Given the important relationship between age and asset accumulation, portraits of low-income families, or any other type of family, are improved by accounting for age. Lermans (2005) age by net worth profiles for families with different characteristics, as presented in exhibit 13 of this report, provides an excellent example of how to account for age. Exhibits 3, 10, and 12, for example, show the distinct life-cycle pattern of asset, debt, and net worth accumulation.
More detailed work on creating portraits of low-income families from national surveys like SIPP, PSID, SCF, and HRS would be beneficial. More detailed portraits can examine the assets and liabilities of particular types of families, such as low-income, single-headed families who do and do not own a home; low-income, married-couple families who do and do not own a home; and low-income families who do and do not participate in welfare programs, for example. A challenge for some surveys like the Survey of Consumer Finances (SCF) and perhaps the Health and Retirement Study (HRS) is insufficient sample size when multiple classifiers are used in combination.
It is also important to provide greater detail on the role that bankruptcy may play in the asset accumulation of low-income families. Extant research (known to the authors) provides little information on the descriptive relationship between bankruptcy, income, education, minority status, and marital status.
Important to the portraits of low-income families are family holdings of consumer durables such as furniture, appliances, and equipment, since they may be important time saving and income-generating assets for low-income families.
Future portraits of low-income families can access the role that region and rural status play on asset accumulation and the types of assets families accumulate. Furthermore, these portraits could also consider the assets and liabilities of families below the median. How different is the portrait for families at the 10th, 20th, 30th, and 40th percentiles of the income distribution from those at the median? Alternatively, deciles rather than quintiles could be used to expand the reach of medians.
In addition to considering asset holding rates and values, computations of expected levels of assets and liabilities, using asset and liability holding rates and median and mean levels of such assets or liabilities could prove useful. These expected levels may better illustrate the dual disadvantage faced by many low-income families: not only do they tend to hold lower levels of assets and liabilities than higher-income families, they are also much less likely to hold these assets or liabilities.
Future research could also examine the role that Social Security, Medicare, and defined-benefit plans play in asset accumulation for low-income families and how best to value these important programs alongside more traditional concepts of assets, such as homes and bank accounts.
Less important, but also worth exploring, is undertaking an analysis that relies on family classifier composites based on age, education, marital status, and race or ethnicity to recognize that the distribution of family characteristics within a family may differ from those of the household head or survey respondent. Portraits based on family classifier composites may tell a more nuanced asset-liability story than portraits based on classifiers of the family head alone.