Income Inequality, Income Mobility, and Economic Policy: U.S. Trends in the 1980s and 1990s

Income Inequality, Income Mobility, and Economic Policy: U.S. Trends in the 1980s and 1990s

Order Code RL34434
Income Inequality, Income Mobility, and Economic
Policy: U.S. Trends in the 1980s and 1990s
April 4, 2008
Thomas L. Hungerford
Specialist in Public Finance
Government and Finance Division

Income Inequality, Income Mobility, and Economic
Policy: U.S. Trends in the 1980s and 1990s
Summary
Income inequality has been increasing in the United States over the past 25
years. Several factors have been identified as possibly contributing to increasing
income inequality. Some researchers have suggested the decline in unionization and
a falling real minimum wage as the primary causes. Others have argued that rising
returns to education and skill-biased technological change are the important factors
explaining rising inequality. Most analysts agree that the likely explanation for rising
income inequality is due to skill-biased technological changes combined with a
change in institutions and norms, of which a falling minimum wage and declining
unionization are a part.
Since most people are concerned with upward mobility, and given the central
importance of income mobility to the debate over income inequality, this report
examines the relation between income mobility and inequality. Income mobility
studies are an important complement to income inequality studies — income
inequality does not address the issue of whether or not the poor are getting poorer,
whereas income mobility does.
While there appears to be considerable relative income mobility (about 60% of
individuals change income quintiles over 10 years), it is not far — about 60% of
those individuals who changed income quintile in the 1980s or 1990s only moved to
the next quintile. But most individuals in the poorest quintile in 1980 experienced
an increase in their real income between 1980 and 1989 — half saw their real income
increase by more than 36%. Of those in the richest quintile, almost half saw their
real income fall by 10% or more during the 1980s. But there are differences in
income changes between the 1980s and the 1990s: those in the poorest income
quintile may have done slightly better in the 1990s than in the 1980s, while
individuals higher up in the income distribution (quintiles 2-5) appear to have done
better in the 1980s than in the 1990s.
In both the 1980s and 1990s, income growth was progressive and had an
equalizing effect on the income distribution, but the equalizing effect had a larger
absolute value in the 1990s than in the 1980s. Mobility, however, had a
disequalizing effect and, in fact, outweighed the progressivity effect, thus increasing
the annual inequality. In both decades, the long-term income inequality is lower than
the income inequality in the first year of the decade. The results suggest that mobility
had a greater equalizing effect on long-term inequality in the 1990s than in the 1980s.
Three broad types of government economic policy affect income growth and
mobility, and hence income inequality: (1) regulation, (2) the tax system, and (3)
government transfers. Economic policies to reduce the growth of income inequality
may work, in part, through their effects on income mobility. Reducing income
mobility (that is, stabilizing incomes) may reduce the rising trend in income
inequality, but it could also increase inequality of longer-term income.

Contents
What is Income? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Income Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Income Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Previous Studies of Income Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Relative Income Mobility in the 1980s and 1990s . . . . . . . . . . . . . . . . . . . . . 9
Absolute Income Mobility in the 1980s and 1990s . . . . . . . . . . . . . . . . . . . 12
The Effect of Mobility on Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
U.S. Economic Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Effects of Mobility on Inequality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Multivariate Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
List of Figures
Figure 1. Income Inequality, 1980-1999 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
List of Tables
Table 1. How Changes in Income from Different Sources Affects
Income Inequality, 1980-1999 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Table 2. Relative Income Mobility — Transition Matrices for the
1980s and 1990s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Table 3. How Demographic Variables Affect Percentage Change in
Probability of Upward, No, and Downward Mobility . . . . . . . . . . . . . . . . . 11
Table 4. Absolute Income Mobility — Real Income Growth in the
1980s and 1990s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Table 5. Decomposition of Change in Gini Coefficient into Progressivity
Effect and Reranking Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Table 6. Effect of Mobility on Inequality of Longer-term Income . . . . . . . . . . . 15
Table A1. Quintile Breaks — Real Equivalence-adjusted Family Income . . . . . 20
Table A2. Coefficient Estimates — Multinomial Logit . . . . . . . . . . . . . . . . . . . 22

Income Inequality, Income Mobility, and
Economic Policy: U.S. Trends in the
1980s and 1990s
Income inequality has been increasing in the United States over the past 25
years.1 Several factors have been identified as possibly contributing to increasing
income inequality. Some researchers have suggested the decline in unionization and
a falling real minimum wage as the primary causes.2 Others have argued that rising
returns to education and skill-biased technological change are the important factors
explaining rising inequality.3 Tax policy, especially the Tax Reform Act of 1986, has
also been identified as a possible cause for rising income inequality.4 Most analysts
agree that the likely explanation for rising income inequality is due to skill-biased
technological changes combined with a change in institutions and norms of which a
falling minimum wage and declining unionization are a part.5 Research suggests that
tax policy, while possibly having short-term effects on inequality, does not have
much impact on longer-term inequality trends.6
1 See CRS Report RL34155, Income Inequality and the U.S. Tax System, by Thomas L.
Hungerford.
2 See David S. Lee, “Wage Inequality in the United States During the 1980s: Rising
Dispersion or Falling Minimum Wage?” Quarterly Journal of Economics, vol. 114, no. 3
(Aug. 1999), pp. 977-1023; and John DiNardo, Nicole M. Fortin, and Thomas Lemieux,
“Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric
Approach,” Econometrica, vol. 64, no. 5 (Sept. 1996), pp. 1001-1044.
3 See John Bound and George Johnson, “Changes in the Structure of Wages in the 1980s:
An Evaluation of Alternative Explanations,” American Economic Review, vol. 82, no. 3
(Jan. 1992), pp. 371-392; David H. Autor, Lawrence F. Katz, and Melissa S. Kearney, “The
Polarization of the U.S. Labor Market,” American Economic Review, papers and
proceedings, vol. 96, no. 2 (May 2006), pp. 189-194; and Thomas Lemieux, “Postsecondary
Education and Increasing Wage Inequality,” American Economic Review, papers and
proceedings, vol. 96, no. 2 (May 2006), pp. 195-199.
4 See Daniel R. Feenberg and James M. Poterba, “Income Inequality and the Incomes of
Very High-Income Taxpayers: Evidence from Tax Returns,” in James M. Poterba, ed., Tax
Policy and the Economy, vol. 7 (Cambridge, MA: MIT Press, 1993); and Roger H. Gordon
and Joel B. Slemrod, “Are “Real” Responses to Taxes Simply Income Shifting Between
Corporate and Personal Tax Bases?” in Joel B. Slemrod, ed., Does Atlas Shrug? The
Economic Consequences of Taxing the Rich (New York and Cambridge, MA: Russell Sage
Foundation and Harvard University Press), pp. 240-280.
5 See, for example, Frank Levy and Peter Temin, Inequality and Institutions in 20th Century
America, National Bureau of Economic Research, Working Paper no. 13106, May 2007; and
Autor, Katz, and Kearney.
6 See Joel Slemrod and Jon M. Bakija, “Growing Inequality and Decreased Tax
(continued...)

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Arguments are offered for and against reducing income inequality. The classic
argument against rising income inequality is the rich get richer and the poor get
poorer. This can increase poverty, reduce well-being, and reduce social cohesion.
Consequently, many argue that reducing income inequality may reduce various social
ills. Some researchers are concerned about the consequences of rising income
inequality. Research has demonstrated that large income and class disparities
adversely affect health and economic well-being.7
In contrast, there are those arguing that rising inequality is nothing to worry
about and point out that average real income has been rising, so while the rich are
getting richer, the poor are not necessarily getting poorer. In addition, many argue
that some income inequality is necessary to encourage innovation and
entrepreneurship — the possibility of large rewards and high income are incentives
to bear the risks. Furthermore, many argue that income or social mobility reduces
income inequality and increases well-being. Milton Friedman argued in 1962 that
mobility is an important determinant of well-being:
A major problem in interpreting evidence on the distribution of income is the
need to distinguish two basically different kinds of inequality; temporary, short-
run differences in income, and differences in long-run income status. Consider
two societies that have the same distribution of annual income. In one there is
great mobility and change so that the position of particular families in the income
hierarchy varies widely from year to year. In the other, there is great rigidity so
that each family stays in the same position year after year. Clearly, in any
meaningful sense, the second would be the more unequal society.8
Since Congress and most people are concerned with upward mobility and, given
the central importance of income mobility to the debate over income inequality, this
report examines the relation between income mobility and inequality.9 Income
mobility studies are an important complement to income inequality studies.
6 (...continued)
Progressivity,” in Kevin A. Hassett and R. Glenn Hubbard, Inequality and Tax Policy
(Washington, DC: AEI Press, 2001), pp. 192-226; Levy and Temin; Thomas Piketty and
Emmanuel Saez, “Income Inequality in the United States, 1913-1998,” Quarterly Journal
of Economics, vol. 118, no. 1 (Feb. 2003), pp. 1-39; and Edward M. Gramlich, Richard
Kasten, and Frank Sammartino, “Growing Inequality in the 1980s: The Role of Federal
Taxes and Cash Transfers,” in Sheldon Danziger and Peter Gottschalk, eds., Uneven Tides:
Rising Inequality in America (New York: Russell Sage Foundation, 1993), pp. 225-249.
7 Michael Marmot, The Status Syndrome: How Social Standing Affects Our Health and
Longevity (New York: Henry Holt and Co., 2004); Richard G. Wilkinson, Unhealthy
Societies: The Afflictions of Inequality (New York: Routledge, 1996); Robert Frank, Falling
Behind: How Rising Inequality Hurts the Middle-Class (Berkeley, CA: University of
California Press, 2007); and Gopal K. Singh and Mohammad Siahpush, “Widening
Socioeconomic Inequalities in US Life Expectancy, 1980-2000,” International Journal of
Epidemiology, vol. 35 (May 2006), pp. 969-979.
8 Milton Friedman, Capitalism and Freedom (Chicago: University of Chicago Press, 1962),
p. 171.
9 For example, the Subcommittee on Income Security and Family Support of the House
Ways and Means Committee held a hearing on economic opportunity in Feb. 2007.

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Examining income inequality provides information on the dispersion of income and
a snapshot of well-being. It does not provide dynamic information on well-being
over a period of time — are the same people always at the bottom of the income
distribution? Income inequality does not address the issue of whether or not the poor
are getting poorer, whereas income mobility does.
What is Income?
A precise definition of income is important in studying inequality and mobility.
Most people think of income as the salary they receive from their employer or
adjusted gross income as reported on their income tax return. A broader definition
of income is the Haig-Simons concept of income. Henry Simons started from the
proposition that “[p]ersonal income connotes, broadly, the exercise of control over
the use of society’s scarce resources.”10 Robert Haig defined “income in terms of
power to satisfy economic wants rather than in terms of satisfactions themselves.”11
Both economists argue that income is the sum of consumption and additions to
wealth.12 There are some who argue that only consumption should be considered
because, they claim, it is a better measure of well-being.13 But additions to wealth
reflect rights that could have been exercised in consumption and may be so exercised
in the future.14
For analytic purposes, income has to be measured and expressed in numerical
terms in terms of national currency. Consequently, consumed goods and services
produced through home production (such as child care services provided by family
members and food grown by family members) are not included in income, since a
monetary value is difficult to calculate. In this analysis, income is measured in
dollars and includes earnings, asset income (interest and dividends), government cash
transfers, pension payments, the face value of food stamps, and transfers from private
individuals. Realized capital gains are not included since they are not an annual
income flow and vary greatly from year to year. Taxes (which may be negative) are
subtracted to produce what is called post-government income.15
10 Henry C. Simons, Personal Income Taxation: The Definition of Income as a Problem of
Fiscal Policy (Chicago: University of Chicago Press, 1938), p. 49.
11 Robert Murray Haig, “The Concept of Income — Economic and Legal Aspects,” in R.M.
Haig, ed., The Federal Income Tax (New York: Columbia University Press, 1921).
12 Simons, p. 50, states that “[p]ersonal income may be defined as the algebraic sum of (1)
the market value of rights exercised in consumption and (2) the change in the value of the
store of property rights between the beginning and end of the period in question.”
13 See, for example, W. Michael Cox and Richard Alm, “You Are What You Spend,” New
York Times, Op-Ed Contribution, Feb. 10, 2008, p. 14.
14 The same reasoning would apply to an increase in debt which is a subtraction from wealth.
15 See Appendix for more information on the definition of income used in the analysis. Pre-
government income includes only income from non-governmental sources and excludes any
adjustment for taxes.

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Income Inequality
Earnings and income inequality has been rising in the United States since
1980.16 The evidence suggests that the increase in inequality is primarily due to those
at the top of the income distribution pulling away from households lower down in the
distribution. Furthermore, it appears that the real incomes of the poor have been
roughly steady over the past 25 years. The United States is not the only industrial
country experiencing rising income inequality. Income inequality also increased in
most developed countries throughout the 1980s and 1990s, though at different rates
and starting from different levels.17
One common measure to characterize income inequality is the Gini coefficient,
which varies from 0 to 1. A Gini coefficient of 0 indicates that income is evenly
distributed among the population (that is, everyone has the same income) while a
value of 1 indicates perfect income inequality (that is, one individual has all the
income). The 20-year trend from 1980 to 1999 of the Gini coefficient for
equivalence-adjusted family income is displayed in Figure 1.18
Figure 1. Income Inequality, 1980-1999
0.55
0.5
Pre-Government Income
0.45
0.4
Gini
0.35
Post-Government Income
0.3
0.25
0.2
1
3
4
5
6
8
0
1
2
5
7
9
1980
198
1982
198
198
198
198
1987 198
1989
199
199
199
1993
1994 199
1996
199
1998
199
Year
Source: Author’s calculations of the Panel Study of Income Dynamics (PSID).
16 See CRS Report RL34155, Income Inequality and the U.S. Tax System, by Thomas L.
Hungerford.
17 Peter Gottschalk and Timothy M. Smeeding, “Cross-National Comparisons of Earnings
and Income Inequality,” Journal of Economic Literature, vol. 35, no. 2 (June 1997), pp. 633-
687.
18 See the Appendix for a description of how equivalence-adjusted income is calculated.

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The top solid line in the figure shows the trend in pre-government (before taxes
and receipt of government transfers) income inequality. Between 1980 and 1999 the
Gini coefficient increased by 22.4% from 0.408 to 0.500. The bottom dashed line
shows the 20-year trend in the post-government (after taxes and receipt of
government transfers) income inequality. The post-government family income Gini
coefficient was also steadily increasing over this period — increasing by 33% from
0.307 in 1980 to 0.408 in 1999. The trends in the two Gini coefficients increase
roughly in tandem.
The post-government income Gini coefficient is about 30% lower than the pre-
government income Gini coefficient. This strongly indicates that government
transfers and taxes have a leveling effect on the distribution of income. This leveling
effect, however, appears to have not changed over this period — tax and transfers
were equally progressive throughout the period.
Not all sources of post-government income, however, have a leveling effect.
For example, research has shown that rising male earnings inequality is a significant
source of the rise in family income inequality, while changes in female earnings have
had an equalizing effect on family income inequality.19 Table 1 reports the estimated
effect on the Gini coefficient arising from a 1% increase in an income source
(holding the level of other income sources constant) for selected years.20
Increasing both labor income and asset income by 1% would lead to an increase
in the Gini coefficient. In interpreting this result, it must be kept in mind that if labor
or asset income are zero then a 1% increase is also zero — only individuals with
positive labor or asset income would benefit from this hypothetical increase.21
Consequently, individuals at the top of the income distribution would benefit the
most from such an increase. Overall, the estimated results are qualitatively similar
across the selected years.
19 See, for example, Deborah Reed and Maria Cancian, “Sources of Inequality: Measuring
the Contributions of Income Sources to Rising Family Income Inequality,” Review of Income
and Wealth, series 47, no. 3 (Sept. 2001), pp. 321-333.
20 The years chosen are the beginning and end years for the two time periods examined in
the next section.
21 If, on the other hand, individuals with no labor income in 1999 were given a full-time
minimum wage job, the Gini coefficient would be reduced by 1.4%.

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Table 1. How Changes in Income from Different Sources Affects
Income Inequality, 1980-1999
Percent Change in Gini from 1% Increase in Income
Source
Income Source
1980
1989
1990
1999
0.1988
0.1901
0.1420
0.1193
Labor Income
(0.0028)
(0.0022)
(0.0026)
(0.0046)
0.1043
0.1050
0.0933
0.1207
Asset Income
(0.0018)
(0.0014)
(0.0018)
(0.0025)
-0.0124
-0.0101
-0.0139
0.0033
Private Transfers
(0.0002)
(0.0001)
(0.0002)
(0.0006)
Private Retirement
0.0107
-0.0026
0.0083
-0.0048
Income
(0.0004)
(0.0005)
(0.0007)
(0.0005)
-0.0551
-0.0356
-0.0335
-0.0179
Public Transfers
(0.0004)
(0.0002)
(0.0004)
(0.0002)
Social Security
-0.0577
-0.0828
-0.0365
-0.0755
Pension
(0.0005)
(0.0005)
(0.0005)
(0.0006)
-0.1886
-0.1640
-0.1597
-0.1451
Total Taxes
(0.0010)
(0.0006)
(0.0005)
(0.0025)
0.0007
-0.0012
0.0036
0.0057
Payroll Taxes
(0.0002)
(0.0002)
(0.0002)
(0.0004)
-0.0187
-0.0238
-0.0217
-0.0171
State Taxes
(0.0002)
(0.0002)
(0.0002)
(0.0004)
-0.1706
-0.1389
-0.1416
-0.1336
Federal Taxes
(0.0011)
(0.0003)
(0.0003)
(0.0019)
Source: Author’s analysis of the PSID.
Note: Bootstrap estimated standard errors in parenthesis.
Three sources of post-government income (public transfers, social security
pension, and total taxes) have the effect of reducing income inequality as measured
by the Gini coefficient. These are the income sources that lead to the difference
between pre-government income and post-government income. The estimated effect
is negative in all of the selected years, but the values vary somewhat from year to
year. Total taxes appear to have the largest progressive effect on income inequality.
Different taxes, however, have different effects on inequality. Payroll taxes (such as
Social Security taxes), state income taxes, and federal income taxes are separated and
the individual effects on inequality are estimated (see the last three rows of Table 1).
Payroll taxes have the smallest effect on inequality and the effect varies around zero
(positive and negative) over the selected years suggesting these taxes have very little
equalizing effects on income. Federal and state income taxes have a consistent
negative effect on inequality (that is, reduces income inequality) with federal taxes

CRS-7
having the greatest equalizing effect. This is arguably due to the refundable tax
credits, especially the earned income credit.
Income Mobility
Most people are concerned with upward mobility. But upward mobility means
different things to different people. Many think of upward mobility as increasing
inflation-adjusted or real income. Others think of it as upward movement in the
income distribution — not only keeping up with the Jones but surpassing them.
Social scientists have developed several measures to examine the different concepts
of income mobility.22
Two methods are used in this study to examine income mobility. The first is a
transition matrix, which compares a person’s place in the income distribution in the
base year to his or her place in the distribution in the final year of the period under
consideration. The second is the difference between real income in the base year and
the final year. These two measures provide information on relative and absolute
changes in well-being. The two measures, however, may or may not provide a
consistent picture of income mobility. For example, it is possible for someone to
experience downward relative mobility (that is, fall in the income distribution) even
though their real income is increasing — it just didn’t increase as much as other
people’s income.
Income mobility studies also can provide information on the relation between
(1) inequality in one year with inequality in another, and (2) short-term inequality and
long-term inequality. The trend in inequality is affected by income growth and
reranking or mobility within the income distribution — whose income grows and by
how much affects inequality. Additionally, Peter Gottschalk notes that “inequality
in each subperiod and mobility across subperiods would both impact inequality of
permanent (or average) earnings.”23
Previous Studies of Income Mobility
Several studies have examined income mobility over the past 20 years using a
variety of methods and longitudinal data sources.24 Researchers at the Department
of the Treasury have produced three of these studies. In each study, the researchers
use a 10-year sample of individual tax returns. The earliest Treasury study limited
the sample to taxpayers who filed a tax return in each of the 10 years between 1979
22 See Gary S. Fields and Efe A. Ok, “The Measurement of Income Mobility: An
Introduction to the Literature,” in Jacques Silber, ed., Handbook of Income Inequality
Measurement (Boston, MA: Kluwer Academic Publishers, 1999), pp. 557-596 for a
discussion of the different concepts of mobility and measurement issues.
23 Peter Gottschalk, “Inequality, Income Growth, and Mobility: The Basic Facts,” Journal
of Economic Perspectives, vol. 11, no. 2 (Spring 1997), p. 24.
24 Longitudinal data contain information about a sample of individuals and families over a
period of time, which is collected from periodic surveys.

CRS-8
and 1988, the observation period.25 Their results show that 40% of the taxpayers who
started out in the poorest income quintile in 1979 ended in the richest two income
quintiles 10 years later, while only 14% remained in the poorest quintile. This
sample selection criteria, however, eliminates many lower income individuals who
do not file a tax return in one or more years (for example, many elderly families are
not included) who tend to remain near the bottom of the income distribution. The
Treasury sample, therefore, is statistically biased towards finding little downward
mobility.
The next two Treasury studies focus on the 10-year period 1987 to 1996 or 1996
to 2005.26 The sample is limited to taxpayers who filed a tax return in the first year
(1987 or 1996) and the final year of the period (1996 or 2005). Consequently, the
sample is a little more representative of the U.S. population than in the first Treasury
study, but taxpayers under age 25 in the first year are eliminated from the analysis.
The sample selection criteria still omits many lower income individuals and families
who do not file a tax return such as the elderly. The two studies find that lifetime
income is more equally distributed (that is, inequality is lower) than income in a
single year because of considerable mobility. For example, both studies find that
more than half of those taxpayers in the poorest income quintile move up to higher
quintiles by the final year. The upward movement, however, is not far — about half
of those who move up in the distribution only move to the next income quintile. As
with the first Treasury study, the sample selection criteria yields a sample that is
statistically biased toward finding upward mobility and little downward mobility.
Several studies have used the University of Michigan’s Panel Study of Income
Dynamics (PSID) to examine income mobility. The PSID is well suited for studying
income mobility because: (1) it is representative of the broader U.S. population
(rather than of taxpayers); (2) it includes sources of income not reported on tax
returns (but not capital gains); and (3) it includes detailed demographic information
on the individuals and families in the sample. All the studies find considerable
income mobility, but less than was found in the three Treasury studies, and the
movement is not very far. One researcher concludes that, “the rags to riches success
stories are fairly rare as well as riches to rags sob stories.”27 The same researcher also
found that when a longer term measure of income is considered, individuals appear
less mobile within the income distribution. Another study found that mobility
increases when the length of the time period under consideration increases, but again
observed mobility rates are lower than those found in the Treasury studies.28
25 U.S. Department of Treasury, Office of Tax Analyst, “Household Income Changes Over
Time: Some Basic Questions and Facts,” Tax Notes (Aug. 24, 1992), pp. 1065-1074.
26 Gerald E. Auten and Geoffrey Gee, Income Mobility in the U.S.: Evidence from Income
Tax Returns for 1987 and 1996, U.S. Department of Treasury, Office of Tax Analysis, OTA
working paper 99, May 2007; and U.S. Department of Treasury, Income Mobility in the U.S.
from 1996 to 2005, Report of the Department of Treasury, Nov. 13, 2007.
27 Thomas L. Hungerford, “U.S. Income Mobility in the Seventies and Eighties,” Review of
Income and Wealth, vol. 39, no. 4 (Dec. 1993), p. 414.
28 Maury Gittleman and Mary Joyce, “Have Family Income Mobility Patterns Changed?”
(continued...)

CRS-9
Furthermore, the authors find that families headed by people lacking a college degree
are less likely to experience upward mobility. A study comparing mobility between
time periods found less income mobility in the 1990s than in the 1970s.29 A study
of income mobility in Britain finds many of the same results as for the U.S. — much
income mobility, but it tends to be not very far.30
A few studies have examined earnings mobility. One of the studies finds that
earnings mobility has declined significantly over the years.31 Another study finds that
changes in earnings mobility have been smaller than changes in inequality and the
authors conclude that “changes in mobility have not substantially affected the
evolution of inequality, so that annual snapshots of the distribution provide a good
approximation of the evolution of the longer term measures of inequality.”32
Relative Income Mobility in the 1980s and 1990s
Transition matrices of relative income mobility are reported in Table 2 for the
1980s (panel A) and the 1990s (panel B). Two summary measures of association are
shown in the last two rows of each panel. One measure is the immobility ratio,
which shows the proportion of individuals not changing income quintiles between
the first and final year. The other measure is Cramér’s V, which has a range of -1 to
+1 with a value of +1 indicating perfect association between the income quintile in
the first year and the final year quintile (that is, no mobility).
The first row in panel A of Table 2 shows that of the poorest 20% of individuals
(the poorest quintile) in 1980, 53% were still in the poorest quintile 10 years later,
while 2.5% made it to the richest quintile (the traditional Horatio Alger rags to riches
success story). The immobility ratio is 0.377, indicating that overall 37.7% of the
individuals remained in the same income quintile between the two years. While there
appears to be considerable mobility, it is not far — about 60% of those individuals
who changed income quintile between 1980 and 1989 only moved to the next
quintile. The same overall pattern is seen for the 1990s in panel B, but both the
immobility ratio and Cramér’s V are larger, suggesting relative income mobility was
lower in the 1990s than in the 1980s.
28 (...continued)
Demography, vol. 36, no. 3 (Aug. 1999), pp. 299-314.
29 Katherine Bradbury and Jane Katz, “Are Lifetime Incomes Growing More Unequal?
Looking at New Evidence on Family Income Mobility” Regional Review, Q4, Federal
Reserve Bank of Boston (2002), pp. 3-5.
30 Sarah Jarvis and Stephen P. Jenkins, “How Much Income Mobility is There in Britain?”
Economic Journal, vol. 108 (Mar. 1998), pp. 428-443.
31 Moshe Buchinsky and Jennifer Hunt, “Wage Mobility in the United States,” Review of
Economics and Statistics, vol. 81, no. 3 (Aug. 1999), pp. 351-368.
32 Wojciech Kopczuk, Emmanuel Saez, and Jae Song, Uncovering the American Dream:
Inequality and Mobility in Social Security Earnings Data Since 1937, National Bureau of
Economic Research, Working Paper no. 13345, Aug. 2007.

CRS-10
Table 2. Relative Income Mobility — Transition Matrices for the
1980s and 1990s
A. 1980-1989
1989 Income Ranking
1
2
3
4
5
Total
1
53.0
27.2
11.6
5.8
2.5
100.0
2
22.4
30.0
25.5
15.6
6.5
100.0
3
12.2
21.4
26.0
25.5
15.0
100.0
4
8.9
13.2
22.8
29.2
25.9
100.0
5
3.5
8.2
14.2
24.1
50.1
100.0
1980 Income Ranking
Total
100.0
100.0
100.0
100.0
100.0
Cramér’s V
0.311
Immobility Ratio
0.377
B. 1990-1999
1999 Income Ranking
1
2
3
4
5
Total
1
53.2
23.9
13.7
6.4
2.8
100.0
2
25.9
32.9
23.5
13.1
4.5
100.0
3
9.0
23.4
30.3
23.4
13.9
100.0
4
7.7
13.8
20.4
33.0
25.1
100.0
5
4.1
6.2
11.9
24.0
53.8
100.0
1990 Income Ranking
Total
100.0
100.0
100.0
100.0
100.0
Cramér’s V
0.335
Immobility Ratio
0.406
Source: Author’s analysis of the PSID.
The transition matrices show the extent of relative income mobility but not who
is likely to move up or down in the income distribution. Table 3 presents the results
of a multivariate analysis of the likelihood of upward, no, and downward mobility.33
The entries in the table show the percentage differences in the probability of mobility
between individuals with the indicated characteristic and other individuals. For
33 See the Appendix for a description of the multinomial logit analysis, which is the
multivariate analysis method used to estimate the effects of the demographic characteristics
on the likelihood of moving up in the income distribution, no movement, or moving down.

CRS-11
example, the first entry of 0.1447 in the table shows that an individual with a high
school diploma has a 14.5% higher probability of experiencing upward mobility than
an individual with less than a high school education (the omitted educational
category in the analysis). The top panel reports results for the 1980s and the bottom
panel for the 1990s.
Overall, the qualitative results for the two decades are similar. Individuals with
more than a high school education are more likely to experience upward mobility
than others, while older individuals and African-Americans are less likely to
experience upward mobility and more likely to experience downward mobility (all
the marginal effects are statistically significant). The results also suggest that
individuals from larger families are more likely to experience upward mobility.
The quantitative results for the two decades, however, are quite different. The
estimated effect of having more than a high school education on upward mobility is
considerably lower in the 1990s than in the 1980s (33.4% for the 1980s versus 10.2%
for the 1990s). The individuals in the oldest age group (65 or older) were much less
likely to experience upward mobility (compared to the youngest age group) in the
1990s than in the 1980s (-52.9% for the 1990s versus -39.7% for the 1980s). Lastly,
blacks, while less likely to experience upward mobility than others, appeared to do
less badly in the 1990s than in the 1980s.
Table 3. How Demographic Variables Affect Percentage Change
in Probability of Upward, No, and Downward Mobility
Downward
Upward Mobility
No Mobility
Mobility
A. 1980 to 1989
High School
0.1447***
-0.3186***
0.0149
Education
More than High
0.3344***
-0.1113*
-0.2866***
School
Age 18-24
0.0163
-0.1998**
0.0859
Age 25-39
0.0818
0.1161
-0.1437***
Age 40-54
-0.0692
0.1452
-0.0035
Age 55-64
-0.4327***
-0.0119
0.4387***
Age 65 or older
-0.3972***
0.2894**
0.2596***
Female
-0.0137
0.0724
-0.0231
Black
-0.2645***
0.2088***
0.1646***
Family Size
0.0955***
0.0067
-0.1016***

CRS-12
Downward
Upward Mobility
No Mobility
Mobility
B. 1990 to 1999
High School
-0.1094*
0.0236
0.0864
Education
More than High
0.1016*
-0.0401
-0.0706
School
Age 18-24
0.1370
-0.3232***
0.0463
Age 25-39
0.2176***
0.0825
-0.2401***
Age 40-54
0.1069
0.1312
-0.1656***
Age 55-64
-0.3460***
-0.0836
0.3567***
Age 65 or older
-0.5293***
0.3835**
0.2764***
Female
0.0007
-0.0433
0.0221
Black
-0.1617***
0.1306*
0.0773
Family Size
0.0234*
0.0344
-0.0419***
Source: Author’s analysis of the PSID.
Notes: *** significant at 1% level; ** significant at 5% level; * significant at 10% level. Standard errors
in parentheses. See Appendix for full set of coefficient estimates upon which the marginal effects are
based.
Absolute Income Mobility in the 1980s and 1990s
Relative income mobility is concerned with the extent to which individuals
change places in the income distribution (that is, reranking). Absolute income
mobility is concerned with the extent to which an individual’s real income changes.
Table 4 displays how real income changed in the 1980s (panel A) and 1990s (panel
B). The tables show the proportion of individuals in each first year income quintile
whose real income changed by the indicated percentage between the first (1980 or
1990) and final (1989 or 1999) year. Also shown is the median percentage change
in real income for each income quintile.

CRS-13
Table 4. Absolute Income Mobility — Real Income Growth in the
1980s and 1990s
A. 1980-1989
Median
Proportion of Quintile Within Range
Quintile
Percentage
Change
<-10%
-10-0%
0-10%
>10%
1
36.6
22.1
7.1
8.0
62.9
2
23.4
25.3
6.1
7.4
61.1
3
17.0
29.5
7.8
7.8
55.0
4
4.5
37.0
9.2
9.2
44.6
5
-9.4
49.3
8.5
8.8
33.4
B. 1990-1999
Median
Proportion of Quintile Within Range
Quintile
Percentage
Change
<-10%
-10-0%
0-10%
>10%
1
35.5
23.8
7.0
4.9
64.3
2
9.9
31.6
9.8
8.6
50.0
3
5.5
36.0
8.5
10.3
45.2
4
-2.8
42.5
10.1
9.0
38.4
5
-12.0
51.7
8.9
10.1
29.3
Source: Author’s analysis of the PSID.
Most individuals in the poorest quintile in 1980 experienced an increase in their
real income between 1980 and 1989 — half saw their real income increase by more
than 36%. However, over one in five individuals in the poorest quintile in 1980 saw
their real income decrease by over 10%. Of those in the richest quintile, almost half
saw their real income fall by 10% or more during the 1980s. The same patterns are
evident for the 1990s.
There are differences, however, in absolute mobility between the 1980s and the
1990s. First, those in the poorest income quintile may have done slightly better in
the 1990s than in the 1980s — a larger proportion saw their real income increase by
more than 10% in the 1990s than in the 1980s but the median percentage change was
slightly lower in the 1990s than in the 1980s. Second, individuals higher up in the
income distribution (quintiles 2-5) appear to have done worse in the 1990s than in
the 1980s.

CRS-14
The Effect of Mobility on Inequality
Income growth and mobility contribute to changes in income inequality over
time. Income growth can have an equalizing (progressive) effect on incomes if the
income of those individuals at the bottom of the income distribution grows at a
greater rate than for those at the top of the distribution. This is what happened in
both the 1980s and 1990s (see Table 4). But income inequality increased in both
decades (see Figure 1). The reshuffling or reranking (income mobility) in the
income distribution between two points in time affects inequality through a
disequalizing effect.34 The progressivity effect focuses solely on the change in
income holding an individual’s rank or place in the income distribution constant. Of
course, when income changes an individual’s place in the income distribution is also
likely to change. The reranking effect focuses on how far an individual’s change in
rank is from his or her original position, holding income constant at the final year
level.
The decomposition of the increase in the Gini coefficient into a progressivity
effect and a reranking effect over the 1980s and 1990s is reported in Table 5. The
Gini coefficient increased by 0.0579 points (19%) over the 1980s and by 0.0466
points (13%) over the 1990s. In both decades, income growth was progressive (the
progressivity effect) and had an equalizing effect on the income distribution (that is,
reducing the Gini coefficient). The equalizing effect had a larger absolute value in
the 1990s than in the 1980s. The reranking effect, however, had a disequalizing
effect and, in fact, outweighed the progressivity effect. In both decades, reranking
(or relative income mobility) had the effect of increasing the annual Gini coefficient.
Income mobility can reduce long-term income inequality, however. With high
income mobility, individuals at the top or the bottom of the income distribution will
not necessarily be there in the future, suggesting that longer-term incomes may be
more equal than annual incomes. Consequently, long-term inequality will be lower
than short-term inequality.35 It is possible, though, that high income mobility implies
income instability — some individuals may face a high likelihood of a large fall in
income.
34 Stephen P. Jenkins and Philippe Van Kerm, “Trends in Income Inequality, Pro-poor
Income Growth, and Income Mobility,” Oxford Economic Papers, vol. 58 (2006), pp. 531-
548.
35 Gary S. Fields, Does Income Mobility Equalize Longer-term Incomes? New Measures of
an Old Concept, Cornell University, ILR working paper, Aug. 2007.

CRS-15
Table 5. Decomposition of Change in Gini Coefficient into
Progressivity Effect and Reranking Effect
Progressivity
Reranking
Year
Gini
Difference
Effect
Effect
0.3053
1980
(0.0009)
0.0579
-0.0694
0.1273
1980s
(0.0012)
(0.0017)
(0.0007)
0.3632
1989
(0.0011)
0.3502
1990
(0.0011)
0.0466
-0.0844
0.1310
1990s
(0.0025)
(0.0036)
(0.0009)
0.3968
1999
(0.0024)
Source: Author’s analysis of the PSID.
Note: Bootstrap estimated standard errors in parenthesis.
Table 6 reports results of the comparison of short-term and long-term income
inequality in the 1980s and 1990s. The long-term Gini coefficient is estimated using
income for each individual averaged over each decade (1980 to 1989 or 1990 to
1999). In both decades, the long-term Gini coefficient is lower than the Gini
coefficient of income in the first year of the decade. The equalization measure is
shown in the last column of the table. The results suggest that mobility had a greater
equalizing effect on long-term inequality in the 1990s than in the 1980s.
Table 6. Effect of Mobility on Inequality of Longer-term Income
Equalization
Decade
Year
Gini
Measure
0.3053
1980
(0.0009)
0.0209
1980s
(0.0021)
0.2989
Long-term
(0.0008)
0.3502
1990
(0.0011)
0.0819
1990s
(0.0022)
0.3215
Long-term
(0.0009)
Source: Author’s analysis of the PSID.
Note: Bootstrap estimated standard errors in parenthesis.

CRS-16
U.S. Economic Policy
Both income growth and mobility affect the trend in inequality. In the 1980s
and 1990s, the equalizing effect of income growth was more than offset by the
disequalizing effect of mobility. Three broad types of government economic policy
affect income growth and mobility, and hence income inequality: (1) regulation and
legislation, (2) the tax system, and (3) government transfers. Each of these three
types of economic policy affect inequality through different mechanisms.
Regulation and legislation can affect income inequality directly by reducing the
extreme ranges of the income distribution. For instance, increasing in the minimum
wage increases the earnings of low-wage workers who are often near the bottom of
the income distribution.36 Efforts to reduce excessive executive pay could reduce the
growth in executive pay and could reduce or limit increases in income inequality by
affecting the upper tail of the income distribution.37 Enforcement of anti-
discrimination laws can help keep workers from falling to the bottom of the income
distribution by safeguarding wages and employment opportunity for the aged,
women, and minorities. It is often argued, however, that these policies have
unintended consequences. Many claim that minimum wage hikes, for example,
reduce employment among low-skilled individuals, although recent empirical
research shows there is little or no disemployment effect from raising the minimum
wage.38
Earnings and income can be quite volatile, and large reductions in a family’s
income reduce economic well-being. It is this income volatility that causes mobility
within the income distribution which, in turn, contributes to rising income inequality.
The progressive personal income tax system is part of a redistributive tax-transfer
system that insures individuals and families against risks of volatile income
associated with human capital and random events.39 One analyst shows that
redistributive taxation can reduce variation in after-tax income, and that the optimal
income tax may be a progressive tax.40 Progressive taxation may be more effective
at raising revenue than a proportional tax, and, combined with transfers, may be
36 The minimum wage was most recently increased in three steps by the Defense
Supplemental Appropriations (P.L. 110-28). The minimum wages was increased from $5.15
per hour to $5.85 in July 2007. It will increase to $6.55 in July 2008 and to $7.25 in July
2009.
37 For specific policy proposals, see CRS Report RS22604, Excessive CEO Pay: Background
and Policy Approaches, by Gary Shorter, Mark Jickling, and Alison Raab.
38 David Card and Alan Krueger, Myth and Measurement: The New Economics of the
Minimum Wage (Princeton, N.J.: Princeton University Press, 1995).
39 See, for example, Jonathan Eaton and Harvey S. Rosen, “Taxation, Human Capital, and
Uncertainty,” American Economic Review, vol. 70, no. 4 (Sept. 1980), pp. 705-715; and
Robert J. Shiller, Macro Markets (Oxford: Oxford University Press, 1993).
40 Hal Varian, “Redistributive Taxation and Social Insurance,” Journal of Public Economics,
vol. 14 (1980), pp. 49-68.

CRS-17
effective in reducing income inequality.41 Recent research, however, shows that the
federal income tax system, while progressive, has become less progressive at the top
of the income distribution since 1960.42 Additionally, many tax deductions,
exclusions, and exemptions are targeted to higher income taxpayers, thus further
reducing the progressivity of the income tax system.43
Government transfers — both social insurance (for example, Social Security and
unemployment compensation) and public assistance (for example, Temporary
Assistance for Needy Families (TANF), Supplement Security Income (SSI), and food
stamps) — also insure individuals and families against large income reductions due
to risks associated with human capital and random events.44 Protected by this
insurance function, individuals may engage in risky but profitable economic activities
they would otherwise not undertake in the absence of such protection, which can
increase economic growth.
The insurance protection of the redistributive tax-transfer system, however, may
create a moral hazard problem — individuals may recklessly engage in risky
economic activities or neglect to take necessary precautions in their economic
activities. In addition, the redistribution may also involve some inefficiencies, the
so-called “leaky bucket” of Arthur Okun.45 The inefficiencies, the leaks in the bucket
carrying money from the rich to the poor, include the administrative costs of
collecting taxes and operating the social welfare programs, as well as the
disincentives associated with taxes and government transfers. The disincentive
effects, while real and measurable, are often not as large as expected, but research
continues on the estimation of these effects.46
Concluding Remarks
Income mobility can affect income inequality in two ways. First, the
disequalizing effect of reranking or mobility has contributed to rising inequality since
41 Howell H. Zee, “Inequality and Optimal Redistributive Tax and Transfer Policies,” Public
Finance Review, vol. 32, no. 4 (July 2004), pp. 359-381.
42 Thomas Piketty and Emmanuel Saez, How Progressive is the U.S. Federal Tax System?
A Historical and International Perspective, National Bureau of Economic Research,
Working Paper no. 12404, July 2006.
43 CRS Report RL33641, Tax Expenditures: Trends and Critiques, by Thomas L.
Hungerford.
44 See, for example, Hans-Werner Sinn, “A Theory of the Welfare State,” Scandinavian
Journal of Economics, vol. 97, no. 4 (1995), pp. 495-526.
45 Arthur M. Okun, Equality and Efficiency: The Big Tradeoff (Washington: The Brookings
Institution, 1975).
46 See, for example, Robert A. Moffitt, “The Temporary Assistance for Needy Families
Program,” in Robert A. Moffitt, ed., Means-tested Transfer Programs in the United States
(Chicago: University of Chicago Press, 2003); and Seth H. Giertz, “The Elasticity of
Taxable Income over the 1980s and 1990s,” National Tax Journal, vol. 60, no. 4 (Dec.
2007), pp. 743-768.

CRS-18
1980. Second, mobility has an equalizing effect on longer-term income, though the
effect appears to be small. Economic policies to reduce the growth of income
inequality may work, in part, through their effects on income mobility. Reducing
income mobility (that is, stabilizing incomes) may reduce the rising trend in income
inequality, but it could also increase inequality of longer-term income. The specific
effect on longer-term inequality, however, depends on how the policy affects
mobility. It is possible, for example, that policies establishing an income floor could
reduce both the rising trend in income inequality and inequality of longer-term
incomes.

CRS-19
Appendix
Data
The University of Michigan’s Panel Study of Income Dynamics (PSID) is
employed to study income inequality and income mobility since 1980. The specific
data file was obtained from Cornell University’s Cross-National Equivalent File.
The data contain income and tax information that is comparably defined every year
for the PSID. The tax information is estimated using the National Bureau of
Economic Research’s TAXSIM model.47 Taxes are estimated for each tax unit
within the household and then summed over all tax units within the household to
arrive at a total household tax burden. Payroll taxes are calculated from reported
earnings and legislated payroll tax rates.
The Panel Study of Income Dynamics (PSID) is a nationally representative
longitudinal data set of the U.S. population that has been ongoing since 1968. The
replacement mechanism of the PSID for births is designed to yield a representative
sample of the nonimmigrant population in each year. The PSID oversamples low-
income households because it was created by combining the Survey of Economic
Opportunity (SEO), a survey of low-income households, with a representative group
of households from the Survey Research Center (SRC) national sampling frame.
Consequently, family weights are used throughout the analysis.48
The measure of income used for this study is family post-government income.
This measure includes all cash income from public and private sources except
realized capital gains. Realized capital gains are not an annual income flow and vary
greatly from year to year. The measure does, however, include the face value of food
stamps. Federal, state, and payroll taxes are subtracted. Family income is adjusted
for family size and composition using an equivalence scale proposed by the National
Research Council.49
Two periods are examined: 1980 to 1989 and 1990 to 1999. The individual is
the unit of observation in this analysis. The individual is the focus of the analysis
because family composition changes from year to year as people are born or marry
into a family and people die, couples separate or children leave home. Equivalence-
adjusted family income is used because well-being is based on the fortunes of the
family the individual lives in.
47 See Barbara A. Butrica and Richard V. Burkhauser, Estimating Federal Income Tax
Burdens for Panel Study of Income Dynamics (PSID) Families Using the National Bureau
of Economic Research TAXSIM Model, Syracuse University, Maxwell School of Citizenship
and Public Affairs, Aging Studies Program Paper no. 12, Dec. 1997.
48 See Martha S. Hill, The Panel Study of Income Dynamics: A User’s Guide (Newbury
Park, CA: Sage Publications, 1992).
49 See Constance F. Citro and Robert T. Michael, eds., Measuring Poverty: A New Approach
(Washington: National Academy Press, 1995).

CRS-20
Inequality
Taxes and transfer payments (e.g., Social Security benefits and Temporary
Assistance to Needy Families (TANF) benefits) affect the distribution of income.
Furthermore, increasing any one source of post-government income will affect the
income distribution differently than any other income source. The method used to
estimate the marginal effect of income changes on the Gini coefficient was developed
by Robert Lerman and Shlomo Yitzhaki.50 Standard errors are obtained using
bootstrap resampling methods.
Mobility
When examining income mobility between two years, the individuals had to be
in the sample both years. In each year, the individuals are ranked by their
equivalence-adjusted income and divided into five groups or quintiles. Quintile 1
contains the poorest 20% of individuals, while quintile 5 contains the richest 20%.
Mobility within the income distribution is determined by comparing the individual’s
income quintile in the first year (1980 or 1990) to the individual’s quintile in the
second year (1989 or 1999). The income quintile breaks for the various years are
reported in Table A1.
Table A1. Quintile Breaks — Real Equivalence-adjusted Family
Income
1980
1989
1990
1999
1
$12,912
$13,166
$14,265
$13,900
2
$18,306
$19,662
$20,661
$20,998
3
$23,699
$26,870
$27,413
$28,803
4
$31,637
$36,833
$37,735
$41,800
Source: Author’s analysis of PSID.
50 See Robert I. Lerman and Shlomo Yitzhaki, “Effect of Marginal Changes in Income
Sources on U.S. Income Inequality,” Public Finance Quarterly, vol. 22, no. 4 (Oct. 1994),
pp. 403-417.

CRS-21
Effects of Mobility on Inequality
The change in the Gini coefficient between two years is decomposed into two
additive components using the method described in an article by Stephen Jenkins and
Philippe Van Kerm.51 The decomposition of the change in the Gini coefficient is
given by:
G − G = 2 × cov(s − s , F ) + 2 × cov(s , F − F )
2
1
2
1
1
2
2
1
where s =y /: , y is annual income, : is average annual income, and F is the
t
t
t
t
t
t
cumulative distribution of income. The change in inequality is a directional change
and compares inequality in the base year with inequality in the final year. The choice
of a reference point (the base year or the final year) gives rise to an index number
issue. In the present case, the forward-looking perspective is the natural one to use.
Consequently, the base year (1980 or 1990) is the reference point.52
The first component is the progressivity effect of income growth between the
two years. For example, if the income of those individuals at the bottom of the
income distribution grows faster than for those individuals at the top, then income
inequality will decrease, holding other factors constant. In this case, the income
growth effect will be negative indicating income growth is progressive.
The second component is the effect of reranking or mobility within the income
distribution. This component is an average of changes in income ranks (i.e., place
in the income distribution) weighted by relative income. It will be equal to zero
when there is no reranking and positive otherwise.
Income mobility also affects long-term income inequality. An equalization
measure developed by Gary Fields is used to quantify the equalizing effect of income
mobility on long-term income inequality.53 The formula for the measure is:
G(l)
Ε = 1− G(s)
where G(l) is the Gini coefficient of long-term income (the average of income over
the relevant period) and G(s) is the Gini coefficient of income in the first year of the
period.
51 See Stephen P. Jenkins and Philippe Van Kerm, “Trends in Income Inequality, Pro-Poor
Income Growth, and Income Mobility,” Oxford Economic Papers, vol. 58 (2006), pp. 531-
548. The method used to estimate the components is derived in Robert I. Lerman and
Shlomo Yitzhaki, “Changing Ranks and The Inequality Impacts of Taxes and Transfers,”
National Tax Journal, vol. 48, no. 1 (Mar. 1995), pp. 45-59.
52 This issue is discussed in Lerman and Yitzhaki (1995), and Jenkins and Van Kerm (2006).
53 See Gary S. Fields, Does Income Mobility Equalize Longer-tem Incomes? New Measures
of an Old Concept, Cornell University, ILR working paper, Aug. 2007.

CRS-22
Multivariate Analysis
The multinomial logit procedure is employed to estimate the effects of the
explanatory variables on the distribution of individuals across the three income
mobility states. Consequently, the focus is on the proportion of individuals falling
into each of these categories. The three categories examined are upward mobility
(moving up one or more deciles), no mobility, and downward mobility (moving
down one or more deciles).
The multinomial logit model in this case takes the form of:
exp(β ′ X )
Pr( y = k)
k
i
=
i
∑3 exp(β′ X )
k
i
k =1
where X is the vector of explanatory variables and $ is the vector of parameters to
i
k
be estimated. Since the regressors in the multinomial logit do not vary across the
three alternatives, a normalization is required to identify the parameters — the
coefficients corresponding to upward mobility are set to zero; the coefficient
estimates and standard errors are reported in Table A2. As a result of the
normalization, the signs and magnitudes of the coefficient estimates may not bear any
relation to the marginal effect of a variable change on the probability of being in a
particular category.54 Consequently, marginal effects (the partial derivatives of the
probabilities with respect to the independent variables evaluated at the means) along
with the associated standard errors are calculated and reported in Table 3.
Table A2. Coefficient Estimates — Multinomial Logit
1980 to 1989
1990 to 1999
No Mobility
-1.3199***
-1.4999***
Decile 2
(0.1264)
0.1496
-1.6712***
-1.6861***
Decile 3
(0.1456)
0.1635
ecile
-1.8018***
-1.6576***
Decile 4
(0.1535)
0.1647
Initial D
-1.6738***
-1.7010***
Decile 5
(0.1482)
0.1701
-2.0254***
-1.6797***
Decile 6
(0.1620)
0.1681
54 William H. Greene, Econometric Analysis, 3rd Ed. (Upper Saddle River, NJ: Prentice
Hall, 1997).

CRS-23
1980 to 1989
1990 to 1999
-1.3453***
-1.3904***
Decile 7
(0.1492)
0.1644
-1.0408***
-0.9883***
Decile 8
(0.1475)
0.1687
-0.5939***
-0.3801**
Decile 9
(0.1503)
0.1709
-0.4633***
0.1330
High School Education
(0.0978)
0.1175
-0.4457***
-0.1417
More than High School
(0.1022)
0.1196
-0.2161
-0.4602**
Age 18-24
(0.1397)
0.1859
0.0343
-0.1350
Age 25-39
(0.1148)
0.1286
0.2144*
0.0242
Age 40-54
(0.1201)
0.1348
0.4446***
0.2623
Age 55-64
(0.1688)
0.1822
0.6866
0.9128***
Age 65 or older
(0.1752)
0.1929
0.0860
-0.0441
Female
(0.0744)
0.0836
0.4734***
0.2923***
Black
(0.1002)
0.1073
-0.0888***
0.0080
Family Size
(0.0226)
0.0310
0.8589***
0.5154***
Constant
(0.1657)
0.1974
Downward Mobility
-1.4802***
-1.1833***
Decile 2
(0.1425)
0.1558
-0.7891***
-0.5090***
Decile 3
ecile
(0.1277)
0.1453
-0.7556***
-0.4855***
Decile 4
Initial D
(0.1245)
0.1439
-0.3399***
-0.3466***
Decile 5
(0.1253)
0.1453

CRS-24
1980 to 1989
1990 to 1999
-0.3429***
-0.0718
Decile 6
(0.1200)
0.1410
0.2780**
0.0976
Decile 7
(0.1206)
0.1398
0.4360***
0.5581***
Decile 8
(0.1267)
0.1487
0.8758***
0.9618***
Decile 9
(0.1331)
0.1595
-0.1298
0.1958*
High School Education
(0.0846)
0.1056
-0.6210***
-0.1722
More than High School
(0.0916)
0.1078
0.0696
-0.0907
Age 18-24
(0.1099)
0.1422
-0.2255**
-0.4577***
Age 25-39
(0.0989)
0.1120
0.0657
-0.2725**
Age 40-54
(0.1045)
0.1201
0.8714***
0.7027***
Age 55-64
(0.1391)
0.1520
0.6568***
0.8057
Age 65 or older
(0.1616)
0.1735
-0.0095
0.0214
Female
(0.0632)
0.0717
0.4291***
0.2390**
Black
(0.0951)
0.1000
-0.1971***
-0.0682**
Family Size
(0.0216)
0.0285
0.9676***
0.4329**
Constant
(0.1593)
0.1853
Log Likelihood
-8289.14
-5616.83
P2
1024.97***
662.10***
Pseudo R2
0.10
0.08
Source: Author’s analysis of PSID.
Notes: *** significant at 1% level; ** significant at 5% level; * significant at 10% level. Robust standard
errors in parentheses.