We use two approaches to measuring the income of the poor, where we define the poor as the poorest 20% of the population.7 For 796 country-year observations covering 137 countries, we are able to obtain information on the share of income accruing to the poorest quintile constructed from nationally representative household surveys that meet certain minimum quality standards. For these observations, we measure mean income in the poorest quintile directly, as the share of income earned by the poorest quintile times mean income, divided by 0.2. For a further 158 country-year observations we have information on the Gini coefficient but not the first quintile share. For these observations, we assume that the distribution of income is lognormal, and we obtain the share of income accruing to the poorest quintile as the 2 0 th percentile of this distribution.<sup>8</sup> Our data on income distribution are drawn from four different sources. Our primary source is the UN-WIDER World Income Inequality Database, which is a substantial extension of the income distribution dataset constructed by Deininger and Squire (1996). A total of 706 of our country-year observations are obtained from this source. In addition, we obtain 97 observations originally included in the sample designated as 'high-quality" by Deininger and Squire (1996) that do not appear in the UN-WIDER dataset. Our third data source is Chen and Ravallion (2000) who construct measures of income distribution and poverty from 265 household surveys in 83

> 7 An altemative would be to define 'the poor' as those below a fixed poverty line such as the dollar-a-day poverty line used by the World Bank. We do not follow this approach for two reaons. First, constructing this measure requires information on the shape of the entire lower tail of the income distribution, and we only have at most five points on the Lorenz curve for each country. Second, even if this information were available or were obtained by some kind of interpolation ofthe Lorenz curve, the relationship between growth in average incomes and growth in this measure of average incomes of the poor is much more difficult to interpret. For example, ifthe distribution of income is very steep near the poverty line, distribution-neutral growth in average incomes will lift a large fraction of the population from just below to just above the poverty line with the resul **t** hat average incomes of those below the poverty line fall. Ali and Elbadawi (2001) provide results using this measure of incomes of the poor and unsurprisingly find that incomes of the poor according to this measure rise less than proportionately with average incomes. Another altemative would not be to examine average incomes of the poor, but rather the fraction of the population below some prespecified poverty line. In this case, it is well-known that the elasticity of the poverty headcount with respect to average income varies widely across countries and depends among other things on the level and distribution of income.

8 If the distribution of income is lognormal, i.e. log per capita income - N(p,a), and the Gini coefficient on a scale from 0 to 100 is G, the standard-deviation of this lognormal distribution isgiven by o = a 2_.(_1+GI100) where o(.) denotes the cumulative normal distribution function. (Aitcheson and Brown (1966)). Using the properties of the mean of the truncated lognormal distribution (e.g. Johnston, Kotz and Balakrishnan (1994)) it can be shown that the 20' percentile of this distribution is given by 0(q-1 (0. 2) - c).

12