a certain number of unskilled workers migrate from one segment to the other in response to changes of the wage differentials across the segments.<sup>9</sup>

**This rich set-up can capture the changes in wages for different types of workers** . Since skilled-unskilled and agriculture–non-agriculture wage gaps represent important drivers of inequality, the set-up enables the delineation of changes in the income distribution of each economy. The evolution of the labor force is in line with the change of working age population.<sup>10</sup> The growth of supply of skilled and unskilled workers is consistent with this overall growth of the labor force present in the microsimulation model.

**Capital accumulation, labor expansion, and productivity changes determine the growth dynamics in the model** . In each period, capital stock is equal to the depreciated capital stock from the previous period augmented with the investment. Investment is driven by savings which, in turn, depend on per capita income growth and the population youth and elderly dependency ratios. In contrast to national savings, foreign savings are exogenous.

**The model assumes heterogeneity of productivity trends across sectors** . Productivity in agriculture is assumed to be factor-neutral and exogenous and is set to estimates from empirical studies (e.g., Martin and Mitra (1999)). Productivity in manufacturing and services is labor-augmenting; it is skill-neutral but sector-biased. In the case of agriculture, productivity growth averages 2.5% per annum for all countries. In manufacturing and services changes in productivity are country-specific and based on past trends. Following the broad findings of Bosworth and Collins (2003) productivity growth in manufacturing is assumed to grow about three percentage points faster than in services.<sup>11</sup>

**This model calculates wages of skilled and unskilled workers by combining firms’ demand for labor with the aggregate change in supply of these two types of workers** . In addition to the changes in rewards, the computable general equilibrium model also calculates the overall economic growth and sectoral reallocation of labor.<sup>12</sup> As already described above, changes of these variables – the Linkage Aggregate Variables (LAVs) – are used as inputs in the final step of the microsimulation.

9 We do not distinguish agricultural sector and rural regions, which may neglect the presence of rural non-farm activities. See, for example, Reardon, Stamoulis, and Pingali (2007).

10 This means that participation rates are constant. This is a conservative assumption given that the overall labor force participation rates are likely to increase as more women enter the labor force in developing countries or due to the changes in the retirement age in high income countries. The United Nations population projections are disaggregated by age groups and gender. They include net migrations per 1,000 people by country/region. We do not provide alternative migration scenarios, which can be complex and beyond the scope of the paper. However, migration can certainly be handled by the methodology, see for example, Ahmed et al. (2016).

11 The dynamic calibration allows the model to reproduce, in its baseline scenario, GDP growth rate trends that are in line with available long-term projections such as, for example, the growth projections under the Shared Socio-Economic Pathway Scenario 2 of Dellink et al. (2017). In fact, during the dynamic calibration the model targets these exogenous growth rates of GDP by endogenously determining the labor augmenting productivity factor described in the text. In the counterfactual scenarios, the real GDP growth rate is endogenized, and the uniform productivity variable is exogenized, and simulated to grow at the rate determined in the baseline scenario. The real growth rate can thus change depending on the other shocks considered in the scenario.

12 In the GIDD microsimulations, workers move across sectors to achieve the proportions of employment in agriculture and non-agriculture calculated by the CGE model. Note that these inter-sectoral movements are net of the sectoral shifts already generated by the reweighting procedure. The microsimulation procedure to select which specific worker moves is based on a probabilistic model described in detail in (Bussolo, De Hoyos, and Medvedev 2010).

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