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Author: Lisa A. Keister, Stephanie Moller

Works Cited Keister, Lisa A., and Stephanie Moller. “Wealth Inequality in the United States.” Annual Review of Sociology, vol. 26, Aug. 2000, p. 63. EBSCOhost, doi:10.1146/annurev.soc.26.1.63.


Wealth ownership in the United States has long been concentrated in the hands of a small minority of the population. Since the early 1920s, the top 1% of wealth holders has consistently owned an average of 30% of total household sector wealth. During economic downturns, the distribution of wealth has appeared more equal. However, studies of wealth mobility suggest that upward movement is rare and that eras of relative equality reflect deflated asset prices more than they do improvements in the financial well-being of the majority of the population. Recent trends in wealth inequality have been particularly startling. The top 1% of wealth owners owned nearly 40% of net worth and nearly 50% of financial assets in the late 1980s and 1990s. During this same period, the top 1% enjoyed two thirds of all increases in household financial wealth, and movement into the top segments of the distribution was nearly nonexistent. Moreover, while inequalities of wealth were consistently more extreme throughout Europe for many decades, by the early 1990s, the United States had surpassed all industrial societies in the extent of inequality of family wealth.Despite extreme inequalities in wealth ownership, however, researchers have paid relatively little attention to wealth inequality and its causes. There are important exceptions, but wealth has largely been ignored in studies of inequality. Sociologists typically focus on income, or the flow of money received by an individual or household, as an indicator of financial well-being. In contrast, wealth, or net worth, is the value of assets owned by the household. More precisely, net worth is the difference between total assets (including real assets such as houses, real estate, and vehicles; and financial assets such as checking and savings accounts, stocks, and bonds) and total liabilities or debt (such as mortgages, car loans, student loans, and credit card debt). Researchers have documented that income inequality is extreme (Danziger & Gottschalk 1993), but recent evidence suggests that inequality is much worse if wealth is taken into account as there are advantages associated with wealth ownership that income alone cannot provide. Wealth provides for both short- and long-term financial security, bestows social prestige, contributes to political power, and can be used to produce more wealth (Domhoff 1970, 1990, Henretta & Campbell 1978, Oliver & Shapiro 1995). Moreover, the correlation between income and wealth ownership is relatively weak, suggesting that income tells only part of the financial story. In this chapter, we review recent literature that describes trends in wealth ownership and the distribution of wealth among households. We also examine research that proposes explanations of wealth inequality. We begin with an examination of the reasons that wealth inequality has received little empirical attention from sociologists, including difficulties that arise in the collection of data on wealth ownership. We then discuss methods of creating empirical estimates of wealth accumulation and distribution, and we present some estimates of trends in wealth inequality in the United States. We review empirical research and literature that explains trends in wealth inequality, including both treatments of aggregate influences, such as stock market and real estate market fluctuations, and individual- and householdlevel factors, such as race, age, and divorce. We conclude the chapter with a review of research on intergenerational processes and wealth mobility. Much of the literature we review is from economics and policy studies as these are the disciplines most active in wealth research. Sociologists have relatively recently begun to study wealth (Jackman & Jackman 1980, Keister 2000b, Parcel 1982). However, we believe that sociological studies of inequality would benefit from a more thorough examination of wealth. We also believe that the study of wealth could be improved by the application of sociological theory and methods, particularly in the study of mobility and related processes.

MEASURES OF INEQUALITY: Wealth Versus Income Because accurate data on wages and salaries are widely available, income is perhaps the most commonly studied indicator of financial well-being. The advent of the income tax, increasingly comprehensive census data, and advances in survey data collection have made accurate, longitudinal data on income widely available (Winnick 1989). Using income alone to indicate the financial well-being of families would be adequate if income and wealth were highly correlated. In reality, however, the correlation between the two indicators is relatively low. Estimates from survey data during the 1980s suggested that the correlation between income and wealth was about 0.50, and that much of this already-weak correlation was attributable to the inclusion of asset income (income generated by wealth) in the definition of total income. When asset income was removed from total income, the correlation between income and net worth dropped to 0.26 (Lerman & Mikesell 1988:779). Family wealth is a critical component of well-being. Omitting wealth from studies of inequality leaves an important part of the stratification story untold. There are several reasons that wealth and income are not more highly correlated. Many of the truly wealthy have rather low earnings because they are able to support current consumption with income derived from assets (Wolff 1995a). In addition, retired persons often have low incomes but substantial net worth because their wealth continues to accumulate after retirement when earnings cease (Radner 1989b). Racial differences in savings and asset accumulation also account for some of the weak correlation between wealth and income (Brimmer 1988). In fact, many families, particularly nonwhite families, have zero or negative net worth regardless of income (Radner 1989a, Winnick 1989). For these reasons, many families who are below the poverty line based solely on current income may be living quite comfortably on assets acquired during more prosperous years. Likewise, those with incomes above the poverty line may, in reality, have considerable debt and few assets, making them vulnerable if current income were to be reduced or to cease entirely. Hence, current income may be a poor indicator of true financial stability (Wolff 1990). Moreover, wealth is even more unequally distributed than income. According to Wolff (1995b), in 1989 the top 1% of wealth owners held 38.9% of total household wealth, while the top 1% of income earners received 16.4% of total household income. The top quintile of wealth holders owned almost 85% of total household wealth, and the top quintile of income recipients received just over 50% of total family income. Another report (based on the Survey of Consumer Finances) found that wealth is more highly concentrated than income (Avery et al 1984). This report demonstrated that 28% of total wealth was owned by the top 2% of wealth owners in 1983, and 57% of wealth was owned by the top 10%. In contrast, in the same year, 14% of total income was received by families with the highest incomes and 33% by those in the top 10%. Moreover, the Gini coefficient for wealth increased from 0.80 in 1983 to 0.84 in 1989 (Wolff 1994). In contrast, the Gini coefficient for income in 1989 was 0.52.

EMPIRICAL ESTIMATES OF WEALTH INEQUALITY Past estimates of wealth ownership and distribution have generally come from three sources: survey data, estate tax data, and the government’s aggregate estimates of household wealth ownership. Both the Survey of Income and Program Participation (SIPP) and the Panel Study of Income Dynamics (PSID) contain information on wealth holdings, but the most widely used source of survey information on household wealth holdings is the Survey of Consumer Finances (SCF) (Curtin et al 1989, Kennickell et al 1997). The SCF includes panel estimates, contains the greatest detail on the components of wealth such as the home and stocks, and oversamples high-income households in order to include more top wealth holders (Avery et al 1988). Accurate representation of top wealth holders is particularly important because the distribution of wealth is highly skewed. For this reason, researchers have also drawn on estate tax records to estimate the wealth of this group. These records of the taxes paid by survivors of the wealthy at the time of death are not perfect; there are loopholes that allow some to avoid paying estate taxes, and estate tax estimates do not include the wealthy who are still living. But methods designed to compensate for these shortcomings have allowed researchers to produce highly accurate estimates of the wealth of the rich (Johnson & Woodburn 1994). Finally, aggregate data on household wealth have been used to estimate trends in levels of wealth and to adjust survey estimates of wealth distribution (Antoniewicz 1996, Federal Reserve System 1993). Two problems plague researchers who use these data to estimate the distribution of wealth. First, surveys underestimate the wealth of the rich. Because wealth is highly concentrated, estimates of wealth ownership must include an ample number of top wealth holders. This is difficult because surveys often sample too few wealthy families as they are a small slice of the population. Moreover, wealthy families generally do not welcome queries about the extent of their wealth holdings, and even if they were willing to answer questions openly, they may not be well informed about the details of their portfolios. Oversampling high-income families and using estate tax data reduce this problem but do not eliminate it (Avery et al 1984, Avery et al 1986a, Avery et al 1986b). Second, empirically estimating trends in household wealth distribution requires adequate longitudinal data. There are several crosssectional surveys of wealth holdings, and occasionally these are conducted as panel surveys. However, neither provides long-term coverage of the same individuals that is ideal for addressing long-term trends in accumulation, distribution, mobility, or other intergenerational processes. In addition to using basic data sources, researchers have developed methods of synthesizing data from multiple sources to compensate for the weaknesses and take advantage of the strengths of the various data sources. The goal of such methods is to combine two or more basic data sources to create a joint estimate of wealth ownership. Wolff (1980, 1983) statistically matched census data and tax returns and used income capitalization to estimate values of assets. Similarly, Greenwood (1983, 1987) matched income tax records with data from the Current Population Survey and used income capitalization to obtain asset values. In a more complex matching process, Keister (2000b) used microsimulation modeling to match estimates from Census data, the 1962 Survey of the Financial Characteristics of Consumers (SFCC), SCF panels and cross-sections, estate tax records, aggregate data, and other sources to build synthetic longitudinal estimates of wealth distribution, accumulation, and mobility. Although synthetic methods are resource-intensive and require assumptions that can quickly become prohibitive, they allow researchers to estimate inequality and explore accumulation processes that are evasive in basic data sources.


While their methods may vary, researchers agree that wealth ownership in the United States is extremely unequal and that inequality has worsened in recent decades. Table 1 summarizes some of what is know aout the amount of wealth that Americans have owned. This table illustrates that both mean and median net worth increased from 1962 through 1989 and then began to decline. The table also highlights the gap between mean and median wealth, alluding to the disproportionate control of net worth by wealthy households. Mean net worth increased by more than 50% between 1962 and 1995. In 1962, mean net worth was just over $115,000. By 1983 it had increased to more than $170,000, and the mean was nearly $200,000 by the end of the 1980s. In the early 1990s, however, mean net worth began to decline. By 1992, the mean was just under $190,000, and it fell to about $175,000 by 1995. The trend in median values mirrored trends in mean values but at a much lower level. This table also demonstrates that there was a relatively steady increase in the percentage of households with zero or negative net worth between 1962 and 1995. But how has wealth been distributed? Historical evidence indicates that while levels of inequality in the distribution of household wealth varied dramatically during the first part of the twentieth century, inequality in wealth ownership was consistently severe. Lampman (1962) was one of the first researchers to point to inequalities in wealth distribution as a source of social problems. Using estate tax data and the estate multiplier method (Johnson & Woodburn 1994), Lampman investigated trends in wealth ownership and inequality in the decades between 1920 and 1960. His findings indicated that between 1922 and 1953, the top 1% of wealth holders owned an average of 30% of total household sector wealth. While inequalities varied with macroeconomic trends during the decades Lampman studied, he provided convincing evidence that inequality was consistently extreme throughout that period. Other historical estimates have produced similar evidence of inequality during the early twentieth century. Wolff & Marley (1989) used various data sources to study wealth inequality over the entire 1920–1990 period. For the early part of the century, their results were consistent with Lampman’s findings. They demonstrated that the top 1% of wealth owners owned an average of 30% of total net worth between 1922 and the early 1950s. Between 1922 and the 1929 stock market crash, the share of wealth owned by the top 1% increased from about 29% to about 32%. During the 1930s and 1940s, the concentration of wealth declined, so that the top 1% owned less than 30% by the late 1940s. During the 1950s, economic prosperity brought with it increased wealth inequality, and by the late 1950s, estimates suggest that the top 1% of households owned nearly 35% of total wealth. Wealth data, and the corresponding estimates of wealth distribution, began to improve in the 1960s. In 1962, the Federal Reserve Board’s SFCC became the first comprehensive survey of wealth holdings in the United States. Table 2 contains estimates of wealth distribution from the SFCC and SCF panels for the 1980s and 1990s. These estimates demonstrate that a very small portion of households have consistently owned the vast majority of household wealth. In 1962, the top 1% of wealth owners held 33.5% of total net worth, and the top quintile held more than 80% of total net worth. Wealth inequality remained unequally distributed but relatively constant between 1962 and the mid-1970s due to an extended stock market slump and the growth of welfare programs such as AFDC and Social Security (Smith 1987). Using estate tax data, Smith found evidence that after 1973 wealth inequality began to drop once again. Others using similar methods have found that between 1972 and 1976, the share of total wealth owned by the top 1% of wealth owners declined from 29% to about 19% of total wealth (Smith 1987, Wolff 1992). Wealth inequality began to rise considerably after 1979, a trend that continued throughout the 1980s. By 1983, wealth inequality had returned to, and indeed on some measures had surpassed, 1962 levels. The estimates in Table 2 indicate that the share of wealth owned by the top 1% of wealth holders was 33.8% in 1983 and 37.4% by 1989. Real mean wealth grew at 3.4% annually during this sixyear period, a rate nearly double that of wealth growth between 1962 and 1983. Others have found similar trends (Danziger et al 1989, Wolff 1993). Wolff (1993) found that mean family wealth increased 23% in real terms but that median wealth grew by only 8% over that period. His research also suggested that the share of the top 0.5% of wealth owners rose 5% during this period, from 26.2% of total household sector wealth in 1983 to 31.4% in 1989. The wealth of the next half percent remained relatively constant at about 7.5% of total household wealth, but the share of the next 9% decreased from 34.4% in 1983 to 33.4% in 1989. Most striking is evidence of the decline in the wealth of the poorest 80% of households. The wealth of this group decreased by more than 2 percentage points, from 18.7% of total wealth in 1983 to 16.4% in 1989. Moreover, nearly all growth in real wealth between 1983 and 1989 was accumulated by the top 20% of wealth holders, who gained 2.3 percentage points in their total wealth holdings, from 81.3% to 83.6%. The second 20% lost 0.3 percentage points, the middle 20% lost 0.4 points, and the bottom 40% lost 1.6 percentage points (from 0.9% to negative 0.7%). Wolff (1995b) found similar results in his examination of trends in wealth inequality. Past research has also suggested that in the 1980s, wealth inequality in the United States became severe relative to that found in European nations. Studies of wealth in the 1920s suggested that wealth in the United States was much more equally distributed than in Western European nations. By the late 1980s, however, research suggests that household sector wealth in the United States was considerably more concentrated than in Western Europe (Wolff 1995b). While mean and median household net worth declined during the 1990s, the distribution of wealth continued to worsen. The wealth of the top 1% of wealth holders increased from 37.4% of total wealth in 1989 to 38.5% in 1995. The Gini coefficient, an indicator of the degree of inequality comparable to the Gini coefficient used to measure income inequality, increased from 0.85 in 1989 and 1992 to 0.87 in 1995. The Gini coefficient ranges from 0 to 1, with 0 indicating perfect equality and 1 indicating perfect inequality. Conceptually, if a single household were to own all wealth, the Gini coefficient would equal unity (Weicher 1995, 1997). The estimates of the wealth Gini in Table 2 indicate that wealth inequality is extremely severe, and that it worsened considerably between 1962 and 1995.


Explanations of wealth inequality typically fall into two camps: those that focus on aggregate-, or macro-, level influences, and those that focus on processes at the level of individuals and families. It is nearly impossible to discuss the wealth accumulation of individuals and families without speculating about the implications that this has on inequalities in the macrolevel distribution of wealth. Likewise, it is difficult to discuss the aggregate-level distribution of wealth among families without speculating about how the behavior of members of the society affects this distribution. Although most would agree that processes at both levels of aggregation are important, researchers seldom integrate the two levels. In this section, we review literature that takes each approach, and we conclude with a discussion of efforts to integrate macro and micro approaches.

Aggregate Processes

The impact of market fluctuations, particularly stock and real estate markets, has dominated discussions of the influence of aggregate processes on the distribution of wealth. When the value of a particular asset increases, those who own the asset have more wealth, net of any change in their behavior or other circumstances. Because the wealthy have generally been more likely than the nonwealthy to own stocks (Smith 1987, Winnick 1989), when the stock market booms, the concentration of wealth intensifies (Smith 1987, Wolff 1987, 1992). The concentration of wealth, therefore, tends to follow trends in the stock market, Similarly, when real estate values increase, those who own houses or other real estate enjoy an increase in their net worth. Because the ownership of other assets, such as housing, is more equally distributed, the impact of the ownership of other assets on wealth inequality is less pronounced. Changes in portfolio behavior, that is, the combination of assets families own, thus has important implications for their wealth holdings. Table 3 documents trends in the percentage of gross household assets accounted for by various types of wealth. Until the 1980s in the United States, the single largest component of total wealth for most families was the primary residence (Holloway 1991, Levy & Michel 1991). In the 1990s, an increasing number of Americans began putting their savings into stocks and stock-based mutual funds (including Individual Retirement Accounts and pension plans), hoping to reap some of the benefits of a stock market that experienced record increases in the eight consecutive years between 1988 and 1995 (Kennickell & Starr-McCluer 1997, Kennickell et al 1997). While the stock market spiraled upward, the housing market topped out, making real estate investments less appealing and stock investments more appealing. Because increased numbers of households across the wealth distribution were investing in stocks, stock market booms in the 1990s had a less dramatic effect on inequality than they had in the past. Ownership of other assets has fluctuated less. As stocks gained importance between 1983 and 1995, cash accounts declined in their relative share of the household wealth portfolio. Like housing, cash accounts tended to be owned primarily by the nonwealthy. More specifically, following housing ownership, checking accounts, savings accounts, and other demand deposits tend to dominate the portfolios of the middle and lower middle classes. The wealthy, of course, also own these assets, but their value tends to be overshadowed by more substantial investments in stocks, bonds, business assets, and real estate (Kennickell & Starr-McCluer 1997). Life insurance ownership has had relatively little impact on wealth inequality, and ownership of this asset remained relatively constant between 1962 and 1995 as the estimates in Table 3 indicate. Yet life insurance ownership has attracted the attention of scholars because there are relatively pronounced differences in propensities to buy life insurance. Life insurance tends to comprise a smaller portion of the wealth portfolios of the wealthy than of the middle class and poor. These differences are largely thought to be a function of the size of net worth and of demographics such as family size (Fischer 1973, Lewis 1989, Pissarides 1980, Yaari 1965). There is also some evidence, however, that minorities, households headed by women, and poor families are more likely than others to use life insurance as savings plans (Kennickell & Shack-Marquez 1992, Kennickell et al 1997). As with other types of portfolio behavior, it is difficult to ascertain reasons for these differences given available data, but social pressure, unscrupulous insurance salesmen, and poorly regulated financial markets may contribute to the differences. Individual and Family Processes Individual- and family-level characteristics also influence wealth accumulation. Family income generally has a positive effect on saving and wealth (Atkinson 1980, Bomberger 1993, Greenwood 1987, Radner & Vaughan 1987), and research has shown that other attributes such as age, race, and family structure influence wealth ownership, net of income. The effect of age on wealth ownership has attracted perhaps more attention than any other single process, particularly among economists working from a life cycle model. Keynesian economics, the predominant approach to economic behavior in the 1930s and 1940s, emphasized the role that individual saving played in the larger economy and held that current income was the sole determinant of saving (Modigliani & Brumberg 1952 (unpublished), 1954). Responding to the simplicity of this approach, Modigliani & Brumberg argued that saving is a function not of the absolute income of a family but instead of the family’s income relative to both mean income across families and permanent income (the present value of lifetime labor earnings and bequests). They developed the life cycle hypothesis of saving and wealth accumulation, positing that households will accumulate assets in working years and use these assets to support consumption in old age (Modigliani 1992). According to the life cycle hypothesis, net worth should increase until retirement and then fall sharply (Ando & Modigliani 1963). While the life cycle hypothesis is conceptually appealing, empirical support of the idea was limited from the beginning. Some researchers provided empirical evidence that supported the predictions of the hypothesis by showing that wealth increases until about age 60 or 65 and then declines at a relatively constant and sharp rate (Fisher 1952, King & Dicks-Mireaux 1982, Straw 1956). Yet other empirical studies directly contradicted the life cycle hypothesis by demonstrating that saving continued well after retirement (Bernheim 1987, Danziger et al 1982, David & Menchik 1988, Menchick & David 1983, Torrey 1988, Torrey & Taeuber 1986). After considerable debate, a relatively strong consensus developed around the idea that while the elderly do dissave after retirement, they do so at a rate much less than that predicted by the life cycle hypothesis (Darby 1979, Hurd 1987, 1990, Mirer 1979, 1980, Sheiner & Weil 1992, Shorrocks 1975, White 1978). An important reason for the lack of support of the dissaving hypothesis is that most people experience both a decline in income (minus saving) and a decline in expenditures at retirement. As a result, it is not surprising that the decline in postretirement income is less than predicted by the life cycle hypothesis. There is also evidence that when status attainment variables are controlled, the age at which net worth begins to decline is far beyond the normal retirement age (Land & Russell 1996). There are two reasons that wealth decreases more slowly than predicted by the life cycle hypothesis. First, the timing of death is uncertain, and risk averse households appear to continue to save in preparation for the possibility of a relatively long life. Second, theories of inheritance have suggested that the elderly do not dissave because they are motivated to leave an inheritance to their offspring (Cheal 1983, Davies 1982, Hurd & Mundaca 1989, Osberg 1984). Modigliani’s own research (1988a, 1988b) has found that transfers—both inter-vivos, made between living persons, and bequests, made after the death of the giver—account for only 20% of the net worth of US families. However, the bulk of research on inheritance has demonstrated that inter-vivos transfers and bequests account for at least 50% (Gale & Scholz 1994) and perhaps more than 80% of the net worth of US families (Kotlikoff & Summers 1981:706). Research has also shown that race affects wealth ownership, net of income. Indeed, when wealth is included as an indicator of well-being, racial inequality is considerably more severe than other indicators suggest. Oliver & Shapiro documented that in the late 1980s, median income for blacks was about 60% of that of whites, while median net worth for blacks was only 8% of that of white families ($43,800 for white families versus $3,700 for black families). Similarly, while 25% of white families had zero or negative assets in 1992, more than 60% of black families were in such bleak financial straits in the late 1980s (Oliver & Shapiro 1989, 1990, 1995). Various factors account for racial differences in wealth ownership. Status attainment theorists have argued that educational differences are central to explaining racial differences in wealth (Campbell & Henretta 1980, Henretta 1984, 1979, Henretta & Campbell 1978). Others have argued more generally that structural barriers and discrimination create these differences (Baer 1992, Blau & Graham 1990, Oliver & Shapiro 1995, Parcel 1982). Indeed, social scientists, particularly sociologists, generally agree that redlining in housing, dampened educational and occupational opportunities for minorities, and other structural constraints contribute to inequality (Barth et al 1980, Horton 1992, Jackman & Jackman 1980, Oliver & Shapiro 1989, Ong & Ill 1988, Williams 1975). Others, however, have focused on racial differences in portfolio behavior, that is, decisions about how to save, and have argued that there are systematic racial variations in asset ownership (Galenson 1972, Keister 2000a, Terrel 1971). The reasons that portfolio behavior varies racially, however, are less clear, although the dominant explanation suggests that differences in willingness to postpone consumption are important (Brimmer 1988, Lawrence 1991). Of course, social influences on current consumption (e.g., decisions about whether to save or buy a new car) are likely quite strong, but current data restrict empirical examination of such influences (Keister 2000b). Family structure also plays an important role in creating and maintaining differences in wealth ownership. Some researchers have argued that a relatively small percentage of the increase in poverty in the 1970s through the 1990s was accounted for by changes in family structure (Gottschalk & Danziger 1984). Two separate studies contended that the “feminization of poverty” between 1960 and the mid-1980s was a result of changes in relative poverty rates for various household compositions rather than changes in family structure, particularly for blacks (Bane 1986, Danziger et al 1986). Yet evidence continues to mount that suggests some role for change in family structure. Few wealth researchers address issues of family structure, but both survey and simulated estimates suggest that gender and family structure affect both cross-sectional wealth ownership and longitudinal patterns of wealth mobility (Keister 2000b). These estimates suggest that at any given point in time, family structure is highly correlated with wealth ownership, net of income, education, and race. In particular, there is evidence that marriage and widowhood increase wealth ownership, while increased family size and family dissolution through divorce or separation have the opposite effect (Kennickell & Starr-McCluer 1994, Kennickell et al 1997). Researchers have also shown that family structure continues to affect poverty when it is defined in income terms (see McLanahan & Kelly 1999 for a review of the literature on the feminization of poverty). Integrating Macro and Micro Approaches Past research on wealth accumulation processes and wealth inequality has been concerned either with describing the distribution of wealth among families or with explaining how families acquire their wealth, usually with little regard for how processes at one level of aggregation affect outcomes or processes at other levels. One empirical approach to integrating macro and micro processes is that used by Steckel & Krishman (1992) to estimate changes in individuals’ percentile positions in the wealth distribution based on demographic characteristics. Steckel & Krishman explained changes in individuals’ positions in the wealth distribution (a macro measure) on characteristics of the individuals such as age and gender (micro measures). Angle (1986, 1993) suggested an alternative micro-macro link in his work which argued that the surplus theory of social stratification—the tendency for wealth to flow into the hands of those who already have wealth—could be used to explain wealth inequality. Large-scale modeling efforts that incorporate both aggregate processes and microlevel processes are also able to capture much of the interaction between levels of aggregation (Greenwood 1983, Keister 2000b, Wolff 1980). Ideally, however, these methods would be more clearly linked to theoretical approaches that incorporate both macro and micro processes.


Intergenerational processes and mobility are vital to understanding how to relieve extreme wealth inequality. Literature on intergenerational processes has focused on generational comparisons in wealth ownership, with most of the focus on differences between the well-being of baby boomers and their parents. Relatively unfavorable labor market conditions (Berger 1985, 1989, Easterlin 1987, Easterlin et al 1993, Welch 1979) combined with changes in marriage and fertility patterns among baby boomers have raised suspicions that this may be one of the first generations to do worse than their parents financially (Campbell & O’Rand 1988, Levy & Michel 1986, 1991). Yet empirical estimates have demonstrated that the prospects for baby boomers are not as pessimistic as the simple demographic and economic trends indicate. A number of studies have documented that baby boomers, on average, have had higher incomes and have accumulated more wealth than other generations, including their parents’ generation, at a comparable age (Easterlin et al 1990). One reason for achieving a higher living standards was, indeed, because the baby boomers had altered their demographic behavior from that of earlier generations. If remaining single is combined with child rearing, then parents are unlikely to be able to save for retirement. But it was not just single parenthood that increased among baby boomers. Baby boomers were also more likely than earlier generations to have fewer children or to remain childless, both choices that would increase rather than decrease ability to save for retirement. Others have documented similar patterns (Kingson 1992, Manchester 1993, Sabelhaus & Manchester 1995). Perhaps more crucial to understanding the persistence of wealth inequality is understanding wealth mobility. Unfortunately, while the study of wealth distribution dates back to the writings of Smith, Mill, and Ricardo, discussions of wealth mobility are relatively rare. Early studies of mobility concluded that poverty, and thus wealth as well, was transmitted from parents to their children, typically via education (Blau & Duncan 1967). Other researchers extended Blau & Duncan’s work and found similar patterns around that time (Corcoran 1995, Duncan et al 1972, Featherman & Hauser 1978, Jencks et al 1972, Sewell & Hauser 1975). Studies consistently found relatively weak, although statistically significant, correlations between parents’ and children’s income and concluded that mobility was, indeed, possible in the United States. These studies, however, relied on nonrandom, relatively homogenous samples of white, working men, and the studies typically used earnings or income estimates from a single year to generalize about lifetime earnings. None explicitly measured wealth, but most generalized their findings to wealth inequality as well as income inequality (Corcoran & Datcher 1981). By the late 1980s, new longitudinal data sets became available, and researchers began to uncover more evidence of status inheritance, but even these neglected wealth (Behrman & Taubman 1990, Zimmerman 1992). Indeed, while some researchers attempted to incorporate wealth into their analyses, data limitations made this nearly impossible (Levy 1980). The researchers who have dominated the field of wealth distribution have occasionally used the panel Survey-of-Consumer-Finances data sets to estimate short-term trends in wealth ownership (see, for example, Wolff 1998). Short-term trends in mobility, however, tell us little about the intergenerational or life-course processes that lead to the persistence of either wealth or poverty. What is needed are longitudinal data on wealth ownership that would facilitate studies comparable to those conducted using the PSID’s information on income. Related to intergenerational processes is the inheritance of wealth. We know very little about how much wealth is actually inherited because data on inheritance is virtually nonexistent. Indeed, Menchik & Jianakoplos (1998) estimated that between the 1970s and 1990s, as little as 20% and as much as 80% of total wealth may have been inherited. Those who study inheritance typically refer to three forms of inheritance: inheritance at the death of a parent or other benefactor, inter-vivos transfers of money and other assets, and transfers of cultural capital (Miller & McNamee 1998:3). While we typically think of inheritance as occurring at the death of the benefactor, Kurz (1984) estimated that inter-vivos transfers account for nearly 90% of intergenerational wealth transfers. Miller & McNamee (1998:3) argue that cultural capital, transferred through formal education and informal experiences, is also a vital, inherited resource (Miller & McNamee 1998:3). Inheritance likely explains much of the persistence of wealth inequality. Racial differences in wealth ownership, for example, are bound to be exacerbated across generations if most wealth is inherited (Clignet 1998, Oliver & Shapiro 1995:152– 156). Unfortunately, however, the majority of writing about inheritance processes addresses historical or legal issues because of data limitations (Chester 1998:23). Until such data are available, inheritance will likely remain a black box in most studies of wealth accumulation and distribution.

CONCLUSION In this chapter, we reviewed recent literature that describes trends in wealth ownership and the distribution of wealth among households. We also examined research that proposes explanations of wealth inequality. We explored research that poses explanations for wealth inequality, including those that focus on aggregate explanations, those that concentrate on processes at the micro level, and those that attempt to integrate macro and micro explanations. We then discussed literature on intergenerational processes and mobility. We concluded that data limitations have made the study of wealth mobility nearly impossible, and we called for more comprehensive longitudinal data on asset ownership. Levels of wealth inequality are so extreme that most people register hardly any wealth at all, yet wealth is one of the most central indicators of financial well-being and security. To address this fundamental social problem, we must first acknowledge that it exists, and the first step in acknowledgment is acquiring adequate data to demonstrate the nature and causes of the problem. Only once we understand the problem better, can we decide what we are willing to do to alleviate it.

TABLE 1 Mean and median net worth, 1962–1995 (1990 dollars) 1962 1983 1989 1992 1995 Mean 115,995 170,550 195,382 189,948 175,485 Median 30,996 43,801 46,881 39,995 39,146 Percent with no wealtha 11 16 18 18 19 Data are from the Survey of the Financial Characteristics of Consumers for 1962 and the Survey of Consumer Finances for other years. a Households with zero or negative net worth.

TABLE 2 Percent of total net worth held by position in the wealth distribution, 1962–1995 Gini Bottom coefficient Top 1% Top 20% 2nd 20% 3rd 20% 40% 1962 0.80 33.5 81.2 13.5 5.0 0.3 1983 0.80 33.8 81.3 12.6 5.2 0.9 1989 0.85 37.4 83.6 12.3 4.8 −0.7 1992 0.85 37.2 83.9 11.4 4.5 0.2 1995 0.87 38.5 83.9 11.4 4.5 0.2 Data are from the Survey of the Finance

TABLE 3 Percentage of gross household assets held in various wealth categories, 1962–1995 1962 1983 1989 1995 Primary residence 26 30 29 30 Other real estate 6 15 13 11 Business assets 15 19 16 18 Cash accounts 17 15 14 7 Stocks & mutual funds 20 9 12 12 Bonds 8 4 6 4 Whole life insurance 1 2 2 3 Pension assets 2 2 3 9 Personal trusts 4 3 3 3 Other assets 1 1 2 3 Total assets 100 100 100 100 Data are from the Survey of the Financial Characteristics of Consumers for 1962 and from the Surveys of Consumer Finances for other years.

DMU Timestamp: February 03, 2020 23:30

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