The Image of Our Community: Disaggregate Tufts’s Racial Data by Ethnicity
I happened upon data disaggregation because I felt marginalized during class discussions on race that perpetuated the Model Minority Myth. These discussions invalidated my experience as a person of color, and I set out to research what it meant to be Asian American in a foundationally White institution. My research fundamentally changed how I view race. I realized that the perpetuation of the Model Minority Myth is bolstered by statistics that conceal variations in data for the Asian racial group. It had never occurred to me that the way we present data could be so easily manipulated to be inequitable and have negative ramifications on marginalized people. My faith had always been in data and numbers, having been taught from a young age that data is the definite truth. But data is power––it can be manipulated and abused.
Anyone navigating through the “Diversity Data” on the Tufts Admissions page will not find a wholly accurate representation of the student body. For prospective students like myself who were not able to visit campus, the racial data does not give a holistic picture of the Tufts community.
What does diversity mean if the data collected on our admissions website generalizes the range of identities, experiences, and histories of Tufts students across racial groups? Tufts creates a misleading image of “diversity” by only recognizing the racial makeup of the student body; the university cannot be held accountable for accepting a class of “diverse” students if no one knows who is here in the first place. Barriers to access in higher education are not racially monolithic. To create a more comprehensive picture of the student body, Tufts needs to disaggregate—break down—its racial data by ethnicity.
Demographic data disaggregation is a powerful tool for dismantling systemic racism because it collects data by ethnicity, in addition to the six major racial categories. For example, instead of only having an option to report race as “Latino,” data disaggregation allows reporting on ethnicities including but not limited to: Mexican, Puerto Rican, Cuban, Salvadoran, Dominican and Colombian. All US Census data is disaggregated, demonstrating how important the tool is in allocating resources. In 2015, 132 federally funded programs used US Census data to allocate more than 675 billion dollars of resources. However, the US Census happens only every 10 years. To account for how infrequent US Census data is collected, data disaggregation must be implemented into statewide data collection if state resources are to effectively support and adapt to the needs of changing communities.
According to the Asian Pacific Islanders Civic Action Network, an organization that works to promote equity in API communities in Massachusetts, data disaggregation practices ensure that the data collected can accurately “address social, economic, or educational inequalities which persist but may be invisible in data analyzed only through broad racial categories.” When data is collected and organized solely by race, it effectively hides the data of ethnic minorities who get lost in the averages.
Collecting racial data often marginalizes specific ethnic groups and their experiences because identities are generalized solely to race. This is especially prominent in the Asian American community. For example, government data reporting educational and economic outcomes provided data that portrayed Asian Americans as needing little social or governmental support on average. However, when the racial data is disaggregated to report ethnic data, it shows specific ethnic groups fall well below certain averages. When the data is not disaggregated, these ethnic groups are disregarded, and there is no potential for allocating additional resources.
For example, the average test score data collected in the early 2000s from the Massachusetts Comprehensive Assessment System showed that Asian Americans are academically “successful” based on their high test results. However, when the demographics of school districts were closely analyzed, communities with more Southeast Asians had lower academic success than communities with higher proportions of Chinese and South Asian students. According to University of Wisconsin Professor Stacy J. Lee, this can be attributed to the fact that the greatest disparity exists in educational outcomes within the Asian racial group, meaning that racially-derived averages can be deceiving for ethnicities that report data that is an outlier in a data set.
Massachusetts is taking a stand against this abuse of power by making data inequity a priority. Within the past year, there has been a push to disaggregate racial data on the state level, following activism from minority communities. A public hearing for Bill H.2681 took place two weeks ago, and demanded racial data disaggregation for all six racial groups. In Massachusetts, current marginalized demographics such as “Southeast Asians including Vietnamese and Cambodians; Black and African American including Haitian, Cape Verdean, Ethiopians, and Somalians; Latino groups including Puerto Rican, Salvadoran, Dominicans, and Colombians” would immediately benefit from data disaggregation. In doing so, these communities and their diverse and unique experiences would gaina place in statewide data, after being lost and forgotten in the numbers of traditional racial data collection.
The passage of this Bill would not only provide more equitable opportunities on a large scale through fund allocations in marked areas of need, but also promote equitable access to opportunities by offering services and support in the languages spoken by the people of given communities. These services include but are not limited to healthcare accessibility in native languages, bilingual ballots, and educational support in schools where there may be language barriers.
323 students are enrolled in the class of 2023 from Massachusetts, but how many of those students, if any, are from the marginalized communities detailed above? Tufts, on a micro-level, allocates resources in the form of admissions and aid it gives to students. Elite higher education institutions like Tufts may find it in their interest to not disaggregate racial data, as practicing disaggregation could further exaggerate the lack of diversity there is within the university. Reporting the demographics of current students would likely give us a better picture of accessibility to higher education in America, and how the highest barriers to access affect certain ethnic minorities.
It takes capital and resources to disaggregate racial data, but if Tufts is committed to transparency and inclusion in the admissions process and the image it projects to the public, then data disaggregation is imperative to implement. If Tufts were to fully disaggregate its racial data, it would mark the university’s accountability for the lack of diversity on campus, and furthermore, make a commitment to a future in which Tufts is more accessible to people of all different backgrounds.
Ethnicity is just one variable; it does not wholly define a person’s lived experience. But when attempting to dismantle racism, choosing to not disaggregate racial data makes important resources even more inaccessible as ethnic minorities are left underrepresented. Even with the right terminology and sensitivities when talking about race, meaningful structural changes cannot be made if statistical variations within the community are unknown.
If we are to let institutions further craft the image of our communities, then it will only be at the detriment of those most powerless and ignored. To make space for all people in society, and create a future in which there is equitable access to resources, then we must first acknowledge who is a part of these communities—data disaggregation is the first step.