Industrial Location, Agglomeration, Dynamic Increasing Returns, Markov Chains, Polya Urns.

C1, L6, R1

In this paper we study a class of evolutionary models of industrial agglomeration with local positive feedbacks, which allow for a wide set of empirically-testable implications. Their roots rest in the Generalized Polya Urn framework. Here, however, we build on a birth-death process over a finite number of locations and a finite population of firms. The process of selection among production sites that are heterogeneous in their ?intrinsic attractiveness? occurs under a regime of dynamic increasing returns depending on the number of firms already present in each location. The general model is presented together with a few examples of small economies which help to illustrate the properties of the model and characterize its asymptotic behavior. Finally, we discuss a number of empirical applications of our theoretical framework. The basic model, once taken to the data, is able to empirically disentangle the relative strength of technologically-specific agglomeration drivers (affecting differently firms belonging to different industrial sectors in each location) from site-specific geographical forces (horizontally acting upon all sectors in each location).