HOME: Oakland Equity Indicators | Overview | Background | 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. 

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.

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 6-Neighborhood 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.

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. Click here to see the full structure of the framework with the exact Themes, Topics, and Indicators. 

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).

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 (click here for the ratio-to-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 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 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.

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.

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). Click here to see a list of all data sources.

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.