Estimation of the Inclusive Development Index Based on the REL-PCANet Neural Network Model

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 TRnet 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.