Transcript Economic Growth and Income Distribution: Linking

Economic Growth and Income Distribution:
Linking Macroeconomic Models with
Household Surveys at the Global Level
Mauricio Bussolo, Rafael E. De Hoyos, and
Denis Medvedev
The World Bank
Presented by:
Francisco H. G. Ferreira (The World Bank)
IARIW Conference 2008
Outline
1. Motivation
2. Methodological Approach
1.
2.
3.
4.
Demographic and education projections
Sample re-weighting
The CGE
Microsimulations
3. Applications
1. Global Income Distribution in 2030
2. The Rise of China and India
3. Distributional Implications of Climate Change
4. Comments.
1.
Motivation
• To propose a modeling tool capable of
forecasting changes in the global income
distribution.
– To generate “reasonable” predictions of how global
inequality and poverty might change under different
scenarios.
– “Baseline” growth projections
– Changes in trade agreements
– Climate change
2.
•
Methodological Approach
Use household surveys for 121 countries (90%
of world population).
1. Project forward changes in demographic and
educational structure (from “inertia”).
2. Project changes in occupational structure and
incomes:
•
•
Taking account of (1) and
Forecasting changes in incomes and returns in each sector
from estimates of productivity growth and changes in
demand from a “Global CGE”.
The GIDD method:
A “Global CGE-Microsimulation System”
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 )
1
2
3
4
Household Survey
(Simulated Distribution )
Step 1: Demographic and Education Projections
Age
The changes in demographic structure are taken from WB or UN
population projections
Education
Overall education attainments are assumed to be related with aging
via a “pipeline” effect (Lutz and Goujon, 2001)
2030
2000
Skilled Unskilled
Skilled
Unskilled
Young
60
40
Young
60
40
Old
30
70
Old
60
40
Step 2: Reweighting individual observations in the
surveys
•
Organize sampling weights into a matrix of individuals by partition
cells:
Matrix of “n” individual sampling
weights over “m” characteristics
Population in Subgroup “m”
•
The demographic and educational projections generate the marginal
“projected” densities:
•
System is under-identified. Can be solved in various ways, including
Step 3: The CGE
• There are other changes in the economy, in
addition to the age/education structure.
• These are simulated through a (computable)
general equilibrium model, which incorporates
the population changes from Steps 1 and 2.
– The World Bank’s global LINKAGE model
• Production function is nested CES with five factors:
Unskilled and skilled labor, capital, land, natural resources.
• Demand structure modeled through an ELES, with crossprice and income elasticities.
• Sector-specific productivity growth trends “calibrated to be
consistent with historical evidence”
Step 4: Microsimulations
•
Once shocked with:
1.
2.
3.
•
The CGE generates a set of “aggregate” changes in linkage variables:
•
•
•
•
Age/demographic reweighting;
Sector and factor-specific productivity growth;
Any exogenous changes (in policy or climate)
Worker reallocation flows
Changes in incomes
Changes in prices
These are translated to the counterfactual distributions in the surveys
by:
•
•
•
•
Using probits to identify the most likely individuals to move sectors
Using sector-specific earnings equations to predict their earnings
Scaling resulting sector and skill gaps so that the changes in average
gaps in the survey match the changes in average gaps in the CGE.
Making a final adjustment on overall levels of real aggregate per
capita income
3.
1.
Applications
Global Income Inequality in 2030 (compared to 2000)
•
•
•
Forecast a decline in global income inequality…
…driven entirely by inter-country convergence.
“Global middle class” grows from 7.6% to 16.1% of world
population.
Year
Gini
Theil
Subgroup Decomposition
Between
Within
Countries
Countries
2000
0.68
0.93
0.69
0.23
(75%)
(25%)
0.54
0.23
(70%)
(30%)
2030
0.63
0.77
3.
2.
Applications (ctd.)
The rising influence of China and India
3.
Applications (ctd.)
2.
Distributional Impacts of Climate Change
•
•
A “climate model” links carbon emissions to regional changes in
temperatures.
Use estimates in Cline (2007) to map these changes onto changes in
agricultural productivity. Feed these into agricultural production functions
in the CGE.
•
•
Find (small) increases in global poverty and inequality.
Larger losses among “near poor”.
Comments
1. A plausible list of really difficult things to do in
economics:
Measure global inequality
Account for general equilibrium effects of policy
changes
Predict the future

This paper has it all!

That makes it very easy to criticize.

But if one accepts the premise that the ability to “predict” obviously subject to great uncertainty - the plausible worldwide
distributional implications of large shocks and policy changes in
the future, then it is not easy to propose a clearly superior
alternative to this.
Comments (ctd.)
2.
My comments are mostly presentational:
1.
2.
3.
4.
Reconsider use of the Shorrocks (1982) between-group
decomposition framework as a motivating device. After Steps 4
and 5, within-group inequality is no longer constant, as you
currently appear to claim.
Consider an alternative notation to describe Step 2. I have a
feeling that this can be written down just as formally, but more
clearly.
Spend a little more time spelling out the micro-simulation stage,
with examples.
While this is a nice conference paper, hard to think that
publication won’t require two papers: one on the basic
methodology, and other(s) on the applications.