Zhen, Ni; Tes a, Giuseppina; Compañó, Ramón
Wo king Pape
Small-wo ld ne wo ks, dynamics and p oximi y in
in es men decisions
JRC Wo king Pape s on Co po a e R&D and Inno a ion (CoRDI), No. 2/2025
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Small-Wo ld Ne wo ks, Dynamics and P oximi y in
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JRC141795
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How o ci e his epo : N, Zhen, G, Tes a, R, Compañó, Small-Wo ld Ne wo ks, Dynamics and P oximi y in In es men Decisions, Eu opean
Commission, 2025, Se ille JRC141795.
1
Con en s
Abs ac .................................................................................................................................................................................................................................... 2
Acknowledgemen s ............................................................................................................................................................................................................ 3
Execu i e summa y ............................................................................................................................................................................................................ 4
1 In oduc ion .................................................................................................................................................................................................................... 5
2 Li e a u e e iew ......................................................................................................................................................................................................... 7
3 Theo e ical backg ound and hypo hesis ......................................................................................................................................................... 9
3.1 De e minan s o In es men Decisions in Ven u e Capi al Fi ms ......................................................................................... 9
3.1.1 The ole o cul u al p oximi y .................................................................................................................................................... 9
3.1.2 The ole o spa ial p oximi y ..................................................................................................................................................... 9
3.1.3 The ole o syndica ion and ne wo ks ............................................................................................................................... 10
3.1.4 O he de e minan s ..................................................................................................................................................................... 11
4 Da ase , Ne wo k Analysis, and Adjacency Ma ix ................................................................................................................................. 13
4.1 Da ase .............................................................................................................................................................................................................. 13
4.2 Ne wo k analysis .......................................................................................................................................................................................... 14
5 Econome ic modelling, case con ol and a iables .............................................................................................................................. 17
5.1 Econome ic modelling and a iables ............................................................................................................................................... 17
5.2 Case con ol ..................................................................................................................................................................................................... 18
6 Desc ip i e S a is ics .............................................................................................................................................................................................. 20
7 Es ima ion esul s .................................................................................................................................................................................................... 24
7.1 Main esul s ..................................................................................................................................................................................................... 24
7.2 Robus ness check ......................................................................................................................................................................................... 30
8 Conclusion and Policy Implica ions ................................................................................................................................................................. 34
Re e ences ........................................................................................................................................................................................................................... 36
Lis o ables....................................................................................................................................................................................................................... 41
Annexes ................................................................................................................................................................................................................................. 42
Annex 1. De ini ion o Va iables and Da a Sou ces .............................................................................................................................. 42
Annex 2. De ini ions o Key Concep s in Ne wo k Analysis ............................................................................................................... 43
2
Abs ac
Using deal-le el mic o da a om he Deal oom da abase, we cons uc a dynamic co-in es men
syndica ion ne wo k o examine he in luence o cul u al p oximi y and geospa ial p oximi y
be ween in es o s and s a -ups, as well as he ne wo k posi ion o global VC i ms on in es men
decisions in Eu opean-based s a -ups. By applying a linea p obabili y eg ession model wi h high-
dimensional ixed e ec s o e he pe iod 2015-2022, we con i m ha bo h cul u al and spa ial
p oximi y signi ican ly acili a e VC in es men . Mo eo e , ou analysis e eals ha a p ominen
ne wo k posi ion — cha ac e ized by how well-connec ed (deg ee cen ali y) and how in luen ial
(Ka z cen ali y) wi hin he co-in es men ne wo k— subs an ially enhances VC in es men s on
accoun o he acili a ed sha ing o in o ma ion, con ac s, and esou ces among in es o s.
Fu he mo e, ou indings e eal ha small-wo ld ne wo ks, cha ac e ized by high clus e ing
coe icien s, acili a e in es men s in dis an s a -ups, helping o o e come spa ial cons ain s—an
aspec la gely o e looked in he li e a u e. Small-wo ld syndica ion ne wo ks os e us among
membe s, complemen ing each o he h ough di e en ia ion and specializa ion in indus ial
knowledge and local ma ke s, po en ially al e ing isk-a e se beha iou and enabling in es men s
ha anscend geog aphical bounda ies.
3
Acknowledgemen s
The au ho s would like o acknowledge he aluable discussions and commen s ecei ed om Da io
Dioda o, Lo enzo Napoli ano, Ma ie Lalanne, F ancesco Ren occhini, James Ga igan, Alexande
Tübke (Eu opean Commission, Join Resea ch Cen e), F ancesco Lelli (Uni e si y o T en o), and
Ji se Duijs e s (Uni e si y o G oningen), as well as om pa icipan s a he JRC's Indus ial
Inno a ion Dynamics (IID) semina se ies held in Se ille, on Decembe 16, 2024, and a he 37 h
RSEP In e na ional Con e ence on Economics, Finance and Business, which ook place on No embe
22-23, 2024, a he Uni e si y o Washing on Rome Cen e , Rome. This esea ch was conduc ed
wi hou speci ic unding om public, comme cial, o no - o -p o i sec o s. The au ho s emain
esponsible o all e o s.
4
Execu i e summa y
The EU aces signi ican unding gaps in en u e capi al (VC) in es men , as VC i ms exhibi local
bias and p edominan ly in es in nea by s a -ups. This pape aims o explo e ac o s ha acili a e
easie access o en u e capi al, iden i ying possible a ge s and policy measu es o educe egional
unding gaps. Speci ically, we deepen he analysis by in es iga ing he impac o ne wo k posi ion
o VCs wi hin he syndica ion ne wo k on VC in es men decisions, along wi h closeness o he
in es o o s a -up coun ies in cul u al and legal e ms. This pape is among he i s o examine
VC in es men decisions h ough he lens o small-wo ld ne wo ks, highligh ing he ole o
clus e ing coe icien s wi hin small-wo ld in es o communi ies on he abili y o o e come spa ial
cons ain s and in es in dis an s a -ups, a ac o la gely o e looked in he li e a u e o da e.
U ilizing deal-le el da a om he Deal oom da abase o he pe iod 2015-2022, his s udy ocuses
on s a -ups headqua e ed in he EU27, Iceland, Liech ens ein, No way, Swi ze land, and he UK,
wi h global in es o s om o e 110 coun ies. Based on a linea p obabili y eg ession model wi h
high-dimensional ixed e ec s, we ind ha cul u al p oximi y, spa ial p oximi y, and a p ominen
ne wo k posi ion—measu ed by deg ee and Ka z cen ali y wi hin he co-in es men ne wo k—
signi ican ly acili a e VC in es men s.
In addi ion, we ind ha be e -posi ioned in es o s, in e ms o deg ee and Ka z cen ali y, ha e
less incen i e o in es in dis an deals. High deg ee and Ka z cen ali y migh no compensa e o
he lack o es ablished ela ionships and us wi h dis an en ep eneu s. Be e -ne wo ked VCs
may p e e local o egional in es men s o exe mo e con ol and in luence, o le e age ne wo k
ad an ages.
Ou indings highligh he in luence o high clus e ing coe icien s wi hin small-wo ld in es o
communi ies in o e coming spa ial cons ain s o in es in dis an s a -ups—a ac o ha has been
la gely o e looked in he exis ing li e a u e. Pa icipa ion in in e na ional, closely connec ed
ne wo ks enhances us wi hin he ne wo k, po en ially al e ing isk-a e se beha io s and
anscending geog aphical bounda ies.
The indings o his s udy ha e signi ican policy implica ions. To add ess egional unding
dispa i ies, policymake s should implemen policy ins umen s o os e en u e capi al syndica ion,
pa icula ly wi h well-connec ed and in luen ial in es o s. Policymake s should p omo e amilia ,
us ed pa ne ships. Encou aging syndica ion wi h global in es o s in small-wo ld ne wo ks can
help anscend geog aphical bounda ies, educing egional unding gaps, os e ing a mo e
in eg a ed and collabo a i e global in es men landscape.
5
1 In oduc ion
Ven u e capi al (VC) i ms a e i al sou ces o unding o eme ging companies, new en u es, no el
indus ies, and echnology clus e s (Gi audo e al., 2019; Gompe s and Le ne , 2001; Be oni e al.,
2015; Ghinami and Mon eso , 2023). An e ec i e VC ma ke plays an essen ial ole in p omo ing
sus ainable economic g ow h, c ea ing jobs, os e ing inno a ion, de eloping he en ep eneu ial
ecosys em, and suppo ing egional de elopmen (Ko um and Le ne , 2000; De la Dehesa, 2002;
Chen, 2009; Lu z e al., 2013).
En ep eneu s o en s uggle o secu e ex e nal capi al due o in o ma ion asymme ies and agency
issues (Gompe s and Le ne , 2001). Specialized equi y in es o s, such as en u e capi alis s (VCs)
and business angels (BAs), a e mo e adep a add essing hese asymme ies han adi ional
inancial ins i u ions due o supe io sc eening, moni o ing, and s aging capabili ies (Kaplan and
S ombe g, 2001). VCs ypically engage in close o e sigh o he i ms hey in es in (Le ne , 2022),
helping o mi iga e inancial cons ain s du ing he ea ly s ages o a i m’s de elopmen (Clemen i
and Hopenhayn, 2006; Cooley and Quad ini, 2001; Desai e al., 2003), and con ibu ing o he
p o essionaliza ion o he i m (Hellmann and Pu i, 2002; Pu i and Za u skie, 2012). Consequen ly,
companies backed by VCs ha e lowe ailu e a es wi hin hei i s i e yea s compa ed o hose
wi hou VC inancing (Pu i and Za u skie, 2012).
O e he pas decade, Eu ope has o e all been wi nessing a conside able ise in VC in es men s.
Acco ding o he 2022 Pi chBook Eu opean Ven u e Repo , he olume o VC deals inc eased om
19.2 billion eu os in 2015 o 91.6 billion eu os in 2022 (
1
). This inc ease has howe e been une en.
S a -ups in unde de eloped egions ace g ea challenges in accessing VC esou ces compa ed o
s a -ups in economic hubs. The lack o adequa e inancing op ions hinde s success ul
en ep eneu ship, especially in Eu ope, whe e ex e nal equi y in es o s a e mo e isk-a e se han
hei US coun e pa s (Be oni e al., 2015).
Regional unding gaps imply an unde - ep esen a ion o VC in es men s in ce ain a eas, ela i e o
hei sha e o na ional economic ac i i y (Mason, 2007). These gaps a e pa icula ly e iden in VC
in es men s (Mason, 2007; Ma in e al., 2005, 2002), wi h he equi y gap anging om 0.7% o
GDP in he Ne he lands o 13.05% in Romania (McCahe y e al., 2016). C oss-bo de in es men s
wi hin Eu ope accoun ed o only 23.1% o o al VC ac i i y on a e age om 2007 o 2020
(Asd ubali, 2023).
In ields whe e quali y and isk is di icul o measu e objec i ely, such as en u e capi al
in es men , epu a ion and in luen ial ne wo ks a e essen ial o secu ing access o esou ces and
ewa ds (Za a and Caselli, 2024; F aibe ge e al., 2018). Ven u e capi alis s o en depend on
amilia local ne wo ks o mi iga e unce ain y by building mu ual us h ough epea ed
in e ac ions, and sha ing in o ma ion wi h o he in es o s, consul an s, and accoun an s (Flo ida and
Kenney, 1988; Flo ida and Smi h J , 1993; Mason, 2007). In ac , VCs epo ha 58% o hei deal
low o igina es om hei ne wo ks (Za a and Caselli, 2024; Gompe s e al., 2016). The e o e, he
success o VCs depends on hei ole wi hin syndica ion ne wo ks, pa icula ly on how well-
connec ed and in luen ial hey a e.
By and la ge, VC in es men is cha ac e ized by local bias (Cumming and Dai, 2010; Zook, 2002;
Lu z e al., 2013; Mason, 2007); and clus e ing pa e ns (So enson and S ua , 2001; Mason, 2007).
Local bias e e s o he endency o VC i ms o in es in nea by s a -ups, while clus e ing pa e ns
indica e ha VC i ms a e usually concen a ed in majo inancial cen es and high- ech egions
(
1
) 2022 Annual Eu opean Ven u e Repo by Pi chBook
h ps:// iles.pi chbook.com/websi e/ iles/pd /2022_Annual_ Eu opean_Ven u e_Repo .pd .
6
(Mason, 2007). Local bias p ima ily s ems om he sca ci y o publicly a ailable in o ma ion on
new and eme ging businesses, as his in o ma ion is o en pe sonal, in o mal, and di icul o
access o e long dis ances (Mason, 2007). Spa ial p oximi y enables in es o s o selec and moni o
in es ee companies mo e e ec i ely, leading o a concen a ion o VC in es men s in co e egions
while neglec ing economically lagging a eas (Ha ison e al., 2010). Clus e ing pa e ns in inancial
cen es p o ide VC i ms wi h essen ial access o he knowledge and expe ise needed o iden i y
deals, s uc u e in es men s, and suppo hei po olio companies. P oximi y o o he inancie s,
en ep eneu s, legal, accoun ing, and consul ancy i ms also plays a c ucial ole du ing he
in es men p ocess (Mason, 2007). This pape aims o explo e ac o s ha acili a e easie access
o en u e capi al, iden i ying possible a ge s and policy measu es o educe egional unding gaps.
Speci ically, we deepen he analysis by in es iga ing he impac o ne wo k posi ion o VCs wi hin
he syndica ion ne wo k on VC in es men decisions, along wi h closeness o he in es o o s a -up
coun ies in cul u al and legal e ms. This issue is policy- ele an , as adequa ely unded
en ep eneu ship can con ibu e o economic de elopmen in lagging egions and educe c oss-
egional dispa i ies.
The no el con ibu ion o his pape is wo old. Fi s , o ou knowledge, his s udy is among he i s
o inco po a e ne wo k analysis in o VC in es men decisions wi hin a empo al con ex (So enson
and S ua , 2001; Ghinami and Mon eso , 2023). Unlike p e ious s udies ha p ima ily ocus on US
VC samples, his analysis le e ages he Deal oom da ase (2015–2022), co e ing s a -ups
headqua e ed in he EU27, Iceland, Liech ens ein, No way, Swi ze land, and he UK, wi h global
in es o s om o e 110 coun ies. Second, his s udy is among he i s o examine VC in es men
decisions h ough he lens o small-wo ld ne wo ks, highligh ing he ole o inhe en ne wo k
s uc u es in shaping in es men choices om an empi ical pe spec i e. Small-wo ld ne wo ks,
cha ac e ized by sho pa h leng hs and high local clus e ing coe icien s, ha e gained a en ion as
powe ul o ganizing mechanisms ha enhance pe o mance by acili a ing e icien knowledge
ans e , lea ning, and collabo a ion (Uzzi e al., 2007). While s udied in con ex s such as s a egic
alliances, pa en ing in en o s, co-au ho ship ne wo ks, ac o s and musicians, small-wo ld ne wo ks
emain la gely unexplo ed in en u e capi al co-in es men (Uzzi e al., 2007). To ou knowledge,
his s udy is he i s o iden i y he dis inc impac o high clus e ing coe icien s wi hin small-wo ld
in es o communi ies on he abili y o o e come spa ial cons ain s and in es in dis an s a -ups, a
ac o la gely o e looked in he li e a u e o da e.
The pape is s uc u ed as ollows: Sec ion 2 e iews he key li e a u e. Sec ion 3 iden i ies he
ela ionship be ween cul u al and geospa ial p oximi ies, syndica ion and ne wo k on VC in es men
decisions and pos ula es se e al hypo heses. Sec ion 4 p o ides a summa y o da a cons uc ion
and ne wo k analysis. Sec ion 5 discusses he econome ic model, case con ol and a iable
selec ion. Sec ion 6 p esen s desc ip i e s a is ics. Sec ion 7 p esen s he es ima ion esul s and
addi ional obus ness checks. Finally, Sec ion 8 syn hesizes he main indings, discusses policy
implica ions, and concludes he s udy.
13
4 Da ase , Ne wo k Analysis, and Adjacency Ma ix
4.1 Da ase
This pape uses Deal oom, a comme cial da abase co e ing s a -ups, scale-ups, g ow h and ech
companies om 2015 o 2022. The s udy ocuses on s a -ups headqua e ed in he EU27
coun ies, along wi h Iceland, Liech ens ein, No way, Swi ze land, and he UK, while global in es o s
a e loca ed ac oss 110 coun ies. We speci ically examine equi y inancing ounds classi ied as
Angel, Seed, Ea ly VC, and La e VC (
3
). O he deal ypes such as acquisi ions, buyou s, non-VC
g ow h equi y, IPOs, media o equi y, me ge s, p i a e placemen VC, seconda y deals, SPAC IPOs,
deb inancing, g an s, and con e ibles a e excluded om he analysis (
4
). Addi ionally,
c owd unding i ms a e excluded based on he a iable in es o ype. The inal sample consis s o
en u e capi al (VC) in es o s, including business angels, go e nmen en u e capi al in es o s,
co po a e in es o s and in es men banks (
5
). App oxima ely 83.86% o he deals a e classi ied as
Angel, Seed, o Ea ly VC ounds.
The inal da ase comp ises 40,556 deals in ol ing 20,221 in es o s om 110 coun ies, who ha e
in es ed in 25,687 Eu opean s a -ups ac oss he EU-27, as well as Iceland, Liech ens ein, No way,
Swi ze land, and he UK. I is impo an o no e ha in es o s a e global and ee o o m
syndica ions wi hou dis inguishing be ween EU and non-EU coun ies. The numbe o deals pe
annum ises s eadily om 3,384 in 2015 o 6,691 in 2022, nea ly doubling o e eigh yea s and
unde sco eing he g owing impo ance o he VC indus y.
On a e age, a VC i m in es s in 3.5 companies and pa icipa es in 1.6 ounds, wi h he conside able
a ia ion. No ewo hy examples include Bpi ance, which in es ed in 693 s a -ups du ing he pe iod
om 2015 o 2022. In es o s like Bpi ance, Ba e y Ven u es (US-based), and Goldman Sachs a e
ac i e ac oss all in es men s ages, including Angel, Seed, Ea ly VC, G ow h Equi y VC, and La e VC,
co e ing Se ies A h ough Se ies G ounds.
Ou o he 40,556 o al, 21,761 in ol e solo in es o s, while 46.34% o he deals a e syndica ed.
On a e age, 2.2 in es o s pa icipa e in each deal, wi h he la ges syndica ion in ol ing 44
in es o s. I is impo an o no e ha la ge VC syndica es a e no always ad an ageous. Co-
in es men can in oduce syndica ion cos s, including in o ma ion asymme y, agency issues, and
coo dina ion ic ions among membe s. La ge syndica es o en ace di e ging incen i es, slowe
consensus-building, and educed s a egic agili y (B ande e al., 2002). Addi ionally, VCs a y in
epu a ion among en ep eneu s, om being “en ep eneu - iendly” o “quick o pull he igge ”
(Nanda and Rhodes-K op , 2018; B ande e al., 2002). The choice o ini ial in es o s wi hin a
syndica e has las ing consequences, as hei decision no o ein es may signal isk o po en ial
ollow-up inancie s, signi ican ly a ec ing a s a -up’s success. The e o e, selec ing syndica ion
pa ne s wisely is c ucial—balancing he bene i s o collabo a ion while ca e ully managing
associa ed cos s and isks.
(
3
) The seed s age is de ined as ounds labeled as ‘seed’. The angel s age is de ined as ounds labeled as ‘angel’. Ea ly
VC is de ined as ounds labeled as ‘ea ly VC’ o ‘se ies A’. La e VC encompasses ounds labeled as ‘g ow h equi y VC’,
‘la e VC’, o ‘se ies B’ h ough ‘se ies I’. Since he VC is iden i ied based on he ’ ound’ a iable a he han he
in es o ype, seed accele a o s a e also included in he sample.
(
4
) De ini ions o hese e ms a e a ailable ia Deal oom’s axonomy a : h ps://deal oom.co/blog/glossa y-de ini ions.
(
5
) Ven u e capi al e e s o independen , p o essionally managed unds dedica ed o equi y o equi y-linked in es men s
in p i a ely held, high-g ow h companies (Gompe s and Le ne , 2001).
14
4.2 Ne wo k analysis
Deal oom da a con ains deal-le el in o ma ion wi h iden i iable deal IDs linking o s a -ups,
in es o s, and co-in es o s. This allows o he iden i ica ion and acking o co-in es men
ela ionships wi hin a yea , acili a ing he cons uc ion o syndica ion ne wo ks.
The cons uc ion p ocedu e is es ablished as ollows: he da ase is i s spli on an annual basis. A
bina y ma ix M is hen cons uc ed o indica e he ela ionships among all in es o IDs and all deal
IDs in a gi en yea . The ma ix elemen s ake he alue 1 i he e is an in es men om a speci ic
in es o o a speci ic deal, and 0 o he wise. By applying he ule MM′, a weigh ed adjacency ma ix
is c ea ed, whe e each ma ix elemen indica es he numbe o co-in es men s be ween wo
in es o s in a gi en yea . The analyzed ne wo k is ep esen ed by an undi ec ed adjacency ma ix
due o he absence o da a on he lead ole played by speci ic VC i ms. Consequen ly, he e is no
di e en ia ion be ween he o igina o and he ecei e in e ms o in es men di ec ion.
Ne wo ks a e dynamic en i ies, wi h new syndica ions o ming and e mina ing o e ime (Hochbe g
e al., 2007). The empo al dimension is c ucial due o he dynamic na u e o in es men s, as he
s a -up ecosys em con inuously e ol es wi h eme ging echnologies and ends (Za a and Caselli,
2024). Syndica ion ne wo ks should inco po a e he e ol ing ela ionships among in es o s along
wi h all ma ke de elopmen s (Za a and Caselli, 2024). In o de o cap u e he empo al dimension,
we cons uc ed eigh annual adjacency ma ices om 2015 o 2022. Annual ne wo ks e ec i ely
ep esen he dynamism o co-in es men o ma ion and e olu ion. In a VC co-in es men
syndica ion ne wo k, nodes ep esen en u e capi al i ms, while edges deno e co-in es men
ela ionships wi hin a speci ic yea . A dis ance o 2 indica es an indi ec link h ough a mu ual co-
in es o , and a dis ance o 3 implies a connec ion h ough wo in e media y co-in es o s. The
g ea e he dis ance be ween wo in es o s, he weake hei connec ion.
Ne wo k posi ion ep esen s how cen ally posi ioned an in es o is ela i e o o he in es o s in he
ne wo k. We use a a ie y o cen ali y measu es, including deg ee cen ali y, Ka z cen ali y, and
clus e ing coe icien s. Each highligh s di e en ace s o economic oles o a VC i m wi hin he
syndica ion ne wo k and in e ac ions wi h o he en i ies. Ou analysis employs nwcommands, an
e icien S a a package o ne wo k calcula ions (G und and Heds om, 2015).
We ocus on s a -ups om he EU 27, plus Iceland, Liech ens ein, No way, Swi ze land, and he UK.
Global in es o s a e ee o o m syndica ions wi hou dis inguishing be ween EU and non-EU
coun ies. Mo eo e , cen ali y measu es a y o e ime as ne wo ks a e upda ed annually.
The e o e, each VC i m exhibi s di e en cen ali y measu emen s ac oss yea s.
Weigh ed Deg ee Cen ali y: Deg ee cen ali y assesses he numbe o di ec connec ions a VC
i m main ains. This undamen al measu e indica es he s uc u al signi icance o a VC i m wi hin
he ne wo k. A high deg ee cen ali y sugges s ha a VC i m is well-connec ed, implying
subs an ial ac i i y and in luence wi hin he ne wo k. I such a i m exi s, many o he s would lose a
signi ican co-in es o . Fi ms wi h nume ous connec ions may ely less on any single VC o
in o ma ion o deal low, po en ially o e ing access o a b oade ange o expe ise, con ac s, and
capi al pools (Hochbe g e al., 2007).
Since ou analysis in ol es an undi ec ed, weigh ed adjacency ma ix, we calcula e weigh ed deg ee
cen ali y, which e lec s he in ensi y o coope a ion h ough he equency o co-in es men s
annually. The weigh ed deg ee ep esen s no jus he coun o connec ions bu he o al co-
in es men ac i i ies wi hin a yea wi h all co-in es o s. To ensu e empo al compa abili y, we
no malize he weigh ed deg ee by di iding by he o al numbe o nodes in he ne wo k (i.e., N − 1).
15
In his con ex , we e e o ”isola ed in es o s” as hose who do no syndica e wi h any o he VC
i ms wi hin a gi en yea , e ec i ely emaining ou side he ne wo k. Con e sely, “ solo in es o s ”
a e hose who do no co-in es in speci ic deals. Acco ding o B ande e al. (2002), isola ed
in es o s may signal highly p omising p ojec s, whe e he need o addi ional opinions is minimal,
and hey may be eluc an o sha e po en ially luc a i e deals wi h o he VCs. On he o he hand,
mode a ely p omising p ojec s a e o en syndica ed. Empi ical e idence gene ally suppo s he
alue-added hypo hesis, wi h syndica ed p ojec s yielding highe e u ns han isola ed in es men s.
Ka z Cen ali y: Ka z cen ali y e alua es bo h he quan i y and quali y o a node’s connec ions. I
measu es a node’s ela i e in luence in he ne wo k by conside ing i s neighbou s’ connec ions and
applying an a enua ion ac o o dis an connec ions. This means ha nodes a he away
con ibu e less o he cen ali y measu e. Ka z cen ali y cap u es di e en aspec s o in o ma ion
o knowledge ans e . A VC i m wi h high Ka z cen ali y is in luen ial no only due o i s di ec
connec ions bu also h ough i s indi ec connec ions o o he in luen ial ac o s. This measu e
e lec s he i m’s o e all impo ance in he ne wo k, accoun ing o bo h di ec and ex ended
connec ions. The e is a s ong co ela ion be ween Ka z cen ali y and deg ee cen ali y, wi h a
co ela ion coe icien o 0.9823.
The a enua ion ac o o Ka z cen ali y ensu es ha he in luence o a e ex dec eases wi h
dis ance. This ac o should be s ic ly less han he in e se o he la ges eigen alue o he
adjacency ma ix, which, in ou case, is 57.0793 (
6
). We ocus on he Gian Connec ed Componen
o bo h desc ip i e s a is ics and es ima ion esul s wi h Ka z cen ali y. Due o he complex na u e
o he ne wo k, annual ne wo ks o en con ain mul iple disconnec ed componen s. To ensu e
compa abili y, we ocus on he la ges connec ed subg aph (GCC) wi hin each ne wo k, as he Ka z
cen ali y o nodes in di e en disjoin subg aphs canno be di ec ly compa ed. Connec ed
componen s a e subg aphs whe e nodes can be eached om each o he h ough ne wo k edges
(Coscia, 2021). We p esen an o e iew o componen dis ibu ion in Table 1. The Gian Connec ed
Componen comp ises abou 50% o he sample, whe eas obse a ions o he second-la ges
componen a e limi ed (
7
). Thus, we ocus on he Gian Connec ed Componen o desc ibing and
es ima ing Ka z cen ali y. Ka z cen ali y conside s he o e all ne wo k s uc u e, and ocusing on
he Gian Connec ed Componen p o ides a solid basis o compa ison.
Table 1. Ne wo k Componen Dis ibu ion O e Time
Yea
No.
Obse -
a ions
No. Gian
Connec ed
Componen
No. Second
La ges
Componen
% Gian
Connec ed
Componen
% Second
La ges
Componen
2015
3205
1593
4
49.70%
0.12%
2016
3920
1875
1
47.83%
0.03%
2017
5102
2597
11
50.90%
0.22%
(
6
) Consequen ly, he a enua ion ac o should be less han 1/57.0793=0.01752, and sligh ly below he gi en h eshold,
acco dingly, he a enua ion ac o is se as 0.01.
(
7
) Gian Connec ed Componen e e s o he la ges connec ed subg aph wi hin a ne wo k.
16
2018
4766
2490
3
52.25%
0.06%
2019
4300
2466
3
57.35%
0.07%
2020
5134
3081
1
60.01%
0.02%
2021
8342
5502
2
65.96%
0.02%
2022
9126
6160
9
67.50%
0.10%
Sou ce: JRC analysis
Clus e ing Coe icien : The local clus e ing coe icien o a node quan i ies how close i s
neighbou s a e o o m a clique (e e y wo dis inc e ices in he clique a e adjacen ). The
clus e ing coe icien measu es he deg ee o which nodes in a ne wo k clus e oge he , o en
summa ized by an old adage, ‘The iend o my iend is my iend’. In he con ex o a VC co-
in es men ne wo k, a high clus e ing coe icien sugges s he p esence o a small-wo ld ne wo k,
highligh ing he likelihood ha wo co-in es o s o a pa icula VC i m a e also co-in es o s wi h
each o he . This me ic se es as a p oxy o he le el o in o ma ion sha ing wi hin highly-
connec ed communi ies. In such small-wo ld ne wo ks, deal low in o ma ion is pe cei ed as mo e
eliable as e e als ca y epu a ional isks. Mo eo e , he dense connec i i y acili a es ex ensi e
exchanges o deal low oppo uni ies, indus y expe ise, aluable con ac s, and inancial esou ces
ha VC i ms can le e age (Hochbe g e al., 2007; Wa s, 1999; Uzzi e al., 2007).
In addi ion, ne wo ks wi h high clus e ing coe icien s os e lea ning and knowledge c ea ion,
enabling i ms o e alua e in es men oppo uni ies beyond. While i ms o en ely on local
sea ches, close connec ions p o ide access o no el insigh s, p omo ing explo a o y lea ning and
inno a ion (Ma ch, 1991). The combina ion o us ed collabo a ion and esh, non edundan
in o ma ion imp o es in es men decision-making, enabling i ms o anscend local biases and
geog aphic cons ain s (Sulli an and Tang, 2012; Schilling and Phelps, 2007).
We assume ha cen ali y measu es om annual ne wo ks a e compa able despi e di e ences in
he numbe o nodes and ne wo k s uc u e (Ba bou and Reine , 2003). Exac compa abili y would
equi e ne wo k ma ching o alignmen me hods o iden i y co esponding nodes and subne wo k
ma ches, hough exac g aph ma ching is NP-ha d, and app oxima e me hods a e o en
compu a ionally in ensi e and yield limi ed alignmen (Ba bou and Reine , 2003). Combining
annual ne wo ks in o a single comp ehensi e ne wo k would inc ease disconnec ed componen s,
making Ka z cen ali y compa isons imp ac ical.
To acili a e ne wo k compa isons, we ocus on he ela i e anking o nodes based on cen ali y
measu es wi hin each ne wo k. To s anda dize hese measu es ac oss yea s, we apply min-max
scaling o no malize all measu es o he ange [0,1]. I is wo h no ing ha while deg ee and
clus e ing coe icien s a e node-speci ic p ope ies, Ka z cen ali y e lec s he s uc u e o he
en i e ne wo k.
17
5 Econome ic modelling, case con ol and a iables
5.1 Econome ic modelling and a iables
Ou objec i e is o explain he likelihood o es ablishing an in es men ela ionship be ween a
speci ic VC in es o and a s a -up. The uni o analysis is he sample o he po en ial ma ches
( ealized and un ealized) be ween an indi idual s a -up and an indi idual VC i m. We es ima e he
p obabili y o in es men decision wi h he ollowing econome ic model:
Dealis, =α+β1Cul u al_P oximi yis+β2Geospa ial_P oximi yis+β3Ne wo ki, +γXis, +δi+µs+λ +ϵis,
(1)
Whe e i indexes in es o and s indexes s a -ups. The dependen a iable deal, is a dummy a iable
ha akes he alue 1 i in es o i inances s a -up s. We examine he impac o cul u al p oximi y,
spa ial p oximi y and ne wo k posi ion o in es o s wi hin co-in es men ne wo ks on he
in es men decision, along wi h o he con ol a iables. Cul u al p oximi y is measu ed by wo
ac o s—common language and common legal sys em--which a y a he coun y-dyadic le el
be ween in es o and s a -up, and in a ian o ime. The common language a iable is a dummy
a iable ha akes he alue 1 i he in es o and s a -up coun ies sha e an o icial language.
Simila ly, he common legal a iable is a dummy a iable ha akes he alue 1 i he in es o and
s a -up coun ies sha e he same legal amily o amewo k.
Fo common language, we use he ‘Wo ld Languages’ da ase om he Uni e si y o G oningen,
which eco ds all o icial languages in each coun y as o 2015. Fo he common legal amewo k
be ween coun y pai s, we d aw on he da ase by Po a e al. (2008), which iden i ies wo p ima y
legal adi ions: common law and ci il law, along wi h se e al sub- adi ions, including F ench,
Ge man, socialis , and Scandina ian.
Geospa ial p oximi y e e s o he loga i hmic alue o he geodesic dis ance be ween he in es o
and s a -up’s loca ions in kilome e s, calcula ed as he sho es pa h be ween wo poin s on he
Ea h’s su ace. Missing alues a e supplemen ed using da a om he C unchbase da abase, which
p o ides longi ude and la i ude o in es o s and s a -ups. This geospa ial dis ance a ies a he
in es o -company pai le el. Ne wo k posi ion, which is in es o -speci ic and ime- a ying, cap u es
he annual cen ali y and in e connec edness o VC in es o s wi hin he co-in es men syndica ion
ne wo k. The ec o Xis, includes a se o con ol a iables. Mo e speci ically, Xis, includes con ol
a iables ha a y a he coun y-dyadic le el, such as GDP-di e ence be ween he in es o and
in es ee coun ies. GDP di e ence deno es he di e ence in he loga i hmic alue o annual GDP
pe capi a be ween he in es o and s a -up coun ies, using da a om he Wo ld Bank.
In addi ion, Xis, includes con ol a iables ha a y a he in es o -s a -up pai le el, such as
indus y i , which measu es he deg ee o which he in es men aligns wi h he in es o ’s indus y
ocus. In pa icula , i is calcula ed by he a e age pe cen age o deals made by he VC in he same
indus y as he s a -up. In addi ion, s age i measu es he deg ee o which he in es men aligns
wi h he in es o ’s p e e ed s age o i m de elopmen , which is calcula ed by he a e age
pe cen age o deals a he same ound as he s a -up. I is wo h no ing ha indus y i e e s o
he pe cen age o deals made by he VC in he same indus y as he s a -up o e all ime.
The e o e, i is ime-in a ian , in con as o a iable such as log GDP-di e ence which is ime
a ying.
Las ly, Xis, encompasses a iables ha a y ac oss in es o s and s a -ups, espec i ely. In e ms o
VC-speci ic ac o s, we con ol o VC expe ience cap u ed by he o al numbe o deals by each
18
in es o by he in es men ime (
8
). In e ms o s a -up-speci ic ac o s, we con ol o s a -up age
and in es men s age. Age ep esen s he cu en yea minus he launch yea o s a -ups. We only
e ain hose s a -ups aged be ween 0 o 10 yea s om Deal oom. The in es men s age
ca ego izes deals in o Angel, Seed, Ea ly VC, and La e VC based on he a iable ‘ ound’.
To con ol o sys ema ic di e ences ac oss in es o s and s a -ups, he mos appealing way is o
inco po a e in es o s and s a -ups le el indi idual e ec s. The in es o ixed-e ec s ake ca e o
any sys ema ic di e ences ac oss in es o s, such as sc eening and moni o ing capabili y, isk
p e e ences. The s a -ups le el indi idual e ec s con ol o company cha ac e is ics, such as
capaci y o w i e compelling business p oposals, seek ex e nal unding and engage in a us -based
ela ionship. ϵis, ep esen s he idiosync a ic e o s ha a e iden ically and independen ly dis ibu ed
ac oss deals be ween s a -ups and in es o s o e ime.
The app oach o he ‘ a e e en logi ’ p oposed by King and Zeng (2001) is pa icula ly use ul o
bina y ou comes wi h a e e en s, co ec ing o he unde es ima ion o p obabili ies. Howe e , i
has limi a ions, including a s a ic se ing. To ackle unobse able in es o and company’s
cha ac e is ics, we aim o inco po a e a se o in es o ixed e ec s and s a -up ixed e ec s. The
in oduc ion o excessi e ixed e ec s in he logi model can lead o he inciden al pa ame e
p oblem and biased es ima es (G eene, 2004).
In o de o a o d daun ing amoun s o ixed e ec s on he le el o in es o s and s a -ups, gi en
he la ge numbe o ixed e ec s, we employ linea p obabili y eg ession models wi h high-
dimensional ixed e ec s acco dingly (Guima aes and Po ugal, 2010). The dimensionali y e e s o
he quan i y o ixed e ec s included in he analysis. Speci ically, we accoun o wo high-
dimensional ixed e ec s in he linea eg ession model—s a -ups and in es o s by examining bo h
ealized and un ealized deals o assess he in luencing ac o s o VC in es men decision. In
pa icula , Guima aes and Po ugal (2010) pa i ion he es ima ion equa ion be ween explana o y
a iables and dummies, hen use zigzag algo i hm o he GaussSeidel algo i hm as discussed in
Smy h (1996) o he pa i ioned equa ion o p oduce slow bu s able exac leas squa e solu ions.
In he amewo k o he a e e en model, linea p obabili y model wi h ixed e ec s p oduces mo e
accu a e es ima es and p edic ed p obabili ies han condi ional logi and logi wi h dummies, when
he dependen a iables has less han 25% o ones (Timoneda, 2021).
5.2 Case con ol
Ou uni o analysis is he sample o he po en ial ( ealized and un ealized) ma ches be ween an
indi idual s a -up and an indi idual VC i m. The dependen a iable is a dummy a iable ha
akes he alue 1 o an e ec i e deal and 0 o a case-con ol.
Ideally, we iden i y all VC candida es ha e alua e a s a -up and decide no o make he
in es men . In eali y, as his in o ma ion is una ailable we can only “make up” non-e en s (King
and Zeng, 2001). When he occu ence o he case is a e in he popula ion, conside able esou ces
in da a collec ion can be sa ed by andomly selec ing wi hin ca ego ies o dependen a iable (King
and Zeng, 2001). This is known in econome ics as choice-based o endogenous s a i ied sampling
o case-con ol design in epidemiology (B eslow, 1996; King and Zeng, 2001). The e ec i e s a egy
o case-con ol design o a e e en s is o collec all a e cases and a andom selec ion o
obse a ions o non-e en s. In o he wo ds, in o de o make alid in e ences, mo e e icien
sampling designs exis such as sampling all a ailable e en s and a iny ac ion o non-e en s,
compa ed o commonly used da a collec ion s a egies (King and Zeng, 2001).
(
8
) I should be dis inguished om he o al numbe o deals by each in es o o e he pe iod 2015-2022.
19
We implemen he case con ol based on So enson and S ua (2001) and King and Zeng (2001), by
pai ing VC i ms ha unded a s a -up in a gi en qua e o a calenda yea wi h a s a -up unded
by a di e en en u e capi alis in ha same qua e . This me hod o case-con ol design
gua an ees he andom selec ion o obse a ions o non-e en s. The da ase is he e o e spli in o
32 subsamples based on yea qua e s. Wi hin each qua e , in es o s and s a -ups a e e-
ma ched, inco po a ing ele an in es o -le el and s a -up-le el in o ma ion o ha pe iod.
Ou case con ol esul s in app oxima ely 87,617,723 addi ional po en ial deals. I ep esen s a
andom ac ion o possible deals ha a e likely o happen. Compa ed o 40,556 e ec i e deals, i
quali ies he ac ual da ase as a e e en s. To illus a e, he e a e 1026 s a -ups along wi h 1034
in es o s in he i s qua e o 2015. Pai ing s a -ups wi h in es o s independen ly wi hou ega d
o VC expe ience, in es men his o y in indus y, and geospa ial dis ibu ion would esul in
1026*1034 possibili ies, whe eas he e ec i e deals a e a ound 1037. The e o e, we in la e ou
sample by a ac o o a ound 1000.
20
6 Desc ip i e S a is ics
Table 2 p o ides an o e iew o he desc ip i e s a is ics o he ull sample, including bo h case-
con ol and e ec i e deals. The ne wo k measu es a e cons uc ed based on he e ec i e deals and
ep esen in es o -speci ic cha ac e is ics ha a e ema ched wi h s a -ups du ing he case-con ol
p ocedu e. Compa ed o he e ec i e deal sample, he ull sample ends o o e - ep esen global
in es o s. As a esul , he ull sample, in con as o he e ec i e deals, is cha ac e ized by longe
geodesic dis ances, less commonali ies in language and legal sys ems, g ea e GDP di e ences,
lowe deg ees and Ka z cen ali y, and highe clus e ing coe icien s. This is pa icula ly due o he
case con ols e-ma ching wi h non-EU in es o s, such as hose om China and he es o he
wo ld, who a e associa ed wi h highe clus e ing coe icien s.
Table 2. Summa y S a is ics o Full and E ec i e Deal Samples
Full Sample Mean
E ec i e Deal Mean
Fi m age
3.25
3.33
Log dis ance
7.12
5.05
Common legal
0.28
0.73
Common language
0.22
0.70
GDP di e ence
0.05
0.03
Indus y i
0.07
0.37
S age i
0.27
0.51
Deg ee
0.03
0.08
Ka z cen ali y
0.000033
0.000066
Clus e ing
0.44
0.38
Obse a ions
87,703,908
86,185
Sou ce: JRC analysis
Table 3 p o ides desc ip i e s a is ics o in es o s om a ious global egions, including EU, he
US, China, and he es o he wo ld (ROW) based on e ec i e deals (
9
). The da a e eals a clea
pa e n in ne wo k cen ali y: he EU exhibi s he highes deg ee, ollowed by he US, China, and
inally, he ROW. The Ka z deg ee ollows a simila dis ibu ion ac oss hese egions, unde sco ing
he highly cen al and in luen ial ole o he EU wi hin he ne wo k. In con as , he clus e ing
coe icien s exhibi an in e se pa e n, wi h China showing he highes le el o clus e ing, indica i e
o a s ong communi y-based in es ing app oach whe e co-in es o s equen ly collabo a e,
(
9
) EU27, Iceland, Liech ens ein, No way, Swi ze land and he UK.
21
ollowed by he US, ROW, and he EU. In addi ion, China demons a es he highes indus y i (0.6),
while ROW exhibi s he highes s age i (0.66), indica ing mo e ocused in es men s a egies (
10
).
The US and China also end o ha e mo e co-in es o s pe deal and la ge deal sizes (
11
).
Table 3. Summa y S a is ics by Region (EU, US, China, ROW)
EU
US
China
ROW
Fi m age
3.33
3.39
3.80
3.20
Log dis ance
4.00
8.90
9.04
8.24
Common legal
0.82
0.44
0.30
0.44
Common language
0.79
0.45
0.00
0.30
GDP di e ence
0.01
0.39
-1.47
-0.49
Indus y i
0.32
0.50
0.60
0.56
S age i
0.49
0.54
0.60
0.66
No. o deals pe in es o
32.45
15.40
6.34
10.05
Deg ee
0.09
0.07
0.06
0.03
Ka z cen ali y
0.000071
0.000055
0.000049
0.000032
Clus e ing
0.35
0.51
0.54
0.48
No. o co-in es o s
3.54
5.16
5.14
4.39
Amoun in EUR million
11.55
30.90
33.78
20.59
S age
2.61
2.79
2.95
2.65
Sou ce: JRC analysis
Table 4 summa izes key cha ac e is ics o op in es o s as measu ed by di e en ne wo k
cen ali y. ‘Top Deg ee’ e e s o he mos connec ed in es o s, de ined as hose wi h deg ee
cen ali y abo e he 90 h pe cen ile in he annual ne wo k. ‘Top Ka z’ iden i ies he mos in luen ial
in es o s wi h Ka z cen ali y abo e he 90 h pe cen ile wi hin he annual ne wo k’s Gian
Connec ed Componen (GCC). ‘Top Clus e ing Coe icien ’ highligh s in es o s whose clus e ing
(
10
) Fo gene al compa ison pu poses, we do no es ic he sample o cases whe e he annual numbe o deals in ol ing
a leas wo in es o s exceeds one.
(
11
) Va iables such as isola e and numbe o co-in es o s a e excluded om he es ima ion due o hei high co ela ion
wi h he cen ali y measu es. Addi ionally, he amoun a iable e e s o he o al deal olume; howe e , in cases o
syndica ion, he ac ual in es men amoun is unce ain due o he unclea dis ibu ion o unds among co-in es o s.
22
coe icien anks abo e he 90 h pe cen ile in he annual ne wo k, limi ed o cases whe e he annual
numbe o deals in ol ing a leas wo in es o s exceeds one.
Table 4. Summa y S a is ics o In es o s wi h Top Deg ee Cen ali y, Ka z Cen ali y, and Clus e ing
Coe icien s
Top
Deg ee
Top Ka z
(GCC)
Clus e ing
Coe icien s = 1
Numbe o o al deals
241.82
268.83
19.19
Numbe o in es o s
4.54
4.63
4.82
Deal amoun (EUR million)
24.15
26.93
22.12
Numbe o indus ies
21.86
22.60
5.80
Numbe o obse a ions
8467
6856
4992
Numbe o dis inc
in es o s
132
91
1777
Sou ce: JRC analysis
A no able inding is ha di e en cen ali y measu es cap u e dis inc aspec s o ne wo k s uc u e.
In es o s wi h he highes Ka z cen ali y demons a e he la ges numbe o o al deals pe
in es o o e he pe iod 2015-2022 (268.83), ollowed by op-deg ee in es o s (241.82), o e en
imes he co esponding numbe o he op in es o s in he small-wo ld ne wo ks (19.19). Top
deg ee and op Ka z in es o s also engage in a wide ange o indus ies (21.86 and 22.6 indus ies,
espec i ely). Con e sely, in es o s wi h high clus e ing coe icien s end o ocus on specialized
sec o s (5.8).
Mo eo e , op deg ee and op Ka z in es o s consis o a small numbe o dis inc in es o s o e he
pe iod 2015-2022 (132 and 91, espec i ely). I implies a high pe sis ence in op ne wo k posi ions
o e he pe iod 2015-2022, compa ing o a mo e di e se ep esen a ion in he op clus e ing
coe icien g oup (1,777). The g oup o op clus e ing coe icien in es o s, o alling 1,777, s ill
ep esen s only a small ac ion (8.7%) o he 20,221 in es o s o e all. The compa ison sugges s
ha he componen s o op in es o s wi h he g ea es in luence and connec ions emain s able,
whe eas he op in es o s wi h he highes clus e ing coe icien s luc ua e o e ime. This able
illus a es how ne wo k s uc u e in luences in es men s a egy. In es o s wi h b oad, ex ensi e
ne wo ks (high deg ee and Ka z cen ali y) end o pa icipa e mo e deals and engage in la ge
deals, on accoun o cen al posi ion o access o a wide in o ma ion exchange. In con as , hose
wi hin small-wo ld ne wo ks (high clus e ing coe icien ) may ocus on limi ed in es men s,
p io i ize collabo a i e, long- e m, us -based ela ionships.
Rega ding e ec i e deals, long-dis ance ansac ions (cha ac e ized by abo e-a e age geodesic
dis ances) a e p edominan ly d i en by VC in es o s om he US (67.23%), Singapo e (3.96%),
China (2.84%), and Japan (2.37%). Table 5 lis s he op 10 coun ies in ol ed in hese long-dis ance
deals.
29
clus e ing
-0.00115***
-0.0157***
(0.000052)
(0.000419)
clus e ing# log dis ance
0.00214***
(0.000058)
age
0.000187***
0.000185***
(0.000015)
(0.000015)
GDP di e ence
-0.000621***
-0.000280
(0.000156)
(0.000157)
indus y i
0.0122***
0.0122***
(0.000094)
(0.000094)
s age i
0.00576***
0.00576***
(0.000044)
(0.000044)
numbe deal by in es o
-0.0000363***
-0.0000364***
(0.000001)
(0.000001)
S age and Yea Dummies
YES
YES
N
35904729
35904729
S anda d e o s in pa en heses
* p < 0.05, ** p < 0.01, *** p < 0.001
Sou ce: JRC analysis
Highly in e connec ed VCs o en possess in es men expe ise ha is bo h sec o -speci ic and
loca ion-speci ic (Hochbe g e al., 2007). Di e en VC may ep esen dis inc pools o expe ise,
complemen ing each o he h ough di e en ia ion and specializa ion in indus y knowledge and
30
local ma ke s (Gold a b e al., 2007; So enson and S ua , 2001; Bubna e al., 2020). Small-wo ld
ne wo ks and amilia pa ne s acili a e lea ning by os e ing a be e unde s anding o pa ne s’
no ms and p ocesses (Ge le , 1995; Po e , 2000; Bubna e al., 2020). Incomple e con ac ing
heo ies, which highligh he impossibili y o o eseeing all con ingencies, also sugges a p e e ence
o amilia , us ed pa ne s. This amilia i y can enhance us and ecip oci y, leading o be e
ou comes (Guiso e al., 2004; Bo azzi e al., 2010; Bubna e al., 2020). Leading VC in es o s a e
inc easingly making in es men s ac oss a ious global egions wi hou geog aphic conce ns. This
end is e iden as many leading VC i ms ha e co-in es o s loca ed in di e en coun ies, which
enables hem o ope a e e ec i ely wi hou being con ined o a speci ic geog aphic a ea.
7.2 Robus ness check
In o de o ule ou he possibili y ha in es o s om China and he US—who ha e ela i ely high
clus e ing coe icien s and a e geog aphically dis an om EU-based s a -ups—a e d i ing he
conclusion, we conduc obus ness checks using subsample analyses o in es o s headqua e ed in
Eu ope, he US, China, and he Res o he Wo ld.
Table 9 p esen s he es ima ed esul s o linea p obabili y eg ession model wi h high-dimensional
ixed e ec s on he subsample. The model includes sepa a e e ec s o deg ee cen ali y and Ka z
cen ali y. Consis en ac oss all egions, bo h deg ee and Ka z cen ali y play a posi i e, signi ican
ole in acili a ing in es men . Gi en ha no EU coun y sha es a legal amewo k o language wi h
China, hese a iables a e excluded om he models. The no ably highe coe icien s o Ka z
cen ali y o China and he Res o he Wo ld sugges ha , being in luen ial wi hin he syndica ion
ne wo k is pa icula ly c ucial o Chinese in es o s and hose om he RoW when in es ing in
Eu ope-based s a -ups.
Table 9. Robus ness Check: Reg ession Resul s Based on he Linea P obabili y Model wi h High-Dimensional
Fixed E ec s.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
EU
US
China
ROW
EU
US
China
ROW
log_dis ance
-0.00174***
-0.00113**
0.00473
-0.00456***
-0.00186***
-0.00128*
0.00728
-0.00446***
(0.000013)
(0.000382)
(0.004280)
(0.000221)
(0.000016)
(0.000504)
(0.005705)
(0.000290)
commonlegal
-0.000483***
0
-0.000260***
-0.000437***
0
-
0.000154***
(0.000013)
(.)
(0.000034)
(0.000016)
(.)
(0.000046)
commonlan
0.00157***
0
0.000520***
0.00173***
0
0.000386***
(0.000020)
(.)
(0.000053)
(0.000026)
(.)
(0.000073)
deg ee
0.0107***
0.00966***
0.0155***
0.0130***
(0.000277)
(0.000488)
(0.002876)
(0.000907)
ka z
12.93***
14.52***
32.26***
21.47***
(0.417929)
(0.750110)
(5.487164)
(1.640808)
31
GDP_di e ence
-0.000594***
0.000743
0.00106
0.000804***
-0.000936***
0.000652
-0.000251
0.00156***
(0.000103)
(0.000405)
(0.001684)
(0.000210)
(0.000138)
(0.000570)
(0.002372)
(0.000322)
indus y_ i
0.00983***
0.00835***
0.00949***
0.00928***
0.0110***
0.00926***
0.00968***
0.00977***
(0.000061)
(0.000096)
(0.000484)
(0.000155)
(0.000080)
(0.000121)
(0.000624)
(0.000208)
s age_ i
0.00409***
0.00395***
0.00481***
0.00379***
0.00435***
0.00423***
0.00502***
0.00400***
(0.000027)
(0.000050)
(0.000263)
(0.000069)
(0.000033)
(0.000060)
(0.000339)
(0.000091)
numbe _deal_by_in es o
-
0.0000302***
-
0.0000259***
-
0.0000901***
-
0.0000514***
-
0.0000318***
-
0.0000268***
-
0.000129***
-
0.000111***
(0.000001)
(0.000002)
(0.000018)
(0.000006)
(0.000001)
(0.000002)
(0.000022)
(0.000010)
age
0.000106***
0.0000359
0.000159
0.0000480
0.000137***
0.0000343
0.000425*
0.000185***
(0.000009)
(0.000019)
(0.000118)
(0.000027)
(0.000013)
(0.000028)
(0.000175)
(0.000050)
S age and Yea Dummies
YES
YES
YES
YES
YES
YES
YES
YES
N
63038537
16451770
695778
7434476
43629365
11593069
439645
4388523
S anda d e o s in pa en heses
* p < 0.05, ** p < 0.01, *** p < 0.001
Sou ce: JRC analysis
Table 10 p esen s he es ima ion esul s based on he inclusion o he in e ac ion e m be ween
dis ance be ween s a -up and in es o s. Fo EU in es o s, hese in e ac ion e ms exhibi a
signi ican nega i e impac , suppo ing ou conclusion ha highly cen al EU in es o s end o be
sensi i e o in es men dis ance. This pa e n may e lec a mo e isk-a e se app oach. In es o s
may no al e hei isk-a e se beha iou s ega ding dis an deals despi e hei cen al ne wo k
ad an age. This inding aligns wi h empi ical e idence ha Eu opean VC in es o s a e gene ally
mo e isk-a e se and well-ne wo ked VCs may p e e local o egional in es men s o exe mo e
con ol and in luence. By con as , being in luen ial wi hin he syndica ion ne wo k in o he egions,
such as he Res o he Wo ld may enable VC i ms o pu sue dis an in es men s mo e ac i ely.
Table 10. Robus ness Check: Reg ession Resul s Based on he Linea P obabili y Model wi h High-
Dimensional Fixed E ec s, Including In e ac ion Te ms be ween Deg ee Cen ali y, Ka z Cen ali y, and
Dis ance.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
EU
US
China
ROW
EU
US
China
ROW
log_dis ance
-0.00135***
-0.000930*
0.00463
-0.00461***
-0.00136***
-0.00106*
0.00732
-0.00461***
(0.000014)
(0.000392)
(0.004249)
(0.000225)
(0.000019)
(0.000524)
(0.005666)
(0.000300)
commonlegal
-0.000467***
0
-0.000260***
-0.000428***
0
-0.000155***
(0.000013)
(.)
(0.000034)
(0.000016)
(.)
(0.000046)
commonlan
0.00161***
0
0.000520***
0.00178***
0
0.000386***
32
(0.000020)
(.)
(0.000053)
(0.000026)
(.)
(0.000073)
deg ee
0.0643***
0.0762***
0.0571
-0.0120
(0.001342)
(0.016532)
(0.111115)
(0.018331)
deg ee#
log_dis ance
-0.00866***
-0.00746***
-0.00459
0.00282
(0.000200)
(0.001850)
(0.012266)
(0.002063)
ka z
84.54***
84.70**
15.96
-50.13
(2.010540)
(26.382451)
(187.930915)
(27.045632)
ka z#
log_dis ance
-11.58***
-7.865**
1.801
8.164**
(0.299965)
(2.955387)
(20.791636)
(3.084141)
GDP_di e ence
-0.000653***
0.000761
0.00108
0.000802***
-0.000920***
0.000657
-0.000255
0.00150***
(0.000103)
(0.000405)
(0.001688)
(0.000210)
(0.000138)
(0.000570)
(0.002374)
(0.000322)
indus y_ i
0.00983***
0.00835***
0.00949***
0.00928***
0.0110***
0.00926***
0.00968***
0.00977***
(0.000061)
(0.000096)
(0.000484)
(0.000155)
(0.000080)
(0.000121)
(0.000624)
(0.000208)
s age_ i
0.00408***
0.00395***
0.00481***
0.00379***
0.00435***
0.00423***
0.00502***
0.00400***
(0.000027)
(0.000050)
(0.000263)
(0.000069)
(0.000033)
(0.000060)
(0.000339)
(0.000091)
numbe _deal_
by_in es o
-
0.0000285***
-
0.0000261***
-
0.0000891***
-
0.0000523***
-
0.0000303***
-
0.0000270***
-0.000129***
-0.000114***
(0.000001)
(0.000002)
(0.000018)
(0.000006)
(0.000001)
(0.000002)
(0.000022)
(0.000010)
age
0.0000987***
0.0000359
0.000158
0.0000498
0.000127***
0.0000345
0.000425*
0.000188***
(0.000009)
(0.000019)
(0.000118)
(0.000027)
(0.000012)
(0.000028)
(0.000174)
(0.000050)
S age and Yea
Dummies
YES
YES
YES
YES
YES
YES
YES
YES
N
63038537
16451770
695778
7434476
43629365
11593069
439645
4388523
S anda d e o s in pa en heses
*p < 0.05, ** p < 0.01, *** p < 0.001
Sou ce: JRC analysis
Table 11 displays he esul s based on clus e ing coe icien s, including in e ac ions wi h dis ance.
The main e ec s o clus e ing coe icien s e eal uni e sally nega i e impac s ac oss egions,
consis en wi h p e ious indings ha in es o s in small-wo ld ne wo ks p e e limi ed in es men s,
close collabo a ion, and us -based ela ionships. Mo eo e , he in e ac ion e ms imply ha ou
o iginal conclusion holds s ongly o in es o s in he EU and he US. The posi i e, signi ican
coe icien s o in e ac ion e m be ween clus e ing coe icien s and dis ance sugges ha high
clus e ing coe icien s indeed p omo e dis an in es men s o he EU and US in es o s. Ou
conclusion ha high clus e ing coe icien s enhance he capaci y o VC i ms o in es in dis an
s a -ups is no d i en by he p esence o in es o s om China and he US, who end o ha e high
clus e ing coe icien s and a e geog aphically dis an om EU-based s a -ups.
33
Table 11. Robus ness Check: Reg ession Resul s Based on he Linea P obabili y Model wi h High-
Dimensional Fixed E ec s wi h Clus e ing Coe icien s, Including In e ac ion Te ms be ween Clus e ing
Coe icien s, and Dis ance.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
EU
US
China
ROW
EU
US
China
ROW
log_dis ance
-0.00221***
-0.00133
0.0257**
-0.00536***
-0.00306***
-0.00278***
0.0261*
-0.00533***
(0.000021)
(0.000768)
(0.009644)
(0.000412)
(0.000034)
(0.000820)
(0.010976)
(0.000481)
commonlegal
-0.000535***
0
-
0.000384***
-0.000540***
0
-
0.000384***
(0.000023)
(.)
(0.000083)
(0.000023)
(.)
(0.000083)
commonlan
0.00217***
0
0.000654***
0.00220***
0
0.000653***
(0.000036)
(.)
(0.000128)
(0.000036)
(.)
(0.000129)
clus e ing
-0.00111***
-0.00141***
-0.0289***
-0.00162***
-0.0178***
-0.0243***
-0.0259
-0.000904
(0.000057)
(0.000140)
(0.004034)
(0.000248)
(0.000458)
(0.005014)
(0.039641)
(0.004882)
clus e ing#log_dis ance
0.00260***
0.00256***
-0.000334
-
0.0000859
(0.000067)
(0.000560)
(0.004357)
(0.000576)
GDP_di e ence
-0.00135***
0.000245
-0.00243
0.00323***
-0.00120***
0.000235
-0.00243
0.00324***
(0.000184)
(0.000933)
(0.005137)
(0.000533)
(0.000184)
(0.000933)
(0.005142)
(0.000534)
indus y_ i
0.0128***
0.0104***
0.00953***
0.0120***
0.0129***
0.0104***
0.00953***
0.0120***
(0.000113)
(0.000194)
(0.001146)
(0.000383)
(0.000113)
(0.000194)
(0.001146)
(0.000383)
s age_ i
0.00575***
0.00566***
0.00805***
0.00534***
0.00575***
0.00566***
0.00805***
0.00534***
(0.000051)
(0.000105)
(0.000773)
(0.000166)
(0.000051)
(0.000105)
(0.000772)
(0.000166)
numbe _deal_by_in es o
-
0.0000365***
-
0.0000264***
-
0.000455***
-
0.000115***
-
0.0000365***
-
0.0000264***
-
0.000455***
-
0.000115***
(0.000001)
(0.000002)
(0.000059)
(0.000011)
(0.000001)
(0.000002)
(0.000059)
(0.000011)
age
0.000202***
0.0000458
0.00482***
0.000279**
0.000198***
0.0000451
0.00482***
0.000277**
(0.000017)
(0.000044)
(0.000713)
(0.000091)
(0.000017)
(0.000044)
(0.000713)
(0.000092)
S age and Yea Dummies
YES
YES
YES
YES
YES
YES
YES
YES
N
28116971
5646296
171583
1967403
28116971
5646296
171583
1967403
S anda d e o s in pa en heses
*p < 0.05, ** p < 0.01, *** p < 0.001
Sou ce: JRC analysis
34
8 Conclusion and Policy Implica ions
This s udy in es iga es he impac o cul u al p oximi y, geospa ial p oximi y, and ne wo k posi ion
on he in es men decisions o global en u e capi al (VC) i ms in EU s a ups, u ilizing deal-le el
da a om he Deal oom da abase o he pe iod 2015-2022. By employing a linea p obabili y
eg ession model wi h high-dimensional ixed e ec s, we ind ha cul u al p oximi y, spa ial
p oximi y, and a p ominen ne wo k posi ion—measu ed by deg ee and Ka z cen ali y wi hin he co-
in es men ne wo k—signi ican ly acili a e VC in es men s.
The mos connec ed in es o s (high deg ee cen ali y) and he mos in luen ial in es o s (high Ka z
cen ali y) a e ypically in ol ed in a la ge numbe o deals, highe deal olumes, a g ea e numbe
o co-in es o s, and a b oade ange o indus ies. In con as , in es o s in small-wo ld ne wo ks—
cha ac e ized by high local clus e ing coe icien s— end o concen a e on ewe in es men s wi h
specialized sec o s, p io i izing close collabo a ion and long- e m, us -based ela ionships.
In addi ion, we ind ha in es o s wi h highe deg ee o Ka z cen ali y may no al e hei isk-
a e se beha iou s ega ding dis an deals. This inding aligns wi h empi ical e idence ha Eu opean
VC in es o s a e gene ally mo e isk-a e se compa ed o hei US coun e pa s (Bo azzi e al.,
2010). Well-ne wo ked VCs may p e e local o egional in es men s o exe mo e con ol and
in luence, hus maximizing he ad an ages o hei ne wo k posi ion.
Ou indings highligh he in luence o high clus e ing coe icien s wi hin small-wo ld in es o
communi ies in o e coming spa ial cons ain s o in es in dis an s a -ups—a ac o ha has been
la gely o e looked in he exis ing li e a u e. Pa icipa ion in in e na ional, closely connec ed
ne wo ks enhances us wi hin he ne wo k, po en ially al e ing isk-a e se beha io s and
anscending geog aphical bounda ies.
Meanwhile, we acknowledge ha syndica ion may also b ing abou disad an ages, such as
in o ma ion asymme y, agency cos s, and coo dina ion ic ions wi hin VC syndica es. La ge
syndica es o en en ail di e en incen i es and objec i es, slowe decision-making p ocesses o
each consensus, and educed s a egic esponsi eness (B ande e al., 2002; Nanda and Rhodes-
K op , 2018). VCs also ha e a ying epu a ions among en ep eneu s ega ding hei app oach,
anging om being ‘en ep eneu - iendly’ o being ‘quick o pull he igge ’ (Nanda and Rhodes-
K op , 2018). The selec ion o ini ial VC in es o s wi hin syndica es is c ucial, as hei decision no o
ein es can send ad e se signals o po en ial ollow-up inancie s and signi ican ly impac he
success o s a -up. Thus, he wise selec ion o syndica ion pa ne s is essen ial, balancing he
bene i s o syndica ion while emaining mind ul o he associa ed cos s and isks.
The indings o his s udy ha e signi ican implica ions o ou unde s anding o VC in es men
decisions. Howe e , i is essen ial o no e ha ou esul s show condi ional associa ions bu do no
necessa ily imply causal e ec s. The e o e, cau ion is wa an ed when in e p e ing ou indings, and
u he esea ch is needed o con i m he causal ela ionships be ween he a iables o in e es .
Policies should be designed and implemen ed wi h disc e ion, aking in o accoun he po en ial isks
and unin ended consequences o syndica ion.
In ligh o hese indings, policymake s may conside implemen ing policy ins umen s o os e
en u e capi al syndica ion, pa icula ly wi h well-connec ed and in luen ial in es o s. Policymake s
may p omo e amilia , us ed pa ne ships. To imp o e ou comes in VC syndica es, i is c ucial o
encou age he o ma ion o small-wo ld ne wo ks whe e amilia i y and us can be buil .
Policymake s may p o ide incen i es o VCs o engage in epea ed in e ac ions h ough e en s,
aining sessions, and collabo a i e p ojec s. This can help mi iga e he isks associa ed wi h
incomple e con ac ing and enhance he ecip oci y necessa y o success ul long- e m pa ne ships.
35
Mo eo e , encou aging syndica ion wi h global in es o s in small-wo ld ne wo ks can help anscend
geog aphical bounda ies, educing egional unding gaps, os e ing a mo e in eg a ed and
collabo a i e global in es men landscape. I is i al o encou age egional and sec o al ne wo ks o
VCs. Policymake s could suppo he c ea ion o specialized syndica ion pla o ms whe e VCs can
collabo a e based on indus y expe ise and egional knowledge. By os e ing hese connec ions, VCs
can le e age hei sec o -speci ic and loca ion-speci ic insigh s, leading o mo e in o med
in es men decisions and be e ou comes o s a -ups. This app oach can also acili a e
knowledge sha ing among VCs wi h complemen a y skills, enhancing he o e all e iciency and
e ec i eness o he syndica ion p ocess.
In conclusion, his s udy sheds ligh on he ole o cul u al p oximi y, spa ial p oximi y, and a
p ominen ne wo k posi ion—cha ac e ized by how well-connec ed (deg ee cen ali y), how
in luen ial (Ka z cen ali y), and he ex en o clus e ing (clus e ing coe icien s) wi hin he co-
in es men ne wo k—in shaping VC in es men decisions.
While ou indings p o ide aluable insigh s o bo h in es o s and policymake s, i is c ucial o
ecognize he limi a ions o ou s udy and he need o u he esea ch o con i m he causal
ela ionships be ween he a iables o in e es . By le e aging ne wo k posi ions, in es o s can mo e
e ec i ely na iga e he in es men landscape, and policymake s can de elop s a egies o add ess
egional unding gaps, which a e pa icula ly p onounced in VC in es men s o Eu ope-based s a -
ups.
By le e aging ne wo k posi ions, in es o s can mo e e ec i ely na iga e he in es men landscape,
while policymake s can de elop s a egies o add ess egional unding gaps, which a e pa icula ly
p onounced in VC in es men s o Eu ope-based s a -ups. This esea ch con ibu es equally o he
academic discou se on en u e capi al and p o ides p ac ical insigh s o bo h in es o s and
policymake s.
36
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