“…In effect, the derived principal component is the variable that captures variations in data to the maximum extent possible" (Huh and Park, 2017). PCA is a familiar method for constructing indexes (Huh and Park, 2017;Park and Claveria, 2018), so we forego further explanation of the initial estimation to focus on what is known as "two-stage PCA" (Huh and Park, 2017). Here, PCA is first employed to find the relevant principal components for each dimension, and then, in the second stage, PCA is used again to estimate the composite index from the components.…”