In 2023, the population of the Seattle-Tacoma-Bellevue metropolitan area in the United States was about 4.04 million people. This was a slight decrease from the previous year, when the population was about 4.03 million.

Graph and download economic data for Resident Population in Seattle-Tacoma-Bellevue, WA (MSA) (STWPOP) from 2000 to 2024 about Seattle, WA, residents, population, and USA.

Historical dataset of population level and growth rate for the Seattle metro area from 1950 to 2025.

In 2023, the GDP of the Seatle-Tacoma-Bellevue metro area amounted to ****** billion U.S. dollars, an increase from the previous year. The GDP of the United States since 1990 can be accessed here. Seattle metro area The Seattle metropolitan area in the U.S. state of Washington includes the city of Seattle, King County, Snohomish County, and Pierce County within the Puget Sound region. About **** million people were living in the Seattle metro area, which is more than half of Washington's total population in 2021 (about **** million people). This makes the Seattle metro area the **** largest metropolitan area in the United States, by population. However, Seattle is in fourth place among the 20 largest metro areas in terms of household income, which stood at ****** U.S. dollars in 2019. This is by far more than the average household income in the United States. Household income in Washington is on a similar high level. In 2021, the federal state of Washington was ranked **** in terms of household income among the states of the U.S. The city of Seattle is the largest city in the Pacific Northwest region of North America. It has about ******* residents and is among the ** largest cities in the United States. Seattle has always been an important coastal seaport city and a gateway to Alaska. The importance of the city and metro area is also due to fact that some of the biggest companies worldwide started in Seattle during the 1980s. Companies like Amazon and Microsoft are still based in the Seattle area in the state of Washington.

Graph and download economic data for Employed Persons in Seattle-Tacoma-Bellevue, WA (MSA) (LAUMT534266000000005) from Jan 1994 to Jul 2025 about Seattle, WA, persons, household survey, employment, and USA.

Graph and download economic data for Employed Persons in Seattle-Tacoma-Bellevue, WA (MSA) (LAUMT534266000000005A) from 1994 to 2024 about Seattle, WA, household survey, persons, employment, and USA.

A dataset listing Washington cities by population for 2024.

Table from the American Community Survey (ACS) 5-year series on income and earning related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B19025 Aggregate Household Income, B19013 Median Household Income, B19001 Household Income, B19113 Median Family Household Income, B19101 Family Household Income, B19202 Median Nonfamily Household Income, B19201 Nonfamily Household Income, B19301 Per Capita Income/B19313 Aggregate Income/B01001 Sex by Age, C24010 Sex by Occupation of the Civilian Employed Population 16 years and Over, B20017 Median Earnings by Sex by Work Experience for the Population 16 years and over with Earnings, B20001 Sex by Earnings for the Population 16 years and over with Earnings. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B19013, B19001, B19113, B19101, B19202, B19201, B19301, B19313, B01001, C24010, B20017, B20001, B19025Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

About this dataset

Context

The dataset presents the median household income across different racial categories in Seattle. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Seattle population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 61.84% of the total residents in Seattle. Notably, the median household income for White households is $130,622. Interestingly, despite the White population being the most populous, it is worth noting that Asian households actually reports the highest median household income, with a median income of $133,340. This reveals that, while Whites may be the most numerous in Seattle, Asian households experience greater economic prosperity in terms of median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Seattle.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Seattle median household income by race. You can refer the same here

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Including Some Other Race (5-year estimate) in King County, WA (B03002010E053033) from 2009 to 2023 about King County, WA; Seattle; non-hispanic; WA; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Unemployment Rate in Seattle-Tacoma-Bellevue, WA (MSA) (LAUMT534266000000003A) from 1994 to 2024 about Seattle, WA, household survey, unemployment, rate, and USA.

Graph and download economic data for Civilian Labor Force in Seattle-Tacoma-Bellevue, WA (MSA) (LAUMT534266000000006A) from 1994 to 2024 about Seattle, WA, civilian, labor force, labor, household survey, and USA.

The greater Seattle Coronavirus Assessment Network (SCAN) study is a response to the novel coronavirus outbreak (COVID-19). Since March 23rd, 2020, SCAN has worked in collaboration with Public Health Seattle & King County to deliver and collect at-home COVID-19 tests. The SCAN study is focused on testing people who are experiencing symptoms of COVID-19, and is working to increase testing in underrepresented communities and populations.

The SCAN dashboard provides geographic and demographic information from King County about who is ordering a test kit (individuals, contacts and groups) and may differ from the testing data which includes all final results (positive, negative and inconclusive). Reported positives and positivity rate are a combination of general SCAN enrollment and contact testing results, and are not representative of overall population frequency. There was a pause in testing from May 13th through June 9th, during which time SCAN worked with the FDA to update procedures and certifications.

Data is updated daily, subject to change and may vary across other technical reports due to the specific analyses being performed.

Graph and download economic data for Unemployed Persons in Seattle-Tacoma-Bellevue, WA (MSA) (LAUMT534266000000004) from Jan 1994 to Jun 2025 about Seattle, WA, household survey, unemployment, persons, and USA.

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Including Some Other Race (5-year estimate) in Pierce County, WA (B03002010E053053) from 2009 to 2023 about Pierce County, WA; Seattle; WA; non-hispanic; estimate; persons; 5-year; population; and USA.

Design Review Equity Areas are areas of Seattle where applicants for development projects going through the City’s Design Review program are required to work with staff from the Department of Neighborhoods (DON) to customize their community outreach plan to the needs of historically underrepresented communities. Equity Areas are identified based on local demographic and socioeconomic characteristics from the US Census Bureau. Equity Areas are census tracts having a census-tract average greater than the city-as-a-whole average for at least two of the following characteristics: 1. Limited English proficiency, identified as percentage of households that are linguistically isolated households. 2. People of Color, identified as percentage of the population that is not non-Hispanic white; and 3. Income, identified as percentage of population with income below 200% of the federal poverty level. For more information please see Director’s Rule for Early Community Outreach for Design Review. Additional resources and FAQs are available on DON’s Early Community Outreach webpage. Data Source: US Census Bureau’s American Community Survey 2016 Five-Year Estimates. This map will be evaluated and updated every three years.<span style='font-size:11.0pt;line-height:107%;font-family:"Calibri",sans-serif; mso-ascii

Background Rural U.S. communities are at risk from COVID-19 due to advanced age and limited access to acute care. Recognizing this, the Vashon Medical Reserve Corps (VMRC) in King County, Washington, implemented an all-volunteer, community-based COVID-19 response program. This program integrated public engagement, SARS-CoV-2 testing, contact tracing, vaccination, and material community support, and was associated with the lowest cumulative COVID-19 case rate in King County. This study aimed to investigate the contributions of demographics, geography and public health interventions to Vashon’s low COVID-19 rates. Methods This observational cross-sectional study compares cumulative COVID-19 rates and success of public health interventions from February 2020 through November 2021 for Vashon Island with King County (including metropolitan Seattle) and Whidbey Island, located ~50 km north of Vashon. To evaluate the role of demography, we developed multiple linear regression models of COVID-19 rates using metrics of age, race/ethnicity, wealth and educational attainment across 77 King County zip codes. To investigate the role of remote geography we expanded the regression models to include North, Central and South Whidbey, similarly remote island communities with varying demographic features. To evaluate the effectiveness of VMRC’s community-based public health measures, we directly compared Vashon’s success of vaccination and contact tracing with that of King County and South Whidbey, the Whidbey community most similar to Vashon. Results Vashon’s cumulative COVID-19 case rate was 29% that of King County overall (22.2 vs 76.8 cases/K). A multiple linear regression model based on King County demographics found educational attainment to be a major correlate of COVID-19 rates, and Vashon’s cumulative case rate was just 38% of predicted (p<.05), so demographics alone do not explain Vashon’s low COVID-19 case rate. Inclusion of Whidbey communities in the model identified a major effect of remote geography (-49 cases/K, p<.001), such that observed COVID-19 rates for all remote communities fell within the model’s 95% prediction interval. VMRC’s vaccination effort was highly effective, reaching a vaccination rate of 1500 doses/K four months before South Whidbey and King County and maintaining a cumulative vaccination rate 200 doses/K higher throughout the latter half of 2021 (p<.001). Including vaccination rates in the model reduced the effect of remote geography to -41 cases/K (p<.001). VMRC case investigation was also highly effective, interviewing 96% of referred cases in an average of 1.7 days compared with 69% in 3.7 days for Washington Department of Health investigating South Whidbey cases and 80% in 3.4 days for Public Health–Seattle & King County (both p<0.001). VMRC’s public health interventions were associated with a 30% lower case rate (p<0.001) and 55% lower hospitalization rate (p=0.056) than South Whidbey. Conclusion While the overall magnitude of the pre-Omicron COVID-19 pandemic in rural and urban U.S. communities was similar, we show that island communities in the Puget Sound region were substantially protected from COVID-19 by their geography. We further show that a volunteer community-based COVID-19 response program was highly effective in the Vashon community, augmenting the protective effect of geography. We suggest that Medical Reserve Corps should be an important element of future pandemic planning. Methods The study period extended from the pandemic onset in February 2020 through November 2021. Daily COVID-19 cases, hospitalizations, deaths and test numbers for King County as a whole and by zip code were downloaded from the King County COVID-19 dashboard (Feb 22, 2022 update). Population data for King County and Vashon are from the April 2020 US Census. Zip code level population data are the average of two zip code tabulation area estimates from the WA Office of Financial Management and Cubit (a commercial data vendor providing access to US Census information). The Asset Limited, Income Constrained, and Employed (ALICE) metric, a measure of the working poor, was obtained from United Way.

In 2022, San Francisco had the highest median household income of cities ranking within the top 25 in terms of population, with a median household income in of 136,692 U.S. dollars. In that year, San Jose in California was ranked second, and Seattle, Washington third.

Following a fall after the great recession, median household income in the United States has been increasing in recent years. As of 2022, median household income by state was highest in Maryland, Washington, D.C., Utah, and Massachusetts. It was lowest in Mississippi, West Virginia, and Arkansas. Families with an annual income of 25,000 and 49,999 U.S. dollars made up the largest income bracket in America, with about 25.26 million households.

Data on median household income can be compared to statistics on personal income in the U.S. released by the Bureau of Economic Analysis. Personal income rose to around 21.8 trillion U.S. dollars in 2022, the highest value recorded. Personal income is a measure of the total income received by persons from all sources, while median household income is “the amount with divides the income distribution into two equal groups,” according to the U.S. Census Bureau. Half of the population in question lives above median income and half lives below. Though total personal income has increased in recent years, this wealth is not distributed throughout the population. In practical terms, income of most households has decreased. One additional statistic illustrates this disparity: for the lowest quintile of workers, mean household income has remained more or less steady for the past decade at about 13 to 16 thousand constant U.S. dollars annually. Meanwhile, income for the top five percent of workers has actually risen from about 285,000 U.S. dollars in 1990 to about 499,900 U.S. dollars in 2020.

Demographics of King County, Vashon Island, and Island County communities.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Seattle. The dataset can be utilized to gain insights into gender-based income distribution within the Seattle population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Seattle, among individuals aged 15 years and older with income, there were 312.52 thousand men and 289.09 thousand women in the workforce. Among them, 189,091 men were engaged in full-time, year-round employment, while 140,215 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 2.74% fell within the income range of under $24,999, while 3.56% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 57.49% of men in full-time roles earned incomes exceeding $100,000, while 45.01% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Seattle median household income by race. You can refer the same here