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README.md

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+---
+license: other
+license_name: us-government-public-domain
+license_link: https://www.usa.gov/government-works
+language:
+  - en
+size_categories:
+  - 1M<n<10M
+task_categories:
+  - tabular-classification
+pretty_name: "OPM FedScope Federal Employment — February 2026 (Part 1 of 3)"
+tags:
+  - government
+  - federal-workforce
+  - foia
+  - sampling-frame
+  - opm
+  - fedscope
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: "employment_202602_part1.parquet"
+---
+
+# OPM FedScope Federal Employment — February 2026 (Part 1 of 3)
+
+## Why this dataset exists
+
+We are building **FOIA requests** for federal agencies. To do that responsibly we need to
+identify and sample the **custodians of records** likely to hold the documents we want —
+i.e. the actual federal employees whose mailboxes, drives, and chat logs are responsive.
+
+A naive list of senior officials over-samples the small visible top of an agency and
+misses the bulk of work, which sits in the GS‑13/14/15 mass and the technical career
+series. To get a defensible sample we want **stratification weights anchored in real
+employment data**, so the FOIA targets reflect the actual distribution of staff across
+sub-agencies, occupational series, pay plans, and locations.
+
+This dataset is that anchor: OPM's record-level (PII-redacted) federal civilian
+employment snapshot for **February 28, 2026** — every row is one employee.
+
+> **Scope:** This file is **part 1 of 3** of OPM's Feb‑2026 Employment release. OPM
+> splits the full ~6M‑row monthly snapshot into three roughly equal text files for
+> distribution; this repo currently contains part 1 only (~2.03M rows, ~1.5 GB raw,
+> compressed to 54 MB Parquet). Parts 2 and 3 are on the OPM site at
+> [data.opm.gov/explore-data/data/data-downloads](https://data.opm.gov/explore-data/data/data-downloads).
+
+## What's in here
+
+- `employment_202602_part1.parquet` — Snappy-ZSTD Parquet, **2,028,138 rows × 61 columns**, all string typed.
+- `employment_202602_1_2026-05-04.txt` is the original pipe-delimited source from OPM (not uploaded; reproducible from OPM).
+
+### Schema (61 columns, all from OPM's published dictionary)
+
+Identifiers / org:
+`agency`, `agency_code`, `agency_subelement`, `agency_subelement_code`,
+`cfo_act_agency_indicator`, `personnel_office_identifier_code`
+
+Position:
+`occupational_category` (P/A/T/C/B), `occupational_category_code`,
+`occupational_group`, `occupational_group_code`,
+`occupational_series`, `occupational_series_code`,
+`pay_plan`, `pay_plan_code`, `grade`, `step_or_rate_type`, `step_or_rate_type_code`,
+`position_occupied`, `position_occupied_code`,
+`supervisory_status`, `supervisory_status_code`,
+`appointment_type`, `appointment_type_code`,
+`tenure`, `tenure_code`, `work_schedule`, `work_schedule_code`,
+`flsa_category`, `flsa_category_code`,
+`bargaining_unit`, `bargaining_unit_code`, `bargaining_unit_status`,
+`nsftp_indicator`, `stem_occupation`, `stem_occupation_type`
+
+Person attributes (coarse, no PII):
+`age_bracket`, `length_of_service_years`, `education_level`, `education_level_bracket`,
+`education_level_code`, `veteran_indicator`
+
+Compensation (often `REDACTED`):
+`annualized_adjusted_basic_pay`, `pay_basis`, `pay_basis_code`
+
+Location:
+`duty_station_code`, `duty_station_country`, `duty_station_country_code`,
+`duty_station_county`, `duty_station_county_code`,
+`duty_station_state`, `duty_station_state_abbreviation`,
+`duty_station_state_country_territory_code`,
+`core_based_statistical_area`, `core_based_statistical_area_code`,
+`consolidated_statistical_area`, `consolidated_statistical_area_code`,
+`locality_pay_area`, `locality_pay_area_code`,
+`service_computation_date_leave`
+
+Snapshot key:
+`snapshot_yyyymm` (always `202602` here), `count` (always `1`)
+
+### What this dataset will **not** give you
+
+- **Free-text position titles** (e.g. "Chief of Staff", "Senior Advisor for Policy") — OPM strips these. Closest proxy is `occupational_series` (job family).
+- **Personally identifiable information** — no names, no employee IDs.
+- **Sub-office / front-office breakdown** — granularity stops at `agency_subelement` (e.g. all of "Office of the Secretary of the Interior" is one `IN01` bucket regardless of which office an employee actually sits in).
+- **Adjusted basic pay** for many records (~122K of 418K Feb‑2026 VA records are null per OPM's release notes; redactions also common elsewhere).
+
+For the named-position layer (Secretary, Deputy Secretary, Assistant Secretaries,
+Schedule C / SES non-career, etc.) supplement this with the
+[Plum Book](https://www.govinfo.gov/app/collection/plumbook), the agency org chart, and
+SES/SL/ST listings.
+
+---
+
+## Recipe: building a FOIA custodian sampling frame with SQL
+
+All examples use **DuckDB** against the Parquet file. Install with `pip install duckdb`
+(or `uv run --with duckdb python ...`).
+
+```python
+import duckdb
+con = duckdb.connect()
+con.execute("CREATE VIEW emp AS SELECT * FROM 'employment_202602_part1.parquet'")
+```
+
+If you prefer the CLI:
+```bash
+duckdb -c "SELECT count(*) FROM 'employment_202602_part1.parquet'"
+```
+
+### 1. Pick the agency you're FOIA'ing
+
+```sql
+-- Find every subelement under DOI
+SELECT DISTINCT agency_subelement, agency_subelement_code
+FROM emp
+WHERE agency_code = 'IN'
+ORDER BY agency_subelement;
+```
+
+For DOI you'll see 14 subelements: `IN01` Office of the Secretary, `IN05` BLM, `IN06`
+Indian Affairs, `IN07` Reclamation, `IN08` USGS, `IN10` NPS, `IN15` FWS, `IN21`
+Solicitor, `IN22` OSMRE, `IN24` OIG, `IN26` BSEE, `IN27` BOEM, `IN28` BIE, `IN29` BTFA.
+
+### 2. Get the headcount-per-subelement denominator (top of the stratification tree)
+
+```sql
+SELECT agency_subelement_code,
+       agency_subelement,
+       count(*) AS employees,
+       round(100.0 * count(*) / sum(count(*)) OVER (), 2) AS pct_of_agency
+FROM emp
+WHERE agency_code = 'IN'
+GROUP BY agency_subelement_code, agency_subelement
+ORDER BY employees DESC;
+```
+
+Use the `pct_of_agency` column directly as your sub-agency sampling weight.
+
+### 3. Within a subelement, stratify by tier and job family
+
+A reasonable **Tier** proxy from `pay_plan_code` and `grade`:
+
+| pay_plan_code | tier              |
+|---|---|
+| `EX`            | Presidentially appointed (PAS) |
+| `ES`            | SES |
+| `SL`, `ST`      | Senior Level / Senior Scientific |
+| `GS` (grade ≥ 14), `GL` (≥ 14) | Senior career |
+| `GS` (grade 12‑13)             | Mid career |
+| `GS` (grade ≤ 11), `WG`, `WS`, `WL`, `WD` | Rank-and-file |
+| else            | Other |
+
+```sql
+WITH tiered AS (
+  SELECT *,
+    CASE
+      WHEN pay_plan_code = 'EX' THEN 'PAS'
+      WHEN pay_plan_code = 'ES' THEN 'SES'
+      WHEN pay_plan_code IN ('SL','ST') THEN 'SL_ST'
+      WHEN pay_plan_code IN ('GS','GL') AND TRY_CAST(grade AS INT) >= 14 THEN 'Senior_career'
+      WHEN pay_plan_code IN ('GS','GL') AND TRY_CAST(grade AS INT) BETWEEN 12 AND 13 THEN 'Mid_career'
+      WHEN pay_plan_code IN ('GS','GL') AND TRY_CAST(grade AS INT) <= 11 THEN 'Rank_and_file'
+      WHEN pay_plan_code IN ('WG','WS','WL','WD') THEN 'Rank_and_file'
+      ELSE 'Other'
+    END AS tier
+  FROM emp
+  WHERE agency_subelement_code = 'IN01'      -- swap to whatever you're FOIA'ing
+)
+SELECT tier,
+       occupational_series_code,
+       occupational_series,
+       count(*) AS employees,
+       round(100.0 * count(*) / sum(count(*)) OVER (PARTITION BY tier), 2) AS pct_within_tier
+FROM tiered
+GROUP BY tier, occupational_series_code, occupational_series
+ORDER BY tier, employees DESC;
+```
+
+### 4. Build your sampling frame as a single tidy table
+
+```sql
+COPY (
+  SELECT
+    agency_code,
+    agency_subelement_code   AS office_code,
+    agency_subelement        AS office,
+    pay_plan_code,
+    grade,
+    occupational_category_code,
+    occupational_series_code AS series_code,
+    occupational_series      AS series,
+    duty_station_state_abbreviation AS state,
+    count(*)                 AS employees
+  FROM emp
+  WHERE agency_code = 'IN'
+  GROUP BY ALL
+) TO 'doi_sampling_frame.parquet' (FORMAT PARQUET);
+```
+
+Now you can allocate FOIA-custodian sample slots proportional to `employees` (or any
+weighted scheme — e.g. over-sample SES tiers, then use this frame to generate quotas for
+the GS‑13/14/15 mass).
+
+### 5. Sanity-check the frame against what OPM publishes
+
+```sql
+SELECT count(*) AS rows, sum(count(*)) OVER () AS employees
+FROM emp WHERE agency_code = 'IN';
+```
+
+`employees` should match OPM's published DOI total for Feb 2026 (within whatever rows
+fell into parts 2 or 3 of the release — see the scope note above).
+
+---
+
+## Recreating this file from OPM
+
+1. Visit <https://data.opm.gov/explore-data/data/data-downloads>.
+2. Click **DOWNLOAD** under "Federal Employment Raw Data (February 2026)". Note: this is
+   a Blazor Server app that streams the file over WebSocket; large transfers may
+   disconnect under headless automation. Real browsers work.
+3. The download is a `.txt` file with `|` delimiters and a header row, named
+   `employment_202602_<part>_<download-date>.txt`.
+
+To regenerate the Parquet:
+```bash
+uv run --with duckdb python -c "
+import duckdb
+duckdb.sql('''
+  COPY (SELECT * FROM read_csv(\"employment_202602_1_*.txt\",
+                               delim=\"|\", header=true, all_varchar=true))
+  TO \"employment_202602_part1.parquet\" (FORMAT PARQUET, COMPRESSION ZSTD)
+''')
+"
+```
+
+## Provenance & license
+
+- **Source:** U.S. Office of Personnel Management, Enterprise Human Resources
+  Integration (EHRI) Status snapshot, published via
+  [data.opm.gov](https://data.opm.gov/).
+- **Coverage:** Federal civilian workforce snapshot as of **2026-02-28**, published
+  **2026-03-30**, version 1.
+- **PII:** OPM redacts personal identifiers and many compensation values. No employee
+  names, no employee IDs.
+- **License:** U.S. federal government work — public domain in the U.S. under
+  [17 U.S.C. § 105](https://www.usa.gov/government-works). Not endorsed by or affiliated with OPM.
+
+## Citation
+
+```
+U.S. Office of Personnel Management (2026). Federal Employment Raw Data — February 2026.
+Enterprise Human Resources Integration (EHRI) Status dataset.
+https://data.opm.gov/explore-data/data/data-downloads
+```