Transcript Slide 1

Estimating the Inequality of Household Incomes:
A Statistical Approach to the Creation of a Dense and Consistent
Global Data Set
A presentation prepared for the
International Association for Research on Income and Wealth
Cork, Ireland
August 23, 2004
by
James K. Galbraith and Hyunsub Kum
The University of Texas Inequality Project
http://utip.gov.utexas.edu
Basic Question: Has Inequality
been Rising or Falling?
Three ways to measure it, per Milanovic, 2002
• Un-weighted Between-Country
(has been rising in all studies)
• Weighted Between-Country
(has fallen because of China)
• Within-country “True”
(disputed territory)
?
The Economist compares
inequality types 1 and 2,
1980-2000.
(from Stanley Fischer,
2003 Ely Lecture)
Existing studies of “true” world income inequality give
conflicting results, recently surveyed by B. Milanovic
Including Sala-i-Martin’s claim
that inequality has been
steadily declining…based on
Deininger and Squire.
Figure borrowed
from Milanovic
Key Questions
for comparing global data sets when little
is known about their quality in advance
• How good is the coverage?
• Are the numbers accurate
and comparable?
Comparing Coverage: Deininger and Squire
Number of Observations Per Country,
1950-1997
DK Observations
1 - 10
11 - 20
21 - 30
31 - 40
41 - 50
N
W
7000
0
7000
E
14000 Miles
S
Version of D&S used by Dollar and Kraay, “Growth is good for the poor.”
The D&S data are heterogeneous for North America and Europe, but homogeneous for
Asia
World Bank Inequality
D&S Gini Coefficients, 1950-1997
<= 30.06
30.06 - 34.66
34.66 - 39
39 - 44.2
44.2 - 51.51
51.51 - 62.3
Note the low inequality registered for Indonesia and India, comparable to Europe and Canada.
The fact that South Asia uses expenditure surveys while Europe uses income surveys is clearly
relevant, but how to make an adjustment?
Inequality (Gini)
<= 30.06
30.06 - 34.66
34.66 - 39
39 - 44.2
44.2 - 51.51
51.51 - 62.3
Elementary economics suggests these differences in inequality are implausible. Europe has an
integrated economy with free trade, free capital flow, nearly equal average incomes (between,
say, France and Germany) and factor mobility.
Inequality (Gini)
<= 30.06
30.06 - 34.66
34.66 - 39
39 - 44.2
44.2 - 51.51
51.51 - 62.3
Inequality (G ini)
<= 30.06
30.06 - 34.66
34.66 - 39
39 - 44.2
44.2 - 51.51
51.51 - 62.3
Indonesia and India have highly unequal manufacturing pay. So how do they
arrive at highly equal D&S measures – more equal than Australia or Japan?
Through strong redistributive welfare states? Probably not. Or, if low Ginis in
those countries reflect egalitarian but impoverished agriculture – as many who
use these data believe -- then why are the D&S Ginis so high in agrarian Africa?
Table 1. Different Types of Inequality in the DS Data
Reference unit
Household
Person
Household
Person
Total
equivalent
equivalent
Source
Gross* Net Gross Net Gross Net Gross Net Gross Net
Expenditure**
23
104
1
128
Income
254
72
12 108
46
34 362 164
* Indicates whether the measure of income is gross or net of taxes.
** Indicates whether the survey measure is of expenditure or income
Inequality in Spain, as reported by D&S
40
HGI
Gini from D&S
35
HGI
30
HNE
HNE
HNE
HNE
25
HNE
HNE
1960
1970
year
HGI: Household Gross Income
HNE: Household Net Expenditure
1980
1990
Rank and Distribution of D&S Gini for 20 OECD countries
BEL
ESP
FIN
CAN
DNK
NZL
JPN
USA
PRT
FRA
Gini coefficient
50
1956
1990
40
1962
1967 1992 1991
1951 1992
1984
1966
1976
1965
30
1991
1975
1979
1992 1985
1962
1973
1991
1984
1947 1987
1991
1969
1973
1963 1991
1991
1989
1961
1973
1991
1988
1974 1990
1991
1990
1974
20
GBR
LUX
NLD
DEU
SWE
NOR
GRC
ITA
IRL
AUS
The U.T. Inequality Project
• Measures Global Pay Inequality
• Uses Simple Techniques that Permit Up-to-Date
Measurement at Low Cost
• Uses International Data Sets for Global
Comparisons, especially UNIDO’s Industrial
Statistics
• Has Many Regional and National Data Sets as
well, including for Europe, Russia, China, India,
and the U.S.
General Technique
We use Theil’s T statistic, measured across sectors within each
country, to show the evolution of economic inequality. You can
do this with many different data sets, including at the regional or
provincial level. International comparisons are facilitated by
standardized categories, for which sources include UNIDO and
Eurostat. Our global pay inequality data set is calculated from
UNIDO’s Industrial Statistics, and gives us ~3,200 country-year
Observations.
A brief review of the Theil Statistic:
The “Between-Groups Component”
m
m

T   p j R j log R j   p j R j Tj
j 1
j 1



1
Tj 
ri log ri


n
j i g j

pj 
nj
n
j
Rj 
Y
n ~ employment; mu ~ average income; j ~ subscript denoting group
The UTIP-UNIDO Data Set for Pay Inequality
has fewer gaps ….
Number of Observations per Country,
1963-1999
Note: Observation count for Russia includes USSR
1963-1991; China and Brazil blended from multiple
editions of UNIDO ISIC; all others based on 2001
edition only.
UTIP Observations
1 - 10
11 - 20
21 - 30
31 - 40
41 - 50
Inequality in Income and in Manufacturing Pay, US and UK
GBR
USA
38.16
Income Inequlaity: D&S Gini
Income Inequlaity: D&S Gini
32.4
22.9
33.5
1963
1968
1973
1978
1983
1988
1993
1963
GBR
1968
1973
1978
1983
1988
1993
1968
1973
1978
1983
1988
1993
USA
.029
Pay Inequality: UTIP-UNIDO Theil
Pay Inequality: UTIP-UNIDO Theil
.019
.012
.018
1963
1968
1973
1978
1983
1988
1993
1963
Correspondence to known events…
F ig u r e 7
Inequality in Iran and Iraq
Inequality in China
and Hong Kong
160
140
300
120
100
200
Revolution
80
60
40
100
War
20
Tiananmen
0
0
63
China
67
65
72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98
71
69
75
79
73
77
83
81
87
85
91
89
93
Hong Kong
Iran
Inequality in the Southern Cone
300
Iraq
Inequality in North America
20 0
250
15 0
200
Banking
Crisis
150
10 0
GATT Entry
100
50
Falklands War
50
Military Coup
0
0
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
Chile
Argentina
Brazil
Data for China drawn partly from State Statistical Yearbook
United States
Canada
Mexico
92
93
94
95
96
Inequality in Scandinavia
150
100
50
0
71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
Finland
Sweden
Norway
Denmark
Inequality in Central Europe
250
200
150
100
50
0
63 64 6566 67 68 6970 71 72 7374 75 76 7778 79 80 8182 83 84 8586 87 88 8990 91 92 93 9495
Czechoslovakia
Hungary
Poland
Consistency across space…
Global Inequality
UTIP Rankings
1963-1999 Averages
These maps rank countries by comparative measures of
inequality over a long historical period, with red and
orange indicating relatively low inequality, yellow and
green in the middle, and light and dark blue indicating the
highest values.
< = 0 .0 1 78
0 .0 1 78 - 0 .03 55 6
0 .0 3 55 6 - 0.0 51 5 8
0 .0 5 15 8 - 0.0 74 3 9
0 .0 7 43 9 - 0.0 98 7 2
0 .0 9 87 2 - 0.8 92 6
Global Inequality
UTIP Rankings
1963-1999 A verages
<= 0. 0178
0. 0178 - 0. 03556
0. 03556 - 0. 05158
0. 05158 - 0. 07439
0. 07439 - 0. 09872
0. 09872 - 0. 8926
Note: Data for Balkans, Czech Republic, Slovakia and post-Soviet states are
post-1991 only. Earlier data for prior boundaries are available from UTIP.
Global Inequality
UTIP Rankings
1963-1999 A verages
<= 0. 0178
0. 0178 - 0. 03556
0. 03556 - 0. 05158
0. 05158 - 0. 07439
0. 07439 - 0. 09872
0. 09872 - 0. 8926
Note that the UTIP-UNIDO measures are homogeneous
for Europe, North America, and South America, but highly
heterogeneous for Asia.
With the UTIP data, we can review changes in
global inequality both across countries and
through time. Nothing comparable can be
done with the Deininger and Squire data set,
for the measurements are too sparse and too
inconsistent.
The Scale
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. h
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 Debt
Note 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…
Simon Kuznets in 1955 argued that while
inequality could rise in the early stages of
industrialization, in the later stages it should be
expected to decline. This is the famous “inverted
U” hypothesis.
Recent studies based on Deininger & Squire find
almost no support for any relationship between
inequality and income levels.
We believe, however, that in the modern
developing world the downward sloping
relationship should predominate, particularly in
data drawn from the industrial sector.
3D Surf ace Plot (Tngall4ax.STA 3v *5360c)
z=0.05+0.001*x+-3.974e-6*y
A regression of pay inequality on
GDP per capita and time, 1963-1998.
The downward sloping income-inequality relation holds, but with an upward shift
over time…
0.008
0.016
0.025
0.033
0.041
0.049
0.057
0.065
0.074
0.082
above
Global Pay Inequality
Time Effect, 1963-1997
T im e e f f e c t
0
-0.1
Milanovic Unweighted
Inequality Between Countries
-0.2
-0.3
Time Effects
Dollar & Kraay data set
0.5
-0.4
0.4
63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97
Year
0.3
0.2
0.1
0
1950
1960
1970
1980
1990
The time effect from a two-way fixed effects panel data analysis
of inequality on GDP per capita, with time and country effects.
2000
Estimating the DS Gini Coefficients from Pay Inequality and other variables.
Income
Household
Gross
Model 1
0.272
(9.20)**
-0.145
(7.02)**
-0.179
(8.01)**
Model 2
-0.015
(0.49)
-0.121
(7.12)**
-0.086
(4.47)**
0.165
(15.56)**
Model 3
-0.139
(4.70)**
-0.081
(5.18)**
-0.042
(2.39)*
0.118
(11.30)**
-0.002
(10.72)**
Model 4
-0.124
(4.07)**
-0.072
(4.37)**
-0.048
(2.69)**
0.117
(11.20)**
-0.002
(10.72)**
0.001
(2.00)*
3.611
(247.48)**
484
0.24
4.249
(99.50)**
484
0.49
4.205
(108.93)**
484
0.59
4.156
(91.86)**
481
0.59
Ln(Theil)
MFGPOP
URBAN
POPGRTH
Constant
Observations
R-squared
Dependent variable is log(DSGini)
Model 5
-0.146
(5.02)**
-0.081
(5.12)**
-0.025
(1.42)
0.106
(10.51)**
-0.002
(8.32)**
0.001
(2.74)**
5.687
(7.18)**
3.984
(80.93)**
481
0.63
EHII -- Estimated Household Income Inequality for OECD Countries
GBR
GRC
ESP
USA
ITA
BEL
AUT
FRA
NLD
DEU
LUX
1999
45
1963
1999 1998
1996
1963
1999
Gini coefficient
40
1963 1989
1967
1999
1999
1999
1997
1999
1999
35
1968
1994
1998
1977
1998
1963
1998
1999
1999
1992
1963
1996
1963 1963
ISL
NZL
1963
1963
1963
1963 1994
1963
1963
1998
1963
30
1963
1963
1963
1963
1963
25
SWE
Low
DNK
FIN
NOR
AUS
CAN
JPN
IRL
PRT
High
Mean Value and Confidence Interval of Differences
lower 95%
mean
upper 95%
eap
eca
lac
mena
na
sas
ssa
we
-6
-2
2
6
D&S Gini - EHII2.1
eap: East Asia and Pacific
eca: Eastern Europe and Central Asia
lac: Latin and Central America
mena: Middle East and North Africa
na: North America
sas: South Asia
ssa: Sub Saharan Africa
we: Western Europe
10
14
Major Differences Between D&S Gini and EHII Gini
30
ZWE
ZAF
20
CAF
SEN
MEX
PRI
PAN
MYS CMR
10
SVK
BGD PAK
ESP
IND
CAN BGR
NLD
0
COL
RUS
LUX
IDN
DZA
ETH HND
SYC HKG BWA KEN MWI
LKA
BHS KOR UGA
-10
BEL
1
34
id
Trends of Inequality in the D&S Data
Non-OECD
OECD
55
D&S Gini
45
35
25
1963
1968
1973
1978
1983
Non-OECD vs OECD
1988
1993
1998
Trends of Inequality in subset of EHII 2.2 Data matched to D&S
Non-OECD
OECD
EHII2.2 Gini: matched to D&S
50
45
40
35
30
25
1963
1968
1973
1978
1983
Non-OECD vs OECD
1988
1993
1998
Trends of Inequality in Full EHII 2.2 Dataset (N=3,179)
Non-OECD
OECD
EHII2.2 Gini
45
40
35
30
1963
1968
1973
1978
1983
Non-OECD vs OECD
1988
1993
1998
Trends of Inequality in the EHII 2.2 Dataset by Income Level
Deininger & Squire Reported Inequality
60
55
50
45
40
35
30
25
63
G in i C o e f f ic ie n t
G in i C o e f f ic ie n t
Income Inequality in North America
UTIP Estimated Inco me Ineq uality
46
44
42
68
73
40
78
Canada
38
83
88
Mexico
93
98
USA
36
34
32
63
Canada
68
73
78
Mexico
83
88
93
United States
98
For more information:
The University of Texas Inequality Project
http://utip.gov.utexas.edu
Type “Inequality” into Google to find us on the Web