their impact on savings as a share of GDP is the same. Investment tends to be lower in the low growth case, because savings as a share of GDP also depends on the real GDP per capita growth rate, and this latter variable is lower by construction than in the high growth scenario.

# **4.2 Impacts on Poverty**

Considering the impacts of demographic change on poverty reduction, a few patterns can be noted from the estimates of the poverty headcount and headcount rate for SSA (Table 5). When the high growth baseline is considered, the poverty rate falls from 51.8 percent in 2007 to about 17 percent by 2030. This is mirrored by a decrease in the absolute number of poor as well, even though the region’s population is doubling in the 23 year period. About 3.25 percentage points of the decline can be attributed to the demographic dividend effects. When the low growth baseline is considered, the demographic dividend for poverty reduction is greater. However, poverty reduction is much smaller. The headcount rate declines by only 16 percentage points, and the poverty headcount rises. For comparison purposes, Basu (2013) estimated the 2030 poverty headcount of Sub-Saharan Africa to be 36.5 percent if African countries grew at the average annual rate of the past 20 years and 26.4 percent if the growth rate followed the performance of the past 10 years.

**Table 5: Poverty in Sub-Saharan Africa* under Alternative Scenarios at $1.25 a day** **<u>poverty line</u>**

|**Scen**|**ario**<br>**Poverty Headcount**<br>**(millions)**|**Poverty Headcount rate**<br>**(%)**|
|---|---|---|
|Base Ye|ar, 2007<br>361|51.9|
||Baseline<br>452|36.7|
|Low Growth, 2030|Age Structure Freeze<br>512|41.6|
||Dividend<br>-60|-4.9|
||Baseline<br>210|17.1|
|High Growth, 2030|Age Structure Freeze<br>250|20.3|
||Dividend<br>-40|-3.2|

Note: * The country coverage in GIDD is slightly smaller than the full set used by the World Bank’s PovcalNet tool for official estimates. Hence, the total populations and poverty headcount may differ from what would be obtained through PovcalNet. The African countries covered by the GIDD database are Angola, Burkina Faso, Botswana, Côte d'Ivoire, Cameroon, Cape Verde, Ethiopia, Gabon, Ghana, Gambia, Kenya, Madagascar, Mozambique, Mauritania, Mauritius, Malawi, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, Swaziland, Chad, Togo, Tanzania, Uganda, South Africa, DR Congo, and Zambia. Source: Simulation results For the region as a whole, Table 5 suggests that poverty reduction is more sensitive to demographic change even though the growth dividends seem similar across the two baselines (columns II and IV of Table 4). This is explained by two factors: the shape of the 2030 income distributions in the high and low growth baselines and the means of those distributions.

The headcount poverty rate is determined from the estimated income distribution of the economy, by estimating the mass of the distribution that is below the $1.25 a day poverty line. So when the mean of a given distribution falls by a certain amount, there is a leftward shift in the mass of the left tail of the distribution that is below the poverty line. The fatter is the left tail of the distribution – that is, the greater the inequality in the economy – the more people will fall into poverty for the same reduction in mean income. At the same time, the poverty rate in the initial distribution is also important. If the initial distribution is for an economy with very little inequality (i.e. a thin left tail), then the same shift in mean will lead to a smaller change in poverty than for an economy with much more inequality (i.e. a fat left tail).

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