/education-data-source-pseo
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.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/education-data-source-pseo
Context 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.
SKILL.md
education-data-source-pseo.SKILL.mdname: 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"
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 | 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 |
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.mdWhat 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 attachmentQuick Reference: PSEO Variables
Earnings Variables
| 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
Read more
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"
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 | 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 |
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.mdWhat 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 attachmentQuick Reference: PSEO Variables
Earnings Variables
| 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
📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md(扩展正文,总表行内的 → 直接跳转到对应锚点)。 English version: README-en.md · 中文扩展正文:docs/CONTENT_ZH.md · README-zh-CN.md 已弃用(重定向占位) 🌐 语言: English |
Other skills on auto-empirical-research-skills.
- /pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) —
Open skill - /pipeline
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid
Open skill - /pipeline
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc +
Open skill - /00-Full-empirical-analysis-skill_StatsPAI
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 /
Open skill - /00.1-Full-empirical-analysis-skill_Python
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) —
Open skill - /00.2-Full-empirical-analysis-skill_Stata
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc +
Open skill

