The DHS typically collect information on whether or not any household members own each of a set of basic assets (radio, refrigerator, television, bicycle, motorcycle, car) and basic characteristics of the dwelling in which the household lives (whether or not the house has electricity, the number of rooms per person, and whether or not the dwel:ling has floors made of a "finished" surface). In order to use these variables to rank households by their economic status, they need to be aggregated into an index, and a major problem in constructing such an index is choosing appropriate weights.<sup>8</sup> This is done here using the statistical technique of principal components. Principal components is a technique for summarizing the information contained in a large number of variables to a smaller number by creating a set of mutually uncorrelated components of the data. Intuitively, the first principal component is that linear index of the underlying variables that captures the most common variation among them.

The details of the methodology are in Filmer and Pritchett (2001). That paper also describes how in three datasets (from Indonesia, Nepal, and Pakistan) where there was both consumption expenditure and asset data, Spearman rank correlation coefficients between ranking households by the asset index and ranking them by expenditures adjusted for household size were 0.64 in Nepal, 0.56 in Indonesia, and 0.43 in Pakistan. In the context of education outcomes, Filmer and Pritchett (2001) argue that in the three countries studied the wealth index performs as well as household-size-adjusted consumption expenditures, in predicting educational enrollment and attainment.<sup>9</sup> Table 4 reports the "factor scores" for the first principal component in the analysis of the Western and Central Africa and Eastern and Southern Africa data. In both regions, the first principal component captures about 30 percent of the variation in the data. The factor scores, which are in essence the weights in the aggregation of the assets into the index, all have the expected sign (except for owning a bicycle in Western and Central Africa, which could reflect the fact that poorer households are more likely to own a bicycle whereas richer households may be more likely to own a motorcycle or a car instead). Although the results imply that the second eigenvalue is greater than one (the usual value used as a cutoff for "relevant" components) the difference between the first and second eigenvalues is large, lending support to the notion that the first is capturing a significantly larger part of the important information in the asset and housing variables.<sup>10</sup> 8. If these assets were only to be used to examine the impact of some other factor (e.g., maternal education) as a "control" for wealth in a multivariate regression we would not need to aggregate the variables (see Montgomery and others 2000).

9. Unlike Filmer and Pritchett (2001) water and sanitation variables have not been included in the set of variables used to derive the index as these likely have direct effects on health status. They are included as individual variables in the multivariate analysis.

10. Principal components in typically derived for continuous variables. Monte Carlo simulations comparing the first principal component derived from (1) continuous variables to (2) dummy variables with the same mean (derived from the ranking of the continuous variables) yields very similar factor loaclings _8_