Page 110 - 1-Entrepreneurship and Local Economic Development by Norman Walzer (z-lib.org)

The Drivers of Regional Entrepreneurship in Rural and Metro Areas 99

3. It is important to remember that the self-employed are not the only entrepre-
neurs, however. Aspiring entrepreneurs would not be identified as self-employed be-
cause they have not started a business to employ themselves. In some cases, entre-
preneurs start by doing a part-time business before becoming fully self-employed.
Thus, the self-employed are best recognized as a subset of entrepreneurs in the
United States.
4. Location quotients (LQs) are an economic analysis tool which compares a lo-
cal measure to a reference measure. In this case, county proprietor breadth is com-
pared to national proprietor breadth: if the county’s breadth is the same as the na-
tional average, the LQ equals one.
5. The LQ for all nonmetropolitan counties was 1.213. Proprietors accounted
for 19.4 percent of nonfarm employment in nonmetropolitan counties.
6. Calculations were based on County Business Patterns data.
7. The value-added measure is constructed as the ratio of nonfarm proprietor
income over the nonemployer receipt data. Nonfarm proprietor income data for
2001 was obtained from BEA–REIS. Proprietor receipt data were obtained from U.S.
Census Nonemployer Receipts, 2001, which stems from the receipts reported by
proprietors to the Internal Revenue Service on Schedule C.
8. The U.S. Department of Agriculture categorizes manufacturing industries
into high-tech, value-added, and routine categories. In high-tech industries, almost
27 percent of the jobs were in skilled occupations compared to 9.3 percent in value-
added industries and 10.3 percent in routine industries (Henderson 2004b).
9. The U.S. Department of Agriculture categorizes service producing industries into
consumer, producer, recreation, and transportation, utilities, and wholesale categories.
In producer industries, almost 37 percent of the jobs were in skilled occupations com-
pared to 25 percent in other service-producing industries (Henderson 2004b).
10. To test for differences in the marginal impacts of human capital, amenities,
financial capital, and infrastructure across rural and metropolitan counties, regres-
sion models that included an interaction term created from a metropolitan dummy
variable and the four drivers are estimated.
11. The human capital and metropolitan interaction variables are positively as-
sociated with entrepreneurial depth, suggesting that the marginal income and value-
added impacts associated with high levels of educational attainment, foreign popu-
lation, and information and arts employment are larger in the more agglomerated
metropolitan areas. The infrastructure and metro interaction variables are also pos-
itive and significantly associated with entrepreneurial depth.
12. The regressions show that the human capital, topography, and the interstate
interaction terms are negative and significant, indicating that these factors’ impacts
are lower in metropolitan areas and stronger in nonmetropolitan or rural areas.

REFERENCES

Acs, Zoltan, and Catherine Armington. 2004. Employment growth and entrepre-
neurial activity in cities. Discussion Paper on Entrepreneurship, Growth and Pub-
lic Policy, Max Planck Institute for Research into Economic Systems, Group En-
trepreneurship, Growth and Public Policy, Jena, Germany.