inequality in the united states: a brief tour of some facts james k. galbraith lyndon b. johnson...
TRANSCRIPT
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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
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The University of Texas Inequality Project
http://utip.gov.utexas.edu
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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 GreenspanTestimony to CongressMarch 5, 1997
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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 GreenspanTestimony to CongressMarch 5, 1997
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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
If technology and trade affect anything, they would affect manufacturing pay
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.02
.03
.04
.05
.06
.07
.08
.09
.10
.11
.010
.012
.014
.016
.018
.020
.022
.024
.026
.028
55 60 65 70 75 80 85 90 95 00 05
Unemployment rate (left)Inequality of manufacturing pay (Theil index, right)
Inequality in Manufacturing Pay and Unemployment in the U.S.1953-2005, Monthly Data
Shaded areas show recessions.
Inequality
Unemployment
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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.
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Inequality and the participation rateInequality and the participation rate
.58
.60
.62
.64
.66
.68
.040
.045
.050
.055
.060
.065
50 55 60 65 70 75 80 85 90 95 00
Inequality for 203 sectorsparticipation rate
source: BLS data and author's calculations
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Participation rates also determine the famous “stagnating median wage”
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Classic argument: **stagnating** median wage
Source: CEPR report, April 2007, p.10
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But, not for women …
Real median income by gender2001 Dollars, GDP deflator
$0
$5,000
$10,000
$15,000
$20,000
$25,000
$30,000
$35,000
19
53
19
56
19
59
19
62
19
65
19
68
19
71
19
74
19
77
19
80
19
83
19
86
19
89
19
92
19
95
19
98
20
01
20
04
MALE
ALL
FEMALE
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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, non-Hispanic 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.
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Thus, when you break out the workforce by race, the stagnation goes away
Real median earnings Full Time 50-52 workweek, year-round,
2001 Dollars, GDP deflator
$20,000
$25,000
$30,000
$35,000
$40,000
19
74
1
97
6
1
97
8
1
98
0
1
98
2
1
98
4
1
98
6
1
98
8
1
99
0
1
99
2
1
99
4
1
99
6
1
99
8
2
00
0
2
00
2
2
00
4
ASIAN
WHITE
ALL
BLACK
HISPANIC
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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.
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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.
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How about the stock market?
That works fine.
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4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
0.015
0.02
0.025
0.03
0.035
0.04
0.045
1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005
Nat
ura
l Log
of N
asd
aq M
onth
ly C
lose
Bet
wee
n-C
oun
ty In
com
e In
equ
alit
y -T
hei
l's T
Sta
tist
ic (
1yr
lag)
U.S. Income Inequality Between Counties 1969 – 2005 Plotted Against the NASDAQ Composite, with Three Counterfactual Scenarios of Inequality Growth from 1994 – 2000
Piketty-Saez data would give essentially the same answer.
Inequality
It’s the stock market, s&%#*d
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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.
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4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
0.015
0.02
0.025
0.03
0.035
0.04
0.045
1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005
Nat
ura
l Log
of N
asd
aq M
onth
ly C
lose
Bet
wee
n-C
oun
ty In
com
e In
equ
alit
y -T
hei
l's T
Sta
tist
ic (
1yr
lag)
U.S. Income Inequality Between Counties 1969 – 2005 Plotted Against the NASDAQ Composite, with Three Counterfactual Scenarios of Inequality Growth from 1994 – 2000
Without Manhattan
Without Silicon ValleyWithoutTop 15
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Counties with the largest positive changes in Theil Elements 1994 - 2000
Counties with the largest negative changes in Theil Elements 1994 – 2000
County, State Theil Element
Change 1994 - 2000 County, State Theil Element
Change 1994 - 2000
New York, New York 0.00517211 Los Angeles, California -0.00089362
Santa Clara, California 0.00468738 Queens, New York -0.00070519
San Mateo, California 0.00208153 Honolulu, Hawaii -0.00065515
King, Washington 0.00169613 Broward, Florida -0.00056938
San Francisco, California 0.00148821 Cuyahoga, Ohio -0.00036473
Harris, Texas 0.00147724 Kings, New York -0.00034178
Middlesex, Massachusetts 0.00131529 Miami-Dade, Florida -0.00032742
Fairfield, Connecticut 0.00099520 San Bernardino, California -0.00031665
Alameda, California 0.00088503 Genesee, Michigan -0.00031147
Westchester, New York 0.00086216 Clark, Nevada -0.00030658
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-0.01
-0.005
0
0.005
0.01
0.015
0.02
0.02519
6919
7019
7119
7219
7319
7419
7519
7619
7719
7819
7919
8019
8119
8219
8319
8419
8519
8619
8719
8819
8919
9019
9119
9219
9319
9419
9519
9619
9719
9819
9920
0020
0120
0220
0320
04
Con
trib
utio
n of
NY
Cou
ntie
s to
US
Inco
me
The
il In
dex
New York Nassau Westchester Suffolk Rockland Richmond Albany MonroePutnam Dutchess Saratoga Schenectady Hamilton Schuyler Columbia WarrenSchoharie Yates Lewis Montgomery Seneca Ontario Essex GreeneFulton Genesee Tioga Sullivan Delaware Wyoming Madison OrleansCortland Rensselaer Chenango Onondaga Livingston Steuben Otsego CayugaFranklin Allegany Herkimer Washington Clinton Wayne Tompkins CattaraugusChemung Jefferson Ulster Oswego St. Lawrence Orange Erie BroomeChautauqua Niagara Oneida Queens Bronx Kings
Contribution of New York Counties to U.S. Income Inequality, 1969-2004
Bar-height is the contribution of the county to the Theil T-Statistic
Manhattan
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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
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Per Capita Income Inequality Across US Counties Over Time
1969 – 2004
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Contribution to Inequality between Counties (Components of the Theil T Statistic)
Relatively Impoverished
Neutral
Prosperous (income above national mean)
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1969
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1970
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1971
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1972
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1973Nixon’s Soviet Wheat Deal
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1974
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1975
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1976
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1977
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1978
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1979
Watch The West
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1980
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1981The Big Recession
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1982
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1983
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1984
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1985
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1986
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1987
The OilBust
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1988
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1989
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1990
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1991
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1992
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1993
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1994
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1995
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1996
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1997
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1998The Tech Bubble
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1999
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2000
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2001
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2002
BeltwayBubble
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2003CheneyDoes Wyoming?
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2004
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Does monetary policy influence pay inequality?
It is obvious that monetary policy influences income inequality: any policy that affects the stock market will affect income inequality.
The Federal Reserve denies any effect, blaming technological change.
Let’s test it
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The VAR modelThe 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.
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The yield curveThe yield curve
Source: U.S. Department of Commerce. 30-day T-Bill vs. 10-year bond rate
-2
-1
0
1
2
3
4
70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02
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After accounting for breaks and outliers, and having checked for stability:
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
Inequality
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Dummy regressions on the Taylor rule
• 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.
log log *, *t t tTS f CPI CPI U U
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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)
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Results for 1984-2006 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.
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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)?
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Tests of a political monetary policyTests of a political monetary policyPartial results
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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.
Conclusions
Monetary policy appears to be driven mainly by fear of low unemployment, and by political considerations.
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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
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For more information:
The University of Texas Inequality Project
http://utip.gov.utexas.edu
Type “Inequality” into Google to find us on the Web