recent flattening in the higher education wag · education, skills, and technical change...

Recent Flattening in the Higher Education Wag...

Education, Skills, and Technical Change

Studies in Income and Wealth

Volume 77

National Bureau of Economic Research

Conference on Research in Income and Wealth

Education, Skills, and Technical ChangeImplications for Future US GDP Growth

Edited by Charles R. Hulten and Valerie A. Ramey

The University of Chicago Press

Chicago and London

The University of Chicago Press, Chicago 60637The University of Chicago Press, Ltd., London© 2019 by the National Bureau of Economic ResearchAll rights reserved. No part of this book may be used or reproduced in any manner whatsoever without written permission, except in the case of brief quotations in critical articles and reviews. For more information, contact the University of Chicago Press, 1427 E. 60th St., Chicago, IL 60637.Published 2019Printed in the United States of America

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ISBN- 13: 978- 0- 226- 56780- 8 (cloth)ISBN- 13: 978- 0- 226- 56794- 5 (e- book)DOI: https:// doi .org /10 .7208 /chicago /9780226567945 .001 .0001

Library of Congress Cataloging- in- Publication Data

Names: Education, Skills, and Technical Change: Implications for Future U.S. GDP Growth (Conference) (2015 : Bethesda, Maryland) | Hulten, Charles R., editor. | Ramey, Valerie A. (Valerie Ann), editor.

Title: Education, skills, and technical change : implications for future US GDP growth / edited by Charles R. Hulten and Valerie A. Ramey.

Other titles: Studies in income and wealth ; v. 77.Description: Chicago : The University of Chicago Press, 2019. | Series:

Studies in income and wealth ; v. 77 | “This volume contains revised versions of the papers presented at the Conference on Research in Income and Wealth titled “Education, Skills, and Technical Change: Implications for Future U.S. GDP Growth,” held in Bethesda, Maryland, on October 16–17, 2015”—Publisher info. | Includes bibliographical references and index.

Identifi ers: LCCN 2018013221 | ISBN 9780226567808 (cloth : alk. paper) | ISBN 9780226567945 (e- book)

Subjects: LCSH: Labor supply—Eff ect of education on—United States—Congresses. | Labor supply—Eff ect of technological innovations on—United States—Congresses. | Education—Eff ect of technological innovations on—United States—Congresses. | Gross domestic product—Social aspects—United States—Congresses. | Human capital—United States—Congresses.

Classifi cation: LCC HD5724 .E28 2019 | DDC 338.973—dc23LC record available at https:// lccn .loc .gov /2018013221

This paper meets the requirements of ANSI/NISO Z39.48- 1992 (Permanence of Paper).

DOI: 10.7208/chicago/9780226567945.003.0009


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vii

Prefatory Note ix

Introduction 1Charles R. Hulten and Valerie A. Ramey

I. The Macroeconomic Link between Education and Real GDP Growth

1. Educational Attainment and the Revival of US Economic Growth 23Dale W. Jorgenson, Mun S. Ho, and Jon D. Samuels

2. The Outlook for US Labor- Quality Growth 61Canyon Bosler, Mary C. Daly, John G. Fernald, and Bart HobijnComment on Chapters 1 and 2: Douglas W. Elmendorf

3. The Importance of Education and Skill Development for Economic Growth in the Information Era 115Charles R. Hulten

II. Jobs and Skills Requirements

4. Underemployment in the Early Careers of College Graduates following the Great Recession 149Jaison R. Abel and Richard Deitz

Contents

viii Contents

5. The Requirements of Jobs: Evidence from a Nationally Representative Survey 183Maury Gittleman, Kristen Monaco, and Nicole Nestoriak

III. Skills, Inequality, and Polarization

6. Noncognitive Skills as Human Capital 219Shelly LundbergComment: David J. Deming

7. Wage Inequality and Cognitive Skills: Reopening the Debate 251Stijn Broecke, Glenda Quintini, and Marieke VandeweyerComment: Frank Levy

8. Education and the Growth- Equity Trade- Off 293Eric A. Hanushek

9. Recent Flattening in the Higher Education Wage Premium: Polarization, Skill Downgrading, or Both? 313Robert G. VallettaComment: David Autor

IV. The Supply of Skills

10. Accounting for the Rise in College Tuition 357Grey Gordon and Aaron HedlundComment: Sandy Baum

11. Online Postsecondary Education and Labor Productivity 401Caroline M. HoxbyComment: Nora Gordon

12. High- Skilled Immigration and the Rise of STEM Occupations in US Employment 465Gordon H. Hanson and Matthew J. SlaughterComment: John Bound

Contributors 501Author Index 505Subject Index 513

ix

This volume contains revised versions of the papers presented at the Confer-ence on Research in Income and Wealth titled “Education, Skills, and Tech-nical Change: Implications for Future U.S. GDP Growth,” held in Bethesda, Maryland, on October 16–17, 2015.

We gratefully acknowledge the fi nancial support for this conference pro-vided by the Bureau of Economic Analysis. Support for the general activities of the Conference on Research in Income and Wealth is provided by the fol-lowing agencies: Bureau of Economic Analysis, Bureau of Labor Statistics, Bureau of the Census, Board of Governors of the Federal Reserve System, Statistics of Income/Internal Revenue Service, and Statistics Canada.

We thank Charles R. Hulten and Valerie A. Ramey, who served as confer-ence organizers and as editors of the volume.

Executive Committee, December 2016

John M. Abowd Barry JohnsonKatharine Abraham (chair) André LorangerSusanto Basu Brian MoyerAndrew Bernard Valerie A. RameyErnst R. Berndt Mark J. RobertsCarol A. Corrado Peter SchottJohn C. Haltiwanger Daniel SichelMichael W. Horrigan William WascherRonald Jarmin

Prefatory Note

313

9Recent Flattening in the Higher Education Wage PremiumPolarization, Skill Downgrading, or Both?

Robert G. Valletta

9.1 Introduction

Holding a four- year college degree confers a distinct advantage to work-ers in the US labor market. The wage gaps between college- educated work-ing adults and those with a high school degree—higher education wage premiums—are large and have grown substantially over the past thirty- fi ve years. These gaps may have been bolstered by technological advances in the workplace, notably the growing reliance on computers and related technologies, because the skills that are needed to master and apply these technologies are often acquired through or associated with higher education (Krueger 1993; Autor, Katz, and Krueger 1998; Autor, Levy, and Murnane 2003; Acemoglu and Autor 2011).

The expansion of the higher education wage premium has not been com-pletely uniform over time, however, with rapid growth in the 1980s followed by progressively slower growth (“fl attening”). During the years 2000 through 2010, the wage premium for college- educated workers rose by only a small amount. Most recently, from 2010 to 2015, the wage premium for those with

Robert G. Valletta is a vice president and economist at the Federal Reserve Bank of San Francisco.

The author thanks David Autor for his highly constructive and detailed discussant com-ments; the editors of this volume, Charles Hulten and Valerie Ramey, for their guidance with revisions; and participants in this conference and also the May 2016 Society of Labor Econo-mists annual meetings for additional helpful comments. He also thanks Catherine van der List for outstanding research assistance. The views expressed in this chapter are solely those of the author and are not attributable to the Federal Reserve Bank of San Francisco or the Federal Reserve System. For acknowledgments, sources of research support, and disclosure of the author’s material fi nancial relationships, if any, please see http:// www .nber .org /chapters /c13705 .ack.

314 Robert G. Valletta

college and graduate degrees was largely unchanged, suggesting that the factors propelling its earlier rise have disappeared.

While the wage advantage associated with higher education remains large, the lack of growth in recent years represents a departure from the earlier pat-tern. This change may have important implications for the value of higher education as an individual and social investment, and consequences for economic growth as well. Despite the voluminous literature on returns to education, little attention has been paid to slower growth in the college wage premium and diff erences between these higher education groups (Lindley and Machin [2016] is an exception).

In this chapter, I assess and attempt to explain the stalling of the higher education wage premium and its variation across the college- only and graduate- degree groups. I focus on two primary, related explanations for changing returns to higher education.

The fi rst potential explanation is labor market “polarization” (Acemoglu and Autor 2011). This theory emphasizes a shift away from medium- skill occupations driven largely by technological change. It provides a broad, cohesive explanation for changes in employment patterns in the United States and other advanced economies in recent decades. Polarization may account for the slowdown in the college wage premium through a shift in the occupational distribution of college graduates toward jobs that are being displaced by automation technologies and related factors (such as outsourc-ing and rising trade). At the same time, rising demand for the cognitive skills possessed by graduate- degree holders may help maintain and expand their wage advantage relative to those holding a four- year college degree only (Lindley and Machin 2016).

I will refer to the second broad potential explanation for the fl attening of higher education wage premiums as “skill downgrading,” based on the recent work of Beaudry, Green, and Sand (2016). They emphasize a gen-eral weakening since the year 2000 in the demand for cognitive tasks in the workplace, refl ecting a maturation in the information technology (IT) revo-lution and consequent slowdown in workplace IT investments. Skill down-grading in their framework refers to the process by which weaker demand for advanced cognitive skills cascades down the skill distribution as highly skilled workers, such as those possessing advanced degrees, increasingly compete with and replace lower- skilled workers in occupations that rely less heavily on advanced cognitive skills.

I begin my empirical assessment in the next section by establishing the basic facts regarding changes in educational attainment and the higher edu-cation wage premiums, distinguishing between individuals with a four- year college degree and those with graduate degrees. The analyses throughout are based primarily on data from the Current Population Survey (CPS) monthly earnings fi les (monthly outgoing rotation groups, or MORG) spanning the

Recent Flattening in the Higher Education Wage Premium 315

period 1979–2015. I also conduct selected parallel analyses using the CPS Annual Social and Economic Supplement fi les (March CPS- ASEC), which at the time of this writing provide earnings data through 2014. Standard wage regressions that adjust for changing workforce composition highlight the fl attening of the higher education wage premiums noted above.

To help interpret these empirical fi ndings, I then discuss the polarization and skill- downgrading arguments in more detail. Observed occupational employment shifts indicate the potential importance of polarization for the fl attening of the college wage premium. The Beaudry, Green, and Sand skill- downgrading narrative takes polarization as its starting point but empha-sizes diff erent dynamics over time, with weaker demand for cognitive skills arising as a consequence of a slowdown in technology investment.

To assess the eff ects of polarization and skill downgrading on higher edu-cation wage premiums, I examine changing premiums within and between the broad occupation categories that are used to identify the extent of polar-ization. The results of these analyses suggest that polarization and skill downgrading have both contributed to the fl attening of the wage premium for individuals with a four- year college degree or postgraduate degree. Con-sistent with the polarization story, the fl attening in the wage premium is partly explained by shifting employment and relative wages across broad occupation groups, mainly for those with a college degree but no graduate degree. However, a substantial contribution also comes from the slowdown in the wage premium within broad occupation categories, consistent with skill downgrading and heightened competition between educational groups for similar jobs. In the conclusion, I discuss the implications of these fi nd-ings for future research on the returns to higher education and its role in economic growth.

9.2 Changes in the Higher Education Wage Premium

The wage premium earned by individuals with higher educational attain-ment is commonly attributed to the more extensive skills that they possess (Card 1999; Goldin and Katz 2008). To save space, I will not review the voluminous and well- known literature on estimating and interpreting the returns to education, but will instead turn directly to updated estimates of the returns to higher educational attainment (college degrees and above).

9.2.1 Data and Descriptive Statistics

Because the data and processing procedures I use are well known, I describe them only briefl y here, with additional details relegated to appen-dix A. The primary data used are from the CPS MORG fi les, compiled by the National Bureau of Economic Research (NBER) and available for the years 1979–2015 when this chapter was written. These fi les contain data for the


quarter sample of the monthly CPS that receives survey questions regarding earnings and related variables in currently held jobs. I also use the complete monthly CPS fi les for selected tabulations that do not involve wages.

The data handling and processing procedures largely follow those detailed in Lemieux (2006a, 2010). These include elimination of observations with imputed values of earnings or hours and adjustments for changing top- codes. I use hourly wages as my earnings measure, either reported directly by hourly workers or formed as usual weekly earnings divided by usual weekly hours worked for salaried workers. All wage and earnings variables are defl ated by the annual average value of the gross domestic product (GDP) defl ator for personal consumption expenditures (and expressed in 2015 terms for ease of interpretation). For all of the analyses in this chap-ter, the samples are restricted to wage and salary workers age twenty- fi ve to sixty- four (with farming and resource occupations excluded).

The basis for the measurement of educational attainment in the CPS switched in 1992 from the highest grade attained and completed to the high-est degree received. I formed educational categories that are largely consis-tent over time following the guidance of Jaeger (1997).1 Individuals with a graduate degree, along with information about the type of degree, are directly identifi ed beginning in 1992. Graduate- degree holders prior to 1992 are identifi ed as those reporting at least eighteen years of completed educa-tion. I code individuals who report seventeen years of completed schooling in the pre- 1992 period as possessing a four- year college degree, but not a graduate degree.2

For comparison purposes, I also use data from the March CPS fi les to estimate changes in the higher education earnings premium. Compared with the MORG data, which provides information on earnings in the current reference week, the March CPS data refer to earnings in the complete prior calendar year. Following standard practice, I restrict the March CPS sample to full- time, full- year workers and use weekly earnings (annual labor earn-ings divided by weeks worked) as the earnings measure, once again dropping observations with imputed earnings or hours and adjusting for changing top- codes (e.g., Autor, Katz, and Kearney 2008). These fi les are currently available through 2015. Since the data refer to the prior calendar year, the reference period for the March data ends one year earlier than the MORG data (2014 rather than 2015).

1. Relative to Jaeger (1997), in the 1992-forward data I include individuals who report twelve years of schooling but no diploma in the “no degree” group rather than the “high school degree” group, to be consistent with the emphasis on degree attainment beginning in 1992.

2. Lindley and Machin (2016) take a similar approach, which groups individuals who drop out of a graduate program after one year or complete a one-year master’s degree program with those who complete a four-year college degree only. This approach generates a slight discontinu-ity in the relative college/graduate shares in 1992, but the discontinuity is larger if instead such individuals are treated as having a graduate degree.


Table 9.1 displays descriptive statistics for employment shares (panel A) and average real wages (panel B) by educational attainment, calculated using the full monthly CPS fi les for the employment shares and the MORG fi les for the wage data. These are provided for ten- year intervals that largely span the sample frame. The table also lists statistics for selected other years, including the year that the education variables changed (1992) to bridge the gap in defi nitions, and a listing for the fi nal data year (2015).

Panel A of table 9.1 illustrates the well- known, steady decline in the employment share of individuals whose educational attainment is a high school degree or less accompanied by a steady rise in the share of individuals possessing a four- year college degree or graduate degree. As of 2015, nearly 40 percent of employed individuals age twenty- fi ve to sixty- four held at least a college degree, and one in seven held a graduate degree, accounting for slightly more than a third of employed college graduates. Master’s degrees (which include MBAs) account for most of the level and change in the frac-tion holding graduate degrees, along with a large proportional increase for the small share of doctoral degrees.

Panel B of table 9.1 illustrates the large wage gaps between the educational

Table 9.1 Educational attainment shares and real hourly wages

1980 1990 1992 2000 2010 2015 (1) (2) (3) (4) (5) (6)

Panel A. Employment shareNo degree (< 12 yrs. education) 0.197 0.130 0.115 0.099 0.082 0.077High school degree 0.371 0.368 0.358 0.314 0.280 0.256Some college 0.205 0.238 0.259 0.280 0.280 0.278College only (4- year) 0.158 0.183 0.177 0.205 0.232 0.247Graduate degree 0.069 0.081 0.090 0.103 0.126 0.143Graduate degree by type

Master’s 0.068 0.075 0.094 0.107Professional 0.012 0.014 0.016 0.016Doctoral 0.010 0.013 0.016 0.019

Panel B. Real hourly wage (2015$) (averages by group)No degree (< 12 yrs. education) 14.19 12.84 12.47 13.03 13.22 13.56High school degree 16.33 15.99 15.87 17.20 17.77 17.98Some college 18.80 19.29 19.16 20.84 21.47 21.59College only (4- year) 22.85 25.32 25.18 28.98 30.49 30.93Graduate degree 27.27 31.43 31.66 36.40 39.70 39.48Graduate degree by type

Master’s 29.94 33.99 36.85 36.83Professional 38.32 45.01 50.75 50.51Doctoral 35.83 41.44 46.43 45.70

Notes: Author’s calculations from CPS monthly fi les (panel A) and MORG fi les (panel B); sample weights used. See table 9.2 note for MORG sample description and counts. Master’s degrees include MBAs along with a wide set of other master’s degrees; professional degrees are JD, MD, and related.


attainment groups, with the spread in real wages between the graduate- degree group and those with less than a high school degree widening approx-imately from a factor of two to a factor of three over the sample frame. Average real wages changed little over the sample frame for those with a high school degree or less. For those with at least some college education, aver-age real wages rose somewhat between 1980 and 2000, with larger increases evident for those with higher educational attainment. Between 2000 and 2010, only holders of graduate degrees saw any meaningful increase in real wages. Between 2010 and 2015, real wages were fl at to down slightly for all groups. The gap in average real wages between individuals with a four- year college degree or graduate degree and high school graduates rose from 40 to 67 percent in 1980 to 72 to 120 percent as of 2015.

9.2.2 Composition- Adjusted Estimates of Wage Gaps

To assess the changing wage premium associated with higher educational attainment, I estimate standard log- wage equations of the following form (where i indexes individuals):

(1) Ln(wi) = Xi + Si + i,

where Xi represents a set of demographic controls and Si represents edu-cational attainment (measured in discrete categories). This equation is estimated separately for each year using the MORG and March CPS data as described above. The control variables in the vector X include dummy variables for seven age groups (e.g., thirty to thirty- four, etc., with twenty- fi ve to twenty- nine omitted), three racial/ethnic groups, gender, marital status, gender ∗ marital status, and geographic location (nine census divi-sions). These controls adjust for the changing composition of the estimation sample, so that the results for the education categories refl ect the average wage premium associated with educational attainment for an individual with a fi xed set of demographic characteristics (X ).3

Our interest centers on the estimated vector of coeffi cients (Γ) on a set of dummy variables representing discrete categories of educational attainment (S). Table 9.2 lists the numerical results for selected years, while fi gure 9.1 displays the results for the complete sample period of 1979 through 2015 (2014 for the March CPS).4 For both displays, panel A lists the results for the MORG data, while panel B lists the results for the March CPS. The results are expressed in natural log terms. These conditional wage gaps are displayed

3. The results reported below are very similar when this set of control variables is replaced by complete interactions between four decadal age categories, four race/ethnic categories, the two genders, and marital status (married spouse present or not), for a total of sixty-four demo-graphic cells.

4. The estimated coeffi cients for college and postgraduate educational attainment are highly statistically signifi cant in virtually all cases reported below, with the exception of a few group-specifi c estimates reported in table 9.3.


for three educational groupings: the broad group of all workers with at least a four- year college degree, and the two subgroups consisting of those with a four- year degree only (“college only”), and those who hold a postgraduate degree as well. The results for the “college degree or higher” group are based on regressions that are estimated separately from the one used to estimate the returns for the two subgroups (as indicated by the horizontal lines in

Table 9.2 Composition- adjusted wage/earnings diff erentials (log points, relative to high school graduates)

1980 1990 1992 2000 2010 2015 (1) (2) (3) (4) (5) (6)

Panel A. CPS MORG dataFull sampleCollege degree or higher 0.304 0.449 0.464 0.518 0.566 0.566 (.003) (.003) (.003) (.004) (.004) (.005)

College only (4- year) 0.270 0.402 0.403 0.451 0.475 0.477(.004) (.004) (.004) (.005) (.005) (.005)

Graduate degree 0.383 0.553 0.581 0.648 0.727 0.712(.005) (.005) (.005) (.006) (.006) (.006)

Observations 121,001 123,111 119,014 83,314 85,397 76,789

College degree or higher sampleGraduate degree 0.111 0.149 0.170 0.194 0.245 0.226

(.006) (.006) (.006) (.007) (.006) (.006)

Observations 27,042 33,334 32,684 26,789 32,305 31,572

Panel B. CPS March dataFull sampleCollege degree or higher 0.293 0.449 0.477 0.538 0.579 0.576 (.006) (.006) (.006) (.006) (.006) (.007)

College only (4- year) 0.260 0.400 0.415 0.468 0.488 0.488(.007) (.007) (.007) (.006) (.007) (.007)

Graduate degree 0.368 0.557 0.593 0.680 0.740 0.725(.009) (.009) (.009) (.008) (.008) (.008)

Observations 34,258 38,123 37,143 52,489 45,575 43,435

College degree or higher sampleGraduate degree 0.102 0.155 0.174 0.206 0.244 0.230

(.011) (.010) (.010) (.009) (.008) (.009)

Observations 8,184 10,630 10,709 16,350 17,608 17,540

Notes: Estimated coeffi cients from ln(wage or earnings) regressions for the years indicated in the column labels; horizontal lines identify coeffi cients obtained from separate regressions. Standard errors in parentheses. Samples are wage and salary workers age twenty- fi ve to sixty- four for both data sources, restricted to full- time, year- round workers (annual hours ≥ 1,750) in the CPS March data. Dependent variable is ln(hourly earnings) for the MORG data and ln(weekly earnings) for the CPS March data, with allocated values dropped and top- code adjustments (see the text and appendix). Composition adjustment relies on the inclusion of the following control variables (all categorical): seven age, three race/ethnic, married, female, married × female, and eight geographic divisions.


the table). The higher education wage premiums are fi rst expressed relative to the wages of high school graduates. In addition, separate estimates are pro-vided for those holding a graduate degree. These are based on the restricted sample of individuals who have at least a college degree, hence they repre-sent the graduate wage premium relative to the wages of the college- only group.

The estimates in table 9.2 and fi gure 9.1 show that the wage premiums for higher education generally have been rising over time. However, both data sets show that the growth has slowed in recent decades, with the slowdown for the graduate group lagging behind that for the college- only group. The

A

B

Fig. 9.1 Estimated higher education wage premium, 1979–2015. A, CPS MORG data (1979–2015); B, March CPS data (1979–2014).Notes: Author’s calculations using CPS MORG and March data (see table 9.2 note). Diff er-entials expressed relative to high school graduates.


rate of growth in the college- only wage premium was cut approximately in half between the 1980s and 1990s and then slowed virtually to a standstill after 2000. It rose about 2 to 2.5 log points through 2010 and then was unchanged between 2010 and 2015.

For the graduate- degree group, the slowdown over time is most evident based on the results for the college or higher sample. These are displayed at the bottom of both panels in table 9.2 and also in fi gure 9.2, where the results for the MORG and March data sets are directly compared. The esti-mated wage premiums are very similar in the two data sources, with some-what greater annual volatility evident in the March data for the college- only sample in fi gure 9.2 due to its smaller sample. Relative to the college- only group, individuals with a graduate degree saw consistent wage premium gains of about 4 to 5 log points in each of the decades of the 1980s, 1990s, and fi rst decade of the twenty- fi rst century. During this time frame, their wage advantage over college- only workers grew steadily, reaching nearly 25 log points by 2010. However, since 2010, the graduate- degree premium is down slightly in both data sources (through 2015 in the MORG data and 2014 in the March data).

9.2.3 Robustness Checks and Disaggregation by Age and Gender

One potential concern with respect to these results is the possibility that they refl ect underlying changes in employment conditions among narrow worker groups or industries. Such narrow changes may be independent of the broad occupational changes and shifting labor market competition

Fig. 9.2 Estimated graduate- degree wage premiumNotes: Author’s calculations using CPS MORG and March data (see table 9.2 note). Diff er-entials expressed relative to four- year college graduates.


related to polarization and skill downgrading (which are discussed and ana-lyzed below, in sections 9.3 and 9.4). One such narrow group is teachers, who constitute a substantial but declining share of employed college graduates.5 Excluding educator and librarian occupations from the regressions raises the estimated higher education wage premiums by 2–4 log points in gen-eral. However, the pattern over time is unchanged relative to the full sample results, with progressive fl attening in the wage premiums and no change from 2010 forward.

It is also important to consider the potential infl uence of changing condi-tions in key industries that employ large numbers of college graduates. One such industry is the fi nancial sector, for which the housing bust and fi nancial crisis tied to the Great Recession of 2007–2009 destroyed a disproportionate number of jobs. Many fi nance- sector jobs are highly paid, and their disap-pearance may have aff ected the higher education wage premium. However, exclusion of workers employed in the fi nancial, insurance, and real estate sectors from the regression analysis has virtually no impact on the estimated wage premiums and their pattern over time.6 Similarly, Beaudry, Green, and Sand (2016) highlight the role of the business and management services industries for their fi ndings, emphasizing substantial employment changes for young college graduates in this sector. Exclusion of individuals employed in these industries does not aff ect the estimated college- only wage premium. It does raise the level of the graduate school wage premium, suggesting a relatively low value for graduate degrees in this industry. Nonetheless, the pattern of the higher education wage premiums over time, as refl ected in the results from table 9.2 and fi gure 9.1, is unaff ected.

It is also instructive to examine the higher education wage premium decomposed by age group and gender. Analyses of employment and wage patterns for the college educated often highlight younger workers, who are likely to experience the most immediate eff ects of changing employment conditions across educational attainment groups (e.g., Beaudry, Green, and Sand 2014). Figure 9.3 parallels fi gure 9.1 (panel A, MORG), but dis-plays wage premiums for the youngest decadal age group in my sample (age twenty- fi ve to thirty- four) in panel A and an older group (age forty- fi ve to fi fty- four) in panel B.7 For younger workers, movements in the wage premi-

5. Among workers with at least a four-year college degree in the MORG data, the fraction of educators and librarians declined by a third over my sample frame, from 24 percent in 1979 to about 16 percent in 2015.

6. Separate analyses by broad industry, also noted in section 9.4, show that the higher educa-tion wage premiums within the fi nance sector broadly track the patterns evident for the overall economy.

7. The underlying regressions used to produce the results in fi gure 9.3 are identical to those used for table 9.1 and fi gure 9.1, except the samples are restricted to the indicated age groups and the age category controls are adjusted accordingly. I use age forty-fi ve to fi fty-four rather than the oldest group in my sample, age fi fty-fi ve to sixty-four, to minimize the infl uence of partial retirement decisions on relative earnings over time.


ums over time largely parallel those for the complete sample in fi gure 9.1, with large gains in the 1980s followed by slower gains in the 1990s and the fi rst decade of the twenty- fi rst century, and no change since 2010. By contrast, for older workers the college- only premium was largely fl at in the 1990s, perhaps because this group did not readily adapt to the new informa-tion technologies introduced during that decade. The college- only premium for older workers picked up in the early twenty- fi rst century, although like the graduate- degree premium, it has been fl at since 2010. Comparison across the two panels in fi gure 9.3 also indicates that the higher education wage premiums are larger for older than for younger workers, by about 5 to 10 log points, on average. This likely arises due to important interaction or

A

B

Fig. 9.3 Estimated higher education wage premium by age group, 1979–2015. A, age twenty- fi ve to thirty- four; B, age forty- fi ve to fi fty- four.Notes: Author’s calculations using CPS MORG data (see table 9.2 note). Diff erentials ex-pressed relative to high school graduates.


reinforcing eff ects between higher education and the subsequent acquisition of on- the- job skills that raise wages as workers age.

Given the well- known increase in the attainment of higher education for women relative to men, it is also informative to examine the wage premiums by gender. These are displayed in fi gure 9.4 (panel A for men, panel B for women). The series represent the composition- adjusted higher education wage premiums by gender; as such, they refl ect relative wages within gender group and hence should not be interpreted as capturing wage diff erences between men and women. The higher education wage premiums are larger for women than for men, although the gap has closed over time, especially for graduate degrees. The pattern over time for both genders is similar to that

A

B

Fig. 9.4 Estimated higher education wage premium by gender, 1979–2015. A, men; B, women.Notes: Author’s calculations using CPS MORG data (see table 9.2 note). Diff erentials ex-pressed relative to high school graduates.


for the overall sample in fi gure 9.1 (panel A), with a fl attening of the wage premiums over time and essentially no change since 2010.

9.2.4 Summing Up: Higher Education Wage Premiums over Time

The results presented in this section indicate general fl attening in the wage premiums associated with four- year college and graduate degrees. The sharp increases observed in the 1980s have been followed by much slower gains. Since the year 2000, the wage premium associated with a four- year college degree has changed little. By contrast, from 2000 to 2010, the wage pre-mium for holders of graduate degrees relative to those with four- year college degrees continued to grow at its previous pace, contributing to increasing “convexifi cation” in the returns to higher education (Lemieux 2006b; Lind-ley and Machin 2016). Since 2010, however, wage premiums for both groups have sputtered. They remain large but were essentially unchanged for the college- only group and down slightly for holders of graduate degrees. These patterns indicate that the factors propelling earlier increases in the returns to higher education have dissipated.

Because of the signifi cant time required for individual investments in higher education—four years or more—the fl atter wage premiums may refl ect a delayed response of the supply of college- educated individuals to earlier increases in demand (Acemoglu and Autor 2011; Autor 2014). How-ever, given the relatively consistent increase over time in the college- educated employment share listed in table 9.1 (panel A), factors on the demand side that aff ect relative productivity and employers’ preference for workers with higher education merit further consideration.

9.3 Potential Explanations: Polarization and Skill Downgrading

The slower growth and eventual fl attening in the wage premium for higher education documented in the preceding section raises the possibility that the factors propelling rising wage premiums for highly skilled workers have dis-sipated. Past accounts of rising wage premiums for skilled workers generally revolved around the skill- biased technological change (SBTC) explanation of labor market developments. Under SBTC, rising reliance on sophisti-cated workplace technologies boosts the employment and wages of workers, mainly the highly educated, whose skills enable them to apply those technol-ogies (e.g., Bound and Johnson 1992; Autor, Katz, and Krueger 1998). Recent research has pointed to factors that may alter or off set this process. I focus on two broad explanations: labor market polarization and skill downgrading.

9.3.1 Polarization and Skill Downgrading: The Basics

The “polarization” hypothesis is a leading explanation for recent employ-ment developments in the United States and other advanced countries (Goos and Manning 2007; Acemoglu and Autor 2011; Autor 2015; Goos, Manning, and Salomons 2014). This is a refi nement of the SBTC story that


accounts for excess employment growth in the top and bottom portions of the wage distribution, with erosion in the middle.

In the polarization framework, evolving workplace technologies under-mine demand for “routine” jobs, in which workers and the tasks they per-form are readily substituted by computer- intensive capital equipment and processes. They include white- collar offi ce jobs (e.g., bookkeeping and cler-ical work), termed “routine cognitive” jobs, and blue- collar occupations that involve repetitive production or monitoring activities, termed “routine manual” jobs. These routine jobs are concentrated toward the middle of the wage and skill distribution. By contrast, workers in high- wage “non-routine cognitive” (or “abstract”) jobs tend to have skills that are comple-mentary with computer- based technologies, while low- wage service workers in “nonroutine manual” jobs are neither substitutes nor complements with computer- based technologies. Polarization arising from changes in domestic production technologies may be reinforced by related changes in overseas production technologies through the impact of off shoring and import com-petition (see, e.g., Autor, Dorn, and Hanson 2013).

Beaudry, Green, and Sand (2016) provide a related but alternative frame-work for understanding changing occupational employment patterns over the past few decades. They rely on a basic variant of the polarization hypothesis as their starting point, but they emphasize a slowdown in IT investments that has undermined the demand for cognitive skills since the year 2000. In their narra-tive, weaker demand for cognitive skills and the consequent impact on highly skilled workers has cascaded down the skill distribution, undermining the demand for lesser skilled workers as well. They refer to this process as “skill downgrading,” which contrasts with the opposite pattern of “skill upgrading” that occurs during the initial period of accelerating IT investments.

The similarities and contrasts between the polarization and Beaudry, Green, and Sand skill- downgrading scenarios can be readily summarized with reference to the production functions and associated objective func-tions that underlie the two models. In a basic model of polarization, fi rms rely on cognitive and routine task inputs supplied by workers for production, combined with inputs of computer capital (see, e.g., Autor, Levy, and Mur-nane 2003).8 The fi rm aims to maximize profi ts π by choosing appropriate input combinations given its production function F:

(2) max,Lc,Lr

= p F( , Lc,Lr, ) r wcLc wrLr

where p is the price of the fi rm’s output, Ω is a form of technological (com-puter) capital with per- unit rental rate r, Lc and Lr are inputs of cognitive

8. This representation is adapted from Autor, Levy, and Murnane (2003), modifi ed to be broadly consistent with the notation and framework in Beaudry, Green, and Sand (2016). Nonroutine manual jobs are largely ignored here for simplicity and because they have limited relevance for college-educated workers.


and routine labor with wage rates wc and wr, and θ is a technology parameter that shifts the level of output for a given set of inputs (assumed constant in this basic version of the model, but allowed to change in the Beaudry, Green, and Sand variant below). The production function F( ) is assumed to refl ect constant returns to scale and hence diminishing marginal productivity for individual inputs.

Production effi ciency requires hiring labor inputs up to the point where each input’s marginal product equals its market wage or rental rate. Impor-tantly, Autor, Levy, and Murnane (2003) assume that computer capital is perfectly substitutable with routine labor inputs, implying complementarity between computers and cognitive (nonroutine) labor in their setting. In this framework, as the price of computer capital falls, production techniques shift toward greater reliance on cognitive labor inputs and less on routine labor inputs, with corresponding reductions in the relative wage paid for routine labor inputs. Because routine tasks are common among many jobs toward the middle of the wage distribution, polarization will tend to erode or “hollow out” middle- class jobs and wages.

Beaudry, Green, and Sand extend the basic polarization model by incor-porating the key feature that cognitive labor inputs create a stock of orga-nizational capital for fi rms, which enables them to develop and utilize new technologies. This is captured in the following modifi cation of equation (2), which is a discrete- time version of the objective function from equation (1) in Beaudry, Green, and Sand (2016):

(3) maxLc,Lr

= p F( , Lr, ) wcLc wrLr

s.t. = Lc 1.

Relative to the production function in equation (2), Ω in equation (3) rep-resents intangible “organizational capital” rather than tangible computer capital. In this modifi ed framework, cognitive labor inputs do not directly aff ect current production but instead contribute to output through the accumulation of organizational capital (which depreciates at the rate δ). The fi rst- order conditions for production effi ciency are similar to the basic polarization model from equation (2).

This modifi ed model is distinguished by its dynamic properties in response to a technological shift, or change in θ. Beaudry, Green, and Sand assume that an increase or improvement in the technology factor θ raises the produc-tivity of the organizational capital accumulated through the use of cognitive labor inputs but has no direct eff ect on the productivity of routine labor inputs. These model features generate a “boom- bust cycle” in the demand for cognitive tasks and overall labor demand in response to technological improvement. In particular, the dynamics of the model predict that the stock of cognitive tasks/skills grows during the boom, as the economy adjusts to the need for additional organizational capital to manage the new technology.


Once the level of organizational capital becomes suffi ciently large for appro-priate use of the new technology, the demand for cognitive tasks declines as their use is shifted from expanding organizational capital to maintaining it by off setting depreciation (similar to the pattern in existing models of technology diff usion and capital investment).

The Beaudry, Green, and Sand model can predict the strong growth in demand and wages for workers in jobs that rely heavily on cognitive tasks/skills up to the year 2000—the boom phase—followed by a decline thereafter—the bust phase. The demand reversal during the bust phase causes high- skilled workers to move down the occupational ladder and replace lower- skilled workers, pushing the latter group further down the occupational ladder (“skill downgrading”) and perhaps out of the labor market entirely.9

9.3.2 Descriptive Evidence

Broad empirical evidence suggests that polarization and skill downgrad-ing are both contributing to changing employment patterns and hence may be aff ecting higher education wage premiums.

Patterns of occupational job growth in recent decades confi rm the rel-evance of the polarization narrative. Labor demand and job growth have been relatively rapid in the high- wage nonroutine cognitive and low- wage nonroutine manual categories, with the middle- wage routine jobs experi-encing downward pressure. This pattern can be seen in fi gure 9.5, which displays annual rates of job growth for the four broad polarization catego-ries over four subperiods (classifi ed using the broad occupational scheme from Acemoglu and Autor [2011]; see appendix B for the correspondence).10 The fi gure shows substantial growth in the 1980s, followed by a slowdown in the 1990s for all groups (refl ecting in part the impact of the early 1990s recession).11 Polarization is evident in the 1990s, refl ected in a sharper slow-down for the routine versus the nonroutine categories. This process appeared to accelerate after the year 2000, with substantial gains for nonroutine jobs and substantial net losses for routine jobs, particularly during the Great Recession of 2007–2009 and the subsequent recovery.12

9. As Beaudry, Green, and Sand note in their introduction: “In this maturity stage, having a college degree is only partly about obtaining access to high-paying managerial and technol-ogy jobs—it is also about beating out less educated workers for barista and clerical-type jobs.”

10. Autor (2015) relabeled the nonroutine categories and collapsed the two routine categories into a single one. I maintain the original four-group categorization based on the cognitive/manual and routine/nonroutine distinctions due to the preponderance of college graduates in each of the cognitive categories.

11. The start year of 1983 was dictated by the availability of offi cial BLS occupational employment data beginning in that year, and the change between 1999 and 2000 is omitted to eliminate the infl uence of a discontinuity in occupation category defi nitions.

12. The diff erential growth rates across the broad occupation categories have generated signifi cant changes in their employment shares over time. Nonroutine cognitive jobs are the largest category: their share rose from about 30 percent to slightly over 40 percent of all jobs during the sample frame. The share of routine jobs declined from nearly 60 percent to about 45 percent. The share of nonroutine manual jobs rose from about 12 to about 15 percent, mostly since the year 2000.


Polarization will diff erentially aff ect highly educated and less educated groups due to their very diff erent occupational distributions. Figure 9.6 shows the shares of the college- only and graduate- degree groups in the nonroutine cognitive (panel A) and routine cognitive (panel B) categories. Workers with at least a college degree account for a large and rising share of nonroutine cognitive jobs, reaching nearly 70 percent by 2014 (panel A). Underlying this pattern is a signifi cant rise in the share of nonroutine cogni-tive jobs held by individuals possessing a graduate degree, with little change in the share from the college- only group. This pattern is consistent with rising demand for the most highly educated individuals in jobs that require extensive nonroutine cognitive skills. The college- only group share also has grown in the routine cognitive category (panel B), commensurate with their rising share of the overall workforce.

Figure 9.7 reverses the fi gure 9.6 calculations by displaying the share of nonroutine cognitive jobs within the college- only and graduate- degree groups. Among the college- only group, the fraction employed in nonroutine cognitive jobs declined between 2000 and 2015, from about 68 to 64 percent. By contrast, the share of graduate- degree holders employed in nonroutine cognitive jobs has been largely stable at about 90 percent in recent years, while their overall workforce share has grown.

These tabulations suggest that polarization may be an important factor underlying the rising relative return to postgraduate education. As discussed

Fig. 9.5 Employment growth by broad occupation category, subperiods from 1983 to 2015Notes: Author’s calculations from Bureau of Labor Statistics data. See text and appendix table 9B.1 for occupation category defi nitions.


by Autor (2015), the wage impacts of polarization depend not only on skill/technology complementarity, but also on (a) the demand elasticity for prod-ucts and services that rely heavily on the diff erent skill/task groups, and (b) labor supply elasticities for the diff erent skill/task groups. In regard to nonroutine cognitive jobs, both factors imply that workers in these jobs are likely to see their wages rise in response to rising reliance on computer and automation technologies (assuming that their skills are complementary with computers). Demand for their output is relatively elastic, and an inelastic supply response due to the time required for acquiring additional education implies that the supply of such workers does not respond quickly to rising

A

B

Fig. 9.6 Higher educational attainment shares by occupation category (selected), 1992–2015. A, nonroutine cognitive; B, routine cognitive.Notes: Author’s calculations using monthly CPS fi les. See text and appendix table 9B.1 for occupation category defi nitions. Series are shares of educational attainment groups in the broad occupation categories.


demand. As such, ongoing polarization should put upward pressures on the relative wages of individuals employed in nonroutine cognitive jobs, most of whom have college or graduate degrees.13

As discussed above, the Beaudry, Green, and Sand “skill downgrading” alternative takes polarization as its starting point, but emphasizes a more general decline in demand for cognitive skills, which may aff ect all educa-tional attainment groups. Beaudry, Green, and Sand present evidence to support the claim that the demand for cognitive and technological skills in the US labor market has weakened since the year 2000. They focus on broad patterns in employment across occupational and educational attain-ment groups, distinguishing between jobs that are intensive in cognitive versus routine or manual skills. The patterns in employment growth that they document are consistent with a reversal in the demand for cognitive skills, notably a slowdown in the relative rate of employment growth for occupations that are toward the top end of the wage distribution. They also use a more detailed identifi cation scheme for cognitive- task- intensive jobs and confi rm the shift out of such jobs by college graduates implied by my fi gure 9.7 (see their fi gure 10).

13. Based on these considerations, Autor (2014) notes that while polarization is likely to lower wages of workers in routine skill/task occupations, wages for workers in nonroutine manual jobs are likely to be relatively unaff ected by polarization, despite the favorable polarization eff ects on employment for that group.

Fig. 9.7 Share of nonroutine cognitive employment by educational attainment, 1992–2015Notes: Author’s calculations using monthly CPS fi les. See text and appendix table 9B.1 for occupation category defi nitions. Series are nonroutine cognitive jobs as a share of employ-ment within each educational attainment group.


One key element of the Beaudry, Green, and Sand framework and pre-dictions is a pickup followed by a decline in technological advance, which generates the boom- bust cycle for cognitive employment and eventual skill downgrading in their model. This assumption is supported by pat-terns in US productivity growth in recent decades, depicted in fi gure 9.8.14 The growth in output per worker measured by productivity gains generally refl ects improvements in production technologies. Figure 9.8 shows a sharp productivity acceleration from 1995 to 2004, which corresponds roughly to the period of diff usion for new IT technologies that motivated the Beaudry, Green, and Sand model.15 This was followed by an even more pronounced downshift in productivity growth. Productivity gains were especially slow from 2010 forward, the period during which the wage premium for higher education was fl at or down (as discussed earlier in section 9.2). This cor-respondence suggests that the Beaudry, Green, and Sand narrative of a technology slowdown is relevant for understanding the recent pattern in the higher education wage premium.

Beaudry, Green, and Sand note that their model has limited implications for relative wages across skill groups. They also note, however, that a simple

14. I thank my colleague John Fernald for his advice with this display; see also Fernald (2015).15. Beaudry, Green, and Sand focus on the year 2000 as a dividing line for the slowdown in

demand for cognitive skills. However, it is likely that fi rms’ ability to utilize new organizational capital associated with the IT revolution, and hence increase measured productivity, continued for a time after investment in that capital and corresponding rapid expansion of cognitive jobs largely came to an end.

Fig. 9.8 US productivity growth, 1973–2016:Q1 (with period averages) Notes: Author’s calculations from US Bureau of Labor Statistics data. Series displayed is la-bor productivity in the nonfarm business sector, percentage change from four quarters earlier. Gray areas denote NBER recession dates.


parametrization of their model generates the slowdown in wage growth across the skill spectrum observed during the “bust” phase, consistent with the observed slowdown in real wages beginning in the year 2000 (see my table 9.1, panel A).16 The skill- downgrading narrative also can explain the recent elevated level of “underemployment” of young college graduates, defi ned as the tendency for them to work in jobs that do not strictly require a college degree (see Abel and Deitz, chapter 4, this volume).

9.4 Wage Eff ects of Polarization and Skill Downgrading

The confl uence of polarization and skill- downgrading infl uences on the labor market in recent years has been noted by others. Autor (2015), Lind-ley and Machin (2016), and Beaudry, Green, and Sand (2016) all provide a balanced, informed discussion and interpretation of labor market develop-ments from 2000 forward and acknowledge the possibility that polarization and skill downgrading may both be playing a role. Each may have contrib-uted to the fl attening of the higher education wage premiums documented in section 9.2.

No sharp dividing lines between the two explanations are readily appar-ent. However, some insight can be gleaned by examining the wage premium patterns within and across the four broad occupation groups used in the polarization typology. The descriptive evidence presented in section 9.3.2 showed complex changes in the employment and wage patterns of highly educated individuals across the broad polarization occupation grouping in recent years. A within- between analysis is a relatively straightforward means for combining these changes into a single set of summary results.

This analysis begins with the same wage regressions as reported in sec-tion 9.2, but with separate regressions run for each of the four broad occu-pation groups from the polarization typology. Let γ represent a higher edu-cation wage premium (college or more, college only, or graduate degree) estimated for a specifi c year based on equation (1) and reported in table 9.2. The overall premium estimate can be decomposed as follows:

(4) = (within effect) + (between effect)

=j=1

4

wj j + (between effect)

where j subscripts the four broad occupation groups in the polarization typology, the γj’s are occupation- specifi c estimates of the higher education premium, and the weights wj are set equal to the share of each occupation group in total employment.

The within component in equation (4) is defi ned as the employment-

16. The slowdown in real wage growth for all educational groups displayed in table 9.1 is maintained when the data are adjusted for the same individual characteristics as used for the regression analyses in tables 9.2 and 9.3 (using a reweighting methodology).


weighted sum of the estimated occupation- specifi c wage premiums. It rep-resents the higher education wage premium conditional on occupational skill/task group. It can be interpreted as the competitive advantage enjoyed by individuals with higher educational attainment when competing directly with less educated individuals for similar jobs (within the broad polariza-tion occupation groupings). As such, a decline in the within component likely refl ects a Beaudry, Green, and Sand skill- downgrading eff ect, which causes enhanced competition across educational groups for similar jobs. The between component is obtained as the diff erence between the total estimate and the within component.17 It does not have a precise interpretation in the context of the polarization and skill- downgrading narratives: a relative increase in the shares of college- educated workers in routine jobs could refl ect ongoing polarization in the distribution of jobs or the process of skill downgrading. However, it is informative nonetheless to assess whether the changes in the wage premium are associated with shifts in the occupational distribution of employment by education group.

I conduct this analysis by fi rst estimating higher education wage premiums within each of the four broad polarization occupation groups, which provide the inputs into equation (4) above. The regressions are otherwise identical to those reported in table 9.2. Table 9.3 lists the regression results, focusing on the college- only premium (measured relative to high school graduates) and the graduate- degree premium (measured relative to the college- only group), with results for the same set of years as table 9.2 listed. The panel immediately below the regressions lists the decomposition of the “total” eff ect into “within” and “between” components.

The regression results in table 9.3 indicate that the higher education wage premiums are widely dispersed and their changes over time have been rel-atively consistent across the occupation groups. The exception is routine manual jobs, in which the higher education wage premium is relatively small for both education groups: the college- only premium is about half its size relative to the estimates for the other three groups, and the graduate pre-mium is not statistically diff erent from zero.

These patterns imply that increases in the total eff ect over the complete sample frame have been primarily driven by changes in the within compo-nent, with limited movement in the between component. This is confi rmed by the decomposition results listed in table 9.3 for selected years and dis-played in fi gure 9.9 for the complete sample frame. The within component, representing a competitive advantage to higher education within broad occupation groups, accounts for virtually all of the increase in the higher education wage premiums over time. However, the between component con-

17. Note that the total eff ect corresponds to the full-sample estimates from table 9.2. For example, the fi rst total eff ect listed in column (1) of table 9.3, 0.270, corresponds to the college-only estimate from column (1) of panel A in table 9.2.


tributed to a slight increase in the wage premiums for both higher education groups up to the year 2000, indicating an ongoing shift toward higher- paid cognitive jobs for college- educated workers.

Our primary goal is to understand and interpret the changes in the within and between components since the year 2000. For the college- only group, the within component continued to grow after the year 2000 at

Table 9.3 Within- between analysis of higher education wage premiums (CPS MORG data, regressions by broad occupation groups)

1980 1990 1992 2000 2010 2015 (1) (2) (3) (4) (5) (6)

College only versus high school degree (full sample)Regressions

Nonroutine cognitive 0.215 0.303 0.305 0.350 0.378 0.392(.007) (.007) (.007) (.009) (.009) (.011)

Routine cognitive 0.134 0.255 0.265 0.309 0.346 0.327(.007) (.007) (.007) (.009) (.009) (.010)

Routine manual 0.056 0.142 0.134 0.131 0.163 0.160(.010) (.011) (.011) (.014) (.014) (.014)

Nonroutine manual 0.166 0.246 0.256 0.286 0.297 0.325(.014) (.014) (.014) (.017) (.013) (.014)

DecompositionWithin component share of total 0.135 0.237 0.241 0.278 0.314 0.322

0.501 0.590 0.598 0.616 0.662 0.674Between component share of total 0.135 0.165 0.162 0.173 0.160 0.155

0.499 0.410 0.402 0.384 0.338 0.326Total 0.270 0.402 0.403 0.451 0.475 0.477

Graduate degree (college degree or higher sample)Regressions

Nonroutine cognitive 0.068 0.096 0.109 0.128 0.170 0.154(.006) (.006) (.006) (.007) (.007) (.007)

Routine cognitive 0.101 0.085 0.112 0.157 0.147 0.135(.021) (.018) (.018) (.025) (.022) (.022)

Routine manual 0.034 0.032 0.033 0.072 0.041 0.017(.031) (.033) (.037) (.040) (.042) (.042)

Nonroutine manual −0.032 −0.008 0.015 0.151 0.194 0.130(.048) (.046) (.044) (.050) (.035) (.036)

DecompositionWithin component share of total 0.068 0.087 0.103 0.131 0.163 0.144

0.612 0.587 0.605 0.674 0.667 0.637Between component share of total 0.043 0.061 0.067 0.063 0.081 0.082

0.388 0.413 0.395 0.326 0.333 0.363Total 0.111 0.149 0.170 0.194 0.245 0.226

Notes: See note to table 9.2 for basic data and specifi cations. Coeffi cients listed with standard errors in pa-rentheses; regressions run separately for each of the broad occupation groups listed, by year. See the text for a description of the decomposition.


nearly the same rate as in the 1990s, rising by about 3.5 log points through 2010 and another 1.0 points through 2015. However, this was off -set by a decline in the between component of about 2 log points from 2000 through 2015. This suggests that despite the increase in the college- only group’s competitive advantage over lesser educated individuals within broad occupation groups, their overall wage advantage has been eroded slightly by a shift toward routine jobs. This is consistent with polarization or skill downgrading.

For the graduate- degree group, the between eff ect did not decline after 2000, indicating that their occupational distribution has not shifted away

A

B

Fig. 9.9 Total and within/between wage premiums, 1979–2015. A, college only (relative to high school graduates); B, graduate degree (relative to college graduates).Notes: See text and table 9.3 for data and methods. Based on broad polarization occupation grouping (nonroutine cognitive, routine cognitive, routine manual, nonroutine manual).


from highly paid cognitive jobs. However, between 2010 and 2015, the wage gap between graduate- degree holders and the college- only group within the same broad occupations fell by about 2 log points, suggesting that the direct competitive edge aff orded by graduate training may be eroding. This erosion of the within eff ect for the graduate group suggests that skill downgrading may be playing an increasingly important role at the top of the skill distri-bution.

These results are robust to alternative defi nitions for the four broad occu-pations groups, including reorganization of the routine and nonroutine manual categories and separate treatment of selected services industries.18 I also investigated alternative decompositions based on industry rather than occupation categories. These analyses indicated that the level and changes in higher education wage premiums are almost entirely determined within industries, with virtually no contribution coming from diff erences or changes over time in the wage premium and higher education shares across industries.

Overall, the results suggest rising competition between education groups for increasingly scarce well- paid jobs. Some of this is refl ected in the move-ment of individuals holding only a college degree into routine jobs, con-sistent with polarization or skill downgrading, and some is refl ected in the wage advantage of those with graduate degrees over the college- only group within broad occupations, suggesting skill downgrading.

9.5 Discussion and Conclusions

I have documented a fl attening in the US higher education wage pre-mium over the last few decades. In particular, after rising substantially in the 1980s, growth in the wage gap between individuals with a four- year college or graduate degree and those with a high school degree slowed pro-gressively. The gaps have changed little since the year 2000, and they were fl at to down during the period 2010–2015. These patterns suggest that the previously growing complementarity between highly educated labor and new production technologies, especially those that rely on computers and related organizational capital, may be leveling off .

I investigated these patterns with reference to two related explanations for changing US employment patterns: (a) a shift away from medium- skill occupations driven largely by technological change (“polarization”; e.g., Acemoglu and Autor 2011), and (b) a general weakening in the demand for advanced cognitive skills that cascades down the skill distribution (“skill downgrading”; Beaudry, Green, and Sand 2016). Descriptive evidence and comparison of the higher education wage premiums within and between

18. I thank my discussant David Autor for emphasizing the importance of investigating alternative occupational groupings, based on his recent research (e.g., Autor and Dorn 2013).


broad occupation groups suggests that both factors have played a role in the fl attening of the overall premiums. Occupational employment shifts have held down the college- only premium somewhat since the year 2000, suggesting that college- educated workers are increasingly sliding down into routine jobs. This is consistent with polarization or skill downgrading. More recently, since 2010, the wage gap between graduate- degree holders and the college- only group within the same broad occupations has declined some-what, suggesting that graduate training may be providing less of a competi-tive edge than it has in the past. This suggests that skill downgrading may be playing an increasingly important role at the top of the skill distribution. Overall, the results suggest rising competition between education groups for increasingly scarce well- paid jobs.

These fi ndings should not be interpreted as suggesting that college and graduate training are no longer sound fi nancial investments, from an indi-vidual or social perspective. Recent analyses indicate that relative to fi nanc-ing costs, higher education yields positive net returns for most individuals who complete college (Abel and Deitz 2014; Autor 2014; Daly and Cao 2015). On the other hand, it is important to note that the wage premiums to higher education are likely to vary substantially across individuals. Although higher education may be fi nancially advantageous on average, the fl atten-ing of returns as costs have continued to rise suggests that college may be an unfavorable fi nancial investment for rising numbers of individuals. In these circumstances, individual variation in returns looms as an increasingly important issue for future research.

I have focused on demand- side factors propelling the relative wages of college graduates, but supply- side factors may be important as well (Acemo-glu and Autor 2011; Autor 2014). Sorting out the relative contributions of a demand slowdown and supply speed- up may be a worthwhile endeavor for future research. Related to overall supply trends, the composition of the college- educated workforce may have shifted in important ways. Enrollment at for- profi t colleges has expanded rapidly since the year 2000, and subse-quent wage increases appear limited, even for those who complete four- year undergraduate and graduate degrees at for- profi t institutions (Cellini and Turner 2016). This may be holding down the returns to college estimated from the population of employed college graduates.

With these caveats in mind, I will conclude by noting that my fi ndings raise the possibility of an eroding relationship between technological advance and the returns to investment in higher education. If this interpretation proves to be correct and durable, it has potentially important implications for this volume’s primary themes. Human capital has been a key engine of growth in developing and advanced economies alike. Slower growth in the returns to higher education suggest that this connection may be fraying, raising the possibility of continued slow growth ahead.


Appendix A

MORG and March CPS Data

The data handling and defi nitions for the CPS MORG and March data gen-erally follow Lemieux (2006a, 2010) and Autor, Katz, and Kearney (2008; see also Buchmueller, DiNardo, and Valletta 2011). All analyses are limited to wage and salary workers age twenty- fi ve to sixty- four (with farming and resource occupations excluded), and appropriate survey weights are used for all tabulated results.

MORG Data (Defi nitions, Top- Coding, and Imputation)

As noted in the text, I use hourly wages as my earnings measure, either reported directly by hourly workers or formed as usual weekly earnings divided by usual weekly hours worked for salaried workers. Wage levels are expressed in real terms using the GDP defl ator for personal consumption expenditures.

Following Lemieux (2006a), the wage analyses are limited to individuals whose hourly wage is greater than $1 and less than $100 (in 1979 dollars); only a small number of observations are dropped due to this restriction. Recorded earnings are subject to maximum limits (“top- codes”) in the public- use data fi les, which change over time. I multiplied the value of top- coded earnings observations by 1.4. This largely follows Lemieux (2006a), with the exception that for the sake of consistency over time, I did not rely on the higher top- code enabled by the use of unedited earnings values for the years 1989–1993.

As noted in past research, nonresponse to the earnings and hours ques-tions in the CPS data and the consequent need to impute their values is sub-stantial and has grown over time, potentially distorting analyses of wage dif-ferentials. Following common practice, I dropped observations with imputed values of earnings or hours worked from all wage analyses. I followed the procedures outlined in Lemieux (2006a) for identifying imputed earnings observations. This includes the comparison of unedited and edited earnings values during the years 1989–1993, when the earnings imputation fl ags are incorrect. Imputation fl ags are missing for 1994 and most of 1995, which precludes dropping observations with imputed values during this period.

CPS March Annual Demographic Supplement Data (Defi nitions, Top- Coding, and Imputation)

I supplement the CPS MORG analyses using data from the CPS March Annual Demographic Supplement fi les. These fi les are currently available through 2015. Income data from the annual March supplement refer to the


prior calendar year, so the reference period for the March data that I use ends one year earlier than the MORG data (2014 rather than 2015).

As noted in the text, following standard practice, I restrict the March CPS sample to full- time, full- year workers and use weekly earnings (annual labor earnings divided by weeks worked) as the earnings measure. The sample restriction with respect to real hourly wages (in 1979 dollars), the treatment of top- coded values, and the elimination of imputed earnings values are the same as described for the MORG data.

Appendix B

Polarization Occupational Coding (Excluding Agriculture/Resources)

References

Abel, Jaison R., and Richard Deitz. 2014. “Do the Benefi ts of College Still Outweigh the Costs?” Federal Reserve Bank of New York Current Issues in Economics and Finance 20 (3). https:// www .newyorkfed .org /medialibrary /media /research /current _issues /ci20 - 3 .pdf.

Acemoglu, Daron, and David Autor. 2011. “Skills, Tasks and Technologies: Implica-tions for Employment and Earnings.” In Handbook of Labor Economics, vol. 4b,

Table 9B.1 Polarization occupational coding

Broad polarization categoryStandard Occupational Classifi cation (SOC)

major occupation groupings

Nonroutine cognitive Management, business, and fi nancial operations (SOC 11, 13)Professional/technical (SOC 15–29)

Routine cognitive Sales and related (SOC 41)Offi ce and administrative support (SOC 34)

Routine manual Construction and extraction (SOC 47)Installation/maintenance/repair (SOC 49)Production (SOC 51)Transportation and material moving (SOC 53)

Nonroutine manual Health care support (SOC 31)Protective services (SOC 33)Food preparation and serving (SOC 35)Building and grounds (SOC 37)

Personal care and service (SOC 39)

Note: 2010 SOC codes listed; earlier period codes harmonized.


edited by Orley Ashenfelter and David Card, 1043–171. Amsterdam: Elsevier- North Holland.

Autor, David H. 2014. “Skills, Education, and the Rise of Earnings Inequality among the ‘Other 99 Percent.’” Science 344 (6186): 843–51.

———. 2015. “Polanyi’s Paradox and the Shape of Employment Growth.” Federal Reserve Bank of St. Louis: Economic Policy Proceedings, Reevaluating Labor Market Dynamics, 129–77. http:// economics .mit .edu /fi les /9835.

Autor, David H., and David Dorn. 2013. “The Growth of Low- Skill Service Jobs and the Polarization of the US Labor Market.” American Economic Review 103 (5): 1553–97.

Autor, David H., David Dorn, and Gordon H. Hanson. 2013. “The China Syn-drome: Local Labor Market Eff ects of Import Competition in the United States.” American Economic Review 103 (6): 2121–68.

Autor, David H., Lawrence F. Katz, and Melissa S. Kearney. 2008. “Trends in U.S. Wage Inequality: Revising the Revisionists.” Review of Economics and Statistics 90 (2): 300–323.

Autor, David H., Lawrence F. Katz, and Alan B. Krueger. 1998. “Computing Inequality: Have Computers Changed the Labor Market?” Quarterly Journal of Economics 113 (4): 1169–214.

Autor, David H., Frank Levy, and Richard J. Murnane. 2003. “The Skill Content of Recent Technological Change: An Empirical Exploration.” Quarterly Journal of Economics 118 (4): 1279–333.

Beaudry, Paul, David A. Green, and Benjamin M. Sand. 2014. “The Declining For-tunes of the Young Since 2000.” American Economic Review 104 (5): 381–86.

———. 2016. “The Great Reversal in the Demand for Skill and Cognitive Tasks.” Journal of Labor Economics 34 (S1, pt. 2): S199–247.

Bound, John, and George Johnson. 1992. “Changes in the Structure of Wages in the 1980s: An Evaluation of Alternative Explanations.” American Economic Review 83:371–92.

Buchmueller, Thomas C., John DiNardo, and Robert G. Valletta. 2011. “The Eff ect of an Employer Health Insurance Mandate on Health Insurance Coverage and the Demand for Labor: Evidence from Hawaii.” American Economic Journal: Economic Policy 3:25–51.

Card, David. 1999. “The Causal Eff ect of Education on Earnings.” In Handbook of Labor Economics, vol. 3, edited by Orley Ashenfelter and David Card. Amster-dam: Elsevier.

Cellini, Stephanie Riegg, and Nicholas Turner. 2016. “Gainfully Employed? Assess-ing the Employment and Earnings of For- Profi t College Students Using Admin-istrative Data.” NBER Working Paper no. 22287, Cambridge, MA.

Daly, Mary C., and Yifan Cao. 2015. “Does College Pay?” In Does College Mat-ter? Federal Reserve Bank of San Francisco 2014 Annual Report. http:// sff ed - education .org /annualreport2014 / #college - essays /does - college - pay.

Fernald, John. 2015. “Productivity and Potential Output before, during, and after the Great Recession.” NBER Macroeconomics Annual 2014, vol. 29, edited by Jona-than Parker and Michael Woodford, 1–51. Chicago: University of Chicago Press.

Goldin, Claudia, and Lawrence F. Katz. 2008. The Race between Education and Technology. Cambridge, MA: Harvard University Press.

Goos, Maarten, and Alan Manning. 2007. “Lousy and Lovely Jobs: The Rising Polar-ization of Work in Britain.” Review of Economics and Statistics 89 (1): 118–33.

Goos, Maarten, Alan Manning, and Anna Salomons. 2014. “Explaining Job Polar-ization: Routine- Biased Technological Change and Off shoring.” American Eco-nomic Review 104 (8): 2509–26.

342 David Autor

Jaeger, David. 1997. “Reconciling the Old and New Census Bureau Education Ques-tions: Recommendations for Researchers.” Journal of Business and Economic Sta-tistics 15 (3): 300–309.

Krueger, Alan. 1993. “How Computers Have Changed the Wage Structure: Evidence from Microdata, 1984–1989.” Quarterly Journal of Economics 108 (1): 33–60.

Lemieux, Thomas. 2006a. “Increasing Residual Wage Inequality: Composition Eff ects, Noisy Data, or Rising Demand for Skill?” American Economic Review 96 (3): 461–98.

———. 2006b. “Postsecondary Education and Increasing Wage Inequality.” Ameri-can Economic Review 96 (2): 195–99.

———. 2010. “What Do We Really Know About Changes in Wage Inequality?” In Labor in the New Economy, edited by Katharine G. Abraham, James R. Spletzer, and Michael Harper. Chicago: University of Chicago Press.

Lindley, Joanne, and Stephen Machin. 2016. “The Rising Postgraduate Wage Pre-mium.” Economica 83:281–306.

Comment David Autor

Robert Valletta’s chapter illuminates one of the leading puzzles for contem-porary US labor economics: the unexpected “fl attening” of the premium to higher education in the United States in the fi rst decade of the twenty- fi rst century. This single metric—the college/high school wage premium—has been the North Star guiding neoclassical analysis of the evolution of wage inequality during a period of rapidly shifting wage structures. Two impactful papers by Beaudry, Green, and Sand (2014, 2016,) argue that since approxi-mately the year 2000, this North Star has become an increasingly dubious point of navigation. Specifi cally, Beaudry, Green, and Sand highlight the failure of the college premium to rise in the fi rst decade of the twenty- fi rst century following two decades of steep increases. They interpret this deceler-ation as refl ecting the maturation of the information technology revolution, which in turn has spurred a slackening in the pace of workplace IT invest-ments and a consequent slowdown in the trend of rising demand for highly educated labor. A key piece of evidence favoring Beaudry, Green, and Sand’s narrative is the precipitous fall in US investment in information- processing equipment and software in the United States after 1999 (fi gure 9C.1), which seems to have precisely the right timing to explain a falloff in IT augmenta-tion of skilled labor demand.

Valletta’s careful analysis extends and probes the Beaudry, Green, and Sand fi ndings, verifi es their robustness, and considers their interpretation

David Autor is the Ford Professor of Economics and associate head of the Department of Economics at the Massachusetts Institute of Technology and a research associate and codirec-tor of the Labor Studies Program at the National Bureau of Economic Research.

For acknowledgments, sources of research support, and disclosure of the author’s material fi nancial relationships, if any, please see http:// www .nber .org /chapters /c13706 .ack.

Comment 343

in the light of both their conceptual framework and an alternative framing off ered by Acemoglu and Autor (2011). There are many things to admire about Valletta’s chapter: it is empirically rigorous, intellectually ecumeni-cal, and commendably ambitious in synthesizing and adjudicating between two conceptual models that are not, to a fi rst approximation, speaking the same language. My remarks focus exclusively on one question that is core to both Valletta’s and Beaudry, Green, and Sand’s work: When did rising demand for college- educated labor decelerate? I argue below that (a) the recent fl attening of the skill premium in the fi rst decade of the twenty- fi rst century is not surprising in light of the canonical supply- demand model, and (b) what is surprising is that the underlying demand for college labor decelerated sharply and (to date) inexplicably almost a decade beforehand. These observations render the phenomenon that Valletta tackles no less consequential, but they may suggest a diff erent set of explanations for the slowdown than those focusing on discontinuous changes in economic trends in the fi rst decade of the twenty- fi rst century.

Modeling School

Following an extraordinarily infl uential series of papers that includes Goldin and Margo (1992), Katz and Murphy (1992), Murphy and Welch

Fig. 9C.1 Private fi xed investment in information- processing equipment and soft-ware as a percentage of gross domestic product, 1949–2014Source: FRED, Federal Bank of St. Louis (http:// research .stlouisfed .org /fred2 /graph / ?g = GXc; accessed 8/3/2014). This graphic originally appeared in Autor (2015).

344 David Autor

(1992), Card and Lemieux (2001), and Goldin and Katz’s magisterial 2008 volume The Race between Education and Technology, labor economists have applied a remarkably simple and surprisingly powerful calibrated supply- demand model (the “canonical model”) to rationalize the fl uctuations over time in the skill premium and the accompanying evolution of wage inequality. This so- called canonical model takes its inspiration from the observation by Nobel Laureate Jan Tinbergen in 1974 that there appears to be an ongoing “race” between technology and schooling, with techno-logical advancements progressively raising the demand for educated labor and the school system simultaneously secularly raising its supply. When technological advancement surges faster than educational production, the relative scarcity of educated labor rises, and the skill premium rises with it—that is, technology pulls ahead of education in this two- person race. Conversely, when educational production surges ahead of technologically induced demand shifts, the skill premium falls.

While many elements of this description seem far too simple (e.g., history provides many examples of technologies that replace rather than comple-ment skills), this framework provides a surprisingly good high- level descrip-tion of what we see in the data. The canonical model provides a benchmark for interpreting the evolution of the skill premium. I apply this model here to address the question of whether we should be surprised—and if so, how much—by the slowdown in the skill premium after 2000. Before applying the model, I review its rudiments, and I refer readers to Acemoglu and Autor (2011) for a fuller development.

The canonical model posits two skill groups, high and low. It draws no distinction between skills and occupations (tasks), so that high- skilled work-ers eff ectively work in separate occupations (perform diff erent tasks) from low- skilled workers. In most empirical applications of the canonical model, it is natural to identify high- skilled workers with college graduates (or in dif-ferent eras, with other high- education groups), and low- skilled workers with high school graduates (or in diff erent eras, those with less than high school). Critical to the two- factor model is that high- and low- skilled workers are imperfect substitutes in production. The elasticity of substitution between these two skill types is central to understanding how changes in relative sup-plies aff ect skill premiums.

Suppose that the total supply of low- skilled labor is L and the total supply of high- skilled labor is H. Naturally not all low- (or high- ) skilled workers are alike in terms of their marketable skills. As a simple way of introducing this into the canonical model, suppose that each worker is endowed with either high or low skill, but there is a distribution across workers in terms of effi ciency units of these skill types. In particular, let ℒ denote the set of low- skilled workers and denote the set of high- skilled workers. Each low- skilled worker i has li effi ciency units of low- skilled labor and each high- skilled worker i has hi units of high- skilled labor. All workers supply

Comment 345

their effi ciency units inelastically. Thus the total supply of high- skilled and low- skilled labor in the economy can be written as

L =i

lidi and H =i

hi di.

The production function for the aggregate economy takes the following constant elasticity of substitution form

(1) Y = [(ALL)( 1)/ + (AHH)( 1)/ ] /( 1),

where ( 0, ) is the elasticity of substitution between high- skilled and low- skilled labor, and AL and AH are factor- augmenting technology terms.1 The elasticity of substitution between high- and low- skilled workers plays a pivotal role in interpreting the eff ects of diff erent types of technological changes in this canonical model. We refer to high- and low- skilled work-ers as gross substitutes when the elasticity of substitution σ > 1, and gross complements when σ < 1.

In this framework, technologies are factor augmenting, meaning that technological change serves to increase the productivity of either high- or low- skilled workers (or both). This implies that there are no explicitly skill- replacing technologies. Depending on the value of the elasticity of substi-tution, however, an increase in AH or AL can act either to complement or (eff ectively) substitute for high- or low- skilled workers (see below).

Assuming that the labor market is competitive, the low- skill unit wage is simply given by the value of the marginal product of low- skilled labor, which is obtained by diff erentiating equation (1) as

(2) wL = YL

= AL( 1)/ [AL

( 1)/ + AH( 1)/ (H /L)( 1)/ ]1/( 1).

Similarly, the high- skill unit wage is

(3) wH = YH

= AH( 1)/ [AL

( 1)/ (H /L) [( 1)/ ] + AH( 1)/ ]1/( 1).

Combining equations (2) and (3), the skill premium—the high- skill unit wage divided by the low- skill unit wage—is

(4) = wH

wL

= AH

AL

( 1)/HL( ) (1/ )

.

Equation (4) can be rewritten in a more convenient form by taking logs

(5) ln = 1ln

AH

AL

1ln

HL( ).

1. This production function is typically written as Y = [γ(ALL)(σ–1)/σ + (1 – γ)(AHH)(σ–1)/σ]σ/(σ–1), where AL and AH are factor-augmenting technology terms and γ is the distribution parameter. I suppress γ (i.e., set it equal to 1/2) to simplify notation.

346 David Autor

The log skill premium, ln ω, has been a central object of study in the empiri-cal literature on the changes in the earnings distribution. Equation (5) shows that there is a simple log- linear relationship between the skill premium and the relative supply of skills as measured by H/L. Equivalently, equation (5) implies

(6) lnln H /L

= 1 < 0.

This relationship corresponds to the second of the two forces in Tinbergen’s race (the fi rst being technology, the second being the supply of skills): for a given skill bias of technology, captured here by AH /AL, an increase in the relative supply of skills reduces the skill premium with an elasticity of 1/σ. Intuitively, when high- and low- skilled workers are producing the same good but performing diff erent functions, an increase in the number of high- skilled workers will necessitate a substitution of high- skilled workers for the func-tions previously performed by low- skilled workers.2 The downward- sloping relationship between relative supply and the skill premium implies that if technology, in particular AH /AL, had remained roughly constant over recent decades, the remarkable increase in the supply of skills (seen, e.g., in table 9.1 of Valletta’s chapter) would have led to a signifi cant decline in the skill premium. The lack of such a decline is a key reason why economists believe that the fi rst force in Tinbergen’s race—changes in technology increasing the demand for skills—must have also been important throughout the twentieth century (cf. Goldin and Katz 2008).

More formally, diff erentiating equation (5) with respect to AH /AL yields

(7) ln

ln AH /AL( )= 1

.

Equation (7) implies that if σ > 1, then relative improvements in the high- skill- augmenting technology (i.e., in AH /AL) increase the skill premium. This can be seen as a shift out of the relative demand curve for skills. The converse is obtained when σ < 1: that is, when σ < 1, an improvement in the produc-tivity of high- skilled workers, AH, relative to the productivity of low- skilled workers, AL, shifts the relative demand curve inward and reduces the skill premium. Nevertheless, the conventional wisdom is that the skill premium increases when high- skilled workers become relatively more—not relatively less—productive, which is consistent with σ > 1. Most estimates put σ in this

2. In this interpretation, we can think of some of the “tasks” previously performed by high-skilled workers now being performed by low-skilled workers. Nevertheless, this is simply an interpretation, since in this model there are no tasks and no endogenous assignment of tasks to workers. One could alternatively say that the H and L tasks are imperfect substitutes, and hence an increase in the relative supply of H labor means that the H task is used more intensively but less productively at the margin.

Comment 347

context to be somewhere between 1.4 and 2 (Johnson 1970; Freeman 1986; Heckman, Lochner, and Taber 1998).

The key equation of the canonical model links the skill premium to the relative supply of skills, H/L, and to the relative technology term, AH /AL. This last term is not directly observed. Nevertheless, the literature has made considerable empirical progress by taking a specifi c form of Tinbergen’s hypothesis, and assuming that there is a log- linear increase in the demand for skills over time coming from technology, captured in the following equation:

(8) lnAH,t

AL,t

= 0 + 1t,

where t is calendar time and variables written with t subscripts refer to these variables at time t. Substituting this equation into equation (8), we obtain

(9) ln t =1

0 +1

1t1

lnHt

Lt

.

Equation (9) implies that “technological developments” take place at a constant rate, while the supply of skilled workers may grow at varying rates at diff erent points in times. Therefore, changes in the skill premium will occur when the growth rate of the supply of skills diff ers from the pace of technological progress. In particular, when H/L grows faster than the rate of skill- biased technical change, (σ – 1)γ1, the skill premium will fall. And when the supply growth falls short of this rate, the skill premium will increase. Surprisingly, this simple equation provides considerable explana-tory power for the evolution of the skill premium—though its limitations are also immediately evident.

Doing the Katz- Murphy

Using data from Autor (2014), I fi t this simple model to fi fty years of US data for 1963–2012. Figure 9C.1 provides the key input into this estimation: the observed log relative supply of US college versus noncollege labor for years 1963–2012, measured in effi ciency units and normalized to zero in the base year.3 Figure 9C.2 highlights the steep rise in production of college- educated labor in the United States in the postwar period—specifi cally until the late 1970s—followed by a sharp deceleration after 1980. This decelera-tion is frequently interpreted as the key driver of the rapid rise in the skill premium after 1980 (Katz and Murphy 1992). Notably, there is also a steep acceleration of supply after 2004. All else equal, one would except this supply acceleration to depress the skill premium absent any slowdown of the secular

3. Extensive details on the calculation of these series are provided in Acemoglu and Autor (2011).

348 David Autor

trend rise in relative demand after 2004. This observation highlights that the evolution of the skill premium is not a suffi cient statistic for fl uctuations in demand for skilled labor; one must also account for supply.

Using the data series in fi gure 9C.2, I fi t equation (9) to obtain the fol-lowing estimate:

(10) ln t = constant + .0151 t 0.302 lnHt

Lt

.(0.0013) (0.0429)

This simple ordinary least squares (OLS) model implies that (a) the rela-tive demand curve for college versus noncollege labor is shifting outward by approximately 1.5 log points per year, and (b) that increases in the rela-tive supply of skilled labor buff er the impact of shifting demand on wage inequality. Specifi cally, the point estimate of −0.30 on the relative supply term implies an elasticity of substitution of ˆ = 1/3.31. While the explana-tory power of this time- series model is high (R2 = 0.94), the point estimate for the elasticity of substitution is more than twice as high as Katz- Murphy’s 1992 estimate of 1.41. This implies that either the elasticity of substitution is changing over time or that the linear time trend is not doing an adequate job of capturing trends in relative demand.

Figure 9C.3 explores these possibilities. The series plotted with circular markers corresponds to the measured (i.e., observed) skill premium in each year. This series depicts the now familiar rise in the skill premium from the

Fig. 9C.2 Effi ciency units of college versus noncollege labor supply, 1963–2012Source: Data from Autor (2014).

Comment 349

early 1960s (start of the series) to the early 1970s, the sharp fall between 1971 and 1981, the steep and continuous rise from 1982 to 1999, and then the much shallower rise from 2000 to 2012 (end of the series). The series with diamond markers performs a within- series extrapolation by reestimating equation (10) using only data from 1962 to 1992 (the period of best fi t), and recovering estimates of the time trend and the elasticity of substitution (ˆ = 0.028, ˆ = 1/–0.631 = –1.59). The plotted series then projects this estimate forward to 2012 using the estimated parameters from the 1962 to 1992 fi t in combination with the observed evolution of aggregate skill supplies (ln Ht /Lt). Notably, the time trend and elasticity recovered from this procedure are extremely similar to those obtained by Katz- Murphy’s in 1992, and using data for 1963 through 1987. The similarity of the current estimates implies that Katz- Murphy’s within- sample point estimates continue to closely track the observed data for an additional fi ve years out of sample.

As the fi gure reveals, however, this projection badly misses the mark after 1992. Adjusting for the evolution of aggregate skill supplies, the growth in the skill premium is far more modest after 1992 than the extrapola-tion projects. Between 1992 and 2012, the observed college/noncollege log earnings gap rises by 11.6 log points. But the projection based on data to 1992—applying the observed evolution of skill supplies to 2012—predicts an increase of 30.4 log points, nearly three times as large as what occurred. A summary judgment is that the evolution of the skill premium has been surprising since 1992.

Fig. 9C.3 Observed, predicted, and fi tted evolution of the log college/noncollege hourly earnings gap, 1963–2012Source: Data from Autor (2014).

350 David Autor

The Element of Surprise

Economic literature noted this surprise some time ago. Card and DiNardo (2002) fi rst pointed out this discrepancy in their broad critique of the skill- biased technical change literature. Autor, Katz, and Kearney (2008) pro-posed an ad hoc workaround, which was to allow for a trend deceleration in the evolution of skill demands after 1992. Goldin and Katz (2008) and Autor (2014) pursue a related approach by applying a quadratic time trend in the time- series model, thereby allowing a smooth deceleration of the trend demand shift. The series in fi gure 9C.3 labeled “Fitted gap: quadratic trend” (triangular marker) shows just how well this works. Conditional on the quadratic trend the fi t is impressively close. But of course, this is simple reverse engineering. This fl exibility was added to the model because the data demanded it, not because the theory suggested it.

These various exercises raise an urgent question: After accounting for fl uctuations in the supply of skilled labor, when did the “fl attening” of demand for skill commence? Here, I draw a distinction between fl attening in the skill premium and fl attening (or deceleration) in the movement of the underlying demand schedule. As noted above, it would be entirely possible for the skill premium to decline even as demand was accelerating—if skill supplies rose fast enough. Figure 9C.1 makes clear that skill supplies accel-erated after 2004. Was this supply- side change an important contributor to the observed fl attening of the skill premium?

The series plotted in square markers in fi gure 9C.3 addresses this question. The log relative supply of college workers (fi gure 9C.2) rose at an annual rate of 4.31 log points between 1963 and 1982, by 1.79 log points between 1982 and 2004, and by 2.61 log points between 2004 and 2012 (i.e., a 45 percent increase after 2004). The series with square markers in fi gure 9C.3 (labeled “Supply trend 1984–2004 continues post- 2004”) replaces the observed val-ues of ln(Ht/Lt) with a counterfactual series in which log relative supply rises at the 1963–1982 of 1.79 log points per annum. Surprisingly (at least to me), this substitution makes a substantial diff erence for inference. The estimated college premium rose by only 1.65 log points between 2004 and 2012. This exercise implies that had the relative supply of college- educated labor not accelerated after 2004, the skill premium would have risen by 5.47 log points rather than a measly 1.65 log points. I submit based on this evidence that had there been no supply acceleration after 2004, Beaudry, Green, and Sand would have had a more diffi cult time making the argument that there was a demand deceleration in the fi rst decade of the twenty- fi rst century.

How Long Has This Been Going On?

The evidence in fi gure 9C.3 in no way obviates the claim that demand for college workers fl attened according to the canonical model. It instead under-

Comment 351

scores that the raw skill premium, not purged of the impact of supply forces, could generate misleading inferences about the trajectory of the demand for skilled labor.

To address this shortcoming, fi gure 9C.4 plots the implied log relative demand shift favoring college versus noncollege labor for 1962–2012, again using the estimated value of σ = 1.59 based on fi tting equation (9) to data for 1962–1992. The plotted (scatter) points in fi gure 9C.4 are not regres-sion estimates. They correspond instead to the calculated values of γt = ωt – (1/ ˆ )ln(Ht /Lt) in each year, where we treat σ as known.4 To guide inter-pretation of these data points, the fi gure also contains three regression lines. The solid line depicts a pure linear extrapolation, fi tted and projected using data for 1962–1992. This corresponds to the implied path of relative demand from 1992 through 2012 had there been no deviation after 1992. The short- dashed series is the quadratic fi t to this set of scatter points. The long- dashed series is a linear spline that allows for a discrete slope shift in 1992 (and otherwise fully overlays the initial trend from 1963 to 1992).

This plot highlights three key patterns. A fi rst is that the trajectory of (implied) relative demand for educated labor is astonishingly linear for the initial thirty years of the series, 1963–1992. This linearity is in no sense

4. Equivalently, they are the time dummies from a saturated regression (no error term) of t (1/ ˆ ) ln (Ht /Lt) on a full set of year indicators.

Fig. 9C.4 Implied evolution of the demand for college versus noncollege labor using 𝛔 = 1.59, 1963–2012Source: Data from Autor (2014).

352 David Autor

mechanical: the relative demand shift estimates plotted in fi gure 9C.4 are extracted from a college wage premium series that fl uctuates dramatically over three decades, rising for the fi rst ten years of the time interval, falling for the next nine, and then increasing with remarkable rapidity thereafter. The linearity of the (implied) underlying demand trend therefore refl ects the uncanny success of the relative supply term ln Ht/Lt in explaining the fl uctua-tions in the premium, leaving little behind but a smooth secular underlying demand shift favoring college- educated labor.

The second pattern immediately visible in fi gure 9C.4 is that the steady secular demand shift favoring college- educated labor decelerates after 1992, and does so abruptly. Estimates of equation (9), fi t using a linear spline (long- dashed series), imply that the relative demand for college labor rose by 2.80 log points per year between 1963 and 1992 and then decelerated to 1.84 points thereafter (a fall of one- third). This pattern, while occasionally noted in the literature (cf. Acemoglu and Autor 2011), has not been rigor-ously explained by any formal model—though of course there are many informal explanations.

The third takeaway from fi gure 9C.4 is that it is hard to see any evidence of a discontinuous deceleration in the demand for educated labor in the fi rst decade of the twenty- fi rst century. Whether fi t using the linear spline, allowing all the post- 1992 points to cluster along one axis, or a quadratic trend, allowing the deceleration to cumulate over the full sample, there is almost nothing in this fi gure that suggests a trend break in demand in the fi rst decade of the twenty- fi rst century.5 Rather, this evidence suggests that the trend movements in relative demand early in the twenty- fi rst century were a continuation of those commencing circa 1992.

Conclusion: Timing Is Everything

These fact patterns lead me to draw a distinct inference from Beaudry, Green, and Sand: we should not be surprised by the evolution of the skill premium—or even the weaker job prospects of college- educated work-ers—in the early twenty- fi rst century. These outcomes are consistent with steadily rising demand for college- educated labor and a surprising surge in new college entrants in the US labor market after 2003, which depressed the skill premium as it would be predicted to do. We should however be deeply puzzled by the sudden trend shift in demand after 1992, which ushered in (at least) twenty years of slower (though still nonnegligible) growth in demand for skilled labor.

5. If one squints, it is possible to see that some of the points immediately after 2000 fall slightly below the regression line, whereas those immediately before fall slightly above it—implying a possible further deceleration after 2000. But then the last three points in the series (2010–2012) again lie slightly above the regression line, suggesting a tiny reacceleration. This is pretty thin evidence.

Comment 353

This development is not altogether bad news, however. Had demand for skilled labor continued to rise after 1992 at its pre- 1992 pace, the estimates in fi gure 9C.3 suggest that the United States would have seen substantially more growth of between- group inequality—specifi cally, a meteoric 30 log point rise in the college premium between 1992 and 2012, nearly three times as large as the economically signifi cant rise of 11 log points that actually occurred. This “good news” is at best partial, however. In the canonical model, relative demand shifts intrinsically convey good economic news because they imply ongoing factor- augmenting technological progress.6 Thus, this slowdown may be read to support Beaudry, Green, and Sand’s view that as information technology has matured, the pace of accompanying labor augmentation has slackened. If so, however, we would want to caveat their conclusion to note that this slowdown started about ten years prior to the date that Beaudry, Green, and Sands pinpoint, and that it occurred during a period in which aggregate productivity growth was robust and US IT investment was rising extraordinarily rapidly (fi gure 9C.1).

References

Acemoglu, Daron, and David H. Autor. 2011. “Skills, Tasks and Technologies: Implications for Employment and Earnings.” Handbook of Labor Economics 4:1043–171.

Autor, David H. 2014. “Skills, Education, and the Rise of Earnings Inequality among the ‘Other 99 Percent.’” Science 344 (6186): 843–51.

———. 2015. “Why Are There Still So Many Jobs? The History and Future of Workplace Automation.” Journal of Economic Perspectives 29 (3): 3–30.

Autor, David H., Lawrence F. Katz, and Melissa S. Kearney. 2008. “Trends in U.S. Wage Inequality: Revising the Revisionists.” Review of Economics and Statistics 90 (2): 300–323.

Beaudry, Paul, David A. Green, and Benjamin M. Sand. 2014. “The Declining For-tunes of the Young Since 2000.” American Economic Review 104 (5): 381–86.

———. 2016. “The Great Reversal in the Demand for Skill and Cognitive Tasks.” Journal of Labor Economics 34 (S1, pt. 2): S199–247.

Card, David, and John E. DiNardo. 2002. “Skill- Biased Technological Change and Rising Wage Inequality: Some Problems and Puzzles.” Journal of Labor Econom-ics 20 (4): 733–83.

Card, David, and Thomas Lemieux. 2001. “Can Falling Supply Explain the Rising Return to College for Younger Men? A Cohort- Based Analysis.” Quarterly Journal of Economics 116 (2): 705–46.

Freeman, Richard B. 1986. “Chapter 6 Demand for Education.” Handbook of Labor Economics 1:357–86.

Goldin, Claudia, and Lawrence F. Katz. 2008. The Race between Education and Technology. Cambridge, MA: Harvard University Press.

Goldin, Claudia, and Robert A. Margo. 1992. “The Great Compression: The Wage

6. This is also true for technological progress that raises AL or both AH and AL. Presuming as the model does that technological progress is always factor augmenting—there are no factor-retarding technological regresses—any movement of AH or AL must correspond to an increase of either or both terms and hence rising productivity.

354 David Autor

Structure in the United States at Mid- Century.” Quarterly Journal of Economics 107 (1): 1–34.

Heckman, James J., Lance Lochner, and Christopher Taber. 1998. “Explaining Ris-ing Wage Inequality: Explorations with a Dynamic General Equilibrium Model of Labor Earnings with Heterogeneous Agents.” Review of Economic Dynamics 1 (1): 1–58.

Johnson, George E. 1970. “The Demand for Labor by Educational Category.” South-ern Economic Journal 37 (2): 190–204.

Katz, Lawrence F., and Kevin M. Murphy. 1992. “Changes in Relative Wages, 1963–1987: Supply and Demand Factors.” Quarterly Journal of Economics 107 (1): 35–78.

Murphy, Kevin M., and Finis Welch. 1992. “The Structure of Wages.” Quarterly Journal of Economics Structure 107 (1): 285–326.

Tinbergen, Jan. 1974. “Substitution of Graduate Labor by Other.” Kyklos 27 (2): 217–26.

501

Jaison R. AbelFederal Reserve Bank of New York50 Fountain Plaza, Suite 1400Buff alo, NY 14202

David AutorDepartment of Economics, E52- 438Massachusetts Institute of Technology77 Massachusetts AvenueCambridge, MA 02139

Sandy BaumUrban Institute2100 M Street, NWWashington, DC 20037

Canyon BoslerDepartment of EconomicsUniversity of Michigan238 Lorch Hall611 Tappan AvenueAnn Arbor, MI 48109- 1220

John BoundDepartment of EconomicsUniversity of MichiganAnn Arbor, MI 48109- 1220

Stijn BroeckeOrganisation for Economic Co-

operation and Development2, rue André Pascal75775 Paris Cedex 16France

Mary C. DalyFederal Reserve Bank of San Francisco101 Market StreetSan Francisco, CA 94105

David J. DemingHarvard Graduate School of

EducationGutman 411Appian WayCambridge, MA 02138

Richard DeitzFederal Reserve Bank of New York50 Fountain Plaza, Suite 1400Buff alo, NY 14202

Douglas W. ElmendorfHarvard Kennedy School79 John F. Kennedy StreetCambridge, MA 02138

Contributors

502 Contributors

John G. FernaldProfessor of EconomicsINSEADBoulevard de Constance77305 Fontainebleau, France

Maury GittlemanUS Bureau of Labor Statistics2 Massachusetts Avenue NE, Suite

4130Washington, DC 20212

Grey GordonDepartment of EconomicsWylie Hall, Room 105Indiana University100 S Woodlawn AveBloomington, IN 47403

Nora GordonMcCourt School of Public PolicyGeorgetown University306 Old North37th and O Streets NWWashington, DC 20057

Gordon H. HansonIR/PS 0519University of California, San Diego9500 Gilman DriveLa Jolla, CA 92093- 0519

Eric A. HanushekHoover InstitutionStanford UniversityStanford, CA 94305- 6010

Aaron HedlundDepartment of EconomicsUniversity of Missouri118 Professional Building909 University AvenueColumbia, MO 65211

Bart HobijnDepartment of EconomicsArizona State UniversityPO Box 879801Tempe, AZ 85287- 9801

Caroline M. HoxbyDepartment of EconomicsStanford UniversityLandau Building, 579 Serra MallStanford, CA 94305

Charles R. HultenDepartment of EconomicsUniversity of MarylandRoom 3114, Tydings HallCollege Park, MD 20742

Dale W. JorgensonDepartment of EconomicsLittauer Center, Room 122Harvard UniversityCambridge, MA 02138

Mun S. HoResources for the Future1616 P Street, NWWashington, DC 20036

Frank LevyDepartment of Urban Studies and

PlanningBuilding 9- 517Massachusetts Institute of TechnologyCambridge, MA 02139

Shelly LundbergDepartment of EconomicsUniversity of California, Santa

BarbaraSanta Barbara, CA 93106- 9210

Kristen MonacoUS Bureau of Labor Statistics2 Massachusetts Avenue NE, Suite


Nicole NestoriakUS Bureau of Labor Statistics2 Massachusetts Avenue NE, Suite


Contributors 503

Glenda QuintiniOrganisation for Economic Co-


Valerie A. RameyDepartment of Economics, 0508University of California, San Diego9500 Gilman DriveLa Jolla, CA 92093- 0508

Jon D. SamuelsBureau of Economic Analysis4600 Silver Hill RoadWashington, DC 20233

Matthew J. SlaughterTuck School of BusinessDartmouth College100 Tuck HallHanover, NH 03755

Robert G. VallettaEconomic Research DepartmentFederal Reserve Bank of San Francisco101 Market StreetSan Francisco, CA 94105

Marieke VandeweyerOrganisation for Economic Co-


505

Author Index

Aaronson, D., 23n1, 71, 72, 76n22, 78, 82, 85, 85n33, 88, 88n36, 93, 95, 100, 101, 102

Abbott, B., 364Abel, J. R., 7, 150, 163n12, 174, 338Abraham, K. G., 200, 204Acemoglu, D., 10, 117, 120, 125, 132, 141,

183, 183n1, 313, 314, 325, 328, 337, 338, 343, 344, 347n3, 486

Achenbach, T. M., 228Aizer, A., 240Almlund, M., 224, 227Altonji, J. G., 69, 151, 301n7Andrews, R. J., 363Anger, S., 224, 225Arcidiacono, P., 163Aronson, J., 234Arum, R., 179Ashenfelter, O., 403n6Aslin, R. N., 235Athreya, K., 364Autor, D. H., 10, 15, 116n2, 117, 120, 125,

132, 133, 134, 137f, 141, 158n8, 183, 183n1, 186, 198, 200, 200n16, 204, 209, 254, 264, 270n16, 278, 288, 303n8, 304, 308, 313, 314, 316, 325, 326, 326n8, 327, 328, 328n10, 330, 331n13, 333, 337, 338, 343, 344, 347, 347n3, 350, 358–59, 360, 363, 373

Avery, C., 424n28

Bacow, L. S., 403n5Baily, M., 52

Baron- Cohen, S., 247Barro, R. J., 295, 295n2Barrow, L., 97Barsky, R., 82Bartel, A. P., 127Baum, S., 397fBaumeister, R. F., 234Baumol, W. J., 358, 361Beato, G., 402n1Beaudry, P., 7, 15, 140n19, 154, 155n5, 178,

290, 314, 322, 326n8, 333, 337, 342Becker, A., 227Bell, B., 276Belley, P., 364Benhabib, J., 5n5, 295Bennett, W. J., 358, 360Benson, A., 290Berg, A., 252, 293Bertrand, M., 69Besharov, D. J., 303n10Bettinger, E., 403n5Betts, J. R., 151, 388n26Biddle, J. E., 70Bils, M. J., 82Blank, R. M., 69Blau, F. D., 251, 252, 255, 258, 261, 261n8,

263, 264–65, 266n10, 269n14, 270n17Blaug, M., 69n13Blinder, A., 183Blom, E., 151Bloom, N., 129Boatman, A., 416n19

506 Author Index

Boeri, T., 69n10Bol, T., 306Bonikowski, B., 69Borghans, L., 183, 234Borjas, G., 468, 486, 489Bouchard, T. J., Jr., 225Bound, J., 183, 325, 466, 495, 497Bowen, H. R., 359Bowen, W. G., 358, 361, 403n5Bowles, S., 219, 226Brown, C., 202Brueckner, M., 293Brunello, G., 306Brynjolfsson, E., 52, 143Burtless, G., 88, 100Byrne, D., 52

Cadena, B., 224Cajner, T., 23n1Caliendo, M., 224Cao, Y., 338Cappelli, P., 133n14Card, D., 203, 315, 344, 350, 363, 403n6Carneiro, P., 203Cascio, E. U., 303Casselman, B., 149n1Castelló- Climent, A., 293–94Cellini, S. R., 338, 363, 462Chakrabarty, R., 362Charles, K. K., 98n47Chaudhary, L., 462Checchi, D., 252Chetty, R., 226, 303Chevalier, A., 151Cho, D., 34Christensen, C. M., 402n1Cingano, F., 252Clotfelter, C. T., 359Cobb- Clark, D., 224Cohen, D., 295n2Coleman, J. S., 303Corrado, C. A., 136, 136f, 138, 138n16, 140fCowen, T., 52, 54, 403n5Cramer, J., 34Cunha, F., 233, 298, 307Cunningham, A. F., 362, 363Currie, J., 225

Dabla Norris, E., 293Daly, M. C., 338Datta Gupta, N., 230Davis, J., 97

Davis, L. S., 363Deitz, R., 7, 150, 163n12, 174, 338Dellas, H., 388n26Deming, D. J., 70, 183, 222, 248, 403n5,

406n9, 408n12, 414n18, 416Demirci, M., 466Denhart, C., 153Dennett, J., 88, 100Devroye, D., 251, 258, 264Dickens, W., 70Diewert, W. E., 78DiNardo, J., 252, 258, 259, 270n16, 270n17,

350Dohmen, T., 233Domar, E., 41Doménech, R., 294Doran, K., 468, 486Dorn, D., 116n2, 158n8, 254, 264, 278, 326Duggan, M., 209Duncan, G. J., 224, 226n1Dunstan, D. W., 209

Ebbesen, E. B., 224Eberly, J., 364Edelbrock, C., 228Ehrenberg, R. G., 359, 362Elsby, M. W., 151, 167Epple, D., 359, 377Esteva, R., 290Eyring, H. J., 402n1

Feenstra, R. C., 65n7Feldman, D. C., 150Fernald, J. G., 27, 52, 62, 62n3, 81, 92,

332n14Ferraro, D., 82Figlio, D., 403n5Figueroa, E. B., 445n42Filer, R. K., 223Fillmore, I., 364Firpo, S., 183, 258, 258n4, 270n17Fisher, F. M., 121n3, 126n9Fogg, N. P., 153Forster, A. G., 306Fortin, N. M., 164n13, 183, 252, 258, 258n4,

259, 270n16, 270n17Fraumeni, B. M., 24, 65n7, 67, 71Frederick, A. B., 363Freeman, R., 251, 253, 258, 264, 276, 347,

497Friedmann, E., 403n5Fryer, R. G., Jr., 303

Author Index 507

Fu, C., 364Fukao, K., 25, 25n2, 54

Galor, O., 294n1Galston, W. A., 149n1Gandal, N., 486Garnero, A., 270Garriga, C., 364Gauthier- Loiselle, M., 467Gertler, P., 226Gibbons, R., 70n15Gintis, H., 219, 226Gittleman, M., 200, 202Goldberg, L. R., 223Goldin, C. D., 3, 35n6, 62, 87, 132, 164n13,

264, 278, 315, 343, 344, 346, 350, 363, 403n5, 414n18

Gollop, F. M., 24, 65n7, 67, 71Goos, M., 288, 325Gordon, R., 52, 54Gottfredson, M. R., 236Gradstein, M., 293Green, D. A., 7, 15, 140n19, 154, 155n5,

178, 290, 314, 322, 326n8, 333, 337, 342Green, F., 151Griffi th, E., 465n1Griliches, Z., 4, 9, 10, 61, 119, 120Grogger, J., 479Gupta, A., 362Gupta, S., 52

Hall, B., 141Haltiwanger, J. C., 142Hamermesh, D. S., 70Handel, M. J., 134, 184, 185, 190, 200n16, 204Hanson, G. H., 116n2, 326, 479, 486Hanushek, E. A., 3, 120, 219, 277, 294, 295,

296, 296n3, 297, 298, 300, 301, 301f, 302, 303n8, 304, 304n12, 305, 305n14, 306, 307, 308

Harcourt, G. C., 124Harrington, P. E., 153Hart, B., 303Hart, C., 403n5Harvey, J., 150Haskel, J., 116n2Hathaway, I., 142Hecker, D., 445n43Heckman, J. J., 7n6, 69n11, 70, 132n12,

134n15, 203, 220, 223, 226, 233, 298, 307, 347

Heineck, G., 223, 224

Heisz, A., 151Heller, D. E., 362Hellerstein, J. K., 69, 69n12Hennessey, J. L., 52, 52n9Hersch, J., 151Hill, M., 403n5Hirschi, T., 236Ho, A. D., 403n5Ho, M. S., 24, 25, 25n2, 26, 27n4, 28, 30, 39,

40, 41n7, 52n8, 54, 62, 63n4, 65n7, 71Hobijn, B., 151, 167Hoekstra, M., 363Holdren, A. E., 27Holmes, C., 298, 307n16Horn, M. B., 402n1Howell, D. R., 276Hoxby, C. M., 403n5, 405n8, 418, 424n28,

426Hoynes, H., 167Hu, L., 23n1Huebler, F., 276Hulten, C. R., 122n4, 123, 131, 136, 136f,

137, 138, 138n16, 140fHumphries, J. E., 7n6, 132n12Hungerford, T., 70Hunt, J., 467Hurst, E., 98n47

Ichniowski, C., 127Ingram, B. F., 200, 204Inklaar, R., 65n7Ionescu, F., 364, 373, 374, 374n17Isaac, M., 287

Jaeger, D., 316, 316n1Jaggars, S. S., 403n5Jarmin, R. S., 142Jasso, G., 466Jensen, J. B., 183John, O. P., 244, 245Johnson, G. E., 183, 325, 347, 497Johnson, M. T., 97Jones, C. I., 62, 62n3, 92, 467Jones, J. B., 364Jones, P., 69n12Jordan, M., 466n3Jorgenson, D. W., 4, 9, 10, 24, 25, 25n2, 26,

26n3, 27n4, 28, 30, 39, 40, 41n7, 52n8, 54, 54n12, 61, 62, 63n4, 65n7, 67, 71, 119, 120

Jovicic, S., 252, 253, 258, 263–64, 276, 277Juhn, C., 258, 264, 278

508 Author Index

Kahn, L. B., 151Kahn, L. M., 251, 252, 255, 258, 261, 261n8,

263, 264, 265, 266n10, 269n14, 270n17Kampelmann, S., 270Katz, L. F., 3, 15, 35n6, 62, 70, 87, 132,

164n12, 183, 253, 254, 264, 265, 278, 313, 315, 316, 343, 344, 346, 347, 350, 358–59, 360, 363, 373, 403n5, 414n18

Kautz, T., 134n15, 220, 225, 233, 235, 245, 246

Keane, M. P., 364Kearney, M. S., 15, 254, 264, 278, 316, 350,

358–59, 360, 363, 373Keightley, M. P., 364Kerr, S. P., 8, 142, 467, 486Kerr, W. R., 8, 142, 485, 486, 497Keys, B., 224Khanna, G., 497, 498Kidd, C., 235Kim, D. D., 27Kimko, D. D., 219Kletzer, L. G., 183Koshal, M., 362Koshal, R. K., 362Koubi, V., 388n26Kroft, K., 34Krueger, A. B., 34, 70, 223, 252, 313, 325Krueger, D., 306Krugman, P., 497Kumar, K. B., 306Kuziemko, I., 164n13Kuznets, S., 25, 293

Landes, D. S., 3Lang, K., 69n14Langdon, D., 469Lausten, M., 230Lee, D. S., 270n16Lee, J.- W., 295, 295n2Lemieux, T., 69n11, 70, 183, 252, 258,

258n4, 259, 260, 270n16, 270n17, 316, 325, 344, 363

Lerman, R. I., 305Lettau, M. K., 202Leuven, E., 251, 252, 253, 255, 264, 265,

266n10, 269n13, 269n14, 287Levine, R., 296Levitt, S. D., 303Levy, F., 117, 133, 143, 183, 186, 198, 200,

254, 264, 288, 290, 313, 326n8, 327Lewis, E., 486Li, J., 363Light, A., 69

Lincoln, W. F., 467, 485, 486, 497Lindley, J., 151, 314, 316n2, 325, 333Lindqvist, E., 224Litan, R. E., 142Lochner, L. J., 69n11, 347, 364Loehlin, J. C., 225Long, B. T., 363, 416n19Looney, A., 402n1Lovenheim, M. F., 363, 403n5, 406n9,

408n12, 416Lowell, B. L., 495, 498Lucas, R. E., Jr., 5, 123, 132n13, 141, 294Lucca, D. O., 363Lundberg, S., 134n15, 224, 235Lyndaker, A. S., 27

Ma, J., 388n26, 395f, 396f, 397Mabutas, M., 263Machin, S., 314, 316n2, 325, 333Maestas, N., 209Magnuson, K., 224, 226n1Mairesse, J., 141Manchester, J., 209Mani, A., 234Mankiw, N. G., 294Manning, A., 270n16, 288, 325Manyika, J., 52Margo, R. A., 343Markoff , J., 52n9Mayerhauser, N. M., 27McAfee, A., 52, 143McCall, B., 305n13McCulla, S. H., 27McFarland, L., 388n26McKee- Ryan, F. M., 150McPherson, M. S., 362, 403n5Medoff , J., 202Meghir, C., 151Meijers, H., 234Memoli, M. A., 287Mian, A., 162n12Miller, D. L., 167Miller, T., 436n34Mincer, J. A., 67, 300Miranda, J., 142Mischel, W., 224Modestino, A. S., 88, 100Mohnen, P., 141Monge- Naranjo, A., 364Morales, N., 497, 498Mueller, G., 223Mullainathan, S., 69Mullen, K. J., 209

Author Index 509

Munnell, A., 200Muraven, M., 234, 313Murnane, R. J., 117, 133, 143, 183, 186, 198,

200, 254, 264, 288, 326n8, 327Murphy, K. M., 183, 253, 258, 264, 265,

343–44, 347, 363

Nadauld, T., 363Nandi, A., 223Nelson, R. R., 5, 124n6, 130, 302Neumann, G. R., 200, 204Neumark, D., 69, 69n12Nickell, S., 276Nicoletti, C., 223Notowidigdo, M. J., 98n47Nunley, J. M., 167Nyhus, E. K., 223

Oaxaca, R., 69Oldenski, L., 183Oliner, S., 52O’Mahony, M., 65n7Oosterbeek, H., 251, 252, 253, 255, 264, 265,

266n10, 269n13, 269n14, 287Oreopoulos, P., 151, 164n13Osborne, M., 226Ostry, J. D., 252, 293

Paccagnella, M., 252, 258, 261, 263, 264, 277, 287

Pager, D., 69Palmeri, H., 235Papageorge, N. W., 229n4, 235Parker, J. A., 82Patrinos, H. A., 69, 300n6Patterson, D. A., 52, 52n9, 408n12Patterson, R., 403n5, 406n9, 416Pena, A. A., 252, 255, 258, 264, 277, 287Peri, G., 467Peterson, N., 153n3Petty, W., 300Phelps, E. S., 5, 130, 302Phipps, S., 164n13Pickett, K., 252Pierce, B., 192, 200, 202, 258, 264Pierret, C. R., 301n7Pillai, U., 52Pinto, R., 226Plug, E., 223Pons, E., 223Porter, E., 486n8Pozzoli, D., 230Prescott, E. C., 129

Price, B., 133, 137fProper, K. I., 209Psacharopoulos, G., 69, 300n6Puma, M., 304

Ramey, G., 6Ramey, V. A., 6Ransom, M., 69Raskoff Zeiss, A., 224Reardon, S. F., 303Renelt, D., 296Reynolds, A. J., 303Risley, T. R., 303Rizzo, M. J., 362Robe, J., 153Rocco, L., 306Roksa, J., 179Romano, R., 359, 377Romer, D., 294Romer, P. M., 5, 130, 141, 294Ronda, V., 229n4, 235Rosen, S., 497Rosenthal, S., 25n2Rubinstein, Y., 223Ruggles, S., 156Rush, M., 403n5Rustichini, A., 227Ryan, P., 306Rycx, F., 270Ryoo, J., 497

Sadeghi, A., 142Şahin, A., 151, 167Salomons, A., 288, 325Salverda, W., 252Samuels, J., 25, 25n2, 27n4, 28, 39, 40, 54,

65n7, 71Samuelson, P. A., 121, 123, 497Sand, B. M., 7, 15, 140n19, 154, 155n5, 178,

290, 314, 322, 326n8, 333, 337, 342Savelyev, P., 226Schaller, J., 167Schanzenbach, D. W., 303Schettkat, R., 253, 276Schkade, D., 223Schmidt, S. J., 363Schreyer, P., 24, 25, 28, 29Schultz, T. W., 302Schweinhart, L. J., 303Schwerdt, G., 301, 301f, 302, 304, 306, 307Segal, C., 224Shapiro, M. O., 362Shaw, K. L., 127

510 Author Index

Shen, K., 363Sherk, J., 97Shonkoff , J. P., 225Sichel, D., 52, 136, 138Sieg, H., 359, 377Silva, O., 70Simone, S., 362Simpson, N., 364, 373Singell, L. D., Jr., 362Singer, N., 287Skrentny, J. D., 69n14Slaughter, M., 486Smith, A., 300Smith, C. L., 270n16Smith, J., 305n13Smith, S., 27Solis, B., 398Solon, G., 70, 82Solow, R. M., 9, 25, 119, 121, 126n9, 131,

131n10Song, J., 209Soto, M., 295n2Speer, J. D., 151Spiegel, M. M., 5n5, 295Spletzer, J. R., 142, 200, 204Srivastava, S., 244, 245Staff ord, F. P., 497Steele, C. M., 234Stiglitz, J., 252Stinebrickner, R., 164Stinebrickner, T. R., 164Stiroh, K. J., 24, 25, 26, 30, 39, 41n7, 52n8,

54Stixrud, J., 7n6, 70, 220, 223Stone, J. A., 362Storesletten, K., 373, 373n14Strand, A., 209Strangler, D., 142Strassner, E. H., 27Streich, F. E., 403n5Su, Y., 150, 174Suen, W., 258Sufi , A., 162n12Sullivan, D., 71, 72, 76n22, 78, 85, 85n33,

88n36, 93, 95, 101, 102Summers, L. H., 70

Tabarrok, A., 403n5Taber, C., 347Talan, D. M., 142Telmer, C., 373, 373n14Terrell, D., 445

ter Weel, B., 183, 234Timmer, M. P., 25, 25n2, 54, 65n7Tinbergen, J., 264, 344Titus, M. A., 362Todd, P. E., 69n11Troske, K. R., 69Tsangarides, C. G., 252, 293Turner, N., 338, 363Turnley, W. H., 150

Uhlendorff , A., 224Ureta, M., 69Urzua, S., 7n6, 70, 220, 223

van der Ploeg, H. P., 209van de Werfhorst, H. G., 306van Ophem, H., 251, 252, 253, 255, 264, 265,

266n10, 269n13, 269n14, 287van Ours, J., 69n10Van Reenen, J., 129Vedder, R., 153Veramendi, G., 7n6, 132n12Vestman, R., 224Visscher, M., 129von Wachter, T., 151, 209Vu, K. M., 26n3, 54n12Vytlacil, E. J., 203

Waldrop, M., 402n1Wasshausen, D. B., 27Weil, D., 294Weinberg, B. A., 183Weinberger, C., 183Weiss, A., 69Welch, F., 302, 343–44West, M. R., 245Western, B., 69Wiederhold, S., 301, 301f, 302, 304Wilkinson, R., 252Willett, J. B., 183Winston, G. C., 360, 376Winter, S. G., 124n6Wiswall, M., 151Witte, J. F., 303Woessmann, L., 3, 120, 219, 294, 295, 296,

296n3, 297, 298, 301, 301f, 302, 304, 305, 305n14, 306, 307, 308

Wolf, M., 445n43Wolpin, K. I., 364Wolverton, M., 367n10Woods, R. A., 445n42Woolley, A. W., 247

Author Index 511

Wright, B. R., 236Wunsch, C., 305n13

Xu, D., 403n5

Yang, F., 364Yannelis, C., 402Yaron, A., 373, 373n14Yen, H., 153

Yin, L., 403n5Yun, M., 258

Zafar, B., 151, 163, 362Zhang, L., 306, 307Zheng, Y., 229n4, 235Zhu, Y., 151Zoghi, C., 65n7, 66, 77, 80, 80n28

513

Page numbers followed by “f” or “t” refer to fi gures or tables respectively.

activity- analysis approach to production: activities and measurement of GDP and, 130–31; aggregation and dynamics of, 128–30; model for, 123–28

Add Health study (National Longitudinal Study of Adolescent to Adult Health), 227, 231–32, 231n5

administrative records, 222–23age, of worker: compensation by, 38, 38f;

during Investment Boom, 38–39

BEA. See Bureau of Economic Analysis (BEA)

Bennett hypothesis, 362–63Big Five personality inventory, 223Bureau of Economic Analysis (BEA):

benchmark revision of, in 2014, 27; extension of output and intermediate accounts, 27–28; system of industry accounts developed by, 27

capital, composition of, structural change in, 135–38

capital, human. See human capitalcapital, knowledge. See knowledge capitalcapital, role of: postwar US economic

growth and, 26; and US economic growth since 1947, 25

capital inputs, changing structure of, 28–30Carolina Abecedarian Project, 303

cognitive skills: assessment of, in PIAAC, 254–55; data used for study, 253–58; role of, in explaining income inequality, 251–52; vs. task- based skills, 254–55. See also noncognitive skills; skills

cognitive skills, wage inequality and, back-ground information, 251–53

Coleman Report, 302–3college earnings premiums. See higher edu-

cation wage premiumscollege graduates: college majors and under-

employment, 168–74, 169–70t; demand for, after Great Recession, 155–56, 155f; labor market for, following Great Reces-sion, 151–52; low- skilled jobs taken by, by major, 170–71t; probability of underemployment among, by selected majors, 172f; probability of working low- skilled jobs among, by selected majors, 173f; share of underemployed recent, by occupation category, 159t; transitioning to better jobs and, 174–77; types of, prone to under employment, 160–68; types of jobs held by under-employed, 156–59, 157t; types of jobs taken by, 159–60; underemployment among, 153–55, 154f; underemploy-ment and, introduction, 149–51; unem-ployment among, 152–53. See also higher education wage premiums

Subject Index

514 Subject Index

college tuition: data and estimation, 371–79; empirical literature, 361–64; introduc-tion to, 357–59; literature employing quantitative models of higher edu-cation, 364; model constructed for, 359–61, 364–70; quantitative fi ndings, 359; results of model, 379–91

crime, male impulsivity and, 236–39

Dictionary of Occupational Titles (DOT), 184–86

disability determination: defi ned, 185; SSA process for, 185–86

Domar weight, defi ned, 41

early childhood education, growth- equity outcomes and, 302–4

Early Training Project, 303economic growth, US: endogenous growth

models of, 294–95; future, 48–53; growth of labor input, as determinant of, 26; human capital and, 219–22, 294, 295–96; human capital development and, 3–4; income distribution and, 293; neoclassical models of, 294–95; skill development and, 117; sources of, 39–47; subperiods of, 25. See also GDP growth; postwar US economic growth

education: importance of, 117–18; macro-economic link between GDP growth and, 9–11; supply and demand for, 5–8; vocational, growth- equity outcomes and, 305–7. See also online postsecond-ary education; skill development

educational attainment, 23; compensation by, 35, 35f; economic growth and, 117; as measure of human capital, 219–20; structural changes in, 131–32; of twenty- fi ve to thirty- four age group, 32, 33f; of US workers, 32, 33f

employment participation rates, by gender, age, and education, 33–34, 34f

employment rates: by average skills and, 277–78, 277t; defi ned, 23; by skill group and country, 275–76, 275t. See also unemployment rates

engineering. See STEM (science, technol-ogy, engineering, and mathematics) activities

foreign- born workers, in STEM occupa-tions, 477–86; age of US entry in, 483–

84; explanations for comparative ad-vantage of, 484–86; revealed comparative advantage of, 479–83; in US economy, 477–79; wage diff erences between native- workers and, 486–92

GDP growth: channels through which hu-man capital aff ects, 4–5; human capi-tal’s contribution to, 3–4; macro-economic link between education and, 9–11. See also economic growth, US

globalization, structural changes in US economy and, 115–17

Great Recession period (2007–2014), 25; educational attainment and, 32; labor market for college graduates following, 151–56; labor- quality growth and, 32

Growth and Recession period (1995–2014), 25

growth- equity outcomes, 293; early child-hood education and, 302–4; higher edu-cation and, 307–9; Kuznets curve and, 293–94; lifelong learning and, 304–5; vocational education and, 305–7

growth models, long- run, 294–99

Head Start programs, 304, 304n11higher education, growth- equity outcomes

and, 307–9higher education wage premiums: assessing

changes in, 318–21; college tuition and, 363–64; data and descriptive statistics for, 315–18; expansion of, 313–14; explanations for stalling of, 314–15; potential explanations for fl attening of, 325–33; robustness checks and disag-gregation by age and gender, 321–25; summary of, over time, 325. See also college graduates; polarization; skill downgrading; wage gaps

human capital: accumulation of, and recent US growth, 1–2; contribution of, to GDP growth, 3–4; economic growth and, 219–22, 294, 295–96; educational attainment as measure of, 219–20; including noncognitive skills to, 239–40; types of channels of, which aff ects GDP growth, 4–5. See also knowledge capital; noncognitive skills

immigrant workers. See foreign- born work-ers, in STEM occupations

Subject Index 515

impulsivity, male, and crime, 236–39income distribution: cognitive achievement

and, 300–301; factors of, 299–300; numeracy skills and, 301–2, 301f; school attainment and, 300

income inequality. See wage inequalityindividual earnings, 299–302inequality. See wage inequalityinformation technology (IT), structural

changes in US economy and, 115–17information technology (IT) industries, eco-

nomic impact of, 39–47innovation, 5; relative unimportance of, 25institutional characteristics, wage inequality

and, 270–74intellectual property investments, share of,

in GDP, 31fInvestment Boom period (1995–2000), 25;

compensation of workers, by age, dur-ing, 38

Jamaican Supplementation Study, 226Jeff erson, Thomas, 5Jobless Recovery period (2000–2007), 25jobs: higher productivity, attaining, after

online enrollment, 445–53; skills requirements and, 11–12; types of, held by underemployed college gradu-ates, 156–59, 157t; types of, prone to underemployment of college gradu-ates, 160–68; types of, taken by college graduates, 159–60

knowledge capital: estimated growth im-pacts of, 298–99; long- run growth and, 296–98. See also human capital

knowledge economy, 120knowledge spillovers, 5Kuznets curve, growth- equity outcomes

and, 293–94

labor characteristics, by industry, 36–37tlabor inputs: changing structure of, 30–39;

growth of, as determinant of US economic growth, 26; postwar US economic growth and, 26; and US eco-nomic growth since 1947, 25

labor market institutions: skills and, 270–74; wage inequality and, 274–77

labor- quality growth: contributions of education, age, and gender to, 31f, 32f; defi ned, 63–65; historical, 78–83; mea-

surement of, 65–78; overview of, 61–63; in Postwar Recovery period (1947–1973), 30–31; projecting, 83–92

lifelong learning, growth- equity outcomes and, 304–5

literature: economics, on noncognitive skills, 223; empirical, on college tuition, 361–64; employing quantitative models of higher education, 364; human capi-tal, and noncognitive skills, 222–27; on noncognitive skills, 220

Long Slump period (1973–1995), 25; labor- quality growth and, 31–32

male impulsivity, crime and, 236–39mathematics. See STEM (science, technol-

ogy, engineering, and mathematics) activities

Mischel, Walter, 224models: activity- analysis approach to pro-

duction, 123–28; for college tuition, 359–61, 364–70, 379–91; endogenous growth, of US economic growth, 294–95; long- run growth, 294–99; neo-classical growth- accounting, 119–23; quantitative, literature employing, 364; sources- of- growth, 138–43

National Income and Product Accounts (NIPAs), 24; benchmark revision of, in 2013, 27

National Longitudinal Study of Adolescent to Adult Health. See Add Health study (National Longitudinal Study of Ado-lescent to Adult Health)

National Longitudinal Survey of Youth (NLSY97), noncognitive skills and adult outcomes in, 227–30

“Nation’s Report Card” survey, 2neoclassical growth- accounting model,

119–20; parables about, 120–23noncognitive skills: adding, to human capital,

239–40; adult outcomes in NLSY97 and Add Health, 227–30; alternative terms for, 220–21; defi ned, 225; economic out-comes and, 219–20; economics literature on, 223; human capital literature and, 222–27; literature on, 220; measurement of, 233–39; metric categories of, 222–23; personality and, 223–24; self- control and, 224; sources of, 225–26. See also cognitive skills; human capital; skills

516 Subject Index

Occupational Information Network (O*NET) database, 153, 185

Occupational Requirements Survey (ORS): Dictionary of Occupational Titles and disability determination, 184–86; evidence from preproduction sample, 188–99; potential of, for research, 208–10; procedures and sampling, 186–88; research potential of, 208–19; safety outcomes and, 205–8; wages and, 199–204

online postsecondary education: attaining higher productivity jobs after enroll-ment in, 445–53; characteristics of students enrolled in, 409–12; costs and payments for, undergraduates vs. graduate students, 421–26, 421t, 422t; data for, 406–9; earnings before and after enrollment in, 426–34, 427f, 429f, 430f, 431f, 432f; empirical strategy for estimating return on investment to, 434–39; federal taxpayer’s point of view of, 453–54; fi ndings on returns on return on investment to, 440–45; highest degree off erings and character-izations of, 414–18; length of enroll-ment in, 418–20; locations of students enrolled in, 412–14, 415t; promise and perils of, 401–3; recent, explosive growth in, 403–6. See also education; skill development

ORS. See Occupational Requirements Sur-vey (ORS)

parent/teacher reports, 222Perry Preschool Project, 226, 303personality, economic studies of, 223–44PIAAC (Programme for the International

Assessment of Adult Competencies), 2, 253–58, 300–301; assessment of cogni-tive skills in, 254–55

polarization, 314, 315; basics of, 325–28; descriptive evidence of, 328–33; inequality, skills, and, 12–15; wage eff ects of, 333–37. See also higher edu-cation wage premiums

Postwar Period (1947–2014), 25; Growth and Recession (1995–2014), 25; Long Slump (1973–1995), 25; Postwar Recov-ery (1947–1973), 25

Postwar Recovery period (1947–1973), 25; labor- quality growth and, 30–31

postwar US economic growth: analysis of, 25; human capital accumulation and, 1–2; productivity growth as determi-nant of future, 26–27; role of growth in capital and labor inputs and, 26. See also economic growth, US

preschool programs, growth- equity out-comes and, 302–4

production, activity- analysis approach to, 123–31

productivity, 4; international standards for measuring, 24; measurement, 24; proto-type for US national accounts, 24–25; and US economic growth since 1947, 25

productivity growth, as determinant of future US economic growth, 26–27

Project Star, 226

return on investment (ROI), to online edu-cation: empirical strategy for estimat-ing, 434–40; fi ndings on, 440–45

safety outcomes, Occupational Require-ments Survey and, 205–8

SBTC. See skill- biased technological change (SBTC)

science. See STEM (science, technology, engineering, and mathematics) activi-ties

self- assessments, 222self- control: economic studies of, 224; male

impulsivity and, 236–39; noncognitive skills and, 236–39

skill- biased technological change (SBTC), 4–5, 117–18, 325–26

skill development: economic growth and, 117; importance of, 117. See also edu-cation; online postsecondary education

skill downgrading, 314, 315, 326–28; descriptive evidence, 328–33; wage eff ects of, 333–37. See also higher edu-cation wage premiums

skills: cross- country, 296; inequality, polar-ization, and, 12–15; labor market insti-tutions and, 270–74; requirements, and jobs, 11–12; role of, and skills prices, 258–64; role of demand and supply and, 264–69; supply and demand for, 5–8; supply of, 15–17; task- related, structural changes in, 133–34. See also cognitive skills; noncognitive skills

skills prices, role of skills and, 258–64

Subject Index 517

social capital, 5Social Security Administration (SSA), 184sources- of- growth model: fi rm dynamics,

141–43; with intangible capital, 138–41specifi c vocational determination, 185STEM (science, technology, engineering,

and mathematics) activities, 134–35STEM (science, technology, engineering,

and mathematics) occupations, in US: comparative advantage in, 474–77; data for, 469–70; employment in, 470–74; foreign- born workers in, 477–86; intro-duction to, 465–69; wage diff erences between native- and foreign- born work-ers, 486–92

structural changes: in composition of capi-tal, 135–38; in educational attainment, 131–32; in task- related skills, 133–34; in US economy, 115–17, 116f

task- based skills, vs. cognitive skills, 254–55task- related skills, structural changes in, 133technical change, skill- biased, 4–5technology. See STEM (science, technology,

engineering, and mathematics) activitiestuition. See college tuition

underemployment, introduction to, 149–51. See also college graduates

unemployment rates, 275t; by skill group and country, 276. See also employment rates

US economy: foreign- born workers in, 477–79; structural changes in, 115–17, 116f

vocational education, growth- equity out-comes and, 305–7

wage compression, employment eff ects and, 274–77

wage gaps, 313; composition- adjusted esti-mates for, 318–21; between native- and foreign- born workers in STEM occupa-tions, 486–92. See also higher education wage premiums

wage inequality, 270; cognitive skills and, background information, 251–53; con-trolling for institutional characteristics in explaining, 270–74; data used for study, 253–58; labor market polices and, 274–77; polarization, skills, and, 12–15; role of cognitive skills in explaining, 251–52; role of demand and supply in explaining, 264–69; role of diff erences in skills in explaining, 258–64; role of skills endowments and skills prices in explaining, 261–64, 262t

wage premiums. See higher education wage premiums

wages, Occupational Requirements Survey and, 199–204

worker productivity, 4. See also productivityworkers, foreign- born, in STEM occupa-

tions, 477–86

recent flattening in the higher education wag · education, skills, and technical change...

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