King's College London

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 rst time
information ltering and hierarchical clustering, that these composite indicators
miss key information on the relationship between dierent indicators. In
particular, the grouping of indicators via topics is not re
ected 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.