Reduce the size of the federal workforce to save billions of dollars.

And so if you reduce the size of those workforces, that saves billions and billions of dollars that you can use to put more money in the pockets of families and get the economy moving again.

Scalise proposed reducing federal agency workforces as a way to save billions and redirect money toward families and economic growth.

Scalise Discusses Trump, House GOP Economic Agenda on Squawk Box | Majority Leader
secondary · other · model gpt-5.5

Series CES9091000001 lists federal employment at 2,757 thousand in May 2008 and 2,870 thousand in December 2010; later it lists 3,010 thousand in January 2025 and 2,677 thousand in May 2026.

The federal workforce did not shrink during Scalise's first House term; it grew. A large later-term reduction occurred between January 2025 and May 2026.

partial later_term

FRED / U.S. Bureau of Labor Statistics: All Employees, Federal
secondary · model gpt-5.5 · confidence 93%

The memorandum froze federal civilian hiring and directed OMB, OPM, and USDS to submit a plan to reduce the size of the Federal Government's workforce through efficiency improvements and attrition.

The Trump administration took concrete executive action in 2025 to reduce the federal workforce, but this was an executive action in a later term rather than a Scalise-authored law.

partial later_term

Federal Register: Hiring Freeze, Presidential Memorandum of January 20, 2025
secondary · model gpt-5.5 · confidence 90%

EO 14210 required agencies to hire no more than one employee for every four employees that depart and directed agency heads to prepare large-scale reductions in force, subject to exclusions.

This official order directly advanced workforce reduction through attrition and RIF planning, supporting partial later-term fulfillment of the workforce-size component.

partial later_term

Federal Register: Executive Order 14210, DOGE Workforce Optimization Initiative
secondary · model gpt-5.5 · confidence 91%

The House passed H. Con. Res. 14, establishing budget levels for FY2025-FY2034, by 217-215; the roll call records Steve Scalise, Republican of Louisiana, voting Yea.

Scalise supported the 2025 House Republican budget framework tied to spending cuts, showing legislative effort, though this vote itself did not enact workforce reductions or prove savings.

partial later_term A for effort

Office of the Clerk, U.S. House: Roll Call 50, H. Con. Res. 14
secondary · model gpt-5.5 · confidence 84%

DOGE reported estimated savings of $215B from asset sales, contract and lease cancellations, fraud and improper payment deletion, grant cancellations, programmatic changes, regulatory savings, and workforce reductions.

The executive branch claimed savings well above billions and included workforce reductions among the sources, but the page is a self-reported estimate and not an independently audited budget outcome.

partial later_term

DOGE: Department of Government Efficiency Savings Page
secondary · model gpt-5.5 · confidence 62%

POLITICO reported that DOGE claimed $54.2 billion in contract-cancellation savings but found verifiable savings of about $1.4 billion, with many claims based on ceiling values rather than actual spending.

This undermines a full-delivered finding for the savings portion: workforce reductions occurred, but the claimed billions in savings were disputed and not clearly proven.

partial later_term

POLITICO: DOGE-flation: DOGE's actual savings are a fraction of what it claims
secondary · model gpt-5.5 · confidence 78%

POLITICO reported that House Republicans were generally giving the administration leeway to reduce the federal workforce, while Scalise said many departments had large COVID-era increases, some of which were not justified.

This shows Scalise publicly defending the rationale for workforce reductions in 2025, while also noting concern about preserving veterans' services. It supports effort, not full delivery.

partial later_term A for effort

POLITICO: Veterans cuts spark GOP backlash on Capitol Hill
secondary · model gpt-5.5 · confidence 74%