employment via LEHD wage records. Earnings percentiles at 1/5/10 years post-graduation by institution, degree, CIP. Use for graduate earnings analysis. Coverage: ~29% of graduates from ~31 states.
name: education-data-source-pseo description: >- PSEO — Census data linking graduates to employment via LEHD wage records. Earnings percentiles at 1/5/10 years post-graduation by institution, degree, CIP. Use for graduate earnings analysis. Coverage: ~29% of graduates from ~31 states. metadata: audience: any-agent domain: data-source skill-authored: "2026-02-09" skill-last-updated: "2026-02-09"
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.
| File | Purpose | When to Read | |------|---------|--------------| | `lehd-methodology.md` | How LEHD produces tabulations, data matching process | Understanding data creation | | `earnings-data.md` | Percentile earnings, cohort definitions, labor attachment | Analyzing graduate earnings | | `geographic-flows.md` | Where graduates work by Census Division | Studying migration patterns | | `industry-flows.md` | What industries graduates enter by NAICS sector | Career pathway analysis | | `variable-definitions.md` | All variables, codes, and status flags | Building queries or interpreting values | | `state-coverage.md` | Participating states, coverage rates, data partners | Understanding limitations |
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.mdDegree 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.
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| Portal Variable | Description | |-----------------|-------------| | `p25_earnings` | 25th percentile earnings (2022 dollars) | | `p50_earnings` | Median earnings (2022 dollars) | | `p75_earnings` | 75th percentile earnings (2022 dollars) | | `years_after_grad` | Years post-graduation: `1`, `5`, or `10` | | `employed_grads_count_e` | Graduate count with earnings data | | `total_grads_count` | To
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