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.Demographic and education projections 2.Sample re-weighting 3.The CGE 4.Microsimulations 3.Applications 1.Global Income Distribution in 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”

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) SkilledUnskilled Young6040 Old3070 SkilledUnskilled Young6040 Old

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”

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”

Step 4: Microsimulations Once shocked with: 1.Age/demographic reweighting; 2.Sector and factor-specific productivity growth; 3.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

3.Applications 1.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. Subgroup Decomposition YearGiniTheil Between Countries Within Countries (75%)(25%) (70%)(30%)

3.Applications (ctd.) 2.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.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. 2.Consider an alternative notation to describe Step 2. I have a feeling that this can be written down just as formally, but more clearly. 3.Spend a little more time spelling out the micro-simulation stage, with examples. 4.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.