Race and Data: Co-creating Solutions
This is the third and final blog post in our three-part Race and Data series. In part one, we reflected on the potential dangers of over-relying on easily accessible data due to systemic racism, flawed collection, or biased analysis. In part two, we explored how thoughtful disaggregation can begin to mitigate those dangers. And finally, in part three we will explore how adding meaning to data, developed by engaging community voice, can tell an even more accurate and holistic story about what quantitative data are saying and, even more importantly, why.
Imagine you’re a government leader who would like to increase the number of people enrolling in high-quality, educational child care. Which of these issues would you want to tackle?
- Increase resources for those with non-traditional work hours
- Offer ways for people to identify facilities along their route to work
- Improve trust in child care facilities
- Do further analysis regarding utilization disparities in the demographic data
A comprehensive understanding of people and the challenges they face is essential to catalyzing long-term change. Demographic analysis of government data is critical, but it is only a piece of the puzzle. Data are most meaningful when considered in context. Stories and details illuminate influential historical and systematic barriers. Understanding this, the Iowa Family Development and Self Sufficiency (FaDSS) program aimed to improve 2Gen outcomes (think: better family well-being, the educational and health outcomes for both parent and child) by going beyond typical data points like demographics and participation. FaDSS invited families and service providers to dig into issues, identify participation challenges, and co-create solutions. In one instance, Iowa families explained that different struggles amplified their challenges to enrolling in child care, these struggles included: non-traditional work hours, transportation, and cultural differences (hint: the answer to the opening question is all of the above). Service providers appreciated that innovations must transcend silos and address issues holistically. Furthermore, there may be a need for multiple solutions because challenges may be different for different groups. Co-investigating barriers is helping Iowa to be well-informed as they improve 2Gen outcomes, avoid assigning blame to individual actors, and ensure newly-developed solutions overcome systemic barriers.
Why do we need an inclusive approach? Because community needs are diverse.
Knowing that outcomes are worse for a select demographic group does not explain the causes or inform the ways to change course. Some of the potential for quantitative analysis to lead systems change depends on the myth of homogeneous experience. People in one racial group do not experience the same barriers or struggles. Moreover, intersecting identities, such as race and ability, can lead to different or exacerbated outcomes. Government needs new ways to investigate impact.
How do we do this? Segment to customize.
One approach is to banish our assumptions and let the people who are impacted tell their stories. Empathetic research like ethnography or community-based participatory research shifts power and tries to avoid researcher bias while uncovering critical factors and systemic barriers. By understanding the diversity of people’s experiences, government can learn if a one-size-fits-all service model is leaving people behind, ignoring their needs, or stigmatizing their cultures.
The next step is to identify patterns in people’s stories and form customer segments or user personas to describe common experiences. Getting personal, by supplementing the variables available in quantitative data, improves the accuracy of analysis. Although segments or personas usually define common attitudes and behaviors to contextualize actions and reactions, a social service will also want to include intersecting aspects of need or qualities of circumstance to develop robust solutions. GovLoop’s Customer Service Playbook is a great place to find tips for building practical personas.
Learning by example: FaDSS advances 2Gen outcomes
Third Sector is currently working with FaDSS, a program of the Iowa Department of Human Rights, to launch a pilot testing 2Gen program and system improvements within Iowa’s TANF system, in order to achieve better outcomes for families. A key part of the pilot development process included family focus groups to gather input, uncover challenges and barriers, and solicit improvement ideas. During the design, implementation, and analysis of the focus groups and their feedback, the project team created participant profiles that spanned beyond race to include factors such as family structure, geography, gender, age, program participation status, primary language, and barriers such as child care and transportation access.
Those diverse profile criteria guided the focus group participant selection process and analysis of the results. Primary themes emerged, including differences in services and satisfaction according to geography (rural vs. urban) and primary barriers (e.g., child care, transportation, mental health). Using those different lenses to code the data helped account for other functional population characteristics, in addition to race, leading to meaningful recommendations that help address differing lived experiences and needs. For example, it became clear that those in rural areas were reporting a higher level of service due to the smaller caseloads of rural staff in a partner program. Another insight was that cultural factors, which were not discernible in quantitative data, were impacting high-quality child care utilization rates for many non-white families.
The focus groups are a first step in the right direction. Iowa’s data were primarily examined by geographic location and primary barrier. Third Sector project teams are now working to build off of that by further broadening the scope of factors and influences that projects take into account when creating personas and examining both quantitative and qualitative data. Projects are seeking to elevate community voice, recognizing that community members can provide context that clarifies the interpretation of quantitative data analysis.
What can we gain? Awareness and accountability
This approach to synthesizing data is just one strategy for helping systems become more inclusive and more accountable to the people they serve. The next step is strategizing with government partners about how to establish solution-oriented processes for continuous improvement that use an equity lens. For one jurisdiction, this looks like pulling in community voices by including qualitative data from clients in provider-government feedback loops. For another, government contracts are offering providers larger payments to focus on people who face more than the typical obstacles when trying to reach their goals. Another government agency is digging into data to find performance measures related to removing obstacles and that are correlated with better outcomes. We’re excited to be working aside these committed government partners who are leading the way!