D'Alfonso Giannangeli

8/13/2019 D'Alfonso Giannangeli

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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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1WORKING PAPER SERIES N. 28 - MAY 2012

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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financial services firms, Journal of Small Business Management, 43, 4, pp. 432452.

Veugelers, R., 1997, Internal R&D expenditures and external technology sourcing, Research Policy,

26, pp. 303-315.

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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