An Application of Partial Least Squares to the Construction of the Social Institutions and Gender Index (SIGI) and the Corruption Perception Index (CPI)

Composite indices used in social science research often rely on principal components analysis (PCA) as a way to derive weights for component variables, which emphasizes the largest variations in the variables in a composite index. However, PCA may not work when the informative variations account for only a small share of the variance in the variables; also, the best weighting scheme may also depend on the use of a particular composite index. We consider partial least squares (PLS) as an alternative weighting scheme, which takes advantage of the relationship between outcome variables of interest and the variables in a composite index. In this paper, the Social Institutions and Gender Index (SIGI), a composite index produced by the OECD, is re-constructed using weights generated by PCA and PLS. Using the revised SIGIs and female education, fertility, child mortality, and corruption as outcome variables, we investigate the relationship between social institutions related to gender inequality and these development outcomes, controlling for relevant other determinants. We find that gender inequality in social institutions has a significant correlation with fertility and corruption regardless of the weighting procedure, while for female education and child mortality only the SIGIs based on PLS show significant results. Additionally, PLS brings benefits in terms of prediction compared to PCA for female education and child mortality. In our analysis of corruption, we consider not only the Corruption Perception Index (CPI) as our measure of corruption, but also create new reweighted CPIs again using PLS and PCA as weighting procedures. The CPI based on PCA shows a significant correlation with gender inequality, while the correlation is only marginally significant when using the PLS.