Intergenerational Economic Mobility in the United States, 1940 to 2000

TL;DR: In this paper, the authors estimate trends in intergenerational economic mobility by matching men in the Census to synthetic parents in the prior generation, finding that mobility increased from 1950 to 1980 but has declined sharply since 1980.

Abstract: We estimate trends in intergenerational economic mobility by matching men in the Census to synthetic parents in the prior generation. We find that mobility increased from 1950 to 1980 but has declined sharply since 1980. While our estimator places greater weight on location effects than the standard intergenerational coefficient, the size of the bias appears to be small. Our preferred results suggest that earnings are regressing to the mean more slowly now than at any time since World War II, causing economic differences between families to become more persistent. However, current rates of positional mobility appear historically normal.

Summary

Introduction

• The authors find that mobility increased from 1950 to 1980 but has declined sharply since 1980.
• How these countervailing trends have impacted changes in intergenerational mobility is ultimately an empirical question.
• In the main analysis, the authors calculate average family income by state of birth for the relevant cohort and Census year and take the log of this as their predicted value of parent income, rather than running TSIV using Equations 2 and 3.17.
• There is a drop in the IGE for the late 1960s and early 1970s cohorts, but these cohorts are observed only at very young ages, so these estimates may be biased downward despite their attempts to model the age bias.
• The results here suggest that the particular cohorts and the years used are also important factors.

A. Discussion of Results

• In their view, the key finding is the decline in intergenerational mobility after 1980.
• This matches the trends in crosssectional inequality that the authors discuss in greater detail in Section VI.
• The 1980s change in the year-specific IGE is somewhat less pronounced than before, though still statistically significant, rising from 0.24 to 0.32.
• Since their estimates of rt are based on pooled samples of multiple birth cohorts, family income is measured in different years, complicating the calculation of the ratio of the s’s.
• While intergenerational mobility unambiguously fell in the 1980s, how the authors interpret this change is dependent on which mobility measure is the focus.

Figures

Figure 2 Layout of Personal Income Per Capita (PIPC) Data by Birth Cohort and Year Notes: Bolded area indicates years used for state PIPC for parent generation. Shaded area indicates years used to calculate son’s earnings.

Figure 4 Comparison of Trends in Inequality and the IGE

Table 3 presents the results using our most flexible specification that includes both cohort and year interactions with family income. Recall that this model requires summing up the relevant components of the IGE by birth cohort and outcome year. As before, we construct the implied IGE for a 40-year-old in each Census year. The first two columns of Table 3 show the baseline results. Column 1 indicates a statistically significant increase in the year effect of 0.09 for 1990 (relative to 1980) and no further increase from 1990 to 2000. Much larger trends are picked up by the birth cohort interactions with family income. Individuals born in the 1930s have an IGE about 0.1 greater than those born between 1926 and 1930. However, this effect rises

Figure 6 Comparisons of the Intergenerational Elasticity and Intergenerational Correlation

Figure 5 Estimates of the IGE with and without Education

Figure 3 Comparisons of IGE Computed From the IPUMS and Income Per Capita Data

Figure 1 Layout of Census IPUMS Data by Birth Cohort and Year Notes: Census income data begins in 1940. Bolded area indicates years used to calculate family income for each cohort. Shaded area indicates years used to calculate son’s earnings.

Table 4 Estimates of the Intergenerational Correlation

Full text

Intergenerational Economic Mobility
in the United States, 1940 to 2000
Daniel Aaronson
Bhashkar Mazumder
abstract
We estimate trends in intergenerational economic mobility by matching men
in the Census to synthetic parents in the prior generation. We find that
mobility increased from 1950 to 1980 but has declined sharply since 1980.
While our estimator places greater weight on location effects than the
standard intergenerational coefficient, the size of the bias appears to be
small. Our preferred results suggest that earnings are regressing to the mean
more slowly now than at any time since World War II, causing economic
differences between families to become more persistent. However, current
rates of positional mobility appear historically normal.
I. Introduction
Is the United States a less economically mobile society than it was a
half-century or more ago? Have economic and policy changes over this period
changed the impact of parental influences in determining one’s future earnings?
These questions have a long and notable history in the social sciences, as well as
in popular discussion. Recent attention may be partly driven by studies over the past
15 years (for example, Solon 1992; Mazumder 2005) demonstrating that income
Daniel Aaronson is an economic advisor in the economic research department at the Federal Reserve
Bank of Chicago. Bhashkar Mazumder is a senior economist in the economic research department of
the Federal Reserve Bank of Chicago and executive director of the Chicago Census Research Data
Center. The authors thank Merritt Lyon for excellent research assistance. They are especial ly thankful
to Tom Hertz who offered several very helpful suggestions. They also acknowledge Anders Bjo
¨
rklund,
Kristin Butcher, John DiNardo, Greg Duncan, David Levine, Bruce Meyer, Gary Solon, Dan Sullivan,
Chris Taber, and seminar participants at several conferences and universities. The views presented here
are not necessarily those of the Federal Reserve Bank of Chicago or the Federal Reserve System. The
data used in this article can be obtained beginning August 2008 through July 2011 from Bhashkar
Mazumder, Research Department, Federal Reserve Bank of Chicago, 230 S. LaSalle St, Chicago IL
60604, bmazumder@frbchi.org.
[Submitted June 2006; accepted March 2007]
ISSN 022-166X E-ISSN 1548-8004 Ó 2008 by the Board of Regents of the University of Wisconsin
System.
THE JOURNAL OF HUMAN RESOURCES
d
XLIII
d
1

persists across generations at a far higher rate than previously believed by economists
(for example, Becker and Tomes 1986) and, perhaps, the public.
1
Most studies have measured intergenerational income mobility at a point in time
and, typically, for a limited group of cohorts. Therefore, it is unclear whether current
estimates of mobility have characterized the U.S. economy for some time. The few
studies that have examined long-term trends in intergenerational mobility (Mayer
and Lopoo 2005; Hertz 2007; Lee and Solon 2006)
2
suffer from two basic data short-
comings. Namely, the intergenerational samples that they use do not go very far back
in time and are based on small samples. Given the pronounced changes in inequality
and the returns to schooling over the century, it is important to have reliable esti-
mates of mobility for more than just the most recent decades. Moreover, small sam-
ple sizes make it difficult to identify precise trends in the time-series.
In addition to filling an important void in the literature, greater knowledge of
trends in intergenerational mobility can potentially lead to a deeper understanding
of the underlying mechanisms by which income is transmitted across generations.
The development of a time series on intergenerational mobility provides a source
of variation for researchers to exploit to improve our understanding of intergenera-
tional linkages. For example, Solon (2004) extends the Becker-Tomes model and
shows that intergenerational mobility is driven by factors that have undergone di-
verging trends. All else equal, the fact that the returns to human capital have risen
in recent decades (Katz and Autor 1999; Goldin and Katz 1999) implies that inter-
generational mobility should have fallen.
3
On the other hand, the emergence of the
Great Society programs in the 1960s (for example, food stamps, WIC) and the de-
segregation of schools should have fostered greater equality of opportunity. How
these countervailing trends have impacted changes in intergenerational mobility is
ultimately an empirical question.
In this paper, we take advantage of the large samples available in the decennial
Censuses. Because parents and children cannot be linked across Censuses, we em-
ploy an approach analogous to a two-sample instrumental variables (TSIV) estimator
to develop a consistent intergenerational mobility series back to 1940.
4
Our primary
approach uses state of birth to match adult sons’ earnings with the income of
1. Both the New York Times and Wall Street Journal printed a series on economic mobility in May and June
of 2005. The New York Times articles can be found at http://www.nytimes.com/pages/national/class/. The
initial WSJ article is at http://www.post-gazette.com/pg/05133/504149.stm (the WSJ site is subscription
only). The public’s beliefs are described in a New York Times poll asking ‘‘Is it possible to start out poor,
work hard, and become rich?’’ (http://www.nytimes.com/packages/html/national/20050515_CLASS_
GRAPHIC/index_04.html) The share answering affirmatively increased from 60 percent in 1983 to 80 per-
cent in 2005. The General Social Survey also asks about social mobility (Questions 1058 and 1059).
Although the questions are somewhat ambiguous, they suggest little change, and perhaps a slight improve-
ment, since the mid-1980s in the belief that upward mobility is possible.
2. Related, there is a large literature, primarily in sociology, on intergenerational occupational mobility.
Ferrie and Long (2005), for example, compares intergenerational occupational mobility during the second
half of the 19
th
and early 20
th
centuries to estimates derived from modern data sets, such as the National
Longitudinal Surveys.
3. A constant relationship between parent income and children’s schooling would lead to a stronger inter-
generational association in income if the returns to schooling rise.
4. The approach is not exactly equivalent to two sample instrumental variables because we use the log of
average parent income rather than average of log parent income. This is described in footnote 17.
140 The Journal of Human Resources

synthetic families, developed from the age of their children and their state of resi-
dence, in a previous generation. This estimator is roughly equivalent to using dummy
variables for state of birth as instruments for parental income.
Our measure of economic mobility is based on the relationship between adult
men’s log annual earnings and log of annual family income in the previous genera-
tion. This regression coefficient, commonly known as the intergenerational elasticity
(IGE), describes how much economic differences between families persist. Since the
IGE measures quantitative movements across the income distribution, it can be used
to ask questions such as how quickly families can move from the poverty level to the
mean level of income. Our preferred estimates of the IGE suggest that economic mo-
bility was relatively low in 1940 but increased over the subsequent four decades.
However, economic mobility fell sharply during the 1980s and failed to revert, per-
haps even continued to decline, in the 1990s.
We also produce estimates of the IGE that include birth cohort effects and find that
mobility has declined for more recent birth cohorts, especially men born in the late
1950s and the 1960s. These time patterns may partly reconcile the results from pre-
vious studies that have used different birth cohorts observed in different decades (for
example, Altonji and Dunn 1991; Solon 1992; Mazumder 2005; Bratsberg et al.
2007), although we certainly acknowledge that differences across surveys and econo-
metric methodology play a key role as well (Mazumder 2005).
We explicitly show that trends in the IGE are similar to those in cross-sectional
inequality over the 20th century (Katz and Autor 1999; Piketty and Saez 2003).
As a brief example, wage dispersion, as measured by the difference in the 90th
and 10th percentiles of men’s hourly wages, fell during the great wage compression
of the 1940s and rose sharply between the late 1970s and mid 1990s (Katz and Autor
1999). That this pattern has similarities to our estimates of intergenerational mobility
is not wholly surprising. Cross-sectional measures of inequality provide a ‘‘snap-
shot’’ of inequality at a moment in time while measures of intergenerational persis-
tence of inequality provide one version of a ‘‘moving picture.’’ It could be that the
same underlying factors that lead to changes in traditional measures of short-term
inequality, such as changes in the returns to skill, also result in changes to long-term
inequality measures. In fact, the time pattern in the returns to education bears a strik-
ing resemblance to our measure of intergenerational persistence. Nevertheless, years
of schooling only partly explains the time pattern in the IGE. We find, for example,
that even after accounting for changes in the return to education that the IGE is sig-
nificantly higher after 1980.
Some researchers prefer to use the intergenerational correlation (IGC) rather than
the IGE as a measure of intergenerational mobility. In principle, the two measures
could show different time patterns. The IGC is roughly a measure of positional mobil-
ity, the likelihood an adult son moves position in the income distribution relative to
his parent’s place a generation prior. An IGC of 1, for example, implies that a child’s
position in the income distribution perfectly replicates that of their parent’s in the prior
generation. That is, there is no intergenerational mobility in rank or position.
We find that the IGE’s time-series pattern differs from the IGC, particularly prior
to 1980. Consequently, how we think about the decline in intergenerational mobility
exhibited by both the IGE and IGC during the 1980s depends to a degree on which
measure is emphasized. The IGC suggests that the 1980s change is a return to earlier,
Aaronson and Mazumder 141

pre-1970s, norms. By contrast, the high rate of intergenerational income persistence
exhibited by the IGE in the 1980s and 1990s may reflect a more pronounced change
from the rest of the post-WWII period. Accordingly, at the close of the twentieth cen-
tury, the rate of positional movement of families across the income distribution
appears historically normal, but, at the same time that cross-sectional inequality
has increased, earnings are regressing to the mean at a slower rate, causing economic
differences between families to persist longer than they had several decades ago.
Finally, it is important to highlight that our two-sample estimator likely produces
an upward biased estimate of the IGE. This bias may be large if state-specific
factors—such as differences in endowments,
5
policies, cost of living, or local neigh-
borhood, school, or peer conditions related to state of birth—are a large part of what
the IGE measures.
6
Therefore, our estimates may exaggerate the importance of birth-
location factors relative to the traditional IGE, which places less, although still pos-
itive, weight on these factors. However, for more recent decades, we use a separate
identification strategy that purges our estimate of state-level geographic effects and
find very similar trends. This exercise reveals that the effects of state-specific factors
are not large enough to account fully for the decline in mobility we identify in recent
decades and for more recent birth cohorts. This finding should not be taken to mean
that there are no effects on the IGE arising from the public provision of investment in
human capital as in Solon (2004). Rather, any variation arising from state differences
in public investment have not had meaningful effects on our estimates of the trend in
the IGE. Similarly, results based on the Panel Study of Income Dynamics (PSID) and
National Longitudinal Survey of Youth (NLSY) also indicate that the state-specific
effects are relatively small compared to the overall IGE, although these results are
less conclusive due to the small samples used.
7
We also explicitly show that state
cost-of-living differences are not an important explanatory factor.
Regardless of the size of the bias, our broader descriptive measure is still informa-
tive about trends in the importance of average family income in one’s state of birth
on children’s economic success. Although strictly speaking, this alternative measure
should be given a different interpretation than the IGE, our results still provide one
useful gauge of intergenerational mobility.
II. Empirical methods
The standard statistical model of intergenerational income mobility
relates a child’s (usually son’s) permanent log income or earnings, y
i
, to his parent’s
(usually father’s) permanent log income, X
i
:
5. These could include differences in physical capital or agglomeration effects, which may be autocorre-
lated over time. Since children tend to stay in their birth state, persistent stat e differences in factors of pro-
duction will bring about an association between parent’s and their adult children’s productivity and hence
income. We consider the parent’s residential choice, which encompasses these factors, to be one aspect of
the intergenerational transmission process.
6. It is important to emphasize that the traditional IGE measure is not a causal estimate of the effect of
parent income on children’s earnings but rather captures all factors (including birth-location factors), that
are correlated with parent income and children’s future earnings.
7. These results are described in the web Appendix A available online linked to the abstract of this article at
www.ssc.wisc.edu/jhr/.
142 The Journal of Human Resources

y
i
¼ a + rX
i
+ f ðchild
0
s ageÞ + f ðparent
0
s ageÞ + e
i
ð1Þ
Since each generation’s income measure is expressed in logs, r is the intergenera-
tional elasticity (IGE). Equation 1 is left intentionally sparse so that r captures the
full association between the parent’s economic status and their children’s later out-
comes. So, for example, any effect related to birth location that is correlated with
X
i
will be included in r. The only controls typically included are the age at which
income is measured in each generation in order to control for life-cycle effects. It
is now well established (Solon 1992) that a consistent estimate of r must account
for measurement error in parent permanent income. In practice, X
i
is usually proxied
by multiyear averages in order to smooth out the transitory component of earnings.
8
Furthermore, Haider and Solon (2006) show that, as a result of heterogeneous pat-
terns in life-cycle earnings profiles, OLS and IV estimates may be inconsistent due
to the age at which the child’s earnings are measured. They find that estimates are
biased downward (upward) when the income of the children is measured at a young
(old) age. The bias is minimized around age 40.
Our goal is to estimate a time-series of r. We begin with a regression equation that
is similar in spirit to Lee and Solon (2006), in that it offers a time-varying estimate of
the intergenerational elasticity while also addressing various statistical issues identi-
fied in the literature. Our most complete specification is:
y
ibst
¼ a + g
1t
ðage-40Þ + g
2t
ðage-40Þ
2
+ g
3t
ðage-40Þ
3
+ g
4t
ðage-40Þ
4
+ u
t
+ v
b
+ d
1
ðage-40ÞX
ibs
+ d
2
ðage-40Þ
2
X
ibs
+ d
3
ðage-40Þ
3
X
ibs
+ d
4
ðage-40Þ
4
X
ibs
+ u X
ibs
+ b
b
X
ibs
+ r
t
X
ibs
+ e
ibst
ð2Þ
where the dependent variable y
ibst
is the log annual earnings of child i, born in birth co-
hort b (measured in five year bands), in state s, at time t. The key independent variable is
X
ibs
, the log of family income for individual i born in birth cohort b and state s.
Following Lee and Solon (2006), we address the problem of bias stemming from het-
erogeneous age-earnings profiles by interacting parent income with a quartic in sons’
age minus 40 (d
1
to d
4
)
9
. Other coefficients on parent income will then reflect effects
for 40-year-olds. We also control for year effects (u
t
), cohort effects (v
b
), and for a quar-
tic in child’s age (minus 40) that might affect the level of earnings. Unlike Lee and
Solon, we allow for this age profile to vary by year (g
1t
to g
4t
) because the age-earnings
profile is likely to have changed substantially over the time period we are analyzing.
10
In order to measure changes in intergenerational mobility over time, we include
additional terms involving parent income. One way to measure time trends in the
IGE is simply to include an interaction between parent’s income and the outcome year.
8. Mazumder (2005) shows that long time averages are needed to fully solve the problem. Other
approaches have used instrumental variables (Solon 1992; Zimmerman 1992) or method of moments
(Altonji and Dunn 1991; Zimmerman 1992).
9. More recently, Bo
¨
hlmark and Linquist (2006) have found changes over time in the pattern of the life
cycle bias in Sweden. We found that our results are barely affected by including these interaction terms
suggesting that lifecycle bias is not much of an issue in our sample.
10. For example, Altonji and Williams (2005) find that the combined returns to tenure and experience in-
creased somewhat over time, especially during the 1980s.
Aaronson and Mazumder 143

Frequently asked questions

Q1. What are the contributions in "Intergenerational economic mobility in the united states, 1940 to 2000" ?

In this paper, the authors estimate trends in intergenerational economic mobility by matching men in the Census to synthetic parents in the prior generation, finding that mobility increased from 1950 to 1980 but has declined sharply since 1980. 

Q2. What are the future works mentioned in the paper "Intergenerational economic mobility in the united states, 1940 to 2000" ?

However, the authors must leave to future research the problem of identifying race-specific effects. Of course, years of education is only a blunt measure of skill and future research is needed to better understand the extent to which the changing returns to cognitive and noncognitive skills may have led to the changing pattern in mobility the authors observe. Fourth, the authors find that the results are insensitive to how they handle potential issues stemming from life-cycle bias. 35 This suggests that both traditional measures of short-term and long-term inequality tend to move together.