# Early-life disadvantage and parent-to-child financial transfers
Kent Jason Go Cheng Center for Healthy Aging, The Pennsylvania State University, University Park, Pennsylvania, USA

## Abstract

Objective: To determine (1) how parental childhood disadvantage is associated with parentto-child financial transfers for schooling and other unspecified purposes and (2) whether the association holds when parental socioeconomic status (years of education, family income, wealth) is considered.

## Background

Cumulative inequality theory posits that early life disadvantage may not only adversely affect one’s resource accumulation across the life course, but it may also shape one’s ability to provide assistance to offspring.

## Method

Data from the Panel Study of Income Dynamics were used to estimate zero-inflated negative binomial regression models to predict the amount of parent-to-child transfers (N = 2364 for school transfers, N = 3618 for other transfers), controlling for parents’ and children’s sociodemographic factors. Childhood disadvantage score (0 as reference, 1, 2, 3, 4+) was constructed using 13 items that reflect the economic, psychosocial, environmental, and health domains of early life. The associations of each domain with financial transfers were also estimated.

## Results

Parents with 4+ disadvantages gave about $2200 less for schooling. Economic and environmental disadvantage lessened levels of school transfers, while environmental disadvantage decreased the odds of being a non-provider of other transfers. The disparity in transfer amounts generally narrowed when the mediating role of parental socioeconomic status was accounted for.

## Conclusion

Higher education has become one of the major mechanisms through which class stratification, social inequality, and health disparities ensue. Educational outcomes among current cohorts of young adults could have been shaped by the preceding generation’s childhood through intergenerational exchanges.

## Keywords

childhood; education; intergenerational relationships; parent–child relationships
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. Correspondence: Kent Jason Go Cheng, Center for Healthy Aging, The Pennsylvania State University, 422 Biobehavioral Health Building, 296 Henderson Drive, University Park, PA 16802, USA. kgcheng@psu.edu. SUPPORTING INFORMATION Additional supporting information can be found online in the Supporting Information section at the end of this article.

## INTRODUCTION

Early-life experiences can etch an indelible mark on one’s later life circumstances. Earlylife disadvantage—be it in terms of health, family background, violence or abuse, or socioeconomic status—is associated with the onset of cardiovascular and cardiometabolic diseases, cognitive health, poor self-rated health, functional impairments, and even earlier death compared with those from more advantaged upbringing (Ferraro et al., 2016; Laditka & Laditka, 2018; Montez & Hayward, 2014). Effects of these disadvantages faced during younger years go beyond health and negatively shape various labor market outcomes like educational attainment, employment, and compensation, as well as wealth and other resource indicators (Clarke et al., 2024; Fronstin et al., 2001; Haas et al., 2011; Lorenti et al., 2020). Studies on childhood circumstances mostly focus on their consequences at the personal level—rarely the family level—despite the consensus that an individual’s life course is intimately linked with the life course of those close to them like family members and friends (Elder, 1974; Ferraro et al., 2009). One’s childhood circumstances undoubtedly shape their life course trajectories in ways that may also impact their family. This study fills this gap in the literature by assessing how early-life circumstances of parents shape parent-to-child (i.e., downward) financial transfers.

Cumulative inequality theory: Tracing the path from childhood to adulthood socioeconomic attainment and intergenerational support
The cumulative inequality theory (CIT) (Ferraro et al., 2009)—a middle-range theory that extends the life course perspective (Elder, 1974) and the cumulative (dis)advantage (CAD) theory (Dannefer, 2003; O’Rand, 1996)—gives explicit attention to the intergenerational feature of disadvantage and inequality, that family lineage and childhoods are major pathways leading to various outcomes and their inequities across the life course. CIT posits that social systems produce inequality, which manifests over the life course through demographic and developmental processes. Through these pathways and processes, individuals accrue risks and resources, develop perceptions, and enact human agency that influences their life course. Like the life course and CAD, CIT asserts that one’s childhood circumstances start the path leading to adulthood outcomes and their effect can potentially extend to the next generation. Early life (i.e., usually measured first 18 years of life), is considered critical because of sensitive developmental periods in which “insults” that occur can have long-lasting and severe impacts throughout one’s life (Ben-Shlomo & Kuh, 2002).
The pathways through which family lineage and childhood influence one’s life course include the biological, social psychological, economic, and ecological (Ferraro et al., 2009). Genes and nutrition are part of the biological while modeling and norms form the social psychological. Economic refers to one’s family resources while ecological pertains to the environment where the family lives. Therefore, studies that assess the impact of early life (and family life in one’s tender years) on an array of outcomes must consider the multidimensionality of childhoods. Families are nested in communities, and excluding factors brought about by the setting in which the child and family grew up may grossly underestimate early disadvantage and its association with an array of outcomes such as parent-to-child transfers.

It follows from CIT/CAD and a plethora of evidence that controlling for other factors, individuals whose childhoods were disadvantageous are less likely to accrue more resources and lower socioeconomic status in adulthood. For instance, parental marital disruption, either due to divorce or the death of a parent, as well as having low socioeconomic status during childhood can result in lowered odds of being employed and lower wages (Clarke et al., 2024; Fronstin et al., 2001). Poor childhood health can lead to lower educational attainment and an earlier onset of comorbidities in adulthood, resulting in lower earnings from early to mid-career compared with those who were healthier from birth to age 16 (Haas et al., 2011). Using a multidimensional measure of childhood experiences that incorporates both health and socioeconomic status, Lorenti et al. (2020) found that more childhood disadvantages are linked to fewer years spent working and more years living with a disability. Given that individuals who grew up disadvantaged are less likely to accrue more resources in adulthood, it follows that they will have less to pass on to their children. In other words, the direct influence of parental childhood experiences on parent-to-child transfers can be attenuated by parental resources reflected by socioeconomic status. Tracing the influence of parents’ childhoods on parents’ capabilities to assist their adult children financially expands our understanding of the resource, risk, and opportunity accumulation pathways across generations as per the CIT.

Heterogeneity of parent-to-child financial transfers by purpose of transfers
Parental financial transfers to children can either be for human capital formation or fallbacks during difficult life course transitions. This current study examines how parents’ childhood circumstances influence parent-to-child large transfers for schooling purposes, a human capital investment, as well as transfers for unspecified purposes which can be interpreted as contingency-driven transfers.
Researchers and social observers consider college education as one of the most potent forces of stratification in American society (Edsall, 2022). Empirical evidence suggests that college graduates fare significantly better in an array of life course outcomes compared with those without a bachelor’s degree. College graduates earn more money, accrue more wealth, and live longer and healthier lives (Amorim & Deming, 2022; Fomby & Kravitz-Wirtz, 2019; Montez & Hayward, 2014; Nam, 2020; Rauscher, 2016). Over the years, it has become increasingly challenging to successfully complete college, largely due to the rising costs. For the 2022–2023 academic year, the annual average cost of undergraduate tuition, fees, room, and board is $27,673, which is ~$7400 higher than the rates from a decade earlier (National Center for Education Statistics, 2023). In this context, having parents who can provide financial assistance for college enrollment serves as a pathway to gaining an advantage across the life course and as another vehicle through which inequalities prevail (Amorim & Deming, 2022; Fomby & Kravitz-Wirtz, 2019; Nam, 2020; Rauscher, 2016).
Aside from human capital investment reasons, parental transfers can be motivated by children’s needs as per the contingency exchange theory (Davey & Eggebeen, 1998; Fingerman et al., 2009, 2015). Achieving traditional markers of adulthood has become increasingly difficult, making adult children more dependent on their parents. Delayed entry into the labor market due to the pursuit of higher education, unemployment spells, and

transitioning in and out of romantic relations elicit a need for parental support (Fingerman et al., 2009, 2015). Therefore, holding constant children’s sociodemographic characteristics which may reflect their needs for assistance, children with parents who grew up advantaged and have more resources to provide in times of need have a significant advantage compared to those without affluent parents.

Past studies
In the few studies that seek to describe the nexus of childhoods and intergenerational transfers, to the best of my knowledge, only the social psychological (Fomby & Kravitz-Wirtz, 2019; Henretta et al., 2014; Rauscher, 2016; Wiemers et al., 2019) and economic (Henretta et al., 2014; Van Herreweghe & Van Lancker, 2021) facets of early-life experiences were considered. Although all these studies controlled for parental socioeconomic status, they did not test its mediating effect on the relationship between childhood experiences and parent-to-child transfers.
In terms of the psychosocial, the difference in parental support by types of parents’ unions has recently received a lot of attention in the literature on intergenerational transfers. Generally, children whose parent/s had unions other than being married to a spouse for a stable period of time tend to receive less parental support as the family safety net was weakened by union dissolution and re-partnering. Families with stepkin tend to be bigger in size—that is, have better kin availability—yet intergenerational transfers occur less often than families without stepkin (Wiemers et al., 2019). Parents who have at least one stepchild have lower chances of providing time transfers to their children compared to parents who do not have any stepchildren, although the odds of giving money to children were the same regardless of whether the parents have a stepchild or none (Wiemers et al., 2019). Upon studying the intergenerational transfer pattern heterogeneity by blended versus unblended families, Henretta et al. (2014) found that stepchildren—especially those of the wife’s—were less likely to receive financial transfers from their parents than biological children. However, whether the child was born to parents who divorced or became widowed did not affect the likelihood of receiving parental money transfers. Relative to children whose parents co-resided, children with unpartnered parents gave 44%–90% less in financial transfers for schooling (Fomby & Kravitz-Wirtz, 2019). Repartnered parents provided one to two-thirds less parental support for education compared with parents who consistently co-resided with the child’s other parent (Fomby & Kravitz-Wirtz, 2019). Mothers who were married when the child was aged 17 gave more transfers to assist their children in schooling compared with their unwed counterparts, although maternal socioeconomic indicators like income and wealth were stronger predictors of the transfer (Rauscher, 2016).
Regarding childhood economic circumstances, the scant literature addressed parental socioeconomic status, experiences of economic hardship in early life, and parental receipt of bequests. Henretta et al. (2014) found that US older adults who received financial assistance from relatives before the age 16—considered a marker of childhood socioeconomic disadvantage (e.g., Montez & Hayward, 2014)—were more likely to give financial aid to their adult children—step or biological. A study on parent–adult–child dyads in 18 European countries conducted by Van Herreweghe and Van Lancker (2021) found that receiving gifts

or bequests of ≥€5000 from their parents (i.e., the grandparent generation) almost doubled the odds of the respondent giving financial transfers to their child. These findings suggest that transfers from the grandparent generation improved parents’ capacities to pass on resources to their children.

The current study
In line with the existing theory and work on this area described above, the following hypotheses were tested in this study (see Figure 1 for the path model):

Hypothesis 1. Parents who experienced disadvantage in economic, psychosocial, environmental, and health domains during childhood are less likely to provide financial transfers and provide less financial transfers to their adult children compared with those with more advantageous early-life circumstances.

Hypothesis 2. The influence of disadvantage in economic, psychosocial, environmental, and health domains during childhood on parent-to-child financial transfers persists but diminishes when parental socioeconomic status (i.e., education, income, and wealth) are controlled for. Specifically, higher socioeconomic position may attenuate the negative association between parental childhood disadvantage and parental financial transfers.

Possible confounders
The association between childhood disadvantage and parent-to-child financial transfers can be confounded by socioeconomic and demographic characteristics that reflect givers’ ability to provide intergenerational assistance and the receivers’ need for help following contingency theory (see, e.g., Fingerman et al., 2020 and Wiemers & Park, 2021 for recent reviews).

Other parental characteristics
Financial transfers tend to flow from parents to children, but the direction typically reverses when parents reach older adulthood—a life course stage marked with declining health and a higher need for assistance (Fingerman et al., 2010; McGarry, 2016). Thus, younger parents can be expected to provide more transfers to their children compared with their older counterparts. Compared with non-Hispanic whites, intergenerational financial transfers among racial minorities are less prevalent and less in magnitude (Amorim & Deming, 2022; Nam, 2020). This racial gap can be attributed to oppressive systems that restrict minorities from amassing resources and power, leaving them less able to support family members (Amorim & Deming, 2022; Nam, 2020). A large family can create a higher demand for support, which, in turn, limits the amount of assistance one is able to provide and receive (Fingerman et al., 2015). Single parents tend to give less financial support to their children, largely due to lower resources compared with married or partnered parents (Fingerman et al., 2015; Fomby & Kravitz-Wirtz, 2019).

Children’s characteristics
Older offspring tend to receive less support from parents, likely because their need for parental assistance declines with age (Hartnett et al., 2012). The literature has also shown

that generally, adult children in better socioeconomic standing are less likely to receive parental transfers (Fingerman et al., 2009, 2015). While marriage confers material benefits such as dual incomes which may translate to a lesser need for parental assistance, married adult children may still require parental support as they establish their own families (Fingerman et al., 2015; McGarry, 2016). Additionally, there is ample evidence that parents provide more assistance to their adult children who are about to or already have children (McGarry, 2016).

## METHODS

Data
For this study, I used the Panel Study of Income Dynamics (PSID, https:// psidonline.isr.umich.edu/default.aspx), the longest-running longitudinal study that was first fielded in 1968 and included 18,230 people living in 4802 households. The PSID collects information on respondents’ and their descendants’ employment, income, wealth, expenditures, health, marriage, childbearing, child development, philanthropy, education, and numerous other topics. The PSID was an annual survey until it became a biennial starting in 1997. The PSID follows a genealogical sample wherein subjects recruited in 1968 gain the PSID “gene.” The biological or adopted descendants of those with the PSID gene also obtain the gene and are recruited to be members of the PSID sample for the remainder of their lives. Individuals without the gene are included in the study for as long as they co-reside with someone who has the PSID gene. As of the 2013 wave, the PSID had almost 25,000 respondents living in 9107 households. A recent overview of the PSID is provided by Pfeffer et al. (2020).
I merged the following PSID modules: the Childhood Retrospective Circumstances Study (CRCS) and the Rosters and Transfers (R&T). The CRCS, fielded in 2014 primarily via the internet (with a mailed self-administered instrument as a secondary option), asked the head of the household or her/his spouse/partner about their early life, gathering information on parental/guardian relationship quality and history, health, socioeconomic status, neighborhood quality, friendships, school experiences, involvement with the criminal justice system, parent/guardian mental health, and young adult mentoring. The CRCS had 8072 respondents aged 19 and above during the time of the interview (67% response rate, 75% of cases completed using the internet).
In 2013, the PSID fielded the R&T, which gathered the names and key characteristics of all the living adult biological or adopted children and biological or adaptive parents of all the PSID heads and their spouses/partners, spanning 9107 families. Note that the rosters cover those related to the interviewees regardless of whether they have the PSID gene. To ensure that respondents provide a fuller account of their children, the study preloaded fertility histories and the interviewers probed about children whom the subject failed to spontaneously report. The R&T also asks whether parents have retained their partners or have remarried, which allows researchers to map out step-relations. The R&T, therefore, allows for analyses of the determinants of intergenerational transfers that account for the giver and the recipients’ characteristics as well as their relationship with each other. That way, both the recipients’ need for assistance and the givers’ capabilities to provide help are

taken into account. The respondents were then asked about the time and money transfers given to or received from the living parent or adult children they have enumerated. See Schoeni et al. (2015) for more information about the R&T.
The analytic sample was based on the CRCS sample of 8072 (100%) individuals which I merged with the household-level version of the R&T file. First, I removed those without at least one adult child (3997 or 49.5%), those who were not present in the Family File (31 or 0.4%) and those with missing values in the variables of interest (426 or 5.3%). I arrived at an analytic sample of 3618 (44.8% of the CRCS sample or 88.8% of CRCS sample with at least one adult child) for the models that predict other transfers. For the school transfer regressions, following Rauscher (2016), I excluded those who have at least one child younger than 22—the age at which college education is most likely to be completed for those who pursue tertiary education (984 or 12.2) since it is crucial to focus the analysis on respondents whose children have likely completed their tertiary education. Doing so resulted in the analytic sample of 2364 individuals (29.3% of the CRCS sample or 58.0% of CRCS sample with at least one adult child).

Parent-to-child financial transfers
The R&T contains large transfers given by the respondents when their children were aged 18 or older. Respondents were asked: “Since turning 18, have any of [your/HEAD’s] (or spouses) children received any of the following from you (or spouse)? (1) help paying for school, including tuition, room and board, and books and (2) financial help for other expenses.” The respondents were further tasked to report the dollar amounts.

Childhood disadvantageous circumstances
Following the CIT (Ferraro et al., 2009) and taking advantage of the breadth of variables in the PSID-CRCS, I included the economic, psychosocial, environmental, and health dimensions of early life (Table 1). In total, I included 13 dichotomized measures which I scored as 1 if the respondent experienced the particular disadvantage in childhood at least once and 0 otherwise. I summed these dichotomized disadvantage indicators and expressed them as the following categories based on their frequency distribution: no disadvantage (reference), 1 disadvantage, 2, 3, and 4+. Converting the summed scores into categories is common practice in the literature (Laditka & Laditka, 2018; Lorenti et al., 2020; Montez & Hayward, 2014), as it enables the testing of nonlinearities of the relationships of interest. I also ran the same empirical models but replaced childhood disadvantage with each of the four domain scores entered separately to avoid multicollinearity and overadjustment bias. Similar to the overall childhood disadvantage variable, the domains’ scores were converted to categorical variables depending on their frequency distribution. Appendix 1 provides more details on how the measures of childhood disadvantage and its four domains were constructed.

Parental socioeconomic status
Parental socioeconomic status was measured through years of education (mean of respondent’s and their spouse’s years of schooling if married), family income in $1000, and household net wealth with equity in $1000. I intentionally did not include all mediators

simultaneously due to their high correlation, which could lead to multicollinearity and overadjustment bias.

Control variables
The main goal of this study is to ascertain the association of the giver’s early-life disadvantage—be it as a whole or by domains—with the money transfers they provide to their adult children for schooling and other purposes, net of factors that may reflect the capacities of the parents to provide transfers, and adult children’s potential need for parental assistance (see Fingerman et al., 2020 and Wiemers & Park, 2021 for recent reviews). The parental characteristics I controlled for include the mean age of the head and spouse or the age of the unpartnered respondent, as well as the head’s marital status (whether married or partnered), race-ethnicity (whether not non-Hispanic white), the total number of children, and the total number of grandchildren. Average characteristics of the respondents’ adult children I included were average age; mean years of education; sex ratio of total children (% female-to-total children); dichotomized indicators of whether at least one child is a student, unemployed, has low income (<$25,000), has high income (>$75,000), has missing income, is a homeowner, and is partnered following Wiemers et al. (2019) whose study also used the PSID R&T.

Empirical strategy
Since financial transfers are highly skewed with a high prevalence of $0’s, I employed zeroinflated negative binomial (ZINB) regressions (Cameron & Trivedi, 2022). ZINB separately predicts (1) the likelihood of making no transfers (i.e., binary indicator: no transfers = 1, provided transfers = 0) and (2) the dollar amount of transfers conditional on a transfer being made. I used the same covariates to predict the likelihood of no transfers and transfer amount >0. ZINB models were preferred over zero-inflated Poisson regression models since the overdispersion parameter α > 1 across the majority of models ran. I clustered the standard errors by household level, given that almost a third of the analytic sample share the same household, and transfers are asked in such a way that they pertain to joint gifts between the head and their spouse.

Sensitivity analyses
Some concerns with the way the analytic sample was derived may arise. First, although conducting listwise deletion may raise some concerns about selection bias, the final analytic sample and the eligible sample did not differ substantially in childhood circumstances, the sociodemographic information of the respondents, and the characteristics of the respondents’ adult children (see Appendix 2). Note that compared with the eligible sample, the analytic sample provided slightly higher transfers to their children. However, this difference (about $800 for school transfers, $90 for other transfers) is quite small relative to the overall size of the transfers. Second, for models predicting financial transfers for schooling, restricting the sample to parents whose children are older than the typical college completion age of 22 may still introduce the following bias: (1) from disadvantaged children requiring additional time to finish their education, and (2) from children who may receive assistance from their affluent parents for the pursuit of graduate school (Nam, 2020; Rauscher, 2016). To ensure robustness of the results, I conducted sensitivity analyses, replicating the main analyses to

samples confined to those with children older than 25, 30, and 34 (see Appendix 3). Results from the sensitivity analyses remained largely quantitatively similar to the main results, except that the statistical significance was lost for the subsample with children older than 34. This is not concerning or surprising, given that this subsample’s size is about half that of the analytic sample.
Despite constructing childhood disadvantage based on CIT (Ferraro et al., 2009) and previous research (Ferraro et al., 2016; Laditka & Laditka, 2018; Montez & Hayward, 2014), the coding of early-life disadvantage and its individual domains as categorical variables remains a concern. To address this, I ran models using childhood disadvantage as an index ranging from 0 to 13 (see Appendix 4). The index was rarely statistically significant across all models run, implying that the relationship between parents’ earlylife disadvantage and parent-to-child transfers cannot be described as a dose–response relationship. Rather, the association showed nonlinearities, justifying coding childhood circumstances categorically.

## RESULTS

Descriptive statistics
Table 2 shows the descriptive statistics. Having at least one childhood disadvantage and economic disadvantage were the most common, while being in poor/fair health in childhood was the least common experience across all study subsamples. School-related transfers generally had a higher prevalence and average amount compared with other transfers. Since the school transfers subsample was limited to respondents with children past the age of typical college completion (i.e., 22 years), this group was unsurprisingly older and had less family income, probably due to retirees, but more wealth compared with the other transfers subsample. Children of the school transfers subsample differed from the children of the other transfers subsample, with the former being generally more likely to have low income but also more likely to be partnered and homeowners. Both subsamples did not differ much in terms of other respondent demographic characteristics.
Parents’ childhood disadvantage and parent-to-child financial transfers
Bivariate analyses between overall childhood disadvantage and transfers (not shown) suggest that the prevalence of transfer provision was higher among those without disadvantage, and this is especially true for school transfers (e.g., the prevalence of school transfer givers among those without disadvantage = 44.9%, i.e., Pearson χ2 p < .001). Those with lower disadvantage were more likely to provide other transfers, but the bivariate association was not statistically significant.
Tables 3–5 provide the zero-inflated negative binomial (ZINB) regression results. The first column of each model, labeled count, pertains to the negative binomial portion of the regression which predicts the amount of transfers when transfers >0. The second column of each model, labeled inflate, pertains to the logit portion of the regression which predicts the dichotomous outcome of whether transfers did not occur. To save space, I am only presenting the coefficients for the key independent variables of interest—childhood

disadvantage and the parental socioeconomic status mediators. Recall that these associations between parents’ childhood disadvantage and parent-to-child financial transfers are net of respondent and adult child characteristics that reflect the need for assistance or capacities of giving. To facilitate interpretation, I presented the count portion coefficients as incidence risk ratios (IRRs) and the logit portion coefficients as odds ratios (ORs). I also provide predicted amounts of transfers (i.e., marginal effects at means of the other independent variables) for the models that yielded statistically significant associations between the key variables in Table 6, combining predictions from both the count and inflate portions of the ZINB models. The predicted amounts were compared against the no disadvantage group.
Table 3, Panel A (B) provides the regressions predicting school (other unspecified) transfers. Conditional on a transfer being made, those with four or more disadvantages provided 24% (IRR = 0.76) less school transfers compared with those with no disadvantage (Model 1). Those with one or two disadvantages were about 1.4–1.6 times more likely to be non-provider of school transfers than those with zero disadvantage (Model 1). As a result, on average, those with four or more childhood disadvantages gave $2261 less in school transfers compared with those with no disadvantage ($4597 vs. $6858, Model 1, Table 6), supporting H1.
Compared with Model 1, when years of education were controlled for in Model 2, the statistical significance of the association of four or more disadvantages in the count portion disappeared, as well as the statistical significance of two disadvantages in the inflate portion. Education increased the amount of transfers when they occurred and each additional year of schooling was associated with 0.81 times lower odds of zero transfer provision. This equates to those with one disadvantage providing about $1744 less in school transfers compared with those without childhood disadvantage ($4080 vs. $5824, Model 2, Table 6), in support of H2. Model 1’s patterns of disparities in school transfers by childhood disadvantage levels remained the same but their magnitude slightly shrank when family income (Model 3) and net wealth with equity (Model 4) were included as mediators, supporting H2.
Note that while generally, disadvantage was negatively associated with educational transfers, the marginal effects (Table 6) demonstrated that the relationship does not follow a dose– response function. Specifically, the transfer amounts of those with three disadvantages generally did not differ from the no-disadvantage group.
While parental socioeconomic status indicators were associated with other transfers in a manner quite similar to their association with school transfers, there were no statistically significant differences in other transfers by childhood disadvantage (Panel B), contradicting both H1 and H2.

Childhood disadvantage domains and parental financial transfers
Table 4 provides the associations between various childhood disadvantage domains (i.e., economic, psychosocial, environmental, and health) and parent-to-child money transfers for school purposes. Having one economic disadvantage was associated with about 1.7 times higher odds of not providing school transfers compared with having no economic disadvantage, in line with H1. This is equivalent to those with no economic disadvantage

transferring about $2263 more than those with one economic disadvantage ($6338 vs. $4075, Table 6, Model 9).
Adding the socioeconomic indicators as mediators attenuated the positive association between childhood economic disadvantage and zero school transfers in the inflate portion (Models 10–12), following H2. Education had the greatest attenuating effect, lowering the disparity in school transfers by about $1760 (Table 6, Model 10). Another noteworthy finding is that the school transfers of those with two or three or more disadvantages did not differ from the no-disadvantaged group, implying that the relationship between economic disadvantage and transfers does not follow dose–response pattern.
Model 17 supported H1, that when compared with the respondents without environmental disadvantage, those who have one disadvantage in this domain provided about 30% less in school transfers (IRR = 0.70), conditional on transfers being made. Those with two or more disadvantages in their environment growing up were 1.75 times more likely to be non-providers of school transfers to their offspring. These findings suggest that on average, those without a disadvantage gave $5454 while those with one disadvantage in this domain gave $4082 and those with two or more disadvantages gave $3200 (Table 6, Model 17).
In support of H2, adding mediators decreased the differences in transfer amounts by environmental disadvantage levels, conditional on a transfer being made. The mediators also attenuated the odds of being non-providers (Models 18–20). Additionally, the predicted transfer amounts were only statistically significantly different between the two extreme groups (i.e., no disadvantage and two or more disadvantage group) when the mediators were included (Table 6, Model 18–20), partially supporting H2. Psychosocial and health disadvantages were not statistically significant predictors of parent-to-child school transfers (Panel B and D, respectively).
Table 5 provides the associations between various childhood disadvantage domains and parent-to-child money transfers for other unspecified purposes. The economic (Panel A), psychosocial (Panel B) and health (Panel D) domains were not associated with transfers for other purposes, rejecting H1. Environmental disadvantage (Panel C) only became statistically significant in predicting no transfers in the inflate portion of ZINB when parental socioeconomic indicators were added in the models, partially supporting H2 but in direct contrast to H1. Specifically, when education, income, and wealth were part of the models, those with one disadvantage in their growing up environment were 0.77–0.81 times less likely to be non-providers of other transfers compared with those with zero disadvantage (Models 34–36). In the count portion of the models, levels of other transfers conditional on a transfer being made did not differ by environmental disadvantage. Combining the inflate and count portions of the model, results suggest that those with no environmental disadvantage gave about $371–$557 more than those with one disadvantage (Table 6, Panel B). The difference was highest for Model 35 wherein family income acted as the mediator (Table 6, Panel B, H2). Note, however, that the differences of predicted other transfers between the disadvantaged versus not disadvantaged group were not statistically significant (Table 6, Panel B).

## DISCUSSION

Exposures to misfortunes during one’s childhood can potentially set the stage for a multitude of deleterious outcomes in the future (Ben-Shlomo & Kuh, 2002; Clarke et al., 2024; Ferraro et al., 2009, 2016; Fronstin et al., 2001; Haas et al., 2011; Laditka & Laditka, 2018; Lorenti et al., 2020; Montez & Hayward, 2014). Many previous studies have documented the negative effects of childhood adversity on health and socioeconomic standing in adulthood, yet little has been done to assess early life’s family-related outcomes. This study is one of the first to test whether parents’ childhood disadvantage is negatively associated with parent-to-child large money transfers, and whether this association holds when parental socioeconomic status is controlled for.
Taking into account the multidimensionality of childhood experiences and controlling for a comprehensive set of parent and adult children characteristics that represent ability to give and need for assistance, respectively, I found that in line with Hypothesis 1 (H1), generally, parents with more disadvantaged childhoods were more likely to be non-providers of school transfers. And those who grew up disadvantaged that do provide school transfers to their successors gave less compared with their advantaged counterparts (Table 3 Panel A and Table 6, Panel A). Those with four or more childhood disadvantages gave about $2200 less in terms of school transfers compared with those with no disadvantage. In support of Hypothesis 2 (H2), early life disadvantage remained a statistically significant predictor of school transfers, but controlling for parental socioeconomic status reduced the differences in school transfer levels. Education emerging as the most significant attenuating factor. To provide context, if the average annual cost of undergraduate tuition, fees, room, and board was around $20,200 (National Center for Education Statistics, 2023) in 2013 (the year the R&T data was collected), those without disadvantages contributed about 30% of the cost of college for a year, whereas those with four or more disadvantages contributed around 20%.
The second part of this study seeks to tease out the domain of childhood disadvantage that is most relevant to parent-to-child large money transfers (Tables 4 and 5). In the rare occasion that early origins of parental assistance to adult children are assessed in the literature, only the socioeconomic and the family-related disadvantage were included. Past research suggests that transfers between generations seem rarer and smaller among those who were economically disadvantaged in childhood (Henretta et al., 2014; Rauscher, 2016; Van Herreweghe & Van Lancker, 2021). Stepchildren and children born of parents with a history of union instability were found to benefit less from parental transfers (Fomby & KravitzWirtz, 2019; Henretta et al., 2014; Rauscher, 2016; Wiemers et al., 2019). Cumulative Inequality Theory (CIT) identifies four domains of childhood experiences, namely, economic (e.g., parental education), psychosocial (e.g., parental divorce), environmental (e.g., whether neighborhood was safe), and health (i.e., self-assessed poor/fair health) (Ferraro et al., 2009). Individuals’ life course trajectories and intergenerational relations are nested within their family and environments. Excluding the setting in which the child and family grew up may grossly underestimate early disadvantage and its association with an array of outcomes such as ability to transfer resources to offspring.

Early-life disadvantage in the economic and environmental domains was negatively associated with school transfers (Table 4 and Table 6, Panel A) in line with H1. Compared with those without economic disadvantage, those who had one disadvantage provided about $2260 less, although the disparity generally decreased but remained statistically significant when parental socioeconomic status’ mediating effect was accounted for, following H2. A similar pattern and amount of transfer difference held for environmental disadvantage. Note that environmental disadvantage was all about the respondents’ neighborhoods growing up. A very likely mechanism behind this finding is housing value. Neighborhood quality determines house value (e.g., Ioannides, 2011), and there is evidence to suggest that parents use their housing wealth to help put their children through college (e.g., Lovenheim, 2011).
Environmental disadvantage was only modestly negatively associated with the likelihood of being a non-provider of other unspecified transfers (i.e., disadvantage was only statistically significant in the inflate portion of the model, Table 5), despite controlling for a comprehensive set of socioeconomic indicators that may reflect children’s need for parental assistance. The direction of their relationship was contrary to H1. Although this may seem to challenge CIT, I propose that this may not necessarily be the case. This additional transfer by the disadvantaged group may be reflective of actualized contingency exchange motive (Davey & Eggebeen, 1998; Fingerman et al., 2009, 2015). Parents who have experienced disadvantage in their upbringing may inadvertently transmit that disadvantage to their offspring, leading the latter to require ongoing parental support even into adulthood. This is evidenced by environmental disadvantage being statistically significant only when parental socioeconomic status is accounted for. Somewhat parallel to Fingerman et al.’s (2015) finding, parents who grew up disadvantaged may experience a “double jeopardy”: they have fewer means to give, yet their offspring have greater needs for support. Future studies that distinguish between children’s met and unmet needs for parental support are better suited to fully examine whether parents’ early life disadvantage set the stage for parents to experience this double jeopardy and how this double jeopardy affects children’s various outcomes.
Unlike other studies that assess the association between childhood disadvantage to various negative outcomes such as health and adulthood socioeconomic standing, I found that early-life misfortune of the giver does not necessarily have a straightforward dose–response relationship with financial transfers to children. Rather, most of the models show that: (1) those who have the highest level of disadvantage were mostly the ones statistically different from those who have no disadvantage, (2) some domains of childhood disadvantage matter more than others, and (3) the associations differ by the purpose of money transfer, with early-life disadvantage more strongly linked to school transfers than other unspecified transfers. In other words, as per the CIT, there are nonlinearities through which advantage/ disadvantage is transmitted across generations, and this study barely pierced the veil of the early origins of disparities in intergenerational exchanges.
Perhaps the most important finding in this research is the stark disparity of parental transfers for education by childhood disadvantage. Researchers and social observers have pointed to an American society divided by one with a college degree and one without (Edsall, 2022). Those without a college degree are more at risk for experiencing a life course full of adversity—from the lack of career and social relationships prospects, up to more severe

infirmities and early mortality (Amorim & Deming, 2022; Fomby & Kravitz-Wirtz, 2019; Montez & Hayward, 2014; Nam, 2020; Rauscher, 2016). Parental influences on educational outcomes are well-studied, with those whose parents are more affluent tending to have a higher educational attainment. This current study has made it apparent that parents’ childhoods have a long reach to the aid for schooling their children receive, despite the attenuating effect of parental socioeconomic status. For a more equitable society, public policies that ease access to human capital investment for young adults, especially those from disadvantaged backgrounds and deprived of familial assistance for schooling, should be expanded (Nam, 2020). It is not enough for states to reverse their disinvestment in higher education; information asymmetries regarding how to access financial aid (e.g., Free Application for Federal Student Aid or FAFSA), which are highly stratified by socioeconomic status, must also be addressed (Page & Scott-Clayton, 2016). Progressive and universal Child Development Accounts (CDAs) (i.e., savings or investment accounts where parents can deposit funds for their child’s future use) can encourage disadvantaged parents to save money for their offsprings’ education, especially if additional incentives for parents of low-income backgrounds are made available (Nam, 2020). Future research should examine whether this parental childhood influence holds for realized educational outcomes of their descendants, and what that means for various layers of social inequities.
Despite this study’s unique contributions, readers should be aware of the following limitations of the study. First, retrospective surveys, especially those that ask about experiences incurred decades before the survey period, could be marred with recall bias. Although this is a concern, past studies using different samples have documented the reliability of early-life experience questionnaires, especially in terms of the economic domain (Warren et al., 2022). Second, despite efforts to construct the disadvantage measure to align with the premise of CIT, the CRCS measures—especially childhood health—do not fully capture the biological explanation posited by CIT. Third, the cross-sectional nature of the data does not allow the full exploration of period and cohort effects which are salient in the life course and cumulative inequality theory (Elder, 1974; Ferraro et al., 2009). Different cohorts may have varying degrees and perceptions of early-life misfortunes, as well as capacities for providing assistance to children. Finally, and somewhat related to the third limitation, the R&T data used were collected in 2013, just 5 years after the Great Recession of 2008. Negative macroeconomic shocks that occur periodically could affect the capabilities of parents in providing transfers to offspring, especially retired older adults who may be dependent on financial market performance or younger individuals who have employment that is cyclical.

## CONCLUSION

This study has uncovered that parents’ monetary assistance to their adult children vary by levels of their early-life disadvantages. Financial transfers earmarked for schooling purposes was at most $2260 less for those with the worst level of early-life misfortunes compared with those who experienced affluent upbringing. Higher education has become one of the major mechanisms through which class stratification, social inequality, and health disparities ensue. This study has demonstrated that educational outcomes among current cohorts of young adults could have been shaped by the preceding generation’s childhood through

intergenerational exchanges. And some childhood domains matter more (i.e., economic, environmental) than others (i.e., psychosocial, health). Indeed, intergenerational exchanges can either promote or set back the next generation’s success. To further understand societal inequality, researchers should continue to uncover life course determinants of the variegated experiences in intergenerational transfers.

## Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

## ACKNOWLEDGMENTS

Cheng is currently supported by the U.S. National Institute on Aging Grant T32 AG049676 to the Center for Healthy Aging at the Pennsylvania State University. This article formed part of his dissertation conducted at Syracuse University (SU), and he is extremely grateful to his adviser Janet Wilmoth, as well as committee members Jennifer Karas Montez, Emily Wiemers, Merril Silverstein, and Shannon Monnat. He is also grateful to the members of SU’s Policy, Place, and Population Health Lab (P3H Lab); Alexis Santos-Lozada; and the attendees of the 2023 Panel Study of Income Dynamics User Conference, 2022 Gerontological Society of America (GSA) Annual Scientific Meeting, and 2022 Population Association of America Annual Meeting (especially Sarah Patterson and Sung Park) for their useful feedback. Finally, he is also indebted to the following for providing him resources to present his work in various avenues: GSA’s Mentoring and Career Development Technical Assistance Workshop Diversity Fellow Award, the Centers on the Demography and Economics of Aging – Coordinating Center Travel Award Program (R24 AG066588), and SU’s Aging Studies Institute, Social Science Department, and Graduate School. The errors remain the author’s alone.

## REFERENCES

Amorim M, & Deming S (2022). Black–white disparities in nuclear family trajectories and parents’ postsecondary transfers to adult children. Journal of Marriage and Family, 84, 1024–1045. 10.1111/ jomf.12845
Ben-Shlomo Y, & Kuh D (2002). A life course approach to chronic disease epidemiology: Conceptual models, empirical challenges and interdisciplinary perspectives. International Journal of Epidemiology, 31, 285–293. 10.1093/ije/31.2.285 [PubMed: 11980781]
Cameron AC, & Trivedi PK (2022). Microeconometrics using Stata (2nd ed.). Stata Press. Clarke C, Bonnet J, Flores M, & Thevenon O (2024). What are the economic costs of childhood
socio-economic disadvantage? Evidence from a pathway analysis for 27 European countries. The Journal of Economic Inequality, 22, 472–494. 10.1007/s10888-023-09603-8 Dannefer D (2003). Cumulative advantage/disadvantage and the life course: Cross-fertilizing age and social science theory. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 58(6), S327–S337. 10.1093/geronb/58.6.S327 [PubMed: 14614120] Davey A, & Eggebeen DJ (1998). Patterns of intergenerational exchange and mental health. Journal of Gerontology: Psychological Sciences, 53B(2), 86–95. 10.1093/geronb/53B.2.P86 Edsall TB (2022, October 5). There are two Americas now: One with a B.A. And one without. New York Times. https://www.nytimes.com/2022/10/05/opinion/mortality-penaltyinequality-education.html Elder G (1974). Children of the great depression: Social change in life experience. University of Chicago Press. Ferraro KF, Schafer MH, & Shippee TP (2009). Cumulative inequality theory for research on aging and the life course. In Bengtson VL & Settersten R (Eds.), Handbook of theories of aging. Springer. Ferraro KF, Schafer MH, & Wilkinson LR (2016). Childhood disadvantage and health problems in
middle and later life: Early imprints on physical health? American Sociological Review, 81(1), 107–133. 10.1177/0003122415619617 [PubMed: 27445413]

Fingerman KL, Huo M, & Birditt KS (2020). A decade of research on intergenerational ties: Technological, economic, political, and demographic changes. Journal of Marriage and Family, 82, 383–403. 10.1111/jomf.12604 [PubMed: 38831801]
Fingerman KL, Kim K, Davis EM, Furstenberg FF, Birditt KS, & Zarit SH (2015). “I’ll give you the world”: Socioeconomic differences in parental support of adult children. Journal of Marriage and Family, 77, 844–865. 10.1111/jomf.12204 [PubMed: 26339102]
Fingerman KL, Miller L, Birditt KS, & Zarit SH (2009). Giving to the good and the needy: Parental support of grown children. Journal of Marriage and Family, 71, 1220–1233. 10.1111/ j.1741-3737.2009.00665.x [PubMed: 20161599]
Fingerman KL, Pitzer LM, Chan W, Birditt K, Franks MM, & Zarit S (2010). Who gets what and why? Help middle-aged adults provide to parents and grown children. Journal of Gerontology: Social Sciences, 66B(1), 87–98. 10.1093/geronb/gbq009
Fomby P, & Kravitz-Wirtz N (2019). Family systems and parents’ financial support for education in early adulthood. Demography, 56(5), 1875–1897. 10.1007/s13524-019-00807-0 [PubMed: 31420843]
Fronstin P, Greenberg DH, & Robins PK (2001). Parental disruption and the labour market performance of children when they reach adulthood. Journal of Population Economics, 14, 137– 172. 10.1007/s001480050163
Haas SA, Glymour MM, & Berkman LF (2011). Childhood health and labor market inequality over the life course. Journal of Health and Social Behavior, 52(3), 298–313. 10.1177/0022146511410431 [PubMed: 21896684]
Hartnett CS, Furstenberg FF, Birditt KS, & Fingerman KL (2012). Parental support during young adulthood. Journal of Family Issues, 34(7), 975–1007. 10.1177/0192513X12454657
Henretta JC, Van Voorhis MF, & Soldo BJ (2014). Parental money help to children and stepchildren. Journal of Family Issues, 35(9), 1131–1153. 10.1177/0192513X13485077 [PubMed: 24976667]
Ioannides YM (2011). Chapter 25—Neighborhood effects and housing. In Handbook of social economics Elsevier, 1, pp. 1281–1340. 10.1016/S0169-7218(11)01030-6
Laditka JN, & Laditka SB (2018). Adverse childhood circumstances and functional status throughout adult life. Journal of Aging and Health, 30(9), 1347–1368. 10.1177/0898264317715976 [PubMed: 28643582]
Lorenti A, Dudel C, Hale JM, & Myrskyla M (2020). Working and disability expectancies at older ages: The role of childhood circumstances and education. Social Science Research, 91(102447), 1–17. 10.1016/j.ssresearch.2020.102447
Lovenheim MF (2011). The effect of liquid housing wealth on college enrollment. Journal of Labor Economics, 29(4), 741–771. 10.1086/660775
McGarry K (2016). Dynamic aspects of family transfers. Journal of Public Economics, 137, 1–13. 10.1016/j.jpubeco.2016.03.008
Montez JK, & Hayward MD (2014). Cumulative childhood adversity, educational attainment, and active life expectancy among U.S. adults. Demography, 51(2), 413–435. 10.1007/ s13524-013-0261-x [PubMed: 24281740]
Nam Y (2020). Parents’ financial assistance for college and black-white disparities in postsecondary educational attainment. Children and Youth Services Review, 110, 104828. 10.1016/j.childyouth.2020.104828
National Center for Education Statistics. (2023). Digest of education statistics. https://nces.ed.gov/ programs/digest/2023menu_tables.asp
O’Rand AM (1996). The precious and the precocious: Understanding cumulative disadvantage and cumulative advantage over the life course. The Gerontologist, 36(2), 230–238. 10.1093/geront/ 36.2.230 [PubMed: 8920094]
Page LC, & Scott-Clayton J (2016). Improving college access in the United States: Barriers and policy responses. Economics of Education Review, 51, 4–22. 10.1016/j.econedurev.2016.02.009
Pfeffer FT, Fomby P, & Insolera N (2020). The longitudinal revolution: Sociological research at the 50-year milestone of the panel study of income dynamics. Annual Review of Sociology, 46, 83– 108. 10.1146/annurev-soc-121919-054821

Rauscher E (2016). Passing it on: Parent-to-adult child financial transfers for school and socioeconomic attainment. RSF: The Russell Sage Foundation Journal of the Social Sciences, 2(6), 172. 10.7758/rsf.2016.2.6.09
Schoeni RF, Bianchi SM, Hotz VJ, Seltzer JA, & Wiemers EE (2015). Intergenerational transfers and extended family roster: A new substudy of the panel study of income dynamics. Longitudinal and Life Course Studies, 6(3), 319–330. 10.14301/llcs.v6i3.332 [PubMed: 26322132]
Van Herreweghe L, & Van Lancker W (2021). The accumulation of (dis)advantages: Downward parent-child financial transfers across European countries (CeSO/SPSW/2021–03; SPSW working paper). Centre for Sociological Research. https://soc.kuleuven.be/ceso/spsw/workingpaperseries/ 2021/ceso-spsw-2021-03
Warren JR, Lee M, & Osypuk TL (2022). The validity and reliability of retrospective measures of childhood socioeconomic status in the health and retirement study: Evidence from the 1940 U.S. census. The Journals of Gerontology: Series B, 77(9), 1661–1673. 10.1093/geronb/gbac045
Wiemers EE, & Park SS (2021). Intergenerational transfers of time and money over the life course. In Ferraro KF & Carr D (Eds.), Handbook of aging and the social sciences (9th ed., pp. 201–220). Academic Press. 10.1016/B978-0-12-815970-5.00013-9
Wiemers EE, Seltzer JA, Schoeni RF, Hotz VJ, & Bianchi SM (2019). Stepfamily structure and transfers between generations in U.S. families. Demography, 56(1), 229–260. 10.1007/ s13524-018-0740-1 [PubMed: 30535653]

FIGURE 1. Path model based on cumulative inequality theory. Solid lines represent the associations of primary interest, while dotted lines indicate associations documented in the literature but not the primary focus of this article.

TABLE 1 Dichotomized measures of childhood disadvantages.

Variables

Scoring scheme

Economic 1. One or both parents finished 8th grade or less 2. One or both parents were unemployed 3. Whether the family’s financial situation was worse than the
average family 4. Whether the family faced financial troubles 5. Whether the family used welfare or food stamps for three or
more months Psychosocial
1. Whether parents ever got divorced
2. Whether respondent perceived their parents as having unsatisfactory relationship with each other
3. Whether respondents ranked the relationship with either parent as poor
4. Whether the respondent was raised by only one parent or no parent Environmental
1. Whether the neighborhood was unsafe for children day or night 2. Whether the neighborhood was clean 3. Whether the respondent worry a lot about the safety of school Health: self-rated health before age 17

1 = yes, 0 = no 1 = yes, 0 = no 1 = somewhat, a lot worse off; 0 = a lot, somewhat better off, same as average 1 = yes, 0 = no 1 = yes, 0 = no
1 = yes, 0 = no, not raised by married parents or by two parental figures 1 = not at all satisfied; 0 = very satisfied, satisfied, somewhat satisfied, not raised by married parents or by two parental figures 1 = poor; 0 = excellent, very good, good, fair
1 = yes, 0 = no
1 = not very true, not true at all; 0 = very true, somewhat true 1 = not very true, not true at all; 0 = very true, somewhat true 1 = a lot, sometimes; 0 = rarely, never 0 = excellent, very good, good; 1 = fair, poor

Unweighted descriptive statistics.
Variables
School transfers (if >0) School transfers ($) Other transfers (if >0) Other transfers ($) Overall childhood disadvantage
0 1 2 3 4+ Economic disadvantage 0 1 2 3+ Psychosocial disadvantage 0 1 2+ Environmental disadvantage 0 1 2+ Poor/ fair childhood health Control variables Respondent level Years of education Family income (in $000) Net wealth with equity (in $000) Mean age Whether head is female Race of the head
Non-Hispanic white Others Whether head is married Number of children Number of grandchildren Adult child level

0–1 $0–700,000 0–1 $0–400,000 0–13 0–1 0–1 0–1 0–1 0–1 0–5 0–1 0–1 0–1 0–1 0–4 0–1 0–1 0–1 0–3 0–1 0–1 0–1 0–1

0.32 18,992
2.20 0.20 0.26 0.19 0.13 0.23 1.40 0.27 0.29 0.21 0.23 0.30 0.74 0.22 0.04 0.25 0.82 0.11 0.07 0.04

0.47 56,018
2.01 0.40 0.44 0.39 0.34 0.42 1.11 0.44 0.45 0.41 0.42 0.54 0.44 0.42 0.20 0.57 0.39 0.32 0.25 0.21

0.22 2945 2.26 0.21 0.24 0.18 0.13 0.24 1.37 0.29 0.28 0.20 0.23 0.35 0.70 0.24 0.06 0.29 0.79 0.12 0.08 0.04

0.41 15,447 2.08 0.40 0.43 0.39 0.33 0.43 1.13 0.45 0.45 0.40 0.42 0.58 0.46 0.43 0.23 0.61 0.41 0.33 0.28 0.20

### TABLE 3
*Zero-inflated negative binomial regressions estimating the association of childhood disadvantage and financial transfers for school and other unspecified purposes.*

```
Panel A: Predicting school transfers (N = 2364)
Model 1
Count IRR
Inflate OR
Model 2 Count IRR
Inflate OR
Model 3 Count IRR
Inflate OR
Model 4 Count IRR
Inflate OR
```

Childhood disadvantage No disadvantage (reference)
4+
Years of education Family income (in $000) Net wealth with equity (in $000)

0.948 (0.112)
0.803 (0.111)
1.077 (0.176)
0.756* (0.115)

1.592*** (0.247)
1.381* (0.241)
1.290 (0.244)
1.188 (0.202)

0.953 (0.113)
0.826 (0.112)
1.137 (0.181)
0.786 (0.119)
1.103*** (0.039)

1.511*** (0.235)
1.275 (0.226)
1.167 (0.223)
1.061 (0.181)
0.812*** (0.033)

0.902 (0.100)
0.870 (0.114)
0.962 (0.125) 0.710*** (0.092)
1.003*** (0.001)

Childhood disadvantage No disadvantage (reference)
4+
Years of education
Family income (in $000)
Net wealth with equity (in $000)

1.010 (0.151)
0.811 (0.134)
0.935 (0.163)
0.890 (0.160)

1.158 (0.151)
1.016 (0.143)
0.966 (0.151)
0.905 (0.123)

1.013 (0.153)
0.869 (0.148)
0.998 (0.178)
0.945 (0.172)
1 097*** (0.036)

1.102 (0.143)
0.954 (0.135)
0.888 (0.140)
0.824 (0.113)
0.810*** (0.027)

1.036 (0.155) 0.867 (0.146) 0.984 (0.178) 0.937 (0.173)
1.001 (0.001)

1.128 (0.148) 0.989 (0.141) 0.947 (0.149) 0.881 (0.121)
0.999* (0.001)

1.144 (0.160)
0.916 (0.140)
1.036 (0.183)
1.039 (0.183)

1.152 (0.150)
1.010 (0.142)
0.965 (0.151)
0.898 (0.122)

1.002*** (0.000)

0.999 (0.000)

Note: Data came from the Panel Study of Income Dynamics Childhood Retrospective Circumstances Module and the Rosters and Transfers Module. Standard errors clustered by household ID in parentheses. ***p < .01 **p < .05 *p < .1. Abbreviations: IRR, incidence risk ratio; OR, odds ratio.

TABLE 4 Zero-inflated negative binomial regressions estimating the association of each childhood disadvantage domain and financial transfers for school (N = 2364).

No disadvantage (reference)
3+
Years of education Family income (in $000) Net wealth with equity (in $000)

0.934 (0.120)
0.903 (0.121)
0.914 (0.124)

1.652*** (0.237)
1.276 (0.204)
1.074 (0.168)

0.932 (0.117)
0.894 (0.117)
0.987 (0.131)
1.106*** (0.039)

1.536*** (0.223)
1.170 (0.190)
0.944 (0.148)
0.812*** (0.033)

No disadvantage (reference)
2+
Years of education
Family income (in $000)
Net wealth with equity (in $000)

Note: Data came from the Panel Study of Income Dynamics Childhood Retrospective Circumstances Module and the Rosters and Transfers Module. Standard errors clustered by household ID in parentheses. ***p < .01 **p < .05 *p < .1. Abbreviations: IRR, incidence risk ratio; OR, odds ratio.

TABLE 5 Zero-inflated negative binomial regressions estimating the association of each childhood disadvantage domain and financial transfers for other unspecified purposes (N = 3618).

1.064 (0.138)
0.862 (0.131)
1.136 (0.181)
1.100*** (0.035)

1.128 (0.133)
0.953 (0.126)
0.812 (0.103)
0.810*** (0.027)

Note: Data came from the Panel Study of Income Dynamics Childhood Retrospective Circumstances Module and the Rosters and Transfers Module. Standard errors clustered by household ID in parentheses. ***p < .01 **p < .05 *p < .1. Abbreviations: IRR, incidence risk ratio; OR, odds ratio.

### TABLE 6
*Predicted parent-to-child transfers (in $) from selected zero-inflated negative binomial regressions.*

```
Panel A: School transfers (2364)
Model 1 Model 2 Model 3 Model 4
Overall childhood disadvantage
No disadvantage (reference) 6858.3
4625.6**
4368.5**
4+
4597.0**
5824.0 4079.7** 4028.4** 5925.5 4390.8
6208.6 4010.9*** 4328.6** 5006.7 3960.4**
6235.6 4064.2*** 4284.3** 4886.8 4203.1**
Model 9 Model 10 Model 11 Model 12
Economic disadvantage No disadvantage (reference) 1 2 3+
4074.8*** 3635.4**
5825.2 3610.1*** 4191.8** 4902.3
5791.2 3656.2*** 4214.9** 5060.5
Model 17 Model 18 Model 19 Model 20
Environmental disadvantage No disadvantage (reference) 1 2+
5454.4 4082.0* 3199.1**
4894.5 4024.1 3243.9*
4787.0 4153.9 3126.7*
4844.5 3991.6 3293.7*
Panel B: Other transfers (N = 3618)
Model 33 Model 34 Model 35 Model 36
Environmental disadvantage No disadvantage (reference) 1 2+
2634.9 1940.5 2085.1
2304.8 1804.6 2415.7
2474.0 1916.5 2203.1
2367.2 1996.4 1981.8
```

Note: All the predicted values were compared with the no disadvantage group. ***p < .01 **p < .05 *p < .1.
