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Measuring Distribution and
Mobility of Income and Wealth

Studies in Income and Wealth
Volume 80

Measuring Distribution
and Mobility of
Income and Wealth
Edited by Raj Chetty, John N. Friedman,
Janet C. Gornick, Barry Johnson,
and Arthur Kennickell
The University of Chicago Press
Chicago and London

The University of Chicago Press, Chicago 60637
The University of Chicago Press, Ltd., London
© 2022 by the National Bureau of Economic Research
All 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 2022
Printed in the United States of America
30 29 28 27 26 25 24 23 22 21 1 2 3 4 5
ISBN- 13: 978- 0- 226- 81603- 6 (cloth)
ISBN- 13: 978- 0- 226- 81604- 3 (e- book)
DOI: https:// doi .org /10 .7208 /chicago /9780226816043 .001 .0001
Library of Congress Cataloging- in- Publication Data
Names: Chetty, Raj, editor. | Friedman, John N., editor. | Gornick,
Janet C., editor. | Johnson, Barry (Economist), editor. | Kennickell,
Arthur B., editor.
Title: Measuring distribution and mobility of income and wealth /
[edited by] Raj Chetty, John N. Friedman, Janet C. Gornick, Barry
Johnson, Arthur Kennickell.
Other titles: Studies in income and wealth ; v. 80.
Description: Chicago ; London : The University of Chicago Press,
2022. | Series: Studies in income and wealth; volume 80 | Revised
versions of papers presented at the Conference on Research in
income and Wealth titled “Measuring and understanding the
distribution and intra/inter- generational mobility of income and
wealth,” held in Bethesda, Maryland, on March 5– 6, 2020. | Includes
bibliographical references and index.
Identifiers: LCCN 2022026311 | ISBN 9780226816036 (cloth) |
ISBN 9780226816043 (ebook)
Subjects: LCSH: Income distribution— Congresses. | Wealth—
Congresses. | BISAC: BUSINESS & ECONOMICS / Econometrics |
BUSINESS & ECONOMICS / Economics / General | LCGFT:
Conference papers and proceedings.
Classification: LCC HC79.I5 M37 2022 | DDC 339.2— dc23/eng/
20220607
LC record available at https://lccn.loc.gov/2022026311
♾ This paper meets the requirements of ANSI/NISO Z39.48- 1992
(Permanence of Paper).

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vii
Prefatory Note xi
Acknowledgments xiii
Introduction 1
Raj Chetty, John N. Friedman, Janet C. Gornick,
Barry Johnson, and Arthur Kennickell
I. Income Inequality
1. In Search of the Roots of American Inequality
Exceptionalism: An Analysis Based on
Luxembourg Income Study (LIS) Data 19
Janet C. Gornick, Branko Milanovic,
and Nathaniel Johnson
2. Rising Between- Firm Inequality and Declining
Labor Market Fluidity: Evidence of a Changing
Job Ladder 45
John Haltiwanger and James R. Spletzer
3. United States Earnings Dynamics: Inequality,
Mobility, and Volatility 69
Kevin L. McKinney, John M. Abowd,
and John Sabelhaus
4. Evidence from Unique Swiss Tax Data on the
Composition and Joint Distribution of Income
and Wealth 105
Isabel Z. Martínez
Contents

viii Contents
II. Wealth Inequality
5. The Wealth of Generations, with Special
Attention to the Millennials 145
William G. Gale, Hilary Gelfond,
Jason J. Fichtner, and Benjamin H. Harris
6. Wealth Transfers and Net Wealth at Death:
Evidence from the Italian Inheritance Tax
Records, 1995– 2016 175
Paolo Acciari and Salvatore Morelli
7. On the Distribution of Estates and the
Distribution of Wealth: Evidence from the Dead 205
Yonatan Berman and Salvatore Morelli
8. Structuring the Analysis of Wealth
Inequality Using the Functions of Wealth:
A Class- Based Approach 221
Pirmin Fessler and Martin Schürz
9. Social Security Wealth, Inequality, and
Life- Cycle Saving 249
John Sabelhaus and Alice Henriques Volz
III. Income and Wealth Mobility
10. Parental Education and the Rising Transmission
of Income between Generations 289
Marie Connolly, Catherine Haeck,
and Jean- William Laliberté
11. Inequality of Opportunity for Income in
Denmark and the United States: A Comparison
Based on Administrative Data 317
Pablo A. Mitnik, Anne- Line Helsø,
and Victoria L. Bryant
12. Presence and Persistence of Poverty in US
Tax Data 383
Jeff Larrimore, Jacob Mortenson,
and David Splinter
13. Intergenerational Home Ownership in France
over the Twentieth Century 411
Bertrand Garbinti and Frédérique Savignac

Contents ix
14. Inequality and Mobility over the Past
Half- Century Using Income, Consumption,
and Wealth 437
Jonathan D. Fisher and David S. Johnson
IV. Mitigating Inequality
15. The Accuracy of Tax Imputations: Estimating
Tax Liabilities and Credits Using Linked Survey
and Administrative Data 459
Bruce D. Meyer, Derek Wu, Grace Finley,
Patrick Langetieg, Carla Medalia, Mark Payne,
and Alan Plumley
16. Geographic Inequality in Social Provision: V
ariation across the US States 499
Sarah K. Bruch, Janet C. Gornick,
and Joseph van der Naald
17. Inequality and the Safety Net in American
Cities throughout the Income Distribution,
1929– 1940 529
James Feigenbaum, Price Fishback,
and Keoka Grayson
18. The EITC and Linking Data for Examining
Multigenerational Effects 569
Randall Akee, Maggie R. Jones,
and Emilia Simeonova
V. Distributional National Accounts
19. Distributing Personal Income: Trends
over Time 589
Dennis Fixler, Marina Gindelsky,
and David S. Johnson
20. Developing Indicators of Inequality and
Poverty Consistent with National Accounts 605
Richard Tonkin, Sean White, Sofiya Stoyanova,
Aly Youssef, Sunny Valentineo Sidhu,
and Chris Payne
21. Distributional National Accounts:
A Macro- Micro Approach to Inequality
in Germany 625
Stefan Bach, Charlotte Bartels,
and Theresa Neef

x Contents
22. The Distributional Financial Accounts of
the United States 641
Michael Batty, Jesse Bricker, Joseph Briggs,
Sarah Friedman, Danielle Nemschoff,
Eric Nielsen, Kamila Sommer,
and Alice Henriques Volz
23. Using Tax Data to Better Capture Top Incomes
in Official UK Income Inequality Statistics 679
Dominic Webber, Richard Tonkin,
and Martin Shine
Contributors 701
Author Index 707
Subject Index 717

xi
This volume contains revised versions of the papers presented at the Confer-
ence on Research in Income and Wealth titled “Measuring and Understand-
ing the Distribution and Intra/Inter- Generational Mobility of Income and
Wealth,” held in Bethesda, Maryland, on March 5– 6, 2020.
We gratefully acknowledge the financial support for this conference pro-
vided by the Stone Center on Socio- Economic Inequality at the CUNY
Graduate Center, the Stone Wealth and Income Inequality Project at Brown
University, Statistics of Income (SOI)/Internal Revenue Service (SOI),
Opportunity Insights, and the NBER. 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,
the Census Bureau, the Board of Governors of the Federal Reserve System,
SOI/IRS, and Statistics Canada.
We thank Raj Chetty, John N. Friedman, Janet C. Gornick, Barry John-
son, and Arthur Kennickell, who served as conference organizers and as
editors of the volume.
Executive Committee, November 2020
Katharine G. Abraham (chair)J. Bradford Jensen
John M. Abowd Barry Johnson
Susanto Basu Greg Peterson
Ernst R. Berndt Valerie A. Ramey
Alberto Cavallo Peter K. Schott
Carol A. Corrado Daniel E. Sichel
Lucy Eldridge Erich H. Strassner
John C. Haltiwanger William Wascher
Ron S. Jarmin
Prefatory Note

xiii
The conference organizers thank those who contributed to making the
conference successful, especially Brett Maranjian and the other staff mem-
bers at the National Bureau of Economic Research (NBER) conference
department. The authors of the chapters, the discussants, and the inquisi-
tive audience played the most essential role. Discussants for the conference
sessions were Pirmin Fessler, William Gale, David Johnson, Maggie R.
Jones, Bruce D. Meyer, Frédérique Savingnac, and Alexander Yuskavage.
Katharine Abraham, director of the Conference on Research in Income and
Wealth (CRIW), provided helpful guidance. The organizers are also grateful
to Helena Fitz- Patrick at NBER and to the staff at the University of Chicago
Press (UCP) for guiding the volume through the publication process, and to
anonymous referees from the NBER and the UCP.
The conference would not have been possible without generous finan-
cial support from the Stone Center on Socio- Economic Inequality at the
CUNY Graduate Center, the Stone Wealth and Income Inequality Project
at Brown University, Statistics of Income/Internal Revenue Service, Oppor-
tunity Insights, and the NBER.
The conference was part of a commemoration of the 100th anniversary
of the NBER. The CRIW was launched as an NBER initiative in 1935.
NBER president and CEO James Poterba delivered at lunchtime talk at the
conference, explaining how income distribution and the share of national
income received by labor and capital were among the key issues that led to
the founding of the NBER.
Acknowledgments

1
Economic research on the efficient allocation of resources has a very long
history— for many it defines the core of the field. Increasingly over time,
however, attention has also turned to inequality in the distribution of per-
sonal resources and outcomes, as well as to the related question of whether
individuals are locked in their respective initial place in this distribution or
whether there is the broadly shared possibility for mobility. Research has
focused not only on measuring inequality and mobility but also on under-
standing its historical, economic, and social determinants, and on how poli-
cies might affect these distributions. In addition, it is now recognized with
increased clarity that distributional differences may affect the transmission
of macroeconomic shocks or responses to fiscal or monetary stimulus.
In March 2020, the Conference on Research in Income and Wealth
(CRIW) convened a meeting held in Bethesda, Maryland, to explore the
latest developments in our understanding of issues related to income and
Introduction
Raj Chetty, John N. Friedman, Janet C. Gornick,
Barry Johnson, and Arthur Kennickell
Raj Chetty is the William A. Ackman Professor of Economics at Harvard University, direc-
tor of Opportunity Insights, and a research associate and director of the Public Economics
Program at the National Bureau of Economic Research.
John N. Friedman is a professor of economics and international and political affairs at Brown
University, and a research associate of the National Bureau of Economic Research.
Janet C. Gornick is a professor of political science and sociology, director of the Stone Center
on Socio- Economic Inequality, and holds the James M. and Cathleen D. Stone Distinguished
Chair in Socio- Economic Inequality at the City University of New York.
Barry Johnson is Deputy Chief Data and Analytics Officer and Director of the Statistics of
Income Division at the Internal Revenue Service.
Arthur Kennickell is a Stone Center Affiliated Scholar at the City University of New York,
and a member of the board of directors of the National Bureau of Economic Research.
For acknowledgments, sources of research support, and disclosure of the authors’ material
financial relationships, if any, please see https:// www .nber .org /books -and -chapters /measuring
-distribution -and -mobility -income -and -wealth /introduction .

2 Chetty, Friedman, Gornick, Johnson, and Kennickell
wealth distribution and mobility. This was the last NBER- affiliated meeting
in 2020 to be held in- person, before COVID- 19 concerns made virtualization
of later meetings a necessity. Disruptions caused by quarantine shutdowns
shortly after the conference prevented several of the conference authors
from fully realizing their research goals by the publication date of this vol-
ume. The worldwide economic impact of the pandemic certainly makes
the topics presented at this conference all the more relevant. The chapters
included here highlight new findings, which push forward our knowledge
in this area, but also bring new challenges to the fore that the next wave of
scholars in this area must confront.
A starting point for many of these chapters is an exploration of the dif-
ficulties that arise in the definition of income and wealth. Scholars often
study these variables to stand as proxies for deeper aspects of inequality
that are far more difficult to define and measure consistently. But however
straightforward income and wealth may seem at first glance, they also entail
many such problems. At the basic level of definition, there is a broad range
of possibilities. In the case of income, should one include, for example, ser-
vice flows from durables and owner- occupied housing or withdrawals from
tax- deferred retirement accounts? Similarly, with wealth, should one include
contingent assets, such as pension rights, and how should one treat income-
producing assets in which there is no right to the underlying assets, such
as some types of trust, or strongly illiquid assets? These questions require
serious thought, especially as the appropriate definition may vary according
to the particular intended analytical purpose.
Whose income or wealth is often a critical question. Ownership within a
household, or an extended family, is sometimes a fuzzy notion. Even when
exact ownership can be determined, it may not be relevant— for example, in
the case of jurisdictions with community property laws. Ownership rights
through legal entities, whether businesses of some sort or trust arrange-
ments, also may substantially veil some types of income or wealth.
Even with clear definitions suitable to purpose, there remains the thorny
question of how to measure income and wealth, and how to track changes
in these variables over time to measure mobility. At least limited informa-
tion on some measures of income is reasonably available, but wealth data at
the individual or household level are much more limited in most countries.
While many countries collect income data as part of administrative sources,
including tax registers, and some collect partial elements of wealth, only a
small number of countries collect broad wealth measures for the full popu-
lation. While researchers have developed methods to impute wealth from
capital income flows, these can be quite noisy. As a result, in many countries
survey datasets continue to play a more important role in the measurement
of wealth than income, alongside government and private sources. There
is also information from sources such as financial institutions and invest-

Introduction 3
ment advisors, but at least for now, this information is not available for the
full spectrum of the population and the data elements available are often
fragmentary. Increasingly, momentum has been building to link multiple
sources of survey, administrative, and other data in order to mitigate mea-
surement problems in single sources or to provide more comprehensive data
on income and wealth.
While traditional research on income and wealth mobility uses data col-
lected from surveys, recent research has highlighted the fragilities of this
data source. Wealthy or high- income households are generally less likely to
participate in surveys, and some evidence also suggests that poor households
are also less likely to participate. Only in a small number of surveys, such as
the US Survey of Consumer Finances, is it even feasible currently to detect
and potentially address this deficiency directly. Reporting errors in surveys,
driven perhaps by low financial literacy or privacy fears, add noise to the
data. Moreover, surveys face two potentially fatal trends: declining response
rates in many cases and escalating costs. In particular, the public’s declin-
ing willingness to participate complicates the use of survey data to study
income or wealth mobility, since it is often difficult to follow individuals or
households over successive rounds of a survey without serious attrition,
which may bias the results. These pressures add to the incentives to merge
and exploit multiple sources of data.
More recently, the focus of the income and wealth inequality and mobility
literature has turned to the use of administrative datasets. In principle, these
sources eliminate some problems inherent in survey datasets— for instance,
noisy individual recall and measurement error, or attrition over time— but
they raise new issues as well. The contents of administrative datasets are
defined by their administrative purposes. Importantly, variables are included
or defined by the governing law or regulations, which may change over time.
For example, individual income tax data are considered very important for
the study of income, but laws and regulatory decisions may have great influ-
ence on what is reported, when it is reported, and how it is reported. What is
reported may change over time, as a result of changes in the administrative
needs. Such considerations may even affect incentives about who reports
the information— whether a different person or a legal entity. Sometimes
administrative data serve as a basis for projecting patterns for other variables
or populations. As noted, under some modeling assumptions about rates of
return and other factors, income tax data may be used to project patterns
of wealth holding. Similarly, estate tax data have been used to project pat-
terns of wealth holding among the full population; such projection requires
assumptions about the “selection probability” appropriate to decedents, the
stationarity of the underlying processes, and the parts of the population
not covered by such taxation. However, for most countries, the absence in
administrative data of full direct measures of all relevant economic and

4 Chetty, Friedman, Gornick, Johnson, and Kennickell
contextual variables for the full spectrum of the population indicates that
for at least the intermediate term, both survey and administrative data in
blended or other complementary form will be needed to further research.
Finally, it is worth noting that privacy concerns often limit what infor-
mation can be collected accurately or shared. As noted, privacy concerns
may affect the incentives persons providing data face to respond at all and
to answer faithfully. But data holders also face important privacy consider-
ations. For example, government agencies typically cannot release personal
data that can be identified with specific individuals. Agencies may address
such constraints by limiting access or by using disclosure limitation tech-
niques to reduce privacy risks by reducing the information content of the
data.
This volume contains revised versions of most of the papers presented at
the conference. They cover an array of topics; some are primarily substan-
tive, others focus more on advancements related to data, measurements, and
methods. The 23 chapters are organized into five sections: income inequality,
wealth inequality, income and wealth mobility, mitigating inequality, and
distributional national accounts. Below, we provide an overview of these five
sections and offer previews of the 23 chapters.
I. Income Inequality
For most households, income is the principal driver of consumption and
wealth accumulation. Thus, changes in the distribution of income and the
transitions of individual income over time have important implications for
both short- term and long- term welfare. Income has components derived
from labor supply, capital returns, and transfers. Differences in capital
income explain much of the inequality observed at the very top of the income
distribution. However, labor income is the largest component of personal
income and its path over time is therefore a key determinant of inequality
and welfare among working households. Unlike the straightforward hump-
shaped pattern of income in the simplest life- cycle models, labor income may
have a variety of trajectories over time, depending on personal choices, labor
market fluidity, returns to skills, and larger social forces. The four chapters
in this first section address the observed patterns of income inequality and
shifts in compensation and fluidity that drive or reinforce income inequality.
Gornick, Milanovic, and Johnson (“In Search of the Roots of American
Inequality Exceptionalism: An Analysis Based on Luxembourg Income
Study (LIS) Data”) assess cross- national variation in households’ market
income, focused on the question of what is driving the unusually high level
of inequality observed in the US. Using micro data on labor income from 24
OECD countries, they disaggregate the working- age population into house-
hold types, defined by the number and gender of the household’s earners and
the partnership and parenting status of its members. The authors find that

Introduction 5
the pattern for the US is explained more by relatively high inequality within
groups rather than variation in mean income across groups.
Haltiwanger and Spletzer (“Rising Between- Firm Inequality and Declin-
ing Labor Market Fluidity: Evidence of a Changing Job Ladder”) look
at potential connections between the observed rise in earnings inequality
and declining labor market fluidity, building on earlier evidence of a rise
in between- firm inequality and other work on labor market fluidity. The
authors bring data from the Longitudinal Employer Household Dynamics
(LEHD) data and other contextual information to bear on the question of
the extent to which the observed patterns reflect changes in hiring across
industries with different earnings profiles. They find that such changes have
made it more difficult for workers both to get on a career ladder and to
proceed up the ladder.
McKinney, Abowd, and Sabelhaus (“United States Earnings Dynam-
ics: Inequality, Mobility, and Volatility”) look at earnings inequality and
dynamics at the subnational level, focusing on for large metropolitan statisti-
cal areas (Detroit, Los Angeles, New York, and San Francisco), using data
from 1998 to 2017 from the LEHD through the new Earnings and Mobility
Statistics (EAMS) application developed by the US Census Bureau. They
find an upward shift toward greater concentration among the top of the
wage distribution, though with differing trends across these areas. Among
other findings, they also report a marked decline in earnings mobility in
Detroit and New York. The results in the chapter exemplify analysis that
will be possible using the new EAMS web application.
Martínez (“Evidence from Unique Swiss Tax Data on the Composition
and Joint Distribution of Income and Wealth”) uses administrative data for
eight Swiss cantons to examine the joint distribution and composition of
income and wealth, revealing both substantial heterogeneity of composition
across the distribution and a high correlation of income and wealth at the
top. The author finds that age is a powerful determinant of wealth holdings,
that gender shapes income more than it does wealth, and that an exception-
ally low level of real estate wealth among the bottom 50 percent renders
Switzerland distinct from other high- income countries.
II. Wealth Inequality
There appears to be a broad trend across many countries toward an
increase in wealth inequality. Understanding the drivers and deeper pat-
terns of inequality is often limited by the availability of data. In part to cope
with measurement difficulties, wealth is often treated as a household- level
phenomenon, thus obscuring other dimensions of inequality and conse-
quent differences in bargaining power within households. Moreover, the very
definition of wealth affects what can be said. While market- based contingent
assets are usually included as a part of wealth, there is no definitive rule for

6 Chetty, Friedman, Gornick, Johnson, and Kennickell
how to include a value of contingent economic income- flow entitlements,
such as forms of social benefits, pensions, or social security. In addition, it
may be that focusing on accounting measures of the value of wealth over-
looks the instrumentality of wealth in a social context. The five chapters in
this second section address all these questions.
Gale, Gelfond, Fichtner, and Harris (“The Wealth of Generations, with
Special Attention to the Millennials”) use the US Survey of Consumer
Finances for the years 1989– 2016 to investigate the demographic structure
of the observed increase in the concentration of wealth over this period.
Among other results, they find an upward shift in wealth for older age groups
and a decline for the young.
Acciari and Morelli (“Wealth Transfers and Net Wealth at Death: Evi-
dence from the Italian Inheritance Tax Records, 1995– 2016”) use data from
inheritance tax files to study the concentration of wealth in Italy. Inferring
the wealth distribution from estate data requires a means of mapping the
wealth of the dead to that of the living. As is usual with such data, they take
the form of a “multiplier,” which is the inverse of the probability of death
of the decedent. The authors document a substantial rise in the total value
of inheritance and gifts as a share of national income, from 8.4 percent in
1995 to 15.1 percent in 2016. At the same time, there was a marked decline
in tax revenues linked to these wealth transfers.
Berman and Morelli (“On the Distribution of Estates and the Distri-
bution of Wealth: Evidence from the Dead”) look more generally at what
can be learned from estate tax data. In particular, they consider how sensi-
tive wealth estimates by this method are to the multipliers typically used to
extrapolate estate wealth to the general population. They conclude for the
set of countries examined that wealth estimates are sufficiently insensitive
to plausible variations in the multipliers that unadjusted estate tax data can
give a good indication of wealth among the living.
Fessler and Schürz (“Structuring the Analysis of Wealth Inequality Using
the Functions of Wealth: A Class- Based Approach”) consider inequality
from the perspective of a decomposition of the wealth distribution that
relies on a categorization that focuses on the social implications of wealth.
The categories are renters (who mainly hold wealth for “precautionary”
reasons), homeowners who occupy the homes that they own, and home-
owners who also own a business or real estate other than a home. Based on
these measures, and analyses of US and European data, the authors propose
new measures of inequality they believe are more directly linked to social
dynamics and choices.
Sabelhaus and Volz (“Social Security Wealth, Inequality, and Life- Cycle
Saving”) consider the distributional implications of incorporating measures
of net Social Security wealth as part of household net worth. Including such
a measure adds substantially to the wealth of otherwise low- wealth house-

Introduction 7
holds. They conclude that including Social Security wealth in an overall
wealth measure generally reduces estimated levels of wealth inequality but
it does not reverse the upward trend in top wealth shares.
III. Income and Wealth Mobility
Research indicates that inequality has a strong element of persistence
across generations. Understanding the intergenerational transmission of
inequality requires sorting out what factors reflect innate characteristics,
which are the result of effort, and which are the result of actions by others.
Families with large material resources may pass assets directly to subsequent
generations through gifts or bequests. Financial investment in the human
capital of children is another way of transmitting advantage. Relative advan-
tage for children later in life may also stem from the nature of their home
life. For example, a stable home, well- educated parents, or simply a caring
and engaged parent may provide the support with which a person may more
easily develop to their potential. Discrimination of many sorts is also an
important factor. The five chapters in this section provide new evidence on
the intergenerational patterns of inequality and the mechanisms that sustain
those patterns.
Connolly, Haeck, and Laliberté (“Parental Education and the Rising
Transmission of Income between Generations”) investigate the causal link
between the education of parents and the future income of their children.
Using linked Canadian census data and intergenerationally linked tax return
data, they show that income mobility has declined, especially for children of
mothers without a high- school diploma. They claim that encouraging higher
educational attainment among the young has the effect of increasing their
earning potential as well as the prospects of their children.
Mitnik, Helsø, and Bryant (“Inequality of Opportunity for Income in
Denmark and the United States: A Comparison Based on Administrative
Data”) use administrative data for Denmark and the US on the 1972– 73
birth cohort to study inequality of long- run income. Taking care to apply
a coherent and consistent analytical framework to each country, they are
able to characterize inequality in the two countries and bound key estimates
of the extent to which observed inequality is a function of people’s initial
conditions over which they have no control.
Larrimore, Mortenson, and Splinter (“Presence and Persistence of Pov-
erty in US Tax Data”) use linked US tax return data from 2007 to 2018
to study incidence and persistence of poverty among households since the
Great Recession. Over 40 percent of the households were in poverty in at
least one of those years. Although there is considerable mobility in and out
of poverty, there is also substantial persistence, with about a third of those
in poverty in 2007 being so in at least half of the years studied. The authors

8 Chetty, Friedman, Gornick, Johnson, and Kennickell
also find important age effects, with older people showing lower rates of
poverty but relatively greater persistence, and younger people experiencing
the opposite.
Garbinti and Savignac (“Intergenerational Home Ownership in France
over the Twentieth Century”) consider the correlation of housing tenancy
across parents and children, using data from the French Wealth Survey. Their
analysis shows that the intergenerational correlation of home ownership is
increasing, as children of homeowners have a stable probability of owner-
ship while children of nonowners have a declining probability of ownership.
Although receipt of an inheritance or intergenerational transfers tends to
be associated a higher level of ownership in general, the effect of parental
home ownership remains strong. The authors suggest that their results may
be explained by intergenerational correlations in income or preferences.
Fisher and Johnson (“Inequality and Mobility over the Past Half- Century
Using Income, Consumption, and Wealth”) use consumption, income, and
wealth data from the Panel Study of Income Dynamics (PSID) from 1968 to
2017 to construct a multidimensional portrait of the inequality and mobil-
ity of individuals and families. They find that, while resources are increas-
ing overall, inequality is also increasing and intragenerational mobility is
falling or flat. They conclude that their study provides further evidence for
the existence of the Great Gatsby Curve— the negative correlation between
inequality and mobility.
IV. Mitigating Inequality
Most high- income countries have some policies in place that mitigate
extreme inequality by providing income support, housing, food, or other
resources. Such support has its most direct effect near the time it is delivered,
but it may also have lasting effects, by helping people to avoid sinking into a
state harder to escape, by providing a more stable environment for the long-
run development of children, or by triggering other persistent behavioral
or psychological reactions. To design effective interventions in the face of
the harsh budgetary constraints, it is important to understand the nature
of interventions and their short- and long- term effects. The four chapters
in this section address variations in intervention strategies across time and
geography, and assess the effects of diverse policies for supplementing the
income of low- wage workers and low- income households.
Meyer, Wu, Finley, Langetieg, Medalia, Payne, and Plumley (“The Accu-
racy of Tax Imputations: Estimating Tax Liabilities and Credits Using
Linked Survey and Administrative Data”) use a data set linking a wide
variety of US administrative sources with the Current Population Survey
to construct a comprehensive picture of the distributional effects of transfer
and tax- credit policies. The data linkage is especially important for capturing
income sources missed in surveys and for addressing measurement error in

Introduction 9
survey variables. The chapter provides improved measures for the US of net
redistribution and poverty reduction.
Bruch, Gornick, and van der Naald (“Geographic Inequality in Social
Provision: Variation across the US States”) assess the role of state govern-
ments, in the United States, in the design and provision of social policies,
directing attention to the consequences of decentralization. Using a unique
cross- state, over- time policy dataset, they examine the magnitude of cross-
state variation in benefit generosity and program inclusiveness. They find
substantial cross- state inequality states in social provision and conclude that
this constitutes a meaningful form of inequality: inequality in the treatment
of similar needs and claims by people who happen to live in different states.
Feigenbaum, Fishback, and Grayson (“Inequality and the Safety Net in
American Cities throughout the Income Distribution, 1929– 1940”) look at
the period after the Great Depression in the US to examine the effects of that
economic collapse and the programs of the New Deal on income inequality.
To do so, they piece together micro data collected in a large number of cities
by the Civil Works Administration and the decennial census. They conclude
that inequality increased broadly, but that the shift was most notable in cities
where per capita income fell the most. Among other results, they find that
some New Deal Programs had a mitigating effect on inequality.
Akee, Jones, and Simeonova (“The EITC and Linking Data for Examin-
ing Multigenerational Effects”) link US demographic micro data with time
series data derived from individual income tax returns to study the effects
on intergenerational mobility of the Earned Income Tax Credit (EITC),
a refundable credit available to low- income workers first enacted in 1975.
Using information on dependents on tax returns of workers claiming the
EITC, the authors track outcomes for children who were exposed to differ-
ing intensities or durations of the EITC. Their findings suggest significant
and mostly positive effects of more generous EITC refunds on the next
generation; those effects vary substantially depending on the child’s gender
and their household type.
V. Distributional National Accounts
For researchers and policymakers trying to use micro data in conjunction
with more frequently available aggregate data, differences in the alignment
of totals in the two sources have long been an obstacle. Conceptual dif-
ferences are an important explanation and they are often quite difficult to
address. Errors may also play a role, as survey respondents may not answer
accurately or nonrandom nonresponse may skew the observed population,
and/or projections or other estimates used in construction of aggregates may
be inadequate or erroneous. Nonetheless, the benefits of being able to pair
such data, especially in considering macroeconomic policy— and, increas-
ingly, for inequality studies— have driven researchers to design strategies for

10 Chetty, Friedman, Gornick, Johnson, and Kennickell
achieving sufficient comparability. The final section in this volume includes
five chapters focused mainly on creating ways of placing surveys on a com-
parable basis with national accounting data.
Fixler, Gindelsky, and Johnson (“Distributing Personal Income: Trends
over Time”) use publicly available micro data to construct a time series of
the distribution of income as defined in the National Income and Product
Accounts. Focusing on the period 2007– 16, they consider trends in growth
and in inequality over this especially volatile period, including the Great
Recession. They find that inequality changed little during the 2007– 16
period, aside from a slight increase derived from growth in the top quintile;
that there was substantial change in the composition of personal income dur-
ing the study years, with compensation decreasing as a share of income and
transfers increasing; and that both mean and median real income increased
during the period, with gains in every income quintile.
Tonkin, White, Stoyanova, Youssef, Sidhu, and Payne (“Developing
Indicators of Inequality and Poverty Consistent with National Accounts”)
address the differences between survey measurements and national account-
ing measures of income for the UK. They note the importance of concep-
tual and coverage differences, but identify underreporting among survey
households at the top of the income distribution as the largest source of
discrepancies. Taking into account both conceptual differences and under-
reporting, they propose a method for adjusting survey measures to develop
plausible indicators of inequality, poverty, and shared prosperity based on
and consistent with national accounts. They also introduce the possibility
of using a microsimulation approach to update survey measurements to
support more frequent monitoring of distributional trends, given the most
recent aggregate data.
Bach, Bartels, and Neef (“Distributional National Accounts: A Macro-
Micro Approach to Inequality in Germany”) pursue a strategy for creating
distributional national accounts following, with necessary adaptations, the
approach of the World Inequality Database. They combine survey data,
tax- based data, and national accounts data for Germany to bridge gaps in
any one source alone, in order to create a consistent time series of income
data, together with a variety of distributional, geographic, and demographic
indicators.
Batty, Bricker, Briggs, Friedman, Nemschoff, Nielsen, Sommer, and Volz
(“The Distributional Financial Accounts of the United States”) describe
the development of system of quarterly distributional accounts for wealth,
blending data from the Federal Reserve Board’s triennial Survey of Con-
sumer Finances (SCF) and a version of the quarterly Financial Accounts
of the United States (FAOTUS) that includes nonprofits in service to the
household sector (NPISH). A particular advantage of the SCF in this con-
text is that it provides an implied value of aggregate wealth that is generally

Introduction 11
close to the FAOTUS estimates, most likely because the SCF has unusually
good effective coverage of the top of the wealth distribution. There are, how-
ever, many differences in the two data sources at a more disaggregated level.
The authors address the range of differences, and even develop a means of
distributing FAOTUS values for estimates that are not directly collected in
the SCF, such as the value of assets underlying defined benefit pensions due
to households. Given the fully reconciled survey data, the authors develop a
system for incorporating more frequently observed information in order to
update the distributional characteristics in the survey. The resulting linkage
provides policymakers with a timely basis for judging the effects of macro-
economic changes on households at a more detailed level.
Finally, Webber, Tonkin, and Shine (“Using Tax Data to Better Capture
Top Incomes in Official UK Income Inequality Statistics”) address the prob-
lem of random and nonrandom effective undercoverage of the top of the
income distribution in surveys, with data from a sample of administrative
records for taxpayers in the UK. Such differences greatly complicate the
ability to use survey data to integrate survey information with data from
national accounting systems. The authors investigate two methods: one
using the administrative data to directly replace survey data on top values
of gross income with values from equivalent quantile groups and the other
reweighting the survey data according to the population observed in groups
in the administrative data. They find that the reweighting method is prefer-
able and that its use is most compelling for the top few percent of the income
distribution.
Papers Presented but not Included in This Volume
Three additional papers were presented at the conference, but for a vari-
ety of reasons were not included in this volume. Because these papers, like
those included, were selected to represent an important topic in inequality
research, for each of those papers we provide a short description and an
external link to a subsequent version of the paper.
Meriküll, Kukk, and Rõõm (“What Explains the Gender Gap in Wealth?
Evidence from Administrative Data”) are able to look at the patterns of
wealth holdings at the individual level, thus allowing insight into gender
differences within and across households.
1
Using Estonian administrative
data together with the Household Finance and Consumption Survey for
Estonia, the authors find a very substantial unconditional gender wealth gap
in favor of men, though much of the gap is driven by the top of the wealth
1. A published version of the paper by Jaanika Meriküll, Merika Kukk, and Tairi Rõõm,
“What Explains the Gender Gap in Wealth? Evidence from Administrative Data,” may be
found in the Review of Economics of the Household 19 (2021): 501– 47; https:// doi .org /10 .1007
/s11150 -020 -09522 -x.

12 Chetty, Friedman, Gornick, Johnson, and Kennickell
distribution. In general, men tend to have somewhat more diversified assets
than women and men are more likely to own personal businesses, one of the
sources of large wealth disparity in Estonia.
Guyton, Langetieg, Reck, Risch, and Zucman investigate the question of
tax evasion through a variety of mechanisms such as offshore accounts, shell
companies and trusts, as well as through financial engineering and other
means.
2
The paper combines random audit data with new data on offshore
bank accounts to estimate the size and distribution of individual income tax
evasion in the US. They find that evasion through offshore financial institu-
tions is highly concentrated at the very top of the income distribution. Thus,
measures of income inequality based on typically observed sources are likely
to be biased downward.
Asher, Novosad, and Rafkin focus on educational mobility across genera-
tions, as a proxy for income mobility, which is substantially more difficult to
observe clearly in many countries.
3
They develop a methodology allowing
for the use of coarsely binned education data, which they apply to the US
and India to make estimates of educational mobility for subgroups.
Directions for Additional Work
Throughout the past decade, in the United States and abroad, there has
been an explosion of interest in high and rising economic inequality. A
broad national and international conversation has developed, one that has
included academics, journalists, policymakers, political figures, NGOs, and
general publics. The global financial crisis of 2007– 9, and the Occupy move-
ments that unfolded shortly after, provided crucial sparks. Since then, this
intensified interest has driven— and has been driven by— methodological
advances, new research institutes, enlarged data options, expanded media
coverage, and a mountain of scholarship. Inequality had, in fact, been stud-
ied in select corners of academia for decades— but the current level of inter-
est is of a different order. Our hope is that this volume will make a notable
contribution to this rapidly growing field.
The 23 chapters in this volume have covered extensive ground— cross- cutting
income and wealth, as well as poverty, inequality, and mobility. The studies
included here address policy impacts, geographic variation, change over
time, and a multitude of issues related to data, measures, and methods. Yet,
2. A revised version of the paper by John Guyton, Patrick Langetieg, Daniel Reck, Max
Risch, and Gabriel Zucman, “Tax Evasion at the Top of the Income Distribution: Theory and
Evidence,” is available as NBER Working Paper No. 28542, at http:// nber .org /papers /w28542.
3. A paper by the authors, Sam Asher, Paul Novosad, and Charlie Rafkin, that addresses
the methodology in this presentation, “Intergenerational Mobility in India: New Methods and
Estimates across Time, Space, and Communities,” is available at http:// paulnovosad .com /pdf
/anr -india -mobility .pdf.

Introduction 13
as always, for each research question addressed here, countless more come
to mind.
In this brief section, we raise some potential areas for future work. We
first turn our attention to possible directions for extending the substance
covered. We envision future lines of work aimed at assessing the effects
of structural changes, disaggregating national populations, and expanding
country coverage with respect to both geography and economic develop-
ment. We close with some remarks about future directions with regard to
data, measures, and methods.
Substantive Extensions
An array of structural changes seems likely to be an important factor in
shaping and sustaining patterns of inequality and mobility, and more work
on them would be welcome. For several decades, the bargaining power of
labor has declined in the US and elsewhere. In the US, labor union mem-
bership has decreased and, in real terms, the federal minimum wage peaked
before 1970. At the same time, the composition of occupations shifted more
in the direction of service jobs. Technology and offshoring eliminated many
types of jobs while creating others. The industrial structure has shifted as
well, including the emergence of some entirely new industries. In recent
years, “gig” work has become more common, appearing similar in some
ways to patterns of self- employment in less developed countries. The effects
of all of these shifts call out for further research.
Increasingly, scholars and practitioners engaged with economic inequal-
ities have called for further disaggregation of populations. The United
Nations Sustainable Development Goals (SDGs), adopted in 2015, empha-
size moving “beyond averages.” SDG Goal 10, reducing inequality, calls
for promoting inclusion “irrespective of age, sex, disability, race, ethnicity,
origin, religion or economic or other status.” Other supranational organiza-
tions have followed suit. Several of the chapters in this volume include anal-
yses of intergroup disparities— mostly comparing age groups or cohorts,
and, in some cases, disaggregating by gender, family structure, or level of
educational attainment. Much more work is needed to assess how earnings,
income, and wealth— levels, trends, and mobility (both “intra and inter”)—
vary across other crucial axes of disparity, including race, ethnicity, religion,
citizenship, sexual orientation, disability status, and urbanicity.
A large share of research on income and wealth inequality, and mobility,
focuses on the US or on other high- income countries. Many cross- national
studies— including those in this volume— include groups of relatively homo-
geneous countries. That homogeneity is understandable; cross- national
variation is more easily interpreted when national/economic contexts are
reasonably similar, and many sources of high- quality data are available only
for one or more high- income countries. Research on economic inequality

14 Chetty, Friedman, Gornick, Johnson, and Kennickell
and intergenerational mobility in middle- and low- income countries has,
of course, been carried out— much of it by development economists— and
a growing literature assesses global inequality, where the unit of analysis
is the whole world. Still, among inequality scholars, silos persist, with one
set of scholars/institutions mainly addressing (essentially) the high- income
Global North and another, the lower- income Global South. We urge schol-
ars of poverty, income and wealth inequality, and mobility to bridge these
geographic and economic divides to more fully assess the extent to which
lessons learned in rich/northern countries do, and do not, apply in less afflu-
ent countries or regions— and vice versa.
Data, Measures, Methods
Several chapters in this volume highlight the value of linking various
sources of data, especially administrative data, in order to increase the accu-
racy and power of analysis. More progress is needed in this area, especially
in linking various government data sources, where agency- specific rules and
differing views of their mandates may be inhibiting. Among the more prom-
ising signs of progress in this area, in the US, is work under the Foundations
for Evidence- Based Policymaking Act and the Federal Data Strategy, aimed
at making more federal data available for research purposes and exploring
potential structural changes, such as a US National Secure Data Service,
as envisioned by the Congressionally Chartered Commission on Evidence-
Based Policymaking. Our hope is that inequality scholars, including the
authors in this volume, will engage in efforts to create new sources of linked
data, to raise the availability of these linked data, and to aim for widespread
and equitable data access.
Surveys, and the challenges that they face, demand continued attention.
Survey data remain an important source for studying inequality but, as
noted earlier, data providers face serious challenges related to cost and data
quality. To support the collection and dissemination of survey data and to
anticipate future difficulties, urgent attention should be given to developing
linkages between survey data and other types of data, and to improving tools
for measuring the impact of nonlinkages and incorrect linkages on infer-
ences. In the short run, more linkage would facilitate new lines of research
and would allow potential improvements in data editing and nonresponse
adjustment. Over the longer run, linkage of survey data on wealth with con-
temporaneous income data would allow a more detailed evaluation of mod-
els used to project wealth information from income data and other sources.
Linkage with multiple years of nonsurvey data might support simulation of
wealth beyond the survey year, as well as research into other questions that
require panel data to place the survey data in context. The US Survey of
Consumer Finances, which employs data based on individuals’ income tax
in its sample design, is a natural candidate for such work. Our overarching

Introduction 15
hope is that diverse scholars and practitioners will commit to supporting the
production, improvement, expansion, and analysis of survey data in new
and innovative ways. Despite the well- known flaws of survey data, research
on inequality and mobility would suffer immeasurably if the volume, quality,
and/or accessibility of survey data were to decline substantially.

I
Income Inequality

19
1.1 Introduction
1.1.1 Background
It has been known for at least two decades that disposable house-
hold income— income after accounting for transfers and taxes— is more
unequally distributed in the United States than in comparable high- income
economies (see, e.g., Brandolini and Smeeding 2006; Gornick and Jäntti
2013; OECD 2009, 2011; Piketty and Saez 2006). Broadly speaking, there
are two possible underlying explanations. First, market income inequality
(i.e., income before direct taxes and transfers are taken into account) may be
similar in the US as elsewhere, but US taxes and transfers are less redistribu-
tive, either because the overall size of the welfare state is smaller or because
the redistribution is less progressive. Second, market income inequality may
itself be higher in the US than in many other countries, thus driving up the
high level of inequality even after redistribution is taken into account. The
first explanation has generally held sway because US market income inequal-
ity calculated across households— importantly, households of all ages—
1
In Search of the Roots of American
Inequality Exceptionalism
An Analysis Based on Luxembourg
Income Study (LIS) Data
Janet C. Gornick, Branko Milanovic,
and Nathaniel Johnson
Janet C. Gornick is a professor of political science and sociology, director of the Stone Center
on Socio- Economic Inequality, and the James M. and Cathleen D. Stone Distinguished Chair
in Socio- Economic Inequality, at the Graduate Center at the City University of New York.
Branko Milanovic is a senior scholar at the Stone Center on Socio- economic Inequality at
the Graduate Center at the City University of New York.
Nathaniel Johnson is a data scientist at Amenity Analytics.
For acknowledgments, sources of research support, and disclosure of the authors’ material financial
relationships, if any, please see https:// www .nber .org /books -and -chapters /measuring -distribution
-and -mobility -income -and -wealth /search -roots -american -inequality -exceptionalism -analysis
-based -luxembourg -income -study -lis -data.

20 Janet C. Gornick, Branko Milanovic, and Nathaniel Johnson
is not especially exceptional, across the OECD countries, while disposable
income inequality is substantially greater.
Recent work, however, by Gornick and Milanovic (2015) shifts that con-
clusion about the market income inequality in the US, in comparative per-
spective. They begin with the insight that market income inequality, when
calculated across households of all ages, may be depressed— especially rela-
tive to many European countries— because Americans tend to stay in the
labor market until later in life compared with their counterparts elsewhere.
Because the market income in pensioners’ households is often very small
or zero, the existence of a developed system of social protection paradoxi-
cally exaggerates market income inequality (among older households) in
other OECD countries and brings the overall market income inequality in
line with that reported in the US. Thus, the comparatively high level of US
market income inequality— net of older households— is obscured. Gornick
and Milanovic’s main conclusion is that, for persons under 60 years of age,
weaker US redistribution is not the main cause of greater inequality at the
disposable income stage. The “problem” is that the distribution of “origi-
nal” labor and capital incomes is substantially more unequal in the US than
elsewhere, and government redistribution, at the average OECD level, does
not compensate for the inequality generated in the market.
Gornick and Milanovic’s (2015) analysis had precursors in the work of
scholars of earnings distributions, who argued that weaker redistribution
in the US could not alone explain the entire disposable income inequality
gap between the US and the rest of the OECD countries. Mishel (2015),
for example, argues that the underlying market income distribution, most
importantly the earnings distribution, in the US, is highly unequal in cross-
national terms. He and others point to, on the bottom end of the earnings
distribution, the low US minimum wage and the high prevalence of low- paid
jobs (Gautié and Schmitt 2009; Lucifora and Salverda 2009), and, on the
upper end, the extremely high earnings of managers, doctors, lawyers, CEOs
and the financial sector (Gabaix and Landler 2008). The exceptionally large
gap between CEOs’ salaries in the US and in the rest of OECD countries
is well documented (see Mishel and Davies 2015; Piketty 2014). Indeed,
the findings in Gornick and Milanovic (2015) confirm that market income
inequality is a major explanation for comparatively high levels of disposable
income inequality in the US, among working- age households.
1.1.2 Objective
The objective of this chapter is to further investigate the nature of the
high level of market income inequality found among US working- age house-
holds, compared to their counterparts in several other affluent countries.
Because the major component of market income is labor income, we focus
exclusively on it— disregarding income from capital, which is a relatively

In Search of the Roots of American Inequality Exceptionalism 21
minor component in the market income package of working- age households
in these countries.
1
Our main strategy is to disaggregate working- age households— in the
US and in the comparison countries— into household subgroups. These
subgroups are distinguished by the number and gender of earners in the
household, and (subsequently) by the partnership and parenting status of
the household. Clearly, a household’s labor income is shaped by the number
of earners present. The logic of further disaggregating by gender, partner-
ship, and parenting is rooted in the labor economics literature, which has
long established that individuals’ earnings (gross and net of other worker-
and job- level characteristics) are affected by their gender and whether they
have partners and/or children (for a review, see Blau and Winkler 2017).
We assess inequality that exists both within and between various house-
hold types and we compare the results for the US with those in other OECD
countries. Our objective is to establish whether the greater underlying US
market income inequality is the result of (1) higher earnings inequality
within each of the relevant groups, (2) an unusual composition (for example,
a high share of groups where earnings inequality is high), or (3) large gaps
between groups in mean earnings.
2
A substantial prior literature on economic inequality in the US addresses
the question of the levels, and/or drivers, of within- group versus between-
group inequality. Much of this literature focuses on earnings, and most of
it locates the question of within- versus- between in the context of change
over time.
Two decades ago, McCall (2000) observed that most research on (earnings)
inequality in the US was concerned with growing gaps between groups—
with workers differentiated by race, age, education, and income. She noted
that, in fact, a large share of rising inequality had occurred within these
groups. Her own study assessed variation in within- group inequality across
500 local labor markets. Western, Bloome, and Percheski (2008) assessed
rising income inequality among US families, between 1975 and 2005. They
concluded that most of the increase in family income inequality during that
period was driven by rising within- group inequality; their disaggregation
combined family type and educational attainment.
Introducing his own study of the drivers of within- group inequality
between 1970 and 2001, VanHeuvelen (2018, 1– 65) summarized the litera-
ture as follows: “An increasing number of studies have begun to note that
within- group inequality— or the inequality that remains after account-
ing for average between- group pay differences . . . such as human capital,
1. Among the working- age population, and in the countries included here, income from labor
accounts for 97 percent of total market income, on average. In no country is the labor income
share of market income less than 93 percent.
2. In this chapter, we use the terms “labor income,” “earnings,” and “wages” interchangeably.

22 Janet C. Gornick, Branko Milanovic, and Nathaniel Johnson
occupational characteristics, sex, socio- demographics, and household
composition— is of growing importance for overall inequality.”
While these and other earlier studies influenced our analytic strategy,
our study is a departure. First, no earlier research disaggregates household
types as we do. Our typology includes unusually finely drawn categories;
our groups are defined by the number and gender of a household’s earners,
further disaggregated by partnership and parenting status.
Second, we depart from earlier research with respect to our income mea-
sure and unit of analysis. Most existing within- versus- between research
assesses either earnings at the individual level, or posttax, posttransfer
income at the household (or family) level. In contrast, we focus on earnings
(what we call market income) at the household level. Our framework allows
us to place our work in the large cross- national literature, much of it using
the same data that we use, concerned with the extent to which inequality
in disposable household income is driven by inequality in household- level
market income.
1.1.3 Analytic Strategy
To carry out our analyses, we use microdata, drawn from household sur-
veys, contained in the Luxembourg Income Study (LIS) Database Wave
VIII, which is centered on the year 2010.
3
We include 24 OECD countries:
4

Australia, Canada, Czech Republic, Denmark, Estonia, Finland, France,
Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Luxembourg,
Netherlands, Norway, Poland, Russia, Slovakia, Slovenia, Spain, the UK,
and the US.
5
In all cases, but one, the data are from the year 2010; the excep-
tion is Hungary, for which we have 2009 data. Appendix table 1A.1 reports
the list of countries and datasets used.
Our analysis is conducted across households whose members are all
below age 60 and which have at least one member reporting labor income. To
assess labor income, we use LIS’s harmonized variable hil (that is, household
income from labor). This variable includes: (1) cash wage and salary income,
and the value of nonmonetary goods and services received as a substitute for
cash; (2) monetary supplements to the basic wage and the value of nonmon-
etary supplements; (3) cash wage and salary income, and the value of non-
monetary goods and services, received by directors of their own enterprise;
(4) monetary payments and the value of nonmonetary goods and services
3. This means that the datasets report income earned in the year 2010; the surveys may have
been fielded in the subsequent year.
4. Russia is not officially an OECD member state, but a “roadmap to accession” has been
approved. For convenience, when we use the term “OECD countries” in this chapter, we include
Russia.
5. The LIS data are available from LIS, the cross- national data center in Luxembourg. Exten-
sive documentation is available on the website: www .lisdatacenter .org (multiple countries;
microdata runs carried out April 2017 to December 2019).

In Search of the Roots of American Inequality Exceptionalism 23
received from casual, irregular, or occasional dependent employment; and
(5) profits/losses from self- employment activities.
Because one of our motivating interests is the relationship, at the
household- level, between earnings inequality and disposable income
inequality, our unit of observation is not an individual worker (earner)
but the household. Total household earnings are adjusted for household
size, using the well- known “square- root adjustment.” In other words, total
household earnings are divided by the square root of the number of house-
hold members.
6
Thus, we arrive at a variable that measures potential indi-
vidual welfare (assuming equal division of earnings within the households)
derived from labor income.
As our measure of inequality, we mainly use the Gini coefficient. The Gini
is preferred largely because it enables us to easily relate our results about
inequality within different demographic subgroups to the well- known Gini
values of market and disposable income inequality seen in the US and else-
where. In one part of our analysis, we use two Theil indices.
1.2 Labor Income Inequality across Various Household Types
In figure 1.1, we report inequality, across households, of labor incomes.
The four countries with the most unequal earnings distributions (at the
6. This assumes economies of scale midway between perfect economies of scale (param-
eter = 0) and no economies of scale (parameter = 1).
Fig. 1.1 Inequality of labor income across working- age households, in 24 OECD
countries
Note: Ginis based on equivalized labor income.

24 Janet C. Gornick, Branko Milanovic, and Nathaniel Johnson
household level) are Israel and three Anglophone countries; the US is
ranked second highest. These labor income Ginis range from 0.277– 0.311
for the highly egalitarian Slovenia and Slovakia, respectively, to 0.436– 0.442
in the US and Israel. The median and mean labor income Gini is about 0.36.
Thus, we establish immediately that labor income inequality in the US is,
relative to other OECD countries, on the high end.
What lies behind this comparatively high level of earnings inequality
among US households? Our approach, as already mentioned, is to dis-
aggregate working- age households into several demographic groups (defined
below) and to assess labor income inequality within each of them.
The Gini decomposition when the population is divided into different
groups has three terms: a weighted sum of within- group inequalities (nar-
rowly defined within-inequality), inequality that is the result of differences
in mean incomes between the groups, and an overlap (residual) term that
reflects the homogeneity of the underlying populations. To understand the
meaning of the last, note that when incomes of the groups into which we
have divided the population are so different that there is absolutely no over-
lap (e.g., all individuals from a mean- richer group have higher incomes than
all individuals from a mean- poorer group), the overlap term becomes zero.
It increases as there is more overlap between the incomes of individuals
belonging to different groups. The overlap terms move together with the
narrowly defined within- inequality, and we shall treat them together.
We can write the Gini decomposition across recipients belonging to
groups i (1, 2,. . . r) as
(1.1)
G=
1
ffi
i=1
r
j>i
r
(y
j
y
i
)p
i
p
j
+
i=1
r
p
i
s
i
G
i
+L,
where μ = overall mean income, y
i
= mean income of i- th group, p
i
= popula-
tion share of i- th group, s
i
= share of i- th group in total income, G
i
= Gini
of i- th group, and L = the overlap term. The first term in equation (1.1) is
the between- group inequality; the second term, the narrowly defined within- group inequality; the third, the overlap term. The second and third terms are in the further text considered as “within- group inequality.”
We can now see that higher overall US labor income Gini (G ) may be the
result of greater group Ginis (G
i
), or greater share (s
i
) of groups that have
higher inequality of earnings, or finally, may be due to large mean income gaps between the groups (that is, to the between- component).
1.2.1 Disaggregating into Household Types, Based on the Number and
Gender of Earners
In all countries, we first divide the population into six main groups, based
on the number and the gender of the earners in these households: house-
holds that contain (1) one female earner, (2) one male earner, (3) one male
and one female earner, (4) two female earners, (5) two male earners and,

In Search of the Roots of American Inequality Exceptionalism 25
finally, (6) three or more earners. Later in the chapter, groups (1), (2), and (3)
will be further subdivided into demographic groups, based on partnership
and parenting status. Throughout this chapter, results are presented at the
person level— albeit drawing on their household characteristics. When we
refer to various household types, either their prevalence or their outcomes,
we are reporting results about the persons who live in those household types.
Figure 1.2 summarizes our typology of households. Earners are defined
as people who report having received nonzero labor income during the sur-
vey’s reference period. Table 1.1 reports the composition of the working- age
population, across the six household types, in these study countries.
7
As can be expected, three household types dominate to the extent that
they include more than 80 percent of all persons in all countries— except
for Hungary, Ireland, and Russia.
8
The three dominant groups are: the
“traditional”
9
two- earner households composed of one female and one male
earner (with a cross- country average share of 46 percent), one- male- earner
households with an average share of 21 percent, and households with three
or more earners, with 16 percent. The other three groups are less prevalent,
although households with only one female earner (cross- country average
share of 12 percent) do play, as we shall see below, an important role.
7. It should be kept in mind that the typology presented in table 1.1 takes no account of part-
nership status. For example, in households with a one female earner, those female earners may
or may not have partners. Later in the chapter, we integrate partnership and parenting status.
8. In all three countries, the reason is a relatively high presence of one- female- earner house-
holds (17– 18 percent).
9. When referring to two- earner households, we use the term “traditional” to denote that
one of these earners is male is one is female (as opposed to two earners of the same gender).
Fig. 1.2 Typology of households based on number and gender of earners, further
disaggregated by demographic groups based on partnership and parenting status
Note: The six main types of households are indicated by numbers 1– 6.

26 Janet C. Gornick, Branko Milanovic, and Nathaniel Johnson
In figure 1.3, we take a first look at US labor income inequality within each
of these household types in comparative context. For each type, the figure
indicates the position of the US Gini (in black) compared to the other 23
countries. For three household types (one- male- earner, one male and one
female earner, and two male earners), the US has the most unequal distribu-
tion of all countries; for the other three household types, the US distribution
is the second most unequal.
10
In no case, is the US Gini even close to the
median Gini for a given household type, much less lower than it.
Therefore, breaking the overall labor earnings distribution into household
10. Note that the Ginis of these various household types differ substantially in countries
considered here. Labor income inequality among “traditional” two- earner households is within
a rather narrow range between 0.22 and 0.36 whereas, for example, one- female- earner and
one- male- earner households display much greater ranges of inequality. However, this is not
the topic with which we are concerned here. Our objective is to find the sources of differences
between the US and comparable countries.
Table 1.1 Composition of working- age population, across six main household types (where
household types are based on the number and gender of earners)
Country/group
1 female
earner
1 male
earner
1 male,
1 female
earner
2 female
earners
2 male
earners
3+
earners
Sum of
columns
2 + 3 + 6
1 2 3 4 5 6 7
Australia 9.2 21.6 39.7 2.3 3.6 22.7 83.9
Canada 9.5 14.7 43.5 2.5 3.2 25.5 83.8
Czech Republic 8.6 23.3 47.7 1.5 2.2 16.8 87.7
Denmark 11.8 13.4 47.8 2.1 2.1 22.1 83.3
Estonia 16.0 20.3 47.4 2.7 1.3 12.3 79.9
Finland 12.0 15.5 53.1 1.4 0.7 17.2 85.8
France 14.7 19.7 55.8 1.1 1.4 6.8 82.3
Germany 14.0 19.7 48.6 1.0 1.7 15.0 83.3
Greece 8.2 30.9 48.6 0.9 2.3 7.3 86.8
Hungary 17.6 24.7 39.6 1.6 0.7 9.1 73.4
Iceland 10.1 11.1 45.3 2.1 1.0 30.4 86.8
Ireland 18.2 23.6 41.1 2.2 3.9 11.0 75.7
Israel 10.7 24.1 40.8 1.9 3.1 19.2 84.1
Italy 10.1 34.0 44.8 0.8 4.0 6.3 85.1
Luxembourg 10.7 25.0 51.5 0.7 2.3 9.7 86.2
Netherlands 9.3 15.6 51.7 1.3 2.2 18.8 86.1
Norway 12.0 15.0 48.3 1.4 1.5 20.2 83.5
Poland 14.0 28.7 42.3 1.5 3.3 10.2 81.2
Russia 16.9 17.3 39.6 2.9 2.6 20.7 77.6
Slovakia 8.3 14.4 43.4 1.4 1.9 30.5 88.3
Slovenia 9.3 15.8 50.6 1.4 1.9 21.1 87.4
Spain 10.8 25.7 46.6 1.5 2.9 10.0 82.3
United Kingdom 13.2 21.2 46.6 1.8 2.2 14.7 82.5
United States 14.8 22.1 42.2 2.3 3.0 15.3 79.6
Unweighted means 12.1 20.7 46.1 1.7 2.3 16.4 83.2

In Search of the Roots of American Inequality Exceptionalism 27
types reinforces our previous finding: US labor income is very unequally
distributed, not only in the aggregate, but within each household type.
We need to also look at between- group inequality (that is, between the six
household types). Consider now figure 1.4, which is constructed similarly to
figure 1.3 but where we look at relative earning levels of household types. For
Fig. 1.3 Inequality in six main household types (where household types are based
on the number and gender of earners)
Notes: Each bar shows the Gini of a given group and country. The US Gini is black. Ginis are
ordered from the highest to the lowest.
Fig. 1.4 Relative income of six main household types
Notes: Each bar shows mean income of a group compared to the mean income of the country. The US values are black. Values are ordered from the highest to the lowest.

28 Janet C. Gornick, Branko Milanovic, and Nathaniel Johnson
example, the one- female- earner households’ mean earnings
11
are in relative
terms the lowest in Israel (only 45 percent of the country mean) and the highest
in Hungary (75 percent of the country mean). The US at 54 percent is some-
what below the median for OECD countries included here. A look at figure
1.4 shows that the US position, with the exception of the two- female- earner
households (relatively low) and the one- male- earner households (relatively
high) is not exceptional. In other words, when it comes to the relative earnings
of various demographic groups, the US is far from being a cross- national out-
lier: groups’ relative earning levels track closely other high- income countries’
relative earnings levels. This, in turn, implies that the origin of high labor
income inequality in the US is not to be found in unusually high earnings of
some demographic groups and unusually low earnings of others, but in sys-
tematically high earnings inequalities within each individual household type.
We confirm this conclusion by looking at the results of the decomposi-
tion exercise using equation (1.1). Each country’s overall inequality is bro-
ken into between- and within- inequalities (vis- à- vis the six groups). The US
within- inequality Gini (shown in table 1.2, column 3) is 0.311. This means
that if the mean earnings of the six household types were exactly equal, the
overall labor income inequality would be 0.311, which is by far the highest
value among the countries considered here. Canada and Luxembourg have
the second highest within- inequality, with a Gini of 0.282, some 10 percent
lower than the US. When we look at the between- inequality, however, the US
is far from exceptional. Although the within- inequality of the US is 34 per-
cent higher than the mean of the other 23 countries, the between- inequality
is practically the same as the mean for other countries.
Finally, we can assess this from another vantage point by using the Theil
index instead of the Gini. The advantage of the Theil, in this particular case,
is that it is exactly decomposable between different components.
Table 1.3 reports the results of two Theil decompositions for the US case.
The first column presents the Theil T— or the GE(1)— where the weights are
income shares. The second column presents the Theil L, or the GE(0)— the
mean log deviation— where the weights are population shares.
When we assume that the US has both the same demographic structure
and the same relative group incomes as the average of the other 23 OECD
countries, the Theil index, in its two variants, is reduced by either 3 or 6 per-
cent. The changes seem minimal and reinforce our view that the dominant
factor explaining high market income inequality in the US is high inequality
within each demographic group.
12
11. Note that this is household- size- adjusted (equivalent) labor income.
12. The two Theil indexes, because of their different weighting structures, give different
answers as to the relative importance of demographics versus relative group incomes. According
to Theil L, US demographic structure (in the sense that it is different from the OECD average)
contributes more to high US inequality. According to Theil T, the divergence of US relative
group incomes from the OECD average pattern is more important.

Table 1.2 Decomposition: Between- group and within- group components (for six
household types)

Overall
labor Gini
(1)
Between Gini
component
(2)
Within Gini
component
(3)
Australia 0.357 0.119 0.238
Canada 0.394 0.112 0.282
Czech Republic 0.323 0.129 0.193
Denmark 0.323 0.112 0.211
Estonia 0.368 0.124 0.245
Finland 0.335 0.103 0.232
France 0.365 0.114 0.251
Germany 0.363 0.109 0.254
Greece 0.365 0.127 0.238
Hungary 0.394 0.149 0.245
Iceland 0.330 0.127 0.202
Ireland 0.430 0.186 0.243
Israel 0.442 0.184 0.258
Italy 0.320 0.149 0.171
Luxembourg 0.366 0.084 0.282
Netherlands 0.336 0.100 0.236
Norway 0.337 0.119 0.218
Poland 0.358 0.135 0.223
Russia 0.368 0.156 0.212
Slovakia 0.311 0.136 0.175
Slovenia 0.277 0.128 0.149
Spain 0.366 0.136 0.230
United Kingdom 0.400 0.124 0.277
United States 0.436 0.125 0.311
Non- US mean 0.358 0.129 0.229
US/non- US mean 1.21 0.97 1.34
Note: Within- inequality includes the narrowly defined within- inequality and the overlap com-
ponent; see equation (1.1).
Table 1.3 Theil counterfactual: US inequality with OECD average demographic
structure and relative mean group incomes
Theil T— GE(1) Theil L— GE(0)
Actual US inequality 0.342 0.380
US inequality if demographic structure were as OECD
average (change)
0.364 0.334
(+6%) (−12%)
US inequality if relative group incomes were as OECD
average (change)
0.312 0.334
(−9%) (+6%)
US inequality if both demographic structure and relative
group incomes were as OECD average (change)
0.333 0.360
(−3%) (−6%)

30 Janet C. Gornick, Branko Milanovic, and Nathaniel Johnson
We have thus established that US labor income inequality is, together
with Israel’s, the highest among all of the OECD countries included here
and that the source of that inequality is not to be found in vastly different
mean labor incomes across different household types, but in the consis-
tently higher inequality with which labor incomes are distributed within
each household type. We now continue by looking in greater detail into three
prevalent household types: one- female- earner households, one- male- earner
households, and two- earner “traditional” households (which contain one
female and one male earner).
1.3 Earnings Inequality within One- Earner and “Traditional”
Households: Further Disaggregation by Partnership and
Parenting Status
1.3.1 One- Female- Earner Households
We begin by looking at households that contain only one earner— one
who is female. The prevalence of these households across the countries
included here is very uneven: at the low end are Greece, Slovakia, and the
Czech Republic where fewer than 9 percent of households contain only one
earner, who is female. At the other end are Estonia and (as mentioned ear-
lier) Hungary, Ireland, and Russia, which each contain more than 16 percent
of households of this type. The US falls in the upper range, with the share
of one- female- earner households being 15 percent.
In our next analysis, we divide one- female- earner households into five
demographic subgroups, corresponding to the households in which they
live: couple- headed households with one or more children, couple- headed
households without children, single- headed
13
households with children,
single- headed household without children, and others.
14
As we did before
for all households, here we look first at inequality levels within each house-
hold type and then at the relative incomes of each type. The most common
type among one- female- earner households in the US, and across these 24
countries, is a household headed by a single woman with children. The next
most prevalent types are couple- headed households with children (where, by
definition, a female is the only earner) and single- female- headed households
13. We use the word “single” to mean, exclusively, a person who is not married/partnered.
We do not use it to refer to the number of earners or persons in a household.
14. Throughout the chapter, households are defined as “coupled” if the head reports having
a partner in the household and there are no other adults in the household. Households are
further coded as having “children” if they contain children (under age 18) who are the children
of the household head. Households are classified as “other” if the household— with or without
children— contains adults who are not the head or the head’s partner (for example, the head’s
parent or sibling, or a roommate).

In Search of the Roots of American Inequality Exceptionalism 31
without children. In the US, these three household types comprise over
80 percent of one- female- earner households.
But is the distribution of labor income in such American households more
unequal than in the other countries? Figure 1.5, with the same interpretation
as figure 1.3, provides an answer. In all cases, US inequality is greater than
the median inequality among 24 countries, and is always ranked either the
fourth or the fifth from the top. Particularly interesting is the situation of
single- headed one- female- earner households with children, where the US
Gini is (a high) 0.48 while the mean Gini for this type of household, is 0.40.
Very high inequality among single- headed one- female- earner households,
both with and without children, in the US clearly implies that they are eco-
nomically and socially diverse. We shall find similar high heterogeneity
among single one- male- earner households without children.
Next, we look at relative incomes (see figure 1.6). The situation here
is familiar: US subgroup mean relative incomes are not dissimilar to the
median relative incomes across the 24 countries. The differences are minimal
(e.g., for a couple with a child, the average labor income is 41 percent of US
overall mean vs. 45 percent across the 24 countries). The exception is the low
income level of one- female- earner households with children (that is, single
mothers): their relative income in the US is 40 percent of the overall mean
while the countries’ average is 50 percent. An ethnic/racial component may
be important here, as we find (not reported here) that these households, when
headed by Hispanics and African Americans, have mean labor incomes that
are only about 30 percent of the overall US mean.
Fig. 1.5. Inequality of five subgroups among one- female- earner households
Notes: Each bar shows the Gini of a given group and country. The US Ginis are black. Ginis
are ordered from the highest to the lowest.

32 Janet C. Gornick, Branko Milanovic, and Nathaniel Johnson
1.3.2 One- Male- Earner Households
We now move to one- male- earner households, where we keep the same
household classification as for one- female- earner households. The preva-
lence of these households varies markedly across countries. At the low end,
in Iceland, Denmark, Canada, and Slovakia, their share is less than 15 per-
cent. But at the high end, Italy and Greece— with comparatively low levels
of female employment— have more than 30 percent of one- male- earner
households. The US result (22 percent) falls near the cross- national mean
(21 percent).
15
The results for inequality are familiar (see figure 1.7): US households have
a much greater labor income inequality than the rest of the countries, and
for two groups in particular (couple- headed households with and without
children) US inequality is the highest of all. But it is among the highest in
the other three types of one- male- earner households as well.
Figure 1.8 shows the results for the relative income of single one- male-
earner households. In three out of five types here, US relative mean income is
around the cross- country median. The exceptions are one- male- earner house-
holds (couples with or without children) whose relative income is among the
highest. These two groups are interesting because they display unusually high
relative mean incomes with similarly unusually high inequality.
15. Note that the share of one- female- earner households across these OECD countries ranges
from 8 to 18 percent. The share of one- male- earner households varies from 11 to 31 percent.
The corresponding US values are 15 and 22 percent. Thus, neither US value is exceptional.
Fig. 1.6 Relative income of five subgroups among one- female- earner households
Notes: Each bar shows mean income of a subgroup compared to the mean income of the country. The US values are black. Values are ordered from the highest to the lowest.

In Search of the Roots of American Inequality Exceptionalism 33
1.3.3 “Traditional” Households
“Traditional” (one male earner and one female earner) households com-
prise the largest share of all households, from just under 40 percent in Aus-
tralia, Hungary, and Russia to 56 percent in France. (The US with 42 percent
is on the low side, modestly below the unweighted mean of 46 percent).
Fig. 1.7 Inequality of five subgroups among one- male- earner households
Notes: Each bar shows the Gini of a given group and country. The US Ginis are black. Ginis
are ordered from the highest to the lowest.
Fig. 1.8 Relative income of five subgroups among one- male- earner households
Notes: Each bar shows mean income of a group compared to the mean income of the country. The US values are black. Values are ordered from the highest to the lowest.

34 Janet C. Gornick, Branko Milanovic, and Nathaniel Johnson
Here, we look at only two subgroups: “traditional” households with and
without children.
US inequality is again very high (see figure 1.9). US inequality is the high-
est of all countries, among these two- earner couples with children— with a
Gini of 0.37 compared to the cross- country median Gini of just less than
0.30. US inequality is second highest, among two- earner couples without
children.
When it comes to relative incomes (see figure 1.10), US relative labor
income for two- earner households with children is very close to the median
for the 24 countries; it is higher than the cross- country median, however, for
two- earner couples without children.
1.3.4 Regression Analysis
To tease out the specificity of US inequality, we estimate regressions
where the Gini coefficient for each country/group is regressed on groups’
relative mean income (i.e., relative to the mean of that country) and dummy
variables for the subgroups (N = 15) and countries (N = 24). The omitted
household type is one- male- one- female- earner with children and the omit-
ted country is Denmark (with low inequality).
We use two specifications of the regression: an unweighted one, and a
weighted regression where each group is weighted by its share in the popula-
tion of a given country. The latter adjusts for variation in household com-
positions across countries. We are, of course, interested in the coefficient on
the dummy variable for the US. The results are reported in table 1.4.
Compared to the omitted country (Denmark), the coefficient on the US
dummy is 0.069 in the unweighted formulation, and 0.101 in the weighted
Fig. 1.9 Inequality of two subgroups of “traditional” households
Notes: Each bar shows the Gini of a given group and country. The US Ginis are black. Ginis
are ordered from the highest to the lowest.

In Search of the Roots of American Inequality Exceptionalism 35
formulation. It is statistically significant at less than 0.01 in both cases. This
means that, on average (whatever demographic group we take), US inequal-
ity is between 6.9 and 10.1 Gini points greater than Denmark’s. Perhaps
more revealing is the fact that in both formulations, the US coefficient is the
largest of all country dummies. The next largest positive coefficient in the
unweighted formulation is Canada’s (5.4 Gini points more unequal than
Denmark) and, in the weighted formulation, Israel’s (8.2 Gini points more
unequal than Denmark). So, in terms of within- group inequalities, the US
is, on average, more unequal than the second most unequal OECD country
by between 1.5 and 1.9 Gini points.
1.3.5 Robustness of the Results
There are two possible limitations of our results that need to be addressed.
The first refers to the composition of the population (i.e., shares of different
demographic groups); the second to the year of study (2010) selected here.
Consider group composition first. Earlier in this chapter, we noted that
the higher overall labor income Gini in the US, compared to other relatively
similar countries, could be the result of
(1) greater group Ginis (the “within” component);
(2) larger mean income gaps between the groups (the “between” compo-
nent); and/or
(3) greater shares of groups that have higher level of inequality.
Throughout this chapter, we formally assessed the contributions of the
first two of these three factors— the “within” and “between” components of
inequality— but we did not present a detailed look at the third. The regres-
Fig. 1.10 Relative income of two subgroups of “traditional” households
Notes: Each bar shows mean income of a group compared to the mean income of the country.
The US values are black. Values are ordered from the highest to the lowest.

Table 1.4 US income inequality exceptionalism (dependent variable: Gini
coefficient of household type/country)
Variable

Coefficient
(p- value)
* = significance < 0.05
** = significance < 0.01
Unweighted
regression
Population- share
weighted regression
Relative group mean −0.036 −0.003
(0.20) (0.89)
Three or more earners −0.028 −0.034**
(0.09) (0.00)
Two earners Female 0.022 0.035*
(0.23) (0.02)
Male 0.034* 0.033**
(0.04) (0.01)
One female earner Couple with children 0.099** 0.136**
(0.00) (0.00)
Couple without children0.048* 0.065**
(0.03) (0.00)
Other 0.057* 0.081**
(0.03) (0.00)
Single with children 0.082** 0.098**
(0.00) (0.00)
Single without children0.066** 0.078**
(0.00) (0.00)
One male earner Couple with children 0.089** 0.097**
(0.00) (0.00)
Couple without children0.054** 0.067**
(0.00) (0.00)
Other 0.049* 0.074**
(0.05) (0.00)
Single with children 0.086** 0.117**
(0.00) (0.00)
Single without children0.087** 0.094**
(0.00) (0.00)
One male one female earner Couple without children0.104 0.002
(0.54) (0.80)
US dummy 0.069** 0.101**
(0.00) (0.00)
Adjusted R- squared (F) 0.59 0.82
(12.3) (38.9)
Number of observations 360 360
Note: The regression is based on 360 observations, i.e., 24 countries × 15 subgroups. The omit-
ted household type is one male, one female earner with children, and the omitted country is
Denmark. Coefficients on dummy variables for countries other than the US are not shown.

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Measuring Distribution And Mobility Of Income And Wealth Raj Chetty John N Friedman Janet C Gornick Barry Johnson Arthur Kennickell

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