This statistic shows the ** places in the United States where the average household income was highest in 2015. In 2015, the average household income in Atherton, California was ******* U.S. dollars per year.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in California, PA, 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 9,382, while the mean income for the highest quintile (20% of households with the highest income) is 156,401. This indicates that the top earners earn 17 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 225,152, which is 143.96% higher compared to the highest quintile, and 2399.83% 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 California median household income. You can refer the same here

Comprehensive dataset containing 4 verified Mission Wealth locations in California, United States with complete contact information, ratings, reviews, and location data.

The median house prices in the most expensive zip codes in California reached as high as *** million U.S dollars. Atherton (94027), had the most expensive median house price, followed by Santa Barbara (93108), and Beverly Hills (90210). Six of the ranked zip codes were among the top ten most expensive zip codes in the United States in 2023.

The Home Owners’ Loan Corporation (HOLC) was a U.S. government-sponsored program initiated in the 1930s to evaluate mortgage lending risk. The program resulted in hand-drawn ‘security risk’ maps intended to grade sections of cities where investment should be focused (greenlined areas) or limited (redlined zones). The security maps have since been widely criticized as being inherently racist and have been associated with high levels of segregation and lower levels of green amenities in cities across the country. Our goal was to explore the potential legacy effects of the HOLC grading practice on birds, their habitat, and the people who may experience them throughout a metropolis where the security risk maps were widely applied, Greater Los Angeles, California (L.A.). We used ground-collected, remotely sensed, and census data and descriptive and predictive modeling approaches to address our goal. Patterns of bird habitat and avian communities strongly aligned with the luxury-effect phenomenon, where green amenities were more robust, and bird communities were more diverse and abundant in the wealthiest parts of L.A. Our analysis also revealed potential legacy effects from the HOLC grading practice. Associations between bird habitat features and avian communities in redlined and greenlined zones were generally stronger than in areas of L.A. that did not experience the HOLC grading, in part because redlined zones, which included some of the poorest locations of L.A., had the highest levels of dense urban conditions, e.g., impervious surface cover. In contrast, greenlined zones, which included some of the city's wealthiest areas, had the highest levels of green amenities, e.g., tree canopy cover. The White population of L.A., which constitutes the highest percentage of a racial or ethnic group in greenlined areas, was aligned with a considerably greater abundance of birds affiliated with natural habitat features (e.g., trees and shrubs). Conversely, the Hispanic or Latino population, which is dominant in redlined zones, was positively related to a significantly greater abundance of synanthropic birds, which are species associated with dense urban conditions. Our results suggest that historical redlining and contemporary patterns of income inequality are associated with distinct avifaunal communities and their habitat, which potentially influence the human experience of these components of biodiversity throughout L.A. Redlined zones and low-income residential areas that were not graded by the HOLC can particularly benefit from deliberate urban greening and habitat enhancement projects, which would likely carry over to benefit birds and humans. Methods We used point count data to collect bird data, remote sensing, and field approaches for the predictor data. We also used Census data from existing products. Please reference our paper for the full methodology.

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

Dataset of feature classes for Conservation Lands of the California Desert within NLCS. In 1976, Congress designated a 25-million acre expanse of resource-rich desert lands in southern California as the California Desert Conservation Area (CDCA) through the Federal Land Policy and Management Act. In 2009, Congress, passed the Omnibus Public Land Management Act, which directed the BLM to include lands managed for conservation purposes within the CDCA as part of the National Conservation Lands. To protect this area's natural resources and facilitate development of its energy resources, the Desert Renewable Energy Conservation Plan was undertaken in 2013. This collaborative, multi-stakeholder, landscape-scale planning effort comprises 22.5 million acres in the desert regions of seven California counties, 10.8 million acres of which are BLM lands. Phase I of the DRECP was completed in September 2016. It designated 4.2 million acres as part of the National Conservation Lands of the California Desert. Much of this land was already a part of the National Conservation Lands (in particular, large portions of the Mojave Trails and Sand to Snow National Monuments), but 2.89 million acres were a new addition to the system. National Conservation Lands of the California Desert are closed to all energy development.

An exposure of a creeping segment of the Bartlett Springs Fault (BSF), part of the San Andreas system in northern California, is a ~1.5 m-wide zone of serpentinite-bearing fault gouge cutting through late Pleistocene fluvial deposits. The fault gouge consists of porphyroclasts of antigorite serpentinite, talc, chlorite, and tremolite-actinolite, along with some Franciscan metamorphic rocks, in a matrix of the same materials. The Mg-mineral assemblage is stable at temperatures above 250°-300°C. The BSF gouge is interpreted to have been tectonically incorporated into the fault from depths near the base of the seismogenic zone, and to have risen buoyantly to the surface where it is now undergoing right-lateral displacement. The ultramafic-rich composition, frictional properties, and inferred mode of emplacement of the BSF serpentinitic gouge correspond to those of the creeping traces of the San Andreas Fault identified in the SAFOD (San Andreas Fault Observatory at Depth) drillhole. This suggests a common origin for creep at both locations. A tectonic model for the source of the ultramafic-rich materials in the BSF is proposed that potentially could explain the distribution of creep throughout the northernmost San Andreas system.

California's commercial and recreational fisheries support vibrant coastal economies and communities. Maintaining healthy fishing communities into the future requires a detailed understanding of their past. The California Department of Fish and Wildlife (CDFW) has been monitoring statewide fisheries landings and participation since 1916 and releases confidential versions of this data through authorized data requests and non-confidential summaries of this data in its quasi-annual landings reports. The non-confidential data published in the landings reports provide a rich history of California's fisheries but are scattered across 1000s of tables in 100 s of documents, limiting their accessibility to researchers, fishers, and other interested stakeholders. We reviewed the 58 landings reports published from 1929 to 2020 and extracted and carefully curated 13 datasets with long time series and wide public interest. These datasets include: (1) annual landings in pounds and value by port and species from 1941 to 2019; (2) annual number of commercial fishing vessels by length class from 1934 to 2020; (3) annual number of licensed commercial fishers by area of residence from 1916 to 2020; and (4) annual number of party boat (CPFV) vessels, anglers, and their total catch by species from 1936 to 2020. Notably, we harmonized port names, species common names, and species scientific names across all years and datasets. We make these curated datasets, collectively called the CALFISH database, publicly available to any interested stakeholder in the supplementary materials of this paper, on an open-access data-repository, and in the wcfish R package. These datasets can be used (1) to understand the historical context of California's fisheries; (2) for original research requiring only summaries of historical landings and participation data; and (3) to anticipate the likely characteristics of confidential data requested from the state. We conclude the paper by identifying key principles for increasing the accessibility and utility of historical fisheries landings and participation data. Methods The California Department of Fish and Wildlife (CDFW) has been monitoring statewide fisheries landings and participation since 1916 and releases confidential versions of this data through authorized data requests and non-confidential summaries of this data in its quasi-annual landings reports. The non-confidential data published in the landings reports provide a rich history of California’s fisheries but are scattered across 1000s of tables in 100s of documents, limiting their accessibility to researchers, fishers, and other interested stakeholders. We reviewed the 58 landing series reports published by CDFW from 1928 to 2020 and extracted and curated 13 datasets of long length (years) and wide public interest. In general, these datasets describe landings and participation in commercial fishing and the CPFV sector of recreational fishing (i.e., recreational fishing from private boats and shore are not described in these reports). We rigorously quality controlled all of the extracted data and enhanced the datasets with additional attributes of interest where possible. Notably, these enhancements included harmonizing common names across years and datasets and linking common names with scientific names.

Graph and download economic data for Estimate of Median Household Income for San Francisco County/City, CA (MHICA06075A052NCEN) from 1989 to 2023 about San Francisco County/City, CA; San Francisco; CA; households; median; income; and USA.

Key findings in the Struggling to Get By report show that one in three California households (31%) do not have sufficient income to meet their basic costs of living. This is nearly three times the number officially considered poor according to the Federal Poverty Level.Families with inadequate incomes are found throughout California, but are most concentrated in the northern coastal region, the Central Valley, and in the southern metropolitan areas.The costs for the same family composition in different geographic regions of California also vary widely. In expensive regions such as the San Francisco Bay Region and the Southern California coastal region, the Real Cost Budget, a monthly budget calculation of what is needed to meet basic needs, can range from 32% to 48% more (depending on family type) than in less expensive counties such as Kern, Tulare, and Kings counties. Nevertheless, incomes in the higher cost regions are also higher, relatively and absolutely, so that the proportions below the Real Cost Measure are generally lower in high-cost than low-cost regions.

Wealth and its subcomponent distributions, dollar values and dollar value per household, by household characteristics such as income quintile, age, housing tenure and composition, Canada, regions and provinces, annual 2010 to 2019 and quarterly starting 2020.

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.

Canada CA: Land Area data was reported at 8,788,700.000 sq km in 2022. This stayed constant from the previous number of 8,788,700.000 sq km for 2021. Canada CA: Land Area data is updated yearly, averaging 8,965,590.000 sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 8,965,590.000 sq km in 2020 and a record low of 8,788,700.000 sq km in 2022. Canada CA: Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.;Food and Agriculture Organization, electronic files and web site.;Sum;

As of March 2024, California was the U.S. state with most billionaires, with *** billionaires calling the state home. New York was second, with *** resident billionaires.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

The mission of the USGS Gap Analysis Program (GAP) is providing state, regional and national assessments of the conservation status of native vertebrate species and natural land cover types and facilitating the application of this information to land management activities. The PAD-US geodatabase is required to organize and assess the management status (i.e. apply GAP Status Codes) of elements of biodiversity protection. The goal of GAP is to 'keep common species common' by identifying species and plant communities not adequately represented in existing conservation lands. Common species are those not currently threatened with extinction. By identifying their habitats, gap analysis gives land managers and policy makers the information they need to make better-informed decisions when identifying priority areas for conservation. In cooperation with UNEP-World Conservation Monitoring Centre, GAP ensures PAD-US also supports global analyses to inform policy decisions by maintaining World Database for Protected Areas (WDPA) Site Codes and data for International Union for the Conservation of Nature (IUCN) categorized protected areas in the United States. GAP seeks to increase the efficiency and accuracy of PAD-US updates by leveraging resources in protected areas data aggregation and maintenance as described in "A Map of the Future", published following the PAD-US Design Project (July, 2009). While PAD-US was originally developed to support the GAP Mission stated above, the dataset is robust and has been expanded to support the conservation, recreation and public health communities as well. Additional applications become apparent over time.

Canada CA: Rural Land Area data was reported at 9,197,138.473 sq km in 2015. This records a decrease from the previous number of 9,198,346.026 sq km for 2000. Canada CA: Rural Land Area data is updated yearly, averaging 9,198,346.026 sq km from Dec 1990 (Median) to 2015, with 3 observations. The data reached an all-time high of 9,199,344.420 sq km in 1990 and a record low of 9,197,138.473 sq km in 2015. Canada CA: Rural Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Rural land area in square kilometers, derived from urban extent grids which distinguish urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.;Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.;Sum;

This dataset provides information about the number of properties, residents, and average property values for Rich Field Drive cross streets in Carlsbad, CA.

In 2017, the California Tax Credit Allocation Committee (CTCAC) and the Department of Housing and Community Development (HCD) created the California Fair Housing Task Force (Task Force). The Task Force was asked to assist CTCAC and HCD in creating evidence-based approaches to increasing access to opportunity for families with children living in housing subsidized by the Low-Income Housing Tax Credit (LIHTC) program.

This feature set contains Resource Opportunity Areas (ROAs) that are the results of the Task Force's analysis for the two regions used for the San Francisco Bay Region; one is for the cities and towns (urban) and the other is for the rural areas. The reason for treating urban and rural areas as separate reasons is that using absolute thresholds for place-based opportunity could introduce comparisons between very different areas of the total region that make little sense from a policy perspective — in effect, holding a farming community to the same standard as a dense, urbanized neighborhood.

ROA analysis for urban areas is based on census tract data. Since tracts in rural areas of are approximately 37 times larger in land area than tracts in non-rural areas, tract-level data in rural areas may mask over variation in opportunity and resources within these tracts. Assessing opportunity at the census block group level in rural areas reduces this difference by 90 percent (each rural tract contains three block groups), and thus allows for finer-grained analysis.

In addition, more consistent standards can be useful for identifying areas of concern from a fair housing perspective — such as high-poverty and racially segregated areas. Assessing these factors based on intraregional comparison could mischaracterize areas in more affluent areas with relatively even and equitable development opportunity patterns as high-poverty, and could generate misleading results in areas with higher shares of objectively poor neighborhoods by holding them to a lower, intraregional standard.

To avoid either outcome, the Task Force used a hybrid approach for the CTCAC/HCD ROA analysis — accounting for regional differences in assessing opportunity for most places, while applying more rigid standards for high-poverty, racially segregated areas in all regions. In particular:

Filtering for High-Poverty, Racially Segregated Areas The CTCAC/HCD ROA filters areas that meet consistent standards for both poverty (30% of the population below the federal poverty line) and racial segregation (over-representation of people of color relative to the county) into a “High Segregation & Poverty” category. The share of each region that falls into the High Segregation & Poverty category varies from region to region.

Calculating Index Scores for Non-Filtered Areas The CTCAC/HCD ROAs process calculates regionally derived opportunity index scores for non-filtered tracts and rural block groups using twenty-one indicators (see Data Quality section of metadata for more information). These index scores make it possible to sort each non-filtered tract or rural block group into opportunity categories according to their rank within the urban or rural areas.

To allow CTCAC and HCD to incentivize equitable development patterns in each region to the same degree, the CTCAC/HCD analysis 20 percent of tracts or rural block groups in each urban or rural area, respectively, with the highest relative index scores to the "Highest Resource” designation and the next 20 percent to the “High Resource” designation.

The region's urban area thus ends up with 40 percent of its total tracts with reliable data as Highest or High Resource (or 40 percent of block groups in the rural area). The remaining non-filtered tracts or rural block groups are then evenly divided into “Low Resource” and “Moderate Resource” categories.

Excluding Tracts or Block Groups The analysis also excludes certain census areas from being categorized. To improve the accuracy of the mapping, tracts and rural block groups with the following characteristics are excluded from the application of the filter and from categorization based on index scores: ● Areas with unreliable data, as defined later in this document; ● Areas where prisoners make up at least 75 percent of the population; ● Areas with population density below 15 people per square mile and total population below 500; and ● Areas where at least half of the age 16+ population is employed by the armed forces, in order to exclude military base areas where it is not possible to develop non-military affordable housing.

Excluded tracts and rural block groups are identified as “nan” in the attribute table.

The full methodology used by the Task Force can be found in the California Fair Housing Task Force Opportunity Mapping Methodology report (https://www.treasurer.ca.gov/ctcac/opportunity/2022/2022-hcd-methodology.pdf) on the California Office of State Treasurer website.

Source data and maps can be found on the CTCAC/HCD Opportunity Area Maps page (https://www.treasurer.ca.gov/ctcac/opportunity.asp).