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![Page 1: Estimating the Inequality of Household Incomes: A Statistical Approach to the Creation of a Dense and Consistent Global Data Set A presentation prepared.](https://reader033.fdocuments.in/reader033/viewer/2022051417/5697c02c1a28abf838cd8e3a/html5/thumbnails/1.jpg)
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
![Page 2: Estimating the Inequality of Household Incomes: A Statistical Approach to the Creation of a Dense and Consistent Global Data Set A presentation prepared.](https://reader033.fdocuments.in/reader033/viewer/2022051417/5697c02c1a28abf838cd8e3a/html5/thumbnails/2.jpg)
byJames K. Galbraith and Hyunsub Kum
The University of Texas Inequality Project
http://utip.gov.utexas.edu
![Page 3: Estimating the Inequality of Household Incomes: A Statistical Approach to the Creation of a Dense and Consistent Global Data Set A presentation prepared.](https://reader033.fdocuments.in/reader033/viewer/2022051417/5697c02c1a28abf838cd8e3a/html5/thumbnails/3.jpg)
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)
?
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The Economist compares inequality types 1 and 2,
1980-2000.
(from Stanley Fischer, 2003 Ely Lecture)
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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
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Key Questionsfor comparing global data sets when little
is known about their quality in advance • How good is the coverage?
• Are the numbers accurate and comparable?
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DK Observations1 - 1011 - 2021 - 3031 - 4041 - 50
7000 0 7000 14000 Miles
N
EW
S
Number of Observations Per Country, 1950-1997
Comparing Coverage: Deininger and Squire
Version of D&S used by Dollar and Kraay, “Growth is good for the poor.”
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<= 30.06
30.06 - 34.66
34.66 - 39
39 - 44.2
44.2 - 51.51
51.51 - 62.3
World Bank InequalityD&S Gini Coefficients, 1950-1997
The D&S data are heterogeneous for North America and Europe, but homogeneous for Asia
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?
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Inequality (Gini)<= 30.0630.06 - 34.6634.66 - 3939 - 44.244.2 - 51.5151.51 - 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.
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Inequality (Gini)<= 30.0630.06 - 34.6634.66 - 3939 - 44.244.2 - 51.5151.51 - 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?
Inequality (Gini)<= 30.0630.06 - 34.6634.66 - 3939 - 44.244.2 - 51.5151.51 - 62.3
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Table 1. Different Types of Inequality in the DS Data Reference unit
Household Household equivalent
Person Person
equivalent Total
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
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Inequality in Spain, as reported by D&S
HGI: Household Gross IncomeHNE: Household Net Expenditure
Gin
i fro
m D
&S
year1960 1970 1980 1990
25
30
35
40
HGI
HGI
HNE
HNE
HNEHNE
HNE
HNE
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Rank and Distribution of D&S Gini for 20 OECD countries
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
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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.
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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-yearObservations.
General Technique
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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 R j
j
Y
A brief review of the Theil Statistic:
n ~ employment; mu ~ average income; j ~ subscript denoting group
The “Between-Groups Component”The “Between-Groups Component”
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The UTIP-UNIDO Data Set for Pay Inequality has fewer gaps ….
UTIP Observations1 - 1011 - 2021 - 3031 - 4041 - 50
Number of Observations per Country,1963-1999
Note: Observation count for Russia includes USSR1963-1991; China and Brazil blended from multipleeditions of UNIDO ISIC; all others based on 2001edition only.
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Inequality in Income and in Manufacturing Pay, US and UK
GBRIn
com
e In
eq
ula
ity: D
&S
Gin
i
1963 1968 1973 1978 1983 1988 1993
22.9
32.4
USA
Inco
me
Ine
qu
laity
: D&
S G
ini
1963 1968 1973 1978 1983 1988 1993
33.5
38.16
GBR
Pa
y In
eq
ua
lity:
UT
IP-U
NID
O T
he
il
1963 1968 1973 1978 1983 1988 1993
.012
.019
USA
Pa
y In
eq
ua
lity:
UT
IP-U
NID
O T
he
il
1963 1968 1973 1978 1983 1988 1993
.018
.029
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0
20
40
60
80
100
120
140
160
6365
6769
7173
7577
7981
8385
8789
9193
Iran Iraq
Inequality in Iran and IraqFigure 7
0
50
100
150
200
250
300
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
Inequality in the Southern Cone
0
50
100
150
200
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
United StatesCanada Mexico
Inequality in North America
Revolution
Military Coup
GATT Entry
Falklands War
BankingCrisis
War
0
100
200
300
727374757677787980818283848586878889909192939495969798
China Hong Kong
Inequality in Chinaand Hong Kong
Tiananmen
Data for China drawn partly from State Statistical Yearbook
Correspondence to known events…
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0
50
100
150
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 Scandinavia
0
50
100
150
200
250
636465666768697071727374757677787980818283848586878889909192939495
Czechoslovakia Hungary Poland
Inequality in Central Europe
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Consistency across space…
1963-1999 Averages<= 0.01780.0178 - 0.035560.03556 - 0.051580.05158 - 0.074390.07439 - 0.098720.09872 - 0.8926
Global InequalityUTIP Rankings
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.
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1963-1999 Averages<= 0.0178
0.0178 - 0.03556
0.03556 - 0.05158
0.05158 - 0.07439
0.07439 - 0.09872
0.09872 - 0.8926
Global InequalityUTIP Rankings
Note: Data for Balkans, Czech Republic, Slovakia and post-Soviet states are post-1991 only. Earlier data for prior boundaries are available fromUTIP.
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1963-1999 Averages<= 0.0178
0.0178 - 0.03556
0.03556 - 0.05158
0.05158 - 0.07439
0.07439 - 0.09872
0.09872 - 0.8926
Global InequalityUTIP Rankings
Note that the UTIP-UNIDO measures are homogeneous for Europe, North America, and South America, but highly heterogeneous for Asia.
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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.
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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
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1963 to 1969
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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.
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1977 to 1983
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1981 to 1987
… the Age of DebtNote the exceptions to rising inequality are mainly India and China, neither affected by the debt crisis…
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1984 to 1990
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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…
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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.
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0.008 0.016 0.025 0.033 0.041 0.049 0.057 0.065 0.074 0.082 above
3D Surface 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…
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-0.4
-0.3
-0.2
-0.1
0
Tim
e e
ffect
6364656667686970717273747576777879808182838485868788899091929394959697
Year
Global Pay InequalityTime Effect, 1963-1997
The time effect from a two-way fixed effects panel data analysis of inequality on GDP per capita, with time and country effects.
0
0.1
0.2
0.3
0.4
0.5
1950 1960 1970 1980 1990 2000
Time EffectsDollar & Kraay data set
Milanovic UnweightedInequality Between Countries
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Model 1 Model 2 Model 3 Model 4 Model 5 Income 0.272 -0.015 -0.139 -0.124 -0.146
(9.20)** (0.49) (4.70)** (4.07)** (5.02)** Household -0.145 -0.121 -0.081 -0.072 -0.081
(7.02)** (7.12)** (5.18)** (4.37)** (5.12)** Gross -0.179 -0.086 -0.042 -0.048 -0.025
(8.01)** (4.47)** (2.39)* (2.69)** (1.42) Ln(Theil) 0.165 0.118 0.117 0.106
(15.56)** (11.30)** (11.20)** (10.51)** MFGPOP -0.002 -0.002 -0.002
(10.72)** (10.72)** (8.32)** URBAN 0.001 0.001
(2.00)* (2.74)** POPGRTH 5.687
(7.18)** Constant 3.611 4.249 4.205 4.156 3.984
(247.48)** (99.50)** (108.93)** (91.86)** (80.93)**
Observations 484 484 484 481 481 R-squared 0.24 0.49 0.59 0.59 0.63
Estimating the DS Gini Coefficients from Pay Inequality and other variables.
Dependent variable is log(DSGini)
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EHII -- Estimated Household Income Inequality for OECD Countries
Gin
i co
effi
cie
nt
SWE DNK FIN NOR AUS ISL NZL CAN JPN IRL PRT
GBR LUX DEU NLD FRA AUT BEL ITA USA ESP GRC
25
30
35
40
45
1963
1963
1963
19631963
1963
1963 19631963
1977
19681963
1963 1963
1963
1967
1963
1963
1963
1963
1963
1963
1999
1999
1998
1994
1999
1994
1998
1999
1997
1998
1996
1999
1996
1992
19991998
1999
1999 1998
1999
1989
1999
Low High
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Mean Value and Confidence Interval of Differences
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
-6 -2 2 6 10 14
eap
eca
lac
mena
na
sas
ssa
we
lower 95% mean upper 95%
D&S Gini - EHII2.1
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Major Differences Between D&S Gini and EHII Gini
id1 34
-10
0
10
20
30
SVK
BEL
BGD
BHS
PAK
KOR
ESP
UGA
IND
RUS
CAN
LUX
BGR
IDN
NLD
LKA
COL
MYS
DZA
CMR
ETH
PAN
HND
PRI
SYC
MEX
HKG
CAF
BWA
SEN
KEN
ZAF
MWI
ZWE
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Trends of Inequality in the D&S Data
D&
S G
ini
Non-OECD vs OECD
Non-OECD OECD
1963 1968 1973 1978 1983 1988 1993 1998
25
35
45
55
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Trends of Inequality in subset of EHII 2.2 Data matched to D&SE
HII2
.2 G
ini:
ma
tche
d to
D&
S
Non-OECD vs OECD
Non-OECD OECD
1963 1968 1973 1978 1983 1988 1993 1998
25
30
35
40
45
50
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Trends of Inequality in Full EHII 2.2 Dataset (N=3,179)E
HII2
.2 G
ini
Non-OECD vs OECD
Non-OECD OECD
1963 1968 1973 1978 1983 1988 1993 1998
30
35
40
45
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Trends of Inequality in the EHII 2.2 Dataset by Income Level
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25
30
35
40
45
50
55
60
Gin
i C
oeffic
ient
63 68 73 78 83 88 93 98
CanadaMexico USA
Deininger & Squire Reported Inequality
32
34
36
38
40
42
44
46
Gin
i C
oeffic
ient
63 68 73 78 83 88 93 98
Canada Mexico United States
UTIP Estimated Income Inequality
Income Inequality in North America
![Page 44: Estimating the Inequality of Household Incomes: A Statistical Approach to the Creation of a Dense and Consistent Global Data Set A presentation prepared.](https://reader033.fdocuments.in/reader033/viewer/2022051417/5697c02c1a28abf838cd8e3a/html5/thumbnails/44.jpg)
For more information:
The University of Texas Inequality Project
http://utip.gov.utexas.edu
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