by the poor, so poverty rates are very unlikely to influence financial depth in a significant manner. Anyway, although it is rarely possible to exclude the possibility of reverse causality altogether, we will proceed on the basis that assuming its absence is likely to be better than working with weak instruments.

Thus, the way is open to a fairly straightforward approach to seeing if there might be a possible causal link. Accordingly, drawing on a cross-section of some 70-odd developing countries<sup>6</sup> for which poverty data are available, we now show that a striking empirical relationship seems to emerge: deep financial systems appear to be associated with lower poverty.

The major determinant of variations in poverty incidence across developing countries is, not surprisingly, the mean level of GDP per capita: an unevenly-divided small cake leaves more people hungry than a large one. As shown in Equation A of Table 1, this variable alone explains 47 per cent of cross-country variation in absolute poverty (share of population below $1 a day). The point estimate implies that a 10 percent increase in mean per capita income translates into a 1.6 percentage point reduction in absolute poverty. Naturally, the way in which this income is distributed also matters: for example, calculating the mean income of the bottom 90% of the population (by subtracting the income earned by the top 10% income earners) greatly improves the fit – the RSQ now is 54% (Equation B). Interestingly, the income share of the top 10% also contributes to explaining cross-country poverty variance (likely because it also predicts a higher share for the _non_ -poor that are in the lower 90%).<sup>7</sup> Anyway with this addition, the RSQ is up to 58% (Equation C).

Now it’s time to add in the financial variables. Starting with the standard nongovernment (“private”) credit variable. Here we prefer to exclude three countries which are outliers in a cross-country regression of credit on inflation and per capita GDP – their data for non-government credit are more than three standard errors away from the regression line.<sup>8</sup> One is Panama, an offshore center, the other two are Thailand and China. (We will return to China below, observing that most Chinese “non-government” credit is to state-owned or controlled firms.) With the remaining data, a striking negative coefficient is observed in the regression explaining poverty (Equation D). The additional variable improves the fit to 64%. Taken literally – and we should not do this except as a rough indication of the size of the effect – the estimates imply that a 10 percentage point in the ratio of private credit to GDP should (even at the same mean income level) reduce poverty ratios by 2.5 to 3 percentage points.

6 Note that the finance and growth studies typically study both rich and poor countries. Poverty shares in the advanced countries are all measured at essentially zero for these international cut-off lines of $1 and $2 a day.

7 Though the Gini coefficient does not enter significantly. Alternative specifications using the share of the top 20%, or the bottom 10 or 20 percent, are inferior in fit. Results from including alternative aspects of the distribution as explanatory variables are reported in Table 6. The link between income inequality and poverty is also discussed in Besley and Burgess (2003), though by looking at standard deviation of log income (rather than, as here, the top decile share) their estimates are influenced by the mechanical partial induced between inequality and absolute poverty by conditioning on mean income.

8 Results for the full sample are reported as Table 1 (Alternate).

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