Class or Race: The Factor that Matters More for Equity

Mikaela Pitcan
Aug 24, 2016 · 4 min read

Should efforts to promote equity in schooling and criminal justice focus on economic class or race or both? Data shows stark disparity between the haves and the have nots, and as a result, there has been a shift from discussion of race inequality to discussion of class inequality in both K-12 and higher education. However, valuable information is missed when the focus of analysis is either class or race rather than a lens that considers how the two intersect.

Class has been shown to have strong predictive value in terms of educational outcomes. Because of this, researchers and politicians contend that instead of race, class should be the key factor when pursuing diversity, inclusion, and equity in schools. But when race and class are considered simultaneously in understanding school disciplinary practices, educational outcomes, and upward mobility, data indicates that race and class intersect to produce unique experiences.

Stemming from the “Broken Windows” criminal justice theory that was applied to policing in New York City when Giuliani was mayor, in recent decades schools have implemented zero tolerance policies focused on swift responses to rule violations under the assumption that this punishment will deter future rule breaking. “Broken Windows” policing deemed markers of poverty undesirable and promoted severe punishment for minor offenses that overwhelmingly impacted low-income communities. But deeper analysis showed that not only were those impacted low-income, but they were also overwhelmingly people of color.

People of color have long been aware of differential treatment by authorities. But this collective awareness goes unaddressed without data to back it up. Findings of an investigation of the Baltimore Police Department showed that black people were frequently arrested for insufficient causes, were stopped at disproportionate rates by police, and that police used excessive force against them. These findings are mirrored in data on school discipline. A US Department of Education report showed that although black students represent 16% of all students, they represent between 32% and 42% of suspensions and expulsions, 27% of referrals to law enforcement, and 31% of students that are arrested for a school-related offense.

CC BY 2.0-licensed scales by Tom Magliery.

There are several factors that may contribute to disproportionate rates of discipline and criminalization in schools for low-income students and students of color; two of which could be race-based stereotypes and the presence of law enforcement in schools. Educators are more likely to view Black students as loud, unruly, and threatening than White students, and educators’ internal beliefs about students affect the way they treat them. These biases manifest in increased rates of discipline and harsher discipline for students of color than their White counterparts. Furthermore, the presence of police in schools may increase students’ risk of having an encounter with law enforcement. Criminal justice security measures are more likely to be used in schools with high rates of poverty than in schools where the majority of children are not low-income. These low-income schools also serve student populations that are predominantly students of color.

These school disciplinary practices have been linked to poor educational outcomes, such as risk of dropping out in high school. Economic class has also been linked to risk of dropping out, with research showing that low income students drop out at higher rates than their high income counterparts in both K-12 and higher education. But research that only accounts for class in its analysis of dropout rates fails to reveal important differences related to race and ethnicity. In 2014, 65% of Black children, 62% of Hispanic/Latino children, and 62% of Native American children lived in low-income families as opposed to 31% of White children.

Just as in criminal justice, vulnerability to unjust practices in schools is not a simple matter of one’s class or race in isolation. For someone who is both black and poor, poverty is not the only barrier to school success. In real life, these aspects of a person’s identity interact and lead to unique vulnerabilities that can only be understood by considering different combinations of these identities. Talking about class instead of race might feel less abrasive but discussing inequality in terms of class while neglecting race and other aspects of identity (sexuality, gender, etc.) subtly erases the history of economic disenfranchisement that has impacted these communities in the United States.

Discussions of class and race attempt to figure out which one should be prioritized over the other, rather than diving deeper into how the two intersect. This is reflected in research that shows data based on income or data based on race, but rarely breaks up these categories to account for differences across individuals who belong to multiple, overlapping groups. The question remains — how are these findings the same or different when interactions between race and class are accounted for? How does the experience of a student who is low-income, of color, and gay or lesbian differ from that of a low-income student of color who is straight? How does the experience of a low-income student of color, differ from that of a low-income White student?

Nuance is complicated and complexity can be daunting, but what will be found when complexity is confronted and acknowledged? In the age of “big data,” researchers have the opportunity to investigate these intersecting identities in new and profound ways. Perhaps these massive amounts of data can be used to understand how these factors combine to impact outcomes, shifting away from practices that focus on an attempt to isolate the effects of one aspect of identity or another.

Enabling Connected Learning

data in education and learning

Mikaela Pitcan

Written by

Research Analyst at Data & Society Research Institute, Counseling Psychology Doctoral Candidate, & Mental Health Clinician.

Enabling Connected Learning

data in education and learning