Inequality in the United States:
A brief tour of some facts
James K. Galbraith
Lyndon B. Johnson School of Public Affairs
The University of Texas at Austin
McCormick Tribune Foundation Conference
Series
Chicago Federal Reserve Bank
April 2, 2008
The University of Texas Inequality Project
http://utip.gov.utexas.edu
The Official Story
Second, however, there has been, as we know and
discussed over the years, a significant opening up of
income spreads, largely as a function of technology and
of education with the increased premium of college
education over high school, and high school over high
school dropouts becoming stronger. The whole spread
goes right through the basic system. It is a development
which I feel uncomfortable with. There is nothing monetary
policy can do to address that, and it is outside the scope, so
far as I am concerned, of the issues with which we deal.
Alan Greenspan
Testimony to Congress
March 5, 1997
The Official Story
Second, however, there has been, as we know and
discussed over the years, a significant opening up of
income spreads, largely as a function of technology and
of education with the increased premium of college
education over high school, and high school over high
school dropouts becoming stronger. The whole spread
goes right through the basic system. It is a development
which I feel uncomfortable with. There is nothing
monetary policy can do to address that, and it is
outside the scope, so far as I am concerned, of the
issues with which we deal.
Alan Greenspan
Testimony to Congress
March 5, 1997
If technology and trade affect anything, they
would affect manufacturing pay
The idea that inequality in the structure of
manufacturing pay has increased systematically is
a myth. It has risen and fallen.
Inequality in manufacturing pay can be measured directly,
easily and accurately. It closely tracks the unemployment rate.
This measure peaked in the early 1990s and declined
sharply as the economy moved toward full employment
Inequality in Manufacturing Pay and Unemployment in the U.S.
1953-2005, Monthly Data
.11
.028
.10
.026
.09
.024
.022
Inequality
.08
.07
.020
.06
.018
Unemployment
.05
.016
.04
.014
.03
.012
Unemployment rate (left)
Inequality of manufacturing pay (Theil index, right)
.02
55
60
65
70
75
80
85
90
95
Shaded areas show recessions.
00
.010
05
The best explanation for inequality in manufacturing pay is, it
is almost exactly the same thing as unemployment.
Looking beyond manufacturing, inequality in pay more
generally, including in services, depends mainly on the
participation rate. As the proportion of workers in the
population has risen, so has inequality.
Overall pay inequality is a combination of two factors: the
effect of participation rates and the effect of unemployment
rates.
Inequality and the participation rate
.065
Inequality for 203 sectors
participation rate
.060
.68
.055
.66
.050
.64
.045
.62
.040
.60
.58
50
55
60
65
70
75
80
85
90
source: BLS data and author's calculations
95
00
Participation rates also determine the famous
“stagnating median wage”
Classic argument: **stagnating** median wage
Source: CEPR report, April 2007, p.10
But, not for women …
$35,000
Real median income by gender
2001 Dollars, GDP deflator
$30,000
MALE
$25,000
ALL
$20,000
$15,000
FEMALE
$10,000
$5,000
2004
2001
1998
1995
1992
1989
1986
1983
1980
1977
1974
1971
1968
1965
1962
1959
1956
1953
$0
Between 1965 and 2000, labor force participation increased
by nine percent, creating about nine million jobs, or fifteen
percent of total job creation. The share of women in the
labor force rose eleven percentage points. That of Hispanics
rose ten percentage points. That of African-Americans rose
three percentage points. That of white non-Hispanic males
fell eighteen percentage points.
To be clear, much of this was the consequence of disruptive
economic events – including especially vast macroeconomic
disruptions in the 1970s and 1980s, and institutional change,
including the attack on unions. Many older, white, nonHispanic male workers were forced from work.
Nevertheless, the transition in the structure of the workforce
is an essential component of the rise of measured inequality
in the structure of pay.
Thus, when you break out the workforce by race, the stagnation goes away
Real median earnings
$40,000
ASIAN
Full Time 50-52 workweek, year-round,
2001 Dollars, GDP deflator
WHITE
$35,000
ALL
$30,000
BLACK
$25,000
HISPANIC
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
$20,000
Conclusion: real median incomes rose for all
groups in the late 1990s.
Full employment is good for median wages.
They were stagnant for a period starting around
1971 and ending in 1983 for whites, 1992 for
blacks and around 1995 for Hispanics.
The stagnation of aggregate median incomes
through 1997 is a composition effect. The
hourglass phenomenon has much to do with the
rising labor force role of women and minorities.
And especially with the rising role of new
immigrants in the Hispanic workforce.
The problem is not whether people start at the
bottom. It is whether they end there. This
depends very much on how we treat those
groups, as they move into jobs previously held by
unionized male, Anglo workers.
Inequality in INCOME, on the other hand, has risen
substantially. This too can be measured quite precisely,
from income tax and other data sources.
It is obvious that the explanation for rising income inequality
must come from some other source, than rising inequalities
in the structure of pay.
How about the stock market?
That works fine.
U.S. Income Inequality Between Counties 1969 – 2005 Plotted
Against the NASDAQ Composite, with Three Counterfactual
Scenarios of Inequality Growth from 1994 – 2000
9
8.5
0.04
It’s the stock market, s&%#*d
8
7.5
0.035
7
0.03
Inequality
6.5
6
0.025
0.02
Piketty-Saez data
would give essentially
the same answer.
5.5
5
4.5
0.015
4
1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005
Natural Log of Nasdaq Monthly Close
Between-County Income Inequality - Theil's T Statistic (1yr lag)
0.045
If you remove a handful of counties, related to
information technology and finance, most of the
rise in income inequality in the late 1990s would
not have occurred.
U.S. Income Inequality Between Counties 1969 – 2005 Plotted
Against the NASDAQ Composite, with Three Counterfactual
Scenarios of Inequality Growth from 1994 – 2000
9
8.5
0.04
8
7.5
0.035
Natural Log of Nasdaq Monthly Close
Between-County Income Inequality - Theil's T Statistic (1yr lag)
0.045
Without7 Manhattan
0.03
6.5
6
0.025
Without Silicon Valley
Without
5.5
Top 15
5
0.02
4.5
0.015
4
1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005
Counties with the largest positive changes in
Theil Elements 1994 - 2000
Counties with the largest negative changes in
Theil Elements 1994 – 2000
Theil Element
County, State
Change 1994 - 2000
New York, New York
0.00517211
Santa Clara, California
0.00468738
San Mateo, California
0.00208153
King, Washington
0.00169613
San Francisco, California
0.00148821
Harris, Texas
0.00147724
Middlesex, Massachusetts
0.00131529
Fairfield, Connecticut
0.00099520
Alameda, California
0.00088503
Westchester, New York
0.00086216
Theil Element
County, State
Change 1994 - 2000
Los Angeles, California
-0.00089362
Queens, New York
-0.00070519
Honolulu, Hawaii
-0.00065515
Broward, Florida
-0.00056938
Cuyahoga, Ohio
-0.00036473
Kings, New York
-0.00034178
Miami-Dade, Florida
-0.00032742
San Bernardino, California
-0.00031665
Genesee, Michigan
-0.00031147
Clark, Nevada
-0.00030658
0.025
0.02
0.015
0.01
Manhattan
0.005
0
-0.005
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
Contribution of NY Counties to US Income Theil Index
Contribution of New York Counties to U.S. Income Inequality, 1969-2004
-0.01
New York
Putnam
Schoharie
Fulton
Cortland
Franklin
Chemung
Chautauqua
Nassau
Dutchess
Yates
Genesee
Rensselaer
Allegany
Jefferson
Niagara
Westchester
Saratoga
Lewis
Tioga
Chenango
Herkimer
Ulster
Oneida
Suffolk
Schenectady
Montgomery
Sullivan
Onondaga
Washington
Oswego
Queens
Rockland
Hamilton
Seneca
Delaware
Livingston
Clinton
St. Lawrence
Bronx
Richmond
Schuyler
Ontario
Wyoming
Steuben
Wayne
Orange
Kings
Albany
Columbia
Essex
Madison
Otsego
Tompkins
Erie
Monroe
Warren
Greene
Orleans
Cayuga
Cattaraugus
Broome
Bar-height is the contribution of the county to the Theil T-Statistic
No good jobs for the unskilled?
“The spiraling crisis in the credit and housing markets has
kept [Phil] Gramm in focus, fairly or not. His employer,
UBS, revealed yesterday that investment losses tied to
the U.S. housing market reached $37 billion over the last
six months. For the last three months, UBS posted a $12
billion loss.
“Gramm, UBS's vice chairman, said yesterday he was
"totally unaware" of his bank's massive holdings of
securities tied to subprime mortgages, but, he added,
"I'm confident we'll recover."
Washington Post, April 2. 2008
Per Capita Income
Inequality Across US
Counties Over Time
1969 – 2004
Contribution to Inequality between Counties
(Components of the Theil T Statistic)
Relatively Impoverished
Neutral
Prosperous
(income above national mean)
1969
1970
1971
1972
1973
Nixon’s Soviet Wheat Deal
1974
1975
1976
1977
1978
1979
Watch
The West
1980
1981
The Big Recession
1982
1983
1984
1985
1986
1987
The Oil
Bust
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
The Tech
Bubble
1998
1999
2000
2001
2002
Beltway
Bubble
Cheney
Does
Wyoming?
2003
2004
It is obvious that monetary policy influences
income inequality: any policy that affects the
stock market will affect income inequality.
Does monetary policy influence pay inequality?
The Federal Reserve denies any effect,
blaming technological change.
Let’s test it
The VAR model
The VAR model is a very standard model to
analyze covariances and “causality;” our
approach is entirely conventional. Like all
VAR analysis, it makes no theoretical
prediction in advance.
Our model features the yield curve,
manufacturing pay inequality,
unemployment and inflation
The yield curve is an attractive, stable measure of
monetary policy stance, well established in the
literature. It’s also a good predictor of recessions.
The yield curve
4
3
2
1
0
-1
-2
70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02
Source: U.S. Department of Commerce. 30-day T-Bill vs. 10-year bond rate
After accounting for breaks and outliers, and having
checked for stability:
Inequality
1. The term structure is the most causal force
2. The term structure does affect inequality
3. The term structure is affected by unemployment but not by
inflation
Dummy regressions on the Taylor rule
TSt  f   log CPIt   log CPI *,U * Ut 
• Unemployment target set at 5.5%
• Inflation target set at 2%
• Several cases considered:
–
–
–
–
Inflation/Unemployment above or below target
Inflation/Unemployment rising or falling
Above and rising or below and falling
…
• Dummy regressions done before and after break in
1983.
Results for 1969- 1983
Shows the working of
the Taylor rule:
-Tightening if inflation is
above target (but not
rising)
-Easing if inflation is
below target (but not
falling)
-But the explanatory
power is very low (5% at
best)
Results for 1984-2006
Contradicts the
Taylor rule:
-The Fed does NOT react
to inflation (rising or
falling, above or below)
-The Fed reacts only to
low (and falling)
unemployment by
tightening… and inviting
recessions.
-The explanatory power
has improved
dramatically.
Politics and the Fed
• There is a well respected theory of the political cycle (Hibbs 1974,
Nordhaus 1975, Tufte 1978, Alesina and Sachs 1988, Greider 1988,
Abrams and Iossifov 2006, Hellerstein 2007).
• To test our version, we define separate dummy variables for the four
quarters preceding a presidential election, depending on which party
holds the presidency: REPUP and DEMUP
• Do those two political dummies affect our the yield curve (and therefore
monetary policy)?
Tests of a political monetary policy
Partial results
Conclusions
Inequality in pay is a macroeconomic phenomenon. It
is strongly influenced by monetary policy, as well as by
other policies affecting unemployment and the
participation rate.
Inequality in income is largely a financial phenomenon.
It is mainly driven by the stock market.
Monetary policy appears to be driven mainly by fear
of low unemployment, and by political considerations.
Reference: UTIP Working Paper No. 42
The Fed’s Real Reaction Function: Monetary
Policy, Inflation, Unemployment, Inequality
– and Presidential Politics
By James K. Galbraith, Olivier Giovannoni and Ann J. Russo
July 17, 2007
http://utip.gov.utexas.edu/papers/utip_42.pdf
For more information:
The University of Texas Inequality Project
http://utip.gov.utexas.edu
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