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Children and the Elderly: Wealth Inequality Among America’s Dependents

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.

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Notes

  1. 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. 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. 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. 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. 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. 6.

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

  7. 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. 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. 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. 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. 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. 12.

    Information on types of debt were not collected in 1989.

  13. 13.

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

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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.

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Correspondence to Christina M. Gibson-Davis.

Appendix

Appendix

Fig. 5
figure5

Income by source for the bottom 50 % of the wealth distribution, by household type and year. Income is reported in constant 2013 dollars. All estimates are weighted

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Gibson-Davis, C.M., Percheski, C. Children and the Elderly: Wealth Inequality Among America’s Dependents. Demography 55, 1009–1032 (2018). https://doi.org/10.1007/s13524-018-0676-5

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Keywords

  • Wealth
  • Inequality
  • Elderly
  • Children