Asia-Pacific Regional Integration Index: Construction, Interpretation, and Comparison

Abstract

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A new index of globalisation: Measuring impacts of integration on economic growth and income inequality

“…Huh and Park (2018) and Park and Claveria (2018a) applied a two‐stage PCA analysis to generate regional integration indexes encompassing six socioeconomic dimensions. In particular, these new indexes include movement of people as well as institutional and social integration which have been overlooked in most previous economic integration measures.…”

Asia Small and Medium-Sized Enterprise Monitor 2020 Volume IV–Technical Note:

“…PCA is a statistical technique that, "partitions the variance in a set of variables and uses it to determine weights that maximize the resulting principal component's variation. 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).…”

“…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.…”

“…Given a sufficient sample size of enterprise data, we use two-stage PCA to create the composite index or SME-DI, referring to Bo and Woo (2008), Huh and Park (2017), and Park and Claveria (2018). If panel data are available, we assume X txp as multidimensional data for obtaining the weights for estimating the SME-DI, where t is total number of periods and p is the number of dimensions.…”

“…We calculated θ as the proportion of respective λ (λ 1 , λ 2 ,… λ i ) to the total λ (λ 1 + λ 2 + …+ λ i ). Then, "squared loadings to the unity sum (η)" (Huh and Park, 2017) were calculated as the proportion of the squared loading to the λ in each component (Z i ). In the next step, we calculated weights of respective variables of the dimension as the sum of "η by θ" of each component (Z i ).…”