Modeling industrial location decisions in U.S. counties



& Woodward (2002), a result not corroborated by other studies [Carlton
(1983), Hansen (1987)and Levinson (1996)]. None of the above domestic
studies tested market size.

5 Conclusion

As one of the central concerns of regional analysis, location studies require a
sound empirical and theoretical foundation. Given its microfoundation, the
CLM has been the most promising econometric approach for modeling in-
dustrial location decisions under profit maximization. This CLM established
a solid methodological basis for applied location research. However, related
research on this topic has been unable to fully accommodate the problem
posed by the IIA assumption. This assumption becomes even more problem-
atic when dealing with complex choice scenarios where the decision-maker
confronts a large number of narrowly defined spatial alternatives.

In this paper we show that by taking advantage of the equivalence re-
lation between the log-likelihood functions of the CLM and the Poisson
regression one can more effectively control for the potential IIA violation
resulting from omitted attribute characteristics. Both the random and the
fixed effects versions of the Poisson regression can be used to introduce an
additional effect specific to each spatial alternative. The introduction of
these specific effects should absorb all the unaccounted for factors affecting
the firm location decision and thus provide a control for the potential IIA
violation. Meanwhile, the implementation of the fixed-effects version of the
Poisson regression requires time series data exhibiting sufficient temporal

19



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