View original document

The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.

ESSAYS ON ISSUES
	

	 THE FEDERAL RESERVE BANK	
OF CHICAGO

2015
	 NUMBER 330

Chicag­ Fed Letter
o
Inequality in skills and the Great Gatsby curve
by Bhashkar Mazumder, senior economist and research advisor

This article presents evidence relating cross-country differences in intergenerational
mobility to differences in inequality of skills.
In recent years, concerns about inequality

of opportunity have risen to the forefront
of policy discussions in the United States.
This is due in part to a growing body of
evidence showing that intergenerational
economic mobility is lower in the U.S. than
in most other advanced
economies. In the U.S.
1. Inequality and intergenerational mobility
more than elsewhere,
intergenerational earnings elasticity
where you are in the in0.6
come distribution reflects
United
Kingdom
where your parents were
0.5
Italy
in the previous generaUnited
States
tion. What is it about the
France
0.4
U.S. that makes it less
Germany
economically mobile?
0.3
Sweden
One prominent hypothesis
Canada
0.2
is that low mobility is
Norway
Finland Denmark
related to the especially
0.1
high level of inequality.
R2 = 0.65
Indeed, there appears to
0.0
be a striking correlation
0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.36
Gini coefficient
between the levels of inequality across countries
Source: Corak (2013).
and rates of intergenerational mobility. In 2012,
Alan Krueger, then the chair of the President’s
Council of Economic Advisers, referred to
this relationship as the “Great Gatsby
curve” and warned that rising inequality
could lead to reduced intergenerational
mobility in the future.
In this Chicago Fed Letter, I examine
one particular aspect of the cross-country
inequality–mobility relationship, namely
whether it may reflect underlying differences
in inequality of skills. I use data from the
Programme for the International Assessment

of Adult Competencies (PIAAC) survey,
conducted by the Organisation for Economic
Co-operation and Development (OECD),
and show that there is a strong cross-country
relationship between intergenerational mobility and inequality in skills. In particular,
I find that inequality in an index of “noncognitive skills” explains as much or more
of the variation in intergenerational mobility
than inequality in traditional measures of
cognitive skills such as numeracy, literacy,
and problem solving. An emerging line of
research has argued that personality traits
such as perseverance and grit play an important role in socioeconomic success. These
results are consistent with the idea that the
large gaps in skills in the U.S. population
are part of what is driving both higher inequality and lower intergenerational mobility.
At a minimum, these new descriptive findings should help inform the ongoing policy
debate about what, if anything, should be
done to improve equality of opportunity.
The Great Gatsby curve

Figure 1 shows the relationship between
inequality and intergenerational mobility
based on a chart from a recent paper by
Miles Corak.1 The x-axis plots the Gini
coefficient, which is one commonly used
measure of inequality. The y-axis plots what
is known as the intergenerational elasticity
or IGE. The IGE is an estimate of intergenerational persistence that describes the
degree to which an increase in parental
income is associated with an increase in a
child’s income. For example, an IGE of
0.4 implies that a 10% increase in parental
income is associated with a 4% increase

	 2. Inequality in skills and intergenerational mobility
A. Numeracy

B. Literacy

intergenerational earnings elasticity

intergenerational earnings elasticity
0.6

0.6
United
Kingdom

Italy

United
States

United
Kingdom

0.5

France

0.5

0.4

0.4

Italy

United States

France
Spain

Spain
Germany

0.3

0.3
Canada

Sweden

0.2

0.2

Finland
Denmark

Norway

R2 = 0.50
1.50

1.55

Canada

Finland
Norway

Denmark

0.1

0.1
0.0
1.45

Germany

Sweden

1.60

1.65

1.70

1.75

R2 = 0.38
0.0
1.40

1.45

1.50

1.55

1.60

1.65

90/10 ratio of literacy skills

90/10 ratio of numeracy skills
C. Problem solving

D. Index of non-cognitive skills

intergenerational earnings elasticity

intergenerational earnings elasticity

0.6

0.6

0.5

United Kingdom

Italy

0.5

United States

0.4

France

0.4
Germany

0.3
0.2

0.2

Finland

Norway

Denmark

0.1

Spain
Germany
Sweden

0.3

Sweden
Canada

Canada

Finland
Norway
Denmark

0.1
R = 0.22

R2 = 0.51

2

0.0
1.38

1.40

1.42

1.44

1.46

1.48

1.50

1.52

1.54

United
Kingdom

United States

0.0
1.35

1.40

1.45

1.50

1.55

1.60

1.65

1.70

90/10 ratio of non-cognitive skills

90/10 ratio of problem-solving skills
Source: Author’s calculations using PIAAC and Corak (2013).

in child’s income. A higher IGE suggests
a closer association across generations and
less mobility. Therefore, the positive relationship between income inequality and
the IGE shown in figure 1 implies that
higher income inequality is associated with
less intergenerational mobility. What is
striking is that the explanatory power is
quite high, as income inequality differences
explain about 65% of the variation in intergenerational mobility.
Of course, the relationship is an association
and may or may not reflect a true causal
relationship. One could imagine that there
might be some third factor (or set of factors)
that leads countries to exhibit both high
income inequality and low intergenerational
mobility. Indeed, if income inequality rose
for reasons unrelated to this third factor, it

might have no effect at all on intergenerational mobility. There are also many different issues concerning measurement,
methodology, and data quality that could
affect the data points shown in figure 1;
and one might be skeptical about whether
the relationship is robust to all of the issues.
In this article, I do not explore these questions and simply take the data as given and
assume it is reasonably accurate. In recent
work, Chetty et al. (2014) and Bradbury and
Triest (2014)2 have also shown that this
relationship between inequality and intergenerational mobility holds within the
U.S. across commuting zones. In any
event, at a minimum one can simply view
the figure as an interesting descriptive device that ought to motivate further exploration and research.

In that spirit, one might begin by asking
what possible mechanisms could lead to
such a strong cross-country association
between inequality and intergenerational
mobility. Economic studies of inequality
and intergenerational mobility often emphasize human capital as a key driver of
both outcomes. One simple story could be
that countries that do a good job of equalizing educational opportunities will exhibit
greater income equality. This could arise,
for example, if the quality of schools is
uniformly high throughout a country. Such
countries might also be expected to be
successful in weakening the connection
between parental economic success and
children’s future economic status leading
to a lower intergenerational elasticity. This
would be one plausible scenario under

which we could observe the relationship
in figure 1. In this case, we would also expect to see a strong relationship between
intergenerational mobility and inequality
in measures of human capital.
Measuring inequality in skills

The PIAAC survey took place between 2011
and 2012 and collected data on approximately 166,000 adults between the ages of
16 and 65 in 24 OECD countries. The survey included about 5,000 U.S. adults. The
purpose of the PIAAC is to understand how
countries compare in their skill levels, given
the rapid acceleration of the use of technology in the modern economy. The three
primary domains of skill that are measured
by the PIAAC are numeracy, literacy, and
problem solving. On all three domains, the
U.S. is below the OECD average; and in
numeracy, the U.S. scores close to the bottom.
One striking pattern across all three measures is the greater inequality in skills in
the U.S. While the percentage of U.S. adults
who score at the highest proficiency is
similar to or only somewhat lower than
average, the percentage of U.S. adults who
score at the lowest levels of proficiency is
significantly higher than average—among
the highest of all the countries surveyed.
In order to argue that high inequality leads
to low intergenerational mobility, we need
to consider the timing of when each is measured. Ideally, one would prefer to use a
measure of inequality that covers a time
period before children’s income is measured.
Since the PIAAC is a recent survey, I only
measure inequality in skills for those between the ages of 40 and 65, thereby capturing the inequality in skills for cohorts
born between 1946 and 1971. The income
of adult children used in the intergenerational elasticity estimates is typically measured in the 1990s, although this varies
somewhat across countries.
Intergenerational mobility and
skill inequality

In order to measure inequality in skills, I
use the ratio of the 90th percentile of the
skill distribution to the 10th percentile in
each country. Figure 2 plots the 90–10 ratios
for various skill measures against the intergenerational elasticity for a similar set of
countries as that in figure 1.3 Panel A of

figure 2 shows the relationship between the
intergenerational elasticity and the 90–10
ratio in numeracy. Similar to the results in
figure 1, there is a striking positive relationship and the R-squared of the regression
line is reasonably high at 0.50. As with
figure 1, not all countries are close to the
regression line. For example, Canada has
a relatively low intergenerational elasticity
despite having a high degree of inequality
in numeracy. Panel B shows the relationship when using inequality in literacy on
the x-axis. The relationship remains positive
but the R-squared falls to 0.38. The difference in explanatory power between numeracy and literacy might not be so surprising.
For example, Arcidiacono (2004)4 found
that in the U.S., math scores on the SAT
can help explain earnings differences but
verbal scores cannot. Panel C plots the
relationship using inequality in problemsolving skills for a slightly smaller sample
of countries. In this case, the explanatory
power falls considerably as the R-squared
is reduced to 0.22.
A growing literature in developmental
psychology and economics has highlighted
the role of certain personality traits, such
as openness to experience and conscientiousness, as playing an important role in determining socioeconomic success. Economists
refer to such traits as non-cognitive skills.
An underutilized feature of the PIAAC is
that it asks several questions concerning
the ability to learn that correspond to some
of these personality traits. I construct an
index of non-cognitive skills by averaging
the responses to six questions that assess
capabilities related to learning.5 Panel D
of figure 2 shows the relationship between
the 90–10 ratio in this index and the intergenerational elasticity. The figure demonstrates that inequality in non-cognitive skills
explains even more of the cross-country
variation in intergenerational mobility
than numeracy, with an R-squared of 0.51.
Explanations

One straightforward explanation for these
findings is that societies in which opportunities for human capital development are
unequal will exhibit a high degree of skill
inequality and experience less intergenerational economic mobility. This could
arise for a number of reasons, including

differences in access to health care early
in life (including the prenatal period), unequal access to preschool, disparities in
the quality of elementary or secondary
school education, or lack of affordability
of higher education. It could be that lack
of opportunity for human capital development also leads to greater income inequality,
explaining some of the pattern shown in
the Great Gatsby curve. However, there
could be a bi-directional relationship as
well, whereby greater inequality leads to
disparities in skill formation due to inequality in opportunity.
Of course, the relationships shown in these
figures could also be consistent with other
hypotheses. It is conceivable that other
factors—such as demographics, neighborhood characteristics, or the presence of
national institutions—may combine to lead
countries with high degrees of inequality of
skill to also exhibit low intergenerational
mobility. Much more detailed research,
using many other sources of variation and
more sophisticated research designs, is probably needed to arrive at fully convincing
explanations for these findings. Still, it
appears that health and education policies
that may improve equality of opportunity
are a natural starting place for U.S. policymakers seeking to address this issue. 	
Charles L. Evans, President  Daniel G. Sullivan,
;
Executive Vice President and Director of Research;
Spencer Krane, Senior Vice President and Economic
Advisor ; David Marshall, Senior Vice President, financial
markets group  Daniel Aaronson, Vice President,
;
microeconomic policy research; Jonas D. M. Fisher,
Vice President, macroeconomic policy research; Richard
Heckinger,Vice President, markets team; Anna L.
Paulson, Vice President, finance team; William A. Testa,
Vice President, regional programs, and Economics Editor ;
Helen O’D. Koshy and Han Y. Choi, Editors  ;
Rita Molloy and Julia Baker, Production Editors 
;
Sheila A. Mangler, Editorial Assistant.
Chicago Fed Letter is published by the Economic
Research Department of the Federal Reserve Bank
of Chicago. The views expressed are the authors’
and do not necessarily reflect the views of the
Federal Reserve Bank of Chicago or the Federal
Reserve System.
© 2015 Federal Reserve Bank of Chicago
Chicago Fed Letter articles may be reproduced in
whole or in part, provided the articles are not
reproduced or distributed for commercial gain
and provided the source is appropriately credited.
Prior written permission must be obtained for
any other reproduction, distribution, republication, or creation of derivative works of Chicago Fed
Letter articles. To request permission, please contact
Helen Koshy, senior editor, at 312-322-5830 or
email Helen.Koshy@chi.frb.org. Chicago Fed
Letter and other Bank publications are available
at www.chicagofed.org.
ISSN 0895-0164

and Katharine Bradbury and Robert K.
Triest, 2014, “Inequality of opportunity
and aggregate economic performance,”
available at www.bostonfed.org/
inequality2014/papers/bradbury-triest.pdf.

	 Miles Corak, 2013, “Income inequality,
equality of opportunity, and intergenerational mobility,” Journal of Economic Perspectives,
Vol. 27, No. 3, Summer, pp. 79–102.

1

	 Raj Chetty, Nathaniel Hendren, Patrick
Kline, and Emmanuel Saez, 2014, “Where
is the land of opportunity? The geography
of intergenerational mobility in the
United States,” available at http://obs.rc.
fas.harvard.edu/chetty/mobility_geo.pdf;

2

	 Compared to figure 1, figure 2 adds Spain
in panels A, B, and D and drops France
and Italy from panel C.

3

	 Peter Arcidiacono, 2004, “Ability sorting
and the returns to college major,” Journal

4

of Econometrics, Vol. 121, Nos. 1–2,
July–August, pp. 343–375.
	 For example, respondents are shown a
statement such as: “If I don’t understand
something, I look for additional information to make it clearer,” and are asked to
select from the following responses: not at
all, very little, to some extent, to a high
extent, or to a very high extent.

5


Federal Reserve Bank of St. Louis, One Federal Reserve Bank Plaza, St. Louis, MO 63102