States Are Feeling the Economic Toll of Trump’s War on the Federal Government 71%

By Layla A. Jones86%

7/20/2026, 8:19:58 PM

BS Summary: This article contains 23 faulty reasoning types, including Negativity Bias, Ambiguity (Equivocation), and Biased Writer Voice, with Post Hoc (False Cause) as the most egregious example at 39.3% saturation with 261 hits. Analysis detected 2,048 faulty-reasoning hits from 664 analyzed words, generating a BS Score of 63.3% and a BS Rank of 71% (6,528 of 21,886 articles). This article is worse (more manipulative) than 70.20% of the article peer group.

A new report shows how President Donald Trump’s administration is harming state economies nationwide as several states that rely on federal government jobs and spending landed on a list of the top 10 worst economies in the nation. 
And the Trump administration’s sweeping cuts to the federal workforce are continuing to acutely impact the Washington, D.C., Maryland, Virginia region. 
Maryland has the second worst economy in America behind Rhode Island, coming in at 49th out of 50, according to an annual CNBC report measuring the best and worst states for business. 
It’s a big dropoff and the worst-ever showing for the state since CNBC began its tracking in 2007. 
The report uses federal government data sources from institutions like the Bureau of Economic Analysis and the Bureau of Labor Statistics, along with private company data, to track various measures of business friendliness. 
States are ranked for things like workforce, overall economy, cost of living, infrastructure and quality of life. 
Maryland was 25th in 2025, hovered between 30th and 31st place between 2022 and 2024, and saw a five-year economic ranking high of 20th place in 2021. 
“Economic growth and job growth nearly flatlined in Maryland over the past year,” the CNBC report wrote, adding that the state’s “deep connection with the federal government next door has a lot to do with that.” 
In total, Marylanders lost 29,200 federal jobs between January 2025 and April 2026, according to an analysis by Brookings, an independent, D.C.-based research institution. 
That’s an 18% decrease in federal employment in the state, and about 9% of the total 335,000 federal jobs lost between all 50 states under the Trump administration and its Department of Government Efficiency, or DOGE. 
The state’s unemployment rate spiked to 4.4% in May, up from 4% the same time last year, and from 3% in January 2025, according to data from the BLS and the state. 
And it’s not just jobs and the hit to workers’ direct income that’s tanking economies nationwide. 
Several of the top 10 worst state economies have budgets that are heavily reliant on federal government funding, meaning cuts to federal spending nationally have shown up at the state and local levels. 
Maryland has a disproportionately high share of federal workers, but a relatively average percentage of the state’s budget comes from federal spending. 
Other states on the list of top 10 worst economies owe far greater proportions of their state budgets to the federal government. 
Oklahoma has the 10th worst economy and relies on the feds for more than 40% of state spending, CNBC reported. 
Alaska has the seventh worst economy and owes 45% of its state spending to the federal government, while the state with the fourth worst economy, Louisiana, relies on the feds for more than 48% of its state spending. 
CNBC’s best states for business ranking uses economic rankings as one part of its overall rating of a state’s business environment. 
For that part, Maryland fared a bit better, coming in at 36th overall. 
Its southern neighbor, Virginia, has long been among the top best states for business and was named third best for 2026. 
The Trump effect is still being felt there, though. 
In 2025, Virginia fell out of the top three for the first time since 2018, CNBC reported. 
Its economy ranked 23rd this year, down from 14th the year before and 11th in 2024. 
The business news agency’s annual rankings join a mounting number of sources reflecting the cataclysmic, concentrated effect of Trump on the DMV economy. 
Last June, a survey of DMV-area realtors found that 40% reported having transactions affected by federal job cuts. 
In March, one year after Trump’s billionaire tech ally Elon Musk took the helm at DOGE, dismantled independent agencies, and enacted devastating, AI-driven cuts to fed jobs and government spending, realtors told TPM the middle class was being locked out of housing opportunities in the nearby D.C. area. 
Article reasoning-pattern comparisonThis article: 14.3%Layla A. Jones: 7.8%Talking Points Memo: 5.0%Confirmation Bias14.3%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 1.4%Anchoring Bias0.0%This article: 14.3%Layla A. Jones: 4.8%Talking Points Memo: 2.7%Availability Heuristic14.3%This article: 5.0%Layla A. Jones: 1.7%Talking Points Memo: 1.5%Representativeness Heuristic5.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.7%Hindsight Bias0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 1.4%Overconfidence Bias0.0%This article: 2.0%Layla A. Jones: 12.3%Talking Points Memo: 12.6%Framing Effect2.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.3%Loss Aversion0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.6%Status Quo Bias0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.1%Sunk Cost Effect0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 1.1%Optimism Bias0.0%This article: 5.6%Layla A. Jones: 1.9%Talking Points Memo: 2.6%Pessimism Bias5.6%This article: 32.1%Layla A. Jones: 20.2%Talking Points Memo: 12.7%Negativity Bias32.1%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.5%Self-Serving Bias0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 1.0%Fundamental Attribution Error0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.1%Actor-Observer Bias0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.9%In-Group Bias0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.7%Out-Group Homogeneity Bias0.0%This article: 5.1%Layla A. Jones: 1.7%Talking Points Memo: 1.1%Halo Effect5.1%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.7%Horn Effect0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.0%Dunning-Kruger Effect0.0%This article: 5.3%Layla A. Jones: 2.7%Talking Points Memo: 1.0%Recency Bias5.3%This article: 7.2%Layla A. Jones: 2.4%Talking Points Memo: 0.7%Primacy Effect7.2%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.1%Blind-Spot Bias0.0%This article: 0.0%Layla A. Jones: 2.4%Talking Points Memo: 3.3%Ad Hominem0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.3%Straw Man0.0%This article: 25.0%Layla A. Jones: 8.3%Talking Points Memo: 4.0%Appeal to Authority25.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 1.2%False Dilemma0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 1.1%Slippery Slope0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.2%Circular Reasoning0.0%This article: 19.3%Layla A. Jones: 8.4%Talking Points Memo: 5.6%Hasty Generalization19.3%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.1%Red Herring0.0%This article: 3.5%Layla A. Jones: 1.2%Talking Points Memo: 0.4%Bandwagon3.5%This article: 11.6%Layla A. Jones: 8.1%Talking Points Memo: 6.1%Appeal to Emotion11.6%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 1.7%Begging the Question0.0%This article: 39.3%Layla A. Jones: 31.8%Talking Points Memo: 4.0%Post Hoc (False Cause)39.3%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.2%Tu Quoque0.0%This article: 3.6%Layla A. Jones: 1.2%Talking Points Memo: 0.8%Burden of Proof3.6%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.1%Appeal to Nature0.0%This article: 5.0%Layla A. Jones: 1.7%Talking Points Memo: 0.5%Composition/Division5.0%This article: 9.9%Layla A. Jones: 5.1%Talking Points Memo: 2.0%Anecdotal9.9%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.0%No True Scotsman0.0%This article: 28.8%Layla A. Jones: 9.6%Talking Points Memo: 2.7%Ambiguity (Equivocation)28.8%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.1%Middle Ground0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.1%Personal Incredulity0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.1%Special Pleading0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 1.0%Genetic Fallacy0.0%This article: 12.7%Layla A. Jones: 4.2%Talking Points Memo: 1.3%Unattributed Quote12.7%This article: 7.2%Layla A. Jones: 4.8%Talking Points Memo: 2.3%Quote-first Misdirection7.2%This article: 25.5%Layla A. Jones: 22.9%Talking Points Memo: 14.1%Biased Writer Voice25.5%This article: 3.5%Layla A. Jones: 1.2%Talking Points Memo: 0.5%Indoctrination3.5%This article: 22.9%Layla A. Jones: 17.9%Talking Points Memo: 6.7%Politically Left Leaning Bias22.9%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.3%Politically Right Leaning Bias0.0%This article: 0.0%Layla A. Jones: 0.0%Talking Points Memo: 0.1%Attempt to Sell a Product or S…0.0%

664 words analyzed.

Speakers

1speaker11%attributed speech591writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 13 words • 100.0% coverageWriter's voice • 38 words • 100.0% coverageWriter's voice • 21 words • 100.0% coverageWriter's voice • 32 words • 0.0% coverageWriter's voice • 18 words • 100.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageCNBC • 36 words • 100.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 36 words • 100.0% coverageWriter's voice • 32 words • 0.0% coverageWriter's voice • 16 words • 100.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageCNBC • 20 words • 0.0% coverageWriter's voice • 38 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 9 words • 100.0% coverageCNBC • 17 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 23 words • 100.0% coverageWriter's voice • 18 words • 100.0% coverageWriter's voice • 48 words • 100.0% coverage
Selected voice

CNBC

100%flagged-word coverage
73 attributed words100% of attributed speech100% writer coverage
0%25.0%50.0%Unattributed Quote+41.2 ptsWriter: 8.1%CNBC: 49.3%49.3%Biased Writer Voice-28.6 ptsWriter: 28.6%CNBC: 0.0%0.0%Politically Left Leaning B-25.7 ptsWriter: 25.7%CNBC: 0.0%0.0%Quote-first Misdirection-8.1 ptsWriter: 8.1%CNBC: 0.0%0.0%Indoctrination-3.9 ptsWriter: 3.9%CNBC: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.