Modeling industrial location decisions in U.S. counties

as an explanatory variable (MARKET SIZE).

Over the years theoretical models have incorporated agglomeration or
spatial externalities along with factor costs and market dimension. Agglom-
eration includes both localization economies and urbanization economies.
Urbanization economies, i.e. externalities that are common to all firms, are
proxied by the county density of manufacturing and service establishments
per squared kilometer in 1988 and 1996 (URBANIZATIONECONOMIES).¹⁷
Localization economies, external economies that benefit firms in the same
industry, are measured by the number of establishments in the same 2-digit
SIC industry as the investor per squared kilometer for the same years (LO-
CALIZATION ECONOMIES).¹⁸

Additional regressors include dummy variables for the states (to account
for observable and unobservable state level characteristics) as well as a set
of dummies for each combination of year and 2-digit SIC sector to ensure
compatibility between the CLM and Poisson approaches.

4.2 Empirical Results

In Table 3 we present the results of our regression analysis. We ran several
models. The first one, corresponding to columns 1 and 2, is a standard CLM
estimated by means of the equivalence relation with the Poisson regression.
In the first specification (column 1) all variables are highly significant and
with the expected signs. We find evidence that the costs of production fac-
tors (labor costs, land costs and taxes) impact negatively on the probability
of location in a given county. Of all these costs, the cost of land has the
highest impact. Everything else constant, a 1 percent increase in land costs

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