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

Abstract

A growing body of research documents that the size and growth of an industry in a location 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 to best operationalize it empirically or how this empirical regularity can help inform local industrial policy. We try to make progress by performing a structured search over tens of thousands of specifications to identify robust procedures to determine how well industries fit the local economies of US cities that perform well in terms of out-of-sample predictions. 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. Portfolios of these different productive entities yield different relatedness matrices, each of which helps 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. We use these insights to deepen our theoretical understanding of what underlies path-dependent development in cities and expand existing policy frameworks that leverage information from inter-industry relatedness analysis.