share of the lowest quintile, may be more appropriate. Data availability, however, dictates the choice.<sup>12</sup> The growth determinants come from Loayza, Fajnzylber, and Calderon (2002).

Our regressions are conducted using non-overlapping five-year periods spanning the years 1960-2000. Income and inequality data are taken from the latest available date within the given period. Growth and inequality determinants are averages for the fiveyear period in question. These transformations would reduce the sample to 291 observations for 87 countries for which we have at least 2 consecutive Gini coefficients. If we also eliminate countries with less than 3 observations (note that as implied by (16) and (17) we need a minimum of three observations per cross-section unit to run the GMM estimator outlined above), the sample is further reduced to 194 inequality observations for 61 countries.

The original sample of growth determinants is higher and includes data for 78 countries and the total number of observations is 350. When both databases are merged, however, the number of observations available for estimation purposes is reduced to 134 cases and 41 countries. Working with five-year averages reduces the problems associated with the original panel where 24 countries would account for more than half of the 953 observations. Further, the use of at least three observations for each country (i.e. 15 years) gives a long-run perspective to the problem that is lost with the original panel, where more than 30 countries have only one observation.

Admittedly, using five-year periods also brings some complications. For example, the sample size is notoriously reduced from the original inequality database. The reduction in the number of observations is particularly important when one considers the estimator proposed above, since we are working with a large number of variables and a small crosssection dimension (41 countries in some cases). For example, if we were to consider the explanatory variables as predetermined, even limiting the maximum number of lagged levels to be used as instruments to two, we would still end up with 170 instruments in some of our specifications. Not only does the problem then become too large to estimate, but also the excessive number of overidentifying restrictions relative to the sample would dramatically affect the performance of the GMM estimator. Against this background, the results presented below for the models including all the policies treat as a predetermined variable only the lagged dependent variable. Since the model is still overidentified, we can test the validity of the hypothesis that the proposed instrument set is uncorrelated with the error term, using the Sargan test for overidentifying restrictions. It must be noted in this regard that the employed specification tests generally support the econometric models. That is, Sargan and second-order correlation tests cannot reject the hypothesis that the models are well specified and that our instrument set is valid.<sup>13</sup> Table 2 presents some descriptive statistics for the data. The logged Gini coefficient, would have a mean value of -.977 (equivalent to a Gini of about .37) and show considerable dispersion (the 2 standard deviation confidence interval would range from about -.43 to -1.51). The maximum logged Gini in the sample is -.296 and the minimum is -1.79. There is also some evidence of a skewed distribution with a long and thin upper

> <sup>12</sup> For example, using the growth rate in the income of the lowest quintile of the population would leave us with only one third of the observations we manage to use for the Gini regressions. 13 The only exception to this rule is the regression of growth on the initial value of income.

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