KUOW73%

‘Seattle's lost its economic mojo.’ Downtown Seattle Association report claims taxes are driving out jobs 76%

By Monica Nickelsburg0%

6/17/2026, 10:14:52 PM

BS Summary: This article contains 34 faulty reasoning types, including Negativity Bias, Confirmation Bias, and Self-Serving Bias, with Appeal to Emotion as the most egregious example at 21.4% saturation with 95 hits. Analysis detected 1,323 faulty-reasoning hits from 444 analyzed words, generating a BS Score of 67.5% and a BS Rank of 76% (5,371 of 21,886 articles). This article is worse (more manipulative) than 75.50% of the article peer group.

Seattle is losing jobs to Bellevue. 
That’s the assertion in a new report from the Downtown Seattle Association, an advocacy group focused on economic development. 
DSA estimates that between 2023-2025, Seattle lost 1.3% of its jobs, while Bellevue’s labor pool increased 12.6%. 
 My top takeaway is Seattle's lost its economic mojo,” DSA President and CEO Jon Scholes said. 
“It's been five years since the city imposed a significant set of new business taxes, and in that time, we've lost about 30,000 jobs in downtown Seattle. 
Across the lake in Bellevue, a city dealing with similar issues coming out of the pandemic, whether it's hybrid work or shifts in the tech economy, well, they've grown jobs over that same period.” 
Those numbers come from the Puget Sound Regional Council and Placer.ai. 
DSA also estimates that Seattle’s downtown office vacancy rate increased to 32% from 6.7% in 2019. 
Bellevue’s office vacancy rate has also risen from 2.5% to 24% over that time. 
New taxes in Seattle were not the only economic variable during the period the report covers. 
Lasting impacts of the pandemic, widespread tech layoffs, and stubborn inflation have also created economic uncertainty for businesses. 
 Those are variables that Bellevue's dealing with as well, and they've attracted an incredible amount of job growth in the last five years from Amazon, from companies out of the Bay Area that have considered a second or a Pacific Northwest.” 
Scholes said. 
“And a majority of those companies, a vast majority, have chosen the east side of the lake, not the west side.” 
The report calls out Seattle’s JumpStart payroll tax as a particular pain point for employers. 
Passed in 2020 to fund affordable housing and pandemic relief, JumpStart taxes the salaries of high earners at the city’s largest companies. 
King County Councilmember Teresa Mosqueda helped architect JumpStart when she sat on Seattle City Council. 
She called the DSA report “revisionist history” that ignores the fallout of Covid. 
 I think it's a lazy talking point to try to point fingers when truly this was the source that kept Seattle's budget in the black for the first four years of the pandemic,” she said. 
Mosqueda also noted that JumpStart is now the largest source of funding for permanent supportive housing, making Seattle a leader in the region. 
 Let's get away from this lazy politics of pitting one city against another, and recognize that we, in this region, are all better when we invest in housing, in workforce housing, and in permanent supportive housing to keep people off the street,” Mosqueda said. 
Article reasoning-pattern comparisonThis article: 19.1%Monica Nickelsburg: 2.6%KUOW: 2.6%Confirmation Bias19.1%This article: 3.4%Monica Nickelsburg: 1.7%KUOW: 1.3%Anchoring Bias3.4%This article: 7.0%Monica Nickelsburg: 4.1%KUOW: 3.4%Availability Heuristic7.0%This article: 7.7%Monica Nickelsburg: 1.0%KUOW: 1.2%Representativeness Heuristic7.7%This article: 0.0%Monica Nickelsburg: 0.4%KUOW: 0.7%Hindsight Bias0.0%This article: 4.7%Monica Nickelsburg: 1.7%KUOW: 1.4%Overconfidence Bias4.7%This article: 3.4%Monica Nickelsburg: 12.0%KUOW: 7.4%Framing Effect3.4%This article: 0.0%Monica Nickelsburg: 0.7%KUOW: 1.0%Loss Aversion0.0%This article: 3.4%Monica Nickelsburg: 0.9%KUOW: 1.0%Status Quo Bias3.4%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.2%Sunk Cost Effect0.0%This article: 16.9%Monica Nickelsburg: 4.1%KUOW: 3.8%Optimism Bias16.9%This article: 0.0%Monica Nickelsburg: 1.8%KUOW: 1.8%Pessimism Bias0.0%This article: 20.9%Monica Nickelsburg: 9.9%KUOW: 8.0%Negativity Bias20.9%This article: 18.2%Monica Nickelsburg: 3.3%KUOW: 2.0%Self-Serving Bias18.2%This article: 8.1%Monica Nickelsburg: 1.1%KUOW: 0.9%Fundamental Attribution Error8.1%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.2%Actor-Observer Bias0.0%This article: 14.4%Monica Nickelsburg: 2.0%KUOW: 2.0%In-Group Bias14.4%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.5%Out-Group Homogeneity Bias0.0%This article: 14.6%Monica Nickelsburg: 1.7%KUOW: 2.7%Halo Effect14.6%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.2%Horn Effect0.0%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.0%Dunning-Kruger Effect0.0%This article: 9.5%Monica Nickelsburg: 0.9%KUOW: 1.1%Recency Bias9.5%This article: 0.0%Monica Nickelsburg: 0.5%KUOW: 0.4%Primacy Effect0.0%This article: 3.6%Monica Nickelsburg: 0.1%KUOW: 0.0%Blind-Spot Bias3.6%This article: 8.1%Monica Nickelsburg: 1.2%KUOW: 0.5%Ad Hominem8.1%This article: 2.9%Monica Nickelsburg: 0.2%KUOW: 0.3%Straw Man2.9%This article: 16.2%Monica Nickelsburg: 5.4%KUOW: 4.3%Appeal to Authority16.2%This article: 17.8%Monica Nickelsburg: 2.3%KUOW: 1.4%False Dilemma17.8%This article: 0.0%Monica Nickelsburg: 0.7%KUOW: 0.9%Slippery Slope0.0%This article: 0.0%Monica Nickelsburg: 0.5%KUOW: 0.1%Circular Reasoning0.0%This article: 12.2%Monica Nickelsburg: 4.5%KUOW: 4.1%Hasty Generalization12.2%This article: 0.0%Monica Nickelsburg: 0.3%KUOW: 0.3%Red Herring0.0%This article: 0.0%Monica Nickelsburg: 0.9%KUOW: 0.8%Bandwagon0.0%This article: 21.4%Monica Nickelsburg: 4.9%KUOW: 6.1%Appeal to Emotion21.4%This article: 3.8%Monica Nickelsburg: 0.7%KUOW: 0.8%Begging the Question3.8%This article: 9.7%Monica Nickelsburg: 4.4%KUOW: 2.2%Post Hoc (False Cause)9.7%This article: 8.1%Monica Nickelsburg: 0.3%KUOW: 0.1%Tu Quoque8.1%This article: 0.0%Monica Nickelsburg: 0.2%KUOW: 0.3%Burden of Proof0.0%This article: 5.2%Monica Nickelsburg: 0.1%KUOW: 0.2%Appeal to Nature5.2%This article: 5.2%Monica Nickelsburg: 0.4%KUOW: 0.2%Composition/Division5.2%This article: 0.0%Monica Nickelsburg: 3.6%KUOW: 3.3%Anecdotal0.0%This article: 0.0%Monica Nickelsburg: 0.2%KUOW: 0.1%No True Scotsman0.0%This article: 4.7%Monica Nickelsburg: 2.4%KUOW: 1.4%Ambiguity (Equivocation)4.7%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.0%Gambler’s Fallacy0.0%This article: 3.2%Monica Nickelsburg: 0.2%KUOW: 0.1%Middle Ground3.2%This article: 0.0%Monica Nickelsburg: 0.1%KUOW: 0.1%Personal Incredulity0.0%This article: 3.4%Monica Nickelsburg: 0.3%KUOW: 0.2%Special Pleading3.4%This article: 5.0%Monica Nickelsburg: 0.1%KUOW: 0.2%Genetic Fallacy5.0%This article: 3.8%Monica Nickelsburg: 1.8%KUOW: 1.0%Unattributed Quote3.8%This article: 1.1%Monica Nickelsburg: 1.1%KUOW: 0.8%Quote-first Misdirection1.1%This article: 1.1%Monica Nickelsburg: 3.3%KUOW: 3.2%Biased Writer Voice1.1%This article: 10.1%Monica Nickelsburg: 0.6%KUOW: 1.5%Indoctrination10.1%This article: 0.0%Monica Nickelsburg: 1.8%KUOW: 1.1%Politically Left Leaning Bias0.0%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.2%Politically Right Leaning Bias0.0%This article: 0.0%Monica Nickelsburg: 0.9%KUOW: 1.3%Attempt to Sell a Product or S…0.0%

444 words analyzed.

Speakers

3speakers73%attributed speech121writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 5 words • 100.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageDowntown Seattle Association • 17 words • 100.0% coverageJon Scholes • 17 words • 0.0% coverageJon Scholes • 27 words • 0.0% coverageJon Scholes • 34 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageDowntown Seattle Association • 16 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageJon Scholes • 42 words • 0.0% coverageJon Scholes • 2 words • 0.0% coverageJon Scholes • 21 words • 0.0% coverageDowntown Seattle Association • 15 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageTeresa Mosqueda • 15 words • 0.0% coverageTeresa Mosqueda • 13 words • 0.0% coverageTeresa Mosqueda • 36 words • 0.0% coverageTeresa Mosqueda • 23 words • 0.0% coverageTeresa Mosqueda • 45 words • 100.0% coverage
Selected voice

Teresa Mosqueda

100%flagged-word coverage
132 attributed words41% of attributed speech100% writer coverage
0%17.5%35.0%Indoctrination+34.1 ptsWriter: 0.0%Teresa Mosqueda: 34.1%34.1%Quote-first Misdirection-4.1 ptsWriter: 4.1%Teresa Mosqueda: 0.0%0.0%Biased Writer Voice-4.1 ptsWriter: 4.1%Teresa Mosqueda: 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.