Why Has Income Inequality Been Rising Since the 1970’s?
Due in NetTutor through the link in Blackboard (learn.wsu.edu) by February
18, by 11:50 pm (PST)
Below are two competing views (Proposition #1 and Proposition #2) on why income inequality
has been rising (even among the lower 99%) in the United States since the 1970’s.
Reading: “Skills, education, and the rise of earnings inequality among the “other 99 percent,”
David H. Autor, Science 23 May 2014: 843-851. [DOI:10.1126/science.1251868] (see below)
Proposition #1:
“More people are falling behind today because they’re lazy. In the U.S. anyone willing to work can
earn a decent living. The income distribution is the same as the effort distribution. People who work
hard earn a good living. Lazy people stay poor. The income distribution has been skewing because of
a growing culture of dependency. It used to be shameful to accept handouts from the government, but
now it’s perfectly acceptable. So more people opt out of the labor force and sit around collecting
handouts from the rest of us. Of course their incomes are going to be lower than those in the top
tiers.”
Proposition #2:
“The incomes of the rich and poor have been growing farther apart due to purposeful policy
decisions that have favored the rich at the expense of the poor. Starting with Ronald Reagan’s
financial deregulation, and continuing with successive Republican attacks on New Deal and Great
Society social programs to finance tax cuts for their Wall Street cronies, government policy in this
country has systematically channeled the rewards of our immensely productive economy towards the
rich and away from the poor. America needs to open the gateway to prosperity for all through fair tax
policies, decent minimum wages, better public schools, aid to low-income college students, and a
universal health care system.”
EconS 101 WA 1
Page 2 of 3
Assess the degree to which each of those propositions is actually consistent with David H.
Autor’s well-researched explanation of the same phenomenon. That is, for each of the
propositions tell me whether or not Autor would agree that the underlying factor described in
the proposition is the major cause of the pattern of income inequality since the 1970’s. (2
paragraphs)
1) Begin with a topic sentence that concisely states your judgment about how consistent the two
propositions are with Autor’s analysis, and complete that paragraph with a summary of
Autor’s explanation. Be sure to mention some of the factors Autor identifies as having
contributed to an increase in the demand for highly-educated workers and a decrease in the
demand for less-educated workers.
2) In your second paragraph, discuss the degree to which the two propositions are consistent
with Autor’s analysis. Is one more consistent than the other?
EconS 101 WA 1
Page 3 of 3
Grading Rubric
Please include your name, instructor’s name (Dr. Prera), course and section number (i.e. EconS
101.01 [MWF 9:10-10 am] or 101.03 [TTh 10:35-11:50 am]), and writing assignment
(Assignment 1) on the top of your assignment.
This is the first of four writing assignments. After receiving feedback, you may re-submit the
essay to NetTutor for another try – but you only get one second chance on each writing
assignment. If you don’t get an acceptable score, even after revising and resubmitting this
assignment, you can still rescue your writing assignment average by doing well on the next three
assignments. I will take the three highest writing assignment scores to compute your writing
assignment average.
Criteria Meets Expectations Needs Improvement
Writing
Ideas are well-organized.
Transition sentences effectively
connect one idea to the next. The
essay is free of typos and
grammatical errors.
The writing is difficult to
follow and/ or poorly
organized.
Transition sentences are
absent or ineffective.
Typos and/ or grammatical
errors distract the reader.
Economic analysis
The topic sentence clearly states
the student’s judgment about the
degree to which Propositions #1
and #2 are consistent with
Autor’s study.
The topic sentence does not
state the student’s judgment
about the degree to which
Propositions #1 and #2 are
consistent with Autor’s study.
Correctly identifies the major
cause of rising inequality in
Autor’s analysis.
Autor’s argument is
misrepresented, and/or the
summary is too skimpy.
Explicitly state the degree to
which the two propositions are
consistent with Autor’s research,
and supports his or her claims
with examples from the reading.
The question of the degree to
which the two propositions
are consistent with Autor’s
research is not addressed
and/or, is not supported with
examples.
DOI: 10.1126/science.1251868
Science 344, 843 (2014);
David H. Autor
”other 99 percent”
Skills, education, and the rise of earnings inequality among the
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of rising or shrinking inequality. Which one
dominates depends on the institutions and policies
that societies choose to adopt.
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Personal Wealth from a Global Perspective (Oxford Univ. Press,
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SUPPLEMENTARY MATERIALS
www.sciencemag.org/content/344/6186/838/suppl/DC1
Supplementary Text
Figs. S1 and S2
References (31, 32)
10.1126/science.1251936
REVIEW
Skills, education, and the rise of
earnings inequality among
the “other 99 percent”
David H. Autor
The singular focus of public debate on the “top 1 percent” of households overlooks the
component of earnings inequality that is arguably most consequential for the “other
99 percent” of citizens: the dramatic growth in the wage premium associated with higher
education and cognitive ability. This Review documents the central role of both the supply
and demand for skills in shaping inequality, discusses why skill demands have persistently
risen in industrialized countries, and considers the economic value of inequality alongside
its potential social costs. I conclude by highlighting the constructive role for public policy in
fostering skills formation and preserving economic mobility.
Public debate has recently focused on a
subject that economists have been analyzing
for at least two decades: the steep,
persistent rise of earnings inequality in
the U.S. labor market and in developed
countries more broadly. Much popular discussion
of inequality concerns the “top 1 percent,”
referring to the increasing share of national income
accruing to the top percentile of households.
Although this phenomenon is undeniably
important, an exclusive focus on the concentration
of top incomes ignores the component
of rising inequality that is arguably even more
consequential for the “other 99 percent” of
citizens: the dramatic growth in the wage premium
associated with higher education and,
more broadly, cognitive ability. This paper considers
the role of the rising skill premium in
the evolution of earnings inequality.
There are three reasons to focus a discussion
of rising inequality on the economic payoff
to skills and education. First, the earnings
premium for education has risen across a large
number of advanced countries in recent decades,
and this rise contributes substantially to
the net growth of earnings inequality. In the
United States, for example, about two-thirds
of the overall rise of earnings dispersion between
1980 and 2005 is proximately accounted
for by the increased premium associated with
schooling in general and postsecondary education
in particular (1, 2). Second, despite a
lack of consensus among economists regarding
the primary causes of the rise of very top
incomes (3–6), an influential literature finds
that the interplay between the supply and
demand for skills provides substantial insight
into why the skill premium has risen and fallen
over time—and, specifically, why the earnings
gap between college and high school graduates
has more than doubled in the United States over
the past three decades. A third reason for focusing
on the skill premium is that it offers broad
insight into the evolution of inequality within a
market economy, highlighting the social value of
inequality alongside its potential social costs and
illuminating the constructive role for public policy
in maximizing the benefits and minimizing the
costs of inequality.
The rising skill premium is not, of course, the
sole cause of growing inequality. The decadeslong
decline in the real value of the U.S. minimum
wage (7), the sharp drops in non-college
employment opportunities in production, clerical,
and administrative support positions stemming
from automation, the steep rise in international
competition from the developing world,
the secularly declining membership and bargaining
power of U.S. labor unions, and the
successive enactment of multiple reductions in
top federal marginal tax rates, have all served to
magnify inequality and erode real wages among
less educated workers. As I discuss below, the
foremost concern raised by these multiple forces
is not their impact on inequality per se, but
rather their adverse effect on the real earnings
and employment of less educated workers.
I begin by documenting the centrality of the
rising skill premium to the overall growth of
earnings inequality. I next consider why skills
are heavily rewarded in advanced economies
and why the demand for them has risen over
time. I then demonstrate the substantial explanatory
power of a simple framework that
embeds both the demand and supply for skills
in interpreting the evolution of the inequality
over five decades. The final section considers
the productive role that inequality plays in a
market economy and the potential risks attending
very high and rising inequality; evidence on
whether those risks have been realized; and
the role of policy and governance in encouraging
skills formation, fostering opportunity,
Department of Economics and National Bureau of Economic
Research, Massachusetts Institute of Technology, 40 Ames
Street, E17-216, Cambridge, MA 02142, USA. E-mail: dautor@
mit.edu
SCIENCE sciencemag.org 23 MAY 2014 • VOL 344 ISSUE 6186 843
and countering the possibility that extremes of
inequality erode economic mobility and reduce
economic dynamism.
The Critical Role of Skills in the
Labor Market
There is no denying the extraordinary rise in
the incomes of the top 1% of American households
over the past three decades. Between
1979 and 2012, the share of all household income
accruing to the top percentile of U.S.
households rose from 10.0% to 22.5% (8, 9). To
get a sense of how much money that is, consider
the conceptual experiment of redistributing
the gains of the top 1% between 1979 and
2012 to the bottom 99% of households (10).
Howmuchwould this redistribution raise household
incomes of the bottom 99%? The answer
is $7107 per household—a substantial gain, equal
to 14% of the income of the median U.S. household
in 2012. (I focus on the median because it
reflects the earnings of the typical worker and
thus excludes the earnings of the top 1%.)
Now consider a different dimension of inequality:
the earnings gap between U.S. workers
with a 4-year college degree and those with
only a high school diploma (11). Economists frequently
use this college/high school earnings
gap as a summary measure of the “return to
skill”—that is, the gain in earnings a worker
can expect to receive from investing in a college
education. As illustrated in Fig. 1, the earnings
gap between the median college-educated
and median high school–educated among U.S.
males working full-time in year-round jobs was
$17,411 in 1979, measured in constant 2012 dollars.
Thirty-three years later, in 2012, this gap
had risen to $34,969, almost exactly double its
1979 level. Also seen is a comparable trend among
U.S. female workers, with the full-time, fullyear
college/high school median earnings gap
nearly doubling from $12,887 to $23,280 between
1979 and 2012. As Fig. 1 underscores, the
economic payoff to college education rose steadily
throughout the 1980s and 1990s and was
barely affected by the Great Recession starting
in 2007.
Because the earnings calculations in Fig. 1 reflect
individual incomes while the top 1% calculations
reflect household incomes, the two
calculations are not directly comparable. To
put the numbers on the same footing, consider
the earnings gap between a college-educated
two-earner husband-wife family and a high school–
educated two-earner husband-wife family, which
rose by $27,951 between 1979 and 2012 (from
$30,298 to $58,249). This increase in the earnings
gap between the typical college-educated
and high school–educated household earnings
levels is four times as large as the redistribution
that has notionally occurred from
the bottom 99% to the top 1% of households.
What this simple calculation suggests is that
the growth of skill differentials among the “other
99 percent” is arguably even more consequential
than the rise of the 1% for the welfare of
most citizens.
The median earnings comparisons in Fig. 1 also
convey a key feature of rising inequality that
cannot be inferred from trends in top incomes:
Wage inequality has risen throughout the earnings
distribution, not merely at the top percentiles.
Figure S1 documents this pattern by plotting,
for 12 Organization for Economic Cooperation
and Development (OECD) member countries over
three decades (1980 to 2011), the change in the
ratio of full-time earnings of males at the 90th
percentile relative to males at the 10th percentile
of the wage distribution. Although the 90/10
earnings ratio differed greatly across countries
at the earliest date of the sample—from a low
of 2.0 in Sweden to a high of 3.6 in the United
States—this earnings ratio increased substantially
in all but one of them (France) over the
next 30 years, growing by at least 25 percentage
points in 10 countries, by at least 50 percentage
points in 8 countries, and by more than 100 percentage
points in three countries (New Zealand,
the United Kingdom, and the United States).
How much does the rising education premium
contribute to the increase of earnings inequality?
Although data limitations make it difficult to
answer this question for most countries, we do
know the answer for the United States. Goldin
and Katz (1) found that the increase in the education
wage premium explains about 60 to 70%
of the rise in the dispersion of U.S. wages between
1980 and 2005 and, similarly, Lemieux (12)
calculated that higher returns to postsecondary
education can account for 55% of the rise in
male hourly wage variance from 1973–1975 to
2003–2005. Firpo et al. (13) found that rising
returns to education can explain just over 95% of
the rise of the U.S.male 90/10 earnings ratio between
1984 and 2004. That is, holding the expanding
education premium constant over this
period, there would have been essentially no increase
in the relative wages of the 90th-percentile
worker versus the 10th-percentile worker.
I have so far used the terms education and
skill interchangeably.What evidence do we have
that it is skills that are rewarded per se, rather
than simply educational credentials? The Program
for the International Assessment of Adult
Competencies (PIAAC) provides a compelling
data source for gauging the importance of
skills in wage determination. The PIAAC is an
internationally harmonized test of adult cognitive
and workplace skills (literacy, numeracy,
and problem-solving) that was administered
by the OECD to large, representative samples
of adults in 22 countries between 2011 and
2013 (14). Figure 2, sourced from (15), plots the
relationship between adults’ earnings and their
PIAAC numeracy scores across these 22 countries.
The length of each bar reflects the average
percentage earnings differential between
full-time workers ages 35 to 54 who differ by
one standard deviation in the PIAAC score.
The whiskers on each bar provide the 95%
confidence intervals for the estimates.
College/high school median annual earnings gap, 1979–2012
In constant 2012 dollars
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000 dollars
1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012
Household gap
$30,298 to $58,249
Male gap
$17,411 to $34,969
Female gap
$12,887 to $23,280
Fig. 1. College/high school median annual earnings gap, 1979–2012. Figure is constructed using
Census Bureau P-60 (1979–1991) and P-25 (1992–2012) tabulations of median earnings of full-time,
full-year workers by educational level and converted to constant 2012 dollars (to account for
inflation) using the CPI-U-RS price series. Prior to 1992, college-educated workers are defined as
those with 16 or more years of completed schooling, and high school–educated workers are those
with exactly 12 years of completed schooling. After 1991, college-educated workers are those who
report completing at least 4 years of college, and high school–educated workers are those who
report having completed a high school diploma or GED credential.
844 23 MAY 2014 • VOL 344 ISSUE 6186 sciencemag.org SCIENCE
This figure conveys three points. First, cognitive
skills are substantially rewarded in the labor
market across all 22 economies. The average
wage premium corresponding to one “unit” (i.e.,
one standard deviation) increase in measured
cognitive skills is 18%. In addition, cognitive earnings
premiums differ substantially across countries.
The premium is below 13% in Sweden,
the Czech Republic, and Norway. It is above
20% in six countries. The United States stands
out as having the highest measured return to
skill, with a premium of 28% per unit increment
to cognitive ability. Concretely, comparing two
U.S. workers who are one standard deviation
above and one standard deviation below the
population average of cognitive ability, we would
expect their full-time weekly earnings to differ
by 50 to 60%. Notably, the high return to
cognitive ability in the United States does not
follow automatically from high levels of U.S.
earnings inequality. If U.S. wages were determined
mainly by luck, beauty, or family connections,
we would expect little connection
between workers’ cognitive ability and their labor
market rewards (16). Figure 2 demonstrates
that this is not the case.
Of course, these data do not explain why
the skill premium has risen over time, nor
why the United States has a higher skill premium
than so many other advanced nations.
The next section considers the supply and demand
for skill in the labor market—specifically, why
they fluctuate over time and how their interaction
helps to determine the skill premium. I
focus on the United States in this section to allow
a deeper exploration of the data.
Education and Inequality
Workers’ earnings in a market economy depend
fundamentally (some economists would
say entirely) on their productivity—that is, the
value they produce through their labor. And in
turn, workers’ productivity depends on two factors.
One is their capabilities, concretely, the
tasks they can accomplish (i.e., their skills). A
second is their scarcity: The fewer workers that
are available to accomplish a task, and the more
employers need that task accomplished, the
higher is workers’ economic value in that
task. In conventional terms, the skill premium
depends uponwhat skills employers require (skill
demand) and what skills workers have acquired
(skill supply). To interpret the evolution of this
premium, we need to account for both forces.
Skill Demands: The Long View
A technologically advanced economy requires
a literate, numerate, and technically and scientifically
trained workforce to develop ideas,manage
complex organizations, deliver healthcare
services, provide financing and insurance, administer
government services, and operate critical
infrastructure. This was not always the case. In
1900, 4 in 10 U.S. jobs were in agriculture, 11%
of the population was illiterate, a substantial
fraction of economic activity required hard physical
labor, and workers’ strength and physical
stamina were key job skills (17, 18). Few citizens
would have predicted at the time that a century
later, health care, finance, information technology,
consumer electronics, hospitality, leisure,
and entertainment would employ farmoreworkers
than agriculture—which employed only 2%
of U.S. workers in 2010. As physical labor has
given way to cognitive labor, the labor market’s
demand for formal analytical skills, written communications,
and specific technical knowledge—
what economists often loosely term cognitive
skills—has risen spectacularly.
The central determinant of the supply of
skills available to an advanced economy is its
education system. In 1900, the typical young,
native-born American had only a common school
education, about the equivalent of six to eight
grades (19). By the late 19th century, however,
many Americans recognized that farm employment
was declining, industry was rising, and
their children would need additional education
to earn a living. Over the first four decades of the
20th century, the United States became the first
nation in the world to deliver universal high
school education to its citizens. Tellingly, the high
school movement was led by the farm states.
As the high school movement reached its
conclusion, postsecondary education became
increasingly indispensable to the growing occupations
of medicine, law, engineering, science,
and management. In 1940, only 6% of
Americans had completed a 4-year college
degree. From the end of the Second World
War to the early 1980s, however, the ranks of
college-educated workers rose robustly and
steadily, with each cohort of workers entering
the labor market boasting a proportionately
higher rate of college education than
the cohort that preceded it. This intercohort
pattern, which was abetted by the Second
World War and Korean War GI Bills (20) and
by huge state and federal investments in public
college and university systems, is depicted in
Fig. 3A. From 1963 through 1982, the fraction
of all U.S. hours worked that were supplied
by college graduates rose by almost 1 percentage
point per year, a remarkably rapid gain.
After 1982, however, the rate of intercohort
increase fell by almost half—from0.87 percentage
points to 0.47 percentage points per year—and
did not begin to rebound until 2004, nearly
two decades later. As shown in fig. S2, this deceleration
in the supply of college graduates is
particularly stark when one focuses on young
adults with fewer than 10 years of experience—
that is, the cohorts of recent labor market
entrants at each point in time. Although the
supply of young college-educated males relative
to young high school–educated males increased
rapidly in the 1960s and early 1970s
(and indeed throughout the postwar period), this
rising tide reached an apex in 1974 from which
Fig. 2. Cross-national differences
in wage returns to skills,
2011–2013. Reproduced with
permission from Hanushek et al.
[(15), table 2]. Estimates are
obtained by regressing the
natural logarithm of workers’
weekly full-time earnings on test
scores while controlling for sex
and labor market experience
(both a linear and a quadratic
term). Regression estimates are
performed separately for each
country and test scores are
normalized with mean zero and
unit standard deviation within
each country. Estimates that
normalize test scores on a
common basis across countries,
or that use literacy or
problem-solving scores rather
than numeracy scores,
yield qualitatively similar patterns.
Cross-national differences in wage returns
to skills, 2011–2013
Percentage increase for a one standard deviation
increase in skill
0 5 10 15 20 25 30 percent
Sweden
Czech R.
Norway
Italy
Denmark
Cyprus
Finland
Belgium
France
Estonia
Slovak R.
Austria
Netherlands
Japan
Poland
Canada
Korea
U.K.
Spain
Germany
Ireland
U.S.
Earnings
gain
95% confidence
interval
SCIENCE sciencemag.org 23 MAY 2014 • VOL 344 ISSUE 6186 845
it barely budged for the better part of the next
30 years. Among young females, the deceleration
in supply was also unmistakable, although not as
abrupt or as complete as for males.
The counterpart to this deceleration in the
growth of supply of college-educated workers
is the steep rise in the college premium commencing
in the early 1980s and continuing for
25 years. Concretely, when the influx of new
college graduates slowed, the premium that a
college education commanded in the labor market
increased. The critical role played by the
fluctuating supply of college education in the
rise of U.S. inequality is documented in Fig. 3B,
which plots the college wage premium from
1963 through 2012 (blue line). This premium
fluctuated in a comparatively narrow band during
the 1960s and 1970s, as rising demand for
educated workers was met with rapidly rising
year-over-year increases in supply. In 1981, the
average college graduate earned 48% more per
week than the average high school graduate—a
significant earnings gap but not an earnings
gulf. When the supply deceleration began in
1982, however, the college premium hit an inflection
point. This premium notched remarkably
rapid year-over-year gains from 1982 forward,
reaching 72% in 1990, 90% in 2000, and 97% in
2005 (21, 22). Thus, the average earnings of college
graduates were 1.5 times those of high school
graduates in 1982 but were double those of high
school graduates by 2005.
Why is this deceleration in supply relevant
to the college premium? After all, although the
growth of supply slowed in 1982, it was still
rising. A likely answer is that the demand for
college workers rose in the interim. Throughout
much of the 20th century, successive waves
of innovation—electrification, mass production,
motorized transportation, telecommunications—
have reduced the demand for physical labor
and raised the centrality of cognitive labor in
practically every walk of life. The past three
decades of computerization, in particular, have
extended the reach of this process by displacing
workers from performing routine, codifiable
cognitive tasks (e.g., bookkeeping, clerical work,
and repetitive production tasks) that are now
readily scripted with computer software and
performed by inexpensive digital machines. This
ongoing process of machine substitution for routine
human labor complements educated workers
who excel in abstract tasks that harness
problem-solving ability, intuition, creativity, and
persuasion—tasks that are at present difficult
to automate but essential to perform. Simultaneously,
it devalues the skills of workers, typically
those without postsecondary education,
who compete most directly with machinery in
performing routine-intensive activities. The net
effect of these forces is to further raise the demand
for formal education, technical expertise,
and cognitive ability (23–27).
Bringing the Supply-Demand
Framework to the Data
The persistently rising demand for educated
labor in advanced economies was first noted
by the Nobel Prize–winning economist Jan
Tinbergen (28) and is often referred to as the
“education race” model (19). Its primary implication
is that if the supply of educated labor
does not keep pace with persistent outward
shifts in demand for skills, the skill premium
will rise. In the words of the Red Queen in
Lewis Carroll’s Alice in Wonderland, “…it takes
all the running you can do, to keep in the same
place.” Thus, when the rising supply of educated
labor began to slacken in the early 1980s,
a logical economic consequence was an increase
in the college skill premium.
To more formally account for the impact of
the fluctuating growth rate of supply of collegeeducated
workers on the college wage differential,
Fig. 3B depicts the fit of a simple regression
model that predicts the college wage premium
in each year as a function of two factors: (i) the
contemporaneous supply of college graduates,
and (ii) a time trend, which serves as a proxy for
the secularly rising demand for college-educated
15
20
25
30
35
40
45
50 percent
1964 1970 1976 1982 1988 1994 2000 2006 2012
35
45
55
65
75
85
95
105 percent
1964 1970 1976 1982 1988 1994 2000 2006 2012
The supply of college graduates and the U.S. college/high school premium, 1963–2012
College share of hours worked (%), 1963–2012:
All working-age adults
College versus high school
wage gap (%)
A B
Predicted by Supply-
Demand Model
Measured Gap
Fig. 3. The supply of college graduates and the U.S. college/high school
premium, 1963–2012. (A) College share of hours worked in the United
States, 1963–2012: All working-age adults. Figure uses March CPS data for
earnings years 1963 to 2012. The sample consists of all persons aged 16 to
64 who reported having worked at least 1 week in the earnings years,
excluding those in the military. Following an extensive literature, collegeeducated
workers are defined as all of those with four or more completed
years of college plus half of those with at least 1 year of completed college.
Non-college workers are defined as all workers with high school or less
education, plus half of those with some completed college education. For
each individual, hours worked are the product of usual hours worked per
week and the number of weeks worked last year. Individual hours worked
are aggregated using CPS sampling weights. (B) College versus high school
wage gap. Figure uses March CPS data for earnings years 1963 to 2012.The
series labeled “Measured Gap” is constructed by calculating the mean of
the natural logarithm of weekly wages for college graduates and non–
college graduates, and plotting the (exponentiated) ratio of these means for
each year. This calculation holds constant the labor market experience and
gender composition within each education group. The series labeled
“Predicted by Supply-Demand Model” plots the (exponentiated) predicted
values from a regression of the log college/noncollege wage gap on a
quadratic polynomial in calendar years and the natural log of college/
noncollege relative supply. See text and supplementary material for further
details.
846 23 MAY 2014 • VOL 344 ISSUE 6186 sciencemag.org SCIENCE
workers (29). Comparing the fitted values (red
series) from this simple supply-demand model
alongside the actual data (blue series) reveals an
extremely tight correspondence over the course
of five decades and three distinct eras: a declining
skill premium in the 1970s; an explosive rise in
the premium during the 1980s, 1990s, and early
2000s; and,most recently, a plateau commencing
after 2005. A key implication of this figure is that
a central causal factor behind rising inequality
in the United States has been the slowdown in
the accumulation of skills by young adults almost
30 years ago. Had the supply of college graduates
risen as rapidly in the decades after 1980 as it did
in the decades immediately before, it is quite plausible
that there would have been no sustained
rise in the skill premium in the U.S. labormarket.
Of course, this set of facts raises another puzzle:
If slackening college supply sparked rising
inequality, what caused rising U.S. postsecondary
achievement to grind to a sudden halt in 1982?
Work by Card and Lemieux (30) highlights that
one critically important factor was the United
States’ involvement in the Vietnam War. Because
draft-eligible males in the Vietnamera were often
able to defer their military service by enrolling
in postsecondary schooling, the war artificially
boosted college attendance. This created something
of a glut of college enrollments in the late
1960s and early 1970s, which in turn depressed
the college earnings premium in the 1970s (see
Fig. 3) and likely reduced the attractiveness of
college-going absent themilitary draft. Thus, when
the war ended in the early 1970s, college enrollment
rates dropped sharply, particularly among
males. The fall in enrollment produced a corresponding
decline in college completions half a
decade later, and a surge of inequality followed.
This supply-demand explanation for the rise of
U.S. inequality may appear almost too simple to
be credible. After all, we are comparing just two
economic variables: the college wage premium
and the supply of college graduates in the U.S.
workforce. But a host of rigorous studies commencing
with Katz and Murphy (31) confirmthe
remarkable explanatory power of this simple
supply-demand framework for explaining trends
in the college versus high school earnings gap
over the course of nine decades of U.S. history, as
well as across other industrialized economies (most
notably, the United Kingdom and Canada) and
among age and education groups within countries
(19, 31–36). The United States was far from the
only Western country to experience this surge.
One should not, of course, take this model as
irrefutable. A puzzling pattern evident in the
data is that the rising demand for skilledworkers
appears to have slowed in the early 1990s, a
phenomenon that is not anticipated by the “education
race” model (37). This discrepancy underscores
that the supply-demand model is
necessarily incomplete—in part for the sake of
expositional clarity and, in larger part, because
our understanding of macroeconomic phenomena
is typically imperfect. Nevertheless, the data
speak sufficiently clearly to warrant two economic
inferences. The first is that although popular
accounts frequently assert that the United
States is in the midst of a “college bubble”—too
many students going to college at too high a
cost—abundant economic evidence strongly suggests
otherwise. Yes, college tuitions have risen
far faster than inflation, and indeed, student
debt has risen rapidly, with more than $100
billion in federal student aid dollars loaned in
2012–2013 alone (38). But the doubling of the
college weekly wage differential over the past
30 years also implies that there have been sizable
increases in the lifetime earnings of college graduates
relative to high school graduates. How large
are these gains? Figure 4, reproduced from (39),
reports the estimated lifetime college earnings
differential net of tuition for cohorts of students
entering the labor market between 1965 and
2008. For both males and females, the expected
net present value of a college degree relative to a
high school diploma roughly tripled in this period,
with the fastest gains accruing during the
1980s and 1990s. Note that this growing college/
high school gap reflects the rising payoff to the
4-year college degree, the even steeper rise in the
premium associated with graduate and professional
degrees (see below), and the growing fraction of
college graduateswho obtain higher degrees; thus,
an additional payoff to the college degree is that
it opens the door to further specialization. This
lifetime earnings differential would, of course, have
risen further still if college tuitions had held steady
rather than rising. But the inevitable sticker shock
that households feel when confronting the cost of
college should not obscure the fact that the real
lifetime earnings premium to college education
has likely never been higher (40).
The second positive economic news implied
by Fig. 3 above is that the ongoing rise
of skill differentials is not inevitable. Prior cohorts
of U.S. students, particularly males, were
slow to react to the rising return to education
during the 1980s and 1990s, but the message
appears to have finally gotten through. During
the first decade of the 21st century, the U.S.
high school graduation rate rose sharply after
having been essentially stagnant since the late
1960s (41). This unanticipated rise was followed
just a few years later by a surge in college completions.
Between 2004 and 2012, the supply of
new college graduates to the U.S. labor market
rose at a rate not seen in several decades (Fig.
3A). As this influx of supply took hold, the college
wage premium halted its enduring rise (Fig.
3B). What these observations and our simple
213K
368K
261K
385K
439K
582K
590K
129K
198K
138K
225K
284K
387K 370K
Present discounted value of college relative to high school degree
net of tuition, 1965–2008
College/high school difference, 2009 dollars
100,000
0
200,000
300,000
400,000
500,000
600,000 dollars
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Men
Women
Fig. 4. Present discounted value of college relative to high school degree net of tuition,
1965–2008. Reproduced from Avery and Turner with permission of the American Economic Association
(39). Expected earnings are calculated from the March Current Population Survey files
for full-time, full-year workers using sample weights. The estimates equal what a man or woman
would expect to earn working full-time, full-year over a career of 42 years, with a discount rate of
3%, assuming that college graduates delay the start of earnings for 4 years while in school.
Earnings expectations are formed in each year by assuming that future high school and college
graduates will have future earnings at each age equal to the average earnings of high school and
college graduates (respectively) currently observed at each age; for example, expected earnings in
1980 are based on data across ages for 1980. Results for college-educated workers are net of 4 years
of tuition and fees associated with appropriate year-specific values for public universities. Plotted
points show the difference between expected earnings for college graduates and for high school
graduates.
SCIENCE sciencemag.org 23 MAY 2014 • VOL 344 ISSUE 6186 847
supply-demand model suggest is that the flattening
of the college premium after 2005 is in
large part a consequence of the quickening pace
of educational attainment.
Inequality: Causes for Concern?
A market economy needs some inequality to
create incentives. If, for example, students were
not ultimately rewarded for spending their early
adulthoods pursuing undergraduate, graduate,
and professional degrees, or if the hardest-working
and most productive workers were paid the
same as the median worker, then citizens would
have little incentive to develop expertise, to exert
effort, or to excel in their work (42). Having acknowledged
that some inequality is necessary,
however, how can we gauge whether there is
too much of it? I offer two analytical perspectives
on this question.
Earnings Mobility
One metric by which to evaluate the consequences
of inequality is via its relationship
with economic mobility—that is, the degree to
which individual economic fortunes change
over time. Of particular interest is the degree of
intergenerational mobility, meaning the likelihood
that children born to low-income families
become high-income adults and vice versa.
High levels of economic inequality at a given
point in time are not intrinsically inimical to
economic mobility; a society with high inequality
may be dynamic, with lots of movement up
and down the economic ladder, and one with
low inequality may be dynastic. But a natural
concern is that high inequality at a point in time
may serve to reduce mobility over time. If, for
example, adults who became wealthy through
hard work are able to “buy” success for their
children through outsized investments and personal
connections, while adults who are unproductive
or unlucky in their careers are unable
to muster the resources to foster their children’s
potential, then inequality of incomes could become
self-perpetuating even if it originally emanates
from high market returns to skill (43).
To understand the importance of high and
rising U.S. inequality, it is therefore useful to
ask how U.S. economic mobility compares to
that of other developed countries, and whether
U.S. mobility has fallen as inequality has risen.
The answers to both questions will surprise
many. Contrary to conventional civic mythology,
U.S. intergenerational mobility is relatively
low. The left panel of Fig. 5, reproduced from
(44), which plots the relationship between crosssectional
inequality (x axis) and earnings mobility
(y axis) among a set of 13 OECD member
countries for which consistent data are available,
documents that the United States has both the
lowest mobility and highest inequality among
all wealthy democratic countries. The right panel
of Fig. 5, also sourced from (44), suggests one
proximate explanation for this pattern: Countries
with high returns to education tend to
have relatively low mobility.Why, if education is
“the great equalizer” in the words of Horace
Mann, do high educational returns predict low
mobility? A key reason is that educational attainment
is highly persistent within families.
Indeed, two of the strongest predictors of children’s
ultimate educational attainment are parental
education and parental earnings (45, 46).
Hence, when the return to education is high,
children of better-educated parents are doubly
advantaged—by their parents’ higher education
and higher earnings—in attaining greater education
while young and greater earnings in
adulthood. Figure 5 therefore lends credence
to the concern that rising inequality may erode
economic mobility.
Has this erosion occurred? Surprisingly, the
best evidence to date suggests that it has not.
Evidence from Chetty et al. (46), documented
in the supplementary material, underscores the
message from Fig. 5 that there is substantial
economic immobility in the United States. Children
born three deciles apart in the household
income distribution are on average one decile
apart in the earnings distribution at age 29 or
30. Similarly, children born three deciles apart
in the household income distribution differ by
20 percentage points in their probability of attending
college at age 19 (relative to a mean of
approximately 55%). Yet these data offer no
evidence that mobility has appreciably changed
among children born prior to the historic rise
of U.S. inequality (1971–1974) and those born
afterward (1991–1993). As far as we can measure,
rising U.S. income inequality has not reduced
intergenerational mobility so far. These findings,
which also appear to hold over a longer
historical time frame (47), suggest that U.S.
mobility has not trended downward as many
social scientists would have anticipated, and as
Canada
Australia
New Zealand
Germany
Germany
Japan
France France
United
Kingdom
Italy United Kingdom
0.1
0.2
0.3
0.4
0.5
0.1
0.2
0.3
0.4
0.5
20 25 30 35
Income inequality (more inequality )
Denmark
Norway Finland
Canada
Australia
New Zealand
Spain
Italy
100 120 140 160 180
College earnings premium (men 25 to 34)
Earnings inequality and economic mobility: cross-national relationships
Generational earnings elasticity
(higher values imply lower mobility)
Generational earnings elasticity
(higher values imply lower mobility) A B
Sweden
Norway
Denmark
Finland
United States United States
Sweden
Fig. 5. Earnings inequality and economic mobility: Cross-national relationships.
Reproduced from Corak [(44), figs. 1 and 4] with permission of
the American Economic Association. In both panels, the mobility measure is
equal to the intergenerational earnings “elasticity,” meaning the average
proportional increase in a son’s adult earnings predicted by his father’s
adult earnings measured approximately three decades earlier. A higher intergenerational
earnings elasticity therefore implies lower intergenerational
mobility. In the left panel, cross-sectional income inequality is measured
using a “Gini” index that ranges from 0 to 100, where 0 indicates complete
equality of household incomes and 100 indicates maximal inequality (all
income to one household). In the right panel, the college earnings premium
refers to the ratio of average earnings of men 25 to 34 years of age with a
college degree to the average earnings of those with a high school diploma,
computed by the OECD using 2009 data. See (44) for further details.
848 23 MAY 2014 • VOL 344 ISSUE 6186 sciencemag.org SCIENCE
many policymakers and popular accounts frequently
assume.
It is important to interpret these results in
context. The most recent birth cohorts whose
adult outcomes can be observed at present
were born no later than the early 1990s, which
is still relatively early in the rise of U.S. inequality.
Another 10 years of data, focusing
on children born since 2000, may suggest a
different conclusion. Moreover, the fact that
mobility has stayed constant while inequality
has risen means that the lifetime relative disadvantage
of children born to low- versus highincome
families has increased substantially;
concretely, the rungs of the economic ladder
have pulled farther apart but the chance of
ascending the ladder has not improved. Finally,
it is possible to interpret the fact that
mobility has remained unchanged as evidence
that U.S. mobility would have declined had it
not been for the other compensatory steps
taken by the federal government during this
period, including, for example, expanding the
Earned Income Tax Credit for low-income workers
in the 1980s, enlarging the early childhood
education Head Start program in the 1990s,
and increasing federal student grant and loan
programs to support college-going (48). Declines
in racial and gender discrimination during this
period likely also complemented these policies
(49). A cautious read of the evidence is that although
the United States is not a “land of opportunity”
by conventional economic mobility metrics,
it has not become less so in recent decades.
Real Earnings
A second gauge of economic health is the trajectory
of earnings and employment. Here, the
data present substantial cause for concern. Although
the substantial college wage premium
conveys the positive economic news that educational
investments offer large returns, this wage
premium also masks a discouraging truth: The
rising relative earnings of workers with postsecondary
education is not simply due to rising
real earnings among college-educated workers
but is also due to falling real earningsamong non–
college-educated workers. Between 1980 and
2012, real hourly earnings of full-time collegeeducated
U.S. males rose anywhere from 20% to
56%, with the greatest gains among those with
a postbaccalaureate degree (Fig. 6A). During the
same period, real earnings of males with high
school or lower educational levels declined substantially,
falling by 22% among high school dropouts
and 11% among high school graduates. Although
the picture is generally brighter for females (Fig.
6B), real earnings growth among females without
at least some college education over this threedecade
interval was extremely modest.
Accompanying the fall in real wages among
less educated workers has been a pronounced
drop in their labor force participation rates,
particularly among less educated males. Between
1979 and 2007, prior to the onset of the
Great Recession, the fraction of working-age
males in paid employment fell by 12 percentage
points among high school dropouts and 10 percentage
points among those with exactly a high
school diploma. Conversely, employment rates were
generally stable for males with postsecondary
education and rose for females of all education
levels except for high school dropouts.
The causes for the sharp falls in real earnings
among non–college-educated workers are multiple.
One likely force, as noted above, is the
ongoing substitution of computer-intensive machinery
for workers performing routine taskintensive
jobs. This has depressed demand for
workers in both blue-collar production andwhitecollar
office, clerical, and administrative support
positions, and has reduced the set of middleskill
career jobs available to non–college-educated
workers more generally (25). A second factor
is the globalization of labor markets, seen particularly
in the greatly increased U.S. trade
integration with developing countries. Globalization
has become particularly important for
U.S. labor markets since the early 1990s, when
China began its extremely rapid integration
into the world trading system. The influx of
Chinese goods lowered consumer prices but
also fomented a substantial decline in U.S. manufacturing
employment, contributing directly
to the decline in production worker employment
(50). A third factor impinging on the earnings
of non–college-educated males is the decline in the
penetration and bargaining power of labor unions
in the United States, which have historically
obtained relatively generous wage and benefit
packages for blue-collar workers. Over the past
three decades, however, U.S. private-sector union
density—that is, the fraction of private-sector
workers who belong to labor unions—has fallen
by approximately 70%, from24%in 1973 to 7%in
2011 (51, 52).
Notably, these three forces—technological
change, deunionization, and globalization—
work in tandem. Advances in information and
communications technologies have directly
changed job demands in U.S. workplaces while
simultaneously facilitating the globalization of
production by making it increasingly feasible
and cost-effective for firms to source, monitor,
and coordinate complex production processes
at disparate locations worldwide. In turn, the
globalization of production has increased competitive
conditions for U.S. manufacturers and
U.S. workers, eroding employment at unionized
establishments and decreasing the capability
1.0
1.2
1.4
1.6
1.8
2.0
1964 1968 1972 1976 1980 1984 1988 1992 1996 200020042008 2012
Some college
> Bachelor’s
degree
Bachelor’s
degree
High school
graduate
High school
dropout
A B
Changes in real wage levels of full-time U.S. workers by sex and education, 1963–2012
Real weekly earnings relative to 1963 (men)
Real weekly earnings relative to 1963 (women)
1964 1968 1972 1976 1980 1984 1988 1992 1996 200020042008 2012
1.0
1.2
1.4
1.6
1.8
2.0
Fig. 6. Change in real wage levels of full-time workers by education, 1963–2012. (A) Male workers, (B) female workers. Data and sample construction are
as in Fig. 3.
SCIENCE sciencemag.org 23 MAY 2014 • VOL 344 ISSUE 6186 849
of unions to negotiate favorable contracts, attract
new members, and penetrate new establishments.
In all cases, the foremost concern raised by
these multiple forces impinging on the earnings
of workers at different skill levels is not their
impact on inequality per se, but rather their adverse
effect on the real earnings and employment
of less educated workers. These declines in both
earnings and employment bode ill for the welfare
of non–college-educated U.S. adults and are likely
to have broader detrimental social consequences
that frequently accompany non-employment:
greater criminality, increased social dependency,
and (more mundanely) reduced tax receipts.
Do Supply and Demand Make
Policy Irrelevant?
One potential interpretation of the evidence
above is that, because rising inequality is substantially
a consequence of the impersonal forces
of supply and demand, public policy has no role
to play in shaping the trajectory of inequality or
its social impact. This conclusion is incorrect for
two reasons. First, there are multiple channels
by which policy has contributed to the rise of
U.S. inequality, many of which are not fully
evident in the education earnings premium.
These include the fall over several decades in
the real value of the U.S. minimum wage (7); the
declining prevalence and bargaining power
of U.S. labor unions; mounting international
competition that places particular pressure on
the wages and employment of less educated
workers; and sharp reductions in top federal
marginal tax rates that have raised after-tax
inequality and increased the incentive of highly
paid workers to seek still higher compensation.
As discussed in the companion paper by
Piketty and Saez, there is also disagreement
among economists about whether the rising
share of household incomes accruing to the top
few percentiles of households in numerous
developed countries over the past several decades
is also primarily a market phenomenon, or
instead reflects changing social norms, growing
corporate misgovernance, slackening regulatory
oversight, or increasing political capture of
the policymaking process by elites (3–6). It would
therefore be a vast overstatement to conclude that
the rise of U.S. inequality is exclusively due to
conventional market forces, or that public policy
has not played a role.
But let us assume for the sake of argument
that the rise of income inequality is entirely a
market phenomenon. Would this imply that
there is no role for public policy? A moment’s
reflection suggests otherwise. As the economist
Arthur Goldberger once famously observed, the
fact that nearsightedness is substantially a genetic
disorder has no bearing on whether doctors
should prescribe eyeglasses (53). What is relevant
is whether the benefits of addressing myopia
exceed the costs. In the case of myopia, the availability
of eyeglasses make this an easy call.
Although there is no “remedy” for inequality
that is as swift or cheap as eyeglasses, prosperous
democratic countries have numerous effective
policy levers for shaping inequality’s trajectory
and socioeconomic consequences. Policies that
appear most effective over the long haul in raising
prosperity and reducing inequality are those
that cultivate the skills of successive generations:
excellent preschool through high school education;
broad access to postsecondary education; and
good nutrition, good public health, and highquality
home environments. Such policies address
inequality from two directions: (i) enabling a larger
fraction of adults to attain high productivity,
rewarding jobs, and a reasonable standard of
living; and (ii) raising the total supply of skills
available to the economy, which in turnmoderates
the skill premium and reduces inequality (54).
Of course, building skills is a multigenerational
process and thus has little impact on inequality
in the short term. There are, however,
numerous nearer-term levers that moderate
inequality directly without imposing substantial
economic costs: applying progressive tax and
transfer policies that fund public investments
and foster opportunities for children of all socioeconomic
backgrounds; applying well-crafted
labor regulations that ensure safe and nonexploitive
working conditions; providing wage
subsidies such as the Earned Income Tax Credit
that increase the payoff to employment for those
with limited skills; setting modest but nonzero
minimum wage rules; and offering numerous
social insurance policies (health and disability
insurance, flood insurance, disaster assistance,
food assistance) that buffer misfortune for the
unfortunate. Although it is outside the scope of
this article to evaluate these policies, it is critical
to underscore that policy and governance
has played and should continue to play a central
role in shaping inequality—even when a central
cause of rising inequality is the changing supply
and demand for skills.
REFERENCES AND NOTES
1. C. D. Goldin, L. F. Katz, Brookings Pap. Econ. Act. (fall), 135 (2007).
2. Goldin and Katz (1) found that the increase in the education
wage premium, particularly the college premium, explains
about 60 to 70% of the rise in wage inequality (variance)
between 1980 and 2005.
3. F. Alvaredo et al., J. Econ. Perspect. 27, 3–20 (2013).
4. J. Bivens, L. Mishel, J. Econ. Perspect. 27, 57–78 (2013).
5. A. Bonica et al., J. Econ. Perspect. 27, 103–124 (2013).
6. S. N. Kaplan, J. Rauh, J. Econ. Perspect. 27, 35–56 (2013).
7. D. Autor et al., The Contribution of the Minimum Wage to U.S.
Wage Inequality over Three Decades: A Reassessment (NBER
Working Paper 16533, Cambridge, MA, 2010).
8. T. Piketty, E. Saez, Q. J. Econ. 118, 1–41 (2003).
9. These calculations use data from (8), with data updated to
2012 available at http://elsa.berkeley.edu/saez/~Tab-
Fig.2012prel.xls. Average U.S. household incomes, including
the top 1%, rose by 20.2%, while the average household
income of the bottom 99% of households rose by only 3.5%.
10. Thus, the top 1% maintains its share of household income at a
constant 10.0% while average household incomes rise by
20.2%, as actually occurred.
11. This point is due to Lawrence Katz of Harvard University, who
offers these calculations in his graduate labor economics
lecture notes.
12. T. Lemieux, Post-Secondary Education and Increasing Wage
Inequality (Working Paper 12077, National Bureau of Economic
Research, 2006).
13. S. Firpo et al., Decomposition methods in economics. In
Handbook of Labor Economics, D. Card, O. Ashenfelter, Eds.
(Elsevier-North Holland, Amsterdam, 2011), vol. 4, pp. 1–102.
14. See www.oecd.org/site/piaac/surveyofadultskills.htm for more
information. The PIAAC program will encompass 33 countries, but
data for only 22 were available at this writing.
15. E. A. Hanushek, G. Schwerdt, S.Wiederhold,
L.Woessmann, Returns to Skills Around the World:
Evidence from PIAAC (NBER Working Paper 19762,
Cambridge, MA, 2013).
16. Hanushek et al. (15) also found that the correlation between
numeracy skills and years of schooling is 0.45. When including
both numeracy skills and years of schooling in an earnings
regression, they found that both are substantial and significant
predictors of earnings, although each is attenuated relative
to a model where only one factor is included at a time. This
pattern of results suggests, logically, that neither test scores
nor years of schooling is a complete measure of labor
market skills.
17. T. D. Snyder, 120 Years of American Education: A Statistical
Portrait (National Center for Education Statistics, U.S.
Department of Education, 1993).
18. L. D. Johnston, “History lessons: Understanding
the decline in manufacturing.” MinnPost, 22 February
2012; www.minnpost.com/macro-micro-minnesota/
2012/02/history-lessons-understanding-declinemanufacturing.
19. C. Goldin, L. F. Katz, The Race Between Education and
Technology (Harvard Univ. Press, Cambridge, MA, 2008).
20. M. Stanley, Q. J. Econ. 118, 671–708 (2003).
21. B. Pierce, in Labor in the New Economy, K. G. Abraham,
J. R. Spletzer, M. Harper, Eds. (Univ. of Chicago Press, Chicago,
2010), pp. 63–98.
22. These comparisons hold labor market experience and gender
constant. This doubling of the college premium very likely
understates the magnitude of the increase in inequality
between college-educated and non–college-educated workers.
Alongside higher hourly earnings, college-educated workers
enjoy greater job stability, lower rates of unemployment, more
generous fringe benefits, and better working conditions;
Pierce (21) found that these differentials have generally increased
in the same time period.
23. D. H. Autor et al., Q. J. Econ. 118, 1279–1333 (2003).
24. D. Acemoglu, D. H. Autor, Skills, tasks and technologies:
Implications for employment and earnings. In Handbook of
Labor Economics, D. Card, O. Ashenfelter, Eds. (Elsevier-North
Holland, Amsterdam, 2011), vol. 4, pp. 1043–1171.
25. D. H. Autor, D. Dorn, Am. Econ. Rev. 103, 1553–1597 (2013).
26. M. Goos et al., www.aeaweb.org/forthcoming/output/
accepted_AER.php
27. Extensive recent literature, commencing with Autor et al. (23)
and summarized in Acemoglu and Autor (24), considers the
role of technological change in displacing workers performing
routine tasks and complementing workers performing
nonroutine tasks. An additional implication of this framework is
that an increasing share of employment will be found in
comparatively low-skill nonroutine manual tasks that require
situational adaptability, visual and language recognition,
and in-person interactions but limited formal education
(e.g., janitors and cleaners, home health aides, construction
laborers, and security personnel). See Autor and Dorn (25) and
Goos et al. (26) for evidence that employment in the
U.S. and among OECD member countries has increasingly
polarized into high-paid, abstract-intensive occupations and
low-paid, manual-intensive occupations.
28. J. Tinbergen, Kyklos 27, 217–226 (1974).
29. Details of this model are given in the online supplement.
30. D. Card, T. Lemieux, Am. Econ. Rev. 91, 97–102
(2001).
31. L. F. Katz, K. M. Murphy, Q. J. Econ. 107, 35–78
(1992).
32. L. Katz, D. H. Autor, Changes in the wage structure and
earnings inequality. In Handbook of Labor Economics,
D. Card, O. Ashenfelter, Eds. (Elsevier-North Holland, Amsterdam,
1999), vol. 3, pp. 1463–1555.
33. D. Card, T. Lemieux, Q. J. Econ. 116, 705–746 (2001).
34. D. H. Autor et al., Rev. Econ. Stat. 90, 300–323 (2008).
35. E. Crivellaro, “College wage premium over time: Trends in
Europe in the last 15 years.” University Ca’ Foscari
of Venice, Department of Economics Research Paper
Series no. 03/wp01/2014 (2014); http://dx.doi.org/10.2139/
ssrn.2383795.
36. Summarizing evidence on the college premium in 12 European
countries between 1994 and 1999, Crivellaro (35) found a
pattern of increasing skill differentials except in countries that
have had a large increase in the relative supply of college
850 23 MAY 2014 • VOL 344 ISSUE 6186 sciencemag.org SCIENCE
graduates, a pattern consistent with the conceptual model laid
out below.
37. Although this deceleration is not evident from Fig. 3, it is
detected by the regression equation, as discussed in the online
supplement.
38. College Board, Trends in Student Aid: 2013 (College Board,
New York, 2013).
39. C. Avery, S. Turner, J. Econ. Perspect. 26, 165–192 (2012).
40. Three sources of uncertainty should be kept in mind when
interpreting these estimates. First, they encompass
substantial heterogeneity. Although the average college
graduate earns substantially more than the average high
school graduate, the least successful college graduates may
earn substantially less than the median among high school
graduates, and the most successful high school graduates
may earn substantially more than the median among college
graduates. Second, for students who acquire substantial
student debt but do not complete the college degree, it is
far from certain that college will prove a good investment.
Finally, these calculations assume that the lifetime profile of
earnings observed in the year of college graduation will
persist throughout the career. As Fig. 3 indicates, this
premium has changed substantially over time, so this
assumption is only a rough approximation. However, the
college premium is so high at present that even with a
substantial decline, college would remain an attractive
financial proposition on average from a lifetime earnings
perspective.
41. R. J. Murnane, J. Econ. Lit. 51, 370–422 (2013).
42. D. Acemoglu, J. Robinson, Why Nations Fail (Crown, New York,
2012).
43. As with cross-sectional inequality, there is no economically
“ideal” level of intergenerational mobility. Even in a society
with perfect equality of opportunity, one would expect children
of successful parents to have above-average success as
adults, simply because many attributes that contribute to
success (appearance, intellect, athleticism) are partly
heritable.
44. M. Corak, J. Econ. Perspect. 27, 79–102 (2013).
45. S. F. Reardon, The widening academic achievement gap
between the rich and the poor: New evidence and possible
explanations. In Whither Opportunity? Rising Inequality,
Schools, and Children’s Life Chances, G. J. Duncan,
R. J. Murnane, Eds. (Russell Sage Foundation, New York, 2011),
pp. 91–115.
46. R. Chetty et al., Is the United States Still a Land of Opportunity?
Recent Trends in Intergenerational Mobility (NBER Working
Paper No. 19844, Cambridge, MA, 2014).
47. C.-I. Lee, G. Solon, Rev. Econ. Stat. 91, 766–772 (2009).
48. Between 2002–2003 and 2012–2013, the sum of federal
Pell Grants and loans for higher education increased by 105%,
from $83 billion to $170 billion in constant 2012 dollars
[(38), table 1].
49. C.-T. Hsieh et al., The Allocation of Talent and U.S. Economic
Growth (NBER Working Paper No. 18693, Cambridge, MA, 2013).
50. D. H. Autor et al., Am. Econ. Rev. 103, 2121–2168 (2013).
51. D. Card et al., J. Labor Res. 25, 519–559 (2004).
52. B. T. Hirsch, J. Econ. Perspect. 22, 153–176 (2008).
53. A. S. Goldberger, Economica 46, 327 (1979).
54. The extensive involvement of state and federal government in
education at all levels also underscores the fact that the
distribution of education and skills today is in no sense a
“free market” outcome; it is a consequence of both individual
and public choices.
ACKNOWLEDGMENTS
I thank D. Acemoglu, L. Katz, J. Van Reenen, M. Tatsutani, and
two anonymous referees for valuable comments and advice, and
C. Patterson and B. Price for expert research assistance.
Supported by NSF grant SES-1227334, Russell Sage Foundation
grant 85-12-07, and Alfred P. Sloan Foundation grant 2011-10-12.
All data and code that are unique to this article (Figs. 1, 3,
and 6; fig. S2) are available from the author. All other figures
(Figs. 2, 4, and 5; figs. S1 and S3) are reproduced from
other publications, as noted, with permission of the authors.
SUPPLEMENTARY MATERIALS
www.sciencemag.org/content/344/6186/843/suppl/DC1
Supplementary Text
Figs. S1 to S3
References (55–61)
10.1126/science.1251868
REVIEW
Income inequality in the
developing world
Martin Ravallion
Should income inequality be of concern in developing countries? New data reveal less
income inequality in the developing world than 30 years ago. However, this is due to falling
inequality between countries. Average inequality within developing countries has been
slowly rising, though staying fairly flat since 2000. As a rule, higher rates of growth in
average incomes have not put upward pressure on inequality within countries. Growth
has generally helped reduce the incidence of absolute poverty, but less so in more
unequal countries. High inequality also threatens to stall future progress against
poverty by attenuating growth prospects. Perceptions of rising absolute gaps in living
standards between the rich and the poor in growing economies are also consistent
with the evidence.
Development economics emerged as a subdiscipline
of economics in the 1950s, and
its initial focus was on economic growth,
with inequality as a secondary concern.
The prevailing orthodoxy for many decades
was that a period of rising inequality was to
be expected in growing poor countries. Rising
inequality was seen to be more or less inevitable
and not something to worry about, particularly
if the incidence of poverty was falling. Another
commonly held view was that policy efforts to
reduce inequality were likely to impede growth
and (hence) poverty reduction.
The existence of high inequality within many
developing countries, side by side with persistent
poverty, started to attract attention in the early
1970s. Nonetheless, through the 1980s and
well into the 1990s, the mainstream view in
development economics was still that high
and/or rising inequality in poor countries was
a far less important concern than assuring sufficient
growth, which was the key to poverty
reduction. The policy message for the developing
world was clear: You cannot expect to have
both lower poverty and less inequality while
you remain poor, and, if you choose to give poverty
reduction highest priority, then focus on
growth.
Other objections could still be raised to
high income inequality. The classical utilitarian
formulation—whereby social welfare is judged
by the sum of utilities, assuming diminishing
marginal utility of income—pointed to social welfare
losses from high inequality at a given mean.
But that did not persuade those who believed
that there was a trade-off between equity and
growth. A moral defense could also be mounted
for the view that inequality is not an important
issue for a growing developing country by appeal
to John Rawls’s “difference principle” that (subject
to assuring liberty and equal opportunity) higher
inequality can be justified as long as it benefits the
worst-off group in society (1).
The period since 2000 has seen a deeper and
morewidespread questioning of this long-standing
view of pro-poor inequality. New concerns have
emerged about the instrumental importance of
equity to other valued goals, including poverty
reduction and human development more broadly.
It appears more likely today that high inequality
will be seen as a threat to future development
than as an inevitable and unimportant consequence
of past progress. The long-standing idea
of a substantial growth-equity trade-off has come
to be seriously questioned.
This paper reports new estimates of the levels
and changes in income inequality measures for
the developing world. The new estimates take
us up to 2010, embracing the period of higher
growth rates in the developing world since the
turn of the millennium. In the light of these new
data, I revisit past and ongoing debates on inequality
in developing countries and the tradeoffs
with growth and poverty reduction.
Income Inequality Measures
To measure inequality in the developing world
as a whole, one ignores country borders—pooling
all residents and measuring inequality among
them. This overall measure will naturally depend
on the inequality between countries as well as
that within them. Thus, its evolution over time
will depend on whether poorer countries are
seeing lower growth rates as well as the things
happening within countries—economic changes
and policies—that affect inequality.
If we are comparing country or regional performance,
then we want to isolate the withincountry
component of inequality as distinct from
that between countries. Although there are many
inequality measures, not all of themallow a clean
separation of the between and within components.
For example, such a decomposition is
Department of Economics, Georgetown University,
Washington, DC 20057, and National Bureau of Economic
Research, Cambridge, MA 02138, USA. E-mail: mr1185@
georgetown.edu
SCIENCE sciencemag.org 23 MAY 2014 • VOL 344 ISSUE 6186 851

Why Has Income Inequality Been Rising Since the 1970’s?
Due in NetTutor through the link in Blackboard (learn.wsu.edu) by February
18, by 11:50 pm (PST)
Below are two competing views (Proposition #1 and Proposition #2) on why income inequality
has been rising (even among the lower 99%) in the United States since the 1970’s.
Reading: “Skills, education, and the rise of earnings inequality among the “other 99 percent,”
David H. Autor, Science 23 May 2014: 843-851. [DOI:10.1126/science.1251868] (see below)
Proposition #1:
“More people are falling behind today because they’re lazy. In the U.S. anyone willing to work can
earn a decent living. The income distribution is the same as the effort distribution. People who work
hard earn a good living. Lazy people stay poor. The income distribution has been skewing because of
a growing culture of dependency. It used to be shameful to accept handouts from the government, but
now it’s perfectly acceptable. So more people opt out of the labor force and sit around collecting
handouts from the rest of us. Of course their incomes are going to be lower than those in the top
tiers.”
Proposition #2:
“The incomes of the rich and poor have been growing farther apart due to purposeful policy
decisions that have favored the rich at the expense of the poor. Starting with Ronald Reagan’s
financial deregulation, and continuing with successive Republican attacks on New Deal and Great
Society social programs to finance tax cuts for their Wall Street cronies, government policy in this
country has systematically channeled the rewards of our immensely productive economy towards the
rich and away from the poor. America needs to open the gateway to prosperity for all through fair tax
policies, decent minimum wages, better public schools, aid to low-income college students, and a
universal health care system.”
EconS 101 WA 1
Page 2 of 3
Assess the degree to which each of those propositions is actually consistent with David H.
Autor’s well-researched explanation of the same phenomenon. That is, for each of the
propositions tell me whether or not Autor would agree that the underlying factor described in
the proposition is the major cause of the pattern of income inequality since the 1970’s. (2
paragraphs)
1) Begin with a topic sentence that concisely states your judgment about how consistent the two
propositions are with Autor’s analysis, and complete that paragraph with a summary of
Autor’s explanation. Be sure to mention some of the factors Autor identifies as having
contributed to an increase in the demand for highly-educated workers and a decrease in the
demand for less-educated workers.
2) In your second paragraph, discuss the degree to which the two propositions are consistent
with Autor’s analysis. Is one more consistent than the other?
EconS 101 WA 1
Page 3 of 3
Grading Rubric
Please include your name, instructor’s name (Dr. Prera), course and section number (i.e. EconS
101.01 [MWF 9:10-10 am] or 101.03 [TTh 10:35-11:50 am]), and writing assignment
(Assignment 1) on the top of your assignment.
This is the first of four writing assignments. After receiving feedback, you may re-submit the
essay to NetTutor for another try – but you only get one second chance on each writing
assignment. If you don’t get an acceptable score, even after revising and resubmitting this
assignment, you can still rescue your writing assignment average by doing well on the next three
assignments. I will take the three highest writing assignment scores to compute your writing
assignment average.
Criteria Meets Expectations Needs Improvement
Writing
Ideas are well-organized.
Transition sentences effectively
connect one idea to the next. The
essay is free of typos and
grammatical errors.
The writing is difficult to
follow and/ or poorly
organized.
Transition sentences are
absent or ineffective.
Typos and/ or grammatical
errors distract the reader.
Economic analysis
The topic sentence clearly states
the student’s judgment about the
degree to which Propositions #1
and #2 are consistent with
Autor’s study.
The topic sentence does not
state the student’s judgment
about the degree to which
Propositions #1 and #2 are
consistent with Autor’s study.
Correctly identifies the major
cause of rising inequality in
Autor’s analysis.
Autor’s argument is
misrepresented, and/or the
summary is too skimpy.
Explicitly state the degree to
which the two propositions are
consistent with Autor’s research,
and supports his or her claims
with examples from the reading.
The question of the degree to
which the two propositions
are consistent with Autor’s
research is not addressed
and/or, is not supported with
examples.
DOI: 10.1126/science.1251868
Science 344, 843 (2014);
David H. Autor
”other 99 percent”
Skills, education, and the rise of earnings inequality among the
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of rising or shrinking inequality. Which one
dominates depends on the institutions and policies
that societies choose to adopt.
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Personal Wealth from a Global Perspective (Oxford Univ. Press,
Oxford, 2008), pp. 42–63.
20. D. Waldenstrom, Lifting all Boats? The Evolution of Income and
Wealth Inequality Over the Path of Development (Lund
University, Sweden, 2009)
21. T. Piketty, Q. J. Econ. 126, 1071–1131 (2011).
22. R. Goldsmith, Comparative National Balance Sheets: A Study of
Twenty Countries, 1688-1978 (Univ. of Chicago Press, Chicago,
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23. T. Piketty, G. Zucman, Q. J. Econ. 129, in press
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PikettyZucman2013WP.pdf.
24. T. Piketty, Capital in the Twenty-first Century (Harvard Univ.
Press, Cambridge, MA, 2014).
25. J. Stiglitz, Econometrica 37, 382–397 (1969).
26. M. Nirei, “Pareto Distributions in Economics Growth Models,”
Institute of Innovation Research Working Paper No. 09-05,
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(Harvard Univ. Press, Cambridge, MA, 2008).
SUPPLEMENTARY MATERIALS
www.sciencemag.org/content/344/6186/838/suppl/DC1
Supplementary Text
Figs. S1 and S2
References (31, 32)
10.1126/science.1251936
REVIEW
Skills, education, and the rise of
earnings inequality among
the “other 99 percent”
David H. Autor
The singular focus of public debate on the “top 1 percent” of households overlooks the
component of earnings inequality that is arguably most consequential for the “other
99 percent” of citizens: the dramatic growth in the wage premium associated with higher
education and cognitive ability. This Review documents the central role of both the supply
and demand for skills in shaping inequality, discusses why skill demands have persistently
risen in industrialized countries, and considers the economic value of inequality alongside
its potential social costs. I conclude by highlighting the constructive role for public policy in
fostering skills formation and preserving economic mobility.
Public debate has recently focused on a
subject that economists have been analyzing
for at least two decades: the steep,
persistent rise of earnings inequality in
the U.S. labor market and in developed
countries more broadly. Much popular discussion
of inequality concerns the “top 1 percent,”
referring to the increasing share of national income
accruing to the top percentile of households.
Although this phenomenon is undeniably
important, an exclusive focus on the concentration
of top incomes ignores the component
of rising inequality that is arguably even more
consequential for the “other 99 percent” of
citizens: the dramatic growth in the wage premium
associated with higher education and,
more broadly, cognitive ability. This paper considers
the role of the rising skill premium in
the evolution of earnings inequality.
There are three reasons to focus a discussion
of rising inequality on the economic payoff
to skills and education. First, the earnings
premium for education has risen across a large
number of advanced countries in recent decades,
and this rise contributes substantially to
the net growth of earnings inequality. In the
United States, for example, about two-thirds
of the overall rise of earnings dispersion between
1980 and 2005 is proximately accounted
for by the increased premium associated with
schooling in general and postsecondary education
in particular (1, 2). Second, despite a
lack of consensus among economists regarding
the primary causes of the rise of very top
incomes (3–6), an influential literature finds
that the interplay between the supply and
demand for skills provides substantial insight
into why the skill premium has risen and fallen
over time—and, specifically, why the earnings
gap between college and high school graduates
has more than doubled in the United States over
the past three decades. A third reason for focusing
on the skill premium is that it offers broad
insight into the evolution of inequality within a
market economy, highlighting the social value of
inequality alongside its potential social costs and
illuminating the constructive role for public policy
in maximizing the benefits and minimizing the
costs of inequality.
The rising skill premium is not, of course, the
sole cause of growing inequality. The decadeslong
decline in the real value of the U.S. minimum
wage (7), the sharp drops in non-college
employment opportunities in production, clerical,
and administrative support positions stemming
from automation, the steep rise in international
competition from the developing world,
the secularly declining membership and bargaining
power of U.S. labor unions, and the
successive enactment of multiple reductions in
top federal marginal tax rates, have all served to
magnify inequality and erode real wages among
less educated workers. As I discuss below, the
foremost concern raised by these multiple forces
is not their impact on inequality per se, but
rather their adverse effect on the real earnings
and employment of less educated workers.
I begin by documenting the centrality of the
rising skill premium to the overall growth of
earnings inequality. I next consider why skills
are heavily rewarded in advanced economies
and why the demand for them has risen over
time. I then demonstrate the substantial explanatory
power of a simple framework that
embeds both the demand and supply for skills
in interpreting the evolution of the inequality
over five decades. The final section considers
the productive role that inequality plays in a
market economy and the potential risks attending
very high and rising inequality; evidence on
whether those risks have been realized; and
the role of policy and governance in encouraging
skills formation, fostering opportunity,
Department of Economics and National Bureau of Economic
Research, Massachusetts Institute of Technology, 40 Ames
Street, E17-216, Cambridge, MA 02142, USA. E-mail: dautor@
mit.edu
SCIENCE sciencemag.org 23 MAY 2014 • VOL 344 ISSUE 6186 843
and countering the possibility that extremes of
inequality erode economic mobility and reduce
economic dynamism.
The Critical Role of Skills in the
Labor Market
There is no denying the extraordinary rise in
the incomes of the top 1% of American households
over the past three decades. Between
1979 and 2012, the share of all household income
accruing to the top percentile of U.S.
households rose from 10.0% to 22.5% (8, 9). To
get a sense of how much money that is, consider
the conceptual experiment of redistributing
the gains of the top 1% between 1979 and
2012 to the bottom 99% of households (10).
Howmuchwould this redistribution raise household
incomes of the bottom 99%? The answer
is $7107 per household—a substantial gain, equal
to 14% of the income of the median U.S. household
in 2012. (I focus on the median because it
reflects the earnings of the typical worker and
thus excludes the earnings of the top 1%.)
Now consider a different dimension of inequality:
the earnings gap between U.S. workers
with a 4-year college degree and those with
only a high school diploma (11). Economists frequently
use this college/high school earnings
gap as a summary measure of the “return to
skill”—that is, the gain in earnings a worker
can expect to receive from investing in a college
education. As illustrated in Fig. 1, the earnings
gap between the median college-educated
and median high school–educated among U.S.
males working full-time in year-round jobs was
$17,411 in 1979, measured in constant 2012 dollars.
Thirty-three years later, in 2012, this gap
had risen to $34,969, almost exactly double its
1979 level. Also seen is a comparable trend among
U.S. female workers, with the full-time, fullyear
college/high school median earnings gap
nearly doubling from $12,887 to $23,280 between
1979 and 2012. As Fig. 1 underscores, the
economic payoff to college education rose steadily
throughout the 1980s and 1990s and was
barely affected by the Great Recession starting
in 2007.
Because the earnings calculations in Fig. 1 reflect
individual incomes while the top 1% calculations
reflect household incomes, the two
calculations are not directly comparable. To
put the numbers on the same footing, consider
the earnings gap between a college-educated
two-earner husband-wife family and a high school–
educated two-earner husband-wife family, which
rose by $27,951 between 1979 and 2012 (from
$30,298 to $58,249). This increase in the earnings
gap between the typical college-educated
and high school–educated household earnings
levels is four times as large as the redistribution
that has notionally occurred from
the bottom 99% to the top 1% of households.
What this simple calculation suggests is that
the growth of skill differentials among the “other
99 percent” is arguably even more consequential
than the rise of the 1% for the welfare of
most citizens.
The median earnings comparisons in Fig. 1 also
convey a key feature of rising inequality that
cannot be inferred from trends in top incomes:
Wage inequality has risen throughout the earnings
distribution, not merely at the top percentiles.
Figure S1 documents this pattern by plotting,
for 12 Organization for Economic Cooperation
and Development (OECD) member countries over
three decades (1980 to 2011), the change in the
ratio of full-time earnings of males at the 90th
percentile relative to males at the 10th percentile
of the wage distribution. Although the 90/10
earnings ratio differed greatly across countries
at the earliest date of the sample—from a low
of 2.0 in Sweden to a high of 3.6 in the United
States—this earnings ratio increased substantially
in all but one of them (France) over the
next 30 years, growing by at least 25 percentage
points in 10 countries, by at least 50 percentage
points in 8 countries, and by more than 100 percentage
points in three countries (New Zealand,
the United Kingdom, and the United States).
How much does the rising education premium
contribute to the increase of earnings inequality?
Although data limitations make it difficult to
answer this question for most countries, we do
know the answer for the United States. Goldin
and Katz (1) found that the increase in the education
wage premium explains about 60 to 70%
of the rise in the dispersion of U.S. wages between
1980 and 2005 and, similarly, Lemieux (12)
calculated that higher returns to postsecondary
education can account for 55% of the rise in
male hourly wage variance from 1973–1975 to
2003–2005. Firpo et al. (13) found that rising
returns to education can explain just over 95% of
the rise of the U.S.male 90/10 earnings ratio between
1984 and 2004. That is, holding the expanding
education premium constant over this
period, there would have been essentially no increase
in the relative wages of the 90th-percentile
worker versus the 10th-percentile worker.
I have so far used the terms education and
skill interchangeably.What evidence do we have
that it is skills that are rewarded per se, rather
than simply educational credentials? The Program
for the International Assessment of Adult
Competencies (PIAAC) provides a compelling
data source for gauging the importance of
skills in wage determination. The PIAAC is an
internationally harmonized test of adult cognitive
and workplace skills (literacy, numeracy,
and problem-solving) that was administered
by the OECD to large, representative samples
of adults in 22 countries between 2011 and
2013 (14). Figure 2, sourced from (15), plots the
relationship between adults’ earnings and their
PIAAC numeracy scores across these 22 countries.
The length of each bar reflects the average
percentage earnings differential between
full-time workers ages 35 to 54 who differ by
one standard deviation in the PIAAC score.
The whiskers on each bar provide the 95%
confidence intervals for the estimates.
College/high school median annual earnings gap, 1979–2012
In constant 2012 dollars
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000 dollars
1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012
Household gap
$30,298 to $58,249
Male gap
$17,411 to $34,969
Female gap
$12,887 to $23,280
Fig. 1. College/high school median annual earnings gap, 1979–2012. Figure is constructed using
Census Bureau P-60 (1979–1991) and P-25 (1992–2012) tabulations of median earnings of full-time,
full-year workers by educational level and converted to constant 2012 dollars (to account for
inflation) using the CPI-U-RS price series. Prior to 1992, college-educated workers are defined as
those with 16 or more years of completed schooling, and high school–educated workers are those
with exactly 12 years of completed schooling. After 1991, college-educated workers are those who
report completing at least 4 years of college, and high school–educated workers are those who
report having completed a high school diploma or GED credential.
844 23 MAY 2014 • VOL 344 ISSUE 6186 sciencemag.org SCIENCE
This figure conveys three points. First, cognitive
skills are substantially rewarded in the labor
market across all 22 economies. The average
wage premium corresponding to one “unit” (i.e.,
one standard deviation) increase in measured
cognitive skills is 18%. In addition, cognitive earnings
premiums differ substantially across countries.
The premium is below 13% in Sweden,
the Czech Republic, and Norway. It is above
20% in six countries. The United States stands
out as having the highest measured return to
skill, with a premium of 28% per unit increment
to cognitive ability. Concretely, comparing two
U.S. workers who are one standard deviation
above and one standard deviation below the
population average of cognitive ability, we would
expect their full-time weekly earnings to differ
by 50 to 60%. Notably, the high return to
cognitive ability in the United States does not
follow automatically from high levels of U.S.
earnings inequality. If U.S. wages were determined
mainly by luck, beauty, or family connections,
we would expect little connection
between workers’ cognitive ability and their labor
market rewards (16). Figure 2 demonstrates
that this is not the case.
Of course, these data do not explain why
the skill premium has risen over time, nor
why the United States has a higher skill premium
than so many other advanced nations.
The next section considers the supply and demand
for skill in the labor market—specifically, why
they fluctuate over time and how their interaction
helps to determine the skill premium. I
focus on the United States in this section to allow
a deeper exploration of the data.
Education and Inequality
Workers’ earnings in a market economy depend
fundamentally (some economists would
say entirely) on their productivity—that is, the
value they produce through their labor. And in
turn, workers’ productivity depends on two factors.
One is their capabilities, concretely, the
tasks they can accomplish (i.e., their skills). A
second is their scarcity: The fewer workers that
are available to accomplish a task, and the more
employers need that task accomplished, the
higher is workers’ economic value in that
task. In conventional terms, the skill premium
depends uponwhat skills employers require (skill
demand) and what skills workers have acquired
(skill supply). To interpret the evolution of this
premium, we need to account for both forces.
Skill Demands: The Long View
A technologically advanced economy requires
a literate, numerate, and technically and scientifically
trained workforce to develop ideas,manage
complex organizations, deliver healthcare
services, provide financing and insurance, administer
government services, and operate critical
infrastructure. This was not always the case. In
1900, 4 in 10 U.S. jobs were in agriculture, 11%
of the population was illiterate, a substantial
fraction of economic activity required hard physical
labor, and workers’ strength and physical
stamina were key job skills (17, 18). Few citizens
would have predicted at the time that a century
later, health care, finance, information technology,
consumer electronics, hospitality, leisure,
and entertainment would employ farmoreworkers
than agriculture—which employed only 2%
of U.S. workers in 2010. As physical labor has
given way to cognitive labor, the labor market’s
demand for formal analytical skills, written communications,
and specific technical knowledge—
what economists often loosely term cognitive
skills—has risen spectacularly.
The central determinant of the supply of
skills available to an advanced economy is its
education system. In 1900, the typical young,
native-born American had only a common school
education, about the equivalent of six to eight
grades (19). By the late 19th century, however,
many Americans recognized that farm employment
was declining, industry was rising, and
their children would need additional education
to earn a living. Over the first four decades of the
20th century, the United States became the first
nation in the world to deliver universal high
school education to its citizens. Tellingly, the high
school movement was led by the farm states.
As the high school movement reached its
conclusion, postsecondary education became
increasingly indispensable to the growing occupations
of medicine, law, engineering, science,
and management. In 1940, only 6% of
Americans had completed a 4-year college
degree. From the end of the Second World
War to the early 1980s, however, the ranks of
college-educated workers rose robustly and
steadily, with each cohort of workers entering
the labor market boasting a proportionately
higher rate of college education than
the cohort that preceded it. This intercohort
pattern, which was abetted by the Second
World War and Korean War GI Bills (20) and
by huge state and federal investments in public
college and university systems, is depicted in
Fig. 3A. From 1963 through 1982, the fraction
of all U.S. hours worked that were supplied
by college graduates rose by almost 1 percentage
point per year, a remarkably rapid gain.
After 1982, however, the rate of intercohort
increase fell by almost half—from0.87 percentage
points to 0.47 percentage points per year—and
did not begin to rebound until 2004, nearly
two decades later. As shown in fig. S2, this deceleration
in the supply of college graduates is
particularly stark when one focuses on young
adults with fewer than 10 years of experience—
that is, the cohorts of recent labor market
entrants at each point in time. Although the
supply of young college-educated males relative
to young high school–educated males increased
rapidly in the 1960s and early 1970s
(and indeed throughout the postwar period), this
rising tide reached an apex in 1974 from which
Fig. 2. Cross-national differences
in wage returns to skills,
2011–2013. Reproduced with
permission from Hanushek et al.
[(15), table 2]. Estimates are
obtained by regressing the
natural logarithm of workers’
weekly full-time earnings on test
scores while controlling for sex
and labor market experience
(both a linear and a quadratic
term). Regression estimates are
performed separately for each
country and test scores are
normalized with mean zero and
unit standard deviation within
each country. Estimates that
normalize test scores on a
common basis across countries,
or that use literacy or
problem-solving scores rather
than numeracy scores,
yield qualitatively similar patterns.
Cross-national differences in wage returns
to skills, 2011–2013
Percentage increase for a one standard deviation
increase in skill
0 5 10 15 20 25 30 percent
Sweden
Czech R.
Norway
Italy
Denmark
Cyprus
Finland
Belgium
France
Estonia
Slovak R.
Austria
Netherlands
Japan
Poland
Canada
Korea
U.K.
Spain
Germany
Ireland
U.S.
Earnings
gain
95% confidence
interval
SCIENCE sciencemag.org 23 MAY 2014 • VOL 344 ISSUE 6186 845
it barely budged for the better part of the next
30 years. Among young females, the deceleration
in supply was also unmistakable, although not as
abrupt or as complete as for males.
The counterpart to this deceleration in the
growth of supply of college-educated workers
is the steep rise in the college premium commencing
in the early 1980s and continuing for
25 years. Concretely, when the influx of new
college graduates slowed, the premium that a
college education commanded in the labor market
increased. The critical role played by the
fluctuating supply of college education in the
rise of U.S. inequality is documented in Fig. 3B,
which plots the college wage premium from
1963 through 2012 (blue line). This premium
fluctuated in a comparatively narrow band during
the 1960s and 1970s, as rising demand for
educated workers was met with rapidly rising
year-over-year increases in supply. In 1981, the
average college graduate earned 48% more per
week than the average high school graduate—a
significant earnings gap but not an earnings
gulf. When the supply deceleration began in
1982, however, the college premium hit an inflection
point. This premium notched remarkably
rapid year-over-year gains from 1982 forward,
reaching 72% in 1990, 90% in 2000, and 97% in
2005 (21, 22). Thus, the average earnings of college
graduates were 1.5 times those of high school
graduates in 1982 but were double those of high
school graduates by 2005.
Why is this deceleration in supply relevant
to the college premium? After all, although the
growth of supply slowed in 1982, it was still
rising. A likely answer is that the demand for
college workers rose in the interim. Throughout
much of the 20th century, successive waves
of innovation—electrification, mass production,
motorized transportation, telecommunications—
have reduced the demand for physical labor
and raised the centrality of cognitive labor in
practically every walk of life. The past three
decades of computerization, in particular, have
extended the reach of this process by displacing
workers from performing routine, codifiable
cognitive tasks (e.g., bookkeeping, clerical work,
and repetitive production tasks) that are now
readily scripted with computer software and
performed by inexpensive digital machines. This
ongoing process of machine substitution for routine
human labor complements educated workers
who excel in abstract tasks that harness
problem-solving ability, intuition, creativity, and
persuasion—tasks that are at present difficult
to automate but essential to perform. Simultaneously,
it devalues the skills of workers, typically
those without postsecondary education,
who compete most directly with machinery in
performing routine-intensive activities. The net
effect of these forces is to further raise the demand
for formal education, technical expertise,
and cognitive ability (23–27).
Bringing the Supply-Demand
Framework to the Data
The persistently rising demand for educated
labor in advanced economies was first noted
by the Nobel Prize–winning economist Jan
Tinbergen (28) and is often referred to as the
“education race” model (19). Its primary implication
is that if the supply of educated labor
does not keep pace with persistent outward
shifts in demand for skills, the skill premium
will rise. In the words of the Red Queen in
Lewis Carroll’s Alice in Wonderland, “…it takes
all the running you can do, to keep in the same
place.” Thus, when the rising supply of educated
labor began to slacken in the early 1980s,
a logical economic consequence was an increase
in the college skill premium.
To more formally account for the impact of
the fluctuating growth rate of supply of collegeeducated
workers on the college wage differential,
Fig. 3B depicts the fit of a simple regression
model that predicts the college wage premium
in each year as a function of two factors: (i) the
contemporaneous supply of college graduates,
and (ii) a time trend, which serves as a proxy for
the secularly rising demand for college-educated
15
20
25
30
35
40
45
50 percent
1964 1970 1976 1982 1988 1994 2000 2006 2012
35
45
55
65
75
85
95
105 percent
1964 1970 1976 1982 1988 1994 2000 2006 2012
The supply of college graduates and the U.S. college/high school premium, 1963–2012
College share of hours worked (%), 1963–2012:
All working-age adults
College versus high school
wage gap (%)
A B
Predicted by Supply-
Demand Model
Measured Gap
Fig. 3. The supply of college graduates and the U.S. college/high school
premium, 1963–2012. (A) College share of hours worked in the United
States, 1963–2012: All working-age adults. Figure uses March CPS data for
earnings years 1963 to 2012. The sample consists of all persons aged 16 to
64 who reported having worked at least 1 week in the earnings years,
excluding those in the military. Following an extensive literature, collegeeducated
workers are defined as all of those with four or more completed
years of college plus half of those with at least 1 year of completed college.
Non-college workers are defined as all workers with high school or less
education, plus half of those with some completed college education. For
each individual, hours worked are the product of usual hours worked per
week and the number of weeks worked last year. Individual hours worked
are aggregated using CPS sampling weights. (B) College versus high school
wage gap. Figure uses March CPS data for earnings years 1963 to 2012.The
series labeled “Measured Gap” is constructed by calculating the mean of
the natural logarithm of weekly wages for college graduates and non–
college graduates, and plotting the (exponentiated) ratio of these means for
each year. This calculation holds constant the labor market experience and
gender composition within each education group. The series labeled
“Predicted by Supply-Demand Model” plots the (exponentiated) predicted
values from a regression of the log college/noncollege wage gap on a
quadratic polynomial in calendar years and the natural log of college/
noncollege relative supply. See text and supplementary material for further
details.
846 23 MAY 2014 • VOL 344 ISSUE 6186 sciencemag.org SCIENCE
workers (29). Comparing the fitted values (red
series) from this simple supply-demand model
alongside the actual data (blue series) reveals an
extremely tight correspondence over the course
of five decades and three distinct eras: a declining
skill premium in the 1970s; an explosive rise in
the premium during the 1980s, 1990s, and early
2000s; and,most recently, a plateau commencing
after 2005. A key implication of this figure is that
a central causal factor behind rising inequality
in the United States has been the slowdown in
the accumulation of skills by young adults almost
30 years ago. Had the supply of college graduates
risen as rapidly in the decades after 1980 as it did
in the decades immediately before, it is quite plausible
that there would have been no sustained
rise in the skill premium in the U.S. labormarket.
Of course, this set of facts raises another puzzle:
If slackening college supply sparked rising
inequality, what caused rising U.S. postsecondary
achievement to grind to a sudden halt in 1982?
Work by Card and Lemieux (30) highlights that
one critically important factor was the United
States’ involvement in the Vietnam War. Because
draft-eligible males in the Vietnamera were often
able to defer their military service by enrolling
in postsecondary schooling, the war artificially
boosted college attendance. This created something
of a glut of college enrollments in the late
1960s and early 1970s, which in turn depressed
the college earnings premium in the 1970s (see
Fig. 3) and likely reduced the attractiveness of
college-going absent themilitary draft. Thus, when
the war ended in the early 1970s, college enrollment
rates dropped sharply, particularly among
males. The fall in enrollment produced a corresponding
decline in college completions half a
decade later, and a surge of inequality followed.
This supply-demand explanation for the rise of
U.S. inequality may appear almost too simple to
be credible. After all, we are comparing just two
economic variables: the college wage premium
and the supply of college graduates in the U.S.
workforce. But a host of rigorous studies commencing
with Katz and Murphy (31) confirmthe
remarkable explanatory power of this simple
supply-demand framework for explaining trends
in the college versus high school earnings gap
over the course of nine decades of U.S. history, as
well as across other industrialized economies (most
notably, the United Kingdom and Canada) and
among age and education groups within countries
(19, 31–36). The United States was far from the
only Western country to experience this surge.
One should not, of course, take this model as
irrefutable. A puzzling pattern evident in the
data is that the rising demand for skilledworkers
appears to have slowed in the early 1990s, a
phenomenon that is not anticipated by the “education
race” model (37). This discrepancy underscores
that the supply-demand model is
necessarily incomplete—in part for the sake of
expositional clarity and, in larger part, because
our understanding of macroeconomic phenomena
is typically imperfect. Nevertheless, the data
speak sufficiently clearly to warrant two economic
inferences. The first is that although popular
accounts frequently assert that the United
States is in the midst of a “college bubble”—too
many students going to college at too high a
cost—abundant economic evidence strongly suggests
otherwise. Yes, college tuitions have risen
far faster than inflation, and indeed, student
debt has risen rapidly, with more than $100
billion in federal student aid dollars loaned in
2012–2013 alone (38). But the doubling of the
college weekly wage differential over the past
30 years also implies that there have been sizable
increases in the lifetime earnings of college graduates
relative to high school graduates. How large
are these gains? Figure 4, reproduced from (39),
reports the estimated lifetime college earnings
differential net of tuition for cohorts of students
entering the labor market between 1965 and
2008. For both males and females, the expected
net present value of a college degree relative to a
high school diploma roughly tripled in this period,
with the fastest gains accruing during the
1980s and 1990s. Note that this growing college/
high school gap reflects the rising payoff to the
4-year college degree, the even steeper rise in the
premium associated with graduate and professional
degrees (see below), and the growing fraction of
college graduateswho obtain higher degrees; thus,
an additional payoff to the college degree is that
it opens the door to further specialization. This
lifetime earnings differential would, of course, have
risen further still if college tuitions had held steady
rather than rising. But the inevitable sticker shock
that households feel when confronting the cost of
college should not obscure the fact that the real
lifetime earnings premium to college education
has likely never been higher (40).
The second positive economic news implied
by Fig. 3 above is that the ongoing rise
of skill differentials is not inevitable. Prior cohorts
of U.S. students, particularly males, were
slow to react to the rising return to education
during the 1980s and 1990s, but the message
appears to have finally gotten through. During
the first decade of the 21st century, the U.S.
high school graduation rate rose sharply after
having been essentially stagnant since the late
1960s (41). This unanticipated rise was followed
just a few years later by a surge in college completions.
Between 2004 and 2012, the supply of
new college graduates to the U.S. labor market
rose at a rate not seen in several decades (Fig.
3A). As this influx of supply took hold, the college
wage premium halted its enduring rise (Fig.
3B). What these observations and our simple
213K
368K
261K
385K
439K
582K
590K
129K
198K
138K
225K
284K
387K 370K
Present discounted value of college relative to high school degree
net of tuition, 1965–2008
College/high school difference, 2009 dollars
100,000
0
200,000
300,000
400,000
500,000
600,000 dollars
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Men
Women
Fig. 4. Present discounted value of college relative to high school degree net of tuition,
1965–2008. Reproduced from Avery and Turner with permission of the American Economic Association
(39). Expected earnings are calculated from the March Current Population Survey files
for full-time, full-year workers using sample weights. The estimates equal what a man or woman
would expect to earn working full-time, full-year over a career of 42 years, with a discount rate of
3%, assuming that college graduates delay the start of earnings for 4 years while in school.
Earnings expectations are formed in each year by assuming that future high school and college
graduates will have future earnings at each age equal to the average earnings of high school and
college graduates (respectively) currently observed at each age; for example, expected earnings in
1980 are based on data across ages for 1980. Results for college-educated workers are net of 4 years
of tuition and fees associated with appropriate year-specific values for public universities. Plotted
points show the difference between expected earnings for college graduates and for high school
graduates.
SCIENCE sciencemag.org 23 MAY 2014 • VOL 344 ISSUE 6186 847
supply-demand model suggest is that the flattening
of the college premium after 2005 is in
large part a consequence of the quickening pace
of educational attainment.
Inequality: Causes for Concern?
A market economy needs some inequality to
create incentives. If, for example, students were
not ultimately rewarded for spending their early
adulthoods pursuing undergraduate, graduate,
and professional degrees, or if the hardest-working
and most productive workers were paid the
same as the median worker, then citizens would
have little incentive to develop expertise, to exert
effort, or to excel in their work (42). Having acknowledged
that some inequality is necessary,
however, how can we gauge whether there is
too much of it? I offer two analytical perspectives
on this question.
Earnings Mobility
One metric by which to evaluate the consequences
of inequality is via its relationship
with economic mobility—that is, the degree to
which individual economic fortunes change
over time. Of particular interest is the degree of
intergenerational mobility, meaning the likelihood
that children born to low-income families
become high-income adults and vice versa.
High levels of economic inequality at a given
point in time are not intrinsically inimical to
economic mobility; a society with high inequality
may be dynamic, with lots of movement up
and down the economic ladder, and one with
low inequality may be dynastic. But a natural
concern is that high inequality at a point in time
may serve to reduce mobility over time. If, for
example, adults who became wealthy through
hard work are able to “buy” success for their
children through outsized investments and personal
connections, while adults who are unproductive
or unlucky in their careers are unable
to muster the resources to foster their children’s
potential, then inequality of incomes could become
self-perpetuating even if it originally emanates
from high market returns to skill (43).
To understand the importance of high and
rising U.S. inequality, it is therefore useful to
ask how U.S. economic mobility compares to
that of other developed countries, and whether
U.S. mobility has fallen as inequality has risen.
The answers to both questions will surprise
many. Contrary to conventional civic mythology,
U.S. intergenerational mobility is relatively
low. The left panel of Fig. 5, reproduced from
(44), which plots the relationship between crosssectional
inequality (x axis) and earnings mobility
(y axis) among a set of 13 OECD member
countries for which consistent data are available,
documents that the United States has both the
lowest mobility and highest inequality among
all wealthy democratic countries. The right panel
of Fig. 5, also sourced from (44), suggests one
proximate explanation for this pattern: Countries
with high returns to education tend to
have relatively low mobility.Why, if education is
“the great equalizer” in the words of Horace
Mann, do high educational returns predict low
mobility? A key reason is that educational attainment
is highly persistent within families.
Indeed, two of the strongest predictors of children’s
ultimate educational attainment are parental
education and parental earnings (45, 46).
Hence, when the return to education is high,
children of better-educated parents are doubly
advantaged—by their parents’ higher education
and higher earnings—in attaining greater education
while young and greater earnings in
adulthood. Figure 5 therefore lends credence
to the concern that rising inequality may erode
economic mobility.
Has this erosion occurred? Surprisingly, the
best evidence to date suggests that it has not.
Evidence from Chetty et al. (46), documented
in the supplementary material, underscores the
message from Fig. 5 that there is substantial
economic immobility in the United States. Children
born three deciles apart in the household
income distribution are on average one decile
apart in the earnings distribution at age 29 or
30. Similarly, children born three deciles apart
in the household income distribution differ by
20 percentage points in their probability of attending
college at age 19 (relative to a mean of
approximately 55%). Yet these data offer no
evidence that mobility has appreciably changed
among children born prior to the historic rise
of U.S. inequality (1971–1974) and those born
afterward (1991–1993). As far as we can measure,
rising U.S. income inequality has not reduced
intergenerational mobility so far. These findings,
which also appear to hold over a longer
historical time frame (47), suggest that U.S.
mobility has not trended downward as many
social scientists would have anticipated, and as
Canada
Australia
New Zealand
Germany
Germany
Japan
France France
United
Kingdom
Italy United Kingdom
0.1
0.2
0.3
0.4
0.5
0.1
0.2
0.3
0.4
0.5
20 25 30 35
Income inequality (more inequality )
Denmark
Norway Finland
Canada
Australia
New Zealand
Spain
Italy
100 120 140 160 180
College earnings premium (men 25 to 34)
Earnings inequality and economic mobility: cross-national relationships
Generational earnings elasticity
(higher values imply lower mobility)
Generational earnings elasticity
(higher values imply lower mobility) A B
Sweden
Norway
Denmark
Finland
United States United States
Sweden
Fig. 5. Earnings inequality and economic mobility: Cross-national relationships.
Reproduced from Corak [(44), figs. 1 and 4] with permission of
the American Economic Association. In both panels, the mobility measure is
equal to the intergenerational earnings “elasticity,” meaning the average
proportional increase in a son’s adult earnings predicted by his father’s
adult earnings measured approximately three decades earlier. A higher intergenerational
earnings elasticity therefore implies lower intergenerational
mobility. In the left panel, cross-sectional income inequality is measured
using a “Gini” index that ranges from 0 to 100, where 0 indicates complete
equality of household incomes and 100 indicates maximal inequality (all
income to one household). In the right panel, the college earnings premium
refers to the ratio of average earnings of men 25 to 34 years of age with a
college degree to the average earnings of those with a high school diploma,
computed by the OECD using 2009 data. See (44) for further details.
848 23 MAY 2014 • VOL 344 ISSUE 6186 sciencemag.org SCIENCE
many policymakers and popular accounts frequently
assume.
It is important to interpret these results in
context. The most recent birth cohorts whose
adult outcomes can be observed at present
were born no later than the early 1990s, which
is still relatively early in the rise of U.S. inequality.
Another 10 years of data, focusing
on children born since 2000, may suggest a
different conclusion. Moreover, the fact that
mobility has stayed constant while inequality
has risen means that the lifetime relative disadvantage
of children born to low- versus highincome
families has increased substantially;
concretely, the rungs of the economic ladder
have pulled farther apart but the chance of
ascending the ladder has not improved. Finally,
it is possible to interpret the fact that
mobility has remained unchanged as evidence
that U.S. mobility would have declined had it
not been for the other compensatory steps
taken by the federal government during this
period, including, for example, expanding the
Earned Income Tax Credit for low-income workers
in the 1980s, enlarging the early childhood
education Head Start program in the 1990s,
and increasing federal student grant and loan
programs to support college-going (48). Declines
in racial and gender discrimination during this
period likely also complemented these policies
(49). A cautious read of the evidence is that although
the United States is not a “land of opportunity”
by conventional economic mobility metrics,
it has not become less so in recent decades.
Real Earnings
A second gauge of economic health is the trajectory
of earnings and employment. Here, the
data present substantial cause for concern. Although
the substantial college wage premium
conveys the positive economic news that educational
investments offer large returns, this wage
premium also masks a discouraging truth: The
rising relative earnings of workers with postsecondary
education is not simply due to rising
real earnings among college-educated workers
but is also due to falling real earningsamong non–
college-educated workers. Between 1980 and
2012, real hourly earnings of full-time collegeeducated
U.S. males rose anywhere from 20% to
56%, with the greatest gains among those with
a postbaccalaureate degree (Fig. 6A). During the
same period, real earnings of males with high
school or lower educational levels declined substantially,
falling by 22% among high school dropouts
and 11% among high school graduates. Although
the picture is generally brighter for females (Fig.
6B), real earnings growth among females without
at least some college education over this threedecade
interval was extremely modest.
Accompanying the fall in real wages among
less educated workers has been a pronounced
drop in their labor force participation rates,
particularly among less educated males. Between
1979 and 2007, prior to the onset of the
Great Recession, the fraction of working-age
males in paid employment fell by 12 percentage
points among high school dropouts and 10 percentage
points among those with exactly a high
school diploma. Conversely, employment rates were
generally stable for males with postsecondary
education and rose for females of all education
levels except for high school dropouts.
The causes for the sharp falls in real earnings
among non–college-educated workers are multiple.
One likely force, as noted above, is the
ongoing substitution of computer-intensive machinery
for workers performing routine taskintensive
jobs. This has depressed demand for
workers in both blue-collar production andwhitecollar
office, clerical, and administrative support
positions, and has reduced the set of middleskill
career jobs available to non–college-educated
workers more generally (25). A second factor
is the globalization of labor markets, seen particularly
in the greatly increased U.S. trade
integration with developing countries. Globalization
has become particularly important for
U.S. labor markets since the early 1990s, when
China began its extremely rapid integration
into the world trading system. The influx of
Chinese goods lowered consumer prices but
also fomented a substantial decline in U.S. manufacturing
employment, contributing directly
to the decline in production worker employment
(50). A third factor impinging on the earnings
of non–college-educated males is the decline in the
penetration and bargaining power of labor unions
in the United States, which have historically
obtained relatively generous wage and benefit
packages for blue-collar workers. Over the past
three decades, however, U.S. private-sector union
density—that is, the fraction of private-sector
workers who belong to labor unions—has fallen
by approximately 70%, from24%in 1973 to 7%in
2011 (51, 52).
Notably, these three forces—technological
change, deunionization, and globalization—
work in tandem. Advances in information and
communications technologies have directly
changed job demands in U.S. workplaces while
simultaneously facilitating the globalization of
production by making it increasingly feasible
and cost-effective for firms to source, monitor,
and coordinate complex production processes
at disparate locations worldwide. In turn, the
globalization of production has increased competitive
conditions for U.S. manufacturers and
U.S. workers, eroding employment at unionized
establishments and decreasing the capability
1.0
1.2
1.4
1.6
1.8
2.0
1964 1968 1972 1976 1980 1984 1988 1992 1996 200020042008 2012
Some college
> Bachelor’s
degree
Bachelor’s
degree
High school
graduate
High school
dropout
A B
Changes in real wage levels of full-time U.S. workers by sex and education, 1963–2012
Real weekly earnings relative to 1963 (men)
Real weekly earnings relative to 1963 (women)
1964 1968 1972 1976 1980 1984 1988 1992 1996 200020042008 2012
1.0
1.2
1.4
1.6
1.8
2.0
Fig. 6. Change in real wage levels of full-time workers by education, 1963–2012. (A) Male workers, (B) female workers. Data and sample construction are
as in Fig. 3.
SCIENCE sciencemag.org 23 MAY 2014 • VOL 344 ISSUE 6186 849
of unions to negotiate favorable contracts, attract
new members, and penetrate new establishments.
In all cases, the foremost concern raised by
these multiple forces impinging on the earnings
of workers at different skill levels is not their
impact on inequality per se, but rather their adverse
effect on the real earnings and employment
of less educated workers. These declines in both
earnings and employment bode ill for the welfare
of non–college-educated U.S. adults and are likely
to have broader detrimental social consequences
that frequently accompany non-employment:
greater criminality, increased social dependency,
and (more mundanely) reduced tax receipts.
Do Supply and Demand Make
Policy Irrelevant?
One potential interpretation of the evidence
above is that, because rising inequality is substantially
a consequence of the impersonal forces
of supply and demand, public policy has no role
to play in shaping the trajectory of inequality or
its social impact. This conclusion is incorrect for
two reasons. First, there are multiple channels
by which policy has contributed to the rise of
U.S. inequality, many of which are not fully
evident in the education earnings premium.
These include the fall over several decades in
the real value of the U.S. minimum wage (7); the
declining prevalence and bargaining power
of U.S. labor unions; mounting international
competition that places particular pressure on
the wages and employment of less educated
workers; and sharp reductions in top federal
marginal tax rates that have raised after-tax
inequality and increased the incentive of highly
paid workers to seek still higher compensation.
As discussed in the companion paper by
Piketty and Saez, there is also disagreement
among economists about whether the rising
share of household incomes accruing to the top
few percentiles of households in numerous
developed countries over the past several decades
is also primarily a market phenomenon, or
instead reflects changing social norms, growing
corporate misgovernance, slackening regulatory
oversight, or increasing political capture of
the policymaking process by elites (3–6). It would
therefore be a vast overstatement to conclude that
the rise of U.S. inequality is exclusively due to
conventional market forces, or that public policy
has not played a role.
But let us assume for the sake of argument
that the rise of income inequality is entirely a
market phenomenon. Would this imply that
there is no role for public policy? A moment’s
reflection suggests otherwise. As the economist
Arthur Goldberger once famously observed, the
fact that nearsightedness is substantially a genetic
disorder has no bearing on whether doctors
should prescribe eyeglasses (53). What is relevant
is whether the benefits of addressing myopia
exceed the costs. In the case of myopia, the availability
of eyeglasses make this an easy call.
Although there is no “remedy” for inequality
that is as swift or cheap as eyeglasses, prosperous
democratic countries have numerous effective
policy levers for shaping inequality’s trajectory
and socioeconomic consequences. Policies that
appear most effective over the long haul in raising
prosperity and reducing inequality are those
that cultivate the skills of successive generations:
excellent preschool through high school education;
broad access to postsecondary education; and
good nutrition, good public health, and highquality
home environments. Such policies address
inequality from two directions: (i) enabling a larger
fraction of adults to attain high productivity,
rewarding jobs, and a reasonable standard of
living; and (ii) raising the total supply of skills
available to the economy, which in turnmoderates
the skill premium and reduces inequality (54).
Of course, building skills is a multigenerational
process and thus has little impact on inequality
in the short term. There are, however,
numerous nearer-term levers that moderate
inequality directly without imposing substantial
economic costs: applying progressive tax and
transfer policies that fund public investments
and foster opportunities for children of all socioeconomic
backgrounds; applying well-crafted
labor regulations that ensure safe and nonexploitive
working conditions; providing wage
subsidies such as the Earned Income Tax Credit
that increase the payoff to employment for those
with limited skills; setting modest but nonzero
minimum wage rules; and offering numerous
social insurance policies (health and disability
insurance, flood insurance, disaster assistance,
food assistance) that buffer misfortune for the
unfortunate. Although it is outside the scope of
this article to evaluate these policies, it is critical
to underscore that policy and governance
has played and should continue to play a central
role in shaping inequality—even when a central
cause of rising inequality is the changing supply
and demand for skills.
REFERENCES AND NOTES
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2. Goldin and Katz (1) found that the increase in the education
wage premium, particularly the college premium, explains
about 60 to 70% of the rise in wage inequality (variance)
between 1980 and 2005.
3. F. Alvaredo et al., J. Econ. Perspect. 27, 3–20 (2013).
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6. S. N. Kaplan, J. Rauh, J. Econ. Perspect. 27, 35–56 (2013).
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Wage Inequality over Three Decades: A Reassessment (NBER
Working Paper 16533, Cambridge, MA, 2010).
8. T. Piketty, E. Saez, Q. J. Econ. 118, 1–41 (2003).
9. These calculations use data from (8), with data updated to
2012 available at http://elsa.berkeley.edu/saez/~Tab-
Fig.2012prel.xls. Average U.S. household incomes, including
the top 1%, rose by 20.2%, while the average household
income of the bottom 99% of households rose by only 3.5%.
10. Thus, the top 1% maintains its share of household income at a
constant 10.0% while average household incomes rise by
20.2%, as actually occurred.
11. This point is due to Lawrence Katz of Harvard University, who
offers these calculations in his graduate labor economics
lecture notes.
12. T. Lemieux, Post-Secondary Education and Increasing Wage
Inequality (Working Paper 12077, National Bureau of Economic
Research, 2006).
13. S. Firpo et al., Decomposition methods in economics. In
Handbook of Labor Economics, D. Card, O. Ashenfelter, Eds.
(Elsevier-North Holland, Amsterdam, 2011), vol. 4, pp. 1–102.
14. See www.oecd.org/site/piaac/surveyofadultskills.htm for more
information. The PIAAC program will encompass 33 countries, but
data for only 22 were available at this writing.
15. E. A. Hanushek, G. Schwerdt, S.Wiederhold,
L.Woessmann, Returns to Skills Around the World:
Evidence from PIAAC (NBER Working Paper 19762,
Cambridge, MA, 2013).
16. Hanushek et al. (15) also found that the correlation between
numeracy skills and years of schooling is 0.45. When including
both numeracy skills and years of schooling in an earnings
regression, they found that both are substantial and significant
predictors of earnings, although each is attenuated relative
to a model where only one factor is included at a time. This
pattern of results suggests, logically, that neither test scores
nor years of schooling is a complete measure of labor
market skills.
17. T. D. Snyder, 120 Years of American Education: A Statistical
Portrait (National Center for Education Statistics, U.S.
Department of Education, 1993).
18. L. D. Johnston, “History lessons: Understanding
the decline in manufacturing.” MinnPost, 22 February
2012; www.minnpost.com/macro-micro-minnesota/
2012/02/history-lessons-understanding-declinemanufacturing.
19. C. Goldin, L. F. Katz, The Race Between Education and
Technology (Harvard Univ. Press, Cambridge, MA, 2008).
20. M. Stanley, Q. J. Econ. 118, 671–708 (2003).
21. B. Pierce, in Labor in the New Economy, K. G. Abraham,
J. R. Spletzer, M. Harper, Eds. (Univ. of Chicago Press, Chicago,
2010), pp. 63–98.
22. These comparisons hold labor market experience and gender
constant. This doubling of the college premium very likely
understates the magnitude of the increase in inequality
between college-educated and non–college-educated workers.
Alongside higher hourly earnings, college-educated workers
enjoy greater job stability, lower rates of unemployment, more
generous fringe benefits, and better working conditions;
Pierce (21) found that these differentials have generally increased
in the same time period.
23. D. H. Autor et al., Q. J. Econ. 118, 1279–1333 (2003).
24. D. Acemoglu, D. H. Autor, Skills, tasks and technologies:
Implications for employment and earnings. In Handbook of
Labor Economics, D. Card, O. Ashenfelter, Eds. (Elsevier-North
Holland, Amsterdam, 2011), vol. 4, pp. 1043–1171.
25. D. H. Autor, D. Dorn, Am. Econ. Rev. 103, 1553–1597 (2013).
26. M. Goos et al., www.aeaweb.org/forthcoming/output/
accepted_AER.php
27. Extensive recent literature, commencing with Autor et al. (23)
and summarized in Acemoglu and Autor (24), considers the
role of technological change in displacing workers performing
routine tasks and complementing workers performing
nonroutine tasks. An additional implication of this framework is
that an increasing share of employment will be found in
comparatively low-skill nonroutine manual tasks that require
situational adaptability, visual and language recognition,
and in-person interactions but limited formal education
(e.g., janitors and cleaners, home health aides, construction
laborers, and security personnel). See Autor and Dorn (25) and
Goos et al. (26) for evidence that employment in the
U.S. and among OECD member countries has increasingly
polarized into high-paid, abstract-intensive occupations and
low-paid, manual-intensive occupations.
28. J. Tinbergen, Kyklos 27, 217–226 (1974).
29. Details of this model are given in the online supplement.
30. D. Card, T. Lemieux, Am. Econ. Rev. 91, 97–102
(2001).
31. L. F. Katz, K. M. Murphy, Q. J. Econ. 107, 35–78
(1992).
32. L. Katz, D. H. Autor, Changes in the wage structure and
earnings inequality. In Handbook of Labor Economics,
D. Card, O. Ashenfelter, Eds. (Elsevier-North Holland, Amsterdam,
1999), vol. 3, pp. 1463–1555.
33. D. Card, T. Lemieux, Q. J. Econ. 116, 705–746 (2001).
34. D. H. Autor et al., Rev. Econ. Stat. 90, 300–323 (2008).
35. E. Crivellaro, “College wage premium over time: Trends in
Europe in the last 15 years.” University Ca’ Foscari
of Venice, Department of Economics Research Paper
Series no. 03/WP/2014 (2014); http://dx.doi.org/10.2139/
ssrn.2383795.
36. Summarizing evidence on the college premium in 12 European
countries between 1994 and 1999, Crivellaro (35) found a
pattern of increasing skill differentials except in countries that
have had a large increase in the relative supply of college
850 23 MAY 2014 • VOL 344 ISSUE 6186 sciencemag.org SCIENCE
graduates, a pattern consistent with the conceptual model laid
out below.
37. Although this deceleration is not evident from Fig. 3, it is
detected by the regression equation, as discussed in the online
supplement.
38. College Board, Trends in Student Aid: 2013 (College Board,
New York, 2013).
39. C. Avery, S. Turner, J. Econ. Perspect. 26, 165–192 (2012).
40. Three sources of uncertainty should be kept in mind when
interpreting these estimates. First, they encompass
substantial heterogeneity. Although the average college
graduate earns substantially more than the average high
school graduate, the least successful college graduates may
earn substantially less than the median among high school
graduates, and the most successful high school graduates
may earn substantially more than the median among college
graduates. Second, for students who acquire substantial
student debt but do not complete the college degree, it is
far from certain that college will prove a good investment.
Finally, these calculations assume that the lifetime profile of
earnings observed in the year of college graduation will
persist throughout the career. As Fig. 3 indicates, this
premium has changed substantially over time, so this
assumption is only a rough approximation. However, the
college premium is so high at present that even with a
substantial decline, college would remain an attractive
financial proposition on average from a lifetime earnings
perspective.
41. R. J. Murnane, J. Econ. Lit. 51, 370–422 (2013).
42. D. Acemoglu, J. Robinson, Why Nations Fail (Crown, New York,
2012).
43. As with cross-sectional inequality, there is no economically
“ideal” level of intergenerational mobility. Even in a society
with perfect equality of opportunity, one would expect children
of successful parents to have above-average success as
adults, simply because many attributes that contribute to
success (appearance, intellect, athleticism) are partly
heritable.
44. M. Corak, J. Econ. Perspect. 27, 79–102 (2013).
45. S. F. Reardon, The widening academic achievement gap
between the rich and the poor: New evidence and possible
explanations. In Whither Opportunity? Rising Inequality,
Schools, and Children’s Life Chances, G. J. Duncan,
R. J. Murnane, Eds. (Russell Sage Foundation, New York, 2011),
pp. 91–115.
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Recent Trends in Intergenerational Mobility (NBER Working
Paper No. 19844, Cambridge, MA, 2014).
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48. Between 2002–2003 and 2012–2013, the sum of federal
Pell Grants and loans for higher education increased by 105%,
from $83 billion to $170 billion in constant 2012 dollars
[(38), table 1].
49. C.-T. Hsieh et al., The Allocation of Talent and U.S. Economic
Growth (NBER Working Paper No. 18693, Cambridge, MA, 2013).
50. D. H. Autor et al., Am. Econ. Rev. 103, 2121–2168 (2013).
51. D. Card et al., J. Labor Res. 25, 519–559 (2004).
52. B. T. Hirsch, J. Econ. Perspect. 22, 153–176 (2008).
53. A. S. Goldberger, Economica 46, 327 (1979).
54. The extensive involvement of state and federal government in
education at all levels also underscores the fact that the
distribution of education and skills today is in no sense a
“free market” outcome; it is a consequence of both individual
and public choices.
ACKNOWLEDGMENTS
I thank D. Acemoglu, L. Katz, J. Van Reenen, M. Tatsutani, and
two anonymous referees for valuable comments and advice, and
C. Patterson and B. Price for expert research assistance.
Supported by NSF grant SES-1227334, Russell Sage Foundation
grant 85-12-07, and Alfred P. Sloan Foundation grant 2011-10-12.
All data and code that are unique to this article (Figs. 1, 3,
and 6; fig. S2) are available from the author. All other figures
(Figs. 2, 4, and 5; figs. S1 and S3) are reproduced from
other publications, as noted, with permission of the authors.
SUPPLEMENTARY MATERIALS
www.sciencemag.org/content/344/6186/843/suppl/DC1
Supplementary Text
Figs. S1 to S3
References (55–61)
10.1126/science.1251868
REVIEW
Income inequality in the
developing world
Martin Ravallion
Should income inequality be of concern in developing countries? New data reveal less
income inequality in the developing world than 30 years ago. However, this is due to falling
inequality between countries. Average inequality within developing countries has been
slowly rising, though staying fairly flat since 2000. As a rule, higher rates of growth in
average incomes have not put upward pressure on inequality within countries. Growth
has generally helped reduce the incidence of absolute poverty, but less so in more
unequal countries. High inequality also threatens to stall future progress against
poverty by attenuating growth prospects. Perceptions of rising absolute gaps in living
standards between the rich and the poor in growing economies are also consistent
with the evidence.
Development economics emerged as a subdiscipline
of economics in the 1950s, and
its initial focus was on economic growth,
with inequality as a secondary concern.
The prevailing orthodoxy for many decades
was that a period of rising inequality was to
be expected in growing poor countries. Rising
inequality was seen to be more or less inevitable
and not something to worry about, particularly
if the incidence of poverty was falling. Another
commonly held view was that policy efforts to
reduce inequality were likely to impede growth
and (hence) poverty reduction.
The existence of high inequality within many
developing countries, side by side with persistent
poverty, started to attract attention in the early
1970s. Nonetheless, through the 1980s and
well into the 1990s, the mainstream view in
development economics was still that high
and/or rising inequality in poor countries was
a far less important concern than assuring sufficient
growth, which was the key to poverty
reduction. The policy message for the developing
world was clear: You cannot expect to have
both lower poverty and less inequality while
you remain poor, and, if you choose to give poverty
reduction highest priority, then focus on
growth.
Other objections could still be raised to
high income inequality. The classical utilitarian
formulation—whereby social welfare is judged
by the sum of utilities, assuming diminishing
marginal utility of income—pointed to social welfare
losses from high inequality at a given mean.
But that did not persuade those who believed
that there was a trade-off between equity and
growth. A moral defense could also be mounted
for the view that inequality is not an important
issue for a growing developing country by appeal
to John Rawls’s “difference principle” that (subject
to assuring liberty and equal opportunity) higher
inequality can be justified as long as it benefits the
worst-off group in society (1).
The period since 2000 has seen a deeper and
morewidespread questioning of this long-standing
view of pro-poor inequality. New concerns have
emerged about the instrumental importance of
equity to other valued goals, including poverty
reduction and human development more broadly.
It appears more likely today that high inequality
will be seen as a threat to future development
than as an inevitable and unimportant consequence
of past progress. The long-standing idea
of a substantial growth-equity trade-off has come
to be seriously questioned.
This paper reports new estimates of the levels
and changes in income inequality measures for
the developing world. The new estimates take
us up to 2010, embracing the period of higher
growth rates in the developing world since the
turn of the millennium. In the light of these new
data, I revisit past and ongoing debates on inequality
in developing countries and the tradeoffs
with growth and poverty reduction.
Income Inequality Measures
To measure inequality in the developing world
as a whole, one ignores country borders—pooling
all residents and measuring inequality among
them. This overall measure will naturally depend
on the inequality between countries as well as
that within them. Thus, its evolution over time
will depend on whether poorer countries are
seeing lower growth rates as well as the things
happening within countries—economic changes
and policies—that affect inequality.
If we are comparing country or regional performance,
then we want to isolate the withincountry
component of inequality as distinct from
that between countries. Although there are many
inequality measures, not all of themallow a clean
separation of the between and within components.
For example, such a decomposition is
Department of Economics, Georgetown University,
Washington, DC 20057, and National Bureau of Economic
Research, Cambridge, MA 02138, USA. E-mail: mr1185@
georgetown.edu
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