Economic Growth and Income Distribution: Linking Macroeconomic Models with Household Surveys at the Global Level

1. 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

2. Outline • Motivation • Methodological Approach • Demographic and education projections • Sample re-weighting • The CGE • Microsimulations • Applications • Global Income Distribution in 2030 • The Rise of China and India • Distributional Implications of Climate Change • Comments.

3. 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

4. 2. Methodological Approach • Use household surveys for 121 countries (90% of world population). • Project forward changes in demographic and educational structure (from “inertia”). • 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”.

5. The GIDD method:A “Global CGE-Microsimulation System” 1 2 3 4

6. 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

7. Step 2: Reweighting individual observations in the surveys • Organize sampling weights into a matrix of individuals by partition cells: • The demographic and educational projections generate the marginal “projected” densities: • System is under-identified. Can be solved in various ways, including Matrix of “n” individual sampling weights over “m” characteristics Population in Subgroup “m”

8. 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 cross-price and income elasticities. • Sector-specific productivity growth trends “calibrated to be consistent with historical evidence”

9. Step 4: Microsimulations • Once shocked with: • Age/demographic reweighting; • Sector and factor-specific productivity growth; • Any exogenous changes (in policy or climate) • The CGE generates a set of “aggregate” changes in linkage variables: • 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

10. 3. 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.

11. 3. Applications (ctd.) • The rising influence of China and India

12. 3. Applications (ctd.) • 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”.

13. Comments • 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.

14. Comments (ctd.) • My comments are mostly presentational: • 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.