Demography (2014) 51:1729–1753 DOI 10.1007/s13524-014-0330-9

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

Published online: 12 September 2014 # Population Association of America 2014

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. Keywords Inequality . Latent class . Life course . Stratification . Work

J. Pais (*) Department of Sociology, University of Connecticut, 344 Mansfield Road, Unit 2068, Storrs, CT 06269-2068, USA e-mail: [email protected]

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Introduction Racial health disparities coincide with racial differences in labor force participation (Bound et al. 1996, 2003; Brown and Warner 2008; Mutchler et al. 1999), contributing to lost workplace productivity (Kessler et al. 2001; Stewart et al. 2003) and the perpetuation of racial socioeconomic inequality. Yet, the connection between racial disparities in health and racial socioeconomic inequality is complicated by the selective and reciprocal relationship between health and employment that transpires over the life course. Healthadverse occupations can lead to poor health outcomes, but poor health can also constrain an individual’s socioeconomic mobility (Halleröd and Gustafsson 2011; McDonough and Amick 2001; Strully 2009). This is especially problematic when assessing racial health disparities because the endogenous selection of individuals with disadvantaged backgrounds into health-adverse occupations could be greater among minority groups. The combination of a reciprocally casual relationship between health and employment together with differential group selection into health-adverse occupations makes racial health disparities exceedingly difficult to explain. Longitudinal studies of the developmental progression of different health trajectories are getting scholars closer to understanding the mechanisms that drive the reciprocal relationship between health and socioeconomic status (SES) (Haas and Rohlfsen 2010; Halleröd and Gustafsson 2011; Kaplan et al. 2007; Kim and Miech 2009; Liang et al. 2010; Taylor 2008; Warner and Brown 2011). However, population researchers have not fully analyzed longitudinal health patterns in ways that inform our understanding about the role of endogenous selection in generating racial health disparities. Cumulative structural disadvantage theory guides this study’s expectations concerning the role of endogenous selection in shaping racial health disparities. Cumulative structural disadvantage is conceptualized as a selective process where health insults tend to accumulate with greater frequency and magnitude among socially and economically disadvantaged populations (Case et al. 2002; Geronimus 2001; Hayward and Heron 1999; Marmot 2005). Two versions of cumulative structural disadvantage theory provide specific guidance. The first version relies primarily on race-based explanations for racial health disparities by emphasizing a host of systematic disadvantages experienced uniquely by blacks that would also facilitate a racially distinct nexus between health and employment outcomes (cf. Brondolo et al. 2009). The second version draws on research pertaining to the effects of childhood socioeconomic background on health outcomes (e.g., Haas 2007, 2008; Luo and Waite 2005), where disproportionate childhood disadvantages experienced by blacks are posited as the primary factor leading to adult racial health disparities. These two mutually reinforcing explanations for racial health disparities are examined through a longitudinal analysis of blacks and whites in the United States that (1) profiles the development of different health impairment trajectories, where the impairment is significant enough to limit one’s ability to work, and (2) examines the role of childhood background characteristics in channeling individuals into these different health trajectories. This analysis will answer questions concerning the extent of racial similarity and difference in the developmental progression of work-limiting health impairments, as well as the extent to which racial health disparities result from the selection of disadvantaged adolescents into more difficult career paths or from other health-adverse factors.

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Race, Cumulative Disadvantage, and Health Impairment Trajectories Rates of labor force participation drop faster among aging blacks than among aging whites, and racial differences in health are largely responsible for this disparity (Bound et al. 1995, 1996; Hayward et al. 1996). Among aging black women, who typically participate in the labor force at higher rates than white women, the labor force participation gap converges with age because black women are more likely to develop diabetes, hypertension, and arthritis (Brown and Warner 2008; Hayward et al. 2000; Kaiser Family Foundation 2004). Although higher rates of diabetes and hypertension among blacks may have sociobiological causes (Ferraro et al. 1997; Fuchs 2011), racial health disparities also stem from group differences in class backgrounds, social milieus, and work environments (Ferraro and Kelley-Moore 2003; see Fine et al. 2005 for a constructive framing of the gene/environment question). Blacks, for instance, have higher rates of employment in dangerous and physically demanding jobs (Park et al. 1993), are employed in more precarious and unstable jobs (Tausig 1999; Wilson 1997), and have jobs that provide less access to health insurance and health care than whites with commensurate skills (Smart and Smart 1997). Able-bodied blacks may also be more likely to report themselves as disabled because their arduous jobs may provide less monetary incentive for continued work (Parsons 1980, 1982). Although few scholars have questioned the linkage between racial health disparities and differential rates of workforce participation, understanding the development of this relationship is problematic because the connection between health status and employment is selective and reciprocal: those with poor health are more likely to have employment difficulties (McDonough and Amick 2001), yet those with employment difficulties and greater job-related stressors are more likely to suffer health impairments (Strully 2009). For example, Halleröd and Gustafsson (2011) found that those with more-prestigious occupations experience a slower rate of health deterioration, but those with morbidity experience less upward occupational mobility. Researchers address these endogeneity problems via methodological innovations, such as the use of instrumental variables (Cawley 2000) or natural experiments (Petticrew et al. 2005). However, these methodological solutions only seek to establish robust estimates of the health/employment relationship: these studies do not provide adequate insight into development of the health–employment nexus. More closely examining the linkage between SES and racial health disparities requires that the process of endogenous selection be the object of study, not something that is treated as a methodological nuisance (cf. Sampson and Sharkey 2008). Endogenous selection begins long before racial disparities in work-limiting health impairments reach their peak in mid-adulthood. Endogenous selection starts with structural disadvantages experienced during childhood, which can place individuals on a pathway of poor health via a lack of access to health services, the early socialization into an unhealthy lifestyle, the selection of health-adverse careers rife with employment-related hardships, continual exposure to unhealthy work and neighborhood environments, and recurrent financial struggles and everyday life stressors (Marmot 2005). These disadvantages start accumulating during childhood, resulting in the unequal buildup of health insults by mid-adulthood (Dannefer 2003)—a process that Geronimus (2001) and Geronimus et al. (2001) refer to as “weathering” in describing the accumulation of health insults among black women. In support of the

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cumulative disadvantage perspective, Case et al. (2002) found that children from lowincome households accumulate more adverse health effects as they age, and Turrell and colleagues (2007) found that childhood SES affects mortality propensities and functional health limitations later in life. Moreover, adult racial health disparities are often explained by the fact that minority children are disproportionally represented among those in poverty (Clark and Maddox 1992; Haas and Rohlfsen 2010; Taylor 2008). Eventually, cumulative disadvantages affect an individual’s ability to function in the workforce, and in this way, cumulative structural disadvantage is an important form of endogenous selection. Health Trajectories Profiling latent trajectories of various developmental phenomenon is a demographic technique that is useful in examining population heterogeneity, especially when incidents of endogenous selection are cumulative and unobserved (e.g., Dariotis et al. 2011; Hamil-Luker and O’Rand 2007). Insights into the selective process of cumulative structural disadvantage can be gained through identifying different types of health trajectories, given that divergent trajectories between relatively advantaged and disadvantaged groups indicate a greater accumulation of health insults over time (DiPrete and Eirich 2006). Health trajectories also have several features that can reveal details about the developmental process (e.g., Kaplan et al. 2007; Liang et al. 2010). First, the duration of a health impairment can be prolonged, isolated, or episodic (Krause et al. 2001), which will influence the directionality and functional form of a health trajectory. Long durations are reflected by a persistent state of health with little or no change, short durations create idiosyncratic departures from a persistent state, and recurrent durations create oscillations between health states. Second, health trajectories accelerate or decelerate at a gradual or a rapid rate of change, as evident in “normal aging” processes that gradually limit people’s physical functionality versus “pathological aging,” where disease and trauma increase the rate of health depreciation (Dodge et al. 2006; Kane et al. 1999). A third key characteristic involves the timing of the onset. Early onset is often associated with a persistence, and in some cases an acceleration, of poor health after the impairment begins (Geronimus 2001; Taylor 2008); and early onset often leads to greater health complications over the life course, resulting in lower long-term employment rates (Jenkins and Rigg 2004). These three features are points of variation that differentiate health trajectories and, by extension, reveal developmental differences in the process of cumulative structural disadvantage. Race and Childhood Socioeconomic Background Cumulative structural disadvantage is an apt perspective for explaining racial health disparities given the known extent of racial childhood socioeconomic disadvantage (Drake and Rank 2009; Wildeman 2009), the extent of racial labor market inequalities (Bound et al. 1996; Bound et al. 2003; Brown and Warner 2008; Fullerton 1999), and occurrences of racial discrimination (Krieger and Sidney 1996). However, contentious debate continues around the role of racial exceptionalism versus class background in causing racial health disparities. A strong version of cumulative disadvantage theory suggests that racial differences in the accumulation of health insults is so extensive and

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pronounced that a population profile of work-limiting health impairment trajectories among blacks will be fundamentally unique from whites in terms of an earlier onset of an impairment, a faster rate of acceleration of an impairment, and longer durations of impairment after onset. The strong version questions whether the process of endogenous selection among blacks and whites is similar enough to warrant a joint comparison in the first place. Conversely, a weaker version of cumulative disadvantage theory posits a similar endogenous selection process among blacks and whites leading to similar health trajectory profiles. According to the weak version, racial differences will be primarily in degree rather than in kind, with blacks being underrepresented among the relatively healthy trajectories and overrepresented among the health impairment trajectories.1 Another key dimension of cumulative structural disadvantage involves the way in which individuals get channeled (i.e., select) into these different types of health trajectories. Theoretically, there are two leading pathways through which racial differences in the development of work-limiting health impairment trajectories can be explained. One pathway is indirect through the effects that childhood background has on educational attainment,2 occupational socialization (e.g., the formation of career aspirations and expectations), actual employment opportunities, and ultimately a career path. Occupational hazards are a major source of health impairment, and there are particular health risks associated with different occupations (Blanc et al. 2004). Childhood background affects an individual’s career path by shaping early occupational opportunities and choices (Blau and Duncan 1967:48–49, 170), and accordingly, a primary way that an impairment develops is through the health implications associated with specific job tasks and/or through the development of unhealthy lifestyles that are fostered at work. The second pathway is direct: childhood background affects the development of a health impairment independent from the workplace and apart from a career path. This second pathway involves the impact of childhood background on the development of lifestyle behaviors, habits, and routines that are enabled through family ties, lifelong peer group affiliations, and other social relations that constitute an individual’s milieu. Environmental factors growing up, such as exposure to neighborhood pollution (Crowder and Downey 2010) and multigenerational exposure to neighborhood poverty (Sharkey 2008), also play a potentially important role in long-term health statuses. The direct pathway could remain as relevant, or more relevant, as the indirect pathway if the primary influence of childhood background on adult health is delivered through other means than adult occupational attainment.

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.

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Main Hypotheses This cumulative structural disadvantage perspective is amendable to two sets of hypotheses concerning racial differences in the development of work-limiting health impairment trajectories. The first set of hypotheses speaks to racial differences and similarities in the development of different types of health impairment trajectories. The strong version anticipates a racially unique process of cumulative structural disadvantage whereby the health trajectory profile among blacks and whites are expected to be visibly and statistically different. The weak version of cumulative structural disadvantage anticipates a generally similar developmental profile among blacks and whites. The weak version maintains that black representation among the relatively unhealthy trajectories will be significantly greater than whites. A second set of hypotheses speaks to the role of childhood background in explaining racial differences in the development of work-limiting health impairment trajectories. First, childhood socioeconomic characteristics are expected to either eliminate or significantly attenuate any gross (i.e., unadjusted) racial disparity among the unhealthy trajectories. Failure to explain gross racial differences with childhood background covariates will suggest some degree of racial exceptionalism in the cumulative structural disadvantage process. Second, childhood background is expected to matter (1) indirectly, because childhood SES helps channel individuals into particular career paths, and/or (2) directly through the effect of childhood background on the longterm accumulation of health insults, apart from occupation-related influences. To the extent that racial labor-market disadvantages among adults cause racially disparate hardships and health problems, we should expect the indirect pathway to account for the majority of the racial disparity among those with mid-to-late adulthood onset of work-limiting health impairments. The direct influence of childhood background should be more relevant for those trajectories with an early onset impairment that occur prior to, or relatively soon after, entering the workforce.

Data and Methods Data for this study are from the National Longitudinal Survey of Youth, 1979 Cohort (NLSY79). The NLSY79 began in 1979 with 12,686 respondents ages 14–22. The respondents were followed annually through 1994 and biannually thereafter. When observed for this study in 2010, the NLSY79 respondents were between the ages of 44 and 52. The response rate (defined as the percentage of initial respondents remaining eligible to be reinterviewed) through the most recent rounds of interviews has remained around 80 %. The NLSY79 sample frame consists of three subsamples taken in 1979: a nationally representative cross-sectional sample of respondents; an oversample of respondents enlisted in the military; and an oversample of blacks, Hispanics, and poor non-Hispanic whites. After 1984, the NLSY79 no longer followed the military sample; and after the 1990 round of interviews, the NLSY79 also dropped the oversample of poor whites from its sampling frame. Because these two subsamples are not present for the duration of the study, they are excluded from the analysis. The effective sample for this study consists of 9,715 respondents who were born between 1957 and 1965, resided in the United States in 1979, and have now aged from

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adolescence to middle adulthood. The multigroup analysis includes only non-Hispanic whites (i.e., whites: N = 3,937) and non-Hispanic blacks (i.e., blacks: N = 2,913). An advantage of the NLSY79 relative to other data sources used to study health trajectories is the length of time that individuals are observed in the study. Every two years, starting in 1980 and ending in 2010, this study observes the respondent’s self-reported health information, which provides 16 waves of data covering a 30-year period. NLSY79 sample weights are used in the latent class analyses and descriptive statistics to account for the unequal probability of selection into the sample. 3 Full information maximum likelihood (FIML) estimation is used to handle missing data, which ensures that all available information is retained in the analysis (Enders and Bandalos 2001). Health Impairment Trajectories and Latent Class Analysis The key NLSY79 variables of interest for this study capture an individual’s health status as it relates to her/his ability to work. Any work-limiting health impairment constitutes a health-related issue that prevents someone from working given that s/he would be able to work had her/his health not interfered. Mental health issues, chronic disease, short-term physical injuries, long-term disabilities, and other health-related factors are potential work-limiting impairments. At each wave of the NLSY79, respondents are asked whether their health prevents them from “working at a job for pay now,” whether they are “limited in the kind of work you do on a job for pay because of your health,” and whether they are “limited in the amount of work you do because of your health.” Any respondent answering affirmative to any of these three questions is coded 1 for having a health impairment and coded 0 if no impairment is present. These three survey questions combined capture any health impairment that hinders an individual’s capacity for doing paid work.4 How the pattern of response to this dichotomous variable changes over the duration of the study, and how that pattern differs by race, is evaluated via a latent class analysis (LCA). LCA is ideally suited for this study because it allows researchers to (1) identify subgroups of NLSY79 respondents who share similar health trajectories; (2) examine 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-andunderstanding-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).

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the shape, severity, and extent of these distinct trajectories; and (3) determine the racial and childhood background characteristics associated with each latent health trajectory. LCA is a classification tool that is useful for studying unobserved and potentially heterogeneous types of trajectories when categorical data are provided longitudinally (Collins and Lanza 2009). When the characteristics of individual trajectories are suspected of being markedly different—for example, with different timings of onset, durations, and rates of change—LCA can infer from the observed patterns in the longitudinal data the key distinguishing characteristics. This approach allows researchers to identify salient types of trajectories by assessing the commonalty and differences in the item response probabilities (i.e., probability of an impairment) across each wave of data. After an optimal population profile of trajectories is determined through model selection techniques (discussed later), further analyses can be conducted to assess racial disparities via a multigroup analysis. If the general profile is invariant across racial groups, then a multinomial regression analysis as well as a decomposition of direct and indirect effects can determine the pathways from childhood background to adult health disparities. Multigroup Analysis After a population profile is selected from the full NLSY79 sample, steps are taken to examine measurement invariance across racial groups. Establishing measurement invariance is important because it seeks to validate a universal profile from which meaningful racial comparisons can be drawn. Measurement invariance requires the item response probabilities among those with similar health trajectories be near equal. Only if these item response probabilities are equivalent can the underlying latent class profile be valid for both groups. At that point, meaningful comparisons of the latent class prevalences—that is, the share of the population represented among each unique health trajectory—are justified. Three latent class models are estimated to test for measurement invariance: (1) a fully constrained model, (2) a semiconstrained model, and (3) an unconstrained model. The most optimal global latent class solution is used as the base model, and race (nonHispanic white vs. non-Hispanic black) is used as the multigroup variable. To determine whether the latent class trajectories are statistically equivalent for whites and blacks, we compare the model fit of the semiconstrained model with the fit of the unconstrained model. If the semiconstrained model is more parsimonious than the unconstrained model, this will indicate measurement invariance. Measurement invariance leads us to reject the strong version of cumulative structural disadvantage. One can then test for racial disparities by comparing the semiconstrained model with the fully constrained model. If the semiconstrained model fits better than the fully constrained model, then there is evidence of unequal latent class prevalences between whites and blacks. More details of the multigroup analysis are provided in the Results section (also see Collins and Lanza 2009). Multinomial Regression Analysis and Childhood Background Variables Multinomial regression models are used to assess the direct and indirect pathways from childhood to a health trajectory of work-limiting impairment. The NLSY79 contains a

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rich battery of information about the respondents’ childhood background. Although the NLSY79 does not contain specific questions about childhood health status, it does ask respondents when their health limitations began. From this information, it is possible to construct a childhood health status variable based on whether the respondent had any health impairments prior to turning age 18. This variable is coded 1 if the respondent had a health impairment prior to turning 18, and 0 otherwise. The education level of the mother and the father’s occupational status is used to assess childhood socioeconomic background. Where data are missing for mother’s education or father’s occupational status, the father’s education and mother’s occupational status is imputed. Occupational status is measured using Duncan’s socioeconomic index (SEI). Other important variables are whether the child was raised in an intact household (Biblarz et al. 1997; McLanahan and Sandefur 1994), whether the mother worked outside the home (Haveman et al. 1991),5 and the number of siblings in the household utilizing family resources (Conley 2004). An intact household is defined as living with both biological parents at age 14 and is dummy coded [1, yes; 0, otherwise]. Whether the mother worked outside the home at age 14 is also dummy coded [1, yes; 0, otherwise].6 Number of siblings is measured with data from the 1979 interview. Regression models also include the median income level of the county where the respondent lived at age 14 as a proxy measure of the local opportunity structure. Although certainly not exhaustive, these variables do capture several essential aspects of the environment that children experience while growing up that affect adult outcomes later in life (e.g., Fischer et al. 1996). These core childhood background characteristics are expected to influence an individual’s health impairment trajectory in several ways. One key pathway is through the influence of childhood background on career opportunities and occupational attainments. However, because of the selective and reciprocal relationship between health and career achievement, it is a challenge to assess the influence of one’s occupational life on health outcomes. For this reason, the three measures that capture the occupational pathway are assessed in young adulthood, rather than later in life, when the relationship between health and occupation is confounded. The three measures are (1) an SEI score for the respondent’s occupational aspiration during late adolescence, (2) the occupational status of the respondent’s first job after leaving school, and (3) the average rate of occupational mobility for that first occupation. The occupational status of the first job is operationalized by taking the first occupational code in the respondent’s record after a break in their consecutive annual school enrollment. The average rate of occupational mobility is an occupational-level measure (not an individual-level measure) based on the empirical Bayes estimates from an auxiliary linear growth curve model of occupational status mobility (not shown). 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.

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Operationalizing the career influences in this manner is beneficial because it captures a period when young adults are likely seeking employment for the purpose of gaining independence from their parental household; it is when family status likely has the greatest influence on employment opportunities; and importantly, the potential influences of health on occupational attainment are at the lowest point, thus minimizing (but not eliminating) other endogeneity concerns. Direct and Indirect Pathways Last, assessing the relative importance of the direct and indirect pathway from childhood disadvantage to racial health disparities requires an appropriate decomposition method. The method typically used for assessing mediation from a path analysis using linear regressions cannot be used in the context of nonlinear probability models (e.g., those using a logit link) because the change in the mediated coefficient (e.g., race) is influenced not only by the mediators (e.g., childhood background) but also by a rescaling of the logit coefficients in relation to the error variance. The Karlson-HolmBreen (KHB) method is appropriate for assessing mediation in the context of multinomial regressions (Breen et al. 2013). The KHB method compares the adjusted model (i.e., full model) with the unadjusted model (i.e., reduced model) by calculating a correction factor that makes the mediated coefficient from the unadjusted model comparable to the adjusted model. The KHB method distinguishes the change in the coefficient that is due to true mediation from the change that is due to rescaling.

Results Table 1 provides the results from the LCA. Following the recommendations in Nylund et al. (2007), the analysis begins by sequentially fitting models, beginning with two latent classes and progressing upward until there is a lack of improvement in model fit according to Lo, Mendell, and Rubin likelihood ratio test (LMR-LRT).7 The LMR test uses an approximation to the LRT distribution to compare nested LCA models with k latent classes to a model with k – 1 latent classes. The Bayesian information criterion (BIC) and entropy measure are provided as secondary considerations in determining the optimal number of latent classes. The results from the LMR test indicate that a five-class solution is ideal. With each additional latent class up to five latent classes, model fit improves over the previous model. Using a probability level of .05 to reject the null hypothesis of no difference between a model with k and k – 1 latent classes, the five-class solution is a significant improvement over the four-class solution (p = .019). The LMR test fails to reject the null of no difference between the six-class solution and the five-class solution (p = .490).

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.

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Table 1 A latent class analysis of health trajectories: National Longitudinal Survey of Youth, 1980–2010 A. Model Fit Statistics No. of Latent Classes

No. of Parameters

LogLikelihood

LMR LRT

χ2 df

LMR p Value

BIC

Entropy

2-Class Solution

33

–27,202.4

17,869.7

17

.000

54,707.6

.904

3-Class Solution

50

–26,271.3

1,850.3

17

.000

53,001.6

.848

4-Class Solution

67

–25,734.2

1,067.2

17

.002

52,083.6

.841

5-Class Solution

84

–25,506.6

452.3

17

.019

51,784.5

.843

101

–25,395.5

220.9

17

.490

51,718.3

.848

6-Class Solution

B. Descriptive Statistics for the 5-Class Solution

Healthy

Latent Class Prevalence

Average Probability of Latent Class Membership

N

Healthy

%

Disabled

Early Onset

Late Onset

Unhealthy Repeaters .10

7,725

79.5

.94

.00

.01

.06

Disabled

312

3.2

.00

.89

.05

.00

.02

Early Onset

401

4.1

.01

.08

.83

.06

.02

Late Onset

618

6.4

.02

.00

.08

.80

.07

Unhealthy Repeaters

659

6.8

.03

.03

.03

.08

.79

9,715

100.0

Total

Note that the BIC and entropy statistics favor the six-class solution over the fiveclass solution, but five of the health trajectories in the six-class solution model are very similar in functional form to the five-class solution. The key difference is the addition of a sixth trajectory that appears to be made up of individuals that overcome an earlyin-life impairment as their trajectories move from a high initial probability of impairment to a lower probability of impairment over time (downward slope). However, the remainder of the analyses relies on the five-class solution because the LMR is a formal statistical test, and the prevalence of a sixth trajectory is small (less than 2 %). 8 The bottom of Table 1 provides the latent class prevalences and the average probability of latent class membership for the five-class solution. This information supplements the entropy statistic to more transparently assess the quality of the model solution in terms of within-class homogeneity and classification error. The average probabilities for class membership in Table 1 are near or all above .80 (along the diagonal), which means that the probability of incorrect classification is generally less than 20 %—a desired threshold in LCA analysis (Collins and Lanza 2009).

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 sixclass 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.

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Probability of Health Limitation Affecting Work Ability

Figure 1 illustrates the five latent health trajectories and makes apparent how the duration of impairment, the rate of change between health states, and the timing of onset influence the functional form of a health trajectory. By far, the majority of respondents are classified as “healthy” (79.5 %). Those in the healthy class have a very low probability of having a health issue that limits their ability to work over a 30year period. The second most common trajectory falls into the “unhealthy repeaters” classification (6.8 %), in which people over time have periodic spells with one or more health issues that limit their ability to work. It is worth noting that when the trajectories for a random subset of individual cases in the unhealthy repeaters class are plotted, the plot resembles an up-and-down saw-tooth pattern (not shown). In the aggregate depicted in Fig. 1, the random up-and-down pattern among different individuals produces an elevated average probability of impairment for the group that fluctuates only slightly. Unhealthy repeaters have a modest but consistent risk of impairment over a 30-year period that is notably higher than the average risk among the healthy class. The third most prevalent class are those with a “late onset” impairment (6.4 %), followed by the “early onset” class (4.1 %). Unlike the unhealthy repeaters, after a health impairment is initiated among the late and early onset classes, their health limitation quickly increases and appears to be fixed at a high level of limitation for the duration of the study period, especially among those with early onset. Last, a “disabled” class (3.2 %) has the lowest representation among the five classes. Members of the disabled class have a higher probability of a health impairment at the start of the study; on average, though, the probability of a work-limiting impairment for this group does not reach the peak until roughly 15 years after the start of the study. Perhaps some disabled respondents in their late teens and early 20s with limited experience in the labor force have yet to recognize (and thus report) the ways in which their health may affect their ability to work. Overall, the LCA results provide a population profile that incorporates heterogeneous health histories by grouping individuals into parsimonious categories that reflect common experiences via their shared health trajectory characteristics. This profile can now be used as a heuristic to further study the process of endogenous selection among different groups. Table 2 proceeds with the multigroup analysis. The aim of the multigroup analysis is to test for measurement invariance by race across a general health trajectory profile. 1.0 0.8 Disabled Early onset

0.6

Late onset

0.4 Unhealthy repeaters

0.2

Healthy

0.0 1980 15–23

1985 20–28

1990 25–33

1995 30–38

2000 35–43

2005 40–48

2010 45–53

Year/Age

Fig. 1 A latent class analysis of health impairment trajectories: National Longitudinal Survey of Youth 1980–2010

Cumulative Disadvantage and Health Trajectories

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Using only non-Hispanic white (N = 3,937) and non-Hispanic black (N = 2,913) NLSY respondents, the multigroup analysis compares three nested models: (1) a fully constrained model that sets the item response probabilities to equality (i.e., equal probability of a health impairment for whites and blacks that are in the same year and in the same latent class) and the latent class prevalences to equality (i.e., equal share of white and black respondents in each latent class); (2) a semiconstrained model that frees the equality constraint on the latent class prevalences; and (3) an unconstrained model that frees the equality constraint on both the latent class prevalences and the item response probabilities. According to the likelihood ratio tests and the BIC criteria, the semiconstrained model is the best-fitting model. The semiconstrained model has the lowest BIC score, indicating a more parsimonious model fit relative to the fully constrained and unconstrained model. More formally, according to the LRT, the semiconstrained model is an improvement over the fully constrained model (p < .001); by comparison, at a probability level of .05, the unconstrained model is deemed statistically equivalent to the more parsimonious semiconstrained model (p = .093). Therefore, we conclude that the general shape and pattern of the five health trajectories are statistically equivalent among whites and blacks, but that there are unequal prevalences among the five latent class trajectories. Figure 2 illustrates the similarity in the trajectories between whites and blacks using the estimates from the unconstrained model in which, each racial group’s item response pattern is free to vary. Two features are apparent in Fig. 2: (1) the profile for whites and blacks are remarkably similar even without the use of constraints, and (2) the profile for whites and blacks closely resembles the pattern observed for the full sample in Fig. 1. The modest exception is that the average timing of the onset of a health impairment is typically later among blacks than among whites. Perhaps racial differences in access to health care (Smart and Smart 1997) delay diagnoses for blacks relative to whites, thus delaying the recognition and acceptance of a health impairment. The fact that the general profile is replicated on two NLSY subsamples also adds confidence in the validity of the five-class solution. Table 3 examines the extent of racial disparity across the five health trajectories and across several important childhood background characteristics. As anticipated, blacks are underrepresented in the healthy class (72.0 % of blacks vs. 81.2 % of whites), and blacks are overrepresented among all the trajectories with higher probabilities of a health impairment relative to whites: disabled (4.1 % vs. 2.5 %), early onset (6.5 % vs. 4.0 %), late onset (8.6 % vs. 5.2 %), and unhealthy repeaters (8.9 % vs. 7.0 %). These disparities are statistically significant. When considering the societal impact of having nearly one-third of blacks (100 – 72.0 = 28.0 %) eventually experiencing at least some Table 2 Multigroup latent class analysis of health trajectories: Non-Hispanic black and non-Hispanic white respondents from the National Longitudinal Survey of Youth, 1980–2010 No. of Parameters

Log-Likelihood

LRT

χ2 df

p Value

BIC

Fully Constrained

85

–21,308.496

Semiconstrained

89

–21,277.770

61.452

4

.000

43,341.59

169

–21,229.168

97.204

80

.093

43,950.95

Unconstrained

43,367.71

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Probability of Health Limitation Affecting Work Ability

1.0

0.8

0.6

0.4

0.2

0.0 1980 15–23

1985 20–28

1990 25–33

1995 30–38

2000 35–43

2005 40–48

2010 45–53

Year/Age

Fig. 2 White (gray lines) and black (black lines) health impairment trajectories from an unconstrained LCA: National Longitudinal Survey of Youth, 1980–2010

form of a work-limiting impairment during their prime working years, and considering that the overall racial disparity is almost a full 10 percentage points (28.0 % among blacks vs. 18.8 % among whites), the consequential health effects on racial socioeconomic inequality, and on the productivity level of a society in general, are potentially large. Blacks are also significantly disadvantaged relative to whites on several important socioeconomic background characteristics. Parental education is more than two years less for blacks than for whites; parental occupational status is more than 10 points less for blacks than for whites; blacks are considerably less likely than whites to come from an intact household at age 14 (.51 vs. .81); and blacks have significantly more siblings, have more working mothers, and grow up in poorer counties. Blacks have lower occupational aspirations, and they attain lower levels of occupational status upon entry into the workforce. Racial differences in childhood health statuses are nether statistically nor practically significant. This nonsignificance adds support to the idea that racial health disparities develop primarily via a cumulative process that transpires over the life course. Table 4 provides the decomposition analysis from a series of multinomial regression models that include childhood background characteristics as mediators of the four types of racial health disparities. The multinomial logit coefficients for the covariates in all the estimated models are provided in Table 5 in the Appendix. Briefly, several variables have consistent and noteworthy main effects. Intact household and the occupational status of the respondent’s first job are statistically significant predictors of all four trajectory contrasts (disabled vs. healthy; early onset vs. healthy; late onset vs. healthy; and unhealthy repeaters vs. healthy). It is noteworthy that living in an intact household at age 14 is protective of health even after several socioeconomic background characteristics are controlled for. Also worth noting is that the negative effect of first job occupational status on selecting into the disabled health trajectory versus the healthy trajectory (β = –.012) could reflect additional endogeneity concerns not captured by the childhood health status indicator. That said, childhood health status is a very strong predictor of the selection into all the unhealthy trajectories relative to the healthy

Cumulative Disadvantage and Health Trajectories

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Table 3 Racial differences in latent class prevalence and childhood background characteristics: National Longitudinal Survey of Youth, 1980–2010 Latent Class Prevalence Non-Hispanic White

Non-Hispanic Black

Healthy (%)

81.2

72.0***

Disabled (%)

2.5

4.1**

Early Onset (%)

4.0

6.5***

Late Onset (%)

5.2

8.6**

Unhealthy Repeaters (%)

7.0

8.9**

Background Characteristics

Mean

SD

Mean

SD

Women

.50

.50

.51

Child’s health status

.05

.22

.04

.50

Parental education

12.13

2.35

10.87

2.55***

Parental occupational SEI

37.84

19.87

25.72

13.76***

Intact household at age 14

.81

.39

.51

.50***

Working mother at age 14

.51

.50

.60

.49***

Number of siblings

2.97

1.88

4.58

2.97***

Median county income at age 14 (in thousands, 1979 USD)

9.85

1.94

9.15

2.07***

.20

Occupational aspiration, SEI

48.73

20.09

44.74

19.76***

Occupational status of first job

47.37

24.71

39.51

22.96***

.07

.56

Average occupational mobility of first job N

3,899

.05

.49

2,851

**p < .01; ***p < .001 (two-tailed independent sample t test for racial difference)

trajectory, except for the late onset trajectory, for which I anticipate and find stronger occupation-related effects. The upper half of Table 4 reports the total mediation effect on the racial health disparities. The unadjusted logits reported in the upper half capture the gross racial differences in the prevalences reported in Table 3. For example, blacks are 1.76 times (exp.564) more likely than whites to be in the disabled class relative to the healthy class; 1.87 times (exp.627) more likely than whites to be in the early onset class relative to the healthy class; 1.79 times (exp.585) more likely than whites to be in the late onset class relative to the healthy class; and 1.46 times (exp.376) more likely than whites to be in the unhealthy repeaters class relative to the healthy class. After controls are added for childhood background characteristics, the racial health disparities are no longer statistically significant, and the difference between the unadjusted estimates and the adjusted estimates are all highly significant beyond the p < .001 level. Using the KHB method of decomposition (Breen et al. 2013), I find that the background characteristics account for 74 % of the initial racial disparity in the disabled class, 64 % of the disparity in the early onset class, 64 % of the disparity of the late onset class, and 78 % of the initial racial

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Table 4 Direct and indirect pathways of childhood background to a racial disparity among health impairment trajectories: National Longitudinal Survey of Youth, 1980–2010

Race Effect: Non-Hispanic Black

Disabled vs. Healthy

Unadjusted Logits (SE)

.564*** (.142)

Adjusted Logits (SE)

.627*** (.113)

.585*** (.101)

Repeaters vs. Healthy .376*** (.093)

.139

.228

.209

.084

(.132)

(.121)

(.108)

.425*** (.093)

Correction Factor

Late Onset vs. Healthy

(.170) Difference in Logits (SE) Percentage Change

Early Onset vs. Healthy

74.41 1.110

.399*** (.074) 63.60 1.123

.376*** (.060) 64.31 1.076

.292*** (.057) 77.68 1.111

Mediation Effects as a Percentage of the Unadjusted Racial Difference Components of the Difference

% Reduction

% Reduction

% Reduction

% Reduction

Child’s health status

ns

ns

ns

ns

Parental education

12.52

ns

ns

ns

Parental occupational SEI

ns

22.52

ns

23.60

Intact household at age 14

22.87

12.83

11.71

16.61

Direct Pathway

Working mother at age 14

ns

ns

ns

ns

Number of siblings

24.61

ns

ns

ns

Median county income

ns

ns

ns

ns

35.35

11.71

40.21

Total % from significant effects 60.00 Indirect Pathway Occupational aspiration, SEI

ns

6.71

3.38

7.97

Occupational status of first job

16.67

14.33

25.60

15.81

Avg. occupational mobility of first job

ns

ns

ns

ns

21.04

29.98

23.78

Total % from significant effects 16.67

Notes: Estimates are based on the models in Table 5 in the Appendix and the KHB method of decomposition of categorical data (see Breen et al. 2013). ns = not a statistically significant mediator at p < .05 (two-tailed test). ***p < .001 (two-tailed test)

disparity in the unhealthy repeaters class.9 For a modest set of background measures, these are substantial mediation effects. The bottom half of Table 4 provides further details about the explanatory power of each specific background variable and its direct and indirect contributions. Among the background characteristics that directly mediate racial health disparities are parental education and parental occupational status (except among those with late onset 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).

Cumulative Disadvantage and Health Trajectories

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impairment);10 the number of siblings is a statistically significant mediator for the disabled class; and being raised in an intact household consistently emerges as a significant mediator. Neither childhood health status (because little racial disparity exists in this variable) nor median county income emerge as significant mediators. Combined, the statistically significant childhood background characteristics account for 60 % of the mediation effect among the disabled, 35 % among those with early onset, 12 % among those with late onset, and 40 % among the unhealthy repeaters group. Comparatively, the mediation effects stemming from the indirect pathway account for 17 % of the racial disparity among the disabled class, 21 % among the early onset class, 30 % among the late onset class, and 24 % among the unhealthy repeaters class. Occupational status of respondents’ first job is consistently a statistically significant mediator; and among those with late onset impairment, the indirect pathway accounts for more than twice the mediation effect of the direct pathway (11.71 % vs. 29.98 %). Together, these findings indicate the significant importance of childhood background in directly and indirectly affecting racial health disparities among adults.

Conclusion Researchers face considerable challenges in disentangling the selective and reciprocal relationships between race, SES, and health. Cumulative structural disadvantage theory coupled with health trajectory research is capable of making major strides in addressing this challenge. Studying the development of different types of health impairment trajectories and the pathways through which childhood background characteristics channel individuals into these different trajectories illuminate the role of cumulative structural disadvantage in shaping racial health disparities. The results from this study should influence cumulative structural disadvantage theory and future health trajectory research in at least three notable ways. First, this study employs a latent class analysis to derive a population profile of health impairment trajectories among individuals in their prime working years. The most reliable profile consists of five common health trajectories: (1) the large majority of people in their prime working years—that is, late teens/early 20s to their late 40s/ early 50s—don’t experience a health impairment that limits their ability to work; (2) there are individuals that are disabled, or chronically unhealthy, at an early age, which affects their employment throughout adulthood; (3) a notable share of individuals experience periodic health problems that temporarily affect their ability to work; and last, there are those that experience a relatively early (4) or relatively late (5) onset of a health impairment that, once established, becomes a permanent hindrance to their employment. This profile of five different types of health trajectories suggests that the selective process of cumulative structural disadvantage—the idea that health insults tend to accrue with greater frequency and magnitude among those structurally positioned toward the bottom of a social hierarchy (Marmot 2005)—becomes manifest at different times during the life course, with different rates of acceleration and for varying durations of time. But far from being a bewildering array of heterogeneous health

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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experiences, the selective process appears to unfold largely in a patterned and thus predictable fashion. Second, by comprehensively examining the health trajectory differences between whites and blacks via a multigroup invariance analysis, this study offers insight into the processes of cumulative structural disadvantage as it relates to racial and ethnic stratification more generally. The strong version of cumulative disadvantage theory suggests that blacks face such unparallel disadvantages and such unique historical antecedents and cultural norms regarding lifestyles, health habits, and work attitudes as to render the profile of health trajectories between whites and blacks of an entirely different making. This does not appear to be the case. The health trajectory profiles for whites and blacks are remarkably similar visibly and essentially equivalent statistically. However, the analysis here finds the extent of the racial disparity in the prevalences among health impairment trajectories to be quite large. In all instances, blacks are far more likely than whites to experience trajectories involving health problems. The estimated racial disparity across a commonly shared profile of health trajectories supports a weaker version of cumulative disadvantage theory, which tends to underscore the structural disadvantages associated with the disproportionate burden of poverty among blacks. Third, this study takes advantage of the common profile of health trajectories to study the pathways through which childhood socioeconomic background affects adult racial health disparities. Knowledge about how childhood advantages and disadvantages accumulate to affect health and work outcomes helps explain health disparities among adults (Case et al. 2002; Haas 2007, 2008; Haas and Rohlfsen 2010; Luo and Waite 2005). A goal of this study is to assess the extent to which childhood background affects adult racial health disparities either indirectly through its influence on occupational attainment or directly through earlylife exposure to health-adverse environments. The results indicate that both pathways matter for explaining racial health disparities. The direct pathway matters more among those in the disabled trajectory and among those in the early onset trajectory; the indirect pathway accounts for more of the racial disparity among those with a late onset impairment trajectory. In total, racial differences in childhood background account for most, if not all, of the racial disparity among the unhealthy trajectories. These findings work against any theory that privileges explanations for poor minority health and poor minority employment productivity based on notions of racial exceptionalism regarding sociobiological and/or cultural differences. Alternatively, this study suggests that what requires more concerted attention are the institutional and structural conditions that continue to disproportionately subject black children to impoverished environments growing up. Although this study advances prior work in several important ways, it is not without limitation. Implicit in why childhood socioeconomic advantages and disadvantages affect an individual’s health trajectory is the assumption that the social environment and employment options leading into adulthood matter. This research, however, is unable to fully untangle the relative import of the specific mechanisms that are responsible for the observed trajectories, such as identifying work hazards that are specific to certain occupations, identifying specific reasons for economic and social vulnerabilities that are known to induce stress, accounting for longstanding hereditary health issues, and accounting for actual lifestyle behaviors that inevitably affect who develops what kind of health trajectory. In other words, we know that children from disadvantaged backgrounds from all races disproportionately experience health impairments, but this study cannot speak to the salience of these numerous mechanisms that make this relationship so durable.

Cumulative Disadvantage and Health Trajectories

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Similarly, this study cannot speak to the severity of specific health problems because the measurement of health focuses on the broadest possible range of potential health impairments. Additional research could be conducted to determine what kinds of health problems lead to particular kinds of health impairment trajectories and whether specific health problems are related to these trajectories by affecting the duration, rate of change, and/or timing of the impairment. Moreover, without detailed health histories, some childhood health information is inevitably omitted. This is problematic if the child’s health issue is severe enough to have influenced the parents’ socioeconomic position or significant enough to have shaped the young adult’s employment prospects. The disabled trajectory and the early onset trajectory are likely to be the most affected by this omission. This study controls for the presence of a child health problem to minimize this concern, but having more information about the severity of the health problem would give the exogeneity assumption concerning childhood SES and young adult employment options greater credibility. This study could also be improved with more data about the accumulation of specific health insults. Detailed information about specific types of health insults could help further our understanding of how cumulative structural disadvantage fosters the selective and reciprocal relationships among race, SES, and health.

Appendix Table 5 Mulitnoimial logistic coefficients for the determinants of latent class membership into five health impairment trajectories: NLSY79 1980–2010 (N = 6,750) Model 1

Model 2

Model 3

Model 4

β(SE)

β(SE)

β(SE)

β(SE)

Disabled vs. Healthy Non-Hispanic black Women

0.508***

0.137

(0.139)

(0.170)

0.300*

0.243

(0.139)

(0.142)

0.391** (0.140) 0.410** (0.144)

0.139 (0.171) 0.322* (0.147)

Background characteristics Child’s health status Parental education

2.193***

2.161***

(0.190)

(0.193)

–0.071*

–0.054

(0.031)

(0.031)

Parental occupational SEI

–0.008

–0.005

(0.005)

(0.005)

Intact household at age 14

–0.463***

–0.430***

(0.150)

(0.149)

Working mother at age 14

–0.162

–0.158

(0.144)

(0.144)

Number of siblings

0.091***

0.082**

(0.026)

(0.026)

0.060

0.066

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Table 5 (continued) Model 1

Model 2

Model 3

Model 4

β(SE)

β(SE)

β(SE)

β(SE)

Median county income at age 14 (in thousands, 1979 USD)

(0.035)

(0.035)

Occupational aspiration, SEI

–0.005 (0.004)

(0.004)

Occupational status of first job

–0.016***

–0.012***

(0.004)

(0.004)

Average occupational mobility of first job

–0.404**

–0.341*

Intercept

–0.002

(0.139)

(0.141)

–3.567***

–3.107***

–2.651***

–2.846***

(0.121)

(0.546)

(0.216)

(0.554)

Early Onset vs. Healthy Non-Hispanic black

0.558*** (0.111)

Women

0.503*** (0.113)

0.211 (0.132) 0.486*** (0.114)

0.426*** (0.112) 0.619 (0.116)***

0.228 (0.132) 0.586*** (0.117)

Background characteristics Child’s health status

0.967***

Parental education

0.931***

(0.216)

(0.218)

–0.070**

–0.044

(0.024)

(0.025)

Parental occupational SEI

–0.016***

–0.012**

(0.004)

(0.004)

Intact household at age 14

–0.299*

–0.268*

(0.120)

(0.121)

Working mother at age 14

–0.203

–0.189

(0.115)

(0.115)

Number of siblings Median county income at age 14 (in thousands, 1979 USD)

0.027

0.014

(0.022)

(0.023)

0.011

0.022

(0.028)

(0.029) –0.014***

Occupational aspiration, SEI

–0.011***

(0.003)

(0.003)

Occupational status of first job

–0.014***

–0.012***

(0.003)

(0.003)

Average occupational mobility of first job

–0.393**

–0.336*

Intercept

(0.131)

(0.131)

–3.231***

–1.752***

–2.011***

–1.331***

(0.105)

(0.386)

(0.179)

(0.397)

Late Onset vs. Healthy Non-Hispanic black

0.544*** (0.099)

0.208 (0.120)

0.389*** (0.100)

0.209 (0.121)

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1749

Table 5 (continued)

Women

Model 1

Model 2

Model 3

Model 4

β(SE)

β(SE)

β(SE)

β(SE)

0.118

0.102

(0.099)

(0.099)

0.278** (0.102)

0.248* (0.103)

Background characteristics Child’s health status

0.317

0.265

(0.248)

(0.247)

Parental education

–0.072***

–0.045*

(0.022)

(0.023)

Parental occupational SEI

–0.006

–0.002

(0.003)

(0.003)

Intact household at age 14

–0.273*

–0.228*

(0.109)

(0.110)

Working mother at age 14

–0.160

–0.148

(0.101)

(0.102)

0.034

0.019

Number of siblings Median county income at age 14 (in thousands, 1979USD)

(0.019)

(0.019)

–0.056*

–0.043

(0.025)

(0.026)

Occupational aspiration, SEI

–0.007** (0.003)

(0.003)

Occupational status of first job

–0.022***

–0.020***

(0.003)

(0.003)

Average occupational mobility of first job

–0.386***

–0.349**

(0.112)

(0.112)

Intercept

–0.005

–2.761***

–0.961**

–1.561***

–0.553

(0.087)

(0.340)

(0.153)

(0.342)

Repeaters vs. Healthy Non-Hispanic black

0.338*** (0.092)

Women

0.434*** (0.093)

0.080

0.237*

0.084

(0.108)

(0.094)

(0.108)

0.406*** (.094)

0.529*** (0.095)

0.488*** (0.096)

Background characteristics Child’s health status

1.334*** (.163)

(.167)

Parental education

–.055**

–.038

(.021)

(.021)

Parental occupational SEI

–.010**

–.007*

(.003)

(.003)

Intact household at age 14

–.238*

–.208*

(.101)

(.102)

.057

.067

Working mother at age 14

1.302***

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Table 5 (continued) Model 1

Model 2

Model 3

Model 4

β(SE)

β(SE)

β(SE)

β(SE)

Number of siblings Median county income at age 14 (in thousands, 1979 USD)

(.094)

(.095)

0.027

0.017

(0.019)

(0.019)

0.016

0.026

(0.023)

(0.023)

Occupational aspiration, SEI

–0.010*** (0.003)

(0.003)

Occupational status of first job

–0.010***

–0.008***

(0.002)

(0.002)

Average occupational mobility of first job Intercept AIC

0.022

0.066

(0.098)

(0.099) –1.558***

–2.680***

–1.832***

–1.822***

(0.083)

(0.327)

(0.150)

11,288.34

11,032.23

–0.008**

11,094.66

(0.333) 10,921.12

*p < .05; **p < .01; ***p < .001 (two-tailed tests)

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