a presentation at the international labor organization united nations geneva, switzerland april 16,...
TRANSCRIPT
A presentation at the International Labor OrganizationUnited NationsGeneva, Switzerland
April 16, 2012
byJames K. Galbraith
Inequality and InstabilityA Study of the World Economy Just Before
the Great Crisis
“Kepler undertook to draw a curve through the places of Mars, and his greatest service to science was in impressing on men's minds that this was the thing to be done if they wished to improve astronomy; that they were not to content themselves with inquiring whether one system of epicycles was better than another, but that they were to sit down to the figures and find out what the curve, in truth was.”
-- Charles Sanders Peirce (1877)
A research initiative since ~1996 Emphasis on accurate measurement Global and national coverage 60 working papers; six books 15 data sets on-line Highly ranked in Google under “Inequality” Supported by the Ford Foundation
The University of Texas Inequality Project
DK Observations1 - 1011 - 2021 - 3031 - 4041 - 50
7000 0 7000 14000 Miles
N
EW
S
Number of Observations Per Country, 1950-1997
Deininger and Squire: Data Set of Choice?
Version of D&S used by Dollar and Kraay, “Growth is good for the poor.”
Gin
i coe
ffici
ent
GBR LUX NLD DEU SWE NOR GRC ITA IRL AUS
BEL ESP FIN CAN DNK NZL JPN USA PRT FRA
20
30
40
50
1961
19791985
1965
1975
1966
1963
19511967
1976
1962
1973
1974
1962
1974
1947
1973
1973
1969
1956
1991
1992 19851989
1991
1991
1984
1991
19921992 1991
1990
1988 1990
1991
1991
19871991
1990
1984
Deininger & Squire: Inequality Measures for the OECD
Countries ranked by average value, first and last dates shown
Trends of Inequality in the Deininger-Squire Data Set
D&
S G
ini
Non-OECD vs OECD
Non-OECD OECD
1963 1968 1973 1978 1983 1988 1993 1998
25
35
45
55
T p R R p R T
Tn
r r
j jj
m
j jj
m
j j
jj
ii g
i
j
1 1
1
log
log
pn
njj
A brief review of the Theil Statistic:
n ~ employment; ~ average income; j ~ subscript denoting group
The “Between-Groups Component”
R jj
Within-Group Inequality
Weighted Sum of Within-GroupsComponents
r yi i j
Table 2. Distribution of UTIP-UNIDO Inequality Measures
Year East
Asia & Pacific
East & Central
Asia
Latin & Central America
Middle East & North Africa
North America
South Asia
Sub-Saharan Africa
Western Europe
Total
Country 19 24 28 7 2 8 38 19 145
1960s 49 52 75 62 14 25 83 113 473 1970s 118 80 162 102 20 46 205 183 916 1980s 142 93 192 96 20 64 198 190 995 1990s 125 161 162 97 19 43 147 173 927 2000s 21 40 10 20 4 9 18 19 141
Total 455 426 601 377 77 187 651 678 3,452
The UTIP-UNIDO TheilMeasure of Inequality
SWE
DNK
CZE
NLD
NOR
FIN
POL
DEU
AUS
HKG
LUX
TWN
FRA
SVK
ITA
GBR
AUT
HUN
CAN
KOR
BEL
USA
ESP
MEX
MYS
GRC
CRI
SGP
COL
JPN
PRT
IRL
ARG
VEN
PHL
CHL
BRA
BOL
IDN
THA
IND
PER
-.05
0.0
5.1
.15
UT
IP-U
NID
O I
nequ
ality
0 10 20 30 40Rank by mean of Theil
* Bar indicates 1 standard deviation across years** Cross line indicates overall mean
China: Contributions to inequality by Province
Beijing
Contributions to a Theil T-Statistic, measured across provinces
4/16/12
ImpoverishedFar Below AverageBelow AverageLow low NeutralLow NeutralNeutralHigh NeutralAbove AverageProsperousWealthy
Contribution of European Provinces in Inequality Across the European continent, late 1990s.
China
Brazil
Korea
Denmark
USA
Kuwait
Inequality
Income
A Stylized “Augmented Kuznets Curve”
Inequality in the world economy is dominated by a common trend, with turning points around 1971, 1980, 2000
The most-likely explanation is changing global financial regimes.
Key Global Finding
Brown: Very large decreases in inequality; more than 8 percent per year.
Red Moderate decreases in inequality. Pink: Slight Decreases. Light Blue: No Change or Slight increases Medium Blue: Large Increases -- Greater than 3 percent per
year. Dark Blue: Very Large Increases -- Greater than 20 percent per
year.
Global Movement of Inequality
1963 to 1969
1970 to 1976
The oil boom: inequality declines in the producing states, but rises in the industrial oil-consuming countries, led by the United States.
1977 to 1983
1981 to 1987
… the Age of DebtNote the exceptions to rising inequality are mainly India and China, neither affected by the debt crisis…
1984 to 1990
1988 to 1994
The age of globalization…Now the largest increases in inequality in are the post-communist states; an exception is in booming Southeast Asia, before 1997…
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Debt Crisis
End of BrettonWoods
9/11
“Concept 4” Inequality: The Common Movement of Inequality Measured within Countries, Across Time
Note: The vertical axis represents the time element in a two-way fixed effects panel regression, across the panel of country-year observations. Vertical scale is log(T) units. Source: Kum 2008.
Milanovic Concept 1
Profit Share in OECD
The Super Bubble
UTI
P-U
NID
O L
og(T
)
Non-OECD vs. OECD
Non-OECD OECD
1963 1968 1973 1978 1983 1988 19931998-4.5
-4
-3.5
-3
-2.5
UTI
P-U
NID
O L
og(T
)
Non-OECD vs. OECD
Non-OECD OECD
1963 1968 1973 1978 1983 1988 19931998
-4.5
-4
-3.5
-3
-2.5
(Actual Values) (After Global Effect Removed)
Note: Bands indicate two standard deviations of country observations within each year shown. OECD and non-OECD countries shown separately. Vertical scale is log(T) units. Source: Galbraith and Kum, 2003.
What would have happened without the Global Element?
This broad picture of the world economy from the standpoint of inequality measure suggests that the “super-bubble” was also, for most of the world’s population, a “super-crisis.”
The super-bubble came to a peak in 2000-20001.
The period since then was marked in the United States by efforts to keep the bubble going, in part through aggressive efforts to relax standards, which may be described as the growth of a “predator state.”
This led to the corruption of the financial markets whose collapse produced the great crisis.
Pay inequality in the US depends mainly on economic conditions, especially the unemployment rate.
Income inequality in the US depends mainly on the stock market, and is driven by top-level incomes in highly concentrated small areas.
For example: Manhattan, Silicon Valley in the 1990s, Washington DC in the 2000s
Key US Findings
.0 2
.0 3
.0 4
.0 5
.0 6
.0 7
.0 8
.0 9
.1 0
.1 1
.0 1 0
.0 1 2
.0 1 4
.0 1 6
.0 1 8
.0 2 0
.0 2 2
.0 2 4
.0 2 6
.0 2 8
55 60 65 70 75 80 8 5 9 0 95 00 05
Unemployment rate (left)Inequality of manufacturing pay (Theil index, right)
US Pay Inequality and Unemployment, 1952-2005
Inequality measured on earnings across industries in manufacturing, monthly data; recessions entered in gray.
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
1 9 6 9 1 9 7 0 1 9 7 1 1 9 7 2 1 9 7 3 1 9 7 4 1 9 7 5 1 9 7 6 1 9 7 7 1 9 7 8 1 9 7 9 1 9 8 0 1 9 8 1 1 9 8 2 1 9 8 3 1 9 8 4 1 9 8 5 1 9 8 6 1 9 8 7 1 9 8 8 1 9 8 9 1 9 9 0 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5
N a t ur al L o g o f N a s d a q M on t hly C lo s e
B e t w ee n
- C o u nt y I n c om e I n eq u alit y
- T h eil's T S t a tis ti c ( 1y r la g )
US Income Inequality and the NASDAQ, 1969-2006
Income inequality measured between counties, from tax data
Tax Reform Act
Internet Bubble
InequalityLog of NASDAQ
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
Income Inequality in the United States, 1969-2009Measured Between Counties
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.018
0.020
Calculation by Amin Shams
Contribution of Each County to Income Inequality, Late 2000s
Contribution to inequality is presented as shading and as height above or below the zero plane. Calculation and map by Amin Shams.
Contribution of each Municipality to Inequality in Brazil
Contribution to inequality is presented as shading and as height above or below the zero plane. Calculation and map by Laura Spagnolo.
Further Research Results
Inequality and Unemployment Inequality and Political Regime Types Inequality and Voting: Participation Inequality and Voting: Outcomes Extent of wage flexibility in Europe Declining Inequality in Latin America Contribution of Financial Sectors to
Inequality
Needs Going Forward...
Staff regular updating of data sets Fill historical and geographic gaps Extend the record back in time Mapping Software Tools Spreading the Word.
Oxford University Press, 2012
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