Transcript Is the Developing World Catching Up? Global Convergence and National Rising Dispersion Maurizio Bussolo, Rafael E.

Is the Developing World Catching Up?
Global Convergence and National Rising
Dispersion
Maurizio Bussolo, Rafael E. De Hoyos, and
Denis Medvedev
The World Bank
Paper prepared for the World Bank’s 2008 Economists’ Forum, April 17,
Washington, DC
Why global inequality matters?
1. Is globalization increasing global income
disparities?
2. Closer international economic relationships
has increased global awareness (Milanovic,
2006)
•
•
No global representative government
More awareness creates more demand for
redistribution
“International policies are decided on the street” (Collier,
2007 “The bottom billion”)
Objective: Analyze, ex-ante, the potential trends in
global income distribution
Outline
1. Antecedents to global income distribution
analysis
2. Methodology: Global Income Distribution
Dynamics (GIDD)
3. The global income distribution in 2030
4. Conclusions
Antecedents to global income distribution
analysis
Inconclusive evidence on recent trends (last 20
years) on global income distribution
1.
Divergence “big time” (Pritchett,1997; Quah, 1996)
2.
Bourguignon and Morrison (2002): global
inequality has not changed in the last 20 years
3.
Sala-i-Martin (2006): global inequality has reduced
4.
Milanovic (2002): global income distribution
deteriorated
Sources behind the discrepancies
Data sources, country coverage, time period
Different concepts of inequality:
Decomposing Global Distribution
Global inequality can be defined as a sum of inequality
between countries and inequality within countries
I ( global)  I (between)  I (within)
The changes in global distribution are also a function
of these two components:
I ( global)  I (between)  I (within)  interaction
Convergence
Component
Dispersion
Component
Ex-ante analysis of global inequality
This is the first attempt to make an ex-ante
analysis of global income distribution
Create counterfactual analysis of different
policy scenarios (e.g. what if
globalization continues?)
Our approach takes into account the
convergence (between) and dispersion
(within) components of global inequality
Methodology: GIDD
Population Projection by
Age Groups
(Exogenous )
Education Projection
(Semi- Exogenous )
New Population Shares or
Sampling Weights by Age
and Education
CGE
(New Wages, Sectoral
Reallocation )
Household Survey
(Simulated Distribution )
Education forecasts
– Demographic dimensions in forecasting: Adding
education to age and gender
•
Lutz, Goujon and Dobhammer-Reither (1999) and
Lutz and Goujon (2001)
2000
2030
UnSkilled
skilled
UnSkilled
skilled
Young
60
40 Young
60
40
Old
30
70 Old
60
40
Methodology: GIDD
Population Projection by
Age Groups
(Exogenous )
Education Projection
(Semi- Exogenous )
New Population Shares or
Sampling Weights by Age
and Education
CGE
(New Wages, Sectoral
Reallocation )
Household Survey
(Simulated Distribution )
GIDD: From country-specific to global
analysis
1. Global CGE  LINKAGE
2. Global household data
•
•
•
Collection of all available household surveys
around year 2000 (120 countries covering 91
percent of the world population)
Standardise incomes using PPP
Standardise linking variables: education and
sector of employment
The Global Distribution in 2030
Global income inequality reduces
Gini (2000) = 0.68
Gini (2030) = 0.63
Decomposing the inequality levels
Subgroup
Decomposition
Year
Gini
Theil
Between
Within
2000
0.68
0.93
0.69
(75%)
0.23
(25%)
2030
0.63
0.77
0.54
0.23
(70%)
(30%)
Rising intra-country inequality
What if the only change observed was the withincountry changes in inequality?
-0.06
Haiti
Bolivia
Jamaica
Brazil
Dominican Republic
Panama
Argentina
El Salvador
Lithuania
Peru
Uruguay
Chile
Ecuador
Turkey
Paraguay
Costa Rica
Moldova
Nicaragua
Honduras
Venezuela, RB
Kyrgyz Republic
Russian Federation
Romania
Colombia
Mexico
Guyana
Sierra Leone
Armenia
Guatemala
Cambodia
Vietnam
South Africa
Hungary
Bangladesh
Indonesia
Mozambique
Tanzania
Thailand
Mauritania
Yemen, Rep.
Egypt, Arab Rep.
Niger
Ethiopia
India
Bulgaria
Pakistan
Poland
Benin
Zambia
Nigeria
Nepal
Cote d'Ivoire
Madagascar
Uganda
Cameroon
Burundi
Sri Lanka
Albania
Rwanda
Burkina Faso
Guinea
Senegal
Jordan
Demographic and economic shifts and
income distribution
Variation of Gini coefficient between base year and 2030
0.08
Total change
0.06
Change due to demographic shift
0.04
0.02
Change due to
economic shift
0.00
-0.02
-0.04
More equal
Total change in income inequality
Less equal
Convergence Component
What if the only change observed was the
between-country changes growth rates?
Poor Populous Countries are Catching Up
.4
Density
.3
Global 2000
Global 2030
.2
.1
China 2000
China 2030
0
1
3
5
7
9
Monthly household per capita income (1993 PPP, log)
Authors' own calculation using data from GIDD
The Emergence of a Global Middle Class
• Definition: Individuals whose income is between the
per-capita income of Brazil and Italy, i.e. between 10 and
20 dollars a day, 1993 PPP
• Increase in the global middle class
– 2000 = 460 million members (8 % of global population)
– 2030 = 1.2 billion members (14 % of global population)
• How much is due to China and India?
– ½ of the total change in the global middle class (GMC) is
explained by China
The Importance of China and India in the
Global Middle Class
Conclusions
1. GIDD contributes to our understanding of the
dynamics behind changes in global distribution
2. We show that the changes in global
distribution are the outcome of several offsetting changes
•
•
The reduction in global inequality depends on the
convergence of poor and populous countries (China
and India)
Inequality within-countries is expected to increase
due a rise in the skilled-to-unskilled wage premium
Conclusions
3. The rise in the middle class can provide
support for further globalization
4. Specific groups of people within countries may
not be able to adjust to the new wave of
globalization
5. Governments should be able to design equity
enhancing policies to compensate the losers