Cross Border Venture Capital SyndicationA Network of Emerging Trust

May 18, 2014

Daniel S. Hain 1,2 Sofia A. Johan 3 Daojuan Wang 1

1Department of Business and Management, Aalborg University

2Scancor, Stanford University (Visiting Scholar)

3Schulich School of Business, York University

1

BackgroundPositioning Venture Capital

Venture Capital and its ImpactI Venture capitalists: Financial intermediaries acting as a blend of technological and

financial competences, specialized on providing financial and managerial supportfor knowledge based entrepreneurship.

I Catalyst for commercialization (Samila & Sorenson, 2010), facilitating innovationand entrepreneurship (Kortum & Lerner, 2000), and bringing innovation to marketrapidly (Bygrave, 1992; Cumming & Johan, 2007)

Recent trends in VCI Shift towards globally distributed investment pattern (Aizenmann & Kendall, 2008;

Guler & Gullien, 2010; Baygan & Freudenberg, 2000)I Explanations of cross-border VC flows by macro and financial economists:

I Market capitalization (Black & Gilson, 1998)I Growth rates (Romain & Van Pottelsberghe, 2004)I Institutional environment (Guler & Gullien,2005; 2010)

I Recently, attention of research and VC investment community turns to emergingeconomies (Dai et al., 2012; Bruton et al., 2004)

2

backgroundMotivation

MotivationI Wile promising VC investment opportunities nowadays spring up everywhere

around the globe, we need to understand what enables the formerly more localVC industry to invest in geographically, culturally and institutionally very distanttargets.

I Prosmising candidates suggested by former research (e.g. Tykvova & Scherter,2013): (I) Institutional trust, (II) Teaming up with local VCs

I VC investments in emerging economies steadily increase, yet well lack inempirical evidence regarding the determinants on investment and entry modedecisions in such settings. Furthermore, emerging economies are oftencharacterized by weaker legal institution and property rights.

3

Theory and HypothesesGeographical, Institutional & Cultural Distance

I Interpersonal interaction necessary in pre-deal due diligence and post dealmonitoring and value adding.

I VC investment profit from spatial proximity, hence geographical concentration ofinvestments

I VC might also suffer from higher operating costs in foreign environments with verydifferent institutions, business practices, cultures and ethics

I However, teaming up with a local venture capitalists can be used to bridge highgeographical, cultural and institutional distance

Hypothesis 1HYPOTHESIS 1A - Geographical, cultural and institutional distance negatively affect venture capitalinvestment activity between countries.HYPOTHESIS 1B - The negative effects of geographical, cultural and institutional distance negativelyare less pronounced in cross border investments syndicated with a domestic VC.

4

Theory and Hypothesesrelational & Institutional Trust

I VCs do their best to mitigate agency costs of venture (Fiet, 1995a,b; Shepherdand Zacharakis, 2001) using effective contracts and governance structures toprotect themselves against opportunistic behavior

I Such risks can never be totally eliminated, especially in destination countries withless efficient laws and corporate structures (Cumming and Johan, 2006; La Portaet al., 1998, 2000, 1997)

I Institutional trust in destination country might decrease the investors needs to usemore resources to protect themselves, even in such less efficient regulatoryenvironments

Hypothesis 2HYPOTHESIS 2A - Institutional and relational trust positively affects bilateral venture capitalinvestment activity and diminishes the negative effects of geographical, social and institutionaldistance.HYPOTHESIS 2B - The positive effects of institutional trust appears stronger for investments inemerging vis-á-vis investments in developed economies.HYPOTHESIS 2C - The positive effects of institutional trust appears weaker for cross-borderinvestments syndicated with domestic venture capitalists.HYPOTHESIS 2D - The positive effects of relational trust appears stronger for cross-borderinvestments syndicated with domestic venture capitalists.

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Empirical StrategyData & Model

Data & Model SetupI Data Source: Zephir M&A Database, extracting only deals including VC

investmentsI Period 2000–2013, ca. 30,650 deals, 78 countriesI Deploying a 2-stage GLS model, correcting for zero-inflation of the many

zero-investment-pairs of country dyads

Dependent VariablesVenture Capital Investment Propensity: Measure of bilareral VC investments relative tothe size of the two economies, and the size of the VC market in the destination country

VCproptj ,i =

VCflow tj ,i/VCinvest t

i

GDP tj /GDP t

i(1)

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Empirical StrategyIndependent Variables

DistanceI Geographical: ln(km, population density adjusted)I Institutional: same (i.) language (CEPII), (ii.) legal system (La Porta, 1996l)I Cultural: Composed index of cultural difference (Kogut & Singh, 1988)

CDj , i =

5∑

u=1

Iuj − Iuivar(Iu )

2

5(2)

I Technological: Euclidian distance between the sectoral VC investment pattern inSC and DC

dist techti ,j =

√√√√√√√√ 7∑

s=1

(VCt

i ,sVCt

i total−

VCtj ,s

VCtj total

)2

7(3)

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Empirical StrategyIndependent Variables, cont’d

Relational & Institutional TrustI Institutional Trust (World Value Survey)I Bilateral Trade

tradet−1i→j =

export t−1i→j ∗export t−1

j→i

gdpt−1i ∗gdpt−1

j

(4)

I Share of syndicated VC deals in all VC deals between source and destinationcountry

Destination Country institutionsI Institutional Stability (CEPII)I Corruption Perception Index (Transparency International)

ControlsI ∆ GDP, GDP growth, market capitalization

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Empirical StrategyDescriptives

Table: Descriptive Statistics – Dyadic Venture Capital Counts

Variable Mean Std. Deviation Minimum Maximum N

DependentVC countti→j 0.568 7.301 0.000 763.000 24,596

VC propti→j 0.019 0.244 0.000 19.947 24,596

Distancedist geoi ,j 8.495 0.990 5.081 9.880 24,596dist culti ,j 0.061 0.023 0.006 0.133 20,280dist techi ,j 0.131 0.169 0.000 1.000 24,596same legali ,j 0.225 0.418 0.000 1.000 24,596same langi ,j 0.119 0.324 0.000 1.000 24,596

Institutions & Relational TrustVC syndt

i→j 0.048 0.198 0.000 1.000 24,596

cpitj * 0.631 0.236 0.170 1.000 23,822

inst stabtj 0.454 0.854 -2.118 1.668 24,596

trustj 0.494 1.199 -1.478 3.459 23,478

tradeti ,j * -0.005 0.003 -0.007 0.131 21,174

Controlsgdpt

j * 4.219 8.82 0.019 59.778 24,596

gdp growthtj 3.119 3.519 -14.072 14.781 24,553

capitalizationtj 80.371 74.504 3.640 606.001 24,037

stockstj 67.387 89.028 -0.800 741.584 24,037

The source country is denoted with subscript i , the destination country with j* indicates the variable is normalized

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Results & DiscussionAll vs. hosted deals

Figure: GLS model with endogeneous selection - VC propensity between country dyads

all Only foreign-domestic

Variable Model I Model II Model III Model IV Hypotheses

VC propt-1 + * + * + *** + ***

Distance

Dist geo - ** - ** - * - *

Dist cult - ** - ** - * *

Dist tech + + + +

Same legal + * + ** + * + *

Same lang + + + +

Institutional & Relational trust

Trust DC + *** +

Tradet-1 + + Hyp 2a, 2d: -

Institutions

Inst. stabDC - - * - -

CPIDC + + + +

Emerging Economy DC - + - +

Controls

Δ GDPDC - SCt-1 - - - -

Δ GDP growthDC - SCt-1 - * - * - -

Market CapDC - SCt-1 - * - * - ** - **

… … … … …

Year / industry yes yes yes yes

N 20.053 20.053 20.053 20.053

R2 0.13 0.13 0.42 0.42

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Results & DiscussionDeveloped vs. Emerging Economies

Figure: GLS model with endogeneous selection - VC propensity between country dyads

Dev. Emerg. Dev. Emerg. Dev. Emerg.

Variable I II III IV V VI Hypotheses

VC propt-1 + * + + *** + + *** +

Distance

Dist geo - ** - - * - - ** -

Dist cult - ** - - * - - * -

Dist tech - - - - - -

Same legal + * + + * + + * +

Same lang - - - - - -

Institutional & Relational trust

Trust DC + + ***

VC synd + *** +

Tradet-1 + + - + + + Hyp 2b: -

Institutions

Inst. stabDC - + - + - +

CPIDC - - - - - -

Controls

Δ GDPDC - SCt-1 - - - - - -

Δ GDP growthDC - SCt-1 - *** - - *** - - *** -

Market CapDC - SCt-1 - * - - * - - * -

… … … … … … …

Year / industry yes yes yes yes yes yes

N 11.080 8.973 11.080 8.973 11.080 8.973

R2 0.28 0.04 0.29 0.06 0.28 0.04

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ConclusionConcluding Remarks

Destination Country institutionsI We provide a nuanced analysis on the effects of geographical, cultural and

institutional distance on cross-border VC dealsI We indeed find these distances to negatively affect VC investment activity

between country dyadsI In case the deal includes at least one local VC from the destination country, these

effects diminishI Institutional trust in the destination country facilitates cross-border deals, but effect

looses significance for deals with local co-investor→ substitution effect ofrelational and institutional trust

I However, that all changes substantially in case the destination country is aemerging economy→ all well established determinants loose their explanatorypower, model yet unable to explain these pattern

Avenues for further researchI Analyzing the micro foundations of cross-border VC dealsI Shed light on investment decisions and investor composition in emerging

economies

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ConclusionPreview: New Micro Model on Domestic Participation in cross border deals

Figure: Probit - Cross-Border Deal includes at least one Local Investor

all Developed DC Emerging DC

Variable I II III IV V VI Hypotheses

Destination Country

GDPDC + *** + *** + *** + *** + ** + *

GDP growthDC - - - * - * - ** - **

Market CapDC + ** + ** + *** + *** - *** - ***

CPIDC - *** - * - *** - * + +

TrustDC - - - - + *** + ***

Emerging EconomyDC - *** - ***

Dyad

Dist geomean + ** + ** + + + *** + ***

Dist cultmean - ** - - * - - *** - ***

Same legalmax + ** + ** + ** + ** + ** + **

Same langmax

Aquiring foreign VC

Exp sectormax - *** - *** -

Exp countrymax + *** + *** -

Exp targetmax + *** + *** + **

Controls

Year / industry yes yes yes yes yes yes

N 7,251 7,251 6,056 6,056 1,195 1,195

Pseudo R2 0.10 0.13 0.04 0.07 0.11 0.12

Log Likelyhood - 4,375 - 4,257 -3,704 -3,582 -618 -614

13Feedback

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