parameter of interest is a2 which measures the impact of other determinants of income of the poor _over and above their impact on mean income,_ i.e. the effects of these variables on the income share of the poorest quintile. In particular, we can use this regression framework to systematically examine whether increases in trade volumes (or any other variable) are systematically associated with changes in the income share of the poorest quintile.

Estimating Equation (3) poses a variety of econometric difficulties that we address in detail in our other paper. Here we briefly note that we estimate this equation using a system generalized method of moments estimator which optimally combines information in the levels of the data with the withincountry variation in the data. As discussed in the other paper, this strategy allows us to address as best we know how problems of measurement error in the income distribution data (and other variables), possible omitted variables, and the possibility of reverse causation from income distribution to average incomes.

Table 7 shows a typical set of results from that paper, regressing average incomes of the poorest quintile on average incomes and several additional control variables that have been identified as important for growth in the larger empirical growth literature. We typically find a point estimate of a, which is slightly larger than, but not statistically significantly different from, 1, indicating that incomes in the bottom quintile on average rise one-for-one with average incomes (alternatively, that changes in income distribution are not significantly associated with changes in average incomes). In addition, we rarely find that any of the additional control variables enter significantly, indicating that these variables have no systematic effect on income distribution. The only exception is government consumption, which at times enters significantly. Neither of these two results should be all that surprising. Various authors, including Chen and Ravallion (1997) and Deininger and Squire (1996) have documented the striking absence of any correlation between (changes in) income and (changes in) inequality, albeit with smaller samples and different econometric techniques. Our lack of systematic significant effects of policies and institutions on inequality mirrors the dearth of similar robust results in the small empirical literature on determinants of income inequality.

For the purposes of this paper, the most interesting results are those relating to trade volumes. Our results indicate that there is no significant correlation between changes in inequality and changes in trade volumes, controlling for changes in average incomes (first column of Table 7). This can be seen quite clearly in Figure 5, which reports the simple correlation between changes in trade volumes and changes in inequality as measured by the Gini coefficient (in the top panel) and the logarithm of the first quintile share (in the bottom panel). In our other paper we also subject this basic result to a wide variety of robustness checks and also consider several other measures of international economic integration. Our

> _15_ It is straightforward to generalize the discussion to include year effects. We do not do so here because in our empirical results we do not find time effects to be significant.

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