per capita growth is a function of several demographic effects on growth. These effects include the impact of the congestion of fixed factors, capital dilution, dependency ratios, experience, lifecycle labor, childcare, and child quality.<sup>7</sup> Demographic projections from different fertility scenarios are presented as exogenous shocks that determine economic outcomes through these seven channels. Another recent simulation model of demography and growth is the DemDiv model of Moreland et al. (2014), which links several statistically estimated reduced form economic relationships to demographic variables (such as total fertility rates).

While these models clearly delineate specific channels by which demographic variables can influence economic outcomes, they are unable to capture certain effects which may be important from a development policy perspective. Specifically, they do not account for general equilibrium effects (GE) in the economy which are bound to arise from the interaction of different demographic and economic factors, and from the interaction of different economic agents that may lead to structural shifts in the economy. For example, the interaction of labor force participation rates and labor demand from different sectors for different kinds of workers will interact with demographically influenced changes in the quantity and quality of the labor supply, as well as the rate of capital formation, to influence factor returns, and hence growth.

In contrast, computable general equilibrium (CGE) analyses are able to capture these effects, and provide insights on structural shifts in economies over time.<sup>8</sup> McKibbin (2006), Tyers and Shi (2007), and World Bank (2015a) use dynamic CGE analyses to examine the contribution of future demographic change to growth. McKibbin (2006) finds that demographic change leads to lower GDP for many high-income countries. For example, Japan’s GDP in 2050 is expected to be 28 percent smaller than it was in 1985. Focusing on Sub-Saharan Africa, Tyers and Shi (2007) find that demographic change could lead to a 15 percent higher GDP over 1995 and 2030. Also focusing on Africa, Ahmed et al. (2016) goes further than these CGE analyses and integrate a household microsimulation model to global dynamic model to examine the contribution of demographic change to poverty reduction, as well as growth. In all these models, demographic changes are presented as exogenous changes in different scenarios, thus ignoring the endogeneity between economic growth and demographic variables, such as fertility rates.

This paper will build on Ahmed et al. (2016), by considering the interaction of policy outcomes under different economic scenarios rather than just decompose the possible contributions of demographics. Moreover, it will estimate and compare the effects on demographic change on poverty reduction across those different economic scenarios.

> 7 Ashraf, Weil, and Wilde (2013) illustrate the model by examining the specific case of Nigeria and also provides a very useful review of the growth and demographics literature. In the paper, the congestion of fixed factors and capital dilution are also referred to as “Malthus” and “Solow” effects, respectively.

> 8 Devarajan and Robinson (2013) provide a survey of how computable general equilibrium models built on microeconomic theory have been applied to other development issues. Recently, Devarajan et al. (2015) used the LINKAGE-GIDD modeling framework to stress-test the sensitivity of income per capita growth in African economies to different exogenous shocks, such as drought and conflict.

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