Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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. © 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.