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Cumulative Structural Disadvantage and Racial Health Disparities: The Pathways of Childhood Socioeconomic Influence

Demography

Abstract

Cumulative structural disadvantage theory posits two major sources of endogenous selection in shaping racial health disparities: a race-based version of the theory anticipates a racially distinct selection process, whereas a social class-based version anticipates a racially similar process. To operationalize cumulative structural disadvantage, this study uses data from the 1979 National Longitudinal Survey of Youth in a Latent Class Analysis that demographically profiles health impairment trajectories. This analysis is used to examine the nature of selection as it relates to racial differences in the development of health impairments that are significant enough to hinder one’s ability to work. The results provide no direct support for the race-based version of cumulative structural disadvantage theory. Instead, two key findings support the social class–based version of cumulative disadvantage theory. First, the functional form of the different health trajectories are invariant for whites and blacks, suggesting more racial similarly in the developmental process than anticipated by the race-based version of the theory. The extent of the racial disparity in the prevalences across the health impairment trajectories is, however, significant and noteworthy: nearly one-third of blacks (28 %) in the United States experience some form of impairment during their prime working years compared with 18.8 % of whites. Second, racial differences in childhood background mediate this racial health disparity through the indirect pathway of occupational attainment and through the direct pathway of early-life exposure to health-adverse environments. Thus, the selection of individuals into different health trajectories, based largely on childhood socioeconomic background, helps explain racial disparities in the development of health impairments.

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Notes

  1. The distinction between strong and weak versions of cumulative disadvantage theory is similar to the distinction between strong and weak versions of place stratification theory (see Logan and Alba 1993).

  2. Educational attainment is intentionally omitted from the regression models because it is difficult to justify it as a direct or indirect mediator. Education influences occupational attainment, but education also has additional indirect implications for long-term health apart from employment that confounds the explanatory relevance of the direct and indirect pathways (see Frisvold and Golberstein 2013:1991). For this reason, education is treated as an overcontrol and is omitted. In supplemental models, the number of years of schooling significantly mediates racial health disparities, and schooling also modestly reduces the explanatory power of parental education, parental occupational status, and the occupational status of the first job. The main conclusions, however, remain unaltered when schooling is included in the analysis.

  3. This study uses the initial (Round 1) sample weights provided by the NLSY that adjust for the inverse probability of selection into the sample. The NLSY does readjust the sample weights after every new data collection to account for survey nonresponse (see http://nlsinfo.org/content/cohorts/nlsy79/using-and-understanding-the-data/sample-weights-clustering-adjustments). The readjusted survey weights are designed to give approximate population estimates for the 1957–1964 birth cohort for that given year. When using the weights to adjust the sample longitudinally (as is the case here), the researcher must design a custom set of weights using a program provided by the NLSY (https://www.nlsinfo.org/weights/nlsy79). The custom weights, however, apply only to those that are observed in all selected years. When the sample weights are longitudinally customized through the selection of “any or all” respondents that contributed information over the study period (as is the case with an unbalanced design like the one used in this study), then the custom weights are the Round 1 sample weights. This is the reason behind using the Round 1 weights: they are the same as the custom weights when listwise deletion is avoided. See the documentation in the links provided for further information. The regression analyses employ robust standard errors to account for the design effects.

  4. In addition to creating a conceptually well-rounded yet parsimonious health measure, collapsing these variables into one measure also helps to minimize any measurement error stemming from a changing skip pattern in the data. Between 1998 and 2000, the NLSY changed the preceding survey questions in a way that affected who was assigned a valid skip for the first question, and by extension, the pool of eligible respondents for the subsequent health questions is affected. Reliably studying these questions longitudinally requires combining them into one measure (see NLSY79 errata item 10: https://www.nlsinfo.org/content/cohorts/nlsy79/other-documentation/errata/errata-1979-2010-data-release).

  5. Expectations concerning the effect of working mothers on child development are mixed. On one hand, loss of time with children may lead to developmental problems. On the other hand, mother’s paid work contributes to the family’s pool of resources, thus potentially improving the child’s outlook. See Haveman et al. (1991) for a framing of the “working-mother hypothesis.” The present study includes working mother as a covariate because positive, negative, and/or null findings can inform further development of the working-mother hypothesis.

  6. The reference age of 14 is how the NLSY collected retrospective information for respondents that were older than 14 when the study began in 1979. The reference age of 14 is meaningful to the extent that it is the age at which strong peer group ties tend to form and gain more influence over the family.

  7. According to Nylund et al.’s (2007:565) simulation study, the bootstrapped likelihood ratio test (BLRT) performed better than the LMR test under some circumstances, but the BLRT isn’t tenable when using sampling weights with complex survey data like the NLSY. For this situation, Nylund et al. recommended using the LMR test.

  8. As a sensitivity check, the LCA was rerun using listwise deletion and custom NLSY sample weights to adjust for the inverse probability of selection into the sample and sample attrition. In this supplementary analysis, the four-class solution is the preferred model by the LMR test, but the five-class solution has the lowest BIC value. As with the main analysis, there are only minor distinctions among the four-, five-, and six-class solutions. The four-class solution tends to combine early and late onset classes into one onset trajectory, and the six-class solution adds a downward sloping trajectory. Rerunning the subsequent regression analyses based on the four- rather than the five-class solution yields the same substantive conclusions as the analysis in the text. The seven- and eight-class LCA will not properly converge. This lack of convergence suggests that noteworthy prevalence beyond six latent classes is doubtful.

  9. Note the small differences between the unadjusted coefficients reported in Table 4 and those reported in the Appendix. These differences are attributed to the rescaling that occurs via the KHB method. The unadjusted logit must be rescaled by a correction factor so that the true difference between the unadjusted and adjusted coefficients is attributed only to the mediation effect (e.g., .508 × 1.110 = .564).

  10. Stronger parental education effects and weaker parental SEI effects emerge in the models that use father’s education rather than mother’s education as the primary source.

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Correspondence to Jeremy Pais.

Appendix

Appendix

Table 5 Mulitnoimial logistic coefficients for the determinants of latent class membership into five health impairment trajectories: NLSY79 1980–2010 (N = 6,750)

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Pais, J. Cumulative Structural Disadvantage and Racial Health Disparities: The Pathways of Childhood Socioeconomic Influence. Demography 51, 1729–1753 (2014). https://doi.org/10.1007/s13524-014-0330-9

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