Abstract
This essay reviews the evidence from the mid-1970s up to 2010 of the increasing differentiation of the richest people from the rest of the population, a phenomenon designated as “fracturing.” Indicators under review include not just income and wealth, but also residential location, education, and health. Most of the indicators indicate that fracturing is increasing, that this process is cumulative, and that it will continue in the future. Public opinion data show that the attitudes and opinions of the richest are also deviating from the rest of the population on many (but not all) social and economic issues, and it seems likely that economic fracturing has had an important influence on this split, but the social implications of fracturing are, however, difficult to isolate.
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Notes
For the purposes of this discussion, more technical definitions of these concepts are not necessary; they can, however, be found in Duclos et al. [2004]. Unlike fracturing, measurement of inequality and polarization requires knowledge of the full distributions, which in many cases is unavailable.
For instance, average wages in the US have remained roughly constant since 1970, increasing only by an average of 0.1 percent a year, while productivity has risen considerably more during the period. At the same time, the top 100 executive salaries increased by an average of 11.0 percent a year [Piketty and Saez 2001, 2011], Data on the income shares of the top 10 percent, 1 percent, and 0.1 percent come from the same source.
These estimates are based primarily on IRS publications, which in turn derive their data from income tax returns. They assume that the share of income hidden from tax authorities remained relatively constant over the period. Such estimates allow a closer insight into very high incomes and differ somewhat from those produced by other government agencies (based on survey data), which unfortunately have a very small sampling of the highest incomes and are also top coded. Nevertheless, these other estimates and the Piketti-Saez data show the same general trends since the 1970s.
Between 1995 and 2007, this fall in tax rates, when graphed by income quintile, reveals the greatest decline in the tax burden at the two ends of the income distribution. More specifically, the tax burden of state and local taxes on families in the lowest-income quintile fell 1.4 percentage points; for families in the third income quintile, only 0.9 percentage points; and for families of the richest 1 percent, 2.6 percentage points. In brief, the highest income groups gained the most in the fall in state and local tax rates in the 2 years under examination.
These are rough estimates only since definitive numbers on gated communities do not seem available. Although many scholars refer to data from the American Housing Survey to estimate the population living in gated communities, the AHS estimates are misleading. For instance, for 2009 the AHS said that 6,091,000 people (roughly 5.3 percent of the population) lived in communities surrounded by fences or walls and with special entry systems. Since 15.3 percent of the population living in such communities reported incomes below the poverty limit and gated communities are primarily upper-income enclaves, the AHS data include many neighborhoods that are not “gated” under the common definition of the term.
One impact on adults of living in poor neighborhoods on adults is a greater probability of being a victim of a violent crime. It is difficult, however, to know if this problem has been growing and has been a factor in the increasing concentration of high-income families in common-interest communities.
The most extensive study is by Backlund et al. [2007], whose sample covered 521,248 individuals and holds constant age, sex, race, Hispanic origin, urbanization, the log of family income, household size, education, employment status, and marital status (but not income inequality or fracturing). They find the expected inverse relation between mortality rates and income.
These estimates are calculated in the same manner and come from the same source as the data presented in Table 3.
Lynch et al. [2004a, 2004b] surveys 98 studies; since then others have been published.
Another similarly inclusive survey is by Skinner and Zhou [2006].
Low-weight babies tend to require much higher medical costs than babies with a normal weight. This problem can be alleviated relatively inexpensively, but public authorities have usually not taken such measures. A private charitable organization in Mansfield, Ohio, the Community Health Access Program (CHAP), attacked this problem and for a relatively small sum brought the percentage of low weight babies in this high risk group down from 23 to 5 percent [personal communication from a director of the program]. Public health authorities, however, have been slow to adopt this or similar programs.
These data are drawn from the same source as used in Table 2.
Other recent studies linking attitudes and opinions with income include those by Page et al. [2013] and Gilens [2012].
From the calculation of the average scores, I also eliminated all answers from consideration of “don’t know.” Such data can, of course, be analyzed from a variety of perspectives, for instance, a liberal-conservative scale, but such exercise are irrelevant to the analysis of fracturing.
These data come from US Bureau of Labor Statistics, series WSU002 http://data.bls.gov/cgi-bin/surveymost?ws.
The data for 2010 can be found in ftp://ftp.bls.gov/pub/special.requests/lf/aat46.txt; data for other years have related URLs.
Hertz [2007] also uses data from the PSID, although for fewer years, and finds relatively little change in inter-generational income mobility.
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Acknowledgements
I thank Philip Jefferson, David Smith, and Victoria Wilson-Schwartz for helpful comments on an early draft of this essay. They are, of course, not responsible for any errors.
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Appendices
Appendix A
Evidence of income mobility
Four empirical questions arise in looking at income mobility: (1) Do people tend to remain in the same income quintile over time or do they change their relative income position over their work life? (2) If such intra-generational mobility exists, is it changing over time? (3) Do children tend to remain in the same income quintile, as their parents? (4) If not, is such inter-generational mobility changing over time? This discussion summarizes some of the relevant literature on these topics.34
Intra-generational income mobility
Data relevant to intra-generational mobility are scarce and, for the purpose of this discussion I focus first on labor earnings derived from the Kopczuk et al [2007], rather than on total income. Measured in terms of a Gini coefficient, the inequality of the labor incomes of full-time workers has increased steadily from the early 1970s through the first few years of the twenty-first century, just as total income did, but we need more detailed data to examine fractionalization.
Looking at 11-year average earnings, the probability of upper mobility (moving from the 0–40th percentile to the 80–100th percentile of income) was less than 10 percent and fell somewhat after 1970 when measured 10, 15, and 20 years after the initial income calculation [Kopczuk et al. 2007: Figure 7]. Using 3, 5, and 10-year intervals, the probability of staying in the top 0.1 percent remained, however, roughly constant after 1970 [Kopczuk et al. 2007, Figure 4A].
The authors of this study conclude that short-term and long-term mobility among all workers has been quite stable since the 1950s and that mobility at the top of the earnings distribution, measured by the probability of staying in a top income group after 1, 3, or 5 years, has been very stable since 1978. The dramatic increase in annual earnings concentration is, therefore, not mitigated by increased mobility into the top income group.
Inter-generational income mobility
Determining whether sons or daughters are differently placed on the income distribution than their parents is also difficult because of the paucity of relevant data. The first step is to obtain data on the average incomes of parents and children over a number of years in order to compute lifetime income, but to be meaningful the two generations must be compared at the same ages and that the averages be fairly long for this approximation. Since such data are not readily available, investigators must make a number of approximations, and any results must be interpreted cautiously. A common statistic of such calculations is the inter-generational income elasticity, the relation of the log of the child’s income to the log of his/her parent’s income. A high elasticity indicates that a large share of the latter’s income is passed on to the child and that income mobility is low; a low elasticity indicates little relation between their incomes, that is, that intergenerational income mobility is high.
From an international perspective, recent studies [e.g., Corak 2013] show that of 13 OECD nations, the US have less inter-generational income mobility than all but one nation in this sample. In comparison to other OECD countries the US also has a higher correlation in the long-run earnings of brothers [Bjorklund and Jantti 2009]. Although this indicates that inter-generational income mobility is lower in the US than in other developed nations, such international comparisons must be viewed cautiously because the results are so sensitive to the manner in which the estimates were made.
In the United States, various economists [for instance Harding et al. 2005; Hertz 2007; Aaronson and Mazumder 2008; Bjorklund and Jantti 2009] have estimated this inter-generational income elasticity to lie between 0.4 and 0.6, which appears higher than that of most economically developed nations. The relatively low inter-generational mobility by these estimates is also reflected in the relationship between the income of siblings, who presumably inherit the same economic status and whose incomes are correlated.
To gain a sharper focus on fracturing in the US and the hardening of class lines, it is necessary to know the trends of such inter-generational income mobility coefficients. Lee and Solon [2009] attack the problem in an ingenious fashion using data from the Panel Study on Income Dynamics, which covers the income of parents and their children from 1968 up to the present. They estimate that from 1977 through 2000, the relative income status of parents and offspring showed no significant trend for sons; for daughters, they obtain similar results after 1984 (in the earlier years their sample size was too small for the results to be taken into account). This suggests that inter-generational income mobility did not greatly change over the last quarter of the twentieth century, that is, that in this respect the degree of fracturing remained constant.Footnote 17, 5 Furthermore, Aaronson and Mazumder [2008] estimate that such intra-generational mobility in the US actually decreased between 1980 and 2000 and, depending on their sample, from 1950 to 2000 as well. This study suggests fractionalization through an increasing hardening of class lines. A very recent study by Chetty et al. [2013] also shows that intergenerational mobility has remained stable for the 1971–1993 birth cohorts.
Certain results from education data, however, suggest that inter-generational mobility has decreased. Since income is related to education, inter-generational income mobility should be inversely related to the correlation between the education of parents and children. Of industrialized nations the US has one of the highest correlation of the educational levels of parents and children, which confirms the results of studies noted above [Hertz et al. 2007, p. 5]. Of particular relevance, this correlation also appears to be increasing over time [Hertz et al. 2007 p. 41], which supports the result that inter-generational income mobility is decreasing.
Appendix B
Variables used in Table 3
From the General Social Survey the following variables were selected (listed in the order in which they appear in Table 3): TRUST, JOBLOSE, NATSPACY, NATMASS, NATSOC, RACHOME, NATRACE, CONFINAN, HEALTH, ABANY, DIVLAW, NATCRIME, NATENVIR, CONLABOR, SOCFRIEND, HAPPY, ATTEND, NATCITYY, FEPRES, NATEDUY, NATFAREY, AGED.
