have taken the average over the period).<sup>5</sup> GDP per capita data were available, however, for these countries for 1999 from the World Bank's World Development Indicators (WDI) 2001 (World Bank 2001) (both for PPPs and $US) though not for the CAR or Comoros. We therefore report some results for all four income measures, and in the regressions opt for the WDI data to limit the loss of observations. We have also taken our income inequality data from the WDI. These are for the latest year available (though coincidentally the year is typically not far from the year of the survey), but data were not available for Benin, Cameroon, Chad, Comoros, Haiti, Malawi, Namibia or Togo. Income inequality is measured using the Gini coefficient, which is derived from a diagram like Figure 1. In the case of the Gini coefficient, the horizontal axis plots the cumulative share of people or households (ranked by their income), and the vertical axis plots the cumulative share of income received. The resultant curve is the Lorenz curve and it lies below the line of equality, reflecting the fact that the poorest quintile, say, receives less than 20 percent of the country's income. The Gini coefficient is defined as twice the area between the Lorenz curve and the diagonal, or equivalently as the area between the Lorenz curve and the diagonal, expressed as a proportion of the area under the diagonal. The closer the Gini coefficient is to zero, the lower the level of income inequality. The other variable we are interested in capturing is the extent to which policies are in place that reduce the price of medical care at the point of use. We capture this using the share of health spending financed publicly. Estimates of this are also available from the WDI and again the most recent data are used. No data are available from the WDI for this variable for the CAR or Comoros.

**Table** 5 shows the Pearson correlations between the (negative of the) concentration index for under-five mortality, various measures of income per capita, income inequality and the share of health spending publicly financed. Under-five mortality inequality is strongly and significantly correlated with all measures of income per capita. The strongest correlation is with income per capita measured at the survey year in PPPs, but bear in mind that the sample size differs across correlations. The scatter plot of under-five mortality inequality against income per capita (in 1999 in PPPs) in **Figure 9** gives the reader a better sense of the strength of the relationship and the locations of the different countries that underlie the relationship. The correlations of under-five mortality inequality with income inequality and the public share of health spending, although of the expected sign, are much weaker and neither is significant. These correlations are also sensitive to the omission of the four countries for which the historical data on income per capita are missing. Omitting these countries turns the positive correlation between under-five mortality inequality and the Gini coefficient into a negative correlation and reduces the correlation between underfive mortality inequality and the public share of health spending. The influence of these countries is evident from the scatter plots in **Figures 10 and 11.**

> 5 The data are from Global Development Network Growth Database http://www.worldbank.org/researcb/growth/GDNdata.htm.

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