Abstract
Anti-black bias is an important focal point in conversations about the sources of racial inequality in schools. Much of the empirical research on this issue has focused on the racial biases of individual teachers, finding that racial inequality in student outcomes is generally worse when teachers have more racial bias. Less is known, however, about how racial inequality in schools relates to anti-black biases that play out at a larger scale within communities. This study begins to fill this gap by examining the relationship between county-level estimates of racial bias and black-white test score gaps in U.S. schools. Data from over 1 million respondents from across the United States who completed an online survey of explicit and implicit racial attitudes were combined with data from the Education Opportunity Project covering over 300 million test scores from U.S. schoolchildren in grades 3 through 8. Results indicated that counties with higher levels of racial bias had larger black-white test score disparities. The magnitude of these associations was on par with other widely accepted predictors of racial test score gaps, including racial gaps in family income and racial gaps in single parenthood. This study also found that the observed relation between collective rates of racial bias and racial test score gaps was largely accounted for when controlling for between-school segregation and racial gaps in discipline, gifted assignment, and special education placement. This pattern is consistent with a theoretical model in which collective rates of racial bias relate to educational opportunity through sorting mechanisms that operate both within and beyond schools.
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Notes
For critiques of the reliability and predictive validity of implicit bias, see Greenwald et al. (2009) and Arkes and Tetlock (2004). Moreover, prior work has suggested that measures of implicit racial bias can proxy an individual’s awareness that a social group faces discrimination rather than the degree to which an individual harbors anti-black bias (see Uhlmann et al., 2006). Nevertheless, substantive conclusions from the current study are consistent across implicit and explicit measures of racial bias, indicating that findings transcend any single concern about the measurement and operationalization of implicit racial bias used in this study.
Collective rates of explicit racial bias were unrelated to racial disparities in gifted assignment; therefore, racial gaps in gifted assignment were not a significant mediator of the relation between collective rates of explicit racial bias and racial test score disparities. See Tables A.1–A.6 in the Appendix for complete results from mediation models.
References
Arkes, H. R., & Tetlock, P. E. (2004). Attributions of implicit prejudice, or “would Jesse Jackson ‘fail’ the implicit association test?” Psychological Inquiry, 15(4), 257–278. https://doi.org/10.1207/s15327965pli1504_01
Ashenfelter, O., Collins, W. J., & Yoon, A. (2006). Evaluating the role of Brown v. Board of Education in school equalization, desegregation, and the Income of African Americans. American Law and Economics Review, 8(2), 213–248. https://doi.org/10.1093/aler/ahl001
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research. Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology. https://doi.org/10.1037/0022-3514.51.6.1173
Blanton, H. (2015). Toward a meaningful metric of implicit prejudice. Journal of Applied Psychology, 100(5), 1468. https://doi.org/10.1037/A0038379
Bui, S. A., Craig, S. G., & Imberman, S. A. (2014). Is gifted education a bright idea? Assessing the impact of gifted and talented programs on students. American Economic Journal: Economic Policy, 6(3), 30–62. https://doi.org/10.1257/pol.6.3.30
Card, D., & Giuliano, L. (2016). Universal screening increases the representation of low-income and minority students in gifted education. Proceedings of the National Academy of Sciences of the United States of America. https://doi.org/10.1073/pnas.1605043113
Carter, P. L., Skiba, R., Arredondo, M. I., & Pollock, M. (2017). You can’t fix what you don’t look at. Urban Education, 52(2), 207–235. https://doi.org/10.1177/0042085916660350
Chetty, R., Hendren, N., Kline, P., & Saez, E. (2014). Where is the land of opportunity? The geography of intergenerational mobility in the United States. The Quarterly Journal of Economics, 129(4), 1553–1623. https://doi.org/10.1093/qje/qju022
Chetty, R., Hendren, N., Jones, M. R., & Porter, S. R. (2020). Race and economic opportunity in the United States: An intergenerational perspective. The Quarterly Journal of Economics, 135(2), 711–783. https://doi.org/10.1093/qje/qjz042
Chin, M. J., Quinn, D. M., Dhaliwal, T. K., & Lovison, V. S. (2020). Bias in the air: A nationwide exploration of teachers’ implicit racial attitudes, aggregate bias, and student outcomes. Educational Researcher. https://doi.org/10.3102/0013189X20937240
Cook, L. (2014). Violence and economic activity: Evidence from African American patents, 1870–1940. Journal of Economic Growth, 19(2), 221–257.
Copur-Gencturk, Y., Cimpian, J. R., Lubienski, S. T., & Thacker, I. (2019). Teachers’ bias against the mathematical ability of female, black, and hispanic students. Educational Researcher. https://doi.org/10.3102/0013189X19890577
Dovidio, J. F., Kawakami, K., & Gaertner, S. L. (2002). Implicit and explicit prejudice and interracial interaction. Journal of Personality and Social Psychology, 82(1), 62–68. https://doi.org/10.1037/0022-3514.82.1.62
Eberhardt, J. (2019). Biased: Uncovering the hidden predudice that shapes what we see, think, and do. Penguin Random House.
Fahle, E., Shear, B., Kalogrides, D., Reardon, S., Chavex, B., & Ho, A. (2019). Stanford Education Data Archive Technical Documentation. (Version 3.0). Retrieved from http://purl.stanford.edu/db586ns4974
Fiscella, K., & Kitzman, H. (2009). Disparities in academic achievement and health: The intersection of child education and health policy. Pediatrics, 123(3), 1073–1080. https://doi.org/10.1542/PEDS.2008-0533
Gawronski, B., & Creighton, L. A. (2013). Dual process theories. In D. E. Carlston (Ed.), The Oxford handbook of social cognition (pp. 282–312). Oxford University Press.
Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464–1480. https://doi.org/10.1037/0022-3514.74.6.1464
Greenwald, A. G., Poehlman, T. A., Uhlmann, E. L., & Banaji, M. R. (2009). Understanding and using the implicit association test: III. Meta-analysis of predictive validity. Journal of Personality and Social Psychology, 97(1), 17–41. https://doi.org/10.1037/a0015575
Gregory, A., Skiba, R. J., & Noguera, P. A. (2010). The achievement gap and the discipline gap. Educational Researcher, 39(1), 59–68. https://doi.org/10.3102/0013189X09357621
Guryan, J. (2004). Desegregation and black dropout rates. American Economic Review, 94(4), 919–943. https://doi.org/10.1257/0002828042002679
Hehman, E., Flake, J. K., & Calanchini, J. (2018). Disproportionate use of lethal force in policing is associated with regional racial biases of residents. Social Psychological and Personality Science, 9(4), 393–401. https://doi.org/10.1177/1948550617711229
Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005). A meta-analysis on the correlation between the implicit association test and explicit self-report measures. Journal of Personality and Social Psychology, 31(10), 1369–1385. https://doi.org/10.1177/0146167205275613
Imai, K., & Yamamoto, T. (2010). Causal inference with differential measurement error: Nonparametric identification and sensitivity analysis. American Journal of Political Science, 54(2), 543–560. https://doi.org/10.1111/j.1540-5907.2010.00446.x
Jacoby-Senghor, D. S., Sinclair, S., & Shelton, J. N. (2016). A lesson in bias: The relationship between implicit racial bias and performance in pedagogical contexts. Journal of Experimental Social Psychology, 63, 50–55. https://doi.org/10.1016/j.jesp.2015.10.010
Jennings, J. L., Deming, D., Jencks, C., Lopuch, M., & Schueler, B. E. (2015). Do differences in school quality matter more than we thought? New evidence on educational opportunity in the twenty-first century. Sociology of Education, 88(1), 56–82. https://doi.org/10.1177/0038040714562006
Johnson, R. C. (2011). Long-run impacts of school desegregation and school quality on adult attainment. NBER Working Paper. https://doi.org/10.1017/CBO9781107415324.004
Kaufman, R. (2010). Race, gender, and the labor market: Inequalities at work. Lynne Rienner Publishers.
Ladson-Billings, G., & Tate, W. (2006). Toward a critical race theory of education. In A. Dixson & C. Rousseau (Eds.), Critical race theory in education: All God’s children got a song. Routledge.
Leitner, J. B., Hehman, E., Ayduk, O., & Mendoza-Denton, R. (2016). Blacks’ death rate due to circulatory diseases is positively related to whites’ explicit racial bias. Psychological Science, 27(10), 1299–1311. https://doi.org/10.1177/0956797616658450
McKown, C., & Weinstein, R. S. (2008). Teacher expectations, classroom context, and the achievement gap. Journal of School Psychology, 46(3), 235–261. https://doi.org/10.1016/j.jsp.2007.05.001
Milner, H. R. (2015). Rac(e)ing to class: Confronting poverty and race in schools and classrooms. Harvard Education Press.
Okonofua, J. A., Paunesku, D., & Walton, G. M. (2016). Brief intervention to encourage empathic discipline cuts suspension rates in half among adolescents. Proceedings of the National Academy of Sciences of the United States of America, 113(19), 5221–5226. https://doi.org/10.1073/pnas.1523698113
Oswald, F. L., Mitchell, G., Blanton, H., Jaccard, J., & Tetlock, P. (2015). Using the IAT to predict ethnic and racial discrimination: Small effect sizes of unknown societal significance. Journal of Personality and Social Psychology, 108(4), 571. https://doi.org/10.1037/PSPA0000023
Parekh, G., & Brown, R. S. (2019). Changing lanes: The relationship between special education placement and students’ academic futures. Educational Policy, 33(1), 111–135. https://doi.org/10.1177/0895904818812772
Park, D. K., Gelman, A., & Bafumi, J. (2004). Bayesian multilevel estimation with poststratification: State-level estimates from national polls. Political Analysis, 12(4), 375–385. https://doi.org/10.1093/pan/mph024
Pearman, F. A., Curran, F. C., Fisher, B., & Gardella, J. (2019). Are achievement gaps related to discipline gaps? Evidence from national data. AERA Open. https://doi.org/10.1177/2332858419875440
Quillian, L. (2003). How long are exposures to poor neighborhoods? The long-term dynamics of entry and exit from poor neighborhoods. Population Research and Policy Review, 22(3), 221–249. https://doi.org/10.1023/A:1026077008571
Reardon, S. F. (2016). School segregation and racial academic achievement gaps. The Russell Sage Foundation Journal of the Social Sciences, 2(5), 34–57.
Reardon, S. F., Kalogrides, D., & Shores, K. (2019). The geography of racial/ethnic test score gaps. American Journal of Sociology, 124(4), 1164–1221. https://doi.org/10.1086/700678
Riddle, T., & Sinclair, S. (2019). Racial disparities in school-based disciplinary actions are associated with county-level rates of racial bias. Proceedings of the National Academy of Sciences of the United States of America, 116(17), 8255–8260. https://doi.org/10.1073/pnas.1808307116
Rubie-Davies, C., Hattie, J., & Hamilton, R. (2006). Expecting the best for students: Teacher expectations and academic outcomes. British Journal of Educational Psychology, 76(3), 429–444. https://doi.org/10.1348/000709905X53589
Schmidt, K., & Nosek, B. A. (2010). Implicit (and explicit) racial attitudes barely changed during Barack Obama’s presidential campaign and early presidency. Journal of Experimental Social Psychology, 46(2), 308–314. https://doi.org/10.1016/j.jesp.2009.12.003
Shores, K., Kim, H. E., & Still, M. (2020). Categorical inequality in black and white: Linking disproportionality across multiple educational outcomes. American Educational Research Journal, 57(5), 2089–2131. https://doi.org/10.3102/0002831219900128
Siegel-Hawley, G., Diem, S., & Frankenberg, E. (2018). The disintegration of memphis-shelby county, tennessee: School district secession and local control in the 21st century. American Educational Research Journal, 55(4), 651–692. https://doi.org/10.3102/0002831217748880
Sosina, V. E., & Weathers, E. S. (2019). Pathways to inequality: Between-district segregation and racial disparities in school district expenditures. AERA Open, 5(3), 233285841987244. https://doi.org/10.1177/2332858419872445
Starck, J. G., Riddle, T., Sinclair, S., & Warikoo, N. (2020). Teachers are people too: examining the racial bias of teachers compared to other American adults. Educational Researcher. https://doi.org/10.3102/0013189X20912758
Tamborini, C. R., Kim, C., & Sakamoto, A. (2015). Education and lifetime earnings in the United States. Demography, 52(4), 1383. https://doi.org/10.1007/S13524-015-0407-0
Uhlmann, E. L., Brescoll, V. L., & Paluck, E. L. (2006). Are members of low status groups perceived as bad, or badly off? Egalitarian negative associations and automatic prejudice. Journal of Experimental Social Psychology, 42(4), 491–499. https://doi.org/10.1016/J.JESP.2004.10.003
van den Bergh, L., Denessen, E., Hornstra, L., Voeten, M., & Holland, R. W. (2010). The implicit prejudiced attitudes of teachers. American Educational Research Journal, 47(2), 497–527. https://doi.org/10.3102/0002831209353594
VanderWeele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press.
Warikoo, N., Sinclair, S., Fei, J., & Jacoby-Senghor, D. (2016a). Examining racial bias in education: A new approach. Educational Researcher. https://doi.org/10.3102/0013189X16683408
Warikoo, N., Sinclair, S., Fei, J., & Jacoby-Senghor, D. (2016b). Examining racial bias in education. Educational Researcher, 45(9), 508–514. https://doi.org/10.3102/0013189X16683408
Xu, K., Nosek, B., & Greenwalk, A. (2014). Data from the race implicit association test on the project implicit demo website. Journal of Open Psychology Data, 2(1), e3. https://doi.org/10.5334/jopd.ac
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Pearman, F.A. Collective Racial Bias and the Black-White Test Score Gap. Race Soc Probl 14, 283–292 (2022). https://doi.org/10.1007/s12552-021-09347-y
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DOI: https://doi.org/10.1007/s12552-021-09347-y