The LINKAGE model has a 2001 base year and relies on the Global Trade Analysis Project (GTAP) 6.1 database<sup>1</sup> to calibrate initial parameters. A scenario is developed by solving for a new equilibrium in each subsequent year through 2030. The growth in the labor force is driven by demographics—essentially given by the growth of the working age population. Differentiated growth of skilled versus unskilled workers is partly driven by demographics and partly driven by changes in education rates. As education levels rise (in the younger populations), they eventually increase relative growth of skilled workers once they enter the labor force (and older unskilled workers retire). Savings decisions are partly driven by demographics—rising as youth dependency ratios fall and falling as elderly dependency ratios rise. Investment rates are driven by changes in growth rates (the accelerator mechanism) and differential rates of return to capital. Net foreign savings is the difference between domestic savings and investment.

Productivity is derived by a combination of factors, but is also partially judgmental. First, agricultural productivity is assumed to be factor-neutral and exogenous and is set to estimates from empirical studies. Productivity in manufacturing and services is laboraugmenting and a constant wedge is imposed between productivity growth in the two broad sectors with the assumption that productivity growth is higher in manufacturing than in services. Finally, the model assumes that energy efficiency improves autonomously by 1 percent per year in all regions and that international trade costs also decline by 1 percent per year.

# 2.2 GIDD: linking macroeconomic outcomes to micro survey data

The GIDD framework is based on micro-simulation methodologies developed in the recent literature, including Bourguignon and Pereira da Silva (2003); Ferreira and Leite (2003, 2004); Chen and Ravallion (2003); and Bussolo, Lay, and van der Mensbrugghe (2005). The starting point is the global income distribution in 2000, assembled using data from household surveys for 84 countries and data on income groups (usually vintiles) for the remaining countries; the final sample covers 91 percent of the world population (see Annex 1 for a full detailed list).<sup>2</sup> The hypothetical 2030 distribution is then obtained by applying three main exogenous changes to the initial distribution: (a) demographic changes, including aging and shifts in the skill composition of the population; (b) shifts in the sectoral composition of employment; and (c) economic growth, including changes in relative wages across skills and sectors.

The empirical framework is depicted in Figure 1. Our simulations will include the expected changes in the shares of population by groups formed by age and education characteristics (top boxes of Figure 1). The future changes in population shares by age (upper left part of Figure 1) are taken as exogenous from the population projections provided by the World Bank’s Development Data Group. Therefore, we assume that fertility decisions and mortality rates are determined outside the model. The change in shares of the population by education groups incorporates the expected demographic changes (linking arrow from top left box to top right box in Figure 1). Next, new sets of

> 1 See <u>www.gtap.org</u> for details.

> 2 Throughout the paper, when we talk about the global distribution, we are indeed referring to the GIDD’s sample covering 91 percent of the world population.

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