Using these results, Tables 9 and 10 report the simulated values of expression (15) for alternative values of the Gini coefficient and the mean income / poverty line ratio, using the variance and covariance patterns of growth and inequality changes shown in the preceding table. Table 9 uses the values from the Dollar and Kraay sample, while Table 10 uses the values from Povmonitor. In both tables, a number close to 1 means that in the scenario in question changes in the poverty measure of interest are mainly driven by growth, whereas a number close to zero means that they are mainly driven by changes in inequality.<sup>24</sup> The simulations in Tables 9 and 10 suggest that, consistent with the previous discussion, in poorer and more equal countries growth accounts for a larger share of the variance of poverty changes. Conversely, inequality changes tend to play a more prominent role in richer and/or more unequal countries. This again suggests that in the former countries growth should be expected to be the main driver of poverty reduction, while the opposite would happen in the latter countries.

Notice also that, for any given configuration of Gini coefficient and per capita income / poverty line ratio, the relative contribution of growth to the overall variance of poverty changes declines as the poverty measure of interest varies from headcount poverty to the poverty gap and then its square. As noted by Kraay (2005), this is a natural consequence of the fact that more bottom-sensitive poverty measures place more weight on changes in the distribution of income than on changes in average income.

So far we have implicitly viewed alternative values of (ν _/z_ ) as reflecting different levels of average per capita income with a given poverty line. But they could also be interpreted the other way around, namely reflecting alternative poverty lines with a given level of average per capita income. In this view, the numerical results above imply that as the relevant poverty line _z_ becomes more generous -- i.e., as (ν _/z_ ) declines -- the relative role of growth in the overall variation of poverty changes must go up as well, and other things equal this offers a rationale for shifting poverty reduction priorities in favor of growth-oriented policies. In the limit, as (ν _/z_ ) falls to zero – so that the poverty rate approaches 1 – distributional change becomes completely ineffective for poverty reduction. In other words, given the choice of poverty measure _P_ α and the initial conditions in terms of average income and inequality, the location of the poverty line is a key determinant of the relative effectiveness of growth and redistribution for poverty reduction.

# **V. Conclusions**

The focus on poverty reduction as the key objective of development policy has opened a debate on the relative merits of aggregate growth and distributional change as anti-poverty strategies, and the conditions under which one may be more effective than the other. In this paper we have reexamined that question using a parametric approach to model the distribution of per capita income. The parametric approach has a long tradition in the literature, going back over a century. One of its key advantages is that it allows a

> 24Note that in this calculation we are implicitly assuming that the same variance / covariance pattern applies regardless of the particular configuration of Gini coefficient and mean income/ poverty line ratio.

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