probability of an investor selecting location j can be expressed as,
_ exp(θ0 yj + β0Zjk + Yj )
(6)
Pj/kY PJ=1 eχp(θ0yj + β0zjk + Yj )
The above formulation may be interpreted as a variant of the mixed logit
model, where the attributes of the characteristics which are not explic-
itly modeled are assumed to reside in the error terms.11 On the other
hand, in light of their relation between the CLM and the Poisson regres-
sion, one can estimate the model above by means of a Poisson model with
random effects.12 If we assume that exp(Y j) follows an i.i.d. gamma dis-
tribution with (δ-1 , δ-1) parameters and consequently that E (exp(Y j )) = 1
and V (exp(Y j)) = δ, then, as shown by Hausman, Hall & Griliches (1984),
the resulting Poisson model with gamma distributed random effects has
an analytically tractable log-likelihood. In the pure cross-section case, this
later model collapses to a standard negative binomial regression [Cameron
& Trivedi (1998)]. Thus, if our specification does not include sectorial effects
(i.e. zjk variables) one can estimate (6) by applying the negative binomial
model. More recently, there have been studies using the negative binomial
regression to model location decisions [Wu (1999)and Coughlin & Segev
(2000)] but the authors failed to note the compatibility of their approach
with the Random Utility Maximization framework.
The CLM with random effects relies on the assumption that the alter-
native specific effects are uncorrelated with the explanatory variables. This
is a questionable assumption for dealing with the IIA problem in location
studies. Omitted factors which are supposedly accounted for by the random
11