Modeling industrial location decisions in U.S. counties

of more detailed micro data sets. Increasingly, the empirical literature has
turned to model location probabilities against many spatial choices, just as
firms face when making site selection decisions. Potentially, these studies
contain important findings that can be confirmed or rejected through stud-
ies in both similar and different spatial contexts. Reliable estimates across
studies can help inform important public policy debates; for example, by
assessing the influence of local taxes compared with other regional factors.²

Given extensive analysis, the determinants of firm and plant location
decisions should be well established. We should know a lot about the rela-
tive importance of economic factors (such as factor costs and agglomeration
economies) vis-à-vis policy influences (eg. taxes and promotional policies).
But the results of the vast location empirical literature vary widely.³ More-
over, the basic questions keep getting recast in different models. Is agglom-
eration really the dominant force in location that theory would predict? Do
labor and land costs matter? What is the real efficacy of tax abatements on
location? Almost invariably, the motivation for more empirical research is
that these and other major questions remain unanswered.

Unfortunately, then, a systematic approach to empirical location mod-
eling has not been found. One reason is that the spatial scale tested in
the empirical literature extends from neighborhoods to nation states. Lo-
cation factors (wages and taxes, for example) exert distinct influences on
intraurban and international decisions. Even within interregional location
studies, however, there seems to be little commonality among the estimates.
In part, this is because various econometric approaches have been employed
(linear regression models, limited dependent models, and categorical mod-

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