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In 2018, at the World Economic Forum in Davos it was presented a newcountries' economic performance metric named the Inclusive Development Index(IDI) composed of 12 indicators. The new metric implies that countries mightneed to realize structural reforms for improving both economic expansion andsocial inclusion performance. That is why, it is vital for the IDI calculationmethod to have strong statistical and mathematical basis, so that results areaccurate and transparent for public purposes. In the current work, we propose anovel approach for the IDI estimation - the Ranking Relative PrincipalComponent Attributes Network Model (REL-PCANet). The model is based on RELARMand RankNet principles and combines elements of PCA, techniques applied inimage recognition and learning to rank mechanisms. Also, we define a newapproach for estimation of target probabilities matrix to reflect dynamicchanges in countries' inclusive development. Empirical study proved thatREL-PCANet ensures reliable and robust scores and rankings, thus is recommendedfor practical implementation.

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