Rodrigo Lovaton Davila, University of Minnesota
The construction of wealth indices based on housing characteristics and asset ownership has been widely used when other measures of socioeconomic status are not available. A popular approach has been to apply principal components analysis (PCA) on data recoded to binary indicators (Filmer and Pritchett, 2001). However, this procedure has been criticized since standard PCA methods are not designed to handle discrete data. In this paper, I compare alternative aggregation procedures that have been proposed to overcome this issue. The paper uses data from twelve developing countries. The evidence indicates that methods based on ordinal data have high agreement in rankings, but the PCA procedure on dichotomized data also has reasonable agreement with these measures. The alternative measures do not have striking differences in their relationship with a set of education, fertility, and mortality outcomes, both based on wealth index quintiles and on regression analysis. Finally, none of the asset-based indices outperformed the rest in terms of similarities of rankings with the logarithm of income per capita. In this sense, despite recommendations given by previous research (Howe et al., 2008; Kolenikov and Angeles, 2009), results suggest a relatively similar performance of the PCA procedure on dichotomized data with respect to methods based on ordinal data.
Presented in Session P2. Poster Session 2