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I N C O M E

D I S T R I B U T I O N ,

I N E Q U A L I T Y ,

A N D

T H O S E

L E F T

B E H I N D

Box 3.2 Aggregate economic performance: distribution matters

I

n measuring social welfare, economists have struggled to provide simple statistics that reflect changes in both aggregate income (that is, the gross national product—GNP) and distribution. This box presents a graphic approach—the growth incidence curve (GIC)—that, by jointly measuring size and distribution effects, provides an intuitive evaluation of welfare changes. The basic idea behind the GIC was already present more than 30 years ago in a well-known study entitled “Redistribution with Growth.” In this study, Chenery and others (1974) proposed to use the weighted sum of the growth of all income groups as a summary measure for changes in social welfare. In a typical developing country the top two quintiles—the richest 40 percent of the population—would normally account for about three-quarters of total GNP. Therefore the GNP growth rate, the most commonly used index of performance, measures the income growth of the richer minority and “is not much affected by what happens to the income of the remaining 60 percent of the population” (Chenery and others 1974: 40). The trends observed in aggregate economic performance will differ according to the weights associated to the various income groups. Chenery and others (1974) found that when using GNP growth rates, where the weights are income shares of the initial distribution,

Brazil, Mexico, Panama, and República Bolivariana de Venezuela showed strong positive growth. However, because of their worsening income distributions, when equal weights (0.2 for each quintile) or poverty weights (0.6 for the poorer 40 percent, 0.3 for the next 40 percent, and 0.1 for the richest quintile) are used, these countries display much lower welfare increases. Conversely, countries enjoying improving income distribution during the 1960s and 1970s, such as Colombia, El Salvador, Sri Lanka, and Taiwan (China), scored better when their performance was measured with indicators that gave more weight to poorer individuals. This weighting idea underlies the GIC, originally proposed by Ravallion and Chen (2003). The GIC is a graphical representation of the growth rate in income or consumption at each percentile of the distribution. It can summarize the distributional effects of income growth by plotting the cumulative share of the population (the x-axis) against the income growth rate of the nth percentile of the distribution (the y-axis) when the population percentiles are ranked in ascending order of income. Ravallion and Chen (2003) show that a measure of pro-poor growth can be obtained by integrating under the GIC. However, a simple comparison of the growth rate of the poorest percentiles against the mean

Growth incidence curves Mexico

Brazil

% per capita income growth

% per capita income growth

0

15 Doha rural FTAA urban

⫺1

FTAA all

10 Doha urban Doha all

FTAA rural

⫺2

5

⫺3

0 0

20

40

60

80

% population, ranked by per capita income

100

0

20

40

60

80

100

% population, ranked by per capita income

Source: Bussolo and Medvedev 2006. Note: FTAA ⫽ Free Trade Area of the Americas.

(continued)

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Global Economic Prospects 2007  

Managing the Next Wave of Globalization

Global Economic Prospects 2007  

Managing the Next Wave of Globalization

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