Modeling industrial location decisions in U.S. counties

distributions of these new plants. As can be seen, the distributions are rela-
tively stable over time and exhibit a substantial degree of concentration. For
both years, the same five most important sectors account for approximately
57 percent of all investments. A similar pattern can be found for the spatial
distribution, as the same ten states concentrate 56 percent of new plants
births for any of the considered years.¹⁵ .

[insert Tables 1 and 2]

The independent variables include the county characteristics that can
affect the firm profit function. These characteristics can affect profits both
from the cost and revenue side. On the cost side of the profit function we test
the cost of labor, land, and capital. The county labor cost is measured by
the wage and salary earnings per job in 1988 and 1996 (LABOR COSTS).¹⁶
Since industrial and residential users compete for land, when modeling with
small areas, as in our case, land costs can be proxied by population density.
Consequently, we use population density for the years of 1988 and 1996 to
approximate land costs (LAND COSTS). Per capita property taxes for 1987
and 1997 are included in the model to account for the tax business climate in
each county (TAXES). Property taxes affect all private investments made in
United States, and vary significantly across counties. Incentives can change
effective payments to local governments in some cases, but for the majority
of the new plants in our dataset the average county property tax captures
a relevant cost of doing business. To account for the revenue (demand) side
of the profit function, the model needs to include a measure of market size.
As such, we use total county personal income for the years of 1988 and 1996

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