  Economic transformation -- change in what an economy produces -- is
foundational to development and rising standards of living. Our understanding
of this process has been propelled recently by two branches of work in the
field of economic complexity, one studying how economies diversify, the other
how the complexity of an economy is expressed in the makeup of its output.
However, the connection between these branches is not well understood, nor how
they relate to a classic understanding of structural transformation. Here, we
present a simple dynamical modeling framework that unifies these areas of work,
based on the widespread observation that economies diversify preferentially
into activities that are related to ones they do already. We show how stylized
facts of long-run structural change, as well as complexity metrics, can both
emerge naturally from this one observation. However, complexity metrics take on
new meanings, as descriptions of the long-term changes an economy experiences
rather than measures of complexity per se. This suggests relatedness and
complexity metrics are connected, in a hitherto overlooked way: Both describe
structural change, on different time scales. Whereas relatedness probes
transformation on short time scales, complexity metrics capture long-term
change.
