A new set of cluster driven composite development indicators

TL;DR

This paper introduces a novel, data-driven approach to creating composite development indicators using clustering techniques, which better capture relationships among indicators and improve policy relevance.

Contribution

The paper presents a new set of cluster-driven composite indicators derived from indicator clustering, enhancing objectivity, comparability, and interpretability over traditional methods.

Findings

Cluster-driven indicators outperform traditional ones in dataset reconstruction.

The new indicators reveal relationships missed by existing composite measures.

They provide more objective and comparable insights for policy making.

Abstract

Composite development indicators used in policy making often subjectively aggregate a restricted set of indicators. We show, using dimensionality reduction techniques, including Principal Component Analysis (PCA) and for the first time information filtering and hierarchical clustering, that these composite indicators miss key information on the relationship between different indicators. In particular, the grouping of indicators via topics is not reflected in the data at a global and local level. We overcome these issues by using the clustering of indicators to build a new set of cluster driven composite development indicators that are objective, data driven, comparable between countries, and retain interpretabilty. We discuss their consequences on informing policy makers about country development, comparing them with the top PageRank indicators as a benchmark. Finally, we demonstrate that our new set of composite development indicators outperforms the benchmark on a dataset reconstruction task.