- Central: Same as in the preceding simulations, i.e. an initial urban-rural per-capita health service gap of 1.77 and an initial rural-urban cost ratio of 1.14;

- Large urban-rural service gap: 25 percent increase in the initial urban-rural per-capita health service gap (from 1.77 to 2.22)

- Small urban-rural service gap: 25 percent decrease in the initial urban-rural per-capita health service gap (from 1.77 to 1.33)

- Large rural-urban cost gap:  50 percent increase in the initial rural-urban unit cost ratio (from 1.14 to 1.71).<sup>15</sup>

For each variant of the six simulations, each of the 22 indicators covered in Tables A3.1 (macro growth rates) and A3.5 (MDG values in 2030) was ranked across the 6 simulations. After this, the resulting rankings for the four variants were compared. It turned out that, out of 132 (22 times 6) rankings, 126 were identical across the 4 variants; the ranking deviations for the remaining 6 rankings were all by one point (where simulations were ranked from 1 to 6) and for deviations in outcomes that were so small that they would be considered identical in a discussion of results.<sup>16</sup> Thus, the qualitative findings, based on comparisons across simulations, are extremely insensitive even to fairly large changes in assumptions with regard to initial cost and service gaps.

However, the magnitudes of initial service and cost gaps obviously have a strong bearing on the requirements for and repercussions of realizing rural-urban equality in terms of health services or the U5MR. In order to gauge the quantitative implications of alternative assumptions, the simulations underlying Figure 3.5 were implemented for each of the above four variants. The results are summarized in Figure 3.6. For the central variant, reported above, an average aid level of close to US$22 per capita or close to 5 percent of health service efficiency growth is required to bring about rural-urban U5MR equality. For the variants with a large or small urbanrural service gap, the requirements are roughly US$27 or US$15 per capita in aid or efficiency growth rates of 6 or 4 percent, respectively. The figures for the variant with a large rural-urban cost gap are similar to those of a large urban-rural service gap. The important implication of these is that requirements in individual country settings would vary quite widely depending on their initial conditions. Initial country data for the rural and urban U5MR and the degree of equalizing that is targeted would also influence the magnitude of the policy and financing challenge. Better data on household use of health services and the health sector (including its cost structure) would be needed to generate reliable assessments in a country setting.

> 15 The rural-urban cost ratio was not varied downwards since it did not seem likely that it would be less than unity (as this would indicate that it is cheaper to provide health services in rural than in urban areas).

> 16 The ranking gaps were due to outcome result differences smaller than 0.1 (using the same units as in Tables A3.1 and A3.5). For the largest deviation, the gap between rankings was due to that, for the central case, the 2030 unemployment rate was 0.03 percent higher for _mdg-eq+tx_ than for _ser-eq+db_ whereas, for the case with a large urban-rural service gap, this unemployment rate was 0.1 percent higher for _ser-eq+db_ than for _mdg-eq+tx_ .

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