The rising demand for skills and faster growth of skilled wages have important consequences for the distribution of income and poverty reduction. Growing wage differentials increase income inequality, which means that much of the inequality reduction recorded in the baseline scenario is reversed. In the MDG achievement scenario, the Gini coefficient reaches 0.386 in 2015, up from 0.380 in the baseline, although still down from 0.389 in the base year.<sup>17</sup> This is one of the reasons why the MDG achievement scenario fails to deliver more impressive poverty reduction: the poverty headcount is equal to 9.9% in 2015, very close to the 10.3% of the accelerated growth scenario.

The distribution of gains in per capita consumption, shown by the higher of the two growth incidence curves in Figure 3, makes it clear that, adjusting for the differences in mean growth, the MDG-achievement scenario is _less_ pro-poor than the baseline. Although many more people escape poverty in the MDG achievement scenario than in the baseline and the gains among the poorest of the poor are comparable across the two scenarios, the above-average gains enjoyed by the poor in the former simulation begin to converge towards the average much quicker than in the latter scenario. However, this slight increase in inequality relative to the accelerated scenario must be viewed in proper context. First, as it was done for the baseline case, it is possible to simulate the effect of the MDG achievement scenario on poverty in a case where the distribution of the gains is equal for all individuals (i.e., the original distribution of 2004 is unchanged). In this case poverty reduction would be less pronounced and the headcount for 2015 would be equal to 10.2%. Second, it is important to emphasize that the results presented here do not necessarily not imply a “worsening” of income inequality. These outcomes are underpinned by rising premiums for education, which in the long term will encourage more children to attend school and potentially raise economy-wide productivity levels: there is such thing as a “good” increase in income inequality justified by more productive human capital At the same time, the results highlight the potential need for public programs to help individuals cope with rising income disparities, particularly in the short term.

Despite the many complimentary relationships between human development and growth, important trade-offs exist between these two broad public policy objectives. The allocation of public expenditure across various activities becomes particularly relevant under fiscal space constraints. One may broadly distinguish between investing in activities that are beneficial to growth over the short to medium term as well as the long term (such as infrastructure) and HD-related activities that do not have immediate feedbacks to growth, although are beneficial to growth in the long term. As already mentioned, MAMS does not include an explicit policy tool for the government to target the poverty MDG, although expenditure shifting from HD-intensive activities to infrastructure may result in accelerated growth that is normally associated with a faster rate of poverty reduction. Starting from the baseline scenario, Figure 5 presents a series of MAMS simulations aimed at quantifying this HD-poverty trade-off for Ghana.<sup>18</sup> For

> 17 Similarly, the Theil index in the MDG achievement scenario rises to 0.260 from 0.250 in the baseline (both numbers refer to 2015), although still down from 0.263 in 2004.

> 18 This implies that MDG-related foreign grants are kept at zero (their initial level) throughout the model horizon, while foreign and domestic borrowing as well as tax rates remain at baseline levels. Public

19