On Discrete Location Choice Models

Within the context of the firm location choice problem, Guimara?es et al. (2003) have shown that a Poisson count regression and a conditional logit model yield identical coefficient estimates. Yet, the corresponding interpretation differs since these discrete choice models reflect polar cases as regards the degree with which the different locations are similar. Schmidheiny and Brulhart (2011) have shown that these cases can be reconciled by adding a fixed outside option to the choice set and transforming the conditional logit into a nested logit framework. This gives rise to a dissimilarity parameter that equals 1 for the Poisson count regression (where locations are completely dissimilar) and 0 for the conditional logit model (where locations are completely similar). Though intermediate values are possible, the nested logit framework does not permit the dissimilarity parameter to be pinned down. We show that, with panel data and adopting a choice consistent normalisation, the fixed outside option can also be introduced into the Poisson count framework, from which the estimation of the dissimilarity parameter is relatively straightforward. The different location choice models are illustrated with an empirical application using cross-border acquisitions data.