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Why Has Income Inequality Been Rising Since the 1970\u2019s
\nDue in NetTutor through the link in Blackboard (learn.wsu.edu) by February
\n18, by 11:50 pm (PST)
\nBelow are two competing views (Proposition #1 and Proposition #2) on why income inequality
\nhas been rising (even among the lower 99%) in the United States since the 1970\u2019s.
\nReading: \u201cSkills, education, and the rise of earnings inequality among the \u201cother 99 percent,\u201d
\nDavid H. Autor, Science 23 May 2014: 843-851. [DOI:10.1126\/science.1251868] (see below)
\nProposition #1:
\n\u201cMore people are falling behind today because they\u2019re lazy. In the U.S. anyone willing to work can
\nearn a decent living. The income distribution is the same as the effort distribution. People who work
\nhard earn a good living. Lazy people stay poor. The income distribution has been skewing because of
\na growing culture of dependency. It used to be shameful to accept handouts from the government, but
\nnow it\u2019s perfectly acceptable. So more people opt out of the labor force and sit around collecting
\nhandouts from the rest of us. Of course their incomes are going to be lower than those in the top
\ntiers.\u201d
\nProposition #2:
\n\u201cThe incomes of the rich and poor have been growing farther apart due to purposeful policy
\ndecisions that have favored the rich at the expense of the poor. Starting with Ronald Reagan\u2019s
\nfinancial deregulation, and continuing with successive Republican attacks on New Deal and Great
\nSociety social programs to finance tax cuts for their Wall Street cronies, government policy in this
\ncountry has systematically channeled the rewards of our immensely productive economy towards the
\nrich and away from the poor. America needs to open the gateway to prosperity for all through fair tax
\npolicies, decent minimum wages, better public schools, aid to low-income college students, and a
\nuniversal health care system.\u201d
\nEconS 101 WA 1
\nPage 2 of 3
\nAssess the degree to which each of those propositions is actually consistent with David H.
\nAutor\u2019s well-researched explanation of the same phenomenon. That is, for each of the
\npropositions tell me whether or not Autor would agree that the underlying factor described in
\nthe proposition is the major cause of the pattern of income inequality since the 1970\u2019s. (2
\nparagraphs)
\n1) Begin with a topic (sample nursing essay examples by the best nursing assignment writing service) sentence that concisely states your judgment about how consistent the two
\npropositions are with Autor\u2019s analysis, and complete that paragraph with a summary of
\nAutor\u2019s explanation. Be sure to mention some of the factors Autor identifies as having
\ncontributed to an increase in the demand for highly-educated workers and a decrease in the
\ndemand for less-educated workers.
\n2) In your second paragraph, discuss the degree to which the two propositions are consistent
\nwith Autor\u2019s analysis. Is one more consistent than the other
\nEconS 101 WA 1
\nPage 3 of 3
\nGrading Rubric
\nPlease include your name, instructor\u2019s name (Dr. Prera), course and section number (i.e. EconS
\n101.01 [MWF 9:10-10 am] or 101.03 [TTh 10:35-11:50 am]), and writing assignment
\n(Assignment 1) on the top of your assignment.
\nThis is the first of four writing assignments. After receiving feedback, you may re-submit the
\nessay to NetTutor for another try \u2013 but you only get one second chance on each writing
\nassignment. If you don\u2019t get an acceptable score, even after revising and resubmitting this
\nassignment, you can still rescue your writing assignment average by doing well on the next three
\nassignments. I will take the three highest writing assignment scores to compute your writing
\nassignment average.
\nCriteria Meets Expectations Needs Improvement
\nWriting
\nIdeas are well-organized.
\nTransition sentences effectively
\nconnect one idea to the next. The
\nessay is free of typos and
\ngrammatical errors.
\nThe writing is difficult to
\nfollow and\/ or poorly
\norganized.
\nTransition sentences are
\nabsent or ineffective.
\nTypos and\/ or grammatical
\nerrors distract the reader.
\nEconomic analysis
\nThe topic (sample nursing essay examples by the best nursing assignment writing service) sentence clearly states
\nthe student\u2019s judgment about the
\ndegree to which Propositions #1
\nand #2 are consistent with
\nAutor\u2019s study.
\nThe topic (sample nursing essay examples by the best nursing assignment writing service) sentence does not
\nstate the student\u2019s judgment
\nabout the degree to which
\nPropositions #1 and #2 are
\nconsistent with Autor\u2019s study.
\nCorrectly identifies the major
\ncause of rising inequality in
\nAutor\u2019s analysis.
\nAutor\u2019s argument is
\nmisrepresented, and\/or the
\nsummary is too skimpy.
\nExplicitly state the degree to
\nwhich the two propositions are
\nconsistent with Autor\u2019s research,
\nand supports his or her claims
\nwith examples from the reading.
\nThe question of the degree to
\nwhich the two propositions
\nare consistent with Autor\u2019s
\nresearch is not addressed
\nand\/or, is not supported with
\nexamples.
\nDOI: 10.1126\/science.1251868
\nScience 344, 843 (2014);
\nDavid H. Autor
\n”other 99 percent”
\nSkills, education, and the rise of earnings inequality among the
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\nwww.sciencemag.org (this information is current as of August 5, 2014 ):
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\nof rising or shrinking inequality. Which one
\ndominates depends on the institutions and policies
\nthat societies choose to adopt.
\nREFERENCES
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\nSavings (National Bureau of Economic Research, Cambridge,
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\nl’enregistrement des d\u00e9clarations de successions (Mouton,
\nParis, 1973).
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\n6. P. Lindert, J. Polit. Econ. 94, 1127\u20131162 (1986).
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\nsi\u00e8cle\u2014In\u00e9galit\u00e9s et redistributions, 1901\u20131998
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\n10. A. B. Atkinson, J. R. Stat. Soc. Ser. A Stat. Soc. 168, 325\u2013343
\n(2005).
\n11. T. Piketty, E. Saez, Q. J. Econ. 118, 1\u201341(2003).
\n12. A. B. Atkinson, T. Piketty, Eds., Top Incomes over the
\n20th Century\u2014A Contrast Between Continental European
\nand English Speaking Countries (Oxford Univ. Press, New York,
\n2007).
\n13. A. B. Atkinson, T. Piketty, Eds., Top Incomes\u2014A Global
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\n14. A. B. Atkinson, T. Piketty, E. Saez, J. Econ. Lit. 49, 3\u201371
\n(2011).
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\nPerspect. 27, 3\u201321 (2013).
\n16. W. Kopczuk, E. Saez, Natl. Tax J. 57, 445\u2013487 (2004).
\n17. T. Piketty, G. Postel-Vinay, J. L. Rosenthal, Am. Econ. Rev.
\n96, 236\u2013256 (2006).
\n18. J. Roine, D. Waldenstrom, Scand. J. Econ. 111, 151\u2013187
\n(2009).
\n19. H. Ohlson, J. Roine, D. Waldenstrom, in J. B. Davies, Ed.,
\nPersonal Wealth from a Global Perspective (Oxford Univ. Press,
\nOxford, 2008), pp. 42\u201363.
\n20. D. Waldenstrom, Lifting all Boats The Evolution of Income and
\nWealth Inequality Over the Path of Development (Lund
\nUniversity, Sweden, 2009)
\n21. T. Piketty, Q. J. Econ. 126, 1071\u20131131 (2011).
\n22. R. Goldsmith, Comparative National Balance Sheets: A Study of
\nTwenty Countries, 1688-1978 (Univ. of Chicago Press, Chicago,
\nIL, 1985)
\n23. T. Piketty, G. Zucman, Q. J. Econ. 129, in press
\n(2014); http:\/\/piketty.pse.ens.fr\/files\/
\nPikettyZucman2013WP.pdf.
\n24. T. Piketty, Capital in the Twenty-first Century (Harvard Univ.
\nPress, Cambridge, MA, 2014).
\n25. J. Stiglitz, Econometrica 37, 382\u2013397 (1969).
\n26. M. Nirei, \u201cPareto Distributions in Economics Growth Models,\u201d
\nInstitute of Innovation Research Working Paper No. 09-05,
\nHitotsubashi University, Tokyo (2009)
\n27. T. Piketty, G. Postel-Vinay, J. L. Rosenthal, Explor. Econ. Hist.
\n51, 21\u201340 (2014).
\n28. J. Davies, S. Sandstrom, T. Shorrocks, E. Wolff, Econ. J. 121,
\n223\u2013254 (2011).
\n29. G. Zucman, Q. J. Econ. 128, 1321\u20131364 (2013).
\n30. C. Goldin, L. Katz, The Race Between Education and Technology
\n(Harvard Univ. Press, Cambridge, MA, 2008).
\nSUPPLEMENTARY MATERIALS
\nwww.sciencemag.org\/content\/344\/6186\/838\/suppl\/DC1
\nSupplementary Text
\nFigs. S1 and S2
\nReferences (31, 32)
\n10.1126\/science.1251936
\nREVIEW
\nSkills, education, and the rise of
\nearnings inequality among
\nthe \u201cother 99 percent\u201d
\nDavid H. Autor
\nThe singular focus of public debate on the \u201ctop 1 percent\u201d of households overlooks the
\ncomponent of earnings inequality that is arguably most consequential for the \u201cother
\n99 percent\u201d of citizens: the dramatic growth in the wage premium associated with higher
\neducation and cognitive ability. This Review documents the central role of both the supply
\nand demand for skills in shaping inequality, discusses why skill demands have persistently
\nrisen in industrialized countries, and considers the economic value of inequality alongside
\nits potential social costs. I conclude by highlighting the constructive role for public policy in
\nfostering skills formation and preserving economic mobility.
\nPublic debate has recently focused on a
\nsubject that economists have been analyzing
\nfor at least two decades: the steep,
\npersistent rise of earnings inequality in
\nthe U.S. labor market and in developed
\ncountries more broadly. Much popular discussion
\nof inequality concerns the \u201ctop 1 percent,\u201d
\nreferring to the increasing share of national income
\naccruing to the top percentile of households.
\nAlthough this phenomenon is undeniably
\nimportant, an exclusive focus on the concentration
\nof top incomes ignores the component
\nof rising inequality that is arguably even more
\nconsequential for the \u201cother 99 percent\u201d of
\ncitizens: the dramatic growth in the wage premium
\nassociated with higher education and,
\nmore broadly, cognitive ability. This paper considers
\nthe role of the rising skill premium in
\nthe evolution of earnings inequality.
\nThere are three reasons to focus a discussion
\nof rising inequality on the economic payoff
\nto skills and education. First, the earnings
\npremium for education has risen across a large
\nnumber of advanced countries in recent decades,
\nand this rise contributes substantially to
\nthe net growth of earnings inequality. In the
\nUnited States, for example, about two-thirds
\nof the overall rise of earnings dispersion between
\n1980 and 2005 is proximately accounted
\nfor by the increased premium associated with
\nschooling in general and postsecondary education
\nin particular (1, 2). Second, despite a
\nlack of consensus among economists regarding
\nthe primary causes of the rise of very top
\nincomes (3\u20136), an influential literature finds
\nthat the interplay between the supply and
\ndemand for skills provides substantial insight
\ninto why the skill premium has risen and fallen
\nover time\u2014and, specifically, why the earnings
\ngap between college and high school graduates
\nhas more than doubled in the United States over
\nthe past three decades. A third reason for focusing
\non the skill premium is that it offers broad
\ninsight into the evolution of inequality within a
\nmarket economy, highlighting the social value of
\ninequality alongside its potential social costs and
\nilluminating the constructive role for public policy
\nin maximizing the benefits and minimizing the
\ncosts of inequality.
\nThe rising skill premium is not, of course, the
\nsole cause of growing inequality. The decadeslong
\ndecline in the real value of the U.S. minimum
\nwage (7), the sharp drops in non-college
\nemployment opportunities in production, clerical,
\nand administrative support positions stemming
\nfrom automation, the steep rise in international
\ncompetition from the developing world,
\nthe secularly declining membership and bargaining
\npower of U.S. labor unions, and the
\nsuccessive enactment of multiple reductions in
\ntop federal marginal tax rates, have all served to
\nmagnify inequality and erode real wages among
\nless educated workers. As I discuss below, the
\nforemost concern raised by these multiple forces
\nis not their impact on inequality per se, but
\nrather their adverse effect on the real earnings
\nand employment of less educated workers.
\nI begin by documenting the centrality of the
\nrising skill premium to the overall growth of
\nearnings inequality. I next consider why skills
\nare heavily rewarded in advanced economies
\nand why the demand for them has risen over
\ntime. I then demonstrate the substantial explanatory
\npower of a simple framework that
\nembeds both the demand and supply for skills
\nin interpreting the evolution of the inequality
\nover five decades. The final section considers
\nthe productive role that inequality plays in a
\nmarket economy and the potential risks attending
\nvery high and rising inequality; evidence on
\nwhether those risks have been realized; and
\nthe role of policy and governance in encouraging
\nskills formation, fostering opportunity,
\nDepartment of Economics and National Bureau of Economic
\nResearch, Massachusetts Institute of Technology, 40 Ames
\nStreet, E17-216, Cambridge, MA 02142, USA. E-mail: dautor@
\nmit.edu
\nSCIENCE sciencemag.org 23 MAY 2014 \u2022 VOL 344 ISSUE 6186 843
\nand countering the possibility that extremes of
\ninequality erode economic mobility and reduce
\neconomic dynamism.
\nThe Critical Role of Skills in the
\nLabor Market
\nThere is no denying the extraordinary rise in
\nthe incomes of the top 1% of American households
\nover the past three decades. Between
\n1979 and 2012, the share of all household income
\naccruing to the top percentile of U.S.
\nhouseholds rose from 10.0% to 22.5% (8, 9). To
\nget a sense of how much money that is, consider
\nthe conceptual experiment of redistributing
\nthe gains of the top 1% between 1979 and
\n2012 to the bottom 99% of households (10).
\nHowmuchwould this redistribution raise household
\nincomes of the bottom 99% The answer
\nis $7107 per household\u2014a substantial gain, equal
\nto 14% of the income of the median U.S. household
\nin 2012. (I focus on the median because it
\nreflects the earnings of the typical worker and
\nthus excludes the earnings of the top 1%.)
\nNow consider a different dimension of inequality:
\nthe earnings gap between U.S. workers
\nwith a 4-year college degree and those with
\nonly a high school diploma (11). Economists frequently
\nuse this college\/high school earnings
\ngap as a summary measure of the \u201creturn to
\nskill\u201d\u2014that is, the gain in earnings a worker
\ncan expect to receive from investing in a college
\neducation. As illustrated in Fig. 1, the earnings
\ngap between the median college-educated
\nand median high school\u2013educated among U.S.
\nmales working full-time in year-round jobs was
\n$17,411 in 1979, measured in constant 2012 dollars.
\nThirty-three years later, in 2012, this gap
\nhad risen to $34,969, almost exactly double its
\n1979 level. Also seen is a comparable trend among
\nU.S. female workers, with the full-time, fullyear
\ncollege\/high school median earnings gap
\nnearly doubling from $12,887 to $23,280 between
\n1979 and 2012. As Fig. 1 underscores, the
\neconomic payoff to college education rose steadily
\nthroughout the 1980s and 1990s and was
\nbarely affected by the Great Recession starting
\nin 2007.
\nBecause the earnings calculations in Fig. 1 reflect
\nindividual incomes while the top 1% calculations
\nreflect household incomes, the two
\ncalculations are not directly comparable. To
\nput the numbers on the same footing, consider
\nthe earnings gap between a college-educated
\ntwo-earner husband-wife family and a high school\u2013
\neducated two-earner husband-wife family, which
\nrose by $27,951 between 1979 and 2012 (from
\n$30,298 to $58,249). This increase in the earnings
\ngap between the typical college-educated
\nand high school\u2013educated household earnings
\nlevels is four times as large as the redistribution
\nthat has notionally occurred from
\nthe bottom 99% to the top 1% of households.
\nWhat this simple calculation suggests is that
\nthe growth of skill differentials among the \u201cother
\n99 percent\u201d is arguably even more consequential
\nthan the rise of the 1% for the welfare of
\nmost citizens.
\nThe median earnings comparisons in Fig. 1 also
\nconvey a key feature of rising inequality that
\ncannot be inferred from trends in top incomes:
\nWage inequality has risen throughout the earnings
\ndistribution, not merely at the top percentiles.
\nFigure S1 documents this pattern by plotting,
\nfor 12 Organization for Economic Cooperation
\nand Development (OECD) member countries over
\nthree decades (1980 to 2011), the change in the
\nratio of full-time earnings of males at the 90th
\npercentile relative to males at the 10th percentile
\nof the wage distribution. Although the 90\/10
\nearnings ratio differed greatly across countries
\nat the earliest date of the sample\u2014from a low
\nof 2.0 in Sweden to a high of 3.6 in the United
\nStates\u2014this earnings ratio increased substantially
\nin all but one of them (France) over the
\nnext 30 years, growing by at least 25 percentage
\npoints in 10 countries, by at least 50 percentage
\npoints in 8 countries, and by more than 100 percentage
\npoints in three countries (New Zealand,
\nthe United Kingdom, and the United States).
\nHow much does the rising education premium
\ncontribute to the increase of earnings inequality
\nAlthough data limitations make it difficult to
\nanswer this question for most countries, we do
\nknow the answer for the United States. Goldin
\nand Katz (1) found that the increase in the education
\nwage premium explains about 60 to 70%
\nof the rise in the dispersion of U.S. wages between
\n1980 and 2005 and, similarly, Lemieux (12)
\ncalculated that higher returns to postsecondary
\neducation can account for 55% of the rise in
\nmale hourly wage variance from 1973\u20131975 to
\n2003\u20132005. Firpo et al. (13) found that rising
\nreturns to education can explain just over 95% of
\nthe rise of the U.S.male 90\/10 earnings ratio between
\n1984 and 2004. That is, holding the expanding
\neducation premium constant over this
\nperiod, there would have been essentially no increase
\nin the relative wages of the 90th-percentile
\nworker versus the 10th-percentile worker.
\nI have so far used the terms education and
\nskill interchangeably.What evidence do we have
\nthat it is skills that are rewarded per se, rather
\nthan simply educational credentials The Program
\nfor the International Assessment of Adult
\nCompetencies (PIAAC) provides a compelling
\ndata source for gauging the importance of
\nskills in wage determination. The PIAAC is an
\ninternationally harmonized test of adult cognitive
\nand workplace skills (literacy, numeracy,
\nand problem-solving) that was administered
\nby the OECD to large, representative samples
\nof adults in 22 countries between 2011 and
\n2013 (14). Figure 2, sourced from (15), plots the
\nrelationship between adults\u2019 earnings and their
\nPIAAC numeracy scores across these 22 countries.
\nThe length of each bar reflects the average
\npercentage earnings differential between
\nfull-time workers ages 35 to 54 who differ by
\none standard deviation in the PIAAC score.
\nThe whiskers on each bar provide the 95%
\nconfidence intervals for the estimates.
\nCollege\/high school median annual earnings gap, 1979\u20132012
\nIn constant 2012 dollars
\n0
\n10,000
\n20,000
\n30,000
\n40,000
\n50,000
\n60,000
\n70,000 dollars
\n1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012
\nHousehold gap
\n$30,298 to $58,249
\nMale gap
\n$17,411 to $34,969
\nFemale gap
\n$12,887 to $23,280
\nFig. 1. College\/high school median annual earnings gap, 1979\u20132012. Figure is constructed using
\nCensus Bureau P-60 (1979\u20131991) and P-25 (1992\u20132012) tabulations of median earnings of full-time,
\nfull-year workers by educational level and converted to constant 2012 dollars (to account for
\ninflation) using the CPI-U-RS price series. Prior to 1992, college-educated workers are defined as
\nthose with 16 or more years of completed schooling, and high school\u2013educated workers are those
\nwith exactly 12 years of completed schooling. After 1991, college-educated workers are those who
\nreport completing at least 4 years of college, and high school\u2013educated workers are those who
\nreport having completed a high school diploma or GED credential.
\n844 23 MAY 2014 \u2022 VOL 344 ISSUE 6186 sciencemag.org SCIENCE
\nThis figure conveys three points. First, cognitive
\nskills are substantially rewarded in the labor
\nmarket across all 22 economies. The average
\nwage premium corresponding to one \u201cunit\u201d (i.e.,
\none standard deviation) increase in measured
\ncognitive skills is 18%. In addition, cognitive earnings
\npremiums differ substantially across countries.
\nThe premium is below 13% in Sweden,
\nthe Czech Republic, and Norway. It is above
\n20% in six countries. The United States stands
\nout as having the highest measured return to
\nskill, with a premium of 28% per unit increment
\nto cognitive ability. Concretely, comparing two
\nU.S. workers who are one standard deviation
\nabove and one standard deviation below the
\npopulation average of cognitive ability, we would
\nexpect their full-time weekly earnings to differ
\nby 50 to 60%. Notably, the high return to
\ncognitive ability in the United States does not
\nfollow automatically from high levels of U.S.
\nearnings inequality. If U.S. wages were determined
\nmainly by luck, beauty, or family connections,
\nwe would expect little connection
\nbetween workers\u2019 cognitive ability and their labor
\nmarket rewards (16). Figure 2 demonstrates
\nthat this is not the case.
\nOf course, these data do not explain why
\nthe skill premium has risen over time, nor
\nwhy the United States has a higher skill premium
\nthan so many other advanced nations.
\nThe next section considers the supply and demand
\nfor skill in the labor market\u2014specifically, why
\nthey fluctuate over time and how their interaction
\nhelps to determine the skill premium. I
\nfocus on the United States in this section to allow
\na deeper exploration of the data.
\nEducation and Inequality
\nWorkers\u2019 earnings in a market economy depend
\nfundamentally (some economists would
\nsay entirely) on their productivity\u2014that is, the
\nvalue they produce through their labor. And in
\nturn, workers\u2019 productivity depends on two factors.
\nOne is their capabilities, concretely, the
\ntasks they can accomplish (i.e., their skills). A
\nsecond is their scarcity: The fewer workers that
\nare available to accomplish a task, and the more
\nemployers need that task accomplished, the
\nhigher is workers\u2019 economic value in that
\ntask. In conventional terms, the skill premium
\ndepends uponwhat skills employers require (skill
\ndemand) and what skills workers have acquired
\n(skill supply). To interpret the evolution of this
\npremium, we need to account for both forces.
\nSkill Demands: The Long View
\nA technologically advanced economy requires
\na literate, numerate, and technically and scientifically
\ntrained workforce to develop ideas,manage
\ncomplex organizations, deliver healthcare
\nservices, provide financing and insurance, administer
\ngovernment services, and operate critical
\ninfrastructure. This was not always the case. In
\n1900, 4 in 10 U.S. jobs were in agriculture, 11%
\nof the population was illiterate, a substantial
\nfraction of economic activity required hard physical
\nlabor, and workers\u2019 strength and physical
\nstamina were key job skills (17, 18). Few citizens
\nwould have predicted at the time that a century
\nlater, health care, finance, information technology,
\nconsumer electronics, hospitality, leisure,
\nand entertainment would employ farmoreworkers
\nthan agriculture\u2014which employed only 2%
\nof U.S. workers in 2010. As physical labor has
\ngiven way to cognitive labor, the labor market\u2019s
\ndemand for formal analytical skills, written communications,
\nand specific technical knowledge\u2014
\nwhat economists often loosely term cognitive
\nskills\u2014has risen spectacularly.
\nThe central determinant of the supply of
\nskills available to an advanced economy is its
\neducation system. In 1900, the typical young,
\nnative-born American had only a common school
\neducation, about the equivalent of six to eight
\ngrades (19). By the late 19th century, however,
\nmany Americans recognized that farm employment
\nwas declining, industry was rising, and
\ntheir children would need additional education
\nto earn a living. Over the first four decades of the
\n20th century, the United States became the first
\nnation in the world to deliver universal high
\nschool education to its citizens. Tellingly, the high
\nschool movement was led by the farm states.
\nAs the high school movement reached its
\nconclusion, postsecondary education became
\nincreasingly indispensable to the growing occupations
\nof medicine, law, engineering, science,
\nand management. In 1940, only 6% of
\nAmericans had completed a 4-year college
\ndegree. From the end of the Second World
\nWar to the early 1980s, however, the ranks of
\ncollege-educated workers rose robustly and
\nsteadily, with each cohort of workers entering
\nthe labor market boasting a proportionately
\nhigher rate of college education than
\nthe cohort that preceded it. This intercohort
\npattern, which was abetted by the Second
\nWorld War and Korean War GI Bills (20) and
\nby huge state and federal investments in public
\ncollege and university systems, is depicted in
\nFig. 3A. From 1963 through 1982, the fraction
\nof all U.S. hours worked that were supplied
\nby college graduates rose by almost 1 percentage
\npoint per year, a remarkably rapid gain.
\nAfter 1982, however, the rate of intercohort
\nincrease fell by almost half\u2014from0.87 percentage
\npoints to 0.47 percentage points per year\u2014and
\ndid not begin to rebound until 2004, nearly
\ntwo decades later. As shown in fig. S2, this deceleration
\nin the supply of college graduates is
\nparticularly stark when one focuses on young
\nadults with fewer than 10 years of experience\u2014
\nthat is, the cohorts of recent labor market
\nentrants at each point in time. Although the
\nsupply of young college-educated males relative
\nto young high school\u2013educated males increased
\nrapidly in the 1960s and early 1970s
\n(and indeed throughout the postwar period), this
\nrising tide reached an apex in 1974 from which
\nFig. 2. Cross-national differences
\nin wage returns to skills,
\n2011\u20132013. Reproduced with
\npermission from Hanushek et al.
\n[(15), table 2]. Estimates are
\nobtained by regressing the
\nnatural logarithm of workers\u2019
\nweekly full-time earnings on test
\nscores while controlling for sex
\nand labor market experience
\n(both a linear and a quadratic
\nterm). Regression estimates are
\nperformed separately for each
\ncountry and test scores are
\nnormalized with mean zero and
\nunit standard deviation within
\neach country. Estimates that
\nnormalize test scores on a
\ncommon basis across countries,
\nor that use literacy or
\nproblem-solving scores rather
\nthan numeracy scores,
\nyield qualitatively similar patterns.
\nCross-national differences in wage returns
\nto skills, 2011\u20132013
\nPercentage increase for a one standard deviation
\nincrease in skill
\n0 5 10 15 20 25 30 percent
\nSweden
\nCzech R.
\nNorway
\nItaly
\nDenmark
\nCyprus
\nFinland
\nBelgium
\nFrance
\nEstonia
\nSlovak R.
\nAustria
\nNetherlands
\nJapan
\nPoland
\nCanada
\nKorea
\nU.K.
\nSpain
\nGermany
\nIreland
\nU.S.
\nEarnings
\ngain
\n95% confidence
\ninterval
\nSCIENCE sciencemag.org 23 MAY 2014 \u2022 VOL 344 ISSUE 6186 845
\nit barely budged for the better part of the next
\n30 years. Among young females, the deceleration
\nin supply was also unmistakable, although not as
\nabrupt or as complete as for males.
\nThe counterpart to this deceleration in the
\ngrowth of supply of college-educated workers
\nis the steep rise in the college premium commencing
\nin the early 1980s and continuing for
\n25 years. Concretely, when the influx of new
\ncollege graduates slowed, the premium that a
\ncollege education commanded in the labor market
\nincreased. The critical role played by the
\nfluctuating supply of college education in the
\nrise of U.S. inequality is documented in Fig. 3B,
\nwhich plots the college wage premium from
\n1963 through 2012 (blue line). This premium
\nfluctuated in a comparatively narrow band during
\nthe 1960s and 1970s, as rising demand for
\neducated workers was met with rapidly rising
\nyear-over-year increases in supply. In 1981, the
\naverage college graduate earned 48% more per
\nweek than the average high school graduate\u2014a
\nsignificant earnings gap but not an earnings
\ngulf. When the supply deceleration began in
\n1982, however, the college premium hit an inflection
\npoint. This premium notched remarkably
\nrapid year-over-year gains from 1982 forward,
\nreaching 72% in 1990, 90% in 2000, and 97% in
\n2005 (21, 22). Thus, the average earnings of college
\ngraduates were 1.5 times those of high school
\ngraduates in 1982 but were double those of high
\nschool graduates by 2005.
\nWhy is this deceleration in supply relevant
\nto the college premium After all, although the
\ngrowth of supply slowed in 1982, it was still
\nrising. A likely answer is that the demand for
\ncollege workers rose in the interim. Throughout
\nmuch of the 20th century, successive waves
\nof innovation\u2014electrification, mass production,
\nmotorized transportation, telecommunications\u2014
\nhave reduced the demand for physical labor
\nand raised the centrality of cognitive labor in
\npractically every walk of life. The past three
\ndecades of computerization, in particular, have
\nextended the reach of this process by displacing
\nworkers from performing routine, codifiable
\ncognitive tasks (e.g., bookkeeping, clerical work,
\nand repetitive production tasks) that are now
\nreadily scripted with computer software and
\nperformed by inexpensive digital machines. This
\nongoing process of machine substitution for routine
\nhuman labor complements educated workers
\nwho excel in abstract tasks that harness
\nproblem-solving ability, intuition, creativity, and
\npersuasion\u2014tasks that are at present difficult
\nto automate but essential to perform. Simultaneously,
\nit devalues the skills of workers, typically
\nthose without postsecondary education,
\nwho compete most directly with machinery in
\nperforming routine-intensive activities. The net
\neffect of these forces is to further raise the demand
\nfor formal education, technical expertise,
\nand cognitive ability (23\u201327).
\nBringing the Supply-Demand
\nFramework to the Data
\nThe persistently rising demand for educated
\nlabor in advanced economies was first noted
\nby the Nobel Prize\u2013winning economist Jan
\nTinbergen (28) and is often referred to as the
\n\u201ceducation race\u201d model (19). Its primary implication
\nis that if the supply of educated labor
\ndoes not keep pace with persistent outward
\nshifts in demand for skills, the skill premium
\nwill rise. In the words of the Red Queen in
\nLewis Carroll\u2019s Alice in Wonderland, \u201c\u2026it takes
\nall the running you can do, to keep in the same
\nplace.\u201d Thus, when the rising supply of educated
\nlabor began to slacken in the early 1980s,
\na logical economic consequence was an increase
\nin the college skill premium.
\nTo more formally account for the impact of
\nthe fluctuating growth rate of supply of collegeeducated
\nworkers on the college wage differential,
\nFig. 3B depicts the fit of a simple regression
\nmodel that predicts the college wage premium
\nin each year as a function of two factors: (i) the
\ncontemporaneous supply of college graduates,
\nand (ii) a time trend, which serves as a proxy for
\nthe secularly rising demand for college-educated
\n15
\n20
\n25
\n30
\n35
\n40
\n45
\n50 percent
\n1964 1970 1976 1982 1988 1994 2000 2006 2012
\n35
\n45
\n55
\n65
\n75
\n85
\n95
\n105 percent
\n1964 1970 1976 1982 1988 1994 2000 2006 2012
\nThe supply of college graduates and the U.S. college\/high school premium, 1963\u20132012
\nCollege share of hours worked (%), 1963\u20132012:
\nAll working-age adults
\nCollege versus high school
\nwage gap (%)
\nA B
\nPredicted by Supply-
\nDemand Model
\nMeasured Gap
\nFig. 3. The supply of college graduates and the U.S. college\/high school
\npremium, 1963\u20132012. (A) College share of hours worked in the United
\nStates, 1963\u20132012: All working-age adults. Figure uses March CPS data for
\nearnings years 1963 to 2012. The sample consists of all persons aged 16 to
\n64 who reported having worked at least 1 week in the earnings years,
\nexcluding those in the military. Following an extensive literature, collegeeducated
\nworkers are defined as all of those with four or more completed
\nyears of college plus half of those with at least 1 year of completed college.
\nNon-college workers are defined as all workers with high school or less
\neducation, plus half of those with some completed college education. For
\neach individual, hours worked are the product of usual hours worked per
\nweek and the number of weeks worked last year. Individual hours worked
\nare aggregated using CPS sampling weights. (B) College versus high school
\nwage gap. Figure uses March CPS data for earnings years 1963 to 2012.The
\nseries labeled \u201cMeasured Gap\u201d is constructed by calculating the mean of
\nthe natural logarithm of weekly wages for college graduates and non\u2013
\ncollege graduates, and plotting the (exponentiated) ratio of these means for
\neach year. This calculation holds constant the labor market experience and
\ngender composition within each education group. The series labeled
\n\u201cPredicted by Supply-Demand Model\u201d plots the (exponentiated) predicted
\nvalues from a regression of the log college\/noncollege wage gap on a
\nquadratic polynomial in calendar years and the natural log of college\/
\nnoncollege relative supply. See text and supplementary material for further
\ndetails.
\n846 23 MAY 2014 \u2022 VOL 344 ISSUE 6186 sciencemag.org SCIENCE
\nworkers (29). Comparing the fitted values (red
\nseries) from this simple supply-demand model
\nalongside the actual data (blue series) reveals an
\nextremely tight correspondence over the course
\nof five decades and three distinct eras: a declining
\nskill premium in the 1970s; an explosive rise in
\nthe premium during the 1980s, 1990s, and early
\n2000s; and,most recently, a plateau commencing
\nafter 2005. A key implication of this figure is that
\na central causal factor behind rising inequality
\nin the United States has been the slowdown in
\nthe accumulation of skills by young adults almost
\n30 years ago. Had the supply of college graduates
\nrisen as rapidly in the decades after 1980 as it did
\nin the decades immediately before, it is quite plausible
\nthat there would have been no sustained
\nrise in the skill premium in the U.S. labormarket.
\nOf course, this set of facts raises another puzzle:
\nIf slackening college supply sparked rising
\ninequality, what caused rising U.S. postsecondary
\nachievement to grind to a sudden halt in 1982
\nWork by Card and Lemieux (30) highlights that
\none critically important factor was the United
\nStates\u2019 involvement in the Vietnam War. Because
\ndraft-eligible males in the Vietnamera were often
\nable to defer their military service by enrolling
\nin postsecondary schooling, the war artificially
\nboosted college attendance. This created something
\nof a glut of college enrollments in the late
\n1960s and early 1970s, which in turn depressed
\nthe college earnings premium in the 1970s (see
\nFig. 3) and likely reduced the attractiveness of
\ncollege-going absent themilitary draft. Thus, when
\nthe war ended in the early 1970s, college enrollment
\nrates dropped sharply, particularly among
\nmales. The fall in enrollment produced a corresponding
\ndecline in college completions half a
\ndecade later, and a surge of inequality followed.
\nThis supply-demand explanation for the rise of
\nU.S. inequality may appear almost too simple to
\nbe credible. After all, we are comparing just two
\neconomic variables: the college wage premium
\nand the supply of college graduates in the U.S.
\nworkforce. But a host of rigorous studies commencing
\nwith Katz and Murphy (31) confirmthe
\nremarkable explanatory power of this simple
\nsupply-demand framework for explaining trends
\nin the college versus high school earnings gap
\nover the course of nine decades of U.S. history, as
\nwell as across other industrialized economies (most
\nnotably, the United Kingdom and Canada) and
\namong age and education groups within countries
\n(19, 31\u201336). The United States was far from the
\nonly Western country to experience this surge.
\nOne should not, of course, take this model as
\nirrefutable. A puzzling pattern evident in the
\ndata is that the rising demand for skilledworkers
\nappears to have slowed in the early 1990s, a
\nphenomenon that is not anticipated by the \u201ceducation
\nrace\u201d model (37). This discrepancy underscores
\nthat the supply-demand model is
\nnecessarily incomplete\u2014in part for the sake of
\nexpositional clarity and, in larger part, because
\nour understanding of macroeconomic phenomena
\nis typically imperfect. Nevertheless, the data
\nspeak sufficiently clearly to warrant two economic
\ninferences. The first is that although popular
\naccounts frequently assert that the United
\nStates is in the midst of a \u201ccollege bubble\u201d\u2014too
\nmany students going to college at too high a
\ncost\u2014abundant economic evidence strongly suggests
\notherwise. Yes, college tuitions have risen
\nfar faster than inflation, and indeed, student
\ndebt has risen rapidly, with more than $100
\nbillion in federal student aid dollars loaned in
\n2012\u20132013 alone (38). But the doubling of the
\ncollege weekly wage differential over the past
\n30 years also implies that there have been sizable
\nincreases in the lifetime earnings of college graduates
\nrelative to high school graduates. How large
\nare these gains Figure 4, reproduced from (39),
\nreports the estimated lifetime college earnings
\ndifferential net of tuition for cohorts of students
\nentering the labor market between 1965 and
\n2008. For both males and females, the expected
\nnet present value of a college degree relative to a
\nhigh school diploma roughly tripled in this period,
\nwith the fastest gains accruing during the
\n1980s and 1990s. Note that this growing college\/
\nhigh school gap reflects the rising payoff to the
\n4-year college degree, the even steeper rise in the
\npremium associated with graduate and professional
\ndegrees (see below), and the growing fraction of
\ncollege graduateswho obtain higher degrees; thus,
\nan additional payoff to the college degree is that
\nit opens the door to further specialization. This
\nlifetime earnings differential would, of course, have
\nrisen further still if college tuitions had held steady
\nrather than rising. But the inevitable sticker shock
\nthat households feel when confronting the cost of
\ncollege should not obscure the fact that the real
\nlifetime earnings premium to college education
\nhas likely never been higher (40).
\nThe second positive economic news implied
\nby Fig. 3 above is that the ongoing rise
\nof skill differentials is not inevitable. Prior cohorts
\nof U.S. students, particularly males, were
\nslow to react to the rising return to education
\nduring the 1980s and 1990s, but the message
\nappears to have finally gotten through. During
\nthe first decade of the 21st century, the U.S.
\nhigh school graduation rate rose sharply after
\nhaving been essentially stagnant since the late
\n1960s (41). This unanticipated rise was followed
\njust a few years later by a surge in college completions.
\nBetween 2004 and 2012, the supply of
\nnew college graduates to the U.S. labor market
\nrose at a rate not seen in several decades (Fig.
\n3A). As this influx of supply took hold, the college
\nwage premium halted its enduring rise (Fig.
\n3B). What these observations and our simple
\n213K
\n368K
\n261K
\n385K
\n439K
\n582K
\n590K
\n129K
\n198K
\n138K
\n225K
\n284K
\n387K 370K
\nPresent discounted value of college relative to high school degree
\nnet of tuition, 1965\u20132008
\nCollege\/high school difference, 2009 dollars
\n100,000
\n0
\n200,000
\n300,000
\n400,000
\n500,000
\n600,000 dollars
\n1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
\nMen
\nWomen
\nFig. 4. Present discounted value of college relative to high school degree net of tuition,
\n1965\u20132008. Reproduced from Avery and Turner with permission of the American Economic Association
\n(39). Expected earnings are calculated from the March Current Population Survey files
\nfor full-time, full-year workers using sample weights. The estimates equal what a man or woman
\nwould expect to earn working full-time, full-year over a career of 42 years, with a discount rate of
\n3%, assuming that college graduates delay the start of earnings for 4 years while in school.
\nEarnings expectations are formed in each year by assuming that future high school and college
\ngraduates will have future earnings at each age equal to the average earnings of high school and
\ncollege graduates (respectively) currently observed at each age; for example, expected earnings in
\n1980 are based on data across ages for 1980. Results for college-educated workers are net of 4 years
\nof tuition and fees associated with appropriate year-specific values for public universities. Plotted
\npoints show the difference between expected earnings for college graduates and for high school
\ngraduates.
\nSCIENCE sciencemag.org 23 MAY 2014 \u2022 VOL 344 ISSUE 6186 847
\nsupply-demand model suggest is that the flattening
\nof the college premium after 2005 is in
\nlarge part a consequence of the quickening pace
\nof educational attainment.
\nInequality: Causes for Concern
\nA market economy needs some inequality to
\ncreate incentives. If, for example, students were
\nnot ultimately rewarded for spending their early
\nadulthoods pursuing undergraduate, graduate,
\nand professional degrees, or if the hardest-working
\nand most productive workers were paid the
\nsame as the median worker, then citizens would
\nhave little incentive to develop expertise, to exert
\neffort, or to excel in their work (42). Having acknowledged
\nthat some inequality is necessary,
\nhowever, how can we gauge whether there is
\ntoo much of it I offer two analytical perspectives
\non this question.
\nEarnings Mobility
\nOne metric by which to evaluate the consequences
\nof inequality is via its relationship
\nwith economic mobility\u2014that is, the degree to
\nwhich individual economic fortunes change
\nover time. Of particular interest is the degree of
\nintergenerational mobility, meaning the likelihood
\nthat children born to low-income families
\nbecome high-income adults and vice versa.
\nHigh levels of economic inequality at a given
\npoint in time are not intrinsically inimical to
\neconomic mobility; a society with high inequality
\nmay be dynamic, with lots of movement up
\nand down the economic ladder, and one with
\nlow inequality may be dynastic. But a natural
\nconcern is that high inequality at a point in time
\nmay serve to reduce mobility over time. If, for
\nexample, adults who became wealthy through
\nhard work are able to \u201cbuy\u201d success for their
\nchildren through outsized investments and personal
\nconnections, while adults who are unproductive
\nor unlucky in their careers are unable
\nto muster the resources to foster their children\u2019s
\npotential, then inequality of incomes could become
\nself-perpetuating even if it originally emanates
\nfrom high market returns to skill (43).
\nTo understand the importance of high and
\nrising U.S. inequality, it is therefore useful to
\nask how U.S. economic mobility compares to
\nthat of other developed countries, and whether
\nU.S. mobility has fallen as inequality has risen.
\nThe answers to both questions will surprise
\nmany. Contrary to conventional civic mythology,
\nU.S. intergenerational mobility is relatively
\nlow. The left panel of Fig. 5, reproduced from
\n(44), which plots the relationship between crosssectional
\ninequality (x axis) and earnings mobility
\n(y axis) among a set of 13 OECD member
\ncountries for which consistent data are available,
\ndocuments that the United States has both the
\nlowest mobility and highest inequality among
\nall wealthy democratic countries. The right panel
\nof Fig. 5, also sourced from (44), suggests one
\nproximate explanation for this pattern: Countries
\nwith high returns to education tend to
\nhave relatively low mobility.Why, if education is
\n\u201cthe great equalizer\u201d in the words of Horace
\nMann, do high educational returns predict low
\nmobility A key reason is that educational attainment
\nis highly persistent within families.
\nIndeed, two of the strongest predictors of children\u2019s
\nultimate educational attainment are parental
\neducation and parental earnings (45, 46).
\nHence, when the return to education is high,
\nchildren of better-educated parents are doubly
\nadvantaged\u2014by their parents\u2019 higher education
\nand higher earnings\u2014in attaining greater education
\nwhile young and greater earnings in
\nadulthood. Figure 5 therefore lends credence
\nto the concern that rising inequality may erode
\neconomic mobility.
\nHas this erosion occurred Surprisingly, the
\nbest evidence to date suggests that it has not.
\nEvidence from Chetty et al. (46), documented
\nin the supplementary material, underscores the
\nmessage from Fig. 5 that there is substantial
\neconomic immobility in the United States. Children
\nborn three deciles apart in the household
\nincome distribution are on average one decile
\napart in the earnings distribution at age 29 or
\n30. Similarly, children born three deciles apart
\nin the household income distribution differ by
\n20 percentage points in their probability of attending
\ncollege at age 19 (relative to a mean of
\napproximately 55%). Yet these data offer no
\nevidence that mobility has appreciably changed
\namong children born prior to the historic rise
\nof U.S. inequality (1971\u20131974) and those born
\nafterward (1991\u20131993). As far as we can measure,
\nrising U.S. income inequality has not reduced
\nintergenerational mobility so far. These findings,
\nwhich also appear to hold over a longer
\nhistorical time frame (47), suggest that U.S.
\nmobility has not trended downward as many
\nsocial scientists would have anticipated, and as
\nCanada
\nAustralia
\nNew Zealand
\nGermany
\nGermany
\nJapan
\nFrance France
\nUnited
\nKingdom
\nItaly United Kingdom
\n0.1
\n0.2
\n0.3
\n0.4
\n0.5
\n0.1
\n0.2
\n0.3
\n0.4
\n0.5
\n20 25 30 35
\nIncome inequality (more inequality )
\nDenmark
\nNorway Finland
\nCanada
\nAustralia
\nNew Zealand
\nSpain
\nItaly
\n100 120 140 160 180
\nCollege earnings premium (men 25 to 34)
\nEarnings inequality and economic mobility: cross-national relationships
\nGenerational earnings elasticity
\n(higher values imply lower mobility)
\nGenerational earnings elasticity
\n(higher values imply lower mobility) A B
\nSweden
\nNorway
\nDenmark
\nFinland
\nUnited States United States
\nSweden
\nFig. 5. Earnings inequality and economic mobility: Cross-national relationships.
\nReproduced from Corak [(44), figs. 1 and 4] with permission of
\nthe American Economic Association. In both panels, the mobility measure is
\nequal to the intergenerational earnings \u201celasticity,\u201d meaning the average
\nproportional increase in a son\u2019s adult earnings predicted by his father\u2019s
\nadult earnings measured approximately three decades earlier. A higher intergenerational
\nearnings elasticity therefore implies lower intergenerational
\nmobility. In the left panel, cross-sectional income inequality is measured
\nusing a \u201cGini\u201d index that ranges from 0 to 100, where 0 indicates complete
\nequality of household incomes and 100 indicates maximal inequality (all
\nincome to one household). In the right panel, the college earnings premium
\nrefers to the ratio of average earnings of men 25 to 34 years of age with a
\ncollege degree to the average earnings of those with a high school diploma,
\ncomputed by the OECD using 2009 data. See (44) for further details.
\n848 23 MAY 2014 \u2022 VOL 344 ISSUE 6186 sciencemag.org SCIENCE
\nmany policymakers and popular accounts frequently
\nassume.
\nIt is important to interpret these results in
\ncontext. The most recent birth cohorts whose
\nadult outcomes can be observed at present
\nwere born no later than the early 1990s, which
\nis still relatively early in the rise of U.S. inequality.
\nAnother 10 years of data, focusing
\non children born since 2000, may suggest a
\ndifferent conclusion. Moreover, the fact that
\nmobility has stayed constant while inequality
\nhas risen means that the lifetime relative disadvantage
\nof children born to low- versus highincome
\nfamilies has increased substantially;
\nconcretely, the rungs of the economic ladder
\nhave pulled farther apart but the chance of
\nascending the ladder has not improved. Finally,
\nit is possible to interpret the fact that
\nmobility has remained unchanged as evidence
\nthat U.S. mobility would have declined had it
\nnot been for the other compensatory steps
\ntaken by the federal government during this
\nperiod, including, for example, expanding the
\nEarned Income Tax Credit for low-income workers
\nin the 1980s, enlarging the early childhood
\neducation Head Start program in the 1990s,
\nand increasing federal student grant and loan
\nprograms to support college-going (48). Declines
\nin racial and gender discrimination during this
\nperiod likely also complemented these policies
\n(49). A cautious read of the evidence is that although
\nthe United States is not a \u201cland of opportunity\u201d
\nby conventional economic mobility metrics,
\nit has not become less so in recent decades.
\nReal Earnings
\nA second gauge of economic health is the trajectory
\nof earnings and employment. Here, the
\ndata present substantial cause for concern. Although
\nthe substantial college wage premium
\nconveys the positive economic news that educational
\ninvestments offer large returns, this wage
\npremium also masks a discouraging truth: The
\nrising relative earnings of workers with postsecondary
\neducation is not simply due to rising
\nreal earnings among college-educated workers
\nbut is also due to falling real earningsamong non\u2013
\ncollege-educated workers. Between 1980 and
\n2012, real hourly earnings of full-time collegeeducated
\nU.S. males rose anywhere from 20% to
\n56%, with the greatest gains among those with
\na postbaccalaureate degree (Fig. 6A). During the
\nsame period, real earnings of males with high
\nschool or lower educational levels declined substantially,
\nfalling by 22% among high school dropouts
\nand 11% among high school graduates. Although
\nthe picture is generally brighter for females (Fig.
\n6B), real earnings growth among females without
\nat least some college education over this threedecade
\ninterval was extremely modest.
\nAccompanying the fall in real wages among
\nless educated workers has been a pronounced
\ndrop in their labor force participation rates,
\nparticularly among less educated males. Between
\n1979 and 2007, prior to the onset of the
\nGreat Recession, the fraction of working-age
\nmales in paid employment fell by 12 percentage
\npoints among high school dropouts and 10 percentage
\npoints among those with exactly a high
\nschool diploma. Conversely, employment rates were
\ngenerally stable for males with postsecondary
\neducation and rose for females of all education
\nlevels except for high school dropouts.
\nThe causes for the sharp falls in real earnings
\namong non\u2013college-educated workers are multiple.
\nOne likely force, as noted above, is the
\nongoing substitution of computer-intensive machinery
\nfor workers performing routine taskintensive
\njobs. This has depressed demand for
\nworkers in both blue-collar production andwhitecollar
\noffice, clerical, and administrative support
\npositions, and has reduced the set of middleskill
\ncareer jobs available to non\u2013college-educated
\nworkers more generally (25). A second factor
\nis the globalization of labor markets, seen particularly
\nin the greatly increased U.S. trade
\nintegration with developing countries. Globalization
\nhas become particularly important for
\nU.S. labor markets since the early 1990s, when
\nChina began its extremely rapid integration
\ninto the world trading system. The influx of
\nChinese goods lowered consumer prices but
\nalso fomented a substantial decline in U.S. manufacturing
\nemployment, contributing directly
\nto the decline in production worker employment
\n(50). A third factor impinging on the earnings
\nof non\u2013college-educated males is the decline in the
\npenetration and bargaining power of labor unions
\nin the United States, which have historically
\nobtained relatively generous wage and benefit
\npackages for blue-collar workers. Over the past
\nthree decades, however, U.S. private-sector union
\ndensity\u2014that is, the fraction of private-sector
\nworkers who belong to labor unions\u2014has fallen
\nby approximately 70%, from24%in 1973 to 7%in
\n2011 (51, 52).
\nNotably, these three forces\u2014technological
\nchange, deunionization, and globalization\u2014
\nwork in tandem. Advances in information and
\ncommunications technologies have directly
\nchanged job demands in U.S. workplaces while
\nsimultaneously facilitating the globalization of
\nproduction by making it increasingly feasible
\nand cost-effective for firms to source, monitor,
\nand coordinate complex production processes
\nat disparate locations worldwide. In turn, the
\nglobalization of production has increased competitive
\nconditions for U.S. manufacturers and
\nU.S. workers, eroding employment at unionized
\nestablishments and decreasing the capability
\n1.0
\n1.2
\n1.4
\n1.6
\n1.8
\n2.0
\n1964 1968 1972 1976 1980 1984 1988 1992 1996 200020042008 2012
\nSome college
\n> Bachelor’s
\ndegree
\nBachelor’s
\ndegree
\nHigh school
\ngraduate
\nHigh school
\ndropout
\nA B
\nChanges in real wage levels of full-time U.S. workers by sex and education, 1963\u20132012
\nReal weekly earnings relative to 1963 (men)
\nReal weekly earnings relative to 1963 (women)
\n1964 1968 1972 1976 1980 1984 1988 1992 1996 200020042008 2012
\n1.0
\n1.2
\n1.4
\n1.6
\n1.8
\n2.0
\nFig. 6. Change in real wage levels of full-time workers by education, 1963\u20132012. (A) Male workers, (B) female workers. Data and sample construction are
\nas in Fig. 3.
\nSCIENCE sciencemag.org 23 MAY 2014 \u2022 VOL 344 ISSUE 6186 849
\nof unions to negotiate favorable contracts, attract
\nnew members, and penetrate new establishments.
\nIn all cases, the foremost concern raised by
\nthese multiple forces impinging on the earnings
\nof workers at different skill levels is not their
\nimpact on inequality per se, but rather their adverse
\neffect on the real earnings and employment
\nof less educated workers. These declines in both
\nearnings and employment bode ill for the welfare
\nof non\u2013college-educated U.S. adults and are likely
\nto have broader detrimental social consequences
\nthat frequently accompany non-employment:
\ngreater criminality, increased social dependency,
\nand (more mundanely) reduced tax receipts.
\nDo Supply and Demand Make
\nPolicy Irrelevant
\nOne potential interpretation of the evidence
\nabove is that, because rising inequality is substantially
\na consequence of the impersonal forces
\nof supply and demand, public policy has no role
\nto play in shaping the trajectory of inequality or
\nits social impact. This conclusion is incorrect for
\ntwo reasons. First, there are multiple channels
\nby which policy has contributed to the rise of
\nU.S. inequality, many of which are not fully
\nevident in the education earnings premium.
\nThese include the fall over several decades in
\nthe real value of the U.S. minimum wage (7); the
\ndeclining prevalence and bargaining power
\nof U.S. labor unions; mounting international
\ncompetition that places particular pressure on
\nthe wages and employment of less educated
\nworkers; and sharp reductions in top federal
\nmarginal tax rates that have raised after-tax
\ninequality and increased the incentive of highly
\npaid workers to seek still higher compensation.
\nAs discussed in the companion paper by
\nPiketty and Saez, there is also disagreement
\namong economists about whether the rising
\nshare of household incomes accruing to the top
\nfew percentiles of households in numerous
\ndeveloped countries over the past several decades
\nis also primarily a market phenomenon, or
\ninstead reflects changing social norms, growing
\ncorporate misgovernance, slackening regulatory
\noversight, or increasing political capture of
\nthe policymaking process by elites (3\u20136). It would
\ntherefore be a vast overstatement to conclude that
\nthe rise of U.S. inequality is exclusively due to
\nconventional market forces, or that public policy
\nhas not played a role.
\nBut let us assume for the sake of argument
\nthat the rise of income inequality is entirely a
\nmarket phenomenon. Would this imply that
\nthere is no role for public policy A moment\u2019s
\nreflection suggests otherwise. As the economist
\nArthur Goldberger once famously observed, the
\nfact that nearsightedness is substantially a genetic
\ndisorder has no bearing on whether doctors
\nshould prescribe eyeglasses (53). What is relevant
\nis whether the benefits of addressing myopia
\nexceed the costs. In the case of myopia, the availability
\nof eyeglasses make this an easy call.
\nAlthough there is no \u201cremedy\u201d for inequality
\nthat is as swift or cheap as eyeglasses, prosperous
\ndemocratic countries have numerous effective
\npolicy levers for shaping inequality\u2019s trajectory
\nand socioeconomic consequences. Policies that
\nappear most effective over the long haul in raising
\nprosperity and reducing inequality are those
\nthat cultivate the skills of successive generations:
\nexcellent preschool through high school education;
\nbroad access to postsecondary education; and
\ngood nutrition, good public health, and highquality
\nhome environments. Such policies address
\ninequality from two directions: (i) enabling a larger
\nfraction of adults to attain high productivity,
\nrewarding jobs, and a reasonable standard of
\nliving; and (ii) raising the total supply of skills
\navailable to the economy, which in turnmoderates
\nthe skill premium and reduces inequality (54).
\nOf course, building skills is a multigenerational
\nprocess and thus has little impact on inequality
\nin the short term. There are, however,
\nnumerous nearer-term levers that moderate
\ninequality directly without imposing substantial
\neconomic costs: applying progressive tax and
\ntransfer policies that fund public investments
\nand foster opportunities for children of all socioeconomic
\nbackgrounds; applying well-crafted
\nlabor regulations that ensure safe and nonexploitive
\nworking conditions; providing wage
\nsubsidies such as the Earned Income Tax Credit
\nthat increase the payoff to employment for those
\nwith limited skills; setting modest but nonzero
\nminimum wage rules; and offering numerous
\nsocial insurance policies (health and disability
\ninsurance, flood insurance, disaster Helpance,
\nfood Helpance) that buffer misfortune for the
\nunfortunate. Although it is outside the scope of
\nthis article to evaluate these policies, it is critical
\nto underscore that policy and governance
\nhas played and should continue to play a central
\nrole in shaping inequality\u2014even when a central
\ncause of rising inequality is the changing supply
\nand demand for skills.
\nREFERENCES AND NOTES
\n1. C. D. Goldin, L. F. Katz, Brookings Pap. Econ. Act. (fall), 135 (2007).
\n2. Goldin and Katz (1) found that the increase in the education
\nwage premium, particularly the college premium, explains
\nabout 60 to 70% of the rise in wage inequality (variance)
\nbetween 1980 and 2005.
\n3. F. Alvaredo et al., J. Econ. Perspect. 27, 3\u201320 (2013).
\n4. J. Bivens, L. Mishel, J. Econ. Perspect. 27, 57\u201378 (2013).
\n5. A. Bonica et al., J. Econ. Perspect. 27, 103\u2013124 (2013).
\n6. S. N. Kaplan, J. Rauh, J. Econ. Perspect. 27, 35\u201356 (2013).
\n7. D. Autor et al., The Contribution of the Minimum Wage to U.S.
\nWage Inequality over Three Decades: A Reassessment (NBER
\nWorking Paper 16533, Cambridge, MA, 2010).
\n8. T. Piketty, E. Saez, Q. J. Econ. 118, 1\u201341 (2003).
\n9. These calculations use data from (8), with data updated to
\n2012 available at http:\/\/elsa.berkeley.edu\/saez\/~Tab-
\nFig.2012prel.xls. Average U.S. household incomes, including
\nthe top 1%, rose by 20.2%, while the average household
\nincome of the bottom 99% of households rose by only 3.5%.
\n10. Thus, the top 1% maintains its share of household income at a
\nconstant 10.0% while average household incomes rise by
\n20.2%, as actually occurred.
\n11. This point is due to Lawrence Katz of Harvard University, who
\noffers these calculations in his graduate labor economics
\nlecture notes.
\n12. T. Lemieux, Post-Secondary Education and Increasing Wage
\nInequality (Working Paper 12077, National Bureau of Economic
\nResearch, 2006).
\n13. S. Firpo et al., Decomposition methods in economics. In
\nHandbook of Labor Economics, D. Card, O. Ashenfelter, Eds.
\n(Elsevier-North Holland, Amsterdam, 2011), vol. 4, pp. 1\u2013102.
\n14. See www.oecd.org\/site\/piaac\/surveyofadultskills.htm for more
\ninformation. The PIAAC program will encompass 33 countries, but
\ndata for only 22 were available at this writing.
\n15. E. A. Hanushek, G. Schwerdt, S.Wiederhold,
\nL.Woessmann, Returns to Skills Around the World:
\nEvidence from PIAAC (NBER Working Paper 19762,
\nCambridge, MA, 2013).
\n16. Hanushek et al. (15) also found that the correlation between
\nnumeracy skills and years of schooling is 0.45. When including
\nboth numeracy skills and years of schooling in an earnings
\nregression, they found that both are substantial and significant
\npredictors of earnings, although each is attenuated relative
\nto a model where only one factor is included at a time. This
\npattern of results suggests, logically, that neither test scores
\nnor years of schooling is a complete measure of labor
\nmarket skills.
\n17. T. D. Snyder, 120 Years of American Education: A Statistical
\nPortrait (National Center for Education Statistics, U.S.
\nDepartment of Education, 1993).
\n18. L. D. Johnston, \u201cHistory lessons: Understanding
\nthe decline in manufacturing.\u201d MinnPost, 22 February
\n2012; www.minnpost.com\/macro-micro-minnesota\/
\n2012\/02\/history-lessons-understanding-declinemanufacturing.
\n19. C. Goldin, L. F. Katz, The Race Between Education and
\nTechnology (Harvard Univ. Press, Cambridge, MA, 2008).
\n20. M. Stanley, Q. J. Econ. 118, 671\u2013708 (2003).
\n21. B. Pierce, in Labor in the New Economy, K. G. Abraham,
\nJ. R. Spletzer, M. Harper, Eds. (Univ. of Chicago Press, Chicago,
\n2010), pp. 63\u201398.
\n22. These comparisons hold labor market experience and gender
\nconstant. This doubling of the college premium very likely
\nunderstates the magnitude of the increase in inequality
\nbetween college-educated and non\u2013college-educated workers.
\nAlongside higher hourly earnings, college-educated workers
\nenjoy greater job stability, lower rates of unemployment, more
\ngenerous fringe benefits, and better working conditions;
\nPierce (21) found that these differentials have generally increased
\nin the same time period.
\n23. D. H. Autor et al., Q. J. Econ. 118, 1279\u20131333 (2003).
\n24. D. Acemoglu, D. H. Autor, Skills, tasks and technologies:
\nImplications for employment and earnings. In Handbook of
\nLabor Economics, D. Card, O. Ashenfelter, Eds. (Elsevier-North
\nHolland, Amsterdam, 2011), vol. 4, pp. 1043\u20131171.
\n25. D. H. Autor, D. Dorn, Am. Econ. Rev. 103, 1553\u20131597 (2013).
\n26. M. Goos et al., www.aeaweb.org\/forthcoming\/output\/
\naccepted_AER.php
\n27. Extensive recent literature, commencing with Autor et al. (23)
\nand summarized in Acemoglu and Autor (24), considers the
\nrole of technological change in displacing workers performing
\nroutine tasks and complementing workers performing
\nnonroutine tasks. An additional implication of this framework is
\nthat an increasing share of employment will be found in
\ncomparatively low-skill nonroutine manual tasks that require
\nsituational adaptability, visual and language recognition,
\nand in-person interactions but limited formal education
\n(e.g., janitors and cleaners, home health aides, construction
\nlaborers, and security personnel). See Autor and Dorn (25) and
\nGoos et al. (26) for evidence that employment in the
\nU.S. and among OECD member countries has increasingly
\npolarized into high-paid, abstract-intensive occupations and
\nlow-paid, manual-intensive occupations.
\n28. J. Tinbergen, Kyklos 27, 217\u2013226 (1974).
\n29. Details of this model are given in the online supplement.
\n30. D. Card, T. Lemieux, Am. Econ. Rev. 91, 97\u2013102
\n(2001).
\n31. L. F. Katz, K. M. Murphy, Q. J. Econ. 107, 35\u201378
\n(1992).
\n32. L. Katz, D. H. Autor, Changes in the wage structure and
\nearnings inequality. In Handbook of Labor Economics,
\nD. Card, O. Ashenfelter, Eds. (Elsevier-North Holland, Amsterdam,
\n1999), vol. 3, pp. 1463\u20131555.
\n33. D. Card, T. Lemieux, Q. J. Econ. 116, 705\u2013746 (2001).
\n34. D. H. Autor et al., Rev. Econ. Stat. 90, 300\u2013323 (2008).
\n35. E. Crivellaro, \u201cCollege wage premium over time: Trends in
\nEurope in the last 15 years.\u201d University Ca\u2019 Foscari
\nof Venice, Department of Economics Research Paper
\nSeries no. 03\/WP\/2014 (2014); http:\/\/dx.doi.org\/10.2139\/
\nssrn.2383795.
\n36. Summarizing evidence on the college premium in 12 European
\ncountries between 1994 and 1999, Crivellaro (35) found a
\npattern of increasing skill differentials except in countries that
\nhave had a large increase in the relative supply of college
\n850 23 MAY 2014 \u2022 VOL 344 ISSUE 6186 sciencemag.org SCIENCE
\ngraduates, a pattern consistent with the conceptual model laid
\nout below.
\n37. Although this deceleration is not evident from Fig. 3, it is
\ndetected by the regression equation, as discussed in the online
\nsupplement.
\n38. College Board, Trends in Student Aid: 2013 (College Board,
\nNew York, 2013).
\n39. C. Avery, S. Turner, J. Econ. Perspect. 26, 165\u2013192 (2012).
\n40. Three sources of uncertainty should be kept in mind when
\ninterpreting these estimates. First, they encompass
\nsubstantial heterogeneity. Although the average college
\ngraduate earns substantially more than the average high
\nschool graduate, the least successful college graduates may
\nearn substantially less than the median among high school
\ngraduates, and the most successful high school graduates
\nmay earn substantially more than the median among college
\ngraduates. Second, for students who acquire substantial
\nstudent debt but do not complete the college degree, it is
\nfar from certain that college will prove a good investment.
\nFinally, these calculations assume that the lifetime profile of
\nearnings observed in the year of college graduation will
\npersist throughout the career. As Fig. 3 indicates, this
\npremium has changed substantially over time, so this
\nassumption is only a rough approximation. However, the
\ncollege premium is so high at present that even with a
\nsubstantial decline, college would remain an attractive
\nfinancial proposition on average from a lifetime earnings
\nperspective.
\n41. R. J. Murnane, J. Econ. Lit. 51, 370\u2013422 (2013).
\n42. D. Acemoglu, J. Robinson, Why Nations Fail (Crown, New York,
\n2012).
\n43. As with cross-sectional inequality, there is no economically
\n\u201cideal\u201d level of intergenerational mobility. Even in a society
\nwith perfect equality of opportunity, one would expect children
\nof successful parents to have above-average success as
\nadults, simply because many attributes that contribute to
\nsuccess (appearance, intellect, athleticism) are partly
\nheritable.
\n44. M. Corak, J. Econ. Perspect. 27, 79\u2013102 (2013).
\n45. S. F. Reardon, The widening academic achievement gap
\nbetween the rich and the poor: New evidence and possible
\nexplanations. In Whither Opportunity Rising Inequality,
\nSchools, and Children\u2019s Life Chances, G. J. Duncan,
\nR. J. Murnane, Eds. (Russell Sage Foundation, New York, 2011),
\npp. 91\u2013115.
\n46. R. Chetty et al., Is the United States Still a Land of Opportunity
\nRecent Trends in Intergenerational Mobility (NBER Working
\nPaper No. 19844, Cambridge, MA, 2014).
\n47. C.-I. Lee, G. Solon, Rev. Econ. Stat. 91, 766\u2013772 (2009).
\n48. Between 2002\u20132003 and 2012\u20132013, the sum of federal
\nPell Grants and loans for higher education increased by 105%,
\nfrom $83 billion to $170 billion in constant 2012 dollars
\n[(38), table 1].
\n49. C.-T. Hsieh et al., The Allocation of Talent and U.S. Economic
\nGrowth (NBER Working Paper No. 18693, Cambridge, MA, 2013).
\n50. D. H. Autor et al., Am. Econ. Rev. 103, 2121\u20132168 (2013).
\n51. D. Card et al., J. Labor Res. 25, 519\u2013559 (2004).
\n52. B. T. Hirsch, J. Econ. Perspect. 22, 153\u2013176 (2008).
\n53. A. S. Goldberger, Economica 46, 327 (1979).
\n54. The extensive involvement of state and federal government in
\neducation at all levels also underscores the fact that the
\ndistribution of education and skills today is in no sense a
\n\u201cfree market\u201d outcome; it is a consequence of both individual
\nand public choices.
\nACKNOWLEDGMENTS
\nI thank D. Acemoglu, L. Katz, J. Van Reenen, M. Tatsutani, and
\ntwo anonymous referees for valuable comments and advice, and
\nC. Patterson and B. Price for expert research Helpance.
\nSupported by NSF grant SES-1227334, Russell Sage Foundation
\ngrant 85-12-07, and Alfred P. Sloan Foundation grant 2011-10-12.
\nAll data and code that are unique to this article (Figs. 1, 3,
\nand 6; fig. S2) are available from the author. All other figures
\n(Figs. 2, 4, and 5; figs. S1 and S3) are reproduced from
\nother publications, as noted, with permission of the authors.
\nSUPPLEMENTARY MATERIALS
\nwww.sciencemag.org\/content\/344\/6186\/843\/suppl\/DC1
\nSupplementary Text
\nFigs. S1 to S3
\nReferences (55\u201361)
\n10.1126\/science.1251868
\nREVIEW
\nIncome inequality in the
\ndeveloping world
\nMartin Ravallion
\nShould income inequality be of concern in developing countries New data reveal less
\nincome inequality in the developing world than 30 years ago. However, this is due to falling
\ninequality between countries. Average inequality within developing countries has been
\nslowly rising, though staying fairly flat since 2000. As a rule, higher rates of growth in
\naverage incomes have not put upward pressure on inequality within countries. Growth
\nhas generally helped reduce the incidence of absolute poverty, but less so in more
\nunequal countries. High inequality also threatens to stall future progress against
\npoverty by attenuating growth prospects. Perceptions of rising absolute gaps in living
\nstandards between the rich and the poor in growing economies are also consistent
\nwith the evidence.
\nDevelopment economics emerged as a subdiscipline
\nof economics in the 1950s, and
\nits initial focus was on economic growth,
\nwith inequality as a secondary concern.
\nThe prevailing orthodoxy for many decades
\nwas that a period of rising inequality was to
\nbe expected in growing poor countries. Rising
\ninequality was seen to be more or less inevitable
\nand not something to worry about, particularly
\nif the incidence of poverty was falling. Another
\ncommonly held view was that policy efforts to
\nreduce inequality were likely to impede growth
\nand (hence) poverty reduction.
\nThe existence of high inequality within many
\ndeveloping countries, side by side with persistent
\npoverty, started to attract attention in the early
\n1970s. Nonetheless, through the 1980s and
\nwell into the 1990s, the mainstream view in
\ndevelopment economics was still that high
\nand\/or rising inequality in poor countries was
\na far less important concern than assuring sufficient
\ngrowth, which was the key to poverty
\nreduction. The policy message for the developing
\nworld was clear: You cannot expect to have
\nboth lower poverty and less inequality while
\nyou remain poor, and, if you choose to give poverty
\nreduction highest priority, then focus on
\ngrowth.
\nOther objections could still be raised to
\nhigh income inequality. The classical utilitarian
\nformulation\u2014whereby social welfare is judged
\nby the sum of utilities, assuming diminishing
\nmarginal utility of income\u2014pointed to social welfare
\nlosses from high inequality at a given mean.
\nBut that did not persuade those who believed
\nthat there was a trade-off between equity and
\ngrowth. A moral defense could also be mounted
\nfor the view that inequality is not an important
\nissue for a growing developing country by appeal
\nto John Rawls\u2019s \u201cdifference principle\u201d that (subject
\nto assuring liberty and equal opportunity) higher
\ninequality can be justified as long as it benefits the
\nworst-off group in society (1).
\nThe period since 2000 has seen a deeper and
\nmorewidespread questioning of this long-standing
\nview of pro-poor inequality. New concerns have
\nemerged about the instrumental importance of
\nequity to other valued goals, including poverty
\nreduction and human development more broadly.
\nIt appears more likely today that high inequality
\nwill be seen as a threat to future development
\nthan as an inevitable and unimportant consequence
\nof past progress. The long-standing idea
\nof a substantial growth-equity trade-off has come
\nto be seriously questioned.
\nThis paper reports new estimates of the levels
\nand changes in income inequality measures for
\nthe developing world. The new estimates take
\nus up to 2010, embracing the period of higher
\ngrowth rates in the developing world since the
\nturn of the millennium. In the light of these new
\ndata, I revisit past and ongoing debates on inequality
\nin developing countries and the tradeoffs
\nwith growth and poverty reduction.
\nIncome Inequality Measures
\nTo measure inequality in the developing world
\nas a whole, one ignores country borders\u2014pooling
\nall residents and measuring inequality among
\nthem. This overall measure will naturally depend
\non the inequality between countries as well as
\nthat within them. Thus, its evolution over time
\nwill depend on whether poorer countries are
\nseeing lower growth rates as well as the things
\nhappening within countries\u2014economic changes
\nand policies\u2014that affect inequality.
\nIf we are comparing country or regional performance,
\nthen we want to isolate the withincountry
\ncomponent of inequality as distinct from
\nthat between countries. Although there are many
\ninequality measures, not all of themallow a clean
\nseparation of the between and within components.
\nFor example, such a decomposition is
\nDepartment of Economics, Georgetown University,
\nWashington, DC 20057, and National Bureau of Economic
\nResearch, Cambridge, MA 02138, USA. E-mail: mr1185@
\ngeorgetown.edu
\nSCIENCE sciencemag.org 23 MAY 2014 \u2022 VOL 344 ISSUE 6186 851