Disaggregating Data by Race: Context Matters

Last week I had the opportunity to attend the Annie E. Casey Foundation’s conference for KIDS COUNT grantees on Race Equity and Inclusion in San Antonio, Texas. As inspiring as it was challenging, the conference exposed me to diverse perspectives and experiences of thought leaders, organizers and advocates from across the country.

 

It is by no accident that I labeled the conference as both inspiring and challenging. As plenary speaker john a powell put it, “race is like gravity” – we all have it, but few fully understand it, thought it affects us greatly. Many will admit that, until recently, race has been like gravity for a majority of child advocates in the field as well. While advocates may be well aware that outcomes are not equal for children across race, we as a community of advocates have struggled to find ways to talk about race and incorporate data disaggregated by race into our reports and communications in a way that is authentic, meaningful and historically-rooted. Other advocates have felt intense pressure to focus analysis exclusively on a more neutrally perceived indicator, socioeconomic status, and to avoid mention of race altogether. How to authentically incorporate race equity as a foundation to all child advocacy work, then, was at the core of the conference.

 

Specifically, the question on many of our minds was: Will disaggregating data by race reinforce negative stereotypes when I advocate?

 

Director of the Haas Institute for a Fair and Inclusive Society john powell addressed this concern by challenging the deficit-minded frame of focusing on disparities by race in the first place. Admitting that focusing on disparities in isolation will likely unjustly stigmatize racial groups, powell stressed how important it is to setup and frame the disaggregated data in a structural context. That is, because the differences we see in child outcomes by race were created by a long history of unequal opportunity and access, we as advocates must be explicit about naming the specific laws and policies that created this unequal “situated-ness,” as well as articulating such policies’ effects. Because structures are not neutral, but humans are “structurally blind,” I am struck by how critical it is for me as an advocate, specifically an advocate who is tasked with a sizeable amount of communications works, to take up this charge to thoughtfully and intentionally give nuanced and historical context to data as I advocate for DC children.

 

This of course sounds great in theory, but what does providing context to disaggregated data look like in practice? Representing the Texas KIDS COUNT, Dr. Frances Deviney shared one of her approaches to framing data by explaining how she framed Texas graduation rates, disaggregated by race. Alongside graphs showing graduation rates by rate, Deviney also included data in the report on turnover rates and the percentages of inexperienced teachers in these Texas schools, also disaggregated by race. This pairing of data, coupled with short, specific explanatory paragraphs, have the power to prompt report readers to think less on the individual student level and more on the structural level.

 

disaggregated

 

Plenary speaker and founders of Sojourners Jim Wallis expressed an undertone of the conference well: it’s not just about “all” children, it’s about our children. As we at DC Action continue our commitment to advocate for children with data disaggregated by race, I am inspired that this type of data, coupled with intentional framing and presentation of the data, has the power to shift mindsets to think about what resources and supports we would want for all children, as our own, so that they can thrive.

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