# Annex 1 Global Income Distribution Dynamics Model

We use the Global Income Distribution Dynamics (GIDD) model, developed by Bussolo, De Hoyos and Medvedev (2010), to estimate distributional effects. GIDD is a “top-down” micro simulation framework that exploits heterogeneity observed in household surveys to distribute macroeconomic shocks. These shocks are aligned with a macroeconomic model such as the CGE model used in this paper. More specifically, we impose consistency between the GIDD and the CGE models in this paper in various ways. First, both use the same United Nations projections in aggregate population and age and education structures. The GIDD then uses estimates from the CGE model as inputs into the household model. In particular, as inputs into the calculation of changes in per capita household income, the GIDD uses CGE model estimates of differentiated wages for skilled, unskilled, agricultural and non-agricultural labor and changes in the prices of agricultural and non-agricultural goods.<sup>20</sup> Finally, all household incomes are adjusted proportionally so that the percentage change in the aggregate of household incomes in the GIDD is consistent with the CGE model’s estimate of the percentage change in real income.

GIDD was developed by the World Bank and was inspired by previous efforts involving simulation exercises (Francois Bourguignon, Bussolo, & Pereira da Silva, 2008; François Bourguignon, Ferreira, & Leite, 2008; Davies, 2009). Previous examples of application using CGE outputs and GIDD include the effect of agriculture distortions in the global economy (Bussolo et al., 2010; Dessus, Herrera, & de Hoyos, 2008), the effect of demographic change on Africa (Ahmed, Cruz, Go, Maliszewska, & Osorio Rodarte, 2014) and external and internal shocks in Africa (Devarajan et al., 2015).

The first step in the microsimulation exercise is to implement a set of changes in the household surveys’ demographic structure. The population growth adjustment is particularly important in countries with high fertility rates, such as those in Sub-Saharan African. In practical terms, the adjustment for population growth allows the analysis to explicitly take into account changes in the size of the working-age population. We perform population and education projections during the first stage of the micro-simulation model and in creating the Business as Usual scenario for the CGE model. For each country, we construct the demographic profile in two steps. First, the age and gender composition is exogenously determined following medium variant estimates from the World Population Prospects (United Department of Economic and Social Affairs Population Division, 2015). In a second step, following Bourguignon and Bussolo (2013), country-specific educational profiles are constructed using initial educational achievement levels observed in the household surveys with some conservative yet simple assumptions about educational progress.

More specifically, starting with the household surveys, the country-specific demographic profiles are constructed by partitioning each country’s total population into: (1) 16 age-groups (0-4, 5-9, 10-14, ..., 6569, 70-74, 75; (2) two gender groups; and (3) three different levels of educational attainment: (i) Noeducation or primary; (ii) secondary; and (iii) tertiary education. As mentioned earlier, age and gender totals are based on data from the United Nations’ (2015) medium variant population projections. In terms of education, we assume that as the population ages, the average educational attainment in a country increases through a pure pipeline effect, as younger and more educated cohorts replace older cohorts. For example, if at time _t_ half of the population in the cohort formed by individuals between 25 and 30 years of age have post-secondary education, then after 10 years (at _t_ +10), half of the population between 35 and 40 will have post-secondary education. Furthermore, for younger cohorts we imposed the assumption that

> 20 The aggregate returns to capital and labor in the CGE model, however, are not aligned with the household data. See Rutherford & Tarr (2008) for a discussion of the impact of reconciliation of the factor returns in the household data with the input-output table.

30