Data Disaggregation, Let’s Taco About It

Editor’s Note: This week we host a piece by Carlos Sánchez Huizar who explores Hispanic/Latinx disaggregated data. He writes about two of my favorite topics — disaggregated data and tacos. As a quick reminder race is the broader group and ethnicities are the smaller groups under it. After you read this post, revisit this post about Asian disaggregated data


By Carlos Sánchez Huizar

tj-dragotta-wGm2-XxaDCY-unsplash

Photo by TJ Dragotta on Unsplash

According to the American Council on Education (ACE)’s Race and Ethnicity in Higher Education report (2016), “as the U.S. population increased, the nation became more racially and ethnically diverse” (para. x). So, what does it mean to have a nation that has grown in diversity? On the positive side, it means you get to decide whether you want tacos, Korean BBQ, or a gyro for lunch. Beyond food, how much do we really know about the diversity of our nation? Let’s take the Hispanic/Latinx[1] population as an example. According to the 2010 Census, the U.S. has a total population of just over 3 million, of which 16.3% are Hispanics/Latinxs. Besides being the second-largest racial-ethnic population after white, ACE concluded that Hispanics/Latinxs, “had the largest increase in their total share of the population, increasing from 11.1 percent in 1997 to 18.0 percent in 2017” (ACE, 2016, para. 1). In other words, over a span of 20 years, Hispanics/Latinxs have had a 6.9% population increase. Do you think tacos represent the entire 16.3% of this population? The answer is unequivocally, no. Empanadas, arepas, and pupusas are also representative of the Hispanic/Latinx community and just as good as the tacos from your favorite lonchera.[2]

The wide range of Hispanic/Latinx food is mirrored by the significant and growing population of different communities among Hispanics/Latinxs in the U.S. In fact, the 2010 Census has reported of the 16.3% Hispanics/Latinxs, 10% are Mexican, 1.5% are Puerto Rican, 0.6% are Cuban, 0.5% are Dominican, 0.5% are Salvadoran, 0.3% Guatemalan, 0.2% Honduran, and the list continues with Nicaraguans, Panamanians, Argentinians, Bolivians, Chileans, Colombians, Ecuadorians, Venezuelans, Spanish, and other Hispanics/Latinxs. These data point out the increasing diversity is not only happening across racial groups but also within them. Beyond capturing within-group diversity, how is this detailed data collection significant?

The U.S. Census has stated that race and ethnicity data collection is, “critical to policymakers who use the information to make funding decisions that affect educational opportunities, assess equal employment practices, and ensure equal access to health care for everyone” (U.S. Census Bureau, 2010). Data collection enables one to answer relevant questions and evaluate outcomes. For example, how are educational gaps affecting the Hispanic/Latinx community? As diversity increases, equity gaps for ethnic sub-groups become more difficult to follow. Data collection gives us the opportunity to identify these gaps in people, school systems, and districts. For instance, data disaggregation——the breaking down of large racial categories into smaller ethnic sub-groups——shows us although the majority of Hispanics/Latinxs living in the U.S. are Mexican, only 9.93% (ACS, 2011-2013) of Mexicans have obtained a bachelor’s degree compared to 50.9% of Venezuelans who represent 0.1% (ACS, 2011-2013) of Hispanics/Latinxs. In other words, data disaggregation is allowing us to better understand and track the complexity of racial heterogeneity——diversity within racial groups——as well as the educational disparities among ethnic sub-groups. Data disaggregation gives us the ability to distinguish which ethnic groups within the Hispanic/Latinx population need more attention and resources. Understanding the disparities between ethnic groups is critical to making decisions that are socially just.

As the use of data disaggregation becomes more common, engaging community leaders and constituents, as well as data experts—in better data collection (adding more categories to race-ethinic categories)—is fundamental to advancing equity in education. In other words, those who know have the responsibility to teach those who do not know. It is important for marginalized communities to be part of data dialogue, as they:

[H]ave the critical context expertise that can lead to meaningful insights and provide critical input into the design of social change effort…communities of color directly to unearth the root causes of inequalities and source potential solutions to authentically unpack the “why” behind disparities revealed by disaggregated data (Arias, 2015, p. x).

In order to develop initiatives for more equitable educational opportunities, work must be grounded in the use of data disaggregation and the participation of communities; community-based organizations, districts, state agencies, and data experts. Data disaggregation helps us understand the circumstances of our population. It offers the opportunity to revise our educational infrastructure, as well as inform policy makers on decisions grounded in equity. In brief, disaggregating data offers a more precise approach to identifying differences between ethnic sub-groups.

What are the next steps in the data disaggregation movement?

First, the discussion about data disaggregation must expand beyond those who hold knowledge (e.g. districts, data experts, policy makers, etc.). Not only is data disaggregation as a discussion necessary, there is a need to amplify the significance of data disaggregation as a common practice across communities, in schools, and within families. Next, inequities cannot be addressed if they remain unseen. Thus, we must re-evaluate the collection and use of data. What is the landscape of data? How can it be improved? Finally, we have to apply the findings that emerge from using disaggregated data to address actual gaps. It seems like a complex and tedious process, but it may not even be as complicated as topping off your taco with the right amount of cilantro, onions, limón, and salsa. It requires some commitment and attention to detail, but you will soon be able to garnish your own tacos and arrive at a solution with a bit of practice.


Carlos Sánchez HuizarCarlos Sánchez Huizar is a graduate student in the MA in Student Affairs Administration at Lewis & Clark Graduate School of Education and Counseling.

[1] Not all Latinx(a,o) identify as Hispanic and not all Hispanic identify as Latinx. Hispanic/Latinx as a term acknowledges ties, changes, adaptation, invention and reinvention of different ethinic generations within the group.

[2] Food-truck/taco-truck


References

American Council on Education (ACE). (2016). Educational Attainment of Adults Ages 25 and Older, by Race and Ethnicity: 2017 [Data file]. Retrieved from https://www.equityinhighered.org/indicators/u-s-population-trends-and-educational-attainment/educational-attainment-by-race-and-ethnicity/

U.S. Census Bureau (2010), Race and Hispanic or Latino Origin: 2010.

Sebastian Arias, J. (2015, April 14). “Working with Communities to Advance Racial Equity and Eliminate Disparities”. Livingcities.org. Retrieved from https://www.livingcities.org/blog/812-working-with-communities-to-advance-racial-equity-and-eliminate-disparities


Thank you to our Patreon subscribers who help to keep the blog going: Adrienne, Aimie,Ali, Aline, Alissa, Amy, Amy R., Andrea, Angie, Anh-Chi, Annie, Annie G.,  Ashlie, Ben, Betsy, Brooke, Brian, C+C, Calandra, Carolyn C., Carolyn M., Carrie, Carrie S., Casey, Chandra, Chelsea, Chicxs Happy Brownies, Claudia, Cierra, Clark, Colleen, Crystal, Dan, Danya, Darcie, Dawnnesha, Dean, Debbie (x2), Denise, Denyse, Donald, Edith, Elena, emily, Erica (2), Erica R.B., Erin, Evan, eve, Freedom, Greg, Hannah, Heather, Heidi, Heidi and Laura, Heidi S., Jake, Janis, Jean, Jena, Jennet, Jennifer C., Jennifer M., Jennifer T., Jessica, Jessica G., Jessie, Jillian, Jody, John, Julia, Julie Anne, K.T., Kari, Karen, Katheryn, Kathi, Katie, Keisha, Kelli, Kristen, Kristen C., Kristen D., Kumar, Laura, Laurel, Laurie, Leah, Lisa, Lisa C., Liz, Lori, Lynn, Lynn D., Makeba, Marc, Maria, Matthew, Maura, McKenzie, Megan, Melissa, Michael, Michelle, Mikaela, Mike, Milo, Minesh, Miranda, Miriam, Misha, Molly, Natasha, Nathan, Nathan H., Nicole, Norah, Norrie, Paola, Patrick, Priya, Rebecca, Risa, Rise Up for Students, Robin, Ruby, Sarah, Sarah S., Sean, SEJE Consulting, Shannon, Shaun, Shawna, Shelby, Stephanie, Stephanie O., Stephanie S., Susan, Tana, Tania, Tara, Terri, Tracy, and Vivian. If I missed anyone my apologies and thank you for your support. Support the blog by becoming a Patreon supporter.

If you subscribe to the blog, thank you. Please check fakequity.com for the most up to date version of the post. We often make grammatical and stylistic corrections after the first publishing which shows up in your inbox. Please subscribe, the sign-up box on the right sidebar (desktop version).