Page 86 - 1-Entrepreneurship and Local Economic Development by Norman Walzer (z-lib.org)

Entrepreneurship and Small Business Growth 75

in the innovation sense, they are associated with microenterprise growth
in the counties and, for this reason, can add to rural economic develop-
ment.
There have been major changes in approaches to local economic de-
velopment with much more attention now paid to seeking out local
businesses with the potential to expand with technical support. Identify-
ing new markets or helping small businesses stay current on technologi-
cal advances is at the heart of the widely recognized Economic Garden-
ing concept pioneered by Christian Gibbons in Littleton, Colorado, for
example (City of Littleton 2006). This shift away from an industrial at-
traction focus and more focus on assessing the potential for local firms
to prosper with management or marketing assistance will probably in-
crease in the future. The analysis suggest that business structure is im-
portant for successful entrepreneurship activity although it may not
be widely recognized by incoming entrepreneurs before they a start a
business.
In the past, microenterprises have been an important component in the
economies of many, if not most, small rural counties. There is every indica-
tion that these businesses will become even more important in the future,
and more research is needed regarding factors that create the most suitable
climate for these businesses to prosper. Identifying potential entrepreneurs
interested in starting microenterprises and creating a supportive environ-
ment may well be one of those elements.

APPENDIX: THE STRUCTURAL EQUATION MODEL

Structural equation models enable one to specify theoretical frameworks
and test causal relationships. Consider the construct economic climate of a
region. It can be measured using a variety of indicators such as per capita
income, unemployment rate, etc. Since these indicators are often measured
with error, linking these indicators with other variables such as new firm
starts is bound to yield statistical estimates of association that involve mea-
surement error. Structural equation models can overcome this limitation
because relationships among variables can be estimated after adjusting for
measurement errors.
The causal model in figure 4.1 uses two sets of equations: (1) measurement
equations, and (2) structural equations. The first set of equation specifies the
observed variables used to measure concepts. Specifically, it describes the
reliabilities of the observed variables—the degree of correspondence be-
tween the theoretical constructs and their indicators of measurement. If
reliability is less than one in magnitude, then the indicator contains mea-
surement errors.