Does syndication with local venture capitalists moderate the effects of
geographical and institutional distances and experience?
Abstract:
Drawing on a novel dataset of worldwide venture capital deals, we investigate venture capitalists’
(VCs) participation likelihoods and put particular emphasis on how syndication with a local VC
moderates the negative effects of geographical and institutional distances and low experience. Our
results indicate that syndication with local VCs is a common way for foreign VCs to gain deal access,
overcome the complexity of investing in geographically distant regions and offset their lack of withincountry experience. The VCs’ geographical (but not institutional distance) from the portfolio company
ceases to be a serious investment obstacle when they can rely on a highly experienced local VC or
when they join a syndicate that invests in late-stage deals, large deals or subsequent rounds.
Institutional distance is more difficult to overcome through syndication, as experienced local VCs
prefer to invite VCs with similar institutional backgrounds.
Keywords: Venture Capital, Internationalization, Syndication, Geographical and Institutional
Distance, Experience, Moderating Effect
JEL Classification: F21, G24.
1. INTRODUCTION
In the last decade, more than one third of worldwide venture capital investments have been crossborder deals and every third venture capitalist (VC) has invested abroad. In many of these deals VCs
invest over long geographical distances and in countries with substantially different legal and cultural
institutions. Information and agency problems inherent in venture capital transactions become more
pronounced as geographical and institutional distances increase (e.g. Wright et al. 2005), giving rise to
the question which VCs manage to overcome these distances and how. Studies in venture capital have
already investigated cross-border activity (Aizenman and Kendall 2008, Guler and Guillén 2010a,
2010b, Iriyama and Madhavan 2009). Our study’s first important contribution to this literature is that,
to the best of our knowledge, we are the first to investigate how local VCs moderate (i) the relation
between foreign VCs’ investment chances and their geographical and institutional distance from
portfolio companies (PCs) as well as (ii) the relation between foreign VCs’ investment chances and
their experience. To determine these local VC’s moderating effects we employ a novel approach with
which we determine VCs’ participation likelihood in cross-border deals. This approach provides
insights into the various internationalization strategies VCs may employ and allows us to draw
conclusions on how VCs increase their chances to invest across borders. The moderating effects of
local VCs help us particularly to assess whether teaming up improves cross-border chances of less
experienced VCs aiming at expanding their business activities. For this analysis, we employ a
comprehensive dataset of worldwide deals and VCs.
These moderating effects of the local VC come into play since foreign VCs often syndicate with VCs
located in the PC country instead of investing there alone. Investigating these moderating effects is
important to better understand the high intensity of such cross-border syndication. In our dataset,
foreign VCs syndicate with local VCs in 57% of all cross-border deals. 35% of all cross-border deals
are stand-alone transactions and the remaining 8% are transactions syndicated among foreign VCs
only. To model foreign VCs’ participation likelihood in (syndicated and stand-alone) cross-border
deals, we consider all participating and non-participating foreign VCs (potential VCs). In Figure 1, we
depict the different deal types we observe in our worldwide dataset. In Panel A we visualize the two
major ways how VCs invest across borders, and given these cross-border deals we exemplify
participating and non-participating foreign VCs. This approach captures the foreign VC’s perspective
and allows us to address the moderating effects of a local VC in two respects. First, syndication with a
local VC may reduce the negative effects of geographical and institutional distances on foreign VCs’
participation likelihood, since information and agency problems are mitigated when a geographically
close VC monitors and supports the PC. Second, syndication with a local VC may help offset foreign
VCs’ lack of general and within-country experience. It is difficult for VCs lacking general and withincountry experience to directly access investment opportunities in a foreign country, to evaluate
investment opportunities and, later on, to monitor and support their PCs (e.g. Mäkelä and Maula
2008). Therefore, we expect inexperienced foreign VCs to rely on a local syndication partner more
often than experienced foreign VCs.
1
The challenge inherent in the approach of modeling foreign VCs’ participation likelihoods in crossborder deals is that the two major types of cross-border deals are not directly comparable. In standalone deals VCs directly access PCs in foreign countries whereas in syndicated deals a local VC
typically invites a foreign VC to participate and form a joint syndicate (e.g. Mäkelä and Maula 2008).
Therefore, we also investigate the participation likelihood of (foreign and local) VCs in syndicated
deals. Taking on the local VC’s perspective, we determine the likelihood of a local VC inviting
another VC, distinguishing between potential domestic VCs, potential foreign VCs with experience in
the respective PC country, and potential foreign VCs without such within-country experience. In
Panel B of Figure 1, we depict participating (domestic and foreign) VCs as well as non-participating
(domestic and foreign) VCs in syndicated deals. To the best of our knowledge, our study is the first to
consider syndication behavior in worldwide venture capital deals. We focus on the moderating effects
related to the local VC’s experience, which is the second important contribution of this paper to the
literature on venture capital internationalization. Our approach allows us to put particular emphasis on
these moderating effects in two respects: First, the local VC’s experience may mitigate the negative
effects of the partner’s geographical and institutional distances since experienced local VCs manage
information and agency problems (arising from great distances between syndication partners) better
than local VCs that lack experience (e.g. Mäkelä and Maula 2008). In addition, as the local VC’s
experience increases, distant VCs are more willing to rely on the local VC’s expertise in screening,
monitoring and supporting the PC, which renders their own distance less important. Second, the local
VC’s experience may affect the selection of a foreign syndication partner in terms of the partner’s
within-country experience. We do not expect that an experienced local VC invites inexperienced
foreign partners, since these partners can only contribute little to such syndicates (e.g. Casamatta and
Haritchabalet 2007, Cestone et al. 2007). However, we conjecture that an inexperienced local VC may
invite inexperienced foreign partners and thus offers them internationalization opportunities.
Our study addresses several research gaps in the emerging literature on internationalization in venture
capital industries. Addressing these research gaps not only improves our understanding of VCs’
internationalization but also delivers deeper insights for VCs planning an international expansion. The
recent literature shows that geographical and institutional distances negatively affect the intensity of
bilateral venture capital flows between countries (e.g. Aizenman and Kendall 2008). We add to this
finding first by showing that syndication with local VCs moderates the negative effects of foreign
VCs’ geographical and institutional distances. Second, we demonstrate that the local VC’s experience
moderates the discouraging impact of geographical distance on partners’ participation likelihoods. Our
analyses, third, show that geographical distance of the potential syndication partner is not a serious
investment obstacle in late-stage deals, in large deals and in subsequent rounds. The fourth interesting
finding from our analyses of partners’ participation likelihoods is that moderating effects of local VC’s
experience do not fulfill the same task in partners’ handling of geographical and institutional
distances. Institutional proximity seems to matter more for the participation decision than the
geographical one and institutional distance seems to be more difficult to overcome.
2
Another strand of the literature points out that VC experience plays a decisive role in cross-border
investments. Recent evidence suggests that US VCs with more international experience (Guler and
Guillén 2010a) and European VCs and private equity investors with more international human capital
(Prijcker et al. 2009) expand faster and more often into foreign countries. We complement these
findings by showing that syndication with a local VC and the local VC’s experience moderate these
effects. Our results suggest that foreign VCs with within-country experience have higher participation
likelihoods in cross-border and syndicated deals than foreign VCs that lack within-country experience.
However, foreign VC’s that lack within-country experience increase their chances of participation
relative to foreign VC’s that have within-country experience when they invest alongside a local VC
rather than alone.
Finally, the recent literature also examines the ways investors participate in cross-border deals.
Meuleman and Wright (2011) investigate syndication between foreign and local investors in a related
asset class, private equity. They analyze the characteristics of UK private equity investors investing
alone in Continental Europe versus those syndicating with a local investor. In terms of Panel A in
Figure 1, their approach differs from ours since they consider only participating VCs, while we build
our analyses on both participating and non-participating VCs. Our model of VCs’ participation
likelihood in cross-border deals allows us to extend Meuleman and Wright’s findings not only with
respect to geographical and institutional distances but also with respect to repeated relationships. The
recent theoretical literature suggests that repeated relationships reduce informational frictions between
the contracting parties in a syndicate (Chemmanur and Tian 2009, Tykvová 2007) and recent
empirical work from UK private equity investors is in line with this reasoning (Meuleman et al. 2009).
Our paper is the first to show that repeated relationships work over long geographical distances.
We draw on a comprehensive dataset covering VCs from 48 countries, whereas most other studies at
the micro level only focus on VCs or private equity investors from one country (e.g. Guler and Guillén
2010a, 2010b, Iriyama and Madhavan 2009, Meuleman and Wright 2011).
2. BACKGROUND AND HYPOTHESES
2.1. Foreign VC’s perspective
VCs may strive to invest in foreign countries to exploit profitable investment opportunities in these
countries or to geographically diversify their portfolios. However, investments abroad potentially
incur higher costs than local deals due to greater geographical and institutional distances. An extant
literature supports the view that investors geographically close to the investment opportunity face
lower information costs than more distant investors (e.g. Agarwal and Hauswald 2010, Coval and
Moskowitz 1999, Ivkovic and Weisbenner 2005, Kang and Kim 2008, 2010). Empirical research on
venture capital finance suggests that geographically close PCs are less costly to find (Wright et al.
2005), to screen (Cumming and Johan 2006) as well as to monitor and support (Lerner 1995, Sorenson
and Stuart 2001) than distant ones. The reason is that local VCs are familiar with local practices, have
regional business experience and access to soft information through their managers’ interactions in
3
social, civic and business meetings and their participation in formal and informal networks.
Consequently, VCs tend to invest in geographically close PCs (Florida and Kenney 1988, Powell et al.
2002, Sorenson and Stuart 2001). When investing across borders, they prefer geographically close
countries to distant ones (Aizenman and Kendall 2008).
Institutional distance, i.e., differences in legal systems, legal institutions and culture (Boschma 2005),
also hinders cross-border venture capital investments. If the VCs are not familiar with the PC
country’s legal institutions, they experience difficulties to transfer and enforce the governance
structure and contract design they typically use in their home countries (e.g. Bottazzi et al. 2009).
Governance structure and contract design are essential in venture capital finance because they
incentivize the PCs’ managers to perform well (Kaplan and Strömberg 2003, 2004) and, consequently,
play a decisive role for the PCs’ success (e.g. Cumming et al. 2006). Evidence from cross-border
venture capital flows suggests that US VCs prefer to invest in countries with the same law tradition
(Guler and Guillén 2010a) and that flows are much larger between countries with similar legal
institutions (Aizenman and Kendall 2008). Not only legal differences affect economic interactions,
cultural differences (differences in habits, attitudes, etc.) are crucial as well. In a cross-country
analysis, cultural characteristics help explain why some countries have more venture capital finance
than others (Li and Zahra 2012). Recent literature also outlines how cultural differences affect crossborder interactions. Cultural differences impair communication and increase failure rates in crossborder mergers (Weber and Camerer 2003, Weber et al. 1996). They also influence how banks design
borrowers’ loan conditions in foreign countries (Giannetti and Yafeh 2012) and determine how direct
investors enter foreign markets (Kogut and Singh 1998). Recent literature also outlines that cultural
differences between countries affect how intensively foreign VCs support their portfolio companies
(Mäkelä and Maula 2006).
Our aim is to determine how geographical and institutional distances affect foreign VCs’ participation
likelihood in cross-border deals. VCs typically invest across borders either by carrying out a standalone deal or by forming a syndicate with a local VC. These two alternatives are visualized in Panel A
of Figure 1, in which we also depict investment activities of domestic VCs in domestic PCs. When
VCs invest in foreign PCs alone, they do not have to share benefits from this investment (Wilson
1968), but they have to carry all costs themselves. Our previous discussions lead to the conclusion that
these costs increase as geographical and institutional distances increase. When VCs team up with local
VCs, they likely have to carry lower costs because they profit from an easier access to investment
opportunities and less pronounced agency problems when the local VC monitors and supports the PC.
Mäkelä and Maula (2008) demonstrate that in such cross-border syndicates, the local VC usually
precedes the foreign one. They also argue that local VCs typically lead the syndicate because they
have more local contacts and knowledge and because it is easier to manage a company from a
proximate than from a distant location. Consequently, we expect a local VC to have a moderating
effect in the sense that foreign VCs invest over greater geographical and institutional distances if a
local VC is on board and over shorter distances if they invest alone. Hence, we postulate:
4
Hypothesis 1. An increase in geographical and institutional distances between the potential
foreign VC and a PC …
a) decreases this VC’s participation likelihood.
b) decreases this VC’s participation likelihood less strongly when it syndicates with a local VC
than when it invests alone.
Besides distance, VCs’ experience affects their ability to exploit investment opportunities abroad and
to manage the costs of investing abroad. We follow the literature (e.g. Meuleman and Wright 2011,
Sorenson and Stuart 2001) and distinguish between general and within-country experience. Greater
general experience increases VCs’ likelihood to invest, even outside the countries in which they have
accumulated experience, for at least three reasons. First, the traditional learning perspective (for an
overview see Barkema and Schijven 2008) suggests that VCs profit from general experience they have
gathered in their previous investments as they may use some of the acquired general skills in future
deals. Even if some aspects of deal selection differ across countries, a part of the selection process of
any PC involves aspects of the business plan evaluation not specific to any particular country.
Similarly, some aspects of monitoring and support require knowledge specific to a particular country,
but others likely apply to all PCs. With increasing experience, VCs’ ability to perform these tasks
improves and they more likely dare to cross borders. Second, experienced VCs have developed
relationships with other VCs that have experience in foreign countries and that may provide them with
access to investment opportunities in these countries. In addition, such relationships may render
screening, monitoring and support of foreign PCs easier. Third, experienced VCs obtain more business
proposals from foreign PCs than inexperienced VCs because PCs benefit from the certification and
value-adding role of experienced VCs (Hsu 2004). Empirical studies lend support to these arguments.
As an example, Powell et al. (2002), who investigate US VCs’ local and non-local investments in
biotech firms, find that VCs investing both locally and non-locally are those that have greater general
experience.
The three arguments above for why general experience positively affects VCs’ participation in crossborder deals should apply even more strongly for specific within-country experience. According to the
learning perspective, VCs profit from their experience in deal selection, screening, monitoring and
support within the particular PC country because they are more familiar with particular local practices
and habits. Also, local VCs more likely invite foreign VCs that have some within-country experience
and are possibly embedded in local networks than VCs investing in the PC country for the first time,
which are hence newcomers in this country. Finally, a foreign VC with within-country experience
receives more business proposals from local PCs. In line with these arguments, Mäkelä and Maula
(2006) find that foreign VCs with past investments in a country, who are embedded locally, behave
differently towards new PCs than newcomers.
The way in which foreign VCs participate in cross-border deals likely moderates the effect of VCs’
general and within-country experience on their participation likelihoods in cross-border deals. A crossborder stand-alone deal typically requires both high general and within-country experience to gain
5
deal access in a specific foreign country and to successfully screen, monitor and support the PCs
(Meuleman and Wright 2011). Consequently, inexperienced foreign VCs benefit more strongly from a
local VC than highly experienced foreign VCs. We expect a moderating effect of the local VC’s
presence in the sense that foreign VCs need less general and within-country experience when
syndicating with a local VC than when investing alone. This implies that syndication with a local VC
may partly offset a lack of foreign VCs’ general and within-country experience. Hence, we expect:
Hypothesis 2. An increase in general and within-country experience of the potential foreign VC…
a) increases this VC’s participation likelihood.
b) increases this VC’s participation likelihood less strongly when it syndicates with a local VC
than when it invests alone.
The idea of syndication with a local VC as a way for foreign VCs to gain access to cross-border deals
and mitigate the negative effects of distances and a lack of experience is appealing, but the underlying
story is more complex because syndication has benefits and costs. From the resource-based
perspective, syndication is beneficial because VCs combine their complementary resources (e.g.
Bygrave 1987, Manigart et al. 2004). Such combination of resources has several facets far beyond
pooling financial resources from the participating VCs. Syndication enables a combination of
information resources. Such “second opinions” on the future prospects of the company are particularly
useful in the screening phase since it reduces the risk of funding bad deals (e.g. Casamatta and
Haritchabalet 2007, Lerner 1994). In addition, syndication combines VCs’ skills, experience, and
contact networks. Hereby, it may create an additional value through better monitoring and support
during the investment phase (e.g. Brander et al. 2002, Cumming and Walz 2010, Tian 2012). These
benefits may be more pronounced in cross-border syndication because VCs from different countries
have access to different business ecosystems and may thus combine more complementary resources
than VCs from one country. For example, foreign VCs may play a key role in PCs’
internationalization efforts (e.g. Mäkelä and Maula 2005) because the PCs may benefit from the VCs’
access to foreign human capital and business networks and from their knowledge of foreign product
and capital markets (e.g. Hursti and Maula 2007). Another benefit from syndication is an increased
VC portfolio diversification as VCs spread their limited resources over more companies (e.g. Manigart
et al. 2004). Through cross-border syndication diversification gains an additional dimension.
As to the costs, syndication gives rise to new agency problems emerging from information
asymmetries within the syndicate (Wright and Lockett 2003). There are at least two sources of these
problems. First, the less informed foreign VC may suffer from adverse selection if the local VC, that
possesses more information about the deal quality, only invites foreign VCs for low quality deals.
Second, foreign VCs may suffer from moral hazard and free riding problems if they are unable to
observe the local VC’s efforts in monitoring and supporting the PC. Thus, teaming up with
syndication partners over long distances might substantially raise information costs while local
syndication is much easier (Hochberg et al. 2007).
6
One mechanism which helps reduce informational frictions within a syndicate is joint investments in
the past (e.g. Cai 2009, Gopalan et al. 2008 and Pichler and Wilhelm 2001 for empirical studies;
Chemmanur and Tian 2009 and Tykvová 2007 for theoretical works). Empirical evidence from the
United States suggests that if VCs recently experienced a successful cooperation within a syndicate,
they are more willing to cooperate again (e.g. Chemmanur and Tian 2009, Hochberg et al. 2007). We
suggest that such reduction in informational frictions through repeated relationships should reach
across borders as well as over long geographical distances. Hence, we predict:
Hypothesis 3. The potential foreign VC’s participation likelihood increases when this VC has
invested with the participating local VC in the past.
2.2. Local VC’s perspective
One drawback of the preceding arguments is that we cannot directly compare the two ways in which
VCs participate in cross-border deals because foreign VCs have direct access to PCs when they invest
alone, while they typically are invited to participate in syndicated deals by a local VC. We follow
Mäkelä and Maula (2008) and conjecture that the local VC typically has deal access, invites other
domestic or foreign VC to participate, and leads the syndicate. We use the local VC’s perspective to
gain insights into whether and how the local VC’s experience determines which VCs, in terms of their
distance and experience in particular, the local VC chooses as syndication partners. Thus, in our local
VC perspective, potential VCs are not only all VCs located outside the country in which the deal takes
place but also all domestic VCs as visualized in Panel B of Figure 1.
Experienced local VCs are probably better able to set up and manage syndicates with geographically
and institutionally distant partners than inexperienced local VCs. Moreover, distant partners
themselves are more willing to participate in syndicates led by experienced local VCs, as more
experienced VCs make more successful investments (e.g. Sorensen 2007). They are better able to
perform information gathering and processing. Consequently, experienced local VCs may more
accurately signal the PC’s quality to distant syndication partners (e.g. Mäkelä and Maula 2008).
Experienced local VCs may also better monitor and support the PC than their inexperienced
counterparts, so that their distant syndication partners will be less actively involved in these tasks.
Consequently, we expect a moderating effect of the local VC’s experience in the sense that VCs may
dare to invest over great geographical and institutional distances when they can rely on the experience
of local VCs. Hence, we postulate:
Hypothesis 4. An increase in geographical and institutional distances between the potential
syndication partner and a PC decreases this partner’s participation likelihood less strongly when
the local VC is experienced than when the local VC is inexperienced.
In the local VC’s perspective, we have to distinguish between general and within-country experience
more strictly than in the foreign VC’s perspective, since the local VC may invite not only foreign but
also domestic VCs to participate in a deal. We first focus on the general experience of (foreign or
domestic) potential syndication partners. From the local VC’s point of view, an inexperienced partner
would contribute less to the PC development than an experienced one (Dimov and Milanov 2010).
7
Recent theoretical literature suggests that syndicate formation depends on the experience of both VCs.
In the theoretical work by Casamatta and Haritchabalet (2007), VCs screen projects to generate an
information signal about the true quality of the PC. The precision of this signal increases with a VC’s
experience. Syndication does allow the local VC to obtain a second evaluation but it also gives rise to
costs, which depend on the local VC’s experience. Casamatta and Haritchabalet conclude that
experienced VCs either invite experienced VCs or forgo syndication. Also, Cestone et al. (2007) argue
that experienced VCs only invite experienced, but not inexperienced, syndication partners. Lerner
(1994) offers some empirical evidence in line with these arguments for local (US) but not cross-border
syndicates. Our paper is the first study to consider syndicate composition (in terms of experience of
the different syndicate members) in cross-border deals and the moderating effect of the local VC’s
experience. We expect that experienced local VCs invite experienced syndication partners. Potential
partners with little experience would like to join syndicates led by highly experienced local VCs in
order to benefit from their know-how and their screening, monitoring and value-adding abilities.
However, experienced local VCs will not invite these VCs to participate. Thus inexperienced
syndication partners have a better chance to syndicate with an inexperienced local VC than with an
experienced one.
A local VC has three categories of potential syndication partners in terms of within-country
experience: a domestic VC, a foreign VC that is a newcomer in the respective country, and a foreign
VC that has already gathered within-country experience, which we call old hand. Since syndication
across borders may entail higher costs, we expect that both newcomers and old hands have lower
participation likelihood than domestic VCs and that old hands have a higher participation likelihood
than newcomers. If VCs build homogenous syndicates with respect to experience, as the theoretical
literature argues, old hands’ participation likelihood is higher when the local VC is experienced than
when the local VC is inexperienced. But another effect might be at work and this effect goes in the
opposite direction. It may pay for an old hand to accept the invitation of an inexperienced local VC to
gain deal access. Given these two countervailing effects, we do not know whether and how the local
VC’s experience moderates old hands’ participation likelihood. Newcomers, on the contrary, are not
attractive for experienced local VC. Therefore, we expect their chances to be invited to participate in a
deal to be higher when the local VC is inexperienced than when it is experienced. Hence, we predict:
Hypothesis 5.
a) An increase in the general experience of the potential syndication partner increases this
partner’s participation likelihood more strongly when the local VC is experienced than when
the local VC is inexperienced.
b) A newcomer has a higher participation likelihood when the local VC is inexperienced than
when the local VC is experienced.
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3. DATA AND METHODS
3.1. Data on cross-border venture capital deals
We identify worldwide venture capital deals and all participating VCs from the dataset used in
Schertler and Tykvová (2011), which was extracted from the Bureau van Dijk Zephyr database. Our
final dataset contains 23,826 deals completed between the beginning of 2000 and the end of 2008. A
deal is an investment in a PC by one or several VCs. The number of deals is larger than the number of
PCs since several PCs receive more than one round of financing. Our sample corresponds to 58,377
dyads between VCs and deals. A dyad is a connection of each individual participating VC to a deal
(e.g. De Clercq and Sapienza 2010). For example, a deal financed by three VCs contains three VCdeal dyads.
Table 1 displays number and volume of local and cross-border deals VCs carried out in each country
during the period 2000 to 2008. In local deals, all VCs are located in the PC country. In cross-border
deals, at least one VC is located outside the PC country. The by far highest number of venture capitalbacked PCs is located in the United States. In this PC country, we count 9,370 local and 2,854 crossborder deals, resulting in an internationalization share (number of cross-border to total deals) of 23%.
The country with the second largest VC industry is the United Kingdom with 1,540 local and 1,214
cross-border deals. It has an above-average internationalization share of 44.1%. While the average
worldwide internationalization share is 33.5% based on number of deals, it is 49.8% based on volume.
Thus, cross-border deals have, on average, a higher deal volume than local deals.
Table 2 provides further insights into worldwide internationalization and syndication patterns. In total,
we count 15,879 local deals, 7,474 of which are local stand-alone deals and 8,405 are syndicated
among local VCs. We count 7,947 cross-border deals, 2,779 of which are cross-border stand-alone
deals, 645 are financed by syndicates of foreign VCs, and 4,523 are financed by a foreign-local
syndicate. The number of syndicate members differs across the various syndicate types. The median
syndicate size is 3 for local syndicates, 4 for foreign-local syndicates, and 2 for foreign syndicates.
Foreign-local syndicates typically consist of more local than foreign VCs (2 local and 1 foreign VCs in
the median deal). In the median deal of foreign syndicates, the foreign VCs come from two different
countries.
3.2. Dependent variables
We use two dependent variables. Both are binary variables that equal one if a particular VC
participates in a deal, and zero otherwise. These binary variables are not directly observable in our
data. Rather, we generate them by combining each deal with all potential VCs, i.e. with those VCs
participating in this deal as well as those VCs that do not participate. The two dependent variables
differ because we consider different types of deals and potential VCs when generating them. A similar
approach building all potential pairwise combinations has been used, for example, by Corwin and
Schultz (2005) and Song (2004) who investigate the likelihood that a given bank participates in an
underwriting syndicate.
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Our first dependent variable is participation in cross-border deals, which we use to investigate how
the presence of a local VC influences the participation likelihoods of foreign VCs coming from
different distances (Hypothesis 1) and having different experience levels (Hypothesis 2). We also use
participation in cross-border deals to investigate whether repeated relationships between foreign and
local VCs work effectively over long geographical distances (Hypothesis 3). We include all types of
cross-border deals carried out between 2003 and 2008:1 stand-alone deals of foreign VCs, deals
financed by foreign syndicates, and deals financed by foreign-local syndicates. We then combine each
of these cross-border deals with all potential (participating and non-participating) VCs located outside
the country in which the deal takes place (see Panel A of Figure 1). Thus, we only focus on potential
foreign VCs. Our first dependent variable equals one if a foreign VC participates in this cross-border
deal (participating potential VC), and zero otherwise (non-participating potential VC). The
combination of all cross-border deals with all potential (participating and non-participating) foreign
VCs delivers nearly 28 million observations. The number of observations for each deal varies since the
number of potential foreign VCs depends on the country in which the deal takes place. For example,
since most VCs are located in the United States, the number of potential foreign VCs is much lower
when the deal takes place in the United States than when it takes place in another country.
Our second dependent variable is participation in syndicated deals, which allows us to investigate
how the local VC’s experience influences the participation likelihoods of potential syndication
partners coming from different distances (Hypothesis 4) and having different experience levels
(Hypothesis 5). We use deals financed by foreign-local syndicates and local syndicates, and combine
each of these deals with all (local and foreign) potential (participating and non-participating)
syndication partners. We include all VCs except the participating local VC, hence focusing on the
likelihood to be invited by the local VC (see Panel B of Figure 1). The dependent variable equals one
if a potential syndication partner participates in this syndicated deal and zero otherwise. If there are
two or more participating local VCs in one deal, we consider the local VC located most closely to the
PC the “local” one (that is assumed to invite the others).2 Since the sample of potential syndication
partners is of the same size for each deal and does not vary with the country in which the deal takes
place (all VCs in the dataset with the exception of the participating local one), the number of
observations is identical for each deal.
3.3. Econometric specifications
We use two types of econometric specifications to estimate a VC’s likelihood to participate in either
cross-border or syndicated deals. Both specifications are adequate for binary variables. Since we have
limited information on the deals and the PCs, we use a conditional logit specification (Andersen 1970,
Chamberlain 1980) as our work horse. This approach allows us to benefit from multiple observations
1
2
We need the first three years of data to generate variables related to VCs’ general and within-country experience and
repeated relationships.
However, our results are robust towards other specifications, e.g., choosing the “local” VC randomly.
10
for each deal (equal to the number of potential VCs in each deal) by modeling an individual dealspecific fixed effect.
The conditional logit specification provides a semi-parametric estimation of the logit model without
estimating the individual deal fixed effects. Individual deal fixed effects control, as effectively as
possible, for all deal-specific characteristics (such as deal volume and the year in which the deal is
closed). Moreover, the individual deal-fixed effects also capture all PC-specific characteristics (such
as age and financial characteristics), all industry-specific characteristics (such as asset tangibility and
market to book ratios in this industry) and all characteristics of the country in which the PC is located
(such as the availability of investment opportunities and the stance of the capital market), which might
affect investment and syndication decisions.
The individual deal fixed effects in the conditional logit specification also capture deal characteristics
related to two central issues, namely the participation of a local VC and this VC’s experience, which
both do not vary over a single deal. A cross-border deal either does or does not have a participating
local VC. In a similar vein, a syndicated deal either has an experienced or an inexperienced
participating local VC. Consequently, we cannot include a dummy for the local VC’s participation or
the local VC’s experience. To measure how the local VC’s participation and the local VC’s experience
influence participation likelihoods, an alternative approach is warranted, which we outline next.
In our first model, which analyzes the participation likelihood in cross-border deals, we interact a
dummy variable equal to one if a local VC participates (and zero otherwise) with the geographical
distance between the potential foreign VC and the PC.3 This interaction term allows us to investigate
how the local VC’s presence is related to the participation of foreign VCs coming from different
geographical and institutional distances and having different levels of general and within-country
experience (Hypotheses 1b and 2b). In our second model, which investigates the participation
likelihood in syndicated deals, we interact the local VC’s experience with the geographical distance
between the potential syndication partner and the PC. This interaction term allows us to investigate
how the local VC’s experience is related to the participation of (local or foreign) syndication partners
from different geographical and institutional distances and with different levels of general and withincountry experience (Hypotheses 4 and 5). We evaluate the impact of the potential VC’s distance and
experience separately for the situation with/without a local VC and for the situation with an
inexperienced/experienced local VC, since we cannot infer from the estimated interaction term
coefficients whether an increase in a variable increases or decreases the likelihood of a potential VC’s
participation (Ai and Norton 2003).
The second econometric specification is a simple logit specification without deal fixed effects, in
which we use PC and deal characteristics as separate regressors. In addition, we employ PC and VC
country-specific characteristics, industry characteristics and year dummies to account for the multitude
of effects that may affect VC’s participation likelihoods. Since we gain information on PC and deal
3
We have used alternative interaction terms, such as an interaction between the dummy variable for a local VC and the
general experience of the foreign VC. These alternative interaction terms do not change our main results.
11
characteristics from our dataset (particularly: first vs. subsequent round), we include only PCs with
complete histories in the logit specification.
3.4. Main independent variables
Our independent variables capture the geographical and institutional distances between VC and PC as
well as the characteristics of VC and PC. The appendix provides definitions and sources of all
independent variables employed. To measure the geographical distance between each potential VC
and each PC, we use individual address data on the zip code, city, and country of all VCs and PCs.
From http://www.batchgeocode.com, we obtain the latitude and longitude for the center of each zip
code–city–country combination. We employ Vincenty’s (1975) formula4 and calculate the distance
between the centers of the two zip code–city–country combinations for each potential VC-deal pair.
We only include deals in our main analyses for which we are able to obtain the geographical distances
between the PC and each of the VCs participating in this deal. Table 3 indicates that, on average, the
foreign VC is located approximately 2,700 miles from the PC. The median distance between foreign
VC and PC is 1,200 miles for stand-alone deals, whereas it amounts to more than 3,200 miles when a
local VC participates. In deals in which local and foreign VCs form a syndicate, the median distance
between the local VC and the PC is 22 miles. Throughout the empirical analysis, we use the logarithm
of the geographical distance to capture its non-linear effect on cross-border activity documented in
earlier studies (e.g. Grinblatt and Keloharju 2001).
In our main analyses, we employ several variables to account for institutional distance. A dummy
variable same law equals one if the two countries share the same law tradition, and zero otherwise.
Countries’ law traditions are based on the categorization by La Porta et al. (1998 and 1999) who
distinguish five law traditions (English, French, German, Scandinavian, and Socialist). Table 3 shows
that the foreign VC country has the same law tradition as the PC country in 48% of deals syndicated
among foreign and local VCs, in 35% of stand-alone deals, and in 42% of deals syndicated among
foreign VCs. Legal distance is the difference in the two countries’ levels of the legality index based on
Berkowitz et al. (2003) and employed in the context of VC by e.g. Cumming and Walz (2010).
Cultural distance is based on all five Hofstede’s cultural dimensions: power distance index,
individualism, masculinity, uncertainty avoidance index and long-term orientation. We adapt the fourdimensions’ distance index developed by Kogut and Singh (1988), which has already been used in the
context of cross-border venture capital flows (e.g. Mäkelä und Maula 2006). In extensions of our main
analyses, we add dummy variables same language and colonial ties (e.g. Buch and DeLong 2004).
Moreover, we include disclosure distance, because Cumming and Knill (2012) find disclosure laws to
be very important for the supply and performance of VCs and demand by PCs, and Spamann distance,
built from a recently developed revisited antidirector rights index (Spamann 2010), as further
measures of institutional proximity. We also build an alternative index of cultural distance (cultural
distance alt) based on the societal cultural practices reported in Globe culture scales (see House et al.
4
Vincenty (1975) developed a formula for calculating geodesic distances between a pair of points on the surface of the
Earth using an accurate ellipsoidal model of the Earth.
12
2004) as an alternative to Hofstede’s measures. Institutional distance variables are highly correlated.
This particularly concerns the variables cultural distance, cultural distance alt, disclosure distance,
same law and same language. In addition, the dummy variable colonial ties is also correlated with the
same law and same language dummy variables. We will put particular emphasis on these correlations
in our robustness checks.
We follow the recent literature and measure general and within-country experience as past deal counts.
For general experience we count the number of each VC’s deals in the previous three years.5 Table 3
reveals that VCs participating in cross-border deals have greater experience than VCs that invest
locally. Table 3 also displays the different attitudes of experienced VCs towards syndication in local
and cross-border deals. While stand-alone local deals are carried out by inexperienced VCs, greater
general experience seems to be necessary when VCs invest alone in cross-border deals. Moreover, the
experience of the closest local VC in cross-border deals is greater than that of the closest VC in local
deals.
To capture within-country experience, we classify foreign VCs as either newcomers or old hands. If
the foreign VC has not invested in the country under consideration during the previous three-year
window, the dummy variable newcomer equals one, and zero otherwise. If the foreign VC has invested
in the country under consideration during the previous three years, the dummy variable old hand
equals one, and zero otherwise. Newcomers supposedly face higher obstacles than old hands, that
already possess valuable within-country experience, contacts and, in some cases, even local offices or
subsidiaries in the PC country. Table 3 reveals that 48% of VCs in stand-alone cross-border deals,
47% of foreign VCs in foreign-local syndicates and 63% of VCs in foreign syndicates are newcomers.
Due to data limitations we are not able to measure some other factors which may affect how VCs
invest across borders. For instance, those VCs that opened subsidiaries or offices in foreign countries
in which they invest may be located very closely to their PCs, rendering the geographical distance
between their headquarters and the PC irrelevant for their investment and syndication decisions.
Meuleman and Wright (2011) use a dummy variable capturing the existence of local offices but,
surprisingly, do not find any significant effect of this variable on the likelihood that a UK private
equity investor syndicates a deal with a local investor in Continental Europe. Unfortunately, we cannot
follow their strategy since we do not have information on whether, at the date of the particular
investment, the potential VC has a subsidiary or a local office in the PC country. However, we do
believe that our dummies newcomer and old hand at least partially account for these effects.
To measure repeated relationships, we use a dummy variable repeated which equals one if the
potential VC has invested with the participating local VCs in the previous three years.6 59% of all
syndicated deals are formed on the basis of repeated relationships; in foreign-local syndicates this
fraction even amounts to 67% (Table 3). We find similar results based on the participating VCs: 54%
5
6
The period length used in the literature to calculate experience differs substantially. For example, Guler and Guillén
(2010a and 2010b) use only one prior year, while Cumming and Dai (2010) count deals over the whole VC’s history.
E.g., Meuleman et al. (2009) use a five-year window to obtain information on repeated relationships. However, given the
short time period covered by our data, we opt for this shorter window.
13
of VCs join a syndicate if they have repeated relationships to one of the other syndicate members.
Again, the fraction of VCs with repeated relationships is highest within deals financed by foreign-local
syndicates (58%).
As to the PC characteristics, we employ the logarithm of the deal volume. We also add a dummy
variable early stage capturing whether, at the time of investment, the company is in the early or late
stage. We finally use a dummy variable subsequent round indicating whether there has been another
investment round before the investment round under consideration. We only construct this variable for
companies founded after 1999. The reason is that we do not observe investments until 2000.
Consequently, when our sample includes an investment round for older companies (founded before the
year 2000), we do not know whether this investment round is the first round or a subsequent round.
Table 3 suggests that foreign VCs tend to participate in larger deals, in later stage deals and in
subsequent rounds. In addition, syndicated deals have larger median volumes than stand-alone deals.
We finally control for the legal environment, taxation, market capitalization, innovativeness, expected
growth in the VC and PC country, which may be important country characteristics that affect venture
capital investments and cross-border flows (see Schertler and Tykvová 2011). Finally, we also add the
size of the local venture capital industry in the VC and PC country.
4. RESULTS
4.1 Participation in cross-border deals
Table 4 delivers the main patterns we find for marginal effects in conditional logit estimations for the
VCs’ participation likelihood in cross-border deals. We evaluate the marginal effects at the sample
means of all variables except for dummy variables, for which we evaluate them by changing the
variable from 0 to 1. The marginal effects suggest that the geographical distance between the potential
VC and the PC is significantly negatively related to the likelihood of this VC’s participation. Given
that all variables are at their means and the deal fixed effect equals zero, this baseline specification
reveals that the likelihood of a VC’s participation in cross-border deals decreases by 0.21 percentage
points when this VC’s geographical distance from the PC doubles.7 The economic effect seems to be
small at first glance. However, when interpreting it, one has to take into account that the unconditional
participation likelihood is very low, as this regression includes 3,811,567 potential VC-deal pairs,
from which 2,647 are participating VC-deal pairs. Switching from a different to the same law tradition
increases the likelihood of participation by 0.4 percentage points. Legal distance matters as well, while
cultural distance does not have any significant effect on participation likelihood, which may be due to
the fact that the variables same law and cultural distance are highly correlated. Consistently with
Hypothesis 1a, we find evidence for a discouraging impact of a VC’s geographical and institutional
distances on this VC’s participation likelihood in cross-border deals.
7
In the regression, we use log(distance). Doubling the mean distance results in log(2×meandistance)=log (2) + log
(meandistance). Extracting the mean distance from this expression gives log(2)=0.69, which is the change in the
transformed distance variable (i.e., log(2×meandistance)- log(meandistance)). The marginal effect amounts to
-0.003. Thus, if the distance doubles, the probability change equals approximately: -0.003*0.69 = -0.0021.
14
As expected, VCs’ general and within-country experience shapes the likelihood to participate in crossborder deals. AVC’s participation likelihood increases by 0.28 percentage points when this VC’s
general experience doubles. Being a newcomer (having no within-country experience) reduces
participation likelihood by almost 4.2 percentage points. These results lend support to Hypothesis 2a.
In line with the arguments on the positive role of repeated relationships in mitigating agency costs, we
find that the participation likelihood of potential foreign VCs that have previously invested with one of
the participating local VCs is 1.5 percentage points higher than that of VCs without repeated
relationships. However, this finding does not imply that repeated relationships to local VCs really
work over long distances, since we evaluate the marginal effect at the sample mean. To investigate this
issue, we re-evaluate the marginal effect of repeated relationships to a local VC for different distance
percentiles. The marginal effect of repeated relationships to a local VC on the potential VC’s
participation likelihood is statistically significant at all distance deciles and it declines as the distance
increases. At almost the maximum distance within Europe, which is the 4th distance decile in our data,
it shows a bend. At the 10% distance decile, the existence of repeated relationships to a participating
local VC increases the participation likelihood by 3 percentage points. At the 90% distance decile, the
effect is reduced by 1.8 percentage points to 1.2 percentage points, but it remains significant. Thus, in
line with Hypothesis 3, repeated relationships work over long geographical distances; although they
are more effective over short distances.
Table 4 also delivers the marginal effects from our baseline regression conditional on the absence of a
local VC (we set the local dummy to 0), and conditional on the presence of a local VC (we set the
local dummy to 1). The last column presents test results on whether the marginal effects of our
variables differ significantly when a local VC participates and when a local VC is absent.
The results indicate that the local VC’s presence moderates the marginal effect of geographical
distance. A doubling in the geographical distance between the PC and the potential foreign VC
decreases this VC’s participation likelihood by 0.07 percentage points when it syndicates with a local
VC and by 0.35 percentage points when it does not syndicate with a local VC. This difference is
statistically significant at the 1.3% level. A local VC thus significantly moderates the negative effect
of geographical distance on the likelihood of the foreign VC’s participation. The moderating effect of
the local VC on institutional distance is, however, not as clear cut as the effect on geographical
distance. Having the same law tradition increases participation likelihood by 0.2 percentage points
when the potential foreign VC syndicates with a local VC and by 1.0 percentage points when this VC
does not invest alongside a local VC. This difference is, however, not statistically significant. Legal
distance negatively affects participation likelihoods irrespective of whether or not a local VC is
present. The difference in marginal effects of legal distance is only weakly statistically significant at
the 8.4% level. The effect of cultural distance is insignificant irrespective of whether a local VC is
present or not. All in all, these findings strongly support the part of Hypothesis 1b related to
geographical distance and less so the part related to institutional distance.
15
The marginal effects of potential VCs’ general as well as within-country experience on their
participation likelihood are also moderated by the participation of a local VC. The results indicate that
VCs investing across borders without a local VC have much greater experience than those relying on
local VCs. The local VCs’ know-how and expertise are of particular importance for inexperienced
VCs and newcomers: Being a newcomer (instead of an old hand) decreases the participation
probability by about 10 percentage points when a local VC is absent, while it is only 2 percentage
points lower when the newcomer syndicates with a local VC. This difference is highly statistically
significant. This result indicates, in line with Hypothesis 2b, that VCs with little general experience
and without within-country experience have more difficulties than experienced VCs to participate in
stand-alone cross-border deals.
In Table 5, we put forward several alterations of our baseline specification, the results of which are
repeated in Column (1) of Panel A, to show that our baseline specification delivers the main patterns.
In Column (2) we add further variables related to the institutional distance (colonial ties, same
language, Spamann distance, disclosure distance) to the baseline specification. These variables are
highly correlated with the other variables measuring institutional proximity (in particular with same
law), which is the reason why we did not include them in our baseline specification. A higher
Spamann distance between the VC and PC country decreases a foreign VC’s participation likelihood.
As a robustness check (results not depicted), we include our three main measures (same law, legal
distance and cultural distance) and our four additional measures (colonial ties, same language,
Spamann distance, and disclosure distance) one-by-one in a regression and find most of them
significant with the right sign. A particularly interesting finding is that cultural distance, which is
insignificant in the baseline specification, becomes significant when it is included separately. It also
becomes significant when we include its alternative specification based on the Globe survey instead of
the specification based on Hofstede’s index in the baseline specification; in this case, however, the
variable legal distance turns insignificant.
Columns (3)-(5) are dedicated to additional PC and/or VC variables. In Column (3) we include VC
country characteristics instead of dummy variables for VC countries. Our main findings do not
change. We only observe a loss of statistical significance for the variable same law. In Columns (4)
and (5), we include VC country, PC country, and PC and deal characteristics and use simple logit
regressions. While standard errors are clustered on PC country and year in Column (4), they are
clustered on VC in Column (5). It is worth mentioning that the likelihood of a foreign VC to
participate in a cross-border deal increases as deal volume increases. Also, the likelihood of a foreign
VC to participate in a cross-border deal increases in subsequent rounds, although this effect is only
significant when we cluster standard errors on PC countries and years.
Columns (1)-(5) in Panel B of Table 5 are dedicated to regional differences. Columns (1) and (2)
provide separate analyses for PCs located in the United States and in Europe. The results suggest some
regional differences. Legal and cultural distances in cross-border deals play a negative role in Europe,
but not in the United States. In other words, when investing in the United States, foreign VCs more
16
easily overcome great institutional distances than when investing in Europe. While the effect of
geographical distance is larger in Europe than in the United States, the difference is not statistically
significant. Column (3) excludes all PCs and Column (4) all VCs from the United Kingdom and
United States to dismiss concerns that the two countries with the largest and most important venture
capital industries drive our results. When we exclude the United States and the United Kingdom as VC
countries, we merely observe that the marginal effect of the variable newcomer is only significant at
the 10.6% level. In Column (5) we exclude all VCs and all PCs from the United States. Most marginal
effects become more pronounced when the United States are excluded from the sample.
The sample we used so far has been subject to two confinements. The first confinement is that using
individual addresses of the VCs and PCs to calculate geographical distance generates a great loss of
observations. The reason is that the information on individual addresses is missing for a remarkable
number of VCs and PCs and we only included those deals for which we know all individual addresses
(that of the PC and all participating VCs) in our sample. Although we do not know the exact address
for many VCs and PCs, we know the country of origin for most of them. Therefore, we alternatively
use country distances from CEPII (http://www.cepii.fr/anglaisgraph/bdd/distances.htm) to check
whether our main results are sensitive to the underlying sample. This confinement also addresses
concerns related to the irrelevance of within country location in distant deals. For example, it probably
does not matter whether a Route 128 or a Silicon Valley VC invests in Europe, even if the two are
more than 3,000 miles apart. Similarly, for European VCs it might be irrelevant whether they have to
overcome the geographical distance to a PC in Silicon Valley or Route 128. The second confinement
is that measures of institutional distances are unavailable for some countries, especially for developing
ones. Therefore, the specification in Column (6) is built on bilateral country distance instead of the
individual VC-PC distance and it excludes variables related to institutional distance, which makes it
possible to also include PC and VC countries not covered in La Porta (1998 and 1999), Berkowitz et
al. (2003) and Hofstede. Hence, Column (6) includes the whole universe of countries and deals, which
results in nearly 28 million observations. Column (7) adds variables related to institutional distance,
which reduces the number of countries from 48 to 32 and the number of observations drops from 27.7
to 23.4 million. The unavailability of institutional measures shrinks the number of observations only
by about 10% because the number of deals that take place in these countries as well as the number of
VCs located there are both small. The effect of geographical distance, general experience, newcomers
and repeated relationships is highly statistically significant with the right sign in all specifications.
4.2. Participation in syndicated deals
The marginal effects from the baseline specification of our second model, which determines the
likelihood of participation in syndicated deals, are presented in Table 6. We evaluate the marginal
effects at the sample means of all variables except for dummy variables, for which we evaluate them
by changing the variable from 0 to 1. The results can be summarized as follows. The geographical and
institutional distances between the PC and the potential VC significantly decrease this VC’s
participation likelihood in syndicated deals. The results suggest that a greater general experience
17
increases the likelihood to be invited to participate in a syndicated deal. From the marginal effects on
the dummy variables old hand and newcomer we obtain information not only on the differences
between potential syndication partners that hold or lack within-country experience, but also on the
differences between foreign and local VCs, since all foreign VCs are classified as either newcomer or
old hand. Local VCs thus constitute the reference category. Local VCs have the highest likelihood of
matching because the effects of both newcomer and old hand dummies are negative and highly
statistically significant. Thus, foreign VCs have to overcome obstacles local VCs do not face. The
marginal effect of the newcomer dummy is much higher than that of the old hand dummy. This
indicates that having some within-country experience reduces these obstacles, but it does not
completely eliminate them.
To investigate the moderating effect of the local VC’s experience, we present marginal effects from
the baseline specification (i) conditional on an inexperienced local VC (we set the local VC’s
experience to its value at the 25% percentile), and (ii) conditional on an experienced local VC (we set
the local VC’s experience to its value at the 75% percentile). We test whether the marginal effects of
our variables differ significantly when an inexperienced local VC leads the syndicate and when an
experienced local VC leads the syndicate, test results are displayed in the last column.
The results indicate that the local VC’s experience moderates how geographical distance is related to
the potential syndication partner’s participation likelihood. The marginal effect of geographical
distance is only significantly negative when the local VC is inexperienced, while it is insignificant
when the local VC is experienced. Thus, the geographical distance of the syndication partner becomes
irrelevant when an experienced local VC leads the syndicate. In Table 6, the effects are evaluated for a
marginal change at the sample mean of geographical distance between a VC and a PC, which, in many
cases, will be a distance within a country, since our sample is dominated by local syndicates.
However, we have checked that the effect of a marginal change in geographical distance is
insignificant at all distance deciles when an experienced local VC manages the syndicate. Thus, we
can rule out the possibility that this insignificant effect of geographical distance is driven by local
deals of VCs in the United States or other countries. This result strongly supports the prediction of
Hypothesis 4 related to geographical distances. Participation likelihood is always significantly affected
by institutional distance (captured via the same law dummy variable and legal distance), irrespective
of whether the local lead VC is experienced or inexperienced. It is even more affected when an
experienced rather than an inexperienced local VC leads the syndicate. This result suggests that VCs
(and in particular experienced VCs) tend to choose partners with a similar institutional background.
These results contradict what Hypothesis 4 states about institutional distances. Institutional proximity
seems to be more important for the participation decision than the geographical one, and institutional
distance is more difficult to overcome by teaming up with an experienced local VC.
In addition, a comparison of marginal effects lends support to the hypothesis that local VCs’
experience moderates how the potential VCs’ general and within-country experience is related to their
participation likelihood. The results indicate that experienced VCs invest together. The potential
18
syndication partner’s general experience is more important when the local VC is experienced than
when the local VC is inexperienced and the difference is statistically significant. This result on the
local VC’s experience is further supported by the results on within-country experience. The
significantly differing marginal effects on the newcomer dummy variable (when the local VC is
inexperienced vs. experienced) suggest that foreign VCs without any within-country experience have a
much lower chance to be invited to participate in a deal when the local VC is experienced than when
the local VC is inexperienced. These findings lend support to Hypotheses 5a and 5b.
We also perform a number of alterations that confirm that our baseline specification describes the
main patterns. Table 7 has the same set up as Panel A of Table 5. In Column (1) we repeat the results
from our baseline regression, all other columns deliver modifications of this baseline specification. For
the likelihood of participating in syndicated deals we cannot perform estimations using the country
distance, since we need exact distances to determine the closest local VC.
In Table 8 we investigate whether deal- and PC-related characteristics, which have been included in
the deal fixed effects in our baseline regression, influence how distance is related to the participation
likelihood in syndicated deals. To this end, we divide our sample into early-stage and late-stage deals,
into first round and subsequent round deals, and, finally, into small and large deals and employ our
baseline specification within these subsamples. The results suggest that a VC’s likelihood to
participate in a syndicated deal is negatively affected by this VC’s geographical distance from the PC
only in the first financing round, if the PC is in the early stage and if the deal is small. The
geographical distance between the PC and the potential VC does not have any negative effects on this
VC’s participation likelihood in subsequent rounds, late-stage deals and large deals. In Table 8, the
effects are evaluated for a marginal change at the sample mean of geographical distance between a VC
and a PC, which, as we argued above, will in many cases be a distance between a PC and a local VC.
However, we can again exclude the explanation that this insignificant effect of geographical distance
only holds for local deals because this effect is insignificant at all distance deciles in all three
subsamples. On the contrary, institutional distance has a significant and negative effect on
participation likelihood, irrespective of the subsample. These results are in line with our previous
finding that VCs handle geographical and institutional distances differently and that they may be able
to overcome geographical distances more easily than institutional distances.
5. SUMMARY AND CONCLUSION
This paper contributes to a better understanding of international venture capital investments as, to the
best of our knowledge, we provide the first integrated analysis on how geographical and institutional
distances, general and within-country experience, and repeated relationships affect venture capitalists’
participation in worldwide deals. By applying a new approach formerly employed in the literature
analyzing the issue of how underwriters build syndicates, we are able to determine which venture
capitalists participate in which deals. To investigate the relevant spectrum, we employ two different
models. With the model of participation in cross-border deals, we investigate which foreign venture
capitalists participate in cross-border deals and put particular emphasis on the moderating effects of
19
local venture capitalists’ presence. With the model of participation in syndicated deals, we investigate
which (local and foreign) venture capitalists are invited by a local lead venture capitalist to form a
syndicate and put particular emphasis on the moderating effects of the local venture capitalists’
experience.
Our results lend support to the hypothesis that syndication with local venture capitalists helps foreign
venture capitalists in overcoming the complexity of investing in geographically distant regions. We
even identified some circumstances in which the geographical distance between the venture capitalist
and the portfolio company does not hinder this venture capitalist’s participation in syndicated deals.
More specifically, geographical distance becomes irrelevant in subsequent rounds (but it is highly
statistically and economically significant in the first round), in late-stage deals (in contrast to earlystage deals), and in large deals (in contrast to small deals). In addition, the geographical distance does
not hinder participation in syndicated deals when an experienced local venture capitalist takes the
syndicate lead. For example, when an experienced US venture capitalist leads a syndicate that invests
in a US portfolio company, a potential syndication partner from the United Kingdom has the same
participation likelihood as a potential syndication partner from Canada (assuming the two potential
partners do not differ in any other relevant aspect).
We focused not only on geographical distance between the company and the venture capitalist, but we
also analyzed how differences in countries’ legal and cultural institutions (institutional distance) affect
participation likelihoods. Another new finding from our paper is that venture capitalists handle
geographical and institutional distances differently. Institutional distance appears to be more difficult
to overcome. While, for example, experienced venture capitalists do not seem to attribute any
importance to the geographical distance of their syndication partners, they do find the institutional
background of their partners to be crucial and they tend to choose syndication partners with a similar
institutional background.
Our results also support the hypothesis that syndication with local venture capitalists compensates a
lack of foreign venture capitalists’ experience. Foreign venture capitalists need less general and
within-country experience when they syndicate with local partners than when they invest alone in a
foreign portfolio company. Our findings suggest that not only large, but also smaller and less
experienced venture capitalists are able to exploit the advantages associated with internationalization.
They increase their chances to invest across borders when they cooperate with local partners that are
equally inexperienced. This result of our analysis is particularly interesting for venture capitalists with
little general and within-country experience seeking to expand their activities beyond their countries’
borders but that have not (yet) had direct access to deals in foreign countries. These investors may start
their international expansion via syndication with inexperienced local venture capitalists. In the course
of time, as they become more and more experienced and establish better contacts to other venture
capitalists through repeated interactions, they may not only gain access to cross-border deals on their
own but they may also be able to join cross-border syndicates led by more experienced venture
capitalists.
20
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23
Table 1
PC country
Australia
Austria
Belgium
Brazil
Bulgaria
Canada
Chile
China
Czech Republic
Denmark
Egypt
Estonia
Finland
France
Germany
Greece
Hong Kong
Hungary
India
Ireland
Israel
Italy
Japan
Jordan
Lithuania
Luxembourg
Malaysia
Netherlands
New Zealand
Nigeria
Norway
Philippines
Poland
Portugal
Russia
Saudi Arabia
Singapore
South Africa
Spain
Sweden
Switzerland
Thailand
Turkey
Ukraine
United Kingdom
United States
Uruguay
Vietnam
Total
Number and volume (bn EUR) of deals carried out in each country
Cross-border
deals
201
44
111
12
10
387
1
270
7
107
6
6
111
503
390
1
17
10
184
129
298
155
28
1
7
15
8
146
9
1
77
2
24
26
38
0
32
9
117
244
115
5
7
3
1,214
2,854
0
5
7,947
NUMBER
VOLUME
Local
deals
92
45
152
10
2
686
2
84
3
112
1
3
162
1,140
620
9
2
6
75
84
198
124
24
1
5
2
8
187
20
1
80
0
13
21
19
2
8
26
489
380
39
1
8
1
1,540
9,370
1
21
15,879
Local
deals
0.444
0.125
0.523
1.031
na
2.142
na
0.677
na
0.331
0.030
0.004
0.129
13.553
2.693
0.573
0.004
0.005
0.550
0.252
1.405
0.858
0.235
na
0.009
0.002
0.016
0.346
0.023
0.009
0.278
0
0.010
0.056
0.155
na
0.028
0.392
4.472
0.963
0.204
na
0.031
0.022
6.904
117.193
0.001
0.051
156.728
Cross-border to Cross-border
total deals (%)
deals
68.6
1.231
49.4
0.396
42.2
0.923
54.5
1.454
83.3
0.133
36.1
6.543
33.3
0.043
76.3
4.679
70.0
0.013
48.9
1.298
85.7
0.234
66.7
0.004
40.7
0.849
30.6
21.483
38.6
8.499
10.0
na
89.5
0.564
62.5
0.093
71.0
5.515
60.6
0.940
60.1
3.200
55.6
4.406
53.8
5.856
50.0
0.106
58.3
0.028
88.2
0.962
50.0
0.600
43.8
3.211
31.0
0.035
50.0
0.033
49.0
0.843
100.0
0.001
64.9
0.043
55.3
0.416
66.7
1.161
0.0
0
80.0
0.297
25.7
0.691
19.3
5.586
39.1
3.146
74.7
1.637
83.3
0.029
46.7
0.397
75.0
0.085
44.1
14.157
23.3
53.578
0.0
0
19.2
0.033
33.5
155.432
Cross-border to
total deals (%)
73.5
76.0
63.9
58.5
na
75.3
na
87.4
na
79.7
88.6
53.8
86.8
61.3
75.9
na
99.3
95.1
90.9
78.9
69.5
83.7
96.1
na
75.3
99.8
97.3
90.3
60.5
78.2
75.2
100.0
80.7
88.2
88.2
na
91.4
63.8
55.5
76.6
88.9
na
92.8
79.6
67.2
31.4
0
39.0
49.8
Source: Authors’ calculation from Zephyr data (2000-2008).
24
Table 2
Internationalization and syndication
Number of deals
Deals
No.
%
No.
%
Dyads
Number of VCs
Mean
Median
Local deals
15,879
StandLocal
alone
syndicates
7,474
8,405
31.4
35.3
7,474
26,731
12.8
45.8
1
1
3.2
3
Standalone
2,779
11.7
2,779
4.8
Cross-border deals
7,947
Foreign-local
Foreign
syndicates
syndicates
4,523
645
19.0
2.7
19,675
1,718
33.7
2.9
1
1
4.3
4
2.7
2
Notes: In local deals all VCs are local. In cross-border deals, at least one foreign VC participates.
Source: Authors’ calculation from Zephyr data (2000-2008).
Table 3
Independent variables
Local deals
StandLocal
alone
syndicates
Distance of foreign VCs
Distance of local VCs
Distance of the closest local VC
Same law trad. in VC and PC country
Legal distance of foreign VCs
Cultural distance of foreign VCs
General experience of VCs
General experience of the closest local
VC
Newcomers
Deals with repeated relationship
Dyads with repeated relationship
Deal volume (mil. Euro)
Early stage
Subsequent round
Standalone
Cross-border deals
ForeignForeign
local synd. syndicates
GEOGRAPHICAL DISTANCE BETWEEN VC and PC* (miles)
Mean
2,724
2,774
2,702
Median
1,205
3,272
2,431
Std dev
2,696
2,295
2,383
Mean
487
727
741
Median
79
251
207
Std dev 1,165
944
1,024
Mean
487
215
266
Median
79
18
22
Std dev 1,165
500
605
INSTITUTIONAL DISTANCE BETWEEN VC and PC**
35%
48%
42%
Mean
1.46
1.22
1.75
Median
0.44
0.70
0.84
Std dev
2.06
1.48
2.04
Mean
1.28
1.31
1.46
Median
0.90
1.25
1.41
Std dev
1.10
1.06
1.17
Mean
Median
Std dev
Mean
Median
Std dev
16.2
4
39.1
16.2
4
39.1
-
EXPERIENCE OF VCs***
21.9
82.2
24.7
10
17
11
31.0
114.9
46.1
25.2
27.0
11
13
31.0
35.1
48%
47%
25.7
7
50.0
-
63%
REPEATED RELATIONSHIPS BETWEEN VCs***
56%
67%
41%
52%
58%
36%
PC AND DEAL CHARACTERISTICS****
Mean
11.4
12.9
30.8
20.7
31.9
Median
3.0
7.7
5.2
11.7
10.0
Std dev
56.8
28.1
132.1
78.8
160.5
36%
46%
21%
44%
41%
21%
47%
38%
59%
47%
Notes: In local deals all VCs are local. In cross-border deals, at least one foreign VC participates. * only for deals for which
each VC’s and the PC’s geographical location (latitude and longitude) are available. ** only for deals for which VC country
and PC country characteristics are available. *** between 2003 and 2008. **** only for companies whose characteristics
are available; fraction of subsequent round deals only for companies founded after 1999.
Source: Authors’ calculation from Zephyr data and other sources (2000-2008).
25
Table 4
Moderating effect of the local VC’s presence in cross-border deals
Sample
Local VC
Local VC
means
does not participate
participates
Difference test
log geo distance
-0.0025
-0.0045
-0.0014
0.013**
(0.000)***
(0.001)***
(0.000)***
same law
0.0040
0.0105
0.0017
0.155
(0.002)**
(0.006)*
(0.001)***
0.084*
legal distance
-0.0012
-0.0031
-0.0005
(0.001)**
(0.002)**
(0.000)*
0.537
cultural distance
-0.0003
-0.0009
-0.0001
(0.001)
(0.001)
(0.000)
0.047**
log experience
0.0038
0.0100
0.0016
(0.001)***
(0.004)**
(0.001)***
0.030**
newcomer
-0.0419
-0.1024
-0.0185
(0.013)***
(0.039)***
(0.008)**
repeated
0.0150
(0.004)***
Notes: The table reports marginal effects of the baseline specification evaluated at the sample means of all variables, when a
local VC does not participate, and when a local VC participates. The regression includes deal fixed effects and VC country
dummies (see also Column 1 in Table 5 for more details). Dependent variable is the participation in cross-border deals. It
takes the value of one if VC I (located outside the country in which the deal takes place) participates in deal J, and zero
otherwise. Independent variables are defined in the appendix. The last column indicates whether the difference in the
marginal effects is statistically significant. For the variable repeated, a separate evaluation (when a local VC does not
participate) does not make sense since this variable captures repeated relationships to a local VC. Standard errors (in
parentheses) are clustered at PC-country and year level (see Petersen 2009). *p<0.10, **p<0.05, ***p<0.01.
26
Table 5
Participation in cross-border deals: alternative specifications
Panel A
log geo distance
same law
legal distance
cultural distance
log experience
newcomer
repeated
(1)
-0.003
(0.000)***
0.004
(0.002)**
-0.001
(0.001)**
0.000
(0.001)
0.004
(0.001)***
-0.042
(0.013)***
0.015
(0.004)***
colonial ties
same language
Spamann distance
disclosure distance
(2)
-0.003
(0.001)***
0.004
(0.002)**
-0.001
(0.001)***
0.000
(0.001)
0.004
(0.001)***
-0.046
(0.014)***
0.016
(0.005)***
0.002
(0.001)
0.003
(0.002)
-0.001
(0.000)*
0.001
(0.003)
(3)
-0.013
(0.006)**
0.023
(0.015)
-0.006
(0.003)**
0.003
(0.005)
0.020
(0.010)**
-0.201
(0.077)***
0.077
(0.037)**
log deal volume
early stage
subsequent round
mcapVC
0.011
(0.009)
0.001
(0.003)
0.001
(0.001)
0.001
(0.003)
0.002
(0.003)
0.000
(0.000)
R&DVC
taxVC
vci_indexVC
growthVC
VCsizeVC
mcapPC
R&DPC
taxPC
vci_indexPC
growthPC
VCsizePC
deal fixed effects
VC country dummies
industry dummies
year dummies
Pseudo R2
Wald test
Number of obs. (VC-deal pairs)
… Prob=0
… Prob=1
Number of VCs
Number of deals
Number of countries
yes
yes
no
no
0.243
6,208.14
3,811,567
3,808,920
2,647
2,288
2,064
25
yes
yes
no
no
0.243
7,715.68
3,811,567
3,808,920
2,647
2,288
2,064
25
yes
no
no
no
0.239
4,872.53
3,811,567
3,808,920
2,647
2,288
2,064
25
(4)
-0.081
(0.015)***
0.189
(0.041)***
-0.021
(0.021)
0.033
(0.023)
0.151
(0.029)***
-0.483
(0.090)***
0.285
(0.033)***
(5)
-0.081
(0.024)***
0.189
(0.063)***
-0.021
(0.024)
0.033
(0.031)
0.151
(0.042)***
-0.483
(0.145)***
0.285
(0.045)***
0.046
(0.012)***
0.007
(0.017)
0.038
(0.021)*
0.008
(0.033)
0.035
(0.023)
0.006
(0.006)
0.031
(0.019)*
0.039
(0.020)**
-0.004
(0.002)*
0.017
(0.025)
0.176
(0.037)***
-0.006
(0.002)***
-0.039
(0.022)*
-0.011
(0.024)
-0.003
(0.001)**
no
no
yes
yes
0.277
17,154.24
1,335,937
1,335,135
802
2,288
786
25
0.046
(0.022)**
0.007
(0.025)
0.038
(0.026)
0.008
(0.055)
0.035
(0.034)
0.006
(0.006)
0.031
(0.033)
0.039
(0.031)
-0.004
(0.002)*
0.017
(0.034)
0.176
(0.057)***
-0.006
(0.003)*
-0.039
(0.029)
-0.011
(0.031)
-0.003
(0.002)
no
no
yes
yes
0.277
2,137.52
1,335,937
1,335,135
802
2,288
786
25
27
Panel B
log geo distance
same law
legal distance
cultural distance
log experience
newcomer
repeated
(1)
(2)
US PCs
only
-0.002
(0.001)***
0.008
(0.006)
-0.001
(0.000)
0.000
(0.001)
0.003
(0.001)**
-0.013
(0.009)
0.019
(0.009)**
yes
yes
no
no
0.247
2,851.01
European
PCs only
-0.003
(0.001)***
0.008
(0.004)**
-0.003
(0.001)**
-0.002
(0.001)*
0.007
(0.002)***
-0.091
(0.028)***
0.017
(0.005)***
yes
yes
no
no
0.262
2,625.67
(3)
(4)
(5)
Without
Without
Without US
UK and US UK and US
VCs and
PCs
VCs
PCs
-0.002
-0.003
-0.004
(0.000)*** (0.001)*** (0.001)***
0.005
0.010
0.017
(0.003)**
(0.004)**
(0.009)*
-0.001
-0.002
-0.004
(0.001)*
(0.001)*
(0.002)*
-0.001
-0.001
-0.002
(0.000)
(0.001)
(0.002)
0.004
0.005
0.012
(0.001)*** (0.002)**
(0.005)**
-0.078
-0.030
-0.096
(0.024)***
(0.019)
(0.039)**
0.009
0.017
0.019
(0.004)**
(0.007)**
(0.008)**
yes
yes
yes
yes
yes
yes
no
no
no
no
no
no
0.251
0.260
0.302
2,509.21
3,509.04
2,931.41
(6)
(7)
Country
distance
-0.003
(0.000)***
Country
distance
-0.005
(0.002)***
0.004
(0.002)*
-0.001
(0.001)*
0.000
(0.000)
0.003
(0.001)**
-0.023
(0.011)**
0.631
(0.120)***
yes
yes
no
no
0.436
36,037.73
0.001
(0.000)***
-0.014
(0.001)***
0.469
(0.025)***
yes
yes
no
no
0.430
112,192.38
deal fixed effects
VC country dummies
industry dummies
year dummies
Pseudo R2
Wald test
Number of obs.
866,320
2,442,496
2,244,183
1,001,719
1,020,073 27,670,651 23,355,017
(VC-deal pairs)
… Prob=0
865,497
2,440,955
2,242,814
1,000,356
1,018,923 27,651,407 23,338,990
… Prob=1
823
1,541
1,369
1,363
1,150
19,244
16,027
Number of VCs
1,274
2,288
2,288
912
1,274
6,258
6,031
Number of deals
680
1,160
1,020
1,141
915
5,441
5,065
Number of countries
25
25
25
25
24
48
32
Notes: The table reports marginal effects of conditional logit estimations (Columns 1-3 in Panel A and 1-7 in Panel B) and
simple logit estimations (Columns 4 and 5 in Panel A) evaluated at the sample means of all variables. Dependent variable is
the participation in cross-border deals. It takes the value of one if VC I (located outside the country in which the deal takes
place) participates in deal J, and zero otherwise. Independent variables are defined in the appendix. Standard errors (in
parentheses) are clustered at PC-country and year level (see Petersen 2009) except in Column 5 of Panel A, where we cluster
at the VC level. *p<0.10, **p<0.05, ***p<0.01.
Table 6
The moderating effect of local VC’s experience in syndicated deals
Sample
Inexperienced Experienced
Difference
means
lead
lead
test (p-value)
log geo distance
-0.0033
-0.0063
0.0001
0.000***
(0.0019)*
(0.0017)***
(0.0021)
same law
0.0635
0.0481
0.0756
0.039**
(0.0278)**
(0.0209)**
(0.0334)**
legal distance
-0.0236
-0.0179
-0.0282
0.001***
(0.0058)***
(0.0046)***
(0.0069)***
cultural distance
-0.0010
-0.0007
-0.0011
0.928
(0.0105)
(0.0080)
(0.0125)
log experience
0.0647
0.0490
0.0771
0.000***
(0.0070)***
(0.0063)***
(0.0081)***
newcomer
-0.3608
-0.2938
-0.4073
0.000***
(0.0370)***
(0.0326)***
(0.0378)***
old hand
-0.0622
-0.0464
-0.0752
0.000***
(0.0106)***
(0.0081)***
(0.0131)***
repeated
0.4939
0.4398
0.5223
0.000***
(0.0232)***
(0.0283)***
(0.0195)***
Notes: This table reports marginal effects of the baseline specification evaluated at the sample means of all variables with
local VC’s experience set at the 25% percentile (inexperienced lead), and at the 75% percentile (experienced lead). The
regression includes deal fixed effects and VC country dummies (see also Column 1 in Table 7 for more details). Dependent
variable is the participation in syndicated deals. It takes the value of one if VC I participates in the syndicated deal J, and zero
otherwise. Independent variables are defined in the appendix. The last column indicates whether the difference in the
marginal effects is statistically significant. Standard errors (in parentheses) are clustered at PC-country and year level (see
Petersen 2009). *p<0.10, **p<0.05, ***p<0.01.
28
Table 7
Participation in syndicated deals: alternative specifications
log geo distance
same law
legal distance
cultural distance
log experience
newcomer
old hand
repeated
(1)
-0.003
(0.002)*
0.063
(0.028)**
-0.024
(0.006)***
-0.001
(0.011)
0.065
(0.007)***
-0.361
(0.037)***
-0.062
(0.011)***
0.494
(0.023)***
colonial ties
same language
Spamann distance
disclosure distance
(2)
-0.003
(0.001)**
0.059
(0.020)***
-0.021
(0.007)***
0.001
(0.010)
0.060
(0.010)***
-0.36
(0.048)***
-0.064
(0.007)***
0.481
(0.025)***
0.032
(0.015)**
0.018
(0.020)
-0.000
-0.004
-0.002
(0.034)
(3)
-0.005
(0.003)**
0.091
(0.042)**
-0.028
(0.009)***
0.019
(0.019)
0.082
(0.020)***
-0.446
(0.072)***
-0.084
(0.028)***
0.534
(0.030)***
log deal volume
early stage
subsequent round
mcapVC
0.019
(0.024)
-0.012
(0.011)
0.002
(0.002)
-0.005
(0.006)
-0.007
(0.010)
0.000
(0.000)***
R&DVC
taxVC
vci_indexVC
growthVC
VCsizeVC
mcapPC
R&DPC
taxPC
vci_indexPC
growthPC
VCsizePC
deal fixed effects
VC country dummies
industry dummies
year dummies
Pseudo R2
Wald test
Number of obs. (VC-deal pairs)
… Prob=0
… Prob=1
Number of VCs
Number of deals
Number of countries
yes
yes
no
no
0.273
10,585.52
7,647,729
7,641,470
6,259
2,288
3,344
25
yes
yes
no
no
0.273
18,330.03
7,647,729
7,641,470
6,259
2,288
3,344
25
yes
no
no
no
0.271
12,295.66
7,647,729
7,641,470
6,259
2,288
3,344
25
(4)
-0.004
(0.002)*
0.059
(0.036)
-0.019
(0.013)
0.016
(0.013)
0.061
(0.041)
-0.336
(0.166)**
-0.045
(0.032)
0.460
(0.123)***
(5)
-0.004
(0.003)*
0.059
(0.048)
-0.019
(0.012)
0.016
(0.018)
0.061
(0.038)
-0.336
(0.155)**
-0.045
(0.032)
0.460
(0.126)***
0.037
(0.026)
-0.010
(0.007)
0.043
(0.030)
-0.005
(0.008)
-0.005
(0.006)
0.001
(0.001)
0.001
(0.003)
0.002
(0.003)
0.000
(0.000)
-0.013
(0.011)
0.022
(0.017)
-0.002
(0.001)
-0.005
(0.004)
-0.001
(0.005)
0.000
(0.000)
no
no
yes
yes
0.278
57,987.71
2,895,342
2,892,938
2,404
2,288
1,266
25
0.037
(0.024)
-0.010
(0.008)
0.043
(0.027)
-0.005
(0.008)
-0.005
(0.007)
0.001
(0.001)
0.001
(0.004)
0.002
(0.005)
0.000
(0.000)
-0.013
(0.013)
0.022
(0.020)
-0.002
(0.001)
-0.005
(0.006)
-0.001
(0.006)
0.000
(0.000)
no
no
yes
yes
0.278
6,185.80
2,895,342
2,892,938
2,404
2,288
1,266
25
29
Notes: This table reports marginal effects of conditional logit estimations (Columns 1-3) and simple logit estimations
(Columns 4 and 5) evaluated at the sample means of all variables. Dependent variable is the participation in syndicated deals.
It takes the value of one if VC I participates in the syndicated deal J, and zero otherwise. Independent variables are defined in
the appendix. Standard errors (in parentheses) are clustered at PC-country and year level (see Petersen 2009) in Columns 1-4
and at the VC level in Column 5. *p<0.10, **p<0.05, ***p<0.01.
Table 8
Company characteristics and the participation in syndicated deals
(1)
(2)
(3)
(4)
(5)
(6)
Early
Late
First
Subsequent
Small
Large
stage
stage
round
round
deals
deals
log geo distance
-0.005
-0.003
-0.001
-0.002
-0.003
-0.003
(0.003)*
(0.002)
(0.001)*
(0.003)
(0.001)**
(0.003)
same law
0.073
0.056
0.002
0.056
0.023
0.097
(0.038)*
(0.026)**
(0.010)
(0.032)*
(0.018)
(0.050)**
legal distance
-0.032
-0.020
-0.023
-0.017
-0.022
-0.016
(0.009)*** (0.007)*** (0.008)***
(0.014)
(0.008)*** (0.008)**
cultural distance
0.001
-0.003
-0.007
0.001
-0.010
0.005
(0.012)
(0.011)
(0.004)
(0.015)
(0.004)***
(0.021)
log experience
0.067
0.062
0.025
0.070
0.027
0.107
(0.011)*** (0.008)*** (0.010)** (0.012)*** (0.009)*** (0.009)***
newcomer
-0.331
-0.368
-0.189
-0.358
-0.262
-0.408
(0.053)*** (0.041)*** (0.067)*** (0.060)*** (0.063)*** (0.033)***
old hand
-0.063
-0.061
-0.022
-0.048
-0.029
-0.083
(0.015)*** (0.011)*** (0.010)** (0.014)*** (0.010)*** (0.016)***
repeated
0.429
0.514
0.085
0.575
0.357
0.532
(0.037)*** (0.026)*** (0.036)** (0.028)*** (0.079)*** (0.016)***
deal fixed effects
yes
yes
yes
yes
yes
yes
VC country dummies
yes
yes
yes
yes
yes
yes
industry dummies
no
no
no
no
no
no
year dummies
no
no
no
no
no
no
Pseudo R2
0.267
0.276
0.286
0.320
0.301
0.262
Wald test
4,552.59
13,122.44
5,434.87
32,821.22
5596.1
15,870.09
Number of obs. (VC-deal pairs)
2,460,813
5,186,916
937,670
2,131,484
2,657,494
4,432,206
… Prob=0
2,459,001
5,182,469
937,063
2,129,579
2,655,771
4,427,994
… Prob=1
1,812
4,447
607
1,905
1,723
4,212
Number of VCs
2,288
2,288
2,288
2,288
2,288
2,288
Number of deals
1,076
2,268
410
932
1,550
1,550
Number of countries
25
25
25
25
25
25
Notes: This table reports marginal effects of conditional logit estimation evaluated at the sample means of all variables for
the subsamples of early-stage and late-stage deals, first and subsequent financing rounds, large and small deals. Dependent
variable is the participation in syndicated deals. It takes the value of one if VC I participates in the syndicated deal J, and zero
otherwise. Independent variables are defined in the appendix. Standard errors (in parentheses) are clustered at PC-country
and year level (see Petersen 2009). *p<0.10, **p<0.05, ***p<0.01.
30
Figure 1
Panel A
Deals and VCs considered
Participation in cross-border deals
PC
VC
VC
PC
VC
PC
VC
PC
VC
Local VC
PC
VC
VC
PC
PC
PC country
Panel B
VC
Other country
Participation in syndicated deals
PC
VC
VC
PC
VC
PC
VC
PC
Local VC
Local VC
PC
VC
VC
PC
PC
PC country
VC
Other country
Notes: This figure visualizes which deals from all available worldwide deals we consider in our model of participation in
cross-border deals (Panel A) and in our model of participation in syndicated deals (Panel B). It also depicts how we identify
potential participating and non-participating VCs. The various PCs and VCs are categorized as follows:
PC not considered
PC considered
VC not considered
participating VC
non-participating VC
potential VC
31
Appendix: Data description and sources
geo distancePC,VC
same lawPC,VC
legal distancePC,VC
cultural distancePC,VC
Geographical and institutional distances
Distance between VC and PC based on individual addresses in miles (source: Zephyr).
Dummy variable equal to one if the VC and PC countries have the same law tradition based on
French, German, English, Scandinavian or Socialist law; zero otherwise (source: La Porta et al.
1998 and 1999).
Difference between the legality index in the VC and PC countries. The legality index is a
weighted average of the following factors: efficiency of judicial system, rule of law, corruption,
risk of expropriation, risk of contract repudiation, and shareholder rights (source: Berkowitz et
al. 2003).
Difference between Hofstede’s five dimensions index in the VC and PC countries. The five
dimensions index includes: the power distance index (D1), individualism (D2), masculinity (D3),
uncertainty avoidance index (D4), and long-term orientation (D5). We calculate it as:
5
( DdVC  DdPC ) 2
/ 5 (source: http://www.geert-hofstede.com/hofstede_dimensions.php).

d 1
Var ( Dd )
colonial tiesPC,VC
Dummy variable equal to one if the VC and PC countries were previously under common
imperial rule (source: CIA World Factbook).
same languagePC,VC
Dummy variable equal to one if the same language is spoken in the VC and PC countries
(source: www.cepii.fr).
Spamann distancePC,VC Difference between the revisited antidirector rights index in the VC and PC countries (source:
Spamann 2010).
disclosure distancePC,VC Difference between the disclosure index in the VC and PC countries (source: Cumming and
Knill 2012).
cultural distance altPC,VC Difference between index of societal cultural practices in the VC and PC countries reported in
Globe culture scales. The index includes the following societal practices: uncertainty avoidance
(D1), future orientation (D2), power distance (D3), institutional collectivism (D4), human
orientation (D5), performance orientation (D6), in-group collectivism (D7), gender egalitarianism
(D8), assertiveness (D9). We calculate it as:
9

d 1
( DdVC  DdPC ) 2
/9
Var ( Dd )
(source: House et al. 2004).
experienceIt
newcomerIJt
old handIJt
repeatedIJt
deal volumeJ
early stageJ
subsequent roundJ
localJ
mcap
R&D
vci_index
tax
growth
VCsize
VC characteristics
Number of deals carried out by VC I during the previous three years (source: Zephyr).
Dummy variable equal to one if a foreign VC I has not invested in the country in which deal J
takes place during the previous three years; zero otherwise (source: Zephyr).
Dummy variable equal to one if a foreign VC I has already invested in the country in which deal
J takes place; zero otherwise (source: Zephyr).
Dummy variable equal to one if VC I has invested together with one of the local VCs
participating in deal J (participation in cross-border deals) or with the local participating VC
closest to the deal J (participation in syndicated deals) during the previous three years, zero
otherwise (source: Zephyr).
PC and deal characteristics
Volume of deal J in mil. Euro (source: Zephyr).
Dummy variable equal to one if deal J is an early-stage deal; zero otherwise. We consider all
deals with companies older than three years as late-stage deals; the remaining deals are
classified as early-stage deals (source: Orbis).
Dummy variable equal to one if the particular investment round was preceded by another
investment round; zero otherwise. This variable is only defined for companies founded after
1999 (source: Zephyr).
Dummy variable equal to one if a local VC participates in cross-border deal J; zero otherwise
(source: Zephyr).
VC and PC country characteristics
Stock market capitalization / GDP in the country of the VC or PC (source: Worldbank).
Business R&D expenditures / GDP in the country of the VC or PC (source: IMD World
Competitiveness Online).
Venture capital legal index in the country of the VC or PC, higher value is better (source: IMD
World Competitiveness Online).
Effective personal income tax rate in % in the country of the VC or PC (source: IMD World
Competitiveness Online).
Expected real GDP growth rate for the next 3-5 years in the country of the VC or PC (source:
Datastream).
Number of VC firms located in the country of the VC or PC (source: Zephyr).
32