Governments collect a lot of data — program participation, event attendance, advisory board applicants, satisfaction with public services, hiring and promotions, contractors and vendors, and more. How these data are collected and used can determine whether the jurisdiction is advancing racial equity or perpetuating the white supremacist status quo.
We know that many public institutions in the United States were systematically created to advantage white groups, resulting in long-term inequities for communities of color. As Rothstein writes in The Color of Law(2017): “Without our government’s purposeful imposition of racial segregation, the other causes—private prejudice, white flight, real estate steering, bank redlining, income differences, and self-segregation—still would have existed but with far less opportunity for expression.” Thus, we cannot advance racial equity without the transformation of government into an inclusive democracy. Purposeful collection and use of data can help governments understand their role and how they could track their performance, and move forward to advance equity. However, governments may not have expertise or capacity to use data to inform policy and services (Lovelace & Shah, 2016). Often, data are not disaggregated, by race/ethnicity and income, to look at how services are provided for different populations. Data that are not disaggregated mask important underlying patterns that contribute to adverse impacts (Brawley, 2016; Ford & Russo, 2016; Gomez et al., 2010; Yoon & Gentry, 2009).
Key examples of government policies that created and perpetuated segregation and racism are Urban Renewal and Redlining. Starting in the 1950s, cities across the country cleared areas designated as “slums” as part of the federally-subsidized urban renewal program. The Hayti neighborhood in Durham, NC is a prime example of this practice. Redlining emerged as an outgrowth of the federal government’s attempt to stem the tide of residential foreclosures during the Great Depression (De Marco & Hunt, 2018). As part of its charge to stabilize and secure home mortgages, the newly-created Home Owners’ Loan Corporation (HOLC) was tasked in the late 1930s with producing residential maps that showed lending risk by neighborhood in cities across the United States. HOLC recruited local representatives to evaluate neighborhoods for credit worthiness using a range of criteria, including race, immigration status and class. This map of Durham provides an excellent, interactive visual. The impact of redlining is felt through to today.
A map of Los Angeles was superimposed with the map of the City’s pedestrian deaths revealing that the overwhelming majority of pedestrian deaths are taking place in formerly redlined neighborhoods because of racially disproportionate investments in infrastructure and reliance on other modes of commuting than cars among communities of color. This is why data are so important!
Not all data are high quality data and there is a risk in using bad data. Also, there could be many hidden biases in the data. So, what are the criteria for good data and how do we avoid bias? Here are some thoughts:
- Data collection should be comprehensive – every possible demographic category should be included. You can always collapse categories later but you cannever go the other direction and separate the data into different categories.
- Questions should be clear and at an appropriate reading level. For example, avoid double-barreled questions: “Were the services accessible and helpful?” These should be two questions. Pilot for clarity.
- Use terms that are culturally appropriate. For example, the question “How many members are there in your family?”can lead to responses that might not be comparable across demographic groups because each racial, ethnic, and cultural group defines “family” differently.
Frequently, local governments have to collect new data because existing data may not be sufficient to assess where inequity exists in the government’s policies and operations, and the degree to which progress toward equity has been made.
There are many ways to collect that new data, including focus groups, interviews, and surveys. Each has strengths and weaknesses when the goal is to advance equity. Focus groups are appropriate when sensitive data are not being collected because you can get many responses at one time and one participant’s response may prompt a memory or experience for another participant. However, facilitators need to be aware of who is in the room for potential power differences. For example, you may not want both public housing residents and housing providers in the same group. Interviews are a way to collect data on more sensitive topics and respond to lower literacy levels. They allow for probing for additional information. Yet, staff selecting interview participants need to be clear about who was selected and why. The interviewers must be well trained to be culturally sensitive and respectful.
Surveys can include a larger number of people and cover a larger geographic area — especially if it is an online survey — compared to focus groups and interviews. However, this method may make it difficult for people with low literacy and limited access to technology, and who are distrusting of surveys and research when they don’t know how the findings will be used. Each of these methods can be harmed by poor outreach. If interviews are only offered in person or focus groups are only conducted during business hours they may be biased against those who can’t take time off from work. Moreover, equitable community engagement strategiesare needed to maximize response and center voices that are seldom heard.
Finally, what you do with the data you’ve collected is key. Data analysis should include disaggregating by key community characteristics, if appropriate:race/ethnicity, language, gender, income. However, caution must be taken with disaggregation to avoid inadvertent identification if sample sizes are small. Results should be reported back to communities – this is a toolkit step! Then you can ask the community to help make meaning of results and involve the community in creating an action plan. And last, provide regular updates and avenues for continued involvement.
Want to learn more or bring a workshop to your community? Contact us!
* Based on a workshop presented at the Government Alliance on Race and Equity (GARE) Annual Meeting in Albuquerque, New Mexico, in April 2019
Brawley, O. W. (2016). Some thoughts on health surveillance data, race, and population categorization. CA: a cancer journal for clinicians, 66(3), 179-181.
Ford, D. Y., & Russo, C. J. (2016). Historical and legal overview of special education overrepresentation: Access and equity denied. Multiple Voices for Ethnically Diverse Exceptional Learners, 16(1), 50-57.
Gomez, S. L., Quach, T., Horn-Ross, P. L., Pham, J. T., Cockburn, M., Chang, E. T., & Clarke, C. A. (2010). Hidden breast cancer disparities in Asian women: disaggregating incidence rates by ethnicity and migrant status. American journal of public health, 100(S1), S125-S131.
Lovelace, K. A., & Shah, G. H. (2016). An Iterative, Low-Cost Strategy to Building Information Systems Allows a Small Jurisdiction Local Health Department to Increase Efficiencies and Expand Services. Journal of Public Health Management and Practice, 22(Suppl 6), S95.
Yoon, S. Y., & Gentry, M. (2009). Racial and ethnic representation in gifted programs: Current status of and implications for gifted Asian American students. Gifted Child Quarterly, 53(2), 121-136.
Rothstein, R. (2017). The color of law: A forgotten history of how our government segregated America. Liveright Publishing.