next, but even the most inequality-averse country is likely to be willing to accept some increase in inequality if the rise in average health is sufficiently large.

The second aspect of success whose measurement we explore is the impact of health policies on health inequalities. The fact that rising incomes seem to go hand-in-hand with rising health inequalities doesn't necessarily mean that policymakers are impotent in their fight against health inequality. Policies can be devised and implemented that may enable countries to swim against the tide and not be carried as fast downstream as the tide itself runs. _But the impact of such policies cannot be assessed by looking at simple changes in health inequalities._ The fact that health inequalities rose in Ceara at a time a targeted MCH program was being implemented doesn't necessarily mean that this program failed to have any distributional _impact-health inequalities might have grown even more in its absence._ The fact that under-five mortality inequalities are so much higher in Brazil than in Haiti doesn't necessarily mean that policies aimed at combating health inequalities in Brazil are less effective than those operating in Haiti-they may be _more_ effective, but the effects of income are so strong that we observe higher inequalities in Brazil despite more effective antiinequality policies.

# **6. Trading Off Health Inequality against Average Health**

One very striking thing about the data on health inequalities from the World Bank's study is that, for the most part, it is the countries with the lowest average rates of under-five mortality and malnutrition that have the largest gaps between poor and non-poor children. This is evident in **Figure 12.** The Dominican Republic may have a high level of inequality compared to Haiti, buts average under-five mortality rate is much less (just over 50 per 1,000 compared to nearly 150 per 1,000 in Haiti). It is true that in many of the pair wise comparisons, one country dominates the other on both criteria. Senegal, for example, does much worse on both than Zimbabwe. But the fact that in so many pair wise comparisons one country does better on one criterion and worse on the other makes it highly desirable to have a means of capturing these two dimensions in one overall summary index.

We can think of the problem as one of is to think of the problem as one of constructing a distributionally sensitive measure of population health (Wagstaff 2001). The mean is clearly not appropriate, since it weights everyone's health equally, irrespective of how poor they are. One possible set of weights is the person's rank in the income distribution, or some simple function of it. One such scheme is to assign the poorest person a weight of 2 and then let the weight decline by 2/N (where N is the sample size) for each one-person step up the income distribution. Adopting this set of weights produces a distributionally-sensitive measure of population health, or an "achievement index", that is simply equal to the mean level of health of the population multiplied by the complement of the concentration index. A policy that resulted in the same proportional improvement in everyone's health would raise the value of the achievement index, while a policy that led to the same increase in the mean but a larger (smaller) proportional improvement in the health of the poor would produce a larger (smaller) increase in it.

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