OPM Federal Workforce Data Portal

Enterprise Human Resources Integration (EHRI) datasets and visualization catalog for federal workforce analysis

January - November 2025

Visualizations

22
Interactive dashboards

Categories

6
Analysis themes

Variables Used

15+
Data dimensions

Data Source

OPM
data.opm.gov
📊

Workforce Size & Composition

4 visualizations

The Federal Civilian Workforce Over Time

Variables
Department Agency Subagency Fiscal Year
Example Questions
  • How has the total federal workforce size changed over the past decade?
  • What is the trend in employee count for a specific agency?
View Visualization →

Federal Workforce by Agency and Occupation

Variables
Department Agency Subagency Occupational Series
Example Questions
  • Which agencies employ the most IT specialists?
  • How does workforce distribution vary across different occupational series?
View Visualization →

Federal Workforce by Appointment Type

Variables
Department Agency Service Type Permanent/Temporary
Example Questions
  • What percentage of federal employees are in permanent vs. temporary positions?
  • Which agencies rely most heavily on non-permanent staff?
View Visualization →

Federal Workforce by Bargaining Unit Status

Variables
Department Agency Bargaining Unit Eligibility Date Range
Example Questions
  • What proportion of employees are union-eligible but not in a bargaining unit?
  • How has bargaining unit membership trended over time?
View Visualization →
📈

Workforce Changes

4 visualizations

Federal Workforce Changes Over Time

Variables
Department Agency Subagency Fiscal Year
Example Questions
  • What is the net change in federal employment year over year?
  • During which fiscal years did the workforce grow or shrink the most?
View Visualization →

Separations and Accessions Since January 20, 2025

Variables
Department Agency Occupational Series Action Types
Example Questions
  • What is the net hiring vs. separation balance since the new administration?
  • Which occupational series have seen the most separations?
View Visualization →

Separations and Accessions Over Time

Variables
Department Agency Personnel Action Type Fiscal Year
Example Questions
  • What are the historical trends in new hires vs. retirements?
  • When do agencies typically see the highest turnover?
View Visualization →

Federal Workforce Changes by Agency

Variables
Department Agency Subagency Fiscal Year
Example Questions
  • Which agencies experienced the largest workforce changes?
  • How do workforce changes compare across departments?
View Visualization →
💰

Compensation, Performance & Leave

4 visualizations

Federal Workforce by Pay Plan and Grade

Variables
Department Agency Subagency Fiscal Year
Example Questions
  • What is the distribution of employees across GS grades?
  • How does pay grade composition differ by agency?
View Visualization →

Salary Distribution by Agency and Series

Variables
Department Agency Occupational Series Fiscal Year
Example Questions
  • What is the average salary for a specific occupational series?
  • Which agencies pay the highest salaries for comparable positions?
View Visualization →

Performance Ratings Distribution

Variables
Department Agency Rating Pattern Fiscal Year
Example Questions
  • What percentage of employees receive the highest performance ratings?
  • How do performance ratings vary across departments?
View Visualization →

Use of Administrative Leave

Variables
Department Admin Leave Type Pay Period Date Range
Example Questions
  • How much administrative leave is being used for investigations?
  • What are the trends in administrative leave usage over time?
View Visualization →
👥

Demographics

4 visualizations

Employees by Age

Variables
Department Agency Subagency Age Groups
Example Questions
  • What is the age distribution of the federal workforce?
  • Which agencies have the youngest or oldest workforce?
View Visualization →

Employees by Retirement Eligibility

Variables
Department Agency Occupational Series Eligibility Timeframe
Example Questions
  • What percentage of employees are currently eligible to retire?
  • How many employees will become retirement-eligible in the next decade?
View Visualization →

Employees by Veteran Status

Variables
Department Agency Subagency Veteran Status
Example Questions
  • What proportion of federal employees are veterans?
  • Which agencies have the highest veteran employment rates?
View Visualization →

Employees by Education Level

Variables
Department Agency Occupational Series Education Level
Example Questions
  • What is the educational attainment profile of the federal workforce?
  • Which occupations require the highest education levels?
View Visualization →
🗺️

Location

3 visualizations

Distribution of Employees by Location

Variables
Department Agency Subagency
Example Questions
  • Where are federal employees located geographically?
  • What is the concentration of federal workers by state?
View Visualization →

Location by Agency and State

Variables
Department Agency State Fiscal Year
Example Questions
  • Which states have the largest presence of a specific agency?
  • How has geographic distribution of employees changed over time?
View Visualization →

Telework and Remote Work Over Time

Variables
Department Hours Type Pay Period Date Range
Example Questions
  • How have telework patterns changed since return-to-office directives?
  • What percentage of work hours are performed remotely?
View Visualization →
🎯

Federal Recruitment

3 visualizations

Current Job Announcements by Agency

Variables
Department Agency Occupational Series Hiring Path
Example Questions
  • Which agencies currently have the most open positions?
  • What types of jobs are federal agencies actively recruiting for?
View Visualization →

Current Job Announcements by Location

Variables
Department Agency Occupational Series Location
Example Questions
  • Where are federal jobs currently available geographically?
  • Which regions have the most federal hiring opportunities?
View Visualization →

Job Announcements Over Time

Variables
Department Agency Occupational Series Fiscal Year Range
Example Questions
  • How has federal hiring activity changed over time?
  • Is the government expanding or contracting recruitment efforts?
View Visualization →

Employment Records

24.4M
Total rows across 11 monthly snapshots

Accession Records

118K
New hires and transfers into federal service

Separation Records

335K
Resignations, retirements, and departures

Data Format

CSV
Pipe-delimited (|) with headers

👤 Federal Employment Raw Data

EHRI Status dataset - Monthly snapshots of all active federal employees (in pay or non-pay status) as of the last day of each month.

11 monthly files
~2.1-2.3M
31
780-860 MB each

⚠ Important: Large File Warning

These files exceed 2 million rows and will exceed the row limit for most spreadsheet tools (e.g., MS Excel). Download files and use Python, R, or database tools to process.

Age Distribution (Sample: 500K records)

40-44
71,513
14.3%
45-49
65,635
13.1%
50-54
64,510
12.9%
35-39
63,413
12.7%
55-59
62,365
12.5%
30-34
50,925
10.2%
60-64
45,483
9.1%
25-29
35,902
7.2%
65+
25,616
5.1%
20-24
14,040
2.8%

Top 10 Agencies

Veterans Affairs
83,614
16.7%
Homeland Security
73,367
14.7%
Air Force
56,153
11.2%
Army
45,019
9.0%
Navy
39,259
7.9%
DOD (Other)
32,265
6.5%
Agriculture
28,937
5.8%
HHS
27,297
5.5%
Interior
20,265
4.1%
Treasury
15,990
3.2%

Column Reference

Column Name Type Nulls Unique Description / Sample Values
snapshot_yyyymm int64 0% 1 Month of snapshot (e.g., 202501). Unique identifier for temporal analysis.
agency string 0.01% 62 Parent agency name.
DEPARTMENT OF DEFENSE DEPARTMENT OF VETERANS AFFAIRS
agency_code string 0% 62 2-letter agency code.
DD VA HS AR AF
agency_subelement string 0% 331 Sub-agency or bureau name within parent agency.
agency_subelement_code string 0.01% 346 4-character sub-agency code.
DD16 ARCE VATA
age_bracket string 0% 11 Age range.
20-24 25-29 ... 65 OR MORE LESS THAN 20
annualized_adjusted_basic_pay string 0.01% 5,635 Annual salary. Contains REDACTED for sensitive records. Values like 99036, 168792
appointment_type string 0% 17 Employment classification.
CAREER (COMPETITIVE SERVICE PERMANENT) CAREER-CONDITIONAL
appointment_type_code string 0% 17 2-digit appointment code.
10 15 38 * (invalid)
count int64 0% 1 Always 1. Each row represents one employee record.
duty_station_country string 0% 3
UNITED STATES REDACTED INVALID
duty_station_country_code string 0% 3
US REDACTED *
duty_station_state string 0% 33 State name. ~48% REDACTED for privacy.
MARYLAND VIRGINIA TEXAS
duty_station_state_abbreviation string 0% 33 State abbreviation.
MD VA DC REDACTED
duty_station_state_code string 0% 33 FIPS state code.
11 (DC) 24 (MD) 51 (VA)
education_level string 0% 24
HIGH SCHOOL GRADUATE BACHELOR'S DEGREE MASTER'S DEGREE DOCTORATE DEGREE
education_level_code string 0.03% 23 2-digit education code.
04 13 17 21
grade string 0% 123 Pay grade.
01-15 (GS) 00 F (varies by pay plan)
length_of_service_years float64 0% 552 Federal service tenure in years. Range: 0.0 - 72.5, Mean: 11.94
occupational_group string 0% 58
MEDICAL, HOSPITAL, DENTAL, AND PUBLIC HEALTH GROUP INFORMATION TECHNOLOGY GROUP
occupational_group_code string 0% 59 2-digit group code.
06 22 03
occupational_series string 0% 511 Specific job series.
NURSE CONTRACTING INFORMATION TECHNOLOGY MANAGEMENT
occupational_series_code string 0% 511 4-digit OPM series code.
0610 (Nurse) 2210 (IT) 1102 (Contracting)
pay_plan string 0% 95
GENERAL SCHEDULE FEDERAL WAGE SYSTEM SENIOR EXECUTIVE SERVICE
pay_plan_code string 0.03% 95 2-character pay plan code.
GS WG ES SV
stem_occupation string 0% 4
ALL OTHER OCCUPATIONS (72.8%) STEM OCCUPATIONS (18.4%) HEALTH OCCUPATIONS (8.7%)
stem_occupation_type string 0% 7
ENGINEERING OCCUPATIONS TECHNOLOGY OCCUPATIONS SCIENCE OCCUPATIONS MATHEMATICS OCCUPATIONS
supervisory_status string 0% 6
ALL OTHER POSITIONS (84%) SUPERVISOR OR MANAGER (11.7%) LEADER TEAM LEADER
supervisory_status_code int64 0% 6 Values: 2 (Supervisor/Mgr), 4-8. Mean: 7.21
work_schedule string 0% 8
FULL-TIME (95.4%) PART-TIME INTERMITTENT SEASONAL
work_schedule_code string 0% 8
F (Full-time) P (Part-time) I (Intermittent) G S

➕ Federal Accessions Raw Data

EHRI Dynamics dataset - Personnel actions for individuals entering federal civilian employment, including new hires and transfers.

11 monthly files
118,015
33
2-10 MB each

Accession Categories

New Hire - Competitive
12,000
52.9%
New Hire - Excepted
9,137
40.3%
Transfer In
1,301
5.7%
New Hire - SES
243
1.1%

New Hire Age Distribution (Jan 2025)

25-29
3,625
16.0%
30-34
3,575
15.8%
35-39
3,219
14.2%
40-44
2,878
12.7%
20-24
2,710
11.9%
45-49
2,222
9.8%
50-54
1,827
8.1%
55-59
1,298
5.7%

Unique Columns (vs Employment)

Column Name Type Nulls Unique Description / Values
accession_category string 0% 4 Type of entry.
NEW HIRE - COMPETITIVE SERVICE APPOINTMENT NEW HIRE - EXCEPTED SERVICE APPOINTMENT TRANSFER IN - INDIVIDUAL TRANSFER NEW HIRE - SENIOR EXECUTIVE SERVICE (SES) APPT
accession_category_code string 0% 4
AA (Competitive) AC (Excepted) AD (SES) AE (Transfer)
personnel_action_effective_date_yyyymm int64 0% 2+ Month when personnel action took effect.
202501 202301 (some historical)

Shared Columns

  • All Employment columns except snapshot_yyyymm are present
  • Uses personnel_action_effective_date_yyyymm instead of snapshot_yyyymm
  • 94 unique agencies (vs 62 in Employment) - more granular agency data
  • Mean length of service: 7.22 years (includes rehires and transfers)

➖ Federal Separations Raw Data

EHRI Dynamics dataset - Personnel actions for individuals leaving federal civilian employment, including resignations, retirements, and other separations.

11 monthly files
335,192
33
6-48 MB each

📈 Notable: September 2025 Spike

September 2025 shows 125,589 separations (vs typical ~20K/month), suggesting a significant workforce reduction event or policy change. Contains 48.4 MB of data compared to typical 8-10 MB files.

Separation Categories (Jan 2025)

Quit (Resignation)
9,148
41.0%
Retirement - Voluntary
6,549
29.4%
Transfer Out
3,032
13.6%
Termination (Expired/Other)
1,858
8.3%
Other Separation
1,377
6.2%
Retirement - Other
303
1.4%
Retirement - Early Out
24
0.1%
RIF
1
<0.01%

Separation Age Distribution (Jan 2025)

65+
3,774
16.9%
60-64
3,438
15.4%
55-59
2,258
10.1%
35-39
2,251
10.1%
40-44
2,163
9.7%
30-34
2,099
9.4%
25-29
1,792
8.0%
50-54
1,711
7.7%

Unique Columns (vs Employment)

Column Name Type Nulls Unique Description / Values
separation_category string 0% 9 Type of departure.
QUIT RETIREMENT - VOLUNTARY TRANSFER OUT - INDIVIDUAL TRANSFER TERMINATION (EXPIRED APPT/OTHER) REDUCTION IN FORCE (RIF)
separation_category_code string 0% 9
SA (Other) SB (Quit) SC (RIF) SD (Early Out) SE (Other Retire) SG (Voluntary Retire) SH (Termination) SJ (Transfer) SL (Mass Transfer)
personnel_action_effective_date_yyyymm int64 0% 2+ Month when separation took effect.

Data Quality Notes - Separations

Higher Null Rates

  • annualized_adjusted_basic_pay: 3.32% nulls (vs <1% in Employment)
  • grade: 1.83% nulls
  • occupational_series_code: 1.82% nulls
  • pay_plan_code: 1.91% nulls
  • work_schedule_code: 1.82% nulls
  • Mean length of service: 18.54 years (higher due to retirements)
  • Includes international duty stations: France, Japan, Mexico, India, etc.

📚 Data Quality & Privacy Notes

Privacy Redaction

  • REDACTED values appear in location fields (duty_station_*) and salary for sensitive records
  • ~48% of duty station state data is redacted in Employment files
  • Salary redaction more common in Accessions (0.57%) and Separations (3.32%)

Invalid/Missing Data Markers

  • INVALID - Data validation failed
  • * - Invalid code value
  • NO DATA REPORTED - Missing submission
  • UNSPECIFIED - Not categorized

File Naming Convention

{type}_{yyyymm}_{version}_{download_date}.csv

Example: employment_202501_1_2026-01-08.csv

Recommended Analysis Tools

  • Python: pandas, polars, dask for large file handling
  • R: data.table, arrow for efficient CSV reading
  • SQL: Load into SQLite, DuckDB, or PostgreSQL for queries
  • BI Tools: Tableau, Power BI (may need data sampling for Employment files)

Variables Available for Custom Analysis

These data fields are present in the EHRI raw data files but are not currently featured in the OPM visualization catalog. They represent opportunities for additional analysis and custom visualizations.

🔬

STEM Classification Fields

2 variables

stem_occupation - STEM Occupation Category

High-level classification of whether a position is categorized as STEM, Health, or other occupation types. Useful for analyzing the federal government's science and technology workforce.

All Values

ALL OTHER OCCUPATIONS (72.8%) STEM OCCUPATIONS (18.4%) HEALTH OCCUPATIONS (8.7%) UNSPECIFIED

Potential Analysis Questions

  • Which agencies have the highest concentration of STEM workers?
  • How has STEM employment changed over time across the federal government?
  • What is the separation rate for STEM vs non-STEM positions?
  • Are STEM positions more likely to be filled by new hires or transfers?

stem_occupation_type - Detailed STEM Category

More granular breakdown of STEM occupations into specific disciplines. Enables analysis of engineering, technology, science, and math workforce segments.

All Values

ALL OTHER OCCUPATIONS ENGINEERING OCCUPATIONS TECHNOLOGY OCCUPATIONS SCIENCE OCCUPATIONS MATHEMATICS OCCUPATIONS HEALTH OCCUPATIONS UNSPECIFIED

Potential Analysis Questions

  • Which STEM discipline has the highest representation in federal employment?
  • How do salaries compare across STEM disciplines?
  • What is the age distribution within each STEM category?
  • Which agencies are actively recruiting for specific STEM disciplines?
👔

Supervisory Status Fields

2 variables

supervisory_status - Supervisory Classification

Indicates whether an employee holds a supervisory, managerial, team leader, or non-supervisory position. Critical for understanding organizational structure and leadership distribution.

All Values

ALL OTHER POSITIONS (84%) SUPERVISOR OR MANAGER (11.7%) TEAM LEADER LEADER SUPERVISOR (CSRA) MANAGEMENT OFFICIAL (CSRA)

Potential Analysis Questions

  • What is the supervisor-to-employee ratio across agencies?
  • How does supervisory status correlate with salary and grade?
  • Are supervisors more or less likely to separate than non-supervisors?
  • How has the management layer changed over time?

supervisory_status_code - Supervisory Code

Numeric code representation of supervisory status. Values range from 2-8, with mean of 7.21 indicating most employees are non-supervisory.

Values

2 (Supervisor/Mgr) 4 5 6 7 8 (Non-supervisory)
⏰

Work Schedule Fields

2 variables

work_schedule - Work Schedule Type

Describes the employee's work arrangement including full-time, part-time, intermittent, and seasonal designations. Important for workforce planning and utilization analysis.

Values (Employment Data)

FULL-TIME (95.4%) PART-TIME INTERMITTENT FULL-TIME SEASONAL PART-TIME SEASONAL INTERMITTENT SEASONAL PART-TIME JOB SHARER PHASED RETIREMENT

Potential Analysis Questions

  • Which agencies rely most heavily on part-time or seasonal workers?
  • How does work schedule affect separation rates?
  • What occupational series have the highest proportion of intermittent workers?
  • How many employees are using phased retirement options?

work_schedule_code - Work Schedule Code

Single-letter code for work schedule type.

Values

F - Full-time P - Part-time I - Intermittent G - Full-time Seasonal S - Part-time Seasonal J - Intermittent Seasonal Q - Part-time Job Sharer R - Phased Retirement
📅

Tenure Fields

1 variable

length_of_service_years - Federal Service Tenure

Continuous numeric field representing years of federal service. Ranges from 0 to 72.5 years with mean of 11.94 years for current employees. Separations show higher mean (18.54 years) due to retirements.

Statistics

Min: 0.0 years Max: 72.5 years Mean (Employment): 11.94 Mean (Accessions): 7.22 Mean (Separations): 18.54

Potential Analysis Questions

  • What is the average tenure by agency or occupational series?
  • How does tenure correlate with grade level and salary?
  • At what tenure length do employees most commonly separate?
  • Which agencies have the most experienced workforce?
  • How does new hire tenure (prior federal service) vary by accession type?