# _Cross-country comparisons of the sources of health inequalities_

In a cross-section, one ought in principle to be able to parcel out the effects of policies along the lines of the cross-country regressions above. The results of the analysis reported here are somewhat discouraging, since the effect of the public share of health spending was insignificant and in results not reported above (but predictable on the basis of the scatter plot in Figure 10) was found to be very sensitive to the sample choice. Furthermore, the estimated impact in the full sample is very small. Table 7 shows that public spending on health closed the concentration index gap between Latin America and Sub-Saharan Africa but it did so only by a very small amount-the gap is 0.054, but only -0.002 of this was closed by Latin America's higher public share. To put this in perspective, Latin America would have to increase its public share of health spending from 49 percent to 134 percent to get a predicted under-five mortality concentration index equal to that of Sub-Saharan Africa! These numbers are clearly not definitive, and further work along these lines is required to learn more from cross-sectional data about the impacts on health inequality of anti-inequality policies.<sup>9</sup>

# _Before-and-after country studies with controls_

While the cross-section comparative analysis is certainly worth exploring further, an arguably more fruitful line of enquiry is through the use of country-based program evaluations with controls. For example, the results of the Ceara and Pelotas studies would have been far more compelling if the changes in inequality in these two areas had been compared with changes in inequality during the same period elsewhere. Ideally, the control groups would have been matched to ensure comparability, but even comparisons with Brazil over the period in question would have been more illuminating than simple trends. It might well have been the case that inequalities rose by more in Brazil over the period in question than in Ceara.

Before-and-after comparisons of health inequalities _with control groups_ are few and far between. One recent study that does adopt such an approach is that of Bhuiya and others (2001), who consider the impact on differentials in child mortality (mortality between the ages of one and five) of an MCH program delivered by the International Centre for Diarrhoeal Disease (ICDDRB). In the study, household wealth was measured by dwelling size in square meters-one of the variables used in the PCA-based wealth score in the World Bank study and, according to Bhuiya and others, a reasonably good proxy for household living standards. The MCH program was introduced in 1977 in only half of the area covered by the ICDDRB's demographic surveillance system. The areas not covered by the MCH program (the control group) comprise areas where only government health services were provided, but also areas where, in addition to government health service provision, BRAC (a nongovernmental organization) was operating its socioeconomic development program. The BRAC program was aimed at both poverty alleviation and women's development, and most of its activities (which included essential health care) were targeted on poor women.

9 Bidani and Ravallion (1997) also report the results of cross-country regressions aimed at shedding light on the effects of public expenditure and income on health gaps between the poor and the nonpoor. However, in the absence of data on health indicators by poverty grouping, they were forced to make distributional assumptions to "retrieve" them artificially. They find that at both one-dollar-a-day and two-dollar-a-day poverty lines, public health spending reduces health inequality between the poor and nonpoor, because it has a larger impact among the poor than among the nonpoor.

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