Abstract
With growing wealth inequality in the United States, understanding the implications on individual well-being becomes increasingly important. Prior research has found that economic well-being has a small but significant effect on well-being; however, much of the literature has used income as a proxy for economic well-being. This is a narrow measure of the resources that might influence an individual’s overall well-being, and does not capture an individual’s overall economic security. From a theoretical perspective, wealth is a better indication of life-time financial resources, as compared to income which is recorded at a point in time. More recent research has indicated that wealth, or income and wealth taken together, explain more of the variance in SWB than does income alone. I use the Panel Study of Income Dynamics (PSID) from 1984 to 2015 and the PSID “Well-being and Daily Life” supplement collected in 2016 to examine the relationship between well-being and several types of financial resources to determine which resources matter, and whether they matter separately or in combination. My findings indicate that well-being is associated with both income and wealth, controlling for other known correlates with well-being. When included in the same model both remain statistically significant and comparable in both magnitude and direction. Further, housing wealth itself matters for an individual’s reported satisfaction with overall life, even controlling for income and other assets. These findings further confirm the importance of accounting for the complex financial circumstances when trying to understand the implications of social inequalities on overall well-being.
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Notes
- 1.
- 2.
In the sample used here, the correlation between family income (averaged from 2011 to 2015) and net worth in 2015 is 0.55. The correlation between family income quintiles and net worth categories used in the primary analysis is 0.50.
- 3.
I also tested simpler weighted quintiles of net worth and home equity, and the results were not substantively different, however there is no longer a coefficient separate for negative wealth which masks some of this relationship which is why the category variable is used here.
- 4.
See Killewald et al. (2017) for correlations between family income, family income over a period of time, and measures of wealth.
- 5.
The income quintiles, and quartiles of wealth are weighed using the 2015 cross-sectional family weights.
- 6.
The original sample of the PSID overrepresented the number of low-income households. In addition, attrition is of concern in a longstanding panel study such as the PSID. Weights are included in the regression models to attempt to properly represent the U.S. population, which is appropriate given the purpose of this study is descriptive, rather than causal (Solon et al. 2015). I ran the analysis unweighted as well, and the results without sampling weights were slightly different, but the interpretation remains the same as is presented here. Substantive differences reported in footnote 9.
- 7.
There may also be concerns that the head of household and spouse from the same family responded to the survey which may bias the results. Specifications run with just the heads of household produced substantively similar results.
- 8.
When interpreting the effects of dummy variables in a semi-logarithmic equation, coefficients should be adjusted using the following equation (Wooldridge 2009; see also Kennedy 1981; van Garderen and Shah 2002):
$$ adj\widehat{\beta}=100\cdot \left[\exp \left(\widehat{\beta}-\frac{1}{2}\widehat{Var}\left(\widehat{\beta}\right)\right)-1\right] $$ - 9.
The unweighted regression analysis produces substantively similar results for all variables and models, and the direction of the coefficients remains consistent across the weighted and unweighted analysis. There are only a few coefficients that change substantively enough to report here. The top two categories of net worth have an interpretation approximately two percentage points lower in the unweighted analysis. For home equity, the interpretation for the negative home equity category and the bottom quartile of positive home equity is approximately 2–3 percentage points higher, and statistically significant in the unweighted analysis. Further, in the home equity models, the interpretation for the top family income quintile is approximately 2 percentage points higher.
- 10.
There have been concerns in the literature with including a subjective measure as a control, given that the reporting of health is likely highly correlated with the dependent variable, which is also subjective, therefore the same models reported here were run excluding self-reported health status. The analysis excluding self-reported health status as a control produce substantively similar results.
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Burland, E.C. (2019). Wealth and Well-being in the United States. In: Brulé, G., Suter, C. (eds) Wealth(s) and Subjective Well-Being. Social Indicators Research Series, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-05535-6_11
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