by David Stockman, Activist Post:
The following is Chapter One of David Stockman’s latest book, How To Cut $2 Trillion: A Blueprint From Ronald Reagan’s Budget Cutter To Musk, Ramaswamy And The DOGE Team. We encourage you to buy copies for your Senators and members of Congress and to share the Amazon link with as many influential voices as you can. The book provides detailed insights on the operations of Federal Agencies and how to streamline them.
Under our three savings bucket scheme, “Slashing the Fat” from the Federal payroll and bureaucracy would account for just $400 billion or 20% of DOGE’s $2 trillion per year savings target. Needless to say, however, even that small portion would be far easier said than done.
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That’s because, unlike the case of typical US businesses, where payroll costs can range from 15% to 40% of total costs, such expenses comprise only a tiny fraction of total Federal spending. Setting aside DOD payrolls for the “Downsize the Muscle” bucket, we estimate fully-loaded nondefense employee compensation costs at $215 billion in the target year of FY 2029. That’s just 3.1% of the $7 trillion of nondefense outlays projected under current policy by CBO for what would be the final Trump budget.
So there is a lot of wood to chop in other areas of nondefense spending, but we start with the assumption that $85 billion or 40% of nondefense payroll costs would be a fair component of a broader plan to generate the $400 billion of “Slash the Fat” savings. At the projected FY 2029 cost of $160,000 per Federal employee for payroll, benefits, and fringes, this would require termination of 535,000 positions from the current total of 1,343,000 nondefense employees.
On its face, this headcount reduction target is eminently plausible given that the Washington Swamp is a vast cesspool of padded payrolls, useless projects, endemic inefficiency, and misbegotten government enterprises. But what is especially telling is that our 40% payroll cut would amount to just half of the 80% staff reduction that Elon Musk achieved at the old Twitter. And he did so in the context of a labor-intensive business without missing a beat in terms of operations and customer accommodation at the new “X.”
So we begin the payroll savings analysis by bringing the hammer down terminally on the 16 worst and most unneeded Federal agencies, including the FBI, OSHA, the FTC, and the Department of Education. Eliminating these 16 bureaucracies entirely would reduce Federal employment by 71,000 jobs and save $11.1 billion per year of direct compensation costs. That’s nothing to sneeze at, of course, but to place it in budgetary context it does represent only 13 hours’ worth of the $8.0 trillion per year of total baseline Federal spending for the target budget year of FY 2029.
We also show that cutting 50% of the staff levels at another 9 dubious departments–including the EPA, NASA, and GSA–would shrink the Federal payroll by an additional 93,000. That would save a further $15 billion annually in compensation costs.
Still, we would need an additional $59 billion in nondefense savings to achieve the $85 billion target for direct compensation reductions. Accordingly, upwards of 371,000 positions would need to be eliminated from the balance of the nondefense agencies or about 34% of the 1,084,000 current jobs at everything from the Agriculture Department to the Social Security Administration and Veterans health care system.
In addition, we estimate that $85 billion in compensation cost savings would generate an additional $45 billion of indirect savings in the related costs for agency overhead, occupancy, supplies, and outside contractor services.
In summary, therefore, we’d propose that about one-third of the “Slash the Fat” savings target of $400 billion be obtained from the following areas inside the four walls of nondefense government. Chapter 6 will also outline $270 billion of savings from outside the walls of nondefense government in the form of cuts in corporate welfare, farmer subsidies, the Green New Deal, and other wasteful private sector subventions.
