  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.
