vintages, land, and natural resources. The latter are sector-specific, while land has limited transformation across agricultural uses. New capital vintages and skilled labor are freely mobile across sectors, while the mobility of old vintages is limited. Unskilled workers are freely mobile within farm and non-farm activities, but the movement from farm to nonfarm employment is limited with a Harris-Todaro migration function. Consumer demand is modeled with a nesting of Cobb-Douglas and constant-differences-in-elasticity (CDE) utility functions. International trade is specified with nested CES and CET functions which allow for limited substitution between domestically produced goods and imports or exports (the Armington assumption). The model contains an integrated climate module which links CO2 emissions to changes in global temperature with feedbacks to agricultural productivity (following the approach of Nordhaus and Boyer, 2000, and Nordhaus, 2007, and calibrated with estimates in Cline, 2007).

The current version of the model is based on the GTAP database with a 2004 base year, which has been aggregated to 26 country/regions and 22 sectors (Table 8). The model is solved forward, in recursive fashion, until 2010, with labor force and population growth rates lined up to the UN’s medium variant population forecast. TFP growth in agriculture is set at 2.5 percent per annum with no differentiation across sectors or regions, based on estimates in Martin and Mitra (1999). Labor-augmenting productivity growth in the other sectors is endogenized to achieve the World Bank's forecasted growth of real GDP. The macro closure has government expenditures as a share of GDP fixed at 2004 levels, while a demographically-driven savings function determines the allocation of private expenditures between consumer demand and domestic investment. The manufactured export price index of the high-income countries is the numéraire.

The distributional analysis is carried out with the World Bank’s GIDD model, which generalizes the existing CGE-microsimulation methodologies—e.g., Bourguignon, Bussolo, and Pereira da Silva (2008), Chen and Ravallion (2003), and Bussolo, Lay, and van der Mensbrugghe (2006)—at the global level and is described in detail in Bussolo, De Hoyos, and Medvedev (2008a).<sup>11</sup> The conceptual framework of the model is depicted in Figure 6. The expected changes in population structure by age (upper left part of Figure 6) are exogenous, meaning 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 6). Next, new sets of population shares by age and education subgroups are computed and household sampling weights are re-scaled according to the demographic and educational changes above (larger box in the middle of Figure 6). The impact of changes in the demographic structure on labor supply (by skill level) is incorporated into the CGE model, which then provides a set of link variables for the micro-simulation: (a) change in the allocation of workers across sectors in the economy, (b) change in returns to labor by skill and occupation, (c) change in the relative price of food and non-food consumption baskets, and (d) differentiation in per capita income/consumption growth rates across countries. The final distribution is obtained by applying the changes in these link variables to the re-weighted household survey (bottom link in Figure 6).

> 11 The detailed description of the methodology can also be found at http://www.worldbank.org/gidd

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