Modeling industrial location decisions in U.S. counties

els). Moreover, the research often fails to take advantage of all available
information, including disaggregated data sets that capture microlevel in-
dustrial and spatial characteristics. In many cases, the econometric analysis
lacks a clear theoretical foundation—in particular, profit maximizing behav-
ior.

In this connection, the most appealing approach to recent interregional
location research was pioneered by Carlton (1979, 1983), who tested the
probability that a branch plant (in one of three narrowly defined industries)
would chose a metropolitan location in the United States. Carlton’s sig-
nificant and lasting contributions were two-fold. First, his work was based
on a rich micro data base that focused the location decision problem on
narrowly defined industries and geographic areas. Second, Carlton applied
the conditional logit model (CLM) for the first time, opening up new possi-
bilities for applied location research. Based on McFadden’s (1974) Random
Utility Maximization framework, the paper suggested that location decision
probabilities could be modeled in a partial equilibrium setting, following
a verifiable economic process that results from profit maximizing behavior
across spatial choices.

This paper argues that despite the advantages of the CLM, problems
arose in the aftermath of Carlton’s work. These problems hindered further
progress and refinement in an otherwise promising line of research. Specif-
ically, studies that followed the conditional logit approach had to confront
the Independence of Irrelevant Alternatives (IIA) assumption, which, in a
spatial context, states that decision makers look at all locations as similar,
after controlling for the observable characteristics tested in the model. The

<<   2   3   4   5   6   7   8   >>

More intriguing information