Ex. C - Housing Element 2023-2031 with appendices — South San Francisco City Council, January 25, 2023 (part 3 of 3)

Additionally, the black line within each racial group notes the percentage of the population of City of South San Francisco represented by that group and how that percentage ranks among all 109 jurisdictions. Local staff can use this chart to compare the representation of different racial groups in their jurisdiction to those groups’ representation in other jurisdictions in the region, which can indicate the extent of segregation between this jurisdiction and the region.

11 While comparisons of segregation measures are made only using the 104 jurisdictions with more than one census tract, this comparison of jurisdiction level demographic data can be made using all 109 jurisdictions. 18 Figure 6: Racial Demographics of South San Francisco Compared to All Bay Area Jurisdictions (2020)
Universe: Bay Area Jurisdictions.

Source U.S. Census Bureau, 2020 Census State Redistricting Data (Public Law 94-171) Summary File, 2020 Census of Population and Housing, Table P002.

The map in Figure 7 below also illustrates regional racial segregation between South San Francisco and other jurisdictions. This map demonstrates how the percentage of people of color in South San Francisco and surrounding jurisdictions compares to the Bay Area as a whole:
• Jurisdictions shaded orange have a share of people of color that is less than the Bay Area as a whole, and the degree of difference is greater than five percentage points. • Jurisdictions shaded white have a share of people of color comparable to the regional percentage of people of color (within five percentage points). • Jurisdictions shaded grey have a share of people of color that is more than five percentage points greater than the regional percentage of people of color. 19 Figure 7: Comparing the Share of People of Color in South San Francisco and Vicinity to the Bay Area (2020)
Universe: Population.

Source: U.S. Census Bureau, 2020 Census State Redistricting Data (Public Law 94-171) Summary File, 2020 Census of Population and Housing, Table P002.

Note: People of color refer to persons not identifying as non-Hispanic white. The nine-county Bay Area is the reference region for this map.

Segregation between jurisdictions in the region can also be analyzed by calculating regional values for the segregation indices discussed previously. Table 5 presents dissimilarity index, isolation index, and Theil’s H index values for racial segregation for the entire nine-county Bay Area in 2010 and 2020. In the previous section of this report focused on neighborhood level racial segregation, these indices were calculated by comparing the racial demographics of the census tracts within a jurisdiction to the demographics of the jurisdiction as a whole. In Table 5, these measures are calculated by comparing the racial demographics of local jurisdictions to the region’s racial makeup. For example, looking at the 2020 data, Table 5 shows the white isolation index value for the region is 0.429, meaning that on average white Bay Area residents live in a jurisdiction that is 42.9% white in 2020. An example of regional dissimilarity index values in Table 5 is the Black/white dissimilarity index value of 0.459, which means that across the region 45.9% of Black (or white) residents would need to move to a different jurisdiction to evenly distribute Black and white residents across Bay Area jurisdictions. The dissimilarity index values in Table 5 reflect recommendations made in HCD’s AFFH guidance for calculating dissimilarity at the region level.12 The regional value for the Theil’s H index measures how

12 For more information on HCD’s recommendations regarding data considerations for analyzing integration and segregation patterns, see page 31 of the AFFH Guidance Memo. 20 diverse each Bay Area jurisdiction is compared to the racial diversity of the whole region. A Theil’s H Index value of 0 would mean all jurisdictions within the Bay Area have the same racial demographics as the entire region, while a value of 1 would mean each racial group lives exclusively in their own separate jurisdiction. The regional Theil’s H index value for racial segregation decreased slightly between 2010 and 2020, meaning that racial groups in the Bay Area are now slightly less separated by the borders between jurisdictions.

Table 5: Regional Racial Segregation Measures
Index Group 2010 2020
Isolation Index Regional Level
Asian/Pacific Islander 0.317 0.378
Black/African American 0.144 0.118
Latinx 0.283 0.291
White 0.496 0.429
People of Color 0.629 0.682
Dissimilarity Index Regional Level
Asian/Pacific Islander vs. White 0.384 0.369
Black/African American vs. White 0.475 0.459
Latinx vs. White 0.301 0.297
People of Color vs. White 0.296 0.293
Theil's H Multi-racial All Racial Groups 0.103 0.097
Universe: Population.

Source: IPUMS National Historical Geographic Information System (NHGIS). U.S. Census Bureau, 2020 Census State Redistricting Data (Public Law 94-171) Summary File, 2020 Census of Population and Housing, Table P002. Data from 2010 is from U.S. Census Bureau, 2010 Census of Population and Housing, Table P4. 21 3 INCOME SEGREGATION IN CITY OF SOUTH SAN FRANCISCO
Definition of Terms - Income Groups
When analyzing segregation by income, this report uses income group designations consistent with the Regional Housing Needs Allocation and the Housing Element:

Very low-income: individuals earning less than 50% of Area Median Income (AMI)
Low-income: individuals earning 50%-80% of AMI
Moderate-income: individuals earning 80%-120% of AMI
Above moderate-income: individuals earning 120% or more of AMI Additionally, this report uses the term “lower-income” to refer to all people who earn less than 80% of AMI, which includes both low-income and very low-income individuals.

The income groups described above are based on U.S. Department of Housing and Urban Development (HUD) calculations for AMI. HUD calculates the AMI for different metropolitan areas, and the nine county Bay Area includes the following metropolitan areas: Napa Metro Area (Napa County), Oakland-Fremont Metro Area (Alameda and Contra Costa Counties), San Francisco Metro Area (Marin, San Francisco, and San Mateo Counties), San Jose-Sunnyvale-Santa Clara Metro Area (Santa Clara County), Santa Rosa Metro Area (Sonoma County), and VallejoFairfield Metro Area (Solano County).

The income categories used in this report are based on the AMI for the HUD metro area where this jurisdiction is located.

3.1 Neighborhood Level Income Segregation (within South San Francisco)
Income segregation can be measured using similar indices as racial segregation. Income dot maps, similar to the racial dot maps shown in Figures 1 and 5, are useful for visualizing segregation between multiple income groups at the same time. The income dot map of South San Francisco in Figure 8 below offers a visual representation of the spatial distribution of income groups within the jurisdiction. As with the racial dot maps, when the dots show lack of a pattern or clustering, income segregation measures tend to be lower, and conversely, when clusters are apparent, the segregation measures may be higher as well.
22 Figure 8: Income Dot Map of South San Francisco (2015) Universe: Population.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and ModerateIncome Summary Data.

Note: The plot shows the income group distribution at the census block group level for City of South San Francisco and vicinity.

Dots in each block group are randomly placed and should not be construed as actual placement of individuals.

The isolation index values for all income groups in South San Francisco for the years 2010 and 2015 can be found in Table 6 below.13 Very Low-income residents are the most isolated income group in South San Francisco. South San Francisco’s isolation index of 0.377 for these residents means that the average Very Low-income resident in South San Francisco lives in a neighborhood that is 37.7% Very Low-income. Among all income groups, the Very Low-income population’s isolation index has changed the most over time, becoming more segregated from other income groups between 2010 and 2015.

Similar to the tables presented earlier for neighborhood racial segregation, the “Bay Area Average” column in Table 6 provides the average isolation index value across Bay Area jurisdictions for different income groups in 2015. The data in this column can be used as a comparison to provide context for the levels of segregation experienced by income groups in this jurisdiction. For example, Table 6 indicates the average isolation index value for very low-income residents across Bay Area jurisdictions is 0.269,

13 This report presents data for income segregation for the years 2010 and 2015, which is different than the time periods used for racial segregation. This deviation stems from the data source recommended for income segregation calculations in HCD’s AFFH Guidelines. This data source most recently updated with data from the 2011-2015 American Community Survey 5-year estimates. For more information on HCD’s recommendations for calculating income segregation, see page 32 of HCD’s AFFH Guidelines. 23 meaning that in the average Bay Area jurisdiction a very low-income resident lives in a neighborhood that is 26.9% very low-income.

Table 6: Income Group Isolation Index Values for Segregation within South San Francisco
South San Francisco Bay Area
Average
Income Group 2010 2015 2015
Very Low-Income (<50% AMI) 0.279 0.377 0.269
Low-Income (50%-80% AMI) 0.287 0.235 0.145
Moderate-Income (80%-120% AMI) 0.226 0.235 0.183
Above Moderate-Income (>120% AMI) 0.346 0.282 0.507
Universe: Population.

Figure 9 below shows how income group isolation index values in South San Francisco compare to values in other Bay Area jurisdictions. In this chart, each dot represents a Bay Area jurisdiction. For each income group, the spread of dots represents the range of isolation index values among Bay Area jurisdictions. Additionally, the black line within each income group notes the isolation index value for that group in South San Francisco, and each dashed red line represents the Bay Area average for the isolation index for that group. Local staff can use this chart to contextualize how segregation levels for income groups in their jurisdiction compare to the rest of the region. 24 Figure 9: Income Group Isolation Index Values for South San Francisco Compared to Other Bay Area Jurisdictions (2015)
Universe: Bay Area Jurisdictions.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and ModerateIncome Summary Data.

Table 7 below provides the dissimilarity index values indicating the level of segregation in South San Francisco between residents who are lower-income (earning less than 80% of AMI) and those who are not lower-income (earning above 80% of AMI). This data aligns with the requirements described in HCD’s AFFH Guidance Memo for identifying dissimilarity for lower-income households.14 Segregation in South San Francisco between lower-income residents and residents who are not lower-income increased between 2010 and 2015. Additionally, Table 7 shows dissimilarity index values for the level of segregation in Albany between residents who are very low-income (earning less than 50% of AMI) and those who are above moderate-income (earning above 120% of AMI). This supplementary data point provides additional nuance to an analysis of income segregation, as this index value indicates the extent to which a jurisdiction’s lowest and highest income residents live in separate neighborhoods.

Similar to other tables in this report, the “Bay Area Average” column shows the average dissimilarity index values for these income group pairings across Bay Area jurisdictions in 2015. For example, Table 7 indicates that the average dissimilarity index between lower-income residents and other residents in a Bay Area jurisdiction is 0.198, so on average 19.8% of lower-income residents in a Bay Area jurisdiction would need to move to a different neighborhood within the jurisdiction to create perfect income group integration in that jurisdiction.

14 For more information, see page 32 of HCD’s AFFH Guidance Memo. 25 In 2015, the income segregation in South San Francisco between lower-income residents and other residents was higher than the average value for Bay Area jurisdictions (See Table 7). This means that the lower-income residents are more segregated from other residents within South San Francisco compared to other Jurisdictions in the region.

Table 7: Income Group Dissimilarity Index Values for Segregation within South San Francisco
South San Francisco Bay Area
Average
Income Group 2010 2015 2015
Below 80% AMI vs. Above 80% AMI 0.209 0.235 0.198
Below 50% AMI vs. Above 120% AMI 0.326 0.278 0.253
Universe: Population.

Figure 10 below shows how dissimilarity index values for income segregation in South San Francisco compare to values in other Bay Area jurisdictions. In this chart, each dot represents a Bay Area jurisdiction. For each income group pairing, the spread of dots represents the range of dissimilarity index values among Bay Area jurisdictions. Additionally, the black line within each income group pairing notes the dissimilarity index value in South San Francisco, and each dashed red line represents the Bay Area average for the dissimilarity index for that pairing. Local staff can use this chart to contextualize how segregation levels between lower-income residents and wealthier residents in their jurisdiction compared to the rest of the region.
26 Figure 10: Income Group Dissimilarity Index Values for South San Francisco Compared to Other Bay Area Jurisdictions (2015)
Universe: Bay Area Jurisdictions.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and ModerateIncome Summary Data.

The Theil’s H Index values for neighborhood income group segregation in South San Francisco for the years 2010 and 2015 can be found in Table 8 below. The “Bay Area Average” column in this table provides the average Theil’s H Index value across Bay Area jurisdictions for different income groups in 2015. By 2015, the Theil’s H Index value for income segregation in South San Francisco was about the same amount as it had been in 2010. In 2015, the Theil’s H Index value for income group segregation in South San Francisco was higher than the average value for Bay Area jurisdictions, indicating there is more neighborhood level income segregation in South San Francisco than in the average Bay Area city.

Table 8: Theil’s H Index Values for Income Segregation within South San Francisco
South San Francisco Bay Area
Average
Index 2010 2015 2015
Theil's H Multi-income 0.053 0.050 0.043
Universe: Population.

Source: Data for 2015 is from U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011- 2015 Low- and Moderate-Income Summary Data. Data for 2010 is from U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2006-2010 Low- and Moderate-Income Summary Data. 27 Figure 11 below shows how Theil’s H index values for income group segregation in South San Francisco compare to values in other Bay Area jurisdictions in 2015. In this chart, each dot represents a Bay Area jurisdiction. Additionally, the black line notes the Theil’s H index value for income group segregation in South San Francisco, and the dashed red line represents the average Theil’s H index value across Bay Area jurisdictions. Local staff can use this chart to compare how neighborhood income group segregation levels in their jurisdiction compare to other jurisdictions in the region.

Figure 11: Income Group Theil’s H Index Values for South San Francisco Compared to Other Bay Area Jurisdictions (2015)
Universe: Bay Area Jurisdictions.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and ModerateIncome Summary Data.

3.2 Regional Income Segregation (between South San Francisco and other jurisdictions)
At the regional level, segregation is measured between jurisdictions instead of between neighborhoods.

Income dot maps are not only useful for examining neighborhood income segregation within a jurisdiction, but these maps can also be used to explore income demographic differences between jurisdictions in the region. Figure 12 below presents an income dot map showing the spatial distribution of income groups in South San Francisco as well as in nearby Bay Area jurisdictions. 28 Figure 12: Income Dot Map of South San Francisco and Surrounding Areas (2015) Universe: Population.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and ModerateIncome Summary Data.

Note: The plot shows the income group distribution at the census block group level for City of South San Francisco and vicinity.

Dots in each block group are randomly placed and should not be construed as actual placement of individuals.

When looking at income segregation between jurisdictions in the Bay Area, one can examine how South San Francisco differs from the region. The income demographics in South San Francisco for the years 2010 and 2015 can be found in Table 9 below. The table also provides the income composition of the nine-county Bay Area in 2015. As of that year, South San Francisco had a higher share of very lowincome residents than the Bay Area as a whole, a higher share of low-income residents, a higher share of moderate-income residents, and a lower share of above moderate-income residents.

Table 9: Population by Income Group, South San Francisco and the Region South San Francisco Bay Area
Income Group 2010 2015 2015
Very Low-Income (<50% AMI) 23.37% 32.45% 28.7%
Low-Income (50%-80% AMI) 26.29% 21.76% 14.3%
Moderate-Income (80%-120% AMI) 21.36% 21.2% 17.6%
Above Moderate-Income (>120% AMI) 28.98% 24.59% 39.4%
29 Universe: Population.

Source: Data for 2015 is from Housing U.S. Department of and Urban Development, American Community Survey 5-Year 2011- 2015 Low- and Moderate-Income Summary Data. Data for 2010 is from U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2006-2010 Low- and Moderate-Income Summary Data.

Figure 13 below compares the income demographics in South San Francisco to other Bay Area jurisdictions.15 Like the chart in Figure 3, each dot represents a Bay Area jurisdiction. For each income group, the spread of dots represents the range of that group’s representation among Bay Area jurisdictions. The smallest range is among jurisdictions’ moderate-income populations, while Bay Area jurisdictions vary the most in the share of their population that is above moderate-income.

Additionally, the black lines within each income group note the percentage of South San Francisco population represented by that group and how that percentage ranks among other jurisdictions. Local staff can use this chart to compare the representation of different income groups in their jurisdiction to those groups’ representation in other jurisdictions in the region, which can indicate the extent of segregation between this jurisdiction and the region.

Figure 13: Income Demographics of South San Francisco Compared to Other Bay Area Jurisdictions (2015)
Universe: Bay Area Jurisdictions.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and ModerateIncome Summary Data.

15 While comparisons of segregation measures are made only using the 104 jurisdictions with more than one census tract, this comparison of jurisdiction level demographic data can be made using all 109 jurisdictions. 30 Income segregation between jurisdictions in the region can also be analyzed by calculating regional values for the segregation indices discussed previously. Similar to the regional racial segregation measures shown in Table 5, Table 10 presents dissimilarity index, isolation index, and Theil’s H index values for income segregation for the entire nine-county Bay Area in 2010 and 2015. In the previous section of this report focused on neighborhood level income segregation, segregation indices were calculated by comparing the income demographics of the census tracts within a jurisdiction to the demographics of the jurisdiction as a whole. In Table 10, these measures are calculated by comparing the income demographics of local jurisdictions to the region’s income group makeup. For example, looking at 2015 data, Table 10 shows the regional isolation index value for very low-income residents is

0.315 for 2015, meaning that on average very low-income Bay Area residents live in a jurisdiction that is 31.5% very low-income. The regional dissimilarity index for lower-income residents and other residents is 0.194 in 2015, which means that across the region 19.4% of lower-income residents would need to move to a different jurisdiction to create perfect income group integration in the Bay Area as a whole. The regional value for the Theil’s H index measures how diverse each Bay Area jurisdiction is compared to the income group diversity of the whole region. A Theil’s H Index value of 0 would mean all jurisdictions within the Bay Area have the same income demographics as the entire region, while a value of 1 would mean each income group lives exclusively in their own separate jurisdiction. The regional Theil’s H index value for income segregation decreased slightly between 2010 and 2015, meaning that income groups in the Bay Area are now slightly less separated by the borders between jurisdictions.

Table 10: Regional Income Segregation Measures
Index Group 2010 2015
Isolation Index Regional Level
Very Low-Income (<50% AMI) 0.277 0.315
Low-Income (50%-80% AMI) 0.157 0.154
Moderate-Income (80%-120% AMI) 0.185 0.180
Above Moderate-Income (>120% AMI) 0.467 0.435
Dissimilarity Index Regional Level
Below 80% AMI vs. Above 80% AMI 0.186 0.194
Below 50% AMI vs. Above 120% AMI 0.238 0.248
Theil's H Multi-income All Income Groups 0.034 0.032
Universe: Population.

4.1 Segregation in City of South San Francisco
• The isolation index measures the segregation of a single group, and the dissimilarity index measures segregation between two different groups. The Theil’s H-Index can be used to measure segregation between all racial or income groups across the city at once. • As of 2020, Asian residents are the most segregated compared to other racial groups in South San Francisco, as measured by the isolation index. Asian residents live in neighborhoods where they are less likely to come into contact with other racial groups. • Among all racial groups, the white population’s isolation index value has changed the most over time, becoming less segregated from other racial groups between 2000 and 2020. • According to the dissimilarity index, within South San Francisco the highest level of racial segregation is between Black and white residents.16 However, local jurisdiction staff should note that this dissimilarity index value is not a reliable data point due to small population size. • According to the Theil’s H-Index, neighborhood racial segregation in South San Francisco declined between 2010 and 2020. Neighborhood income segregation stayed about the same between 2010 and 2015.
• Very Low-income residents are the most segregated compared to other income groups in South San Francisco. Very Low-income residents live in neighborhoods where they are less likely to encounter residents of other income groups.
• Among all income groups, the Very Low-income population’s segregation measure has changed the most over time, becoming more segregated from other income groups between 2010 and 2015.
• According to the dissimilarity index, segregation between lower-income residents and residents who are not lower-income has increased between 2010 and 2015. In 2015, the income segregation in South San Francisco between lower-income residents and other residents was higher than the average value for Bay Area jurisdictions.

4.2 Segregation Between City of South San Francisco and Other jurisdictions in the Bay Area Region
• South San Francisco has a lower share of white residents than other jurisdictions in the Bay Area as a whole, a higher share of Latinx residents, a lower share of Black residents, and a higher share of Asian/Pacific Islander residents.

16 The analysis conducted for this report suggests that dissimilarity index values are unreliable for a population group if that group represents approximately less than 5% of the jurisdiction’s total population. ABAG/MTC recommends that when cities have population groups that are less than 5% of the jurisdiction’s population (see Table 15 in Appendix 2), jurisdiction staff could focus on the isolation index or Thiel’s H-Index to gain a more accurate understanding of neighborhood-level racial segregation in their jurisdiction. 32 • Regarding income groups, South San Francisco has a higher share of very low-income residents than other jurisdictions in the Bay Area as a whole, a higher share of low-income residents, a higher share of moderate-income residents, and a lower share of above moderate-income residents.
33 5 APPENDIX 2: SEGREGATION DATA
Appendix 2 combines tabular data presented throughout this report into a more condensed format. This data compilation is intended to enable local jurisdiction staff and their consultants to easily reference this data and re-use the data in the Housing Element or other relevant documents/analyses.

Table 11 in this appendix combines data from Table 1, Table 2, and Table 3 in the body of the report.

Table 12 in this appendix combines data from Table 6, Table 7, and Table 8 in the body of the report.

Table 13 represents a duplication of Table 5 in the body of the report; Table 14 represents a duplication of Table 10 in the body of the report; Table 15 in this appendix represents a duplication of Table 4 in the body of the report, while Table 16 represents a duplication of Table 9 in the body of the report.

Table 11: Neighborhood Racial Segregation Levels in South San Francisco South San Francisco Bay Area
Average
Index Race 2000 2010 2020 2020
Isolation
Asian/Pacific Islander 0.419 0.459 0.488 0.245
Black/African American 0.035 0.027 0.020 0.053
Latinx 0.419 0.445 0.415 0.251
White 0.368 0.262 0.207 0.491
Dissimilarity
Asian/Pacific Islander vs. White 0.359 0.254 0.228 0.185 Black/African American vs. White 0.384* 0.311* 0.302* 0.244 Latinx vs. White 0.298 0.292 0.287 0.207
People of Color vs. White 0.270 0.218 0.202 0.168
Theil's H Multi-racial All 0.104 0.090 0.072 0.042
Universe: Population.

Source: IPUMS National Historical Geographic Information System (NHGIS). U.S. Census Bureau, 2020 Census State Redistricting Data (Public Law 94-171) Summary File, 2020 Census of Population and Housing, Table P002. Data from 2010 is from U.S. Census Bureau, 2010 Census of Population and Housing, Table P4. Data for 2000 is standardized to 2010 census tract geographies and is from U.S. Census Bureau, Census 2000, Table P004.

Note: If a number is marked with an asterisk (*), it indicates that the index is based on a racial group making up less than 5 percent of the jurisdiction population, leading to unreliable numbers. 34 Table 12: Neighborhood Income Segregation Levels in South San Francisco South San Francisco Bay Area
Average
Index Income Group 2010 2015 2015
Isolation
Very Low-Income (<50% AMI) 0.279 0.377 0.269
Low-Income (50%-80% AMI) 0.287 0.235 0.145
Moderate-Income (80%-120% AMI) 0.226 0.235 0.183
Above Moderate-Income (>120% AMI) 0.346 0.282 0.507
Dissimilarity
Below 80% AMI vs. Above 80% AMI 0.209 0.235 0.198
Below 50% AMI vs. Above 120% AMI 0.326 0.278 0.253
Theil's H Multi-racial All 0.053 0.050 0.043
Universe: Population.

Source: Income data for 2015 is from U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and Moderate-Income Summary Data. Data for 2010 is from U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2006-2010 Low- and Moderate-Income Summary Data. 35 Table 13: Regional Racial Segregation Measures
Index Group 2010 2020
Isolation Index Regional Level
Asian/Pacific Islander 0.317 0.378
Black/African American 0.144 0.118
Latinx 0.283 0.291
White 0.496 0.429
People of Color 0.629 0.682
Dissimilarity Index Regional Level
Asian/Pacific Islander vs. White 0.384 0.369
Black/African American vs. White 0.475 0.459
Latinx vs. White 0.301 0.297
People of Color vs. White 0.296 0.293
Theil's H Multi-racial All Racial Groups 0.103 0.097
Universe: Population.

Table 14: Regional Income Segregation Measures
Index Group 2010 2015
Isolation Index Regional Level
Very Low-Income (<50% AMI) 0.277 0.315
Low-Income (50%-80% AMI) 0.157 0.154
Moderate-Income (80%-120% AMI) 0.185 0.180
Above Moderate-Income (>120% AMI) 0.467 0.435
Dissimilarity Index Regional Level
Below 80% AMI vs. Above 80% AMI 0.186 0.194
Below 50% AMI vs. Above 120% AMI 0.238 0.248
Theil's H Multi-income All Income Groups 0.034 0.032
Universe: Population.

Source: Data for 2015 is from U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011- 2015 Low- and Moderate-Income Summary Data. Data for 2010 is from U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2006-2010 Low- and Moderate-Income Summary Data. 36 Table 15: Population by Racial Group, South San Francisco and the Region South San Francisco Bay Area
Race 2000 2010 2020 2020
Asian/Pacific Islander 28.59% 37.68% 42.34% 35.8%
Black/African American 2.68% 2.33% 1.79% 5.6%
Latinx 31.84% 34.02% 32.75% 28.2%
Other or Multiple Races 6.36% 3.95% 5.11% 24.4%
White 30.53% 22.03% 18.01% 5.9%
Universe: Population.

Source: IPUMS National Historical Geographic Information System (NHGIS). U.S. Census Bureau, 2020 Census State Redistricting Data (Public Law 94-171) Summary File, 2020 Census of Population and Housing, Table P002. Data from 2010 is from U.S. Census Bureau, 2010 Census of Population and Housing, Table P4. Data for 2000 is standardized to 2010 census tract geographies and is from U.S. Census Bureau, Census 2000, Table P004.

Table 16: Population by Income Group, South San Francisco and the Region South San Francisco Bay Area
Income Group 2010 2015 2015
Very Low-Income (<50% AMI) 23.37% 32.45% 28.7%
Low-Income (50%-80% AMI) 26.29% 21.76% 14.3%
Moderate-Income (80%-120% AMI) 21.36% 21.2% 17.6%
Above Moderate-Income (>120% AMI) 28.98% 24.59% 39.4%
Universe: Population.

66. Bayer, Patrick, Robert McMillan, and Kim S. Rueben. 2004. “What Drives Racial Segregation? New Evidence using Census Microdata,” Journal of Urban Economics 56(3): 514-535.

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Pendall, Rolf. 2000. “Local Land-Use Regulation and the Chain of Exclusion,” Journal of the American Planning Association 66(2): 125-142.

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7 | HOUSING PLAN – GOALS AND POLICIES
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South San Francisco Housing Element 2023-2031
APPENDIX 6.4 AFFH RESIDENT SURVEY ANALYSIS
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 1
Appendix 6.4
AFFH Resident Survey Analysis
This section reports the findings from the resident survey conducted of San Mateo County residents to support the AFFH analysis of Housing Elements. It explores residents’ housing, affordability, and neighborhood challenges and experiences with displacement and housing discrimination. The survey also asks about residents’ access to economic opportunity, captured through residents’ reported challenges with transportation, employment, and K-12 education. The survey was offered in both English and Spanish.

The resident survey was available online, in both Spanish and English, in a format accessible to screen readers, and promoted through jurisdictional communications and social media and through partner networks. A total of 2,382 residents participated.

The survey instrument included questions about residents’ current housing situation, housing, neighborhood and affordability challenges, healthy neighborhood indicators, access to opportunity, and experience with displacement and housing discrimination.

Explanation of terms. Throughout this section, several terms are used that require explanation.
 “Precariously housed” includes residents who are currently homeless or living in transitional or temporary/emergency housing, as well as residents who live with friends or family but are not themselves on the lease or property title. These residents may (or may not) make financial contributions to pay housing costs or contribute to the household in exchange for housing (e.g., childcare, healthcare services).  “Disability” indicates that the respondent or a member of the respondent’s household has a disability of some type—physical, mental, intellectual, developmental.  “Single parent” are respondents living with their children only or with their children and other adults but not a spouse/partner.
 “Tenure” in the housing industry means rentership or ownership.  “Large households” are considered those with five or more persons residing in a respective household.
 “Seriously Looked for Housing” includes touring or searching for homes or apartments, putting in applications or pursuing mortgage financing.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 2
Sampling note. The survey respondents do not represent a random sample of the county or jurisdictions’ population. A true random sample is a sample in which each individual in the population has an equal chance of being selected for the survey. The selfselected nature of the survey prevents the collection of a true random sample. Important insights and themes can still be gained from the survey results, however, with an understanding of the differences among resident groups and between jurisdictions and the county overall. Overall, the data provide a rich source of information about the county’s households and their experience with housing choice and access to opportunity in the communities where they live.

Jurisdiction-level data are reported for cities with 50 responses or more. Response by jurisdiction and demographics are shown in the figure below. Overall, the survey received a very strong response from typically underrepresented residents including: people of color, renters, precariously housed residents, very low income households, households with children, large households, single parents, and residents with disabilities.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 3
Figure 1.

Resident Survey Sample Sizes by Jurisdictions and Selected Characteristics Note: Numbers do not aggregate either due to multiple responses or that respondents chose not to provide a response to all demographic and socioeconomic questions.

Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 4
Primary Findings
The survey data present a unique picture of the housing choices, challenges, needs, and access to economic opportunity of San Mateo County residents.

Top level findings from residents’ perspectives and experiences:
 The limited supply of housing that accommodates voucher holders presents several challenges. Specifically,
 Eight out of 10 voucher holders represented by the survey find a landlord that accepts a housing voucher to be “difficult” or “very difficult.”  According to the survey data, vouchers not being enough to cover the places residents want to live is a top impediment for residents who want to move in San Mateo County, as well as African American, Asian, and Hispanic residents, households with children under 18, single parents, older adults, households with a member experiencing a disability, and several jurisdictions.
 Low income is a barrier to accessing housing. The impacts are highest for large households, Hispanic households, and residents in South San Francisco and Redwood City.
 Nearly 4 in 10 respondents who looked for housing experienced denial of housing. African American/Black respondents, precariously housed respondents, households with income below $50,000, and single parent respondents reported the highest denial rates.
 1 in 5 residents have been displaced from their home in the past five years. One of the main reasons cited for displacement was the rent increased more than I could pay. The impacts are higher for African American households, single parents, households that make less than $25,000, and precariously housed respondents.  For households with children that were displaced in the past five years, 60% of children in those households have changed schools. The most common outcomes identified by households with children who have changed schools include school is more challenging, they feel less safe at the new school, and they are in a worse school.
 Nearly 1 in 5 residents reported they have experienced discrimination in the past five years. African American, single parent, precariously housed respondents reported the highest rates of discrimination. The most common actions in response to discrimination cited by survey respondents were Nothing/I wasn’t sure what to do and Moved/found another place to live.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 5
 Of respondents reporting a disability, about 25% report that their current housing situation does not meet their accessibility needs. The three top greatest housing needs identified by respondents included installation of grab bars in bathroom or bench in shower, supportive services to help maintain housing, and ramps.
 On average, respondents are fairly satisfied with their transportation situation. Groups with the highest proportion of respondents somewhat or not at all satisfied with their transportation options included African American, single parents, precariously housed, and Brisbane respondents.

There are some housing, affordability, and neighborhood challenges unique to specific resident groups. These include:
 Would like to move but can’t afford it—Most likely to be a challenge for Daly City, East Palo Alto, and Redwood City respondents, as well as Hispanic, renter, precariously housed, households making less than $50,000, and large household respondents.
 My house or apartment isn’t big enough for my family—Most likely to be a challenge for East Palo Alto respondents, as well as Hispanic households, large and single parent households, and households with children under 18.  I’m often late on my rent payments—Most likely to be a challenge for East Palo Alto and renter respondents, as well as households that make less than $25,000.  I can’t keep up with my utility payments—Most likely to be a challenge for Daly City, East Palo Alto, and San Mateo respondents, as well as African American and Hispanic respondents, single parent households, households with children under 18, and households that make less than $50,000.
 Bus/rail does not go where I need to go or does not operate during the times I need— Most likely to be a challenge for African American, precariously housed, single parent household, Brisbane and Pacifica respondents.  Schools in my neighborhood are poor quality—Most likely to be a challenge for East Palo Alto, Redwood City, San Bruno and South San Francisco respondents, as well as Hispanic respondents and households with children under 18.

Resident Survey Findings
Of survey respondents who reported their race or ethnicity, 40% of survey respondents identified as non-Hispanic White, followed by Asian (26%), Hispanic (20%), African American (7%), and Other Minority (8%) residents (Figure 2). Overall, 45% of the survey respondents were homeowners, followed by 42% of renter respondents. Thirteen percent of ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 6
respondents reported they are precariously housed (Figure 3). Four in ten respondents reported having household income greater than $100,000. Nearly 30% of respondents reported a household income between $50,000-99,999, followed by 15% of respondents who made between $25,000-49,999 and 16% of respondents making less than $25,000 (Figure 4).

The survey analysis also included selected demographic characteristics of respondents, including those with children under the age of 18 residing in their household, adults over the age of 65, respondents whose household includes a member experiencing a disability, those who live in large households, and single parents. Thirty five percent of respondents indicated they had children in their household, while 31% indicated they were older adults.

Thirty percent of respondents indicated they or a member of their household experienced a disability, 12% of respondents reported having large households, and 10% were single parents.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 7
Figure 2.

Survey Respondents
by Race/Ethnicity
Note:
n=1,937; 535 respondents did not
indicate their race or ethnicity.

Source:

Root Policy Research from the 2021-
2022 21 Elements AFFH Resident
Survey.

Figure 3.

Survey Respondents
by Tenure
Note:
n=2,426.

Source:

Root Policy Research from the 2021-
2022 21 Elements AFFH Resident
Survey.

Figure 4.

Survey Respondents
by Income
Note:
n=1,785.

Source:

Root Policy Research from the 2021-
2022 21 Elements AFFH Resident
Survey.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 8
Figure 5.

Survey Respondents
by Selected
Household
Characteristics
Note:

Denominator is total responses to the
survey (n=2,382)
Source:

Root Policy Research from the 2021-
2022 21 Elements AFFH Resident
Survey.

Housing, Neighborhood and Affordability Challenges
Housing challenges: overall. Survey respondents were asked to select the housing challenges they currently experience from a list of 28 different housing, neighborhood, and affordability challenges. Figures 6a through 8c present the top 10 housing and neighborhood challenges and top 5 affordability challenges experienced by jurisdiction, race/ethnicity, tenure, income, and selected household characteristics.

These responses allow a way to compare the jurisdictions to the county for housing challenges for which other types of data do not exist. In this analysis, “above the county”—shaded in light red or pink—is defined as the proportion of responses that is 25% higher than the overall county proportion. “Below the county”—shown in light blue—occurs when the proportion of responses is 25% lower than the overall county proportion.

As shown in Figure 6a, residents in Redwood City and East Palo Alto experience several housing challenges at a higher rate than the county overall. Conversely, Foster City and Hillsborough residents experience nearly all identified housing challenges at a lower rate than the county.

Notable trends in housing, neighborhood, and affordability challenges by geographic area include:
 Residents in Daly City, East Palo Alto, and Redwood City are less likely to move due to the lack of available affordable housing options.
 East Palo Alto, Redwood City, and San Mateo residents report living in housing that is too small for their families.
 Millbrae and Pacifica residents report being more reticent to request a repair to their unit in fear that their landlord will raise their rent or evict them.  Nearly 1 in 5 Pacifica survey respondents report that their home or apartment is in bad condition.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 9
 Brisbane residents are more likely to experience a landlord refusing to make repairs to their unit.
 Residents in Daly City and Millbrae are more likely to report that they don’t feel safe in their neighborhood or building
 Half Moon Bay and East Palo Alto expressed the greatest need for assistance in taking care of themselves or their home.

When compared to the county overall, the most common areas where respondents’ needs were higher than the county overall were:
 Overall, half of the jurisdictions’ respondents reported I need help taking care of myself/my home and can’t find or afford to hire someone at a higher rate than the county.  Nearly 40% of jurisdictions’ respondents reported a higher rate than the county for the following housing or neighborhood challenges: My home/apartment is in bad condition, my landlord refuses to make repairs despite my requests, and I don’t feel safe in my neighborhood/building.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 10
Figure 6a.

Top 10 Housing Challenges Experienced by Jurisdiction
Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,159 73 158 118 49 135 59 50 53 79 151 93 163 738
31% 12% 20% 51% 41% 16% 25% 4% 32% 28% 43% 30% 38% 35%
20% 11% 14% 24% 35% 10% 12% 4% 21% 11% 26% 20% 26% 21%
14% 10% 13% 17% 14% 9% 10% 2% 23% 15% 20% 11% 15% 13%
11% 14% 9% 15% 12% 3% 7% 0% 11% 18% 14% 5% 15% 10%
6% 14% 3% 5% 12% 4% 5% 2% 2% 9% 9% 5% 10% 5%
6% 5% 4% 8% 4% 5% 8% 6% 6% 3% 8% 4% 7% 5%
6% 5% 5% 13% 8% 0% 7% 6% 11% 10% 8% 3% 6% 3%
5% 7% 7% 7% 10% 2% 14% 2% 8% 9% 3% 4% 8% 4%
5% 5% 4% 3% 16% 2% 3% 4% 6% 9% 11% 6% 4% 3%
4% 5% 1% 3% 8% 11% 3% 2% 4% 5% 3% 3% 4% 2%
42% 48% 50% 20% 33% 55% 44% 76% 36% 47% 28% 45% 35% 46% Daly
City
East
Palo
Alto
Foster
City
I live too far from family/
friends/my community
I don't feel safe in my building/
neighborhood
I need help taking care of
myself/my home and can't find or
afford to hire someone
South San
Milbrae Francisco
Housing or Neighborhood
Condition
Valid cases
Half
Moon
Bay Hillsborough Pacifica
Redwood
City
San
Bruno
San
County Brisbane Burlingame Mateo
I would like to move but I can't
afford anything that is
available/income too low
My house or apartment isn't big
enough for my family
I worry that if I request a repair it
will result in a rent increase or
eviction
My home/apartment is in bad
condition
My landlord refuses to make repairs
despite my requests
I have bed bugs/insects or rodent
infestation
The HOA in my neighborhood won't
let me make changes to my house
or property
None of the above
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 11
The following three figures segment the answers by:
 Housing affordability challenges only; and
 Neighborhood challenges only.

Housing challenges. As shown in Figure 6b, residents in San Mateo, Daly City, East Palo Alto, and Pacifica experience affordability challenges at a higher rate than the county overall.

Conversely, Hillsborough, Burlingame, and South San Francisco residents experience affordability challenges at a lower rate than the county.

The most significant geographic variations occur in:
 San Mateo city residents experience all five affordability challenges at a greater rate than the county overall. In addition to being less likely to pay utility bills or rent on time, San Mateo residents are more than twice as likely than the average county respondent to have bad credit or a history of eviction/foreclosure that impacts their ability to rent.  San Mateo, East Palo Alto, and Daly City residents are most likely to experience difficulty paying utility bills.
 Residents in East Palo Alto and Redwood City are most likely to be late on their rent payments.
 Millbrae residents experience the greatest difficultly paying their property taxes among jurisdictions in San Mateo County.
 Respondents from Brisbane, Half Moon Bay, and Pacifica are more likely to have trouble keeping up with property taxes.
 City of San Mateo, Daly City and Redwood City respondents are more likely to have bad credit or an eviction history impacting their ability to rent Overall, nearly 40% of jurisdictions’ respondents experienced the following affordability challenges at a higher rate than the county: I can’t keep up with my property taxes and I have bad credit/history of evictions/foreclosure and cannot find a place to rent. .

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 12
Figure 6b.

Top 5 Affordability Challenges Experienced by Jurisdiction Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,130 73 157 115 51 134 58 50 50 77 147 93 160 728
10% 5% 6% 15% 16% 5% 12% 4% 12% 8% 12% 9% 15% 9%
8% 5% 6% 10% 20% 3% 7% 2% 8% 4% 12% 4% 11% 7%
6% 10% 4% 3% 2% 8% 10% 0% 16% 10% 3% 5% 9% 5%
4% 4% 2% 13% 6% 0% 0% 2% 0% 5% 8% 4% 10% 2%
4% 7% 3% 3% 2% 7% 3% 4% 4% 5% 3% 3% 6% 2%
73% 68% 80% 65% 59% 78% 66% 88% 64% 71% 70% 77% 63% 80% South San
Francisco
Valid cases
I can't keep up with my utilities
I'm often late on my rent payments
I can't keep up with my property
taxes
Half
Moon
Bay Hillsborough Milbrae Pacifica
Redwood
City
San
County Brisbane Burlingame Bruno
Daly
City
East
Palo
Alto
I have bad credit/history of
evictions/foreclosure and cannot
find a place to rent
I have Section 8 and I am worried my
landlord will raise my rent higher
than my voucher payment
None of the above
Affordability Challenges
San
Mateo
Foster
City
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 13
Neighborhood challenges. As shown in Figure 6c, residents in East Palo Alto, Brisbane, Daly City, and Pacifica experience neighborhood challenges at a higher rate than the county.

Burlingame and Foster City both experience neighborhood challenges at a lower rate than the county.

Hillsborough residents report divergent experiences related to neighborhood challenges — respondents identified more challenges around neighborhood infrastructure and access to transit but fewer challenges around school quality and job opportunities.

There are a handful of jurisdictions who experience specific neighborhood challenges at a disproportionate rate compared to the county.  For instance, East Palo Alto residents experience neighborhood infrastructure issues (e.g., bad sidewalks, no lighting) more acutely than county residents overall.  Brisbane residents experience transportation challenges in their neighborhoods.  East Palo Alto, Redwood City, and San Bruno experience challenges with school quality in their neighborhoods.
 Residents in Brisbane, Hillsborough, Pacific, and Half Moon Bay report the highest rates of difficulty accessing public transit.
 Daly City, Millbrae, San Mateo, and East Palo Alto residents were more likely to identify the lack of job opportunities available in their neighborhoods.

Over 30% of jurisdictions’ respondents experienced the following neighborhood challenges at a higher rate than the county: I can’t get to public transit/bus/light rail easily or safely and There are not enough job opportunities in the area.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 14
Figure 6c.

Top 5 Neighborhood Challenges Experienced by Jurisdiction Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,079 72 153 116 48 130 56 53 46 75 145 91 151 712
17% 18% 13% 25% 40% 4% 18% 23% 20% 15% 21% 14% 12% 16%
15% 18% 3% 17% 25% 4% 14% 2% 7% 13% 20% 20% 15% 20%
15% 24% 8% 14% 15% 21% 18% 9% 15% 24% 17% 14% 17% 10%
14% 29% 7% 9% 10% 14% 18% 25% 17% 21% 12% 13% 15% 10%
12% 8% 7% 20% 17% 8% 14% 0% 20% 13% 11% 11% 18% 12%
50% 28% 69% 45% 33% 62% 46% 57% 50% 52% 41% 52% 52% 55% South San
Neighborhood Challenges Francisco
Valid cases
My neighborhood does not have good
sidewalks, walking areas, and/or
lighting
Schools in my neighborhood are poor
quality
Half
Moon
Bay Hillsborough Milbrae Pacifica
Redwood
City
San
County Brisbane Burlingame Bruno
Daly
City
East
Palo
Alto
Bus/rail does not go where I need to
go or does not operate during the
times I need
I can't get to public transit/bus/light
rail easily or safely
There are not enough job
opportunities in the area
None of the above
San
Mateo
Foster
City
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 15
Differences in needs by race and ethnicity and housing tenure. As shown in Figure 7a, and compared to the county overall:
 African American, Hispanic, and Other race respondents, and  Renters and those who are precariously housed experience several housing challenges at a higher rate than the county overall.
 Conversely, non-Hispanic White residents and homeowners are less likely to experience housing challenges.

Specifically,
 Black or African American residents are more than three times as likely to have a landlord not make a repair to their unit after a request compared to county residents overall.

Hispanic, Other Race, and Precariously housed residents are also more likely to experience this challenge.
 African American, Asian, Hispanic, Renters, and Precariously Housed groups are more likely to experience bed bugs or rodent infestation in their homes.  African American, Hispanic, Renters, and Precariously Housed groups are also more likely to live further away from family, friends, and their community.  African Americans are three times more likely than the average county respondent to be told by their HOA they cannot make changes to their house or property. Asian households are twice as likely to experience this challenge.
 Hispanic, Other Race, and Renter respondents are more likely to worry that if they request a repair it will result in a rent increase or eviction and to report that their homes are in bad condition.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 16
Figure 7a.

Top 10 Housing Challenges Experienced by Race/Ethnicity and Tenure Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,159 132 489 392 144 734 986 974 301
31% 30% 32% 50% 31% 20% 7% 48% 56%
20% 16% 21% 35% 22% 11% 12% 29% 18%
14% 17% 13% 23% 19% 11% 2% 28% 13%
11% 12% 9% 16% 17% 10% 6% 17% 10%
6% 20% 7% 10% 10% 5% 2% 13% 10%
6% 15% 6% 6% 13% 6% 5% 8% 9%
6% 13% 6% 6% 9% 5% 4% 8% 7%
5% 14% 7% 5% 6% 5% 5% 6% 11%
5% 14% 8% 7% 5% 4% 4% 9% 9%
4% 14% 8% 4% 3% 3% 5% 3% 7%
42% 18% 37% 24% 38% 58% 68% 21% 13%
I need help taking care of myself/my home and can't find or afford to hire someone
I have bed bugs/insects or rodent infestation
The HOA in my neighborhood won't let me make changes
to my house or property
County
African
American Asian Hispanic
I would like to move but I can't afford anything that is available/income too low
Homeowner Renter
Precariously
Housing or Neighborhood Condition Housed
Valid cases
Other
Race
Non-Hispanic
White
None of the above
My house or apartment isn't big enough for my family
I worry that if I request a repair it will result in a rent increase or eviction
My home/apartment is in bad condition
My landlord refuses to make repairs despite my requests I live too far from family/ friends/my community
I don't feel safe in my building/ neighborhood
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 17
The above trends are similar for the most acute housing affordability challenges. As shown in Figure 7b, African American and Hispanic households, as well as renters and those precariously housed, experience affordability challenges at a higher rate than the county overall. Non-Hispanic White residents and homeowners experience these same challenges at a lower rate than the county.
 African American residents experience all five affordability challenges at a greater rate than the county overall.
 In addition to being more likely to not pay utility bills or rent on time, African American residents are more than four times as likely than the average county respondent to have a Section 8 voucher and worry that their landlord will raise their rent more than the voucher payment.
 Along with African American residents, Hispanic households, renters, and precariously housed households are most likely to experience difficulty paying utility bills, as well as have bad credit or eviction/foreclosure history impacting their ability to find a place to rent.  These groups, with the exception of those precariously housed, are also more likely to be late on their rent payments.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 18
Figure 7b.

Top 5 Affordability Challenges Experienced by Race/Ethnicity and Tenure Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,130 132 487 391 146 739 983 953 293
10% 22% 11% 17% 14% 5% 5% 15% 15%
8% 13% 6% 12% 12% 4% 1% 15% 8%
6% 16% 8% 4% 5% 7% 9% 5% 14%
4% 5% 3% 8% 4% 2% 1% 6% 11%
4% 18% 5% 6% 7% 2% 2% 7% 8%
None of the above 73% 32% 70% 63% 64% 83% 84% 61% 54%
County
African
American Asian Hispanic
Valid cases
I can't keep up with my utilities
I'm often late on my rent payments
I can't keep up with my property taxes
I have bad credit/history of evictions/foreclosure
and cannot find a place to rent
I have Section 8 and I am worried my landlord will
raise my rent higher than my voucher payment
Non-Hispanic
White Homeowner Renter
Precariously
Affordability Challenges Housed
Other
Race
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 19
As shown in Figure 7c, African American and precariously housed residents experience neighborhood challenges at a higher rate than the county. These two groups experience neighborhood issues related to transportation more acutely than county residents overall. In addition to Other race respondents, they are also more likely to identify the lack of job opportunities in their respective neighborhoods.

Additionally, Hispanic residents are more likely to live in neighborhoods with poor performing schools than the average county respondent. Homeowners are also more likely to report that they cannot access public transit easily or safely.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 20
Figure 7c.

Top 5 Neighborhood Challenges Experienced by Race/Ethnicity and Tenure Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,079 133 486 389 146 737 975 918 284
17% 14% 17% 19% 16% 18% 18% 15% 18%
15% 13% 18% 20% 17% 13% 18% 13% 13%
15% 33% 16% 13% 17% 17% 17% 14% 24%
14% 24% 15% 11% 16% 16% 18% 11% 19%
12% 22% 14% 12% 19% 9% 9% 15% 20%
50% 23% 46% 48% 45% 53% 49% 51% 36%
My neighborhood does not have good sidewalks, walking
areas, and/or lighting
County
African
American Asian Hispanic Homeowner Renter
Precariously
Neighborhood Challenges Housed
Valid cases
Other
Race
NonHispanic
White
Schools in my neighborhood are poor quality
Bus/rail does not go where I need to go or does not
operate during the times I need
I can't get to public transit/bus/light rail easily or safely There are not enough job opportunities in the area
None of the above
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 21
Differences in needs by household status. As shown in Figure 8a, single parents, households making less than $50,000, households with children under 18 and those with a member experiencing a disability experience the majority of housing challenges are more likely to experience housing challenges. Conversely, households making more than $100,000 experience nearly all specified housing challenges at a lower rate than the county.

Single parents experience all ten housing challenges at a greater rate than the county overall.

Households making less than $25,000 also experience every challenge at a higher rate, with the exception of I worry that if I request a repair it will result in a rent increase or eviction.

Households making less than $50,000, single parents, and households with children under 18 are more likely to experience the following challenges:
 My house or apartment isn’t big enough for my family;  My house or apartment is in bad condition;
 My landlord refuses to make repairs despite my request;  I live too far from family/friends/my community;
 I don’t feel safe in my building/neighborhood;
 I need help taking care of myself/my home and can’t find or afford to hire someone; and  I have bed bugs/insects or rodent infestation.

Households with a member experiencing a disability are also more likely to experience landlords refusing their requests to make repairs, living further away from family/friends/community, and not being able to find or afford someone to help take care of themselves or their homes. These households are also more likely to experience bed bugs, insects, or rodent infestation, as well as HOA restrictions impacting their ability to make changes to their home or property.

Additionally, large households have the highest proportion of respondents among the selected groups that would like to move but can’t afford anything that is available or because their income is too low.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 22
Figure 8a.

Top 10 Housing Challenges Experienced by Income and Household Characteristics Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,159 280 260 505 701 827 278 240 701 709
31% 47% 48% 37% 16% 35% 51% 40% 36% 25%
20% 25% 25% 23% 16% 34% 43% 32% 20% 13%
14% 16% 18% 19% 9% 19% 19% 28% 16% 11%
11% 15% 20% 12% 6% 15% 17% 17% 12% 9%
6% 13% 13% 8% 2% 9% 8% 14% 10% 6%
6% 9% 9% 6% 5% 10% 5% 10% 8% 6%
6% 9% 9% 6% 3% 8% 4% 10% 7% 5%
5% 9% 9% 5% 3% 7% 6% 12% 11% 6%
5% 10% 9% 5% 3% 9% 4% 15% 9% 6%
4% 7% 3% 4% 3% 7% 4% 11% 6% 5%
42% 21% 21% 37% 61% 28% 26% 12% 32% 49%
Above
Housing or Neighborhood Condition $100,000
Valid cases
I would like to move but I can't afford anything
that is available/income too low
My house or apartment isn't big enough for my
family
I worry that if I request a repair it will result in a
rent increase or eviction
My home/apartment is in bad condition
My landlord refuses to make repairs despite my
requests
None of the above
County
Less than
$25,000
$25,000-
$49,999
$50,000-
$99,999
I live too far from family/ friends/my community
I don't feel safe in my building/ neighborhood
I need help taking care of myself/my home and
can't find or afford to hire someone
I have bed bugs/insects or rodent infestation
The HOA in my neighborhood won't let me make
changes to my house or property
Children
under 18
Large
Households
Single
Parent Disability
Adults
(age 65+)
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 23
As shown in Figure 8b, households making less than $50,000, as well as large households, single parents, households with children under 18, and households with a member experience a disability, experience the most acute affordability challenges at a higher rate than the county overall. Households making more than $50,000 and adults over the age of 65 are less likely to experience affordability challenges.

Households making less than $25,000, single parents, and households with children under 18 experience all five affordability challenges at a greater rate than the average county respondent.

Households making less than $25,000 and households with a member experiencing a disability also disproportionately report affordability challenges.

Of households experiencing major affordability issues, single parent households are most acutely impacted. These households are more than three times as likely to have a Section 8 voucher and fear their landlord will raise the rent impacting the viability of their voucher, more than twice as likely to miss utility payments and have bad credit/eviction or foreclosure history impacting their ability to rent, and twice as likely to have trouble keeping up with their property taxes.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 24
Figure 8b.

Top 5 Affordability Challenges Experienced by Income and Household Characteristics Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,130 276 260 509 703 830 279 239 699 716
10% 16% 16% 12% 3% 16% 14% 23% 15% 8%
8% 19% 16% 6% 1% 11% 12% 15% 11% 4%
6% 7% 9% 8% 5% 9% 4% 12% 8% 7%
4% 8% 7% 4% 1% 5% 6% 10% 6% 3%
4% 11% 6% 4% 1% 7% 3% 14% 8% 5%
73% 46% 56% 72% 90% 59% 70% 32% 59% 75%
Adults
(age 65+)
I have Section 8 and I am worried my landlord
will raise my rent higher than my voucher
payment
None of the above
County
Less than
$25,000
$25,000-
$49,999
$50,000-
Affordability Challenges $99,999
Valid cases
I can't keep up with my utilities
I'm often late on my rent payments
I can't keep up with my property taxes
I have bad credit/history of
evictions/foreclosure and cannot find a place
to rent
Above
$100,000
Children
under 18
Large
Households
Single
Parent Disability
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 25
As shown in Figure 8c, households with children under 18, as well as single parents, households with a member experiencing a disability, and households making less than $25,000 are more likely to experience neighborhood challenges. These households are most likely to report that the bus/rail does not go where I need to go or does not operate during the times I need. In addition to households that make between $25,000-$100,000, these groups are more likely to identify the lack of job opportunities in their respective neighborhoods.

Households with children under 18 are more likely to live in neighborhoods with poor quality schools. Large households are more likely to report issues with neighborhood infrastructure (e.g., bad sidewalks, poor lighting) and households with a member experiencing a disability are more likely to report they cannot access public transit easily or safely.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 26
Figure 8c.

Top 5 Neighborhood Challenges Experienced by Income and Household Characteristics Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
County
2,079 273 259 503 709 824 277 234 692 714
17% 17% 15% 18% 17% 19% 22% 16% 19% 14%
15% 17% 14% 11% 19% 24% 19% 17% 14% 9%
15% 19% 16% 15% 16% 19% 11% 28% 19% 16%
14% 15% 12% 14% 14% 15% 12% 15% 19% 17%
12% 21% 17% 16% 6% 17% 12% 19% 15% 11%
50% 40% 45% 51% 53% 38% 48% 31% 41% 53%
Above
Neighborhood Challenges $100,000
Valid cases
My neighborhood does not have good
sidewalks, walking areas, and/or lighting
Schools in my neighborhood are poor
quality
There are not enough job opportunities
in the area
None of the above
Less than
$25,000
$25,000-
$49,999
$50,000-
$99,999
Bus/rail does not go where I need to go or
does not operate during the times I need
I can't get to public transit/bus/light rail
easily or safely
Children
under 18
Large
Households
Single
Parent Disability
Adults
(age 65+)
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 27
Experience Finding Housing
This section explores residents’ experience seeking a place to rent or buy in the county and the extent to which displacement—having to move when they do not want to move—is prevalent.

For those respondents who seriously looked for housing in the past five years, this section also examines the extent to which respondents were denied housing to rent or buy and the reasons why they were denied.

Recent experience seeking housing to rent. Figure 9 presents the proportion of respondents who seriously looked to rent housing for the county, jurisdictions, and selected respondent characteristics, as well as the reasons for denial.

Over half of county respondents (56%) have seriously looked for housing in the past five years.

The most common reasons for denial included:
 Landlord not returning the respondent’s call (26%),
 Landlord told me the unit was available over the phone but when I showed up in person, it was no longer available (22%), and
 Landlord told me it would cost more because of my service or emotional support animal (14%).

Jurisdictions with the highest percentage of respondents who seriously looked for housing include Millbrae (74%), San Mateo (73%), and Redwood City (72%). While all three jurisdictions reported that landlord not returning the respondent’s call was one of their main reasons for denial, 18% of Redwood City respondents identified landlord told me they do not accept Section 8 vouchers as a main reason for denial.

Eighty percent of African American respondents reported that they had seriously looked for housing in the past five years while the lowest percentage of respondents who reported seriously looking for housing were non-Hispanic White (46%). The main reasons for denial experienced by African American respondents included landlord told me the unit was available over the phone but when I showed up in person, it was no longer available (39%), landlord told me it would cost more because of my service or emotional support animal (34%), and landlord told me I couldn’t have a service or emotional support animal (28%).

Among respondents by tenure, renters (75%) and precariously housed (74%) tenants reported the highest rates of seriously looking for housing. Among respondents by income, households making less than $25,000 (71%) had the highest rate. However, the main reasons for denial reported by these households were landlord told me I couldn’t have a service or emotional support animal (36%) and landlord told me it would cost more because of my service or emotional support animal (30%).

Single parents (79%) and households with children under 18 (66%) also reported the highest percentage of those who seriously looked for housing in the past five years among the selected ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 28
household characteristics respondent groups. In addition to sharing the top two reasons for denial with the county, 25% of single parent household respondents also reported they were denied housing because the landlord told me I can’t have a service or emotional support animal.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 29
Figure 9. If you looked seriously for housing to rent in San Mateo County in the past five years, were you ever denied housing?
Note: The "Percent Seriously Looked for Housing" column includes all respondents, not just those who indicated they rent.

Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

Jurisdiction
County 56% 26% 22% 14% 45% 928
Brisbane 59% 41% 22% 26% 27
Burlingame 48% 19% 23% 54% 57
Daly City 63% 33% 16% 16% 44% 61
East Palo Alto 58% 35% 30% 26% 23
Foster City 50% 12% 16% 14% 55% 51
Half Moon Bay 68% 17% 17% 48% 29
Hillsborough 42% 14% 29% 14% 57% 14
Milbrae 74% 25% 46% 36% 28
Pacifica 51% 16% 26% 16% 55% 31
Redwood City 72% 31% 18% 40% 99
San Bruno 57% 22% 22% 39% 36
San Mateo 73% 30% 34% 39% 98
South San Francisco 47% 24% 13% 56% 248
Race/Ethnicity
African American 80% 39% 34% 28% 15% 101
Asian 56% 19% 29% 40% 199
Hispanic 63% 32% 22% 41% 230
Other Race 70% 29% 22% 45% 91
Non-Hispanic White 46% 29% 20% 48% 263
Tenure
Homeowner 36% 25% 15% 54% 183
Renter 75% 29% 22% 43% 641
Precariously Housed 74% 23% 32% 26% 188
Income
Less than $25,000 71% 30% 36% 29% 182
$25,000-$49,999 60% 39% 32% 27% 149
$50,000-$99,999 58% 24% 20% 45% 251
Above $100,000 48% 19% 14% 64% 216
Household Characteristics
Children under 18 66% 30% 29% 33% 447
Large Households 60% 33% 19% 18% 44% 139
Single Parent 79% 25% 35% 25% 19% 173
Disability 63% 24% 24% 34% 386
Older Adults (age 65+) 48% 20% 29% 39% 282
n
Overall
Percent
Seriously
Looked for
Housing
Reason for Denial
Landlord did not
return calls
and/or emails
asking about a
unit
Landlord said unit
was available over
phone, but when I
showed up in
person, it was no
longer available
Landlord told me it
would cost me
more for my
service or
emotional animal
Landlord told me
I can't have a
service or
emotional
support animal
Landlord told
me it would cost
me more to rent
because I have
children
Landlord told me
they don't rent to
families with
children
Landlord told
me they do not
accept Section 8
vouchers
Landlord told me they
couldn't make
changes to the
apartment/ home for
my disability
None
of the
Above
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 30
Recent experience seeking housing to buy. Figure 10 presents the proportion of respondents who seriously looked to buy housing in the county, by jurisdiction, and selected respondent characteristics, as well as the reasons for denial. As noted above, 56% of county respondents have seriously looked for housing in the past five years.

The most common reasons for denial included:
 Real estate agent told me I would need to show I was prequalified with a bank (29%) and  A bank would not give me a loan to buy a home (22%).

For the jurisdictions with the highest percentage of respondents who seriously looked for housing (Millbrae, San Mateo and Redwood City), all three cities shared the same top two reasons for denial as the county. Additionally, 21% of Millbrae respondents reported that the real estate agent would not make a disability accommodation when I asked.

For African American respondents who looked to buy housing in the last five years, the most common reason for denial was the real estate agent would not make a disability accommodation when I asked (47%).

African Americans, along with Other Races, also most commonly reported that they needed a loan prequalification before real estate agents would work with them. While between 43-54% of respondents from other racial/ethnic groups reported they did not experience any reason for denial when seriously looking to buy housing over the past five years, 12% of African American respondents reported similarly.

Among respondents by income, the main reasons for denial for households making less than $25,000 were the real estate agent told me I would need to show I was prequalified with a bank (32%) and real estate agent only showed me or only suggested homes in neighborhoods where most people were of my same race or ethnicity (26%).

Among the selected housing characteristics category, single parent households and households with children under 18 reported shared the same top two reasons for denial as the county. Additionally, 36% of single parent household respondents reported that the real estate agent would not make a disability accommodation when I asked, as well as 25% of respondents over the age of 65.

Residents in Redwood City, Millbrae, and South San Francisco, as well as large households, also reported that a bank or other lender charged me a high interest rate on my home loan as a reason for denial.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 31
Figure 10. If you looked seriously for housing to buy in San Mateo County in the past five years, were you ever denied housing?
Note: The "Percent Seriously Looked for Housing" column includes all respondents, not just those who indicated they rent.

Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. n
Jurisdiction
County 56% 29% 23% 50% 870
Brisbane 59% 36% 30% 42% 33
Burlingame 48% 22% 14% 61% 51
Daly City 63% 19% 27% 56% 52
East Palo Alto 58% 24% 33% 48% 21
Foster City 50% 25% 20% 49% 51
Half Moon Bay 68% 35% 23% 23% 50% 26
Hillsborough 42% 18% 23% 59% 22
Milbrae 74% 25% 29% 21% 21% 54% 28
Pacifica 51% 35% 35% 42% 31
Redwood City 72% 30% 22% 27% 50% 64
San Bruno 57% 14% 21% 62% 42
San Mateo 73% 40% 32% 38% 82
South San Francisco 47% 26% 18% 16% 57% 251
Race/Ethnicity
African American 80% 40% 38% 47% 12% 89
Asian 56% 30% 25% 43% 223
Hispanic 63% 29% 28% 49% 174
Other Race 70% 36% 21% 21% 50% 90
Non-Hispanic White 46% 29% 23% 54% 250
Tenure
Homeowner 36% 29% 17% 54% 332
Renter 75% 32% 27% 46% 467
Precariously Housed 74% 36% 36% 30% 30% 154
Income
Less than $25,000 71% 32% 25% 26% 41% 131
$25,000-$49,999 60% 42% 40% 29% 106
$50,000-$99,999 58% 35% 30% 38% 216
Above $100,000 48% 22% 13% 10% 64% 296
Household Characteristics
Children under 18 66% 33% 28% 40% 443
Large Households 60% 33% 25% 25% 49% 126
Single Parent 79% 38% 43% 36% 24% 143
Disability 63% 35% 26% 38% 330
Older Adults (age 65+) 48% 35% 29% 25% 38% 252
Percent
Seriously
Looked for
Housing
Reason for Denial
The real estate agent told
me I would need to show I
was prequalified with a
bank
A bank or other
lender would not
give me a loan to
buy a home
The real estate agent would
not make a disability
accommodation when I
asked
Only showed homes in
neighborhoods where
most people were same
race/ethnicity
A bank or other lender
charged me a high
interest rate on my
home loan
None of
the
Above
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 32
Denied housing to rent or buy. Figure 11 presents the proportion of those who looked and were denied housing to rent or buy for the county, jurisdictions, and selected respondent characteristics, as well as reason for denial. As shown, nearly 4 in 10 county respondents who looked for housing experienced denial of housing. African American/Black respondents, precariously housed respondents, households with income below $50,000, and single parent respondents have denial rates of 60% or higher. African American (79%) and single parent (74%) respondents report the highest rates of denial.

Among the reasons for denial:
 Income too low was a major reason for denial for all groups except homeowners and households with incomes above $100,000. Additionally, all jurisdictions report this as a common reason for being denied housing with the exception of Foster City, Hillsborough, and San Bruno.  Haven’t established a credit history or no credit history was also a common reason of denial for most groups. The impacts are higher for Asian, Hispanic and African American households, along with renter and precariously housed respondents, households with income below $50,000, and single parent households, households with children under 18, and households with a member experiencing a disability.
 Another top denial reason among certain groups is the landlord didn’t accept the type of income I earn (social security or disability benefit or child support). Source of income was the most common reason for denial among African American households (28%). Other groups with denial rates of 25% or higher for this specific issue include precariously housed respondents, single parent households, and households with a member experiencing a disability, as well as Foster City and San Bruno residents.
 Bad credit is another barrier for accessing housing, particularly for Hispanic and Other Race households, households with income between $50,000-$100,000, and large households. This also impacts East Palo Alto, San Mateo, Daly City, Redwood City, Burlingame, and South San Francisco residents.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 33
Figure 11. If you looked seriously for housing to rent or buy in San Mateo County in the past five years, were you ever denied housing?
Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

Jurisdiction
County 39% 1154 18% 44% 19% 21% 449
Brisbane 42% 38 25% 19% 31% 16
Burlingame 30% 71 24% 29% 21
Daly City 49% 73 28% 53% 28% 19% 36
East Palo Alto 55% 29 38% 44% 25% 16
Foster City 30% 63 25% 40% 30% 19
Half Moon Bay 41% 34 29% 29% 14
Hillsborough 23% 22 40% 5
Milbrae 36% 33 67% 25% 33% 25% 12
Pacifica 38% 39 47% 27% 33% 15
Redwood City 41% 105 28% 63% 26% 26% 43
San Bruno 25% 51 31% 31% 38% 13
San Mateo 48% 112 30% 38% 28% 53
South San Francisco 30% 331 19% 58% 28% 17% 98
Race/Ethnicity
African American 79% 107 25% 25% 25% 28% 27% 85
Asian 42% 281 38% 28% 21% 21% 117
Hispanic 49% 253 28% 60% 26% 26% 125
Other Race 43% 105 22% 49% 24% 45
Non-Hispanic White 31% 351 40% 19% 23% 25% 108
Tenure
Homeowner 26% 348 24% 22% 23% 91
Renter 45% 687 48% 20% 24% 310
Precariously Housed 61% 208 42% 22% 25% 126
Income
Less than $25,000 64% 199 47% 31% 29% 127
$25,000-$49,999 65% 158 48% 21% 20% 20% 103
$50,000-$99,999 38% 302 21% 51% 24% 114
Above $100,000 18% 346 27% 16% 20% 16% 64
Household Characteristics
Children under 18 51% 558 42% 26% 19% 283
Large Households 43% 171 27% 64% 41% 74
Single Parent 74% 189 41% 27% 25% 138
Disability 54% 446 39% 21% 25% 239
Older Adults (age 65+) 44% 350 35% 22% 21% 153
I had/
have
COVID
The
language I
speak Foreclosure n
Reason for Denial
Too many
people in my
household
Other
renter/
applicant
willing to pay
more for rent
Haven't
established a
credit
history/no
credit history
Don't have a
regular/
steady job/
consistent
work history
Landlord didn't
accept the type of
income I earn
(social security or
disability)
Lack of
stable
housing
record
Real or
perceived
sexual
orientation or
gender
identity
Criminal
background
Percent
Denied
Housing Total n
Bad
Credit
Eviction
history
Income
too low
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 34
Experience using housing vouchers. It is “difficult” or “very difficult” for eight out of 10 voucher holders to find a landlord that accepts a housing voucher (Figure 13).

As shown in Figure 12, this is related to the amount of the voucher and current rents and the lack of supply (inability to find a unit in the allotted amount of time). Over half of voucher holders (53%) who experienced difficulty indicated the voucher is not enough to cover the rent for places I want to live and almost half of voucher holders (49%) who experienced difficulty indicated there is not enough time to find a place to live before the voucher expires.

Other significant difficulties using vouchers identified by respondents included landlords have policies of not renting to voucher holders (46%) and can’t find information about landlords that accept Section 8 (36%).

Among respondents by race/ethnicity, African American respondents had the greatest proportion of those with a housing choice voucher (60%). Of those respondents, 76% found it difficult to find a landlord that accepts a housing voucher. While 13% of Hispanic respondents have a housing voucher, 85% have found it difficult to use the voucher. Fourteen percent of Asian respondents have housing vouchers— nearly three quarters of these respondents reported that the voucher is not enough to cover the rent for the places I want to live.

Other groups of respondents with higher proportions of voucher utilization include single parent households (43%), precariously housed respondents (30%), and households with income below $25,000 (29%). For each of the aforementioned groups, more than 75% of their respective respondents reported difficulty in utilizing the housing choice voucher. The voucher is not enough to cover the rent for places I want to live was one of the main reasons cited for not using the voucher.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 35
Figure 12.

Why is it difficult to
use a housing
voucher?
Source:

Root Policy Research from the 2021-
2022 21 Elements AFFH Resident
Survey.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 36
Figure 13. How difficult is it to find a landlord that accepts a housing voucher? ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 37
Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 38
Displacement. Figure 14 presents the proportion of residents who experienced displacement in the past five years, as well as the reason for displacement.  Overall, 21% of survey respondents experienced displacement in the past five years. Among all survey respondents, the main reason for displacement was rent increased more than I could pay (29%).
 Respondents who are precariously housed have higher rates of recent displacement than homeowners or renters; this suggests that when displaced a unit these housing-insecure tenants are more likely to couch surf or experience homelessness for some period of time before securing a new place to live.
 Among respondents by race/ethnicity, African American respondents reported the highest rate of displacement (59%). The primary reason reported by African American respondents for their displacement was housing was unsafe (e.g., domestic assault, harassment).

Twenty eight percent also reported that they were forced out for no reason.  Asian households, as well as homeowners, households that make less than $25,000, single parent households, households that include a member experiencing a disability, and Millbrae, Brisbane and Pacifica residents are also more likely than other respondents to have been displaced due to an unsafe housing situation (e.g., domestic assault, harassment).  Additionally, Asian, precariously housed respondents, households making less than $25,000, and single parent households are more likely than other respondents to have been displaced and not given a reason.

For respondents that had experienced displacements, they were asked to identify which city they moved from and which city they moved to. The most common moves to and from cities included:
 Moved within South San Francisco (28 respondents)
 Moved from outside San Mateo County to San Mateo (10 respondents)  Moved from San Bruno to South San Francisco (9 respondents)  Moved from Daly City to South San Francisco (9 respondents)  Moved within Burlingame (8 respondents)
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 39
Figure 14. Displacement Experience and Reasons for Displacement ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 40
Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 41
Children changing schools after displacement. Overall, for households with children that were displaced in the past five years, 60% of children in those households have changed schools. The most common outcomes reported among these respondents included school is more challenging (28%), they feel less safe at the new school (25%), and they are in a worse school (24%) (Figure 15).

Among respondents by race/ethnicity, non-Hispanic White households (44%) were the only subgroup to report that being displaced resulted in their children being in better schools. Of African American households that were displaced and have children, 87% reported that their children changed schools.

Of these respondents, 32% reported that their children feel safer at the new school but also have fewer activities.

Among respondents by tenure, precariously housed (78%) and homeowner (74%) households had the highest proportion of children who changed schools. The most common outcomes for precariously housed households included School is less challenging/they are bored (35%) and their children feel less safe at school (34%). For homeowner households, 39% reported that school is more challenging, followed by 31% who reported that their children feel less safe at school.

Among respondents by selected household characteristics, older adult (77%), single parent (74%), households with a member experiencing a disability (70%), and households with children under 18 (67%) all reported high proportions of children who changed schools. The most common outcomes for these respondents included School is more challenging and they feel less safe at the new school.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 42
Figure 15. Children Changing Schools and Outcomes, Displaced Households Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 43
Experience with housing discrimination. Overall, 19% of survey respondents felt they were discriminated against when they looked for housing in the area.1 As shown in Figure 16, African American respondents (62%), single parent households (44%) and precariously housed respondents (39%) are most likely to say they experienced housing discrimination. Residents with income above $100,000 and homeowners are least likely (11%).

Respondents who believed they experienced discrimination when looking for housing in the county reported when the discrimination occurred. Nearly half of respondents (45%) reported that the discrimination they experienced occurred between 2 and 5 years ago. Twenty eight percent of respondents reported that the discrimination occurred in the past year, 20% reported more than 5 years ago and 7% of respondents did not remember when the discrimination happened.

How discrimination was addressed. Respondents who believed they experienced discrimination when looking for housing in the county were asked to describe the actions they took in response to the discrimination. Overall, the most common responses to discrimination experienced by survey respondents were Nothing/I wasn’t sure what to do (42%), Moved/found another place to live (30%), and Nothing/I was afraid of being evicted or harassed (20%).

Among top responses for actions taken in response to experienced discrimination, every group reported Nothing/I wasn’t sure what to do with the exception of African American households and Brisbane residents (both groups top response was Moved/found another place to live). Similarly, survey respondents from Foster City and Redwood City were the only groups not to include Moved/found another place to live among their top responses. African American and Asian households, as well as single parent households, were more likely than other groups to contact either a housing authority, local fair housing organization, or the California Department of Housing or Civil Rights to report their discrimination incident.

Reasons for discrimination. Respondents who believed they experienced discrimination when looking for housing in the county provided the reasons why they thought they were discriminated against. Note that the basis offered by residents is not necessarily protected by federal, state, or local fair housing law, as respondents could provide open-ended and multiple reasons why they thought they experienced discrimination.

Examples of how respondents described why they felt discriminated against, which they provided as open-ended responses to the survey, include:
1 Note that this question applies to all respondents, not just those who seriously looked for housing in the past five years.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 44
Appearance/Characteristics
 “Because of my race and ethnicity”
 “[We] were given a subprime loan for home purchase for being Latinx, low-income and primarily Spanish-speaking; refinance last year was lower than expected.”  “It was clear my disability is the reason”
 “I have a child and a couple places told me they wouldn’t rent to me due to my son.”  “The agent asked if I was a tech worker. When I said no, the agent said the place was just rented, even though it was on the listing as active.”
 “I was approved for the unit and when they met my partner, who is Black, they said [the unit] was rented.”
Source of Income/Credit
 “Income was through SSDI”
 “The landlord wanted an excellent credit score…”
 “We were not able to provide all the requirement to rent, like SSN [social security number], income proof, employment, and we don’t make enough income…”
 “They wanted someone with income from employment not due to disability.”  “I was discriminated against because of my race and the fact that I had Section 8 at the time. Being African American and having Section 8 made a lot of people feel like I wouldn’t take care of their property.”
 “I am currently being discriminated against due to my need with rental help and because two of us in our household have a need for an emotional support animal.” Immigration status
 Mi hermana llamo a los departamentos donde yo vivo y la manager le dijo que no había disponible pero no era verdad también le dijo que hablara inglés y le pidió seguro social pensando que no tenia y le dijo que tenía que ganar una cierta cantidad de dinero para poder rentar. (My sister called the apartments where I live and the manager told her that there was no one available but it was not true.

She also told her to speak English and asked for social security thinking that she did not have it and told her that she had to earn a certain amount of money to be able to rent).

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 45
Figure 16. Percent of respondents who felt they were discriminated against and how was it addressed ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 46
Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 47
Experience of persons with disabilities. Overall, 35% of respondents’ households include a member experiencing a disability. Of these households, 26% said their housing does not meet their accessibility needs; 74% report that their current housing situation meets their needs. The three top greatest housing needs expressed by respondents included grab bars in bathroom or bench in shower (34%), supportive services to help maintain housing (33%), and ramps (26%). Other needs expressed by a substantial proportion of groups included wider doorways, reserved accessible parking spot by the entrance, and more private space in the facility in which I live.

Of respondents by jurisdiction, East Palo Alto (64%) has the lowest proportion of respondents with disabilities whose current housing situation meets their needs. Of these respondents, 63% indicated they needed supportive services to help maintain housing.

The highest proportion of respondents by group reporting that they or a member of their household experiences a disability were African American (71%), households making less than $25,000 (59%), single parent households (58%), and precariously housed respondents (56%).

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 48
Figure 17. Respondents experiencing a disability and their top three greatest housing needs Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 49
Transportation. Over 80% of respondents indicated the type of transportation used most often is driving a personal vehicle. This share was relatively similar across the majority of jurisdictions and was the number one type of transportation used across all jurisdictions and demographic characteristics.

The groups with the lowest proportion of those who primarily drive included African American (40%), households making less than $25,000 (53%), single parents (57%), and precariously housed (57%) respondents.

As shown in Figure 18, on average respondents are fairly satisfied with their transportation situation.

Those groups somewhat or not at all satisfied with their transportation options include African American (58%), Brisbane (51%), single parents (45%) and precariously housed (44%) respondents.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 50
Figure 18.

Are you satisfied
with your current
transportation
options?
Source:

Root Policy Research from the
2021-2022 21 Elements AFFH
Resident Survey.

ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 51
Solutions offered by residents. Respondents were asked a series of questions about how to improve their situations related to housing, employment, health, education and neighborhood.

Improve housing security. When asked what could improve a respondent’s housing security, the top answers among respondents by jurisdiction, race/ethnicity, tenure, income, and other selected housing characteristics were none of the above and help me with a downpayment/purchase.

The highest proportion of respondents among groups that selected None of the above includes:
 Hillsborough, 71%
 Owners, 65%
 Income greater than $100,000, 54%
 Residents of Foster City, 53%
 White, 51%
 Residents of Burlingame, 50%
The highest proportion of respondents among groups that selected Help me with a downpayment or purchase includes:
 Renters, 44%
 Large households, 42%
 Residents of Daly City, 41%
 Hispanic, 39%
 Precariously housed, 39%
 Residents of the City of San Mateo, 37%
Other solutions to improve housing security identified by several different groups included Help me with the housing search, help me pay rent each month, and find a landlord who accepts Section 8. The highest proportion of respondents among groups that selected these solutions includes:

Help me with the housing search
 Precariously housed, 39%
 Income less than $25,000, 34%
 Income between $25,000-$50,000, 29%
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 52
 Half Moon Bay residents, 27%
Help me pay rent each month
 Income less than $25,000, 35%
 Single parent, 31%
Find a landlord who accepts Section 8
 Black or African American, 37%
Improve neighborhood situation. When asked what could improve a respondent’s neighborhood situation, nearly every respondent group by jurisdiction, race/ethnicity, tenure, income, and other selected housing characteristics identified Better lighting. Other solutions flagged by multiple respondent groups to improve their neighborhood situations includes Improve street crossings and none of the above.

The highest proportion of respondents among groups that selected Better lighting includes:
 East Palo Alto residents, 45%
 Millbrae residents, 45%
 Other race, 42%
 Daly City residents, 41%
 Hispanic residents, 40%
 Income between $25,000-$50,000, 40%
 Income between $50,000-$100,000, 40%
The highest proportion of respondents among groups that selected Improve street crossings includes:
 San Mateo residents, 34%
 Single parent, 31%
The highest proportion of respondents among groups that selected None of the above includes:
 Foster City residents, 37%
 Hillsborough residents, 36%
 Burlingame residents, 28%
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 53
Additionally, 42% of Millbrae respondents chose Reduce crime, 40% of Brisbane respondents chose More stores to meet my needs, and 33% of Half Moon Bay respondents chose Build more sidewalks.

Improve health situation. When asked what could improve a respondent’s health situation, the majority of respondent groups by jurisdiction, race/ethnicity, tenure, income, and other selected housing characteristics selected Make it easier to exercise, More healthy food and None of the above.

The highest proportion of respondents among groups that selected Make it easier to exercise includes:
 Redwood City residents, 48%
 Hispanic, 42%
 South San Francisco residents, 41%
 City of San Mateo residents, 41%
 Asian, 41%
 Renters, 40%
The highest proportion of respondents among groups that selected More healthy food includes:
 East Palo Alto, residents 48%
 Precariously Housed, 47%
 Single parent, 41%
 Daly City residents, 40%
 Income less than $25,000, 38%
 Black or African American, 37%
 Large Households, 37%
The highest proportion of respondents among groups that selected None of the above includes residents from:
 Hillsborough, 48%
 Burlingame, 47%
 Foster City, 42%
 White, 41%
 Owners, 39%
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 54
Additionally, African American (34%) and San Bruno (29%) respondents identified Better access to mental health care as a solution to help improve their health situations.

Improve job situation. When asked what could improve a respondent’s employment situation, the majority of respondent groups by jurisdiction, race/ethnicity, tenure, income, and other selected housing characteristics selected Increase wages and None of the above.

The highest proportion of respondents among groups that selected Increase wages includes:
 Renters, 52%
 Single parents, 50%
 Hispanic, 49%
 Households with children, 49%
 Daly City residents, 49%
 Income between $50,000-$100,000, 49%
 Large households, 48%
The highest proportion of respondents among groups that selected None of the above includes:
 Hillsborough residents, 76%
 Owners, 58%
 White, 57%
 Over 65+, 53%
 Income greater than $100,000, 53%
 Foster City residents, 53%
Additionally, 29% of households with income less than $25K identified Find a job near my apartment or house as a solution to help improve their situation.

Improve education situation. When asked what could improve a respondent’s education situation for their children, the majority of respondent groups by jurisdiction, race/ethnicity, tenure, income, and other selected housing characteristics selected None of the above, Have more activities, and Stop bullying/crime/drug use at school.

The highest proportion of respondents among groups that selected None of the above includes:
 Burlingame residents, 55%
ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 55
 White, 52%
 Over 65+, 51%
 Hillsborough residents, 49%
 Foster City residents, 46%
 Brisbane residents, 45%
The highest proportion of respondents among groups that selected Have more activities includes:
 Single parent, 45%
 Households with children, 41%
 Large households, 41%
 Other race, 37%
 Daly City residents, 34%
 Hispanic, 34%
The highest proportion of respondents among groups that selected Stop bullying/crime/drug use at school includes:
 East Palo Alto residents, 38%
 Precariously housed, 31%
 Other race, 30%
 Redwood City residents, 29%
 Hispanic, 29%
 San Mateo residents, 28%
Additionally, 29% of Millbrae respondents identified Have better teachers at their schools as a means to improve the education situation in their respective households. 7 | HOUSING PLAN – GOALS AND POLICIES
South San Francisco Housing Element 2023-2031 221
South San Francisco Housing Element 2023-2031
APPENDIX 6.5 DISPARATE ACCESS TO EDUCATIONAL OPPORTUNITIES ROOT POLICY RESEARCH SECTION V, PAGE 1
Appendix 6.5
Disparate Access to Educational
Opportunities
This section examines the extent to which members of protected classes and those in poverty experience disparities in access to opportunity as measured by access to education. This section draws from data provided by the San Mateo Office of Education, the California Department of Education, and U.S. Census American Community Surveys (ACS). This section discusses the following topics:
 Changes in school enrollment during COVID-19 by race and ethnicity, and by groups with extenuating circumstances;
1  Achievement gaps by race and ethnicity and for groups with extenuating circumstances as measured by test scores, California State University or University of California admissions standards, and college-going rates;  Barriers to success measured by chronic absenteeism, dropout rates, and suspension rates.

After describing this section’s primary findings, we describe the county’s school districts before launching into data measuring achievement gaps and barriers to success.

Primary Findings
Student racial and ethnic diversity is modestly increasing. Student bodies in San Mateo County have become increasingly racially and ethnically diverse.  Hispanic students make up the largest ethnic group in the county’s schools, representing 38% of students in the 2020-2021 academic school year. This a slight increase from the 2010-2011 school year, where Hispanic students made up 37% of the population.
 There has been a large increase in Asian students, with 17% identifying as such in 2020-2021, an increase of 5 percentage points from 2010-2011. 1 The term “extenuating circumstances” is used in this section to capture students whose socioeconomic situations and/or disability may make standard educational environments challenging.

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 Students identifying as White (26%) have decreased by 3 percentage points since 2010- 2011.

Free and reduced lunch-qualifying students and English language learners are concentrated in a handful of schools. Overall, 29% of public school students in San Mateo County qualify for reduced or free lunch.  The rate of reduced lunch qualification was highest in Ravenswood City Elementary School District, where 83% of students qualify for reduced lunch. Also in Ravenswood City Elementary, 30% of students are experiencing homelessness. This is a large outlier in the county, where overall just 2% are experiencing homelessness.  Countywide, 20% of public school students are English learners. Again, this rate is highest at Ravenswood City Elementary, where 53% of students are English learners.

La Honda-Pescadero Unified School District, Jefferson Union High School, and Redwood City Elementary also have high rates of English learners, representing more than a third of students.

Enrollment is dropping. Public school enrollment reduced substantially in some areas during the pandemic. Total enrollment decreased by 3% between 2019-2020 and 2020-2021 in San Mateo County, which was the largest decrease of the decade.  Portola Valley and La Honda-Pescadero school districts had the largest enrollment decreases during COVID-19, with a 11% and 10% decline in enrollments, respectively.  Decreased enrollment was especially common among Pacific Islander students.

Between 2019-2021, enrollment among Pacific Islander students decreased by 6% (from 1,581 students in 2019-20 to 1,484 students in 2020-21), substantially higher than the 3% countywide average.
 Enrollment among migrant students decreased drastically by 16% over the same period (from 332 students to 279 students).

Learning proficiency is improving yet disparities exist. Across all racial and ethnic groups, the rate at which students met or exceeded English and mathematics testing standards has increased since the 2014-2015 school year. Students with extenuating circumstances (i.e., disability, facing homelessness, learning English) tend to score lower on English and mathematics tests than the overall student body.  Proficiency gaps are especially pronounced among English learning students in Portola Valley Elementary, Woodside Elementary, Menlo Park City Elementary, and Brisbane Elementary, where students with extenuating circumstances met or exceeded mathematics test standards at a rate at least 50 percentage points below the overall test rate in each district.

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 Students with disabilities in San Carlos Elementary and Las Lomitas Elementary school districts scored far below the overall student body: In these districts, students with disabilities met or exceeded mathematics test standards at 54 percentage points below the overall test rate.

Many students meet admissions standards for CSU or UC schools.  Among the high school districts in San Mateo County, Sequoia Union had the highest rate of graduates who met such admission standards, at 69%. On the other end of the spectrum, Cabrillo Unified and South San Francisco Unified had the lowest rates at 41%.
 Jefferson Union High School District had the most drastic increase in the share of graduates meeting CSU or UC standards: just 21% of students met these standards in 2016-2017 compared to 48% of students in 2019-2020. La Honda-Pescadero Unified School District experienced a 10 percentage point increase in this success rate over the same period.

Most school districts in the county have a college-going rate at 70% or higher—yet there are wide gaps by race and ethnicity.
 In every district, White students have a higher college-going rate than Hispanic students, but the largest gaps are in South San Francisco United, where 91% of White students go to college compared to just 68% of Hispanic students—a 23 percentage point gap.

Students with extenuating circumstances are highly concentrated in a few schools and move schools often due to housing instability.  Students with extenuating circumstances may need additional resources—e.g., onsite health care, free meals, tutoring—to be successful in school. When these students are concentrated into a few schools, the schools bear an unequal responsibility for providing needed resources. K-12 school funding in California has long been inadequate, and, although policymakers have recently allocated additional resources to schools with high proportions of low income children under a “concentration grant” system, funding gaps remain.
 The highest concentration of high needs students is found in Ravenswood City Elementary, where 30% of all students are experiencing homelessness and 83% qualify for free and reduced lunch.
 Currently, students whose families have been evicted do not have protections allowing them to remain in their current school district. This can result in frequent changes in schools for low income children, raising their vulnerability to falling behind in school.

Absenteeism, dropout rates, and discipline rates are highest for students of color, students with disabilities, and students with other extenuating circumstances. While 10% of students were chronically ROOT POLICY RESEARCH SECTION V, PAGE 4
absent during the 2018-2019 school year, chronic absenteeism rates were higher in districts with a large number of students experiencing economic and housing precarity.  For instance, Ravenswood Elementary, which has a 30% rate of homelessness among students, had one of the higher rates of chronic absenteeism at 16%.  Pacific Islander students (26%), Black/African American students (18%), and Hispanic students (15%) had notably higher rates of chronic absenteeism than the overall student population (10%).
 In most districts, chronic absenteeism is higher among students with disabilities. In fact, only Bayshore Elementary’s students with disabilities had a lower rate of chronic absenteeism than the overall student body.

Dropout rates vary across the county:
 Dropout rates were highest in Sequoia Union High School District (10%) and South San Francisco Unified (9%).
 In all school districts in the county, dropout rates are higher for boys than for girls.  Pacific Islander, Black/African American, and Hispanic students in the county often had higher dropout rates than those in other racial and ethnic groups  Students with disabilities, students experiencing homelessness, foster youth, and students learning English had higher dropout rates than the overall population.

Discipline rates also vary by area and race and ethnicity.  In many school districts across San Mateo County, Hispanic students are disciplined at disproportionately higher rates compared to their peers.  In most districts, Black/African American and Pacific Islander students are also overrepresented in terms of suspension rates, but these rates are slight compared to those of Hispanic students.
 Asian and Filipino students were underrepresented in terms of suspension rates.

White students were also underrepresented in discipline rates in most districts except for La Honda-Pescadero.

The demographics of faculty and staff are fairly similar to that of students.  There is a slightly larger share of White and Black/African American staff than students, meaning that Black/African American and White student groups are more likely to interact with same-race staff and faculty than other racial groups.  Asian students are less likely to interact with a same-race staff of faculty member: 17% of the student body is Asian compared to just 8% of staff and faculty.

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Background
This section describes the school districts in San Mateo County, including their geographic boundaries and a brief history of the school districts’ formation. This section also includes details on how districts’ enrollments and student demographic have changed over time.

San Mateo County School Districts. There are three unified school districts in San Mateo County which include both elementary and high schools. These are Cabrillo Unified School District, La Honda-Pescadero Unified School District, and South San Francisco Unified School District.

In addition to the unified school districts, there are three high school districts, which include: Jefferson Union High School District, San Mateo Union High School District, and Sequoia Union High School District. The elementary schools covering these high schools’ district boundaries areas are described below:
 In the Jefferson Union High School District geographic boundary, elementary school districts are the Bayshore Elementary School District, Brisbane School District, Jefferson Elementary School District, and Pacifica School District.  Within the San Mateo Union High School District geographic boundary, elementary school districts include San Mateo-Foster City School District, Hillsborough City School District, Burlingame School District, San Bruno Park School District, and Millbrae School District.
 Within the Sequoia Union High School District geographic boundary, the elementary schools include Belmont-Redwood Shores School District, San Carlos School District, Redwood City School District, Ravenswood City School District, Menlo Park City School District, Woodside Elementary School District, Las Lomitas Elementary School District, and Portola Valley School District.

Geographic boundaries of school districts. Figure V-1 illustrates the geographic boundaries of the unified school districts as well as the three high school districts. Municipal boundaries are overlayed on the map.

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Figure V-1.

Unified School Districts and High School Districts in San Mateo County Source: San Mateo County Office of Education.

As illustrated in the map, Cabrillo Unified School District covers Half Moon Bay and some unincorporated areas of San Mateo County. South San Francisco Unified covers South San ROOT POLICY RESEARCH SECTION V, PAGE 7
Francisco and a small portion of Daly City. La Honda-Pescadero Unified School District covers unincorporated areas of San Mateo County.

The other high school districts, Jefferson Union, San Mateo Union, and Sequoia Union, cover the remaining jurisdictions. Jefferson Union covers Brisbane, Colma, Daly City, and Pacifica. San Mateo Union covers Burlingame, Hillsborough, Millbrae, San Bruno, San Mateo City, and Foster City. Sequoia Union covers Atherton, Belmont, Redwood City, East Palo Alto, Menlo Park, San Carlos, Portola Valley, and Woodside.

The county’s elementary school districts cover the same areas as the three high school districts. Their geographic boundaries are illustrated in the map below.

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Figure V-2.

Elementary School Districts in San Mateo County
Source: San Mateo County Office of Education.

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Because the elementary school districts are much smaller, many jurisdictions have several elementary schools. The table blow shows each jurisdiction and their associated elementary school.

Figure V-3.

School Districts in San Mateo County’s Jurisdictions
Source: San Mateo County Office of Education.

A brief history of district formation. San Mateo County’s numerous school districts were formed over a century ago, when the county was more rural and scattered:
communities needed elementary schools close to home, and only a few students were attending high school. As young people began going to high school, individual districts often found they had too few students and resources to support their own high schools, so Jurisdiction
Atherton Sequoia Union Menlo Park City ; Las Lomitas Elementary; Redwood City
Belmont Sequoia Union Belmont-Redwood Shores
Brisbane Jefferson Union Brisbane; Bayshore Elementary
Burlingame San Mateo Union Burlingame
Colma Jefferson Union Jefferson Elementary
Daly City Jefferson Union; South San Francisco Unified Jefferson; Bayshore Elementary East Palo Alto Sequoia Union Ravenswood City
Foster City San Mateo Union San Mateo-Foster City
Half Moon Bay Cabrillo Unified (none, included in Cabrillo Unified) Hillsborough San Mateo Union Hillsborough City
Menlo Park Sequoia Union Menlo Park City; Las Lomitas Elementary; Ravenswood City
Millbrae San Mateo Union Millbrae
Pacifica Jefferson Union Pacifica
Portola Valley Sequoia Union Portola Valley
Redwood City Sequoia Union Redwood City
San Bruno San Mateo Union San Bruno Park
San Carlos Sequoia Union San Carlos; Redwood City
San Mateo San Mateo Union San Mateo-Foster City
South San Francisco South San Francisco Unified (none, included in South San Francisco Unified) Woodside Sequoia Union Woodside Elementary; Portola Valley; Las Lomitas; Redwood City
Unified or High School District Elementary School District(s) ROOT POLICY RESEARCH SECTION V, PAGE 10
separate high school districts, covering the territories of two or more elementary districts, were established to meet the communities’ needs.2
Once California’s population grew and San Mateo County became more urbanized, “a jigsaw puzzle of overlapping districts evolved haphazardly.” Since 1920, the state has been pushing elementary districts to unify with the high school districts that serve their communities, citing improved educational quality and equity of opportunity. However, there has been limited success and local voters in San Mateo County have consistently resisted unification.3
Early efforts at unification were more successful in the rural communities along the coast— for example, voters approved the new Cabrillo Unified district for the area around Half Moon Bay and the La Honda-Pescadero Unified district in a 1964 election. Unification was not supported by many suburban communities edging the Bay. The county’s school district committee proposed to split each of the three high school districts and feeder schools into two or three smaller unified districts, but the State Board of Education rejected variations of those plans three times. The Board argued that the county committee’s proposals would create districts with widely varying property tax bases and could contribute to racial segregation. The State Board instead devised a plan that would create a single unified district within each of the existing high school district boundaries. Voters turned down the state plans in all three districts in June 1966, and rejected a similar proposal again in 1972.

In 1973, the Mid-Peninsula Task Force for Integrated Education petitioned the county committees to unify the elementary districts of Menlo Park, Las Lomitas, Portola Valley, Ravenswood and a portion of Sequoia Union High School District across county lines with Palo Alto Unified. Their goal was racial integration, but the county committee did not support the effort.4
Efforts against unification have persisted, leaving the county with several elementary school districts which feed into a high school, rather than a unified district. As a result, some elementary school districts have faced waning budgets and administrative hurtles.

For instance, Brisbane and Bayshore elementary school districts, at the northern end of the county, serve a little more than 1,000 students and long have struggled with tight budgets.

To rectify their budgetary concerns, the districts now share both a superintendent and a chief business officer. They also participate in a special education collaborative with the Jefferson elementary and high school districts.

According to the county’s superintendent of schools Anne Campbell, other districts may find themselves pooling their resources in the future: local identification may be strong, 2 Watson, Aleta. “How Did We End Up With 54 School Districts in San Mateo and Santa Clara Counties?” Silicon Valley Community Foundation, 2012. https://www.siliconvalleycf.org/sites/default/files/report-edu.pdf 3 Ibid.
4 Ibid.

ROOT POLICY RESEARCH SECTION V, PAGE 11
she says, but financial reality is hard to ignore: “As we move forward in time, I think it’s going to be interesting to see what school districts are going to do, especially as budgets get more bleak.”5
Enrollment changes. Total public school enrollment in the county has decreased slightly, by just 1%, from the 2010-2011 academic year to 2020-2021. Figure V-4 illustrates enrollment changes by district.

Bayshore Elementary, Ravenswood City, and Portola Valley school districts experienced the largest enrollment decreases (by at least 30%) between 2010-11 and 2020-21. School districts with the largest increases in enrollments were Burlingame (22%) and BelmontRedwood Shores (30%). 5 Ibid.

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Figure V-4.

Enrollment changes by district, 2010-11 to 2020-2021
Source: California Department of Education and Root Policy Research However, it is important to note that many of these enrollment decreases were driven by the pandemic. In fact, total enrollment in these public schools decreased by 3% between School District
Unified School Districts
Cabrillo Unified 3,352 2,934 -12%
La Honda-Pescadero 341 275 -19%
South San Francisco 9,312 8,182 -12%
High & Elementary School Districts
Jefferson Union High School 4,960 4,705 -5%
Bayshore Elementary 543 361 -34%
Brisbane Elementary 545 474 -13%
Jefferson Elementary 6,998 6,653 -5%
Pacifica 3,164 3,006 -5%
San Mateo Union High School 8,406 9,760 16%
Burlingame Elementary 2,771 3,387 22%
Hillsborough City Elementary 1,512 1,268 -16%
Millbrae Elementary 2,222 2,238 1%
San Bruno Park Elementary 2,599 2,275 -12%
San Mateo-Foster City 10,904 10,969 1%
Sequoia Union High School 8,765 10,327 18%
Belmont-Redwood Shores 3,206 4,152 30%
Las Lomitas Elementary 1,336 1,116 -16%
Menlo Park City Elementary 2,629 2,781 6%
Portola Valley Elementary 711 491 -31%
Ravenswood City Elementary 4,285 2,993 -30%
Redwood City Elementary 9,119 8,086 -11%
San Carlos Elementary 3,212 3,265 2%
Woodside Elementary 453 369 -19%
Total Enrollment 91,345 90,067 -1%
2010-2011
Enrollment
2020-2021
Enrollment Percent Change
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2019-2020 and 2020-2021 in San Mateo County: the largest decrease of the decade. As shown in Figure V-5, enrollments actually increased steadily from 2010-2011 to 2017-2018, then began decreasing afterwards.

Figure V-5.

Public School Enrollment Changes, 2010-2011 to 2020-2021 Note: These data exclude enrollments in SBE Everest Public High School District, which in 2015 combined with the Sequoia Union High School District.

Source: California Department of Education and Root Policy Research Portola Valley and La Honda-Pescadero school districts had the largest enrollment decreases during COVID-19, with a 11% and 10% decline in enrollments, respectively. The only school district with increasing enrollments between the 2019-2020 to 2020-2021 school years was Sequoia Union High School District, with a modest 1% increase in enrollments.

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Figure V-6.

Enrollment changes by district during COVID-19, 2019-20 to 2020-21 Source: California Department of Education and Root Policy Research.

Declining enrollments in public schools have been common across the state and country during the COVID-19 pandemic, and enrollment declines in San Mateo County are on par School District
Unified School Districts
Cabrillo Unified 3,136 2,934 -6%
La Honda-Pescadero 306 275 -10%
South San Francisco 8,438 8,182 -3%
High & Elementary School Districts
Jefferson Union High School 4,811 4,705 -2%
Bayshore Elementary 381 361 -5%
Brisbane Elementary 476 474 0%
Jefferson Elementary 6,687 6,653 -1%
Pacifica 3,110 3,006 -3%
San Mateo Union High School 9,885 9,760 -1%
Burlingame Elementary 3,534 3,387 -4%
Hillsborough City Elementary 1,290 1,268 -2%
Millbrae Elementary 2,349 2,238 -5%
San Bruno Park Elementary 2,454 2,275 -7%
San Mateo-Foster City 11,576 10,969 -5%
Sequoia Union High School 10,238 10,327 1%
Belmont-Redwood Shores 4,314 4,152 -4%
Las Lomitas Elementary 1,208 1,116 -8%
Menlo Park City Elementary 2,922 2,781 -5%
Portola Valley Elementary 551 491 -11%
Ravenswood City Elementary 3,269 2,993 -8%
Redwood City Elementary 8,530 8,086 -5%
San Carlos Elementary 3,405 3,265 -4%
Woodside Elementary 376 369 -2%
Total Enrollment 93,246 90,067 -3%
2019-2020
Enrollment
2020-2021
Enrollment Percent Change
ROOT POLICY RESEARCH SECTION V, PAGE 15
with those across the state. According to a study conducted by the Public Policy Institute of California, public K–12 enrollment declined by 3% in California from the 2019-2020 school year to the 2020-2021 school year. 6
As funding is tied directly to the number of enrolled pupils, schools in San Mateo County could suffer fiscal consequences with continued declines. By law, districts are “held harmless” for declines for one year—that is, school budgets for 2020–2021 were unaffected, but continued enrollment declines could mean cuts in future years.7 Reductions in enrollments, and consequently funding, could also worsen economic inequality in the long-term by reducing students’ resources and access to opportunities.

Demographics: race & ethnicity. Over the last decade, San Mateo County’s school districts have diversified in terms of students’ race and ethnicity. Hispanic students make up the largest ethnic group in the county’s schools: 38% of students identified as Hispanic in the 2020-2021 academic school year. This is just a one percentage point increase from 2010-2011. Many other students are White (26%), though this has decreased by 3 percentage points since 2010-2011, The largest increase was in Asian students, with 17% identifying as such in 2020-2021, an increase of 5 percentage points from 2010-2011.

Other students identify as Filipino (8%), or bi- or multi-racial (8%). A small and decreasing percentage of students identify as Black/African American (1%) and Pacific Islander (2%). 6 Lafortune, Julien & Prunty, Emmanuel. “Digging into Enrollment Drops at California Public Schools.” Public Policy Institute of California. May 14, 2021. https://www.ppic.org/blog/digging-into-enrollment-drops-at-california-publicschools/ 7 Ibid.

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Figure V-7.

Changes in Race and
Ethnicity, 2010-2011 to 2020-
2021
Note: These data exclude enrollments in SBE
Everest Public High School District, which
in 2015 combined with the Sequoia Union
High School District.

Source: California Department of Education and
Root Policy Research
:

Figure V-8 shows the racial and ethnic distribution of students enrolled in public schools by jurisdiction in 2020-2021.
 Portola Valley Elementary School District (66%) and Woodside Elementary School District (64%) had the highest share of White students, making them among the least racially and ethnically diverse districts in the county.  Ravenswood City Elementary School District and Redwood City Elementary School District had the highest share of Hispanic students, at 84% and 70%, respectively.  Ravenswood City also had the highest proportion of Pacific Islander students (7%) and Black/African American students (5%) compared to other districts.  Millbrae Elementary (46%), Hillsborough Elementary (32%), and Belmont-Redwood Shores Elementary (32%) had the highest share of Asian students.  Jefferson Elementary School District and Jefferson Union High School District had the highest portion of Filipino students, at 25% and 29% respectively.

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Figure V-8.

Student body by Race and Ethnicity, 2020-2021
Note: In almost all school districts, less than 1% of students were Native American, so they are not included in this table.

Source: California Department of Education and Root Policy Research Enrollment changes due to COVID-19 varied by race and ethnicity. For instance, between 2019-2021, enrollment among Pacific Islander students decreased by 6% (from 1,581 students in 2019-20 to 1,484 students in 2020-21). This is substantially higher than the 3% countywide average. Enrollments among Filipino and Hispanic students decreased by 4% while enrollment among Black/African American students decreased by 2%. On the other School District
Unified School Districts
Cabrillo Unified 1% 0% 1% 52% 0% 40% 5%
La Honda-Pescadero 0% 0% 1% 63% 0% 35% 1%
South San Francisco 14% 1% 23% 48% 2% 6% 6%
High & Elementary School Districts
Jefferson Union High School 15% 1% 29% 31% 1% 14% 7%
Bayshore Elementary 19% 3% 21% 41% 4% 3% 8%
Brisbane Elementary 20% 1% 12% 28% 0% 24% 11%
Jefferson Elementary 19% 2% 25% 36% 1% 11% 5%
Pacifica 8% 1% 9% 26% 0% 39% 16%
San Mateo Union High School 23% 1% 5% 32% 2% 28% 10%
Burlingame Elementary 27% 0% 3% 16% 0% 41% 9%
Hillsborough Elementary 32% 0% 2% 5% 0% 48% 12%
Millbrae Elementary 46% 1% 6% 20% 2% 16% 8%
San Bruno Park Elementary 16% 1% 10% 41% 5% 15% 1%
San Mateo-Foster City 26% 1% 3% 37% 2% 21% 9%
Sequoia Union High School 9% 2% 1% 45% 2% 35% 5%
Belmont-Redwood Shores 32% 1% 3% 12% 1% 34% 14%
Las Lomitas Elementary 18% 1% 1% 13% 0% 53% 14%
Menlo Park City Elementary 13% 1% 1% 17% 1% 55% 11%
Portola Valley Elementary 6% 0% 0% 14% 0% 66% 13%
Ravenswood City Elementary 0% 5% 0% 84% 7% 1% 2%
Redwood City Elementary 4% 1% 1% 70% 1% 19% 4%
San Carlos Elementary 18% 1% 1% 14% 0% 49% 13%
Woodside Elementary 4% 2% 0% 16% 1% 64% 11%
Total 17% 1% 8% 38% 2% 26% 8%
Asian White
Two or
Black Filipino Hispanic more races
Pacific
Islander
ROOT POLICY RESEARCH SECTION V, PAGE 18
end of the spectrum, there was a 3% increase in enrollment among White students (from 22,308 students to 23,055 students) between 2019-20 and 2020-21. Similarly, there was a 1% increase in enrollment among Asian students and a 4% increase among students of two or more races.

Figure V-9.

Enrollment Changes by Race and Ethnicity, San Mateo County, 2019-20 to 2020-21
Source: California Department of Education and Root Policy Research While many of their families may have simply moved out of San Mateo County during the pandemic, it is possible that Black/African American, Filipino, Hispanic, and Pacific Islander students are otherwise slipping through the cracks of the education system during this period.

Demographics: students with extenuating circumstances. Several students in the county’s public schools are facing additional hurtles to educational ease.

Many are English learners, qualify for reduced lunch, are foster children, are experiencing homelessness, have a disability, or are migrants. Students in these groups often have hindrances to excelling in school because of detrimental circumstances beyond their control. These include financial and social hardships as well as problems within students' families.

Qualification for free and reduced lunch is often used as a proxy for extenuating circumstances. Qualifications are determined based on household size and income. For instance, in the 2020-2021 academic year, students from a household of three making less ROOT POLICY RESEARCH SECTION V, PAGE 19
than $40,182 annually qualified for reduced price meals, and those making less than $28,236 in a household of three qualified for free meals.8 Free and reduced lunch disparities. Overall, 29% of public school students in San Mateo County qualify for reduced or free lunch. This rate was substantially lower in districts like Hillsborough Elementary, San Carlos Elementary, Portola Valley Elementary, Las Lomitas Elementary, Belmont-Redwood Shores, and Menlo Park City Elementary, where each had less than 10% of students qualify for free or reduced lunch.

The rate of reduced lunch qualification was far higher in Ravenswood City Elementary School District, where 83% of students qualify for reduced lunch.

Disparities in homelessness. In Ravenswood City Elementary, 30% of students are experiencing homelessness. This is an outlier in the county, where overall just 2% are experiencing homelessness. The school district has received media attention due to its astronomically high rate of students experiencing homelessness. Some have noted that rates of homelessness have increased due to escalating costs of living in an area surrounded by affluence.
9 Others have highlighted that ”Having a roof over your head, having a safe place to sleep and study, is fundamental to absolutely everything," and have noted that students who experience homelessness have higher dropout rates and are more likely to experience homelessness as adults.10
School moves related to evictions. Currently, students whose families have been evicted do not have protections allowing them to remain in their current school district.

This means that precarious housing also means precarious schooling for many of the county’s students. Frequent moves by students are closely related to lower educational proficiency.

In the City of San Francisco, a 2010 ordinance protects some students from being evicted during the school year; however, it only relates to owner/relative move-in evictions.11 Children in families who are evicted for other reasons may need to move schools or districts when their housing is lost.

English language learners. Countywide, 20% of public school students are English learners. Again, this rate is highest at Ravenswood City Elementary, where 53% of students are English learners. La Honda-Pescadero Unified School District, Jefferson Union High 8 “Income Eligibility Scales for School Year 2020-2021.” California Department of Education. 9 Bartley, Kaitlyn. “Homelessness: The shadow that hangs over students in this Bay Area school district.” The Mercury News. December 2018.
10 Jones, Carolyn. “California schools see big jump in homeless students.” Palo Alto Online. October 2020. 11 https://sfrb.org/new-amendment-prohibiting-owner-move-evictions-minor-children-during-school-year ROOT POLICY RESEARCH SECTION V, PAGE 20
School, and Redwood City Elementary also have high rates of English learners, representing more than a third of students.

Less than one percent of students in San Mateo County public school districts are foster youth or migrants. Cabrillo Unified School District had the highest rate of migrant students at 3%. La Honda-Pescadero had the highest rate of foster children at 2%.

School districts without large low income populations also tend to serve very few English language learners. For instance, in Hillsborough Elementary where 0% of students qualify for reduced lunch, only 1% of students are English language learners.

ROOT POLICY RESEARCH SECTION V, PAGE 21
Figure V-10.

Students with Extenuating Circumstances, 2020-2021
Source: California Department of Education and Root Policy Research The overall share of students in these groups has not changed drastically over time. As shown in Figure V-11, there have been slight decreases in the share of students who are English learners and the share of students who qualify for reduced lunch from 2016-2017 School District
Unified School Districts
Cabrillo Unified 20% 37% 0% 2% 3%
La Honda-Pescadero 38% 38% 2% 1% 1%
South San Francisco 21% 34% 0% 1% 1%
High & Elementary School Districts
Jefferson Union High School 36% 44% 0% 0% 0%
Bayshore Elementary 30% 57% 0% 0% 0%
Brisbane Elementary 16% 19% 0% 0% 0%
Jefferson Elementary 14% 27% 0% 1% 0%
Pacifica 9% 18% 0% 1% 0%
San Mateo Union High School 10% 21% 0% 0% 0%
Burlingame Elementary 13% 11% 0% 0% 0%
Hillsborough Elementary 1% 0% 0% 0% 0%
Millbrae Elementary 19% 25% 0% 0% 0%
San Bruno Park Elementary 29% 18% 0% 0% 0%
San Mateo-Foster City 26% 28% 0% 2% 0%
Sequoia Union High School 15% 30% 0% 0% 0%
Belmont-Redwood Shores 10% 7% 0% 0% 0%
Las Lomitas Elementary 7% 6% 0% 0% 0%
Menlo Park City Elementary 6% 7% 0% 0% 0%
Portola Valley Elementary 4% 5% 0% 0% 0%
Ravenswood City Elementary 53% 83% 0% 30% 0%
Redwood City Elementary 38% 56% 0% 2% 1%
San Carlos Elementary 5% 6% 0% 0% 0%
Woodside Elementary 8% 10% 0% 0% 0%
Total 20% 29% <1% 2% <1%
Migrant
Reduced
Lunch
English
Learners
Foster
Children Homeless
ROOT POLICY RESEARCH SECTION V, PAGE 22
to 2020-2021. Around 2% of students in the county are homeless and this has not changed between 2016-2017 and 2020-2021. Foster youth and migrant students are not shown in the figure, as both have hovered at less than 1% from year to year.

Figure V-11.

Changes in rates of English
Leaners, Reduced Lunch,
and Homelessness, 2016-
2017 to 2020-2021
Note: These data exclude enrollments in SBE
Everest Public High School District, which
in 2015 combined with the Sequoia Union
High School District.

Source: California Department of Education and
Root Policy Research
:

During COVID-19, enrollments decreased by 3% between 2019-2020 and 2020-2021 school years, as families withdrew or did not reenroll their children from public schools.

Enrollment among migrant students decreased much more drastically, by 16% (from 332 students to 279 students). Similarly, enrollment among students who qualify for reduced lunch declined at a higher rate (10%) than the overall student population. Foster children and English learners also experienced enrollment decreases at a rate higher than the total population, with 7% and 10% decreases in enrollment, respectively.

ROOT POLICY RESEARCH SECTION V, PAGE 23
Figure V-12.

Enrollment Changes by Extenuating Circumstance, San Mateo County, 2019-2020 to 2020-2021
Source: California Department of Education and Root Policy Research Achievement Gaps
This section details achievement gaps within school districts. Gaps are measured by test scores, meeting California State University or University of California admissions standards, and college-going rates.

Test scores. Figure V-13 indicates the percent of students who met or exceeded English and mathematics testing standards set by the California State Assessment of Student Performance and Progress. Overall, 62% of students in the county met or exceeded English testing standards and 52% met or exceeded mathematics testing standards.

Of all the districts with high schools, San Mateo Union High School District had the highest student pass rates: 70% of their students met or exceeded standards in English testing and 50% met or exceeded standards in mathematics testing.

Among elementary school districts, Portola Valley Elementary School District and Woodside Elementary School District had the highest rates of success in English, with 87% and 88% of students meeting or exceeding English testing standards, respectively. Woodside Elementary School District and Hillsborough Elementary School District had the highest rates of success in mathematics, with 84% and 85% meeting math testing standards, respectively.

In every school district, girls scored higher on English tests than boys. Overall, girls met or exceeded English testing at a rate of 67% while boys met or exceeded English testing at a rate of 57%. The largest gender gap was in Brisbane Elementary School District, where 72% ROOT POLICY RESEARCH SECTION V, PAGE 24
of girls met or exceeded English testing standards and just 56% of boys did: a gap of 16 percentage points.

Gender gaps in mathematics were less pronounced, but largest gender gaps were in Cabrillo Unified School District and in La Honda Pescadero Unified School District. In Cabrillo Unified, girls passed mathematics at a rate 7% higher than boys, while in La Honda-Pescadero, boys passed at a rate 6% higher than girls.

Figure V-14.

Students who Met or Exceeded Testing Standards, by Gender and District, 2018-2019
Source: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research The gender gap in test scores has started to close in recent years, as indicated in Figure V15. In 2014-2015 there was a 11 percentage point gap in girls’ and boys’ English testing pass District
Unified School Districts
Cabrillo Unified 48% 41% 55% 34% 31% 38%
La Honda-Pescadero 43% 36% 49% 31% 34% 28%
South San Francisco 52% 45% 60% 44% 42% 45%
High & Elementary School Districts
Jefferson Union High School 57% 52% 63% 37% 38% 35%
Bayshore Elementary 27% 24% 31% 27% 27% 28%
Brisbane Elementary 64% 56% 72% 54% 56% 53%
Jefferson Elementary 48% 43% 54% 37% 39% 35%
Pacifica 60% 55% 65% 57% 57% 57%
San Mateo Union High School 70% 66% 76% 50% 50% 50%
Burlingame Elementary 80% 75% 84% 78% 78% 78%
Hillsborough Elementary 85% 81% 89% 85% 86% 84%
Millbrae Elementary 63% 57% 70% 58% 58% 58%
San Bruno Park Elementary 50% 47% 53% 41% 43% 38%
San Mateo-Foster City 62% 58% 67% 56% 56% 56%
Sequoia Union High School 68% 64% 72% 50% 50% 50%
Belmont-Redwood Shores 82% 78% 86% 79% 78% 80%
Las Lomitas Elementary 86% 84% 88% 82% 84% 80%
Menlo Park City Elementary 84% 81% 87% 83% 82% 83%
Portola Valley Elementary 87% 83% 91% 83% 84% 82%
Ravenswood City Elementary 22% 20% 23% 15% 16% 13%
Redwood City Elementary 54% 49% 59% 46% 46% 46%
San Carlos Elementary 80% 77% 83% 75% 76% 74%
Woodside Elementary 88% 85% 91% 84% 85% 83%
Total 62% 57% 67% 52% 52% 52%
English Language Arts/Literacy Mathematics
Total Boys Girls Total Boys Girls
ROOT POLICY RESEARCH SECTION V, PAGE 25
rates, and by 2018-2019 this was just a 10 percentage point gap. The figure also indicates that there have been steady gains in the share of students meeting or exceeding testing standards in the county.

Figure V-15.

Students who Met or Exceeded Testing Standards, by Gender, 2014-2015 to 2018-2019
Source: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research Very large gaps in test scores by race and ethnicity exist among students in some areas.

Figure V-16 illustrates the rate at which students of various racial and ethnic groups met or exceeded English testing standards.

For the past five years in San Mateo County, Asian, White, and Filipino students have met or exceeded English testing standards at rates higher than the overall student population.

Hispanic, Black/African American, and Pacific Islander students, on the other hand, have been underserved in this realm and have consistently scored lower than the overall student body.

However, across all groups, the rate at which students met or exceed English testing standards has increased since the 2014-2015 school year. Hispanic students have made the largest percentage point gain: 34% met standards in 2014-2015 and 40% met standards in 2019-19, an increase of six percentage points.

ROOT POLICY RESEARCH SECTION V, PAGE 26
Figure V-16.

Students who Met or Exceeded English Testing Standards, by Race and Ethnicity, 2014-2015 to 2018-2019
Source: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research A similar narrative holds in Math testing standards, where scores have improved among each racial and ethnic group from 2014-2015 to 2018-2019. Again, White and Asian students meet or exceed math testing standards at rates higher than the overall population while Hispanic, Pacific Islander, and Black/African American students scored lower.

White and Hispanic students have seen the biggest increases in rates of mathematics success: both have experienced a five percentage point increase in the percent of students who met or exceeded math testing standards.

ROOT POLICY RESEARCH SECTION V, PAGE 27
Figure V-17.

Students who Met or Exceeded mathematics testing standards, by Race and Ethnicity, 2014-2015 to 2018-2019
Source: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research Figure V-18 illustrates the rates at which students of various racial and ethnic groups met or exceeded mathematics testing standards by district.

There were several districts in which the gaps between the overall test pass rates and a specific racial groups’ pass rates were especially wide. For instance, in San Carlos Elementary School District, 75% of the total student body met or exceeded math testing standards, but only 11% of Black/African American students met or exceeded math testing standards— a gap of 64 percentage points.

Other school districts with wide gaps between Black/African American and overall math testing success were Las Lomitas Elementary (46 percentage point gap), Menlo Park City Elementary (43 percentage point gap), and Belmont-Redwood Shores (42 percentage point gap).

Some school districts also had similar gaps in Pacific Islander students’ math passing rates and overall passing rates. For instance, in Menlo Park City Elementary School District, 83% of the student body met or exceeded mathematics testing standards but just 35% of Pacific Islander students passed or exceeded mathematics testing standards—a gap of 48 ROOT POLICY RESEARCH SECTION V, PAGE 28
percentage points. Millbrae Elementary School District also had a 47 percentage point gap between Pacific Islander students’ and total students’ math test rates.

Figure V-18.

Students who Met or Exceeded Mathematics Testing Standards, by Race/Ethnicity and District, 2018-2019
Source: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research Although racial gaps in English testing were less pronounced, San Carlos Elementary School District also had a wide gap between the total student body and Black/African American School District
Unified School Districts
Cabrillo Unified 34% 65% (no data) 38% 16% (no data) 54% La Honda-Pescadero 31% (no data) (no data) (no data) 20% (no data) 46% South San Francisco 44% 75% 19% 60% 29% 33% 46%
High & Elementary School Districts
Jefferson Union High School 37% 75% (no data) 36% 17% (no data) 42% Bayshore Elementary 27% 44% (no data) 38% 17% 14% (no data) Brisbane Elementary 54% 67% (no data) 65% 38% (no data) 60% Jefferson Elementary 37% 61% 15% 42% 23% 20% 30%
Pacifica 57% 74% 38% 48% 38% (no data) 66%
San Mateo Union High School 50% 84% (no data) 46% 22% 20% 63% Burlingame Elementary 78% 92% 53% 66% 50% (no data) 81% Hillsborough Elementary 85% 92% (no data) (no data) 76% (no data) 82% Millbrae Elementary 58% 75% 31% 63% 27% 11% 51%
San Bruno Park Elementary 41% 69% 23% 64% 25% 27% 50%
San Mateo-Foster City 56% 87% 30% 61% 23% 27% 69%
Sequoia Union High School 50% 81% 18% 53% 22% 11% 76%
Belmont-Redwood Shores 79% 92% 37% 77% 52% 43% 79%
Las Lomitas Elementary 82% 93% 36% (no data) 44% (no data) 87% Menlo Park City Elementary 83% 94% 40% (no data) 55% 35% 88% Portola Valley Elementary 83% 89% (no data) (no data) 56% (no data) 89% Ravenswood City Elementary 15% (no data) 9% (no data) 15% 11% (no data) Redwood City Elementary 46% 92% 22% 76% 34% 44% 75%
San Carlos Elementary 75% 91% 11% 85% 51% (no data) 78% Woodside Elementary 84% 92% (no data) (no data) 52% (no data) 89% Total 52% 82% 18% 50% 27% 21% 71%
Overall Asian Black Filipino Hispanic White
Pacific
Islander
ROOT POLICY RESEARCH SECTION V, PAGE 29
students. Namely, 80% of the student body met or exceeded English testing standards, but only 19% of Black/African American students met or exceeded testing standards—a 61 percentage point gap. Las Lomitas Elementary had a 41 percentage point gap between overall English testing success and Black/African American English testing success.

Other districts had large gaps between the total student body’s English test scores and Pacific Islander students’ test scores. Namely, in Menlo Park City Elementary School District 84% of students met or exceeded English testing standards, but only 40% of Pacific Islander students—a 44 percentage point gap.

ROOT POLICY RESEARCH SECTION V, PAGE 30
Figure V-19.

Students who Met or Exceeded English Testing Standards, by Race/Ethnicity and District, 2018-2019
Source: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research Students with extenuating circumstances across all districts met or exceeded testing standards at lower rates. However, some districts had especially wide disparities between overall test scores and test scores of students with extenuating circumstances.

School District
Unified School Districts
Cabrillo Unified 48% 78% (no data) 54% 28% (no data) 71% La Honda-Pescadero 43% (no data) (no data) (no data) 27% (no data) 61% South San Francisco 52% 76% 36% 66% 38% 44% 56%
High & Elementary School Districts
Jefferson Union High School 57% 81% (no data) 60% 43% (no data) 59% Bayshore Elementary 27% 49% (no data) 33% 20% 14% (no data) Brisbane Elementary 64% 63% (no data) 75% 51% (no data) 79% Jefferson Elementary 48% 62% 28% 59% 34% 33% 43%
Pacifica 60% 65% 32% 52% 45% (no data) 68%
San Mateo Union High School 70% 88% 55% 79% 50% 34% 81% Burlingame Elementary 80% 88% 61% 73% 55% (no data) 83% Hillsborough Elementary 85% 89% (no data) (no data) 77% (no data) 83% Millbrae Elementary 63% 74% 46% 68% 42% 23% 61%
San Bruno Park Elementary 50% 72% 39% 76% 36% 31% 56%
San Mateo-Foster City 62% 85% 41% 68% 34% 37% 77%
Sequoia Union High School 68% 87% 44% 92% 47% 31% 88%
Belmont-Redwood Shores 82% 91% 44% 81% 64% 61% 83%
Las Lomitas Elementary 86% 91% 45% (no data) 65% (no data) 89% Menlo Park City Elementary 84% 92% 60% (no data) 62% 40% 88% Portola Valley Elementary 87% 92% (no data) (no data) 58% (no data) 93% Ravenswood City Elementary 22% (no data) 24% (no data) 21% 18% (no data) Redwood City Elementary 54% 91% 35% 73% 43% 47% 83%
San Carlos Elementary 80% 90% 19% 76% 60% (no data) 83% Woodside Elementary 88% 92% (no data) (no data) 58% (no data) 92% Total 62% 82% 34% 64% 40% 31% 79%
Overall Asian Black Filipino Hispanic White
Pacific
Islander
ROOT POLICY RESEARCH SECTION V, PAGE 31
For example, English learning students in Portola Valley Elementary, Woodside Elementary, Menlo Park City Elementary, and Brisbane Elementary each met or exceeded mathematics test standards at a rate at least 50 percentage points below the overall test rate in each district. English learning students in Las Lomitas Elementary (54%) had the highest mathematics pass rates, followed by those in Belmont-Redwood Shores (42%) and Burlingame Elementary (40%).

Students with disabilities scored especially high on mathematics tests in Hillsborough Elementary, where 48% met or exceeded standards. Others in Belmont-Redwood Shores (43%) and Woodside Elementary (41%) had high pass rates as well. Students with disabilities in San Carlos Elementary and Las Lomitas Elementary school districts scored far below the overall student body: in these districts, students with disabilities met or exceeded mathematics test standards at 54 percentage points below the overall test rate.

In Jefferson Elementary and Ravenswood Elementary students experiencing homelessness passed math tests at a rate similar to their housed peers. In other districts, however, students experiencing homelessness often scored substantially lower. School districts with the widest math testing gaps between the overall student body and students experiencing homelessness were San Mateo-Foster City and Millbrae Elementary, with a 41 percentage point gap and 42 percentage point gap, respectively.

ROOT POLICY RESEARCH SECTION V, PAGE 32
Figure V-20.

Students who Met or Exceeded Math Testing Standards, by Special Case and District, 2018-2019
Source: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research School District
Unified School Districts
Cabrillo Unified 34% 4% 5% 4% 9%
La Honda-Pescadero 31% 4% (no data) (no data) 2%
South San Francisco 44% 20% 25% 4% 18%
High & Elementary School Districts
Jefferson Union High School 37% 5% (no data) (no data) 6% Bayshore Elementary 27% 11% (no data) (no data) 9%
Brisbane Elementary 54% 4% (no data) (no data) 12%
Jefferson Elementary 37% 15% 36% (no data) 11%
Pacifica 57% 22% (no data) (no data) 17%
San Mateo Union High School 50% 10% (no data) (no data) 13% Burlingame Elementary 78% 40% (no data) (no data) 29%
Hillsborough Elementary 85% (no data) (no data) (no data) 48% Millbrae Elementary 58% 26% 16% (no data) 25%
San Bruno Park Elementary 41% 12% (no data) (no data) 9% San Mateo-Foster City 56% 11% 15% (no data) 14%
Sequoia Union High School 50% 3% 33% (no data) 9%
Belmont-Redwood Shores 79% 42% (no data) (no data) 43%
Las Lomitas Elementary 82% 54% (no data) (no data) 28%
Menlo Park City Elementary 83% 31% (no data) (no data) 38% Portola Valley Elementary 83% 14% (no data) (no data) 39% Ravenswood City Elementary 15% 5% 11% (no data) 2%
Redwood City Elementary 46% 14% (no data) 29% 14%
San Carlos Elementary 75% 24% (no data) (no data) 21%
Woodside Elementary 84% 27% (no data) (no data) 41%
English
Learners
Experiencing
homelessness Migrant
With
Overall Disabilities
ROOT POLICY RESEARCH SECTION V, PAGE 33
Students with extenuating circumstances also consistently scored lower in English testing than the overall student body.

For instance, English learning students in San Mateo Union High School District, Hillsborough Elementary School District, Sequoia Union High School District, Menlo Park City Elementary School District, and Portola Valley Elementary School District met or exceeded English test standards at a rate at least 60 percentage points below the overall test rate in each district. Hillsborough Elementary had the largest gap at 85 percentage points. Las Lomitas Elementary had the highest success rate among English learners, where 50% met or exceeded English testing standards.

However, students with disabilities in Las Lomitas Elementary and San Carlos Elementary school districts met or exceeded English test standards at rate 55 and 51 percentage points below the overall test rate, respectively. These were the largest gaps in the county.

Students with disabilities at Woodside Elementary did the best on English testing, where 56% passed or exceeded standards.

Among students experiencing homelessness, those at Sequoia Union High School were most likely to meet English testing standards, with 42% meeting or exceeding standards.

The school district with the widest gap between overall English test scores and scores among students experiencing homelessness was Cabrillo Unified with a 34 percentage point gap.

Just three districts reported English testing scores among migrant students. Redwood City Elementary had the highest pass rate at 34% and Cabrillo Unified had the lowest at 16%.

ROOT POLICY RESEARCH SECTION V, PAGE 34
Figure V-21.

Students who Met or Exceeded English Testing Standards, by Special Case and District, 2018-2019
Source: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research School District
Unified School Districts
Cabrillo Unified 48% 9% 14% 16% 12%
La Honda-Pescadero 43% 9% (no data) (no data) 9%
South San Francisco 52% 21% 35% 20% 18%
High & Elementary School Districts
Jefferson Union High School 57% 3% (no data) (no data) 19% Bayshore Elementary 27% 3% (no data) (no data) 4%
Brisbane Elementary 64% 21% (no data) (no data) 16%
Jefferson Elementary 48% 16% 30% (no data) 15%
Pacifica 60% 12% (no data) (no data) 15%
San Mateo Union High School 70% 11% (no data) (no data) 27% Burlingame Elementary 80% 33% (no data) (no data) 33%
Hillsborough Elementary 85% (no data) (no data) (no data) 47% Millbrae Elementary 63% 19% 34% (no data) 23%
San Bruno Park Elementary 50% 14% (no data) (no data) 12% San Mateo-Foster City 62% 9% 33% (no data) 15%
Sequoia Union High School 68% 8% 42% (no data) 27%
Belmont-Redwood Shores 82% 31% (no data) (no data) 45%
Las Lomitas Elementary 86% 51% (no data) (no data) 31%
Menlo Park City Elementary 84% 21% (no data) (no data) 42% Portola Valley Elementary 87% 17% (no data) (no data) 37% Ravenswood City Elementary 22% 6% 16% (no data) 5%
Redwood City Elementary 54% 13% (no data) 34% 16%
San Carlos Elementary 80% 29% (no data) (no data) 28%
Woodside Elementary 88% 18% (no data) (no data) 56%
English
Learners
Experiencing
homelessness Migrant
With
Overall Disabilities
ROOT POLICY RESEARCH SECTION V, PAGE 35
Students who met university requirements. Many high schoolers in the county met admission standards for a University of California (UC) or California State University (CSU) school. Figure V-22 illustrates the percentage of cohort graduates who met admission requirements for a CSU or UC school according to California Department of Education data.

Of the high school districts in San Mateo County, Sequoia Union had the highest rate of graduates who met such admission standards, at 69%. On the other end of the spectrum, Cabrillo Unified and South San Francisco Unified had the lowest rates at 41%.

Figure V-22.

Students Meeting
California University
Admission
Standards, 2019-
2020
Source:

California Department of Education
and Root Policy Research.

Cabrillo Unified and South San Francisco Unified have experienced a decrease in the share of graduates meeting CSU or UC admission standards in recent years. For instance, in 2016-2017, 57% of South San Francisco Unified graduates met these standards, but this decreased by 16 percentage points by 2019-2020. Cabrillo Unified experienced a less drastic decrease over the same period, but the rate still shrunk by two percentage points.

Jefferson Union High School District had the most drastic increase in the share of graduates meeting CSU or UC standards: just 21% of students met these standards in 2016-2017 compared to 48% of students in 2019-2020. La Honda-Pescadero Unified School District experienced a 10 percentage point increase in this success rate over the same period.

Sequoia Union and San Mateo Union experienced more modest increases, but remain the districts with the highest rates of students meeting CSU and UC standards.

ROOT POLICY RESEARCH SECTION V, PAGE 36
Figure V-23.

Students Meeting
University
Admission
Standards, 2016-
2017 and 2019-2020
Source:

California Department of Education
and Root Policy Research.

Rates at which students met CSU or UC admissions standards varied substantially by race and ethnicity in 2019-2020. In all high school districts in San Mateo County, White and Asian students meet CSU and UC admissions standards at higher rates than the overall student population.

The largest gap is in South San Francisco Unified, where just 41% of students meet CSU or UC admissions standards, but 73% of Asian students meet those standards—a 32 percentage point gap.

On the other end of the spectrum, Black/African American students typically met CSU or UC admissions standards at lower-than-average rates. The largest gap was in San Mateo Union, where just 29% of Black/African American students met CSU or UC standards compared to 68% of students in the district overall.

Filipino students typically met admissions standards at rates similar to the overall student body. For instance, in Jefferson Union, San Mateo Union, and South San Francisco Unified, Filipino students are slightly more likely to have meet CSU and UC standards than the overall student population. In Sequoia Union, they are slightly less likely to have met admission standards than the overall student population.

ROOT POLICY RESEARCH SECTION V, PAGE 37
In La Honda-Pescadero, Hispanic students are slightly more likely to have met CSU or UC standards than the overall student body. However, in all other school districts, Hispanic students are less likely to have met CSU and UC standards than the overall student body.

The largest disparity is in San Mateo Union, where just 46% of Hispanic students meet the university admissions standards compared to 68% of students overall.

Finally, Pacific Islander students in Jefferson Union were slightly more likely to have met California university admissions standards compared to the overall student body, but in Sequoia Union and San Mateo Union they were substantially less likely.

ROOT POLICY RESEARCH SECTION V, PAGE 38
Figure V-24.

Students Meeting University Admission Standards, by Race and Ethnicity, 2019-2020
Source: California Department of Education and Root Policy Research As expected, students with extenuating circumstances were less likely to meet CSU or UC admissions standards than students in the county overall. In all school districts where data are available, students with disabilities, students experiencing homelessness, English learners, foster youth, and migrant students met CSU or UC admission standards at lower rates than the overall student population.

ROOT POLICY RESEARCH SECTION V, PAGE 39
English learners in Sequoia Union and San Mateo Regional met CSU or UC admission standards at higher rates than their peers in other school districts. However, compared to the overall student body within their own school districts, they had a larger gap than other districts. Namely, in Sequoia Union, 69% of students met admissions standards compared to just 32% of students learning English— a 37 percentage point gap.

Similarly, students with disabilities in Sequoia Union had the highest rate of meeting admissions standards (31%) compared to peers with disabilities in other districts, but also had the largest gap (38 percentage points) compared to the district’s overall student body.

Migrant students met admission standards at the lowest rate in South San Francisco Unified (27%) and at the highest rate in Sequoia Union (45%). However, in Cabrillo Unified, their rates were only eight percentage points lower than that of the overall student body, the smallest gap in the county.

Approximately 36% of students experiencing homelessness in Sequoia Union met CSU or UC admission standards, which was higher than rates in San Mateo Union (21%) and Jefferson Union (21%).

Just San Mateo Union and Sequoia Union had enough foster youth to report their rate of meeting CSU or UC admission standards. In Sequoia Union, 29% met admissions standards and 22% in San Mateo Union met admissions standards.

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Figure V-25.

Students Meeting
University
Admission
Standards, 2019-
2020
Source:

California Department of Education
and Root Policy Research.

Notes; La-Honda Pescadero Unified
is excluded from these data as they
do not report admission standards
data for these special groups, likely
due to small sample size.

College-going rates. The college-going rate is defined as the percentage of public high school students who completed high school in a given year and subsequently enrolled in any public or private postsecondary institution (in-state or out-of-state) in the United States within 12 or 16 months of completing high school.

Most school districts in the county have a college-going rate at 70% or higher. San Mateo Union had the highest college-going rate at 77%. La Honda-Pescadero School District is the notable exception, with just 32% of graduates attending college within 12 or 16 months.

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Figure V-26.

College-Going
Rates, 2017-2018
Source:

California Department of Education
and Root Policy Research.

As shown in Figure V-27, La Honda-Pescadero School District previously had the highest college-going rate of all the county’s high school districts, with an 80% college-going rate in 2014-2015 and a 93% college-going rate in 2015-2016. The district experienced a rapid decline in college-going rates, starting in 2016-2017. However, La Honda-Pescadero has especially small sample sizes. For instance, the district had just 26 twelfth-graders in the 2017-2018 school year, meaning that just a couple students going to college (or not) drastically alters the college-going rate in La Honda-Pescadero. All other high school districts in the county have maintained relatively consistent college-going rates.

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Figure V-27.

College-Going
Rates, 2014-2015 to
2017-2018
Source:

California Department of Education
and Root Policy Research.

Within each of the high school districts, college-going rates vary by race and ethnicity.  In every district, White students have a higher college-going rate than Hispanic students, but the largest gaps are in South San Francisco United, where 91% of White students go to college compared to just 68% of Hispanic students, a 23 percentage point gap. Jefferson Union has the smallest gap between the two groups: 77% of White students go to college compared to 71% of Hispanic students.  Among Black/African American students, those at San Mateo Union have the highest college-going rate at 82%. Those at Jefferson Union have the lowest at just 53%, which is 24 percentage points lower than that of White students and 34 percentage points lower than that of Asian students.
 Overall, Asian students have among the highest college-going-rates in the county. The rate is especially high in South San Francisco Unified, where 92% go to college. The rate is lowest in Sequoia Union High School District, where 84% go to college.

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 Filipino students also have generally high rates of college-going. The highest collegegoing rate among Filipino students is in Sequoia Union (86%) and the lowest is in South San Francisco Unified (73%).
 College-going rates for Pacific Islander students vary substantially by district. For instance, in Sequoia Union 54% go to college, but in South San Francisco Unified 92% go to college.

Figure V-28.

College-going Rates by Race and Ethnicity, 2017-18
Note: Cabrillo Unified and La Honda- Pescadero Unified are not included here because they do not report the data, likely due to small sample sizes.

ROOT POLICY RESEARCH SECTION V, PAGE 44
Source: California Department of Education and Root Policy Research College-going rates are lower for students with disabilities and those learning English compared to the overall student population across the county.  For instance, the largest gap between overall college-going rates and English learners’ college-going rates is in South San Francisco Unified, where just 52% of English learning students go to college as opposed to 74% of the overall student population— a 22 percentage point gap. Among English learners, San Mateo Union High School District had the highest college-going rate, where 63% of English learners go to college.  Among students with disabilities, South San Francisco Unified also had the largest gap, where 59% of students with disabilities went to college compared to 74% of the overall student population — a 15 percentage point gap. Jefferson Union, on the other hand, had a relatively high college-going rate among students with disabilities that was not very different from the district’s overall college-going rate: 71% went to college which is just five percentage points lower than the district’s overall student population.

Figure V-29.

College-going Rates
for English Learners
and Students with
Disabilities, 2017-
2018
Note:

Cabrillo Unified and La HondaPescadero Unified are not included here because they do not report the
data, likely due to small sample
sizes.

Source:

California Department of Education
and Root Policy Research.

Gaps in college enrollment by race, ethnicity, disability status, or English learning have stark financial consequences for students in the long-term. Figure V-30 illustrates median annual earnings by educational attainment. College degrees are especially important in San Mateo County: those with a bachelor’s degree in the county earn 115% more than those with a high school diploma. This gap is wider in San Mateo County than in other parts of California and nationwide. The differences between high-school graduate earnings and bachelor's degree earnings are around 100% in California and 76% in the US overall.

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Figure V-30.

Median Annual Earnings by Educational Attainment, 2019
Source: 5-year 2019 American Community Surveys Data.

Unfortunately, the gap between high school graduates’ and college graduates’ earnings have been increasing in San Mateo County. As illustrated in Figure V-31, median earnings for high school graduates increased by just 15% over the last decade (from $31,816 to $36,747) while earnings for college graduates increased by 29% over the same period (from $61,485 to $79,080).

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Figure V-31.

Median Annual Earnings by Educational Attainment in San Mateo County, 2010 to 2019
Source: 5-year American Community Surveys Data.

Because income disparities between college graduates and high school graduates have been increasing, it is increasingly important that school districts in San Mateo County address differences in college-going rates stratified by race, ethnicity, and extenuating circumstances.

Barriers to Success
Many students are unable to achieve academic success because of barriers in home and school. This section explores the available indicators of barriers to success, including chronic absenteeism and dropout rates. It also describes inequities in discipline rates by ROOT POLICY RESEARCH SECTION V, PAGE 47
race and ethnicity, which has been linked both to discrimination by education professionals as well as a major barrier to students’ future success.

Chronic absenteeism. Academic studies have found that if a student is chronically absent, it reduces their math and reading achievement outcomes, educational engagement, and social engagement.12 Chronic absenteeism also has spillover effects and negatively impacts students who themselves are not chronically absent. For instance, one study found that students suffer academically from having chronically absent classmates— as exhibited across both reading and math testing outcomes.13 Students are considered chronically absent if they were absent for 10% or more of the days during a school year. Note, however, students are exempt from chronic absenteeism calculations if they receive instruction through a home or hospital instructional setting, are attending community college full-time, or were not expected to attend more than 31 days.

In the county overall, 10% of students were chronically absent during the 2018-2019 school year.
14 This is a slight increase from the 2016-2017 school year, where just 9% of students overall were chronically absent.

Chronic absenteeism rates were higher in districts with a large number of students experiencing economic and housing precarity. For instance, Ravenswood Elementary, which has a 30% rate of homelessness among students, had one of the higher rates of chronic absenteeism at 16%. La Honda-Pescadero and Sequoia Union high school districts also had high rates of chronically absent students at 16% and 17%, respectively.

When disaggregating by race and ethnicity, just 3% of Asian students were chronically absent, and 7% of White and Filipino students were chronically absent. On the other end of the spectrum, Pacific Islander students (26%), Black/African American students (18%), and Hispanic students (15%) had notably higher rates of chronic absenteeism than the overall student population (10%). Chronic absenteeism among Pacific Islander students has increased in recent years, as illustrated in Figure V-32. 12 Gottfried, Michael A. "Chronic absenteeism and its effects on students’ academic and socioemotional outcomes." Journal of Education for Students Placed at Risk (JESPAR) 19.2 (2014): 53-75. 13 Gottfried, Michael A. "Chronic absenteeism in the classroom context: Effects on achievement." Urban Education 54.1 (2019): 3-34.
14 Because of the physical school closures during the COVID-19 pandemic, the California Department of Education determined that 2019–2020 absenteeism data are not valid, therefore, we present data from the 2018-2019 school year.

ROOT POLICY RESEARCH SECTION V, PAGE 48
Figure V-32.

Chronic
Absenteeism by
Race/Ethnicity,
2016-2017 to 2018-
2019
Source: California Department of
Education and Root Policy
Research
Chronic absenteeism among Pacific Islander students was especially pronounced in San Mateo-Foster City school district where there was a 26 percentage point gap between chronic absenteeism rates for Pacific Islander students (32%) and the overall student body (6%). Other districts had similarly large gaps, including San Bruno Park Elementary (20 percentage points) and South San Francisco Unified (18 percentage points).

Some districts had larger gaps in absenteeism rates between Black/African American students and the overall population. For instance, in San Carlos Elementary, 4% of the overall student body is chronically absent compared to 27% of Black/African American students— a 23 percentage point gap. Jefferson Elementary school district had a 17 percentage point gap between their overall chronic absenteeism rate (12%) and their chronic absenteeism rate among Black/African American students (28%).

Among White students, Bayshore Elementary School District was a major outlier, where 46% of White students were chronically absent compared to just 12% of the total student population. However, it is important to note that this represents a very small sample of White students: just 3% of students at Bayshore Elementary are White, one of lowest in the county.

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Figure V-33.

Chronic Absenteeism by District and Race/Ethnicity, 2018-2019 Source: California Department of Education and Root Policy Research In most districts, chronic absenteeism is higher among students with disabilities. In fact, only Bayshore Elementary’s students with disabilities had a lower rate of chronic absenteeism than the overall student body. In all other districts, students with disabilities were more likely to be chronically absent than the overall student population. This was particularly true in Sequoia Union High School District, Jefferson Union High School District, and San Mateo Union High School District, which had gaps between the overall School District
Unified School Districts
Cabrillo Unified 10% 5% (no data) 5% 11% (no data) 10%
La Honda-Pescadero 16% (no data) (no data) (no data) 14% (no data) 18% South San Francisco 13% 4% 16% 7% 17% 31% 12%
High & Elementary School Districts
Jefferson Union High School 15% 8% 22% 11% 22% 18% 15%
Bayshore Elementary 12% 5% 12% 0% 18% 19% 46%
Brisbane Elementary 12% 3% (no data) 12% 17% (no data) 17% Jefferson Elementary 12% 5% 28% 6% 13% 25% 23%
Pacifica 7% 4% 12% 6% 9% 21% 7%
San Mateo Union High School 10% 3% 18% 4% 17% 21% 9%
Burlingame Elementary 5% 2% 15% 5% 10% 20% 5%
Hillsborough Elementary 4% 1% (no data) 4% 4% (no data) 6% Millbrae Elementary 10% 3% 6% 17% 16% 26% 14%
San Bruno Park Elementary 12% 5% 10% 4% 14% 32% 9%
San Mateo-Foster City 6% 2% 9% 2% 10% 32% 4%
Sequoia Union High School 17% 6% 23% 8% 23% 33% 10%
Belmont-Redwood Shores 5% 3% 8% 5% 12% 17% 5%
Las Lomitas Elementary 4% 2% 0% (no data) 7% (no data) 3% Menlo Park City Elementary 3% 1% 8% 7% 5% 14% 3%
Portola Valley Elementary 4% 0% (no data) (no data) 6% (no data) 3% Ravenswood City Elementary 16% 0% 20% (no data) 15% 24% 21% Redwood City Elementary 10% 2% 19% 3% 12% 18% 4%
San Carlos Elementary 4% 2% 27% 8% 7% (no data) 3%
Woodside Elementary 8% 0% 0% (no data) 12% (no data) 7% Total 10% 3% 18% 7% 15% 26% 7%
Total Asian Black Filipino Hispanic
Pacific
Islander White
ROOT POLICY RESEARCH SECTION V, PAGE 50
absenteeism rate and the absenteeism rate among students with disabilities of 13, 12, and 11 percentage points, respectively.

Rates of chronic absenteeism were also higher among English learners than the general population in most districts (with the exception of Ravenswood City Elementary and Jefferson Elementary). Woodside Elementary and Sequoia Union High School districts both had 14 percentage point gaps between absenteeism rates of English learners and the overall student body.

In every school district where the data are available, foster youth had higher rates of chronic absenteeism than the overall population. This was especially true in Sequoia Union High School District, where 63% of foster youth were chronically absent compared to just 17% of the overall student body.

Similarly, in almost all districts with available data, students experiencing homelessness had higher rates of chronic absenteeism than the overall student body. The chronic absenteeism rate among students experiencing homelessness was highest in Burlingame Elementary at 64%.

Migrant students were chronically absent at rates similar to or lower than the total student body in all districts with reported data.

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Figure V-34.

Chronic Absenteeism by District and Extenuating Circumstance, 2018-2019 Source: California Department of Education and Root Policy Research Dropout rates. As previously indicated, workers without a high school degree have the lowest annual earnings compared to others at higher levels of educational attainment. In addition to the economic and housing precarity associated with low earnings, low earnings also often lead to increased incentives to participate in criminal activity. In fact, one study School District
Unified School Districts
Cabrillo Unified 10% 12% 23% 9% (no data) 18%
La Honda-Pescadero 16% 16% (no data) (no data) (no data) 22% South San Francisco 13% 14% 47% 13% 49% 18%
High & Elementary School Districts
Jefferson Union High School 15% 27% 33% (no data) 36% 28% Bayshore Elementary 12% 19% (no data) (no data) (no data) 11% Brisbane Elementary 12% 18% (no data) (no data) (no data) 18% Jefferson Elementary 12% 10% 21% (no data) 24% 16%
Pacifica 7% 11% (no data) (no data) (no data) 14%
San Mateo Union High School 10% 21% 50% (no data) 53% 21% Burlingame Elementary 5% 8% 64% (no data) (no data) 12% Hillsborough Elementary 4% 6% (no data) (no data) (no data) 8% Millbrae Elementary 10% 12% 5% (no data) (no data) 12%
San Bruno Park Elementary 12% 12% (no data) (no data) 18% 20% San Mateo-Foster City 6% 8% 15% (no data) 17% 13%
Sequoia Union High School 17% 31% 52% 16% 63% 29%
Belmont-Redwood Shores 5% 11% (no data) (no data) (no data) 10% Las Lomitas Elementary 4% 6% (no data) (no data) (no data) 5% Menlo Park City Elementary 3% 5% (no data) (no data) (no data) 9% Portola Valley Elementary 4% 3% (no data) (no data) (no data) 9% Ravenswood City Elementary 16% 16% 19% 17% 23% 21%
Redwood City Elementary 10% 12% 30% 6% 32% 16%
San Carlos Elementary 4% 8% 23% (no data) (no data) 11% Woodside Elementary 8% 22% (no data) (no data) (no data) 10% Total
English
Learners
Experiencing
homelessness Migrant
With
Disabilities
Foster
Youth
ROOT POLICY RESEARCH SECTION V, PAGE 52
suggest that high school dropouts are 3.5 times more likely than high school graduates to be imprisoned at some point during their lifetime.15 Another study found that raising the high school completion rate by one percent for all men ages 20 through 60 would save the US $1.4 billion annually in crime related costs.16 Dropping out of high school also has adverse health costs: for instance, research has shown that high school dropouts are more likely to smoke and have a marijuana disorder in adulthood.17 For these reasons, reducing high school dropout rates in San Mateo County is pivotal to the health and economic prosperity of the community.

In this report, dropout rates shown for high school districts with available data and are defined as the percentage of cohort students who did not graduate with a regular high school diploma, did not complete high school, and are not still enrolled as a "fifth year senior".

In the 2019-2020 academic year, dropout rates were highest in Sequoia Union High School District, where 10% of students dropped out. This is similar to South San Francisco Unified, where 9% of students dropped out. In both these districts, and in Cabrillo Unified, dropout rates have increased since 2016-2017.

Dropout rates have decreased by one percentage point over the same period in San Mateo Union High School District, from 5% to 4%. Jefferson Union had the lowest dropout rate in the county at just 3%, which after slightly higher rates in 2017-18 and 2018-19, is the same as its 2016-2017 rate.
15 Monrad, Maggie. "High School Dropout: A Quick Stats Fact Sheet." National High School Center (2007). 16 U.S. Department of Justice, Bureau of Justice Statistics. (2002). Correctional populations in the United States, 1998 (NCJ-192929). Washington: U.S. Government Printing Office. 17 Gonzalez, Jennifer M. Reingle, et al. "The long-term effects of school dropout and GED attainment on substance use disorders." Drug and alcohol dependence 158 (2016): 60-66.

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Figure V-35.

Dropout Rates by
District, 2016-2017 to
2019-2020
Note: La Honda-Pescadero Unified
School District is excluded
from these data.

Source: California Department of
Education and Root Policy
Research
In all school districts in the county, dropout rates are higher for boys than for girls.

Jefferson Union had the smallest gender gap, where 3% of girls dropped out and 4% of boys dropped out. Sequoia Union had the widest gender gap, where 13% of boys dropped out compared to just 7% of girls.

Figure V-36.

Dropout Rates by
Gender, 2019-2020
Note: La Honda-Pescadero Unified
School District is excluded
from these data.

Source: California Department of
Education and Root Policy
Research
Pacific Islander, Black/African American, and Hispanic students in the county often had higher dropout rates than those in other racial and ethnic groups.

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 In Sequoia Union High School District, dropout rates were highest among Pacific Islander students, where 20% dropped out in the 2019-2020 academic year. Dropout rates were also especially high among Hispanic and Black/African American students in Sequoia Union, at 16% and 12% respectively.
 In districts with lower dropout rates, for instance, Jefferson Union, the highest dropout rates still found among Black/African American (7%) and Hispanic students (6%).  Notably, however, in South San Francisco Unified, White students were more likely to drop out than any other racial or ethnic group. In fact, 12% of White students dropped out compared to 11% of Hispanic students, 5% of Filipino students, and 3% of Asian students. Data for Black/African American and Pacific Islander students were not available for South San Francisco Unified due to small sample sizes.

Figure V-37.

Dropout Rates by Race, 2019-2020
ROOT POLICY RESEARCH SECTION V, PAGE 55
Source: California Department of Education and Root Policy Research In all school districts in the county, students with disabilities, students experiencing homelessness, foster youth, and students learning English had higher dropout rates than the overall population.
 Among students with disabilities, the highest dropout rate was in Sequoia Union, where 24% dropped out. The gap between overall dropout rates and dropout rates among students with disabilities was wide in Sequoia Union at 14 percentage points.  Cabrillo Unified, on the other hand, had less than a one percentage point gap between the dropout rate of overall students (6%) and students with disabilities (6%).  Among students learning English, Sequoia Union had the highest dropout rate at 27%, while Jefferson Union had the lowest dropout rate at 8%.  Sequoia Union also had the highest rate of dropout among students experiencing homelessness at 29% while Jefferson Union, again, had the lowest at 15%.  Foster Youth in Sequoia Union had an exceptionally high dropout rate a t 40%. San Mateo Union is the only other district in the county which reported these data in 2019- 2020, and found only 18% of foster youth dropped out.
 Migrant students at South San Francisco Unified actually dropped out at a rate slightly lower than the general student body: just 8% of migrant students dropped out compared to 9% of the overall student body. However, those in Cabrillo Unified were 11 percentage points more likely than the total student body to dropout.

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Figure V-38.

Dropout Rates by Extenuating Circumstance, 2019-2020
Source: California Department of Education and Root Policy Research Disproportionate discipline rates. Strict discipline policies may stigmatize suspended students and expose them to the criminal justice system at a young age, setting them up for limited economic and social success down the line. Research has found that suspensions not only negatively affect the suspended students, but also their peers.

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Students in schools with higher suspension rates are more likely to drop out or school and less likely to attend a four-year college.18
Other academic studies have found that students from African American and Latino families are more likely than their White peers to receive expulsion or out of school suspension as consequences for the same or similar problem behavior.19 This means that Black/African American and Hispanic students suffer more of the economic and social consequences than their White peers for the same behaviors.

Luckily, in every high school district in San Mateo County, suspension rates have decreased since 2011-2012. La Honda-Pescadero School District experienced the largest decrease: it was the district with the highest suspension rate in 2011-2012 at 10%, but now has the lowest suspension rate at just 1% in 2019-2020. San Mateo Union also experienced a rapid decrease in suspension rates over the same period, with a rate of 9% in 2011-2012 to a rate of 3% in 2019-2020.
18 Bacher-Hicks, Andrew, Stephen B. Billings, and David J. Deming. The school to prison pipeline: Long-run impacts of school suspensions on adult crime. No. w26257. National Bureau of Economic Research, 2019. 19 Skiba, Russell J., et al. "Race is not neutral: A national investigation of African American and Latino disproportionality in school discipline." School Psychology Review 40.1 (2011): 85-107.

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Figure V-39.

Suspension Rates, 2011-2012 to 2019-2020
Source: California Department of Education and Root Policy Research In many school districts across San Mateo County, Hispanic students are disciplined at disproportionately higher rates compared to their peers. Figure V-40 compares each racial/ethnic group’s share of suspensions to their share of the overall student population.  In all districts except for La Honda-Pescadero, Hispanic students make up a larger share of suspensions than their overall share of the student body. For instance, in San Mateo Union, 34% of students are Hispanic, but 66% of suspended students are Hispanic, making a 32 percentage point overrepresentation gap.  In most districts, Black and Pacific Islander students are also overrepresented in terms of suspension rates, but these rates are slight compared to those of Hispanic students.

For instance, in Sequoia Union, just 2% of the student body identified as Pacific Islander but 8% of suspended students were Pacific Islander.  Asian and Filipino students were underrepresented in terms of suspension rates. For example, in Jefferson Union High School District, 31% of students identified as Filipino but just 10% of suspended students were Filipino, a 21 percentage point gap. In San ROOT POLICY RESEARCH SECTION V, PAGE 59
Mateo Union High School, 22% of students identified as Asian but just 5% of suspended students were Asian, a 17 percentage point gap.  White students were also underrepresented in discipline rates in most districts except for La Honda-Pescadero, where they were overrepresented by 30 percentage points.

They were substantially underrepresented in Cabrillo Unified (with a gap of 21 percentage points) and Sequoia Union (18 percentage points).

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Figure V-40.

Suspension Rates by Race and Ethnicity, 2019-2020
Notes: the percentage of suspensions and shares of racial groups do not sum to 100% because we exclude students with no reported race, with more than one reported race, where districts did not report racial/ethnic data due to small sample sizes. Gaps of 15 percentage points or more are highlighted.

Source: California Department of Education and Root Policy Research School District
Asian Students
Share of Student Body 1% 14% 22% 9% 13%
Share of Suspensions 1% 7% 5% 1% 3%
Gap 0% -7% -17% -8% -10%
Black Students
Share of Student Body 1% 1% 3% 1%
Share of Suspensions 5% 1% 6% 2%
Gap 4% 0% 3% 1%
Filipino Students
Share of Student Body 1% 31% 6% 2% 23%
Share of Suspensions 0% 10% 2% 0% 9%
Gap -1% -21% -4% -2% -14%
Hispanic Students
Share of Student Body 52% 32% 61% 34% 41% 48%
Share of Suspensions 79% 46% 33% 66% 62% 69%
Gap 27% 14% -28% 32% 21% 21%
Pacific Islander Students
Share of Student Body 1% 2% 2% 2%
Share of Suspensions 4% 4% 8% 3%
Gap 3% 2% 6% 1%
White Students
Share of Student Body 40% 14% 37% 26% 38% 7%
Share of Suspensions 19% 16% 67% 14% 20% 7%
Gap -21% 2% 30% -12% -18% 0%
Cabrillo
Unified
Jefferson
Union
High
La HondaPescadero
San
Mateo
Union
High
Sequoia
Union
High
South San
Francisco
Unified
ROOT POLICY RESEARCH SECTION V, PAGE 61
Staff demographics. Diversity of school staff has been shown to improve outcomes for students of color. For instance, one recent study found that students are less likely to be removed from school as punishment when they and their teachers are the same race.

This effect is driven almost entirely by black students, especially black boys, who are markedly less likely to be subjected to exclusionary discipline when taught by black teachers. There is little evidence of any benefit for white students of being matched with white teachers.20 Other research in California has found that, when students have a teacher of their race, they are more likely to attend class, therefore reducing chronic absenteeism.21 Even more studies have found that having a teacher of a student’s own race substantially improves their math and reading achievement.22 In San Mateo County, the demographics of faculty and staff are fairly similar to that of its students. Figure V-41 illustrates the share of the county’s faculty and staff who are Asian, Black/African American, Hispanic, Filipino, Pacific Islander, and White, and compares those shares to the racial/ethnic breakdown of the county’s student body.

There is a slightly larger share of White and Black/African American staff than students, meaning that Black/African American and White student groups are more likely to interact with same-race staff and faculty than other racial groups. Asian students are less likely to interact with a same-race staff of faculty member: 17% of the student body is Asian compared to just 8% of staff and faculty.
20 Lindsay, Constance A., and Cassandra MD Hart. "Teacher race and school discipline: Are students suspended less often when they have a teacher of the same race?." Education Next 17.1 (2017): 72-79. 21 Gottfried, Michael, J. Jacob Kirksey, and Tina L. Fletcher. "Do High School Students With a Same-Race Teacher Attend Class More Often?." Educational Evaluation and Policy Analysis (2021): 01623737211032241. 22 Dee, T. S. (2004). Teachers, race, and student achievement in a randomized experiment. Review of economics and statistics, 86(1), 195-210.

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Figure V-41.

Staff and Student
Demographics,
2020-2021
Notes: Percentages do not always
sum to 100% because we
do not show shares of staff
with no reported race, with
more than one reported
race, or Native American
staff.

Source: California Department of
Education and Root Policy
Research
Since 2011-2012, the county’s school districts have diversified in that there has been a 13 percentage point decrease in the share of White faculty and staff and a 10 percentage point increase in Hispanic faculty and staff. However, there has been a slight decrease (by two percentage points) in the share of faculty and staff who identify as Black/African American. There has been a two percentage point increase in the share of Asian and Filipino faculty and staff, and a one percent increase in the share of Pacific Islander faculty and staff.

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Figure V-42.

Faculty and Staff Demographics, 2011-2012 to 2020-2021
Notes: Percentages do not always sum to 100% because we do not show shares of staff with no reported race, with more than one reported race, or Native American staff.

Source: California Department of Education and Root Policy Research Figure V-43 illustrates faculty and staff racial and ethnic diversity for the 2020-2021 school year by district.
 Portola Valley has the least diverse faculty and staff in the county, with 59% identifying as White.
 Ravenswood Elementary has the most diverse faculty and staff: the district has the highest share of Pacific Islander (5%), Black/African American (12%) and Hispanic (72%) faculty and staff.
 South San Francisco Unified School District has the highest share of Asian faculty and staff at 14%.
 Brisbane Elementary and Jefferson Elementary have the highest shares of Filipino faculty and staff at 28%.

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Figure V-43.

Faculty and Staff Race/Ethnicity, by District, 2020-2021 Notes: Percentages do not always sum to 100% because we do not show shares of staff with no reported race, with more than one reported race, or Native American staff.

Source: California Department of Education and Root Policy Research School District
Unified School Districts
Cabrillo Unified 0% 1% 1% 46% 0% 51%
La Honda-Pescadero 0% 5% 5% 39% 0% 51%
South San Francisco 14% 3% 16% 34% 2% 28%
High & Elementary School Districts
Jefferson Union High School 3% 3% 13% 26% 1% 43%
Bayshore Elementary 13% 4% 17% 61% 0% 4%
Brisbane Elementary 7% 0% 28% 20% 4% 42%
Jefferson Elementary 13% 3% 28% 25% 0% 29%
Pacifica 7% 2% 8% 23% 2% 54%
San Mateo Union High School 11% 5% 6% 34% 3% 40%
Burlingame Elementary 8% 5% 11% 27% 3% 45%
Hillsborough Elementary 2% 1% 7% 20% 1% 55%
Millbrae Elementary 13% 3% 9% 25% 0% 48%
San Bruno Park Elementary 4% 2% 13% 26% 4% 48%
San Mateo-Foster City 13% 2% 7% 33% 3% 37%
Sequoia Union High School 2% 12% 2% 54% 4% 26%
Belmont-Redwood Shores 13% 2% 3% 39% 0% 42%
Las Lomitas Elementary 7% 7% 0% 42% 0% 42%
Menlo Park City Elementary 3% 1% 3% 28% 1% 40%
Portola Valley Elementary 4% 4% 0% 33% 0% 59%
Ravenswood City Elementary 2% 12% 1% 72% 5% 3%
Redwood City Elementary 4% 5% 2% 65% 1% 21%
San Carlos Elementary 8% 6% 3% 37% 1% 42%
Woodside Elementary 12% 8% 0% 30% 0% 49%
Total 8% 5% 8% 40% 2% 35%
Asian Black Filipino Hispanic White
Pacific
Islander
ROOT POLICY RESEARCH SECTION V, PAGE 65
Figure V-44 illustrates the gap between faculty/staff representation and the student body.

For instance, at San Bruno Park Elementary, 15% of the students are White while 48% of the faculty/staff are White, leaving a 33 percentage point gap.

If schools are striving for a distribution of faculty/staff that reflects the racial and ethnic distribution of their student body, the closer to a 0 percentage point gap, the better.

Schools like San Bruno Park Elementary fall short of meeting this goal, in that there is a large overrepresentation of White faculty/staff compared to the student body. Many other districts have a large overrepresentation of White faculty/staff, including Millbrae Elementary (32 percentage point gap), Jefferson Union High School District (29 percentage point gap), and South San Francisco Unified School District (22 percentage points). There are just a few school districts where the share of White students is higher than the share of White faculty, particularly Woodside Elementary and Menlo Park City Elementary, both with a 15 percentage point gap.

Across most school districts, the share of Asian students is larger than the share of Asian faculty/staff. This suggests that Asian students are less likely than their peers to interact with a same-race teacher or staff member. The largest disparity is in Millbrae Elementary, where just 13% of the faculty identify as Asian compared to 46% of the student body, a 33 percentage point gap.

In many school districts, there is a dearth of Hispanic faculty and staff. For instance, in La Honda-Pescadero, 63% of students are Hispanic compared to 39% of faculty, a 24 percentage point gap. In other districts, however, there is a larger share of Hispanic faculty/staff than students. In Las Lomitas Elementary, for instance, 13% of students are Hispanic and 42% of faculty/staff are Hispanic. Recall that Las Lomitas Elementary commonly has high-performing English language learnings students. This may be partly due to the district’s large portion of Hispanic faculty/staff.

Though district wide there are approximately the same portions of Filipino students as there are faculty/staff, Jefferson Union High School stands out as a district where Filipino students are less likely to interact with a same-race teacher or staff member. In Jefferson Union, 29% of students are Filipino compared to just 13% of faculty/staff.

In all districts, there only very small gaps in the share of students that identify as Pacific Islander and the share of faculty/staff that identify as Pacific Islander. All in all, they are represented in approximately equal proportions.

ROOT POLICY RESEARCH SECTION V, PAGE 66
Figure V-44.

Difference Between Staff and Student Populations, by District, 2020-2021 Notes: The figure shows percentage point gaps in student representation versus faculty/staff representation (calculated as the share of faculty/staff minus the share of students).

Source: California Department of Education and Root Policy Research School District
Unified School Districts
Cabrillo Unified -1% 1% 0% -6% 0% 11%
La Honda-Pescadero 0% 5% 4% -24% 0% 16%
South San Francisco 0% 2% -7% -14% 0% 22%
High & Elementary School Districts
Jefferson Union High School -12% 2% -16% -5% 0% 29%
Bayshore Elementary -6% 1% -4% 20% -4% 1%
Brisbane Elementary -13% -1% 16% -8% 4% 18%
Jefferson Elementary -6% 1% 3% -11% -1% 18%
Pacifica -1% 1% -1% -3% 2% 15%
San Mateo Union High School -12% 4% 1% 2% 1% 12%
Burlingame Elementary -19% 5% 8% 11% 3% 4%
Hillsborough Elementary -30% 1% 5% 15% 1% 7%
Millbrae Elementary -33% 2% 3% 5% -2% 32%
San Bruno Park Elementary -12% 1% 3% -15% -1% 33%
San Mateo-Foster City -13% 1% 4% -4% 1% 16%
Sequoia Union High School -7% 10% 1% 9% 2% -9%
Belmont-Redwood Shores -19% 1% 0% 27% -1% 8%
Las Lomitas Elementary -11% 6% -1% 29% 0% -11%
Menlo Park City Elementary -10% 0% 2% 11% 0% -15%
Portola Valley Elementary -2% 4% 0% 19% 0% -7%
Ravenswood City Elementary 2% 7% 1% -12% -2% 2%
Redwood City Elementary 0% 4% 1% -5% 0% 2%
San Carlos Elementary -10% 5% 2% 23% 1% -7%
Woodside Elementary 8% 6% 0% 14% -1% -15%
Total -9% 4% 0% 2% 0% 9%
Asian Black Filipino Hispanic
Pacific
Islander White
7 | HOUSING PLAN – GOALS AND POLICIES
South San Francisco Housing Element 2023-2031 223
South San Francisco Housing Element 2023-2031
APPENDIX 6.6 STATE FAIR HOUSING LAWS AND REGULATIONS
ROOT POLICY RESEARCH CA FAIR HOUSING LAWS, PAGE 1
Appendix 6.6
State Fair Housing Laws and Regulations
This appendix summarizes key state laws and regulations related to mitigating housing discrimination and expanding housing choice.

California Fair Employment and Housing Act (FEHA) (Part 2.8 (commencing with Section 12900) of Division 3 of Title 2) is the state fair housing law that prohibits those engaged in the housing business—landlords, real estate agents, home sellers, builders, mortgage lenders, and others—from discriminating against tenants or homeowners.

California law protects individuals from illegal discrimination by housing providers based on:
 Race, color
 Ancestry, national origin
 Citizenship, immigration status
 Primary language
 Age
 Religion
 Disability, mental or physical
 Sex, gender
 Gender identity, gender expression
 Marital status
 Familial status
 Source of income
 Military or veteran status
Government Code section 65008. Covers actions of a city, county, city and county, or other local government agency, and makes those actions null and void if the action denies an individual or group of individuals the enjoyment of residence, landownership, tenancy, or other land use in the state because of membership in a protected class, the method of financing, and/or the intended occupancy.

ROOT POLICY RESEARCH CA FAIR HOUSING LAWS, PAGE 2
 For example, a violation under Government Code section 65008 may occur if a jurisdiction applied more scrutiny to reviewing and approving an affordable development as compared to market-rate developments, or multifamily housing as compared to single family homes.
 Government Code section 65008, subdivision (e), authorizes preferential treatment of affordable housing
Government Code section 8899.50 requires all public agencies to administer programs and activities relating to housing and community development in a manner to affirmatively further fair housing and avoid any action that is materially inconsistent with its obligation to affirmatively further fair housing.

Government Code section 11135 et seq. requires full and equal access to all programs and activities operated, administered, or funded with financial assistance from the state, regardless of one’s membership or perceived membership in a protected class.

Density Bonus Law (Gov. Code, section 65915) requires California jurisdictions to adopt ordinances that specify how density bonuses will be offered to incentivize affordable housing. The state law contains the minimum specifications for density bonuses.

Housing Accountability Act (Gov. Code, section 65589.5) prohibits local agencies from disapproving housing developments, including farmworker housing and emergency shelters, or requiring conditions that make such housing infeasible except under certain conditions specified in the law.

No-Net-Loss Law (Gov. Code, section 65863) is meant to ensure that development opportunities remain available throughout a jurisdiction’s regional housing need allocation (RHNA) period, especially for low and moderate income households. It prohibits jurisdictions from lowering residential densities without substantial evidence.

Least Cost Zoning Law (Gov. Code, section 65913.1) requires jurisdictions to designate and zone sufficient vacant land for residential use with sufficient standards in relation to growth projections.

Excessive subdivision standards (Gov. Code, section 65913.2) prohibits jurisdictions from imposing design criteria that make residential development infeasible.

Limits on growth controls (Gov. Code, section 65302.8) describes how flood plains are used in comprehensive planning and zoning.

Housing Element Law (Gov. Code, section 65583, esp. subds. (c) (5), (c) (10) governs state-required housing elements.
7 | HOUSING PLAN – GOALS AND POLICIES
South San Francisco Housing Element 2023-2031 225

South San Francisco City CouncilJanuary 25, 2023

South San Francisco City Council
January 25, 2023