No abstract

No abstract

No abstract

Abstract-The conditional logit model based on random utility maximization has provided an adequate framework to model firm location decisions. However, in practice, the implementation of this methodology presents problems when one has to handle complex choice scenarios with a large number of spatial alternatives. We posit the Poisson regression as a tractable solution to these problems. We demonstrate that by taking advantage of an equivalence relation between the likelihood function of the conditional logit and the Poisson regression we can, under certain circumstances, easily estimate a conditional logit model regardless of the number of choices. This insight should be particularly useful for studies of economic location.

Given sound theoretical underpinnings, the random utility maximization-based conditional logit model (CLM) serves as the principal method for applied research on industrial location decisions. Studies that implement this methodology, however, confront several problems, notably the disadvantages of the underlying Independence of Irrelevant Alternatives (IIA) assumption. This paper shows that by taking advantage of an equivalent relation between the CLM and Poisson regression likelihood functions one can more effectively control for the potential IIA violation in complex choice scenarios where the decision maker confronts a large number of narrowly defined spatial alternatives. As demonstrated here our approach to the IIA problem is compliant with the random utility (profit) maximization framework.