About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in South San Francisco, CA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 30,039, while the mean income for the highest quintile (20% of households with the highest income) is 422,057. This indicates that the top earners earn 14 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 723,500, which is 171.42% higher compared to the highest quintile, and 2408.54% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 South San Francisco median household income. You can refer the same here

San Francisco County/city, CA - Estimate of Median Household Income for San Francisco County/City, CA was 125456.00000 $ in January of 2023, according to the United States Federal Reserve. Historically, San Francisco County/city, CA - Estimate of Median Household Income for San Francisco County/City, CA reached a record high of 135366.00000 in January of 2022 and a record low of 30166.00000 in January of 1989. Trading Economics provides the current actual value, an historical data chart and related indicators for San Francisco County/city, CA - Estimate of Median Household Income for San Francisco County/City, CA - last updated from the United States Federal Reserve on July of 2025.

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.

Per Capita Personal Income in San Francisco County/city, CA was 164807.00000 $ in January of 2023, according to the United States Federal Reserve. Historically, Per Capita Personal Income in San Francisco County/city, CA reached a record high of 164807.00000 in January of 2023 and a record low of 5926.00000 in January of 1969. Trading Economics provides the current actual value, an historical data chart and related indicators for Per Capita Personal Income in San Francisco County/city, CA - last updated from the United States Federal Reserve on July of 2025.

Personal Income in San Francisco County/city, CA was 133327237.00000 Thous. of $ in January of 2023, according to the United States Federal Reserve. Historically, Personal Income in San Francisco County/city, CA reached a record high of 133327237.00000 in January of 2023 and a record low of 4303674.00000 in January of 1969. Trading Economics provides the current actual value, an historical data chart and related indicators for Personal Income in San Francisco County/city, CA - last updated from the United States Federal Reserve on July of 2025.

In 2021, the per capita income in San Francisco city was at 80,383 U.S. dollars. San Francisco was followed in this regard by Seattle and Washington, D.C. The most populated cities in the U.S. are ranked by per capita income in this statistic. While New York, New York had the highest population, San Francisco had the highest per capita income in 2021. The median household income in San Francisco in 2020 was 119,136 dollars, the highest among the most populated cities in the United States.

San Francisco County/city, CA - Income Inequality in San Francisco County, CA was 28.39933 Ratio in January of 2023, according to the United States Federal Reserve. Historically, San Francisco County/city, CA - Income Inequality in San Francisco County, CA reached a record high of 28.39933 in January of 2023 and a record low of 22.62209 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for San Francisco County/city, CA - Income Inequality in San Francisco County, CA - last updated from the United States Federal Reserve on June of 2025.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in San Francisco Township, Minnesota, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 27,873, while the mean income for the highest quintile (20% of households with the highest income) is 388,805. This indicates that the top earners earn 14 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 743,293, which is 191.17% higher compared to the highest quintile, and 2666.71% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 San Francisco township median household income. You can refer the same here

San Francisco-Oakland-Berkeley, CA - Real Personal Income for San Francisco-Oakland-Hayward, CA (MSA) was 419973150.00000 Mil. of Chn. 2009 $ in January of 2023, according to the United States Federal Reserve. Historically, San Francisco-Oakland-Berkeley, CA - Real Personal Income for San Francisco-Oakland-Hayward, CA (MSA) reached a record high of 439103105.00000 in January of 2021 and a record low of 224826.90000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for San Francisco-Oakland-Berkeley, CA - Real Personal Income for San Francisco-Oakland-Hayward, CA (MSA) - last updated from the United States Federal Reserve on July of 2025.

In 20212, the San Jose-Sunnyvale-Santa Clara metro area in California had the highest per capita income at 64,169 U.S. dollars. The second highest, San Francisco-Oakland-Berkeley metro area is also located in California.

Explore the progression of average salaries for graduates in High School (Also Completed Intensive English At City College San Francisco) from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of High School (Also Completed Intensive English At City College San Francisco) relative to other fields. This data is essential for students assessing the return on investment of their education in High School (Also Completed Intensive English At City College San Francisco), providing a clear picture of financial prospects post-graduation.

Net Income for Commercial Banks Geographically Located in Federal Reserve District 12: San Francisco (DISCONTINUED) was 8531502.00000 Thous. of $ in July of 2020, according to the United States Federal Reserve. Historically, Net Income for Commercial Banks Geographically Located in Federal Reserve District 12: San Francisco (DISCONTINUED) reached a record high of 23439843.00000 in October of 2018 and a record low of -8454748.00000 in October of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for Net Income for Commercial Banks Geographically Located in Federal Reserve District 12: San Francisco (DISCONTINUED) - last updated from the United States Federal Reserve on June of 2025.

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

Background: Sugar-sweetened beverage (SSB) taxes are a promising strategy to decrease SSB consumption, and their inequitable health impacts, while raising revenue to meet social objectives. In 2016, San Francisco passed a one cent per ounce tax on SSBs. This study compared SSB consumption in San Francisco to that in San José, before and after tax implementation in 2018. Methods & findings: A longitudinal panel of adults (n = 1,443) was surveyed from zip codes in San Francisco and San José, CA with higher densities of Black and Latino residents, racial/ethnic groups with higher SSB consumption in California. SSB consumption was measured at baseline (11/17–1/18), one (11/18–1/19), and two years (11/19-1/20) after the SSB tax was implemented in January 2018. Average daily SSB consumption (in ounces) was ascertained using the BevQ-15 instrument and modeled as both continuous and binary (high consumption: ≥6 oz (178 ml) versus low consumption: <6 oz) daily beverage intake measures. Weighted generalized linear models (GLMs) estimated difference-in-differences of SSB consumption between cities by including variables for year, city, and their interaction, adjusting for demographics and sampling source. In San Francisco, average SSB consumption in the sample declined by 34.1% (-3.68 oz, p = 0.004) from baseline to 2 years post-tax, versus San José which declined 16.5% by 2 years post-tax (-1.29 oz, p = 0.157), a non-significant difference-in-differences (-17.6%, adjusted AMR = 0.79, p = 0.224). The probability of high SSB intake in San Francisco declined significantly more than in San José from baseline to 2-years post-tax (AOR[interaction] = 0.49, p = 0.031). The difference-in-differences of odds of high consumption, examining the interaction between cities, time and poverty, was far greater (AOR[city*year 2*federal poverty level] = 0.12, p = 0.010) among those living below 200% of the federal poverty level 2-years post-tax. Conclusions: Average SSB intake declined significantly in San Francisco post-tax, but the difference in differences between cities over time did not vary significantly. Likelihood of high SSB intake declined significantly more in San Francisco by year 2 and more so among low-income respondents. Methods The three waves of the study utilized a “push-to-web” data collection method, in which sampled households were sent an invitation via mail, text, and/or E-mail to complete an online Web questionnaire. Additional web completes were collected using a non-probability web panel. Questionnaires were completed in English, Spanish, or Chinese. Data from each wave were appended together--each row/observation is unique to participant ID and wave. Variables for the study were constructed using Stata. More details on methodology can be found in SSB_Sampling_and_Data_Collection_Methodology.pdf and SSB_Analytic_Sample_Creation_Flowchart.pdf.

The Environmental Justice Communities Map (“EJ Communities Map”) describes areas of San Francisco that have higher pollution and are predominately low-income. This map is based on CalEnviroScreen, a tool created by CalEPA & OEHHA that maps California communities that are most affected by pollution and other health risks. This EJ Communities Map includes additional local data on pollution and demographics, and was refined during the community engagement process based on public feedback. “EJ Communities” are defined as the areas facing the top one-third of cumulative environmental and socioeconomic burdens across the City. The EJ Communities include areas of Bayview Hunters Point, Chinatown, Excelsior, Japantown, Mission, Ocean View-Merced Heights-Ingleside, Outer Mission, Potrero Hill, SoMa, Tenderloin, Treasure Island, Visitacion Valley, and Western Addition.

"EJ Communities” are defined as the areas facing the top one-third of cumulative environmental and socioeconomic burdens across the City, with scores 21-30.

Further information is available here: https://sfplanning.org/project/environmental-justice-framework-and-general-plan-policies#ej-communities

With over ** billion U.S. dollars, the Federal Reserve Bank of New York reported the highest net interest income of the Federal Reserve (Fed) in 2023. It was followed by San Francisco and Richmond, with ***** and ***** billion U.S. dollars. The total net interest income of the Fed amounted to roughly ***** billion U.S. dollars at the end of the year.

The San Francisco Bay Region, not surprisingly, has some of the least affordable housing in the country – both in absolute terms, and in terms relative to income. In San Francisco proper, the median home value is $800,000 with a median income of $81,000, giving a price-to-income ratio of nearly 10 to 1. In Marin County, the median home value is $815,000 with a median income of $93,000. This ratio is 8.8 times the median income of the county. In Silicon Valley, housing is still pricey, but many people are able to make up for it with higher incomes: San Mateo County has a ratio of 8.3, and Santa Clara County has a ratio of 7.3.

This dataset contains replication files for "The Association Between Income and Life Expectancy in the United States, 2001-2014" by Augustin Bergeron, Raj Chetty, David Cutler, Benjamin Scuderi, Michael Stepner, and Nicholas Turner. For more information, see https://opportunityinsights.org/paper/lifeexpectancy/. A summary of the related publication follows. How can we reduce socioeconomic disparities in health outcomes? Although it is well known that there are significant differences in health and longevity between income groups, debate remains about the magnitudes and determinants of these differences. We use new data from 1.4 billion anonymous earnings and mortality records to construct more precise estimates of the relationship between income and life expectancy at the national level than was feasible in prior work. We then construct new local area (county and metro area) estimates of life expectancy by income group and identify factors that are associated with higher levels of life expectancy for low-income individuals. Our findings show that disparities in life expectancy are not inevitable. There are cities throughout America — from New York to San Francisco to Birmingham, AL — where gaps in life expectancy are relatively small or are narrowing over time. Replicating these successes more broadly will require targeted local efforts, focusing on improving health behaviors among the poor in cities such as Las Vegas and Detroit. Our findings also imply that federal programs such as Social Security and Medicare are less redistributive than they might appear because low-income individuals obtain these benefits for significantly fewer years than high-income individuals, especially in cities like Detroit. Going forward, the challenge is to understand the mechanisms that lead to better health and longevity for low-income individuals in some parts of the U.S. To facilitate future research and monitor local progress, we have posted annual statistics on life expectancy by income group and geographic area (state, CZ, and county) at The Health Inequality Project website. Using these data, researchers will be able to study why certain places have high or improving levels of life expectancy and ultimately apply these lessons to reduce health disparities in other parts of the country.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in South San Francisco: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 217(0.99%) households where the householder is under 25 years old, 6,372(29.10%) households with a householder aged between 25 and 44 years, 9,521(43.48%) households with a householder aged between 45 and 64 years, and 5,785(26.42%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of South San Francisco, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 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 $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 South San Francisco median household income by age. You can refer the same here

With over *** billion U.S. dollars, the Federal Reserve Bank of New York reported the highest interest expense among the Federal Reserve Banks in 2023. It was followed by the Federal Reserve Banks of Richmond and San Francisco, with **** billion and ** billion U.S. dollars, respectively. The total net interest expense of the Federal Reserve amounted to approximately *** billion U.S. dollars at the end of 2023, representing a significant increase from the previous year due to sharply rising interest rates throughout the year.