33/53,; Oakland Equity Indicators 00 ~35 MEASURING CHANGE TOWARD GREATER EQUITY IN OAKLAND 2 Page Table of Contents Overview Report Structure  Results ¤ City-wide Result ¤ Highest Scores ¤ Lowest Scores Background Methodology Section 1 Theme 1: Economy 8 8 8 9 10 10 12 15 20 21 Topic 1: Business Development  Business Ownership  Prime Contracts Awarding  Long-term Business Vacancy Topic 2: Employment  Disconnected Youth  Labor Force Participation  Unemployment Topic 3: Financial Health  Access to Healthy Financial Institutions  Median Household Income  Poverty Topic 4: Job Quality  Employment in High Wage Industries  Living Wage  Participation in Workforce Development Programs 22 22 23 25 27 27 28 30 31 32 33 34 36 36 38 38 Section 2 Theme 2: Education 42 43 Topic 1: Enrollment  Preschool Enrollment  Chronic Absenteeism  High School On-Time Completion 44 44 45 46 3 Page Topic 2: Achievement rd  3 Grade ELA Proficiency  High School Readiness  A-G Completion Topic 3: Program Access  AP Course Enrollment  Linked Learning Pathway Enrollment  Suspensions Topic 4: Teachers  Representation of Student Population  Teacher Experience  Teacher Turnover 48 49 50 51 53 53 55 56 57 58 59 61 Section 3 Theme 3: Public Health 63 64 Topic 1: Access to Preventive Care  Acute Preventable Hospitalizations  Chronic Disease Preventable Hospitalizations  Health Insurance Topic 2: Child Health  Childhood Asthma Emergency Department Visits  Physical Fitness  SNAP Recipiency Topic 3: Mortality  Infant Mortality  Life Expectancy  Premature Death Topic 4: Physical and Mental Health  Severe Mental Illness Emergency Department Visits  Substance Abuse Emergency Department Visits  HIV Diagnoses 65 65 67 68 69 70 71 73 74 75 76 77 78 79 80 82 Section 4 Theme 4: Housing 84 85 Topic 1: Affordability  Homeownership  Loan Denial 85 85 87 4 Page Rent Burden Topic 2: Displacement  Homelessness  Homeownership with Mortgage  Notice of Evictions Topic 3: Essential Services  Complete Plumbing Facilities  Energy Cost Burden  High Speed Internet Access Topic 4: Housing Quality  Housing Habitability Complaints  Complete Kitchen Facilities  Overcrowding 89 90 91 92 93 95 95 96 98 99 100 101 102 Section 5 Theme 5: Public Safety 104 105 Topic 1: Incarceration  Adult Felony Arrests  Jail Incarceration  Prison Incarceration Topic 2: Law Enforcement  Police Response Times  Stops  Use of Force Topic 3: Staffing  Representation  Attrition from Academy  Attrition from Field Training Topic 4: Community Stressors  Domestic Violence  Homicides  Juvenile Felony Arrests 106 106 107 109 110 111 112 114 115 115 117 117 121 121 122 123 Section 6 Theme 6: Neighborhood and Civic Life 125 126  5 Page Topic 1: Built Environment  Pedestrian Safety  Soft Story Buildings  Long-term Residential Vacancy Topic 2: Civic Engagement  Adopt a Drain  Voter Turnout  Equal Access Accommodations Topic 3: Environmental Health  Park Quality  Abandoned Trash  Pollution Burden Topic 4: Transportation and Infrastructure  Access to a Car  Bus Frequency  Curb Ramps 127 129 130 130 131 132 133 135 137 138 139 140 142 142 143 145 Section 7 Appendices Appendix A: Full Framework Structure Appendix B: Ratio to Score Conversion Table Appendix C: Data Sources List Appendix D: Full Framework with Scores Appendix E: Racial and Ethnic Disparities by Census Tract and Zip Code 147 Acknowledgements 160 148 150 151 156 158 6 Page “Not everything that is faced can be changed, but nothing can be changed until it is faced.” James Baldwin 7 Page City of Oakland Equity Indicators 2018 Report 33.5 Score Overview Oakland has a long history of activism around issues of inequity and social justice. It is, therefore, not surprising that Oakland was chosen in 2017 to be among the first cohort of five cities to develop local Equity Indicators tools in partnership with the City University of New York’s Institute for State and Local Governance (CUNY ISLG) and with funding from the Rockefeller Foundation. The project began as a joint effort between the Resilient Oakland Office and the Department of Race and Equity. It has resulted in a product that will be useful across City departments as we strive to advance equity by using strategies determined through an intentional focus on racial and ethnic disparities and their root causes. In Oakland, the City defines equity as fairness. It means that identity—such as race, ethnicity, gender, age, disability, sexual orientation or expression—has no detrimental effect on the distribution of resources, opportunities and outcomes for our City’s residents. One key assumption in our work is that race matters, and this assumption is supported by the data: almost every indicator of well-being shows troubling disparities by race. The purpose of Oakland’s Equity Indicators Report is to develop a baseline quantitative framework that can be used by City staff and community members alike to better understand the impacts of race, measure inequities, and track changes in the disparities for different groups over time. This framework can then be used to guide and inform policies that address these disparities. Report Structure The Oakland Equity Indicators framework is structured at four levels: Citywide, Theme, Topic, and Indicator. The Citywide framework consists of six Themes that cover broad areas of people’s lives: 1-Economy, 2-Education, 3-Public Health, 4-Housing, 5-Public Safety, and 6Neighborhood and Civic Life. Within each Theme are four Topics, for a total of twenty-four Topics in the whole framework. Topics allow the broad Themes to be discussed and analyzed at a more detailed level. Within each Topic are Three Indicators, for a total of twelve 8 Page Indicators per Theme and seventy-two Indicators in the whole framework. Indicators are the specific quantifiable metrics that are used to measure equity within each Topic and Theme. See Appendix A for the full framework structure. Every Indicator receives a score, which is created by calculating the ratio between the outcomes for the least and most advantaged racial/ethnic groups (for exceptions, see Methodology). This ratio is then converted to an Equity Score using an algorithm developed by CUNY ISLG. Scores are on a scale from 1 to 100, with 1 representing the highest possible inequity and 100 representing highest possible equity. Scores for Topics are calculated by averaging the three Indicator scores within each Topic, and Theme Scores are calculated by averaging the four Topic Scores within each Theme. Finally, the Citywide score is calculated as the average of the six Theme scores. It is important to remember with this scoring system a high score indicates high levels of equity, not necessarily overall quality of outcomes. If everyone is doing poorly in a particular area but doing equally poorly, that area would get a high equity score, but that does not indicate that outcomes are as good in that area as we might ultimately want them to be. Additionally, low scores mean there is a lot of inequity, but do not directly measure whether the outcomes for the groups are objectively good or bad. This equity baseline measurement can, however, inform our choices and policies so that as our City grows and prospers, all residents are able to benefit from that prosperity. Results City-wide Result 33.5 Oakland’s 2018 Citywide Equity score, which encompasses all Indicators in the framework, is 33.5 (out of 100), demonstrating substantial room for improvement. See Appendix D for the full framework with all the scores. The highest scoring Theme was Neighborhood and Civic Life (50.6), followed by Economy (41.8), then Housing (36.8), Education (29.0), Public Health (25.8), and the lowest scoring Theme was Public Safety (17.3). 9 Page 2018 Theme Scores 100 90 80 70 60 50 50.6 41.8 40 36.8 29 30 25.8 17.3 20 10 0 Economy Education Public Health Housing Public Safety Neighborhood and Civic Life Highest Scores The five highest scoring Topics throughout the framework were Civic Engagement at 75.0 (within the Neighborhood and Civic Life Theme), Job Quality at 51.7 (within the Economy Theme), Employment (also within Economy) and Affordability (within the Housing Theme) both at 49.0, and Staffing (within Public Safety) at 48.3. The five highest scoring Indicators were Equal Access Accommodations at 100 (within Neighborhood and Civic Life Theme: Civic Engagement Topic), Adopt a Drain at 80 (within Neighborhood and Civic Life: Civic Engagement), Homeownership with Mortgage at 78 (within Housing: Displacement), Life Expectancy at 77 (within Public Health: Mortality), and tied for fifth highest scoring were Labor Force Participation (within Economy: Employment) and Participation in Workforce Development Programs (within Economy: Job Quality), both at 72. Lowest Scores There were 12 Indicators that received the lowest possible score of a 1 indicating the most extreme levels of inequity exist between groups for these measures. They were (in the order they appear in the Framework) as follows:     Education: Program Access – Suspensions Education: Teachers – Representation of Student Population Public Health: Child Health – Childhood Asthma Emergency Department Visits Public Health: Physical and Mental Health – Substance Abuse Emergency Department Visits 10 P a g e         Housing: Displacement - Homelessness Public Safety: Incarceration – Adult Felony Arrests Public Safety: Incarceration – Jail Incarceration Public Safety: Incarceration – Prison Incarceration Public Safety: Law Enforcement – Use of Force Public Safety: Community Stressors – Homicides Public Safety: Community Stressors – Juvenile Felony Arrests Neighborhood and Civic Life: Built Environment – Pedestrian Safety These are significant findings, with potentially profound life changing impacts, disproportionately being experienced by our residents of color. In light of the City of Oakland’s commitment to equity, they provide meaningful markers of the greatest opportunities to make a difference for those in our marginalized communities. Next Steps Publishing this first year’s Equity Indicators Report is important because the information positions the City to use data to drive equity outcomes, but it is only a small step in a much larger effort to address these inequities. To complement this quantitative baseline, the Department of Race & Equity is also working with community partners to gather qualitative data from diverse community members in Oakland. This will provide important context and insights into the root causes of these disparities and meaningful solutions to the problems illuminated in the Equity Indicators Report. Data-informed, transparent community involved decision-making is essential to transformational institutional change that will advance equitable outcomes in our communities of color. The City of Oakland is energized to keep building on the foundation of this report, to promote dialogue with Oakland’s diverse communities, and to develop policies, programs and partnerships that reduce these inequities, so we build a future where every Oaklander can thrive. 11 P a g e Background Oakland has a long history of activism around issues of justice and equity. Both oppression and this resistance to oppression have shaped the city’s past and the lives of its residents to this day. It is, therefore, not surprising that Oakland was chosen in 2017 to be among the first cohort of five cities to develop local Equity Indicators tools in partnership with the City University of New York’s Institute for State and Local Governance (CUNY ISLG) and with funding from the Rockefeller Foundation. The Equity Indicators Report originated as an action in the Resilient Oakland Playbook (funded by and created in partnership with 100 Resilient Cities—pioneered by the Rockefeller Foundation). Joining the CUNY ISLG cohort allowed Oakland to implement this action while also learning and collaborating with other cities around the country around best practices in measuring and tracking progress toward increasing equity. The Department of Race and Equity collaborated on the development of this report because access to data is critical to Oakland’s progress toward addressing inequity through systemic, transformational change. The purpose of Oakland’s Equity Indicators Report is to develop a baseline quantitative framework that can be used by City staff and community members alike to better understand the impacts of race and measure inequities. It will enable City departments and staff to make data-driven decisions about programs and policies to address these inequities and ensure people have equitable access to opportunities and services that we administer or deliver, directly or by contract. It will enable community members to monitor our progress or setbacks and advise improvement. Future reports will measure change in the disparities for different groups over time and will offer an opportunity for City staff and community members to work in collaboration to devise and implement course correction and to celebrate progress. A Brief Racial History of Oakland Social inequities in life outcomes that are predictable by race are the inevitable result of our nation's history. Oakland is today one of the most racially and ethnically diverse cities in the country (1). Before the arrival of European explorers, it was the home of one group, the Ohlone, one of the many indigenous tribes who populated the territory that became California. In the late 1700s, California was home to more than 300,000 native people in more than 200 tribes, by 1848, disease spread by contact with outsiders had reduced California's native population by more than two-thirds. This catastrophic decline disrupted families, communities, and trading networks, weakening native resistance to Spanish, Mexican, and American intrusion. 12 P a g e By 1860, the state's native population had been reduced to 30,000, decimated by disease, removal from their land, starvation, poverty, bounty hunters, and other historical mistreatment. Just 40 years later, in 1900, this native population had plummeted to 20,000. Ultimately the fate of local tribes mirrored that of indigenous groups across the country, leading to the commonly unnamed disparity of underrepresentation in the general population, when at one time they were the majority population (2). In more recent history, Oakland was the place where laws like the 1882 Chinese Exclusion Act (the first law to prevent a specific ethnic group from immigrating to the United States) was first tested (3) and where in 1927 William Parker (a known KKK member) was elected to City Council (4). In Oakland, as in cities across the nation, people of color were impacted by the 1940/50s federal housing redlining policy, which excluded communities of color from the wealth building opportunity of homeownership. Their neighborhoods were abandoned to urban decay after “White flight” to the suburbs. Highway 17 (now I-880 or Nimitz Freeway) was built through the heart of the African American community, disrupting community cohesion, and economic viability by cutting it off from Downtown. Many homes and businesses were destroyed to build the Cypress Viaduct and the rest of the Nimitz Freeway. Further urban renewal caused the destruction of the area around Market and 7th streets to make way for the Acorn High Rise apartments. This urban renewal thrust in West Oakland continued into the 1960s with the construction of BART and the Main Post Office Building at 1675 7th Street. Many African American and Latino families were displaced from West Oakland during this period. African Americans relocated to East Oakland (especially the Elmhurst district and surrounding areas) and Latinos moved into the Fruitvale neighborhood. The people of Oakland pushed back. Oakland was at the center of the general strike during the first week of December 1946, one of six cities across the country that experienced such a strike after World War II and marked the beginning of the labor movement. In the 1960s, when massive demonstrations and civil unrest resulted in the Civil Rights Acts (which made it a federal crime to discriminate against someone based on their race, color, sex, religion, or national origin in employment and housing), Oakland was again at the center of change. Community groups born in the 1960s like the Black Panther Party, Oakland Community Organizations (OCO), Unity Council, Intertribal Friendship House and many others continued to organize and demand protections and equal access to jobs, housing, employment, transportation and services (5). These laws and policies helped people to address injustice at an individual level, but it was soon realized that more needed to be done to address the deep inequities created by years of blatantly discriminatory policies and practices and to change the systems that created oppression (6). 13 P a g e In the 1980s and 1990s, community organizations started new efforts to influence and encourage local governments to explore how to undo the legacy of institutionalized racism. In Oakland, PolicyLink, the Green Lining Institute and the Center for Racial Justice Innovation (Race Forward) amongst others led these efforts. By the early 2000s racial equity initiatives and tools began to be used by local government staff and elected government officials to figure out how to change the inequities in outcomes impacting communities of color in multiple cities across the country. In 2016 the City of Oakland launched its own Department of Race and Equity to advance equity change action in the City government. A growing number of local government institutions are realizing the need to measure and account for their progress towards equity and to embrace their responsibility to ensure that their programs serve all populations. Using disparity data to evaluate the impact of activities, set equity outcome goals and do racial equity impact analyses is critical to advancing equitable outcomes for communities of color (6). Although we cannot change the past, we can learn from it to change the future. By focusing on the impacts of race, implementing intentional strategies to address disparities and measuring our progress we can eliminate rather than deepen disparities in our communities (6). If Oakland’s history of struggle to achieve equity teaches us anything, it is that we cannot do this in isolation. We understand the need to work side by side with the community and partner institutions to undo the legacy of racism to create an Oakland where there is equity in opportunity that results in equitable outcomes for all. 14 P a g e 1 Bernanrdo, Richie. (2018.1.13) Most and Least Racial and Ethnically Diverse Cities in the U.S. https://wallethub.com/edu/cities-with-the-most-and-least-ethnoracial-and-linguistic-diversity/10264/. Oakland is the second most diverse City in the U.S. 2 University of California. (2009) Native Americans: Arts and Traditions in Everyday Life. (2009) California Cultures project 3 Zhang, Sheldon (2007). Smuggling and trafficking in human beings: all roads lead to America. Greenwood Publishing Group. p. 69. ISBN 978-0-275-98951-4. 4 Deniels, Roger and Olin, C. Spencer Jr, Editors. Racism in California: A Reader in the History of Oppression. (1972) The Macmillan Company. 5 Zinn, Howard (2003). A People’s History of the United States. Harper-Collins. P. 126-210. ISBN-0-06052842-7 6 Hanks, Angela, Solomon, Danyelle, and Weller, Christine E. Systemic Inequality. (2018) Center for American Progress https://www.americanprogress.org/issues/race/reports/2018/02/21/447051/systematic-inequality/ Methodology The Equity Indicators methodology was originally developed by the City University of New York’s Institute for Local and State Governance (CUNY ISLG) and then adapted for the Oakland context. Process of Developing the Initial Framework The process included the following steps: 1. Research inequities in Oakland, who experiences those inequities, and the City of Oakland’s policy priorities, including the Resilient Oakland Playbook and the work of the Department of Race and Equity. 2. Create a draft framework, based on the research in Step 1. 3. Solicit feedback from a range of stakeholders, including community members, advocacy groups, government agencies, and City leadership. This step included two community workshops held in fall 2017. 4. Revise the draft framework in accordance with the feedback received. 5. Test the Indicators (see section below on How Indicators Were Chosen). 6. Revise the framework and solicit additional feedback as needed. 7. Finalize the tool and publish the first year of findings. Structure of Oakland Equity Indicators Framework The Oakland Equity Indicators framework is structured at four levels: Citywide, Theme, Topic, and Indicator. The Citywide framework consists of six Themes that cover broad areas of people’s lives: 1-Economy, 2-Education, 3-Public Health, 4-Housing, 5-Public Safety, and 6Neighborhood and Civic Life. These Themes are not exhaustive, but were chosen based on areas of inequity in Oakland. They are also not mutually exclusive; there are many relationships between the Themes. For example, education influences economic outcomes, economic status influences housing and health, etc. 15 P a g e Within each Theme are four Topics, for a total of twenty Topics in the whole framework. Topics allow the broad Themes to be discussed and analyzed at a more detailed level. For example, within the Theme of Economy, the four Topics are: Business Development, Employment, Financial Health, and Job Quality. Within each Topic are three Indicators, for a total of twelve Indicators per Theme and seventy-two Indicators in the whole framework. Indicators are the specific quantifiable metrics that are used to measure equity within each Topic and Theme. See Appendix A for the full structure of the framework with the exact Themes, Topics, and Indicators. How Indicators Were Chosen The Indicators chosen represent the best proxies we could find for the complex disparity themes we set out to measure. The following criteria were used to determining the indicators included in each of the topics in the final framework: 1. Data is available, high quality, and from a reliable source. 2. We will be able to calculate change over time (i.e., data is updated and accessible on an annual basis and changes from year to year can be meaningfully interpreted). 3. There is a strong causal model for why this Indicator matters (i.e., we understand the context behind the Indicator and how disparities affect people). 4. The data accurately represents the impact of inequity on people’s lives (e.g., not measuring quantity when what matters is quality). How Indicators Are Scored Per CUNY ISLG, Equity Indicators are designed to be scored in two ways. Static Scores capture findings for a given year, and Change Scores capture change from the baseline to the most recent year. Given that this is the first ever equity indicators report for Oakland, all scores presented will be Static Scores. We intend in future years to include Change Scores to allow for discussions about whether and where progress toward equity is being made. The standard approach for scoring Indicators is to calculate the ratio between the outcomes for the least and most advantaged racial/ethnic groups. This ratio is then converted to an Equity Score using a standard algorithm developed by CUNY ISLG (see Appendix B for the ratioto-score conversion table). Scores are on a scale from 1 to 100, with 1 representing the highest possible inequity and 100 representing highest possible equity. For example, for the Unemployment Indicator, we calculated the ratio between the unemployment rates of African Americans and Whites because these two groups had the highest and lowest rates 16 P a g e respectively. The ratio for this Indicator is 2.12, meaning that African Americans were 2.12 times more likely than Whites to be unemployed. This ratio yields an Equity Score of 40, representing substantial room for improvement. There are some exceptions to this standard approach. While most Indicators measure negative outcomes, some Indicators measure positive outcomes (e.g., business ownership). In this case, the ratio is flipped to compare the most and least advantaged groups so that scores can align on the same scale. Also, whenever possible, data was used that directly contained the reported race/ethnicity of the people affected by that Indicator, however sometimes we used geographic data as a proxy for racial and ethnic groups. Nine of the seventy-two Indicators in the framework measure racial and ethnic disparities based on the majority race/ethnicity of census tracts. Four of the seventy-two Indicators in the framework measure racial and ethnic disparities based on zip code. Due to the low number of zip codes in Oakland, these Indicators compare zip codes in which more than 60% of the population is non-White and zip codes in which more than 60% of the population is White. These demographics are all based on Census Bureau, American Community Survey 5-year estimates, 2012-2016. For full details on census tract and zip code calculations, see Appendix E. In addition, while the vast majority of Indicators measure racial and ethnic disparities, three Indicators measure geographic disparities (1 by Police Area and 2 by City Council District), and two Indicators are citywide measures (equal access accommodations and curb ramps). Finally, there are some exceptions to which racial and ethnic groups are used for the scored comparison (i.e., for some indicators we do not compare the least and most advantaged). Any exception is noted and a reason given. Regardless of any exceptions, within the explanation of each Indicator, data is presented for all available groups or geographic areas, and it is made clear which groups/areas are used for scoring. Scores for Topics are calculated by averaging the three Indicator scores within each Topic, and Theme Scores are calculated by averaging the four Topic Scores within each Theme. Finally, the Citywide score is calculated as the average of the six Theme scores. By having multiple measures, we aim to generate more fair and accurate scores for the broader Topics, Themes, and ultimately the single Citywide Equity Score. By choosing a standard number of Indicators and Topics per Theme, we avoid skewing the results too heavily towards any one area. By using a simple average to calculate higher level scores (as opposed to assigning weights to Indicators or Topics), we also avoid potential personal bias. It is important to remember with this scoring system that a high score indicates high levels of equity, not necessarily overall quality of outcomes. If everyone is doing poorly in a particular area but doing equally poorly, that area would get a high equity score, but that does not 17 P a g e indicate that outcomes are necessarily as good in that area as we might ultimately want them to be. Additionally, low scores mean there is a lot of inequity, but do not directly measure whether the outcomes for the groups are objectively good or bad. This equity baseline measurement can, however, inform our choices and policies so that as our City grows and prospers, all residents are able to benefit from that prosperity. Purpose of Scoring Per CUNY ISLG, “scoring has two important and related benefits. It enables the standardization of data produced in different formats (i.e., percentages, and rates) and from different modes of data collection (i.e., administrative data and survey data). In turn, [scoring] makes it possible to synthesize findings across Indicators, Topics, and Themes to produce higher-level findings,” an important feature of the framework. Without scoring, the only conclusions from this process would be individual results for the seventy-two Indicators. Data Sources The specific data source for each Indicator is noted in the explanation of that Indicator. Generally, data came from two different types of sources: publicly available data and internal City administrative data. The two most frequently used publicly available data sources were the Census Bureau’s American Community Survey and the Oakland Unified School District’s (OUSD) dashboards. We also requested Oakland-specific data from the Alameda County Department of Public Health for many of our Public Health Indicators. Internal City administrative data was either already publicly available or obtained by request from specific departments (such as the Oakland Police Department). For a list of all data sources, see Appendix C. We attempted to use the most recently available data for all Indicators. Usually that meant data from 2016 or 2017, but sometimes data was older than that or aggregated over multiple years. In those cases, the exact timeframe is noted in the explanation of each Indicator. Race Matters 18 P a g e How Indicator Are Presented Equity Indicators Cityw i The Citywide framework consist of 6 themes that cover broad areas of people's lives. 1. Economy 2. Education 3.Public Health 4.Housing 5.Pub ic Safety 6. Neighborhood and Civic Life. Within each theme there are 4 topics. To i CS Whithin each topic there are 3 indicators, for a total of 12 indicators per theme and 72 indicators in the whole framework. Indicators represent the best proxy we could find for the complex disparities we set out to measure. I Cl cato rS Every indicator receives a score, created by calculating the ratio between the outcomes for the least and most advantaged racial/ethnic group. Themes Scores are on a scale from 1 to 100. 1 represents the highest possible level of inequity. 100 represents the highest possible level of equity. The ratio is then converted to an Equity Score using an algorithm developed by CUNY ISLG. Scores Scales 19 Page Theme 1 Economy IN THIS SECTION: Business Development . Business Ownership . Prime Contracts Awarding . Lang?term Business Vacancv Employment . Disconnected Youth . Labor Force Participation . Unemplovment Financial Health . Access to Healthv Financial Institutions . Median Household Income . Povertyr Job Quality . Employment in High 1Wage Industries . Living Wage . Participation in Workforce Development Programs 20 Page Theme 1: Economy Theme Score: 41.8 In Oakland and across the Bay Area, there are wide disparities in economic outcomes for different groups. Structural barriers in society result in some residents having more access than others to economic opportunities that build wealth and financial stability. This Theme not only includes the standard measures of economic well-being (e.g., unemployment, poverty) but it also evaluates racial and ethnic disparities in the quality of jobs, business development, and elements of financial health. Economy ranked second highest compared to the other Themes in the Oakland Equity Indicators framework. The 12 Indicators within the Economy Theme examine inequities faced by racial and ethnic minorities across four Topic areas: Business Development, Employment, Financial Health, and Job Quality. All Topics showed room for improvement. Job Quality had the highest Topic score (51.7), and Employment scored second highest (49.0), followed by Business Development (33.7), and the lowest scoring Topic was Financial Health (32.7). Topics and Indicators within this Theme: Topic Business Development Employment Financial Health Job Quality Score Indicators 33.7 Business Ownership Prime Contracts Awarding Long-term Business Vacancy 49.0 Disconnected Youth Labor Force Participation Unemployment 32.7 Access to Healthy Financial Institutions 51.7 Median Household income Poverty Employment in High Wage Industries Living Wage Participation in Workforce Development Programs Score 36 31 34 35 72 40 31 34 33 54 29 72 21 P a g e Topic 1.1: Business Development Topic Score: 33.7 The Business Development Topic includes three Indicators that measure racial and ethnic disparities in business ownership, prime contracts awarding, and business vacancy. The first Indicator in this Topic measures disparities in business ownership rates between African American and White Oaklanders. The second Indicator measures disparities in the distribution of contracts under $100,000 awarded by the City to African American and White business owners. The third Indicator measures disparities in the location of business addresses that had been vacant for two years or more by majority race/ethnicity of census tracts. Business Development was the second-lowest scoring Topic in the Economy Theme, with a Topic score of 33.7. The Indicator scores were relatively similar, with a score of 34 for longterm business vacancy and business ownership receiving a score of 36. Prime contracts awarding had a slightly lower score (31), but all Indicators in this Topic show room for improvement. Economy: Business Development - Business Ownership Ratio between the percents of Whites and African Americans who are business owners Score: 36 Ratio: 2.70 What is measured? This Indicator measures the percent of employed individuals who are self-employed in their own incorporated business, professional practice, or farm. Why is this important? Business ownership is an important measure of economic development and individual financial empowerment, and it can help alleviate other economic disparities for racial and ethnic minorities, including disparities in income and employment. Self-employment and family-business ownership have been shown to increase economic mobility for workers and their children. (Source: https://www.urban.org/sites/default/files/publication/33841/413134self-employment-family-business-ownership-and-economic-mobility.pdf ) 22 P a g e What did we find? Among White employed individuals, 3.9% were business owners, compared to 1.4% of African American employed individuals. Latino and Asian employed individuals had similar rates of business ownership at 2.4% and 2.3%, respectively. The citywide business ownership rate was 2.6%. Whites were 2.7 times more likely to own their own business than African Americans. Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/mapsdata/maps/2010puma/st06_ca.html) Economy: Business Development - Prime Contracts Awarding Ratio between the percents of Prime construction and professional services contracts under $100,000 received by African Americans and Whites Score: 31 Ratio: 3.42 What is measured? This Indicator measures the percent by race/ethnicity of Prime contractors for construction and professional services contracts who received under $100,000. Additional data is provided on the average amount received by these contractors. Data is from fiscal year 2015-16 and the date used to assign contracts to a fiscal year is the date that the compliance analysis was 23 P a g e completed, or the review date. The dollar amounts represent the initial award; any negotiated bid amounts or change orders are not taken into account. The initial award is adjusted to how much of the total contract went to the Prime as opposed to Subprime contractors. Why is this important? The City of Oakland awarded over $58 million in construction contracts and almost $8.5 million in professional services contracts in fiscal year 2015-16. It is important to understand whether there are disparities by race/ethnicity in who received these contract dollars. The City is in the process of a full disparity study that will provide analysis on this issue as well. Next year, we intend to update this Indicator with the results of that study. In the interim, we used the data currently available to determine whether contractors of certain races were less likely to receive large contracts. What did we find? We found that for Prime construction and professional services contracts, 66.7% of African American contractors received contracts under $100,000, which was 3.42 times as often as White contractors (19.5%). Additionally, White contractors received an average of $1,059,209 per contract which was 11.87 times as much as African American contractors received on average ($89,191). It should be noted that the sample sizes between races were very different with 6 contracts going to African Americans and 41 to Whites. Whether or not this low number awarded to African American contractors was in and of itself an inequity remains to be determined by the full disparity study which will look at the availability of contractors by race/ethnicity. Data: Prime Contracts by Race/Ethnicity Number of Contractors Average Contractor Amount Percent of Contracts Under $100k African American 6 $89,191 66.7% Asian 5 $362,643 20.0% Latino 18 $923,891 44.4% White 41 $1,059,209 19.5% Other/NL 14 $299,175 35.7% Source: Oakland Contracts and Compliance Division by request, Fiscal Year 2015-16 24 P a g e Indicator 3: Economy: Business Development - Long-term Business Vacancy Ratio between the percents of business addresses that have been vacant for 24 months or more in majority Asian and majority White census tracts Score: 34 Ratio: 2.96 What is measured? This Indicator measures the percent of business addresses that have been identified as “vacant” by the United States Postal Service (USPS) for at least two years. Data is collected and aggregated at the census tract level by the USPS on a quarterly basis. Why is this important? High levels of long-term business vacancy are detrimental to the economic vibrancy of neighborhoods. Business addresses can be vacant at any point in time for a variety of reasons, including new construction, renovation, and tenant turnover. Addresses that remain vacant for two years or more, however, may be indicative of economically distressed neighborhoods or areas where rents are too high for businesses to afford them. What did we find? Long-term business vacancy was highest in majority Asian census tracts (4.8%), which represent the Chinatown neighborhood near downtown Oakland. Second highest were majority African American census tracts (3.9%). Long-term business vacancy was lowest in majority White census tracts (1.6%) and second lowest in majority Latino census tracts (2.4%). Majority Asian census tracts were 2.96 times more likely to have long-term business vacancies than majority White census tracts. 25 P a g e Data: Percent of business addresses that have been vacant 2 years or more by majority race/ethnicity of census tracts African American 3.9% Asian 4.8% Latino 2.4% White 1.6% Non-White/Mixed 2.9% Oakland 0.0% 2.7% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% Source: U.S. Department of Housing and Urban Development Aggregated USPS Administrative Data on Address Vacancies, Quarter 3 ending September 30, 2017, https://www.huduser.gov/portal/datasets/usps.html; American Community Survey, 5-year estimates, 2012-2016 Topic 1.2: Employment Topic Score: 49.0 The Employment Topic includes three Indicators that measure participation in the workforce, an essential component of economic wellbeing. The first Indicator in this Topic measures disparities in the rate of disconnectedness from school or work among young people ages 16 to 24 between African Americans and Asians. The second Indicator measures disparities in labor force participation, while the third Indicator focuses on disparities in unemployment, both between African American and White Oaklanders. The Employment Topic scored 49.0, the second highest score in the Economy Theme. The disconnected youth Indicator received the lowest score within the topic at 35. Labor force participation scored the highest within the Topic at 72, and the unemployment score was 40. This indicates that while there are fewer racial and ethnic disparities in who is participating the labor market, African American people within the labor market face greater disadvantage when it comes to securing and maintaining employment. 26 P a g e Economy: Employment - Disconnected Youth Ratio between the percents of African American and Asian youth who are disconnected Score: 35 Ratio: 2.80 What is measured? Youth are considered disconnected if they are out of work and out of school. This Indicator measures the percent of the population aged 16-24 who are neither working nor in school. Why is this important? Between the ages of 16-24, young people are in transition between youth and adulthood, developing the education, networks, confidence, and social-emotional skills to handle stress and prepare for adult independence. Youth that are out of work and out of school face disadvantages in making this transition successfully. They also face a higher risk of involvement with the criminal justice system. What did we find? Citywide, one in ten youth were neither working nor in school (10.3%). African American youth were the most likely to be disconnected (14.8%), followed closely by Latino youth (13.2%). Asian youth were the least likely to be disconnected (5.3%), while 8.8% of White youth were disconnected. African American youth were 2.80 times more likely to be disconnected from both work and school than Asian youth. This outcome tracks with the education data for the groups, with the same groups experiencing the greatest disadvantage. 27 P a g e Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) Economy: Employment - Labor Force Participation Ratio between the percents of African Americans and Whites who are not participating in the labor force Score: 72 Ratio: 1.27 What is measured? This Indicator measures the percent of the population aged 16 and older who are neither working nor looking for work. Employed individuals, unemployed individuals who are looking for work and individuals in the armed forces are not included in this measure. Why is this important? Labor force participation is an important Indicator because unemployment statistics do not capture all individuals who are not working. For example, individuals who are not working and not looking for work are not in the labor force. Some of these individuals are classified as discouraged workers who may have given up seeking work due to prolonged unemployment, 28 P a g e lack of opportunities that match their skills, and education, age, and disability. Other individuals not in the labor force include retired persons, students, and those taking care of children or other family members. (Source: https://www.bls.gov/cps/lfcharacteristics.htm) What did we find? Across all racial and ethnic groups, about one in three individuals aged 16 and older (32.6%) were not in the labor force. Labor force non-participation was less common among Whites and Latinos, 28.9% and 30.9% respectively. Higher percents of African Americans (36.7%) and Asians (36.0%) were not in the labor force. African Americans were 1.27 times more likely than Whites to not be in the labor force. Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) 29 P a g e Economy: Employment - Unemployment Ratio between the unemployment rates for African Americans and Whites Score: 40 Ratio: 2.12 What is measured? Unemployment is measured by the percent of the labor force that is unemployed. The labor force includes all individuals aged 16 and older who are either employed or unemployed and looking for work. Individuals in the armed forces are excluded from this measure. Why is this important? Employment provides the means to participate in the economy and reduces the likelihood of living in poverty. Nationally, unemployment rates are higher among African Americans than their White counterparts. Furthermore, the African American unemployment rate rose more than the rate for Whites during the Great Recession and has been slower to fall as the economy has recovered. Differences across racial and ethnic groups may point to a number of barriers racial and ethnic minorities face to securing and maintaining employment, including job availability, educational attainment, and discrimination in hiring. (Source: https://news.stanford.edu/2017/06/16/report-finds-significant-racial-ethnicdisparities/) What did we find? African Americans were the most likely to be unemployed (8.9%) and Whites the least likely (4.2%). The unemployment rate among Latinos (4.5%) was similar to that of Whites, while a slightly higher percent of Asians were unemployed (5.8%). African Americans were 2.12 times more likely than Whites to be unemployed. 30 P a g e Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) Topic 1.3: Financial Health Topic Score: 32.7 The Financial Health Topic includes three Indicators that consider economic security and stability through measures of banking, income, and poverty. The first Indicator measures disparities in the rate of access to healthy financial institutions in White and non-White zip codes. The second Indicator measures racial and ethnic disparities in median household income, while the third Indicator focuses on poverty. Financial Health had the lowest score in the Economy Theme, at 32.7. The Indicator scores were relatively similar and low, showing room for improvement across the board. Healthy financial institutions had the lowest score at 31. Poverty scored 33, and median household income scored 34. 31 P a g e Economy: Financial Health - Access to Healthy Financial Institutions Ratio between the ratios of bad-to-good financial institutions in non-White and White zip codes Score: 31 Ratio: 3.40 What is measured? This Indicator measures access to different types of financial institutions by zip code. “Good” institutions include banks, credit unions, and savings institutions. “Bad” institutions include check cashing services, money transfer services, and payday loan institutions. The ratio between the number of bad institutions and the number of good institutions is calculated for each zip code. A higher ratio of bad-to-good institutions means that there are disproportionately more bad institutions in a given zip code. The Indicator then measures the ratio of these ratios, comparing zip codes in which more than 60% of the population is nonWhite to those in which more than 60% of the population is White. The third category of zip codes is those in which the population is racially and ethnically mixed. Why is this important? Access to financial institutions can help support the financial health of neighborhoods by providing residents with the resources they need to save and plan for the future. While some types of institutions engage in predatory lending practices, others provide a safe way for customers to build wealth and participate in the local economy. Research has shown that predatory lenders target racial and ethnic minority neighborhoods where there is less access to mainstream financial institutions. Another important factor to consider is the affordability of financial services, even from the “good” institutions. (Source: https://www.reuters.com/article/us-usa-foreclosures-race/racial-predatory-loansfueled-u-s-housing-crisis-study-idUSTRE6930K520101004) What did we find? The ratio of bad-to-good financial institutions in zip codes that are more than 60% non-White was 0.42, compared to 0.13 in zip codes that are more than 60% White. Zip codes that are racially and ethnically diverse had a ratio of 0.25, which was lower than the citywide ratio of 0.38. Majority non-White zip codes had a bad-to-good financial institutions ratio 3.23 times higher than majority White zip codes. See the chart on page 33. 32 P a g e Data: Source: Reference USA, publicly available through the Alameda County Library, http://www.aclibrary.org/atoz/R, data retrieved January 19, 2018; American Community Survey, 5-year estimates, 2012-2016 Economy: Financial Health - Median Household Income Ratio between the median incomes for White and African American households Score: 34 ` Ratio: 2.93 What is measured? This Indicator measures median household income by the race/ethnicity of householders. Why is this important? Median household income is a measure often used by economists to capture how a typical household is faring in a particular area. It is also used to guide certain public policies, including the eligibility requirements for affordable housing. Income is directly tied to many other economic indicators, including poverty, unemployment, educational attainment, and job quality. Differences in median household income may point to disparities in these and other areas. 33 P a g e What did we find? The median income for White households was highest ($110,000) and the median income for African American households was lowest ($37,500). The median income for Asian households ($76,000) was similar to the citywide median income ($73,200), while Latino households fell below the citywide median with a median income of $65,000. The median income for White households was 2.93 times the median income of African American households. Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html Economy: Financial Health - Poverty Ratio between the percents of African Americans and Whites who are living in poverty Score: 33 Ratio: 3.09 What is measured? This Indicator measures the percent of the population living at or below the federal poverty level, as defined by the U.S. Department of Health and Human Services. (Source: https://aspe.hhs.gov/poverty-guidelines) 34 P a g e Why is this important? Individuals and families living in poverty struggle financially but also forego basic necessities in order to make ends meet. Individuals living in poverty may experience hunger, live in lowquality housing, and decide not to seek medical care. Intergenerational poverty can further limit access to opportunity and economic mobility. In addition, when poverty is concentrated geographically, the negative effects on health and wellbeing are compounded at the neighborhood and community level. What did we find? African Americans were most likely to be living at or below the federal poverty level (26.1%), compared to 21.9% of Latinos, 15.0% of Asians, and 8.4% of Whites. This means that more than one in four African Americans and more than one in five Latinos were living at or below the federal poverty level. African Americans were 3.09 times more likely than Whites to be living at or below the federal poverty level. Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html 35 P a g e Topic 1.4: Job Quality Topic Score: 51.7 The Job Quality Topic includes three Indicators that measure access to high quality jobs that pay a living wage and promote career development and long term economic stability. The first Indicator in this Topic measures disparities in employment rates in high wage industries. The second Indicator measures racial and ethnic disparities in the likelihood of having a job that pays at least living wage. The third Indicator measures racial and ethnic disparities in participation rates in workforce development programs intended for unemployed individuals. Job Quality had the highest Topic score in the Economy Theme, at 51.7. The Indicator scores varied widely, with the living wage Indicator receiving the lowest score at 29. Employment in high wage industries scored higher at 54. Participation in workforce development programs had the highest score at 72. Economy: Job Quality - Employment in High Wage Industries Ratio between the percents of Latino and White workers who are not employed in high wage industries Score: 54 Ratio: 1.65 What is measured? This Indicator is measured by the percent of employed individuals who are not employed in industries with a mean annual wage of at least $80,000. In 2016, these industries included management occupations; legal occupations; healthcare practitioners and technical occupations; computer and mathematical occupations; architecture and engineering occupations; life, physical, and social science occupations; and business and financial operations occupations. (Source: Occupational Employment Statistics, CA Employment Development Department https://data.edd.ca.gov/Wages/Occupational-EmploymentStatistics-OES-/pwxn-y2g5) Why is this important? 36 P a g e Employment in high wage industries is an important measure of what kinds of jobs are accessible to individuals of different racial and ethnic groups. Limited access to jobs in high wage industries may be due to several factors, including a mismatch between available jobs and required education or training, discrimination, and other limiting factors that may also contribute to differences in access to quality jobs and overall employment rates. (Source: https://www.stlouisfed.org/publications/regional-economist/july-2011/the-mismatchbetween-job-openings-and-job-seekers) What did we find? Latino workers were the most likely to not be employed in a high wage industry (83.2%), followed closely by African American workers (82.0%). About half of White workers were not employed in a high wage industry (50.4%), and Asian workers fell in the middle (67.5%). Citywide, six out of ten workers were not employed in high wage industries. Latino workers were 1.65 times more likely to not be employed in a high-wage industry than White workers. Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) 37 P a g e Economy: Job Quality - Living Wage Ratio between the percents of Latino and White workers who make less than the living wage Score: 29 Ratio: 3.79 What is measured? This Indicator measures the hourly wage for all workers ages 16 and older and compares it to the 2016 Oakland living wage ($14.86 per hour). Hourly wages are calculated by dividing the total person’s earnings by the product of the weeks worked and the usual hours worked per week during the past 12 months. The weeks worked variable was set to the midpoint of the interval included in the ACS data. Only workers with non-zero earnings, who were not selfemployed or unpaid family workers, and who were at work or had a job but were not at work last week were included in the analysis. (Source for methodology: http://laborcenter.berkeley.edu/pdf/2014/chartbook-data-and-methods.pdf) Why is this important? Living wage is the wage that is necessary to maintain a typical standard of living in a particular place. It is the minimum income that represents the fine line between financial independence and the need to seek out public assistance. Living wage standards are sometimes set by local government to take into account higher costs of living, and they are higher than the state or federal minimum wage. In Oakland, the Living Wage Ordinance requires the City to adjust the living wage annually. The living wage standard used in this year’s Indicator was based on the 2016 wage which was in effect before the wage was raised effective July 1, 2017. (Sources: http://livingwage.mit.edu/pages/about, http://www2.oaklandnet.com/w/DOWD009082) What did we find? Citywide, three in ten workers (30.9%) made less than the living wage. Almost half of Latino workers (46.5%) made less than the living wage compared to 12.3% of their White counterparts. Among African American workers, 37.6% made less than the living wage, which was a similar percent to that of Asian workers (36.4%). Latino workers were 3.79 times more likely than White workers to make less than the living wage. 38 P a g e Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) Economy: Job Quality- Participation in Workforce Development Programs Ratio between the percents of unemployed Asian and African American Oaklanders who did not participate in the City’s Workforce Development Program Score: 72 Ratio: 1.27 What is measured? This Indicator measures the percent of the unemployed population (ages 16 and up) in Oakland by race/ethnicity who did not participate in the City of Oakland’s Workforce Development program between 7/1/2016 and 6/30/2017. The percent that did participate for each race/ethnicity is calculated by dividing number of participants of that race/ethnicity by the number of unemployed people in the labor force in Oakland of that race/ethnicity. Percent that did not participate is 100% minus the percent that did participate. NOTE: Participation is not the most meaningful metric, but was the data available. In the future, we hope to replace this with a measurement of exit outcomes for participants by race/ethnicity (i.e., did participants successfully find jobs?). 39 P a g e Why is this important? The City of Oakland’s Workforce Development programs are a resource for job seekers. Job seekers are assigned a case worker and given supports to secure a job (such as a comprehensive assessment and individual employment plan). The intent is that these supports make it more likely the job seeker will find a job and that the job will be of higher quality than might have been obtained without support. What did we find? Unemployed African American Oaklanders had the highest participation in the City’s program (26.8% participate, 73.2% did not participate). Asian unemployed Oaklanders had the lowest participation rates (7.1% participate, 92.9% did not participate). Therefore, an unemployed Asian person was 1.27 times more likely to not participate in the City’s Workforce Development programs than an African American unemployed person. As shown in our Unemployment Indicator, African Americans have the highest rate of unemployment and Asians the second highest rate. It is, therefore, appropriate that African Americans participate extensively in Workforce Development programs and that participation should continue. However, the results in this Indicator show that Asian participation is an area for improvement as they also experience high unemployment rates, but are the least likely to participate in the City’s Workforce Development programs. Data: Source: Workforce participation data from Oakland Economic and Workforce Development department by request, 7/1/2016-6/30/2017. Data on population by race/ethnicity that was unemployed but in the labor force from American Community Survey, 1-year PUMS, 2016. (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) 40 P a g e A PUBLIC USE MICRODATA AREA, OR PUMA, ARE GEOGRAPHIC UNITS USED BY THE US CENSUS FOR PROVIDING STATISTICAL AND DEMOGRAPHIC INFORMATION. EACH PUMA CONTAINS AT LEAST 100,000 PEOPLE. PUMAS DO NOT OVERLAP, AND ARE CONTAINED WITHIN A SINGLE STATE. SOURCE: CENSUS BUREAU 41 P a g e Theme 2 Educa?on IN THIS SECTION: Enrollment . Preschool . Chronic Absenteeism . High Orr?Time Achievement . 3nd Gracie ELA Prc?ciency . High Readiness . Program Access . AP Course . Linked Lecming . Suscensicns Teachers . Represeniciicn cf Siudeni Pcpulciicn . Teccher Experience . Teccher Tum ever 42 Page Theme 2: Education Theme Score: 29.0 Education has long been an area of well-known disparities in outcomes across racial and ethnic groups, both in Oakland and throughout the country. Yet, education is also perhaps the single most powerful tool to rectify disparities in other Themes, such as Economy. This Theme includes measures that span from traditional measures of student attendance and achievement to measures of teachers, recognizing that teachers play a critical role in a child’s education. Education ranked fourth compared to the other Themes in the Oakland Equity Indicators framework. The 12 Indicators within the Education Theme examine inequities faced by racial and ethnic minorities across four Topic areas: Enrollment, Achievement, Program Access, and Teachers. The lowest scoring Topic was Enrollment (22.3), followed by Teachers (28.3). The other two Topics were slightly higher scoring, Achievement (32.0) and Program Access (33.3). It is important to note that in two of the Topics, Teachers and Program Access, there was large variability in scores between Indicators, so the average Topic score does not tell the full story. Topics and Indicators within this Theme: Topic Enrollment Score Indicator Score 22.3 Preschool Enrollment 22 Chronic Absenteeism 25 High School On-Time Completion 20 Achievement 32.0 3rd Grade ELA Proficiency 20 High School Readiness 37 A-G Completion 39 Program Access 33.3 AP Course Enrollment 37 Linked Learning Pathway Enrollment 62 Suspensions 1 Teachers 28.3 Representation of Student Population 1 Teacher Experience 55 Teacher Turnover 29 43 P a g e Topic 2.1: Enrollment Topic Score: 22.3 The Enrollment Topic includes three Indicators that measure racial and ethnic disparities in preschool enrollment, chronic absenteeism, and high school on-time completion. The first Indicator measures disparities in preschool enrollment between Latinos and Whites. The second Indicator measures disparities in chronic absenteeism between African Americans and Asians. The third Indicators measures disparities in how many high school students are still enrolled after four years between Latinos and Whites. Enrollment is the lowest scoring Topic in the Education Theme with a Topic score of 22.3. The Indicator scores were very similar with chronic absenteeism scoring highest at 25, followed closely by preschool enrollment at 22, and last was high school on-time completion at 20. All the Indicators in this Theme have substantial room for improvement. Education: Enrollment - Preschool Enrollment Ratio between the percents of Latino and White Kindergarten students who were not previously enrolled in preschool Score: 22 Ratio: 4.72 What is measured? The measurement is percent of children entering OUSD Kindergartens without any preschool experience. Preschool experience can be OUSD Transitional Kindergarten (TK), OUSD preschools, Head Start, daycare, as well as subsidized, special education, or private preschool programs. Why is this important? Preschool experience prior to Kindergarten is correlated with greater readiness and success in school, likely because of the academic as well as social skills gained in preschool. What did we find? Latino children were the least likely to have attended preschool with 23.6% not attending preschool of any kind. Asian children were next at 14.9%, followed by African American children at 10.0% not attending preschool. White children in OUSD Kindergartens were the most likely to have attended preschool with only 5% not attending any form. Latino children 44 P a g e were 4.72 times more likely and Asian children almost 3 times more likely to have not attended preschool than White children. Data: Source: OUSD Data Dashboard, 2016-17, https://dashboards.ousd.org/views/PreschoolExperience201617PUBLIC/PreschoolExperience?%3Aembed=yes#1 Education: Enrollment - Chronic Absenteeism Ratio between the percents of African American and Asian students who are chronically absent Score: 25 Ratio: 4.30 What is measured? The measurement is percent of children within OUSD who are chronically absent. Chronic absence is defined as an attendance rate of 90% or less (missing 18 or more days in a 180 day school year), regardless of whether the absences are excused or unexcused. It is not the same as Truancy. Alternative Education schools are not included in the data. 45 P a g e Why is this important? Chronic absences, can severely impact a child’s ability to succeed in school and therefore potentially their opportunities later in life as well. What did we find? Asian students had the lowest chronic absenteeism rates at only 5.2% of students chronically absent. White students were doing almost as well at only 5.6%. African American students had the highest chronic absenteeism rates at 22.2%. Latino students were second highest at 12.6%. African American students were 4.3 times more likely and Latino students 2.4 times more likely than Asian students to be chronically absent from school. Data: Source: OUSD Data Dashboard, 2016-17, https://dashboards.ousd.org/views/ChronicAbsence_0/Comparison?:embed=y&:display_count=no&:render=false#40 Education: Enrollment - High School On-Time Completion Ratio between the percents of Latino and White students who are still enrolled after 4 years Score: 20 Ratio: 5.14 46 P a g e What is measured? This Indicator measures the percent of students who are still enrolled in high school after four years. Data is also provided on percent who graduated/completed high school within four years and percent who dropped out. The most recent year of OUSD graduation data is from 2015-16, so the cohort began 9th grade in 2012-13. Students who leave OUSD (but not from dropping out) are removed from the cohort, and students who enroll after 9th grade are added. GED completion and Special Ed Certification are included as types of completion. Why is this important? Successful and timely completion of high school is a critical step toward opportunity and success later in life. Students who do not complete in four years may either have dropped out or may still be enrolled but have not met graduation requirements. Both of these two outcomes will have negative effects on their future, but looking specifically at those still enrolled allows us to shine light on students still in the education system who have not been set up for success. What did we find? White students were the most likely (77.0%) and Latino students the least likely (60.9%) to graduate/complete within four years. Of those who failed to graduate/complete in four years, the largest disparities arose not with dropouts but with those still enrolled. White students were the least likely to still be enrolled (only 3.1%). Latino students were the most likely (at 15.7%) and African American students the second most likely (at 12.8%) to still be enrolled after 4 years. Asian students followed just behind at 11.2% still enrolled. Latino students were therefore 5.14 times more likely than White students to still be enrolled after four years of high school. All non-White students were taking longer to graduate or potentially dropping out after four years (the data did not tell us what happens to them after four years). 47 P a g e Data: Source: OUSD Data Dashboard, 2015-16, https://dashboards.ousd.org/views/CohortGraduationandDropout_0/Comparison?:embed=y&:display_count=no&:render=false#52 Topic 2.2: Achievement Topic Score: 32.0 The Achievement Topic includes three Indicators that measure racial and ethnic disparities in 3rd grade ELA (English Language Arts) proficiency, high school readiness, and A-G completion. The first Indicator measures disparities in 3rd grade ELA proficiency between Latinos and Whites. The second Indicator measures disparities in high school readiness between African Americans and Asians. The third Indicators measures disparities in A-G completion between African Americans and Whites. Achievement is the second highest scoring Topic in the Education Theme with a Topic score of 32.0. The Indicator scores are somewhat spread out. A-G completion scored highest at 39 and was followed closely by high school readiness at 37. But substantially lower was 3rd grade ELA proficiency at 20. All the Indicators in this Theme have significant room for improvement, but we found the largest disparities arising in the achievement of our youngest age-group (with 3rd grade ELA). 48 P a g e Education: Achievement- 3rd Grade ELA Proficiency Ratio between the percents of Latino and White students who scored “Standard Not Met” on the 3rd grade SBAC (Smarter Balanced Assessment Consortium) ELA (English Language Arts) test. Score: 20 ` Ratio: 5.18 What is measured? The measurement is percent of students by race/ethnicity who scored “Standard Not Met” on their SBAC ELA test in 3rd grade. The SBAC is California's state-mandated test for all students starting in 3rd grade. Scores only include students enrolled in OUSD schools, not charters or private schools. Why is this important? Research has found that reading proficiently in 3rd grade is correlated with likelihood to graduate from high school. Additionally, the effect is unequal across races and income levels. For African American and Latino children as well as low-income children, failure to read proficiently in 3rd grade is even more strongly correlated with a failure to graduate from high school. (Source: http://www.aecf.org/resources/double-jeopardy/) What did we find? In Oakland, White students (11.9%) were the least likely to score “Standard Not Met”. Latino students were the most likely at 61.6% with African Americans close to them at 60.9%. Asian students were in the middle with 31.1% scoring “Standard Not Met”. Latino and African American students were both over five times more likely than White students to score “Standard Not Met”. 49 P a g e Data: Source: OUSD Data Dashboard, 2016-17, https://dashboards.ousd.org/views/SBAC/ComparisonSBAC?:embed=y&:showShareOptions=true&:display_count=no&:render=false Education: Achievement - High School Readiness Ratio between the percents of African American and Asian students who are not high school ready Score: 37 Ratio: 2.50 What is measured? The measurement is percent of students by race/ethnicity who are not high school ready by the end of 8th grade. High school readiness is calculated by OUSD. A student is considered high school ready when all of the following have been met at the end of 8th grade: 1) Total weighted GPA of 2.5 of better, 2) School Attendance 96% or better, 3) No “Ds” or “Fs” in their core English and Math course grades in 8th grade, 4) No suspensions in 8th grade. Why is this important? This measurement gauges “the degree to which 8th grade students are prepared for the rigor and expectations of high school” and therefore their likelihood to succeed in high school, 50 P a g e which in turn is correlated with future income and life prospects. (Source: OUSD Data Dashboard, https://dashboards.ousd.org/views/HighSchoolReadiness_1/Intro?%3Aembed=y&%3Adisplay_count=no&%3A render=false) What did we find? In Oakland, African American students were the least likely to be high school ready by the end of 8th grade with 71% not ready. Latino students were next with 55.4% not ready. White students did somewhat better with 47.9% not ready. Asian students were the least likely to not be high school ready at only 28.4%. African American students were 2.50 times as likely as Asian students to not be high school ready by the end of 8th grade. Data: Source: OUSD Data Dashboard, 2016-17, https://dashboards.ousd.org/views/HighSchoolReadiness_1/Comparison?:embed=y&:display_count=no&:render=false Education: Achievement - A-G Completion (Readiness for UC System) Ratio between the percents of African American and White students who fail to meet A-G requirements Score: 39 Ratio: 2.25 51 P a g e What is measured? The measurement is percent of students by race/ethnicity who have met the California A-G requirements by the end of high school. There are two components to the requirement: 1) Students must enroll in the right sequence of A-G courses, and 2) They must obtain a grade of “C” or better in each required course. Data includes only OUSD students (not charter or private schools) who have graduated. Why is this important? Per OUSD, “meeting A-G requirements is an important step in becoming college ready in California. High school graduates who do not meet the A-G subject breadth requirement are not yet eligible for admission to a University of California (UC) / California state University (CSU) campus.” Additionally, A-G completion is a general proxy for students being college and career ready, regardless of whether they want to enroll in a UC/CSU. (Source: OUSD Data Dashboard, https://dashboards.ousd.org/views/AGCompletion_1/Comparison?:embed=y&:display_count=no&:render=false) What did we find? In Oakland, African American students (68.3%) were the most likely to fail to meet A-G requirements, followed by Latino students at 59.3%. Asian students did better with only 36.7% failing to meet A-G requirements. White students were the least likely to fail to meet A-G requirements at only 30.3% doing so. African American students were 2.25 times more likely to fail to meet A-G requirements than White students. Data: 52 P a g e Source: OUSD Data Dashboard, 2016-17, https://dashboards.ousd.org/views/AGCompletion_1/Comparison?:embed=y&:display_count=no&:render=false Topic 2.3: Program Access Topic Score: 33.3 The Program Access Topic includes three Indicators that measure racial and ethnic disparities in AP course enrollment, Linked Learning Pathway enrollment, and suspensions. The first Indicator measures disparities in AP course enrollment between African Americans and Whites. The second Indicator measures disparities in Linked Learning Pathway enrollment between African Americans and Asians. The third Indicator measures disparities in suspension rates between African Americans and Asians. Program Access is the highest scoring Topic in the Education Theme with a Topic score of 33.3, but the Indicator scores are highly variable. Linked Learning Pathway enrollment scored highest at 62. AP course enrollment was next but substantially lower at 37. Last and dramatically lower was suspensions at a score of 1. All the Indicators in this Theme have room for improvement, but the overall Topic score hides the especially serious disparities in suspensions. If students are suspended, they by definition cannot have access to any educational programs. Education: Program Access - AP Course Enrollment Ratio between the percents of African American and White 12th graders who have not taken any AP courses Score: 37 Ratio: 2.49 What is measured? This Indicator measures what percent of 12th grade students have never taken an AP course throughout high school. OUSD collects data on the cumulative enrollment of 12th graders in AP courses by race/ethnicity, i.e. how many AP courses have they taken at any point in high school? Advanced Placement (AP) is a program created by The College Board offering collegelevel courses and tests in high school. 53 P a g e Why is this important? Though research is limited and mixed, AP courses are generally considered important for getting into and succeeding in college. College admissions officers often view successful completion of AP courses as an indicator that a student is college-ready. Depending on the college, AP courses can be used for college credit, allowing the student to graduate more easily and potentially sooner. (For more information, see here: https://news.stanford.edu/2013/04/22/advanced-placement-courses-032213/) What did we find? We found that African American students had the lowest enrollment with 73.7% having never taken a single AP course in all of high school. Latino students were next lowest at 58.1% having no AP courses. Asian students were doing better at only 35.9% and White students were doing best at only 29.6% having never taken an AP course. African American 12th graders were 2.49 times more likely to have never taken a single AP course than White students. Latino 12th graders were 1.96 times more likely than White students to have never taken an AP course. Data: Source: OUSD by request, 2016-17 54 P a g e Education: Program Access - Linked Learning Pathway Enrollment Ratio between the percents of African American and Asian students who are not enrolled in a Linked Learning Pathway Score: 62 Ratio: 1.48 What is measured? The measurement is percent of OUSD students (in Grades 10, 11, and 12) by race/ethnicity who are not enrolled in a Linked Learning Pathway. According to OUSD, Linked Learning is a successful approach to education based on the idea that students work harder and dream bigger if their education is relevant to them. These pathways, also known as college and career pathways, are a set of high school courses and work-based learning experiences such as internships that link academic learning to real world careers and college majors. Why is this important? OUSD implemented Linked Learning Pathways because of the belief that these pathways will lead to greater college and career readiness and therefore greater opportunities and success later in life. (For more information, see: https://www.ousd.org/linkedlearning) What did we find? We found that African American and Latino students were most likely to not be enrolled in a Linked Learning Pathway at 50.0% of each not enrolled. White students had only 36.2% and Asian students 33.9% not enrolled in a Linked Learning Pathway. An African American or Latino student in 2016-17 was 1.48 times more likely to be a non-pathway student than an Asian student. Data: 55 P a g e Source: OUSD Data Dashboard, 2016-17, https://dashboards.ousd.org/views/PathwayEnrollment_1/Comparison?:embed=y&:display_count=no&:render=false#54 Education: Program Access - Suspensions Ratio between the percents of African American and Asian students who were suspended Score: 1 Ratio: 10.43 What is measured? The measurement is percent of OUSD students by race/ethnicity who were suspended at any point in the school year. This is only measuring out-of-school suspensions, not on-campus suspensions or office referrals that did not result in removing a student from school. It includes all students who were enrolled at any point during the school year for any number of days. Why is this important? Out-of-school suspensions mean the student is not receiving educational hours or even having the benefit of spending the day in the safety of their school building. Suspensions have also been correlated with an increased likelihood to drop out of school. What did we find? 56 P a g e African American students (7.3%) in OUSD were the most likely to be suspended while Asian (0.7%) and White students (0.9%) were the least likely. Latino students fell in the middle with 2.3% suspended. An African American student in OUSD in 2016-17 was 8.11 times more likely to be suspended than a White student and 10.43 times more likely than an Asian student. Data: Source: OUSD Data Dashboard, 2016-17, https://dashboards.ousd.org/views/SuspendedStudents_1/Introduction?%3Aembed=y&%3AshowShareOptions=true&%3 Adisplay_count=no&%3AshowVizHome=no Topic 2.4: Teachers Topic Score: 28.3 The Teachers Topic includes three Indicators that measure racial and ethnic disparities in teacher representation of the student population, teacher experience, and teacher turnover. The first Indicator measures disparities in teacher representation of students between Latinos and Whites. The second Indicator measures disparities in teacher experience between schools that are predominantly African American and Latino vs. schools that are majority White. The third Indicators measures disparities in teacher turnover between majority African Americans and majority White schools. 57 P a g e Teachers is the second lowest scoring Topic in the Education Theme with a Topic score of 28.3, and the Indicator scores are highly variable. Teacher experience scored highest at 55. Teacher turnover was next but substantially lower at 29. Last and dramatically lower was representation of the student population at a score of 1. All the Indicators in this Theme have room for improvement, but the overall Topic score hides the especially serious disparities in teacher representation of the student body. Education: Teachers - Representation of Student Population Ratio between the rates of teachers of the same race/ethnicity for White and Latino students Score: 1 Ratio: 13.09 What is measured? The measurement is the rate of teachers per 1,000 students of the same race/ethnicity in OUSD, in other words how representative are the teachers of the student population? Numbers of teachers and students were calculated from percents of the total number given in OUSD’s Fast Facts report. Why is this important? The Brookings Institute states, “a growing body of literature suggests that outcomes such as test scores, attendance, and suspension rates are affected by the demographic match between teachers and students. Minority students often perform better on standardized tests, have improved attendance, and are suspended less frequently (which may suggest either different degrees of behavior or different treatment, or both) when they have at least one same-race teacher.” (Source: https://www.brookings.edu/research/the-importance-of-a-diverse-teaching-force/) What did we find? Districtwide, there were 67.5 teachers per 1,000 students, so rates less than that indicate that a particular group was underrepresented in the teaching force and rates higher than that indicate overrepresentation in the teaching force. We found that Latino students were greatly underrepresented with only 23.0 Latino teachers per 1,000 Latino students. White students were greatly overrepresented with 301.5 White teachers per 1,000 White students. African American and Asian students were slightly underrepresented at 59.2 and 60.5 teachers per 1,000 students respectively. White students in OUSD had 13.09 times as many teachers of their own race/ethnicity as Latino students did. 58 P a g e Data: Race/Ethnicity Number of Teachers Number of Students Rate of Teachers per 1,000 Students African American 569 9,607 59.2 Asian 302 4,987 60.5 Latino 349 15,144 23.0 White 1,227 4,070 301.5 Districtwide 2,474 36,668 67.5 Source: OUSD Fast Facts report, 2016-17, publicly available on http://www.ousddata.org/ Education: Teachers - Teacher Experience Ratio between the percents of teachers in their first five years of teaching at predominantly African American and Latino schools and majority White schools Score: 55 Ratio: 1.64 What is measured? The measurement is percent of teachers who were paid on salary steps 1-5 at OUSD schools (data from charters and private schools were not available). Salary steps are a proxy for teaching experience, as teachers move one step up the salary schedule for each year of teaching. In theory, teachers on salary steps 1-5 have between 1-5 years of teaching experience (not just in OUSD, but anywhere). However, it is important to note that salary steps are just a proxy and not always exactly equal to years of teaching experience. Percents are then calculated at the school level by dividing the number of teachers on salary steps 1-5 by the total number of teachers at that school. Schools are placed into groups based on the racial and ethnic breakdown of their student population (see note below data table for full explanation of grouping). Average percent of teachers on salary steps 1-5 is then calculated for each group of schools. 59 P a g e Why is this important? Greater teacher experience has long and often been shown to positively impact student success. Though there is of course individual variation and not all novice teachers are worse than all experienced ones, some general conclusions are consistent in the research: “Teaching experience is positively associated with student achievement gains throughout a teacher’s career. Gains in teacher effectiveness associated with experience are most steep in teachers’ initial years, but continue to be significant as teachers reach the second, and often third, decades of their careers. As teachers gain experience, their students not only learn more, as measured by standardized tests, they are also more likely to do better on other measures of success, such as school attendance.”(Source:https://learningpolicyinstitute.org/product/does-teachingexperience-increase-teacher-effectiveness-review-research) What did we find? Schools in OUSD with a predominantly African American and Latino student body had the highest percent of teachers in their first five years of teaching, averaging 48.9% in the 2016-17 school year. Majority Latino schools were second highest at 42.9% and majority African American were next at 38.3%. Schools with a majority Asian student body had the lowest percent of teachers in their first five years of teaching at only 20.3%. However, there are only 2 schools with a majority Asian student body, so this Indicator is scored using majority White schools which had 29.9% of teachers in their first five years of teaching. Students that went to predominantly African American and Latino schools had 1.64 times as many teachers in their first five years of teaching as students at majority White schools. Data: School Group Count of Schools Average % of Teachers on Salary Steps 1-5 in Group (proxy for teaching experience) African American Majority 15 African American and Latino Predominant 15 Latino Majority 33 42.9% Asian Majority 2 20.3% White Majority 5 29.9% Mixed 16 32.5% 38.3% 48.9% 60 P a g e Note on School Groups: Any schools with a student population that is majority (over 50%) one race/ethnicity were placed into a corresponding “Majority” group. Of OUSD’s 86 schools, 31 had no single majority racial or ethnic group. 15 of those 31 had predominantly African American and Latino students (combined percents ranging from 75-90% of the student body) and were placed in the “African American and Latino Predominant” group. The remaining 16 were variable in the racial and ethnic breakdown of their student bodies and were placed together in the “Mixed” group. Source: Student populations by race/ethnicity at schools from California Department of Education, 201617. Teacher salary step percents from OUSD Data Dashboard, 2016-17, https://dashboards.ousd.org/t/HR/views/TeacherDataDashboardPublic/Comparison?%3Aembed=y&%3AshowShareOpti ons=true&%3Adisplay_count=no&%3AshowVizHome=no#2. Education: Teachers - Teacher Turnover Ratio between the percents of teacher turnover at majority African American and majority White schools Score: 29 Ratio: 3.76 What is measured? The measurement is percent of teachers who turned over between the 2016-17 and 2017-18 school years at OUSD schools (data from charters and private schools were not available). Turnover percents are calculated at a school level out of the total number of teachers at that school. Schools are placed into groups based on the racial and ethnic breakdown of their student population (see note below data table for full explanation of grouping). Average teacher turnover percent is then calculated for each group of schools. Why is this important? While some turnover is inevitable and potentially good (if teachers who are ill equipped or ill matched leave a school), high teacher turnover is disruptive to schools and students. Higher teacher turnover has been found to correlate with reduced student achievement. Principals have to find new teachers that (even if not necessarily new to teaching) are new to the school and do not know the students, fellow staff, policies, etc., which harms the school culture and staff morale. Additionally, schools with higher populations of minority and low-income students often experience higher teacher turnover. (For more information see: http://www.ascd.org/publications/researchbrief/v2n19/toc.aspx 61 P a g e http://blogs.edweek.org/edweek/teacherbeat/2012/03/when_teachers_leave_schools_ov.html ) What did we find? Schools in OUSD with a majority African American student body had the highest teacher turnover, averaging 38.3% of teachers leaving between the 2016-17 and 2017-18 school years. Majority Latino schools were second highest at 34.5% of teachers turning over. Schools with a majority White student body had the lowest teacher turnover at only 10.2%. Students that went to majority African American schools experienced 3.76 times more teacher turnover last year than students at majority White schools. Students at majority Latino schools experienced 3.38 times more than those at majority White schools. Data: School Group Count of Schools in Group Average % Teacher Turnover African American Majority 15 38.3% African American and Latino Predominant 15 Latino Majority 33 34.5% Asian Majority 2 14.6% White Majority 5 10.2% Mixed 16 20.1% 26.9% Note on School Groups: Any schools with a student population that is majority (over 50%) one race/ethnicity were placed into a corresponding “Majority” group. Of OUSD’s 86 schools, 31 had no single majority racial or ethnic group. 15 of those 31 had predominantly African American and Latino students (combined percents ranging from 75-90% of the student body) and were placed in the “African American and Latino Predominant” group. The remaining 16 were variable in the racial and ethnic breakdown of their student bodies and were placed together in the “Mixed” group. Source: Student populations by race/ethnicity at schools from California Department of Education, 2016-17. Teacher turnover at schools from OUSD Data Dashboard, baseline year 2016-17, https://dashboards.ousd.org/t/HR/views/RetentionDashboardPublic/TeachersatSites?%3Aembed=y&%3AshowShareOpt ions=true&%3Adisplay_count=no&%3AshowVizHome=no#7 62 P a g e Theme 3 Public Health IN THIS SECTION: Access to Preventive Care . Acute Preventable Hospitalizations . Chronic Disease Preventable Hospitalizations . Health Insurance Child Health . Childhood Asthma Emergencv Department Visits . Physical Fitness . SNAP Recipiencv Mortality . Intont Mortalityr . Life Er-zpectanc'glr . Premature Death Physical and Mental Health . Severe Mental Illness Emergencv Department Visits . Substance Abuse Emergencv Department Visits . HIV Diagnoses 63 Page Theme 3: Public Health Theme Score: 25.8 Public health relates to many of the other Themes in the Equity Indicators framework. Public health outcomes are tied to housing quality, environment, and neighborhood. Social determinants of health include poverty, employment, and educational attainment. The public health field has historically been dedicated to understanding and addressing racial and ethnic disparities in health outcomes, but the complexity of these issues contributes to the persistence of inequity. Public Health ranked second to last compared to the other Themes in the Oakland Equity Indicators framework with only Public Safety receiving a lower overall Theme score. The 12 Indicators within the Public Health Theme examine inequities faced by racial and ethnic minorities across four Topic areas: Access to Preventive Care, Child Health, Mortality, and Physical and Mental Health. The lowest scoring Topic was Physical and Mental Health (4.7), and the highest scoring Topic was Mortality (42.0). The Topic scores for Access to Preventive Care and Child Health were similar (28.7 and 27.7, respectively). Topics and Indicators within this Theme: Topic Score Indicator Score Access to Preventive Care 28.7 Acute Preventable Hospitalizations 39 Chronic Disease Preventable Hospitalizations 26 Health Insurance 21 Child Health 27.7 Childhood Asthma Emergency Department Visits 1 Physical Fitness 63 SNAP Recipiency 19 Mortality 42.0 Infant Mortality 16 Life Expectancy 77 Premature Death 33 Physical and Mental 4.7 Severe Mental Illness Emergency Department 7 Health Visits Substance Abuse Emergency Department Visits 1 HIV Diagnoses 6 64 P a g e Topic 3.1: Access to Preventive Care Topic Score: 28.7 The Access to Preventive Care Topic includes two Indicators that measure preventable hospitalizations related to acute and chronic disease, and one that measures health insurance. Non-White Oaklanders are more likely to be hospitalized for conditions that could have been prevented by having better access to preventive care. They are also more likely to be uninsured, which is a major deterrent to accessing preventive health services. Poverty and immigration status may make it even more likely for racial and ethnic minorities to lack insurance and forego needed care. Access to Preventive Care received a Topic score of 28.7. The Indicator scores were relatively similar, with acute preventable hospitalizations scoring highest at 39, chronic disease preventable hospitalizations scoring in the middle at 26, and health insurance scoring lowest at 21. Public Health: Access to Preventive Health Care- Acute Preventable Hospitalizations Ratio between the average acute preventable hospitalization rates in non-White and White zip codes Score: 39 Ratio: 2.18 What is measured? This Indicator measures the age adjusted rate of acute preventable hospitalizations. “‘Preventable hospitalizations’ are inpatient hospital stays that could have been avoided with improved access to and quality of outpatient care. They are measured by prevention quality indicators (PQIs). PQI #91, the acute composite, is a summary measure which includes bacterial pneumonia, urinary tract infection, and dehydration preventable hospitalizations for adults.”(Source:http://www.healthyalamedacounty.org/indicators/index/view?indicatorId=2480&localeId=2 38) 65 P a g e This Indicator compares zip codes in which more than 60% of the population is non-White to those in which more than 60% of the population is White. The third category of zip codes is those in which the population is racially and ethnically mixed. Data is from 2013 through the third quarter of 2015. Why is this important? Measuring preventable hospitalizations is important for two reasons. One, they can serve as potential markers of health system efficiency. Lack of access to health care and poor-quality care can lead to increases in these types of hospitalizations. Two, hospitalizations create an economic burden, both for covering the hospitalization costs, but also for any missed work or schooling during the hospitalization. What did we find? We found that the average age-adjusted acute preventable hospitalization rate was 360.3 per 100,000 in non-White zip codes and 165.3 per 100,000 in White zip codes. Mixed zip codes fell in the middle with a rate of 256.9 per 100,000, which was slightly lower than the average rate citywide (297.3 per 100,000). The average rate in non-White zip codes was 2.18 times higher than the rate in White zip codes. Data: Source: California Office of Statewide Health Planning and Development by request, 2013-3Q2015; American Community Survey, 5-year estimates, 2012-2016 66 P a g e Public Health: Access to Preventive Care- Chronic Disease Preventable Hospitalizations Ratio between the average chronic disease preventable hospitalization rates in non-White and White zip codes Score: 26 Ratio: 4.12 What is measured? This Indicator measures the age-adjusted rate of chronic diseases preventable hospitalizations. “Preventable hospitalizations” are inpatient hospital stays that could have been avoided with improved access to and quality of outpatient care. They are measured by prevention quality indicators (PQIs). PQI #92, the chronic composite, is a summary measure which captures preventable hospitalizations from diabetes-related, respiratory-related and circulatory systemrelated preventable hospitalizations for adults, such as uncontrolled diabetes, asthma, and heartfailure.(Source:http://www.healthyalamedacounty.org/indicators/index/view?indicatorId=2480&locale Id=238) This Indicator compares zip codes in which more than 60% of the population is non-White to those in which more than 60% of the population is White. The third category of zip codes is those in which the population is racially and ethnically mixed. Data is from 2013 through the third quarter of 2015. Why is this important? Measuring preventable hospitalizations are important for two reasons. One, they can serve as potential markers of health system efficiency. Lack of access to health care and poor-quality care can lead to increases in these types of hospitalizations. Two, hospitalizations create an economic burden, both for covering the hospitalization costs, but also for any missed work or schooling during the hospitalization. What did we find? We found that the average age-adjusted chronic disease preventable hospitalization rate was 1,132.9 per 100,000 in non-White zip codes and 274.8 per 100,000 in White zip codes. Mixed zip codes fell in the middle with a rate of 728.9 per 100,000, which was slightly lower than the average citywide (884.3 per 100,000). The average rate in non-White zip codes was 4.12 times higher than in White zip codes. 67 P a g e Data: Source: California Office of Statewide Health Planning and Development by request, 2013-3Q2015; American Community Survey, 5-year estimates, 2012-2016 Public Health: Access to Preventive Care - Health Insurance Ratio between the percents of Latinos and Whites who do not have health insurance Score: 21 Ratio: 4.92 What is measured? This Indicator measures the percent of the population that does not have health insurance coverage by race/ethnicity. Why is this important? Lack of health insurance negatively affects individuals’ health and wellbeing. People without insurance coverage are much more likely not to seek needed medical care, and they are more likely to have poor health outcomes. Furthermore, the high costs of healthcare services can place great financial burden on uninsured individuals when they access those services. (Source: https://www.ncbi.nlm.nih.gov/books/NBK223643/) 68 P a g e What did we find? Latinos had the highest uninsured rate (13.1%) and were far more likely than Whites (2.7%) to be uninsured. The uninsured rate among African Americans (5.6%) and Asians (4.2%) fell below the citywide rate (6.4%). Latinos were 4.92 times more likely to lack health insurance than Whites. Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) Topic 3.2: Child Health Topic Score: 27.7 Wellness, physical activity, and nutrition are essential for children as they prepare for a healthy lifestyle and positive health outcomes as adults. The Child Health Topic includes three Indicators that measure different aspects of child health and wellness: childhood asthma emergency department visits, physical fitness, and SNAP recipiency. The first Indicator measures asthma-related emergency department visits and is related to the environmental and housing conditions that affect children’s health. The second Indicator, physical fitness, a measure of student fitness levels assessed in schools, tracks physical aptitude and activity. The third Indicator, SNAP recipiency, shows whether families have adequate income to provide healthy food for their children. 69 P a g e Child Health had a Topic score of 27.7. There was a wide variety in the Indicator scores, with childhood asthma emergency department visits receiving the lowest possible score of 1 and physical fitness receiving the highest score in the Topic at 63. SNAP recipiency scored 19, indicating a large disparity between Latino and White Oaklanders’ food security. Public Health: Physical and Mental Health - Childhood Asthma Emergency Department Visits Ratio between the rates of asthma-related emergency department visits for African American and White children under 5 years of age Score: 1 Ratio: 10.05 What is measured? This Indicator measures the age-adjusted rate of asthma-related emergency department visits per 100,000 children under 5 years of age. Data is from 2013 through the third quarter of 2015. Why is this important? Childhood asthma has been linked to poor housing conditions that expose children to asthma triggers, including mold, roaches, and poor ventilation. Living in neighborhoods with poor air quality can also exacerbate asthma in children. Higher rates of emergency department visits lead to missed school and work, as well as hospital expenses that further deplete a family’s resources and ability to pursue better quality housing. What did we find? We found that African American children had much higher rates of asthma-related emergency department visits (4,093.3 per 100,000), compared to White children (407.4 per 100,000). Asian children also had a very low rate (408.0 per 100,000). The rate for Latino children (1,134.0 per 100,000) was also lower than the citywide rate (1,658.0). African American children were 10.05 times more likely than White children to be admitted to the emergency department for asthma-related conditions. 70 P a g e Data: Source: California Office of Statewide Health Planning and Development by request, 2013-3Q2015 Public Health: Child Health- Physical Fitness Ratio between the percents of Latino and White students who are not in the Healthy Fitness Zone in all six fitness areas Score: 63 Ratio: 1.45 What is measured? Physical fitness is measured by whether OUSD students are in the “Healthy Fitness Zone” in all six fitness areas captured by the Physical Fitness Test. The test is administered to students in grades 5, 7, and 9, and it measures abdominal strength, aerobic capacity, body composition, flexibility, trunk extensor strength, and upper body strength. Students are assessed based on whether they meet standards in each of these areas and are categorized as “Healthy Fitness Zone,” “Needs Improvement,” or “Needs Improvement - Health Risk”. 71 P a g e Why is this important? Physical fitness is important for health and wellbeing. Promoting physical fitness in schools can help children form healthy habits early on. These habits help to build the foundation for a healthy lifestyle and can lead to improved health outcomes through adolescence and into adulthood. Physical activity is related to other health outcomes, such as obesity and diabetes, which disproportionately affect children of color in urban areas. What did we find? We found that in Oakland, Latino students were most likely to not be in the Healthy Fitness Zone in all six areas (85.0%). African American students had a similar percent (82.6%). White students were least likely to not be in the Healthy Fitness Zone in all six areas (58.8%), compared to 65.2% of Asian students. Students of other races had a smaller percent (68.5%) than the citywide percent (78.1%). Latino students were 1.45 times more likely than white students to not be in the Healthy Fitness Zone in all six areas (African American students were 1.40 times more likely). Data: Source: OUSD Data Dashboard, 2016-17, https://dashboards.ousd.org/views/PhysicalFitnessTest_1/TotalScore?%3Aembed=y&%3AshowShareOptions=t rue&%3Adisplay_count=no&%3AshowVizHome=no 72 P a g e Public Health: Child Health - SNAP Recipiency Ratio between the percents of Latinos and Whites who are living in households that receive SNAP Score: 19 Ratio: 5.44 What is measured? This Indicator measures the percent of individuals who live in households in which at least one household member received SNAP (Supplemental Nutrition Assistance Program) benefits in the past 12 months, by race/ethnicity. Why is this important? SNAP, formerly known as the Food Stamp Program, helps low-income individuals and families afford food. The amount of benefits received depends on household size, income, and monthly expenses. Almost 70% of SNAP recipients nationally are families with children, and the results of the program include better nutrition and poverty alleviation. While SNAP is an important federal anti-hunger program, recipiency of SNAP benefits suggests that households are experiencing problems with food security and access. Furthermore, SNAP benefits are not always sufficient to eliminate these issues for families. What did we find? Among Latino Oaklanders, 23.3% lived in households that received SNAP benefits, compared to 4.3% of White Oaklanders. Among African Americans, 18.6% lived in households that received SNAP, while the percent for Asians (11.6%) fell below the citywide percent (13.6%). Latinos were 5.44 times more likely than Whites to live in households that receive SNAP benefits. 73 P a g e Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) Theme 3.3: Mortality Topic Score: 42.0 Mortality is a widely recognized measure of population health. This Topic includes an Indicator on infant mortality, which is particularly important to public health and relates to other health outcomes, including access to care. The other two Indicators in this Topic, life expectancy and premature death, reveal disparities in how long individuals are expected to live and whether they are able to meet those expectations. In all three Indicators, African Americans have the worst outcomes. Mortality was the highest-scoring Topic in the Public Health Theme (42.0), but overall, African Americans are twice as likely to have negative mortality outcomes as other racial and ethnic groups. The infant mortality Indicator received the lowest score in the Topic (16), which means this Indicator had the greatest disparity. Life expectancy had the highest score in the Topic (77), but the premature death Indicator had a score of 33. 74 P a g e Public Health: Mortality - Infant Mortality Ratio between the infant mortality rates for African Americans and Whites Score: 16 Ratio: 6.16 What is measured? This Indicator measures the number of infant deaths for every 1,000 live births from 20142016. Why is this important? Infant mortality is widely used as a measure of population health and the quality of healthcare. The infant mortality rate is not only seen as a measure of the risk of infant death but it is used more broadly as a crude indicator of: community health status; poverty and socioeconomic status levels in a community; and availability and quality of health services and medical technology. The five leading causes of infant death in 2015 were: birth defects, preterm birth and low birth weight, sudden infant death syndrome, maternal pregnancy complications, and injuries (e.g., suffocation). (Source: https://www.cdc.gov/reproductivehealth/maternalinfanthealth/infantmortality.htm) What did we find? African Americans had an infant mortality rate of 11.7 per 1,000 live births, whereas Whites had an infant mortality rate of 1.9 per 1,000. The infant mortality rate for Latinos (4.7 per 1,000) and Asians (3.1 per 1,000) fell below the citywide rate (5.1 per 1,000). The African American infant mortality rate was 6.16 higher than the rate for Whites. 75 P a g e Data: Source: Alameda County Public Health Department Community Assessment, Planning, and Evaluation, with data from Alameda County vital statistics files, by request, 2014-2016 Public Health: Mortality - Life Expectancy Ratio between the life expectancy of Asians and African Americans Score: 77 Ratio: 1.18 What is measured? This Indicator measures the life expectancy of Oakland residents from 2014-2016. Why is this important? The term "life expectancy" refers to the number of years a person can expect to live. Life expectancy is one of the key measures of economic prosperity and community health. What did we find? African Americans had the lowest life expectancy at 73.0 years, while Asians had the highest life expectancy at 85.9 years. Latinos had the second highest life expectancy (83.3 years), and the life expectancy for White was 81.6 years. Oaklanders overall had a life expectancy of 80.0. 76 P a g e The life expectancy for Asians was 1.18 times higher than the life expectancy for African Americans; Asians can expect to live more than a decade longer than African Americans. Data: Source: Alameda County Public Health Department Community Assessment, Planning, and Evaluation, with data from Alameda County vital statistics files, by request, 2014-2016 Public Health: Mortality - Premature Death Ratio between the years of lives lost for African Americans and Asians Score: 33 Ratio: 3.15 What is measured? Mortality is measured in years of lives lost (YLL). The YLL Method was developed from the Global Burden of Disease 2015 Study. This method compares the age for each death to the life expectancy for that person. This Indicator then takes the total number of YLL for each category and creates a rate of YLL per 100,000 people per year. Data is from 2014-2016. Why is this important? YLL is important because it is sheds light on the issue of premature death. Premature deaths help indicate communities where there is an increase in environmental, health, and social stressors leading to shortened lives. These stressors include things like air pollution, gun 77 P a g e violence, and chronic diseases. By identifying groups with high levels of premature deaths, we can start looking at the causes of those deaths. What did we find? Oakland as a whole had an age-adjusted YLL rate of 15,479.7 per 100,000 people. Asians had the lowest age-adjusted YLL rate at 8,527.9 per 100,000 people. African Americans had the highest age-adjusted YLL rate at 26,889.6 per 100,000. The YLL rate for Latinos (11,940.9 per 100,000) and Whites (12,592.9 per 100,000) were both lower than the citywide rate (15,479.7 per 100,000). The African American YLL rate was 3.15 times greater than that of Asians. Data: Source: Alameda County Public Health Department Community Assessment, Planning, and Evaluation, with data from Alameda County vital statistics files, by request, 2014-2016 Topic 3.4: Physical and Mental Health Topic Score: 4.7 Physical and mental health encompasses a wide range of health outcomes and can be measured in many different ways. The Indicators in this Topic were chosen to reflect important public health issues that are not captured in other Topics in the Public Health Theme. The first two Indicators measure visits to the emergency department for severe mental illness and for substance abuse. The third Indicator measures new HIV diagnoses in 78 P a g e Alameda County. In all three of these Indicators, the greatest disparities are between African American and Asian Oaklanders. With a score of 4.7 out of 100, the Physical and Mental Health Topic is the lowest scoring Topic in the Public Health Theme, meaning that racial and ethnic disparities are the greatest in this Topic. All three of the Indicators in this Topic had low scores: the severe mental illness emergency department visits Indicator received a score of 7 and the new HIV diagnoses Indicator received a score of 6. The substance abuse emergency department visits Indicator received the lowest possible score of 1. Public Health: Physical and Mental Health - Severe Mental Illness Emergency Department Visits Ratio between the rates of severe mental illness related emergency department visits for African Americans and Asians Score: 7 Ratio: 8.41 What is measured? This Indicator measures severe mental illness related emergency department visits per 100,000 by race/ethnicity, from 2013 through the third quarter of 2015. Severe mental illness is often defined by its length of duration and the disability it produces. These illnesses include disorders that produce psychotic symptoms, such as schizophrenia and schizoaffective disorder, and severe forms of other disorders, such as major depression and bipolar disorder. Why is this important? Often mental illness related emergency department visits are avoidable. When this is the case, these visits take up hospital resources that could otherwise be used for non-preventable visits. Emergency department visits are also very expensive, and these costs get passed down to the patient or to the wider community. If the costs get passed down to the patient, they can cause financial strain and can prevent patients from seeking additional medical treatment, increasing the likelihood of another emergency department visit. What did we find? African Americans in Oakland had the highest emergency department visit rate of 352.2 per 100,000, whereas the lowest rate was among Asians at 41.9 per 100,000. The rates for Latinos 79 P a g e (112.0 per 100,000) and Whites (142.8 per 100,000) were lower than the Oakland rate of 169.0 visits per 100,000. African Americans had a severe mental illness related emergency department visit rate that was 8.41 times higher than the rate for Asians. Data: Source: California Office of Statewide Health Planning and Development by request, 2013-3Q2015 Public Health: Physical and Mental Health - Substance Abuse Emergency Department Visits Ratio between the rates of substance abuse related emergency department visits for African Americans and Asians Score: 1 Ratio: 15.66 What is measured? This Indicator measures substance abuse related emergency department visits per 100,000 by race/ethnicity, from 2013 through the third quarter of 2015. Substance abuse related 80 P a g e emergency department visits refers to cases that involved nonmedical use of pharmaceuticals, illicit drugs, or alcohol in combination with other drugs. Why is this important? Often substance abuse related emergency department visits are avoidable. When this is the case, these visits take up hospital resources that could otherwise be used for non-preventable visits. Emergency department visits are also very expensive, and these costs get passed down to the patient or to the wider community. If the costs get passed down to the patient, they can cause financial strain and can prevent patients from seeking additional medical treatment, increasing the likelihood of another emergency department visit. What did we find? We found that African Americans in Oakland had the highest emergency department visit rate of 5,859.7 per 100,000, whereas the lowest rate was among Asians at 374.2 per 100,000. The rate among Latinos (1858.9 per 100,000) and the rate among Whites (1920.1 per 100,000) were both lower than the citywide rate of 2,604.2 visits per 100,000. African Americans had a substance abuse related emergency department visit rate that was 15.66 times higher than the rate for Asians. Data: Source: California Office of Statewide Health Planning and Development by request, 20133Q2015 81 P a g e Public Health: Physical and Mental Health - HIV Diagnoses Ratio between the rates of new HIV diagnoses for African Americans and Asians Score: 6 Ratio: 8.67 What is measured? This Indicator measures the rate of new HIV diagnoses per 100,000 population for each racial and ethnic group from 2014-2016 in Alameda County. The Alameda County Public Health Department’s HIV Epidemiology and Surveillance Unit is required to report new HIV diagnoses, which is a proxy for new HIV transmissions which are difficult to capture. Why is this important? Understanding which segments of the population have the highest rates of new HIV diagnoses can help to decrease the rate of transmission and treat people living with HIV. Modern treatments are able to stop the progression and spread of HIV, so ensuring that treatment is accessible to those who need it is a public health priority. Nationally, men are more likely to be living with HIV than women, and African Americans and Latinos are more likely to be living with HIV than other races and ethnicities. (Source: http://www.acphd.org/media/493775/hivreport2018.pdf) What did we find? African Americans had the highest rate of new HIV diagnoses (52.0 per 100,000), far higher than any other race or ethnicity. Asians had the lowest rate (6.0 per 100,000), followed by Whites (11.5 per 100,000). The rate for Latinos (18.1 per 100,000) was higher than the rate for Alameda County (15.7 per 100,000). The rate of new HIV diagnoses among American Americans was 8.67 times higher than the rate for Asians. Data: 82 P a g e Source: HIV in Alameda County, 2014-2016, Alameda County Public Health Department HIV Epidemiology and Surveillance Unit, March 2018, http://www.acphd.org/media/493775/hivreport2018.pdf 83 P a g e Theme 4 Hou?ng IN THIS SECTION: A?ordability . Homeownership . Loan Denial . Rent Burden Displacement . Homelessness . Homeownership with Mortgage . Nolioe at Evictions Essential Services . Complete Plumbing Facilities . Energy;r Cost Burden . High Speed lntemet Aooess Housing Quality . Housing Habitability Complaints . Complete Kitchen Facilities . Cwerorawoing 84 Page Theme 4: Housing Theme Score: 36.8 Affordable, stable, and high-quality housing is a basic right of Oakland residents. Many Oaklanders face challenges in accessing adequate housing, and racial and ethnic minorities are even more disadvantaged. The Indicators in this Theme reveal disparities among individuals and neighborhoods and highlight the pressing need for more equitable housing policy. Housing ranked third compared to the other Themes in the Oakland Equity Indicators framework. The 12 Indicators within the Housing Theme examine inequities faced by racial and ethnic minorities across four Topic areas: Affordability, Displacement, Essential Services, and Housing Quality. The lowest scoring Topic was Displacement (29.0), followed by Housing Quality (33.0) and Essential Services (36.0). Affordability was the highest scoring Topic, though the Topic score was still low (49.0). Topics and Indicators within this Theme: Topics Affordability Scores Indicators Scores 49.0 Homeownership 53 Loan Denial 40 Rent Burden 54 Displacement 29.0 Homelessness 1 Homeownership with Mortgage 78 Eviction Notices 8 Essential Services 36.0 Complete Plumbing Facilities 35 Energy Cost Burden 38 High Speed Internet Access 35 Housing Quality 33.0 Housing Habitability Complaints 40 Complete Kitchen Facilities 37 Overcrowding 22 85 P a g e Topic 4.1: Affordability Topic Score: 49.0 The affordable housing crisis affects communities around the country, and Oakland is no exception. While many Oaklanders struggle to afford their homes, racial and ethnic minorities face particular challenges in access to affordable housing. The three Indicators in this Topic measure access to stable housing through homeownership, access to financial resources in the form of home loans, and access to rental housing that does not cost more than 30% of household income. The Affordability Topic received the highest score in the Housing Theme (49.0), and the three Indicator scores fell relatively close to this average. The loan denial Indicator received the lowest score of the three, with a score of 40, meaning that African American Oaklanders were twice as likely as White Oaklanders to have their home loan applications denied. Homeownership and rent cost burden received very similar scores of 53 and 54, respectively. Housing: Affordability - Homeownership Ratio between the percents of African American and White householders who do not own their homes Score: 53 Ratio: 1.70 What is measured? This Indicator measures the percent of householders who do not own their homes. The percent of householders living in owner-occupied units does not consider other properties that may be owned by individuals within the household. This Indicator measures whether the householder owns the unit in which s/he lives. Why is this important? This Indicator is important because it tracks inequalities in homeownership based upon race/ethnicity. Buying a home is one of the largest purchases a person will ever make. In addition to providing housing stability, a home is a major source of wealth for the household. As the household grows, owning a home makes it easier to buy a bigger house, building upon the existing investment. Additionally, a home passed from one generation to the next provides a springboard for the younger generation to begin to build their wealth. Due to the role of 86 P a g e homeownership on household economic security and intergenerational wealth, it is important for equity in other areas beyond housing that homeownership is not concentrated within one racial or ethnic group. What did we find? Among White householders, 43.6% did not own their homes, meaning that over half of White householders were homeowners. Asian householders were also slightly more likely to own their homes than not (48.1% did not own their homes). Conversely, 69.0% of Latino householders did not own their homes, and almost one in four African American householders did not own their homes (74.1%). Citywide, just over half of householders did not own their homes (56.4%). African American householders were 1.70 times more likely to not own their homes than White householders. Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) Housing: Affordability - Loan Denial Ratio between the loan denial rates for African Americans and Whites. Score: 40 Ratio: 2.13 87 P a g e What is measured? This Indicator measures the difference in denial rate of Home Mortgage Disclosure Act (HMDA) loans by race/ethnicity. The HMDA requires that any loan secured by a lien on a dwelling made for the purpose of purchasing a home is reportable on an annual basis to the Federal Financial Institutions Examination Council (FFIEC), which is the federal reporting agency of the Federal Reserve Board. Why is this important? The primary purposes of the HMDA are to help authorities monitor discriminatory and predatory lending practices, as well as to ensure government resources are allocated properly. This Indicator is important because disparities in loan denial rates suggest that disparities in homeownership are related to discrimination and differential rates of financial stability. What did we find? We found that African American loan applicants were much more likely to have their applications denied by the financial institution (25.7%) compared to White applicants (12.1%). One in five Latino applicants had their loan applications denied (19.6%). The percent for Asian applicants (14.3%) was similar to the citywide percent (14.7%). The home loan denial rates for African Americans were 2.13 times higher than the rates for Whites. Data: Source: Home Mortgage Disclosure Act, 2016, https://www.ffiec.gov/HMDA/hmdaproducts.htm 88 P a g e Housing: Affordability - Rent Burden Ratio between the percents of African American and White renter households who pay more than 30% of household income on rent. Score: 54 Ratio: 1.67 What is measured? This Indicator measures the percent of renter households that are considered rent burdened by the race/ethnicity of the householder. Households that rent their homes are considered to be cost burdened when a household spends more than 30% of its annual household income on rent. Why is this important? One of the key factors in determining housing affordability is how much a household pays for their housing, compared to their ability to pay. Housing is considered affordable if the housing costs are less than 30% of the annual household income. The higher percent of income needed to pay for a basic necessity like housing means fewer resources are available to meet other essential needs, such as food and housing utilities, and limits whether a household can spend money in other areas or build up savings in case of emergencies. In a city and region with a high rent market and a housing crisis, rent burden affects individuals across the income spectrum. However, people with lower incomes will experience the brunt of the impact. Households with lower incomes will be forced to spend a higher percent of their incomes on rent due to limited affordable options, compared to households with higher incomes and therefore more housing options. (Source: https://www.mercurynews.com/2017/11/09/high-rents-still-astruggle-for-many-in-bay-area/ ) What did we find? Citywide, almost half of households were rent burdened, meaning they spent more than 30% of their annual income on rent. It was more common among African American and Latino households, with 58.4% and 52.7% respectively. It was slightly less common among Asian households (at 49.2%) while only one in three White households (34.9%) paid more than 30% of their annual income on rent. African American households were 1.67 times more likely to be rent burdened than White households. 89 P a g e Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) Topic 4.2: Displacement Topic Score: 29.0 The influx of development in the Bay Area, soaring housing costs, and a lack of affordable housing, have all contributed to the displacement of Oakland residents. Displacement is difficult to measure, but the three Indicators in this Topic serve as proxies for this larger, more complex issue. Homelessness is one possible consequence of displacement, and it is also a possible consequence of eviction, which is measured in the third Indicator. The second Indicator in this Topic is a measure of housing stability, which may help to prevent displacement among homeowners. The Displacement Topic received a score of 29.0, making it the lowest scoring Topic in the Housing Theme, and the Indicator scores are widely variable. The second Indicator, homeownership with a mortgage, received the highest score of 78. The score for the third Indicator, eviction notices, was dramatically lower at 8. The first Indicator, homelessness, received the lowest possible score of 1 out of 100. 90 P a g e Housing: Displacement - Homelessness Ratio between the rates of homelessness for African Americans and Asians Score: 1 Ratio: 41.76 What is measured? Homelessness is measured by number of homeless individuals per 100,000 individuals in the general population. Homelessness data was available by race and ethnicity, separately. Both sheltered and unsheltered homeless individuals are captured in the homelessness counts, to provide a fuller picture of the homeless population. Why is this important? Homelessness is an important Indicator because shelter is a basic human need. Housing instability exacerbates stress on individuals and families and reduces their ability to plan for the future. Homelessness inhibits one’s ability to maintain stable employment and access services that may be essential to maintain stable housing. This cycle of housing instability is important to examine in Oakland specifically due to gentrification and the related displacement of communities of color, African American households in particular, due to eviction and foreclosure. (Source: http://oaklandlocal.com/2015/03/growing-number-of-oakland-seniorsare-homeless/ ) What did we find? The homelessness rate among African Americans was 1,797.0 per 100,000, compared to 43.0 per 100,000 for Asians. The rate among Whites fell in the middle (268.6 per 100,000) but was still much lower than the rate for African Americans. Looking separately at ethnicity, the homelessness rate among Latinos (329.3 per 100,000) was lower than that for non-Latinos (774.2 per 100,000). African Americans were 41.76 times more likely than Asians to be homeless. 91 P a g e Data: Source: EveryOne Counts! 2017 Homeless Count and Survey, http://everyonehome.org/wpcontent/uploads/2017/11/2017HIRDReport-Oakland.2-2-3.pdf; The 2017 Alameda County Point-in-Time Count was a community-wide effort conducted on January 30, 2017. It uses the 2015 American Community Survey 1year estimates to compare the homeless population to the general city population. Housing: Displacement - Homeownership with Mortgage Ratio between the percents of African American and White homeowners who have a mortgage on their homes Score: 78 Ratio: 1.14 What is measured? This Indicator measures the percent of homeowners who have a mortgage or loan on their homes. Outstanding debt distinguishes these homeowners from those who own their homes free and clear and no longer need to make mortgage or loan payments. Why is this important? Homeowners who own their homes free and clear have a lower risk of displacement from their neighborhoods. Owning a home free and clear greatly reduces housing costs and allows families to stay in their homes even as land and housing values change. Housing stability is 92 P a g e particularly important as Oakland neighborhoods continue to gentrify and residents are priced out of their homes. What did we find? White homeowners were the least likely to still have a mortgage or loan on their homes (69.8%), while African American homeowners were the most likely to have a mortgage (79.7%). The percents of Asian homeowners (71.1%) and Latino homeowners (69.9%) who still had a mortgage were similar to that of White homeowners and the citywide percent (72.2%). African American homeowners were 1.14 times more likely than White homeowners to still have a mortgage on their homes, meaning that they did not own their homes free and clear. Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) Housing: Displacement - Eviction Notices Ratio between the rates of eviction notices in majority African American and majority Asian census tracts. Score: 8 Ratio: 8.14 93 P a g e What is measured? This Indicator measures the rate of eviction notice filings per 1,000 housing units that are renter-occupied. The Indicator compares census tracts by their majority race/ethnicity. Why is this important? Property owners are required to file all notices of eviction with the City of Oakland’s Rent Adjustment Program. Eviction notices are often filed regardless of circumstance, often when a rent payment is late or when there is a breach of the lease. Before a physical eviction takes place, property owners must receive an Unlawful Detainer and have a sheriff conduct an eviction. While these notices may not always end in an actual eviction, they can be indicative of housing instability and tenant harassment. What did we find? Majority African American census tracts had the highest rates of eviction notices (141.6 per 1,000 renter-occupied housing units). This rate was much higher than the rate for majority Asian census tracts (17.4 per 1,000). The rates for majority White census tracts (41.0 per 1,000) and majority Latino census tracts (66.2 per 1,000) fell in the middle. Citywide, 72.7 eviction notices were filed per 1,000 renter-occupied housing units in 2016. Renter-occupied housing units in majority African American census tracts were 8.14 times more likely to receive eviction notices than renter-occupied housing units in majority Asian census tracts. Data: Source: Oakland Rent Adjustment Program by request, 2016; American Community Survey, 5-year estimates, 2012-2016 94 P a g e Topic 4.3: Essential Services Topic Score: 36.0 A lack of essential services can have a significant impact on the daily lives of residents. The three Indicators in this Topic measure three different types of essential services: plumbing facilities, energy (i.e., electricity, gas), and high-speed Internet. Plumbing facilities are necessary for the basic functions of a home, energy costs should not place too large a burden on households and families, and high-speed Internet has become a crucial component of work, school, and social life. The Essential Services Topic received a score of 36.0, and the Indicators in this topic received similar scores. The energy cost burden Indicator received the highest score in the Topic (38), while the complete plumbing facilities and high-speed Internet access indicators both received scores of 35. Housing: Essential Services - Complete Plumbing Facilities Ratio between the percents of African Americans and Whites who live in housing units without complete plumbing facilities Score: 35 Ratio: 2.78 What is measured? This Indicator measures the percent of individuals who live in housing units that do not have complete plumbing facilities. Complete plumbing facilities include hot and cold water and a bathtub or shower. Why is this important? Complete plumbing facilities is a key Indicator of access to essential services and housing quality. In the United States, lacking complete plumbing facilities is more common in certain regions and in rural areas. The lack of complete plumbing facilities is also more common in areas with higher percents of racial and ethnic minorities and higher unemployment rates. (Source: https://www.cambridge.org/core/services/aop-cambridge core/content/view/1F3C7B5E413CBD73BE480E66FABDDD7C/S1742058X16000242a.pdf/basics_inequality.pdf) 95 P a g e What did we find? African American individuals were the most likely to not have complete plumbing facilities in their homes (1.63%). White individuals were the least likely to lack complete plumbing facilities in their homes (0.59%), followed closely by Asian individuals (0.61%) and Latino individuals (0.69%). Citywide, 0.81% of Oaklanders lacked complete plumbing facilities in their homes. African Americans were 2.78 times more likely than Whites to live in housing units without complete plumbing facilities. See data graph on next page Source: American Community Survey, 5-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html); 5-year data was used for this indicator due to the small sample size of individuals without complete plumbing facilities Housing: Essential Services - Energy Cost Burden Ratio between the median energy cost burden for African American and White households Score: 38 Ratio: 2.34 What is measured? Energy cost burden is measured by the amount spent on electricity, gas, and other fuel, as a percent of household income. This Indicator measures the median energy cost burden by the race/ethnicity of householders. Householders whose energy costs were included in rent or condominium fees were excluded from this analysis. 96 P a g e Why is this important? High energy cost burdens can have a number of negative effects on households. Low-income households may have to make trade-offs between energy costs and the costs of other basic necessities such as food and medical care. Households that cut back on energy use due to high cost may experience negative health effects, including asthma and arthritis. High energy cost burden also creates a chronic source of stress, which negatively affects the mental health of household members. Recent research has found that low-income, African-American, and Latino households have higher energy cost burdens than the average households in the same metropolitan area. These disparities point to the need for better energy efficiency in these households. (Source: http://energyefficiencyforall.org/resources/lifting-high-energy-burden-americaslargest-cities) What did we find? The median energy cost burden for African American households was 2.4%, compared to 1.0% for White households. Latino households spent 1.9% of their income on energy costs, on average. The median energy cost burden for Asian households was 1.3%, which was similar to the citywide median of 1.4%. The median energy cost burden for African American households was 2.34 times higher than the cost burden for White households. Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) 97 P a g e Housing: Essential Services - High Speed Internet Access Ratio between the percents of African Americans and Whites who do not have access to high speed Internet at home Score: 35 Ratio: 2.79 What is measured? This Indicator measures the percent of individuals living in housing units without access to high speed Internet, defined as broadband Internet service such as cable, fiber optic, or DSL service. Why is this important? Internet access has become an essential service for U.S. households in recent years. Children need the Internet to complete homework, adults need the Internet to apply for jobs, and families need the Internet to access government, financial, and other services. The quality of Internet access is also an important factor, and this Indicator measures high speed Internet access more specifically. What did we find? African American individuals were the most likely to not have high speed Internet access at home (40.8%), followed by Latino individuals (33.5%). White individuals were least likely to lack high speed Internet access at home (14.6%). Among Asian individuals, 25.2% did not have access to high speed Internet at home, slightly lower than the citywide percent (26.8%). African Americans were 2.79 times more likely than Whites to not have high speed Internet access at home. 98 P a g e Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) Topic 4.4: Housing Quality Topic Score: 33.0 Access to high quality housing is an important component of the Housing Theme. Substandard housing can have negative impacts on residents’ health and their ability to engage in different aspects of their lives outside of the home. The three Indicators in this Topic measure geographic disparities in housing habitability complaints, and racial and ethnic disparities in complete kitchen facilities and overcrowded conditions in Oakland housing units. The Housing Quality Topic received a score of 33.0 out of 100. The first Indicator in this Topic, housing habitability complaints, received the Topic’s highest score of 40. The third Indicator, overcrowding, received the lowest score in the Topic (22). The score for the second Indicator, complete kitchen facilities, fell in the middle (37) 99 P a g e Housing: Housing Quality - Housing Habitability Complaints Ratio between the percents of housing units that have filed complaints in non-White and White zip codes Score: 40 Ratio: 2.03 What is measured? This Indicator measures housing habitability complaints as a percent of total housing units by zip code as reported to the Oakland Code Enforcement Division. This Indicator compares zip codes in which more than 60% of the population is non-White to those in which more than 60% of the population is White. The third category of zip codes is those in which the population is racially and ethnically mixed. Why is this important? Housing habitability complaints are often the first indication the City receives of a potential issue with a housing unit. The severity of the complaint can range from a drafty window all the way to a collapsing roof. What did we find? We found that 1.36% of housing units in zip codes that were more than 60% non-White reported housing habitability complaints, compared to 0.67% of housing units in zip codes that were more than 60% White. Housing units in racially and ethnically mixed zip codes were only slightly more likely than those in White zip codes to report a housing habitability complaints (0.80%). Overall in Oakland, 1.13% of housing units reported a housing habitability complaint. Housing units in predominantly non-White zip codes were 2.03 times more likely to report housing habitability complains than housing units in predominantly White zip codes. 100 P a g e Data: Source: Accela Housing Habitability Complaint Cases Calendar Year 2017, Oakland Planning and Building department by request; American Community Survey, 5-year estimates, 2012-2016 Housing: Housing Quality- Complete Kitchen Facilities Ratio between the percents of African Americans and Latinos who live in housing units without complete kitchen facilities Score: 37 Ratio: 2.55 What is measured? This Indicator measures the percent of individuals who live in housing units that do not have complete kitchen facilities. Complete kitchen facilities include a stove or range, a refrigerator, and a sink with a faucet. Why is this important? Complete kitchen facilities is a key Indicator of housing quality. Stoves and ranges, refrigerators, and sinks are essential for the storage and preparation of food. Being able to cook at home can help households to save money, maintain healthier diets, and spend time with family. 101 P a g e What did we find? African American individuals were the most likely to not have a stove/range, refrigerator, or sink in their homes (2.03%). Latino individuals were the least likely to lack complete kitchen facilities (0.79%), followed closely by White individuals (0.97%) and Asian individuals (1.05%). Citywide, 1.13% of Oaklanders do not have complete kitchen facilities in their homes. African Americans were 2.55 times more likely than Latinos to not have a stove/range, refrigerator, or sink in their homes. Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html); 5-year data was used for this indicator due to the small sample size of individuals without complete kitchen facilities Housing: Housing Quality - Overcrowding Ratio between the percents of Latinos and Whites who live in overcrowded housing Score: 22 Ratio: 4.80 What is measured? This Indicator measures the likelihood of individuals living in overcrowded housing, which is defined as housing units that have more than 1.5 people per room. Persons-per-room is the most common measure for overcrowding in housing, and 1.5 is a widely accepted threshold 102 P a g e above which there are impacts on health and personal safety. (Source: https://www.huduser.gov/publications/pdf/measuring_overcrowding_in_hsg.pdf) Why is this important? Overcrowded housing can have negative impacts on the physical and mental health of children and adults. Overcrowded conditions may lead to increased risk of contracting illnesses, disrupted sleep patterns, and higher levels of stress. In addition, children living in overcrowded housing may have less room to read and study, which can affect their school performance. (Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3805127/) What did we find? Latino individuals were far more likely to live in overcrowded housing (14.2%) than other racial and ethnic groups. White individuals experienced the least overcrowding (3.0%), while 4.5% of African American Oaklanders and 7.0% of Asian Oaklanders lived in overcrowded housing. Citywide, 7.0% of individuals live in overcrowded housing. Latinos were 4.80 times more likely than Whites to live in overcrowded housing. Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) 103 P a g e Theme 5 Public Safety Incarceration I I Law Enforcement Staf?ng IN THIS SECTION: Adult Felony Arrests Jail Incarceration Prison lncarceralion Police Response limes Stops Use of Force Representation Attrition from Academy Attrition from Field Training Community Stressors Domestic 1L.l?ialence Homicides Juvenile Felony Arrests 104 Page Theme 5: Public Safety Theme Score: 17.3 Public Safety is a critical area of concern for any city. Though national conversations have recently brought a lot of attention to racial and ethnic disparities in public safety, this is a longstanding problem area that can have severe negative impacts on communities of color. Public Safety was the lowest scoring of all the Themes in the Oakland Equity Indicators framework. The 12 Indicators within the Public Safety Theme examine inequities faced by racial and ethnic minorities across four Topic areas: Incarceration, Law Enforcement, Staffing, and Community Stressors. The lowest scoring Topic was Incarceration (1.0), followed by Community Stressors (1.7). The other two Topics were somewhat higher scoring, Law Enforcement (18.3) and Staffing (48.3). Incarceration and Community Stressors are the two lowest scoring Topics in the entire report. Though Law Enforcement and Staffing are higher scoring, they still have substantial room for improvement. They are also areas over which the City has somewhat more control and therefore merit further investigation into strategies for change. Note: Whenever the acronym OPD is used, it refers to the Oakland Police Department. Topics and Indicators within this Theme: Topics Incarceration Scores 1.0 Law Enforcement 18.3 Staffing 48.3 Community Stressors 1.7 Indicators Adult Felony Arrests Jail Incarceration Prison Incarceration Police Response Times Stops Use of Force Representation Attrition from Academy Attrition from Field Training Domestic Violence Homicides Juvenile Felony Arrests Scores 1 1 1 48 6 1 45 63 37 3 1 1 105 P a g e Topic 5.1: Incarceration Topic Score: 1.0 The Incarceration Topic includes three Indicators that measure racial and ethnic disparities in adult felony arrests, jail incarceration, and prison incarceration. The first Indicator measures disparities in adult felony arrests between African Americans and Asians. The second Indicator measures disparities in jail incarceration between African Americans and Asians. The third Indicator measures disparities in prison incarceration between African Americans and Asians. In all three indicator areas the disparities between African American and White residents were nearly as large. Incarceration is the lowest scoring Topic in the Public Safety Theme and the entire framework with a Topic score of 1.0, the lowest possible score. The Indicator scores were all the same and all the absolute lowest score possible (1), indicating that this is an area of extreme disparity. Public Safety: Incarceration - Adult Felony Arrests Ratio between the adult felony arrest rates for African Americans and Asians Score: 1 Ratio: 14.24 What is measured? This Indicator measures the rate of adult felony arrests by race/ethnicity adjusted for population. Rate is calculated as the number of adult felony arrests per 100,000 people of each race/ethnicity that are 18 years of age and older. To determine the number of adults of each race/ethnicity in Oakland, we took the population by race/ethnicity for all ages and multiplied by the percent of Oakland’s population that is 18 and over (80.33%). This approach was necessary due to the lack of exact data available on the 18 and over population in Oakland by race/ethnicity. Why is this important? A felony is a serious crime that typically results in a prison sentence of over one year. Individuals arrested on felony charges are thus more likely to face longer sentences and be incarcerated in prison. National studies have shown that black individuals are more likely to 106 P a g e be detained while awaiting trial than white individuals. Black individuals are also more likely to be convicted and face longer prison sentences than white individuals. (Source:http://www.slate.com/articles/news_and_politics/crime/2015/08/racial_disparities_in_the_criminal_ justice_system_eight_charts_illustrating.html) What did we find? In Oakland, the adult felony arrest rate was highest among African Americans at 8,269.1 per 100,000 people. Latinos were second highest at 2,006.3 per 100,000 people. Whites were next at 638.3 per 100,000 people, and Asians had the lowest felony arrest rates at 580.6 per 100,000 people. An African American person was 14.24 times more likely and a Latino person 3.46 times more likely to be arrested for a felony than an Asian person in Oakland. An African American person was also 12.95 times more likely to be arrested for a felony than a White person. Data: Race/Ethnicity of Arrestee Number of Adult Felony Arrests in 2017 Population in Oakland over 18 years old Rate per 100,000 people African American 6,442 77,905 8,269.1 Asian 315 54,251 580.6 Latino 1,769 88,172 2,006.3 White 596 93,368 638.3 Source: Felony arrest data from Oakland Police Department by request, 2017. Population data from American Community Survey, 1-year estimates, 2016. Public Safety: Incarceration - Jail Incarceration Ratio between the jail incarceration rates for African Americans and Asians/Other Score: 1 Ratio: 19.53 What is measured? Jail incarceration rates are measured by the number of incarcerated individuals in jail per 100,000 population aged 18-69. This Indicator is measured for Alameda County, and the most recently available data is from 2015. 107 P a g e Why is this important? Individuals in jails may be incarcerated pre-trial or serving a sentence of less than one year. Even short-term sentences can have negative impacts on individuals, including higher risk of future involvement with the justice system and loss of employment. Families of incarcerated individuals can also be affected by legal costs, increased childcare needs, and loss of income. Disparities in jail incarceration rates reveal the differences in who is affected by minor charges and short-term confinement. (Source: http://www.arnoldfoundation.org/wpcontent/uploads/2014/02/LJAF_Report_state-sentencing_FNL.pdf) What did we find? The jail incarceration rate was by far the highest for African Americans at 974.6 per 100,000 people, followed by Latinos at 257.9 per 100,000 people. Next highest was Whites at 113.3 per 100,000 people. The rate for Asians and other races and ethnicities was lowest at 49.9 per 100,000. The overall jail incarceration rate for Alameda County was 227.9 per 100.000. African Americans in Alameda County in 2015 were 19.53 times more likely than Asians/Other and 8.60 times more likely than Whites to be incarcerated in jails. Data: Source: California Sentencing Institute, Center on Juvenile and Criminal Justice, 2015, http://casi.cjcj.org/ 108 P a g e Public Safety: Incarceration - Prison Incarceration Ratio between the prison incarceration rates for African Americans and Asians/Other Score: 1 Ratio: 24.82 What is measured? Prison incarceration rates are measured by the number of incarcerated individuals in prison per 100,000 population aged 18-69. This Indicator is measured for Alameda County, and the most recently available data is from 2015. Why is this important? Individuals in prison experience the mental and physical toll of confinement, as well as strained relationships and family dynamics. In addition, there are collateral consequences for formerly incarcerated individuals after release, including poorer health outcomes, increased barriers to employment, and disenfranchisement. Disparities in prison incarceration rates show the disproportionate effect of prison on individuals, families, and communities of different racial and ethnic groups. What did we find? The prison incarceration rate for African Americans was by far the highest at 1,856.8 per 100,000 people. The Latino prison incarceration rate was a distant second at 251.5 per 100,000 people. Next came Whites at 92.1 per 100,000 people, while the rate for Asians and other races and ethnicities was 74.8 per 100,000 people. The prison incarceration rate for Alameda County was 332.1 per 100,000. African Americans were 24.82 times more likely to be incarcerated in prisons than Asians/Other and 20.16 times more likely than Whites. 109 P a g e Data: Source: California Sentencing Institute, Center on Juvenile and Criminal Justice, 2015, http://casi.cjcj.org/ Topic 5.2: Law Enforcement Topic Score: 18.3 The Law Enforcement Topic includes three Indicators that measure disparities in police response times, stops, and use of force. The first Indicator measures disparities in police response times between Area 5 and Areas 1/3. This is a placeholder for this year that we plan to replace with an analysis of response times by police beat which can be more closely related to the racial and ethnic demographics of each beat. The second Indicator measures disparities in discretionary stops between African Americans and Asians. The third Indicator measures disparities in use of force between African Americans and Whites. Law Enforcement is the second highest scoring Topic in the Public Safety Theme with a Topic score of 18.3. The Indicator scores were highly variable with police response times scoring highest at 48, followed by discretionary stops at 6, and use of force getting the lowest score of 1. Though police response times bring up the average, this is still a Topic of stark disparities that warrant further investigation into root causes and solutions. 110 P a g e Public Safety: Law Enforcement - Police Response Times Ratio between the median Priority 2 call response times for Area 5 and Area 1/3 Score: 48 Ratio: 1.82 What is measured? This Indicator measures the median response times of calls for service that were routed to patrol. The measurement is broken down between Priority 1 and Priority 2 calls as well as by police area. Priority 1 Calls are defined as those that include potential danger for serious injury to persons, prevention of violent crimes, serious public hazards, felonies in progress with possible suspect on scene. Priority 2 Calls are defined as urgent but not an emergency situation, hazardous / sensitive matters, in-progress misdemeanors and crimes where quick response may facilitate apprehension of suspect(s). There are 5 police areas in Oakland each of which consist of a defined set of police beats and therefore cover a specific geographic part of Oakland. For more information and maps of areas, see here: http://www2.oaklandnet.com/government/o/OPD/o/BFO/index.htm Why is this important? How long it takes for patrol to respond to a call will directly affect whether citizens feel well served and supported by OPD. It is therefore important to understand if there are inequities in these response times. In future years, we plan to update this Indicator to an analysis of response times by police beat, which can more directly be related to the demographics of citizens living in that beat and therefore say whether citizens of different races receive different levels of service in Oakland. However, response time data by police beat was not available at this time, so we used response times by area as a placeholder. What did we find? Priority 2 calls made up the majority of calls for service citywide (and within every area) and therefore affected a greater number of citizens than Priority 1 calls, so we focused our analysis there. The areas with the fastest median response times were Areas 1 and 3 at just about 50 minutes each. The slowest was Area 5 at 1 hour and 31 minutes. If you called for service in Area 5 (and it is a Priority 2 call), you waited 1.82 times longer than if you were calling from Area 1 or 3. 111 P a g e However, it is important to note that Area 5 had the fastest response time for Priority 1 calls, so the disparity goes in a different direction for the two different types of calls. When we replace this with data by beat next year, we may or may not find more consistent patterns across beats and racial/ethnic groups. Data: Police Area Priority 1 Call Volume Priority 1 Call Median Response Time Priority 2 Call Volume Priority 2 Call Median Response Time Area 1 8,829 00:06:39 26,603 00:49:48 Area 2 6,409 00:08:11 24,153 01:06:09 Area 3 6,917 00:07:25 24,129 00:50:40 Area 4 7,803 00:07:53 24,297 01:21:59 Area 5 10,876 00:05:27 34,432 01:30:46 Citywide 41,032 00:07:47 133,623 01:08:04 Source: Oakland Police Department by request, 2017 Public Safety: Law Enforcement - Stops Ratio between the rates of discretionary stops for African Americans and Asians Score: 6 Ratio: 8.60 What is measured? This Indicator measures the rate of discretionary stops per 1,000 people in Oakland by race/ethnicity. “In 2016 through 2017, officers were required to complete stop data forms after every discretionary detention or arrest, and discretionary encounters in which a search or request to search occurred. Discretionary stops and searches exclude detentions and arrests that occurred as the result of a dispatched call for service, a citizen request, or for stops occurring pursuant to search warrants.” (Source: Oakland Police Department’s 20162017 Stop Data Report) Why is this important? “There are profound impacts to local OPD-community relations and to OPD’s mission when stops, stop outcomes, or conduct exhibited during stops are influenced, or are perceived to be influenced, by bias or racial and identity profiling.” (Source: Oakland Police Department’s 2016-2017 Stop Data Report) (For 112 P a g e more information on work OPD has done around racial disparities in stops, specifically traffic stops, see:https://oakland.legistar.com/LegislationDetail.aspx?ID=3110280&GUID=12D3472A-B7D1-4B7D-8A80- 2559AFF08DED) What did we find? We found that African Americans were by far the most likely to experience a discretionary stop with 197.8 out of every 1,000-people stopped in 2017 alone. Latinos were next most likely with a rate of 62.5 out of every 1,000. Whites and Asians were the least likely at rates of 24.1 and 23.0 respectively. African Americans were 8.60 times and Latinos 2.72 times more likely to be stopped than Asians. OPD has been collecting data that can help investigate this disparity. The “2016-2017 Stop Data Report” includes data by race/ethnicity on reasons for stops, whether a search was conducted, and whether anything was recovered. One relevant finding is that probation and parole searches make up a disparately large proportion of searches of African Americans in comparison to other groups (37% as opposed to 23% for Whites, Table 7a-Search Types). This result demonstrates how this Indicator interacts with others (such as jail and prison incarceration) in distinct ways for African Americans. Data: Race/Ethnicity Number of 2017 Stops Population of Oakland Rate per 1,000 people in Oakland African American 19,185 96,981 197.8 Asian 1,553 67,535 23.0 Latino 6,855 109,762 62.5 White 2,805 116,230 24.1 Citywide 31,528 419,987 75.1 Source: Oakland Police Department, 2016-2017 Stop Data Report 113 P a g e Public Safety: Law Enforcement - Use of Force Ratio between the rates of use of force on subjects for African Americans and Whites Score: 1 Ratio: 23.68 What is measured? This Indicator measures the rate of use of force on subjects per 100,000 people in Oakland by race/ethnicity. Note: There were 10 incidents of use of force that were on a crowd. For these incidents, the number and race/ethnicity of subjects were not available. Therefore, each was counted as one incident in the Citywide total. This will somewhat undercount the true total of people subjected to use of force and will potentially misrepresent the true racial and ethnic breakdown as well. Why is this important? Research has found that there are large disparities by race/ethnicity in who experiences use of force from police, that these disparities are not linked to crime rates in different communities, and that they span across many different cities and types of force used. (For more information, see: http://policingequity.org/study-reports-racial-disparities-regarding-police-use-force/, https://www.nytimes.com/2016/07/12/upshot/surprising-new-evidence-shows-bias-in-police-use-of-forcebut-not-in-shootings.html?smid=pl-share) What did we find? African Americans were by far the most likely to experience use of force with a rate of 244.4 people per 100,000 in 2017. Latinos were a distant second at a rate of 70.2. Asians and Whites were the least likely to experience use of force at rates of 14.8 and 10.3 respectively. An African American person in Oakland was 23.68 times more likely than a White person to experience use of force in 2017. (For more information on how OPD defines Use of Force and strategies employed to reduce incidents, see here: http://www2.oaklandnet.com/government/o/OPD/a/data/useofforce/index.htm) 114 P a g e Data: Race/Ethnicity Number of use of force subjects Population of Oakland Rate per 100,000 people in Oakland African American 237 96,981 244.4 Asian 10 67,535 14.8 Latino 77 109,762 70.2 White 12 116,230 10.3 Citywide 353 419,987 84.1 Source: Use of force data from Oakland Police Department by request, 2017. Population data from American Community Survey, 1-year estimates, 2016. Topic 5.3: Staffing Topic Score: 48.3 The Staffing Topic includes three Indicators that measure racial and ethnic disparities in OPD sworn staff representation, attrition from academy, and attrition from field training. The first Indicator measures disparities in how well OPD sworn staff represent the demographics of Oakland, specifically between Whites and African Americans. The second Indicator measures disparities in attrition from academy between African Americans and Whites. The third Indicator measures disparities in attrition from field training between Latinos/Asians and Whites. Staffing is the highest scoring Topic in the Public Safety Theme with a Topic score of 48.3. The Indicator scores were somewhat variable but all higher than the norm in this Theme. Attrition from academy scored highest at 63, followed by representation of Oakland at 45, and attrition from field training scored lowest at 37. Though these scores may be higher than most others in Public Safety, there is still substantial room for improvement. Given that staffing is also an area in which the City can employ direct strategies for change, it merits further investigation and concrete next steps. 115 P a g e Public Safety: Staffing - Representation Ratio between the rates of sworn staff of the same race/ethnicity for Whites and African Americans Score: 45 Ratio: 1.89 What is measured? This Indicator measures the rate of sworn staff in OPD per 100,000 people of the same race/ethnicity in Oakland, in other words how representative are the sworn staff of the Oakland population? Numbers of sworn staff are as of February 28, 2018. Why is this important? Though research on the topic is limited and mixed, we believe that having sworn staff that reflect the diversity of our community is important. It would grow trust and improve relations between OPD and Oaklanders and hopefully ensure fair treatment for all residents. A lack of representation is also a call for further investigation and concern from the perspective of equity in recruitment and hiring. (For more information see: http://www.governing.com/topics/publicjustice-safety/gov-police-departmentdiversity.html,https://www.washingtonpost.com/news/storyline/wp/2014/08/22/do-diverse-police-forcestreat-their-communities-more-fairly-than-all-white-ones-like-fergusons/?utm_term=.c6e4896273ab) What did we find? Citywide there were 733 sworn staff which equals 174.5 sworn staff for every 100,000 people in Oakland. For any given race/ethnicity: if their rate was lower than 174.5, that means they were underrepresented or if it was higher than 174.5, that means they were overrepresented among sworn staff. We found that White people were greatly overrepresented with a rate of 246.1 White sworn staff per 100,000 White people in Oakland. African Americans were the least well represented with only 129.9 African American sworn staff per 100,000 African American people in Oakland. Asians were next least well represented at a rate of 143.6 and Latinos were also slightly underrepresented with a rate of 164.9. The White community in Oakland had 1.89 times as many sworn staff of their own race/ethnicity as African Americans did in Oakland. 116 P a g e Data: Race/Ethnicity Number of Sworn Staff Population of Oakland Rate of Sworn Staff per 100,000 people in Oakland African American 126 96,981 129.9 Asian 97 67,535 143.6 Latino 181 109,762 164.9 White 286 116,230 246.1 Citywide 733 419,987 174.5 Source: Sworn staff demographics from Oakland Police Department Monthly Staffing Report (dated April 4, 2018, with data as of February 28, 2018,). Population data from American Community Survey, 1-year estimates, 2016. Public Safety: Staffing - Attrition from Academy Ratio between the rates of attrition from academy for African Americans and Whites Score: 63 Ratio: 1.46 What is measured? This Indicator measures the attrition rate by race/ethnicity of police officers from the Oakland Police Department’s Academies over the past 3 years (since the start of 2015). This includes the last six OPD academies (172nd-177th) as well as lateral academies and SFPD-managed academies. Why is this important? All non-White groups are underrepresented among OPD sworn staff (see Representation Indicator for more information). There could be many reasons for this underrepresentation, but it is important to look critically at the phases of recruitment, hiring, and training to understand how to improve the diversity of OPD. Academy and Field Training are two important phases in becoming an OPD officer, so we examined both (across two Indicators) to see what patterns emerged in attrition across racial and ethnic groups. However, it is important to note that attrition from either phase is not necessarily bad (no one benefits from ill-prepared people moving on to the next phase). 117 P a g e What did we find? Over the past 3 years, Latinos had the lowest attrition from academy at only 34.8%, with Whites slightly higher at 37.6%. Asians were slightly higher still at 40.0%. African Americans had the highest attrition from academy at 54.8% not completing. Though Latinos had the lowest attrition rates, this Indicator is scored between African Americans and Whites because White officers were the only overrepresented racial or ethnic group among OPD sworn staff (see Representation Indicator for more information). African Americans were 1.46 times more likely than White officers to not complete academy. Data: Race/Ethnicity Began Academy Completed Academy Did Not Complete Academy Attrition Rate African American 62 28 34 54.8% Asian 55 33 22 40.0% Latino 89 58 31 34.8% White 85 53 32 37.6% Other 23 22 1 4.3% Total 314 194 120 38.2% Source: Oakland Police Department Monthly Staffing Reports. Ending numbers were found in Table 5b from the report dated April 4, 2018, with data as of February 28, 2018, starting demographics were collected and aggregated from older staffing reports (2015 to present) and from data supplied by request from OPD. Public Safety: Staffing - Attrition from Field Training Ratio between the rates of attrition from field training for Latinos/Asians and Whites Score: 37 Ratio: 2.56 118 P a g e What is measured? This Indicator measures the attrition rate by race/ethnicity of police officers from the Oakland Police Department’s Field Training Program over the past 3 years (since the start of 2015). This includes the last five OPD academies (172nd-176th) as well as lateral academies and SFPDmanaged academies. Officers are released from the program for different reasons ranging from termination, resignation, and/or failing to meet the performance dimensions required by the Field Training Program and the POST approved guidelines. Why is this important? All non-White groups are underrepresented among OPD sworn staff (see Representation Indicator for more information). There could be many reasons for this under representation, but it is important to look critically at the phases of recruitment, hiring, and training to understand how to improve the diversity of OPD. Academy and Field Training are two important phases in becoming an OPD officer, so we examined both (across two Indicators) to see what patterns emerged in attrition across racial and ethnic groups. However, it is important to note that attrition from either phase is not necessarily bad (no one benefits from ill-prepared people moving on to the next phase). What did we find? Over the past 3 years, White officers had the lowest attrition from field training at only 8.0% not completing. African American officers were almost double that rate at 15.4%. Latino and Asian officers had the highest attrition rate from field training at 20.5% each. Latino and Asian officers were 2.56 times more likely than White officers to not complete field training. This is a different pattern across racial/ethnic groups than attrition from academy (see previous Indicator), and they both warrant further investigation into root causes and potential solutions. 119 P a g e Data: Race/Ethnicity Began Field Training Completed Field Training Did Not Complete Field Training Attrition Rate African American 26 22 4 15.4% Asian 44 35 9 20.5% Latino 44 35 9 20.5% White 50 46 4 8.0% Other 19 15 4 21.1% Total 183 153 30 16.4% Source: Oakland Police Department Monthly Staffing Report (Table 12b from the report dated April 4, 2018, with data as of February 28, 2018,). Topic 5.4: Community Stressors Topic Score: 1.7 The Community Stressors Topic includes three Indicators that measure racial and ethnic disparities in domestic violence, homicides, and juvenile felony arrests. The first Indicator measures disparities in domestic violence victimization between African Americans and Asians. The second Indicator measures disparities in homicides between African Americans and Asians. The third Indicator measures disparities in juvenile felony arrests between African Americans and Whites. Community Stressors is the second lowest scoring Topic in the Public Safety Theme and in the entire report with a Topic score of 1.7. The Indicator scores were all similar and extremely low. Domestic violence scored highest at 3 followed by homicides and juvenile felony arrests both scoring a 1. This is an area of extreme disparity. 120 P a g e Public Safety: Community Stressors - Domestic Violence Ratio between the rates of domestic violence victimization for African Americans and Asians Score: 3 Ratio: 9.45 What is measured? This Indicator measures the rate of domestic violence victimization in Oakland by race/ethnicity. Rate is calculated as the number of domestic violence incidents per 100,000 people of the same race/ethnicity (of any age). Why is this important? Domestic violence has serious negative effects on the lives of the victims (predominantly women and children). These effects range from the physical to emotional, can be long lasting even after the abuse stops, and impact every part of a victim’s life. (For more information, see here: https://www.healthyplace.com/abuse/domestic-violence/effects-of-domestic-violence-domestic-abuseon-women-and-children/) What did we find? In Oakland, the domestic violence victimization rate among African Americans was 2,111.8 per 100,000, compared to 835.4 among Latinos, 321.8 among Whites, and 223.6 among Asians. An African American person was 9.45 times more likely to be a victim of domestic violence than an Asian person and 6.56 times more likely than a White person. A Latino person was 3.74 times more likely to be the victim of domestic violence than an Asian person and 2.60 times more likely than a White person in Oakland. It is important to note, however, that domestic violence is generally under-reported to police, so we cannot be sure that the data we have here reflects the true rate of incidents for people of different races. 121 P a g e Data: Race/Ethnicity of Victim Number of Victims in Population in Oakland 2017 (all ages) Rate per 100,000 people African American 2,048 96,981 2,111.8 Asian 151 67,535 223.6 Latino 917 109,762 835.4 White 374 116,230 321.8 Source: Domestic violence data from Oakland Police Department by request, 2017. Population data from American Community Survey, 1-year estimates, 2016. Public Safety: Community Stressors - Homicides Ratio between the rates of homicides for African Americans and Asians Score: 1 Ratio: 37.62 What is measured? This Indicator measures the rate of homicides in Oakland by race/ethnicity. Rate is calculated as the number of homicides per 100,000 people of the same race/ethnicity (of any age). Why is this important? Homicides have a devastating effect that extends beyond just the victim to the victim’s family, friends, and broader community. It is important to understand how these effects are distributed among different racial/ethnic groups in Oakland. What did we find? African Americans had the highest rate of homicides at 55.7 per 100,000 people in Oakland. The homicide rate for Latinos was next but much lower at 10.9 homicides per 100,000 people. The rate for Whites was 3.4 homicides per 100,000 people and the rate for Asians was the lowest of all at 1.5 homicides per 100,000 people. Because Asians had the lowest rate, this Indicator was scored between African Americans and Asians, but given the extreme disparities the score would still have been 1 had the ratio been between African Americans and Whites. 122 P a g e In Oakland in 2017, an African American person was 37.62 times more likely to be a homicide victim than an Asian person and 16.19 times more likely than a White person. Data: Race/Ethnicity of Victim Number of Homicides in 2017 Population in Oakland (all ages) Rate per 100,000 people African American 54 96,981 55.7 Asian 1 67,535 1.5 Latino 12 109,762 10.9 White 4 116,230 3.4 Source: Homicide data from Oakland Police Department by request, 2017. Population data from American Community Survey, 1-year estimates, 2016. Public Safety: Community Stressors - Juvenile Felony Arrests Ratio between the juvenile felony arrest rates for African Americans and Whites Score: 1 Ratio: 112.63 What is measured? This Indicator measures the rate of felony arrests of juveniles by race/ethnicity adjusted for population. This Indicator is measured for Oakland. Rate is calculated as the number of juvenile felony arrests per 100,000 people of each race/ethnicity that are under 18 years of age. To determine the number of juveniles of each race/ethnicity in Oakland, we took the population by race/ethnicity for all ages and multiplied by the percent of Oakland’s population that is under 18 (19.67%). This approach was necessary due to the lack of exact data available on the under 18 population in Oakland by race/ethnicity. Why is this important? A felony is a serious crime that typically results in a prison sentence of over one year. For young people under age 18, a felony is punishable by a sentence to a Youth Authority facility or adult prison. Young people sentenced to adult prison are more likely to be re-arrested and incarcerated as adults than the general population. (Source: https://www.cdc.gov/mmwr/pdf/rr/rr5609.pdf) 123 P a g e What did we find? The juvenile felony arrest rate among African Americans was 1,971.0 per 100,000, compared to 370.5 among Latinos, 30.1 among Asians, and 17.5 among Whites. It should be noted that the actual number of felony arrests for White and Asian juveniles was very low (4 each). An African American juvenile was 112.63 times more likely to be arrested on felony charges than a White juvenile in Oakland in 2017, which is a truly staggering disparity and by far the most extreme of any Indicator in this entire report. Data: Race/Ethnicity of Arrestee Number of Arrests Population in Oakland in 2017 under 18 years old Rate per 100,000 people African American 376 19,076 1,971.0 Asian 4 13,284 30.1 Latino 80 21,590 370.5 White 4 22,862 17.5 Source: Felony arrest data from Oakland Police Department by request, 2017. Population data from American Community Survey, 1-year estimates, 2016. 124 P a g e Theme 6 Neighborhood and Civic Lite IN THIS SECTION: Built Environment . Pedestrian Safety . Soft Story Buildings . Lang?tenn Residential Vaeaney Civic Engagement . Adopt a Drain . Voter Tumout . Equal Aooess Accommodations Environmental Health . Park Quality . Abandoned Trash . Pollution Burden Transportation and lntrastruoture . Access to a Car . Bus Frequency . Curl:- Ramps 125 Page Theme 6: Neighborhood and Civic Life Theme Score: 50.6 For a city to thrive, it is essential that neighborhoods are safe and accessible, and that residents have the opportunity to access city services and hold the city government accountable. Oakland residents face disparities based on where they live in the city, with majority non-White neighborhoods faring worse than majority White neighborhoods. Neighborhood differences rooted in historical segregation result in disparate outcomes for people of color, especially African American and Latino residents. Neighborhood and Civic Life scored the highest, indicating the most equity, compared to the other Themes in the Oakland Equity Indicators framework. The 12 Indicators within the Neighborhood and Civic Life Theme examine inequities faced by racial and ethnic minorities across four Topic areas: Built Environment, Civic Engagement, Environmental Health, and Transportation and Infrastructure. The lowest scoring Topic was Built Environment (33.3), followed by Environmental Health (46.7) and Transportation and Infrastructure (47.3). Civic Engagement scored the highest at 75.0. Topics and Indicators within this Theme: Topics Built Environment Scores Indicators Scores 33.3 Pedestrian Safety 1 Soft Story Buildings 67 Long-term Residential Vacancy 32 Civic Engagement 75.0 80 Adopt a Drain Voter Turnout 45 Equal Access Accommodations 100 Environmental Health 46.7 Park Quality 57 Abandoned Trash 28 Pollution Burden 55 Transportation and Infrastructure 47.3 Access to a Car 33 Bus Frequency 60 Curb Ramps 49 126 P a g e Topic 6.1: Built Environment Topic Score: 33.3 The Built Environment Topic includes three Indicators that measure the safety, resilience, and quality of Oakland neighborhoods. All three of these Indicators measure geographic disparities based on the majority race/ethnicity of census tracts. The first Indicator in this Topic measures disparities in the location of streets where there have been a high number of pedestrian injuries. The second Indicator measures disparities in the location of soft story buildings, which are prone to collapse in the event of an earthquake. The third Indicator measures disparities in the location of residential addresses that have been vacant for two years or more. Built Environment was the lowest scoring Topic in the Neighborhood and Civic Life Theme, with a Topic score of 33.3. This low score was partially due to the pedestrian safety Indicator, which received the lowest possible score of 1. The soft story buildings Indicator received the highest score in this Topic (67), and the score for long-term residential vacancy fell in the middle (32). Neighborhood and Civic Life: Built Environment - Pedestrian Safety Ratio between the percents of streets in the High Injury Network in majority Asian and majority White census tracts Score: 1 Ratio: 13.16 What is measured? This Indicator measures the percent of streets that are in the High Injury Network for pedestrians over the five year period 2012-2016. This subset of the High Injury Network includes streets with the highest concentration of severe and fatal pedestrian injuries. The Indicator takes into account the length of each street segment in both the High Injury Network and the overall street network. Why is this important? Pedestrian safety is a priority because over 100 Oaklanders are severely injured and 30 Oaklanders are killed in traffic in the average year, and pedestrians make up approximately one-third of those injuries. The High Injury Network allows Oakland to target traffic calming 127 P a g e and other interventions proven to reduce severe and fatal crashes where they are currently concentrated. It also allows Oakland to identify and prioritize inequitable outcomes from preventable traffic crashes. What did we find? We found that majority Asian census tracts had the highest percent of streets in the High Injury Network for pedestrians (30.0%), while majority Latino census tracts had the second highest percent (11.4%). Majority White census tracts had the lowest percent of High Injury Network streets (2.3%), and 5.6% of streets in majority African American census tracts were in the High Injury Network. The percent for non-White/mixed census tracts (6.5%) was similar to the percent of all streets citywide (6.1%). The percent of streets with pedestrian safety concerns in majority Asian census tracts was 13.16 times the percent in majority White census tracts. Data: Source: Oakland Vision Zero Team by request, 2012-2016; American Community Survey, 5-year estimates, 2012-2016 128 P a g e Neighborhood and Civic Life: Built Environment - Soft Story Buildings Ratio between the percents of residential parcels that are soft story in majority nonWhite/mixed and majority White census tracts Score: 67 Ratio: 1.37 What is measured? This Indicator measures the residential parcels that required a Level 1 or Level 2 evaluation of the group floor for a soft-story retrofit. Potential soft story buildings were identified through a 2008 screening of buildings permitted for construction before January 1, 1991, with parking or commercial on the ground floor, with five or more residential units, without previous soft story retrofits, and at least two stories. Some of these buildings were marked exempt or had incomplete evaluations, so this Indicator only measures those that were officially identified as soft story in 2014. In future years, we hope to measure the percent of identified soft story buildings that have been retrofitted. Why is this important? Soft story buildings are “multi-unit, wood-frame, residential buildings with a first story that lacks adequate strength or stiffness to prevent leaning or collapse in an earthquake.” They are dangerous for building tenants and neighborhoods, and they pose challenges to the recovery of the City and region in the event of an earthquake. (Source: http://softstory.openoakland.org/) What did we find? Non-White/mixed census tracts were the most likely to have soft story buildings on their residential parcels (0.85%), followed by majority White census tracts (0.62%). Due to the fact that only certain types of buildings can be designated as soft story, and that these types of buildings are concentrated in certain areas of Oakland, we chose non-White/mixed and White as the comparison groups for this indicator. Majority African American census tracts (0.22%) and majority Latino census tracts (0.27%) had much lower percents of parcels with soft story buildings, while the two majority Asian census tracts had no soft story buildings. The percent of residential parcels that are soft story in majority non-White/mixed census tracts was 1.37 times the percent in White census tracts. 129 P a g e Data: Source: OpenOakland, 2014, http://softstory.openoakland.org/; American Community Survey, 5-year estimates, 2012-2016 Neighborhood and Civic Life: Built Environment - Long-term Residential Vacancy Ratio between the percents of residential addresses that have been vacant for 24 months or more in majority African American and majority Latino census tracts Score: 32 Ratio: 3.21 What is measured? This Indicator measures the percent of residential addresses that have been identified as “vacant” by the United States Postal Service (USPS) for at least two years. Data is collected and aggregated at the census tract level by the USPS on a quarterly basis. Why is this important? Residential addresses can be vacant at any point in time for a variety of reasons, including new construction, renovation, and tenant turnover. Addresses that remain vacant for two years or more, however, are indicative of a more chronic condition that may be more difficult to address. High levels of long-term vacancy can have a negative impact on the safety and quality of neighborhoods, and can contribute to neighborhood blight. 130 P a g e What did we find? Long-term residential vacancy was highest in majority African American census tracts (0.88%), followed by majority Asian census tracts (0.66%). Majority Latino census tracts had the lowest long-term residential vacancy (0.27%), followed closely by majority White census tracts (0.39%). Citywide, 0.47% of residential addresses in Oakland had been vacant for at least two years. The percent of vacant addresses in majority African American census tracts was 3.21 times the percent in majority Latino census tracts. Data: Source: U.S. Department of Housing and Urban Development Aggregated USPS Administrative Data on Address Vacancies, Quarter 3 ending September 30, 2017, https://www.huduser.gov/portal/datasets/usps.html; American Community Survey, 5-year estimates, 2012-2016 Topic 6.2: Civic Engagement Topic Score: 75.0 The Civic Engagement Topic measures three different aspects of an engaged community working to improve conditions and collectively shape the future of Oakland. The first Indicator compares the percents of drains that have been adopted in majority Asian and majority White census tracts through the Adopt a Drain program. The second Indicator compares voter turnout rates among residents in Council District 7 and Council District 1. Finally, the third Indicator compares the percent of the population that is Spanish-speaking with limited English 131 P a g e proficiency (LEP) and the percent of city public contact position (PCP) employees that are bilingual in Spanish and English to ensure that LEP status does not inhibit access to city services. With an overall Topic score of 75.0, Civic Engagement is the highest scoring Topic in the Neighborhood and Civic Life Theme and the whole framework. However, the Indicators within the Topic exhibit a wide range of scores. The first Indicator, Adopt a Drain, scored relatively high at 80. The second Indicator, voter turnout, scored the lowest, at 45. The third Indicator, Equal Access Accommodations, received a perfect score of 100, indicating that the City met the requirements for bilingual PCP employees. Neighborhood and Civic Life: Civic Engagement - Adopt a Drain Ratio between the percents of drains that have not been adopted in majority Asian and majority White census tracts Score: 80 Ratio: 1.12 What is measured? This Indicator measures the percent of storm drains that have not been adopted through Oakland’s Adopt a Drain Program. Through this program, volunteers help to clean storm drain inlets throughout the city. This Indicator measures disparities in program participation by the majority race/ethnicity of census tracts. Why is this important? Adopting a drain can have a number of positive impacts on a neighborhood, including pollution cleanup and storm water management. Adopting a drain is one way to demonstrate civic engagement through volunteerism. Participating in this program can help to strengthen communities and bring neighbors together. One of the goals of the Adopt a Drain program is to promote cleaner, healthier, and more sustainable neighborhoods. Measuring program participation by census tract illuminates neighborhood disparities. These disparities may point to differences in civic engagement, but they may also be due to differences in the need for drains to be adopted (i.e., the quality of drains and the risk of flooding in different neighborhoods). 132 P a g e What did we find? Majority White census tracts had the lowest percent of drains that were not adopted (87.1%), while majority Asian census tracts had the highest percent (97.5%). Majority African American and majority Latino census tracts fell in the middle (94.8% and 96.3%, respectively), but both percents were low compared to the percent in majority White census tracts. The percent of drains that were not adopted in non-White, mixed census tracts (90.8%) was similar to the citywide percent (91.0%). The percent of drains that have not been adopted in majority Asian census tracts was 1.12 times the percent in majority White census tracts. Data: Source: Oakland Environmental Services Division by request, as of February 2018; American Community Survey, 5-year estimates, 2012-2016. Neighborhood and Civic Life: Civic Engagement - Voter Turnout Ratio between the percents of registered voters who did not vote in the 2016 general election in Council Districts 7 and 1 Score: 45 Ratio: 1.89 133 P a g e What is measured? Voter turnout is measured by the percent of registered voters that voted in the 2016 general election. This Indicator measures geographic disparities by City Council District. For a map of the City Council Districts, see here: http://www2.oaklandnet.com/oakca1/groups/ceda/documents/image/oak044524.pdf. Why is this important? Voter turnout is a direct measure of engagement with the political system. Alameda County had higher voter turnout rates than the national average, but the United States lags far behind other democratic countries. Registered voters cite various reasons for not voting, including dislike of candidates or the issues they campaign for, feeling that their vote doesn't matter, and being too busy. Other research has cited the complexity of the voting process as a reason for low voter turnout. (Sources: https://oaklandnorth.net/2016/11/16/voter-turnout-in-alameda-countybeats-the-national-rate-as-groups-push-local-measures/, http://www.pewresearch.org/facttank/2017/06/01/dislike-of-candidates-or-campaign-issues-was-most-common-reason-for-not-voting-in2016/, https://www.pbs.org/newshour/politics/voter-turnout-united-states.) What did we find? The percent of registered voters who did not vote in the 2016 general election was highest in District 7 (34.8%) and was lowest in District 1 (18.5%). The other four districts fell in the middle: 31.5% of registered voters in District 5, 31.1% in District 6, 28.8% in District 3, and 25.5% in District 2 did not vote in the 2016 general election. Citywide, 26.2% of registered voters did not vote. The percent of registered voters in Council District 7 who did not vote in the 2016 election was 1.89 times the percent in Council District 1. 134 P a g e Data: Source: Alameda County Registrar of Voters, 2016, https://data.acgov.org/Voting/AlamedaCounty-Voter-Precincts/q6ek-ybkr/data Neighborhood and Civic Life: Civic Engagement - Equal Access Accommodations Ratio between the percent of the population that is Spanish-speaking with limited English proficiency (LEP) and the percent of City public contact position (PCP) employees who are bilingual in Spanish/English Score: 100 Ratio: 0.75 What is measured? The equal access accommodations Indicator is measured by comparing the percent of public contact position (PCP) employees who speak Spanish to the percent of Spanish speakers who have limited English proficiency (LEP) citywide. The Equal Access to Services Ordinance includes a requirement for City departments to offer bilingual services based on citywide demographics. In FY2016-2017, the two languages required by the ordinance were Spanish and Chinese. We chose to measure Spanish-speaking PCP employees for this Indicator because Spanish speakers comprise a larger proportion of the population. 135 P a g e Why is this important? Equal access accommodations help to ensure that all city residents have access to public services, regardless of language differences. Bilingual public-facing employees help bridge the language gap for individuals who have LEP or who are more comfortable speaking languages other than English. Having bilingual employees on staff helps city agencies deliver services in an equitable and culturally competent matter. What did we find? In FY2016-2017, 15.8% of PCP employees were bilingual in Spanish and English, compared to 11.8% of the general population who spoke Spanish and had LEP. The City, therefore, met and exceeded the minimum requirement for bilingual Spanish speakers. The result was similar for Chinese: 6.0% of PCP employees were bilingual in Chinese and English while 5.1% of the general population spoke Chinese and had LEP. The percent of the city population that was Spanish-speaking with LEP was 0.75 times the percent of bilingual PCP employees, meaning that the number of bilingual PCP employees was more than proportional to the LEP Spanishspeaking population. It should be noted that while this Indicator received a score of 100, there is still room for improvement. Bilingual employees in Spanish and Chinese did not match the percents of the population who spoke those languages overall (22.3% and 6.9%, respectively), including both those with LEP and those without LEP. As Oakland’s population changes over time, the City may need to adjust the minimum requirements for bilingual employees to meet the language needs of all Oaklanders. 136 P a g e Data: Source: Equal Access to Services Annual Compliance Report, FY2016-2017, https://oakland.legistar.com/LegislationDetail.aspx?ID=3152572&GUID=5FF4C646-DD03-41BE-A64AC84153CE7964&Options=&Search= Topic 6.3: Environmental Health Topic Score: 46.7 A safe, clean, and healthy environment is a key component of neighborhood quality. The three Indicators in this Topic explored different attributes of neighborhoods that have an impact on resident health. The first Indicator compared park quality in Council District 1 and Council District 7. The second Indicator compared the rates of service request for illegal dumping in majority Latino and majority White census tracts. The third Indicator compared the pollution burden score in majority Asian and majority White census tracts. Environmental Health scored 46.7, and the individual Indicators showed notable room for improvement. Park quality, the first Indicator, scored the highest in the Topic at just 57. The second Indicator, abandoned trash, scored the lowest in the Topic at 28. The third Indicator, pollution burden, received a score of 55, revealing geographic disparities in different areas of the city. 137 P a g e Neighborhood and Civic Life: Environmental Health - Park Quality Ratio between the average overall park rating scores in Council District 1 and Council District 7 Score: 57 Ratio: 1.59 What is measured? This Indicator measures overall ratings for Oakland parks and compares average scores by City Council District. The overall ratings were based on an annual survey that assigned parks letter grades (A through F), which corresponded to scores (A=4, B=3, C=2, D=1 and F=0). In addition to Council District scores, the scores for parks surrounding Lake Merritt were reported as an average Lakeside score. The survey also included questions about greenery, amenities, recreation areas, and homeless encampments. For a map of the City Council Districts, see here: http://www2.oaklandnet.com/oakca1/groups/ceda/documents/image/oak044524.pdf. Why is this important? Parks provide environmental, recreational, and aesthetic benefits to neighborhoods. They reduce urban heat island effects, contribute to cleaner air, and collect stormwater. They also provide spaces for physical activity and for neighbors to gather. While access to parks is a common Indicator of both neighborhood and civic life, the quality of those parks is also important. Park quality affects how much parks are used and how beneficial they are to the surrounding community. What did we find? Council District 1 received the highest score of 2.9 (C+), while Council District 7 received the lowest score of 1.8 (D+). These scores differed from the citywide average of 2.5 (C). The annual list of Parks in Jeopardy (parks that received an F rating) included parks in all Council Districts except Districts 1 and 6. The data source analyzed Lakeside Park separately, and it received a relatively high score of 3.0 (B). The average overall park rating in Council District 1 was 1.59 times the rating in Council District 7. 138 P a g e Data: Source: 2016 Community Report Card on the State of Maintenance in Oakland Parks, Oakland Parks and Recreation Foundation, www.oaklandparks.org/sites/default/files/2016.Park%20Maintenance%20Report_0.pdf Neighborhood and Civic Life: Environmental Health - Abandoned Trash Ratio between the rates of service requests for illegal dumping in majority Latino and majority White census tracts Score: 28 Ratio: 3.94 What is measured? This Indicator measures the number of service requests received by the Oakland Call Center for illegal dumping as a rate per 1,000 people in each census tract. The census tracts are grouped based on majority race/ethnicity. Service requests that were canceled were excluded from the analysis. Why is this important? Abandoned trash contributes to an unhealthy and unsafe living environment and has a negative impact on neighborhood quality. Abandoned trash can contribute to land, water, and 139 P a g e air pollution in a neighborhood and may contain harmful substances. Piles of abandoned trash can also be fire hazards. The number of piles of abandoned trash that Oakland Public Works picked up in 2016 was 100% more than the number of piled picked up in 2009, and the department reports that it is a systemic, citywide problem. (Source: http://www2.oaklandnet.com/government/o/PWA/o/FE/s/IDR/index.htm) What did we find? Rates of illegal dumping requests per 1,000 population were highest in majority Latino census tracts (102.8) and lowest in majority White census tracts (26.1). Majority African American and majority Asian census tracts had similar rates of illegal dumping service requests (82.6 and 82.0, respectively). The rate for non-White/mixed census tracts (69.4) was similar to the citywide rate (66.9). The rate of illegal dumping service requests in majority Latino census tracts was almost four (3.94) times higher than the rate in majority White census tracts. Data: Source: Service requests received by the Oakland Call Center, 2017, https://data.oaklandnet.com/Infrastructure/Service-requests-received-by-the-Oakland-Call-Cent/quthgb8e/data; American Community Survey, 5-year estimates, 2012-2016 Neighborhood and Civic Life: Environmental Health - Pollution Burden Ratio between the average pollution burden scores in majority Asian and majority White census tracts Score: 55 Ratio: 1.63 140 P a g e What is measured? Pollution burden is calculated by the Office of Environmental Health Hazard Assessment’s CalEnviroScreen 3.0. This tool measures pollution burden as a combined score that includes Indicators of potential exposures to pollutants and environmental conditions (e.g., ozone, pesticides, toxic releases, traffic, hazardous waste). The pollution burden scores are averaged by majority race/ethnicity of Oakland census tracts. Why is this important? Pollution is a major environmental health factor that has negative impacts on a number of health outcomes, including respiratory diseases such as asthma and COPD. Pollution also affects water and soil, impacting drinking water and food. What did we find? The two majority Asian census tracts in Oakland had the highest average pollution burden (51.6), far higher than the average for majority White census tracts (31.8). Majority African American and majority Latino census tracts also had higher average pollution scores than majority White census tracts (37.4 and 40.6, respectively), as did non-White/mixed census tracts (37.9). The pollution burden score in majority Asian census tracts was 1.63 times higher than the score in majority White census tracts. Data: Source: CalEnviroScreen 3.0 Maps, https://oehha.ca.gov/calenviroscreen/maps-data, updated on January 9, 2017; American Community Survey, 5-year estimates, 2012-2016 141 P a g e Topic 6.4: Transportation and Infrastructure Topic Score: 47.3 The three Indicators in the Transportation and Infrastructure Topic measure the different ways Oaklanders travel in the city. The first Indicator measures racial and ethnic disparities in car access, which is important due to the heavy reliance on cars for commuting to work, school, and other places. The second Indicator measures the frequency of buses along each segment of the network and compares majority non-White/mixed and majority African American census tracts. The third Indicator is a citywide measurement of curb ramps that are accessible to individuals with limited mobility, including seniors and those with physical disabilities. The Transportation and Infrastructure Topic score was 47.3, and the Indicator scores were variable. The first Indicator, car access, had the lowest score in the Topic (33). The second Indicator, bus frequency, had the highest score in the Topic (60). The third Indicator, curb ramps, scored in between at 49. Neighborhood and Civic Life: Transportation and Infrastructure - Access to a Car Ratio between the percents of African Americans and Whites who do not have access to a car Score: 33 Ratio: 3.08 What is measured? This Indicator measures the percent of individuals who live in housing units that do not have a car. Why is this important? More than half of Oaklanders (63.8%) commute to work in a car, truck or van, either individually or in a carpool. (Source: https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml) Cars are also an important mode of transportation for traveling to school, appointments, and social gatherings. Access to a car can lower travel distances to important resources and services such as grocery stores and hospitals. Car access is particularly beneficial in areas of the city where public transit is either inconsistent or unavailable. 142 P a g e What did we find? Nearly one in five African American Oaklanders did not have access to a car (18.7%), compared to 6.1% of White Oaklanders. The car ownership rate among Asians (10.0%) was similar to the citywide rate (10.2%), while the rate was lower for Latinos (7.6% did not have a car). The percent of African Americans who did not have access to a car was three (3.08) times higher than the percent of Whites without car access. Data: Source: American Community Survey, 1-year PUMS, 2016 (Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html) Neighborhood and Civic Life: Transportation and Infrastructure - Bus Frequency Ratio between the average numbers of buses per hour in majority non-White/mixed and majority African American census tracts Score: 60 Ratio: 1.52 What is measured? Bus frequency is measured as the average number of buses per hour along routes in and surrounding census tracts. For this Indicator, 100ft buffers were created around each group of census tracts in order to capture bus routes that did not fall completely inside census tract 143 P a g e boundaries but were still important to include because they were very close. For each group of census tracts, bus frequency was averaged, taking into account the length of the route. Why is this important? Buses are an important part of the public transportation network in Oakland, in part because they reach neighborhoods not served by Bay Area Rapid Transit. The frequency of buses on each route can have a great impact on how reliable the bus system is for individuals commuting to work, school, and other appointments. What did we find? Bus frequency was highest in majority Asian census tracts, which had an average of 20.5 buses per hour, and second highest in non-White/mixed census tracts, which had an average frequency of 16.7 buses per hour. Since the two majority Asian census tracts are located near Downtown where bus frequency is expected to be highest and the same level of bus service would be expensive and unnecessary elsewhere, we chose non-White/mixed census tracts as the comparison group for this Indicator. The lowest average frequency was found in majority African American census tracts and majority Latino census tracts (both 11.0 buses per hour), with a slightly lower frequency in African American census tracts before rounding. The average number of buses per hour in majority non-White/mixed census tracts was 1.52 times the buses per hour in majority African American census tracts. Data: Source: Oakland GIS Department by request, 2017; American Community Survey, 5-year estimates, 2012-2016 144 P a g e Neighborhood and Civic Life: Transportation and Infrastructure - Curb Ramps Percent of curbs citywide that are not ADA accessible Score: 49 Percent: 51.6% What is measured? This Indicator measures the percent of curbs with curb ramps that are not modern, indicating that they are non-compliant. Modern curb ramps should have all the proper elements: low slope and cross-slope, adequate width, level top and bottom landings, a detectable warning surface, and they should be directional and clear of obstructions in the curb ramp area. This is a citywide Indicator that measures conditions for individuals with physical disabilities and limited mobility. Why is this important? Curb ramps are necessary for individuals with physical disabilities to fully access sidewalks and streets. Curb ramps are also important for individuals with strollers or carts, as well as seniors and others with limited mobility. Curb ramp accessibility is an issue throughout Oakland, so we decided to measure this Indicator citywide What did we find? More than half of all curbs assessed in Oakland (51.6%) were not modern. Of the total curbs, 16.7% lacked a detectable warning surface (truncated domes), and 1.3% had cracking or damage. 145 P a g e Data: Source: Oakland Curb Ramp Inventory Dashboard, 2017, http://s3.amazonaws.com/oakbec/Dev/CurbrampInventoryDashboard_testCopy.html 146 P a g e Appendices IN THIS SECTION: Appendix A Full Framcwark Siruciure Appendix Raiia ia Scare Canversian Table Appendix [Jaia Saurccs Lisi Appendix Full Framcwarlc with Scares Appendix Racial and Eihnic by Census Traci and Eila Caac 147 Page Appendix A: Full Framework Structure 1-Economy Business Development Employment Business Ownership Affordability Prime Contracts Awarding Long-term Business Vacancy Disconnected Youth Loan Denial Rent Burden Displacement Labor Force Participation Unemployment Topic 1 Economy Financial Health Access to Healthy Financial Institutions Topic 4 Housing Essential Services Median Household income Poverty Job Quality Employment in High Wage Industries Housing Quality Living Wage Enrollment Participation in Workforce Development Programs Preschool Enrollment Topic 2 EducationEducation High School Readiness Teachers AP Course Enrollment Energy Cost Burden High Speed Internet Access Housing Habitability Complaints Complete Kitchen Facilities Overcrowding Law Enforcement Police Response Times Stops Topic 5 Public Safety A-G Completion Program Access Homeownershi p with Mortgage Eviction Notices Complete Plumbing Facilities Adult Felony Arrests Jail Incarceration Prison Incarceration High School On-Time Completion 3rd Grade ELA Proficiency Homelessness Incarceration Chronic Absenteeism Achievement Homeownersh ip Use of Force Staffing Representation Linked Learning Pathway Enrollment Attrition from Academy Suspensions Attrition from Field Training Domestic Violence Homicides Representation of Student Population Teacher Experience Teacher Turnover Community Stressors Juvenile Felony Arrests 148 P a g e 3-Public H Access to Preventive Care Child Health Mortality Acute Preventable Hospitalizations 6- Pedestrian Safety Chronic Disease Preventable Hospitalizations Soft Story Buildings Health Insurance Long-term Residential Vacancy Childhood Asthma Emergency Department Visits Civic Engagement Adopt a Drain Physical Fitness Voter Turnout SNAP Recipiency Equal Access Accommodations Infant Mortality Topic 3 Health Topic 6 Neighborhood and Civic Life Physical and Mental Health Built Environment Environmental Health Park Quality Life Expectancy Abandoned Trash Premature Death Pollution Burden Severe Mental Illness Emergency Department Visits Transportation and Infrastructure Access to a Car Substance Abuse Emergency Department Visits Bus Frequency HIV New Diagnoses Curb Ramps 149 P a g e Appendix B: Ratio to Score Conversion Table Ratio to Score Conversion Table created by the City University of New York Institute for State and Local Governance 150 P a g e Appendix C: Data Sources List Notes: American Community Survey 1-year and 5-year PUMS data was retrieved from DataFerrett, https://dataferrett.census.gov/. Oakland PUMAs extend beyond the city boundaries, see maps here: https://www.census.gov/geo/maps-data/maps/2010puma/st06_ca.html. American Community Survey 1-year and 5-year estimates were retrieved from American FactFinder, https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml. Full hyperlinks for other publicly available data sources are provided in each Indicator explanation. Economy: Business Development  Business Ownership - American Community Survey, 1-year PUMS, 2016  Prime Contracts Awarding - Oakland Contracts and Compliance Division by request, FY2015-16  Long-term Business Vacancy - U.S. Department of Housing and Urban Development Aggregated USPS Administrative Data on Address Vacancies, Quarter 3 ending September 30, 2017; American Community Survey, 5-year estimates, 2012-2016 Employment  Disconnected Youth - American Community Survey, 1-year PUMS, 2016  Labor Force Participation - American Community Survey, 1-year PUMS, 2016  Unemployment - American Community Survey, 1-year PUMS, 2016 Financial Health  Access to Healthy Financial Institutions - ReferenceUSA, data retrieved January 19, 2018; American Community Survey, 5-year estimates, 2012-2016  Median Household Income - American Community Survey, 1-year PUMS, 2016  Poverty - American Community Survey, 1-year PUMS, 2016 Job Quality  Employment in High Wage Industries - American Community Survey, 1-year PUMS, 2016  Living Wage - American Community Survey, 1-year PUMS, 2016  Participation in Workforce Development Programs - Workforce participation data from Oakland Economic and Workforce Development department by request. Data on population by race that was unemployed but in the labor force from American Community Survey, 1-year PUMS, 2016. 151 P a g e Education: Enrollment Preschool Enrollment - OUSD Data Dashboard, 2016-17  Chronic Absenteeism - OUSD Data Dashboard, 2016-17  High School On-Time Completion - OUSD Data Dashboard, 2015-16 Achievement rd  3 Grade ELA Proficiency - OUSD Data Dashboard, 2016-17  High School Readiness - OUSD Data Dashboard, 2016-17  A-G Completion - OUSD Data Dashboard, 2016-17 Program Access  AP Course Enrollment - OUSD by request, 2016-17  Linked Learning Pathways Enrollment - OUSD Data Dashboard, 2016-17  Suspensions - OUSD Data Dashboard, 2016-17 Teachers  Representation of Student Population - OUSD Fast Facts report, 2016-17  Teacher Experience - Student populations by race/ethnicity at schools from California Department of Education, 2016-17. Teacher salary step percents from OUSD Data Dashboard, 2016-17.  Teacher Turnover - Student populations by race/ethnicity at schools from California Department of Education, 2016-17. Teacher turnover at schools from OUSD Data Dashboard, baseline year 2016-17.  Public Health: Access to Preventive Care  Acute Preventable Hospitalizations - California Office of Statewide Health Planning and Development by request, 2013-3Q2015; American Community Survey, 5-year estimates, 2012-2016  Chronic Disease Preventable Hospitalizations - California Office of Statewide Health Planning and Development by request, 2013-3Q2015; American Community Survey, 5-year estimates, 2012-2016  Health Insurance - American Community Survey, 1-year PUMS, 2016 Child Health  Childhood Asthma Emergency Department Visits - California Office of Statewide Health Planning and Development by request, 2013-3Q2015  Physical Fitness - OUSD Data Dashboard, 2016-17  SNAP Recipiency - American Community Survey, 1-year PUMS, 2016 152 P a g e Mortality Infant Mortality - Alameda County Public Health Department Community Assessment, Planning, and Evaluation, with data from Alameda County vital statistics files, by request, 2014-2016  Life Expectancy - Alameda County Public Health Department Community Assessment, Planning, and Evaluation, with data from Alameda County vital statistics files, by request, 2014-2016  Premature Death - Alameda County Public Health Department Community Assessment, Planning, and Evaluation, with data from Alameda County vital statistics files, by request, 2014-2016 Physical and Mental Health  Severe Mental Illness Emergency Department Visits - California Office of Statewide Health Planning and Development by request, 2013-3Q2015  Substance Abuse Emergency Department Visits - California Office of Statewide Health Planning and Development by request, 2013-3Q2015  HIV Diagnoses - HIV in Alameda County, 2014-2016, Alameda County Public Health Department HIV Epidemiology and Surveillance Unit, March 2018  Housing: Affordability Homeownership - American Community Survey, 1-year PUMS, 2016  Loan Denial - Home Mortgage Disclosure Act, 2016  Rent Burden - American Community Survey, 1-year PUMS, 2016 Displacement  Homelessness - EveryOne Counts! 2017 Homeless Count and Survey. The 2017 Alameda County Point-in-Time Count was a community-wide effort conducted on January 30, 2017, and uses the 2015 1 year ACS data to compare to the general city population.  Homeownership with Mortgage - American Community Survey, 1-year PUMS, 2016  Notice of Evictions - Oakland, Rent Adjustment Program by request, 2016; American Community Survey, 5-year estimates, 2012-2016 Essential Services  Complete Plumbing Facilities - American Community Survey, 1-year PUMS, 2016  Energy Cost Burden - American Community Survey, 1-year PUMS, 2016  High Speed Internet Access - American Community Survey, 1-year PUMS, 2016  153 P a g e Housing Quality  Housing Habitability Complaints - Accela Housing Habitability Complaint Cases Calendar Year 2017, Oakland Planning and Building department by request; American Community Survey, 5-year estimates  Complete Kitchen Facilities - American Community Survey, 1-year PUMS, 2016  Overcrowding - American Community Survey, 1-year PUMS, 2016 Public Safety: Incarceration  Adult Felony Arrests - Felony arrest data from Oakland Police Department by request, 2017. Population data from American Community Survey, 1-year estimates, 2016.  Jail Incarceration - California Sentencing Institute, Center on Juvenile and Criminal Justice, 2015  Prison Incarceration - California Sentencing Institute, Center on Juvenile and Criminal Justice, 2015 Law Enforcement  Police Response Times - Oakland Police Department by request.  Stops - Oakland Police Department 2016-2017 Stop Data Report  Use of Force - Use of force data from Oakland Police Department by request, 2017. Population data from American Community Survey, 1-year estimates, 2016. Staffing  Representation - Sworn staff demographics from Oakland Police Department Monthly Staffing Report (dated April 4, 2018, with data as of February 28, 2018). Population data from American Community Survey, 1-year estimates, 2016.  Attrition from Academy - Oakland Police Department Monthly Staffing Reports. Ending numbers were found in Table 5b from the report dated April 4, 2018, with data as of February 28, 2018. Starting demographics were collected and aggregated from older staffing reports (2015 to present) and from data supplied by request from OPD.  Attrition from Field Training - Oakland Police Department Monthly Staffing Report (Table 12b from the report dated April 4, 2018, with data as of February 28, 2018,). Community Stressors  Domestic Violence - Domestic violence data from Oakland Police Department by request, 2017. Population data from American Community Survey, 1-year estimates, 2016. 154 P a g e   Homicides - Homicide data from Oakland Police Department by request, 2017. Population data from American Community Survey, 1-year estimates, 2016. Juvenile Felony Arrests - Felony arrest data from Oakland Police Department by request, 2017. Population data from American Community Survey, 1-year estimates, 2016. Neighborhood and Civic Life: Built Environment  Pedestrian Safety - Oakland Vision Zero Team by request, 2012-2016; American Community Survey, 5-year estimates, 2012-2016  Soft Story Buildings - OpenOakland, 2014; American Community Survey, 5-year estimates, 2012-2016  Long-term Residential Vacancy - U.S. Department of Housing and Urban Development Aggregated USPS Administrative Data on Address Vacancies, Quarter 3 ending September 30, 2017; American Community Survey, 5-year estimates, 2012-2016 Civic Engagement  Adopt a Drain - Oakland Environmental Services Division by request, as of February 2018; American Community Survey, 5-year estimates, 2012-2016  Voter Turnout - Alameda County Registrar of Voters, 2016  Equal Access Accommodations - Equal Access to Services Annual Compliance Report, FY2016-2017 Environmental Health  Park Quality - 2016 Community Report Card on the State of Maintenance in Oakland Parks, Oakland Parks and Recreation Foundation  Abandoned Trash - Service requests received by the Oakland Call Center, 2017; American Community Survey, 5-year estimates, 2012-2016  Pollution Burden - CalEnviroScreen 3.0 Maps, updated on January 9, 2017; American Community Survey, 5-year estimates, 2012-2016 Transportation and Infrastructure  Access to a Car - American Community Survey, 1-year PUMS, 2016  Bus Frequency - Oakland GIS Department by request, 2017; American Community Survey, 5-year estimates, 2012-2016  Curb Ramps - Oakland Curb Ramp Inventory Dashboard, 2017 155 P a g e Appendix D: Full Framework with Scores Theme Theme Score 1-Economy 41.8 2-Education 3-Public Health 29.0 25.8 Topic Topic score Indicator Indicator score Business Development 33.7 Employment 49.0 Financial Health 32.7 Job Quality 51.7 Enrollment 22.3 Achievement 32.0 Program Access 33.3 Teachers 28.3 Access to Preventive Care 28.7 36 31 34 35 72 40 31 34 33 54 29 72 22 25 20 20 37 39 37 62 1 1 55 29 39 26 Child Health 27.7 Business Ownership Contracts Awarding Long-term Business Vacancy Disconnected Youth Labor Force Participation Unemployment Rate Access to Healthy Financial Institutions Median household income Poverty Rates Employment in High Wage Industries Living Wage Workforce Development Programs Preschool Enrollment Chronic Absenteeism High School Completion 3rd grade Reading Proficiency High School Readiness A-G Completion AP Course Enrollment Linked Learning Pathways Enrollment Suspensions Representation of Student Body Teacher Experience Teacher Turnover Acute Preventable Hospitalizations Chronic Disease Preventable Hospitalizations Health Insurance Childhood Asthma Emergency Department Visits Physical Fitness SNAP Recipiency Infant Mortality Life Expectancy Premature Death Rate Severe Mental Illness Emergency Department Visits Substance Abuse Emergency Department Visits HIV New Diagnoses 77 33 7 Mortality Physical and Mental Health 42.0 4.7 21 1 63 19 16 1 6 156 P a g e Theme Theme Score 4-Housing 36.8 Topic Affordability 49.0 Displacement 29.0 Essential Services 36.0 Housing Quality 5-Public Safety 6-Neighborhood and Civic Life 17.3 50.6 Topic score 33.0 Incarceration 1.0 Law Enforcement 18.3 Staffing 48.3 Community Stressors 1.7 Built Environment 33.3 Civic Engagement 75.0 Environmental Health 46.7 Transportation and Infrastructure 47.3 Indicator Homeownership Loan Denial Renter Cost Burden Homelessness Homeownership with Mortgage Notices of Eviction Complete Plumbing Facilities Energy Cost Burden High Speed Internet Access Housing Habitability Complaints Kitchen Facilities Overcrowding Adult Felony Arrests Jail Incarceration Prison Incarceration Police Response Times Stops Use of Force Representation Attrition from Academy Attrition from Field Training Domestic Violence Homicides Juvenile Felony Arrests Pedestrian Safety Soft Story Buildings Long-term Residential Vvacancy Indicator score 53 40 54 1 78 8 35 38 35 40 37 22 1 1 1 48 6 1 45 63 37 3 1 1 1 67 32 80 Adopt a Drain Voter Turnout Equal Access Accommodations Park Quality Abandoned Trash Pollution Burden Access to Car Bus Frequency Curb Ramps 45 100 57 28 55 33 60 49 157 P a g e Appendix E: Racial and Ethnic Disparities by Census Tract and Zip Code Nine of the 72 Indicators in the framework measure racial and ethnic disparities based on the majority race/ethnicity of census tracts. These calculations are based on American Community Survey 5-year estimates, 2012-2016. Majority Race/Ethnicity Number of Census Tracts Total Population in Census Tracts African American 6 17,025 Asian 2 7,326 Latino 16 76,414 White 28 90,289 Non-White/Mixed 61 220,986 Four of the 72 Indicators in the framework measure racial and ethnic disparities based on zip code. Due to the low number of zip codes in Oakland, these Indicators compare zip codes in which more than 60% of the population is non-White and zip codes in which more than 60% of 158 P a g e the population is White. These calculations are based on American Community Survey 5-year estimates, 2012-2016. Non-White/White Number of Zip Codes Total Population in Zip Codes >60% Non-White 10 294,289 >60% White 3 67,735 Mixed 3 83,445 159 P a g e Acknowledgements With deep gratitude to all Dakland residents. community organizations, City Departments, staff and elected pfficialsfur their time, input and data for this report. Special thanks toVlctorla Lawson, Senlcr Research Associate CUNY INSTITUTE - Jacelyn Drummc-nd, Research Associate FOR LOCAL -J I GOVERNANCE Ia cw Ing, esearc na Department of Race and Equity ?s . re "Ref .e?p .Darlene Director .Jacclue Larralnzar, Program Analyst i City of Oakland Resiliency foice CITY OF OAKLAND .Ethan Guy, Acting City of Oakland Resiliency Officer .Ieanette Wickelgren, Intern PIDHEERED THE RDEKEFELLEH FEIUHEIATIEIH 10D CITIES 160 Page