The author finds that about half of earnings inequality in the U.S. can be explained by family and community influences during childhood. He also finds that these influences have become more important in recent decades.Brothers and sisters ; Income ; Wages

Bhashkar Mazumder

English

Research Papers in Economics

Chicago Fed Letter
What similarities between siblings tell us about inequality in the U.S.
by Bhashkar Mazumder, economist
The author finds that about half of earnings inequality in the U.S. can be explained by
family and community influences during childhood. He also finds that these influences
have become more important in recent decades.
ESSAYS ON ISSUES THE FEDERAL RESERVE BANK DECEMBER 2004
OF CHICAGO NUMBER 209
How important is family background
in determining economic success in the
United States? If children from wealthy
families turn out to be only modestly
more successful, on average, than those
from less fortunate backgrounds, this
might be indicative of a fairly econom-
ically mobile society with widespread
opportunities. If on the other hand,
family background turns out to play a
very large role in deter-
mining future economic
success, it raises the ques-
tion of whether there is
sufficient access to oppor-
tunity for all families.
It is extremely difficult, if
not impossible, to accurate-
ly measure the full set of
relevant family background
variables using existing
datasets. It is also often ex-
tremely difficult to collect
useful information for family members
from two generations. For these reasons,
many researchers have used socioeco-
nomic data on siblings as a way to try
to identify the importance of family
background.
If one compared the earnings or wages
of two sets of randomly chosen individ-
uals from the population, one would not
expect to find any correlation between
the groups. In contrast, one would prob-
ably expect to find some positive corre-
lation if the comparison was between
pairs of siblings. Specifically, the corre-
lation between siblings in a particular
outcome measures how much of the
overall variance in that outcome is due
to all of the factors that siblings share in
common—namely the same family and
the same community influences (e.g.,
peers, schools).1
In this Chicago Fed Letter, I discuss some
new research in which I show that the
sibling correlation in economic out-
comes (e.g., annual earnings) is close to
0.5. This suggests that about half of
earnings inequality in the U.S. can be
explained by family and community in-
fluences during childhood. To provide
some context, this is roughly the same
magnitude as the sibling correlation in
height, a characteristic that presum-
ably has a large genetic component.
Given the multitude of factors that are
involved in determining one’s earn-
ings (e.g., schooling, skills, choice of
industry/occupation), one might find
such a high correlation very surprising.
This finding also suggests that inequal-
ities between families persist strongly
from generation to generation and that
the U.S. is a less mobile society than is
commonly believed. I also find that the
sibling correlation has risen in recent
decades suggesting that the U.S. may
have become less mobile.
Measuring permanent economic status
Until the 1990s the studies that attempt-
ed to measure the sibling correlation
1. Sibling correlation in economic outcomes
Family
Earnings income Wages Hours
Brothers
Sibling corr. 0.49 0.47 0.54 0.39
Standard error (0.02) (0.02) (0.02) (0.04)
No. of individuals 5,213 5,164 5,102 5,179
Sisters
Sibling corr. 0.34 0.45 0.36 0.15
Standard error (0.04) (0.03) (0.03) (0.03)
No. of individuals 4,994 5,195 4,956 5,0682. Sibling correlation in noneconomic  outcomes
in economic outcomes typically used
only a single year of annual earnings to
proxy for economic status. However, as
far back as the 1950s, as a result of the
work of Nobel laureates Milton Friedman
and Franco Modigliani, economists have
understood that a one-year snapshot of
income is a poor measure of one’s life-
time income stream or “permanent in-
come.” In a review of studies on the
sibling correlation in earnings, Gary
Solon found that the average estimate
of the sibling correlation from those stud-
ies that only used one-year measures of
earnings was about 0.25. Solon suggests
that these results are biased down by 30%
to 50% and that the true sibling corre-
lation in permanent status is probably
around 0.4 or higher.2 In fact, a few em-
pirical studies in the 1990s that tried to
measure permanent economic status by
using multiple years of data on earnings
did indeed estimate the sibling correla-
tion to be in the 0.4 range. However,
given that there are only a few estimates
and that these studies used relatively
small samples and different methodolo-
gies, considerable uncertainty remains
about the accuracy of these estimates.
New estimates
In a new study I estimate the sibling cor-
relation in a variety of outcomes using
the National Longitudinal Survey of
Youth (NLSY).3 The NLSY contains a
national sample of more than 12,000
young men and women who were be-
tween 14 years old and 22 years old in
1979 and tracks these individuals through
to the present. The sample includes well
over 4,000 sibling pairs. By using many
years of labor market data on these indi-
viduals, I am able to better measure per-
manent economic status. I also employ
a methodology that has better statistical
properties than previous approaches.4
I consider four different economic out-
comes: log annual earnings, log family
income, log hourly wages, and log an-
nual hours worked. I also analyze women
as well as men and consider a variety of
noneconomic outcomes.
The results for economic outcomes are
shown in figure 1. For men, the sibling
correlation in annual earnings is estimat-
ed to be 0.49 and the sibling correlation
in family income is 0.47. Interestingly,
the estimate is even higher for hourly
wages at 0.54. One previous study that
used a much smaller sample also found
that the sibling correlation was over 0.5
for wages.5 This implies that more than
half of the inequality in wages is due to
differences between families and that
less than half to factors that vary within
a family.
The results for women tend to be low-
er. However, this is not surprising giv-
en the more varied labor force
participation patterns for younger
women, largely related to
having and raising children. In
this respect, family income,
which includes spouse’s in-
come, may better capture
economic status for women
than their own earnings. It
turns out that the estimate
for the correlation in family
income among sisters (0.45)
is virtually identical to the
correlation in family income
among brothers.
In a separate study I also
present new evidence show-
ing that these estimates are
significantly higher than for
a cohort who entered the la-
bor market during the 1970s.
This suggests that family back-
ground has become increas-
ingly important in determining
economic success.6
Noneconomic outcomes
The estimates of the sibling
correlation for a variety of
noneconomic outcomes are
shown in figure 2. Of particu-
lar interest to economists are
measures of human capital,
because these play a central
role in economic models of
earnings. The sibling corre-
lation in years of schooling is
estimated to be 0.60 and the
sibling correlation in the
Armed Forces Qualifying
Test (AFQT) is 0.62.7 The high
sibling correlation in human
capital not only demonstrates
the importance of family
background in determining education
and skill levels, but also suggests that
this may account for the high sibling
correlation in economic outcomes.
In the study I also examine a few behav-
ioral measures that have previously
been examined in studies of neighbor-
hood or peer effects. These include use
of illegal drugs, having spent time in
jail, and teenage pregnancy. The corre-
lations are widely dispersed for these
outcomes and vary by gender, but are
roughly in the 0.2 to 0.3 range. I also
estimate sibling correlations in certain
All Brothers Sisters
Yrs. of schooling
Sibling corr. 0.60 0.62 0.60
Standard error (0.01) (0.01) (0.01)
No. of individuals 6,097 3,000 3,097
AFQT scores
Sibling corr. 0.62 0.62 0.62
Standard error (0.01) (0.01) (0.01)
No. of individuals 5,751 2,811 2,490
Illegal drug use
Sibling corr. 0.27 0.30 0.37
Standard error (0.01) (0.02) (0.02)
No. of individuals 5,413 2,639 2,774
Ever in jail
Sibling corr. 0.15 0.26 0.00
Standard error (0.04) (0.04) (0.09)
No. of individuals 6,111 3,003 3,108
Age of pregnancy
Sibling corr. – – 0.18
Standard error (0.02)
No. of individuals 2,495
Height
Sibling corr. – 0.49 0.47
Standard error (0.02) (0.02)
No. of individuals 2,803 2,937
Weight
Sibling corr. – 0.33 0.29
Standard error (0.02) (0.03)
No. of individuals 2,652 2,768
Body mass index
Sibling corr. – 0.27 0.30
Standard error (0.02) (0.03)
No. of individuals 2,580 2,696
Rotter scale
Sibling corr. 0.09 0.07 0.11
Standard error (0.02) (0.02) (0.02)
No. of individuals 6,053 2,974 3,079
Self esteem
Sibling corr. 0.25 0.22 0.28
Standard error (0.01) (0.02) (0.02)
No. of individuals 5,809 2,860 2,949Michael H. Moskow, President; Charles L. Evans,
Senior Vice President and Director of Research;
Douglas Evanoff, Vice President, financial studies;
David Marshall, Vice President, macroeconomic policy
research; Richard Porter, Senior Policy Advisor,
payment studies; Daniel Sullivan, Vice President,
microeconomic policy research;  William Testa, Vice
President, regional programs and Economics Editor;
Helen O’D. Koshy, Editor; Kathryn Moran,
Associate Editor.
Chicago Fed Letter is published monthly by the
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3. Contributions to brother correlation
physical characteristics. The correlations
in both weight and body mass index are
approximately 0.3. The estimates for the
sibling correlations in height are be-
tween 0.45 and 0.5.
A few recent studies by economists have
emphasized the importance of certain
non-cognitive factors (e.g., personality)
in determining earnings. I examine the
sibling correlation in two psychological
measures, the Rotter scale and an index
of self-esteem. The Rotter scale mea-
sures the extent to which individuals
believe that factors outside of their
control determine their fate. The sib-
ling correlation in the Rotter scale is
around 0.1, while the estimates for
self-esteem are between 0.2 and 0.3.
Accounting for the sibling
correlation in earnings
While a sizable sibling correlation in
earnings suggests that family back-
ground plays an important role in de-
termining economic success, it does not
point to any particular factor or policy
prescription. For example, if the correla-
tion largely reflected unequal schooling
levels, then this might suggest that
policymakers should focus on promot-
ing greater educational opportunity. In
order to understand which specific fac-
tors contribute the most to the sibling
correlation in earnings, I undertook
an additional exercise. Here, the
thought experiment is to consider how
much lower the sibling correlation in
earnings would be if we first removed
any variation in earnings that was as-
sociated with the particular factor(s)
in question. This provides an “upper
bound” estimate of how much any par-
ticular factor contributes to the sibling
correlation. Figure 3 shows the results
of this exercise for men for several ex-
planatory variables.
One of the most important family back-
ground characteristics to consider is the
income of parents. Removing the varia-
tion in earnings associated with pa-
rental income reduces the sibling
correlation in earnings from 0.49 to
0.32, or about 36%. While on the one
hand this is quite large, it also leaves a
large part of family background and
community influences that appear to
be independent of parental income.
I show that each human capital measure
(years of schooling and AFQT scores)
taken on its own accounts for nearly
half of the sibling correlation in earn-
ings, and both measures taken together
combine for just over half of the sibling
correlation. This largely confirms the
idea that education and skills are of
crucial importance in addressing ine-
quality. Physical characteristics, in con-
trast, appear to have little effect, although
height is shown to account for about
5% of the correlation. Time spent in
jail has a large effect, accounting for
more than 20% of the correlation be-
tween brothers. This is largely driven by
the effect of jail time on hours worked.
The sibling correlation in hourly wages
is only reduced by 8% when accounting
for jail time.
Interestingly, the two psychological
measures have a non-negligible effect
on the sibling correlation. Including the
effects of both in conjunction lowers
the sibling correlation by about 20%.
Finally, I consider the effects of includ-
ing information about occupation into
the analysis. A long literature in soci-
ology has emphasized occupation as an
important mechanism by which econom-
ic advantages are maintained by fami-
lies. On the other hand, occupation
may also be thought of as an outcome
that is determined by the same factors
(e.g., schooling) that determine earn-
ings. I find that the correlation in
earnings between brothers is reduced
by about 60% when I include occupa-
tion. Finally, I look at all of the mea-
sures in combination. Including all of
the variables except occupation can
explain 65% of the sibling correla-
tion. Adding occupation to the mix
explains about 80% of the overall sib-
ling correlation.
Inequality within families
The sibling correlation could still un-
derestimate the overall role of family
background if there are important ways
in which families reinforce inequality
among siblings within the family.
Economists have developed models to
explain how families divide financial
resources among children, but this lit-
erature is still in its early stages. In a
standard model, parents invest in each
child's education to the point at which
the return to the investment equals the
marginal cost. More able children who
generate a larger return will receive
more schooling. Parents who have a
strong preference for equality in the
economic outcomes of their children
will compensate less-gifted children
with financial transfers. However, fam-
ilies who are financially constrained
Contribution %
Parent income 0.17 36
Human capital 0.25 51
Education 0.21 44
AFQT scores 0.22 45
Physical characteristics 0.03 5
Height 0.02 5
Weight 0.01 1
Body mass index 0.00 0
Illegal behavior 0.11 22
Jail 0.11 23
Illegal drug use 0.00 0
Psychological char. 0.09 20
Rotter scale 0.05 10
Self esteem 0.07 15
Occupation 0.29 59
All except occupation 0.31 65
All 0.38 80
Note: Upper bound estimates of the
contribution of each factor(s) to the 0.49
brother earnings correlation.1 The sibling correlation does not include
some factors that are commonly thought
of as part of family background, such as
genetic traits that are sibling specific.
2 See Gary Solon, 1999, “Intergenerational
mobility in the labor market,” in Hand-
book of Labor Economics, Vol. 3A, Orley C.
Ashenfelter and David Card (eds.), Am-
sterdam: Elsevier.
3 See Bhashkar Mazumder, 2004, “Sibling
similarities, differences, and economic
inequality,” Federal Reserve Bank of Chicago,
working paper, No. 2004-13.
4 The correlation is computed by first esti-
mating a variance component model using
restricted maximum likelihood (REML).
Unlike analysis of variance (ANOVA),
which has been used by some previous
studies, REML produces consistent esti-
mates and has a known asymptotic sampling
dispersion matrix. On the other hand,
REML requires imposing a distributional
assumption on the data. The relevant
variance components on permanent eco-
nomic status (the “between” and “within”
family variance components) are then
used to estimate the sibling correlation.
5 See Gary Solon, Mary Corcoran, Roger
Gordon, and Deborah Laren, 1991, “A
longitudinal analysis of sibling correla-
tions in economic status,” Journal of Hu-
man Resources, Vol. 26, No. 3, pp. 509–534.
6 See Bhashkar Mazumder and David I. Le-
vine, 2003, “The growing importance of
family and community: An analysis of
changes in the sibling correlation in
men’s earnings,” Federal Reserve Bank
of Chicago, working paper, No. 2003-24.
7 The AFQT is part of the Armed Services
Vocational Aptitude Battery (ASVAB) of
ten tests given to applicants to the U.S.
military. The AFQT score is based on
four of the tests that focus on reading
skills and numeracy. The AFQT was ad-
ministered to nearly all respondents in
the NLSY in 1980 to provide new norms
for the test based on a nationally repre-
sentative sample.
from making such transfers, but who
nonetheless value equality, may choose
to invest similar amounts in their chil-
dren's schooling. One implication of
this model is that we would expect to
see greater sibling inequality in school-
ing levels and wages among wealthier
families than poorer families.
Using the same data described earlier,
I actually find little difference in the de-
gree of sibling inequality in schooling
levels between low- and high-income
families (defined by parental income).
On the other hand, I do find evidence
that the variance in AFQT scores among
siblings does increase with parental
income up to a point. This discrepancy
might be explained by the fact that
differences in education levels are easi-
ly observed and may be deemed as “un-
fair.” For example, parents may find it
difficult to justify sending only one child
to college. In contrast, parents may
channel other resources in more subtle
ways to children based on their likeli-
hood for success, and it could be that
these differences are captured by dif-
ferences in test scores. I also find some
important differences in the degree of
sibling inequality depending on the gen-
der of children that are not explained
by the simple theoretical models and
deserve more scrutiny in future re-
search. For example, in high-income
families sisters tend to have much
more similar levels of completed
schooling than brothers.
Conclusion
I present new evidence showing that the
sibling correlation in economic out-
comes is about 0.5. This implies that
family background accounts for half of
the inequality in the U.S. The sibling
correlation also appears to have risen
for more recent cohorts suggesting that
the U.S. may have become less economi-
cally mobile in recent decades. I also
analyze sibling inequality in economic
outcomes and find mixed evidence in
support of economic theories of resource
allocation within families.

What similarities between siblings tell us about inequality in the U.S.

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What similarities between siblings tell us about inequality in the U.S.

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