  An understanding of the economic landscape in a world of ever increasing data
necessitates representations of data that can inform policy, deepen
understanding and guide future research. Topological Data Analysis offers a set
of tools which deliver on all three calls. Abstract two-dimensional snapshots
of multi-dimensional space readily capture non-monotonic relationships, inform
of similarity between points of interest in parameter space, mapping such to
outcomes. Specific examples show how some, but not all, countries have returned
to Great Depression levels, and reappraise the links between real private
capital growth and the performance of the economy. Theoretical and empirical
expositions alike remind on the dangers of assuming monotonic relationships and
discounting combinations of factors as determinants of outcomes; both dangers
Topological Data Analysis addresses. Policy-makers can look at outcomes and
target areas of the input space where such are not satisfactory, academics may
additionally find evidence to motivate theoretical development, and
practitioners can gain a rapid and robust base for decision making.
