Modeling industrial location decisions in U.S. counties

leads to an 0.81 percent decrease in the number of new plant births while the
same elasticities for labor costs and taxes are -0.46 and -0.26, respectively.¹⁹
We also find evidence that the county market size matters and that ag-
glomeration economies (both localization and urbanization) are associated
with higher numbers of plant births. Apparently, of the two agglomeration
measures, urbanization economies have the strongest impact.

It may be argued that investment decisions are also affected by state level
variables. Consequently, our results in column 1 may be substantially biased.
While it could be possible to add some observable state level variables, such
as right-to-work (open shop) legislation or state taxes, we opted instead to
control for these effects by including ”state fixed-effects.” By doing this, we
are also controlling for unobservable state characteristics and, as argued by
some authors, mitigating the IIA problem. The results for this specification
are presented in column 2. As expected, the increase in the log-likelihood
is statistically significant providing evidence on the relevance of state level
characteristics. Notwithstanding, all coefficient estimates remain practically
unchanged.

[insert Table 3]

As argued in section 3, to more effectively control for the potential vi-
olation of the IIA assumption one should include ”county specific-effects.”
In a first step, we estimate a mixed logit model (with and without ”state
fixed-effects”) by means of a Poisson regression with county random effects.
The results are shown in columns 3 and 4. The difference between the log-
likelihoods of the model with random effects and the comparable Poisson

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