Modeling industrial location decisions in U.S. counties

effects, such as natural advantages, may be correlated with, for example,
density of economic activity.

An alternative approach is to assume that γ_j is a fixed effect. This
amounts to including a dummy variable for each elemental alternative (an
alternative specific constant). In this case the dummies absorb the effects
of the yj variables and we may write,

_   exp(β⁰zjk + Yj )

Pj^/k   PJ=1 ^exp(^β'zjk + Yj )

However, in the presence of a large choice set the implementation of this
specification is impractical because of the large number of parameters to be
estimated. On the other hand, in light of the equivalence relation between
the log-likelihoods of the CLM and the Poisson regression, the alternative
specific constant can be viewed as a fixed-effect in a Poisson regression.
Consequently, these effects can be ”conditioned-out”and one can still ob-
tain estimates for the β vector regardless of the number of parameters (see
Appendix B).

The problem with the above approach is that we rely on sectoral variation
to estimate the model and consequently are unable to identify the impact
of variables that only exhibit intraregional variation (i.e. the yj vector).
The marginal impact of these variables is of particular interest in location
studies. However, as long as we have available data for different time periods
exhibiting sufficient time-series variation, one can still obtain estimates for

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