First draft: June 29th 2007
This version: August 24th 2009

Determinants of R&D Cooperationin Small and Medium-Sized Enterprises

Hyunbae Chun* and Sung-Bae Mun**

Abstract
We investigate the determinants of R&D cooperation in small and medium-sized enterprises(SMEs). Using firm-level data from the 2002 Korean Innovation Survey and applying a
probit model with sample selection, we find that incoming spillovers of knowledge have asignificant and positive impact on SMEs’ decisions to engage in cooperative R&D. In
particular, the effect of knowledge spillovers on R&D cooperation is much larger for smallerfirms. Despite the importance of external knowledge for SMEs, the estimation results suggestthat SMEs may have a disadvantage in establishing external R&D linkages because of theirabsolute size limitations.
JEL Classification Numbers: L20, O32
Keywords: R&D Cooperation, Small and Medium-Sized Enterprises, Spillovers_____________________________*

Department of Economics, Sogang University, Seoul, 121-742 Korea. Tel: +82-2-705-8515. Fax: +82-2-7048599. E-mail: hchun@sogang.ac.kr. Corresponding author.
**
Korea Information Society Development Institute, Kwachun, Kyunggi-do, 427-710 Korea. Tel: +82-2-5704071. Fax: +82-2-570-4169. E-mail: mun@kisdi.re.kr.
We thank Jungsoo Park and participants in the Western Economic Association Conference for their helpfulcomments. We also thank the Science and Technology Policy Institute for providing the 2002 Korean
Innovation Survey dataset. Chun acknowledges financial support from the 2007 Sogang University Research
Fund.

1

1. Introduction
Most small and medium-sized enterprises (SMEs) have few formal R&D activities anddepend heavily on external sources of knowledge (Acs, Audretsch, and Feldman, 1994). Thus,
R&D cooperation with other firms, research institutions, or universities can be a viable way
for SMEs to obtain new knowledge. In undertaking cooperative R&D activities, SMEs may
have behavioral advantages over large firms; namely, SMEs might be able to respond more
rapidly to external threats or opportunities and may have more efficient internal
communication.1 However, possible disadvantages for SMEs include insufficient resources
for organizing successful R&D collaboration and the lack of absorptive capacity in gaining
benefits from knowledge spillovers.
Using data from the 2002 Korean Innovation Survey (KIS), we examine thedeterminants of R&D cooperation in SMEs.2 As determinants of cooperative R&D activities,
we consider variables related to information flow (e.g., incoming knowledge spillovers andappropriability) and other factors (e.g., cost, risk, and technological complementarity). In
particular, we focus on the differential effects of incoming knowledge spillovers andappropriability in R&D cooperation according to firm size. We also investigate whether
SMEs have a disadvantage in R&D cooperation because of their absolute size limitations andlack of absorptive capacity.
We find that incoming spillovers have a significant and positive impact on SMEs’ R&D
cooperation. We also find that incoming spillovers have a larger effect on smaller firms,
which suggests that obtaining external knowledge is a key determinant of SMEs’ R&D

1

For example, Link and Rees (1990) show that small firms utilizing university-based research relationships are
as a result more efficient in their internal R&D because they avoid bureaucratic inefficiencies.
2
See Acs and Audretsch (1990) for an overview of factors affecting innovation activities in SMEs. Using the2002 KIS and the CIS2, Mun and Chun (2006) and Vaona and Pianta (2008) examine the relationship between
firm size and innovation activities for Korean and European manufacturing firms, respectively.

2

cooperation. However, neither strategic protection nor industry-level legal protection isrelated to cooperative R&D. Among the traditionally considered factors, both risk andtechnological complementarity are significantly and positively related to SMEs’ R&D
cooperation, but sharing costs is not.
The estimation results for different types of partners suggest that incoming spillovers
stimulate both cooperation with suppliers and customers (vertical cooperation) andcooperation with research institutions and universities (institutional cooperation). Incoming
spillovers have a larger marginal effect on the probability of cooperation with research
institutions and universities than with suppliers and customers. Risk sharing is particularly
important for vertical R&D cooperation with suppliers and customers while seeking
complementary knowledge is a key determinant for institutional R&D cooperation.
Correcting the sample selection bias is econometrically crucial in investigating SMEs’
cooperative R&D activities. Similar to the Community Innovation Survey (CIS), the KIS
questionnaires on R&D cooperation are restricted to innovative firms. 3 Excluding noninnovative SMEs, which make up more than half of the total number of SMEs, may lead tosample selection bias if participation in cooperative R&D is correlated with SMEs’ decisions
to innovate. To address this bias due to simultaneous decision, we apply a bivariate probit
model with sample selection. Estimation results using the sample selection model suggestthat SMEs may have a disadvantage in establishing external R&D linkages because of theirabsolute size limitations, while the results of the simple probit model do not.
The remainder of this paper is organized as follows. Section 2 provides a brief review ofthe literature on the determinants of cooperative R&D. Section 3 describes the 2002 KIS
3Cassiman and Veugelers (2002) and many others analyze only the subsample of innovative firms. If the two
probit equations of innovation activity and R&D cooperation are separately estimated, the estimated coefficients
could be biased unless the error terms are uncorrelated. To address this problem, Cassiman and Veugelers(2002) and others use instrumental variables in their studies.

3

dataset and the construction of variables. Section 4 presents empirical specification dealing
with the simultaneity problem. Section 5 discusses the empirical results, and Section 6presents our conclusions.

2. Related Literature on R&D Cooperation
The growing phenomenon of R&D cooperation has attracted both theoretical and empiricalstudies to identify the determinants of R&D cooperation. Here we briefly discuss the motives
behind cooperative R&D examined in the previous studies.
The industrial organization literature has pointed to technological spillovers as one of theimportant factors influencing firms’ incentives to engage in cooperative R&D (Katz, 1986;
d’Aspremont and Jacquemin, 1988; Kamien, Muller, and Zang, 1992). R&D spillovers arise
when new knowledge created by one firm is also beneficial to other firms. Theoretical studies
suggest that a high level of R&D spillovers can increase firms’ probability of internalizing
the spillovers by participating in R&D cooperation.
The relationship between spillovers and R&D cooperation has also been analyzed using
empirical studies. Cassiman and Veugelers (2002) measure firm-specific incoming spillovers
in terms of the relative importance of publicly available information to the innovation process
and estimate their impact on R&D cooperation.4 They find that higher incoming spillovers
increase the probability of cooperating with research institutions and universities but have noeffect on cooperation with customers or suppliers. In a similar vein, Branstetter andSakakibara (2002) examine the research productivity of a large number of Japanese firms
participating in research consortia and show that research consortia outcomes as measured by
the number of patents are positively associated with the level of potential R&D spillovers.

4

López (2008) also find that spillovers positively affect the probability of R&D cooperation
with research institutions for Spanish manufacturing firms. Belderbos et al. (2004)
distinguish the effects of spillovers from other sources on various types of cooperation: withcompetitors, with suppliers or customers, and with universities or research institutions. They
report that spillovers from one type of R&D partner have a more significant impact on theprobability of R&D cooperation with the same type of partner. They also find that knowledge
spillovers from universities and research institutions facilitate all types of R&D cooperation.
Although empirical studies on the relationship between spillovers and R&D cooperation
are expanding, there are few analyses focusing specifically on SMEs. It is critical for SMEs
to use external knowledge to produce innovative products. Audretsch and Vivarelli (1996)
show that not only are knowledge spillovers important for small firms to be able to innovate,
but they are much more important for small firms than for larger firms. Thus, it is to be
expected that R&D spillovers will positively influence SMEs’ decisions on R&D
collaboration.
The degree of spillovers from external knowledge depends heavily on a firm’s ability tofind, access, and exploit new information, or so-called absorptive capacity. According toCohen and Levinthal (1989), absorptive capacity can be enhanced by investments by firms intheir own innovative activities. 5 Therefore, increased internal R&D efforts can result ingreater incentives for firms to engage in cooperative R&D.
Because data on R&D spending are not often available at the firm-level, many empiricalstudies have used other R&D-related measures to gauge absorptive capacity. For example,
using the number of R&D employees to capture firms’ absorptive capacity, Fritsch and Lukas
4

In addition to R&D cooperation as inter-firm or inter-organization contacts, Simonen and McCann (2008)
examine face-to-face knowledge spillovers from labor acquisition.
5
Human resource and knowledge management are also crucial factors determining firms’ absorptive capacity(Schmidt, 2005).

5

(2001) show that firms with higher shares of R&D employees are more likely to be engaged
in cooperative R&D. Belderbos et al. (2004) include the share of R&D personnel and its
square term in probit regressions. Their findings suggest that R&D intensity is positively
related to the probability of R&D cooperation but that its marginal impact is decreasing.
Tether (2002) and Miotti and Sachwald (2003) use dummy variables for whether or not a
firm conducts R&D on a continuous basis. Their results suggest that firms that undertakeR&D on a continuous basis are more likely to be involved in cooperative arrangements.
Firms’ ability to control the outflows of knowledge can also have a distinct impact ondecisions regarding cooperative R&D (Cassiman and Veugelers, 2002). If firms are not able
to protect their knowledge successfully, the possibility of free-riding within and outside
cooperative agreements increases (Kesteloot and Veugelers, 1995; Greenlee and Cassiman,
1999). Cassiman and Veugelers (2002) show that higher appropriability, as measured by theeffectiveness of strategic protection of product and process such as secrecy, increases theprobability of cooperating with suppliers or customers but has no significant impact oncooperation with research institutions. In contrast, López (2008) find that the effectiveness ofstrategic protection has a positive impact on cooperation with any type of partner.
The management literature often emphasizes firms’ resource constraints as determinantsof R&D collaboration (Hagedoorn, Link, and Vonortas, 2000). The resource-based
perspective suggests that firms utilize R&D partnerships for accessing complementaryknowledge, pooling risks, or sharing costs (Hagedoorn, 1993; Sakakibara, 1997). However,
the empirical results on the effects of these factors on R&D cooperation are mixed. Miotti
and Sachwald (2003) find that a lack of technological information within firms does not havean impact on the probability of R&D cooperation. However, Cassiman and Veugelers (2002)
report that the probability of cooperation is higher for firms with more technological know-

6how. They argue that a higher availability of technological knowledge implies that firms havea wider scope of innovation activities for potential cooperation.
In addition, the results of empirical studies on the hypothesis of cost and risk sharing as
motivations for cooperative R&D do not point in the same direction. Bayona, García-Marco,and Huerta (2001) show that both risks and costs of innovation are significant determinants ofcooperation. In contrast, Miotti and Sachwald (2003) find that neither influences theprobability of cooperation. Cassiman and Veugelers (2002) show that sharing costs is an
important factor affecting the decision to cooperate, whereas reducing risks is not.
Distinguishing R&D cooperation by type of partner, Belderbos et al. (2004) find that risk
constraints positively affect the probability of cooperation with rivals or suppliers, while cost
sharing is only relevant for the decision of cooperating with research institutions.

3. Data
Following the Oslo Manual (OECD and Eurostat, 1997), the Science and Technology Policy
Institute in Korea started the KIS for manufacturing firms in 1997 and continued the survey
in 2000, 2002, and 2005.6 The first two waves are experimental, and data are not available forpublic use. The 2002 KIS includes both privately held firms and corporations, while the 2005
KIS includes only corporations. Since we focus on the cooperative R&D activities in SMEs
consisting mainly of privately-held firms, we choose to use the 2002 KIS rather than the 2005
KIS. Data used in this study contains information about the innovation activities of 3,775
manufacturing firms with at least 10 employees.
To consider simultaneous decisions regarding innovation activity and R&D cooperation,
we construct two dependent variables. The dummy variable of innovation activity is set equal7to 1 if firms are engaged in either product or process innovation activities during the period of
2001–2002. Non-technological innovations such as changes in marketing strategy,organization, or product design are excluded. Product innovation includes both new andimproved products. Moreover, firms are defined as innovative even if their innovation
activities are not successful. Among innovative firms, the R&D cooperation dummy variable
is set to 1 if firms are engaged in at least one cooperative R&D activity with any type ofpartner; otherwise, the variable is set equal to 0. Types of partners include suppliers,
customers, private and public research institutions, and universities.7
Following Cassiman and Veugelers (2002), we consider incoming spillovers to measure
the importance of publicly available information to a firm’s innovation process. Publicly
available information has three types of sources: patent information; specialist conferences,meetings, and publications; and trade shows and seminars. The appropriability variablemeasures the effectiveness of legal and strategic methods for protecting new products andprocesses. We use strategic protection through secrecy, complexity, or lead time as a firmlevel measure for appropriability but define legal protection as that provided by patents,
brand names, or copyrights. The legal protection variable is calculated at the three-digit
industry-level. We also consider other motives for cooperative R&D, such as sharing cost andrisk as well as accessing complementary knowledge. The variables of incoming spillovers,
appropriability, legal protection, cost, risk, and complementarity are defined only forinnovative firms. These variables are classified from 1 (unimportant) to 5 (crucial) and arerescaled from 0 (unimportant) to 1 (crucial).

6

The OECD Frascati Manual focuses on measuring R&D expenditures (input), whereas the Oslo Manual
measures innovation activities (output).
7
We exclude R&D cooperation within a business group (so-called Chaebol). Moreover, we use a businessgroup dummy variable to estimate the determinants of R&D cooperation to control for possibly different
behavior of firms affiliated with the business group.

8Firms continuously involved in R&D can have higher absorptive capacity and thus
obtain more benefits from R&D cooperation. The permanent R&D variable measurescontinuous R&D activity and is set to 1 if a firm has a permanent research department.8
To measure firm size, we construct three dummy variables according to the number of
employees (10–49, 50–99, and 100–299).9 Because firm size can be affected by innovation
activities, we use the number of employees in 1999. Firm size dummy variables enable us toinvestigate the nonlinear effect of firm size on the probability of innovation activity and R&D
cooperation. Other variables are also considered as determinants of R&D cooperation and/or
innovation activity. The export dummy variable is set equal to 1 if a firm has exports in 1999.
The big city variable is a dummy variable set equal to 1 if a firm is located in one of theseven largest cities (including Seoul) in Korea. The three-firm concentration ratio is the sumof the sales of the largest three firms at the four-digit level market.10 The business groupvariable is set equal to 1 if a firm belongs to a domestic or foreign business group and is 0
otherwise.
Table 1 presents descriptive statistics for 2,190 sample firms consisting of 1,147 noninnovative firms (52.4%) and 1,043 innovative firms (47.6%). Among the innovative firms,
approximately 40.7% are engaged in at least one cooperative R&D activity with any type ofpartner. About 34.1% of firms engage in R&D cooperation with suppliers and customers,
whereas about 29.7% of firms cooperate with research institutions and universities. Thus,
about 23.1% of firms have R&D cooperation with both types of partners.

8

Other proxies for permanent R&D may include continuous R&D expenditures and the number of full-time
R&D workers. However, these variables are not available for a substantial number of firms in the sample.
9Under the Framework Act on SMEs in Korea, a company with fewer than 300 employees is classified as an
SME.
10
To obtain the four-digit level three-firm concentration ratio, we aggregate the five-digit level ratio publishedby the Korea Development Institute using the weights of the industry sales.

9[Table 1 about here]

As expected, average firm size is bigger among innovative firms than non-innovative
firms, which indicates possible sample selectivity according to firm size. For example, only
about 40% of firms with fewer than 50 employees have their own innovation activities, whilemore than 65% of firms with more than 100 employees are involved in such activities. If firm
size is associated with a firm’s own innovative activity as well as cooperative R&D activity,
the disproportionate inclusion of innovative and bigger firms could bias the estimated effectof determinants of R&D cooperation.
As discussed earlier, some variables are defined only for innovative firms, such as R&D
cooperation, incoming spillovers, appropriability, cost, risk, and complementarity. However,firm size and other variables are defined for all firms. For example, the average of thepermanent R&D variable is significantly different for innovative and non-innovative firms.
Because the activities of innovation and R&D cooperation can be correlated with each other,an effect of variables such as permanent R&D, which is related to innovative activity, cannot
be consistently estimated in an equation determining R&D cooperation.

4. Empirical Specification
We use a bivariate probit model with sample selection. In the model, R&D cooperation ( Y2 )can be observed only when another variable of innovation activity ( Y1 ) in the selection
equation is equal to 1, given as follows:10⎧ 1 if X 2′i β 2 + u2i > 0
Y2i = ⎨
⎩ 0 otherwise(1)⎧ 1 if X 1′i β1 + u1i > 0
Y1i = ⎨
⎩ 0 otherwise

where Y2i is observed only if Y1i = 1 , while Y1i is always observed; and u1i and u2i have astandard bivariate normal distribution with the correlation coefficient, ρ = corr (u1i , u2i ) .
Constructing the log-likelihood function, we have three types of observations in thesample with unconditional probabilities: Innovation and Cooperation (Y2=1, Y1=1),
Innovation and Non-cooperation (Y2=0, Y1=1), and Non-innovation (Y1=0):L=∑ ln[Φ ( X ′ β , X ′ β )]y1 =1, y2 =1

+

1i

1

2i

2

∑ ln[Φ (X ′ β , − X ′ β )]y1 =1, y2 = 0

+

2

2

1i

1

2i

2

(2)

∑ ln[1 − Φ(X ′ β )]y1 = 01i1where Φ and Φ 2 denote the univariate and bivariate normal cumulative distributionfunctions, respectively. Equation (2) is maximized with respect to the parameters of β1 , β 2 ,and ρ using the full information maximum likelihood method. If the error terms in the two
equations are correlated ( ρ ≠ 0 ), a standard probit model using the subsample of innovative
firms yields biased results.
In the estimation, we include two-digit level industry dummy variables to correct forpossible endogeneity associated with industry-specific factors. 11 This assumes that the
proportions of innovative firms, R&D cooperation, and other variables are substantially11different across industries. In a similar vein, Cassiman and Veugelers (2002) and López(2008) use the industry averages of such variables as incoming spillovers and appropriability
to control for endogeneity in explanatory variables in R&D cooperation equations.

5. Estimation Results
Main Results
Table 2 reports the estimation results for both the (simple) probit model and the bivariate
probit model with sample selection.12 The correlation coefficient between the residuals fromthe two equations in the bivariate probit model is significantly positive, which suggests a
possible complementarity between a firm’s own innovation activity and R&D cooperation.13[Table 2 about here]

Results show that incoming spillovers have a significant and positive effect on theprobability of firms’ R&D cooperation in both the simple probit and bivariate probit withsample selection models. The positive effect of incoming spillovers on R&D cooperation isconsistent with the findings of Cassiman and Veugelers (2002) using Belgian manufacturingfirms and those of Lee and Choe (2006) using Korean manufacturing firms. Moreover, ourresults with a positive correlation coefficient also suggest an overestimated spillover effect in
11

Korean standard industry classification codes include 21 two-digit level manufacturing industries.
The bivariate probit model corrects for the endogeneity problem and also provides more accurate estimates
through the inclusion of non-innovative firms. In fact, the total sample size (2,190) is about twice as large as thenumber of innovative firms (1,043).
13
However, in a study of cooperative R&D behavior in Italian manufacturing firms by Piga and Vivarelli (2004),
estimation results of the bivariate model with sample selection show that the correlation coefficient is positive
but not statistically significant. The simultaneity issue has also been examined in other studies on cooperative
R&D activities. Using the multivariate probit model, Belderbos et al. (2004) show the complementarity(positive correlations) between types of R&D cooperation partners such as competitors, customers, andsuppliers. In contrast, Piga and Vivarelli (2004) use a probit model with sample selection for a firm’s decisions
1212

the simple probit model due to sample selection. Consistently, both the coefficient estimate
and marginal effect is smaller in the latter than in the former. This clearly indicates thepossible endogeneity in the probit model associated with the inclusion of only innovative
firms. For example, firms are more likely to innovate when they expect higher benefits ofincoming spillovers through R&D cooperation.
In contrast to incoming spillovers, neither appropriability nor legal protection isassociated with firms’ R&D cooperation. Higher appropriability through strategic protection
can have a positive effect on R&D cooperation because firms can control the outflow ofcommercially sensitive information. However, small firms often do not possess enoughresources and market power to protect their technological knowledge (Arundel, 2001).
Moreover, SMEs tend to have less bargaining power in cooperative agreements with large
firms, which makes it difficult for SMEs to appropriate benefits from cooperative R&D. Our
results suggest that the appropriability measured by the effectiveness of strategic protection is
not relevant in SMEs’ decisions on R&D collaboration. Legal protection has a negative sign
but is not statistically significant. This variable is also not significant in Cassiman and
Veugelers’s (2002) findings but is negative and significant in the study by López (2008) only
for cooperation with research institutions.14
Table 2 shows that sharing risk and technological complementarity seem to havepositive effects on cooperation, while sharing cost does not. The marginal effects of the risk
and technological complementarity variables in the bivariate probit model are 0.103 and0.106, which is close to the marginal effect of incoming spillovers on R&D cooperation. The
result indicates that not only obtaining complementary technology and knowledge throughregarding internal (making) and external (buying) R&D and find a negative correlation, suggesting
substitutability between internal and external R&D.
14
The negative effect found in the study by López (2008) suggests that stronger legal protection may hamper
the internalization of knowledge flows and therefore decrease the probability of R&D cooperation.

13

incoming spillovers but also sharing risk are key determinants of SMEs’ cooperative R&D
activities. Although cost is an important barrier impeding SMEs’ innovation activities, it does
not seem to be a critical factor influencing cooperation decisions. Collaborative R&D can bebeneficial for both small and large firms because it enables firms to overcome theirdisadvantages in innovation (Rothwell and Dodgson, 1991). However, engaging incooperative R&D is a cautious decision because SMEs undertake fewer R&D projects, which
are the core of the firms’ activity. Our results suggest that SMEs do not participate incooperative R&D simply for the purpose of sharing the cost of innovation.
As pointed out in the study of Cohen and Levinthal (1990), a firm’s R&D has the dualrole of generating new knowledge and enhancing absorptive capacity. The estimation result
suggests that SMEs with more absorptive capacity as measured by the continuity of R&D
activities are more likely to involve R&D cooperation. The absolute size of firms turns out tobe an important determinant of cooperative R&D. The firm size dummy variables arestrongly significant in the sample selection model but are not significant in the simple probit
model.15 Because R&D cooperation is defined only for innovative firms, which are generally
larger in size, the sample selection bias from more observations of bigger firms can mitigate
the firm size effect on R&D cooperation. Correcting for sample selection, we find that small
firms have a disadvantage in establishing external R&D cooperation because of size
limitations, which may be associated with SMEs’ lack of human resources, funding for longterm R&D, and risk spreading (Rothwell and Dodgson, 1991).
Table 3 presents the differential effects of spillovers and appropriability according tofirm size tertile (10–49, 50–99, and 100–299). The importance of incoming spillovers as
15

Instead of the firm size dummy variable, our models include the firm size variable (the number of employees
in hundreds) together with the size-squared variable. The size variable is not significant at the 10% level in thesimple probit model but is highly significant in the selection model. Firm size is reported to be significantly

14

measured by marginal effect decreases as the firm size becomes larger. In particular, we do
not find a statistically significant effect of incoming spillovers on R&D cooperation for firms
with more than 100 employees. The higher spillover effect indicates greater learning throughR&D cooperation, and thus potentially higher marginal profit from cooperation is expected
for smaller firms than for larger firms. Again, knowledge flow is a crucial factor determining
cooperative R&D agreements. This knowledge flow is much more important for smallerfirms’ decisions to engage in R&D cooperation, which is consistent with the findings ofprevious studies that knowledge spillovers through R&D cooperation are a key determinant
for SMEs’ decisions on innovation activities.16[Table 3 about here]

Results for Different Types of Partners
Table 4 shows the estimated results for R&D cooperation with different types of partners. We
find positive and significant effects of incoming spillovers on cooperation with suppliers andcustomers as well as with research institutions and universities. The marginal effect is almost
two times greater for cooperating with research institutions and universities than with
suppliers and customers. Our finding is also consistent with those of Cassiman and Veugelers(2002) and López (2008), who report that incoming spillovers have a significantly positiveeffect on cooperation with research institutions but not on cooperation with suppliers andcustomers or competitors. In a similar vein, Oh (2006) find that Korean firms prefer research
institutions to other types of firms as their R&D partners because they expect greater

positive in Cassiman and Veugelers (2002) for Belgium and in Lee and Choe (2006) for Korea, but not in
Abramovsky et al. (2005) for France, Germany, and the U.K.
16
Consistently, Chun, Mun, and Yoon (2007) find that incoming spillovers constitute a significant factor in
determining cooperative R&D for firms in high-tech industries, but not in low-tech industries.

15

knowledge spillovers from research institutions. 17 Appropriability as measured by theeffectiveness of strategic protection shows a significant and positive sign for cooperation withresearch institutions or universities but not for vertical cooperation. This indicates that firmsthat are good at controlling the outflow of commercially sensitive knowledge are more likely
to engage in cooperative R&D with research institutions and universities.

[Table 4 about here]

In addition to spillovers and protection of knowledge, risk sharing is an importantfactor in cooperating with suppliers and customers, although its effect on cooperation withresearch institutions is smaller and marginally significant. Technological complementarity is
positive and significant only for cooperating with research institutions, not with other firms.
Firms sharing complementary technologies may compete in the market, which may weaken
incentives for R&D collaboration among those firms. In this respect, Branstetter andSakakibara (2002) show that research consortia outcomes are negatively associated with thedegree of product market competition among consortia members. The marginal effect ofincoming spillovers in cooperation with research institutions and universities is the largest of
all variables, whereas the effect in vertical cooperation is closer to those of risk and othervariables.
Overall, the results in Table 4 suggest that obtaining and protecting new knowledge isa key factor in determining R&D cooperation of SMEs, especially with research institutions

17

In addition, Oh (2006) emphasizes trust among partners as an important factor determining the performanceof R&D collaboration and shows that the free-riding problem is significantly reduced as firms’ amount ofinvestment in a project increases.

16

and universities, while strategic business decisions such as sharing risk or obtaining andprotecting new knowledge can be important drivers of vertical R&D cooperation.
Table 5 reports the differential effects of spillovers and appropriability according tofirm size tertile (10–49, 50–99, and 100–299) for two types of partners. R&D cooperation
effects of incoming spillovers for both types of partners are negatively related to firm size.
For each cross-product of size tertile and spillovers, the effect of spillovers is greater incooperation with research institutions than in vertical cooperation. The findings are consistent
with the results shown in Tables 3 and 4 and highlight the fact that incoming spillovers andappropriability are crucial factors in determining R&D cooperation for smaller firms,
especially cooperation with research institutions and universities.

[Table 5 about here]

6. Conclusion
In this paper, we show that incoming spillovers, absorptive capacity, risk, and technologicalcomplementary are important determinants of Korean SMEs’ decisions regarding R&D
cooperation. Moreover, incoming flow of knowledge is a key factor for smaller firms’
cooperation, particularly their cooperation with research institutions and universities rather
than with other firms.
We also find that SMEs face size limitations in R&D cooperation. However, this does
not imply that all SMEs have a disadvantage in R&D cooperation. For example, SMEs intechnology-intensive industries may have a comparatively greater advantage in R&D
cooperation than bigger firms because of their flexibility and ability to respond rapidly.

17Identifying the sources of SMEs’ size-related barriers to collaboration can help clarify
their external knowledge linkages. These sources may be related not only to firm size itself
but also to qualitative aspects of the SMEs such as their human capital and internalorganization. Future data on these variables as collected by the KIS can help researchers tofurther analyze and understand such relationships.

18

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20

Table 1. Descriptive Statistics
Total

Non-innovative
firms

Innovative
firms

N/A

N/A

Cooperation with suppliers and customers

N/A

N/A

Cooperation with research institutions and universities

N/A

N/A

0.407
(0.492)0.341(0.474)0.297(0.457)

N/A

N/A

Appropriability

N/A

N/A

Industry-level legal protection

N/A

N/A

Cost

N/A

N/A

Risk

N/A

N/A

Complementarity

N/A

N/A

Permanent R&D

0.423(0.494)0.065(0.057)0.146(0.353)0.053(0.047)0.454(0.288)0.447(0.288)0.449(0.127)0.575(0.220)0.595(0.259)0.321(0.186)0.729(0.445)0.078(0.064)0.540(0.498)0.253(0.435)0.207(0.405)0.623(0.485)0.240(0.427)0.137(0.344)0.449(0.498)0.267(0.442)0.285(0.452)0.422(0.494)0.432(0.495)0.374(0.224)0.057(0.231)
2,190

0.441
(0.497)0.321(0.467)0.356(0.223)0.049(0.216)
1,147

0.401
(0.490)0.553(0.497)0.393(0.224)0.065(0.247)
1,043

R&D cooperation
Cooperation

Spillovers and other factors
Incoming spillovers

Firm size (Number of employees in hundreds)
Firm size dummySize < 50
50 ≤ Size < 100
100 ≤ Size < 300
Other variables
Big city
ExportThree-firm concentration ratio
Business group
Sample size
Note: Numbers in parentheses are standard deviations.

21

Table 2. Estimation Results for R&D Cooperation

Incoming spillovers
Appropriability
Industry-level legal protection
Cost
Risk
Complementarity
Permanent R&D
Firm size (50 ≤ Size < 100)
Firm size (100 ≤ Size < 300)
Big city
ExportProbit model
Cooperation Eq.
Marginal
Coefficienteffect0.538*** 0.208***(0.158)(0.061)
0.114
0.044(0.153)(0.059)0.431
0.167(0.431)(0.166)–0.2590.100(0.255)(0.099)0.589*** 0.228***(0.217)(0.084)0.590**
0.228**(0.250)(0.097)0.385*** 0.144***(0.103)(0.037)0.067
0.026(0.101)(0.039)0.119
0.046(0.103)(0.040)–0.100–0.039(0.085)(0.032)0.143*
0.055*(0.086)(0.033)Three-firm concentration ratio
Business groupLR test of Rho = 0:
Rho [Chi-squared(1)]Log-likelihood
Sample size

–664.5
1,043

Sample selection model
Cooperation Eq.
Innovation Eq.
Marginal
Coefficient
Coefficienteffect0.400***
0.096***(0.131)(0.035)0.085
0.020(0.114)(0.027)–0.264–0.063(0.334)(0.081)–0.179–0.043(0.193)(0.047)0.429**
0.103**(0.179)(0.046)0.444**
0.106**(0.202)(0.051)0.291***
0.071***(0.087)(0.023)0.184**
0.046**
0.281***(0.085)(0.022)(0.068)0.381***
0.101***
0.618***(0.095)(0.026)(0.078)–0.099–0.024–0.055(0.070)(0.017)(0.059)0.353***
0.087***
0.461***(0.078)(0.018)(0.061)0.451***(0.169)–0.137(0.112)0.880*[2.97]–1,993.9
2,190

Notes: Numbers in parentheses are standard errors. Marginal effects are calculated at the mean values of theregressors. Cooperation, spillovers, appropriability, legal protection, cost, risk, and complementary variables aredefined only for innovative firms. For permanent R&D, firm size, big city, and export dummy variables,
marginal effects are in discrete changes from 0 to 1. All equations include two-digit level industry dummy
variables.
*
Significant at the 10 percent level**
Significant at the 5 percent level***
Significant at the 1 percent level

22

Table 3. Estimation Results for R&D Cooperation: Firm Size, Incoming Spillovers, andAppropriability

Incoming spillovers
× Firm size (Size < 50)
Incoming spillovers
× Firm size (50 ≤ Size < 100)
Incoming spillovers
× Firm size (100 ≤ Size < 300)
Appropriability
× Firm size (Size < 50)
Appropriability
× Firm size (50 ≤ Size < 100)
Appropriability
× Firm size (100 ≤ Size < 300)
Industry-level legal protection
Cost
Risk
Complementarity
Permanent R&D
Firm size (50 ≤ Size < 100)
Firm size (100 ≤ Size < 300)
Big city
ExportProbit model
Cooperation Eq.
Marginal
Coefficienteffect0.839*** 0.324***(0.231)(0.089)0.610**
0.235**(0.290)(0.112)0.012
0.005(0.288)(0.111)0.267
0.103(0.227)(0.087)–0.377–0.146(0.288)(0.111)0.437
0.169(0.291)(0.112)–0.364–0.141(0.436)(0.168)–0.260–0.100(0.256)(0.099)0.599*** 0.231***(0.219)(0.084)0.657*** 0.254***(0.252)(0.097)0.380*** 0.142***(0.104)(0.037)0.450**
0.176**(0.225)(0.088)0.420*
0.164*(0.229)(0.089)–0.104–0.040(0.085)(0.033)0.157*
0.060*(0.087)(0.033)Three-firm concentration ratio
Business groupLR test of Rho = 0:
Rho [Chi-squared(1)]Log-likelihood
Sample size

–659.8
1,043

Sample selection model
Cooperation Eq.
Innovation Eq.
Marginal
Coefficient
Coefficienteffect0.618***
0.143***(0.181)(0.045)0.432*
0.100*(0.221)(0.053)–0.069–0.016(0.230)(0.053)0.190
0.044(0.163)(0.038)–0.243–0.056(0.217)(0.051)0.324
0.075(0.230)(0.054)–0.192–0.045(0.332)(0.078)–0.176–0.041(0.189)(0.044)0.429**
0.100**(0.174)(0.042)0.486**
0.113**(0.200)(0.048)0.285***
0.068***(0.083)(0.020)0.460***
0.119***
0.281***(0.172)(0.049)(0.068)0.647***
0.179***
0.614***(0.181)(0.055)(0.078)–0.104–0.040–0.055(0.085)(0.033)(0.059)0.157***
0.060***
0.459***(0.087)(0.033)(0.061)
0.461***(0.168)–0.138(0.111)0.899*[3.41]–1,989.0
2,190

Notes: Numbers in parentheses are standard errors. Marginal effects are calculated at the mean values of theregressors. Cooperation, spillovers, appropriability, legal protection, cost, risk, and complementary variables aredefined only for innovative firms. For permanent R&D, firm size, big city, and export dummy variables,
marginal effects are in discrete changes from 0 to 1. All equations include two-digit level industry dummy
variables.
*
Significant at the 10 percent level**
Significant at the 5 percent level***
Significant at the 1 percent level23

Table 4. Estimation Results for R&D Cooperation with Different Partners

Incoming spillovers
Appropriability
Industry-level legal protection
Cost
Risk
Complementarity
Permanent R&D
Firm size (50 ≤ Size < 100)
Firm size (100 ≤ Size < 300)
Big city
ExportThree-firm concentration ratio
Business groupLR test of Rho = 0:
Rho [Chi-squared(1)]Log-likelihood
Sample size

Cooperation
with suppliers and customers
ProbitSample selection
Coop. Eq. Coop. Eq. Innov. Eq.
0.576*** 0.433***(0.162)(0.133)0.177
0.138(0.157)(0.118)–0.515–0.358(0.438)(0.337)–0.143–0.105(0.261)(0.198)0.591*** 0.453**(0.223)(0.178)0.396
0.310(0.256)(0.201)0.329*** 0.256***(0.106)(0.085)0.088
0.197** 0.283***(0.104)(0.086)(0.068)0.145
0.388*** 0.620***(0.105)(0.092)(0.078)–0.106–0.102–0.057(0.086)(0.072)(0.059)0.133
0.338*** 0.464***(0.088)(0.078)(0.060)0.416**(0.169)–0.170(0.114)0.900*[3.19]–631.2–1,960.5
1,043
2,190

Cooperation
with research institutions
ProbitSample selection
Coop. Eq. Coop. Eq. Innov. Eq.
1.090*** 0.846***(0.172)(0.134)0.415**
0.318***(0.165)(0.123)–0.249–0.073(0.450)(0.341)–0.031
0.031(0.277)(0.208)0.394*
0.269(0.236)(0.174)0.559**
0.447**(0.276)(0.208)0.515*** 0.397***(0.115)(0.089)0.017
0.143
0.280***(0.109)(0.089)(0.068)0.072
0.325*** 0.624***(0.109)(0.091)(0.078)–0.014–0.033–0.052(0.091)(0.074)(0.059)0.160*
0.365*** 0.461***(0.093)(0.076)(0.060)0.399**(0.169)–0.142(0.115)0.962**[4.24]–566.8–1,895.6
1,043
2,190

Notes: Numbers in parentheses are standard errors. Cooperation, spillovers, appropriability, legal protection,
cost, risk, and complementary variables are defined only for innovative firms. All equations include two-digit
level industry dummy variables.
*
Significant at the 10 percent level**
Significant at the 5 percent level***
Significant at the 1 percent level24

Table 5. Estimation Results for R&D Cooperation with Different Partners: Firm Size,
Incoming Spillovers, and Appropriability

Incoming spillovers
x Firm size (Size < 50)
Incoming spillovers
x Firm size (50 ≤ Size < 100)
Incoming spillovers
x Firm size (100 ≤ Size < 300)
Appropriability
x Firm size (Size < 50)
Appropriability
x Firm size (50 ≤ Size < 100)
Appropriability
x Firm size (100 ≤ Size < 300)
Industry-level legal protection
Cost
Risk
Complementarity
Permanent R&D
Firm size (50 ≤ Size < 100)
Firm size (100 ≤ Size < 300)
Big city
ExportThree-firm concentration ratio
Business groupLR test of Rho = 0:
Rho [Chi-squared(1)]Log-likelihood
Sample size

Cooperation
with suppliers and customers
ProbitSample selection
Coop. Eq. Coop. Eq. Innov. Eq.
0.781*** 0.586***(0.238)(0.179)0.636** 0.459**(0.296)(0.222)0.236
0.111(0.292)(0.238)0.336
0.251(0.234)(0.169)–0.185–0.120(0.294)(0.215)0.337
0.258(0.295)(0.234)–0.484–0.311(0.442)(0.335)–0.147–0.105(0.261)(0.195)0.601*** 0.456***(0.224)(0.172)0.442*
0.345*(0.258)(0.198)0.322*** 0.250***(0.107)(0.082)0.383
0.422** 0.281***(0.233)(0.179)(0.068)0.395* 0.616*** 0.617***(0.236)(0.191)(0.078)–0.109–0.103–0.057(0.087)(0.072)(0.059)0.145
0.350*** 0.463***(0.089)(0.076)(0.060)0.419**(0.168)–0.172(0.114)0.925*[3.65]–629.0–1958.0
1,043
2,190

Cooperation
with research institutions`
ProbitSample selection
Coop. Eq. Coop. Eq. Innov. Eq.
1.431*** 1.077***(0.261)(0.199)1.172*** 0.877***(0.320)(0.244)0.579*
0.441*(0.303)(0.250)0.555**
0.399**(0.251)(0.183)0.263
0.212(0.311)(0.231)0.443
0.362(0.302)(0.245)–0.190–0.035(0.455)(0.344)–0.0310.020(0.278)(0.208)0.394*
0.277(0.237)(0.177)0.613**
0.481**(0.278)(0.212)0.506*** 0.398***(0.116)(0.090)0.275
0.331
0.278***(0.270)(0.213)(0.068)0.553**
0.675*** 0.617***(0.258)(0.212)(0.078)–0.016–0.032–0.052(0.091)(0.075)(0.059)0.167*
0.366*** 0.462***(0.094)(0.077)(0.060)0.398**(0.171)–0.149(0.115)0.941*[3.39]–563.9–1893.1
1,043
2,190

Notes: Numbers in parentheses are standard errors. Cooperation, spillovers, appropriability, legal protection,
cost, risk, and complementary variables are defined only for innovative firms. All equations include two-digit
level industry dummy variables.
*
Significant at the 10 percent level**
Significant at the 5 percent level***
Significant at the 1 percent level25