Introduction

A clear research consensus indicates that household economic well-being plays a prominent role in child and youth behavioral development (Ghandour et al., 2019; Kim et al., 2016; Reising et al., 2013; Slopen et al., 2010). Evidence shows that adolescents raised in households with economic stress are more likely to develop problematic behaviors, such as externalizing behaviors (Ponnet, 2014; Simons et al., 2016). It has been observed that problematic behaviors in adolescence are associated with multiple problems in adulthood, including problems with mental health, physical health, or finances (Odgers et al., 2008), highlighting the need to address it. For the purposes of prevention and alleviation, it is thus important to clarify the mechanisms explaining how household economic well-being is associated with adolescent problematic behavior.

The Theoretical Frameworks of Family Stress Model

The Family Stress Model (FSM) proposes that a lack of sufficient income leads to economic pressures such as unmet material needs, having no money to pay bills or make ends meet, and being forced to cut back on necessary daily needs (e.g., health insurance, medical care). Evidence indicates that these pressures increase caregivers’ risk of experiencing psychological distress (Conger & Conger, 2002; Conger et al., 2010; Lipman & Boyle, 2008; Landers-Potts et al., 2015; Masarik & Conger, 2017). In turn, psychological distress has been shown to affect child and youth developmental outcomes (Simons et al., 2016; Zhang & Han, 2021). Reasons include increases in harsh parenting or reductions in nurturing parenting (Conger & Conger, 2002; Conger et al., 2010; Masarik & Conger, 2017). Recent empirical work has supported the FSM pathway through which low household income could have downstream impacts on child and youth development through caregiver psychological distress (Liu & Merritt, 2018; Sosu & Schmidt, 2017; Thibodeau-Nielsen et al., 2021; Zhang & Han, 2021).

The FSM framework is not inflexible. Rather, it can manifest differently in different families. Sareen et al. (2011) showed that the influence of household income on psychological issues (e.g., depression, bipolar disorder) is stronger in households with lower income. This may be because wider gap between income and daily needs are more commonly seen among lower income people (McKernan & Sherraden, 2008). Studies also show that the association between caregiver depressive symptoms and adolescent externalizing behaviors is statistically significant in low-income families, but not in families who are not in low-income (Ponnet, 2014). Although these findings indicate the importance of improving household economic well-being (i.e., higher income) in order to attenuate the association between household income and caregiver psychological distress as well as the association between caregiver psychological distress and youth behavioral issues, no attention is paid to assets, which are also a key component of household economic well-being (McKernan & Sherraden, 2008).

Importance of Assets

As financial markets continue to grow in modern society, assets (e.g., savings, bounds, stocks) have become an important financial resource which families can use (Miranda-Agrippino & Rey, 2014). When financial needs are greater than current income, assets can buffer such economic stress (McKernan & Sherraden, 2008). For example, McKernan et al. (2011) showed that low-income homebuyers who purchased a home with the help of an Individual Development Account were two to three times less likely to lose their homes to foreclosure. McKernan et al. (2009) observed that households holding $2,000 in liquid assets were less likely to miss doctor visits or utility payments compared to their counterparts with fewer or no such assets. A number of other studies also exist acknowledging the importance of assets in buffering economic stress (Brandolini et al., 2010; Butrica et al., 2010; Chavez et al., 2018; Short & Ruggles, 2005; Wimer & Manfield 2015; Zagorsky, 2005). In addition to its capacity against economic stress, assets are also important to achieve individual and family development goals, such as higher education and microenterprise (Mckernan & Sherraden, 2008; McKernan et al., 2009). Given these features, in addition to income, assets have also been recognized as a key component of household economic well-being that matters one’s daily needs (McKernan & Sherraden, 2008).

Research Gaps

The FSM framework suggests that household income can impact caregiver psychological distress, which in turn can influence child and youth development (Conger & Conger, 2002; Conger et al., 2010; Masarik & Conger, 2017). Prior studies showed that these associations are stronger among households with lower income (Ponnet, 2014; Sareen et al., 2011). These empirical findings suggest that how the FSM framework works could be different by household economic well-being. However, it remains unclear whether strength of these associations varies by assets. In this study, we examine whether household assets moderate the direct and indirect effects of pathways linking household income, caregiver psychological distress, and adolescent problematic behaviors. Addressing this research gap could help practitioners and policymakers design more comprehensive economic interventions to improve child and family well-being.

The Current Study

The proposed model shown in Fig. 1 is built on the FSM, which suggests that caregiver psychological distress mediates the association between household income and adolescent behavioral problems (Conger & Conger, 2002; Conger et al., 2010; Masarik & Conger, 2017). As adolescents may be aware of household economic affairs (Chase-Lansdale et al., 2011), the proposed model also captures direct associations between household income and adolescent behavioral problems. The proposed model includes asset poverty as a moderator to assess whether these indirect and direct effects vary by assets. Although there is no gold standard definition of asset poverty, this study adopts the asset poverty definition pioneered by Caner and Wolff (2004) and Haveman and Wolff (2004), which provides a consensus yardstick. It defines a family as asset poor when household assets are insufficient to cover daily needs for three months, namely a quarter of the official poverty threshold (Brandolini et al., 2010; Gibson-Davis et al., 2021; McKernan & Sherraden, 2008; Nam et al., 2008; Rist, 2022; Rank et al., 2014; Rothwell et al., 2020; Rothwell & Robson, 2018). For a more conservative perspective which is aligned with Shapiro (2006), a six-month threshold and a nine-month threshold are used for sensitivity analyses.

Fig. 1
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The Conceptual Model

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As savings can be quickly converted into cash, people often consume savings to meet needs while preserving other assets (McKernan & Sherraden, 2008). While other assets are also usable, savings are unique in thier relative ease of access and high flexibility and convenience for use (McKernan & Sherraden, 2008). These features make savings distinct from other assets (Nam et al., 2008; McKernan & Sherraden, 2008). While different studies might use different categorizations, in this study, asset poverty is categorized as follows: (1) household savings alone are above the poverty threshold, (2) household savings alone are below the poverty threshold, but sum of savings and other household assets is above the poverty threshold, and (3) sum of savings and other household assets is still below the poverty threshold. By including asset poverty status as a moderator in the proposed model, results can advance our understandings of FSM by clarifying whether family stress processes consisting of household income, caregiver psychological distress, and adolescent problematic behavior could vary by different asset levels.

Research Questions and Hypothesis

Research question: Does asset poverty moderate direct and indirect effects of paths linking household income, caregiver psychological distress, and adolescent problematic behavior?

Within an FSM framework, household economic well-being can have influences on child and youth development through caregiver psychological well-being (Conger & Conger, 2002; Conger et al., 2010; Masarik & Conger, 2017). Empirical evidence shows household income is associated with adolescent problematic behaviors through caregiver psychological distress (Ponnet, 2014; Simons et al., 2016). This study, therefore, hypothesizes that caregiver psychological distress mediates the association between household income and adolescent problematic behavior. This is the pioneer study to empirically model a moderating role of assets in direct and indirect paths linking household income, caregiver psychological distress, and adolescent problematic behavior. Therefore, there is no prior empirical evidence. Still, as prior empirical findings show that the strength of associations between household income, caregiver psychological distress, and adolescent problematic behavior differs by household economic well-being (Ponnet, 2014; Sareen et al., 2011), this study hypothesizes asset poverty could have moderating effects in these associations.

Method

Data

This is a secondary analysis study using the 2017 and 2019 Panel Study of Income Dynamic (PSID) Main Study and 2019 and 2020 Child Development Supplements (CDS), which enable us to establish the temporal order of variables used to build the model. The 2019 PSID Main Study provides household financial resources information between 2017 and 2019, such as 2018 income. The 2019 CDS provides caregiver psychological distress in 2019. The 2020 CDS provides child and youth behavioral outcomes (e.g., internalizing and externalizing behaviors) in 2020. For demographic data, the 2017 PSID Main Study was used to collect demographic information (e.g., age, gender, race). When combining data, the PSID Main Study provides caregiver ID while the CDS provides both caregiver ID and child ID. As every caregiver may have more than one child, the one/many data-merge strategy was applied for data combination. The combined data set that integrates the PSID Main Study and the CDS provided comprehensive measures of child and family well-being, which are suitable for analyses in this study. To ensure a complete picture that includes information about caregiver and their children rather than either one of them alone, families completing the PSID Main Study and CDS were included for analyses. The final sample included 752 adolescents aged 10 to 17 from 565 families. The average child age is 13.1 (SD = 2.1). Children are 52.1% female. Caregivers were 77.1% female. The average age is 41.7 (SD = 7.5). Race and ethnicity include non-Hispanic White American (46.2%), non-Hispanic African American (32.6%), Hispanic (19.0%), and Others (2.2%).

Measures

Behavioral Problems

Internalizing and externalizing behaviors are measured by summing five internalizing behavior items and five externalizing behavior items based on Goodman et al. (1998). Internalizing items measure caregiver-reported child depression, somatic symptoms, worry, nervousness and ease of experiencing fear (0 = not true, 1 = somewhat true, 2 = certainly true). Externalizing behavior assess caregiver-reported child restless, fidgeting, if the child is easily distracted, cannot think things out before acting, or cannot see work through to the end (0 = not true, 1 = somewhat true, 2 = certainly true). A higher score indicates more behavioral problems. Items used to construct internalizing and externalizing behavior were found to have acceptable reliability (α = 0.73 and 0.80, respectively).

Caregiver Psychological Distress

Caregiver psychological distress is measured by summing item scores based on the Kessler Psychological Distress Scale (Kessler et al., 2002). These items assessed whether a caregiver felt nervous, hopeless, restless, that everything was an effort, feeling sad or worthless (0 = none of the time, 1 = a little of time, 2 = some of the time, 3 = most of the time, 4 = all of the time). A higher score indicates higher distress. Items showed acceptable reliability (α = 0.77). Caregivers refers to people who are mainly responsible for raising child and youth in the same household with their children, such as biological parents.

Household Income

Income is measured by the ratio of household income to the official poverty line, accounting for variability in the number of family members. Family members include children and adults.

Asset Poverty

Following asset poverty definitions pioneered by Caner and Wolff (2004) and Haveman and Wolff (2004), households are defined as asset poor if their assets are insufficient for meeting needs for a period of three months, which also can be described as assets below one quarter of the official poverty line. Based on data set availability, household assets include savings and other assets. Savings include saving and checking accounts. Other assets include farm or business, bonds, stocks, vehicles, etc. Asset poverty was categorized into (1) household savings alone are above the poverty threshold, (2) household savings alone are below the poverty threshold, but sum of savings and other household assets is above the poverty threshold, and (3) sum of savings and other household assets is still below the poverty threshold. For sensitivity analyses, six-month and nine-month asset poverty thresholds were also adopted for use.

Covariates

Covariates included caregiver characteristics and child characteristics. Caregiver characteristics included age (years), race (non-Hispanic White American, non-Hispanic African American, Hispanic, and Others), employment status (employed or not), and educational attainment (completed college or not). To avoid the empty cell effect that could severely inflate standard error estimation (Agresti, 2012), race was recoded into three categories (i.e., non-Hispanic White American, non-Hispanic African American, and Others). Child characteristics included age (years) and gender (female or male). Household characteristics used in this study included dual-parenting (dual-parenting or not).

Statistical Analyses

Bivariate analyses for descriptive results were done using Stata 13.0. MP version. This study conducted multi-group path analyses to examine whether asset poverty moderates the direct effects and indirect effects of paths linking household income, caregiver psychological distress, and behavioral outcomes. Multi-group path analyses were done with demographic controls using Mplus 6.12. Full-information maximum likelihood (FIML) estimation was applied for multi-group path analyses in this study. This study utilized the chi-squared test to compare the null model (i.e., path coefficients are constrained to be invariant across groups) with the alternative model (i.e., path coefficients in each group are freely estimated). We observed a statistically significant difference between the null model and the alternative model, justifying the use of multi-group path analysis to investigate the moderating role of asset poverty in the proposed model. A mixed effect model, which accounts for family clustering by adding a random intercept at the family level, was applied for multi-group path analyses.

Missing Data and Model Fitness Estimation

Schafer and Graham (2002) showed that the FIML estimation for treatment of missing data produces unbiased estimates when the pattern of missing data is missing completely at random (MCAR) or missing at random (MAR). The rate of missing variables in this study is 4.7% or lower. In performing Little’s MCAR and the covariate dependent missingness tests, it was confirmed that the missing data pattern in this study is MAR. For model fitness estimation, several indicators of model fit were employed in this study. These include the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI), the Root Mean Square Error of Approximation (RMSEA), and the standardized root mean square residual (SRMR). Values of the CFI and TLI greater than 0.90 indicate acceptably good fit. For the RMSEA, values below 0.06 indicate good fit (Bowen & Guo, 2011). For the SRMR, values less than 0.08 and 0.1 indicate good and acceptable fit, respectively (Wang & Wang, 2012).

Results

Descriptive Results

As shown in Table 1, this study observed that demographic characteristics vary by asset poverty status. Households with a sum of savings and other assets below the asset poverty threshold tend to have younger caregivers. A greater proportion are female or Non-Hispanic Black. In households whose savings are above the poverty threshold, a greater proportion of caregivers are employed and have completed college. These households have higher incomes and more of them are dual-parenting families. For children raised in households with a sum of savings and other assets below the asset poverty threshold, internalizing and externalizing behaviors are more severe.

Table 1 Descriptive results by asset poverty status
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Multi-group Path Analyses: Direct Effects

In Table 2, results show that direct effects vary by asset poverty status. In households with a sum of savings and other assets below the poverty threshold, household income is significantly associated with caregiver psychological distress (β = -0.390, p < 0.01). Caregiver psychological distress is significantly associated with internalizing behaviors (β = 0.089, p < 0.001) and externalizing behaviors (β = 0.140, p < 0.01). In households whose savings are below the poverty threshold while sum of savings and other assets is above it, the association between household income and caregiver psychological distress is non-significant. Caregiver psychological distress is still significantly associated with internalizing behaviors (β = 0.192, p < 0.001) and externalizing behaviors (β = 0.140, p < 0.01). In households where savings are above the poverty threshold, the association between caregiver psychological distress and internalizing behavior is significant (β = 0.144, p < 0.01), while other associations are non-significant.

Table 2 Multi-group Path Analyses: Direct and Indirect Effects by Asset Poverty Status
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Multi-group Path Analyses: Indirect Effects

In Table 2, results also show that indirect effects vary by asset poverty status. In households with a sum of savings and other assets below the poverty threshold, there were significant indirect effects for paths linking household income, caregiver psychological distress, and internalizing externalizing (β = -0.035, p < 0.05) and externalizing behavior (β = -0.055, p < 0.05). Such indirect effects are non-significant in the other two groups. These results are similar when employing a six-month asset poverty threshold or a nine-month asset poverty threshold.

[Insert Table 2 here]

Discussion

In this study, asset poverty status reflects three different asset levels of a household: (1) savings enable a family to meet three-month needs, (2) savings alone cannot support a family to meet three-month needs, but such needs can be met when simultaneously considering savings and other assets, and (3) even simultaneously considering savings and other assets cannot support a family to meet three-month needs. These categories enable this study to clarify whether and how family stress processes consisting of household income, caregiver psychological distress, and adolescent problematic behavior may vary between different asset levels.

While FSM illustrates that lower household income could lead to more severe caregiver psychological distress that influences child and youth development outcomes, this study expands FSM by reporting such family stress processes are not identical across these three different asset levels. More specifically, the family stress processes are strongest for families whose assets cannot support three-month needs, even considering all of their assets. Among these families, lower household income is predictive of higher psychological distress for caregivers, which in turn leads to more severe adolescent problematic behaviors. For families where sum of all assets can support three-month needs, the family stress processes become less intensive. In these families, caregiver psychological distress is not significantly affected by household income. For families where savings alone are sufficient to support three-month needs, the family stress processes are least intensive given that one more family stress process is attenuated, namely that caregiver psychological distress is not significantly associated with adolescent externalizing behavior problems. These findings suggest a dosage effect of household assets, which show that higher assets attenuate more family stress processes.

For practitioners, stronger family stress processes for families with fewer assets suggest that extra support is required to address caregiver psychological distress and adolescent problematic behaviors for families who are more economically vulnerable. This viewpoint is aligned with Chen et al. (2023), who suggest that it is important to provide cross-generational interventions to address child and family well-being, particularly for those experiencing higher economic stress. Linver et al. (2002) also suggest that it is important to offer interventions that simultaneously improve child and caregiver well-being, rather than just either of them alone, for economically vulnerable families. For scholars and policymakers, our findings suggest the need to develop a more nuanced assessment of household asset levels that enables practitioners to have a more detailed observation of child and caregiver well-being.

The core concept of the Family Stress Model is the family stress processes, which suggest that household income has downstream influences on child and youth development through caregiver psychological well-being (Conger & Conger, 2002; Conger et al., 2010; Masarik & Conger, 2017). In other words, within the theoretical frameworks of FSM, the influences of household economic stress are cross-generational given it could influence caregiver psychological well-being, that in turn affects child and youth development (Masarik & Conger, 2017). While such family stress processes have been intensively studied (Masarik & Conger, 2017; Mistry et al., 2008; Zhang et al., 2020), our findings expand FSM by reporting that the family stress processes are less intensive for families with more assets. As a family has more assets, caregiver psychological well-being becomes less affected by household income level. In this case, caregivers are at lower risk of developing psychological distress. Thus, adolescent behavioral development is less affected by it. These findings not only add our knowledge of FSM by taking account the moderating role of assets but also advance our understanding that assets can benefit child and family well-being through alleviating family stress processes.

In addition to assets, financial assistance programs offered by the government also play an important role in addressing financial stress, particularly during economic downturns such as precipitated by Covid-19. Kim (2021) observed that social assistance through the U.S. federal CARES Act during covid-19 helped alleviate food insecurity and housing instability. Johnson and Roberto (2020) suggest that a reduction in taxes and an increase in supplementary cash assistance could also be useful strategies to address financial stress. Reasons are attributed to the fact that these programs are able to shorten the gap between income and daily needs when people facing income shortage (Johnson & Roberto, 2020).

While asset accumulation can benefit household economic well-being (McKernan & Sherraden, 2008), our results suggest that sufficient assets can also benefit caregiver psychological well-being and adolescent behavioral development given less intensive family stress processes in households with more assets. Unfortunately, many current policies exist which result in assets being drawn down and thus could have harmful effects on caregivers and children. For example, the eligibility requirements for SNAP could discourage people from asset accumulation (McKernan & Sherraden, 2008). Thus, loosening the eligibility of social benefits could be important for asset accumulation, and may have broad positive impacts. While there are interventions implemented to reinforce incentives for asset accumulation (e.g., matching one dollar for saving two dollars), there are still barriers, such as insufficient financial literacy (Bell & Lerman, 2005). One study observed that better financial literacy measured by knowledge in interest rate, inflation, and stock risk is associated with subsequent increase in asset accumulation, even after holding income constant (Letkiewicz & Fox, 2014). All of these areas should be considered as we improve current economic and assistance policies (McKernan & Sherraden, 2008).

Limitation

This study’s promising findings are subject to several limitations. It is possible that people could hold savings while using other assets to meet needs. Also, it is possible that people could intentionally preserve assets even when they are facing economic stress. Additionally, when the real estate market is improving, it is possible that people could consume home equity to meet daily needs. Future studies should replicate our findings using other measures of assets, expanding our understanding of how different assets may benefit child and family well-being in different ways. Beyond the factors modeled in this study, factors such as genetic information, community environment, or social networking could also influence adolescent problematic behaviors. To reflect race and ethnicity diversity in our society, future research should replicate our results in a broader range of populations.

While our study investigates the family stress processes using the FSM theoretical framework, there could be other mechanisms explaining these associations. Further analyses using different frameworks are encouraged. For example, it would be valuable to explore reciprocal associations between household economic well-being and caregiver psychological distress. Still, findings from this study’s exploration of the FSM framework across different levels of household assets suggest potentially high-impact ways of improving child and family well-being through asset promotion interventions.

Conclusion

This study observed moderating effects of assets in attenuating family stress processes consisting of household income, caregiver psychological distress, and adolescent problematic behavior. These findings implicated that asset building interventions and policies could benefit child and family well-being given that such family stress processes are less intensive for families with more assets. To promote asset building, interventions such as loosening eligibility assessment, reinforcing incentives for asset accumulation, and improving financial literacy are suggested.