Abstract
Much research documents that middle-income households are facing high prevalence of economic insecurity associated with altruistic transfers to relatives in need. The focus of our examination is across and within generations of the same family tree that have grown up in different public policy regimes. Using panel data on U.S. families, we extend the breadth and depth of the work of Chiteji and Hamilton (2002). We find that, compared to their white counterparts, third-generation, middle-income Black families are disproportionately exposed to relatives who face poverty, unemployment, and wealth disparity. Additionally, we find that economic insecurity in the family tree is one of the largest contributors to the Black-White wealth gap.
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Notes
However, there is a potential flaw with the Chiteji and Hamilton (2002) specification. The inclusion of parental wealth and receipt of inheritance is likely simultaneously correlated with the wealth of their offspring.
Due to low sample sizes for families where the head has a college degree, we do not examine Black-White Oaxaca-Blinder decompositions for this group.
For an orientation on this approach, see Gujarati and Porter (2009).
Further evidence is provided by Hamilton, Darity, Price, Sridharan, and Tippett (2015, 5) using data from the Survey of Income and Program Participation. The authors find that even among Black-White households with equal levels of educational attainment, there are unequal levels of net wealth at the median. They also reveal that White households without a high school degree are in possession of higher levels of median wealth than Black households that are college-educated.
In context, this overall finding is similar to the finding by Chiteji and Hamilton (2002); they report that these factors explain 55%.
One would think that the collective attribute of sibling hardship, parental hardship, grandparental hardship, and cousin need would explain even more of the racial wealth gap. However, sample size limitations prevented us from computing the decomposition with sibling, parental, grandparental, and cousin hardship variables. The sample size reduces greatly, particularly with respect to cousins (see Appendix 12), as the extended kin network is added to the analysis.
The inverse hyperbolic sine log transformation takes the form of sinh^-1 = log(net wealth + (net wealth^2 + 1)^1/2).
Moreover, to better understand the role of contributors, we examine parts of the wealth distribution through quantile regressions (see Appendix 13).
As an alternative approach to the Oaxaca-Blinder decomposition, we present results for average treatment effects (ATE) of kin network economic insecurity on net wealth (see Appendix 13).
Daw et al. (2016) use the PSID data and find that one of the most dramatic decreases in the average number of kin can occur among cousins as they develop from early childhood to middle age. Why do cousin counts rapidly decrease with age? Findings by Daw et al. (2016) indicate that mortality may offer some explanatory power, which provides context for why we run into sample size limitations.
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Acknowledgements
We thank Ngina Chiteji, Samuel Myers Jr., Teresa Ghilarducci, William Darity Jr., Brandon Jordan, Ben Fried, James Kelly, anonymous referees, and seminar participants at the New School for Social Research, the University of Massachusetts, Amherst, 2016 Eastern Economic Association Annual Conference, 2021 Association for Public Policy Analysis and Management Fall Conference for helpful comments. Toney acknowledges that material of this paper was financially supported by the Elinor Goldmark Black Dissertation Fellowship for Advanced Studies in the Dynamics of Social Change (2016-2017 academic year).
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Appendices
Appendix 1. Accounting for Observations Across Multiple Family Generations and Generational Peers
Table 12
Appendix 2. Definitions and Measures
Appendix 2.1. Using Various Definitions of “Middle Income”
There are various ways in which the middle-income group may be empirically defined. Chiteji and Hamilton (2005) offer three approaches: an income approach, defined in terms of the middle 60th percentile of the income distribution, an education approach, defined by individuals who hold a college degree with at least 16 years of education, and a labor occupation approach, defined as whether someone holds a position in the management or professional fields. We examine middle-income families since they are better off than their low-income peers, and thus are more capable of participating in the transfer of resources (Chiteji and Hamilton 2002).
Appendix 2.2. Measures of Variables for Empirical Analysis
Wealth, both with and without home equity, with the latter being a more liquid indicator of household financial well-being, is a core variable of this analysis. Wealth is defined as the sum of household debt subtracted from the sum of household assets, which include owning a farm or business, checking or savings accounts, real estate, stocks, vehicles, bonds, and individual retirement accounts. Home equity is defined as the value of the home subtracted by the amount of mortgage outstanding. Included as part of debt are liabilities, except loans from mortgages or vehicles. Examining wealth in the family tree allows one to see a more expansive view of financial resource or financial drain (un)available to a family and how that connects to race and social stratification. For example, Chiteji (2010) finds that grandparental wealth and college attainment of grandchildren are positively related.
Age of the head of household and age squared of the head of household. The impact of the first degree (i.e., age) is that, at some point, wealth accumulation tends to rise as adults in their prime years gain more experience through work. The impact of the second degree (i.e., age squared) is that wealth accumulation slows, or falls, as the adult ages, moves out of the labor market, and begins to dissave.
Number of children in the household. Wealth can be used to promote the well-being of children, and the presence of children can influence the amount of resources available.
Sex of the head of the household (this is cast as a dummy variable where 1 = female, 0 = otherwise). Social stratification and discrimination tend to be associated with lower wealth for households headed by females.
Marital status of the head of the household (this is coded as a categorical variable where 1 = married, 0 = otherwise). Married couples can benefit from pooling resources among themselves. Greater power to accumulate is analogous to family units experiencing economies of scale.
Years of education attained by the household head. Education is associated with greater wealth accumulation.
Average lifetime income (this variable is the mean of family income over the previous five working years, where data is taken from 2013, 2015, 2017, and 2019 waves of the PSID). Given the volatility of contemporary income and its simultaneity with assets and debts, we use the more robust 5-year average income that is de-trended from year-to-year fluctuations.
Race of the head of the household (where 1 = Black, 0 = otherwise). Scholarship shows that, on average, Black families are more likely to face wealth disparity compared to White families.
Professional or managerial status for head of household (a dummy variable indicating that individual works in a professional or managerial position as defined by the 2000 U.S. Census). The underlying principle of including managers and professionals is that jobs with higher status offer the ability to accumulate wealth.
Economic hardship in the family tree (categorical variable where 1 = receives Temporary Assistance to Needy Families (TANF), or food stamps, or is facing unemployment, 0 = otherwise). Economic hardship measure is computed for adult siblings, parents, grandparents, and cousins. Chiteji and Hamilton (2002, 2005) reveal that hardship can contribute to Black-White wealth disparity among middle-income earners.
Parental wealth during childhood. Both this paper and Chiteji and Hamilton (2002) use the variable of parental wealth as a child in the decomposition. We compile data on all children ages 6–21 living in households with their parents in 1984 but who are then heads or wives in 2019. The “total family wealth” for a child in 1984 is reference to the family in which they are living, as they would not have contributed to this household wealth. Conley and Glauber (2008) also use this approach to explore the intergenerational transmission of financial wealth.
Access to inheritance. In the PSID, participants are surveyed on whether or not they accepted large gifts or inheritances of money or property worth $10,000 or more. This paper supports and maintains the assumption of McKernan et al. (2014) that the gateway for parental wealth to significantly impact their adult children’s wealth is through private transfers. Provided that parental wealth during childhood is a broad indicator of economic transfers, the variable inheritance is no longer needed in the decomposition technique.
Appendix 3. Methodology Used to Explore the Life Course of Cousins
We link the sibling map to the 1984–2019 PSID core family level files to explore cousins who are living with their parents in 1984 and are between the ages of 6 and 21 but who are then household heads in 2019. We match cousins by working from a sibling map in the Family Identification and Mapping System (intra-generational feature of FIMS mapping) that is not restricted to full siblings (same biological father and mother), so it also includes half-siblings.
Once we identify the sibling sample, we then look to their children (who would be the cousin pairs). The cousin sample (1404) in 1984 is identified by looking at the relationship to household head variable, where codes 30 and 33 are “son or daughter of head” or “stepson or stepdaughter of head,” respectively. Cousins who are then adults are identified by looking at the relationship to head variable, where codes 10, 20, and 22 are cousins as household head or wife of household head in 2019. The full sample size for adult cousins in the PSID data is 548.Footnote 10
To our knowledge, there are no studies that explore the life course of cousins in the context of extended family wealth. There are, however, studies that chronicle the wealth accumulation in the life course of older adults by race (Brown 2016) and by race and gender (Brown 2012). We complement previous life course scholarship by focusing on the wealth position of cousin pairs when they are living with their parents and when they are household heads.
Appendix 4. Supplementary Regression Models
Table 13
Appendix 4.1. Pooled OLS Regression Models
Appendix 4.2 Quantile Regressions
To further investigate the role of contributors, we use quantile regressions. We present decomposition results from the 10th percentile in Table 14 and the 90th percentile in Table 15. A common theme that arises from the two tables is that disparities in life cycle, demographic, socioeconomic, and family background variables explain very little of the racial wealth gap. When we adjust for socioeconomic status, we find that Black households and White households exhibit similar behavior (Conley 1999).
To explore the typical experience of a household, we use a quantile regression (median = 0.5) with dichotomous variables for economic insecurity in the extended family. In Table 16, we display the results from the median regression model. We find that there is a negative association between median household wealth and having a sibling or grandparent that is dealing with economic insecurity, which is consistent with our hypothesis.
Table 14
Table 15
Table 16
Appendix 4.3. Average Treatment Effects of Kin Network Economic Hardship on Net Wealth
Oaxaca-Blinder parameter estimates can be equivalent to causal counterfactual treatment effects. However, as An and Glynn (2021) note, this equivalence may not always hold. Estimating a binary treatment effect specification that is altered to vary on covariates of interest (race, income) is recommended to determine if the treatment effect changes.
In Table 17, we present results for average treatment effects (ATE) of kin network economic insecurity on net wealth. Raw net wealth for the middle-income group is the outcome-dependent variable. Meanwhile, grandparental hardship is the binary treatment variable. In the full model that matches on all covariates, the covariates include average lifetime income, parental wealth, age, age squared, educational attainment, children in the household, marital status, managerial or professional occupational status, sex, race, sibling need, and parental need. When there are no omissions of covariates of interest (i.e., full model), the average net wealth for respondents with grandparental hardship is $57,589 less than the average net wealth for respondents without grandparental hardship. We then find that the treatment effect varies with the omissions of covariates of interest (e.g., race, educational attainment, average lifetime income, parental wealth, managerial or professional occupational status, sibling need, parental need). Such a result is suggestive of a causal effect and reinforces the findings from the Oaxaca-Blinder decomposition.
In addition, as both Oaxaca-Blinder and treatment effect estimators are generalized weighting estimators (Kline 2011; Davies et al. 2017), if both methods produce similar results, this can make a case for robustness. Indeed, we find that neglecting to account for variation in average lifetime income can result in an average treatment effect estimator that is biased upward by 30.4%. Meanwhile, neglecting to account for difference in parental wealth can result in an average treatment effect that is biased downward by 9.47%. In contrast, neglecting to adjust for the variation in sibling need and parental need may result in an estimator that is biased downward by 45.06% and 14.39%, respectively. Significantly, results from both ATE and Oaxaca-Blinder methods recognize that kin network economic hardship, parental wealth, and average lifetime income are dominant contributors to differences in net wealth.
Table 17
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Toney, J., Hamilton, D. Economic insecurity in the family tree and the racial wealth gap. Rev Evol Polit Econ 3, 539–574 (2022). https://doi.org/10.1007/s43253-022-00076-5
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DOI: https://doi.org/10.1007/s43253-022-00076-5