Intersectional Inequality: Race, Class, Test Scores and Poverty

The U.S. is becoming more racially and ethnically diverse: according to the PEW Research Center (2015), the racial and ethnic composition of the U.S. population is projected to be 46% white, 13% Black, 24% Hispanic, 14% Asian, and 3% mixed by 2065. Closely associated with this demographic change is increasing social stratification, and of special note are some individuals and groups, such as black women, who experience persistent, acute, and compounding disadvantages. Without effective policy interventions, compounded disadvantages are likely to produce and reproduce life-long deprivation and even generational poverty. For decades, scholars intrigued by the question of who gets what and why have applied various frameworks and methodologies in order to understand and interpret the causal conditions. Conventional quantitative methodology, often operationalized in the form of multivariate analyses, has dominated the scholarly discourse because of its strengths in generating effect size measures, and assessing the relative importance of competing independent variables. Yet, in reality, different social conditions are interdependent, and advantages and disadvantages compound. A more contextual and comparative method that examines the intersection of diverse social conditions and outcomes may be more fitting.

The text, authored by Charles C. Ragin and Peer C. Fiss, offers a methodological guide for scholars who wish to apply diversity-oriented, set-analytic techniques to survey data. By delivering a combination of substantive and methodological materials in an incremental manner, the book offers perspectives in opposition to those expressed in the historically contentious the Bell Curve (1994). The authors argue that the set-analytic method provides new insights and alternative causal interpretations of poverty experienced by the Black community, which have been largely missed by conventional quantitative regression analyses.  Using a stepwise approach, the authors demonstrate how multiple advantages and disadvantages coincide and link to the experience of poverty and poverty avoidance across different race/gender groups. Throughout the book, the authors demonstrate a clear commitment to offering a more nuanced interpretation of the racial gap and to yielding “causal recipes” for alleviating poverty among the group that experiences multiple disadvantages.

Intersectional Inequality: Race, Class, Test Scores and Poverty includes a short introduction and eight chapters. The introduction provides the rationale for why diversity-oriented techniques like set-analytics are more fitting when studying how various social conditions intersect and connect with their consequences.  Following the introduction, the eight short chapters explain how to apply the intersectional methods by using the dataset Herrnstein and Murray and Fischer and colleagues used in their seminal works, The Bell Curve and Inequality by Design (1996). Throughout the text, comparisons are made when appropriate between the authors’ intersectional analyses and the results of these two works.

Specifically, Chapters One to Three provide the contextual and practical significance of intersectional analysis. Chapter Four details how to construct crisp and fuzzy sets using variables such as poverty status, parent income, test scores, parent education, respondent’s education, and household composition. In Chapter Five, the authors run a basic intersectional analysis and reveal that test scores and parent income compound in producing poverty, but pitting test scores against parent income, which was done in the Bell Curve, is substantively problematic. In Chapter Six, the authors add two more variables, parent education and respondent education, and describe how to use the basic mathematical set theory to construct the intersecting sets. They compare set coincidence analysis to bivariate correlation analysis, and demonstrate how the set analytics approach effectively identifies nuanced differences across different race/gender groups in their experience of multiple social conditions and life chances. Chapter Seven describes how to use truth table techniques to derive solutions for avoiding poverty.  Using set analytics, the authors highlight the dilemma that Blacks face in American society: to achieve poverty avoidance comparable to whites, Blacks must combine more advantages; yet, on average, Black Americans possess fewer advantages. In Chapter Eight, the authors summarize the key findings of Chapters Five through Seven and again emphasize the importance of intersectional analysis in addressing social inequality in America.

As a methodological pluralist, I commend the authors for contributing a valuable analytical method that scholars can readily apply in future intersectional analyses using large survey datasets. Their approach is in stark contrast to those held by intersectional theorists who single-mindedly favor qualitative methods (Stewart & McDermott, 2004; Bowleg, 2008). Another virtue of the book, manifested rather implicitly, is its provision of empirical, quantitative evidence that confirms the core principles of intersectional theory: “systems of power … cannot be understood in isolation from one another, systems of power intersect and coproduce one another to result in unequal realities and the distinctive social experiences that characterize them” (Collins & Chepp, 2013, p. 60). Lastly, the authors’ stepwise introduction of set analytics and its key components such as fuzzy sets construction and truth tables makes the technical aspect of this method extremely accessible, even to beginners.

While appreciating the substantive and practical values of this very important work, I do encourage future readers and policy makers to use caution when interpreting the causal claims formed by the authors. For example, their analysis evidences multiple advantages for whites coinciding with multiple disadvantages for Blacks. Accordingly, the authors claim “it is this compounding that explains their potency as causal conditions [of poverty or poverty avoidance].” Such causal claims, which appear in the book repeatedly, are problematic because they essentially claim that when given conditions intersect, they produce causal effects on individuals or groups that they would not produce, or would not produce in the same way, if they do not intersect. Yet, the set analytic results clearly show there exist alternative pathways, albeit not identified in the text, to poverty or poverty avoidance. Additionally, intersectional theorists are interested in and often base their causal claims on demographic categories, but can often be ambiguous in explicating the macro-level social conditions. This text is no exception. It may be useful if future intersectionality theorists build into their discussions a theory of social structural explanation as outlined by Haslanger (2015).

In summary, Intersectional Inequality: Race, Class, Test Scores and Poverty provides valuable substantive and practical knowledge in uncovering nuanced insights associated with the intersectionality of powers and social inequality, and they could help bridge methodological disagreements among intersectionality scholars. Strengthened by a Haslangian social structural explanations, these insights can also inform relevant policy interventions to target the most vulnerable groups or individuals who experience multiple acute disadvantages in an increasingly diverse American society.

References

Bowleg, L. (2008). When black + lesbian + woman black lesbian woman: The methodological challenges of qualitative and quantitative intersectionality research. Sex Roles, 59, 312-25.

Cohn, D., & Caumont, A. (2016). 10 demographic trends that are shaping the U.S. and the world. Retrieved from Pew Research Center website: http://www.pewresearch.org/fact-tank/2016/03/31/10-demographic-trends-that-are-shaping-the-u-s-and-the-world/

Collins, P. H., & Chepp, V. (2013). Intersectionality. In G. Wayla, K. Celis, J. Kantoha, & S. Weldon (Eds.), The Oxford handbook of gender and politics (p. 57– 87). Oxford, UK: Oxford University Press.

Fischer, C. S., Michael, H., Jankowski, M. S., Lucas, S. R., Swidler, A., & Voss, K. (1996). Inequality by design: Cracking the Bell Curve myth. Princeton, NJ: Princeton University Press.

Hernstein, R. J., & Murray, C. A. (1994). The bell curve: Intelligence and class structure in American life. New York: Free Press.

Haslanger, S. (2015). What is (social) structural explanation? Philosophical Studies, 31(8116), 1–18.

Stewart, A. J., & McDermott, C. (2004). Gender in psychology. Annual Review of Psychology, 55, 519– 44.