drb348/opm-fedscope-employment-202602

OPM FedScope Federal Employment — February 2026

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 is the full Feb‑2026 federal civilian workforce snapshot (2,028,138 rows, ~1.5 GB raw, compressed to 54 MB Parquet). The _1_ in the source filename (employment_202602_1_2026-05-04.txt) is a legacy artifact from when OPM split the release into three parts; the current data.opm.gov download ships it as a single file. Source: data.opm.gov/explore-data/data/data-downloads.

What's in here

• employment_202602_part1.parquet — ZSTD-compressed Parquet, 2,028,138 rows × 61 columns, all columns typed as VARCHAR.
• The original pipe-delimited source from OPM (employment_202602_1_2026-05-04.txt) is not uploaded — it's reproducible from OPM and 28× larger.

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, the agency org chart, and SES/SL/ST listings.

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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 ...).

import duckdb
con = duckdb.connect()
con.execute("CREATE VIEW emp AS SELECT * FROM 'employment_202602_part1.parquet'")

If you prefer the CLI:

duckdb -c "SELECT count(*) FROM 'employment_202602_part1.parquet'"

1. Pick the agency you're FOIA'ing

-- 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)

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

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

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

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.

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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:

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.
• 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. 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