Journal of Adolescence 37 (2014) 883e892

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Journal of Adolescence

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Social consequences of early socioeconomic adversity andyouth BMI trajectories: Gender and race/ethnicity differences

Dayoung Bae*, K.A.S. Wickrama, Catherine Walker O'NealDepartment of Human Development and Family Science, The University of Georgia, 107 Dawson Hall, 305 Sanford Drive, Athens, GA30602, USA

Keywords:Early socioeconomic adversityBMI trajectoriesYoung adult socioeconomic attainment

Abbreviations: BMI, body mass index; CAD, cummodeling; FIML, full information maximum likeliho* Corresponding author. Tel.: þ1 706 410 5036.

E-mail addresses: dbae@uga.edu (D. Bae), wickra

http://dx.doi.org/10.1016/j.adolescence.2014.06.0020140-1971/© 2014 The Foundation for Professionals

a b s t r a c t

The present study investigated the mediating effects of adolescent BMI trajectories onsocioeconomic continuity over the early life course using a nationally representativesample of 11,075 respondents. This study considered both the initial severity as well aschange over time in BMI as psycho-physiological mediators. Consistent with the life coursepathway model and the cumulative advantage and disadvantage principle, the resultssuggested that early socioeconomic adversity is associated with youth BMI trajectoriesover time, which in turn, impair young adult socioeconomic attainment. The results alsorevealed important gender and racial/ethnic differences in the hypothesized associations.These findings elucidate how early adversity exerts an enduring long-term influence onsocial attainment in young adulthood. Further, the findings suggest that effective obesityintervention and prevention programs should focus not only on the severity of obesity butalso on growth in BMI over the early years.© 2014 The Foundation for Professionals in Services for Adolescents. Published by Elsevier

Ltd. All rights reserved.

In the United States, national prevalence data show that approximately 34% of adolescents are overweight or obese(Ogden, Carroll, Kit, & Flegal, 2012). The high body mass of children and adolescents has grown into a global major publichealth concern because being overweight in one's early years is a robust predictor of obesity and negative social and healthoutcomes later in life (Ferraro & Kelley-Moore, 2003).

Mountingevidenceindicatesthatyouthobesityorbeingoverweight is, inpart,sociallystructured(Young&Nestle,2002).Previousresearchhasdocumentedyouthobesityorbeingoverweight (asmeasuredusingbodymass index,hereafter,BMI) is affectedbycommunityand familysocioeconomic adversities and produces numerous health and socioeconomic problems in young adulthood (Burdette & Needham, 2012;Crosnoe, 2007;Merten,Wickrama,&Williams, 2008;Wickrama, O'Neal,& Lee, 2013).

Other research has investigated the socioeconomic consequences of adolescent obesity. Obesity, especially when expe-rienced early in the life course, is strongly linked to negative consequences in adulthood (Ferraro & Kelley-Moore, 2003).Particularly, adolescent obesity has a negative effect on young adult socioeconomic status as measured by income andoccupation (Ball, Crawford,& Kenardy, 2004; Conley& Glauber, 2006). Thus, adolescent weight status appears tomediate theeffect of childhood/adolescent socioeconomic experiences on socioeconomic attainment in young adulthood.

ulative advantage and disadvantage principle; LGC, latent growth curve; SEM, structural equationod.

ma@uga.edu (K.A.S. Wickrama), cwalker1@uga.edu (C.W. O'Neal).

in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892884

However, previous research in this area is fragmented, focusing on either antecedents or consequences of youth obesitybut not both. Moreover, most previous studies have used traditional mean comparisons with cross-sectional data orregression models, focusing on discrete obesity status at single points in time. Research that has examined multiple points intime has been limited to relatively short time spans. Therefore, less is known about the continuous life-course processesinvolving youth BMI trajectories stemming from early family socioeconomic circumstances and contributing to young adults'social and health outcomes.

Drawing from the life course developmental perspective (Elder & Giele, 2009) and the existing research, we contend thatsocioeconomic adversity in early years initiates a chain of developmental successes or failures leading to impaired socio-economic attainment in young adulthood (O'Rand & Hamil-Luker, 2005). In the present study, we will analyze a life-coursepathway model to examine potential meditational process (Willson, Shuey, & Elder, 2007); more specifically, we examine ifearly socioeconomic adversity contributes to adverse BMI trajectories of youth and, in turn, if these trajectories impair youngadult socioeconomic attainment. In addition, consistent with the life course cumulative advantage and disadvantage (CAD)principle (Dannefer, 2003), we expect that the influence of early socioeconomic adversity on youth BMI strengthens with age.In order to elucidate this cumulative disadvantage processes over the life course, we investigate how early adversity shapesyouth BMI trajectories over time (Wickrama et al., 2013). With regard to these hypothesized associations, the present studyalso investigates potential gender and race/ethnicity differences. Existing research suggests that such differences are likely(S�anchez-Vaznaugh, Kawachi, Subramanian, S�anchez, & Acevedo-Garcia, 2009), but studies focusing on gender and race/ethnicity differences in these associations are rare.

Early family/community socioeconomic adversity and youth obesity

Numerous studies have found that family economic hardship, low parental education, parents'marital history, and adversecommunity characteristics may impact youths' BMI (Burdette&Needham, 2012; Shin&Miller, 2012;Wickrama,Wickrama,&Bryant, 2006). Adolescents from poor families and single-parent families lack health resources, such as proper food, access torecreation facilities, proper housing, and health services (Fitzgibbon et al., 1998). Furthermore, less educated parents may lackthe health knowledge and information necessary for healthy child rearing. Socioeconomically disadvantaged parents are alsomore likely than others to transmit their unhealthy behaviors and risky lifestyles (e.g., unhealthy eating behaviors, lack ofexercise) to their offspring (Wickrama, Conger, Wallace, & Elder, 1999).

In addition to family socioeconomic characteristics, there appears to be several adverse community processes thatcontribute to obesity. First, poor communities are unable to meet their residents' dietary and health-related needs (Story,Kaphingst, Robinson-O'Brien, & Glanz, 2008). For example, unaffordable prices can limit access to proper food in poorcommunities, and poor communities have a greater number of unhealthy fast food restaurants than do higher incomecommunities (Kipke et al., 2007). In addition, lack of recreational activities, lack of community safety for physical activities,and lack of availability and accessibility of health care services in disadvantaged communities may also contribute to thehigher prevalence of adolescent obesity in these communities (Wen & Maloney, 2011).

In the present study, we will investigate individual trajectories of BMI to explain how socioeconomic adversity in earlyyears influences not only early BMI levels (severity) but also the subsequent individual changes (deteriorations or im-provements) in BMI over an extended period of time. Importantly, the influence of early adversity on health outcomes maygain momentum over time (cumulative effect of early adversity) with increasing age, which may be reflected in changes inBMI over time. Consistent with the life course cumulative advantage and disadvantage principle (Dannefer, 2003), this cu-mulative effect contributes to enlarging socioeconomic BMI inequality over time. Thus, as shown in Fig. 1, we expect earlysocioeconomic adversity, as captured by a composite index of family and community adverse characteristics in early years, toinitiate and shape youth BMI trajectories from adolescence to young adulthood.

Youth obesity and socioeconomic attainment in young adulthood

Several studies have documented that young adults' labor market outcomes are influenced by high BMI or obesity.Obese employees experienced lower wages for the same job, fewer hiring opportunities in high-level positions, and lower

Fig. 1. Conceptual model.

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892 885

promotion prospects than their non-obese counterparts (Puhl & Brownell, 2001). Although both obese men and womenface wage-related obstacles, obesity has a particularly negative effect on the hourly wages for women (Cawley, 2004; Han,Norton, & Powell, 2011). Work-related stereotypes, such as beliefs that overweight employees lack self-discipline, are lazy,less competent and emotionally unstable, could affect wages and promotion opportunities (Roehling, 1999). In addition topotential discrimination based on body size, one of the possible explanations for the association between obesity andnegative labor market outcomes is that obesity adversely affects health, thereby reducing work productivity (Wada &Tekin, 2010). In addition, obese individuals may invest less in accumulating human capital because they mayhave lower expectations about their future (Baum & Ford, 2004) and lower self-esteem (Cawley, 2004; Wada & Tekin,2010).

Educational attainment in adulthood may also be associated with high BMI or obesity. Earlier studies have shown thatobese students often reported lower academic performance and are less likely than their non-obese peers to graduate fromhigh school (Classen, 2009), enter college after high school (Crosnoe, 2007), and attain a college degree (Fowler-Brown, Ngo,Phillips, & Wee, 2009). Several linking mechanisms explain the association between BMI and educational attainment. Obesestudents may experience negative social feedback and discrimination from their classmates and teachers, and internalizethese negative social judgments into their self-concept. The social stigma attached to obesity may limit students' ability toreach their full educational potential (Crosnoe & Muller, 2004). It is also possible that parents may, purposely or sub-consciously, invest less in post-secondary education for their obese children because of the expected lower return fromfuture wages or a shorter lifespan to compensate the investment that they put into their children's education (Classen,2009).

In unraveling the hypothesized associations, a trajectory analysis allows for the examination of more dynamic life courseprocesses; the influence of both the level and change in BMI on subsequent socioeconomic outcomes in young adulthood(Wickrama, Beiser,& Kaspar, 2002). A recent study has shown that BMI trajectories are better predictors of mortality than BMIat one point in time (Zheng, Tumin, & Qian, 2013). Therefore, in the present study, we expect both the initial level and rate ofchange of BMI trajectories to be associated with young adults' socioeconomic attainment, as measured by education, income, andfinancial problems. By investigating BMI trajectories, we will examine how rapid gains in BMI during this period maycontribute to the adverse socioeconomic consequences of young adults.

Gender moderation

Previous studies suggest that there are gender differences in the influence of early socioeconomic conditions on youthBMI. For example, Chang and Lauderdale (2005), using data over a span of more than 30 years, found that low-incomeWhiteand Black women consistently experienced higher BMI than White and Black men. In regard to the socioeconomic conse-quences of obesity, McLaren (2007) found that the majority of studies examining women in industrialized countriesconcluded that, compared to smaller women, women with larger body size had lower socioeconomic status, but this asso-ciationwas less consistent for men. Several other developmental studies have also shown that obese female youth have lowerstatus attainment as young adults than male youth and non-obese females (Merten et al., 2008; S�anchez-Vaznaugh et al.,2009).

To our knowledge, previous studies have primarily examined the association between the level of BMI and the so-cioeconomic characteristics of individuals, but these studies have failed to examine gender differences in the associationbetween changes in BMI over time and the socioeconomic attainment of young adults, independent of the level of BMI.We expect gender differences in the associations between the antecedents and consequences of initial level and changes inBMI.

Race/ethnicity moderation

In regard to the socioeconomic consequences of obesity, studies have documented that the consequences are greater forWhites than members of minority racial/ethnic groups. For example, using a sample of 37,000 respondents, S�anchez-Vaznaugh et al. (2009) found that the shape and the strength of the relationship between BMI and SES differed markedlyby race/ethnicity with larger effects for Whites than Blacks and Hispanics. According to national data, obesity rates tended toincrease with decreased income among women, but this trend was only significant for White women (Freedman, 2011;Ogden, Lamb, Carroll, & Flegal, 2010). In contrast to women and White men, Black and Mexican-American men withhigher incomes had higher BMI levels than those with low income levels.

Most of these findings were drawn from mean comparisons of BMI, which may not reveal the associations betweenthe antecedents and consequences of individual BMI change, independent of the level of BMI. Thus, furtherinvestigation into racial/ethnic and gender differences in early socioeconomic adversity-BMI and BMI-socioeconomicattainment linkages is warranted, particularly with a focus on intra-individual changes in BMI. In the present study, weexamine racial/ethnic differences in the influence of early socioeconomic conditions on both the level and rate of changein youth BMI and in the influence of both the level and rate of change in youth BMI on socioeconomic attainment in youngadulthood.

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892886

Methods

Sample

Data for this study came from a nationally representative sample of adolescents participating in the National LongitudinalStudy of Adolescent Health (Add Health). In 1995, baseline (Wave 1) data were derived from a complex stratified cluster-sampling of middle and high school students, yielding 20,745 respondents (mean age ¼ 15.5 years; range ¼ 12e19 yearsat baseline) from 134middle and high schools. To ensure diversity, the samplewas stratified by region, urbanicity, school type(public vs. private), racial composition, and size. The second, third, and fourth waves of data were collected in 1996, 2001, and2008 (N2 ¼ 14,738; N3 ¼ 15,100; N4 ¼ 15,701). We used inehome interview data from parents who responded to the parents'questionnaire in Wave 1 and adolescents who participated in all four waves. Thus, the study sample included 11,075 re-spondents. The final sample consisted of approximately 54% women, and 38% of respondents reported a minority racial/ethnic status with the largest percentages reported for Black (16%) and Hispanic (13%). Attrition and missing data analysisshowed that adolescents who participated in all four waves were slightly younger but otherwise confirmed that there waslittle difference between adolescents with missing data in our study sample and those with complete data.

Measures

Early socioeconomic adversityWe constructed a composite index for cumulative early socioeconomic adversity by summing dichotomous indicators

capturing different dimensions of adversity. These indicators included low parental education, high family economic hard-ship, low parental marital stability, and high community adversity. Except for marital stability (already a dichotomousmeasure), dichotomous indicators were created by mean splitting the following measures.

Parental education. The responding parent reported both parents' highest level of education obtained at Wave 1 (1995). Re-sponses ranged from: 1 ¼ never went to school to 10 ¼ professional training beyond four-year college or university degree.Mothers' and fathers' educational levels were summed to create an index of parental education. For single-headed families(n ¼ 79) with no available data from fathers, maternal education served as the indicator of parental education.

Economic hardship. Five dichotomous items (0 ¼ no, 1 ¼ yes) assessed whether any member of the household received thefollowing social service benefits: social security, supplemental security income, aid to families with dependent children, foodstamps, or housing subsidies at Wave 1 (1995). Responses to these five items were summed to create an index of economichardship with a range of 0e5.

Parents'marital stability. A binary variablewas used to differentiate parents who had been consistentlymarried to their spouse(or in a marriage-like relationship) for at least 15 years (1) from other parents (0).

Community adversity. The community adversity measure was generated by summing four indicators corresponding to censustract information from the 1990 U.S. Census. Those indicators included (a) the proportion of families living in poverty, (b) theproportion of single-parent families, (c) the proportion of adults employed in service occupations, and (d) the proportion ofunemployed men (adapted from Wickrama & Bryant, 2003). This index had an internal consistency of .78.

Body mass index/obesityRespondents' BMI, the ratio of weight to height squared ([lbs*703]/inches2), was used to assess their degree of being

overweight. At Wave 2 (1995), BMI was calculated using respondents' self-reports of their height and weight. BMI values forWave 3 and Wave 4 (2001 and 2008) were calculated from weight and height measurements obtained by trainedinterviewers.

Young adult socioeconomic attainmentYoung adults' household income, educational attainment, and economic hardship at Wave 4 (2008) were used as multiple

indicators of their socioeconomic attainment.

Young adults' personal earnings. Young adults reported their average annual personal earnings before taxes and deductions(including wages/salaries, tips, bonuses, over-time pay, and self-employment income) for 2006, 2007, and 2008.

Young adults' educational attainment. Young adults reported their highest level of education using an ordinal scale ranging from1 ¼ completed 8th grade to 13 ¼ completed post baccalaureate professional education.

Young adults' economic hardship. Economic hardship was measured by six dichotomous items (0 ¼ no, 1 ¼ yes) indicatingwhether anymember of the household experienced difficulties meeting their basic needs in the previous 12months. Example

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892 887

items include: could not pay the full rent or mortgage, had electricity/gas service turned off or heating oil not delivered, andworried whether food would run out. Responses to these seven items were summed to create an index of young adults'economic hardship ranging from 0 to 7, with higher scores reflecting greater economic hardship.

Race/ethnicityAt Wave 1 (1995), adolescents reported their race/ethnicity. Dichotomous variables were then created to assess Black,

Hispanic, Asian, Native American, and White racial/ethnic statuses. The dichotomous variables for each of the minoritystatuses were included as independent variables in the regression equation resulting in regression coefficients that can beinterpreted with reference to Whites. For multi-racial respondents, only their first choice of race/ethnicity category wasconsidered.

GenderGender was coded as male (0) or female (1).

Analysis strategy

Most previous studies focusing on antecedents and consequences of obesity used traditional mean comparisons withcross-sectional data or regression models focusing on discrete obesity status at a single time point or multiple time pointsover a relatively short time span. Such analyses are unable to reveal antecedents and consequences of individual weight statusand changes in weight status over time. Thus, we tested the theoretical model as a latent growth curve (LGC) in a structuralequation modeling (SEM) framework to estimate individual trajectories using Mplus (version7) (Muthen & Muthen,1998e2013).

We used the TYPE ¼ COMPLEX command, which uses Huber/White correction, to adjust for potential bias in standarderrors and chi-square computation due to the lack of independence between observations within schools in the Add Healthdata. Missing data were accounted for using the Full Information Maximum Likelihood (FIML) procedure. FIML does notimpute missing values; rather, it estimates model parameters and standard errors from all available data, which minimizespotential age-related bias that would have influenced the results (Enders, 2001).

Results

Descriptive statistics

Table 1 presents means, standard deviations, and ranges of the major study variables. On average, BMI levels increasedwith age. By Wave 4, the mean level of BMI was 29.04 (SD ¼ 7.46), which means that the average BMI level in our sample ofyoung adults was close to the cut point for obesity (BMI of 30 or greater). This is similar with the national average (mean adultBMI is 28.7 for adult men and women; Flegal, Carroll, Kit,& Ogden, 2012). The mean yearly personal earnings of young adultswas approximately $35,572 (SD ¼ 44,599), and they, on average, had some college education (M ¼ 5.73, SD ¼ 2.15). The meanfor young adults' economic hardship experience was .72 (SD ¼ 1.27).

Univariate growth curves

To investigate individual changes, a univariate latent growth curve model was estimated for BMI from 1996 to 2008.Unstandardized coefficients and model fit indices for the univariate growth models are presented in Table 2. The total latentgrowth curve model (combining all race/ethnicities, men, and women) for BMI with three successive measurements showedacceptable fit with the data (c2ð1Þ ¼ 74.36; CFI ¼ .96; RMSEA ¼ .08). The mean initial BMI level was 23.20 (p < .001) with

Table 1Descriptive statistics of study variables.

Variable M (or %) SD Range

Early socioeconomic adversity (1995) 1.58 1.11 .00e4.00BMI (Wave 2, 1996) 23.32 4.57 11.63e51.43BMI (Wave 3, 2002) 26.58 6.14 13.46e58.75BMI (Wave 4, 2008) 29.04 7.46 15.40e97.40Young adult personal earnings (2008) 35,572 44,599 0e999, 995Young adult educational attainment (2008) 5.73 2.15 1.00e13.00Young adult economic hardship (2008) .72 1.27 .00e6.00Gender (Female) 53.8%Black 15.8%Hispanic 13.1%Asian 6.0%Native American 2.8%

Table 2Estimates for univariate growth curve model for BMI.

Total Young men Young women

White Black Hispanic Asian Male total White Black Hispanic Asian Female total

InterceptMean 23.20*** 23.36*** 23.81*** 23.47*** 22.77*** 23.41*** 22.67*** 24.62*** 23.84*** 21.40*** 23.10***

Variance 19.90*** 18.56*** 17.99*** 17.43*** 17.16*** 18.29*** 19.69*** 24.56*** 26.36*** 19.14*** 21.41***

SlopeMean .49*** .47*** .44*** .52*** .42*** .48*** .48*** .62*** .52*** .34*** .50***

Variance .18*** .10*** .14*** .16*** .09** .11*** .26*** .20** .26*** .20*** .25***

c2 (df) 74.36(1) 16.32(1) 4.30(1) 5.63(1) 15.34(1) 25.69(1) 71.33(1) .42(1) 22.10(1) 7.71(1) 64.41(1)CFI .96 .96 .99 .98 .94 .96 .96 1.00 .87 .99 .96RMSEA .08 .07 .07 .08 .21 .07 .14 .00 .17 .14 .10

Note: Unstandardized coefficients are shown. *p < .05, **p < .01, ***p < .001.

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892888

significant variation (19.90, p < .001) indicating substantial differences among adolescents in their BMI levels at Wave 2(1995). The average rate of changewas .49 (p< .001) indicating a significant increase in BMI from1996 to 2008. The significantvariation in rates of change (.18, p < .001) showed that the rate of change for some adolescents was significantly steeper orflatter than the average rate of change for the sample as awhole. Table 2 also indicates the estimates for the univariate growthcurve examined separately for men and women of each race/ethnicity. In all race/ethnicity groups, variation in the meaninitial BMI level and the rate of change in BMI was greater for women than men, which indicated a larger variation in theindividual BMI trajectories among women than men. Native American men (.58, p < .001) and Black women (.62, p < .001)showed the fastest growth in BMI while Asian men (.42, p < .001) and women (.34, p < .001) showed the slowest growth.

Antecedents and consequences of youth BMI trajectories

Having estimated univariate growth curves with three repeated BMI measures, the next step involved testing the con-ceptual model depicted in Fig. 1. This model contained cumulative early socioeconomic adversity as a predictor of growthparameters of BMI, which, in turn, were expected to predict young adults' socioeconomic attainment. Fig. 2 provides thestandardized coefficients after controlling for gender and race/ethnicity. The model showed an adequate fit to the data(c2ð26Þ ¼ 474.39; CFI ¼ .94; RMSEA ¼ .04).

Overall, the findings supported our hypotheses. Early socioeconomic adversity was positively associated with the meanlevel and rate of change in BMI (b ¼ .11, p < .001; b ¼ .06, p < .001, respectively). That is, adolescents who experienced higheradversity reported higher beginning levels of BMI at Wave 2, and greater increases in BMI fromWave 2 to Wave 4 than theirpeers who experienced less adversity. In turn, both the initial level and rate of change in BMI subsequently predicted lowersocioeconomic attainment of young adults (b ¼ �.06, p < .01). The total indirect effect of early socioeconomic adversity onyoung adults' socioeconomic attainment through both growth parameters of BMI was significant at p < .01 level. Thus, BMItrajectories served as a mediator between early socioeconomic adversity and socioeconomic attainment.

In addition, the results showed that early socioeconomic adversity directly influenced young adults' socioeconomicattainment (b ¼ �.54, p < .001); high early adversity predicted poor socioeconomic attainment at Wave 4. Thus, our hy-pothesized indirect paths through BMI trajectories did not completely capture the association between early adversity andlater socioeconomic attainment as the direct effect of early adversity remained statistically significant.

Fig. 2. Linking early socioeconomic adversity to young adults' socioeconomic attainment. Notes: c2ð26Þ ¼ 474.39, CFI ¼ .94, RMSEA ¼ .04. Standardized coefficientsare shownwith unstandardized coefficients in parentheses. Gender and race/ethnicity were controlled (not shown) and had a statistically significant influence onthe level and rate of change in BMI and socioeconomic attainment. **p < .01, ***p < .001.

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892 889

We hypothesized that young adults' socioeconomic attainment, as a consequence of their BMI in earlier years, would bemoderated by gender and race/ethnicity. To test this hypothesis, multiple group analyses were conducted for eachmoderator,and the results are presented in Table 3. A significant reduction in chi-square between the constrained path and relaxed pathindicates moderation effects and are shown in bold.

The associations between the increase in early adversity and both level and growth of BMI were significantly strongeramong women compared to men and among Whites compared to Asians, which indicated that women and Whites expe-rienced higher initial levels and steeper increases in BMI when they were exposed to early socioeconomic adversity. Inaddition, the negative effect of BMI growth (increasing rate of change) on young adults' socioeconomic attainment wassignificantly stronger for women than men and for Whites than Blacks. This indicates that the socioeconomic attainment ofwomen andWhites was more sensitive to BMI increases than men and Blacks. The negative effect of BMI level in adolescence(initial level) on socioeconomic attainment in young adulthood was significantly stronger for Asians than Whites; therefore,Asians' socioeconomic attainment was more sensitive to the initial level of BMI than Whites' socioeconomic attainment. ForWhites, early adversity exerted a significantly stronger negative influence on socioeconomic attainment in young adulthoodcompared to Hispanics.

Discussion

The present study examined a dynamic life-course model to more fully understand how early socioeconomic adversitycumulatively influences youth BMI trajectories leading to young adult socioeconomic attainments. A key element in thismodel was the identification of BMI trajectories during this time period as the individual vulnerability mediating the so-cioeconomic continuity over early life course. In addition, the present study examined the gender and racial/ethnic differ-ences in these processes and has produced important findings in regard to these differences.

At a descriptive level, and consistent with expectations, the analyses showed that there is a linear increase in youth BMIover this period. The investigation of individual trajectories of BMI allowed us to preserve the continuity of change in BMI overthe early life course. By doing so, we treated changes in BMI as a continuous process unfolding over time and examined theassociations between different facets of BMI trajectories (the level and rate of change) and their antecedents and conse-quences. Consistent with the life-course path-dependent mechanism (Willson et al., 2007), early socioeconomic adversityexerts a persistent influence on young adult socioeconomic attainment over the early life-course indirectly through both thelevel and change in BMI trajectories. It seems that, in addition to its contemporaneous influence through early disadvantages(i.e., lack of health resources, educational, recreational and health facilities, and constant exposure to early stressful cir-cumstances) that directly influence the initial level of adolescent BMI, early adversity may also lead to the exacerbation ofadverse metabolic processes resulting in increased BMI with increasing age (Dowd, Simanek, & Aiello, 2009).

The influence of early adversity on the growth rate in BMI is consistent with the notion that early socioeconomic adversityexerts a cumulative influence over the life course (posited by the cumulative advantage and disadvantage principle; Dannefer,2003). Statistically, this cumulative effect corresponds to the interaction between early adversity and time. Although notexplicitly tested in the current model, the cumulative influence of early adversity over the life course may be attributed toseveral mechanisms. In summary, thesemechanismsmay include (a) an increase in exposure tomore stressors/disadvantagesdue to the proliferation of early stressors/disadvantages over the life course, (b) an increase in the susceptibility to stressors/disadvantages over the life course (decrease in “biological robustness”), and (c) an intensification of the impact of earlyphysiological damages in the later years. Future research should further elucidate these mechanisms.

The results indicated that the severity (initial level) of adolescent BMI and also the amount of growth or decline (rate ofchange) in BMI independently contribute to the subsequent socioeconomic attainment of young adults. For example, thesocial consequences of an already obese adolescent who has experienced a sharp increase in BMI are different from the socialconsequences of an adolescent with an average BMI who has experienced the same amount of increase in BMI over the sameperiod. In order to gain a deeper understanding about themediational role of BMI trajectories, the different facets of change inBMI must be taken into account because both the level and change in BMI may be differentially influenced by early adversityas well as serve as independent predictors of risk for young adult socioeconomic failures. Thus, the findings of the presentstudy would not have been revealed by traditional regression models and mean comparison analyses, which use discrete

Table 3Standardized path coefficients of multiple group moderation analyses.

Paths Gender Race/Ethnicity

Female (male) Black (White) Hispanic (White) Asian (White)

Early ADV/ BMI Level .12 (.08) .09 (.17) .09 (.11) .08 (.11)Early ADV/ BMI Slope .08 (.04) .04 (.08) .06 (.08) �.04 (.08)Early ADV/ YA SES �.52 (�.55) �.56 (�.53) �.43 (-.54) �.38 (�.54)BMI Level/ YA SES �.07 (�.14) �.15 (�.06) �.14 (�.06) �.19 (-.05)BMI Slope/ YA SES �.13 (.07) .11 (�.10) �.06 (�.10) �.21 (�.10)

Note: Statistically significant differences are indicated in bold. The reference for gender is male and for minority races/ethnicities is White, and coefficientsfor the references are shown in parentheses. Early ADV ¼ Early socioeconomic adversity. YA SES ¼ Young adult socioeconomic status.

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892890

weight status as predictors or outcomes. Particularly, it appears that the negative associations between adult BMI and adultsocioeconomic status documented by previous studies may be attributed to the fact that higher growth in BMI duringadolescence/emerging adulthood contributes to adverse socioeconomic attainment while early BMI level also continues toinfluence adult socioeconomic attainment.

The present study found gender and race/ethnicity significantly moderated associations between BMI growth parametersand socioeconomic attainment in young adulthood. Consistent with the findings of previous studies (McLaren, 2007;S�anchez-Vaznaugh et al., 2009), the adverse socioeconomic consequences of increasing BMI from adolescence to youngadulthood were stronger for women than for men. This findingmay be due to the fact that obese individuals experience moreunfavorable labor market conditions (e.g., wages and promotions) than their non-obese peers, and this unfairness is exac-erbated by gender discrimination. Also, heavier womenmay experiencemore stress and lower self-esteem than other women(Griffiths, Parsons, & Hill, 2010; Perrin, Boone-Heinonen, Field, Coyne-Beasley, & Gordon-Larsen, 2010) due to societal ex-pectations of the ideal female body, which, in turn, may result in lower performance in the labor market. Conversely, womenwith higher social status may place more emphasis on conforming to societal expectations regarding body size and imagethan men and may engage in a healthier lifestyle (S�anchez-Vaznaugh et al., 2009). In addition, the effect of increasing BMI onsocioeconomic attainment was stronger for Whites than Blacks. This association may be attributed to Blacks being moreculturally accepting of “large bodies” (Siegel, Yancey, Aneshensel, & Schuler, 1999) and reporting fewer weight-relatedconcerns and behaviors than Whites (Neumark-Sztainer et al., 2002). Thus, Blacks may experience less pressure toconform to conventional societal norms for weight status. Also, this may be becauseWhites are often in a higher social class orin the process of attaining higher social status, and weight status is an important aspect of conforming tomainstream societalexpectations. Or, due to discrimination and other hardships, Black youth, unlike White youth, may simply be more likely toengage in lower level occupations irrespective of their growing BMI over time.

Therewere no gender differences in BMI growth parameters (the level and rate of change) suggesting that, overall, parallelBMI trajectories existed for men and women. However, the relationship between early adversity and youth BMI trajectoriesvaried by gender. Compared to men, there was a stronger positive relationship between early adversity and the level andgrowth in BMI for women, irrespective of race/ethnicity. These findings suggest that, for all of the races/ethnicities examined,female adolescents are more susceptible to early socioeconomic adversity than male adolescents. It seems that early familyand community adversities create more stressful situations for female adolescents, perhaps because female adolescentdevelopment is more stressful than male adolescent development (Ge, Lorenz, Conger, Elder, & Simons, 1994), exacerbatingfemales' metabolic dysfunctioning and resulting in higher BMI. Furthermore, early socioeconomic adversity influences BMIlevels and BMI growth for White adolescents (both male and females) more than their Asian peers. This may be due toWhiteadolescents experiencing more early socioeconomic affluence, whereas minority adolescents are less likely to receive thebenefits of early socioeconomic affluence, such as recreation and educational facilities.

Independent of BMI trajectories, early socioeconomic adversity exerted “direct” influences on the socioeconomicattainment of young adults. Such strong direct effects are suggestive that there may be other indirect pathways that were notconsidered in the present study. Thesemediating pathwaysmay include adolescent stressful life experiences, such as stressfultransitions to young adulthood and lifestyle factors (e.g., alcohol use), which may also be influenced by early socioeconomicadversity (Wickrama & Baltimore, 2010). In addition, physiological and cognitive damages that occur during infancy, child-hood, or adolescence may have long-term latent effects, whichmaymanifest in the young adulthood years (Conroy, Sandel,&Zuckerman, 2010; Ivanovic et al., 2000; Scrimshaw, 1997).

Several factors potentially limit the scope and the generalizability of the results. First, the present study used self-reportmeasures of young adults' socioeconomic attainment. Replication using more independent reports (e.g., tax returns) wouldalleviate concerns regarding potential self-report biases. Second, we did not differentiate between Mexican, Puerto Rican,Cuban, and other Latino or Spanish origin Hispanics. This may yield mixed-group characteristics regarding personal earnings,educational attainment level, and economic hardship experiences among Hispanic ethnicity because some of these groups(e.g., Cuban) have been shown to have higher levels of income or education than other Hispanics or minority race/ethnicitygroups (Bohon, Johnson, & Gorman, 2006; Williams, Mohammed, Leavell, & Collins, 2010). Third, we did not examine po-tential moderating effects of youth psychosocial resources and life transitions (e.g., “turning points”), which can protect youthfrom the negative influence of early socioeconomic adversity on BMI trajectories, as well as the influence of BMI trajectorieson young adult socioeconomic attainment (Wheaton & Gotlib, 1997). Finally, individual genetic-make up has been shown tohave additive and interactive influences on the study attributes, particularly early adversity and BMI trajectories (Wickramaet al., 2013). Thus, future investigations should be informed by individual genetic characteristics.

Despite these limitations, the present study makes a valuable contribution to existing research by elucidating how earlyadversity initiates an adverse developmental process over the early life course leading to socioeconomic failures in youngadulthood. BMI trajectories appear to be important mediators of the socioeconomic continuity over the early life course.Youth health programs and policies should promote youth competencies that aid to reduce adolescents' BMI and reduce BMIover-growth (i.e., obesity) stemming from early socioeconomic adversity.

Acknowledgment

This research used data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. RichardUdry, Peter S. Bearman, and KathleenMullan Harris at the University of North Carolina at Chapel Hill, and funded byGrant P01-

D. Bae et al. / Journal of Adolescence 37 (2014) 883e892 891

HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperativefunding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and BarbaraEntwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the AddHealthwebsite (http://www.cpc.unc.edu/addhealth). Nodirect supportwas received fromGrant P01-HD31921 for this analysis.

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