Racial Attitudes, Accumulation Mechanisms, and Disparities


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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  1. 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. 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. 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. 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. 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. 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. 7.

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

  8. 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. 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. 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. 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 =.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. 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. 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. 14.

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

  15. 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. 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. 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. 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. 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. 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. 21.

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

  22. 22.

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


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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. (2021). https://doi.org/10.1007/s13164-020-00521-6

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