Abstract

Life cycle theory predicts that elderly households have higher levels of wealth than households with children, but these wealth gaps are likely dynamic, responding to changes in labor market conditions, patterns of debt accumulation, and the overall economic context. Using Survey of Consumer Finances data from 1989 through 2013, we compare wealth levels between and within the two groups that make up America’s dependents: the elderly and child households (households with a resident child aged 18 or younger). Over the observed period, the absolute wealth gap between elderly and child households in the United States increased substantially, and diverging trends in wealth accumulation exacerbated preexisting between-group disparities. Widening gaps were particularly pronounced among the least-wealthy elderly and child households. Differential demographic change in marital status and racial composition by subgroup do not explain the widening gap. We also find increasing wealth inequality within child households and the rise of a “parental 1 %.” During a time of overall economic growth, the elderly have been able to maintain or increase their wealth, whereas many of the least-wealthy child households saw precipitous declines. Our findings suggest that many child households may lack sufficient assets to promote the successful flourishing of the next generation.

Introduction

In his 1984 presidential address to the Population Association of America, demographer Samuel Preston called attention to what he saw as a troubling trend: the transfer of resources to the elderly at the expense of children (Preston 1984). As Preston noted, society bears responsibility for taking care of the elderly and children, who often rely on others for resources. As America’s primary dependents, however, the elderly and children often compete for resources. By prioritizing the elderly over children in the provision of public transfers, Preston argued, society risked negative consequences because subsequent generations would have insufficient resources to thrive.

More than three decades later, little has changed: the United States still directs a disproportionate amount of social welfare dollars to those over the age of 65 relative to those under the age of 18 (Moffitt 2015). The effect of these disparities in social spending on the well-being of U.S. dependent populations may be compounded by disparities in the private resources held by households. Since the 1980s, household income and wealth have become more unequally distributed across households in the United States (Wolff 2016). Although life cycle models predict that elderly households should have higher net worth than households with children (which presumably have younger household heads), the wealth gap between the young and the old is likely dynamic and may have accelerated in recent years (Boshara et al. 2015). Wealth disparities are an important predictor of long-term societal health because wealth plays a critical role in determining the civic, political, and economic life of the next generation (Pfeffer and Schoeni 2016).

Concurrent with a potential rise in wealth disparities between the elderly and child households is a likely rise in wealth inequality within child households (Yellen 2016). The same factors that other scholars have documented as fueling overall increases in wealth inequality in the United States—a top-heavy wage distribution, a skill-driven economy, and globalization (Saez and Zucman 2016)—may have had even larger effects on the wealth distribution of child households. Wealth inequality has likely also risen because child households with higher-skilled and more-educated household heads may have been able to avoid the increase in debt and decreases in home equity that have characterized households headed by individuals with less education (Wolff 2012). Rising wealth inequality among child households is of concern because wealth, above and beyond income, is associated with child development, particularly human capital acquisition (Bond Huie et al. 2003; Conley 2001; Elliott et al. 2011).

Despite the potential consequences of wealth disparities for the long-term health of society and the repercussions for the life chances of children, relatively little research has analyzed temporal changes in the wealth holdings of child households, either relative to the wealth of elderly households or within child households. To address this gap, our study provides the first over-time analysis of trends in wealth, assets, and debts for child households. We concentrate on wealth disparities between child households and elderly households (households in which the head or spouse is aged 65 or older) and on wealth inequality within child households.

Using data from nine waves of the Survey of Consumer Finance (SCF) covering the period 1989–2013, we address several research questions. First, how has the gap in wealth between elderly and child households changed over time?1 Second, how does the wealth gap between the elderly and child households vary across the wealth distribution? Third, how has wealth inequality within each of these population groups changed over time? Finally, how have changes in asset composition and debt accumulation contributed to wealth inequality between and within elderly and child households? By answering these questions, our descriptive study calls attention to an important but heretofore overlooked dimension of the increase in economic inequality: the divergence in wealth holdings of America’s dependents.

Background

Rising Wealth Inequality

Wealth inequality has risen tremendously in the United States over the past half-century.2 In 1962, the top 20 % of the wealth distribution accounted for 81 % of all wealth; by 2013, that share had risen to 89 % (Wolff 2016). Wealth inequality was relatively flat between the 1960s and early 1980s, rose steeply in the 1980s, plateaued in the 1990s and early to mid-2000s, and then increased sharply with the onset of the Great Recession. Increases in wealth inequality appear to be driven by those at the very top of the wealth distribution (the so-called 1 %). Between 1983 and 2013, the top 1 % had net worth gains of 40 %, while those in the bottom 80 % had decreases of –0.01 % (Wolff 2016).

Although likely multiply determined, increasing wealth inequality in the United States can be attributed in large part to a shifting labor market and changes in household debt patterns (Saez and Zucman 2016). The growth in globalization and the pervasiveness of a skill-based economy, coupled with the erosion of labor unions and the real value of the minimum wage, has resulted in stagnating wages for many in the bottom and middle of the income distribution but skyrocketing wages for those at the very top (Autor 2014; Autor et al. 2008). Moreover, most workers have experienced increased earnings volatility, which is partially attributable to the increase in contingent employment and irregular schedules (Dynan et al. 2012; Kalleberg 2009). Although earnings do not translate directly into wealth, households with higher employment-based earnings and less earnings volatility can direct more of their income toward asset accumulation and are less likely to deplete their wealth to pay for living or medical expenses (McGrath and Keister 2008).

Concurrent with the changes in the U.S. labor market are increases in relative indebtedness for households (Saez and Zucman 2016). Debt levels have increased nearly linearly over the past few decades; in 2013, the median debt of $60,400 was more than double the 1992 amount ($27,800) (Bricker et al. 2014). Educational debt, in particular, has risen sharply, both in breadth and depth (Houle 2014). Prior to the 2000s, Americans had been able to offset high debt levels with strong returns to their investments, mainly through increasing home equity and the rising stock market (Saez and Zucman 2016). The collapse of the housing market and the stock market crash during the late 2000s destroyed much of the market value of these goods (Wolff 2016), resulting in high levels of debt and relatively low levels of assets. Increases in debt have not been uniform across the wealth distribution but instead have risen disproportionately faster for more socioeconomically disadvantaged households (Houle 2014; Saez and Zucman 2016).

Figure 2 shows changes in the wealth holdings of the 5th through 50th percentiles of the child and elderly distributions for the years 1989 and 2007. As the figure illustrates, diverging trends in wealth among the least-wealthy households were evident before the Great Recession: in 2007, the median net worth gap between elderly and child households was $156,000, nearly double what it was in 1989.10 Changes in Within-Group Wealth Inequality Child households experienced increasing within-group wealth inequality (Table 2). Gains in net worth were concentrated among the top 10 %, with the losses occurring in the bottom 50 %. Growing disparities between the top and the bottom cannot be explained fully by the Great Recession because child households in the bottom 50 % experienced declines in net worth before 2007. In contrast, child households in the top 10 % enjoyed robust growth in the 1990s and early and mid-2000s, suffering only modest losses in the late 2000s. Irrespective of the Great Recession, then, increasing disparities between the very top and the bottom occurred both because the top gained wealth while the bottom lost wealth. To further examine within-group inequality, we calculated Gini coefficients by household type (Fig. 3). We found that child households had substantially higher levels of within-group wealth inequality than elderly households. In 2013, the net worth Gini coefficient was .92 for child households and .80 among elderly households.11 In contrast with relatively small fluctuations over time in wealth inequality for elderly households, we found steady increases for child households. Between 1989 and 2013, the elderly experienced a very small increase in net worth inequality (Gini coefficient increased from .78 to .80). In contrast, over the same period, child households saw their net worth inequality rise by nearly 11 %, with the Gini coefficient increasing from .83 to .92. In supplementary analyses (not shown), we examined the concentration of wealth for child and elderly households. In 1989, child households in the top 1 % of the wealth distribution accounted for 31 % of net wealth; by 2013, the top 1 % accounted for 42 %. Child households in the bottom 50 % saw what little wealth they had dissipate: they accounted for 0.7 % in wealth in 1989 but had negative 1.5 % of wealth in 2013 (i.e., negative because they held more debt than assets). The distribution of wealth for elderly households, in contrast, changed very little over time, with the top 1 % accounting for 29 % of wealth and the bottom 50 % accounting for 4 % in both 1989 and 2013. Explaining Changes in Net Worth Disparities Between Elderly and Child Households Our next set of analyses examined average differences between child and elderly households in percentile net worth using an ordinary least squares (OLS) framework. Model 1 of Table 3 shows gaps unadjusted for demographic covariates. Elderly households were, on average, 21.7 percentile points higher in the wealth distribution than child households (p < .001). The year fixed effects suggest that child households had a fairly static average percentile net worth throughout the period. Notably, though, the net worth associated with these average positions were not constant throughout the period. The interaction terms between the year and elderly household suggest that the relative gap in percentile net worth between the elderly and child households was smaller in 1989 than in 2007, stable throughout the 1990s and 2000s, and increased during the Great Recession. In 2010, the gap between elderly households and child households was 3 percentile points larger than in 2007. Model 2, which includes demographic covariates, shows largely the same patterns. Associations between sociodemographic characteristics and wealth were as expected; more-disadvantaged households (as measured by racial/ethnic minority status or education level) had lower average levels of net worth. Household heads who were not married also had lower average levels of net worth. Accounting for demographic differences between elderly and child households results in a slightly larger average percentile gap (23.4 vs. 21.7) between household types. The interaction terms between elderly household and year show that wealth gaps were larger in the 1990s (compared with the reference year of 2007) and fairly stable throughout the 2000s before increasing with the onset of the Great Recession. Overall, this analysis suggests that accounting for demographic characteristics does not explain the wealth gaps between elderly and child households. Notably, the relatively stable ranking of child households implied by the ordinary least squares (OLS) regression results mask fluctuating absolute levels of wealth. Figure 4 shows the time-varying correspondence between percentile of net worth and dollar amounts of net worth. (The figure omits the tails of the distribution.) For example, the 42nd percentile, which is the average position of child households across this period, corresponded to a net worth value of$25,813 in 1989; $39,316 in 2007; and$18,165 in 2013. Thus, although the regression model shows that child households occupied a similar relative place in the overall population wealth distribution between 1989 and 2013, this corresponded with a fluctuating absolute value. The increased relative position of elderly households (as indicated by their gain of four percentiles of net worth) combined with the increased absolute value of net worth for the percentiles in the upper half of the wealth distribution (see Fig. 4, panel b), resulted in an absolute gap in net worth between elderly and child households that was smallest in 1989, and despite some fluctuations, generally increased over time.

Results thus far largely confirm our hypothesis: the absolute gap in wealth is growing between elderly and child households, in part because of the effect of the Great Recession. The Great Recession, however, does not explain the growing gap between the least-wealthy elderly and child households, who experienced opposing trends in wealth accumulation over the period from 1989 to 2007. Additionally, we found that inequality was rising faster among child households than among elderly households even before the Great Recession. Wealth increased particularly rapidly among the top 1 % of child households.

Decomposition Analysis

Our next analysis examined how shifts in demographic characteristics accounted for changes in wealth for elderly and child households. The average percentile net worth of child household decreased slightly from 43.9 in 1989 to 42.5 in 2013, but this decrease is not statistically significant (see Table 4). An Oaxaca-Blinder decomposition of the relative stability in the average wealth percentile of child households between 1989 and 2013 (Table 4, panel A) shows the offsetting effects of different demographic changes. The gain in predicted percentile net worth from the increasing educational attainment of household heads (2.08) was offset by the decrease in predicted percentile net worth from an increase in racial/ethnic minority household heads (–0.50) and single-parent households (–1.54), for a net effect of close to 0. Notably, for child households, the returns to education (represented by the educational coefficients) were similar in 1989 and 2013 (B = –0.41, SE = 1.30).

The household structure coefficients (part of the “unexplained” factor) changed such that the average wealth gap between married and unmarried household heads decreased slightly over time. Thus, our findings from the decomposition suggest that the story of a stable relative wealth position (and declining absolute levels of wealth) among child households cannot be attributed to increasing racial/ethnic diversity or increasing shares of single-parent households among families with children.

We also applied an Oaxaca-Blinder decomposition model to changes in the average wealth position of elderly households between 1989 and 2013. Average percentile wealth for elderly households rose by 4.3 percentiles between 1989 and 2013—a change that is statistically significant. In contrast to the results for child households, where demographic factors explained little, results for elderly households indicated that rising educational attainment accounted for most of their increased average wealth position (5.54 percentiles). Child households also experienced large gains in educational attainment (see earlier Table 1); it is unclear why education would explain such wealth gains for one group but not the other.

Changes in Assets and Debts

Our final analyses considered over-time changes in income, assets, and debts. A comparison of elderly to child households between 1989 and 2013 (Table 5, panel A) indicates that the elderly had positive increases in income, assets, homeownership, and home equity. The elderly also experienced a substantial increase in debts, from a median of $0 in 1989 to$900 in 2013. In contrast, child households had declines in income, homeownership, and home equity, as well as sizable increases in debts. Assets in child households rose by 13 %, but debts rose by far more (68 %). Because the elderly, in 1989, had higher levels of assets than child households, their relatively larger increase in assets over the observation period increased the asset disparity. The asset gap was particularly noticeable in terms of homeownership. In 1989, the share of elderly households that owned their own home was approximately 10 percentage points higher than that of child households; by 2013, the gap was 20 percentage points.

Households in the bottom 50 % also had growing disparities between the elderly and child households in the determinants and components of wealth (Table 5, panel B). Between 1989 and 2013, elderly households in the bottom 50 % of the wealth distribution experienced a 38 % increase in income, a 178 % increase in assets, a 26 % increase in homeownership rates, and an 8 % increase in home equity. In contrast, child households saw decreases in income (–5 %), assets (–27 %), and home equity (–41 %), with virtually no change in homeownership rates. Child households in the bottom 50 % also saw their debt increase by 46 % from $12,291 in 1989 to$18,000 in 2013. Elderly households experienced a substantial increase in debts, but their median debt levels in 2013 were quite low at $1,650. Because assets fell and debts rose for child households, by 2013, child households in the bottom 50 % owed more in debts than they could claim in assets. In additional analyses, we investigated the mechanical factors that accounted for the rise in income among the elderly and its decline among child households in the bottom 50 %. Following previous work (Toossi 2009), we looked at changes in Social Security and wages between 1989 and 2013 (see Fig. 5 in the appendix, which also includes other income that was neither Social Security nor wages). Among the elderly, income from Social Security rose by 40 % and from wages by 61 %. In contrast, market income—the largest income source for child households in this part of the distribution—decreased by$267, or 0.6 %. These results suggest that elderly in the bottom half of the wealth distribution have benefitted from the indexing of Social Security payments to inflation (which is partially why Social Security income rose over this time) and have been able to increase their labor market attachment. The decline in market income for child households, however, most likely speaks to the decline in wages for the less-skilled (Toossi 2009).

Comparisons of the determinants and components of wealth for the population of child households (Table 5, panel C) indicate that the top 1 % of child households enjoyed robust growth in earnings, assets, and homeownership. Conditional on owning a home, home equity increased, whereas median debt levels decreased by 38 %. Child households in the bottom 50 % experienced nearly the mirror opposite: declines in earnings, assets, home equity (if they owned homes), and increase in debts. These analyses indicate that rising indebtedness characterized only child households in the bottom half of the wealth distribution.

Supplementary analyses (not shown) indicated that the type of debt with the largest relative increase was educational debt. Between 199212 and 2013, the median amount of educational debt tripled, from $4,874 to$15,000. As a share of debt, it accounted for 14 % of all debt in 2013, up from 5 % in 1992. Credit card debt, vehicle debt, and mortgage-related debt either decreased or remained relatively flat as a proportion of debt.13

Last, we examined whether these changes occurred because of the onset of the Great Recession. We found that the relative gaps in observed components and determinants of wealth were widening between child households and elderly households and within child households prior to 2007. Child households in the bottom 50 % of the wealth distribution had modest gains in income, assets, homeownership, and home equity between 1989 and 2007. These modest gains, however, were far outpaced by gains in the same categories for elderly households, elderly households in the bottom 50 %, and child households in the top 1 %. Furthermore, child households in the bottom 50 % of the wealth distribution had large increases in debt before 2007. Thus, although the least-wealthy child households were disproportionately affected by the Great Recession, they were losing ground vis-à-vis the determinants and components of wealth before its onset.

Conclusions

Among America’s dependents, patterns of wealth accumulation over the past quarter-century have been dynamic. Consistent with recent analyses of trends in wealth accumulation by age of household head (Boshara et al. 2015), we find increasing median levels of wealth for the elderly, but decreasing median levels of wealth for child households. As a consequence, the absolute wealth gap between elderly and child households grew substantially, especially when the least-wealthy elderly households were compared with the least-wealthy child households. In contrast to the relative stability of the wealth distribution among elderly households, the wealth distribution among child households became increasingly top-heavy. Child households in the top 10 %—particularly those in the top 1 %—experienced large increases in net worth. Thus, child households experienced increases in within-group wealth inequality that far outpaced the modest increases in wealth inequality among elderly households.

Consistent with the conclusions of Saez and Zucman (2016), our results suggest that the increased wealth gap between child and elderly households was associated with changes in market income, asset accumulation patterns, and patterns of indebtedness. Elderly households enjoyed the equalizing effects of Social Security income, which increased in real value over our observation period. In contrast, child households were more reliant on market income, which has become more unequal (Saez and Zucman 2016), and median earnings for child households declined. Elderly households also increased their homeownership rate, experienced rising housing values, and maintained very low debt levels. Child households, in contrast, saw no increase in homeownership, a decrease in housing equity for those who owned homes, and a substantial increase in debt, particularly educational debt.

Our findings also suggest that rising inequality within child households occurred because of these same dynamics. The wealthiest child households saw large increases in market income, took on proportionally less debt, and had homes that increased substantially in value, whereas child households in the bottom half of the wealth distribution had large declines in market income, large increases in debt, and losses in home equity.

Other explanations for divergent wealth trends by subgroup received less support. The life cycle hypothesis, a primary explanation for wealth gaps between elderly and child households, suggests that disparities between the elderly and child households arise because the elderly have more years to save. If the hypothesis fully held, then the absolute and relative wealth gap should be relatively stable over time; the hypothesis seems insufficient given that we observed large changes in the size of the (absolute) wealth gap over a relatively short period. The growing wealth gap between elderly and child households also does not appear to have been driven primarily by growing racial and ethnic diversity among child households or by increases in the share of child households headed by single parents. Indeed, our decomposition analyses of changes in the average wealth position of child households between 1989 and 2013 suggest that demographic changes do not explain why child households experienced relative stability in their wealth position in the total population distribution and declines in their absolute levels of wealth.

The Great Recession exacerbated wealth differences between the elderly and child households and contributed to rising inequality within child households. We, like others (Pfeffer et al. 2013; Wolff 2016), found that the largest wealth losses associated with Great Recession accrued to the least-wealthy households. Notably, we showed that the Great Recession exacerbated preexisting disparities in wealth among the least-wealthy households. Although the least-wealthy elderly households enjoyed favorable economic growth before the Great Recession, the least-wealthy child households saw declines in wealth during the recessions of the early 1990s and 2000s, and also lost wealth during the mid-2000s. Thus, the Great Recession contributed to, but was not the origin of, increasing wealth disparities and inequalities over this period (see also Pfeffer and Schoeni 2016).

We found evidence of a “parental 1 %,” an extremely wealthy group of parents who can potentially dedicate high amounts of resources to their children. In 2013, the parental 1 % accounted for 42 % of all wealth among households with children and had approximately \$2.5 million in wealth for each child. With Pfeffer and Schoeni (2016), we worry that the concentration of wealth among so few parents will lead to unequal access for children to the very social institutions that could mitigate wealth inequality (see also Yellen 2016). Moreover, given the rigidity of the wealth distribution—44 % of children who grow up with parents in the top wealth quintile are in the same wealth quintile as adults (Pfeffer and Killewald 2015)—our results suggest that wealth inequality will be high for the foreseeable future.

Diverging wealth trends between elderly and child households underscores Preston’s (1984) theme of decades ago: resources are not equitably distributed among America’s dependents. For the resource of household wealth, we find that some dependents (elderly in the bottom 50 %, child households in the top 1 %) are faring much better than others (children in the bottom 50 %). Substantial wealth increases among the poorest elderly Americans suggest that growing economic inequality does not have uniformly negative effects on (potentially) vulnerable populations. Our description of changes in household wealth over the period from 1989 to 2013 highlights the uneven effects of macro-level economic changes.

Previous research has shown that family wealth affects health and children’s educational attainment (Bond Huie et al. 2003; Conley 2001; Elliott et al. 2011). Given an increasingly porous social safety net for families with children (Moffitt 2015) and growth in inequality in per-pupil educational expenditures (Evans et al. 2017), the declining absolute and relative wealth holdings of most families with children is likely to impede the human capital development of the next generation of Americans. We posit that such underinvestments in children are likely to have negative long-term societal consequences for the United States.

Acknowledgments

We are thankful to a grant from the National Science Foundation (#1459631) for funding this work. We also thank Leslie McCall, Ann Owens, and three anonymous reviewers for their helpful comments and feedback.

Notes

1

Disparities in wealth between two groups, or a gap in wealth between groups, is conceptually distinct from levels of wealth inequality, or the dispersion of the wealth distribution.

2

Wealth, as a measure of stock of resources, is distinguishable from income, which measures the flow of resources. Among families with children, wealth and income are modestly correlated at .50 (Keister 2000). Wealth inequality has increased more rapidly than income inequality (Keister and Moller 2000; Wolff 2016), suggesting that trends in wealth inequality are not reducible to those for income inequality.

3

Two other demographic factors—educational attainment and age—have also shifted over time, as the United States population has become more educated and older (Mather et al. 2015; Ryan and Bauman 2016). Increased educational attainment is evident for both child and elderly households, and therefore is unlikely to contribute to widening gaps between the groups. Increased age among the elderly might downwardly bias gaps if, over time, a larger share of elderly households were in an age range where they were rapidly spending down their assets. The age of household heads for elderly households in our sample increased by only one year between 1989 and 2013, with a similar standard deviation (see Table 1).

4

The SCF describes itself as a sample of “families,” but its definition of family (people at the same address sharing living quarters) is more akin to the U.S. Census definition of households. Following Wolff (2014), we discuss our unit of analysis as households.

5

Cohabiting households with an unmarried man and unmarried woman were classified in the unmarried male head category because the SCF classified all households with a different-sex couple as male-headed.

6

SCF estimates of educational attainment were quite similar to Current Population Survey (CPS) estimates in the same years (results not shown).

7

We investigated using the Survey of Income and Program Participation (SIPP) for this purpose, but SIPP estimates of wealth were inconsistent with SCF estimates and did not seem credible.

8

Estimating household median wealth at different points in the distribution is akin to estimating median wealth at the midpoint of that distribution: for example, household median wealth in the bottom 50 % is the average of the median wealth for households at the 25th and 26th percentile.

9

When all households were compared across time (available upon request), median net worth fell by 5.8 %. Households in the bottom 50 % had declines of 87 %, whereas those in the top 1 % had increases of 82 %.

10

In additional analyses, we divided households into the 0th–25th percentiles and the 26th–50th percentiles. Our main conclusions regarding the gap between elderly and child households remained the same. Notably, child households in both quartiles lost economic ground to elderly households in similar distributional positions between 1989 and 2013.

11

These net worth Gini coefficients may seem implausibly high, but they were consistent with other SCF-based estimates of net worth inequality (e.g., Keister 2014; Wolff 2016).

12

Information on types of debt were not collected in 1989.

13

Home debt rose in the mid-2000s but then fell to 1990s levels after the Great Recession.

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