So you have reduced many original variables to 9 PCA variables, and the question is how to reduce this to one index? Well, as far as I know, there's no universal method or formula that gets you to the next step. Furthermore, the sign on the PCA vectors is arbitrary, and has no real meaning, so PCA vector 1 could be positive to indicate a high wealth index, or it could be negative to indicate a high wealth index, and there's no way in advance to know which it is. Or it could be that the principal components are completely or nearly completely unrelated to wealth; they don't necessarily have to have anything to do with wealth.   Typically, you might want to try to interpret the PCA vectors, gain some understanding of what vector 1 is telling you, and what vector 2 is telling you, etc. by looking to see which variables have the high loadings (either positive or negative) in a given dimension. For example, if dimension 1 has a lot of variables that have big loadings (either positive or negative) that are associated with education, then you could interpret dimension 1 as an education variable. But even after that, how to get a wealth indicator is not clear.   If you want a predictive measure of wealth, then something like Partial Least Squares regression would be useful, its analogous to PCA but tries to find dimensions/vectors that are predictive. This assumes you have a Y variable to predict.\   I would also need to divide the wealth indicator variable into quintiles, indicating household wealth based on the quintile the household belongs to.   PROC RANK with the option GROUPS=5   ... View more