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Racial Attitudes, Accumulation Mechanisms, and Disparities

Review of Philosophy and Psychology volume 12, pages 953–975 (2021)Cite this article

Abstract

Some psychologists aim to secure a role for psychological explanations in understanding contemporary social disparities, a concern that plays out in debates over the relevance of the Implicit Association Test (IAT). Meta-analysts disagree about the predictive validity of the IAT and about the importance of implicit attitudes in explaining racial disparities. Here, I use the IAT to articulate and explore one route to establishing the relevance of psychological attitudes with small effects: an appeal to a process of “accumulation” that aggregates small effects into large harms. After characterizing mechanisms of accumulation and considering some candidate examples, I argue that such mechanisms suggest how a contemporary attitude with small effects could figure in the explanation of large disparities, but they do not vindicate the importance of such an attitude since such mechanisms are typically also determined by competing causes. I close by sketching several strategies for advancing a defense of the relevance of attitudes with small effects.

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Notes

  1. The psychological vs. residue strategy evokes the agency vs. structure divide and is a possible interpretation of that divide. I do not equate them here because “structure” can also be used to pick out recurring features (material and cultural) that form a stable background against which individual, agential choices are made. On this use, stable patterns of biased attitudes and behaviors would figure as part of the structure that an agent faces. Thus understood, the psychological/residue strategy divide would cross-cut the agent/structure divide.

  2. Wording of this and other questions that follow have varied slightly over many years, notably including shifts from “Negroes” to “blacks” or “African Americans.”

  3. According to a number of measures, “baby boomers” are much less racist than their parents, but subsequent generations (Generation Xers, Millennials) have remained about as racist as baby boomers (Clement 2015). Still, other work (Hopkins and Washington 2019) suggests that overall declines in racism appear to be continuing, and, in the U.S., this decline may even have accelerated since the election of Donald Trump in 2016.

  4. One concern about these more circumspect measures of explicit bias is that they may be difficult to distinguish from commitment to other political principles that do not appeal to race (for example, a commitment to race-neutral public policy) that are possibly not a reflection of bias at all (Sniderman and Tetlock 1986; Rabinowitz et al. 2009).

  5. Because indirect measures infer attitudes from behavior, it is possible that such measures could measure unconscious or “implicit” attitudes of which a subject is not aware, and this possibility, together with the prominence of measures like the IAT, has led to discussions of “unconscious bias” becoming widespread. At the same time, empirical work has raised considerable question as to whether subjects are actually unaware of attitudes that are indirectly measured (e.g. Hahn and Gawronski 2019), and recent theoretical work suggests using terms like “indirect” and “implicit” for measures without any commitment to the attitudes they measured being unconscious as I do here (Greenwald and Banaji 2017; Brownstein et al., 2019). Nothing in the present argument hinges upon this issue.

  6. Some have challenged associative accounts of implicit bias (e.g. see Mandelbaum, 2016; De Pinal and Spaulding, 2018) though these issues are orthogonal to my concerns here.

  7. Though test-retest reliability may be greater in specific contexts (e.g. Rae and Olson, 2018).

  8. While low reliability can be evidence of low predictive validity, it need not be. For instance, anger might be well-correlated with angry behavior (e.g. cursing), but anger itself might be a passing state rather than a stable disposition of persons over time. If so, anger could have good predictive validity but not test-retest reliability.

  9. Oswald et al. weighted average computed by Greenwald et al. 2015 (553, 555) for comparison. (Oswald et al. 2013 report correlations using a different meta-analytic technique.) See Oswald et al. 2013, p. 171, 182–183, 186.

  10. Cohen’s influential convention suggests that effect sizes from .1 to .3 are “small,” .3 to .5 are “medium,” and .5 and up are “large” (1988, pp. 79–81).

  11. Indeed, by accounting for various methodological and contextual moderators, some studies have found much higher correlations in the “medium” range. For instance, Kurdi et al. (2019) note that “methodological differences” in studies using implicit measures “produce highly divergent” correlations. In running a limited meta-analysis only upon the 24 effect sizes from 13 studies that “(a) had the relationship between implicit cognition and behavior as their primary focus, (b) used relative or difference score measures of behavior, (c) used an IAT or IRAP, (d) used attributes that were polar opposites of each other, and (e) used highly correspondent implicit and criterion measures,” they find a correlation in the medium range of r̅=.37 (across a range of social categories) (13). For a further discussion as well as a sustained reply and contextualization of skeptical criticisms of implicit bias research, see Brownstein et al., 2020.

  12. To be sure, psychologists are aware of this concern; for example, Hehman et al. (2017, 398) control for a range of other demographic variables that might be thought to predict the disparities in question but do not.

  13. Some writers seem to assume, further, that this follows because the behaviors themselves are in some sense “small.” For example, Sue et al. (2007) link implicit bias with the production of “microaggressions” that result in individually small but cumulatively large harm.

  14. E.g. Jussim (2017) argues that effects of teacher expectations on academic performance dissipate rather than accumulate.

  15. Discussion of “mechanisms” has been a major topic in the philosophy of science in recent decades. In keeping with a central theme of this work, the current discussion appeals to mechanisms as a shorthand for discerning the recurring structures and patterns that underlie generalizations of the special sciences (e.g. Craver 2007, Elster 1989, 1999; Little 1991; see Hedström and Ylikoski 2010 for discussion).

    While little in the present account depends upon the details of recent debates, a few substantive assumptions are needed. Perhaps most the important is that, along with other recent mechanists interested in the special sciences (e.g. Craver 2007), the present discussion avoids any commitment to the sort of “transmission of conserved quantities” view of causal processes that some influential mechanists (e.g. Salmon 1984) have aimed to articulate and defend.

  16. Talk of a “mark” is closely associated with Wesley Salmon’s account of causal processes (1984). Though Salmon later dispenses with the attempt to understand causal processes in terms of marks (1998, Chap. 16), he does not reject the concept itself. (1998, p. 253). Here, I adapt the term because (as it did for Salmon) it suggests an effect of some causal process that may persist over time, and so it could be aggregated with like effects or could have further causal effects.

  17. Comparisons of social accumulation with interest on debt tend to run together what I have here called marking, aggregating, and amplifying. In my terminology, the debt accumulates marks of past events in ways that constitute disadvantage while the interest amplifies this disadvantage further over time.

  18. By “larger,” I mean that they were stronger and more common, not that their connection with behavior was larger than contemporary explicit causes.

  19. While my purpose here is to explore questions about causal explanation, Madva also rightly points out that the question of how best to address social disparities is importantly distinct from the question of what explains them (2016, p. 704). Cf. Madva 2020 for a nuanced discussion of prospects for intervention on social disparities.

  20. It is in part to mark the distinction between inaction-in-the-face-of and action-against structural disparities that the distinction between being simply not racist and being anti-racist has become important in recent social thought (e.g. Kendi 2019).

  21. Greenwald et al. 2015 consider another example: the act of regularly taking aspirin to prevent a heart attack (558).

  22. I am grateful to Victor Kumar and Jacob Beck for pressing me to think more about such cases.

References

  • Abelson, R.P. 1985. A variance explanation paradox: When a little is a lot. Psychological Bulletin 97 (1): 129–133.

    Google Scholar 

  • Alexander, M. 2010. The new Jim crow : Mass incarceration in the age of colorblindness. New York: New Press.

    Google Scholar 

  • Anderson, E. 2010. The imperative of integration. Princeton University Press: Princeton.

    Google Scholar 

  • Arulampalam, W. (2001). "Is Unemployment Really Scarring? Effects of Unemployment Experiences on Wages." The Economic Journal, Vol. 111, No. 475, Features (Nov., 2001), pp. F585-F606.

  • Boissoneault, L. (2017) "what will happen to Stone Mountain, America's largest confederate memorial?" Smithsonian Magazine. August 22, 2017. https://www.smithsonianmag.com/history/what-will-happen-stone-mountain-americas-largest-confederate-memorial-180964588/

  • Brownstein, M., A. Madva, and B. Gawronski. 2019. What do implicit measures measure? WIREs Cognitive Science:1–13.

  • Brownstein, M., A. Madva, and B. Gawronski. 2020. Understanding implicit Bias: Putting the criticism into perspective. Pacific Philosophical Quarterly 101 (2): 276–307.

    Google Scholar 

  • Carlsson, R., and J. Agerström. 2016. A closer look at the discrimination outcomes in the IAT literature. Scandinavian Journal of Psychology 57 (4): 278–287.

    Google Scholar 

  • Chou, T., A. Asnaani, and S.G. Hofmann. 2012. Perception of racial discrimination and psychopathology across three U.S. ethnic minority groups. Cultural Diversity & Ethnic Minority Psychology 18 (1): 74–81.

    Google Scholar 

  • Chugh, D. 2004. Societal and managerial implications of implicit social cognition: Why milliseconds matter. Social Justice Research 17 (2): 203–222.

    Google Scholar 

  • Clement, S. (2015). Millennials are just as racist as their parents. Washington Post. Washington D.C.

  • Cohen, J. 1988. Statistical power analysis for the behavioral sciences. L. Erlbaum Associates: Hillsdale.

    Google Scholar 

  • Correll, J., S.M. Hudson, S. Guillermo, and D.S. Ma. 2014. The police Officer's dilemma: A decade of research on racial Bias in the decision to shoot. Social and Personality Psychology Compass 8: 201–213.

    Google Scholar 

  • Craver, C. 2007. Explaining the brain: Mechanisms and the mosaic Unity of neuroscience. Oxford: Oxford University Press.

    Google Scholar 

  • Del Pinal, G. and S. Spaulding. (2018). "Conceptual centrality and implicit bias." 33(1) 95-111.

  • DiPrete, T.A., and G.M. Eirich. 2006. Cumulative advantage as a mechanism for inequality: A review of theoretical and empirical developments. Annual Review of Sociology 32: 271–297.

    Google Scholar 

  • Dovidio, J. F., Pearson, A. R., and Penner, L. A. (2019) “Aversive racism, implicit Bias, and microaggressions” in Microaggressions Theory: Influence and Implications. Eds G. C. Torino, D. P. Rivera, C.M. Capodilupo, K. L. Nadal, and D.W. Sue. John Wiley & Sons, Inc.

  • Dovidio, J.F., and S.L. Gaertner. 2004. Aversive racism. Advances in Experimental Social Psychology. 26: 4–56.

    Google Scholar 

  • Elster, Jon (1989). Nuts and bolts for the social sciences. Cambridge University Press.

  • Elster, Jon (1999). Alchemies of the mind: Rationality and the emotions. Cambridge University Press.

  • Evans, C. and Mallon, R. (2020)."microaggressions, mechanisms, and harm" Eds. Freeman, L. and Weekes-Schroer, J. Microaggressions and Philosophy. New York. Routledge.

  • Fiske, S. (1998). Stereotyping, prejudice, and discrimination. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology. McGraw-Hill. p. 357–411.

  • Forscher, P. S., Lai, C. K., Axt, J. R., Ebersole, C. R., Herman, M., Devine, P. G., & Nosek, B. A. (2019). A meta-analysis of procedures to change implicit measures. Journal of Personality and Social Psychology. Advance online publication. https://doi.org/10.1037/pspa0000160, 117, 522, 559.

  • Friedlaender, C. 2018. On microaggressions: Cumulative harm and individual responsibility. Hypatia 33 (1): 5–21.

    Google Scholar 

  • Gawronski, B. M. Morrison, C.E. Phills, and S. Galdi. (2017). " Temporal Stability of Implicit and Explicit Measures: A Longitudinal Analysis" Personality and Social Psychology Bulletin, Vol. 43(3) 300–312.

  • Greenwald, A.G., M.R. Banaji, and B.A. Nosek. 2015. Statistically small effects of the implicit association test can have societally large effects. Journal of Personality and Social Psychology 108 (4): 553–561.

    Google Scholar 

  • Greenwald, A.G., T.A. Poehlman, E.L. Uhlmann, and M.R. Banaji. 2009. Understanding and using the implicit association test: III. Meta-analysis of predictive validity. Journal of Personality and Social Psychology 97 (1): 17–41.

    Google Scholar 

  • Greenwald, A.G., D.E. McGhee, and J.K.L. Schwartz. 1998. Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology 74: 1464–1480.

    Google Scholar 

  • Greenwald, A., and M. Banaji. 1995. Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review 102 (1): 4–27.

    Google Scholar 

  • Greenwald, A.G., and M.R. Banaji. 2017. The implicit revolution: Reconceiving the relation between conscious and unconscious. American Psychologist 72 (9):861–871. https://doi.org/10.1037/amp0000238.

  • Hahn, A., and B. Gawronski. 2019. Facing one’s implicit biases: From awareness to acknowledgment. Journal of Personality and Social Psychology 116 (5): 769–794. https://doi.org/10.1037/pspi0000155.

    Article  Google Scholar 

  • Hedström, Peter, and Petri Ylikoski. 2010. Causal mechanisms in the social sciences. Annual Review of Sociology 36: 49–67.

    Google Scholar 

  • Hehman, E., J. Flake, and J. Calanchini 2017. Disproportionate use of lethal force in policing is associated with regional racial biases of residents. 9:4, 393–401. https://doi.org/10.1177/1948550617711229

  • Henry, P., and D.O. Sears. 2002. The symbolic racism 2000 scale. Political Psychology 23: 253–283.

    Google Scholar 

  • Hopkins, Daniel J. and Washington, Samantha. 2019. The Rise of Trump, the Fall of Prejudice? Tracking White Americans' Racial Attitudes 2008-2018 Via a panel survey (April 17, 2019). Available at https://ssrn.com/abstract=3378076

  • Jones, J. (2017). "The racial wealth gap: How African Americans have been shortchanged out of the materials to build wealth." Working Economics Blog http://www.epi.org/blog/the-racial-wealth-gap-how-african-americans-have-been-shortchanged-out-of-the-materials-to-build-wealth/. Accessed November 1, 2017.

  • Jussim, L. (2017). "Précis of social perception and social reality: Why accuracy dominates bias and self-fulfilling prophecy." Behavioral and Brain Sciences 40.

  • Kendi, I. 2019. How to be an anti-racist. One World.

  • Kerckhoff, A.C. 1993. Diverging pathways: Social structure and career deflections. Cambridge: Cambridge Univ. Press.

    Google Scholar 

  • Krysan, M., and K. Crowder. 2017. Cycle of segregation: Social processes and residential stratification. New York: Russell Sage Foundation.

    Google Scholar 

  • Kurdi, B., A.E. Seitchik, J.R. Axt, T.J. Carroll, A. Karapetyan, N. Kaushik, D. Tomezsko, A.G. Greenwald, and M.R. Banaji. 2019. Relationship between the implicit association test and intergroup behavior: A meta-analysis. American Psychologist 74 (5): 569–586. https://doi.org/10.1037/amp0000364.

    Article  Google Scholar 

  • Liévanos RS. (2018) " retooling CalEnviroScreen: Cumulative pollution burden and race-based environmental health vulnerabilities in California." Int J Environ Res Public Health. 15(4).

  • Little, Daniel (1991). Varieties of social explanation: An introduction to the philosophy of social science. Westview Press.

  • Lilienfeld, S.O. 2017. Microaggressions: Strong claims, inadequate evidence. Perspectives on Psychological Science 12 (1): 138–169.

    Google Scholar 

  • Loveless, T. 2017. How well are American students learning? Brown Center Report on American Education. Vol. 3, Number 6.

  • Madva, A. 2016. A Plea for anti-anti-individualism: How Oversimple psychology misleads social policy. Ergo: An Open Access Journal of Philosophy 3: 701–728.

    Google Scholar 

  • Madva, A. 2020. Individual and structural interventions. In An introduction to implicit Bias: Knowledge, justice, and the social mind, ed. Erin Beeghly and Alex Madva. Routledge.

  • Mallon, R. and D. Kelly (2012). Making race out of nothing: Psychologically constrained social roles. The Oxford Handbook of Philosophy of Social Science. H. Kincaid. Oxford, Oxford University Press: 507–532.

  • Mandelbaum, Eric. 2016. Attitude, inference, association: On the propositional structure of implicit Bias. Noûs 50 (3): 629–658.

    Google Scholar 

  • Martell, R.F., D.M. Lane, and C. Emrich. 1996. Male-female differences: A computer simulation. American Psychologist 51 (2): 157–158.

    Google Scholar 

  • Merton, R.K. 1968. The Matthew effect in science. Science 159 (3810): 56–63.

    Google Scholar 

  • McConahay, J. B. (1986). Modern racism, ambivalence, and the modern racism scale. Prejudice, discrimination, and racism. San Diego, CA, US, academic press: 91-125.

  • NAACP. 2005. Interrupting the school to prison pipe-line. Washington DC.

  • Nixon, R. 2011. Slow violence and the environmentalism of the poor. Cambridge: Harvard University Press.

    Google Scholar 

  • Nosek, B.A., F.L. Smyth, J.J. Hansen, T. Devos, N.M. Lindner, K.A. Ranganath, C.T. Smith, K.R. Olson, D. Chugh, A.G. Greenwald, and M.R. Banaji. 2007. Pervasiveness and correlates of implicit attitudes and stereotypes. European Review of Social Psychology 18: 36–88.

    Google Scholar 

  • Nosek B. A., F.L. Smyth, N. Sriram, N.M. Lindner, T. Devos, A. Ayala, Y. Bar-Anan, R. Bergh, H. Cai, K. Gonsalkorale, S. Kesebir, N. Maliszewski, F. Neto, E. Olli, J. Park, K. Schnabel, K. Shiomura, B.T. Tulbure, R.W. Wiers, M. Somogyi, N. Akrami, B. Ekehammar, M. Vianello, M. R. Banaji, and A.G. Greenwald. 2009. National differences in gender-science stereotypes predict national sex differences in science and math achievement. Proceedings of the National Academy of Sciences 106 (26):10593–10597.

  • Ong, A.D., and A.L. Burrow. 2017. Microaggressions and daily experience:Depicting life as it is lived. Perspectives on Psychological Science 12 (1): 173–175.

    Google Scholar 

  • Ong, A.D., A.L. Burrow, T.E. Fuller-Rowell, N.M. Ja, and D.W. Sue. 2013. Racial microaggressions and daily well-being among Asian Americans. Journal of Counseling Psychology 60 (2): 188–199.

    Google Scholar 

  • Oswald, F.L., G. Mitchell, H. Blanton, J. Jaccard, and P.E. Tetlock. 2013. Predicting ethnic and racial discrimination: A meta-analysis of IAT criterion studies. Journal of Personality and Social Psychology 105: 171–192.

    Google Scholar 

  • Oswald, F.L., G. Mitchell, H. Blanton, J. Jaccard, and P.E. Tetlock. 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): 562–571.

    Google Scholar 

  • Parker, Wendy. 2009. Does matter really matter? Computer simulations, experiments, and materiality. Synthese 169 (3): 483–496.

    Google Scholar 

  • Payne, B.K., H.A. Vuletich, and K.B. Lundberg. 2017. The bias of crowds: How implicit bias bridges personal and systemic prejudice. Psychological Inquiry 28 (4): 233–248.

    Google Scholar 

  • Pettit, Becky, and B. Western. 2004. Mass imprisonment and the life course: Race and class inequality in U.S. incarceration. American Sociological Review. 69 (2): 151–169.

    Google Scholar 

  • Rae, J.R., and K.R. Olson. 2018. Test-retest reliability and predictive validity of the implicit association test in children. Developmental Psychology 54 (2): 308–330.

    Google Scholar 

  • Rabinowitz, J.L., D.O. Sears, J. Sidanius, and J.A. Krosnick. 2009. Why do white Americans oppose race-targeted policies? Clarifying the impact of symbolic racism. Political Psychology 30 (5): 805–828.

    Google Scholar 

  • Rosenthal, R., and L. Jacobson. 1968. Pygmalion in the classroom: Teacher expectation and pupils’ intellectual development. New York: Holt, Rinehart and Winston.

  • Rothstein, R. 2017. The color of law : A forgotten history of how our government segregated America. In New York. London: Liveright Publishing Corporation.

    Google Scholar 

  • Rudman, L.A. 2004. Social justice in our minds, homes, and society: The nature, causes, and consequences of implicit Bias. Social Justice Research 17 (2): 129–142.

    Google Scholar 

  • Salmon, Wesley. 1984. Scientific explanation and the causal structure of the world. Princeton University Press.

  • Salmon, Wesley C. 1998. Causality and explanation. Oxford University Press.

  • Sampson, R.J., and J.H. Laub. 1997. A life-course theory of cumulative disadvantage and the stability of delinquency. Adv. Criminol. Theory: Dev. Theories Crime Delinq. 7: 133–161.

    Google Scholar 

  • Schuman, H., et al. (1997). Racial attitudes in America : Trends and interpretations. Cambridge, Mass., Harvard University Press.

  • Singal, J. 2017. Psychology’s favorite tool for measuring racism Isn’t up to the job. New York. January 11, 2017. https://www.thecut.com/2017/01/psychologys-racism-measuring-tool-isnt-up-to-the-job.html.

  • Smith, Tom W, Peter Marsden, Michael Hout, and Jibum Kim. General Social Surveys, 1972–2016 [machine-readable data file] /Principal Investigator, Tom W. Smith; Co-Principal Investigator, Peter V. Marsden; Co-Principal Investigator, Michael Hout; Sponsored by National Science Foundation. -NORC ed.- Chicago: NORC at the University of Chicago [producer and distributor]. Data accessed from the GSS Data Explorer website at gssdataexplorer.norc.org.

  • Sniderman, P.M., and P.E. Tetlock. 1986. Symbolic racism: Problems of motive attribution in political analysis. Journal of Social Issues 42 (2): 129–150.

    Google Scholar 

  • Sue, D.W., C.M. Capodilupo, G.C. Torino, J.M. Bucceri, A.M. Holder, K.L. Nadal, and M. Esquilin. 2007. Racial microaggressions in everyday life: Implications for clinical practice. The American Psychologist 62 (4): 271–286.

    Google Scholar 

  • Sue, D. W. (2019). “Microaggressions and student activism: Harmless impact and victimhood controversies” in Microaggressions Theory: Influence and Implications, first edition. Edited by Gina C. Torino, David P. Rivera, Christina M. Capodilupo, Kevin L. Nadal, and Derald wing Sue. John Wiley & Sons, Inc.

  • Sullivan, S. (2015). The physiology of sexist and racist oppression, Oxford University Press USA.

  • Torres, L., M.W. Driscoll, and A.L. Burrow. 2010. Racial microaggressions and psychological functioning among highly achieving African-Americans: A mixed-methods approach. Journal of Social and Clinical Psychology 29 (10): 1074–1099.

    Google Scholar 

  • Valian, V. (1998). Why so slow? : The advancement of women. Cambridge, Mass., MIT Press.

  • Williams, D.R., and S.A. Mohammed. 2013. Racism and health I: Pathways and scientific evidence. American Behavioral Scientist 57 (8): 1152–1173.

    Google Scholar 

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Acknowledgments

I would like to thank Michael Brownstein, Calvin Lai, and an anonymous referee for this journal as well as audiences at Johns Hopkins University, Oxford University, Washington University in St. Louis, and York University for feedback on earlier versions of this paper.

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Mallon, R. Racial Attitudes, Accumulation Mechanisms, and Disparities. Rev.Phil.Psych. 12, 953–975 (2021). https://doi.org/10.1007/s13164-020-00521-6

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