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The effects of equity financing and debt financing on technological
innovation: Evidence from developed countries
Article in Baltic Journal of Management · October 2019
DOI: 10.1108/BJM-01-2019-0011
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BJM
14,4
The effects of equity financing
and debt financing on
technological innovation
698
Evidence from developed countries
Received 8 January 2019
Revised 15 May 2019
4 August 2019
Accepted 3 September 2019
Ling Zhang
School of Management, Xi’an Polytechnic University, Xi’an, China, and
Sheng Zhang and Yingyuan Guo
School of Public Policy and Administration,
Xi’an Jiaotong University, Xi’an, China
Abstract
Purpose – The purpose of this paper is to compare the effects of equity financing and debt financing
on technological innovation, and prove that the enhancement of a financing system’s risk tolerance
for technological innovation can enhance the innovation risk preference of enterprises and thus
promote innovation.
Design/methodology/approach – This study is based on a transnational sample of 35 developed countries
from 1996 to 2015, by using the panel econometric model to empirically examine the effects of two financing
modes on innovation.
Findings – The findings showed that equity financing, which has higher risk tolerance, has a more
positive impact on innovation than debt financing in terms of both economic uptrend and economic
downtrend, and that government efficiency plays a significant role in supporting the performance of
technological innovation.
Originality/value – The paper provides a research framework for examining how a financing system’s risk
tolerance capacity affects the development of technological innovation through promoting risk preference
among enterprises. This paper provides transnational and cross-cycle comparative evidence that equity
financing with a strong risk tolerance capacity can better support technological innovation, even in periods of
economic downtrend. Moreover, the importance of financing system’s risk tolerance capacity for innovation
during economic crises is discussed.
Keywords Innovation performance, Equity financing, Debt financing, Technological innovation,
Risk tolerance
Paper type Research paper
Baltic Journal of Management
Vol. 14 No. 4, 2019
pp. 698-715
© Emerald Publishing Limited
1746-5265
DOI 10.1108/BJM-01-2019-0011
1. Introduction
Innovation is an increasingly important means by which companies can contribute to
sustainable development (Mousavi and Bossink, 2017). However, technological uncertainty
means the process of innovation is high-risk in terms of technological R&D and technology
commercialization. A good financial system can help support innovation by tolerating and
taking on the risks inherent to technological trial and error and business exploration.
Whereas previous studies have shown that bank debt financing and stock market equity
financing have different risk tolerance for innovation(Caggese, 2012; Nanda and
Rhodes-Kropf, 2017), theoretical contributions have made little headway in exploring how
a financing system’s risk tolerance capacity affects innovation.
There has been a lot of academic debate about which financing system is more likely to
support technological innovation – bank debt financing or stock market equity financing
(Laeven et al., 2015; Rajan and Zingales, 1998). Banks’ inherent aversion to risk leads them to
This work is supported by the National Natural Science Foundation of China (Grant No. 71904152).
avoid investing in high-risk projects with a high probability of failure, which results in
standard debt financing contracts provided by banks being unsuitable for innovation
projects where the potential high returns come with a high failure probability. Moreover, the
bank market itself cannot effectively overcome the information asymmetry and agency
problems that exist in high-tech industries. The stock market has more advantages in terms
of risk management, management incentives and multi-information review, and it is better
able to supervise the company. In addition, the developed stock market provides a good
price discovery mechanism, allowing innovative enterprises to provide a better stock price
and thus encourage innovation. Therefore, increasing the proportion of market financing
may improve the efficiency of R&D funds to promote technological innovation (Brown et al.,
2009). This is particularly true of high-level technology innovations, which particularly
benefits from stock market equity financing, whereas low-tech innovation generally needs
bank credit financing support (Zhang et al., 2016).
While some previous studies support the idea that the key factor in determining which
financial system better supports innovation is the risk tolerance capacity (Beck and
Levine, 2002; Levine, 2002), it is much less clear how it does and the role of risk tolerance
thereof. The existing literature has emphasized that financial markets can affect
innovation in at least two ways – by relieving financial constraints, and by shaping the
incentives of firms to pursue novel rather than routine projects (Atanassov, 2015;
Brown et al., 2013). Only a few studies emphasize difference between the effects of bank
debt financing and stock market equity financing on technological innovation (Brown
et al., 2017; Beck and Levine, 2004; Cornaggia et al., 2015). There is remarkably little
evidence, however, about the role of risk tolerance capacity between debt financing and
equity financing. More recent studies (e.g. Khan et al., 2018; Nanda and Rhodes-Kropf,
2017) have provide some evidence that financing risk has a particularly strong impact on
innovation, but none of them examines why equity financing has a more positive impact
on innovation than debt financing.
We looked to fill that research gap by investigating whether the innovation performance
is related to the risk tolerance capacity of their external financing. We further explore the
effects of financing availability by examining what happens to innovation in times of
economy crisis. This paper uses transnational sample data collected from 35 countries or
regions from 1996 to 2015 to test the hypotheses of the study. This paper finds that equity
financing with higher risk tolerance has a more positive impact on technological innovation
than debt financing in terms of both economic uptrend and economic downtrend.
This paper makes several contributions. First, it provides a research framework for
examining how a financing system’s risk tolerance capacity affects the development of
technological innovation through promoting risk preference among enterprises. Second, it
contributes to our understanding of the influence of external equity and debt financing on
enterprises’ decision making on innovation development, proving that equity financing with
high risk tolerance can endogenously incentivize risk-averse enterprises to become riskpreference enterprises. Third, this paper provides transnational and cross-cycle
comparative evidence that equity financing with a strong risk tolerance capacity can
better support technological innovation, even in periods of economic downtrend.
2. Theoretical framework and research hypothesis
2.1 Theoretical framework
A financial system’s risk tolerance refers to its risk-taking and risk-absorbing ability, which
reflects the risk-bearing capacity of the financial system when it comes to the possibility of
loss through innovation failure. Equity and credit markets could play different roles in
determining financing costs and, ultimately, in influencing innovation (Khan et al., 2018).
Because of their inherent risk aversion, banks need collateral to reduce the risk of corporate
Equity
financing and
debt financing
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700
loans (Brown et al., 2017). Due to their lack of high-value collateral, high-risk technological
innovation enterprises in the seed stage and initial stage cannot easily obtain credit
financing from the bank. However, in the stock market, companies without collateral can get
long-term equity investments from IPO, venture capital (VC), angel investment and private
equity (Drover et al., 2017). Moreover, equity investors regularly utilize risk-mitigation
strategies to manage their investments. Obviously, equity financing in the stock market has
a higher risk tolerance for innovation failure than bank debt financing.
Firms competing on the basis of innovation always make financial slack (low leverage) a
strategic priority (O’Brien, 2003). It is assumed that the risk preference value of enterprises
is usually between the minimum value of risk preference (Bulls) and the maximum value of
risk aversion (Bears) (Laeven et al., 2015). Comparing the critical value of enterprise risk
preference between the bank debt financing and stock equity financing models, we find that
because collateral is required to obtain bank credit, the minimum value of the risk
preference of an enterprise using debt financing is larger than that of an enterprise with
equity financing. Therefore, under the equity financing mode, enterprises tends to become
risk-preferring Bulls while under the debt financing mode, they are more likely to become
risk-averse Bears (Nanda and Rhodes-Kropf, 2017).
In summary, based on the research of King and Zhang on the function of financial risk
management, the risk tolerance of financial systems, and the trial-and-error nature of
technological innovation (King and Levine, 1993; Zhang et al., 2016), this paper considers the
influence of equity and debt financing risk tolerance on enterprises’ expected rates of return,
and puts forward the theoretical analysis framework that the financing system affects the
development of technological innovation through promoting enterprises clusters’ risk
preference. The theoretical analysis model which demonstrates the effect of financing
system on technological innovation is shown in Figure 1.
The impact path of theoretical model is as follows: First, compared with bank debt
financing, stock market equity financing maintains higher risk tolerance and can tolerate
and absorb the failure loss of technological innovation, which improves the financing
system’s risk tolerance and enhances the opportunity to carry out the trial-and-error
practices of technology innovation, thus reducing the technological uncertainty. Second,
along with the reduction of technological uncertainty, the success probability of the
innovation project will be improved, thus increasing the expected return rate of financial
institutions and raising the risk tolerance threshold for the financial system, which means
the financing system can bear more losses associated with innovation failure and therefore
be more daring, taking risks by investing in high-risk innovation projects (Nanda and
Rhodes-Kropf, 2017). Third, the improvement of project success probability will also
increase the expected return rate of enterprises, making the enterprises more optimistic
about technological innovation, and therefore increasing the threshold of enterprises’ risk
Innovation success
probability
Financial system risk
tolerance threshold
Financing system
Bank
debt financing
Increasing
risk tolerance
Figure 1.
Theoretical analysis
model
Stock market
equity financing
Reducing
technical
uncertainty
Enterprises cluster
Risk averse
enterprise
Enterprise’s
expected rate of
return
Enterprise risk
preference
threshold
Technological
innovation
Technology
research
Increasing risk
preference
Risk preference
enterprise
Technology
commercialization
preference. Finally, the improvement of the threshold value of enterprises’ risk preference
will promote some risk-averse enterprises to become risk-preference enterprises, thus
enhancing the risk preference of enterprises clusters on the whole, and thereby improving
the development of technological research and technology commercialization.
To sum up, moving from bank debt financing to equity financing enhances the risk
tolerance of the financial system and thus enhances enterprises clusters’ risk appetite for
technological innovation. Moreover, the accumulation of investment decisions of financial
institutions and the innovation financing decisions of enterprise clusters will ultimately
affect the overall level of a country’s technological innovation capacity at the macro level.
2.2 Hypothesis
The financial system can provide financial support for innovative and adventurous
entrepreneurs (Rajan and Zingales, 1998). In addition, compared with bank credit, stock market
financing can better spread and share the risks of innovation, thus encouraging technological
innovation. Stock markets enable investors to share the high returns of technological
innovation by taking high risks through the mechanism of “risk-sharing and revenue-sharing,”
thereby attracting more funds to invest in technological innovation (Laeven et al., 2015).
For these reasons, stock markets have been said to be uniquely suited for financing
technology-led growth (Brown et al., 2017; Grant and Yeo, 2018). The equity financing model is
more tolerant of technological innovation failure, thus incentivizing innovation (Ferreira et al.,
2014). The risk tolerance of this financial system is reflected in the market’s risk-bearing and
risk-mitigation ability when it comes to the trial-and-error process of technological innovation
and the failure risk associated with technological innovation (Zhang et al., 2016).
Furthermore, changing from debt financing to equity financing has improved the
technological innovation risk preference of enterprises, and even encouraged workers and
researchers to take risks to become entrepreneurs. This encourages more social resources to
invest in high-risk technological innovation activities, promoting the degree of technological
innovation in society as a whole. Several studies show that well-developed stock markets (which
are also pivotal to VC and other forms of private equity finance) have a positive impact on R&D
and innovation, particularly high-tech industries (Brown et al., 2017; Grant and Yeo, 2018).
Compared with bank debt financing, the higher risk tolerance of stock equity financing
endogenously encourages risk-averse enterprises to become more risk-prone. Stock equity
financing can enhance the innovation risk preference of enterprises more than bank debt
financing, and is more conducive to improving the performance of technological innovation
by encouraging enterprises technological innovation and business risk taking. Empirical
studies have also shown that a capital-market-based financial structure supports
technology innovation activity more efficiently than a bank-based financial structure
does (Khan et al., 2018; Brown et al., 2009). Therefore, the following hypotheses are proposed:
H1. Stock market equity financing has a significant positive impact on technological
innovation.
H2. Bank debt financing has a significant negative impact on technological innovation.
Because enterprises are faced with different technological risks and financing needs at
different stages of their development, there are differences in the impacts of different financing
methods on innovation. The main sources of funds for enterprises have shown a changing
trend of “government subsidy→internal financing→equity financing→government
subsidy” (Hsu et al., 2014). Due to the development of financial systems throughout the
world, developed countries often have developed stock market and VC markets. However, the
financial system of developing countries is imperfect, as the stock markets and VC markets
are not yet mature. Increased opening to the outside world and financial development will
Equity
financing and
debt financing
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702
significantly promotes the performance of technological innovation (Narayan and Narayan,
2013). It has also been found that there are differences in the impacts of bank credit on
economic growth in different countries (Owen and Temesvary, 2014). The main reasons for
the differences are bank development, stock market development and the degree of legal
soundness. Both innovation and financial development promote economic growth, but the
short-term effect is not obvious (Pradhan et al., 2016).
Due to the development of the world’s financial systems, developed countries are
generally in the innovation-driven development stage, with a more mature stock market.
Also important are government support for technological innovation and entrepreneurial
support (Hong et al., 2016; Rigotti and Chris, 2012). It has been found that the degree of
development of the stock market and the degree of legal soundness of different countries
have different effects on economic growth (Brown and Osborne, 2013; Souza et al., 2014). It
has been shown that both innovation and financial development can promote economic
growth (Pradhan et al., 2016), so it is necessary for governments to improve the efficiency of
financial resource allocation in order to maintain the competitive advantage of their country
in the scale of innovation.
It is well known that public policy plays an important role in shaping a country’s
national innovative capacity (Furman et al., 2002). The higher the government efficiency is,
the greater the efficiency of resource allocation to the policy, legal, operational and financing
aspects of technological innovation (Bertoni and Tykvová, 2015). However, the government
does not need to try to choose where to innovate but only to support any innovations shown
to work (Nanda and Rhodes-Kropf, 2017). Obviously, good government efficiency can
improve the allocation of financial resources, promoting the development of the stock
market and bank system, thus effectively supporting technological innovation. On the
contrary, low government efficiency reduces the efficiency of the allocation of financial
resources, resulting in the failure to support high-risk technological innovation projects,
thus inhibiting technological innovation. In short, improving government efficiency can
optimize the environment of innovation, and support innovation through laws and policies
that regulate and coordinate innovation activities (Hong et al., 2016). Therefore, we make the
following hypothesis:
H3. Government efficiency has a significant positive impact on technological innovation
in developed countries.
In addition, considering the impact of the economic cycle on the financial markets of
different countries, this paper argues that in different stages of economic development, the
support for technological innovation offered by financial systems differs. In periods of
economic uptrend, with the rapid growth of the real economy and a booming stock market,
it is easier for enterprises to obtain financial support for innovation through external equity
financing, which, of course, leads to improved technological innovation (Hsu et al., 2014). In
periods of economic downtrend, the deterioration of the external business environment
leads to the decline of corporate profitability, which limits the endogenous financing of
technological innovation, and financial crises can lead to a severe “funding gap” in the
financing of technological development and innovation (Block and Sandner, 2009).
Prior research has found evidence that innovative firms have a higher demand for
external capital, and that this demand seems to have increased since the financial crisis (Lee
et al., 2015). However, the availability of finance for these firms worsened considerably in the
crisis (Zouaghi et al., 2018). Several studies indicate that stock market financing has an
anti-cyclical effect because it has greater risk tolerance for technological innovation (Tian
and Wang, 2014; Zhang et al., 2016). Furthermore, stock markets in developed countries
(innovation-intensive economies) offer higher returns and lower risk during crisis episodes,
and investors value innovation more during difficult times (Adcock et al., 2014). And, stock
markets in developed countries have the functions of risk-mitigation and risk absorption,
which can play important roles as financial stabilizers in periods of economic downturn
(Brown et al., 2017; Müller and Zimmermann, 2009). Thus, we argue that stock market
financing can play a positive role in supporting technological innovation regardless of
whether the economy is in a period of uptrend or downtrend. Building on these arguments,
we make the following hypotheses:
Equity
financing and
debt financing
H4. Equity financing in developed countries’ stock markets has a significant positive
impact on technological innovation in both uptrend and downtrend periods.
703
3. Model and data
3.1 Empirical model
In this study, we concentrated on influence mechanism of whole financing system
risk tolerance change on whole enterprises group innovation risk preference change with
cross-country level evidence. The logical relationship from theoretical model to empirical
model is as follows:
First, there is a logical connection between the chosen financing system’s risk tolerance
and enterprises’ decision making on innovation. The core aim of the theoretical model is to
demonstrate that the chosen financing system’s risk tolerance capacity can affect the
innovation risk appetite of enterprise clusters, which in turn affects their innovation
performance. Second, there is a connection between enterprises’ decision making on
innovation and macro-innovation output. There is consistency between the performance of
macro-innovation output and the result of micro-mechanism of enterprise clusters’
innovation decision-making. The accumulated decisions of enterprise clusters will
ultimately affect the development of technology innovation at a macro (cross-country)
level. Third, there is a logical connection between the theoretical mechanism and the
empirical model. The econometric model reflects the theoretical mechanism about the effect
of macro-financial development on macro-innovation output. Transnational data allow the
econometric model to be better measured and tested, ensuring that the applicability of the
empirical research is maintained at the macro level.
A large number of theoretical and empirical analyses have shown that financial development
has a long-term impact on technological innovation. Based on the relevant econometric models
of finance and growth (Beck, 2009; Rajan and Zingales, 1998), we put forward the following
basic econometric model of stock market financing and technological innovation:
Patent i;t ¼ a0 þa1 Patent i;t1 þb1 FDi;t1 þb2 Otheri;t þei;t ;
(1)
where Patenti,t represents the technological innovation variable of country i in the year t. We
choose patent application as the index to measure the development degree of technological
innovation in a country. FDi,t is the financial development index, which indicates the growth of
bank private credit (domestic credits ratio GDP of private department) or stock market
development index (stock market capital value ratio GDP). The other explaining variables
include human capital (the number of R&D staff members), infrastructure construction (the
number of internet users), trade openness (the total GDP ratio of import and export),
governmental efficiency (the efficiency government provides public service) and the national
dummy variable (national economic development level). αi, βi are the estimated coefficient and
ei,t is the residual term.
In order to better reflect the difference between equity financing and debt financing on
technological innovation, the empirical study analyzes the impact of stock and bank
financing systems on technological innovation respectively. In order to investigate the joint
impact of the two financing methods on technological innovation, this paper puts forward a
BJM
14,4
model expressing the joint impact of bank and equity financing on technological innovation.
The regression model is shown as follows:
Patent i;t ¼ a0 þa1 Patent i;t1 þb1 Banki;t1 þb2 Stocki;t1 þb3 Other i;t þei;t :
704
(2)
Considering the lag effect of external financing on technological innovation, Stock and Bank
in this model use a period-lagged index to measure the supporting effect of the former
external financing in supporting technological innovation in the current period. As well, the
models include the lag period for the explained variables, which may lead to a potential
endogenous correlation between financing and innovation. However, when applied to
equations with lagged dependent variables, the traditional fixed and random effects settings
will lead to biased parameter estimates and inconsistency, potentially affecting the accuracy
of the model. To address these issues of endogenous and biases, the regression models have
been estimated using the generalized method of moments (GMM), developed for dynamic
panel models by Arellano and Bover (1995) and Blundell and Bond (1998).
3.2 Variable measurement
3.2.1 Technological innovation. Patent application is one of the key indicators of successful
technological innovation (Ernst, 2001; Cao and Zhao, 2013). Generally, the number of patent
applications, the number of patent authorizations and the number of patent citations are
used to measure the development of technological innovation (Picard, 2012). Therefore, our
dependent variable is patent applications, which is measured as the quantity of patent
applications submitted. Considering the lag effect between R&D input and R&D output,
patent authorization and patent citation cannot accurately reflect the actual development
level of technological innovation in current year, but the number of patent applications can
better reflect the actual level of technological reserves and the technological innovation
strength of a country in a given time period. Therefore, on the basis of empirical research
from Hsu (Hsu et al., 2014) and Furman (Furman et al., 2002), we used patent applications to
measure the development of technological innovation, with the variables used to compare
whole economies.
3.2.2 Financial development. Financial development is the core explanatory variable.
Previous studies have often chosen two kinds of indicators based on the financial
model – bank debt financing and stock equity financing – to measure financial development
(Beck et al., 2010; Pradhan et al., 2016). This paper uses the ratio of stock market
capitalization value to GDP to measure the development degree of stock equity financing
and to reflect the financing ability of stock market. The index measures the support of the
stock market financing model for the technological innovation activities of enterprises.
The calculations are as follows:
Stocki;t ¼ Stock M arket Capitalizationi;t =GDP i;t :
(3)
Based on previous studies (Beck, 2009; Beck et al., 2016), we used the ratio of domestic credit
in the private sector to GDP to measure the development of bank debt financing. The
indicator reflects the direct or indirect support of bank debt financing for technological
innovation. The calculations are as follows:
Banki;t ¼ Bank Credit i;t =GDP i;t :
(4)
3.2.3 Control variables. Based on previous literature (Ruan and Jin, 2017; Bertoni and
Tykvová, 2015; Egger and Keuschnigg, 2015; Furman et al., 2002), we used six control
variables in our analysis: R&D expenditure, which refers to the current expenditure and
capital expenditure on innovation, including basic research, applied research and
experimental development, reflecting the level of investment in technological innovation;
human capital, expressed in terms of the number of R&D staff members, which reflects the
level of human resources involved in technological innovation; infrastructure construction,
represented by the number of internet users, which reflects the support of internet
development for technological innovation in the context of the information age; trade
openness, expressed by the ratio of total imports and exports of goods and services to GDP,
reflecting the level of trade and the degree of openness of a country; degree of knowledge
spillover, expressed by net inflow of foreign direct investment (FDI), which reflects the
extent to which a country absorbs, introduces and utilizes the technological and managerial
advantages from foreign capital; and government efficiency, expressed by the efficiency of
the government’s delivery of public services as determined by the number of civil servants
in the global governance index database, reflecting the quality of public service delivery and
policy formulation and implementation by a government. The data for this are derived from
the government effectiveness index in the data of the Worldwide Governance Index.
In addition, the technological innovation capacity of countries is obviously affected by
their level of economic development. Therefore, in order to control the differences in the
stages of economic development between countries, this paper also sets up a dummy
variable for the degree of national development. The definitions and data sources for each
variable are shown in Table I.
Equity
financing and
debt financing
705
3.3 Data collection
In this paper, panel data are used for empirical analysis. According to the research needs and
the availability of the data, we selected countries with more mature stock markets as research
samples in order to test the role of stock market financing in supporting technological
innovation in these countries. The sample takes transnational dynamic panel data from 35
developed (high-income) countries and territories from 1996 to 2015. These countries and time
period have been carefully selected. Developed countries were selected because they have
well-developed financial markets, good innovation systems and sound government
governance. The World Bank defines economies as “High-income” based on their GNI per
capita was high level (see World Development Indicators). The sample period is determined
by the availability of financial data from Global Financial Development Database, and the
economy has experienced a complete uptrend and downtrend in the studied period.
The countries and territories examined are Australia, Austria, Belgium, Canada,
Switzerland, Chile, the Czech Republic, Germany, Denmark, Spain, Finland, France, the UK,
Greece, the Hong Kong Special Administrative region of China, Hungary, Ireland, Israel,
Variables
Symbol
Definition
Sources
Technological innovation
Patent
Patent application (log)
WDI
Stock equity financing
Stock
Stock market capitalization value to GDP (%)
GFDD
Bank debt financing
Bank
Domestic credit in the private sector to GDP (%)
GFDD
R&D expenditure
Research R&D expenditure to GDP (%)
WDI
Human capital
Human Quantity of R&D staffs (log)
WDI
Infrastructure construction Infra
Internet user (per 100 people)
WDI
Trade openness
Trade
Total imports and exports of goods and services to GDP (%) WDI
Knowledge spillover degree FDI
Net inflow of foreign direct investment($1bn)
WDI
Governmental efficiency
Gov
Efficiency of the government provides public service
WGI
Notes: WDI, World Development Indicators; GFDD, Global Financial Development Database; WGI,
Worldwide Governance Indicators
Table I.
Research variables
and definitions
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706
Italy, Japan, South Korea, Lithuania, Luxembourg, Latvia, the Netherlands, Norway,
New Zealand, Poland, Portugal, Saudi Arabia, Singapore, Slovakia, Slovenia, Sweden and
the USA. Among these, the financial development indexes are mainly established by global
financial development databases (Beck et al., 2010) and the technical innovation index, while
the related indicators of economic development are from the World Bank’s World
Development Index database. The descriptive statistics for the related major variables are
shown in Table II.
The correlation between the main variables is shown in Table III. The data show that there
is a very significant positive correlation between bank credit development and technology
innovation, and the correlation coefficient is 0.4469. However, there is no significant positive
correlation between stock market development (Stock) and technological innovation (Patent).
In addition, there is a significant positive correlation between human capital, infrastructure
construction and technological innovation, and a significant negative correlation between
trade openness, knowledge spillover, government efficiency and technological innovation.
3.4 Methodology
Because the lag term of the dependent variable would lead to problems of endogeny for the
explanatory variables, the GMM is used to estimate the model (Windmeijer, 2005). The
GMM is divided into difference-generalized method of moments (Diff-GMM) and systemgeneralized method of moments (SYS-GMM). As the SYS-GMM method uses more tool
variables than the Diff-GMM, the standard deviation is smaller and its estimation is more
accurate, so the two-step SYS-GMM will be used to test the model.
In order to find the different effects of external financing on technological innovation
under different financing modes, we expand our regression models into four sub-models:
Model (1), original model, without considering external financing; Model (2), debt model,
Table II.
Variable
descriptive statistics
Variables
Mean
SD
Min.
Max.
n
Patent
Stock
Bank
Research
Human
Infra
Trade
FDI
Gov
7.402
0.846
0.926
1.728
7.862
50.965
1.050
6.246
1.369
2.126
1.193
0.449
0.962
0.677
28.737
0.807
13.712
0.591
2.708
0.0138
0.0671
0.294
5.268
0.0267
0.183
−58.323
−0.392
12.858
10.863
2.191
4.405
8.984
97.334
4.426
252.308
2.431
700
700
700
700
700
700
700
700
700
Variables
Table III.
Pearson correlation
coefficient of
major variables
Patent
Stock
Bank
Research
Human
Patent
1
Stock
−0.073
1
Bank
0.446***
0.403***
1
Research
0.570***
0.010
0.383***
1
Human
0.032
−0.227** −0.183*** −0.211***
1
Infra
0.211**
0.173***
0.466***
0.407*
0.139**
Trade
−0.474***
0.546**
0.012
−0.136** −0.026
FDI
−0.212**
0.324**
0.055
−0.091
−0.060
Gov
0.243**
0.321**
0.494**
0.526** −0.281**
Notes: *,**,***Significant at 10, 5 and 1 percent levels, respectively
Infra
Trade
FDI
1
0.160*** 1
0.122** 0.480** 1
0.356** 0.206** 0.152**
Gov
1
only considering debt financing; Model (3), equity model, only considering equity financing;
and Model (4), integral model, considering both debt financing and equity financing. The
sub-models take different financing modes as their core explanatory variables, use the same
control variables, and use the same sample period. This empirical strategy allows us to
better compare the different effects of different financing modes on technological innovation
based on the SYS-GMM.
Equity
financing and
debt financing
707
4. Empirical analysis
4.1 Unit root test and cointegration test
Using the dynamic panel data model, the stability of the panel data must be analyzed before
regression to avoid “spurious regression,” ensuring the validity of the estimation results.
Therefore, the unit root test is needed for the estimated parameters. In keeping with
previous studies, the panel data unit root of each variable was tested by LLC, IPS and
Fisher-ADF test methods (Im et al., 2003; Levin et al., 2002; Maddala and Wu, 1999), and the
results are as shown in Table IV. The results of the unit root test show that the first-order
difference of each variable has no unit root and is a stationary series.
The purpose of the cointegration test is to examine whether there is a long-run equilibrium
relationship between financing variables and technological innovation. Considering the
limitation of the sample size, we proceed to test for the existence of a long-run equilibrium by
using the error correction model (ECM) panel cointegration test (Westerlund, 2007).
The values of the panel cointegration statistics based on 400 bootstrap replications are
presented in Table V. For each cross-section of the interpretive variables Stock and Bank, the
Gt statistics and Ga statistic are based on the OLS regression estimate and the Pt statistics
and Pa statistics are based on the OLS residual regression estimation reject the null
hypothesis, “there is no cointegration relationship,” at a 10 percent significance level.
The results of the cointegration tests provide clear support for the long-run equilibrium
relationships between financing variables and technological innovation.
LLC
Variables
t-value
IPS
p-value
W [t-bar]
Patent
−1.739**
0.041
−11.612***
Stock
−7.965***
0.000
−4.775***
Bank
−4.623***
0.000
−13.286***
Research
−0.878
0.189
−6.824***
Human
−8.475***
0.000
−8.295***
Infra
−8.877***
0.000
−7.742***
Trade
−2.672***
0.003
−8.904***
FDI
−7.271***
0.000
−18.741***
Gov
−0.936
0.174
−10.483***
Notes: **,***Significant at 5 and 1 percent levels, respectively
Statistic
Gt
Ga
Pt
Pa
Independent variable: stock
Value
z-value
p-value
−2.574
−14.503
−10.741
−10.191
−1.605
−2.317
−1.161
−1.225
0.054
0.010
0.000
0.000
Fisher-ADF
p-value
p-value
χ2
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
196.015***
200.536***
327.211***
141.918***
188.102***
271.630***
165.516***
324.002***
170.575***
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
Table IV.
Unit root test results
Independent variable: bank
Value
z-value
p-value
−3.289
−17.440
−17.727
−15.582
−6.872
−4.929
−6.088
−6.565
0.000
0.000
0.000
0.000
Table V.
Panel cointegration
test result based on
error correction model
BJM
14,4
708
4.2 Regression results
We begin our empirical analysis by examining the impact of equity financing and debt
financing on technological innovation. We use Stata 12.0 to estimate the empirical model,
and the results are shown in Table VI. According to the lag term of technological innovation,
the estimated results of all models are significantly positive and the coefficients are large,
indicating that technological innovation itself has a greater inertia, which is consistent with
the reality. Model (3) shows that there is a significant positive correlation between stock
market equity financing and technological innovation at a significance level of 1 percent,
which indicates that stock market equity financing in developed countries is beneficial to
improving the performance of technological innovation. In the case of controlling bank debt
financing, the regression coefficient of equity financing is still significantly positive. Again,
there is a significant positive correlation between equity financing and technological
innovation in the stock market. It is proved that equity financing has a significant positive
effect on technological innovation, which supports H1.
The regression results of Mdel (2) show that there is a significant negative correlation
between bank debt financing and technological innovation, and the regression coefficient of
debt financing in Model (4) is not significantly negative. This indicates that the contribution
of bank debt financing to technological innovation is not obvious or even negative when the
stock market is relatively developed; this result supports H2. These results are consistent
with Hsu et al. (2014), who report a negative and quantitatively important connection
between credit market development and innovation. The above empirical results show that
equity financing in stock markets with high risk tolerance can have a significant positive
impact on technological innovation, while bank debt financing has a negative impact on
technological innovation activities. This conclusion shows that in developed countries
where stock markets are relatively prosperous, equity financing is more conducive to
promoting technological innovation than financing from banks. This finding is similar to
that of Brown et al. (2017), who indicated that better developed stock markets support faster
growth of innovative-intensive, high-tech industries.
Moreover, control variables such as FDI (the degree of knowledge spillover) have a
significant positive impact on technological innovation, indicating that FDI has a positive
Variables
Model (1)
Model (2)
Model (3)
Model (4)
L. Patent
0.967*** (43.45)
0.942*** (37.10)
0.965*** (31.49)
0.933*** (15.01)
Bank
−0.0806*** (−3.39)
−0.00714 (−0.17)
Stock
0.0185*** (4.44)
0.0303** (2.41)
Research
0.0387** (2.32)
0.0398 (0.72)
0.0147 (0.37)
0.0111 (0.20)
Human
0.0362 (1.45)
0.0518 (1.14)
0.0279 (0.53)
0.0501 (0.55)
Infra
−0.000652*** (−2.95)
−0.0000506 (−0.14)
−0.000249 (−0.72)
−0.000371 (−0.77)
Trade
−0.000646 (−0.03)
−0.0285 (−0.76)
−0.0741*** (−2.91)
−0.0563** (−2.21)
FDI
0.00117*** (12.59)
0.00117*** (10.76)
0.000632*** (6.26)
0.000829*** (8.55)
Gov
0.107*** (4.61)
0.0611** (2.30)
0.0388 (1.33)
0.0374 (1.25)
Constant
−0.203 (−0.88)
0.00344 (0.01)
0.0635 (0.16)
0.114 (0.26)
Sargan test
25.473
25.585
25.085
25.608
p-values
1.000
1.000
1.000
1.000
AR(2)
1.128
1.112
1.134
1.139
p-values
0.259
0.266
0.256
0.254
Table VI.
Obs
665
665
665
665
Estimate of the impact
Notes: Numbers in parentheses are t-statistics. The last two rows report the Sargan test and the AR(2) test
of equity financing
results. Sargan test for the over-identification restrictions, AR(2) refers to second-order autocorrelation tests,
and debt financing
and their null hypothesis are “over-identifying restrictions are valid” and “zero autocorrelation in firston technological
differenced errors.” **,***Significant at 5 and 1 percent levels, respectively
innovation
supporting effect on technological innovation. In addition, we have carried out an
autocorrelation test on the estimated residual terms of each model. The results of the AR (2)
test show that there is no second-order autocorrelation in the residual error after the
first-order difference, which indicates that the dynamic panel model set used in this paper is
reasonable. The results of the Sargan test also show that all the tool variables selected by
the model are valid, indicating that the model setting is reasonable.
Furthermore, there is a significant positive correlation between government efficiency
and technological innovation, which indicates that government efficiency in developed
countries have a significant supporting effect on technological innovation performance.
This result supports H3. This indicates that the higher the level of government efficiency,
the more technological innovation is supported. It follows that the improvement of
government efficiency can promote technological innovation by providing a good business
environment and entrepreneurial support.
The above results are in keeping with our expectations, indicating that equity financing in
mature stock markets in developed countries plays a more important role in supporting
technological innovation than debt financing in banks. In fact, equity financing and debt
financing can promote technological innovation to a certain extent. However, when the stock
market is more developed, it has a higher risk tolerance and can better support technological
trial-and-error processes and bear the risk of technological innovation failure. The stock market,
especially the growth enterprise market, provides a channel to obtain long-term equity financing
for high-growth, high-tech and small enterprises. The risk tolerance of the stock market not only
creates technology trial-and-error space for the enterprises to engage in technology innovation,
but also provides more opportunities for the commercialization, growth and maturity of
enterprises’ technological developments. Finally, the stock market helps SMEs rely on
technological innovation to achieve technological upgrading and sustainable development.
4.3 Robustness test
There are some differences in the influence of financial development on technological
innovation under different economic cycles. The American subprime mortgage crisis, which
began in the summer of 2007, has had a serious negative impact on the global economy, and
the support of equity financing and debt financing for the real economy and technological
innovation have also been greatly affected (Block and Sandner, 2009). Therefore, it is
necessary to divide the economic cycle for the period examined in our data into an uptrend
period (1996–2005) and a downtrend period (2006–2015) for comparative analysis, allowing
us to study the different effects of financial development on technological innovation during
periods of economic boom and bust. The empirical results are shown in Table VII.
Comparing the estimation results, we find that the regression results of the two subsamples are consistent with the empirical results of the whole cycle samples, which indicates
that the conclusions of the study are robust. More specifically, the impact of equity
financing on technological innovation during uptrend or downtrend periods are similar to
the sample results for the whole cycle, as shown in Table VI. This indicates that stock
market equity financing has a positive supporting effect on technological innovation, no
matter whether the current period is of economic uptrend or downtrend. These results are
consistent with the previous finding that a higher equity ratio is conducive to a higher R&D
intensity and patenting activity (Müller and Zimmermann, 2009; Brown et al., 2017).
However, the supporting effect of bank debt financing on technological innovation is not
obvious, and even has a negative impact on technological innovation. This result supports
H4. The reason may be that innovative firms are more likely to face absolute credit
rationing during and after financial crises (Lee et al., 2015).
Our findings indicate that stock market equity financing in developed countries can
better support technological innovation than bank debt financing, and this positive effect
Equity
financing and
debt financing
709
Table VII.
The comparison of the
regression results
between periods of
economic uptrend
and downtrend
Economic uptrend (1996–2005)
Model (2)
Model (3)
Model (4)
Model (1)
Economic downtrend (2006–2015)
Model (2)
Model (3)
Model (4)
Notes: **,***Significant at 5 and 1 percent levels, respectively
L. Patent
0.880*** (170.20)
0.912*** (114.58)
0.937*** (51.10)
0.973*** (48.66)
0.966*** (70.01)
0.962*** (68.68)
0.976*** (93.49)
0.973*** (82.67)
Bank
−0.264*** (−7.39)
−0.290*** (−10.99)
−0.0596*** (−3.82)
−0.0790*** (−5.18)
Stock
0.0848*** (5.04)
0.133*** (8.28)
0.0201*** (9.72)
0.0257*** (9.23)
Research
0.0703*** (5.55)
0.0382** (2.14)
−0.0147 (−0.26)
−0.0214 (−0.50)
0.102*** (7.88)
0.0821*** (4.74)
0.0285 (1.04)
0.0122 (0.49)
Human
0.197*** (6.80)
0.171*** (5.94)
0.154*** (2.80)
0.0957 (1.60)
−0.0127 (−0.45)
−0.0214 (−0.75)
0.0284 (0.66)
0.0757** (2.15)
Infra
−0.000967*** (−4.46) −0.0000242 (−0.10) −0.00116*** (−2.85)
−0.000351 (−0.78) −0.00227*** (−3.67) −0.00182*** (−2.61) −0.000689 (−1.44) −0.000515 (−0.82)
Trade
−0.147*** (−7.23) −0.0814*** (−3.13) −0.0957*** (−2.74)
−0.0266 (−0.59)
0.125*** (7.20)
0.117*** (5.96)
−0.00914 (−1.33) −0.0211*** (−3.53)
FDI
−0.00159*** (−5.81) −0.00161*** (−4.48) −0.00187*** (−4.17) −0.00211*** (−6.54)
0.00119*** (15.03)
0.00121*** (14.02) 0.000704*** (7.94) 0.000777*** (11.02)
Gov
0.227*** (11.35)
0.208*** (9.90)
0.0581** (2.52)
0.0311 (0.64)
0.0479 (1.29)
0.0844** (2.38)
−0.0594 (−1.51)
0.00153 (0.07)
Constant
−0.882*** (−3.68)
−0.713*** (−2.90)
−0.715 (−1.57)
−0.360 (−0.81)
0.156 (0.59)
0.281 (1.04)
0.0442 (0.14)
−0.293 (−1.25)
Sargan test
25.125
25.586
25.601
25.608
28.085
29.677
26.892
26.072
p-values
1.000
1.000
1.000
1.000
0.372
0.378
0.891
0.985
AR(2)
1.004
1.112
1.129
1.139
−0.632
−0.733
−0.658
−0.491
p-values
0.277
0.266
0.258
0.2543
0.428
0.463
0.491
0.623
Obs
315
315
315
315
315
315
315
315
Model (1)
710
Variables
BJM
14,4
exists regardless of economic cyclical fluctuations. In fact, financial crises can lead to a
severe “funding gap” in the financing of technological development and innovation (Block
and Sandner, 2009). However, our results suggest that equity financing with powerful risk
tolerance capacity is more likely to accelerate technology development and improve
innovation performance, no matter what the current period is in the economic cycle. There
are three main reasons why equity financing can encourage innovation during periods of
economic downtrend. First, economic crises can reduce the price of capital and accelerate the
optimal allocation of innovation elements. Second, economic downtrend may reduce
investor income expectations and promote companies to pay more attention to long-term
innovation investment (Drover et al., 2017). Third, some pioneering and promising
technology enterprises may engage in talent-showing during periods of crisis, thereby
accelerating the realization of technological track transitions and industrial upgrading.
In addition, the influence of government efficiency on technological innovation is
significantly positive in periods of both economic uptrend and downtrend, which indicates
that government efficiency in developed countries have a significant supporting effect on
technological innovation. It reflects that developed countries tend to have higher
government performance, as well as higher degrees of policy support and business
environment support for technological innovation.
Furthermore, we tested the robustness of the empirical results. First, we adjusted the
research samples by removing those countries with relatively weak stock markets like Greece
and Chile. Then we used the selected sub-samples to re-test the empirical model. It was found
that the regression results were basically consistent with the total sample regression results;
the regression results are shown in Table VIII. Second, the one-step SYS-GMM was used to
estimate the model again. The regression estimation coefficient of the core index showed little
change, and the significance and direction were basically consistent with the above
conclusions. Also, there is a significant positive correlation between government efficiency and
technological innovation. This result supports H3. Therefore, the empirical results and research
conclusions of this paper are robust in terms of time periods and different estimation methods.
Equity
financing and
debt financing
711
5. Conclusions
Based on the current situation of technological innovation financial support systems in
various countries, this paper has compared the risk tolerance to technological innovation of
stock market equity financing and bank debt financing. Based on the transnational sample
Variables
Model (1)
Model (2)
Model (3)
L. Patent
0.999*** (19.65)
0.972*** (18.28)
0.926*** (12.65)
Bank
−0.101* (−1.91)
Stock
0.00931* (1.88)
Research
0.0368 (1.55)
0.0368 (1.46)
0.0277 (0.42)
Human
0.0494* (1.68)
0.0747** (2.42)
0.101 (0.95)
Infra
−0.000923*** (−3.75)
−0.000269 (−0.74)
−0.000487 (−1.25)
Trade
0.0428 (1.08)
0.0285 (0.63)
−0.0708** (−1.98)
FDI
0.00120*** (14.12)
0.00124*** (12.83)
0.000678*** (5.72)
Gov
0.124*** (4.41)
0.106*** (3.71)
0.0670* (1.87)
Constant
−0.606 (−1.58)
−0.501 (−1.37)
−0.274 (−0.45)
Sargan test
27.391
29.480
28.851
p-values
1.000
1.000
1.000
AR(2)
1.141
1.126
1.122
p-values
0.253
0.260
0.261
Obs
627
627
627
Notes: *,**,***Significant at 10, 5 and 1 percent levels, respectively
Model (4)
0.882*** (9.25)
−0.0254 (−0.51)
0.0188*** (3.17)
0.0257 (0.46)
0.0780 (1.04)
−0.000196 (−0.45)
−0.0506 (−1.41)
0.000716*** (6.40)
0.0766** (2.04)
0.208 (0.36)
27.392
1.000
1.102
0.270
Table VIII.
627
Regression result after
adjusting samples
BJM
14,4
712
data from 35 countries from 1996 to 2015, this paper has empirically analyzed the
relationship between equity financing, debt financing and technological innovation by using
the panel econometric model. Our findings provide one explanation for how and why equity
financing can better support innovation than credit debt financing by increasing risk
tolerance capacity. The results reveal that, compared with debt financing, equity financing
is more tolerant of uncertainty and failure of technological innovation, and that it can
enhances enterprises’ risk preference for technological innovation. This study strengthens
the idea that equity financing with high risk tolerance can endogenously incentivize
risk-averse enterprises to become risk-preference enterprises (e.g. Brown et al., 2017; Nanda
and Rhodes-Kropf, 2017). The results also show that there is a significant negative
correlation between bank debt financing and technological innovation, and a significant
positive correlation between equity financing and technological innovation. Therefore, our
findings reinforce the idea that stock market financing can encourage enterprises to carry
out high-risk technological innovation activities (Khan et al., 2018).
Although a few studies have focused on the relationship between external financing and
innovation (Brown et al., 2017), it does not provide sufficient understanding from a risk
tolerance perspective. This study responds to that gap in the literature by investigating why
stock markets appear to be more important for innovation activity than banks. Moreover,
there is a significant positive correlation between government efficiency and technological
innovation, which indicates that government efficiency plays a significant role in supporting
innovation performance and sustainable development in developed countries. In addition,
stock market equity financing can support technological innovation better than bank debt
financing in both periods of economic uptrend and downtrend. The findings reveal that equity
financing with higher risk tolerance can enhance a financial system’s risk tolerance for
technological innovation failure, and thus encourage enterprises to engage in more radical
innovation activities (Khan et al., 2018). Thus, this paper provides a valuable contribution to
understand why stock markets appear to be more important for innovation activity.
Based on the above analysis and conclusions, this paper puts forward the following policy
recommendations for countries to improve the sustainability of technological innovation: first,
enhance the risk tolerance of the stock market and banking system, and optimize the financing
structure. Actively develop the stock market to promote VC with high risk tolerance, improve
the mechanism of risk compensation and risk dispersion for investors and entrepreneurs, as
this can support enterprises’ technological innovations at the seed stage and initial stage.
Second, improve the systems of stock issuance, encourage equity investors to invest in
technology enterprises. Establish a multi-level capital market to attract and encourage angel
investors, VC and private equity investors to make long-term equity investments in
technology enterprises and high-tech startups. Third, improve the efficiency of government
services for innovation. By building a service-oriented government and creating a good
innovative business environment and policy system, governments can stimulate enthusiasm
for innovation and entrepreneurship. Fourth, maintain the stability of the financial system. It
is important to guard against financial risks, maintain the stability of the financial system,
strengthen the risk tolerance and risk-taking capability of the financial system, and enhance
the financial system’s support for the efficiency and sustainability of technological innovation.
This study has several limitations. First, due to the lack of appropriate indicators that
directly reflect the degree of risk tolerance among equity and debt financing methods, this
paper does not empirically test the impact of financing methods’ risk tolerance on
technological innovation. Second, because data on VC financing is difficult to obtain, this
paper does not consider VC financing’s support for enterprises’ technological innovation.
Third, this study chooses the quantity of patents as the variable measure of technological
innovation, but not all patents have successfully been transformed into technological
innovations, as some patents are filed but never make it to the market.
Further research in this field should explore specific indicators to measure the degree of
risk tolerance, construct an overall theoretical model of the impact of bank, stock market
and VC financing on technological innovation, and explore the regional differences and
evolution trends of the impact of financial system risk tolerance on innovation performance.
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Corresponding author
Sheng Zhang can be contacted at: zhangsheng@xjtu.edu.cn
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