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Working Paper Series
n. 28 May 2012
Outsourcing innovation andthe role of bank debt for SMEs
Elena dAlfonsoSilvia Giannangeli
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Statement of Purpose
The Working Paper series of the UniCredit & Universities Foundation is designed to disseminate and
to provide a platform for discussion of either work of UniCredit economists and researchers or outside
contributors (such as the UniCredit & Universities scholars and fellows) on topics which are of special
interest to UniCredit. To ensure the high quality of their content, the contributions are subjected to an
international refereeing process conducted by the Scientific Committee members of the Foundation.
The opinions are strictly those of the authors and do in no way commit the Foundation and UniCredit
Group.
Scientific Committee
Franco Bruni (Chairman), Silvia Giannini, Tullio Jappelli, Catherine Lubochinsky, Giovanna Nicodano,
Reinhard H. Schmidt, Josef Zechner
Editorial Board
Annalisa Aleati
Giannantonio de Roni
The Working Papers are also available on our website (http://www.unicreditanduniversities.eu)
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Contents
Abstract 3
1. Introduction 4
2. Theoretical framework 6
3. Data and empirical methodology 9
4. Results and discussion 12
5. Conclusions 14
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Outsourcing innovation and the role of bank debtfor SMEs
Elena dAlfonso
Silvia Giannangeli
UniCredit
Corporate Analysis
Abstract
This paper extends the extant literature on R&D outsourcing by investigating the role played by the
use of bank debt as a financing source for R&D. In particular, we argue that in imperfect capital
markets, outsourced, contractually stated R&D may expand the borrowing capacity of small and
medium-sized enterprises (SMEs), potentially reducing asymmetric information problems between the
firm and its lenders and increasing asset redeployability. Moreover, we contend that the specific
features of the bank-firm relationship can moderate the relationship between the decision to outsource
R&D and the decision to finance R&D using bank debt. We use a sample of 2549 manufacturing
SMEs located in Italy and find support for our hypotheses.
KEYWORDS: SMEs; innovation; finance; outsourcing
JEL CLASSIFICATION:C25; D22; G30; L24; O30
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1. Introduction
Small- and medium-sized firms (SMEs) envisaged tough times during the current financial crisis. The
credit slowdown and the rapidly changing business environment have created new and difficult
challenges for preserving and expanding the market position of innovative SMEs. To overcome
difficult times and maintain their competitive advantage, SMEs must continually develop new products
and services (OReagan and Kling, 2011). For many SMEs, however, new product development may
be costly due to lacking capabilities; therefore, many SMEs may consider acquiring knowledge and
technology from external sources (Vermeulen, 2005). Given the degree of resource intensity required
for innovation and the high pace of technology diversification, the outsourcing of R&D has become a
common practice (Hagedoorn, 1996; Gassmann et al., 2010: Hsuan and Mahnke, 2011). As a matter
of fact, knowledge sources outside the boundaries of the firm are very important for SMEs. The resultsof the 2008 Community Innovation Survey collected by the European Union in fact confirm that more
than half of the SMEs interviewed consider external information sources highly important for their
innovative activities.
A growing part of the economics and management literature has concentrated on the operational
mode of introducing R&D into the production process, investigating the drivers of the choices between
technology buy or make (Pisano, 1990; Arora and Gambardella, 1990; Veugelers, 1997; Love and
Roper, 2002; Cassiman and Veugelers, 2006; Lokshin et al., 2008; Grimpe and Kaiser, 2010). These
studies have indicated that there is a clear trade-off between the advantages and costs of outsourcing
as opposed to conducting in-house R&D activities. The use of external R&D sources may have
several advantages. For example, outsourcing R&D may reduce the fixed costs of innovation, thus
overcoming the potential limitations of in-house R&D budgets (Love and Roper, 2002). Moreover,
resorting to external research and development allows access to the economies of scale and scope
available to specialist research organizations, thus reducing the time-to-outcome of a research project.
External R&D links may also be a useful method of searching the technological environment, possibly
permitting access to improved technology developed outside the boundaries of the firm (Veugelers,
1997; Cassiman and Veugelers, 2006). However, externalizing R&D also has potential disadvantages.
For example, intellectual property rights and appropriability problems may make external R&D
unattractive (Arora and Gambardella, 1990). Moreover, as emphasized by the transaction cost theory
under the conditions of asymmetric information, which often prevails in the context of research and
innovation, the outsourcing strategy may lead to problems of monitoring costs due to potential
opportunistic behavior of R&D suppliers (Pisano, 1990; Ulset, 1996). A part of the literature focused on
the motives for technology buy rather than make and investigated the complementarity or
substitutability of the two approaches (Arora and Gambardella, 1990; Piga and Vivarelli, 2004;
Cassiman and Veugelers, 2006; Lokshin et al., 2008).
This paper extends the extant literature and contributes to it by investigating a dimension that has
been largely neglected in the studies on R&D outsourcing. Very little attention has been paid to the
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role played by the R&D financing strategy on the choice of a firms R&D mode. Firms engaged in
innovation face several important decisions. First, they must decide how much to invest in R&D, and
second, how to make that investment (Love and Roper, 2002). Firms, however, also need to make a
third important decision, which is how to finance the R&D. The objective of the present paper is to
investigate whether the latter decision has an impact on the choice of the R&D mode. In particular, we
argue that the choice of financing R&D using bank debt will favor the decision to outsource R&D. As
clearly stated by Hall (2002), in perfect capital markets, funding concerns should not affect a firms
R&D choices. However, capital markets are far from perfect (see, for instance, Fazzari et al., 1988 and
the subsequent research). We argue that asymmetric information problems plaguing the relationship
between borrowers and lenders are particularly important in the financing of R&D. In this context,
turning to external, contractually stated R&D may reduce information asymmetries and improve the
borrowing capacity of innovative SMEs.Moreover, we contend that the specific features of the bank-firm relationship can moderate the
decision to outsource R&D and the choice to finance R&D using bank debt. Thus, our hypothesis is
that the use of credit to finance R&D will increase the probability of adopting a technology buy
strategy when the relation between the innovative borrowing SME and its lenders is particularly weak.
The main tenet behind this hypothesis is that a more intense relationship can reduce information
asymmetry problems, thus reducing the role of bank debt as a determinant of R&D outsourcing. The
empirical results corroborate our hypotheses. We find a positive impact of bank debt on outsourcing
R&D. This relationship becomes insignificant for high levels of bank-firm relationship intensity.
The contribution of this paper to the extant literature is twofold. First, it investigates a potential driver of
R&D outsourcing that has been largely neglected by previous studies. Second, the empirical evidence
found by this study sheds light on a potential transmission channel of credit cycles and the banking
system at large on firm innovation and R&D strategies. The empirical evidence may be of particular
interest as the observation period overlaps with the outset and first phase of the current financial
crisis, a period when SMEs are likely to have faced more severe challenges both for their business
environments and their demands for credit.
The paper is organized as follows: Section 2 discusses our theoretical framework and hypotheses.
Section 3 describes the data and explains the empirical strategy. Section 4 discusses the main results,
and Section 5 concludes.
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2. Theoretical framework
2.1 Outsourcing R&D and the use of bank creditA large, well-established theoretical and empirical compilation of literature has indicated that financing
innovation is more difficult than financing ordinary investment. As posited by Hall (2002) and Hall and
Lerner (2009), one of the main problems faced by external investors is the uncertainty of the returns
on innovation investment. R&D investments seem to particularly exacerbate the problems faced by
investors as these types of investments involve assets that are both intangible and, highly firm-
specific. Asymmetric information and moral hazard problems may prevent the financial sector from
properly and accurately evaluating and monitoring firms. As a result, there may be a wedge between
the external and internal cost of capital required for backing R&D investments, eventually limiting a
firms innovation activity.
In the presence of asymmetric information, however, not only the decision about how much to invest in
R&D but also the decision about the mode of R&D activities may depend on the availability and use of
different financial resources. The decision to outsource part of a firms R&D activities may not be the
result only of factors related to the firms technological capabilities, market structure, innovation scale,
or input costs (Love and Roper, 2002), but they may also be the result of the use of credit as a
financing source for innovation.
Evaluation and monitoring problems faced by the financial sector may lessen in the case of external
R&D projects as it may be easier to sort out the quality of projects that are disembodied from the firm
(Cassiman and Veugelers, 2007). Borrowers have a superior set of information about the quality of
their firms innovative projects and may be unwilling to share such information with lenders. Sharing
information about on-going research projects could, in fact, reduce the returns of research output in a
competitive market (Hall, 2002). When R&D is acquired from external suppliers, the final objectives of
the project and the monitoring steps must be clearly stated from the beginning as the costs and the
time-horizon within which the project must be accomplished must be explicitly stated in the contract.
According to transaction cost theory, R&D outsourcing may be vulnerable to principal-agent problems
between the outsourcing firms and the firms suppliers. Furthermore, significant trade-offs affect the
decision of whether to outsource R&D projects (Pisano, 1990; Tapon, 1989). Once R&D projects are
externalized, however, a detailed and careful contractual governance must be instituted, thus
minimizing transaction costs for the outsourcing firms. Cost-minimizing outsourcing contracts are likely
to embody conditions both on the outcome and on the suppliers behavior that will potentially convey
useful information for evaluating the financial risks attached to the projects (Ulset, 1996).
Contrarily, when R&D is conducted in-house, both the objectives and the implementation of innovation
projects are less visible to external financers, and the borders between innovation and ordinary activity
may be blurred, thus potentially making the uncertainty of the results higher. Piga and Atzeni (2007)
find that lenders do not look favorably on large in-house R&D activities of borrowers as they entail a
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large proportion of intangible assets and provide strong incentives to resort to secrecy, thus
exacerbating the information asymmetries between lender and borrower.
Furthermore, disembodied technology acquisition through R&D outsourcing (Cassiman and
Veugelers, 2007) is more likely to involve generic, non-firm-specific, already-sufficient standard
knowledge to minimize both ex-ante search and negotiation and ex-post monitoring and contract
enforcement costs (Mowery and Rosenberg, 1989). This circumstance has clear consequences for
firm lenders. For example, highly firm-specific assets cannot be redeployed readily as they are tailored
to a firms needs and generally do not convey sufficient physical collateral. Hence, they offer poor
guarantees for lenders. Mocnik (2001), with respect to a sample of manufacturing Slovene firms, finds
evidence of a negative relationship between debt ratio and firm-specific assets. More standard and
less firm-specific assets offer lenders some clear advantages in terms of evaluation and
redeployability, thus increasing the firms borrowing capacity (Tirole, 2006).From the viewpoint of the demand for credit for R&D, however, there are those financing instruments
that might fit better than others with the financing needs embedded in an external or an in-house R&D
investment. One of the most relevant characteristics of investments is the time horizon. For creating
internal R&D, for example, innovative firms must establish a department and hire highly skilled
employees. This is clearly a long-term project that involves the entire internal organization and
management of the firm. In such projects, the uncertainty on returns are typically high because they
are often related to the knowledge embedded in the human capital of the employees that would be lost
if they were to leave the firm. Clearly, this requires investors to seek a higher rate of return, which
could induce firms to privilege internal financial sources.
Summarizing, there are a variety of reasons to put forth the following hypothesis:
Hypothesis 1: In the presence of asymmetric information, the use of bank debt for financing research
increases the probability of outsourcing R&D.
2.2 The role of bank-firm relations
The potential economic outcomes of asymmetric information between innovative SMEs and their
lenders can be moderated by the characteristics of the bank-firm relationship. A few studies haveanalyzed, at a micro level, how the banking system can affect a firms innovation decisions (Giannetti,
2009, Herrera and Minetti, 2006, Benfratello et al., 2007), but none of them have considered the
impact on the R&D strategic choices with respect to research externalization.
A large amount of literature has focused on analyzing, at a macro level, the effects of bank-based
versus market-based financial systems on innovation (Carlin and Mayer, 2003 Levine, R. 2002,
Tadesse, 2007). One of the main arguments in favor of the higher suitability of bank-based systems in
fostering innovation is the fact that banks are more capable of preserving confidentiality, thus
increasing a firms willingness to disclose information about technology, knowledge and future
business opportunities (Tadesse, 2007). However, even in bank-based systems, it is not
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straightforward that the appropriate incentives for information sharing between borrowers and lenders
are provided. Information sharing depends on the specific nature of the relationship, which can be
defined by a variety of factors, some of which are the duration, number of banks used and the share of
debt with each bank. If, for example, the firm is financed through multiple banks, information sharing
may not take place. For example, Giannetti (2009) finds evidence that in high-tech sectors, a weak
relation with the bank reduces innovation. The micro level analysis on bank-firm relationships is not,
therefore, an ignorable factor in innovative activity. A strong relation with the bank, in fact, reduces a
firms financial constraints and improves liquidity because it reduces information asymmetries (Castelli
et al. 2006).
A closer relationship with the bank implies a lower impact of asymmetric information, reducing the
above-mentioned asset specificities differences between outsourcing and internal R&D. Even in a
bank-based country such as Italy, the firm-bank relationship can be a relevant factor in reducing theasymmetric information and can moderate the bank debt impact on the strategic choice of outsourcing
research. Thus, in the second part of our analysis we test the following:
Hypothesis 2: A stronger bank-firm relationship moderates the role of bank debt in explaining the
outsourcing of R&D.
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3. Data and empirical methodology
The empirical analysis is based on the EU-EFIGE/Bruegel-UniCredit dataset, which gathersinformation on manufacturing firms located in several European countries. The database contains
qualitative and quantitative information on a wide spectrum of aspects regarding firms activities,
including innovation and R&D choices. The data refer to 2007-2009 and were collected during the first
semester of 2010 through a CATI-based questionnaire filed by 15000 firms in Europe. The present
analysis draws from this large database and analyzes 2549 manufacturing SMEs located in Italy .
Our empirical strategy is based on estimating a discrete choice model for the firms decision to
outsource R&D. In fact, the first hypothesis put forward in Section 2 states that, in the presence of
asymmetric information, the use of bank debt for financing research increases the probability of
outsourcing R&D.
To test whether the outsourcing decision is influenced by the R&D financing mix, we adopt a direct
measure of the share of R&D expenses financed using bank debt (Bank). The dichotomous outcome
variable, D_ExtR&D, is defined as taking a value of 1 if the firm partly or entirely outsources its R&D
activities during the triennium, and zero otherwise.
We estimate a probit equation model taking the following form:
D_ExtR&D = 0 (x+ u1 0) (1)
1 (x+ u1 >0)
with u1 ~ N(0,1)
Clearly, Hypothesis 1 predicts that the coefficient of variable Bank will be positive and significant.
In addition to the variable Bank, the set of explanatory variables in (1) includes several control
variables that have been investigated by previous empirical literature as potential drivers of R&D
outsourcing. These include
- Firm absorptive capacity: A robust literature finds that the more pronounced the ability of an
organization to absorb the new knowledge generated outside its boundaries, the higher the incentives
to externalize R&D activities (Cohen and Levinthal, 1989; Arora and Gambardella, 1994; Schmidt,
2010). In the current analysis, we adopt two different proxies for a firm absorptive capacity: the share
of graduated employees (Grade_employees) and the R&D expenses-to-turnover ratio between 2007
and 2009 (R&Dintensity).
- IPR protection ((D_IPR)): Intellectual property right protection (IPR) could, indeed, indicate
high technology spillovers at the industry level and, therefore, higher concerns at the firm level for
appropriability of innovation output (Levin et al. 1987, Cassiman and Veugelers, 2002). The latter is
likely to reduce the firms propensity for outsourcing R&D (Lai et al., 2009).
- ICT endowment (D_ICT): R&D outsourcing may be favored by a sufficient information and
communications technologies (ICT) endowment. R&D managers increasingly use the possibilities of
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connecting and coordinating R&D initiatives via remote sources of innovation (Oshri et al., 2009). As
with other technological advances that reduce the transaction costs of exchanging innovation
problems and solutions across company boundaries, ICT does promote the emergence of external
markets for innovation (Hsuan and Mahnke, 2011).
- Outsourcing (D_Outsourcing): Transaction costs of organizing external R&D will likely be
higher in smaller firms or those firms in a relatively weak market position. These firms may also find it
more difficult to fully exploit the commercial benefits from successful R&D (Love and Roper, 2002).
Firms that outsource part of their production process approach are likely to own the required capability
for managing external suppliers, thus minimizing principal agent problems. They are also more likely to
externalize R&D (Fritsch and Lukas, 2001).
- Business group (D_Group): Being part of a business group eases outsourcing agreements by
reducing transaction costs within the group and improving appropriability conditions over R&D results.Accordingly, firms belonging to business groups may be less reluctant to buy R&D from structures that
are external to the firm but belong to the same group.
- D_SOUTH: Several studies have noted that localized social capital may influence the
economic behavior of individuals and firms (see, for instance, Guiso et al., 2004 and subsequent
research). In a recent paper Laursen et al. (2011) determined that firm location in a region with high
social capital positively influences the effectiveness of externally acquired R&D on innovation. This
argument may be very relevant in Italys case, where the northern regions highly outperform the
southern regions as for the endowment of localized social capital (Guiso et al., 2004).
We further control for firm size (Size), measured as the natural logarithm of the average number of
employees during the observation period. Finally, we control for industrial sector, defined in
accordance with the OECD technological classification as high-tech (HTECH), medium-high tech
(MHTECH), medium-low tech (MLTECH), and low tech sectors (LTECH) .
To account for censoring problems due to the limited observability of variable D_ExtR&D, we adopt a
probit model with sample selection (also known as Heckman-probit model), where the probability of
performing R&D activities is estimated upon the first step (Heckman 1979). In fact, a firm choice to
outsource part of its R&D projects can be observed only if a firm has chosen to perform some form of
R&D. Not controlling for such sample selection problems would mislead the interpretation of the
results because two different types of zeros in the D_ExtR&D variable would be mixed (i.e., those
firms performing R&D in-house and those firms not performing R&D at all).
The selection equation takes the following form:
D_R&D = 0 (z+ u2 0) (2)
1 (z+ u2 >0)
with u2 ~ N(0,1)
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where variable D_R&D is a dichotomous variable taking value of 1 if the firm performed some form of
R&D during the triennium 2007 to 2009. The explanatory variables in equation (2) are drawn from the
economics of innovation literature, particularly Hall et al. (2008) and include firm size (SIZE), age (Age
Class 0-10 and Age Class 10-20), family ownership (D_FAMILY), firm human capital
(Grade_employees), a dichotomous variable taking value of 1 if the firm has mainly foreign
competitors (D_Foreign) and the export intensity (i.e., the share of turnover sold abroad, or Export).
Finally, we include the dichotomous D_SOUTH location indicator and a set of dummy variables for the
sector technological intensity. Table 1 summarizes the explanatory variables used in the estimationof
model (1)-(2) and the expected signs of explanatory variables in (1), while table 2 shows the
descriptive statistics of the variables discussed thus far.
The second hypothesis put forward in Section 2 posits that a stronger bank-firm relationship
moderates the role of bank debt in explaining the outsourcing of R&D. In order to test Hypothesis 2,we breakdown the full sample into two subsamples and test whether the effect of bank debt is larger
when SMEs lack an intense relationship with their financers,. The EU-EFIGE/Bruegel-UniCredit
dataset offers a nice proxy for the bank-firm relationship, that is, the number of banks used by each
SME in the sample. The number of lending relations has been used as a proxy for the intensity of the
bank-firm relation: borrowing from multiple banks can reduce a banks incentives to generate
information from the relationship with a firm (Herrera and Minetti, 2007; Petersen and Rajan, 1994).
The sample is split into two subgroups identified based on whether the number of banks used by each
SME lies below or above the median value in the sample. Two subsamples are thus identified: the
low intensity group composed of 1254 SMEs (corresponding to firms with more than three banks)
and the high intensity group composed of 1295 firms (corresponding to SMEs maintaining relations
with a maximum of three banks). To empirically test Hypothesis 2, we estimate the abovediscussed
model (1)-(2) and compare the coefficients of the variable Bank, thus obtained. Clearly, we expect that
variable Bank will have a larger effect on the probability of outsourcing R&D in the Low-intensity
subsample. The results are shown in table 3 and table 4 in the next section. Table 5 shows the
correlation matrix among all explanatory variables.
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4. Results and discussion
Table 3 reports the estimation results for the total sample. The coefficient of variable BANK results in a
positive value and is statistically significant. The calculated marginal effect indicates that, on average,
increasing the share of R&D expenditure covered using bank credit would increase the probability of
outsourcing R&D by 1%. While the magnitude of this effect is not very large, it offers support to the
hypothesis that the use of bank debt positively influences the probability of externalizing part of a
firms R&D activities. Among the control variables, we find only weak evidence in favor of the
importance of a firms absorptive capacity as only the variable Grade_Employees results in a positive
value and is statistically significant. In accordance with transaction cost theory, we find that the
probability of outsourcing R&D is higher in SMEs that buy using outsourcing agreements as part of
their production process. Moreover, belonging to a business group and adopting IPRs enhances the
probability of outsourcing R&D, thus suggesting that in these cases, principal-agent problems arising
from suppliers opportunistic behavior may be lower. Contrary to what is emphasized in Hsuan and
Mahnke (2011), ICT endowment is not found to play any role in stimulating R&D outsourcing.
Similarly, our proxy of social capital and the technological intensity classes are found to have no effect
on R&D outsourcing in the sample.
The second column in Table 3 summarizes the estimation results of the selection equation. These
results, although not directly related to the hypotheses put forward in this paper, deserve some
attention. Overall, large firms are found to be more likely to undertake R&D activities, thus confirming
much of the empirical literature on the relationship between size and innovation (Acs and Audretsch,1988 and subsequent research). Moreover, R&D activities are found to be favored by more skilled
human capital and by the firms exposure to international competition. High and medium-high firms are
more likely to conduct R&D activities than low-intensity firms (baseline in the regression). Finally, firms
located in the southern regions of Italy are generally less involved in R&D. The age of the firm seems
not to be associated with R&D activity in the firms of this sample.
Additional support for our hypothesis is delivered by the results obtained after splitting the sample into
the two subgroups defined according to the number of banks used during the observation period. As
already discussed, maintaining borrowing relations with a number of banks reduces a banks incentive
to fully exploit the information regarding firm quality and behavior. The problems of asymmetric
information may be exacerbated in this case, and, according to the arguments discussed in Section
2,,the nay be a positive influence of the use of credit on the probability to outsource R&D. The results
in table 4 lend support to this view because a positive relation between R&D outsourcing and the
share of R&D expenditures covered by bank credit registers only in the low-intensity subsample,
whereas insignificance emerges among firms maintaining a more intense relationship with the banking
system. As for the remainder of the control variables, all of the variables related with transaction cost
reduction (the use of IPR, being part of a group, outsourcing part of the production process and also
the ICT endowment) are found to be notably significant only in the high-intensity subgroup.
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5. Conclusions
This paper builds on the management and economics literature on R&D outsourcing and extends it by
addressing the role played by R&D financing strategies. Namely, we found support for the hypotheses
that, in the presence of asymmetric information, the use of bank debt for financing research increases
the probability of outsourcing R&D. Moreover, this relationship is moderated by the information sharing
between the lender and the borrower, as summarized by a closer bank-firm relationship. Our research
contributes to the extant literature by investigating a potential driver of R&D outsourcing that has been
largely neglected by previous studies. Furthermore, we shed some light on a potential transmission
channel of credit cycles on firm R&D strategies. The findings suggest in fact that challenging credit
market conditions may reduce the viability of the technology buy strategy. The analysis, however,
clearly suffers from some limitations, which are mostly due to the cross-sectional type of data
available. Repeated observations over time would allow to evaluate and compare the robustness of
the relationships highlighted by the present study during different phases of the credit cycle. Yet, the
results found by this study have potentially interesting implications for envisioning new solutions for
overcoming the problems of information asymmetries embedded in innovation financing. In this
context, the use of external R&D can be considered a useful mechanism to lessen the problems
associated with investment evaluation and monitoring by lenders. We believe this result is promising in
that it opens the door for envisaging potential developments in the financing markets aimed to reduce
the opacity of R&D activities from the viewpoint of lenders. Moreover, this result confirms that firms
behavior, and in particular the adoption of some strategies which facilitate the sharing of informationabout technology, knowledge or future business opportunities with lenders, may be effective in reduce
the well-know problems of market failure in the financing of innovation. We consider our contribution a
first step in this area of research, an area that deserves continued investigations.
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Tables:
Table 1
Explanatory variable DescriptionD_R&D selection
equationD_ ExtR&D outcome
equation
Included Included Expected sign
Bankshare of R&D expenses financed through bank
debtx +
Sizenatural logarithm of the average number of
employees in 2007-2009x x
Age Class 0-10dummy variable taking value 1 if the firm is less
than 10 years oldx
Age Class 10-20dummy variable taking value 1 if the firm is more
than 10 and less than 20 years oldx
R&Dintensity R&D expenses-to-turnover ratio during 2007-2009 x +
Grade_employees share of employees devoted to R&D activities x x +
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D_Outsourcing
dummy indicator taking value 1 if the firmpurchases her inputs and services from
subcontractors via an outsourcing agreement in2007-2009
x +
D_ICT dummy indicator taking value 1 if the firm has abroadband connection and use specific softwarefor managing the sales/purchases network
x +
D_IPR
dummy variable taking value 1 if the firm adoptedsome type of protection of intellectual property
right (patent, trademark, industrial design orcopyright).
x +
D_Groupdummy variable taking value 1 if the firm is part of
a groupx +
D_Familydummy variable taking value 1 if the firm is owned
by a familyx
Export share of turnover sold abroad in 2007-2009 x
D-Foreigndummy variable taking value 1 if the firm has
mainly foreign competitorsx
D_Southdummy variable taking value 1 if the firm is
located in the South of Italy (Sardegna, Sicilia,Campania, Calabria, Abruzzo, Basilicata, Molise)
x x -
Table 2 Descriptive statistics of the dependent, explanatory and sorting variables
Mean Std. Dev. Min Max
Dependent variables:
D_ExtR&D 0.13 0.34 0 1
D_R&D 0.54 0.50 0 1
Explanatory varibles:
Size 3.30 0.64 2.30 5.51
Age 28.78 19.50 0 159
Bank 15.04 29.43 0 100
R&Dintensity 3.97 7.46 0 100
Grade_employees 6.54 10.37 0 100
D_Outsourcing 0.66 0.47 0 1
D_ICT 0.81 0.39 0 1
D_IPR 0.22 0.42 0 1
D_Group 0.14 0.35 0 1
D_Family 0.76 0.43 0 1
Export 22.95 28.10 0 100
D_Foreign 0.47 0.50 0 1
D_South 0.14 0.35 0 1
Sorting variable:
Lending relations 4.04 2.57 1 30
High intensity subgroup: 2.32 0.69 1 3
Low intensity subgroup: 5.78 2.60 4 30
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Table 3 Estimation results of model (1) and (2), total sample
Total sample
(2549 obs.)
Outcome equation Selection equation
dep. var.: D_ExtR&D dep. var.:D_R&D
Constant -1.037 ** -1.542***
0.411 0.164
Size -0.051 0.357***
0.081 0.044
Age class 0-10 0.020
0.084
Age class 10-20 -0.025
0.064
Bank 0.002 **
0.001
ReDIntensity 0.001
0.004
Skilled_Employees 0.007 * 0.019***
0.004 0.003
D_Outsourcing 0.167 *
0.089
D_ICT 0.052
0.106
D_IPR 0.165 **
0.080
D_GROUP 0.353 ***
0.102
D_South -0.106 -0.232***
0.130 0.079
D_Family 0.111*
0.062
Export 0.006***
0.001
D_Foreign 0.302***
0.062
htech 0.021 0.439***
0.168 0.138
mhtech -0.172 0.277***
0.110 0.073
mltech -0.054 -0.141***
0.093 0.060
Wald test 37.240***
LR test rho=0 1.670
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Table 4 Estimation results of model (1) and (2), High intensity and Low intensity subsamples
Low intensity subsabpe High intensity subgroup
(1254 obs.) (1295 obs.)
Outcome equation Selection equation Outcome equation Selection equation
dep. var.: D_ExtR&D dep. var.:D_R&D dep. var.: D_ExtR&D dep. var.:D_R&D
Constant -1.281 ** -1.296*** -1.338*** -1.313***
0.506 0.235 0.514 0.246
Size 0.001 0.372*** -0.007 0.197***
0.113 0.062 0.113 0.069
Age class 0-10 -0.156 0.170
0.126 0.110
Age class 10-20 -0.152* 0.084
0.094 0.088
Bank 0.002 ** 0.002
0.001 0.002
ReDIntensity 0.000 0.000
0.005 0.006 0.016***
ReDEmployees 0.013 ** 0.024*** 0.004 0.004
0.005 0.005 0.006
D_Outsourcing 0.120 0.224*
0.117 0.128
D_ICT -0.066 0.291*
0.130 0.173
D_IPR 0.098 0.273**
0.099 0.123
D_GROUP 0.155 0.626***
0.129 -0.472*** 0.162
D_South -0.129 0.125 -0.183 -0.049
0.198 0.170 0.104
D_Family 0.135 0.074
0.092 0.088
Export 0.003 * 0.007***
0.002 0.002
D_Foreign 0.209** 0.379***
0.088 0.088
htech 0.252 0.548** -0.305 0.438**
0.219 0.219 0.261 0.184
mhtech 0.144 0.351*** -0.610*** 0.268***
0.129 0.108 0.188 0.101
mltech -0.015 -0.212** -0.192 -0.056
0.127 0.087 0.135 0.085
Wald test 22.530** 37.240 ***
LR test rho=0 1.330 1.670
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Table 5 Correlation matrix among explanatory variables
Size 1.000
Age class 0-10 -0.069 * 1.000
Age class 10-20 -0.099 * -0.197 * 1.000
D_Family -0.094 * -0.024 -0.026 1.000
Export 0.213 * -0.036 -0.069 * -0.004 1.000
D_Foreign 0.179 * -0.069 * -0.055 * 0.010 0.553 * 1.000
Bank 0.035 -0.004 -0.029 -0.002 0.030 0.012 1.000
R&Dintensity 0.028 0.033 -0.009 -0.009 0.178 * 0.116 * 0.021 1.000
Grade_employees 0.086 * 0.032 -0.033 -0.069 * 0.149 * 0.104 * 0.025 0.199 * 1.000
D_Outsourcing 0.143 * -0.013 -0.004 0.010 0.157 * 0.181 * 0.071 * 0.076 * 0.099 * 1 .000
D_ICT 0.089 * 0.015 0.015 0.061 * 0.115 * 0.099 * -0.025 0.078 * 0.062 * 0.106 * 1.000
D_IPR 0.184 * -0.037 * -0.019 0.006 0.224 * 0.179 * 0.060 * 0.141 * 0.145 * 0.137 * 0.059 * 1.000
D_Group 0.285 * 0.058 * -0.006 -0.227 * 0.091 * 0.054 * -0.046 0.056 * 0.212 * 0.077 * 0.074 * 0.023 1.000
D_South -0.042 * 0.081 * 0.103 * 0.010 -0.155 * -0.141 * -0.045 -0.049 * 0.058 * -0.044 * -0.061 * 0.009 -0.007
htech 0.040 * 0.010 0.010 -0.071 * 0.008 -0.033 -0.074 * 0.170 * 0.249 * 0.002 0.057 * 0.060 * 0.121
mhtech 0.051 * -0.014 -0.016 -0.051 * 0.205 * 0.152 * 0.052 * 0.086 * 0.116 * 0.059 * 0.038 * 0.061 * 0.065mltech 0.014 0.006 0.014 0.039 * -0.150 * -0.095 * -0.029 -0.087 * -0.150 * -0.059 * -0.012 -0.103 * -0.025
D_GrouGrade_em
ployees
D_Outso
urcing
D_ICT D_IPRExport D_Foreign Bank R&Dintens
ity
Size Age class
0-10
Age class
10-20
D_Family
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