Field reference

Every column the generator emits, what type it is, what a real value looks like, and which published dataset decides it. This page is generated from the engine, so it cannot describe a field the files do not have.

Columns
Industries
Merge format
Countries
Sources

Columns

Types are the portable intersection of SQLite and PostgreSQL. Every generated bundle ships a schema.sql with the same definitions, so you do not have to write the CREATE TABLE yourself.

Two things people get wrong. Columns ending _percent hold fractions: 0.40 means 40 percent. And job_title carries an org unit after a comma on leadership rows, so split on the first comma to get the job by itself.

employees 55 columns

Column	Type	Example	Notes
`effective_date`	DATE	`2026-08-31`	Snapshot date. Identical on every row in the file.
`employee_id`	TEXT	`E02067064`
`first_name`	TEXT	`Gerald`
`last_name`	TEXT	`Roy`
`job_title`	TEXT	`Chief Executive Officer`	Leadership rows carry an org-unit qualifier after a comma ('Project Manager, Engineering'). Split on the FIRST comma to get the job by itself.
`job_code`	INTEGER	`75918`
`hierarchy_level`	TEXT	`C`	C, E, D, M, S, L or IC. L is a senior IC and manages nobody.
`job_level`	TEXT	`Executive`
`job_track`	TEXT	`Management`
`job_family`	TEXT	`Administrative`	One of 11 simplified categories, derived from job_sub_family.
`job_sub_family`	TEXT	`Management`	One of the 22 BLS occupation categories.
`job_function`	TEXT	blank
`department`	TEXT	`Engineering`
`division`	TEXT	`Product & Technology`
`business_unit`	TEXT	`Administrative`
`cost_center`	TEXT	`ENG-WA-001`
`remote_status`	TEXT	`Remote`
`hire_date`	DATE	`2022-08-08`
`job_effective_date`	DATE	`2026-02-23`
`date_of_birth`	DATE	`1952-08-29`
`performance_rating`	INTEGER	`3`
`gender`	TEXT	`Male`
`race`	TEXT	`Asian`	US rows only. NULL for employees outside the United States.
`flsa_status`	TEXT	`EXEMPT`
`state`	TEXT	`Minnesota`
`state_abbrev`	TEXT	`MN`
`zip_code`	TEXT	`55402`
`grade_code`	TEXT	`EXEC-C`	G1 to G12. Derived from what the role pays nationally, not from level.
`salary_range_min`	INTEGER	`300000`
`salary_range_mid`	INTEGER	`450000`
`salary_range_max`	INTEGER	`650000`
`fte`	NUMERIC(18, 2)	`1.0`	1.0 is full time. Part-time rates vary by level and job family.
`annualized_base_pay`	INTEGER	`347355`	Pay at 1.0 FTE, before the part-time adjustment.
`hourly_rate`	NUMERIC(18, 2)	`167.0`
`annual_base_pay`	NUMERIC(18, 2)	`347355.0`	Actual pay for this employee's FTE. Differs from annualized_base_pay for anyone below 1.0.
`compa_ratio`	NUMERIC(18, 3)	`0.772`	annualized_base_pay / salary_range_mid. 1.0 sits at band midpoint.
`market_reference_point`	NUMERIC(18, 2)	`452000.0`	NULL where the role has no survey match. Absence is meaningful.
`market_index`	NUMERIC(18, 3)	`0.768`
`bonus_target_percent`	NUMERIC(18, 3)	`0.3`	Fraction, NOT percent. 0.40 means 40%.
`bonus_target_amount`	NUMERIC(18, 2)	`104206.5`
`bonus_actual_percent`	NUMERIC(18, 4)	`0.3293`	Fraction, NOT percent. 0.40 means 40%.
`bonus_actual_amount`	NUMERIC(18, 2)	`114384.0`
`lti_target_percent`	NUMERIC(18, 2)	`0.0`	Fraction, NOT percent. 0.15 means 15%.
`lti_target_amount`	NUMERIC(18, 2)	`0.0`
`lti_actual_percent`	NUMERIC(18, 2)	`0.0`	Fraction, NOT percent. 0.15 means 15%.
`lti_actual_amount`	NUMERIC(18, 2)	`0.0`
`total_target_cash`	NUMERIC(18, 6)	`451561.5`
`total_target_compensation`	NUMERIC(18, 6)	`451561.5`
`total_actual_cash`	NUMERIC(18, 6)	`461739.0`
`total_actual_compensation`	NUMERIC(18, 6)	`461739.0`
`country_code`	TEXT	`USA`
`country`	TEXT	`United States`
`city`	TEXT	`Minneapolis`
`currency`	TEXT	`USD`
`manager_employee_id`	TEXT	`E02067064`	References employees.employee_id. NULL at the top of house.

event_history 50 columns

Column	Type	Example	Notes
`actual_return_date`	DATE	`2023-12-13`
`bonus_actual_percent`	NUMERIC(18, 4)	`0.0535`	Fraction, NOT percent. 0.40 means 40%.
`bonus_payout_amount`	NUMERIC(18, 2)	`4441.62`
`bonus_performance_rating`	TEXT	`4`
`bonus_target_percent`	NUMERIC(18, 3)	`0.05`	Fraction, NOT percent. 0.40 means 40%.
`change_reason`	TEXT	`promotion_performance`
`effective_date`	DATE	`2023-08-28`	Snapshot date. Identical on every row in the file.
`employee_id`	TEXT	`E42382491`
`event_id`	TEXT	`evt_9088786d8f09`
`event_type`	TEXT	`OFF_CYCLE_PROMOTION`
`expected_return_date`	DATE	`2024-01-19`
`grant_annual_value`	TEXT	blank
`grant_type`	TEXT	blank
`grant_value`	TEXT	blank
`leave_start_date`	DATE	`2023-10-13`
`leave_type`	TEXT	`medical`
`new_base_pay`	NUMERIC(18, 2)	`125267.0`
`new_compa_ratio`	NUMERIC(18, 4)	`0.8948`
`new_fte`	NUMERIC(18, 2)	`1.0`
`new_hierarchy_level`	TEXT	`L`
`new_job_code`	NUMERIC(18, 2)	`84537.0`
`new_job_title`	TEXT	`Solutions Architect`
`new_manager_id`	TEXT	`E91596771`
`new_market_index`	NUMERIC(18, 3)	`0.962`
`new_market_reference_point`	NUMERIC(18, 2)	`146000.0`
`new_pay_range_max`	NUMERIC(18, 2)	`182500.0`
`new_pay_range_mid`	NUMERIC(18, 2)	`140000.0`
`new_pay_range_min`	NUMERIC(18, 2)	`98000.0`
`new_performance_rating`	NUMERIC(18, 2)	`4.0`
`new_work_location_type`	TEXT	`ONSITE`
`new_work_state`	TEXT	`Colorado`
`pip_end_date`	DATE	`2023-10-09`
`pip_outcome`	TEXT	`in_progress`
`pip_start_date`	DATE	`2023-09-04`
`previous_base_pay`	NUMERIC(18, 2)	`108509.99`
`previous_compa_ratio`	NUMERIC(18, 4)	`0.9225`
`previous_fte`	NUMERIC(18, 2)	`0.4`
`previous_hierarchy_level`	TEXT	`IC`
`previous_job_code`	NUMERIC(18, 2)	`84537.0`
`previous_job_title`	TEXT	`Previous Solutions Architect`
`previous_manager_id`	TEXT	`E91596771`
`previous_market_index`	NUMERIC(18, 3)	`0.992`
`previous_market_reference_point`	NUMERIC(18, 2)	`122664.0`
`previous_pay_range_max`	NUMERIC(18, 2)	`153330.0`
`previous_pay_range_mid`	NUMERIC(18, 2)	`117623.0`
`previous_pay_range_min`	NUMERIC(18, 2)	`82336.0`
`previous_performance_rating`	NUMERIC(18, 2)	`4.0`
`previous_work_location_type`	TEXT	`ONSITE`
`previous_work_state`	TEXT	`California`
`vesting_years`	TEXT	blank

Industries

Each one maps to a real BLS staffing pattern, so the mix of occupations is what employers in that industry actually hire.

Code	Name	Covers
`agriculture`	Agriculture	Farming, ranching, logging, fishing operations
`energy-mining`	Energy & Mining	Oil/gas, mining, quarrying operations
`utilities`	Utilities	Electric, gas, water, sewage services
`construction`	Construction	Building construction, specialty trades
`manufacturing`	Manufacturing	Production of goods from raw materials
`wholesale`	Wholesale	Wholesale distribution of goods
`retail`	Retail	Consumer retail sales
`transportation`	Transportation & Logistics	Transportation, warehousing, logistics
`technology`	Technology	Software, SaaS, cloud computing, data services
`media`	Media & Publishing	Broadcasting, publishing, telecommunications
`financial-services`	Financial Services	Banking, investment, insurance
`real-estate`	Real Estate	Property management, rentals, leasing
`legal`	Legal Services	Law firms, legal services
`accounting`	Accounting & Tax	CPA firms, bookkeeping, payroll services
`consulting`	Consulting	Management, strategy, IT consulting
`engineering`	Engineering & Architecture	Architecture, engineering, design firms
`research`	Research & Development	Scientific research and development services
`professional-services`	Professional Services	Advertising, PR, other professional services
`holding-companies`	Holding Companies	Corporate headquarters, holding companies
`business-services`	Business Services	Staffing, security, facilities, waste management
`education`	Education	Schools, colleges, training providers
`healthcare`	Healthcare	Hospitals, clinics, medical practices, nursing facilities
`social-services`	Social Services & Nonprofits	Child care, community services, nonprofits
`entertainment`	Entertainment	Performing arts, sports, recreation
`hospitality`	Hospitality	Hotels, restaurants, food service
`other-services`	Other Services	Repair, personal services, religious, civic organizations
`government`	Government	Federal, state, local government

Job title modes

Simple	Familiar job titles only. The same real staffing pattern, filtered to occupations with an authored title.
Realistic	Every role a real employer has, including the long tail.

Merge format

Output shaped for the Merge unified HRIS model, emitting 160 columns across 15 categories. Filter to a single vendor and you get only the fields that vendor's real API populates, which is the difference between testing against the spec and testing against the integration you actually have.

Vendors 72

7shifts, ADP RUN, ADP Workforce Now, AlexisHR, AllianceHCM, Altera Payroll, BambooHR, Breathe, Cezanne HR, Charlie, ChartHop, ClayHR, CyberArk, Darwinbox, Dayforce, Deel, Employment Hero, Factorial, Freshteam, Google Workspace, Gusto, HR Cloud, HR Partner, Hailey HR, Hibob, Humaans, Humi, IRIS Cascade, ISolved, Insperity Premier, IntelliHR, JumpCloud, Justworks, Kallidus, Keka, Kenjo, Lano, Lucca, Microsoft Entra ID, Namely, Nmbrs, Officient, Okta, OneLogin, Oracle Cloud Human Capital Management (HCM), OysterHR, PayCaptain, PayFit, Paychex, Paycom, Paycor, Paylocity, PeopleHR, Personio, PingOne, Planday, Proliant, Remote, Rippling, SAP SuccessFactors, Sage HR, Sage People, Sesame, Square Payroll, TriNet, TriNet HR Platform, UKG Pro, UKG Pro Workforce Management, UKG Ready, Workday, Zelt, Zoho People

Countries

Localised names, addresses and payroll currency. race is United States only, because it is EEO compliance data and collecting it is unlawful in several of these countries.

Australia (AUD), Canada (CAD), China (CNY), Costa Rica (CRC), France (EUR), Germany (EUR), Greece (EUR), Hungary (HUF), India (INR), Ireland (EUR), Israel (ILS), Japan (JPY), Mexico (MXN), New Zealand (NZD), Philippines (PHP), Poland (PLN), Portugal (EUR), Romania (RON), Russia (RUB), Singapore (SGD), Spain (EUR), Sweden (SEK), Switzerland (CHF), United Kingdom (GBP), United States (USD)

Where the numbers come from

Realistic is what every competitor claims, so here is the falsifiable version: the published dataset behind each decision.

Source	What it decides
BLS Occupational Employment and Wage Statistics (OEWS)	Which occupations a company of this industry employs, and in what proportion. A hospital draws nurses at the rate hospitals actually employ them.
EEOC EEO-1 Job Patterns, 2018, by NAICS-3 sector	Race and sex composition, crossed with job category, per industry. Representation changes by level the way a real filer's report does.
BLS Current Population Survey, 2025 annual averages	Age distribution per level and per industry. Hospitality runs twelve years younger than real estate.
SSA national first names, 1935 to 2012	First name drawn from the birth year and sex, so a 58-year-old is not called Aiden.
US Census 2020 surnames by race and ethnicity	Surname drawn conditional on race, so first and last names are not independent draws.
BLS national and state occupational wage data	Salary grade, band midpoints and geography multipliers. A grade reflects what the role pays nationally, not its level.
29 CFR 541, Fair Labor Standards Act exemption tests	flsa_status, from salary, duties and state, including the California computer-software threshold.
NCES Digest 213.10 and Common Core of Data, 2021-22	How a school district is organised: staff per school, principals per school, district-office share.
AHA Fast Facts 2026 and SCCM critical care statistics	How a hospital is organised: beds per site, ICU share, nursing FTE per unit.