Chapter 1: Introduction
If one uses the twin-standards interpretation of inequality, then this approach is equivalent to requiring an associated inequality measure to fall. Let a and b be the two income standards, with a ≤ b, where one of the two is the mean, and let I be an inequality measure based on these twin standards (so that I is a monotonic transformation of b/a). Growth is pro-poor if a grows faster than b, which is equivalent to a falling ratio b/a and, hence, to a decrease in the associated inequality measure I. For example, one might describe growth as pro-poor if the Sen mean grew faster than the mean, and hence the Gini coefficient decreased. Or we could note that the Euclidean mean grew slower than the mean, and hence the coefficient of variation declined. This is basically the inequality-based approach to pro-poor growth we have discussed above. A second poverty-based approach compares the actual change in poverty to the level that might be expected along a counterfactual growth path. Suppose that the distribution of income changes from x to x′ and that this leads to a change in measured poverty from P to P′. Construct a counterfactual income distribution x″ that has the same mean as x′ and the same relative distribution as x, and let P″ be its level of poverty. The growth from x to x′ is then said to be pro-poor if the resulting change in poverty P′ − P exceeds the counterfactual change P″ − P; in other words, the rate of poverty reduction from actual growth is faster than the counterfactual rate from perfectly balanced growth. Of course, the relevance of this conclusion depends on the choice of counterfactual distribution and its assumption that the relative income distribution should not change. A related technique is often used to analyze the extent to which a given change in poverty is primarily due to changes in the mean (the growth effect) or changes in the relative distribution (the distribution effect). As before, let x″ be the counterfactual distribution having the same relative distribution as the initial distribution x and the same mean as the final distribution x′. The overall difference in poverty P′ − P can be expressed as the sum of the growth effect P″− P and the distribution effect P′ − P″. This breakdown first scales up the distribution x to the mean income of x′ to explore how the uniform growth in all incomes alters poverty. Then it redistributes the income to obtain x′, and explores how the distributional change alters poverty. Other breakdowns are possible using a different counterfactual distribution or, indeed, a different order of events (redistribute first, then grow). However, this version has the advantage of being easy to interpret and can be expressed as the sum of two component terms without a troublesome residual term.
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