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A growing body of research documents that the size and growth of an industryin a place depends on how much related activity is found there. This fact iscommonly referred to as the "principle of relatedness". However, there is noconsensus on why we observe the principle of relatedness, how best to determinewhich industries are related or how this empirical regularity can help informlocal industrial policy. We perform a structured search over tens of thousandsof 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 whichindustries co-occur in the portfolios of establishments, firms, cities andcountries. Different portfolios yield different relatedness matrices, each ofwhich help predict the size and growth of local industries. However, ourspecification search not only identifies ways to improve the performance ofsuch predictions, but also reveals new facts about the principle of relatednessand important trade-offs between predictive performance and interpretability ofrelatedness patterns. We use these insights to deepen our theoreticalunderstanding of what underlies path-dependent development in cities and expandexisting policy frameworks that rely on inter-industry relatedness analysis.

Evaluating the principle of relatedness: Estimation, drivers and implications for policy