Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation

TL;DR

This paper introduces REL-PCANet, a novel statistical model combining PCA, image recognition, and ranking techniques to improve the estimation of the Inclusive Development Index, ensuring reliable and transparent country rankings.

Contribution

The paper presents a new REL-PCANet model for IDI estimation, integrating PCA, image recognition, and ranking methods, with a novel probability matrix estimation approach.

Findings

REL-PCANet provides reliable and robust IDI scores.

The model ensures transparent and accurate country rankings.

Empirical results support practical implementation of REL-PCANet.

Abstract

In 2018, at the World Economic Forum in Davos it was presented a new countries' economic performance metric named the Inclusive Development Index (IDI) composed of 12 indicators. The new metric implies that countries might need to realize structural reforms for improving both economic expansion and social inclusion performance. That is why, it is vital for the IDI calculation method to have strong statistical and mathematical basis, so that results are accurate and transparent for public purposes. In the current work, we propose a novel approach for the IDI estimation - the Ranking Relative Principal Component Attributes Network Model (REL-PCANet). The model is based on RELARM and RankNet principles and combines elements of PCA, techniques applied in image recognition and learning to rank mechanisms. Also, we define a new approach for estimation of target probabilities matrix to reflect dynamic changes in countries' inclusive development. Empirical study proved that REL-PCANet ensures reliable and robust scores and rankings, thus is recommended for practical implementation.