  A growing body of research documents that the size and growth of an industry
in a place depends on how much related activity is found there. This fact is
commonly referred to as the "principle of relatedness". However, there is no
consensus on why we observe the principle of relatedness, how best to determine
which industries are related or how this empirical regularity can help inform
local industrial policy. We perform a structured search over tens of thousands
of specifications to identify robust -- in terms of out-of-sample predictions
-- ways to determine how well industries fit the local economies of US cities.
To do so, we use data that allow us to derive relatedness from observing which
industries co-occur in the portfolios of establishments, firms, cities and
countries. Different portfolios yield different relatedness matrices, each of
which help predict the size and growth of local industries. However, our
specification search not only identifies ways to improve the performance of
such predictions, but also reveals new facts about the principle of relatedness
and important trade-offs between predictive performance and interpretability of
relatedness patterns. We use these insights to deepen our theoretical
understanding of what underlies path-dependent development in cities and expand
existing policy frameworks that rely on inter-industry relatedness analysis.
