found that the interaction effect has a significant negative coefficient. However, I also found that this effect was almost entirely attributable to the poverty rate for the $2 line; it is the differences in the extent of poverty by this line that account for the bulk of the cross-country variance in the size of the middle class.

So the (absolute) growth elasticity of poverty reduction tends to be lower in countries with a higher initial poverty rate.<sup>23</sup> Poorer countries tend to experience lower proportionate impacts on their poverty measures from any given rate of growth. Furthermore, the countries with high current poverty rates have tended to experience less middle-income bulge.<sup>24</sup> All this suggests that those countries that have enjoyed the largest expansions in their middle class that will be most vulnerable to aggregate economic downturns in the wake of the crisis.

However, there is an important caveat to this argument. It is not the proportionate impact on poverty that one is most concerned with in this context, but the absolute impact, for that is what matters to the number of people experiencing extreme poverty. When talking about the developing world as a whole, this is not an issue. But it is relevant to cross-country comparisons. Furthermore, the tendency for growth to be less poverty-reducing in poorer countries vanishes when one focuses instead on the absolute impacts. This is evident from the regressions reported in Table 6. The left panel (regressions (1)-(4)) gives regressions of the annualized change in the log of the $2 a day poverty rate against both the annualized growth rate in the mean and its interaction with the initial poverty rate. Both Ordinary Least Squares (OLS) and Instrumental Variables (IV) estimators are given; the IV method uses the growth rate in private consumption per capita from the NAS as the instrument for the growth rate in the survey mean.<sup>25</sup> The table

> 23  Mathematically, this elasticity will be a function of the mean (relative to the poverty line) and the properties of the Lorenz curve, roughly interpretable as “inequality.” Ravallion (1997) argued that in high inequality countries the poor will tend to have a lower share of the gains from growth and showed that this holds empirically; this was substantiated in a larger data set by Ravallion (2007). Ravallion (1997) did not, however, find empirically that the growth elasticity of poverty reduction varied systematically with the mean. Lopez and Serven (2006) show that if incomes are log-normally distributed, then such a variation with the mean is implied theoretically, and they show that log-normality is a good approximation empirically. Easterly (2009) conjectures that the initial poverty rate is likely to be the better predictor of the elasticity though no evidence is provided. The results reported in the present paper, on a larger and better quality data set than used by Ravallion (1997, 2007), suggest that differences in the initial mean are important for explaining differences in the elasticity, and that the initial poverty rate is an even better predictor than the initial mean, consistently with Easterly’s conjecture.

> 24  The correlation coefficient between the log of the $2 a day poverty rate in the most recent survey and the proportionate increase in the share of the population between $2 and $13 a day is 0.620.

25 Following Ravallion (2001), this IV allows for the possibility that a spurious negative correlation exists due to common measurement errors (given that the poverty measure and the mean are calculated from the same surveys) 15