lines (US$ 2, US$ 3 and US$ 4 per person per day). The rest of the variables used in the regressions are taken from Loayza _et al_ . (2002), except for the education variables, which are from Barro and Lee (2001).

Our regressions are conducted using an unbalanced panel of non-overlapping fiveyear periods spanning the years 1960-2000. Income and inequality data pertain to the latest available year within each given period. The original income data is from the PWT 6.1 whereas the original inequality data comes from Dollar and Kraay’s (2002) database on inequality.<sup>19</sup> Table 1 presents summary statistics for income, inequality and the constructed poverty measures.<sup>20</sup> The table shows the wide range of per capita income levels in the sample – from less than $500 (Tanzania in the mid 1990s) to almost $35,000 (Luxembourg in the mid 1990s). The median observation corresponds to Mexico in the mid 1970s, with per capita income about $5,500.<sup>21</sup> Regarding inequality, the Gini indices range from a low 0.17 (the Slovak Republic in the early 1990s) to a high 0.76 (Namibia in the mid 1990s), with a median of 0.38. Regarding the poverty figures, by construction they must rise with the poverty line and decline as the poverty measure changes from P0 to P2 (i.e., as one considers more bottom-sensitive measures). Table 1 shows that, depending on the poverty line used, median headcount poverty ranges from 2.4 percent (using US$2 per day as the poverty line) to about 13 percent (with US$4 per day), whereas the median poverty gap ranges from less than 1 percent (US$2) to about 10 percent (US$4), and the square poverty gap from 0.1 percent (US$2) to slightly above 1 percent (US$4). In turn, the ranges of the various poverty measures run from a minimum of zero (reflecting the presence of some high-income countries in the sample) to a maximum whose value depends on the particular poverty measure under consideration – from 80 to 100 percent for P0, 50 to 60 percent for P1, and 40 to 60 percent for P2.<sup>22</sup>

# **IV. Results**

Table 2 reports estimates of the growth equation using Perotti’s (1996) set of control variables, and with poverty measured by the headcount ratio (P0). The instrument sets for GMM estimation are constructed under the assumption that the time-varying disturbance is serially uncorrelated. The first three columns of the table report the estimates obtained using each of the poverty lines under consideration (US$2, US$3 and US$4 per day, respectively)

> 19 The data sources used to compute the inequality indices show a high degree of diversity across countries. The original data is sometimes based on income figures and other times on expenditure figures; income is net of transfers and taxes in some cases and not in others; the unit of analysis may be the individual or the household, etc. To correct at least in part for this heterogeneity, we adjust the original data as described by Dollar and Kraay (2002).

> 20 Preliminary analysis prompted us to remove two outliers: Sierra Leone (1990-1995) and Moldova (19901995). Their inclusion or exclusion from the sample, however, is of no material consequence for the paper’s main empirical results.

> 21 The figure in the text is the median income from the pooled (unbalanced) sample. However, the crosscountry median (i.e., the median of the country averages) is very similar ($5,400).

> 22 The maximum corresponds in all cases to Tanzania.

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