lower inequality levels). When we consider initial income and its square among the explanatory variables, the last column in table 6 would indicate that there appears to be an inverted-U relationship between the Gini value and income. In fact, this relationship would indicate that the Gini coefficient rises with GDP per capita values of less than US$2940 (1985 dollars) and declines thereafter. We also find that including the initial level of GDP among the regressors does not significantly affect the estimates of the coefficients of the policy variables under consideration. We next consider these.

The results for education would indicate that countries with better education (as measured by secondary enrollment rates) would be less unequal. The estimated coefficients are very similar in the three empirical models for inequality and of a similar order of magnitude to those in the growth model. These results are in line with those of Datt and Ravallion (2002) and Lundberg and Squire (2003). Using 20 household surveys for India’s 15 major states, Datt and Ravallion (2002) also conclude that poor basic education is an impediment to the ability of the poor to participate in opportunities for economic growth. Lundberg and Squire (2003) also find that education is likely to be correlated with both faster growth and lower income inequality.

The estimated coefficient for financial development is always significant and suggests that progress on this front would be associated with increases in inequality. Possible explanations for this finding are: (i) the more educated (and likely richer) are able to exploit the new opportunities better; (ii) adoption of capital intensive technologies is likely to substitute for unskilled labor. Thus to some extent, our results would support the findings of Behrman, Birdsall and Szekely (2001) who used household data over more than two decades for 17 Latin American countries to estimate that the financial sector liberalization reforms that took place during the 1990s negatively affected income distribution. Within a cross-country framework, Dollar and Kraay (2002) also find that financial development negatively affects inequality.

Contrary to financial development, where most of the available empirical results already suggest that more financial development implies more inequality, the available results for trade openness point toward less than unanimous conclusions. Dollar and Kraay (2002) find that trade openness positively affects income distribution. A similar result is obtained by Behrman, Birdsall and Szekely (2001) for a set of Latin American countries. However, Sanchez and Schady (2003) find the opposite result in six Latin American countries, where trade volumes would negatively affect inequality. Spillimbergo et al (1999) and Barro (2000) also find that trade openness would be associated with higher inequality, whereas Lundberg and Squire (2003) conclude that there seems to be a positive correlation between the Sachs-Warner index of trade liberalization and the Gini coefficient. Our results give some support to the view that trade may lead to higher inequality. The estimate of the trade coefficient is always positive and significant. Moreover, its magnitude is sizeable in the sense that it is larger than the coefficient found in the growth regressions.

As with trade openness and financial development, our results suggest that cutting the size of the government is likely to lead to faster growth, but also to increases in inequality. The estimated coefficient for this variable is always statistically significant even at the 1 percent level. Thus, there is at least some evidence that governments may be inefficient (i.e. more government means less growth) but maybe with a benevolent face (i.e. more government increases equality). This result is consistent with the findings of Li

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