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. Author manuscript; available in PMC: 2026 Mar 12.
Published in final edited form as: Longit Life Course Stud. 2024 Dec 12;16(1):23–44. doi: 10.1332/17579597Y2024D000000035

The Effect of Childhood Socioeconomic Status on Adult Self-Rated Health by Age and Race

Emily C Dore 1, Regine Haardörfer 2
PMCID: PMC12977013  NIHMSID: NIHMS2141037  PMID: 39903094

Abstract

Background:

The relationship between childhood socioeconomic status (SES) and adult health is well established. This paper examines the less well-known areas of this research: whether the age of childhood exposure matters, if mediators differ based on age, and if these relationships vary by racialized group.

Methods:

We used multi-group path analysis and data from the Panel Study of Income Dynamics to analyze direct and indirect relationships between a retrospective measure of childhood SES in early, middle, and late childhood and adult self-rated health for non-Hispanic Black and White individuals.

Results:

Childhood SES affected adult health indirectly through each of the three mediators: education, distress, and health behaviors, but only for non-Hispanic Whites. For this group, late childhood SES impacted adult self-rated health through each of the mediators. In addition, early and middle childhood SES (0–5 and 6–12 years old respectively) impacted late childhood SES (13–16 years old), suggesting the importance of cumulative exposure. We found no evidence that childhood SES impacted any of the mediators or adult self-rated health for the non-Hispanic Black sample.

Conclusion:

The findings support the assertion that timing of poverty and possibly increased exposure matter for non-Hispanic Whites, but we found no support for the impact of childhood SES on adult self-rated health for the non-Hispanic Black sample. This study shows the importance of stratifying life course analyses by race and particular periods during childhood, suggesting the need for more targeted interventions based on these factors.

Keywords: life course, structural equation modeling, health disparities, socioeconomic status

Introduction

The lasting impact of childhood socioeconomic status (SES) on adult health and mortality is well documented (Ferraro, 2016). Childhood SES has been linked to adult outcomes including self-rated health, mortality, chronic conditions, and mental health (Brady et al., 2021; Hayward & Gorman, 2004; Luo & Waite, 2005), with children from lower-SES backgrounds experiencing worse health compared to their higher-SES counterparts. This literature points to the need for interventions early in life to disrupt harmful outcomes decades later, even when exposures may appear distally related to health.

While this research has made important contributions to the promotion of population health, appropriate interventions likely differ based on factors that include the specific age of the child when measuring their SES as well as the individual’s racialized identity. The bulk of the life course literature treats childhood as one time period, when these years encapsulate a time of tremendous growth and change. Experiencing poverty may have different long-term effects on health depending on the developmental stage of the individual. The research is so far mixed on this topic, as some studies have shown early childhood to be more predictive of later life health (Li et al., 2018), and other studies showing later childhood to be more important for long-term health (Pudrovska & Anikputa, 2014).

In addition, more recent research seeks to identify the mediators that connect childhood experiences to adult health, allowing for more targeted interventions (e.g. Turner et al., 2016), but these mechanisms that connect childhood SES and adult health may again vary depending on age of exposure. For example, education attainment is often found to mediate the relationship between childhood SES and adult health (Luo & Waite, 2005), but the impact of childhood SES on educational attainment may be more important when experienced during adolescence, when the child is attending school, compared to the effect of poverty experienced in the child’s earliest years.

Finally, given differential exposure to racism, known to impact health (Bailey et al., 2017), White and Black individuals likely have different trajectories linking childhood SES and adult health. For example, studies have shown that Black individuals have worse health outcomes compared to White individuals with the same level of education, suggesting higher levels of SES are not similarly beneficial for health across racialized groups (Brown et al., 2016; Cummings & Jackson, 2008). Age of exposure and differential effects by racialized group are important considerations for this literature so that research findings are more precise and interventions are better designed and targeted appropriately to decrease health disparities rooted in childhood SES.

The goal of this paper is to build on life course research by examining the impact on adult self-rated health of childhood SES at different age ranges in childhood, mediators that facilitate the long-term health impacts of SES at these different ages, and if the effects vary by racialized group. We used a multi-group path analysis and data from the Panel Study of Income Dynamics to examine the impact of a retrospective measure of perceived financial struggle at ages 0–5, 6–12, and 13–16 as the measure of childhood SES, on self-rated health in adulthood for non-Hispanic Black and White individuals. In addition, we analyzed health behaviors, psychological distress, and education as mediators that link childhood SES and adult self-rated health.

Life Course Theories

The life course perspective posits that events throughout one’s life can have a lasting impact (Elder, 1998). This study incorporates two of the most prominent life course hypotheses, directly related to understanding the early origins of disease: critical period (and relatedly, sensitive period) and chains of risks (Kuh & Ben-Shlomo, 2004). Critical period is a time period, such as childhood, during which an exposure will have lasting and irreversible effects. Barker’s well-known fetal origins hypothesis exemplifies this theory, arguing that fetal exposure to malnutrition is associated with cardiovascular disease in adulthood, regardless of possible intervening mechanisms (Barker, 1995). Similarly, an exposure during a sensitive period will have large effects later in life, but these effects are considered more modifiable by later life exposures compared to exposures during a critical period (Power et al., 2013; Stavola et al., 2022). In this study, evidence of a direct relationship between childhood SES, at any age range, and self-rated health in adulthood would provide evidence for the critical or sensitive period model. Chains of risks hypotheses argue events throughout one’s life lead directly to the next, such that advantages lead to more advantages, and disadvantages lead to more disadvantages, increasing inequality over the life span (Luo & Waite, 2005; Meier et al., 2016; Mustillo et al., 2019). In this study, support for the chains of risk model would include indirect pathways linking childhood SES at any age with adult self-rated health through any or all of the mediators.

Timing of Poverty Exposure

An unanswered question in life course literature is whether the specific age in childhood is important for the socioeconomic conditions to have lasting effects. Although the years between 0 and 18 that represent childhood are often conceptualized as a critical period for an individual (Ben-Shlomo & Kuh, 2002), critical or sensitive periods are also sometimes considered to be more narrow age ranges within childhood. For example, Li et al. (2018) found that individuals who experienced early childhood poverty (ages 0–3) were particularly susceptible to being overweight or obese as adults, compared to individuals who experienced poverty in adolescence. Green et al. (2018) found that cumulative and persistent poverty best predicted health outcomes, but that experiencing poverty in early life (ages 0–5) also predicted a higher likelihood of smoking more so than later timepoints. This early childhood period may be especially susceptible to harms, as well as beneficial interventions, due to the rapid physical and mental development during these years (Nores & Barnett, 2010). For example, studies have found that economic interventions in the first few years of a child’s life, including providing tax credits and access to the original Food Stamp Program, have lasting impacts on their health and well-being (Hoynes et al., 2016; Rittenhouse, 2023).

On the other hand, some studies have found adolescence to be an important predictor of health and health behaviors in later life, including obesity and body mass (Gustafsson et al., 2012; Khlat et al., 2009), exercise (Pudrovska & Anishkin, 2013) and mortality (Pudrovska & Anikputa, 2014). However, these studies tend to include only one time period within childhood in the analysis, instead of including multiple age ranges in one model to compare possible differential long-term effects. This limits the ability to make conclusions about whether one time period is more important than another.

Mediating Pathways and Structural Equation Modeling

Mediators that connect childhood SES to adult health include the stress pathway (Turner et al., 2016), adult SES (Hayward & Gorman, 2004; Luo & Waite, 2005; Smith et al., 2019), and health behaviors (Umberson et al., 2010). For example, a child growing up in poverty is more likely to be exposed to stressors that include inconsistent availability of food or safe housing, which can affect their health in older ages through an increased allostatic load (Seeman et al., 2010). Growing up in poverty can also affect an individual’s health in older ages indirectly by affecting their ability to obtain a quality education and thus their earning potential, limiting the resources they have available for maintaining good health in later years (Luo & Waite, 2005).

Related to the research discussed above on age of poverty exposure, mediators between childhood SES and adult health may also depend on the developmental period of the child. Childhood is a time of heightened social, cognitive, and emotional development, as well as when individuals are likely to develop lifelong behaviors (Viner et al., 2015). Toxic stress in childhood is especially impactful on the body and brain in early years, via metabolic or neuroendocrine adaptations (Barker, 1990; McEwen & McEwen, 2017). Whereas adolescence is a time of pursuing educational achievements, solidifying health behaviors, and becoming more aware of social status, all of which can be negatively impacted by financial circumstances (Patton & Viner, 2007; Viner et al., 2015). Thus, experience of poverty in early childhood may affect health through a stress pathway, but experience of poverty in adolescence may impact health through health behaviors and adult SES.

The multitude of studies on this topic of the relationship between childhood SES and adult health suggest a possible causal link. However, much of this research uses regression techniques that do not allow for tests of both indirect and direct effects of childhood SES on adult health. Pudrovska and Anishkin (2013), Nurius et al. (2019), and Mustillo et al. (2019) have furthered this life course literature by using structural equation modeling (SEM) to measure latent constructs and mediation. SEM is a technique that models relationships between several variables as a system, so that instead of modeling only the impact of an independent variable on a dependent variable as in a traditional regression method, hypothesized indirect and direct effects can be estimated simultaneously between multiple variables (Gunzler et al., 2013). Pudrovska and Anishkin (2013) found a positive association between childhood SES (measured at ages 17–18) and later-life physical activity, mediated by socioeconomic resources, health problems, obesity, and depressive symptoms, with some variation by gender. Nurius et al. (2019) found evidence for both direct effects of adverse childhood experiences on adult physical health, as well as indirect effects mediated through adult SES, experiences of adversity, health behaviors, and social support. Finally, Mustillo and colleagues (2019) found that adult SES did not mediate the relationship between a cumulative measure of childhood misfortune and depression, but that it mediated the relationship between a single measure of low-family SES and depressive symptoms experienced as an adult. Identifying mediators is crucial for properly intervening along the life course to buffer negative impacts of childhood adversity on adult health, yet more research is needed on age-specific mediators for the relationship between childhood SES and adult health.

Race and Health Across the Life Course

Relationships between childhood SES and adult health, as well as the relevant mediators, likely vary by race due to racism and its association with other forms of adversity, leading to increased exposure to a wide range of stressors over the life course for people of color. Studies have shown the substantial negative health effects of interpersonal and structural racism through biological adaptation to chronic stress and resulting coping behaviors, poor quality health care due to discriminatory treatment by medical providers, exposure to more environmental toxins and less access to outdoor areas for physical activity due to residential segregation, and racism’s effect on access to socioeconomic resources (Geronimus et al., 2006; Geronimus, 2023; Jackson et al., 2010; Phelan & Link, 2015; Shonkoff et al., 2021; Williams & Sternthal, 2010). Therefore, Black communities tend to experience a wider range of adversities with a higher frequency at multiple time points across the life course, leading to an accumulation of exposure to health risks not typically experienced by White communities (Williams et al., 2016).

As a result of these processes, socioeconomic resources are less protective of health for Black compared to White individuals, a concept known as diminishing returns (Boen, 2016; Farmer & Ferraro, 2005). While diminishing returns is more commonly examined in relation to adult education and income, some scholars have found evidence for this concept in younger populations. For example, Assari and colleagues have found that childhood conditions, including measures of maternal education, income to need ratio, and family structure, were more strongly associated with self-rated health (Assari, Caldwell, et al., 2018) and body mass index (Assari, Thomas, et al., 2018) at age 15 for White compared to Black youth. More research is needed to understand if diminishing returns is relevant to the relationship between childhood SES and health in adulthood.

Another consideration is the non-equivalency of measures of SES across racialized group, meaning that, for example, Black individuals have on average lower levels of income and live in higher poverty neighborhoods compared to White individuals with similar levels of education (Williams et al., 2010, 2016). Thus, a low-SES background likely means greater access to health-related resources for a person racialized as White compared to Black, which affects life course health trajectories differently.

The Current Study

It is unclear whether the age of childhood SES matters for long-term health outcomes, with some literature finding early childhood SES to be more important, and other literature finding later childhood to be more important. Similarly, while theory provides potential explanations for how mediators between childhood SES and later life health outcomes may differ based on the childhood age of SES exposure through different biological and social processes experienced at different time points, there are no studies to our knowledge that have empirically tested these theories. Finally, it is unclear if these relationships differ for White and Black individuals, with some evidence suggesting childhood SES may be less closely tied to adult health for Black individuals. Our study aims to address these gaps by examining 1) the impact on adult self-rated health of childhood SES measured at different ages, 2) education, stress, and health behaviors as mediators that facilitate the long-term health impacts of SES at these different ages, and 3) if the effects vary by racialized group.

Methods

We analyzed data from the Panel Study of Income Dynamics (PSID), the longest running, nationally representative household panel study in the United States that began in 1968. The original PSID sample consisted of about 18,000 people in 5,000 households. Children and grandchildren of the original members are added to the sample when they start their own households. The original sample was drawn from an over-sample of 1,872 low-income families from the Survey of Economic Opportunity, and a nationally representative sample of 2,930 families determined by the Survey Research Center at the University of Michigan. The survey was initiated to capture details on income and poverty, though a question on self-rated health was added in 1984, and more health conditions were added in 1999. The survey has been administered every year between 1968–1997, and biannually ever since (Institute for Social Research, 2021).

The core of the PSID captures some measures of the participants’ childhood circumstances, but in 2014, the PSID also administered the Child Retrospective Circumstances Study (PSID-CRCS), a supplement that included more measures of childhood, including childhood SES at different ages (McGonagle & Freedman, 2015). The PSID-CRCS invited about 13,000 individuals aged 19 years and older who were heads of household or spouses/partners in PSID families that had participated in the 2013 wave of the core PSID survey. We used data from the PSID-CRCS for the childhood SES measures, and data from the main survey in 2013 for most other variables to align with the 2014 PSID-CRCS sample (N=8,072). We excluded individuals missing information on race or ethnicity (N=54), and who were not non-Hispanic White or Black (N=576) because of sample size and theoretical clarity, leaving an analytic sample of N=7,442 individuals. The study was determined not to be human subjects research and was thus exempt from review by the authors’ university Institutional Review Board.

Measures

Childhood SES was measured by three variables from the question that asked whether the family struggled financially when the respondent was 1) 0–5 years old, 2) 6–12 years old, and 3) 13–16 years old. This question was reported retrospectively since participants were adults when they participated in the PSID-CRCS. As this was self-reported, we considered this variable to measure a subjective and relative measure of childhood SES, which we return to in the discussion. Self-rated health was the health measure of interest, based on the survey question that asked, would you say your health in general is excellent, very good, good, fair, or poor? It is an indicator of general health that has been shown to be correlated with current morbidity and predictive of mortality (Idler & Benyamini, 1997). Self-rated health was an ordinal variable coded so that higher values indicated better reported health.

The three mediators were a continuous measure of years of education, the K6 non-specific distress scale, and an aggregate measure of health behaviors. The K6 non-specific distress scale is a validated short form created to screen for mental distress with six questions about the level of distress the participant experienced in the past 30 days (Kessler et al., 2002). The six items were summed and the score ranged from 0 to 24, with higher numbers indicating more distress. The measure of health behaviors was a cumulative index of smoking (1=currently smokes), drinking alcohol (1–3 drinks per day), and exercise (1=exercised less than five times per week). The variable ranged from 0–3 with a higher number indicating more unhealthy behaviors.

The model was grouped by race (non-Hispanic White and non-Hispanic Black), and gender and age were included as covariates due to their association with health.

Analysis

First, we examined the data for systematic missingness using Stata, version 16.1, but found only a small amount of missing data and thus observations with missing data were excluded. Out of the analytic sample, 613 participants (8.2%) were missing one of the variables of interest resulting in sample size of 6,829 (Appendix Table A1). Next, we assessed the three time points of childhood SES. We found that some individuals reported struggling financially for all three time points, but most did not, justifying our analysis of the most impactful time period. For example, 1,466 participants reported having struggled financially at all three time points in their childhood, 371 participants reported struggling financially only from ages 0–5, 228 participants reported struggling financially only from ages 6–12, and 399 participants reported struggling financially only from ages 13–16. We calculated descriptive statistics and correlation matrices for the two samples.

We then conducted the initial path analysis which included the full analytic sample in the lavaan package, version 0.6–8 in R. Path analysis is a type of SEM that allows tests of direct and indirect pathways between different ages of childhood SES and adult health. We used a diagonally weighted least squares estimator since self-rated health was ordinal and the childhood SES variables were right skewed. This estimator makes no assumptions about normality of distribution (Li, 2016). At this point, we estimated model fit and model fit indices. Out of the several suggestions, we made modifications that were theoretically sound. These modifications included adding pathways from education to health behaviors and education to distress, and allowing for distress and health behaviors to covary (Figure 1). After these additions, the model exhibited good fit statistics based on the cut points of chi-squared p>0.05, RMSEA<0.05, CFI >0.90, TLI>0.95, and SRMR<0.08) (Hooper et al., 2007), and we concluded the overall model analysis.

Figure 1.

Figure 1.

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Conceptual Model

Subsequently, we explored results for the different racialized groups using the multi-group path analysis, a modeling approach that allows mechanisms to differ between researcher-defined groups, but models all data together. Our model allowed all estimates to differ between the groups. This capability is an advantage of SEM above other group comparison methods such as stratification because it allows direct comparisons of effect size. We used race (non-Hispanic Black and White) as the grouping variable and ran a group level goodness of fit to assess the model fit of the multi-group model based on Figure 1. We report the results as unstandardized path estimates and the model fit statistics below. As a robustness check, we repeated this analysis but replaced the financial struggle variable with a different measure of childhood SES included in the PSID-CRCS. This variable asked whether the participant’s family received welfare or food stamps from ages 0–5, 6–12, and 13–16.

Results

Table 1 shows descriptive statistics for the Black and White samples. About one third of the samples reported struggling financially in childhood in each age range, which was relatively similar between the two racialized groups. Ages 13–16 stood out, since almost 40% of the Black sample reported experiencing financial struggle compared to about 30% of the White sample. The Black sample was younger (44.3 years old, sd=14.4), had fewer average years of education (12.9, sd=2.2), a higher proportion of women (64.7%), and worse self-rated health (3.4, sd=1.1) and distress (3.5, sd=4.0). Tables A2 and A3 in the Appendix show the correlation matrix for the White and Black samples, respectively.

Table 1.

Descriptive Statistics, Panel Study of Income Dynamics Core Survey, 2013, and Panel Study of Income Dynamics-Child Retrospective Circumstances Study, 2014 (N=6,829)

White Sample (N=4,794) Black Sample (N=2,035)
Mean (SD) or N (%) Mean (SD) or N (%)
Childhood SES
 Financially struggled, ages 0–5 1,586 (33.1%) 678 (33.3%)
 Financially struggled, ages 6–12 1,654 (34.5%) 764 (37.5%)
 Financially struggled, ages 13–16 1,442 (30.1%) 798 (39.2%)
Adult variables
 Education (years) 14.2 (2.2) 12.9 (2.2)
 Distress 2.8 (3.4) 3.5 (4.0)
 Self-rated health 3.7 (1.0) 3.4 (1.1)
 Health behaviors 1.2 (0.7) 1.2 (0.7)
Covariates
 Female 2,604 (54.3%) 1,317 (64.7%)
 Age 48.0 (16.2) 44.3 (14.4)
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Note: Distress scores range between 0–24 with higher values indicating more distress. Self-rated health is coded 1–5 so that 1=poor and 5=excellent. Health behaviors is coded from 0–3, with higher values indicating less healthy behaviors.

Figures 2 and 3 show the significant pathways for each group of the multi-group path analysis. The model fit statistics indicated a good fit with CFI = 0.999; TLI = 0.973; SRMR = 0.005; RMSEA = 0.038. Chi-squared model fit was χ2 = 11.7, df = 2, p=0.003, which was not surprising given the large sample size. The first important finding was that childhood SES was not associated with any later life variables for the Black sample. For the White sample, the only direct pathway from childhood to adulthood stemmed from late childhood SES to education (b= −0.12, p<0.001), indicating that struggling financially between 13–16 years old leads to lower levels of education. We found direct pathways between childhood SES at each age range for both samples (0.67–0.76, p<0.001), showing that struggling financially in early childhood leads to struggling financially in middle childhood, and struggling financially in middle childhood leads to struggling financially in late childhood.

Figure 2.

Figure 2.

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Significant Direct and Indirect Path Coefficients (White Sample)

Note: Childhood SES represents the survey question that asked whether the family struggled financially when the respondent was (1) 0–5 years old, (2) 6–12 years old and (3) 13–16 years old (0=no, 1=yes). Education and distress were coded such that higher values indicated higher levels of educational attainment and higher levels of distress. Health behaviours represent a cumulative index of health behaviours (smoking, alcohol consumption and exercise), coded so that higher values represent more unhealthy behaviours. Self-rated health was based on the survey question that asked, would you say your health in general is excellent, very good, good, fair or poor? It was coded so that higher values indicate better health.

Figure 3.

Figure 3.

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Significant Direct and Indirect Path Coefficients (Black Sample)

Note: Childhood SES represents the survey question that asked whether the family struggled financially when the respondent was (1) 0–5 years old, (2) 6–12 years old and (3) 13–16 years old (0=no, 1=yes). Education and distress were coded such that higher values indicated higher levels of educational attainment and higher levels of distress. Health behaviours represent a cumulative index of health behaviours (smoking, alcohol consumption and exercise), coded so that higher values represent more unhealthy behaviours. Self-rated health was based on the survey question that asked, would you say your health in general is excellent, very good, good, fair or poor? It was coded so that higher values indicate better health.

For the White sample, we found several indirect pathways: in the positive direction, late childhood SES affected self-rated health through education (b=0.02, p=0.001), distress through education (b=0.02, p=0.001) and health behaviors through education (b=0.02, p=0.001). Early childhood SES also indirectly affected late childhood SES through middle childhood SES (b=0.46, p<0.001). In the negative direction, late childhood SES affected self-rated health through both education and distress (b=−0.004, p=0.002) and through education and health behaviors (b=−0.003, p=0.001).

The Black sample had mostly direct pathways that only connected the adult variables, including education, distress, health behaviors, and self-rated health, suggesting childhood SES is more important for White adult health than Black adult health. The coefficients were in the expected direction such that more education resulted in less distress (b=−0.17, p<0.001) and healthier behaviors (b=−0.15, p<0.001). In addition, more distress and more unhealthy behaviors led to worse self-rated health (b=−0.23, p<0.001; b=−0.07, p=0.008 respectively), and more education led to better self-rated health (b=0.10, p=0.001). There was one indirect pathway for the Black sample, which was that early childhood SES impacted late childhood SES through middle childhood SES (b=0.53, p<0.001).

Sensitivity Analyses

In a sensitivity analysis (Figures 1A and 2A in the Appendix) that examined the impact of a different measure of childhood SES, welfare participation at ages 0–5, 6–12, 13–16, we found similar results as the main analyses. The model fit statistics indicated a good fit with CFI = 0.999; TLI = 0.976; SRMR = 0.008; RMSEA = 0.024. Chi-squared model fit was χ2 = 6.0, df = 2, p=0.051. Like the main results, participating in welfare as a child indirectly affected self-rated health for the White sample, but not for the Black sample. Participating in welfare from ages 13–16, as well as ages 0–5 indirectly affected adult health through each of the mechanisms for Whites, which differed slightly from our main results. This difference may be due to welfare participation capturing a more objective measure of financial circumstances, whereas a measure of financial struggle may be more subjective.

Discussion

In this study, we found that childhood SES affected adult health indirectly through the mediators of education, distress, and health behavior, but only for the White sample. The pathways show the importance of SES throughout childhood, as early childhood SES directly affected middle childhood SES which directly affected late childhood SES for both groups. For the White sample, late childhood SES then directly impacted education, which linked childhood SES to self-rated health through distress and health behavior. Although we expected different mediators may be more important for certain time periods, we found that all mediators, education, health behaviors, and distress, in our model mediated the impact of childhood SES on adult health, though again, only for the White sample. For the Black sample, no measures of childhood SES were linked to the mediators or to self-rated health. However, we did find an important direct effect of distress, education, and health behaviors on self-rated health for the Black sample in the expected directions.

The finding that early and middle childhood SES (ages 0–5 and 6–12 respectively) only impacted adult health indirectly through SES in late childhood contrasts with some literature that found early childhood to have a more direct impact on health (Li et al., 2018; Ristikari et al., 2018; Wimer & Wolf, 2020). However, our findings align with the literature on chains of risk, or the process of one adversity leading to another adversity (Ben-Shlomo & Kuh, 2002). Research tends to find that timing of poverty, length of exposure, and sequence of poverty experiences all matter for long-term outcomes (Green et al., 2018; Li et al., 2018; Wimer & Wolf, 2020). Our findings support the assertion that timing of SES and increased exposure matter, as early childhood SES directly affected SES in middle and late childhood, which in turn affected adult health, suggesting the importance of the accumulation of exposure. However, there was no indirect pathway from early or middle childhood SES to adult health (only indirect pathways through late childhood SES), so we cannot say that this is the case in our study. Nonetheless, it is possible that relationship exists, and we are not powered to find the full mediation.

All mediators, education, health behaviors, and distress, were relevant in this study for the significant pathways, suggesting that childhood SES in late childhood directly affects educational attainment, which then affects health behaviors and psychological distress. These findings parallel literature on the importance of adolescence in the pursuit of education, and subsequent experiences of stress and development of health behaviors (Pearlin et al., 2005; Thoits, 2010; Viner et al., 2015). Worth noting is that the effect sizes are small, ranging from about 0.003 to 0.24, suggesting even though these are statistically significant findings, they are not the only factors in determining one’s health.

Lastly, this study’s findings align with the literature on diminishing returns and other work that have shown different effects of SES on health based on racialized group. Scholars have shown that an individual’s own SES, as well as their parent’s, tends to be less protective of health for Black individuals compared to White individuals (Assari, 2018; Assari, Caldwell, et al., 2018; Boen et al., 2020; Hargrove, 2018). These findings suggest that experiences throughout the life course that vary by racialized group, including experiences of racism and other related forms of adversity, shape the relationship between childhood SES and later life health differently for Black individuals compared to White individuals. While childhood SES may trigger a more linear trajectory for a White individual throughout their life, a Black individual is more likely to experience segregation, police violence, and other forms of interpersonal and structural racism that constrain economic resources and negatively impact health (Bailey et al., 2017; Homan & Brown, 2022; Phelan & Link, 2015).

Another consideration is the non-equivalency of SES across racialized group (Williams et al., 2010) and that Black individuals experience more hardships compared to Whites (Iceland & Sakamoto, 2022). “Struggling financially” may mean something different to a Black and White person in a way that effects the relationship with long-term health. For example, a Black family with a higher level of income is more likely to live in a segregated neighborhood, compared to a similarly high-income White family, which can negatively affect access to resources and opportunities for good health.

There were some limitations to this study. First, we used a self-report measure of struggling financially in childhood at three different age ranges. This was a measure of childhood being reported retrospectively as an adult. Retrospective self-report is a common limitation of life course literature due to the potential of recall bias (Schwartz & Glymour, 2023). While other retrospective measures of childhood SES have been validated against prospective measures, such as father’s education (Brady et al., 2022), these particular measures of struggling financially have not been validated to our knowledge. A recent study used these variables (Gowdy et al., 2020), though these age-specific variables are much less common in the literature compared to reports on the whole of childhood. Although this is a notable limitation to the study, we believe examining these age variables is important to better understand the long arm of childhood, and future studies should validate these measures with prospective measures. Also, the subjective nature of self-report means this variable is a measure of perceived financial struggle, and not an objective measure of financial circumstances. Nonetheless, perceptions of one’s social and economic situations can play an important role in their health (Singh-Manoux et al., 2005).

Another limitation is that scholars disagree about comparing self-rated health across racialized groups, which warrants the examination of other health outcomes in future studies (Chandola & Jenkinson, 2000; McGee et al., 1999; Woo & Zajacova, 2017). Finally, we used distress to represent an emotional state that may mediate the relationship between financial hardship and an individual’s physical health. Stress is the more common mediator, but it is not measured in the PSID, thus we used distress as a proxy (Pearlin et al., 2005). This is a measure of acute distress and was measured at the same time as self-rated health, thus an interpretation of causal pathways should be made with caution. However, it measured occurrences of distress thirty days prior, and can be considered to occur first. In addition, this measure of distress has been used in other studies as a mediator (e.g. Nawa et al., 2018).

Future research should examine these relationships using different measures of childhood SES, mediators, health outcomes, and groups to provide more evidence for these questions. Researchers can go beyond individual-level measures to consider contextual factors, such as unemployment rates, education spending, or measures of structural racism and sexism experienced in childhood (Hargrove et al., 2021; Homan, 2019; Homan & Brown, 2022; Kravitz-Wirtz, 2016).

While there are limitations, this study makes important contributions to the life course and health disparity literature by examining different time periods during childhood, relevant mediators, and heterogeneous effects by racialized group. By conceptualizing childhood as one long period and failing to stratify by race, scholars may be missing important details in life course analyses, making the results less generalizable to specific groups. These results can inform future interventions to focus resources appropriately, especially by providing better access to quality education, opportunities to engage in healthy behaviors, and resources to reduce distress, to break the link between childhood SES and adult health, as well as reduce racial and economic health disparities.

Supplementary Material

Appendix

Key Messages.

  1. Childhood socioeconomic status (SES) in later childhood affected adult health through several pathways

  2. These effects were only found in the white sample, but not the black sample

  3. For both samples, SES in early childhood directly impacted SES in late childhood

  4. Studies on the early origins of disease should consider stratifying analyses by racialized group and childhood age

Funding Details

Research reported in this publication was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number F31MD017935. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Conflict of Interest Statement

The Authors declare that there is no conflict of interest.

Data Availability Statement

The data analyzed in the study come from the Panel Study of Income Dynamics, a publicly available dataset accessed through their website: https://psidonline.isr.umich.edu/. The authors take responsibility for the integrity of the data and the accuracy of the analysis.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix
NIHMS2141037-supplement-Appendix.docx (330.5KB, docx)

Data Availability Statement

The data analyzed in the study come from the Panel Study of Income Dynamics, a publicly available dataset accessed through their website: https://psidonline.isr.umich.edu/. The authors take responsibility for the integrity of the data and the accuracy of the analysis.

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