Verifying that a member met an 80-hour monthly activity threshold sounds simple until you look at the data. Wages arrive quarterly, gig income may never touch a state wage file, and a person working 30 hours a week at variable shifts produces numbers that do not map cleanly to a monthly hour count. The quality of a state's data matching determines how many members get cleared automatically versus dumped into manual reporting, where most coverage loss happens.
The core data sources
Several feeds do the heavy lifting. State Unemployment Insurance wage records show quarterly earnings by employer. The National Directory of New Hires captures recent employment faster than quarterly files. SNAP and TANF systems can confirm participation in qualifying activities or signal exemptions. Vocational rehabilitation, workforce-program enrollment, and education-system records can document training or schooling hours. Each source answers a different slice of the question.
The trick is converting earnings into hours. A common approach divides reported wages by an assumed wage rate to estimate hours worked, often using the state minimum wage as a conservative floor so the system does not undercount low earners. A member earning the equivalent of well above 80 hours at minimum wage in a month can be cleared without ever submitting a pay stub.
Where matching breaks down
Three failure modes recur. First, timing lag: quarterly wage data can be months behind, so a person working now may not show recent hours, creating false negatives. Second, invisible income: self-employment, cash work, and many gig platforms do not report to state wage systems at all. Third, identity mismatches, where a name change, transposed digits in a Social Security number, or an employer reporting error breaks the link between a member and their own earnings.
Each false negative is a person who actually qualifies but gets a request to prove it manually. Arkansas in 2018 showed how quickly that friction compounds: about 18,000 people, roughly one in four subject to the rules, lost coverage largely because the reporting process failed them, not because they were ineligible.
Building a resilient pipeline
Strong implementations layer sources rather than relying on one, so a gap in wage data can be filled by new-hire or program data. They use look-back windows that account for reporting lag instead of penalizing recent workers. They flag and route identity-mismatch cases for staff review instead of silently failing them. And critically, they pair every automated clearance with proactive notice to the smaller group that genuinely needs to self-report, so outreach effort lands where it matters most ahead of the January 1, 2027 enforcement date.