County-level Bias Predicts Disciplinary Disparities in two Nationally Representative Datasets

• In Event: 1-188 - Investigating and Ameliorating Teachers’ Racialized Perceptions and the Race Disparities in Students’ Schooling

Integrative Statement

Black students are overrepresented in school suspensions by significant margins which has caused a national call for solutions. In the 2013-2014 school year, Black students were almost 4 times more likely to receive one or more out-of-school suspensions compared to White students, corresponding to 1.1 million suspended Black students (Office of Civil Rights, 2016). These disparities arise over time (Okonofua & Eberhardt, 2015) and contribute to the systematic exclusion of Black students from the learning environment, pushing them towards incarceration, i.e., the school-to-prison pipeline. Biases within geographic regions are predictive of observable racial disparities in health care (Leitner, Hehman, Ayduk, & Mendoza-Denton, 2016) and police use of lethal force (Hehman, Flake, Calanchini, 2018). Regional biases have yet to be tested with discipline disparities.
The present investigation tests if county-level implicit bias 1) predicts teachers’ discipline decisions for misbehaviors by Black or White students in an experimental paradigm and 2) predicts the percent of Black students disciplined in schools using nationwide archival data in two preregistered studies. We hypothesized that county-level implicit bias would predict increased racial disparities in discipline and the proportion of Black students disciplined.
A geographically diverse sample of teachers (N=624) read about a student in class whose name was either stereotypically Black or White. Teachers rated how severely they would discipline the student after 2 misbehaviors and how likely it was that the student was a troublemaker. Responses from Project Implicit were used to assess county-level bias. To test if county-level bias predicts teachers’ responses to students, we used General Estimating Equations (GEEs) in which we predicted teachers’ responses by county-level bias, and responses were nested by county. Implicit bias predicted racial disparities between Black and White students in discipline severity (implicit bias: b=10.97, p=.006; explicit bias: b=1.59, p=.025) and troublemaker ratings (implicit bias: b=10.40, p=.005; explicit bias: b=1.79, p=.002). Black students were rated more negatively as county-level bias increased, while for White students, county-level bias had no impact.
We tested if this effect generalizes systematically across schools by using data from the Department of Education. Data from 3 recent school years (N=82,262 schools) were included. Implicit bias predicted the percent of Black students given in-school suspensions, expulsions, and referrals to law-enforcement, even when controlling for covariates (all ps<.05).
Using two nationally representative data sets, our hypotheses were supported. Implicit bias predicted racial disparities in discipline severity between White and Black students using experimental methods and the proportion Black students were disciplined in school records. By testing this question with a multimethod approach, we have a more thorough understanding for how county-level bias permeates into a classroom. Black students in areas with higher county-level bias are more likely to receive punishment that removes them from the classroom. By receiving fewer days of instruction, students’ ability to learn, grow, and become contributing members of society is hindered. This informs theory by showing how contexts impact punishment and informs policy be enhancing our understanding of factors relevant to race disparities in school discipline across the United States.

Authors

• Shoshana Jarvis, Berkeley
  Presenting Author

• Chrisite Ngo, University of California at Berkely
  Non-Presenting Author

• Jennifer Eberhardt, Standford University
  Non-Presenting Author

• Jason Okonofua, University of California, Berkeley
  Non-Presenting Author