Modeling industrial location decisions in U.S. counties

sion problem caused by the prevalence of zeros [List (2001)] or originated
by an excessive spatial concentration of firms [Wu (1999)and Coughlin &
Segev (2000)]. Papke (1991)use a fixed-effects Poisson regression to control
for unobserved state heterogeneity. Meanwhile, and despite these attractive
features of the Poisson regression model, it lacks a theoretical underpinning
such as the Random Utility Maximization framework for the CLM.

The link between the CLM and the Poisson regression has been addressed
in a recent paper by Guimaraes, Figueiredo & Woodward (2002). Tthe
paper shows that, under some circumstances, the coefficients of the Poisson
model can be given an economic interpretation compatible with the Random
Utility Maximization framework. The next section of this paper explores
this relation’s deeper implications for regional location research, positing
instruments to more effectively control for the potential IIA violation in
complex choice scenarios with a large number of spatial alternatives.

3 Econometric Aspects of Location Modeling

To show the connection between the received empirical location model, we
posit a general profit function for firms in a particular industry and location.
Let us start by considering an economy with K different industrial sectors
(k =1, ..., K). There are N investors (i =1, ..., N) who independently select
a location j from a set of J potential locations (j =1, ..., J). The profit the
investor will derive if he selects sector k and locates at area j is assumed to
be,

∏ijk = γ 0 Xk + θ⁰yj + βZjk + εijk,                                   (1)

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