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

byJames K. Galbraith

and Hyunsub Kum

The University of Texas Inequality Project

http://utip.gov.utexas.edu

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

Key Questions

• Is the coverage sufficient?

• Are the numbers accurate?

• Are the data right for the research question?

• Can we do better?

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.”

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.

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.

Judging AccuracySome Useful Indicators:

• Consistency over time

• Consistency across space

• Correspondence to known events

• Consistency with “common knowledge”

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

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

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.

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

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

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

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

<= 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.

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?

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

Correspondence to known events…

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

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

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”…

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.

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.

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

1984 to 1990

1988 to 1994

The age of globalization…

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.

-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

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.

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.

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.

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).

Conclusion:

Used with care, good pay data are better than bad income data.

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?

For more information:

The University of Texas Inequality Project

http://utip.gov.utexas.edu