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![Page 1: Inequality and Globalization: Judging the Data A Presentation at The World Bank June 18, 2002 A comparison of the UTIP data set on world pay inequalities.](https://reader035.fdocuments.net/reader035/viewer/2022070407/56649e245503460f94b12529/html5/thumbnails/1.jpg)
Inequality and Globalization:Judging the Data
A Presentation at
The World Bank
June 18, 2002
A comparison of the UTIP data set on world pay inequalities with the Deininger-Squire data set on world income
inequalities
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byJames K. Galbraith
and Hyunsub Kum
The University of Texas Inequality Project
http://utip.gov.utexas.edu
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Two Data Sets
• Deininger & Squire• Income inequality• Household surveys• Comprehensive• Official & Unofficial• Bibliographic• Gini coefficient
• UTIP-UNIDO• Pay inequality • Establishment surveys• Narrow• Official Data Only• Calculated “in house”• Theil’s T statistic
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Key Questions
• Is the coverage sufficient?
• Are the numbers accurate?
• Are the data right for the research question?
• Can we do better?
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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
The Deininger-Squire data set suffers from major deficiencies of coverage...
Version of D&S used by Dollar and Kraay, “Growth is good for the poor.”
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The UTIP-UNIDO Data Set 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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Comparing Coverage
Coverage of Inequality Measures: UNIDO-UTIP / D&S Gini (2834/ 653)
Continent Before 1965
1966 - 1970
1971 - 1975
1976 - 1980
1981 - 1985
1986 - 1990
1991 - 1995
1996 -1999
Africa 28/ 3 91/ 3 111/ 3 127/ 6 123/ 6 87/ 14 96/ 25 40/ 3 Central & North America 24/ 19 48/ 12 62/ 16 58/ 20 67/ 16 55/ 27 49/ 14 20/ 0 Asia 33/ 26 73/ 26 87/ 23 99/ 29 104/ 28 100/ 36 86/ 17 33/ 0 Europe 52/ 15 99/ 15 105/ 25 110/ 38 115/ 47 122/ 49 106/ 26 48/ 0 Oceania 9/ 0 17/ 1 20/ 2 20/ 7 24/ 5 24/ 7 16/ 0 5/ 0 South America 11/ 2 21/ 3 27/ 8 35/ 10 41/ 6 46/ 10 43/ 5 17/ 0
UTIP coverage count for this table is based on UNIDO ISIC 2001 edition only, where matching data for GDP per capita also available; total UTIP coverage is about 3200 observations.
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Judging AccuracySome Useful Indicators:
• Consistency over time
• Consistency across space
• Correspondence to known events
• Consistency with “common knowledge”
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log
(T
he
il)
Non-OECD vs. OECD
Non-OECD OECD
19631968197319781983198819931998-4.38301
-2.51034
Gin
iNon-OECD vs. OECD
Non-OECD OECD
1963196819731978198319881993199826.0357
55.1357
Consistency across time…
UTIP-UNIDO D&S
Bars indicate standard deviation around average value for each year
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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
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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
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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
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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
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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
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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
Note the reported heterogeneity of North America and Europe, and the homogeneous measurements for Asia, with low inequality comparable to northern Europe and Canada.
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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 in an integrated region. If inequality were really so much greater in France than in Germany, wouldn’t low-skilled French workers migrate to Germany to sweep the streets?
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Inequality (Gini)<= 30.0630.06 - 34.6634.66 - 3939 - 44.244.2 - 51.5151.51 - 62.3
The UTIP data and the D&S data cannot both be right. If Indonesia or India has highly unequal pay, how does it arrive at highly equal incomes – more equal than Australia? Through a strongly redistributive welfare state? Ha! Alternatively, if low Ginis in those countries reflect egalitarian but impoverished agriculture, then why are 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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Correspondence to known events…
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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
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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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European Countries Ranked in Order of European Countries Ranked in Order of Inequality by the World Bank, Low to HighInequality by the World Bank, Low to High
1970UKSwedenBelgiumNetherlandsFinlandGermanyDenmarkGreeceSpainNorwayPortugalItalyFrance
1992SpainFinlandBelgiumNetherlandsItalyGermanyUKSwedenDenmarkNorwayFranceGreecePortugal
Source: Deininger and SquireData are for nearest available year
Consistency with “common knowledge”…
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Using the UTIP inequality rankings, one finds that countries in Europe that have less inequality also have less unemployment.
European Countries Ranked in Order of IndustrialEarnings Inequality Using the Theil Statistic, Low to High
1970NorwayFinlandDenmarkGermanyNetherlandsUKBelgiumSwedenGreeceFranceAustriaItaly
1992NorwayDenmarkFinlandNetherlandsSwedenUKGermanyBelgiumAustriaGreecePortugalFranceSpainItaly Source: OECD STAN
and authors’ calculationsData are for year reported.
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Consistency over time and space together…
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 Debt
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1984 to 1990
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1988 to 1994
The age of globalization…
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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.
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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
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Globalization and Inequality
Overall, pay data reveal a strong upward trend in inequality across countries, over time, with an inflection point in the early 1980s.
It is a reasonable inference that global macroeconomic forces, notably rising real interest rates and the debt crisis, followed by the implementation under pressure of neoliberal policies, were responsible for this worldwide pattern of rising inequality in pay structures.
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Are Pay Data Appropriate?
• There are some instances of selection bias: where industrial job losses affect mainly low-income workers, increasing inequality will be understated – especially in the UK.
• In very rich countries, trends in capital income can lead to large differences between the trend of pay inequality and of income inequality – especially in the U.S.
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But in General…
• Trade and technology do affect income mainly through pay.
• Manufacturing pay is a fair indicator of the movement of all pay.
• Pay is a large subset of all income.
• Most income dynamics are derived from pay dynamics.
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But in General…
• Trade and technology do affect income mainly through pay. (Galor and Tsiddon 1997, Aghion and Howitt 1997.)
• Manufacturing pay is a fair indicator of the movement of all pay. (Cf. Galbraith & Wang on China, 2001.)
• Pay is a large subset of all income. (Williamson 1982)
• Most income dynamics are derived from pay dynamics. (Acemoglu 1997).
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Conclusion:
Used with care, good pay data are better than bad income data.
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Can we do better?
• UNIDO industrial data has 28 sectors.
• OECD’s STAN has 39 sectors.
• Chinese State Statistics ~ 500 cells
• Russian State Statistics ~ 900 cells
• Brazil, Mexico: monthly observations
• Will the World Bank take up the challenge of collecting national data in detail?
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For more information:
The University of Texas Inequality Project
http://utip.gov.utexas.edu