Graph and download economic data for Percent of Population Below the Poverty Level (5-year estimate) in San Francisco County, CA (S1701ACS006075) from 2012 to 2023 about San Francisco County/City, CA; San Francisco; CA; percent; poverty; 5-year; population; and USA.

This dataset contains R/ECAP data for the nine-county San Francisco Bay Region at the census tract level.

To assist communities in identifying racially/ethnically-concentrated areas of poverty (R/ECAPs), HUD has developed a census tract-based definition of R/ECAPs.

To assist communities in identifying racially/ethnically-concentrated areas of poverty (R/ECAPs), HUD has developed a census tract-based definition of R/ECAPs. The definition involves a racial/ethnic concentration threshold and a poverty test. The racial/ethnic concentration threshold is straightforward: R/ECAPs must have a non-white population of 50 percent or more. Regarding the poverty threshold, Wilson (1980) defines neighborhoods of extreme poverty as census tracts with 40 percent or more of individuals living at or below the poverty line. Because overall poverty levels are substantially lower in many parts of the country, HUD supplements this with an alternate criterion. Thus, a neighborhood can be a R/ECAP if it has a poverty rate that exceeds 40% or is three or more times the average tract poverty rate for the metropolitan/micropolitan area, whichever threshold is lower. Census tracts with this extreme poverty that satisfy the racial/ethnic concentration threshold are deemed R/ECAPs.

Data Source: Decennial census (2010); American Community Survey (ACS), 2006-2010; Brown Longitudinal Tract Database (LTDB) based on decennial census data, 2000 & 1990 References: Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press.

Data Source: American Community Survey (ACS), 2009-2013; Decennial Census (2010); Brown Longitudinal Tract Database (LTDB) based on decennial census data, 1990, 2000 & 2010.

Related AFFH-T Local Government, PHA Tables/Maps: Table 4, 7; Maps 1-17.

Related AFFH-T State Tables/Maps: Table 4, 7; Maps 1-15, 18.

References: Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press.

San Francisco County/city, CA - Percent of Population Below the Poverty Level (5-year estimate) in San Francisco County, CA was 10.60% in January of 2023, according to the United States Federal Reserve. Historically, San Francisco County/city, CA - Percent of Population Below the Poverty Level (5-year estimate) in San Francisco County, CA reached a record high of 13.50 in January of 2013 and a record low of 10.10 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for San Francisco County/city, CA - Percent of Population Below the Poverty Level (5-year estimate) in San Francisco County, CA - last updated from the United States Federal Reserve on July of 2025.

Under 18 years Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in San Francisco, California by age, education, race, gender, work experience and more.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Plan Bay Area 2050 utilized this single data layer to inform the Plan Bay Area 2050 Equity PriorityCommunities (EPC).

This data set was developed using American Community Survey (ACS) 2014-2018 data for eight variables considered.

This data set represents all tracts within the San Francisco Bay Region and contains attributes for the eight Metropolitan Transportation Commission (MTC) Equity Priority Communities tract-level variables for exploratory purposes. These features were formerly referred to as Communities of Concern.

Plan Bay Area 2050 Equity Priority Communities (tract geography) are based on eight ACS 2014-2018 (ACS 2018) tract-level variables:

People of Color (70% threshold) Low-Income (less than 200% of Federal poverty level, 28% threshold) Level of English Proficiency (12% threshold) Seniors 75 Years and Over (8% threshold) Zero-Vehicle Households (15% threshold) Single-Parent Households (18% threshold) People with a Disability (12% threshold) Rent-Burdened Households (14% threshold)

If a tract exceeds both threshold values for Low-Income and People of Color shares OR exceeds thethreshold value for Low-Income AND also exceeds the threshold values for three or more variables, it is a EPC.

Detailed documentation on the production of this feature set can be found in the MTC Equity Priority Communities project documentation.

Average adjusted predicted probability of high SSB consumptiona in San Francisco and San Jose before, one, and two years after San Francisco’s sugar sweetened beverages tax implementation, stratified by federal poverty level (FPL) (n = 1,443).

This data set represents American Community Survey (ACS) 2014-2018 tract information related to Equity Priority Communities (EPCs) for Plan Bay Area 2050+.The Plan Bay Area 2050+ Equity Priority Communities incorporate EPCs identified with 2014-2018 ACS data, as well as EPCs identified with 2018-2022 ACS data into a single consolidated map of Plan Bay Area 2050+ Equity Priority Communities.This data set was developed using American Community Survey 2014-2018 data for eight variables considered.This data set represents all tracts within the San Francisco Bay Region, and contains attributes for the eight Metropolitan Transportation Commission (MTC) Equity Priority Communities tract-level variables for exploratory purposes. Equity Priority Communities are defined by MTC Resolution No. 4217-Equity Framework for Plan Bay Area 2040.As part of the development of the [DRAFT] Equity Priority Communities - Plan Bay Area 2050+ features, the source Census tracts had portions that overlapped either the Pacific Ocean or San Francisco Bay removed. The result is this feature set has fewer Census tracts than the unclipped tract source data.Plan Bay Area 2050+ Equity Priority Communities (tract geography) are based on eight ACS 2014-2018 (ACS 2018) tract-level variables:People of Color (70% threshold)Low-Income (less than 200% of Federal poverty level, 28% threshold)Level of English Proficiency (12% threshold)Seniors 75 Years and Over (8% threshold)Zero-Vehicle Households (15% threshold)Single-Parent Households (18% threshold)People with a Disability (12% threshold)Rent-Burdened Households (14% threshold)If a tract exceeds both threshold values for Low-Income and People of Color shares OR exceeds the threshold value for Low-Income AND also exceeds the threshold values for three or more variables, it is a EPC.Detailed documentation on the production of this feature set can be found in the MTC Equity Priority Communities project documentation.

Difference-in-differences of sugar-sweetened beverage consumption (ounces) pre- and post-tax implementation between San Francisco and San José pre- and post-tax implementation (n = 1,443).

Characteristics of sample at baseline, overall and by city (2017–2018).

This data set represents all urbanized tracts within the San Francisco Bay Region, and contains attributes for the eight Metropolitan Transportation Commission (MTC) Equity Priority Communities (EPC) tract-level variables for exploratory purposes. These features were formerly referred to as Communities of Concern (CoC).MTC 2018 Equity Priority Communities (tract geography) is based on eight ACS 2012-2016 tract-level variables: Persons of Color (70% threshold) Low-Income (less than 200% of Fed. poverty level, 30% threshold) Level of English Proficiency (12% threshold) Elderly (10% threshold) Zero-Vehicle Households (10% threshold) Single Parent Households (20% threshold)Disabled (12% threshold) Rent-Burdened Households (15% threshold) If a tract exceeds both threshold values for Low-Income and Person of Color shares OR exceeds the threshold value for Low-Income AND also exceeds the threshold values for three or more variables, it is a EPC.Detailed documentation on the production of this feature set can be found in the MTC Equity Priority Communities project documentation.

Difference-in-differences of likelihood of high sugar-sweetened beverage (SSB) consumptiona pre- and post-tax implementation between San Francisco and San José (n = 1,443).

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).

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.