Acquisition of institutional capital by niche agricultural producers.
Zhang, Jing ; Van Auken, Howard
Introduction
The acquisition of capital remains one of the most important
challenges for owners of small firms (Black and Strahan, 2002), and is
especially challenging for niche agricultural firms (e.g., small
agricultural producers of products such as organic or locally grown
niche products that include, for example, flowers, seeds, honey, fruit,
beef, vegetables) (Pirog et al., 2006; Richards and Bulkley, 2007).
Their limited access to capital even puts these producers at a
competitive market disadvantage (Wheatley, 2001), resulting from several
main factors. In particular, niche agricultural producers' business
models often do not fit the traditional business models that providers
of capital know and understand (Sherrick, 1998). Products marketed by
niche agricultural producers tend to be subject to uncertain
environmental factors, including the weather. Niche farmers operate
small farms that produce food and fiber product and sell their products
through unique outlets, such as organic and health foods stores,
farmers' markets, and direct consumer channels (McElwee, 2006).
Finally, niche producers often lack equity capital or collateral, which
generally is required to secure loans, and they tend to lack strong
business skills (Richards and Bulkley, 2007).
To address these problems, various programs attempt to improve the
flow of capital to the niche agricultural sector (Kilkenny and Schluter,
2001). Some programs operate through U.S. governmental agencies (e.g.,
United States Department of Agriculture [USDA]), others through state
and local economic development agencies (e.g. revolving loan funds), and
still others through private agencies (e.g. Farm Credit System). In this
article, we refer to capital provided by government or private agencies
as "institutional capital." Despite the expansion of programs
aimed at providing financial resources to the agricultural sector,
Goreham (2005) reports that many sectors remain poorly served.
Similarly, Korsching and Jacobs (2005) have maintained that state and
local organizations need to facilitate the flow of capital to small
agricultural firms.
A possible explanation for this dilemma refers to information
dissemination. Many programs are available, but information about those
programs, including their required criteria, contacts, and
documentation, is not widely disseminated (Richards and Bulkley, 2007).
Prior studies have suggested that poor understanding of both capital
alternatives and the process of acquiring funds erect barriers to
capital acquisition by small firms (Carter and Van Auken, 2008). Without
sufficient information about alternative sources of funding,
owners' search for capital is often inefficient, unorganized, and
unsuccessful (Gibson, 1992). Niche agricultural producers are especially
limited in their access to information. This "knowledge gap"
(Holmes and Kent, 1991) pertains to their awareness about which sources
of capital are available and appropriate, as well as the understanding
by capital providers of how to evaluate requests for funding from these
small, niche market agriculture firms (Fries and Akin, 2004). The
relative isolation of niche agricultural producers likely complicates
their acquisition of capital from institutions, and their rural location
could limit their convenient and consistent access to information and
providers of capital (Van Auken, 2001). As a result, information
spillover is poor, and producers remain unaware of how and where to find
alternative sources of capital.
Although previous studies have suggested that small business
owners' information about institutional capital can influence their
capital acquisition, few studies examine this proposition empirically,
or examine the relationship between financial theory and empirical
findings. In addition, no previous study investigates theoretical issues
related to capital acquisition by niche agricultural firms and where
niche agricultural producers can obtain the information that would help
them acquire institutional capital. We are particularly interested in
local commercial banks in this context, because prior studies suggest
that local commercial banks not only provide external financial capital,
but also function as information hubs that disseminate information about
alternative sources of capital to producers (Berger and Udell, 2002; Van
Auken, 2001). We consider whether and how niche agricultural producers
might achieve a higher propensity to acquire institutional capital when
they obtain their information from local banks.
This study thus makes three significant contributions to the
existing literature. First, we attempt to clarify the relationship
between niche agricultural producers' information about capital and
capital acquisition. Understanding this research question is
particularly important in response to changing governmental support and
volatile market conditions in recent years. Niche agricultural producers
may suffer a significant financial disadvantage if they cannot find
alternative funding sources, and the ineffective dissemination of
information about these alternative sources limits the effectiveness of
funding programs. Better dissemination of information could improve
their effectiveness, if owners of small firms are not currently familiar
with these programs. In particular, we determine whether local
commercial banks may serve as information hubs in this process. The
findings in the study provide insight into finance theory in the context
of niche agricultural firms. The results therefore have key implications
for policymakers, business owners, and other practitioners.
Second, this study provides new insights into the relationship
between the knowledge gap proposed by Gibson (1992) and capital
acquisition. The knowledge gap, caused by a lack of information about
capital acquisition alternatives, may influence information asymmetries,
which represent barriers to capital acquisition (Landstrom, 1992;
Winborg and Landstrom, 2000). However, many prior studies focus solely
on high-technology firms and thus highlight the knowledge gap from the
perspective of capital providers (e.g. Batjargal, and Liu, 2004; Shane
and Cable, 2002; Shane and Stuart, 2002). We instead analyze information
asymmetry from the perspective of firms, because without a solid
understanding of capital acquisition alternatives and the capital
acquisition process, firms suffer a disadvantage in their working
relationship with capital providers. This study reveals comprehensive
details about information asymmetry and capital acquisition.
Third, our empirical work is based on survey data from a sample 169
niche agricultural producers. Prior research has not considered the
acquisition of capital among niche agricultural producers, despite the
important role of this sector for U.S. agriculture. Our theoretical and
empirical understanding of the challenges of raising capital remains
weak; this study offers some important initial findings in this
important yet understudied area.
The remainder of this article is organized as follows: In the next
section, we provide background on niche agricultural producers, followed
by a review of information asymmetry theory for small firm financing. We
propose hypotheses about the relationship between niche agricultural
producers' information about institutional capital and their
acquisition of capital. After describing the data collection and
methodology, we discuss the results of the empirical analysis and
finally conclude.
Background: U.S. Small Farms and Niche Agricultural Producers
The United States currently hosts approximately 2.1 million farms
that operate on one billion acres of land. Approximately 25% of the
farms and 15% of the acreage are located in the Midwest, and
approximately 99% of these farms and farm acres are family-owned
enterprises. Small farms, which earn less than $20,000 in annual sales,
constitute around 60% of all farms and 29% of U.S. agricultural land
held by farmers nationwide (Richards and Bulkley, 2007; Steele, 1997).
Small farms earned total sales of $42.6 billion in 2007 (University of
Illinois Extension, 2009); they clearly are significant to rural life in
the United States.
In contrast with the popular imagination, small farms also can grow
quickly and play an important role in U.S. economic development. For
example, the U.S. organic agricultural industry, an important segment of
the small farm field, accounted for approximately $20 billion in sales
in 2007, and was expected to reach about $23.6 billion in sales in 2008.
Sales of organic food products constituted around 2.8% of total U.S.
food sales in 2006, and are growing at a rate of approximately 20% per
year (Organic Trade Association, 2007).
The importance of small farms is exemplified by the USDA's
recent $65 million initiative, Know Your Farmer, Know Your Food, which
aims to reconnect consumers with local producers (USDA, 2009). Small
farms help maintain the rural economy by creating demand for
labor-intensive goods and services produced in local communities. They
represent important consumers of the services and products produced in
rural communities, and they help maintain a critical population density
to sustain rural services. Small family farms are also perceived as an
attractive, wholesome, and stable way of life.
Financial resources remain the key infrastructure feature that
supports a vibrant, sustainable local/regional food system. Yet,
financing continues to be a challenge for small farmers. With the demise
of publicly funded agricultural development banks, most small farmers
rely on personal or family financing (Hazell, 2005). The lack of
financial resources limits their competitiveness, and issues associated
with capital acquisition demand further research attention (Goreham,
2005).
Theoretical Development and Hypotheses
The finance theory has suggested a list of factors that constrain
small firm owners' ability to access capital from institutional
sources. These include information asymmetry, insufficient collateral,
owner goals, risk preferences, life style preference, and financial
security (Bolton and Freixas, 2000; Landstrom, 1992; Kuratko, Hornsby,
and Naffziger, 1997; Petty and Bygrave, 1993; Xaio et al., 2001). Among
these factors, information asymmetry is one of the most important
factors, and it attracts great research attention.
Information asymmetry refers to the information gap between
external capital providers and small business owners (Winborg and
Landstrom, 2000). According to information asymmetry theory, small
business owners often have insufficient information about capital
alternatives or are unable to articulate the potential of their
business. Some small business owners may be overly cautious about
providing external financiers with detailed information about their
business. As a consequence, capital providers have difficulty obtaining
the information they need, which causes them to perceive a high risk of
offering capital. These capital providers also may have information
about the industry's potential that the firm lacks. These various
types of information asymmetry between capital providers and small
business owners increase the costs of financial transactions; these
costs, in combination with the high perceived risk, can create an
obstacle that prevents capital providers from financing small
businesses. In Figure 1, we depict some barriers to the flow of
information and capital between capital providers and small business
owners; it reflects our premise that the quality of financial decisions
relates directly to the quality and availability of relevant information
(Arthurs and Busenitz, 2003).
[FIGURE 1 OMITTED]
Previous studies have highlighted the lack of information about
small businesses from the perspective of capital providers. For example,
Petersen and Rajan (2002) note that much of the information that small
firms provide is "soft" and difficult to
communicate-particularly in high-technology industries, where small
business owners are apprehensive about disclosing their proprietary
technology and business model (Batjargal and Liu, 2004; Shane and Cable,
2002; Shane and Stuart, 2002; Winborg and Landstrom, 2000).
Yet the other side of information asymmetry--that small business
owners lack sufficient information about capital providers--receives
little attention. Information about capital providers includes
alternative sources of capital and the process for acquiring capital
(Berger and Udell, 1998; Gibson, 1992). Capital providers disseminate
information about their funding programs and fund qualified firms, but
poor dissemination of information bars the flow of capital to small
business owners, who need information to achieve effective usage of the
program (Berger and Udell, 1998). Apparently, small business owners are
not able to access programs if they do not know of the program. They
also may be unlikely to provide the necessary information if they do not
fully understand the capital acquisition process (Van Auken, 2000),
which would worsen the information asymmetry. The availability of
information about alternative sources of capital and the process by
which they can acquire capital, therefore, influence capital acquisition
decisions (Busenitz et al., 2003).
The barriers associated with disseminating information are even
more significant for niche agricultural producers. Korsching and Jacob
(2005) emphasized that poorly disseminated information prevents the
successful flow of capital to agricultural producers, because few
funding programs serve rural markets (Richards and Bulkley, 2007). In
addition, the lack of technical assistance in rural areas constrains
rural entrepreneurs from understanding the process and limits their
ability to acquire capital (Korsching and Jacobs, 2005). McElwee and
Annibal (2010) believed that skill development is important for farmers
to operate and remain competitive.
The information asymmetry theory aforementioned seems to suggest
the first hypothesis--niche agricultural producers' information
about alternative sources of capital should be positively associated
with their capital acquisition. However, one may argue that the
producers with plenty of information about a particular institutional
capital and the capital provider may NOT necessarily be willing to
acquire the capital. The reasons are two-folds. First, bank loans may
dominate the choice of the producers in most states, as they are the
most traditional source of capital and have serviced the rural
communities for the longest time. According to the schemas theory, even
when the producers possess a certain level of information about various
institutional capitals, such information is still considerably new, and
may not fit into their existing schemas of knowledge (Armbruster, 1996).
As a result, the producers will not take actions on the new information.
Second, when the producers collect plenty of information about
institutional capital, they also become aware of the negative
information about the capital, such as the tedious capital application
process, tons of paper work and low successful rate. Such information
may impair the producers' enthusiasm in trying the capital. The two
reasons suggest that the null hypothesis, which predicts no relationship
between producers' information about alternative sources of capital
and their capital acquisition, may hold. Thus, our first hypothesis is
falsifiable and unobvious (Campion, 1993).
To test the hypothesis, we focus on the acquisition of
institutional capital offered by government and private agencies, and we
use familiarity and technical assistance provided by government and
private agencies to measure the level of information that niche
agricultural producers possess. Familiarity refers to the extent of
relevant information owners have about alternative sources of capital
and the process of obtaining that capital; such information may be
obtained from public media or prior experience (Van Auken, 2001). We do
not use "familiarity" to imply any social effects of
established relationships or repeated ties among persons, which is the
usage adopted by Gulati (1995). Furthermore, technical assistance
provided by government and private agencies refers to the business
assistance provided in areas such as business, financial planning, and
marketing. This technical assistance can help producers understand
alternative sources of and requirements for acquiring institutional
capital (Chrisman and McMullan, 2004), and without it, niche
agricultural producers may not have sufficient business knowledge or
experience needed to raise institutional capital. Through their
technical assistance, government agencies can lower risk perceptions,
administer financing programs, and offer opportunities to expand
business networks through capital funding (Beck, 2006). For example, the
Small Business Development Center provides varied technical advice,
ranging from business planning to financial analysis to marketing, to
improve the success rates of small firms. According to the argument
above, we propose the following testable hypotheses:
Hypothesis 1: Niche agricultural producers ' acquisition of
institutional capital is positively associated with their familiarity
with information about institutional capital.
Hypothesis 2: Niche agricultural producers ' acquisition of
institutional capital is positively associated with the technical
assistance provided by government and private agencies.
In addition to obtaining information about institutional capital
from capital providers, niche agricultural producers could obtain such
information from other channels. We focus on the role of local
commercial banks in this aspect, because they are a major source of
capital for all small firms (Report to the Congress on the Availability
of Credit to Small Businesses, 2002). They provide both capital and
information to rural customers (Van Auken, 2001), and facilitating the
flow of information in this way is important, because the capital gap
may result from a lack of knowledge and skills in rural markets
(Drabenstott and Meeker, 1997). Close working relationships with a local
commercial bank (e.g. relationship lending) can improve the flow of
information about business and financial options (Berger and Udell,
2002) and enable bankers to design a tailored program that meets their
small business clients' needs. The bank also may provide technical
assistance, such as details about the range of alternative financing
options. We expect that producers' familiarity with and technical
assistance from local commercial banks increase their chances of
obtaining institutional capital and thus propose:
Hypothesis 3: Niche agricultural producers ' acquisition of
institutional capital is positively associated with their familiarity
with local commercial banks.
Hypothesis 4: Niche agricultural producers ' acquisition of
institutional capital is positively associated with technical assistance
provided by local commercial banks.
Methodology
Sample and Questionnaire
The participants in an initial focus group convened to discuss
issues related to capital acquisition among niche agricultural producers
included owners of niche agricultural businesses, economic development
personnel, and institutional providers of capital. We integrated the
information we gathered from the focus group with findings from previous
research, including Van Auken (2005), Carter and Van Auken (2005),
Busenitz et al. (2003), Kuratko, Hornsby, and Naffiziger (1997), McMahon
and Stanger (1995), Petty and Bygrave (1993), and Ang (1992), to develop
a questionnaire. The questionnaire was initially developed and pretested
in fall 2007. The questionnaires were sent in late fall 2007 because the
harvest and farmers markets were completed.
The questionnaire consisted of two sections: (1) demographic
information and (2) capital acquisition information. In the first
section, respondents identified characteristics of their firms,
including the age of the business, the firm's organizational
structure (e.g. sole proprietorship, C-corp, S-corp, cooperative,
limited liability corporation, partnership, or nonprofit organization),
total assets of the firm (< $25,000, $25,001-$50,000,
$50,000$100,000, and > $100,000), gender of the owner, business
revenue (< $10,000, $10,001$50,000, $50,000-$100,000, or
$100,001-$500,000), stage of business development (still in planning
stage, business plan developed but company not operating, first year of
operation, operating more than one year, or expanding operations), and
education (high school and lower, bachelor's degree, and graduate
degree and higher).
The second section of the questionnaire asked respondents to
indicate their sources of capital and their familiarity with each
source. We identified sources of capital from private and government
agencies during the focus group and through our literature review:
commercial banks, the Small Business Administration (SBA), local
economic development funds, Chambers of Commerce, Iowa Department of
Economic Development, the USDA, Rural Electric Cooperative, Farm Bureau,
Council of Governments, community revolving loan fund, Economic
Development Administrative, State Association for Council of Government,
Iowa Community Development, Rural Development Partners, Iowa Community
Capital, and Grow Iowa fund. Respondents indicated: (1) "Which
source of capital have you used?" and (2) "How familiar are
you with each source of capital?" using a five-point Likert scale
(1 = not familiar to 5 = very familiar). Finally, the respondents
revealed whether they had received technical assistance from commercial
banks, the SBA, Small Business Development Center, Iowa Department of
Economic Development, or a local economic department agency.
The surveys were sent to 663 small, niche agricultural producer
firms in Iowa, whose contact information was obtained from the Leopold
Center for Sustainable Agriculture database of small agricultural
producers in Iowa. Isolating the sample to a single state has several
advantages. First, this focus facilitates data collection. This benefit
is especially relevant in the context of specialized niche businesses
that are more relevant to a particular state than to others. Second, it
minimizes the number of extraneous variables. For example, various
states have different funding programs and provide different levels of
support. We received a total of 169 usable questionnaires, for a
response rate of 25.6%. All responding firms were still in operation. To
test the representativeness of these sample firms, we compared the
one-third of the sample that returned the questionnaires early with the
one-third that returned the questionnaire late using t-tests; we find no
significant differences between early and late respondents on any of the
key variables. Because prior literature suggests that later respondents
are more similar to non-respondents than are early respondents
(Oppenheim, 1966), we believe that response bias is not an issue for the
variables analyzed herein.
Our approach to collecting the data was designed to specifically
obtain information from those who acquire capital. We conducted survey
in this study because no database exists on capital acquisition for
niche agricultural producers, because survey questionnaires can collect
data in a consistent manner (Mathers, Fox and Hunn, 1998) and because
the study specifically focuses on producers' familiarity with
various types of sources of capital. Moreover, we are interested in the
real behavior of where and how the producers approach financial capital.
Given the lack of public data on this information, survey is the only
method that we can use to obtain such information.
Dependent Variable
We use two dependent variables in this study. The first pertains to
whether the firm acquired institutional capital. The variable therefore
takes a value of 1 when the firm has acquired at least one source of
capital and 0 otherwise. The second variable notes how many of the 15
sources of capital the respondent has used, so the value can be 0-15.
Independent Variables
The first group of independent variables refers to the
respondent's familiarity with commercial banks and institutional
capital. We asked the respondents to indicate the extent to which they
were familiar with these sources of capital (1 = not familiar and 5 =
very familiar). The value of [X.sub.commercial] bank equals the level of
the respondent's familiarity with commercial banks; the value of
[X.sub.institutional capital] is the average level of familiarity with
the 15 types of institutional capital.
The second group of independent variables involves whether the
respondent has received technical assistance from commercial banks or
government and private agencies. The value of [X.sub.commercial bank] is
1 if the respondent has received technical assistance from commercial
banks, and 0 otherwise. The value of [X.sub.institutional capital]
equals 1 if the respondent has received technical assistance from at
least one private or government agency, and 0 otherwise. The government
and private agencies include four major government and private
organizations: the SBA, Small Business Development Center (SBDC), Iowa
Department of Economic Development (IDED), and Local Economic Department
Agency (LEDA), as well as other smaller agencies.
Control Variables
As controls, we include age, total assets, gender, education of the
owner, and stage of business development. Age provides a proxy for owner
experience, which is consistent with the approach employed by Honjo
(2000). Experience entails the stock of skills and abilities that a
person acquires over time, and it appears pertinent in entrepreneurship
literature (e.g. MacMillan, Siegel, and Subba Narasimha, 1985; Reuber
and Fischer, 1994; Stuart and Abetti, 1990) for its effect on the
financial strategies of small firms (Chandler and Jansen, 1992). Kim,
Aldrich, and Keister (2006) even find that experience has a positive
influence on capital acquisition.
Total assets indicate whether the owner has previously acquired
capital and thus indicate the owner's previous experience with
raising capital; it also helps control for firm size. Several studies
have suggested that firm size affects the acquisition of capital (Holmes
and Kent, 1991; Lucy and Bhaird, 2006). This variable takes a value of 1
if the firm's total assets are less than $25,000, 2 if they fall
between $25,001 and $50,000, 3 if the assets are $50,000--$100,000, and
4 if firm assets extend beyond $100,000.
Gender frequently has been evaluated relative to capital
acquisition, in that most studies find that women-owned businesses are
undercapitalized and have difficulty raising capital (Carter, 2002;
Verheul and Thruik, 2001; Watson, 2002). The disadvantage for women may
stem from a perceived lack of credibility (Marlow and Patton, 2005). The
value of this variable equals 1 if the business is male- or jointly
owned and 0 if the business is owned solely by a woman.
Education levels may signal greater human capital (Cassar, 2004;
Coleman and Cohn, 2000) and, as Storey (1994) suggests, greater human
capital leads to greater start-up firm viability and greater access to
capital. We use two dummy variables (edubachelor and edu_graduate) to
depict three education levels (high school and lower, bachelor's
degree, and graduate degree and higher). The default is high school.
Finally, we asked the respondents to indicate the stage of their
business development. The variable equals 1 if the business is still in
planning stage, 2 if they had developed a business plan but the company
was not operating, 3 if the firm was in its first year of operation, 4
if the firm has operated more than one year, and 5 if the firm was
expanding its operations. Timmons and Spinelli (2007) convincingly show
that shifts in firm characteristics over time affect the amount and type
of capital that might be accessible. Berger and Udell (1998) also find
that external capital is difficult for small firms to acquire until
their balance sheets start to show substantial tangible assets.
According to Cassar (2004), information asymmetries between the firm and
potential funders limit capital acquisition options, but they can lessen
over time. The decision to acquire capital thus may depend directly on
the firm's stage of development.
Results
Respondent Characteristics
In Table 1, we reveal the demographic characteristics of the
respondents; these firms generally operated as sole proprietorships,
were mid-sized, and were not in the startup phase of their operations.
Almost two-thirds of the responding firms functioned as sole
proprietorships. Furthermore, almost all respondents were jointly owned
(52.4%) or owned by men (41.5%); only 6.1% were owned by women. The
majority of respondents (61.6%) had at least an undergraduate degree.
Approximately 75% of firms had total assets greater than $100,000, and
approximately 14.4% had total assets less than $25,000. Almost one-half
of the firms gained annual revenues of more than $100,000; the
percentages of respondents in the other categories constituted
approximately equal proportions of the remaining half. The vast majority
of firms (91.2%) had been operating more than one year or were in the
process of expanding their operations; only a very small percentage of
firms (6.3%) were in the pre-startup stage.
In Table 2, we provide the descriptive statistics and correlations
for all variables used in the statistical analysis. Respondents were
more familiar with commercial banks (mean = 3.62) than with
institutional capital (mean = 1.57), and commercial banks similarly were
a more common source of technical assistance (47% of firms) than were
private and government agencies (16% of firms). Table 2 also includes
the correlations across all variables. Because the correlations between
the variables are reasonably low, we assert that multicollinearity is
not a problem.
Logistic and Poisson Regressions
We outline the results of the hypotheses tests in Table 3. We used
logistic regression in models 1a, 2a, 3a, 4a and 5a, for which the
dependent, binary variable is whether the firm has acquired
institutional capital. Poisson regression applies in models 1b, 2b, 3b,
4b and 5b, with the dependent variable of how many sources of capital
the respondent has acquired; this variable represents a count of the
sources and, therefore, takes only discrete, nonnegative, integer values
(McCullagh and Nelder, 1989).
Models 1a and 1b and full models 5a and 5b in Table 3 reveal that
institutional capital acquisition is not associated with the average
level of familiarity with the 15 different sources of intuitional
capital. This result holds for both dependent variables--that is,
whether and how many sources of institutional capital the niche
agricultural producer has acquired. Therefore, we cannot support H1 at
the aggregate level of the 15 sources of capital.
Further analysis at the level of the individual sources of capital
creates the results shown in Table 4, though we included only the three
most popular sources of capital: the SBA, USDA, and Farm Bureau (FB).
The remaining capital sources were so rarely used (i.e., 1-4 of the 166
respondents used these sources) that virtually no variables in the
models can explain this form of capital acquisition. The data in Table 4
suggest that for the three main sources of capital, the more familiar
the producers are with them, the more likely they are to use the capital
sources (SBA B = 1.01, p < .05; USDA B = 1.07, p < .001; FB B =
2.04, p < .05). In this sense, we find support for H1 at least for
the three most popularly used sources of capital.
We took each source of capital as a research subject in an
additional analysis and counted the number of respondents who reported
they had used it, then calculated the average level of all
respondents' familiarity with this particular source. As we show in
Table 5, the USDA is the most popular source of capital among
agricultural producers (30 of 166 respondents), but many other sources
of capital were used very rarely (e.g. five sources used only by 1
respondent each). Furthermore, producers were most familiar with the
USDA (2.67 on a five-point scale) but remarkably unfamiliar with all
other sources of capital (all lower than 2). The correlation between the
number of acquisitions and the average level of familiarity with the 15
sources of capital was .920 (p < .001), which suggests a strong,
positive association between the two variables. Overall, if we integrate
Tables 4 and 5 and Models 1a, 1b, 5a and 5b in Table 3, we find support
for H1 at the level of the individual source of capital, but not at the
aggregate level.
Models 2a and 2b and full models 5a and 5b in Table 3 suggest that
the acquisition of capital is more likely if producers receive technical
assistance from at least one of the four major government or private
agencies (SBA, SBDC, IDED, and LEDA). This result holds for both
dependent variables, that is, whether the producer acquires capital (B =
1.85, p < .001 in model 2a; B = 1.82, p < .001 in model 5a) and
how many sources it acquires (B = 1.17, p < .01 in model 2b; B =
1.43, p < .001 in model 5b). We thus find strong support for H2.
At the individual source of capital level, we again tested H2, as
we report in Table 4. This analysis does not issue support for H2: None
of the four technical assistance variables in the three models explains
capital acquisition. We suspect that this problem stems from the very
low frequency of technical assistance usage. We therefore checked the
frequency and percentage of technical assistance from the four agencies,
as we report in Table 6. Only 4 to 8 respondents, out of 166, used any
agencies, in support of our suspicion. Overall, when we combine Tables 4
and 6 and Models 2a, 2b, 5a and 5b in Table 3, we find support for H2 at
the aggregate level of all 15 sources of capital and all four agencies,
but not at the level of individual sources and agencies.
Models 3a and 3b and full models 5a and 5b in Table 3 suggest that
whether a producer acquires capital and how many sources of capital are
acquired are not explained by familiarity with commercial banks. The
results in Table 4 confirm this finding for the acquisition of capital
from the SBA, USDA, and FB. Therefore, H3 does not receive support.
According to Models 4a and 4b and full models 5a and 5b in Table 3,
whether a producer acquires institutional capital or how many sources of
capital are acquired also cannot be explained by whether she or he
receives technical assistance from commercial banks. Although Table 4
confirms this finding for capital from the SBA and FB, it reveals an
interesting finding with regard to the acquisition of capital from the
USDA. Specifically, if a producer receives technical assistance from
commercial banks, he/she is less likely to acquire capital from the USDA
(B = -2.04, p < .01). We discuss this surprising finding in the next
section. However, our combined findings suggest no support for H4.
Discussion
The findings in this article provide greater insight into the
relationship between niche agricultural producers' information
about institutional capital and their capital acquisition. Using a
survey of 169 small niche agricultural producers in Iowa, we find that
their capital acquisition is associated positively with their
familiarity with the particular source of capital, as well as with
technical assistance from major agencies. Contrary to our expectations
though, commercial banks do not seem to provide an information hub that
helps producers acquire institutional capital; the likelihood of
producers' capital acquisition is associated neither with their
familiarity nor with whether they received technical assistance from
commercial banks.
The results of this study enhance our understanding of the
relationship between capital acquisition and information asymmetry. Our
results provide support for the flows depicted in Figure 1 and confirm
that a funding gap can arise from the constrained flow of information
from capital providers to users about alternative financing options
(Berger and Udell, 1998; Busenitz et al., 2003; Holmes and Kent, 1991;
Van Auken, 2001). This funding gap can be filled if small business
owners gain more information about capital providers or receive
technical assistance from government and private agencies. The
association between technical assistance and capital acquisition
supports assumptions about the importance of freely and widely available
information, as exemplified in traditional finance theory.
This study provides new information about capital acquisition in
the niche agricultural sector, an under-researched but important
industry. First, we find that niche agricultural producers are
unfamiliar with most sources of institutional capital. As we show in
Table 5, niche producers are not familiar with 14 sources (familiarity
values less than 2 on a fivepoint Likert scale), though they are
familiar with funding from the USDA. This finding is in line with
Goreham (2005) and Korsching and Jacobs (2005), who have called for
better servicing of the capital flow to small agricultural firms.
Second, this study provides support for the positive relationship
between capital acquisition and producers' familiarity with
particular sources of capital at the individual level. This finding is
valid for the three most popularly used sources of capital (USDA, SBA,
and FB), as we show in Table 4. The finding also is valid when we
compare the 15 sources of capital, as in Table 5. Overall, our finding
is consistent with previous research that examines the effect of
familiarity on capital acquisition (Van Auken, 2001, 2005).
Third, our study suggests that government agencies rarely are used
as sources of technical assistance. As shown in Table 6, less than 5% of
producers rely on the four major agencies for advice. This finding may
explain the very low level of familiarity with institutional capital
among most producers, despite the local community connections that
institutional providers have with government agencies, local economic
development initiatives, or agricultural firms (Richards and Bulkley,
2007). Government agencies have not reached producers and thus cannot
help disseminate information about institutional capital. These agencies
need more extensive connections with local niche agricultural producers.
In addition, we show that at the aggregate level, technical assistance
from government agencies is positively associated with institutional
capital acquisition. Therefore, agencies should facilitate the flow of
information, such as by offering more services, including technical
assistance, to give niche agricultural producers a more comprehensive
understanding of their capital alternatives, which in turn will make
them more likely to acquire institutional capital. Increased flow of
information can provide opportunities for niche agricultural operators
to increase their business skill set and become more entrepreneurial, as
was recommended by McElwee and Bosworth (2010).
Fourth, we do not find that commercial banks assist niche
agricultural producers in their acquisition of institutional capital
from government or other private agencies. This finding challenges the
prevailing belief that local commercial banks act as information hubs
with regard to securing funding and providing general assistance with
capital acquisition. To explain this unexpected finding, we interviewed
bankers from two local commercial banks, who offered the following
explanations: Except for the SBA and UDSA, local community banks do not
work closely with institutional capital providers, which leaves them
relatively unfamiliar with institutional capital as well. Furthermore,
the relatively small number of customers in the niche agricultural
sector, and the various rules and regulations associated with different
forms of institutional capital, leaves managers of community banks
largely unwilling and unable to expend significant time and effort to
gather and offer information about the less frequently used types of
capitals. Finally, banks must focus on generating profits for their
shareholders, while also serving the needs of customers. Bankers'
limited exposure to sources of institutional capital and the limited
requests they receive for niche agricultural financing prevents them
from playing an active role in assisting niche agricultural producers to
acquire institutional capital.
Fifth, this study shows that the likelihood that producers acquire
capital from the USDA is significantly and negatively associated with
their receipt of technical assistance from commercial banks. This
finding is not surprising if we recognize the true roles of commercial
banks; they are not only a central point of contact for issues related
to capital in rural areas, but they also represent profit-seeking
entities with a vested interest in securing profitable customers.
Commercial banks might provide technical assistance only to their best
customers and refer other customers to the USDA for funding. Such a
cooperative arrangement would benefit all stakeholders, including the
communities, because the private sector can serve one segment of
customers while the public sector serves another.
The results of this study also provide managerial implications that
niche agricultural producers can use to develop alternative financial
strategies, and that support agencies (e.g. academics, governments,
consultants) can employ to assist these producers in their financial
planning. For producers, the most important implication is the need to
be aware of financing alternatives and programs other than capital
gained from commercial banks or internal sources, such as savings and
investments from family and friends. Niche agricultural producers also
may need to be proactive in locating, understanding, and securing
alternative sources of capital. They should explore all available
information about government and private agencies instead of relying
only on familiar sources of capital. Providers of institutional capital,
in turn, should focus on providing information more effectively and
marketing their programs to niche agricultural producers better.
Managers of private or government funding opportunities that provide
capital to niche agricultural producers might reevaluate their methods
for disseminating information to obtain greater visibility. For example,
they could work more closely with local commercial banks. Educators also
should help niche agricultural producers understand the various
financing alternatives, where to find information, and how to navigate
through the maze of programs. Finally, commercial banks should play a
more active role in providing information about diverse sources of
capital.
Conclusions
Small firms constantly confront limited access to capital and poor
business skills. Niche agricultural producers may be supremely skilled
in production, but weak in the skills needed to develop a successful
financial acquisition strategy. Yet the importance of agriculture in the
United States and the growth of niche agricultural markets suggest that
communities and governments must develop policies to facilitate the flow
of capital, especially in the face of the modern economic crisis.
Information that is presented in an information packet that provides a
single and unified set of information and agency funding criteria would
be valuable. Such information could be disseminated to providers of
capital so that they could better advise their clients. Alternatively, a
website that presents a flowchart of funding agencies and menu aligned
with criteria would provide owners more transparency into the complex
and confusing array of information. Illiquidity resulting from a failure
to acquire capital can lead to an inability to fund operations and
ultimately to failure.
Producers remain unfamiliar with most sources of institutional
capital, and very rarely do they receive technical assistance from
various agencies. Moreover, we find a positive relationship between
niche agricultural producers' familiarity with and acquisition of
institutional capital from government and private agencies. We uncover
an association between capital acquisition and the receipt of advice
from agencies too. Therefore, our study suggests that producers should
explore more capital funding opportunities, beyond the limited sources
of capital they already know or from which they have previously received
technical assistance. We also call for more effective information
dissemination by institutional capital providers.
The limitations of the study suggest some opportunities for further
research. Our sample is relatively small and only includes firms located
in a specific state in the Midwestern region of the United States.
Additional research should examine the generalizability of these results
by examining similar financing issues in other regions. In addition, we
collected our data at a single point in time, whereas a longitudinal
study might provide evidence about the changing patterns and variables
that affect the acquisition of finance by niche agricultural producers
over time. Because this paper covers a topic not widely discussed in the
literature, more variables and different methodological approaches could
be included in future studies that provide even greater insight into
capital acquisition of this specialized industry. Because niche
agricultural producers operate throughout the world, longitudinal
studies across various regions could offer a good understanding of the
relationship among, for example, economic conditions, economic
development policies, and capital acquisition.
Acknowledgements
This study was funded from a grant by the Leopold Center for
Sustainable Agriculture.
References
Ang, J.S. 1992. "On the Theory of Finance for Privately Held
Firms." Journal of Small Business Finance 1(1): 185-203.
Armbruster, B. 1996. "Schema Theory and the Design of
Content-area Textbooks." Educational Psychologist 21: 253-276.
Arthurs, J.D., and L.W. Busenitz. 2003. "The Boundaries and
Limitations of Agency Theory and Stewardship Theory in the Venture
Capitalist/Entrepreneur Relationship." Entrepreneurship: Theory and
Practice 28: 23-35.
Batjargal, B., and M. Liu. 2004. "Entrepreneurs' Access
to Private Equity in China: The Role of Social Capital."
Organization Science 15(2): 159-172.
Beck, T. 2006. "The Determinants of Financing Obstacles."
Journal of International Money and Finance 26(6): 932-952.
Berger, A., and G. Udell. 1998. "The Economics of Small
Business Finance: The Roles of Private Equity and Debt Markets in the
Financial Growth Cycle." Journal of Banking and Finance 22:
613-673.
Berger, A.N., and G.F. Udell. 2002. "Small Business Credit
Availability and Relationship Lending: The Importance of Bank
Organisational Structure." The Economic Journal 112: 32-53.
Black, S., and P. Strahan. 2002. "Entrepreneurship and Bank
Credit Availability." Journal of Finance 57(6): 2807-2833.
Bolton, P., and X. Freixas. 2000. "Equity, Bonds, and Bank
Debt: Capital Structure and Financial Market Equilibrium Under
Asymmetric Information." Journal of Political Economy 108(2):
324-351.
Busenitz, L., G. West, D. Shepherd, T. Nelson, G Chandler, and A.
Zacharakis. 2003. "Entrepreneurship Research in Emergence: Past
Trends and Future Directions." Journal of Management 29(3):
285-308.
Campion, M. 1993. "Article Review Checklist: A Criterion
Checklist for Reviewing Research Articles in Applied Psychology."
Personnel Psychology 46(3): 705-718.
Carter, S. 2002. "Gender and Enterprise." In S. Carter
and D. Jones-Evans (eds.), Enterprise and Small Business: Principles,
Practice and Policy. Prentice Hall: London.
Carter, R., and H. Van Auken. 2005. "Bootstrap Financing and
Owners' Perception of Their Business Constraints and
Opportunities." Entrepreneurship and Regional Development 17(2):
129-144.
Carter, R., and H. Van Auken. 2008. "Capital Acquisition
Attitudes: Gender and Experience." Journal of Entrepreneurial
Finance and Business Ventures 12(2): 55-73.
Cassar, G. 2004. "The Financing of Business Start-ups."
Journal of Business Venturing 19: 261-283.
Chandler, G.N., and E. Jansen. 1992. "The Founder's
Self-assessed Competence and Venture Performance." Journal of
Business Venturing 7(3): 223-236.
Chrisman, J., and W. McMullan. 2004. "Outsider Assistance as a
Knowledge Resource for New Venture Survival." Journal of Small
Business Management 42(3): 229-244.
Coleman, S., and R. Cohn. 2000. "Small Firms' Use of
Financial leverage: Evidence from the 1993 National Survey of Small
Business Finance." Journal of Business and Entrepreneurship 12(3):
81-98.
Drabenstott, M., and L. Meeker. 1997. "Financing Rural
America: A Conference Summary." Federal Reserve Bank of Kansas
City.
Fries, B., and B. Akin. 2004. "Value Chains and Their
Significance for Addressing the Rural Finance Challenge." ACDI/VOCA
microREPORT #20, Washington, DC.
Gibson, B. 1992. "Financial Information for Decision Making:
An Alternative Small Firm Perspective." Journal of Small Business
Finance 1: 221-232.
Goreham, G. 2005. "Farm Credit Services and Social Capital in
Rural Communities." Farm Credit Horizons, Department of Rural
Sociology, North Dakota State University.
Gulati, R. 1995. "Does Familiarity Breed Trust? The
Implications of Repeated Ties for Contractual Choice in Alliances."
Academy of Management Journal 38(1): 85-112.
Hazell, P. 2005. "Is There a Future for Small Farms?"
Agricultural Economics 32(1): 93-101.
Holmes, S., and P. Kent. 1991. "An Empirical Analysis of the
Financial Structure of Small and Large Australian Manufacturing
Enterprises." Journal of Small Business Finance 1(2): 141-154.
Honjo, Y. 2000. "Business Failure of New Software Firms."
Applied Economics Letters 7: 575-579.
Kilkenny, M., and G. Schluter. 2001. "Value-Added Agriculture
Policies Across the 50 States." Rural America 16(1): 12-18.
Kim, P., H. Aldrich, and L. Keister. 2006. "Access (Not)
Denied: The Impact of Financial, Human, and Cultural Capital on
Entrepreneurial Entry in the United States." Small Business
Economics 27: 5-22.
Korsching, P., and C. Jacobs. 2005. "Farmer Entrepreneurship:
Problems and Prospects of Growing a Business on the Farm."
Sociology Research Briefs, Iowa State University.
Kuratko, D., J. Hornsby, and D. Naffziger. 1997. "An
Examination of Owner's Goals in Sustaining Entrepreneurship."
Journal of Small Business Management 35: 24-33.
Landstrom, H. 1992. "The Relationship Between Private
Investors and Small Firms: An Agency Theory Approach."
Entrepreneurship and Regional Development 4: 199-223.
Lucy, B., and C. Bhaird. 2006. "Capital Structure and the
Financing of SMEs: Empirical Evidence from an Irish Survey."
Working Paper, University of Dublin.
MacMillan, I.C., R. Siegel, and P. Subba Narasimha. 1985.
"Criteria Used by Venture Capitalists to Evaluate New Venture
Proposals." Journal of Business Venturing 1: 119-128.
Marlow, S., and D. Patton. 2005. "All Credit to Men?
Entrepreneurship, Finance, and Gender." Entrepreneurship Theory and
Practice 29(6): 717-735.
Mathers, N., N. Fox, and A. Hunn 1998. "Surveys and
Questionnaires." Trent Focus Group: 1-50.
McElwee, G. 2006. "Farmers as Entrepreneurs: Developing
Competitive Skills." Journal of Developmental Entrepreneurship
11(3): 187-206.
McElwee, G., and I. Annibal. 2010. "Business Support for
Farmers: The Farm Cornwall Project." Journal of Small Business and
Enterprise Development 17(3): 475-491.
McElwee, G., and G. Bosworth. 2010. "Exploring the Strategic
Skills of Farmers Across a Typology of Farm Diversification
Approaches." Journal of Farm Management 13(12): 819-838.
McCullagh, P., and J. Nelder. 1989. Generalized Linear Models.
Chapman and Hall: New York.
McMahon, R., and A. Stanger. 1995. "Understanding the Small
Enterprise Financial Objective Function." Entrepreneurship: Theory
and Practice 19: 21-39.
Oppenheim, A. 1966. Questionnaire Design and Attitude Measurement.
Basic Books: New York.
Organic Trade Association. 2007. Manufacturer Survey. Greenfield,
MA.
Petersen, M., and R. Rajan. 2002. "Does Distance Still Matter?
The Information Revolution in Small." Journal of Finance 57(6):
2533-2570.
Petty, J., and W. Bygrave. 1993. "What Does Finance Have to
Say to the Entrepreneur?" The Journal of Small Business Finance
2(2): 125-137.
Pirog, R., G. Zdorkowski, K. Enshayan, C. Pardee, K. Meter, K.
Beidler, C. Chase, S. Futrell, and A. Hug. 2006. "Developing a
Vibrant and Sustainable Regional Based Food System." Leopold Center
for Sustainable Agriculture, Iowa State University.
Report to the Congress on the Availability of Credit to Small
Businesses. 2002. Board of Governors of the Federal Reserve System,
Washington DC.
Reuber, A.R., and E.M. Fischer. 1994. "Entrepreneurs'
Experience, Expertise, and the Performance of Technology-based
Firms." IEEE Transactions on Engineering Management 41(4): 365-374.
Richards, S., and S. Bulkely. 2007. "Agricultural
Entrepreneurship: The First and the Forgotten?" Hudson Institute
Center for Employment Policy.
Shane, S., and D. Cable. 2002. "Network Ties, Reputation, and
the Financing of New Ventures." Management Science 48(3): 364-381.
Shane, S., and T. Stuart. 2002. "Organizational Endowments and
the Performance of University Start-Ups." Management Science 48(1):
154-170.
Sherrick, B. 1998. "Recent Trends Affecting Farm and Rural
Business Finance." Agricultural Outlook Forum.
Steele, C. 1997. "Why US Agriculture and Rural Areas Have a
Stake in Small Farms." Rural Development Perspectives 12(2): 26-31.
Storey, D. 1994. "The Role of Legal Status in Influencing Bank
Financing and New Firm Growth." Applied Economics 26(2): 129-136.
Stuart, R., and P. Abetti. 1990. "Impact of Entrepreneurial
and Management Experience on Early Performance." Journal of
Business Venturing 3(5): 151-162.
Timmons, J., and J. Spinelli. 2007. New Venture Creation:
Entrepreneurship for the 21st Century (7th ed.). McGraw-Hill/Irwin: New
York, NY.
University of Illinois Extension (2009). The Farm Gate. Available
from World Wide Web: www.farmgate.uiuc.
edu/archive/2009/08/small_farms_who.html.
USDA 2009. "Know Your Farmer, Know Your Food." USDA
Release 0440.09. Available from World Wide Web:
www.usda.gov/wps/portal/!ut/p/_s.7_0_A/7_0_1OB?
contentidonly=true&contentid=2009/09/0440.xml
Van Auken, H. 2001. "Financing Small Technology-Based
Companies: The Relationship Between Familiarity with Capital and Ability
to Price and Negotiate Investment." Journal of Small Business
Management 30(3): 240-258.
Van Auken, H. 2000. "The Familiarity of Small Technology-Based
Business Owners with Sources of Capital: Impact of Location and
Capitalization." Journal of Small Business Strategy 99: 33-47.
Van Auken, H. 2005. "Differences in the Usage of Bootstrap
Financing Among Technology-Based versus Nontechnology Based Firms."
Journal of Small Business Management 43(1): 93-103.
Verheul, I., and R. Thurik. 2001. "Start-Up Capital: Does
Gender Matter?" Small Business Economics 16: 329345.
Watson, J. 2002. "Comparing the Performance of Males and
Female controlled Businesses: Relating Outputs to Inputs."
Entrepreneurship Theory and Practice 26(3): 91-100.
Wheatley, W. 2001. "Consumer Preferences, Premiums, and the
Market for Natural and Organic Pork: Locating a Niche for Small-Scale
Producers." Working Paper, University of Minnesota.
Winborg, J., and H. Landstrom. 2000. "Financial Bootstrapping
in Small Businesses: Examining Small Business Managers' Resource
Acquisition Behaviors." Journal of Business Venturing 16(3):
235-254.
Xaio, J., M. Alhabeeb, S. Hong, and G. Haynes. 2001. "Attitude
Toward Risk and Risk-taking Behavior of Business-owning Families."
Journal of Consumer Affairs 35(2): 307-325.
Jing Zhang, Department of Management, Iowa State University
Howard Van Auken, Department of Management, Iowa State University
Contact
For further information on this article, contact:
Jing Zhang, Department of Management, Iowa State University, Ames,
IA 50011, USA Tel: 1 515 294 7650 E-mail:
[email protected]
Howard Van Auken, Department of Management, Iowa State University,
Ames, IA 50011, USA Tel: 1 515 294 2478
[email protected]
Table 1. Characteristics of Responding Firms (n = 169 (1))
Characteristic Percent
Organization Type Sole Proprietorship 64.4
(n = 163) Corporation 14.7
S-Corp 8.6
Cooperative 4.3
Partnership 6.8
Other 1.2
Gender of Owner Male 41.5
(n = 164) Female 6.1
Joint Ownership 52.4
Education High School 38.4
(n = 164) Bachelor Degree 42.7
Graduate Degree 18.9
Total Assets <$25,000 14.4
(n = 162) $25,001-$50,000 3.8
$50,001-100,000 6.9
>$100,000 75.0
Total Annual <$10,000 19.8
Revenues $10,001-$50,000 16.7
(n = 162) $50,001-$100,000 13.6
>$100,000 50.0
Stage of Business Pre-startup 6.3
Development Pre-Revenue 0
(n = 159) First Year of Operation 2.5
Operating > 1 Year 63.5
Expanding Operations 27.7
Market Served Farmer's Market 9.4
(n = 169) Wholesale to Retailers 4.2
Statewide 15.4
Nationally 6.7
Other 21.1
(1) The total number of respondents differs for each
characteristic, as some respondents did not respond to some
questions.
Table 2. Descriptive Statistics and Correlation
Matrix (n = 69)
Mean s.d. Min Max 1
1. Age 27.39 20.25 21 61 1.00
2. Assets 3.50 1.03 1 4 0.21 *
3. Gender 0.05 0.22 0 1 0.02
4. Edu bachelor 0.46 0.50 0 1 -0.10
5. Edu graduate 0.20 0.40 0 1 -0.03
6. Stage of Development 4.17 0.81 1 5 0.20 *
7. Familiarity with 3.62 1.65 1 5 0.01
commercial banks
8. Familiarity with 1.57 0.68 1 5 0.02
institutional capital
9. Technical 0.47 0.50 0 1 0.10
assistance: Commercial
banks
10. Technical 0.16 0.37 0 1 -0.05
assistance:
Institutional capital
11. Whether acquired 0.35 0.48 0 1 0.07
institutional capital
12. No. of 0.41 1.39 0 14 0.42 **
institutional capital
acquired
2 3 4 5
1. Age
2. Assets 1.00
3. Gender 0.12 1.00
4. Edu bachelor -0.02 -0.08 1.00
5. Edu graduate 0.08 0.05 -0.46 ** 1.00
6. Stage of Development 0.35 ** -0.13 0.15 -0.10
7. Familiarity with 0.16 * 0.03 -0.01 -0.19 *
commercial banks
8. Familiarity with -0.08 -0.14 0.13 -0.08
institutional capital
9. Technical 0.18 * 0.05 -0.04 -0.17 *
assistance: Commercial
banks
10. Technical 0.03 -0.01 -0.19 0.09
assistance:
Institutional capital
11. Whether acquired 0.13 0.04 0.16 0.03
institutional capital
12. No. of -0.03 -0.04 -0.07 -0.03
institutional capital
acquired
6 7 8 9
1. Age
2. Assets
3. Gender
4. Edu bachelor
5. Edu graduate
6. Stage of Development 1.00
7. Familiarity with 0.20 ** 1.00
commercial banks
8. Familiarity with 0.10 0.33 ** 1.00
institutional capital
9. Technical 0.26 ** 0.54 ** 0.16 1.00
assistance: Commercial
banks
10. Technical -0.21 ** 0.06 -0.08 0.13
assistance:
Institutional capital
11. Whether acquired 0.01 0.06 -0.04 -0.11
institutional capital
12. No. of 0.07 -0.08 -0.07 -0.05
institutional capital
acquired
10 11 12
1. Age
2. Assets
3. Gender
4. Edu bachelor
5. Edu graduate
6. Stage of Development
7. Familiarity with
commercial banks
8. Familiarity with
institutional capital
9. Technical
assistance: Commercial
banks
10. Technical 1.00
assistance:
Institutional capital
11. Whether acquired 0.25 ** 1.00
institutional capital
12. No. of -0.10 0.06 1.00
institutional capital
acquired
* p < 05. ** p < 01.
Table 3. Regression Results for Institutional Capital
Acquisition: Aggregate Level a
Y = whether acquired institutional capital
Model 1a Model 2a Model 3a
Age 0.01 (0.01) 0.01 (0.01) 0.01 (0.01)
Asset 0.28 (0.22) 0.25 (0.23) 0.27 (0.22)
Gender 0.23 (0.84) 0.44 (0.89) 0.25 (0.83)
Edu bachelor 1.08 * (0.46) 1.48 ** (0.51) 1.15 * (0.47)
Edu_graduate 0.69 (0.55) 0.84 (0.61) 0.84 (0.57)
Stage of Business -0.18 (0.27) -0.16 (0.30) -0.24 (0.28)
Familiarity with -0.14 (0.30)
institutional
capital
Technical assistance 1.85 *** (0.57)
from institutional
capital providers
Familiarity with 0.13 (0.12)
commercial banks
Technical assistance
from commercial banks
n 133 133 133
[chi square] 8.82 20.15 ** 9.71
Pseudo [R.sup.2] 0.051 0.118 0.057
Y = whether acquired institutional capital
Model 4a Model 5a
Age 0.01 (0.01) 0.01 (0.01)
Asset 0.32 (0.22) 0.29 (0.23)
Gender 0.34 (0.85) 0.28 (0.90)
Edu bachelor 0.98 * (0.46) 1.29 **(0.50)
Edu_graduate 0.56 (0.56) 0.82 (0.61)
Stage of Business -0.13 (0.28) -0.11 (0.31)
Familiarity with -0.18 (0.34)
institutional
capital
Technical assistance 1.82 ** (0.62)
from institutional
capital providers
Familiarity with 0.16 (0.14)
commercial banks
Technical assistance -0.47 (0.40) -0.53 (0.42)
from commercial banks
n 133 133
[chi square] 9.95 18.93 *
Pseudo [R.sup.2] 0.058 0.120
Y = how many sources of
capital were acquired
Model 1b Model 2b
Age 0.00 (0.00) 0.00 (0.00)
Asset -0.09 (0.15) -0.17 (0.14)
Gender 1.39 (1.08) 1.89 (1.11)
Edu bachelor .61 ***(0.44) 1.79 ***(0.43)
Edu_graduate 1.05 (+) (0.55) 0.94 (0.56)
Stage of Business -0.80 ***(0.15) -0.81 ***(0.14)
Familiarity with 0.22 (0.19)
institutional
capital
Technical assistance 1.17 ** (0.35)
from institutional
capital providers
Familiarity with
commercial banks
Technical assistance
from commercial banks
n 133 133
[chi square] 56.21 *** 64.46 ***
Pseudo [R.sup.2] 0.230 0.264
Y = how many sources of
capital were acquired
Model 3b Model 4b
Age 0.00 (0.00) 0.00 (0.00)
Asset -0.12 (0.14) -0.08 (0.15)
Gender 1.45 (1.08) 1.45 (1.09)
Edu bachelor 1.71 ***(0.43) 1.68 ***(0.43)
Edu_graduate 1.14 * (0.55) 1.04 (+) (0.55)
Stage of Business -0.80 ***(0.16) -0.75 ***(0.15)
Familiarity with
institutional
capital
Technical assistance
from institutional
capital providers
Familiarity with 0.03 (0.10)
commercial banks
Technical assistance -0.50 (0.37)
from commercial banks
n 133 133
[chi square] 55 14 *** 56 99 ***
Pseudo [R.sup.2] 0.225 0.233
Y = how many sources of
capital were acquired
Model 5b
Age 0.00 (0.00)
Asset -0.03 (0.15)
Gender 2.11 (+) (1.15)
Edu bachelor 1.60 ***(0.43)
Edu_graduate 0.66 (0.56)
Stage of Business -0.79 *** (0.16)
Familiarity with 0.46 (0.33)
institutional
capital
Technical assistance 1.43 *** (0.37)
from institutional
capital providers
Familiarity with 0.02 (0.12)
commercial banks
Technical assistance -0.57 (0.42)
from commercial banks
n 133
[chi square] 72.20 ***
Pseudo [R.sup.2] 0.295
Notes: Standard errors are in brackets.
(a) Logistic regressions applied in Models 1a- 5a;
Poisson regressions applied in Models 1b-5b.
(+) p <10
* p <05
** p <01
*** p <001
Table 4. Regression Results for Institutional Capital
Acquisition: Individual Level
Model 1:
Small Business
Administration (SBA)
Age 0.03 (0.03)
Asset 0.19 (0.65)
Gender (dummy) -18.18 (11915.32)
Edu bachelor (dummy) 19.63 *** (1.65)
Edu graduate (dummy) 19.36 *** (1.60)
Stage of Business -1.67 (+) (0.87)
Familiarity with SBA 1.01 * (0.51)
Familiarity with USDA
Familiarity with FB
Tech assistance from SBA (dummy) -10.64 (54727.01)
Tech assistance from SBDC (dummy) 31.79 (45275.73)
Tech assistance from LEDA (dummy) -13.23 (24245.47)
Tech Assistance from IDED (dummy) -5.96 (50617.57)
Familiarity with commercial bank -0.09 (0.68)
Tech assistance from commercial
bank (dummy) -0.22 (1.75)
N 133
[chi square] 18.61 *
Nagelkerke [R.sup.2] 0.55
Model 2:
USDA
Age 0.01 (0.01)
Asset 0.47 (0.32)
Gender (dummy) 0.35 (1.58)
Edu bachelor (dummy) 1.20 (0.75)
Edu graduate (dummy) -0.11 (0.88)
Stage of Business -0.68 (+) (0.38)
Familiarity with SBA
Familiarity with USDA 1.07 *** (0.28)
Familiarity with FB
Tech assistance from SBA (dummy) 1.23 (1.51)
Tech assistance from SBDC (dummy) -38.13 (32942.94)
Tech assistance from LEDA (dummy) 20.98 (20927.59)
Tech Assistance from IDED (dummy) 1.99 (1.78)
Familiarity with commercial bank -0.13 (0.23)
Tech assistance from commercial
bank (dummy) -2.04 ** (0.73)
N 133
[chi square] 44.13 ***
Nagelkerke [R.sup.2] 0.46
Model 3:
Farm Bureau (FB)
Age 0.01 (0.05)
Asset -0.33 (0.59)
Gender (dummy) -17.05 (11435.43)
Edu bachelor (dummy) 18.39 *** (3.26)
Edu graduate (dummy) 17.80 *** (3.80)
Stage of Business -1.19 (0.80)
Familiarity with SBA
Familiarity with USDA
Familiarity with FB 2.04 * (0.97)
Tech assistance from SBA (dummy) -50.01 (24543.93)
Tech assistance from SBDC (dummy) 37.07 (35142.73)
Tech assistance from LEDA (dummy) -46.40 (25649.77)
Tech Assistance from IDED (dummy) 35.594 (16467.17)
Familiarity with commercial bank -1.04 (0.80)
Tech assistance from commercial
bank (dummy) -1.14 (2.06)
N 133
[chi square] 26.53 *
Nagelkerke [R.sup.2] 0.66
Notes: Standard errors in brackets.
(+) p < 10. * p < 05. ** p < 01. *** p < 001.
Table 5. Number of Acquisitions and Average Level of Familiarity
for Each Source of Institutional Capital
Source of Institutional Capital Number of Number of
respondents acquisition
cases
Small Business Administration 166 6
Local economic development funds 166 4
Chamber of Commerce 166 1
Iowa Department of Economic Development 166 4
USDA 166 30
Rural Electric Cooperative 166 3
Farm Bureau 166 6
Council of Governments 166 1
Community revolving loan fund 166 3
Economic Development Administrative 166 2
State Association for Council of Government 166 2
Iowa Community Development 166 3
Rural Development Partners 166 1
Iowa Community Capital 166 1
Grow Iowa fund 166 1
Source of Institutional Capital Average level
of familiarity
(out of 5)
Small Business Administration 1.92
Local economic development funds 1.55
Chamber of Commerce 1.46
Iowa Department of Economic Development 1.69
USDA 2.67
Rural Electric Cooperative 1.64
Farm Bureau 1.80
Council of Governments 1.28
Community revolving loan fund 1.34
Economic Development Administrative 1.36
State Association for Council of Government 1.24
Iowa Community Development 1.37
Rural Development Partners 1.30
Iowa Community Capital 1.27
Grow Iowa fund 1.45
Table 6. Number and Percentage of Respondents Using Agencies
for Technical Assistance
Agencies Number of Number of
respondents respondents
receiving
tech assistance
Commercial Bank 166 73
Small Business administration 166 8
(SBA)
Small Business Development 166 4
Center (SBDC)
Local Economic Department 166 4
Agency (LEDA)
Iowa Department of Economic 166 6
Development (IDED)
Other Agencies 166 19
Agencies Percentage of
respondents
receiving
assistance
Commercial Bank 44%
Small Business administration 5%
(SBA)
Small Business Development 2%
Center (SBDC)
Local Economic Department 2%
Agency (LEDA)
Iowa Department of Economic 4%
Development (IDED)
Other Agencies 11%
