Proximity, Social Capital and the Simon Model of Stochastic Growth

It is customary to define economic geography as a discipline that deals with the uneven distribution of economic activity across space. From a historical perspective, stochastic growth models are of particular use (Simon 1955). Such models explain the current distribution of activities from the dynamics of the long historical process that has produced these patterns. This approach might also be labelled “evolutionary economic geography” (Boschma and Frenken 2006), referring to the evolutionary economics tradition, since stochastic growth models account for path dependence in which each event changes the probability of a next event to occur (Arthur 1989; David 1985). In geography, stochastic models of urban growth have a long intellectual history. In particular, The Simon model of the Zipf's rank-size rule is still regarded as one of the canonical models of urban size distribution (Batty 2005). A shortcoming of these urban growth models is that they take spatial entities as the unit of analysis. Since spatial entities are not behavioural entities, the explanation of urban growth in such models is not grounded in the micro-behaviour of agents. What is more, the delineation of spatial entities is a notoriously difficult exercise. Organizational units are less problematic, because these are the agents of change and relatively easy, though by no means trivial, to delineate. More recently, some of the urban growth models have explicit micro-foundations, including neoclassical models (Duranton and Puga 2004) and agent-based models (Batty 2005).