  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.
