Modeling industrial location decisions in U.S. counties

with,

E(njk ) = λjk = exp(αk + θzyj + β⅛ ),                        (4)

where α_k is a dummy taking the value 1 for sector k.

Our main interest centers on the potential problem caused by the omis-
sion of unobserved explanatory variables, which can cause a violation of
the IIA assumption. To address this problem, as indicated before, authors
such as Bartik (1985), Woodward (1992), Levinson (1996)and Head, Ries
& Swenson (1999)have included dummy variables for groups of elemental
alternatives. Within the context of the Poisson regression this amounts to
adding an additional dummy variable for each group and is equivalent to
admitting that each investor restricts his choice set to the group of alterna-
tives where the investment was observed.¹⁰ However, as stated earlier, by
doing this one is still assuming that the IIA assumption holds within the
groups of alternatives.

To more effectively control for the potential violation of the IIA assump-
tion one should include an additional effect specific to each alternative. This
way, we should be able to absorb all the unaccounted for factors affecting
the firm location decision. In terms of our model this amounts to adding an
additional term to the profit function, γ_j , such that,

πijk = γ⁰xk + θ⁰yj + β⁰zjk + γ_j + εijk                            (5)

If we assume that γ_j is a random variable then, conditional on γ_j ,the

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