Description
This criteria with regards to entrepreneurial optimism, financing, and innovation sheryl winston smith.
Paper to be presented at the
35th DRUID Celebration Conference 2013, Barcelona, Spain, June 17-19
Entrepreneurial Optimism, Financing, and Innovation
Sheryl Winston Smith
Temple University
Fox School of Business, Dept. of Strategic Management
[email protected]
Abstract
Successfully launching a new firm fundamentally requires that an entrepreneur garner resources, particularly in the
context of highly innovative new firms. However the type and source of financing may influence subsequent innovation
outcomes, as innovation is inherently characterized by uncertainty. I analyze two key aspects of entrepreneur?s
strategic choice of financing: the role of financial leverage in the early financing of new high-tech firms and the
relationship between initial financing choice and subsequent innovation trajectory. I use the microdata in the confidential
Kauffman Firm Survey (KFS) dataset, a panel of nearly 5,000 US firms started in 2004. In a novel strategy, I exploit the
role of entrepreneurial optimism to discern the relationship between debt financing and innovation outcomes, conditional
on the endogenous choice of financing. The key results from this paper are: 1) optimistic entrepreneurs choose higher
levels of debt financing relative to equity in keeping with behavioral finance expectations; and, 2) after taking into
account the endogeneity of the financing mix, higher leverage (ie higher debt relative to equity) facilitates innovation
using both patent-based and product-based measures. The results suggest that for young, private firms in high-tech
industries, i.e. those that are most information-opaque, access to different sources of financing may serve to provide
greater organizational slack and facilitate innovation.
Jelcodes:L29,O30
Entrepreneurial Optimism, Financing, And Innovation
ABSTRACT
Successfully launching a new firm fundamentally requires that an entrepreneur garner resources,
particularly in the context of highly innovative new firms. However the type and source of
financing may influence subsequent innovation outcomes, as innovation is inherently
characterized by uncertainty. I analyze two key aspects of entrepreneur’s strategic choice of
financing: the role of financial leverage in the early financing of new high-tech firms and the
relationship between initial financing choice and subsequent innovation trajectory. I use the
microdata in the confidential Kauffman Firm Survey (KFS) dataset, a panel of nearly 5,000 US
firms started in 2004. In a novel strategy, I exploit the role of entrepreneurial optimism to discern
the relationship between debt financing and innovation outcomes, conditional on the endogenous
choice of financing. The key results from this paper are: 1) optimistic entrepreneurs choose
higher levels of debt financing relative to equity in keeping with behavioral finance expectations;
and, 2) after taking into account the endogeneity of the financing mix, higher leverage (ie higher
debt relative to equity) facilitates innovation using both patent-based and product-based
measures. The results suggest that for young, private firms in high-tech industries, i.e. those that
are most information-opaque, access to different sources of financing may serve to provide
greater organizational slack by allowing the entrepreneur more “breathing room”.
This version: February 28, 2013
Key words: innovation; new firms and startups; entrepreneurial finance, banks, capital
structure, optimism
2
1 Introduction
Innovation and entrepreneurship are risky and uncertain endeavors, both individually and
in tandem (Knight (1921 )). The significant potential for failure—and the ability to appropriately
weight its likelihood- magnify the importance of early strategic choices. The initial assembly of
resources conditions the dynamic capabilities and future trajectory of a new firm (Helfat and
Peteraf (2003); Zingales (2000 )). For the entrepreneur, some of the earliest strategic choices
surround the decision about which financial resources to assemble and how to most effectively
deploy them. Particularly for high-growth firms—e.g., typically firms in dynamic industries—
the need for external financial capital requires entrepreneurs to apply skills such as ‘practical
intelligence’ (Baum and Bird (2009 )) and outside certification (Burton, Sørensen and Beckman
(2002 )) to gathering sufficient resources to launch and grow the new venture. Indeed, the
entrepreneur’s access to external financial capital often determines why and how the
entrepreneur can exploit potential growth opportunities that existing firms can or will not attempt
(Rajan and Zingales (1998 )). One key aspect is the strategic choice between debt and equity
financing. Importantly, this choice has different implications for entrepreneurs and for
innovation, which will be amplified in the context of new high tech firms (Schmidt (2003); Ueda
(2004); Winton and Yerramilli (2008 )).
The objective of this paper is to probe the relationship between financing choice and the
subsequent innovation trajectory by taking into account both the initial choice of financing and
subsequent decisions to take on additional debt or equity by entrepreneurs in new high-tech
firms. In this paper I utilize the confidential version of the Kauffman Firm Survey (KFS), a rich
panel dataset that tracks a sample of nearly 5,000 new firms on an annual basis from their
common inception in 2004 through 2010. Detailed data were collected on the nature of firm
3
formation activity internal and external sources of financing, measures of innovation related
outcomes, and data related to the characteristics and outlook of the firm founders (Ballou,
Barton, Desroches, Potter, Reedy, Robb, Shane and Zhao (2008); Reedy and Robb (2009 )).
A distinct challenge in addressing this question is that innovation outcomes and
financing choice are likely to be endogenously determined. This paper employs a novel
empirical strategy to address this endogeneity by integrating a key insight from behavioral
finance; specifically, that more optimistic/over-confident individuals exhibit a greater preference
for debt financing (Dai and Ivanov (2010); de Meza and Southey (1996); Hackbarth (2008);
Landier and Thesmar (2009 )). I use this relationship to isolate the relationship between
financing choice and innovation outcomes. More broadly, in addition to providing a powerful
empirical tool, this approach speaks directly to a growing literature addresses the role of
behavioral and psychological considerations in conditioning economic and managerial choices
(Argyres (2011 )). Optimism, in particular, has been shown to influence financial and economic
choices (de Meza and Southey (1996); Dushnitsky (2010); Landier and Thesmar (2009); Puri
and Robinson (2007 )).
The key results from this paper are: 1) optimistic entrepreneurs choose higher levels of
debt financing relative to equity; and, 2) after taking into account the endogeneity of the
financing mix, higher leverage (ie higher debt relative to equity) facilitates innovation using both
patent-based and product-based measures The results suggest that for young, private firms in
high-tech industries, i.e. those that are most information-opaque, access to different sources of
financing may serve to provide greater organizational slack by allowing the entrepreneur more
“breathing room”.
This paper makes several contributions to the entrepreneurship and organization
4
literature. Overall, this paper directly links financing choice and innovation outcomes along
several theoretical dimensions. First, this paper contributes to theory by delineating how the
dynamic innovative capabilities of the new venture are shaped by the source of the
entrepreneurs’ earliest acquisition of external finance. Second, this paper makes a theoretical
contribution by coupling the trajectory of dynamic capability development and organizational
learning to established theories in financial economics that underscore the relationship between
early financing choice and innovation. Third, this paper makes a contribution to organization
theory by linking the development of early entrepreneurial capabilities to organizational slack
and the potential for organizational learning. Finally, this paper contributes to behavioral
theories of entrepreneurship by bringing in insights from behavioral finance. Specifically, this
paper develops theory about over-optimism and financing choices and links it to theories of
entrepreneurial innovation. This paper thus speaks to the literature on the importance of
behavioral influences on managerial actions and the evolution of capabilities in the all-important
realm of financial decision-making.
2 Theoretical Background & Hypotheses Development
2.1 Early Financing and Innovation
To successfully launch and grow a new firm, entrepreneurs require adequate funds. The
literature suggests that entrepreneurs often face a binding liquidity constraint (Black and Strahan
(2002); Cagetti and De Nardi (2006); Carpenter and Petersen (2002); Evans and Jovanovic
(1989); Nanda (2008 )). Theory suggests that the liquidity constraint will be particularly severe
for new high tech firms. Firms in high tech industries are inherently riskier than firms in lower
tech industries. First, latent demand for a new product or process is not known ex ante, resulting
in considerable market uncertainty. Second, introducing a new product or process to market is
5
compounded by technical uncertainty. Moreover, theory suggests that information asymmetry
between the entrepreneur and outside investors is likely to be greatest in firms which are riskiest,
i.e., nascent firms engaged in innovative activity, which face inherently uncertain probability of
success but also offer higher potential reward.
If capital markets were perfect, the relative role of equity and debt in the early mix of
financing should be immaterial to ultimate firm value and success (Modigliani and Miller
(1958 )); however, in the presence of capital market imperfections, this choice of capital
structure becomes a strategic choice which impacts innovation performance (Himmelberg and
Petersen (1994); Titman and Wessels (1988 )). While the initial choice of capital structure—and
subsequent adjustments in the relative use of equity and debt— will have significant implications
for the incentives to innovate, theory is ambiguous as the direction of the effect. Under standard
agency assumptions debt places the risk of failure on the entrepreneur, whereas outside equity
spreads the risk of failure. However, the entrepreneur may be averse to giving up an equity stake
and control, preferring instead to obtain non-dilutive debt financing until a significantly higher
valuation can be attained after milestones have occurred (Ibrahim (2010 )). Furthermore, it can
be argued that debt financing allows greater flexibility to address the idiosyncratic nascent firm
trajectory by providing greater organizational slack, in comparison to equity financing which is
tied to meeting specified milestones.
A substantial literature in management and entrepreneurial finance has examined the
entrepreneur’s ability to garner equity investment as a crucial initial resource, and indeed often
treats this as a binding constraint. [See, for example, Amit ((1990 )), Bruton ((2010 )), Burton et
al.((2002 )).] The literature also considers the impact of equity investment on
innovation(Kortum and Lerner (2000); Wu (2011 )). In small or private firms, VC financing is
6
associated with greater innovation (Hellmann and Puri (2000); Puri and Zarutskie (2008 )). At
the same time theoretical literature in entrepreneurship addresses the entrepreneur’s choice
between debt and equity. Empirically, entrepreneurs choose both (Ibrahim, 2010; Robb &
Robinson, 2012). However, the relationship between choice of debt and equity in earliest years
and implications for subsequent innovation trajectory and development of capabilities is largely
untested empirically.
Overall, higher leverage might be expected to decrease the incentive to pursue a risky
innovation strategy. On balance the literature suggests that the entrepreneur will prefer equity
financing over debt as the riskiness of the venture increases (Carpenter and Petersen (2002); De
Bettignies (2008); Gompers and Lerner (2003); Hall (2002); O'Brien (2003 )). Conversely, if
debt discourages innovation, the entrepreneur whose venture is more highly leveraged would
pursue a less risky strategy and is less likely to develop novel knowledge, which can be
appropriated through intellectual property protection such as patenting or copyright. The
following hypothesis follows:
Hypothesis 1: Greater initial debt financing (relative to equity) deters the likelihood of
innovation.
2.2 Organizational Slack and Innovation
Organizational flexibility or slack enables organizations to address uncertainty (Cyert and
March (1956); Singh (1986 )). Cyert and March ((1956 )) suggest that organizational slack
allows firms to make up in one part/subunit for problems elsewhere in organization. An
entrepreneur in a new innovation-focused venture must reduce technical and market uncertainty
as they go forward, including clearing unanticipated hurdles that may demand significant
financial liquidity. The choice between debt and equity financing has implications for the degree
of organizational slack afforded the entrepreneur. While equity financing is more “risk tolerant”,
7
by virtue of shared potential upside, this risk tolerance comes at the price of control rights.
Control rights may be ceded to the equity investor by the entrepreneur in contractual terms that
either grant these rights explicitly or implicitly through the agreement to staged financing
contingent on meeting specified milestones (Kaplan and Stromberg (2004 )). Venture capitalists,
in particular, are active investors who monitor—and have the right to interfere with—the
entrepreneur’s actions (Bottazzi, Da Rin and Hellmann (2008 )). The entrepreneur is thus
limited in their use of funds—and in their ability to deviate from ex ante targets—when tied too
strongly to equity investors.
The entrepreneur requires organizational slack instead of simply budgeting in advance for
unanticipated possibilities. Budgets reflect “a set of fixed commitments—(and perhaps more
important)—a set of fixed expectations” Cyert and March ((1956, p.50 )). It is precisely the
irreducible uncertainty that justifies slack. It is worth noting that slack can have both positive
and negative consequences, giving the firm/manager/entrepreneur excess resources without
discipline inducing constraint. Singh ((1986 )) finds that organizational slack facilitates risk
taking and positive performance outcomes. Hvide and Moen (Hvide and Møen (2010 )) find that
organizational slack in new firm is essentially a function of the entrepreneur’s liquidity. They
find that startup performance (measured as profitability) increases with entrepreneurial wealth up
to a maximum but decreases in the highest wealth quartile (which they attribute as being
consistent with an interpretation of entrepreneurship as a “luxury good”.)
Taken together, the above arguments suggest that if we take the initial capital structure as
given, this case, debt financing should encourage the riskier innovation focused strategy. The
reasoning suggests the following hypothesis:
Hypothesis 2: Conditional on initial financing, increasing organizational slack increases the
likelihood of subsequent innovation.
8
2.3 Choice of Financing and Optimism
Behavioral finance provides theory and evidence that financial decision-making is
influenced by optimism (de Meza and Southey (1996); Dushnitsky (2010); Landier and Thesmar
(2009); Puri and Robinson (2007 )). The entrepreneur selects financing based on a number of
factors, including her overall assessment of the likelihood of failure. The entrepreneur has
private information regarding the likelihood (or expectation) of success that is essentially
unavailable to outsiders, where “success” in this case can be thought of as successful innovation.
This may arise because the entrepreneur chooses to keep this information hidden (leading to the
archetypical “lemons problem” (Akerlof (1970 )). Alternatively, this information asymmetry
may be a function of the external finance providers’ inability to fully understand the complexities
involved over time (Tversky and Kahneman (1981 )) to the same degree as the entrepreneur. In
either event, if the entrepreneur is able to correctly assess the likely success in innovation, then
she may choose her financing mix accordingly, i.e., relying more heavily on equity if failure is
more likely (in which case there is adverse selection) and relying more heavily on debt if success
is more likely.
In general, the complexity and compounded uncertainty of launching a new venture in an
environment that demands rapid pace innovation (e.g., in a high tech industry) leads to the well-
known cognitive biases that characterize judgment under uncertainty described by Kahneman
and Tversky (1974). More specifically, over-optimism, has been shown to influence financial
and economic choices (Puri and Robinson (2007 )). A growing number of empirical studies link
the characteristics of over-optimism (overconfidence) to greater preference for debt (relative to
equity or overall). Because over-optimistic entrepreneurs underestimate the likelihood of failure,
they are willing to take on more short-term debt (Landier & Thesmar, 2009). Likewise, over-
9
optimistic entrepreneurs will rely upon higher leverage than their more realistic counterparts (Dai
and Ivanov (2010); de Meza and Southey (1996 )). Similarly, Hackbarth (Hackbarth, 2008)
finds that over-optimistic managers overestimate growth potential and profitability while
simultaneously underestimating the risk of failure; in conjunction, these effects lead over-
optimistic managers to issue greater amounts of debt.
Over-optimism may also translate directly into a reluctance to enter into equity
arrangements. Hayward, Shepherd, and Griffin ( 2006) consider a “hubris theory of
entrepreneurship” in which over-confident or over-optimistic entrepreneurs will reduce the
liquidity of ventures by not accepting equity based deals (giving up control), preferring instead to
rely on greater debt (which is subject to default). In a similar vein, technology entrepreneurs
with knowledge-based assets that can be selectively revealed to investors, optimism is associated
with a preference for contingent payment contracting rather than disclosure of knowledge
(Dushnitsky (2010 )).
Taken together, the arguments above lead to the following hypothesis:
Hypothesis 3: Over-optimism of the entrepreneur leads to a preference for greater debt financing
relative to equity.
3 Methodology and Empirical Approach
Data is drawn from the Kauffman Firm Survey (KFS), a longitudinal panel study of 4,928
businesses founded in 2004 and tracked over their early years of operation, with the data in this
paper spanning the first six years of firm life. Detailed data is gleaned on the nature of new
business formation activity including internal and external sources of financing, firm size and
focus, and data related to the characteristics, experience and human capital and of the
entrepreneur (Ballou, Barton, Desroches, Potter, Reedy, Robb, Shane and Zhao (2008); Reedy
and Robb (2009 )). The businesses in this sample all came into existence in 2004, with business
10
start defined in terms of state unemployment insurance paid, FICA, Schedule C income reported
on personal income tax, EIN, or the presence of legal status. The survey utilizes stratified
sampling methodology, oversampled for high-tech firms. Econometrically, survey weights
enable drawing conclusions about population; both cross-sectional and longitudinal weights are
known and estimation techniques are used which take into account survey characteristics. The
panel is inherently unbalanced as firms exit the sample over time and are no longer observed.
Taking into account the survey sampling weights, this translates into a survival rate to the end of
the sixth year of 58.6% for all firms in the sample and 67.0% for high-tech firms in the sample.
3.1 Empirical framework and estimation methodology
The relationship between initial capital structure and innovation performance is likely to
be determined endogenously. In order to separate the effects of choice of capital structure from
the latent/underlying innovativeness of a given entrepreneur I employ a multi-stage approach to
specification and estimation of this relationship. In the first stage, I model the entrepreneur’s
choice between financing sources within the framework of a random utility model (Greene
(2008, chapter 23, section 11 )). In the second stage of the model, the likelihood of innovation by
the nascent firm associated with entrepreneur i is introduced as Pr(I
ij
=1) = X
ij
+Z
j
), where I
ij
= observable innovation by new firm i , X
i j
is the vector of financing characteristics, and Z
j
=
vector of entrepreneur and firm specific attributes of the nascent firm associated with
entrepreneur i.
3.2 Endogeneity of capital structure
The endogeneity of financing choice introduces econometric concerns. One major
econometric concern is that capital structure will be endogenously determined, which would
introduce correlation between the explanatory variables and the error term, biasing the results.
11
Thus, we will need to account separately for the entrepreneur’s choice of financing, distinct from
the actual likelihood of successful innovation. In order to address this concern, I use a two stage
least squares (2SLS) approach in which the endogenous regressors determining capital structure
are regressed in the first stage, and the predicted coefficients used in the second stage regression.
Key to this approach is finding an appropriate instrument or instruments. The instrument must
be uncorrelated with the endogenous variable but correlated with the dependent variable of
interest. I approach this through several facets.
The KFS data provide a measure of the entrepreneur’s personal assessment of her
comparative advantage over rivals. Thus, by construction in the survey, comparative advantage
is a measure of the entrepreneur’s own assessment of their abilities and likelihood of success,
which will be subject to behavorial bias. Other studies suggest that entrepreneurs are
overconfident in their estimation of success (Hvide and Panos (2013 )). Banks are unlikely to be
swayed by this assessment. However, to the extent that it represents the entrepreneurs’ own
understanding of her ability, a belief that she possesses comparative advantage over competitors
may be a predictor of innovation. Optimism can be measured through the divergence between
perception of comparative advantage and performance.
In other specifications, I utilize instruments that capture behavioral optimism more
broadly through measures of expectations regarding the future (Puri and Robinson (2007 )). The
KFS survey includes measures related to entrepreneurs’ general outlook. Finally, the survey also
includes measures of specific expectations related to growth and future revenue associated with
the nascent venture.
In addition to the behavior-based instruments, I also include an instrument that captures
the economic relationship governing leverage but not innovation. Specifically, I include the
12
entrepreneur’s credit rating. An entrepreneur with poor credit will be less able to procure debt
financing; however, her credit rating should not directly influence innovation outcomes. I use a
combination of these behavioral and economic instruments in the analysis.
3.3 Dependent Variables: Innovation
Our first dependent variable captures new innovation as reflected in the production of
patents and copyrights (NewIP) by the end of the current follow-up period. NewIP is a dummy
variable which equals 1 if the firm has generated additional patents or copyrights relative to the
baseline number of patents or copyrights in the founding year. The choice of intellectual property
based measures of innovation is guided by a wide literature on innovation (Hall, Jaffe and
Trajtenberg (2005 )). New innovation is often measured through the production of new patents
or new copyrights by the firm (Acharya and Subramanian (2009); Hall, Jaffe and Trajtenberg
(2005); Hellmann and Puri (2000 )). Distinct questions address how many patents or copyrights
the firm has in a given year and whether or not the firm has any patents or copyrights in a given
year. If the question was answered in the baseline year and in some but not all subsequent years
I imputed the missing number. In additional regressions I also utilize the actual increase in
number of patents and copyrights. I also included specifications with yearly changes in IP.
For a subset of firms, specifically those that survive through year 5, we use a second
dependent variable that more directly captures product market innovation (New Product). This
variable is a dummy variable equal to one if the firm has introduced a new product to market.
3.4 Independent variables
Our focal independent variables measure initial financial leverage and changes in
financial leverage. Our control variables include financial controls, e.g. asset tangibility, bank
loans, outside equity, and initial financing attributes. We control for additional firm attributes,
13
e.g., multiple owners and legal form of organization. We also control for entrepreneurial
attributes, e.g., education, prior work experience, prior entrepreneurial experience, and gender
and race. Industry controls are included.
4 Results
Table 1 provides summary statistics. Table 2 provides a breakdown of debt and equity
financing at founding. As shown in Table 2 nascent high-tech firms rely on both debt and equity
financing at the start.
Table 3 presents results from 2SLS regressions on the probability of new innovation by
the end of last period as a function of leverage, firm characteristics, and characteristics of the
entrepreneur, where the new innovation is defined as an increase in intellectual property from the
baseline period (time t) through the last follow-up period. The dependent variable thus is NewIP.
Regressions are estimated using survey consistent techniques to account for clustered error terms
arising from sample stratification. The results show that initial leverage is associated with a
lower probability of innovation. In each case, the F statistic on the first stage of the 2SLS
regression is significant, suggesting that comparative advantage is not a weak instrument.
Results from the Heckman selection estimation and the 2SLS estimation support the hypothesis
that having a bank loan facilitates subsequent innovation outcomes. Columns 1 and 2 are the
first and second stage regressions, respectively, where the focal independent variable is leverage.
The results in the first stage regression (column 1) confirm that more optimistic entrepreneurs
choose higher levels of debt financing relative to equity. The results in the second stage
regression (column 2) support our hypothesis that after taking into account the endogeneity of
the financing mix, higher leverage (ie higher debt relative to equity) facilitates innovation.
Columns 3 and 4 present results for regressions where the focal independent variable is whether
14
the entrepreneur increases her leverage, i.e. takes on more debt relative to equity over time.
These results are similar.
Table 4 presents similar results from 2SLS regressions on the probability of introducing a
new product to market as our measure of innovation. The dependent variable is thus
NewProduct. Again, the results suggest that once we take into account he endogeneity of the
financing, higher leverage may facilitate innovation. Taken together, the results suggest that for
young, private firms in high-tech industries, access to both debt and equity sources of financing
may facilitate innovation.
5 Discussion and Conclusion
New high-tech firms are heralded as an engine of growth, with the implicit assumption
that innovation accompanies new firm formation. Much of our understanding of the relationship
between financing and innovation has been studied in firms that are significantly more
established compared to truly nascent start-ups. However, the critical link between innovation
and very early financing of entrepreneurial firms is largely unexplored. The results in this paper
provide a window into the relationship between financing and innovation in very early stage
firms. Thus, the findings here have important implications for filling in the rich details of early
stage entrepreneurial innovation.
The results presented in this paper suggest that increasing leverage over time might
provide nascent technology entrepreneurs with financial slack that may enable with innovation.
This implication bears closer attention in further work. The entrepreneur launching a new
technology venture faces significant resource constraints, and the attendant need to secure
financing. For firms with adequate financial resources, lower leverage is associated with
enhanced innovation (O'Brien (2003 )). However, when financial resources are highly
15
constrained, as in a new entrepreneurial venture, the relationship between slack and performance
is nuanced (George (2005 )). While traditionally the finance literature associates increasing
leverage with lower innovation, it seems highly plausible that in truly nascent technology firms
additional debt relaxes the major capital constraints faced by the entrepreneur.
One lens to consider the above results is in differentiating risk and uncertainty, as Knight
does in his seminal discussion of entrepreneurship (Knight (1921 )). For simplicity, risk is
defined in terms of the likelihood of a given outcome, when quantifiable probabilities can be
assigned to multiple, competing outcomes. Risk can be thought of as a draw from an underlying
distribution where the probabilities are known. As economic and financial theory shows, risk
can be pooled and insured, and thus in theory eliminated. On the other hand, uncertainty consists
of the irreducible unknown; exact probabilities thus cannot be assigned and the absolute
likelihood of an uncertain event cannot be known. To make this concrete, contrast the risk
associated with market shifts as a function of macroeconomic circumstances, e.g. recession, with
the idiosyncratic uncertainties such as unexpected and catastrophic defects in raw materials.
Financial slack can provide the agile entrepreneur with funds to combat idiosyncratic
occurrences and move beyond these hurdles towards key innovation milestones.
This paper has important strategic implications for entrepreneurs and for the study of
entrepreneurship and innovation. The starting resources of a firm are crucial to the development
of dynamic capabilities and competitive advantage over the lifecycle of the firm (Helfat and
Peteraf (2003 )). In this paper, I show that the entrepreneur’s strategic choice of initial capital
structure is a key component of innovation success and show how the dynamic innovative
capabilities of the new venture are shaped by the source of the entrepreneurs’ earliest acquisition
of external finance. Moreover, by coupling the trajectory of dynamic capability development and
16
organizational learning to established theories in financial economics that underscore the
relationship between early financing choice and innovation this paper breaks new ground.
Finally, this paper enhances our understanding of the development of early entrepreneurial
capabilities related to organizational slack and the potential for organizational learning.
17
Table 1: Summary Statistics
Variable Mean Lin. SE
Dependent Variables:
New IP 0.064 0.005
New product 0.041 0.003
Focal Independent Variables
Leverage 0.120 0.008
Leverage increases 0.228 0.015
Controls
Female 0.201 0.074
White 0.781 0.427
Education 7.243 0.809
Have outside equity 0.035 0.007
Have business bank loan 0.037 0.005
Have IP at founding 0.202 0.018
18
Table 2. Debt and equity financing at founding
Financing mean Linearized
SE
Panel A: DEBT
Have Non-Bank Loan 0.2369 0.0185
Have Bank Loan 0.1122 0.0135
Panel B: EQUITY
Have Insider Equity 0.7233 0.0173
Have Outside Equity 0.0380 0.0068
19
Table 3. Innovation and leverage: New Intellectual Property
Dependent variable:
New IP
2SLS
First stage
2SLS
Second stage
2SLS
First stage
2SLS
Second stage
Increase in Leverage
0.546
(2.21)
***
Leverage 1.6808
(3.10)
***
1.160
(51.40)
***
-0.645
(-2.22)
***
Have Comparative
Advantage
0.0194
(2.26)
**
0.0348
(2.95)
***
Expect High Revenue
Growth
.0240
(2.63)
***
0.0444
(2.76)
***
HaveIP_0
0.0144
(1.41)
0.0566
(3.10)
***
0.0285
(1.50)
0.0778
(3.57)
***
Female -0.0007
(-0.08)
0.0006
(0.02)
0.0125
(0.67)
-0.0123
(-0.63)
White 0.0130
(1.28)
0.0007
(0.04)
-0.053
(-2.80)
***
0.049
(2.05)
**
Education -0.016
(-7.35)
***
0.0358
(3.80)
***
-0.003
(-0.85)
0.012
(2.94)
***
Have Outside Eq. 0.007 0.110 0.0358 0.051
(0.36) (2.70)
***
(1.32) (1.71)
*
Have Bank Loan 0.2569 -0.435 0.1050 -0.055
(13.29)
***
(-2.99)
***
(4.15)
***
(-1.46)
Major Tech 0.0255
(2.41)
***
-0.036
(-1.41)
0.003
(0.17)
0.009
(0.47)
Random Effects Y Y
Year Dummies N N Y Y
no. obs. 3803 3803 3803 3803
F 12.05 4.57 255.73
Pr>F 0.0000 0.0001 0.0000
R
2
0.0217 0.4474
(two-tailed t-statistics in parentheses) * p < 0.10.,
**
p < 0.05,
***
p < 0.01
Two-stage least squares regressions run using survey estimation techniques controlling for clustered standard
errors.
20
Table 4. Innovation and leverage: New product introduced to market
Dependent variable:
New Product
2SLS
First stage
2SLS
Second stage
2SLS
First stage
2SLS
Second stage
Increase in Leverage
0.5288
(4.74)
***
Leverage 0.6900
(3.29)
***
1.0144
(50.21)
***
-0.5239
(-4.53)
***
Have Comparative
Advantage
0.0214
(2.55)
***
0.0271
(2.55)
***
Expect High Revenue
Growth
.03630
(4.32)
***
0.0664
(6.35)
***
HaveIP_0
0.0240
(2.48)
**
0.1036
(0.84)
0.0165
(1.37)
0.0171
(1.73)
*
Female -0.0049
(-0.51)
-0.0061
(-0.57)
0.0122
(1.02)
-0.0172
(-1.81)
*
White 0.0186
(1.88)
*
-0.0083
(-0.72)
-0.0504
(-4.08)
***
0.0318
(2.75)
***
Education -0.0141
(-6.53)
***
0.0117
(3.15)
***
-0.007
(-2.82)
***
0.0062
(2.75)
***
Major Tech 0.0234
(2.32)
**
0.0012
(0.34)
Random Effects Y Y
Year Dummies N N Y Y
no. obs. 3803 3803 3803 3803
F 12.05 4.57 255.73
Pr>F 0.0000 0.0001 0.0000
R
2
0.0217 0.4474
(two-tailed t-statistics in parentheses) * p < 0.10.,
**
p < 0.05,
***
p < 0.01
Two-stage least squares regressions run using panel estimation techniques controlling for clustered standard errors.
21
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doc_996078180.pdf
This criteria with regards to entrepreneurial optimism, financing, and innovation sheryl winston smith.
Paper to be presented at the
35th DRUID Celebration Conference 2013, Barcelona, Spain, June 17-19
Entrepreneurial Optimism, Financing, and Innovation
Sheryl Winston Smith
Temple University
Fox School of Business, Dept. of Strategic Management
[email protected]
Abstract
Successfully launching a new firm fundamentally requires that an entrepreneur garner resources, particularly in the
context of highly innovative new firms. However the type and source of financing may influence subsequent innovation
outcomes, as innovation is inherently characterized by uncertainty. I analyze two key aspects of entrepreneur?s
strategic choice of financing: the role of financial leverage in the early financing of new high-tech firms and the
relationship between initial financing choice and subsequent innovation trajectory. I use the microdata in the confidential
Kauffman Firm Survey (KFS) dataset, a panel of nearly 5,000 US firms started in 2004. In a novel strategy, I exploit the
role of entrepreneurial optimism to discern the relationship between debt financing and innovation outcomes, conditional
on the endogenous choice of financing. The key results from this paper are: 1) optimistic entrepreneurs choose higher
levels of debt financing relative to equity in keeping with behavioral finance expectations; and, 2) after taking into
account the endogeneity of the financing mix, higher leverage (ie higher debt relative to equity) facilitates innovation
using both patent-based and product-based measures. The results suggest that for young, private firms in high-tech
industries, i.e. those that are most information-opaque, access to different sources of financing may serve to provide
greater organizational slack and facilitate innovation.
Jelcodes:L29,O30
Entrepreneurial Optimism, Financing, And Innovation
ABSTRACT
Successfully launching a new firm fundamentally requires that an entrepreneur garner resources,
particularly in the context of highly innovative new firms. However the type and source of
financing may influence subsequent innovation outcomes, as innovation is inherently
characterized by uncertainty. I analyze two key aspects of entrepreneur’s strategic choice of
financing: the role of financial leverage in the early financing of new high-tech firms and the
relationship between initial financing choice and subsequent innovation trajectory. I use the
microdata in the confidential Kauffman Firm Survey (KFS) dataset, a panel of nearly 5,000 US
firms started in 2004. In a novel strategy, I exploit the role of entrepreneurial optimism to discern
the relationship between debt financing and innovation outcomes, conditional on the endogenous
choice of financing. The key results from this paper are: 1) optimistic entrepreneurs choose
higher levels of debt financing relative to equity in keeping with behavioral finance expectations;
and, 2) after taking into account the endogeneity of the financing mix, higher leverage (ie higher
debt relative to equity) facilitates innovation using both patent-based and product-based
measures. The results suggest that for young, private firms in high-tech industries, i.e. those that
are most information-opaque, access to different sources of financing may serve to provide
greater organizational slack by allowing the entrepreneur more “breathing room”.
This version: February 28, 2013
Key words: innovation; new firms and startups; entrepreneurial finance, banks, capital
structure, optimism
2
1 Introduction
Innovation and entrepreneurship are risky and uncertain endeavors, both individually and
in tandem (Knight (1921 )). The significant potential for failure—and the ability to appropriately
weight its likelihood- magnify the importance of early strategic choices. The initial assembly of
resources conditions the dynamic capabilities and future trajectory of a new firm (Helfat and
Peteraf (2003); Zingales (2000 )). For the entrepreneur, some of the earliest strategic choices
surround the decision about which financial resources to assemble and how to most effectively
deploy them. Particularly for high-growth firms—e.g., typically firms in dynamic industries—
the need for external financial capital requires entrepreneurs to apply skills such as ‘practical
intelligence’ (Baum and Bird (2009 )) and outside certification (Burton, Sørensen and Beckman
(2002 )) to gathering sufficient resources to launch and grow the new venture. Indeed, the
entrepreneur’s access to external financial capital often determines why and how the
entrepreneur can exploit potential growth opportunities that existing firms can or will not attempt
(Rajan and Zingales (1998 )). One key aspect is the strategic choice between debt and equity
financing. Importantly, this choice has different implications for entrepreneurs and for
innovation, which will be amplified in the context of new high tech firms (Schmidt (2003); Ueda
(2004); Winton and Yerramilli (2008 )).
The objective of this paper is to probe the relationship between financing choice and the
subsequent innovation trajectory by taking into account both the initial choice of financing and
subsequent decisions to take on additional debt or equity by entrepreneurs in new high-tech
firms. In this paper I utilize the confidential version of the Kauffman Firm Survey (KFS), a rich
panel dataset that tracks a sample of nearly 5,000 new firms on an annual basis from their
common inception in 2004 through 2010. Detailed data were collected on the nature of firm
3
formation activity internal and external sources of financing, measures of innovation related
outcomes, and data related to the characteristics and outlook of the firm founders (Ballou,
Barton, Desroches, Potter, Reedy, Robb, Shane and Zhao (2008); Reedy and Robb (2009 )).
A distinct challenge in addressing this question is that innovation outcomes and
financing choice are likely to be endogenously determined. This paper employs a novel
empirical strategy to address this endogeneity by integrating a key insight from behavioral
finance; specifically, that more optimistic/over-confident individuals exhibit a greater preference
for debt financing (Dai and Ivanov (2010); de Meza and Southey (1996); Hackbarth (2008);
Landier and Thesmar (2009 )). I use this relationship to isolate the relationship between
financing choice and innovation outcomes. More broadly, in addition to providing a powerful
empirical tool, this approach speaks directly to a growing literature addresses the role of
behavioral and psychological considerations in conditioning economic and managerial choices
(Argyres (2011 )). Optimism, in particular, has been shown to influence financial and economic
choices (de Meza and Southey (1996); Dushnitsky (2010); Landier and Thesmar (2009); Puri
and Robinson (2007 )).
The key results from this paper are: 1) optimistic entrepreneurs choose higher levels of
debt financing relative to equity; and, 2) after taking into account the endogeneity of the
financing mix, higher leverage (ie higher debt relative to equity) facilitates innovation using both
patent-based and product-based measures The results suggest that for young, private firms in
high-tech industries, i.e. those that are most information-opaque, access to different sources of
financing may serve to provide greater organizational slack by allowing the entrepreneur more
“breathing room”.
This paper makes several contributions to the entrepreneurship and organization
4
literature. Overall, this paper directly links financing choice and innovation outcomes along
several theoretical dimensions. First, this paper contributes to theory by delineating how the
dynamic innovative capabilities of the new venture are shaped by the source of the
entrepreneurs’ earliest acquisition of external finance. Second, this paper makes a theoretical
contribution by coupling the trajectory of dynamic capability development and organizational
learning to established theories in financial economics that underscore the relationship between
early financing choice and innovation. Third, this paper makes a contribution to organization
theory by linking the development of early entrepreneurial capabilities to organizational slack
and the potential for organizational learning. Finally, this paper contributes to behavioral
theories of entrepreneurship by bringing in insights from behavioral finance. Specifically, this
paper develops theory about over-optimism and financing choices and links it to theories of
entrepreneurial innovation. This paper thus speaks to the literature on the importance of
behavioral influences on managerial actions and the evolution of capabilities in the all-important
realm of financial decision-making.
2 Theoretical Background & Hypotheses Development
2.1 Early Financing and Innovation
To successfully launch and grow a new firm, entrepreneurs require adequate funds. The
literature suggests that entrepreneurs often face a binding liquidity constraint (Black and Strahan
(2002); Cagetti and De Nardi (2006); Carpenter and Petersen (2002); Evans and Jovanovic
(1989); Nanda (2008 )). Theory suggests that the liquidity constraint will be particularly severe
for new high tech firms. Firms in high tech industries are inherently riskier than firms in lower
tech industries. First, latent demand for a new product or process is not known ex ante, resulting
in considerable market uncertainty. Second, introducing a new product or process to market is
5
compounded by technical uncertainty. Moreover, theory suggests that information asymmetry
between the entrepreneur and outside investors is likely to be greatest in firms which are riskiest,
i.e., nascent firms engaged in innovative activity, which face inherently uncertain probability of
success but also offer higher potential reward.
If capital markets were perfect, the relative role of equity and debt in the early mix of
financing should be immaterial to ultimate firm value and success (Modigliani and Miller
(1958 )); however, in the presence of capital market imperfections, this choice of capital
structure becomes a strategic choice which impacts innovation performance (Himmelberg and
Petersen (1994); Titman and Wessels (1988 )). While the initial choice of capital structure—and
subsequent adjustments in the relative use of equity and debt— will have significant implications
for the incentives to innovate, theory is ambiguous as the direction of the effect. Under standard
agency assumptions debt places the risk of failure on the entrepreneur, whereas outside equity
spreads the risk of failure. However, the entrepreneur may be averse to giving up an equity stake
and control, preferring instead to obtain non-dilutive debt financing until a significantly higher
valuation can be attained after milestones have occurred (Ibrahim (2010 )). Furthermore, it can
be argued that debt financing allows greater flexibility to address the idiosyncratic nascent firm
trajectory by providing greater organizational slack, in comparison to equity financing which is
tied to meeting specified milestones.
A substantial literature in management and entrepreneurial finance has examined the
entrepreneur’s ability to garner equity investment as a crucial initial resource, and indeed often
treats this as a binding constraint. [See, for example, Amit ((1990 )), Bruton ((2010 )), Burton et
al.((2002 )).] The literature also considers the impact of equity investment on
innovation(Kortum and Lerner (2000); Wu (2011 )). In small or private firms, VC financing is
6
associated with greater innovation (Hellmann and Puri (2000); Puri and Zarutskie (2008 )). At
the same time theoretical literature in entrepreneurship addresses the entrepreneur’s choice
between debt and equity. Empirically, entrepreneurs choose both (Ibrahim, 2010; Robb &
Robinson, 2012). However, the relationship between choice of debt and equity in earliest years
and implications for subsequent innovation trajectory and development of capabilities is largely
untested empirically.
Overall, higher leverage might be expected to decrease the incentive to pursue a risky
innovation strategy. On balance the literature suggests that the entrepreneur will prefer equity
financing over debt as the riskiness of the venture increases (Carpenter and Petersen (2002); De
Bettignies (2008); Gompers and Lerner (2003); Hall (2002); O'Brien (2003 )). Conversely, if
debt discourages innovation, the entrepreneur whose venture is more highly leveraged would
pursue a less risky strategy and is less likely to develop novel knowledge, which can be
appropriated through intellectual property protection such as patenting or copyright. The
following hypothesis follows:
Hypothesis 1: Greater initial debt financing (relative to equity) deters the likelihood of
innovation.
2.2 Organizational Slack and Innovation
Organizational flexibility or slack enables organizations to address uncertainty (Cyert and
March (1956); Singh (1986 )). Cyert and March ((1956 )) suggest that organizational slack
allows firms to make up in one part/subunit for problems elsewhere in organization. An
entrepreneur in a new innovation-focused venture must reduce technical and market uncertainty
as they go forward, including clearing unanticipated hurdles that may demand significant
financial liquidity. The choice between debt and equity financing has implications for the degree
of organizational slack afforded the entrepreneur. While equity financing is more “risk tolerant”,
7
by virtue of shared potential upside, this risk tolerance comes at the price of control rights.
Control rights may be ceded to the equity investor by the entrepreneur in contractual terms that
either grant these rights explicitly or implicitly through the agreement to staged financing
contingent on meeting specified milestones (Kaplan and Stromberg (2004 )). Venture capitalists,
in particular, are active investors who monitor—and have the right to interfere with—the
entrepreneur’s actions (Bottazzi, Da Rin and Hellmann (2008 )). The entrepreneur is thus
limited in their use of funds—and in their ability to deviate from ex ante targets—when tied too
strongly to equity investors.
The entrepreneur requires organizational slack instead of simply budgeting in advance for
unanticipated possibilities. Budgets reflect “a set of fixed commitments—(and perhaps more
important)—a set of fixed expectations” Cyert and March ((1956, p.50 )). It is precisely the
irreducible uncertainty that justifies slack. It is worth noting that slack can have both positive
and negative consequences, giving the firm/manager/entrepreneur excess resources without
discipline inducing constraint. Singh ((1986 )) finds that organizational slack facilitates risk
taking and positive performance outcomes. Hvide and Moen (Hvide and Møen (2010 )) find that
organizational slack in new firm is essentially a function of the entrepreneur’s liquidity. They
find that startup performance (measured as profitability) increases with entrepreneurial wealth up
to a maximum but decreases in the highest wealth quartile (which they attribute as being
consistent with an interpretation of entrepreneurship as a “luxury good”.)
Taken together, the above arguments suggest that if we take the initial capital structure as
given, this case, debt financing should encourage the riskier innovation focused strategy. The
reasoning suggests the following hypothesis:
Hypothesis 2: Conditional on initial financing, increasing organizational slack increases the
likelihood of subsequent innovation.
8
2.3 Choice of Financing and Optimism
Behavioral finance provides theory and evidence that financial decision-making is
influenced by optimism (de Meza and Southey (1996); Dushnitsky (2010); Landier and Thesmar
(2009); Puri and Robinson (2007 )). The entrepreneur selects financing based on a number of
factors, including her overall assessment of the likelihood of failure. The entrepreneur has
private information regarding the likelihood (or expectation) of success that is essentially
unavailable to outsiders, where “success” in this case can be thought of as successful innovation.
This may arise because the entrepreneur chooses to keep this information hidden (leading to the
archetypical “lemons problem” (Akerlof (1970 )). Alternatively, this information asymmetry
may be a function of the external finance providers’ inability to fully understand the complexities
involved over time (Tversky and Kahneman (1981 )) to the same degree as the entrepreneur. In
either event, if the entrepreneur is able to correctly assess the likely success in innovation, then
she may choose her financing mix accordingly, i.e., relying more heavily on equity if failure is
more likely (in which case there is adverse selection) and relying more heavily on debt if success
is more likely.
In general, the complexity and compounded uncertainty of launching a new venture in an
environment that demands rapid pace innovation (e.g., in a high tech industry) leads to the well-
known cognitive biases that characterize judgment under uncertainty described by Kahneman
and Tversky (1974). More specifically, over-optimism, has been shown to influence financial
and economic choices (Puri and Robinson (2007 )). A growing number of empirical studies link
the characteristics of over-optimism (overconfidence) to greater preference for debt (relative to
equity or overall). Because over-optimistic entrepreneurs underestimate the likelihood of failure,
they are willing to take on more short-term debt (Landier & Thesmar, 2009). Likewise, over-
9
optimistic entrepreneurs will rely upon higher leverage than their more realistic counterparts (Dai
and Ivanov (2010); de Meza and Southey (1996 )). Similarly, Hackbarth (Hackbarth, 2008)
finds that over-optimistic managers overestimate growth potential and profitability while
simultaneously underestimating the risk of failure; in conjunction, these effects lead over-
optimistic managers to issue greater amounts of debt.
Over-optimism may also translate directly into a reluctance to enter into equity
arrangements. Hayward, Shepherd, and Griffin ( 2006) consider a “hubris theory of
entrepreneurship” in which over-confident or over-optimistic entrepreneurs will reduce the
liquidity of ventures by not accepting equity based deals (giving up control), preferring instead to
rely on greater debt (which is subject to default). In a similar vein, technology entrepreneurs
with knowledge-based assets that can be selectively revealed to investors, optimism is associated
with a preference for contingent payment contracting rather than disclosure of knowledge
(Dushnitsky (2010 )).
Taken together, the arguments above lead to the following hypothesis:
Hypothesis 3: Over-optimism of the entrepreneur leads to a preference for greater debt financing
relative to equity.
3 Methodology and Empirical Approach
Data is drawn from the Kauffman Firm Survey (KFS), a longitudinal panel study of 4,928
businesses founded in 2004 and tracked over their early years of operation, with the data in this
paper spanning the first six years of firm life. Detailed data is gleaned on the nature of new
business formation activity including internal and external sources of financing, firm size and
focus, and data related to the characteristics, experience and human capital and of the
entrepreneur (Ballou, Barton, Desroches, Potter, Reedy, Robb, Shane and Zhao (2008); Reedy
and Robb (2009 )). The businesses in this sample all came into existence in 2004, with business
10
start defined in terms of state unemployment insurance paid, FICA, Schedule C income reported
on personal income tax, EIN, or the presence of legal status. The survey utilizes stratified
sampling methodology, oversampled for high-tech firms. Econometrically, survey weights
enable drawing conclusions about population; both cross-sectional and longitudinal weights are
known and estimation techniques are used which take into account survey characteristics. The
panel is inherently unbalanced as firms exit the sample over time and are no longer observed.
Taking into account the survey sampling weights, this translates into a survival rate to the end of
the sixth year of 58.6% for all firms in the sample and 67.0% for high-tech firms in the sample.
3.1 Empirical framework and estimation methodology
The relationship between initial capital structure and innovation performance is likely to
be determined endogenously. In order to separate the effects of choice of capital structure from
the latent/underlying innovativeness of a given entrepreneur I employ a multi-stage approach to
specification and estimation of this relationship. In the first stage, I model the entrepreneur’s
choice between financing sources within the framework of a random utility model (Greene
(2008, chapter 23, section 11 )). In the second stage of the model, the likelihood of innovation by
the nascent firm associated with entrepreneur i is introduced as Pr(I
ij
=1) = X
ij
+Z
j
), where I
ij
= observable innovation by new firm i , X
i j
is the vector of financing characteristics, and Z
j
=
vector of entrepreneur and firm specific attributes of the nascent firm associated with
entrepreneur i.
3.2 Endogeneity of capital structure
The endogeneity of financing choice introduces econometric concerns. One major
econometric concern is that capital structure will be endogenously determined, which would
introduce correlation between the explanatory variables and the error term, biasing the results.
11
Thus, we will need to account separately for the entrepreneur’s choice of financing, distinct from
the actual likelihood of successful innovation. In order to address this concern, I use a two stage
least squares (2SLS) approach in which the endogenous regressors determining capital structure
are regressed in the first stage, and the predicted coefficients used in the second stage regression.
Key to this approach is finding an appropriate instrument or instruments. The instrument must
be uncorrelated with the endogenous variable but correlated with the dependent variable of
interest. I approach this through several facets.
The KFS data provide a measure of the entrepreneur’s personal assessment of her
comparative advantage over rivals. Thus, by construction in the survey, comparative advantage
is a measure of the entrepreneur’s own assessment of their abilities and likelihood of success,
which will be subject to behavorial bias. Other studies suggest that entrepreneurs are
overconfident in their estimation of success (Hvide and Panos (2013 )). Banks are unlikely to be
swayed by this assessment. However, to the extent that it represents the entrepreneurs’ own
understanding of her ability, a belief that she possesses comparative advantage over competitors
may be a predictor of innovation. Optimism can be measured through the divergence between
perception of comparative advantage and performance.
In other specifications, I utilize instruments that capture behavioral optimism more
broadly through measures of expectations regarding the future (Puri and Robinson (2007 )). The
KFS survey includes measures related to entrepreneurs’ general outlook. Finally, the survey also
includes measures of specific expectations related to growth and future revenue associated with
the nascent venture.
In addition to the behavior-based instruments, I also include an instrument that captures
the economic relationship governing leverage but not innovation. Specifically, I include the
12
entrepreneur’s credit rating. An entrepreneur with poor credit will be less able to procure debt
financing; however, her credit rating should not directly influence innovation outcomes. I use a
combination of these behavioral and economic instruments in the analysis.
3.3 Dependent Variables: Innovation
Our first dependent variable captures new innovation as reflected in the production of
patents and copyrights (NewIP) by the end of the current follow-up period. NewIP is a dummy
variable which equals 1 if the firm has generated additional patents or copyrights relative to the
baseline number of patents or copyrights in the founding year. The choice of intellectual property
based measures of innovation is guided by a wide literature on innovation (Hall, Jaffe and
Trajtenberg (2005 )). New innovation is often measured through the production of new patents
or new copyrights by the firm (Acharya and Subramanian (2009); Hall, Jaffe and Trajtenberg
(2005); Hellmann and Puri (2000 )). Distinct questions address how many patents or copyrights
the firm has in a given year and whether or not the firm has any patents or copyrights in a given
year. If the question was answered in the baseline year and in some but not all subsequent years
I imputed the missing number. In additional regressions I also utilize the actual increase in
number of patents and copyrights. I also included specifications with yearly changes in IP.
For a subset of firms, specifically those that survive through year 5, we use a second
dependent variable that more directly captures product market innovation (New Product). This
variable is a dummy variable equal to one if the firm has introduced a new product to market.
3.4 Independent variables
Our focal independent variables measure initial financial leverage and changes in
financial leverage. Our control variables include financial controls, e.g. asset tangibility, bank
loans, outside equity, and initial financing attributes. We control for additional firm attributes,
13
e.g., multiple owners and legal form of organization. We also control for entrepreneurial
attributes, e.g., education, prior work experience, prior entrepreneurial experience, and gender
and race. Industry controls are included.
4 Results
Table 1 provides summary statistics. Table 2 provides a breakdown of debt and equity
financing at founding. As shown in Table 2 nascent high-tech firms rely on both debt and equity
financing at the start.
Table 3 presents results from 2SLS regressions on the probability of new innovation by
the end of last period as a function of leverage, firm characteristics, and characteristics of the
entrepreneur, where the new innovation is defined as an increase in intellectual property from the
baseline period (time t) through the last follow-up period. The dependent variable thus is NewIP.
Regressions are estimated using survey consistent techniques to account for clustered error terms
arising from sample stratification. The results show that initial leverage is associated with a
lower probability of innovation. In each case, the F statistic on the first stage of the 2SLS
regression is significant, suggesting that comparative advantage is not a weak instrument.
Results from the Heckman selection estimation and the 2SLS estimation support the hypothesis
that having a bank loan facilitates subsequent innovation outcomes. Columns 1 and 2 are the
first and second stage regressions, respectively, where the focal independent variable is leverage.
The results in the first stage regression (column 1) confirm that more optimistic entrepreneurs
choose higher levels of debt financing relative to equity. The results in the second stage
regression (column 2) support our hypothesis that after taking into account the endogeneity of
the financing mix, higher leverage (ie higher debt relative to equity) facilitates innovation.
Columns 3 and 4 present results for regressions where the focal independent variable is whether
14
the entrepreneur increases her leverage, i.e. takes on more debt relative to equity over time.
These results are similar.
Table 4 presents similar results from 2SLS regressions on the probability of introducing a
new product to market as our measure of innovation. The dependent variable is thus
NewProduct. Again, the results suggest that once we take into account he endogeneity of the
financing, higher leverage may facilitate innovation. Taken together, the results suggest that for
young, private firms in high-tech industries, access to both debt and equity sources of financing
may facilitate innovation.
5 Discussion and Conclusion
New high-tech firms are heralded as an engine of growth, with the implicit assumption
that innovation accompanies new firm formation. Much of our understanding of the relationship
between financing and innovation has been studied in firms that are significantly more
established compared to truly nascent start-ups. However, the critical link between innovation
and very early financing of entrepreneurial firms is largely unexplored. The results in this paper
provide a window into the relationship between financing and innovation in very early stage
firms. Thus, the findings here have important implications for filling in the rich details of early
stage entrepreneurial innovation.
The results presented in this paper suggest that increasing leverage over time might
provide nascent technology entrepreneurs with financial slack that may enable with innovation.
This implication bears closer attention in further work. The entrepreneur launching a new
technology venture faces significant resource constraints, and the attendant need to secure
financing. For firms with adequate financial resources, lower leverage is associated with
enhanced innovation (O'Brien (2003 )). However, when financial resources are highly
15
constrained, as in a new entrepreneurial venture, the relationship between slack and performance
is nuanced (George (2005 )). While traditionally the finance literature associates increasing
leverage with lower innovation, it seems highly plausible that in truly nascent technology firms
additional debt relaxes the major capital constraints faced by the entrepreneur.
One lens to consider the above results is in differentiating risk and uncertainty, as Knight
does in his seminal discussion of entrepreneurship (Knight (1921 )). For simplicity, risk is
defined in terms of the likelihood of a given outcome, when quantifiable probabilities can be
assigned to multiple, competing outcomes. Risk can be thought of as a draw from an underlying
distribution where the probabilities are known. As economic and financial theory shows, risk
can be pooled and insured, and thus in theory eliminated. On the other hand, uncertainty consists
of the irreducible unknown; exact probabilities thus cannot be assigned and the absolute
likelihood of an uncertain event cannot be known. To make this concrete, contrast the risk
associated with market shifts as a function of macroeconomic circumstances, e.g. recession, with
the idiosyncratic uncertainties such as unexpected and catastrophic defects in raw materials.
Financial slack can provide the agile entrepreneur with funds to combat idiosyncratic
occurrences and move beyond these hurdles towards key innovation milestones.
This paper has important strategic implications for entrepreneurs and for the study of
entrepreneurship and innovation. The starting resources of a firm are crucial to the development
of dynamic capabilities and competitive advantage over the lifecycle of the firm (Helfat and
Peteraf (2003 )). In this paper, I show that the entrepreneur’s strategic choice of initial capital
structure is a key component of innovation success and show how the dynamic innovative
capabilities of the new venture are shaped by the source of the entrepreneurs’ earliest acquisition
of external finance. Moreover, by coupling the trajectory of dynamic capability development and
16
organizational learning to established theories in financial economics that underscore the
relationship between early financing choice and innovation this paper breaks new ground.
Finally, this paper enhances our understanding of the development of early entrepreneurial
capabilities related to organizational slack and the potential for organizational learning.
17
Table 1: Summary Statistics
Variable Mean Lin. SE
Dependent Variables:
New IP 0.064 0.005
New product 0.041 0.003
Focal Independent Variables
Leverage 0.120 0.008
Leverage increases 0.228 0.015
Controls
Female 0.201 0.074
White 0.781 0.427
Education 7.243 0.809
Have outside equity 0.035 0.007
Have business bank loan 0.037 0.005
Have IP at founding 0.202 0.018
18
Table 2. Debt and equity financing at founding
Financing mean Linearized
SE
Panel A: DEBT
Have Non-Bank Loan 0.2369 0.0185
Have Bank Loan 0.1122 0.0135
Panel B: EQUITY
Have Insider Equity 0.7233 0.0173
Have Outside Equity 0.0380 0.0068
19
Table 3. Innovation and leverage: New Intellectual Property
Dependent variable:
New IP
2SLS
First stage
2SLS
Second stage
2SLS
First stage
2SLS
Second stage
Increase in Leverage
0.546
(2.21)
***
Leverage 1.6808
(3.10)
***
1.160
(51.40)
***
-0.645
(-2.22)
***
Have Comparative
Advantage
0.0194
(2.26)
**
0.0348
(2.95)
***
Expect High Revenue
Growth
.0240
(2.63)
***
0.0444
(2.76)
***
HaveIP_0
0.0144
(1.41)
0.0566
(3.10)
***
0.0285
(1.50)
0.0778
(3.57)
***
Female -0.0007
(-0.08)
0.0006
(0.02)
0.0125
(0.67)
-0.0123
(-0.63)
White 0.0130
(1.28)
0.0007
(0.04)
-0.053
(-2.80)
***
0.049
(2.05)
**
Education -0.016
(-7.35)
***
0.0358
(3.80)
***
-0.003
(-0.85)
0.012
(2.94)
***
Have Outside Eq. 0.007 0.110 0.0358 0.051
(0.36) (2.70)
***
(1.32) (1.71)
*
Have Bank Loan 0.2569 -0.435 0.1050 -0.055
(13.29)
***
(-2.99)
***
(4.15)
***
(-1.46)
Major Tech 0.0255
(2.41)
***
-0.036
(-1.41)
0.003
(0.17)
0.009
(0.47)
Random Effects Y Y
Year Dummies N N Y Y
no. obs. 3803 3803 3803 3803
F 12.05 4.57 255.73
Pr>F 0.0000 0.0001 0.0000
R
2
0.0217 0.4474
(two-tailed t-statistics in parentheses) * p < 0.10.,
**
p < 0.05,
***
p < 0.01
Two-stage least squares regressions run using survey estimation techniques controlling for clustered standard
errors.
20
Table 4. Innovation and leverage: New product introduced to market
Dependent variable:
New Product
2SLS
First stage
2SLS
Second stage
2SLS
First stage
2SLS
Second stage
Increase in Leverage
0.5288
(4.74)
***
Leverage 0.6900
(3.29)
***
1.0144
(50.21)
***
-0.5239
(-4.53)
***
Have Comparative
Advantage
0.0214
(2.55)
***
0.0271
(2.55)
***
Expect High Revenue
Growth
.03630
(4.32)
***
0.0664
(6.35)
***
HaveIP_0
0.0240
(2.48)
**
0.1036
(0.84)
0.0165
(1.37)
0.0171
(1.73)
*
Female -0.0049
(-0.51)
-0.0061
(-0.57)
0.0122
(1.02)
-0.0172
(-1.81)
*
White 0.0186
(1.88)
*
-0.0083
(-0.72)
-0.0504
(-4.08)
***
0.0318
(2.75)
***
Education -0.0141
(-6.53)
***
0.0117
(3.15)
***
-0.007
(-2.82)
***
0.0062
(2.75)
***
Major Tech 0.0234
(2.32)
**
0.0012
(0.34)
Random Effects Y Y
Year Dummies N N Y Y
no. obs. 3803 3803 3803 3803
F 12.05 4.57 255.73
Pr>F 0.0000 0.0001 0.0000
R
2
0.0217 0.4474
(two-tailed t-statistics in parentheses) * p < 0.10.,
**
p < 0.05,
***
p < 0.01
Two-stage least squares regressions run using panel estimation techniques controlling for clustered standard errors.
21
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