Economic Freedom and Cross-Border
Venture Capital Performance∗
Lanfang Wanga and Susheng Wangb
October 2011
Abstract: We investigate the determinants of cross-border venture capital (VC) performance using a large sample of 10,205 cross-border VC investments by 1,906 foreign VC firms
(VCs) in 6,535 domestic portfolio companies. We focus on the impact of a domestic country’s
economic freedom on the performance of both VC investments and portfolio companies using a probit model and the Cox hazard model. After controlling for other related factors of
domestic countries, portfolio companies, VCs and the global VC market, as well as year and
industry fixed effects, we find that a domestic country’s economic freedom is crucial to crossborder VC performance. In particular, in a more economically free country, as measured by
the raw values of, quartiles of or the ranking in the index of economic freedom (IEF), a foreign VC-backed portfolio company is more likely to pull off a successful exit through an IPO
(initial public offering) or an M&A (merger and acquisition), and a foreign VC firm is likely
to spend a shorter investment duration in the portfolio company. We also identify interesting
evidence on the impact of many other level factors of domestic countries, portfolio companies, VCs and the global VC market on cross-border VC performance.
Keywords: Cross-Border VC, Economic Freedom, Legality, Entrepreneurial Activity
JEL Classification: G15, G24, G34
∗
This project was sponsored by the National Natural Science Foundation of China (No. 71102134 and
No. 71072036) and the MOE Project for Key Research Institutes of Humanities and Social Science in Universities
(No. 10JJD630006). We gratefully acknowledge helpful comments and suggestions from Weili Ge, Qinchuan
Hou, Prem C. Jain, Qinglu Jin, Hong Xie, Guochang Zhang, and the anonymous referees.
a Corresponding author. Assistant Professor at Institute of Accounting and Finance, Shanghai University of
Finance and Economics. Email: wang.lanfang@mail.shufe.edu.cn.
b Professor at Hong Kong University of Science and Technology. Email: s.wang@ust.hk.
1. Introduction
Venture capital (VC) is widely regarded as an active driving force in fostering entrepreneurship, financing startups, creating public companies and encouraging innovations in the
U.S. over the past several decades (Barry et al., 1990; Sahlman, 1990; Lerner, 1994; Kortum
and Lerner, 2000; Kaplan and Strömberg, 2003). It is widely believed that VC is instrumental in bringing innovations to markets at a rapid pace, thereby enhancing economic growth.
VC has been increasingly internationalized, as marked by an increasing number of crossborder entries of VCs starting from the mid-1990s. Cross-border VC is crucial to growthoriented but high-risk new ventures, especially in countries where the domestic supply of
private equity is limited.
Our focus is to examine the impact of a domestic country’s economic freedom on crossborder VC performance. The idea that economic freedom is essential for economic efficiency
has been a cornerstone in economic theory since Smith (1776), who argues for the role of
“the invisible hand” in well-functioning markets. In theory, a free economy is defined as the
so-called “Arrow-Debreu world,” where economic efficiency is guaranteed in general equilibrium (Arrow and Debreu, 1954; McKenzie, 1959; Hart, 1980). In empirical studies, economic
freedom has been widely observed to be important to economic growth (Gwartney et al.,
1999; Wu and Davis, 1999; Haan and Sturm, 2000; Heckelman, 2000), income equality
(Berggren, 1999; Scully, 2002) and employment (Feldmann, 2007, 2008).
For VC investments, the role of economic freedom lies in two aspects. On the one hand,
free markets are widely believed to provide more opportunities for early-stage and mostly
not-yet-profitable companies, which is essential to VC investments (Wennekers et al., 2002;
Kreft and Sobel, 2005; Sobel et al., 2007; BjØrnskov and Foss, 2008; Nyström, 2008). The
more constraints there are on the allocation of resources in production and consumption, the
fewer growth opportunities and chances of success for VCs. On the other hand, the government is more likely to act as a “helping hand” instead of a “grabbing hand” in free markets,
which is also essential for the survival and success of VCs. In an economically free market,
the burden of bureaucracy and corruption would be smaller and the government would try to
provide a steady and reliable monetary environment, a free and open investment environment and a transparent and open financial system. Contract enforcement and investor protection, especially regarding private properties, also tend to be better in free markets. For
cross-border VC investments, economic freedom is particularly important since investment
risk, transaction costs, asymmetric information and agency problems are much more severe,
which makes these types of investments more sensitive to the domestic country’s economic
environment (Jeng and Wells, 2000; Gompers et al., 2003; Cumming et al., 2006).
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Using a large sample of 10,205 cross-border VC investments from 1,906 foreign VCs in
6,535 domestic portfolio companies covering 35 countries, we investigate the impact of a
domestic country’s economic freedom on cross-border VC performance. We provide analysis
of both VC investments and portfolio companies using both a probit model and the Cox hazard model. After controlling for other related factors of domestic countries, portfolio companies, VCs and the global VC market, as well as industry and year fixed effects, we find that a
domestic country’s economic freedom is crucial to the performance of cross-border VC. In
particular, in an economically free country, as measured by either the raw values of, quartiles
of or the ranking in economic freedom over the investment period, a foreign VC-backed portfolio company is more likely to pull off a successful exit through an IPO (initial public offering) or an M&A (merger and acquisition), and a foreign VC firm is likely to spend a shorter
investment duration in a portfolio company. Specifically, a one standard deviation increase
in our measure of economic freedom — Index of Economic Freedom, Economic Freedom
Quartile or Ranking in Economic Freedom — at the mean level increases the likelihood of a
successful exit by 2.02%, 2.76% or 3.02%, and the hazard of a successful exit by 5.35%,
12.59% or 13.69%, respectively.
Our main findings are robust to many tests, including tests on a change in economic
freedom, tests on exit choices (IPO versus M&A), tests on decomposed effects of economic
freedom, tests considering domestic country fixed effects and tests using subsamples and
alternative measures.
We further find several interesting results from other related factors of domestic countries, portfolio companies, VCs and the global VC market. For example, we find legal quality
to be positively related to the likelihood of a successful exit through an IPO or an M&A,
which is consistent with the empirical work by Cumming et al. (2006). GDP per capital is
found to be negatively related to cross-border VC performance, which is somewhat consistent with the well-known convergence hypothesis in the exogenous growth literature (Barro,
1991, 1996). Also, a domestic country’s entrepreneurial activity as measured by the number
of VC deals and the number of patents granted over the investment period is positively associated with the likelihood of a successful exit through an IPO or an M&A. Further, our results
provide evidence confirming the positive impact of portfolio company quality and local VCs’
participation and the negative impact of early stage investments and VCs’ portfolio size on
cross-border VC performance, which has been examined in previous studies (Cumming,
2006; Mäkelä and Maula, 2008; Cumming and Dai, 2010, 2011; Dai et al., 2010; Tykvová
and Schertler, 2011). In particular, our finding that market conditions have a strong impact
on the exit choice between an IPO and an M&A sheds light on the exit timing of cross-border
VCs.
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There are few studies on cross-border VC performance that are closely related to ours.1
Wang and Wang (2011) investigate the determinants of cross-border VC performance in
China using a small sample of 495 VC investments in 243 portfolio companies by 84 foreign
VCs. They document that foreign VCs’ human capital has little importance to the likelihood
of successful exits. In contrast, the domestic entrepreneurs’ experience is crucial to VC performance. Our study differs significantly from that of Wang and Wang (2011) in that we provide an international study on cross-border VC performance covering 35 domestic countries,
while Wang and Wang (2011) focus on one domestic country, China. Also, we investigate the
impact of a domestic country’s economic freedom on cross-border VC performance after
controlling for many other level factors of domestic countries, portfolio companies, VCs and
the global VC market, while Wang and Wang (2011) focus on foreign VCs’ human capital and
domestic entrepreneurs’ experience.
Dai et al. (2010) partially address cross-border VC performance using a sample of 4,254
rounds of VC financing by 468 VCs in six Asian countries. They suggest a positive role of
local VCs; that is, cooperation with local VCs helps mitigate information asymmetry and contributes to the likelihood of a portfolio company’s successful exit through an IPO. Different
from Dai et al. (2010), we emphasize the role of a country-level factor, namely the domestic
country’s economic freedom, in cross-border VC performance while controlling for many
other factors including local VCs’ participation which is also examined in their study. Our
results on the impact of local VCs’ participation are quite consistent with those of Dai et al.
(2010), and provide supporting evidence to their finding.
Another closely related study is that of Watson and George (2010), who examine the influence of several country-level factors, such as the size of the economy, business freedom,
trade protection, burden of taxation, government size, price stability, openness, corruption
and cultural distance, on the rate of return in 72 foreign acquisitions from 144 transactions
in 24 different countries. Even though the research question addressed by Watson and
George (2010) is similar to ours, there are substantial differences between the two studies.
First, they use a small sample consisting of only foreign acquisitions of private equity, which
cannot represent the bulk of cross-border VC investments. We use a large sample of 10,205
cross-border VC investments by 1,906 foreign VCs in 6,535 domestic portfolio companies
1 For a comparison of VC investment characteristics in different countries, see Black and Gilson (1998),
Lockett et al. (2002), Cumming and MacIntosh (2003), Bruton et al. (2004), Lerner and Schoar (2005), Mayer et
al. (2005), Ahlstrom and Bruton (2006), Cumming et al. (2006), Schwienbacher (2008), Cumming et al. (2010),
etc. For other aspects of cross-border VC investments (such as economic factors in attracting funding from foreign VCs and the role of local investors), see Mäkelä and Maula (2008), Balcarcel et al. (2009), Schertler and
Tykvová (2010), Chemmanur et al. (2011), Tykvová and Schertler (2011), etc. For determinants of domestic VC
performance, see Hochberg et al. (2007), Zarutskie (2007), Bottazzi et al. (2008), Gompers et al. (2008), Nahata
(2008), etc.
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during 1995–2005 covering 35 domestic countries. Second, their model is quite simple.
Many related factors of countries, investors, investees and the global market that have been
examined in existing studies are not considered. Also, they put all nine country-level factors
into a regression at the same time, which may result in multicollinearity. Lastly, most of the
country-level factors they estimate are captured by the economic freedom variable in our
paper. We have done a robustness study on the decomposed effects of economic freedom
(Section 5.3) and our findings confirm some of their results.
Our study is also related to studies on economic freedom. Economic freedom is widely
investigated in macroeconomic analyses, especially those on long-term economic development across countries. The general equilibrium theory shows that economic efficiency can be
achieved in the Arrow-Debreu world, where the market is complete, perfect and competitive.
Economic freedom measures the degree to which an economy is consistent with these underlying assumptions. Empirical studies have found that economic freedom indeed contributes
significantly to economic growth. In a study of 98 countries for the period 1960–1985, Barro
(1991) suggests that economic growth is inversely related to market distortions. Barro (1996)
further finds that economic freedom has a favorable impact on economic growth. Using an
augmented Solow model with cross-sectional data, Vanssay and Spindler (1994) also find
that economic freedom has a significant and substantial effect on economic growth.
Over the last decade or so, a number of indicators on economic freedom have become
available and they have subsequently been applied to empirical growth models. Wu and Davis (1999) identify the role of economic freedom in enhancing economic growth after controlling for political freedom. In contrast, Gwartney et al. (1999) and Haan and Sturm (2000)
find that the level of economic freedom does not contribute significantly to economic growth;
instead, a positive change in economic freedom does. Also, Heckelman (2000) suggests that
the average level of freedom in a nation, as well as many of the specific underlying components of freedom, precedes economic growth, after addressing the possibility of reverse causality with a series of Granger-causality tests.
The existing studies also explore the role of economic freedom in income equality, employment and welfare. Berggren (1999) finds that during the period 1975–1985, the higher
the degree of economic freedom, the higher the degree of income equality in a country. Scully
(2002) uses a structural model and a reduced-form model to show that economic freedom is
beneficial to both economic growth and income equality. Feldmann (2007, 2008) confirms a
positive role of economic freedom in employment. Esposto and Zaleski (1999) conclude that
the quality of life is positively associated with economic freedom.
However, the empirical literature on economic freedom has focused on its long-term
macroeconomic consequences. This paper investigates the impact of economic freedom on
cross-border VC performance at the VC investment level and the portfolio company level.
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The theoretical link between economic freedom and cross-border VC performance, as implied by the general equilibrium theory, is that an economically free economy ensures free
markets for early-stage companies and in turn improves the economic efficiency of resource
allocation. More specifically, a more economically free country can provide better growth
opportunities, greater chances of success, and more protection with less grabbing from the
government for cross-border VCs. Indeed, we find a positive association between a domestic
country’s economic freedom and cross-border VC performance after controlling for other
related factors of domestic countries, portfolio companies, VCs and the global VC market.
We contribute to the literature in several ways. First, we investigate the role of a domestic country’s economic freedom on cross-border VC performance. This is our main contribution. The literature on economic freedom has focused on long-term effects, typically economic growth, at the macro level. We provide an analysis of economic freedom at the micro
level, specifically on individual cross-border VC investments and domestic portfolio companies. Such an analysis has policy implications for fostering cross-border VC investments.
Second, our study deepens the understanding of cross-border VC investments around the
world and enriches the literature on the internationalization of VC. Studies on cross-border
VC investments are rare. With rapid globalization in recent years, cross-border VC investments have become a significant phenomenon. Such investments can have a huge impact on
the countries involved in innovations, technology transfers and entrepreneurship. Third, we
also document the effects of other country-level factors on cross-border VC performance,
especially such factors as economic development, stock market capitalization, stock market
performance, legal quality and entrepreneurial activity. Our results also address the influence of many other related factors of portfolio companies, VCs and the global VC market.
The remainder of this paper is organized as follows. Section 2 describes the sample and
the research methodology. Sections 3 and 4 provide analyses of cross-border VC performance at the VC investment level and at the portfolio company level, respectively. Section 5
conducts extensive robustness tests. Section 6 concludes the paper with a summary of our
main results.
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2. Sample and Methodology
2.1. Sample
For cross-border VC investments, we rely primarily on the SDC VentureXpert database
from Thomson Financial.2 We focus on the cross-border VC investments made by foreign
VCs in domestic portfolio companies. Here, the VCs and portfolio companies are located in
different countries.3 The primary sample comprises all cross-border VC investments made
during 1995–2005 in portfolio companies that received their initial VC funding in the same
period. We have employed the following sample selection procedure to obtain the final sample of VC investments: (1) we exclude those investments made by angels and buyout funds
and concentrate on the investments made by VC funds; (2) we exclude those investments
made in the buyout/acquisition stage or an unknown stage of portfolio companies; (3) we
focus on the first investment made by each VC firm in a portfolio company, because the performance in later rounds depends heavily on investments in earlier rounds; and (4) we exclude those investments for which we do not have matching data for our regression variables.
We ended up with 10,205 cross-border VC investment observations involving 1,906 foreign
VCs in 6,535 domestic portfolio companies. The sample covers 35 domestic countries—
Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, 4 Denmark, Finland,
France, Germany, Hong Kong, India, Indonesia, Ireland, Israel, Italy, Japan, Malaysia, Mexico, the Netherlands, New Zealand, Norway, Philippines, Portugal, Singapore, South Africa,
South Korea, Spain, Sweden, Switzerland, Thailand, the U.K. and the U.S.
[Insert Table 1]
2 An advantage of using the SDC VentureXpert database is its comprehensive coverage. This database claims
to cover over 2,300 VC funds worldwide. According to Kaplan et al. (2002), the SDC VentureXpert database
captures around 85% of the financing rounds made by American VCs. Du and Vertinsky (2008) point out that
this database also includes most of the venture-related VC deals in Europe and most of the big VC deals in Asia.
Aizenman and Kendall (2008) consider this database to be a comprehensive data source for VC deals worldwide,
with over 30 years of historical data.
3 Since the SDC VentureXpert database only provides updated information on the location of portfolio companies and VCs, we use such information to identify cross-border VC investment observations, an approach that
is consistent with existing studies on cross-border VC investments using public data sources (Balcarcel et al.,
2009; Dai et al., 2010; Schertler and Tykvová, 2010; Watson and George, 2010; Wang and Wang, 2011). We do
not have historical information for each portfolio company at the time of VC funding and the time of exit. Therefore, we cannot define cross-border VC investments as investments by foreign VCs in domestic portfolio companies at the time of investment. There might be a bias here since Cumming et al. (2009) find that relocation to the
U.S. implies much greater returns to Asia-Pacific VCs than investing in companies already based in the U.S. at the
time of VC investment using a hand-collected sample of 468 portfolio companies. As suggested by Cumming et al.
(2009) that most relocations (all but three in their sample) were moves to the U.S., we have tried to rule out the
influence of relocation on our findings by excluding the U.S. portfolio companies in a robustness test.
4 La Porta et al. (1997, 1998) do not provide legality values for China. Considering China’s importance in
cross-border VC investments, and in line with Cumming et al. (2006), we employ the average German legal origin
values for China. These values are somewhat consistent with the legal index estimates for China by Allen et al.
(2005) and Durnev and Kim (2005). In a robustness test, after excluding China from our sample, all our results
remain unchanged.
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Table 1 presents the distribution of 10,205 VC investments by funding year and development stage of a portfolio company, the distribution of 6,535 portfolio companies by country and industry, and the distribution of 1,906 VCs by global region and organizational type.
The number of observations and the corresponding percentages (in parentheses) are given.
As shown, the internet bubble in the late 1990s has affected the timing of cross-border VC
investments. The cross-border VC investments peaked in 2000 and then decreased during
2001–2003. Around 38.1% of VC investments occurred at the early or seed stage of development of a portfolio company. The U.S. is the most active in VC imports, constituting
roughly 40.9% of our sample. Those countries following the U.S., with each constituting just
over 3% of our sample, are the UK, Germany, France, Canada, China, and Israel in that order.
Our sample spans the following six industries based on the Venture Economics Industry
Classification: biotechnology, communication and media, computer-related, medical/health
/life science, semiconductors/other electronics, and non-high technology. Similar to the situation in the U.S., the majority of VC-backed portfolio companies belong to high-tech sectors,
such as the communications and media sector and the computer-related sector, which constitute about 19.5% and 40.1% of our sample, respectively. As indicated in the table, VCs
from Asia, Europe and North America constitute respectively 31.4%, 23.4% and 41.1% of the
sample. VCs from other regions constitute only 4.1% of the sample. Following the literature
on VC strategies (Bottazzi et al., 2008; Hellmann et al., 2008; Nahata, 2008), we identify
four VC firm types: independent VCs, and VCs affiliated with financial institutions, corporations, or governments. For simplicity, we refer to them respectively as traditional VCs, institutional VCs, corporate VCs and government VCs. As presented in the table, around 58.5% of
VCs are traditional VCs, whereas institutional VCs, corporate VCs and government VCs constitute respectively 18.3%, 17.6% and 4.7% of the sample.
2.2. Methodology
We measure VC performance using the likelihood of a successful exit by the end of 2009,
and call this Investment Success. A portfolio company is treated as successful if it went public or was acquired by the end of 2009. We conduct analyses of the likelihood and timing of
successful exits at both the VC investment level and the portfolio company level. Specifically,
we conduct a probit analysis with Investment Success as the dependent variable and a survival analysis using a Cox hazard model with Investment Duration as the dependent variable.
The Cox hazard model allows for right-censored data and time-varying variables, and it is a
semi-parametric model in which the hazard function is not dependent on a specific distribution of the survival time. A positive (or negative) coefficient indicates that the variable increases (or decreases) the hazard of a successful exit and shortens (or lengthens) the expected duration. We take three alternative measures of a domestic country’s economic freePage 8
dom during the investment period—Index of Economic Freedom, Economic Freedom Quartile and Ranking in Economic Freedom—as our primary explanatory variables.
Following the literature and taking into account the characteristics of cross-border VC
markets, we further control for other related factors of domestic countries, portfolio companies, VCs and the global VC market, as well as industry and year fixed effects. Specifically,
the domestic country-related factors are GDP per Capital, Market Capitalization, Market
Return, Legality, Foreign Country Legality, Same Legal Indicator, VC Deals and Patents.
The portfolio company-related factors are Total VC Funding, Valuation Disclosure Indicator,
Early Investment Indicator and Distance. The VC firm-related factors are Local VC Ratio,
VC Experience, VC Size, VC Size Squared, VC Portfolio Size, Institutional VC Indicator,
Corporate VC Indicator, Asia VC Indicator and Europe VC Indicator. The global VC marketrelated factors are VC Industry Competition, IPO Market Conditions and M&A Market Conditions.
The Appendix provides a detailed discussion of our methods and the definitions of all
regression variables. Appendix Tables 1-3 describe the components of economic freedom, the
measures of variables and the correlation matrix, respectively.
We avoid an investigation using aggregate data at the country level, which is often subject to the simultaneous critique. It is much more sensible to examine data at the VC investment level and the portfolio company level where the environment is constant and much
finer controls are available. For the analysis of VC investments, we focus only on each VC
firm at the time of its first investment in a portfolio company, with a sample size of 10,205;
and likewise, for the analysis of portfolio companies, we focus only on each portfolio company at the time of its receiving the initial VC funding, with a sample size of 6,535. The reason for this treatment is that there is typically a substantial time gap between a VC firm’s
first investment and its exit, as well as between a portfolio company’s first VC funding and its
exit. This methodology can effectively address concerns regarding reverse causality where
better cross-border VC performance can conversely lead to more economic freedom in the
domestic country. Similar approaches have been adopted by Hochberg et al. (2007), Nahata
(2008), etc.
In the Cox hazard analysis, we further allow for time variation of the variables Index of
Economic Freedom, Economic Freedom Quartile, Ranking in Economic Freedom, GDP per
Capita, Market Capitalization, Market Return, VC Deals, Patents, IPO Market Conditions
and M&A Market Conditions to alleviate the problem of measuring unsuccessful exits. The
hazard framework has the added advantage of being able to capture the market conditions at
the time of a domestic country’s macroeconomic development and exit to determine their
impact on the likelihood of a successful exit, for which a simple probit model cannot do.
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In summary, our basic regression models are:
The probit model:
Investment Success = a0 + a1 Economic Freedom + a2 Country-related factors
(1)
+a3 Portfolio company-related factors + a4 VC firm-related factors
+a5 Global VC market-related factors + Industry dummies + Year dummies.
The Cox hazard model:
Investment Duration = b1 Economic Freedom + b2 Country-related factors
+b3 Portfolio company-related factors + b4 VC firm-related factors
(2)
+b5 Global VC market-related factors + Industry dummies +Year dummies.
Here, “Economic Freedom” is either Index of Economic Freedom, Economic Freedom Quartile or Ranking in Economic Freedom.
2.3. Summary Statistics and Univariate Analysis
Table 2 presents the summary statistics at the VC investment level. 5 The quartiles,
means, standard deviations and the number of observations are presented. We have very
similar data distributions at the portfolio company level (not reported).
[Insert Table 2]
Only 15.8% of the cross-border VC investments exit successfully through IPOs or acquisitions, a level that is much lower than the 33.5% reported by Nahata (2008) and the 41.8%
reported by Cochrane (2005) for U.S. domestic VC investments. The average Investment
Duration is 2,826 days with a median of 3,072 days.6 The means (medians) of Index of Economic Freedom, Economic Freedom Quartile and Ranking in Economic Freedom are respectively 74.07 (78.57), 2.47 (2) and 0.65 (0.79).
The means (medians) of GDP per Capita, Market Capitalization, Market Return, Legality, Foreign Country Legality, VC Deals and Patents are respectively 33,593 (36,909) US
dollars, 116.57% (127.2%), 1.9% (1.8%), 15.42 (16.45), 15.68 (16.08), 8.43 (9.95) per million
people and 0.42 (0.48) per thousand people. Around 48.2% of domestic countries’ legal systems originate from the same tradition as that of the corresponding foreign countries.
The total VC funding across all financing rounds in a portfolio company is 37.16 million
US dollars on average with a median of about 17.50 million US dollars. About 29.1% of port-
5 We report the original values for all the variables in Table 2, even though we have taken logarithms of
some variables in our regressions (as indicated by “**” in Appendix Table 2).
6 The average Investment Duration of VC investments is only 1,239 days for successful exits and is 2,826
days for all VC investments (taking into account the fact that we do not know the exact time of unsuccessful exits
and that Investment Duration of unsuccessful exits is right-censored at the end of 2009).
Page 10
folio companies voluntarily disclose their market valuation after VCs’ first investment in
them, which is slightly lower than the 34.4% reported by Cumming and Dai (2011) for the
U.S. VC investments made during 1991–2006. Further, 38.1% of first investments by a VC
firm in a portfolio company occur at the seed or early stage of development, which is much
lower than the 63.7% reported by Nahata (2008) and the 62% reported by Gompers et al.
(2010) for the U.S. VC industry. The average geographical distance between a portfolio company and a VC firm is 7,205 kilometers with a median of 7,837 kilometers.
On average, 34.4% of VCs participating in the syndicate of a portfolio company are domestic VCs. The average VC age is about 13.58 years old at the time of its first investment in
a portfolio company with a median of about 8 years old. The aggregate amount of funds
raised by a VC firm over the ten years prior to its first investment in a portfolio company is
about 919 million US dollars with a median of only 143 million US dollars. The mean (median) number of companies in a VC firm’s portfolio is 257.43 (44). The reported portfolio
size is much larger than that suggested by Cumming (2006) since our unit of analysis is VC
firm instead of VC fund. Around 15.4% and 16.8% of VCs are respectively institutional and
corporate VCs; these percentages are slightly larger than those reported by Bottazzi et al.
(2008) for European VCs and by Nahata (2008) for the U.S. VCs. As shown, about 37.8%
and 25.7% of the VCs are respectively located in Asia and Europe.7
The aggregate inflow of VC funds in the world in the year prior to a VC firm’s first investment is around 61.84 billion US dollars with a median of about 43.91 billion US dollars.
Also, there are on average 16.16 (with a median of 13.79) VC-backed IPOs and 110.05 (with a
median of 108.15) M&A transactions with VC-backed portfolio companies as the targets in
the quarter prior to the exit of a portfolio company.
In Table 2, we also report the results of the univariate analysis—the t-tests for the equality of means and Wilcoxon tests for the equality of medians between successful exits and
unsuccessful exits. As shown in the table, successful exits are more likely in domestic countries with higher economic freedom as measured by Index of Economic Freedom, Economic
Freedom Quartile or Ranking in Economic Freedom at the 1% significance level. Also, as
both the t-tests and Wilcoxon tests indicate strong significant associations between most
control variables and the likelihood of successful exits, there is the need to control for these
factors.
Since the summary statistics in Table 2 corresponds to 10,205 VC firm-portfolio company paired observations, the means of Institutional VC Indicator, Corporate VC Indicator, Asia VC Indicator and Europe VC Indicator are slightly different from the distribution of VCs reported in Table 1, which corresponds to 1,906 individual
VCs.
7
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3. Analysis at the VC Investment Level
In this section, we investigate the impact of economic freedom on the performance of
cross-border VC investments. The unit of analysis is VC investment. Our sample consists of
10,205 unique VC firm-portfolio company paired observations. Table 3 reports the results.
All the z-statistics (in parentheses) are adjusted for heteroskedasticity (White, 1980) and
clustered by portfolio company (Petersen, 2009) to account for correlation among multiple
VC investments in the same portfolio company.
[Insert Table 3]
We first present the results of the probit analysis in models 1-3 of Table 3 with Investment Success as the dependent variable using equation (1). Our model fits the data well, with
a high pseudo R-squared of around 0.5. We find that each of the three explanatory variables—Index of Economic Freedom, Economic Freedom Quartile and Ranking in Economic
Freedom—has a significant impact, that is, a domestic country’s economic freedom has a
significant positive effect on Investment Success. The coefficients of Index of Economic
Freedom, Economic Freedom Quartile and Ranking in Economic Freedom are respectively
0.036, 0.569 and 1.760, with the z-statistics respectively at the 5%, 1% and 5% significance
levels. For the marginal effects, a one standard deviation increase in Index of Economic
Freedom, Economic Freedom Quartile or Ranking in Economic Freedom at the mean level
increases the likelihood of a successful exit by 2.02%, 2.76% or 3.02%, respectively.
Some other variables also affect the performance of VC investments, mostly as expected.
First, GDP per Capita is negatively associated with the likelihood of a successful exit, which
is consistent with the well-known convergence hypothesis in the exogenous growth literature
(Barro, 1991, 1996). Market Return is found to be positively associated with the likelihood of
a successful exit, which is consistent with the studies on the impact of stock market performance on IPO volume (Loughran et al., 1994; Lowry, 2003). Legality is found to be significantly positively related to VC performance, which is consistent with Armour and Cumming
(2006), Cumming et al. (2006) and Cumming and Walz (2010). The higher legality is, the
more protected an investor’s legal rights and the higher the likelihood of a successful exit.
We also find that the coefficients of Foreign Country Legality and Same Legal Origin Indicator are typically negative, suggesting that the legal qualities of a domestic country and a
foreign country complement each other. Also, we find a strong positive association between a
domestic country’s entrepreneurial activity and the likelihood of a successful exit. The greater the number of VC deals or patents granted in a domestic country, the higher the likelihood
of a successful exit. However, the relationship between a domestic country’s stock market
capitalization and the likelihood of a successful exit is typically insignificant, which is inconsistent with the findings of Black and Gilson (1998) and Cumming (2008), but consistent
Page 12
with those of Cumming et al. (2006), who show that a country’s stock market capitalization
has no significant impact on exit choices in 12 Asia-Pacific countries.
Second, our results on the coefficients of portfolio company-related factors are quite
consistent with our expectations. Total VC Funding and Valuation Disclosure Indicator positively affect cross-border VC performance at the 1% significance level. The better the quality of a portfolio company, the larger the VC investment amount, the higher the likelihood of
a voluntary disclosure of post-investment valuation, and the higher the likelihood of a successful exit. Also, Early Stage Indicator is found to be strongly negatively associated with the
likelihood of a successful exit at the 1% significance level. Unexpectedly, the variable Distance is found to be positively associated with the likelihood of a successful exit, but at a low
significance level. In later estimations, we generally find insignificant results on Distance at
the portfolio company level. Later in Section 5.2, after a robustness check of the exit choices
between an IPO and an acquisition, we further find a different role of Distance in exits
through IPOs than in exits through acquisitions. 8
Third, Local VC Ratio is positively related to the likelihood of a successful exit at the 1%
significance level. This is consistent with the value-added view of domestic investors on
cross-border VC investments (Mäkelä and Maula, 2008; Dai et al., 2010; Tykvová and
Schertler, 2011). VC Experience is found to be positively related to the likelihood of a successful exit at the 5% significance level, which is consistent with the findings for the U.S. domestic market, such as those by Sorensen (2007), Gompers et al. (2008) and Nahata (2008).
We find a significantly negative relationship between VC Portfolio Size and the likelihood of
a successful exit, which is quite consistent with the findings of Kanniainen and Keuschnigg
(2003, 2004) and Cumming (2006). Also, Institutional VC Indicator and Corporate VC Indicator have positive effects on Investment Success, due possibly to the value-added services
offered by VCs to portfolio companies. As shown, there is no statistically significant association between VC Size or VC Size Squared and the likelihood of a successful exit. Also, neither
Asia VC Indicator nor Europe VC Indicator has a significant effect on the likelihood of a
successful exit.
8 We have tried some alternative measures of Distance, including the geographical distance between the
foreign country and the domestic country, the flight distance between the VC firm and the portfolio company, etc.
The coefficients of Distance are generally positive but not very significant. However, if we define “successful exits”
as IPO exits only, as Tian (2011) does, we find a generally negative effect of Distance on the likelihood and hazard
of IPO exits. By comparing our results with the two related studies focusing on the influence of the geographical
distance on VC investments (Chemmanur et al., 2011; Tian, 2011), we conclude that the coefficient of Distance is
generally found to be positive in our primary regressions because Distance has a different effect on exits through
IPOs than on exits through acquisitions. Also, the inclusion of geographical distance may suffer from an
endogeneity problem due to omitted variables. If we exclude the variable Distance, our results remain very much
unchanged. Since Distance is not the primary focus of our paper, we have not conducted a thorough investigation
of it. It would be interesting and worth exploring in the future.
Page 13
Fourth, the global VC market-related factor VC Industry Competition is negatively associated with Investment Success at the 1% significance level. The greater the worldwide inflow
of VC funds, the more severe VC competition is in the international market and the less likely
it is for a successful exit to occur. However, the global market conditions affect the likelihood
of a successful exit in different ways. Specifically, IPO Market Conditions is negatively related to the probability of a successful exit, contrary to our expectations. M&A Market Conditions is positively related to the probability of a successful exit at the 1% significance level,
consistent with our expectations. Later in Section 5.2, after a robustness check of the exit
choices between an IPO and an M&A, we find that the negative coefficient of IPO Market
Conditions arises from its other role in the choice between an IPO and an M&A.
We next turn to the Cox hazard analysis in models 4-6 of Table 3 with Investment Duration as the dependent variable using equation (2). We find that most results mirror the evidence reported in models 1-3. Taking the right-censored nature of the data into account, we
still find that the coefficients of Index of Economic Freedom, Economic Freedom Quartile
and Ranking in Economic Freedom are significantly positive. Specifically, a one standard
deviation increase in Index of Economic Freedom, Economic Freedom Quartile or Ranking
in Economic Freedom is associated with an increase of 5.35%, 12.59% or 13.69% respectively
in the hazard of a successful exit. The results on the control variables are very similar to our
earlier results with some changes in the significance level. Specifically, GDP per Capita,
Same Legal Origin Indicator, Early Investment Indicator, VC Portfolio Size, VC Industry
Competition and IPO Market Conditions remain negatively associated with the hazard of a
successful exit, while Legality, VC Deals, Patents, Total VC Funding, Valuation Disclosure
Indicator, Local VC Ratio, Corporate VC Indicator and M&A Market conditions remain
significantly positively associated with the hazard of a successful exit. Further, we have
stronger evidence on the impact of the size of a VC firm, with a positive association between
VC Size and the hazard of a successful exit. However, the effects of Market Return, Foreign
Country Legality, VC Experience and Institutional VC Indicator on the hazard of a successful exit are much weaker compared with our earlier estimations. Other variables, such as
Market Capitalization, VC Size Squared, Asia VC Indicator and Europe VC Indicator, remain insignificant as before.
4. Analysis at the Portfolio Company Level
In this section, we conduct a performance analysis at the portfolio company level using
the probit model and the Cox hazard model in equations (1) and (2). The unit of analysis is
portfolio company, with one observation for each portfolio company. The sample size is
6,535. Table 4 reports the results. All the z-statistics (in parentheses) are adjusted for hetPage 14
eroskedasticity (White, 1980) and clustered by domestic country (Petersen, 2009) to account
for correlation among multiple VC investments in the same portfolio company.
[Insert Table 4]
Models 1-3 in Table 4 report the results of our probit analysis of portfolio companies
with Investment Success as the dependent variable using equation (1). Our model fits the
data well, with a high pseudo R-squared of greater than 0.53. The results on a domestic
country’ economic freedom are consistent with the earlier results on VC investments. In particular, a one standard deviation increase in Index of Economic Freedom, Economic Freedom Quartile or Ranking in Economic Freedom at the mean raises the probability of a successful exit by 1.38%, 10.06% or 4.72%, respectively.
The impact of the control variables on Investment Success is largely the same as that
implied by the results in Table 3. We find significantly positive impacts from Legality, VC
Deals, Patents, Total VC Funding, Valuation Disclosure Indicator, Local VC Ratio, Corporate VC Indicator and M&A Market Conditions, and significantly negative impacts from
GDP per Capita, Foreign Country Legality, Same Legal Origin Indicator, Early Investment
Indicator, VC Portfolio Size, VC Industry Competition and IPO Market Conditions. Other
control variables are generally insignificantly associated with Investment Success.
Models 4-6 in Table 4 report the results of our Cox hazard analysis of portfolio companies with Investment Duration as the dependent variable using equation (2). As in the previous section, a domestic country’s economic freedom continues to be a good predictor of Investment Duration with a large marginal effect. Specifically, a one standard deviation increase in Index of Economic Freedom, Economic Freedom Quartile or Ranking in Economic
Freedom is associated with an increase of 1.73%, 10.96% or 12.33% respectively in the hazard
of a successful exit. Taking the right-censored nature of the data into account, we further find
that Legality, VC Deals, Patents, Total VC Funding, Valuation Disclosure Indicator, Local
VC Ratio, Corporate VC Indicator and M&A Market Conditions reduce Investment Duration and hence increase the hazard of a successful exit, while GDP per Capita, Same Legal
Origin Indicator, Early Investment Indicator and VC Portfolio Size increase Investment
Duration and hence decrease the hazard of a successful exit. Other control variables are generally insignificantly associated with the hazard of a successful exit.
In summary, the results of our analyses of VC investments and portfolio companies in
Tables 3 and 4 indicate that the degree of a domestic country’s economic freedom is positively related to cross-border VC performance after controlling for other related factors of
domestic countries, portfolio companies, VCs and the global VC market, as well as year and
industry fixed effects. In addition, we provide interesting evidence on many level factors of
domestic countries, portfolio companies, VCs and the global VC market that affect crossPage 15
border VC performance. This is consistent with existing studies on factors such as a domestic
country’s economic development, legal quality, entrepreneurial activity, portfolio company
quality, local VCs’ participation and VCs’ portfolio size.
Our findings have policy implications for VC imports. One key implication is that a high
degree of freedom is essential for successful investments in a country, especially for foreign
investments.
5. Robustness Checks
In the above analyses, a domestic country’s economic freedom has emerged as a consistent predictor of cross-border VC performance. In this section, we conduct many robustness
tests, other than those mentioned in prior sections, to ensure reliability of our results. To
save space, we report a selection of results only. Details of our other tests are available upon
request.
5.1. Change in Economic Freedom
Up to now, we have applied a level model to investigate the impact of a domestic country’s economic freedom on cross-border VC performance. In other words, we have only focused on the cross sectional difference. In this section, we provide further evidence on effects
of a change in economic freedom to strengthen our findings. This exercise may mitigate the
influence of missing factors and self-selection concerns. We define two variables, Change in
IEF and IEF Growth (%), to measure a change in economic freedom, which are calculated in
a similar way to the three primary independent variables, Index of Economic Freedom, Economic Freedom Quartile and Ranking in Economic Freedom. Specifically, Change in IEF
measures the average yearly change in IEF, and IEF Growth measures the average yearly
growth rate of IEF over the entire investment period. The means (and standard deviations)
of Change in IEF and IEF Growth (%) at the portfolio company level are respectively 0.233
(and 0.436) and 0.339 (and 0.628), which are quite similar to those at the VC investments
level.9 These statistics are consistent with our expectation that economic freedom tends to be
persistent and does not change dramatically over our sample period.
Table 5 reports the probit and Cox hazard analyses at the portfolio company level as in
Table 4 but taking Change in IEF or IEF Growth as the explanatory variable. We find positive coefficients on Change in IEF and IEF Growth in all the models with the z-statistics at
9 Since IEF is only available before 1995, we assume that IEF in 1994 is the same as that in 1995. Hence, the
change in IEF and the change in growth rate of IEF in 1995 are both set to zero.
Page 16
the 1% significance level. For the marginal effects, a one standard deviation increase in
Change in IEF or IEF Growth at the mean level increases the likelihood of a successful exit
by 1.58% or 1.45%, respectively. Also, a one standard deviation increase in Change in IEF or
IEF Growth is associated with an increase of 4.71% or 4.01% respectively in the hazard of a
successful exit. These findings further strengthen our argument that economic freedom plays
a vital role in cross-border VC success.
5.2. IPOs versus Acquisitions
In this section, we focus on the choice of exit between an IPO and an acquisition. IPOs
and acquisitions are the two primary methods by which privately-held firms can become
public companies. Existing studies that focus on exit choices of entrepreneurs and VCs include those of Brau et al. (2003), Cumming et al. (2006), Poulsen and Stegemoller (2008)
and Bayar and Chemmanur (2010). Using a much larger set of sample data that includes
many more countries, we provide further empirical analyses of the determinants of whether
to exit through an IPO or an acquisition.
For brevity, we report only the probit results on portfolio companies in Table 6. In models 1-3, we focus on the subsample consisting of successful portfolio companies only and analyze whether a domestic country’s economic freedom has any impact on the choice of exit
between an IPO and an acquisition. The dependent variable is a dummy variable indicating
whether a VC-backed portfolio company had gone public by the end of 2009. The number of
observations in the subsample is 935. As shown, economic freedom has no significant impact
on the choice between an IPO and an acquisition. The coefficients of Index of Economic
Freedom, Economic Freedom Quartile and Ranking in Economic Freedom are respectively
0.001, 0.164 and 0.143 with z-statistics 0.04, 0.18 and 0.82. In other words, a domestic
country’s economic freedom has an almost equal influence on the decision to exit through an
IPO and the decision to exit through an acquisition.
In addition, the results on some of the other variables are interesting. We find that Total
VC Funding, Valuation Disclosure Indicator, Asia VC Indicator and IPO Market Conditions
have a significantly positive impact on the choice between an IPO and an acquisition, while
GDP per Capita, Legality, Same Legal Origin Indicator, Early Investment Indicator, Distance, Local VC Ratio and M&A Market Conditions have a significantly negative impact.
Most of these results make sense. One result that requires an explanation is the negative impact of Legality. Cumming et al. (2006) claim that Legality is positively related to the decision to exit through an IPO and negatively related to the decision to exit through a private
exit. It seems that we have an opposite result. However, their sample is much smaller and
covers only 12 Asia-Pacific countries. Further, the choice of “private exits” in their study in-
Page 17
cludes acquisitions, secondary sales and buybacks. In contrast, we focus on two successful
exit routes only, namely IPOs and acquisitions, since our attention is on VC performance.
As a further robustness check, we have also investigated the likelihood of a portfolio
company exiting through one of the three major exit routes—IPOs, acquisitions and liquidations—in a multinomial probit model using the full sample. The dependent variable takes
three discrete values corresponding to IPOs, acquisitions and liquidations. The results from
models 4-6 are consistent with those from models 1-3. We see a similar impact of economic
freedom on the decision to exit through an IPO and the decision to exit through an acquisition. Specifically, the coefficients of Index of Economic Freedom (or Economic Freedom
Quartile, or Ranking in Economic Freedom) are respectively 0.069 (or 0.792, or 3.396) and
0.033 (or 0.607, or 2.341) with z-statistics 2.23 (or 5.31, or 2.44) and 1.73 (or 3.81, or 3.28)
for exiting through IPOs and through acquisitions respectively. In addition, we see a different role of IPO Market Conditions and Distance in the two exit choices. Specifically, IPO
Market Conditions (Distance) is positively (negatively) associated with exits through IPOs
and negatively (positively) associated with exits through acquisitions, which may explain the
negative (positive) and sometimes insignificant coefficients of IPO Market Conditions (Distance) in Tables 3 and 4.
5.3. Decomposing the Effects of Economic Freedom
We have provided evidence that economic freedom has a positive impact on crossborder VC performance. However, our analysis is so far based on an integrated index of economic freedom (IEF). One drawback is that a single measure may not be able to properly
represent a complex economic environment, and a highly aggregated index makes it difficult
to draw policy conclusions. In this section, we investigate the effects of the individual components of the IEF on cross-border VC performance.
The IEF consists of 10 different components—business freedom, trade freedom, fiscal
freedom, government spending, monetary freedom, investment freedom, financial freedom,
property freedom, freedom from corruption and labor freedom. A brief description of each
component is presented in Appendix Table 1. Since labor freedom only became available
after 2005, we focus on the other nine components in this section. We define the nine variables in the same way as we did for Index of Economic Freedom—by taking the average of
the raw values of each variable over the investment period.
Table 7 reports the probit analysis of the decomposed economic freedom effects at the
portfolio company level. The results of the Cox hazard analysis are quite similar. Not every
component of the IEF has a positive impact on cross-border VC performance. Specifically,
Trade Freedom, Fiscal Freedom, Government Spending and Freedom from Corruption are
Page 18
negatively but insignificantly associated with cross-border VC performance. Monetary Freedom, Investment Freedom, Financial Freedom and Property Freedom are significantly positively associated with cross-border VC performance, while Business Freedom is positively
but insignificantly associated with cross-border VC performance.
It is clear that those factors that have turned out to be significantly positively related to
VC performance are essential in promoting growth opportunities for early stage VC-backed
private companies, ensuring less grabbing from the state and providing more investor protection. Specifically, Monetary Freedom provides a steady and reliable monetary environment, allowing VCs to raise funds in local currencies and enhance their confidence in longterm investments. Investment Freedom ensures a free and open investment environment for
entrepreneurs, allowing various financial instruments to be deployed to deal with a variety of
investment issues such as risk sharing, asymmetric information and agency problems (Neher,
1999; Cornelli and Yosha, 2003; Wang and Zhou, 2004). Financial Freedom provides a
transparent and open financial system with efficient financial intermediation between VCs
and entrepreneurs and ensures fairness in access to finance and entrepreneurship. Property
Freedom ensures the rights of individuals in acquiring private property and accumulating
wealth, which gives VCs and entrepreneurs the confidence they need to engage in entrepreneurial activities and make long-term plans.
The above positive components of the IEF emphasize the economic environment within
a country. In contrast, Trade Freedom is about competition in international trade. It reflects
the openness of an economy to the imports of goods and services from all over the world and
the ability of citizens to interact freely as buyers and sellers in the international markets.
Protection from international competition may benefit early-stage private companies. Also,
government protection is reflected by Fiscal Freedom, Government Spending and Freedom
from Corruption. These reasons may explain the negative, albeit weak, effects of these factors on cross-border VC performance.
In summary, Monetary Freedom, Investment Freedom, Financial Freedom and Property Freedom provide a transparent and open economic environment for foreign VCs and
allow them to effectively mitigate many investment problems such as risk sharing, asymmetric information and agency problems between themselves and entrepreneurs, which benefits
cross-border VC investments. Less Trade Freedom, Fiscal Freedom, Government Spending
and Freedom from Corruption may mean more government protection for early-stage private companies, which may also benefit cross-border VC investments. In fact, the negative
effect of Trade Freedom, Fiscal Freedom and Government Spending on cross-border VC
performance is consistent with the decomposed effects of economic freedom on economic
growth proposed by Carlsson and Lundstrom (2002).
Page 19
5.4. Other Robustness Tests
We have also done several other robustness tests. Here we report a summary of these
tests.
First, we have conducted a robustness test using an alternative measure of cross-border
VC performance. Following Hochberg et al. (2007), we construct a different success indicator at the portfolio company level as the dependent variable, which equals 1 if a portfolio
company survives a financing round to receive another round of financing or if it exits via an
IPO or an M&A, and 0 otherwise. The sample size decreases from round to round. We use
the same set of explanatory variables as before. The main results remain the same. These
results suggest that our findings are robust to different measures of success.
Second, we further include domestic country fixed effects in all our regressions. Even
though we have tried to control for many factors affecting a domestic country’s investment
environment, we might have missed some factors that are unobservable or hard to measure.
Therefore, we further include domestic country fixed effects to mitigate the influence of these
missing factors. The sample size is reduced to 9,135 for VC investments and 5,746 for portfolio companies. Our findings are consistent with those from earlier regressions. The pseudo
R-squared is substantially improved to above 0.75 in all regressions. The marginal effects of
Index of Economic Freedom, Economic Freedom Quartile and Ranking in Economic Freedom on the likelihood of a successful exit are respectively 0.031, 0.021 and 1.235 for VC investments and 0.026, 0.011 and 0.989 for portfolio companies.
Third, we consider alternative measures of Legality, named the Doing Business Indicators, which are provided by the World Bank and are available at www.doingbusiness.org.
Since these indicators only became available after 2004, we use the most updated information provided by the annual report Doing Business 2011, covering the period from June 2009
through May 2010. For our purpose, we only focus on the factors relating to a country’s legal
quality, including Getting Credit, Protecting Investors and Enforcing Contracts. Similar to
earlier tests, we also control for these factors for each foreign country to capture the differences in the legal systems between domestic and foreign countries. We obtain very similar
findings using these alternative measures.
One other concern of Legality is the potential problem of multicollearity. It is clear that
the index of economic freedom also captures some legal factors such as the protection of a
country’s private property rights, the independence of the judiciary, the ability of individuals
and businesses to enforce contracts, etc. We also find a somewhat high correlation between
Legality and Index of Economic Freedom or Legality and Ranking in Economic Freedom of
about 0.54 or 0.57. The variance inflation factor (VIF) of Legality is also somewhat high at
about 6.48. Although these values are acceptable in most cases, we have tried to rule out a
Page 20
possible influence of multicollinearity. The alternative measures of Legality, named the Doing Business Indicators, are also helpful in dealing with multicollearity. Further, we have
also tried to use the orthogonalized Index of Economic Freedom (or Ranking in Economic
Freedom) and the original Legality, or the original Index of Economic Freedom (or Ranking
in Economic Freedom) and the orthogonalized Legality, or the orthogonalized Index of Economic Freedom (or Ranking in Economic Freedom) and the orthogonalized Legality in all
our analyses. We obtain the same results at even higher significance levels than before.
Fourth, as the U.S. portfolio companies constitute about 40.86% of our sample, we rerun all our regressions using a subsample that excludes these companies. The results are
qualitatively unchanged. As suggested by Cumming et al. (2009), relocation to the U.S. implies much greater returns for Asia-Pacific VCs than investing in companies already based in
the U.S. at the time of VC investment. Due to data limitation, we do not have information on
the location of each portfolio company at the time of VC investments and at the time of exit.
Hence, excluding the U.S. portfolio companies may be helpful in ruling out the influence of
the relocations of portfolio companies in our findings.
Fifth, we have conducted further analyses on the potential problem of a time trend. In
earlier analyses, we have already dealt with the time trend problem by including year dummies to control for year fixed effects. But a number of big events occurred during our sample
period. We re-run all our regressions using a subsample that excludes the data in the period
1999–2000 to rule out the influence of the NASDAQ bubble. The sample size is reduced respectively to 6,562 and 4,127 for the analyses of VC investments and portfolio companies.
Also, the distribution of successful exits (not reported) suggests that the recent financial crisis following year 2007 heavily affected the chances of a successful exit for cross-border VC
investments. Although the method may not be very convincing, we have tried to rule out the
influence of the recent financial crisis by excluding those observations that resulted in a successful exit during 2008–2009 as a robustness check. The sample size is decreased respectively to 10,066 and 6,451 for the analyses of VC investments and portfolio companies. Again,
our results are qualitatively unchanged using these subsamples.
Sixth, some write-offs and liquidations are also recorded as acquisitions in the SDC VentureXpert database. Hence, it is not easy to separate unsuccessful VC investments such as
write offs from successful acquisitions. To rule out the possible influence of this data limitation, we exclude those VC investments that exited through acquisitions with disclosed transaction values of less than $50 million. The sample size is reduced respectively to 9,447 and
6,057 for the analyses of VC investments and portfolio companies. Alternatively, we exclude
those VC investments that exited through acquisitions with disclosed transaction percentages of target portfolio companies less than 100%. The sample size is reduced respectively to
Page 21
10,166 and 6,510 for the analyses of VC investments and portfolio companies. We re-run all
our regressions using these subsamples. Our results again remain qualitatively unchanged.
Seventh, as shown in Table 1, the computer-related and biotechnology industries make
respectively the largest and smallest contributions to our dataset. To examine whether the
inclusion of these industries in our sample influences our results, we re-run all our regressions without these industries. Our results again remain qualitatively unchanged.
Finally, to investigate further whether our results are unduly influenced by outliers, we
re-run all our regressions after winsorizing the top and bottom 1%, 2% and 5% for each continuous variable. The results remain largely unchanged.
6. Conclusion
We investigate the determinants of cross-border VC performance using a sample of
10,205 cross-border VC investments by 1,906 foreign VCs in 6,535 domestic portfolio companies covering 35 domestic countries. In particular, we investigate the impact of a domestic
country’s economic freedom on cross-border VC performance after controlling for other related factors of domestic countries, portfolio companies, VCs and the global VC market,
where economic freedom is measured by either the raw values of, quartiles of or the ranking
in the IEF over the investment period. We provide both probit and Cox hazard analyses at
both the VC investment and portfolio company levels.
We find that a domestic country’s economic freedom is positively related to the likelihood of a successful exit and negatively related to the expected investment duration. This
finding is robust to domestic country fixed effects, subsamples, decomposed economic freedom, and alternative measures of performance, economic freedom and control variables.
We also find cross-border VC performance to be strongly associated with a number of
other characteristics of a domestic country. Specifically, the GDP per capita of a domestic
country is found to be negatively related to the likelihood and hazard of a successful exit,
which is consistent with the convergence hypothesis in the exogenous growth literature. As
expected, legality is found to be positively related to cross-border VC performance, which is
quite consistent with the findings of Cumming et al. (2006). Also, a domestic country’s entrepreneurial activity is found to be positively related to the likelihood and hazard of a successful exit. Our results also contribute to the literature on the role of many other level factors of portfolio companies, VCs and the global VC market in cross-border VC performance.
For example, our findings demonstrate that portfolio company quality and local VCs’ participation have a positive impact, while early stage investments and VCs’ portfolio size have a
negative impact, on the likelihood and hazard of a successful exit. Also, we find that the
Page 22
global market conditions for IPOs and M&As affect the decision to exit through an IPO and
the decision to exit through an M&A differently.
This paper makes three main contributions. First, this paper investigates the impact of
an important country-level factor, namely a domestic country’s economic freedom, on crossborder VC performance, which enriches the literature on economic freedom. Second, our
analysis is helpful in understanding cross-border VC investments around the world. We enrich the literature by exploring the determinants of cross-border VC performance. Third, we
contribute more evidence to the literature on the influence of many other factors of domestic
countries, portfolio companies, VCs and the global VC market on cross-border VC performance.
Appendix: Discussion of Variable Definitions
The Performance Measure
Ideally, we would measure performance directly using the rate of return. However, such
information is not systematically available to the public since VCs generally disclose their
performance only to their investors. The majority of observable VC returns are from successful exits through IPOs or M&As (Cumming and MacIntosh, 2003; Cochrane, 2005). Generally the more successful exits a VC firm makes from its portfolio companies, the larger its
internal rate of return from these investments. Hence, we measure VC performance indirectly using the likelihood of a successful exit by the end of 2009, and call this Investment
Success. A portfolio company is treated as successful if it went public or was acquired by the
end of 2009. We allow a minimum of four years for a successful exit by tracing the performance of a portfolio company until the end of 2009 after an initial investment in or before
2005. A similar approach is adopted by Gompers and Lerner (2000), Hochberg et al. (2007),
Sorensen (2007), Gompers et al. (2008), Nahata (2008), and Cumming and Dai (2010,
2011).
In addition, we also provide a survival analysis using a Cox hazard model. The dependent variable of the Cox hazard model is called Investment Duration.10 For VC investments
(or portfolio companies), Investment Duration is the logarithm of the number of days from
the date when a VC firm first invested in a portfolio company (or when a portfolio company
10 Giot and Schwienbacher (2007) and Cumming and Johan (2010) focus specifically on a survival analysis
of VC investment duration. Giot and Schwienbacher (2007) analyze the time to IPO, trade sales and liquidations
for 6,000 VC-backed companies in the U.S., while Cumming and Johan (2010) relate VC investment duration to
the characteristics of portfolio companies, VCs, deals, and institutional and market conditions. We control for
most of these factors which have been shown to influence VC investment duration.
Page 23
received its first VC funding) to the date of exit. It is right-censored at the end of 2009 for
those investments (or portfolio companies) that had not yet exited successfully by the end of
2009. The Cox hazard model allows for right-censored data and time-varying variables and
is a semi-parametric model in which the hazard function is not dependent on a specific distribution of the survival time.
The Economic Freedom Measure
There are two popular measures of economic freedom: the economic freedom of the
world (EFW) provided by the Fraser Institute at www.freetheworld.com, and the index of
economic freedom (IEF) provided by the Wall Street Journal and Heritage Foundation
(WSJ/HF) at www.heritage.org.
Although these two measures differ somewhat in their coverage, they show similar rankings for the countries in question. The correlation of these two measures is shown to be
around 0.85. In this paper, we make use of the IEF simply because of data availability—the
EFW is only available every 5 years for those years prior to year 2000, while the IEF is available annually for the whole period of 1995–2009. Existing empirical analyses that use the
IEF as a measure of economic freedom include Heckelman (2000), Goel and Nelson (2005),
McGee (2008), McMullen et al. (2008), etc. The IEF is a composite index that indicates the
degree to which an economy is characterized by the free market principle. As described in
Appendix Table 1, the IEF consists of ten components corresponding to ten aspects of economic freedom, each of which is assigned a grade from 0 to 100, where 100 represents maximum freedom. The scores of the ten components are then averaged to give an overall score
of economic freedom for each country.
[Insert Appendix Table 1]
We take the average IEF over the investment period as our primary explanatory variable
and name it Index of Economic Freedom. Specifically, in the analysis of VC investments (or
portfolio companies), Index of Economic Freedom is the average IEF over the investment
period starting from the year when a VC firm first invested in a portfolio company (or the
year when a portfolio company first received VC funding) to the year of exit. For unsuccessful
VC investments (or portfolio companies), Index of Economic Freedom is the average IEF
over the entire time period starting from the year when a VC firm first invested in a portfolio
company (or the year when a portfolio company first received VC funding) to year 2009.
We also conduct analyses using two qualitative measures of economic freedom—
Economic Freedom Quartile and Ranking in Economic Freedom. The Economic Freedom
Quartile is based on the Index of Economic Freedom, which takes the value of 1 if Index belongs to the lowest quartile, 2 if Index belongs to the second quartile, 3 if Index belongs to
Page 24
the third quartile, and 4 if Index belongs to the highest quartile. We have also tried some
other grouping methods to qualitatively measure a country’s economic freedom, such as
adopting a high economic freedom dummy and economic freedom quintiles. All our findings
are quite similar.
To calculate Ranking in Economic Freedom, we sort the IEF in each year for the 35
sample countries and construct a normalized IEF ranking for each country by performing the
calculation of one minus the IEF ranking divided by 35. That is, the normalized IEF ranking
takes a value between 0 and 1. The greater the value of normalized IEF ranking, the more
economically free a country is. For a successful VC investment (or a successful portfolio
company), Ranking in Economic Freedom is the average normalized IEF ranking over the
period starting from the year when the VC firm made the first investment (or the year when
the portfolio company received the first VC funding) to the year of exit. For an unsuccessful
VC investment (or an unsuccessful portfolio company), Ranking in Economic Freedom is the
average normalized IEF ranking over the entire period starting from the year when the VC
firm made the first investment (or the year when the portfolio company received the first VC
funding) to year 2009.
Domestic Country-related Factors
We include country-specific macroeconomic variables from the WDI database to control
for investment opportunities across countries. Specifically, we include the GDP per capita
(named GDP per Capita) to control for a country’s economic development. The size and development stage of financial markets are captured by the market capitalization of listed companies as a percentage of the GDP (named Market Capitalization). The measurements of
these two variables are similar to Index of Economic Freedom and Ranking in Economic
Freedom. For a successful VC investment (or a successful portfolio company), the two variables are measured by their respective averages over the period starting from the year when
the VC firm made the first investment (or the year when the portfolio company received the
first VC funding) to the year of exit. For an unsuccessful VC investment (or an unsuccessful
portfolio company), the two variables are measured by their respective averages over the
entire time period starting from the year when the VC firm made the first investment (or the
year when the portfolio company received the first VC funding) to year 2009.
As suggested by Loughran et al. (1994) and Lowry (2003), we also include stock market
performance (named Market Return), which is measured as a domestic country’s stock market return over a period of three months prior to a VC firm’s exit from a portfolio company.
For unsuccessful exits, it is a domestic country’s average lagged quarterly stock market return over the entire time period starting from the date when a portfolio company received
Page 25
the initial VC funding to the end of 2009. We make use of the stock market index from the
Global Insight database to calculate Market Return.
Another important factor influencing cross-border VC performance is the quality of legal
institutions (Armour and Cumming, 2006; Cumming et al., 2006; Cumming and Walz,
2010). We include a legality index Legality, which is measured as the weighted sum of the
legal factors mentioned in Berkowitz et al. (2003) and derived from La Porta et al. (1997,
1998). Specifically, Legality = 0.381 ´ (Efficiency of Judiciary) + 0.5778 ´ (Rule of Law)
+ 0.5031 ´ (Corruption) + 0.3468 ´ (Risk of Expropriation) + 0.3842 ´ (Risk of Contract
Repudiation). To control further for the difference in the legal systems between a domestic
country and a corresponding foreign country, we also include the foreign country’s legality
index Foreign Country Legality and a dummy variable Same Legal Origin Indicator to indicate whether the legal systems of the domestic country and the corresponding foreign country originated from the same tradition, such as the traditions of the English, the French, the
Germans or the Scandinavians.
To capture the difference between economic freedom and entrepreneurial activity, we
also control for a domestic country’s entrepreneurial activity. In light of the study by Cumming and Li (2009), we use a domestic country’s number of VC deals (VC Deals) and number of patents granted (Patents) over the investment period as proxies for its entrepreneurial
activity. Information on a country’s VC activity is available from the SDC VentureXpert database. To avoid double counting, we consider only the initial VC deal in a portfolio company.
Information on cross-country patents is provided by the WIPO (World Intellectual Property
Organization) database, which is available at www.wipo.int. We count the number of patents
granted by the country of origin. The measurement of these two variables is similar to that
for Index of Economic Freedom, Ranking in Economic Freedom, GDP per Capita and Market Capitalization. Specifically, for a successful VC investment (or a successful portfolio
company), VC Deals is measured as the average number of VC deals per million people in the
domestic country over the investment period starting from the year when a VC firm made
the first investment (or the year when a portfolio company received the first VC funding) to
the year of exit. For an unsuccessful VC investment (or an unsuccessful portfolio company),
VC Deals is the average number of VC deals per million people in the domestic country over
the entire time period starting from the year when a VC firm made the first investment (or
the year when a portfolio company received the first VC funding) to year 2009. Similarly, for
a successful VC investment (or a successful portfolio company), Patents is measured as the
average number of patents granted per thousand people in the domestic country over the
investment period starting from the year when a VC firm made the first investment (or the
year when a portfolio company received the first VC funding) to the year of exit. For an unsuccessful VC investment (or an unsuccessful portfolio company), Patents is the average
Page 26
number of patents granted per thousand people in the domestic country over the entire time
period starting from the year when a VC firm made the first investment (or the year when a
portfolio company received the first VC funding) to year 2009.11
Portfolio Company-related Factors
The greater the total VC funding, the better a portfolio company’s quality is likely to be,
and in turn the higher the likelihood of a successful exit. Following Nahata (2008), we use
the total VC investment amount in a portfolio company across all financing rounds (Total VC
Funding) as a proxy for company quality. Another variable we consider is whether a portfolio
company discloses its post-investment valuation. In the dataset, around 30% of the portfolio
companies voluntarily report their market valuation after an investment round, which is a
good predictor of their quality. Therefore, we use the dummy variable Valuation Disclosure
Indicator to indicate whether a portfolio company discloses its market valuation after a VC
firm has made the first investment in our analysis of VC investments (or after the portfolio
company has received the first VC funding in our analysis of portfolio companies) as one
more proxy for portfolio company quality.
We also include the control variable Early Investment Indicator which indicates whether the first VC investment in a portfolio company occurs at the early or seed stage of development. Companies in their early stages of development are likely to be riskier, which may
affect VC performance.
Due to the information asymmetry, geographical distance may affect VCs’ investment
decision and their successful exit rates. Geographical distance influences VCs’ ability in finding investment opportunities and VCs’ monitoring cost in alleviating moral hazard problems.
Existing studies (Chemmanur et al., 2011; Tian, 2011) have shown that the geographical distance between portfolio companies and VCs matters for the eventual performance of VC investments. To control for a possible influence of geographical distance, we also include geographical distance in our analysis. The variable Distance is measured as the logarithm of the
geographical distance between a portfolio company and a VC firm.
VC Firm-related Factors
As suggested by Mäkelä and Maula (2008), Dai et al. (2010) and Tykvová and Schertler
(2011), local VCs play an important role in boosting cross-border investment initiation and
in alleviating information asymmetry and monitoring problems. Therefore, we control for
11 Since the cross-country patent data in 2009 was still unavailable at the end of 2010 (the time of writing),
we regard the number of granted patents in 2009 as the same as that in 2008 assuming that there is no substantial changes in the number of patents between these two years.
Page 27
the variable Local VC Ratio, which is defined as the proportion of domestic VCs participating
in the syndicate. Also, this variable captures the possible influence of syndication on VC performance, as theoretically suggested by Casamatta and Haritchabalet (2007) and empirically
documented by Brander et al. (2002). As a robustness test, we have also used a dummy variable to indicate whether or not there are local VCs in a syndicate. Our results remain qualitatively unaltered after controlling for this factor.
We also control for VCs’ experience using the logarithm of a VC firm’s age (VC Experience) as a proxy following Lee and Wahal (2004) and Nahata (2008). As a further robustness test, following Sorensen (2007), we use the cumulative number of investment rounds as
a proxy for VC experience. All our findings remain qualitatively unchanged. Further, we have
also calculated a VC firm’s network based on Hochberg et al. (2007) and a VC firm’s reputation based on Nahata (2008) as additional characteristics of VCs and find these two factors
to be highly correlated with VCs’ experience. Our results also remain unaltered when we further control for these two factors.
The size of a VC firm may be a good proxy for its quality (Gompers, 1996; Gompers and
Lerner, 1999; Kaplan and Schoar, 2005). Cumming and Dai (2011) further document that
there is a concave (inverse U-shape) relationship between fund size and a portfolio company’s performance measured as the probability of a successful exit. We control for the logarithm of a VC firm’s aggregate fundraising over the ten years prior to its first investment in a
portfolio company (VC Size) and the squared term (VC Size Squared). As a robustness check,
we also use a VC firm’s capital under management to measure VC Size and its corresponding
squared term, which leads to a reduction of about 2,000 investment observations in sample
size due to data availability. The results are largely the same.
As suggested in the theoretical studies by Kanniainen and Keuschnigg (2003, 2004) and
empirical studies by Cumming (2006) and Cumming and Dai (2011), there is a tradeoff between a VC firm’s portfolio size and the intensity of managerial advice, which will in turn
influence performance. Hence, we further control for a VC firm’s portfolio size measured by
the logarithm of the number of companies in a VC firm’s portfolio (VC Portfolio Size).
In terms of organizational structure, there is a distinction between traditional VCs and
the so-called captive VCs. The latter are owned by financial institutions, corporations or governments (Bottazzi et al., 2008; Hellmann et al., 2008; Nahata, 2008). While traditional
VCs aim to maximize the value of limited partnerships, captive VCs may emphasize the strategic objectives of their parents, sacrificing at times opportunities to maximize the value of
their investments. In order to control for the impact of this difference in objectives on crossborder VC performance, we include two indicator variables on whether a VC firm is a corporate VC firm (Corporate VC Indicator) or an institutional VC firm (Institutional VC Indicator). Since government VCs constitute only a very small proportion of our sample, there is no
Page 28
need to control for them. Also, VCs from different countries may have different business
training, experience and sorting ability. To control for the effect of a VC firm’s country of
origin on its cross-border performance, we include two indicator variables on whether a VC
firm is located in Asia (Asia VC Indicator) or in Europe (Europe VC Indicator).
For the analysis of VC investments, we control for the above VC firm-related factors for
each VC firm at the time of its first investment in a portfolio company. For the analysis of
portfolio companies, these factors are based on the lead VC firm at the time when a portfolio
company received its initial VC funding. Following Nahata (2008), we identify the lead VC
firm for each portfolio company as the VC firm that participated in the first round of financing and made the largest total investment in the portfolio company across all rounds.
Global VC Market-related Factors
Following Hochberg et al. (2007) and Nahata (2008), competition in the VC industry is
expected to be negatively related to VC performance. To control for the effect of competition
in the global VC industry on cross-border VC performance, we include the variable VC Industry Competition measured by the aggregate worldwide inflow of VC funds prior to the funding year.
Lerner (1994) suggests that it is every VC firm’s intention to choose a good time to exit,
especially if it wants to exit through an IPO. Gompers et al. (2008) indicate that VCs actively
react to IPO market signals measured by Tobin-Q or VC-backed IPO activities. Jeng and
Wells (2000) also find that IPOs are the strongest driver of cross-border VC investments.
Considering the fact that many cross-border VCs try to exit through the international market,
we expect the likelihood of a successful exit to be dependent on the conditions in the global
IPO and M&A markets. We use the number of VC-backed IPOs (IPO Market Conditions) and
the number of M&As with VC-backed portfolio companies as the targets (M&A Market Conditions) over a period of three months prior to an exit from a portfolio company as proxies.
These two variables are lagged by a quarter to give a company and its investors a period of
three months to prepare for an impending exit. For unsuccessful VC investments (or unsuccessful portfolio companies), IPO Market Conditions and M&A Market Conditions are
measured respectively by the averages of the lagged quarterly numbers of VC-backed IPOs
and M&A transactions with VC-backed portfolio companies as the targets over the entire
time period starting from when a VC firm made the first investment in a portfolio company
(or when a portfolio company received the first VC funding) to the end of 2009.
We present the definitions, measures and sources of the dependent variables, independent variables and control variables in Appendix Table 2. Appendix Table 3 presents the
Spearman correlation matrix among all the variables. Investment Success (or Investment
Duration) is significantly positively (or negatively) correlated with Index of Economic FreePage 29
dom, Economic Freedom Quartile and Ranking in Economic Freedom. Most of the control
variables show significant correlations with Investment Success and Investment Duration.
Also, there is neither serious correlation among the control variables, nor between the test
and control variables, suggesting that our regression model does not suffer from severe multicollearity.12
[Insert Appendix Table 2]
[Insert Appendix Table 3]
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Tables
Table 1. The Sample Distribution
The sample consists of 10,205 identifiable cross-border VC investments by 1,906 VCs in 6,535 portfolio companies that received their initial VC funding during
1995–2005 and for which relevant data are available. The table presents the distribution of VC investments by funding year and the development stage of portfolio
companies, the distribution of VC-backed portfolio companies by country and industry, and the distribution of VCs by global region and type. The number of observations and the corresponding percentages (in parentheses) are reported.
VC Investments
Portfolio Companies
VCs
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Total
Obs.
134
(1.31)
309
(3.03)
359
(3.52)
531
(5.20)
1187
(11.63)
2456
(24.07)
1601
(15.69)
955
(9.36)
859
(8.42)
1024
(10.03)
790
(7.74)
10205
(100)
Stage
Seed/Startup
Early Stage
Expansion Stage
Later Stage
Total
Obs.
1191
(11.67)
2698
(26.44)
5217
(51.12)
1099
(10.77)
10205
(100)
Country
U.S.
U.K
Germany
France
Canada
China
Israel
India
Sweden
Japan
Other
Total
Obs.
2670
(40.86)
595
(9.10)
323
(4.94)
316
(4.84)
312
(4.77)
274
(4.19)
219
(3.35)
173
(2.65)
169
(2.59)
153
(2.34)
1331
(20.37)
6535
(100)
Industry
Obs.
Biotechnology
Communications
Media
and
Computer Related
Medical/Health/Life
Science
Semiconductors/Other
Electronics
Non-High Technology
Total
503
(7.70)
1273
(19.48)
2621
(40.11)
521
(7.97)
562
(8.60)
1055
(16.14)
6535
(100)
Global Region
Asia
Europe
North America
Other
Total
Obs.
599
(31.43)
445
(23.35)
783
(41.08)
79
(4.14)
1906
(100)
Type
Traditional VC
Institutional VC
Corporate VC
Government VC
Total
Obs.
1114
(58.45)
348
(18.26)
336
(17.63)
89
(4.67)
1906
(100)
Page 36
Table 2. Summary Statistics and Univariate Analysis
This table presents the statistics of 10,205 cross-border VC investments during 1995–2005 in those portfolio companies that received initial VC funding during
1995–2005. The definitions, measures and data sources of the variables are described in Appendix Table 2. The quartiles, means, standard deviations and the number
of observations are presented. The results of the univariate analysis are also reported, which are from the t-tests for equality of the means and Wilcoxon tests for equality of the medians between successful and unsuccessful exits. The significance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.
Successful exits
Unsuccessful exits
Variable
Obs.
Mean
Std.
0.25
0.5
0.75
Mean
Median
Mean
Median
Investment Success
10,205
0.158
0.365
0
0
0
Investment Duration (days)
10,205
2826
1103
2030
3072
3558
Index of Economic Freedom
10,205
74.073
8.681
70.1
78.57
79.64
76.527*** 78.8***
73.612
78.57
Economic Freedom Quartile
10,205
2.467
1.119
1
2
3
2.696***
3***
2.424
2
Ranking in Economic Freedom
10,205
0.648
0.269
0.514
0.790
0.834
0.758***
0.829***
0.627
0.768
Domestic country-related factors
GDP per Capita (US$)
10,205
33593
12397
31988
36909
40815
34002
36381***
33517
37280
Market Capitalization (%)
10,205
116.567
50.325
82.596
127.201
133.97
132***
135.04*** 113.655
125.81
Market Return
10,205
0.019
0.031
0.014
0.018
0.023
0.018
0.019
0.019
0.018
Legality
10,205
15.424
1.848
15.388
16.445
16.506
16.049*** 16.506*** 15.307
16.097
Foreign Country Legality
10,205
15.688
1.335
15.388
16.076
16.506
15.401*** 15.95***
15.742
16.097
Same Legal Origin Indicator
10,205
0.482
0.500
1
0
0
0.466
0
0.484
0
VC Deals (per million people)
10,205
8.432
6.012
3.653
9.949
13.018
14.554*** 12.577*** 7.283
5.994
Patents (per thousand people)
10,205
0.424
0.301
0.218
0.478
0.486
0.440**
0.484***
0.421
0.471
Portfolio company-related factors
Total VC Funding (US$mil)
10,205
37.161
77.736
4.840
17.500
46.608
67.017*** 41.058*** 31.556
14.500
Valuation Disclosure Indicator
10,205
0.291
0.454
1
0
0
0.596***
1***
0.233
0
Early Investment Indicator
10,205
0.381
0.479
1
0
0
0.313***
0***
0.363
0
Distance (kilometers)
10,205
7205
4509
3032
7837
10513
8483***
8409***
6965
7300
VC firm-related factors
Local VC Ratio
10,205
0.344
0.310
0
0.333
0.615
0.554***
0.667***
0.304
0.25
VC Experience (years)
10,205
13.584
16.963
3
8
18
12.081*** 6***
13.866
8
VC Size (US$mil)
10,205
919
2612
0
143
532
628***
100***
974
150
VC Portfolio Size
10,205
257.431
895.336
15
44
113
94.53***
35***
288.013
48
Institutional VC Indicator
10,205
0.154
0.361
0
0
0
0.174**
0**
0.150
0
Corporate VC Indicator
10,205
0.168
0.374
0
0
0
0.234***
0***
0.156
0
Asia VC Indicator
10,205
0.378
0.485
1
0
0
0.524***
1***
0.351
0
Europe VC Indicator
10,205
0.257
0.437
1
0
0
0.262
0
0.256
0
Global VC market-related factors
VC Industry Competition (US$bil)
10,205
61.840
42.976
20.900
43.908
81.299
60.748*
43.908*
62.044
58.940
IPO Market Conditions
10,205
16.158
9.483
12
13.792
18
23.34***
16***
14.809
13.70
M&A Market Conditions
10,205
110.051
11.969
107.52
108.154
112.785
112.25*** 103***
109.639
108.246
Page 37
Table 3. Probit and Cox Hazard Analyses at the VC Investment Level
The sample consists of 10,205 cross-border VC investments during 1995–2005 in those portfolio companies
that received their initial VC funding during 1995–2005. Models 1–3 and 4–6 present probit and Cox hazard
regression results using equations (1) and (2), respectively. The definitions, measures and data sources of the
variables are described in Appendix Table 2. The robust z-values, cluster-adjusted by portfolio company, are in
parentheses. The significance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.
Probit
Cox hazard
Variable
1
2
3
4
5
6
Index of Economic Freedom
0.036**
0.006*
(2.15)
(1.94)
Economic Freedom Quartile
0.569***
0.106***
(5.76)
(7.45)
Ranking in Economic Freedom
1.760**
0.477***
(2.49)
(3.61)
Domestic country-related factors
GDP per Capita
-0.280*** -0.328*** -0.280*** -0.043*** -0.049*** -0.044***
(-16.46)
(-13.29)
(-16.62)
(-24.19)
(-26.10)
(-23.62)
Market Capitalization
0.000
-0.001
-0.000
0.000
0.000
0.000
(0.02)
(-0.45)
(-0.15)
(1.46)
(1.38)
(0.66)
Market Return
2.898*
3.215**
3.015**
0.290
0.323
0.309
(1.92)
(2.15)
(2.01)
(1.35)
(1.60)
(1.45)
Legality
1.270***
1.377***
1.198***
1.398***
1.377***
1.198***
(12.76)
(12.65)
(10.94)
(9.60)
(10.69)
(7.55)
Foreign Country Legality
-0.048**
-0.034*
-0.046**
-0.035
-0.026
-0.034
(-2.37)
(-1.69)
(-2.26)
(-1.54)
(-1.16)
(-1.48)
Same Legal Origin Indicator
-0.117*
-0.121*
-0.137**
-0.164**
-0.162**
-0.215***
(-1.73)
(-1.74)
(-2.02)
(-2.08)
(-2.04)
(-2.66)
VC Deals
0.208***
0.263***
0.204***
0.033***
0.039***
0.033***
(15.31)
(11.61)
(15.25)
(22.14)
(20.74)
(21.94)
Patents
1.909***
2.557***
1.881***
0.328***
0.421***
0.328***
(9.33)
(8.57)
(9.53)
(11.05)
(12.46)
(10.92)
Portfolio company-related factors
Total VC Funding
0.111***
0.098***
0.110***
0.233***
0.220***
0.236***
(4.30)
(3.95)
(4.29)
(4.82)
(4.57)
(4.90)
Valuation Disclosure Indicator
0.245***
0.204***
0.235***
0.230**
0.195**
0.227**
(3.61)
(3.06)
(3.46)
(2.57)
(2.24)
(2.53)
Early Investment Indicator
-0.275*** -0.237*** -0.270*** -0.541***
-0.502*** -0.538***
(-4.18)
(-3.52)
(-4.11)
(-7.35)
(-6.82)
(-7.32)
Distance
0.083**
0.015
0.075*
0.105**
0.027
0.075
(2.25)
(0.38)
(1.96)
(2.24)
(0.57)
(1.54)
VC firm-related factors
Local VC Ratio
0.861***
0.772***
0.847***
0.812***
0.672***
0.780***
(6.69)
(6.01)
(6.60)
(4.58)
(3.71)
(4.42)
VC Experience
0.050**
0.044**
0.049**
0.030
0.022
0.028
(2.33)
(2.02)
(2.28)
(1.21)
(0.91)
(1.13)
VC Size
0.022
0.017
0.022
0.057*
0.047
0.058**
(0.90)
(0.67)
(0.89)
(1.95)
(1.61)
(1.97)
VC Size Squared
-0.000
0.001
-0.000
-0.006
-0.004
-0.005
(-0.09)
(0.15)
(-0.03)
(-1.41)
(-1.06)
(-1.32)
VC Portfolio Size
-0.086*** -0.069*** -0.084*** -0.090*** -0.076*** -0.089***
(-4.46)
(-3.58)
(-4.34)
(-4.15)
(-3.45)
(-4.14)
Institutional VC Indicator
0.119**
0.098*
0.114**
0.097
0.065
0.087
(2.09)
(1.69)
(2.00)
(1.38)
(0.94)
(1.23)
Corporate VC Indicator
0.163***
0.151**
0.163***
0.159**
0.139**
0.166**
(2.80)
(2.57)
(2.79)
(2.23)
(2.00)
(2.34)
Asia VC Indicator
0.024
-0.050
0.018
0.038
-0.034
0.040
(0.32)
(-0.67)
(0.24)
(0.37)
(-0.34)
(0.39)
Europe VC Indicator
0.102
0.010
0.086
0.079
-0.009
0.060
(1.11)
(0.11)
(0.92)
(0.65)
(-0.07)
(0.48)
Global VC market-related factors
VC Industry Competition
-0.330*** -0.335*** -0.323*** -0.171*
-0.242*** -0.163*
(-6.78)
(-7.01)
(-6.65)
(-1.83)
(-2.77)
(-1.74)
IPO Market Conditions
-0.582*** -0.611***
-0.595*** -0.027**
-0.030*** -0.027***
(-4.77)
(-4.95)
(-4.87)
(-2.56)
(-3.24)
(-2.60)
M&A Market Conditions
2.118***
1.847***
2.183***
0.203***
0.146***
0.204***
(5.56)
(4.52)
(5.73)
(4.44)
(3.15)
(4.52)
Industry and year fixed effects
Present
Present
Present
Present
Present
Present
Number of Observations
10,205
10,205
10,205
10,199
10,199
10,199
Pseudo R2
0.499
0.517
0.502
Page 38
Table 4. Probit and Cox Hazard Analyses at the Portfolio Company Level
The sample consists of 6,535 cross-border VC-backed portfolio companies that were initially funded during
1995–2005. Models 1–3 and 4–6 present probit and Cox hazard regression results using equations (1) and (2),
respectively. The definitions, measures and data sources of the variables are described in Appendix Table 2. The
robust z-values, cluster-adjusted by domestic country, are in parentheses. The significance levels at the 1%, 5%
and 10% are identified by ***, ** and *, respectively.
Probit
Cox hazard
Variable
1
2
3
4
5
6
Index of Economic Freedom
0.032**
0.002*
(2.01)
(1.96)
Economic Freedom Quartile
1.939***
0.095***
(2.89)
(8.30)
Ranking in Economic Freedom
0.491***
0.436***
(5.37)
(5.09)
Domestic country-related factors
GDP per Capita
-0.274*** -0.276*** -0.310***
-0.049*** -0.054*** -0.050***
(-15.07)
(-15.05)
(-11.76)
(-28.25)
(-30.11)
(-28.18)
Market Capitalization
-0.001
-0.002
-0.002
0.001***
0.001***
0.001***
(-0.59)
(-0.96)
(-0.91)
(3.98)
(4.41)
(3.17)
Market Return
1.099
1.208
1.334
0.066
0.135
0.096
(0.84)
(0.93)
(1.03)
(0.44)
(0.94)
(0.63)
Legality
1.258***
1.168***
1.321***
1.820***
1.708***
1.540***
(10.67)
(9.47)
(11.28)
(13.21)
(15.74)
(13.24)
Foreign Country Legality
-0.044**
-0.043*
-0.033
-0.025
-0.016
-0.023
(-1.98)
(-1.92)
(-1.49)
(-0.88)
(-0.59)
(-0.83)
Same Legal Origin Indicator
-0.230*** -0.263*** -0.227*** -0.153*
-0.167*
-0.210**
(-2.70)
(-3.09)
(-2.76)
(-1.71)
(-1.86)
(-2.30)
VC Deals
0.224***
0.222***
0.270***
0.037***
0.043***
0.037***
(15.78)
(15.79)
(12.03)
(23.64)
(24.06)
(24.77)
Patents
1.755***
1.742***
2.276***
0.351***
0.454***
0.383***
(8.40)
(8.22)
(7.40)
(12.98)
(16.16)
(15.10)
Portfolio company-related factors
Total VC Funding
0.068***
0.068***
0.055***
0.144***
0.126***
0.144***
(3.37)
(3.33)
(2.76)
(4.62)
(4.11)
(4.58)
Valuation Disclosure Indicator
0.213***
0.205***
0.180***
0.199**
0.169**
0.195**
(3.46)
(3.32)
(2.91)
(2.51)
(2.15)
(2.44)
Early Investment Indicator
-0.207*** -0.198***
-0.166***
-0.440*** -0.405*** -0.430***
(-3.41)
(-3.28)
(-2.76)
(-6.23)
(-5.88)
(-6.06)
Distance
0.070
0.052
0.015
0.118**
0.030
0.065
(1.62)
(1.16)
(0.32)
(2.16)
(0.52)
(1.16)
VC firm-related factors
Local VC Ratio
1.053***
1.042***
0.994***
1.314***
1.261***
1.286***
(9.06)
(8.96)
(8.46)
(8.12)
(7.85)
(7.93)
VC Experience
0.047*
0.046
0.044
0.022
0.012
0.021
(1.66)
(1.62)
(1.57)
(0.66)
(0.36)
(0.62)
VC Size
0.001
-0.000
-0.002
0.049
0.044
0.055
(0.03)
(-0.00)
(-0.07)
(1.29)
(1.17)
(1.43)
VC Size Squared
0.003
0.003
0.003
-0.005
-0.003
-0.005
(0.63)
(0.71)
(0.79)
(-0.96)
(-0.64)
(-0.96)
VC Portfolio Size
-0.087*** -0.085*** -0.072*** -0.081*** -0.075*** -0.083***
(-3.55)
(-3.45)
(-2.93)
(-2.95)
(-2.70)
(-3.01)
Institutional VC Indicator
0.067
0.062
0.037
0.057
0.033
0.044
(0.84)
(0.78)
(0.46)
(0.58)
(0.35)
(0.46)
Corporate VC Indicator
0.130*
0.132*
0.127
0.159*
0.162*
0.170*
(1.69)
(1.71)
(1.64)
(1.76)
(1.82)
(1.89)
Asia VC Indicator
-0.110
-0.105
-0.151*
0.078
0.081
0.106
(-1.32)
(-1.25)
(-1.77)
(0.70)
(0.71)
(0.93)
Europe VC Indicator
-0.014
-0.027
-0.073
0.178
0.162
0.183
(-0.13)
(-0.25)
(-0.69)
(1.36)
(1.19)
(1.33)
Global VC market-related factors
VC Industry Competition
-0.246*** -0.240*** -0.260*** 0.121
-0.010
0.127
(-4.03)
(-3.94)
(-4.38)
(0.91)
(-0.08)
(0.95)
IPO Market Conditions
-0.585*** -0.595***
-0.607*** -0.023**
-0.026*** -0.023**
(-5.81)
(-5.89)
(-5.92)
(-2.53)
(-3.20)
(-2.55)
M&A Market Conditions
1.429***
1.488***
1.127***
0.101**
0.055
0.103**
(4.59)
(4.84)
(3.49)
(2.44)
(1.36)
(2.51)
Industry and year fixed effects
Present
Present
Present
Present
Present
Present
Number of Observations
6,535
6,535
6,535
6,529
6,529
6,529
Pseudo R2
0.532
0.535
0.545
Page 39
Table 5. Analysis of the Change in Economic Freedom at the Portfolio Company
Level
The sample consists of 6,535 cross-border VC-backed portfolio companies that were initially funded during
1995–2005. Models 1–2 (3–4) present probit (Cox hazard) regression results taking Change in IEF or IEF Growth
as the independent variable. Change in IEF (IEF Growth) is measured as the average yearly change in IEF
(growth rate of IEF) during the entire investment period. The definitions, measures and data sources of all the
other variables are described in Appendix Table 2. The robust z-values, cluster-adjusted by domestic country, are
in parentheses. The significance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.
Probit
Cox hazard
Variable
1
2
3
4
Change in IEF
0.713***
0.106***
(5.24)
(4.61)
IEF Growth
0.453***
0.063***
(4.70)
(3.77)
Domestic country-related factors
GDP per Capita
-0.247***
-0.252***
-0.043***
-0.044***
(-13.15)
(-13.52)
(-19.20)
(-19.93)
Market Capitalization
0.001
0.001
0.001***
0.001***
(0.38)
(0.44)
(5.69)
(5.64)
Market Return
1.244
1.260
0.074
0.069
(0.95)
(0.95)
(0.49)
(0.45)
Legality
1.193***
1.227***
1.669***
1.713***
(11.10)
(11.59)
(13.40)
(14.00)
Foreign Country Legality
-0.067***
-0.064***
-0.035
-0.034
(-2.81)
(-2.73)
(-1.23)
(-1.19)
Same Legal Origin Indicator
-0.249***
-0.250***
-0.137
-0.141
(-3.28)
(-3.30)
(-1.53)
(-1.56)
VC Deals
0.202***
0.206***
0.031***
0.033***
(12.69)
(13.21)
(16.10)
(16.92)
Patents
1.520***
1.512***
0.285***
0.289***
(6.99)
(6.87)
(10.38)
(10.04)
Portfolio company-related factors
Total VC Funding
0.079***
0.078***
0.154***
0.153***
(3.91)
(3.90)
(4.95)
(4.89)
Valuation Disclosure Indicator
0.267***
0.261***
0.257***
0.247***
(4.19)
(4.11)
(3.14)
(3.01)
Early Investment Indicator
-0.211***
-0.214***
-0.423***
-0.427***
(-3.40)
(-3.45)
(-6.12)
(-6.15)
Distance
0.130***
0.124***
0.177***
0.168***
(2.86)
(2.77)
(3.26)
(3.09)
VC firm-related factors
Local VC Ratio
1.091***
1.097***
1.328***
1.333***
(8.94)
(8.99)
(8.25)
(8.25)
VC Experience
0.049*
0.049*
0.021
0.022
(1.71)
(1.70)
(0.63)
(0.64)
VC Size
-0.005
-0.005
0.050
0.049
(-0.15)
(-0.16)
(1.29)
(1.27)
VC Size Squared
0.003
0.003
-0.005
-0.005
(0.68)
(0.71)
(-1.02)
(-0.99)
VC Portfolio Size
-0.093***
-0.093***
-0.089***
-0.087***
(-3.75)
(-3.77)
(-3.20)
(-3.14)
Institutional VC Indicator
0.088
0.084
0.080
0.075
(1.09)
(1.04)
(0.81)
(0.77)
Corporate VC Indicator
0.118
0.119
0.145
0.146
(1.49)
(1.51)
(1.58)
(1.60)
Asia VC Indicator
-0.054
-0.063
0.152
0.135
(-0.63)
(-0.74)
(1.38)
(1.22)
Europe VC Indicator
0.072
0.055
0.278**
0.255*
(0.69)
(0.54)
(2.13)
(1.95)
Global VC market-related factors
VC Industry Competition
-0.195***
-0.203***
0.035
0.054
(-3.18)
(-3.32)
(0.27)
(0.41)
IPO Market Conditions
-0.612***
-0.611***
-0.031***
-0.029***
(-6.13)
(-6.08)
(-3.41)
(-3.19)
M&A Market Conditions
1.044***
1.141***
0.066
0.075*
(3.25)
(3.60)
(1.62)
(1.81)
Industry and year fixed effects
Present
Present
Present
Present
Number of Observations
6,535
6,535
6,529
6,529
Pseudo R2
0.547
0.545
Page 40
Table 6. Probit Analysis of IPO and M&A Exits at the Portfolio Company Level
The sample for models 1–3 consists of 935 successful portfolio companies. Models 1–3 are probit models with IPO Indicator as the dependent variable. IPO Indicator
takes the value of 1 for an IPO and 0 for an acquisition. The sample for models 4–6 consists of 6,535 cross-border VC-backed portfolio companies that were initially funded
during 1995–2005 and for which relevant data are available. Models 4–6 are multinomial probit models, for which the dependent variable takes three discrete values corresponding respectively to IPOs, acquisitions and unsuccessful exits. The definitions, measures and data sources of the variables are described in Appendix Table 2. The intercepts are not reported. The robust z-values are in parentheses. The significance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.
1
2
3
4
5
6
Variable
IPO vs. M&A IPO vs. M&A IPO vs. M&A
IPO
M&A
IPO
M&A
IPO
M&A
Index of Economic Freedom
0.001
0.069**
0.033*
(0.04)
(2.23)
(1.73)
Economic Freedom Quartile
0.164
0.792***
0.607***
(0.18)
(5.31)
(3.81)
Ranking in Economic Freedom
0.143
3.396**
2.341***
(0.82)
(2.44)
(3.28)
Domestic country-related factors
GDP per Capita
-0.049
-0.050*
-0.083***
-0.386***
-0.375***
-0.429***
-0.425***
-0.382***
-0.380***
(-1.52)
(-1.92)
(-2.94)
(-9.62)
(-12.03)
(-10.41)
(-9.67)
(-9.60)
(-12.18)
Market Capitalization
0.001
0.001
0.000
-0.009
0.003
-0.010
0.002
-0.010
0.001
(0.34)
(0.33)
(0.16)
(-1.46)
(1.05)
(-1.33)
(0.86)
(-1.53)
(0.59)
Market Return
1.062
1.065
1.013
4.254**
2.290
4.460**
2.624*
4.395**
2.481
(1.19)
(1.19)
(1.12)
(2.45)
(1.56)
(2.56)
(1.69)
(2.44)
(1.61)
Legality
-0.252*
-0.261**
-0.228*
1.506***
1.881***
1.582***
1.948***
1.355***
1.755***
(-1.86)
(-1.96)
(-1.74)
(6.17)
(9.01)
(6.23)
(9.87)
(4.89)
(8.49)
Foreign Country Legality
0.010
0.010
0.006
0.011
-0.069**
0.022
-0.055*
0.012
-0.067**
(0.19)
(0.19)
(0.12)
(0.19)
(-2.02)
(0.39)
(-1.79)
(0.21)
(-1.97)
Same Legal Origin Indicator
-0.372**
-0.375**
-0.361**
-0.672***
-0.174
-0.639**
-0.192
-0.716***
-0.218*
(-2.37)
(-2.39)
(-2.28)
(-2.62)
(-1.36)
(-2.45)
(-1.40)
(-2.87)
(-1.73)
VC Deals
0.011
0.011
0.023*
0.307***
0.309***
0.376***
0.366***
0.304***
0.306***
(1.07)
(1.07)
(1.92)
(6.24)
(8.34)
(6.98)
(7.51)
(6.20)
(8.23)
Patents
0.879
0.893
1.456**
2.362***
2.505***
3.292***
3.151***
2.167***
2.579***
(1.55)
(1.60)
(2.34)
(5.36)
(8.50)
(6.61)
(7.01)
(4.89)
(8.00)
Portfolio company-related factors
Total VC Funding
0.236***
0.236***
0.234***
0.294***
0.045
0.276***
0.029
0.292***
0.045
(3.17)
(3.16)
(3.05)
(4.12)
(1.47)
(3.97)
(0.91)
(4.12)
(1.45)
Valuation Disclosure Indicator
0.745***
0.744***
0.750***
1.000***
0.011
0.963***
-0.031
0.987***
0.000
(5.93)
(5.91)
(5.96)
(6.58)
(0.16)
(6.34)
(-0.51)
(6.60)
(0.01)
Early Investment Indicator
-0.333***
-0.333***
-0.320***
-0.579***
-0.221**
-0.527***
-0.164**
-0.570***
-0.207**
(-2.82)
(-2.82)
(-2.69)
(-7.83)
(-2.44)
(-6.84)
(-2.06)
(-7.90)
(-2.24)
Distance
-0.294***
-0.295***
-0.293***
-0.043
0.139**
-0.108**
0.060
-0.062
0.109*
Page 41
VC firm-related factors
Local VC Ratio
VC Experience
VC Size
VC Size Squared
VC Portfolio Size
Institutional VC Indicator
Corporate VC Indicator
Asia VC Indicator
Europe VC Indicator
Global VC market-related factors
VC Industry Competition
IPO Market Conditions
M&A Market Conditions
Industry and year fixed effects
Number of Observations
Pseudo R2
(-2.73)
(-2.72)
(-2.62)
(-0.87)
(2.11)
(-2.09)
(1.04)
(-1.15)
(1.66)
-0.418*
(-1.72)
-0.061
(-1.16)
0.029
(0.46)
-0.005
(-0.61)
0.002
(0.04)
0.122
(0.70)
-0.088
(-0.58)
0.438**
(2.13)
0.159
(0.70)
-0.418*
(-1.72)
-0.062
(-1.16)
0.028
(0.45)
-0.005
(-0.60)
0.002
(0.04)
0.122
(0.70)
-0.087
(-0.58)
0.437**
(2.11)
0.157
(0.69)
-0.427*
(-1.75)
-0.064
(-1.21)
0.016
(0.25)
-0.004
(-0.46)
0.014
(0.26)
0.103
(0.59)
-0.101
(-0.68)
0.453**
(2.14)
0.187
(0.80)
1.151***
(4.50)
0.010
(0.20)
0.031
(0.42)
-0.001
(-0.12)
-0.090*
(-1.88)
0.138
(0.96)
-0.028
(-0.18)
0.016
(0.07)
-0.173
(-0.64)
1.624***
(10.11)
0.086
(1.25)
0.002
(0.03)
0.004
(0.58)
-0.132***
(-4.35)
0.055
(0.59)
0.241
(1.55)
-0.201*
(-1.92)
0.043
(0.27)
1.035***
(4.19)
0.010
(0.21)
0.021
(0.30)
0.001
(0.05)
-0.069
(-1.35)
0.116
(0.79)
-0.041
(-0.26)
-0.057
(-0.25)
-0.233
(-0.85)
1.561***
(10.54)
0.081
(1.32)
-0.001
(-0.03)
0.005
(0.77)
-0.112***
(-3.91)
0.013
(0.15)
0.239
(1.60)
-0.247**
(-2.04)
-0.043
(-0.27)
1.127***
(4.48)
0.010
(0.21)
0.031
(0.42)
-0.001
(-0.06)
-0.087*
(-1.80)
0.138
(0.96)
-0.016
(-0.10)
0.011
(0.05)
-0.203
(-0.74)
1.614***
(10.22)
0.084
(1.21)
0.001
(0.02)
0.005
(0.64)
-0.129***
(-4.02)
0.046
(0.50)
0.243
(1.57)
-0.188*
(-1.77)
0.032
(0.20)
-0.137
(-1.37)
0.691***
(7.75)
-0.528**
(-1.99)
Present
905
0.356
-0.137
(-1.36)
0.691***
(7.81)
-0.522*
(-1.94)
Present
905
0.356
-0.172*
(-1.70)
0.663***
(7.44)
-0.645**
(-2.42)
Present
905
0.360
-0.304**
(-2.44)
0.514*
(1.76)
1.191
(1.35)
Present
6,535
-0.290**
(-2.38)
-1.088***
(-5.08)
2.368***
(3.79)
-0.376***
(-3.29)
0.460
(1.54)
0.671
(0.80)
Present
6,535
-0.301***
(-2.73)
-1.114***
(-5.19)
1.980***
(3.06)
-0.292**
(-2.34)
0.484
(1.64)
1.285
(1.47)
Present
6,535
-0.285**
(-2.29)
-1.102***
(-5.02)
2.442***
(3.87)
Page 42
Table 7. Probit Analysis of the Decomposed Effects at the Portfolio Company level
The sample consists of 6,535 cross-border VC-backed portfolio companies that were initially funded during 1995–2005. This table presents the probit regression results
with Investment Success as the dependent variable. The independent variables of the nine models are respectively the nine components of economic freedom, which are Business Freedom, Trade Freedom, Government Spending, Monetary Freedom, Investment Freedom, Financial Freedom, Property Freedom and Freedom from Corruption. The
definitions, measures and data sources of the variables are described in Appendix Table 2. The robust z-values, cluster-adjusted by domestic country, are in parentheses. The
significance levels at the 1%, 5% and 10% are identified by ***, ** and *, respectively.
1
2
3
4
5
6
7
8
9
Freedom
Business
Trade Free- Fiscal Free- Government
Monetary
Investment
Financial
Property
Variable
from CorFreedom
dom
dom
Spending
Freedom
Freedom
Freedom
Freedom
ruption
Decomposed Economic Freedom
0.006
-0.069
-0.026
-0.004
0.252*
0.027**
0.072*
0.031**
-0.023
(0.17)
(-1.52)
(-1.31)
(-0.58)
(1.78)
(2.17)
(1.90)
(2.22)
(-1.46)
Domestic country-related factors
GDP per Capita
-0.259***
-0.244***
-0.268***
-0.267***
-0.255***
-0.305***
-0.317***
-0.288***
-0.266***
(-3.17)
(-2.95)
(-3.30)
(-3.14)
(-3.41)
(-3.24)
(-3.00)
(-3.19)
(-3.02)
Market Capitalization
0.000
0.000
0.004
0.001
-0.002
0.001
-0.003
0.000
-0.001
(0.04)
(0.04)
(0.51)
(0.17)
(-0.48)
(0.21)
(-0.50)
(0.06)
(-0.15)
Market Return
0.966
1.762
0.715
0.905
1.295
0.969
1.441
1.150
1.247
(0.78)
(1.49)
(0.56)
(0.75)
(0.94)
(0.74)
(1.01)
(0.95)
(1.02)
Legality
1.331***
1.456**
1.327***
1.326***
1.176***
1.441***
1.070***
1.213***
1.510***
(2.79)
(2.43)
(2.96)
(2.90)
(3.03)
(3.00)
(3.14)
(3.13)
(3.16)
Foreign Country Legality
-0.041***
-0.033
-0.045**
-0.046**
-0.054**
-0.045**
-0.045**
-0.053***
-0.029**
(-2.73)
(-1.54)
(-2.13)
(-2.17)
(-2.53)
(-2.45)
(-2.11)
(-2.75)
(-1.98)
Same Legal Origin Indicator
-0.158
-0.207
-0.133
-0.180
-0.231
-0.239
-0.354**
-0.307*
-0.109
(-1.00)
(-1.02)
(-0.95)
(-1.10)
(-1.28)
(-1.44)
(-2.07)
(-1.79)
(-0.84)
VC Deals
0.223***
0.246***
0.221***
0.222***
0.225***
0.231***
0.231***
0.216***
0.234***
(4.31)
(4.93)
(4.20)
(4.24)
(3.53)
(4.27)
(4.46)
(4.40)
(4.87)
Patents
1.683**
1.633**
1.505*
1.676*
0.350
1.620*
1.867**
1.615*
1.711*
(1.98)
(1.97)
(1.90)
(1.90)
(0.39)
(1.96)
(2.20)
(1.87)
(1.94)
Portfolio company-related factors
Total VC Funding
0.065***
0.062***
0.068***
0.069***
0.077***
0.071***
0.069***
0.078***
0.059***
(3.91)
(4.06)
(3.62)
(3.73)
(3.28)
(3.55)
(3.23)
(3.60)
(3.35)
Valuation Disclosure Indicator 0.204
0.156
0.237*
0.223*
0.254**
0.251*
0.203
0.254*
0.162*
(1.59)
(1.39)
(1.72)
(1.82)
(2.00)
(1.74)
(1.43)
(1.91)
(1.70)
Early Investment Indicator
-0.222*
-0.233*
-0.233*
-0.222*
-0.241*
-0.211
-0.154
-0.210
-0.231*
(-1.80)
(-1.85)
(-1.89)
(-1.81)
(-1.91)
(-1.54)
(-1.22)
(-1.63)
(-1.94)
Distance
0.094
0.064
0.142*
0.106*
0.075
0.087
0.015
0.096
0.067
(1.46)
(0.94)
(1.84)
(1.72)
(0.90)
(1.21)
(0.18)
(1.23)
(1.24)
VC firm-related factors
Local VC Ratio
1.045***
0.981***
1.109***
1.072***
1.178***
1.137***
1.072***
1.107***
0.992***
Page 43
VC Experience
VC Size
VC Size Squared
VC Portfolio Size
Institutional VC Indicator
Corporate VC Indicator
Asia VC Indicator
Europe VC Indicator
Global VC market-related factors
VC Industry Competition
IPO Market Conditions
M&A Market Conditions
Industry and year fixed effects
Number of Observations
Pseudo R2
(4.52)
0.049
(1.46)
0.001
(0.04)
0.002
(0.57)
-0.086*
(-1.92)
0.079
(1.20)
0.128**
(2.09)
-0.133*
(-1.75)
-0.007
(-0.05)
(3.70)
0.056*
(1.67)
0.012
(0.40)
0.001
(0.19)
-0.073*
(-1.70)
0.053
(0.93)
0.122**
(2.27)
-0.236***
(-3.35)
-0.146
(-0.96)
(4.80)
0.053
(1.62)
-0.006
(-0.23)
0.004
(0.80)
-0.100**
(-2.35)
0.091
(1.39)
0.133*
(1.95)
-0.126
(-1.58)
0.001
(0.01)
(4.50)
0.049
(1.45)
-0.001
(-0.02)
0.003
(0.60)
-0.092**
(-2.00)
0.082
(1.29)
0.130*
(1.95)
-0.115
(-1.41)
0.003
(0.02)
(4.76)
0.056*
(1.88)
-0.005
(-0.22)
0.004
(0.72)
-0.097**
(-1.96)
0.036
(0.64)
0.108
(1.34)
-0.033
(-0.38)
0.060
(0.39)
(5.01)
0.046
(1.33)
-0.003
(-0.11)
0.004
(0.79)
-0.097**
(-2.06)
0.068
(1.06)
0.138**
(1.96)
-0.087
(-1.19)
-0.019
(-0.15)
(4.95)
0.045
(1.43)
-0.006
(-0.20)
0.005
(0.87)
-0.076**
(-1.99)
0.027
(0.42)
0.125*
(1.87)
-0.078
(-1.38)
-0.090
(-0.63)
(4.69)
0.047
(1.45)
-0.000
(-0.01)
0.003
(0.66)
-0.099**
(-2.04)
0.078
(1.14)
0.137**
(1.99)
-0.081
(-1.06)
-0.017
(-0.10)
(3.58)
0.051
(1.47)
0.004
(0.14)
0.002
(0.41)
-0.074
(-1.45)
0.069
(1.09)
0.127**
(2.08)
-0.179**
(-2.40)
-0.039
(-0.27)
-0.240***
(-4.54)
-0.581***
(-2.61)
1.430***
(2.81)
Present
6,535
0.532
-0.238***
(-3.97)
-0.643***
(-2.64)
1.483***
(2.58)
Present
6,535
0.551
-0.236***
(-4.37)
-0.595***
(-2.63)
1.485***
(3.04)
Present
6,535
0.539
-0.238***
(-4.28)
-0.584***
(-2.61)
1.444***
(2.83)
Present
6,535
0.532
-0.259***
(-4.40)
-0.612***
(-2.62)
1.475***
(3.10)
Present
6,535
0.569
-0.239***
(-4.09)
-0.609***
(-2.66)
1.548***
(2.93)
Present
6,535
0.541
-0.362***
(-5.79)
-0.587**
(-2.32)
1.408***
(3.11)
Present
6,535
0.570
-0.251***
(-4.74)
-0.606***
(-2.65)
1.548***
(2.84)
Present
6,535
0.538
-0.231***
(-4.30)
-0.586**
(-2.57)
1.402***
(2.61)
Present
6,535
0.536
Page 44
Appendix Tables
Appendix Table 1. Components of the Index of Economic Freedom
This table describes the components of the IEF (Index of Economic Freedom) constructed by the
Wall Street Journal and Heritage Foundation (WSJ/HF). Detailed information is available at website
www.heritage.org.
Components
Description
Business Freedom
Fiscal Freedom
A quantitative measure of the ability to start, operate, and close a business that represents the overall burden of regulation as well as the efficiency of government in the regulatory process.
A composite measure of the absence of tariff and non-tariff barriers that
affect imports and exports of goods and services.
A measure of the tax burden imposed by government.
Government Spending
A measure of government expenditures as a percentage of GDP.
Monetary Freedom
A measure of price stability combined with an assessment of price controls.
A composite measure of a variety of restrictions typically imposed on
investment.
A measure of banking security as well as a measure of independence
from government control.
An assessment of the ability of individuals to accumulate private property, secured by clear laws that are fully enforced by the state.
A measure of the degree to which corruption is perceived to exist among
public officials and politicians.
A quantitative measure that looks into various aspects of the legal and
regulatory framework of a country’s labor market.
Trade Freedom
Investment Freedom
Financial Freedom
Property Freedom
Freedom from Corruption
Labor Freedom
Page 45
Appendix Table 2. Variable Definitions, Measures and Sources13
This table presents the definitions, measures and data sources of the dependent variables, independent variables and control variables. Dummy variables are indicated by *.
Variables in natural logarithm are indicated by **.
Variable
Definition and Measurement
Data Source
Dependent Variables:
Investment Success*
A dummy variable indicating whether a VC-backed portfolio company had gone public or was acquired by the end of VentureXpert
2009.
Investment Duration**
The logarithm of the number of days from the date when a VC firm made the first investment in a portfolio company to VentureXpert
the date of exit. It is right-censored at the end of 2009 for those investments that had yet to exit successfully by the end
of 2009.
Independent Variables:
Index of Economic Freedom
The average IEF of the domestic country over the investment period starting from the year when a VC firm made the WSJ/HF
first investment in a portfolio company to the year of exit. For unsuccessful exits, it is the average IEF of the domestic
country over the entire time period starting from the year when a VC firm made the first investment in a portfolio company to year 2009.
Economic Freedom Quartile
The quartile of Index of Economic Freedom, which takes the value of 1 if Index belongs to the lowest quartile, 2 if Index WSJ/HF
belongs to the second quartile, 3 if Index belongs to the third quartile, and 4 if Index belongs to the highest quartile.
Ranking in Economic Freedom
The average normalized IEF ranking of a domestic country over the investment period starting from the year when a WSJ/HF
VC firm made the first investment in a portfolio company to the year of exit. For unsuccessful exits, it is the average
normalized IEF ranking of a domestic country over the entire time period starting from the year when a VC firm made
the first investment in a portfolio company to year 2009. The normalized IEF ranking of domestic country i in year t is
measured by one minus the IEF ranking of country i in year t divided by the total number of sample countries.
Control Variables:
Domestic country-related factors
GDP per Capita**
The logarithm of a domestic country’s average GDP per capita (US$) over the investment period starting from the year WDI
a VC firm made the first investment in a portfolio company to the year of exit. For unsuccessful exits, it is the logarithm
of a domestic country’s average GDP per capita over the entire time period starting from the year a VC firm made the
first investment to year 2009.
Market Capitalization
A domestic country’s average percentage ratio of market capitalization of listed companies to GDP over the investment WDI
period starting from the year a VC firm made the first investment in a portfolio company to the year of exit. For unsuccessful exits, it is a domestic country’s average percentage ratio of market capitalization of listed companies to GDP
over the entire time period starting from the year a VC firm made the first investment to year 2009.
Market Return
A domestic country’s lagged quarterly stock market return prior to a VC firm’s exit from a portfolio company. For un- Global Insight
successful exits, it is a domestic country’s average lagged quarterly stock market return over the entire time period
starting from the date when a portfolio company received the initial VC funding to the end of 2009.
Legality
Constructed from Berkowitz et al. (2003) to indicate the efficiency of the legal institutions in a domestic country: Le- La Porta et al.
gality=0.381*(Efficiency of Judiciary)+0.5778*(Rule of Law)+0.5031*(Corruption) +0.3468*(Risk of Expropria- (1997, 1998)
tion)+0.3842*(Risk of Contract Repudiation)
13 We present the measures of all the variables for the analysis of VC investments. For the analysis of portfolio companies, we measure all the variables at the time when a
portfolio company received the first VC funding, where the VC firm refers to the lead VC firm.
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Foreign Country Legality
The legality of the foreign country, which is measured by the same method as that for the variable Legality.
Same Legal Origin Indicator*
A dummy variable indicating whether the legal systems of a pair of domestic and foreign countries originate from the
same tradition.
The average number of VC deals per million people in a domestic country over the investment period starting from the
year a VC firm made the first investment in a portfolio company to the year of exit. For unsuccessful exits, it is the
average number of VC deals per million people in a domestic country over the entire time period starting from the year
a VC firm made the first investment to year 2009.
The average number of patents granted per thousand people in a domestic country over the investment period starting
from the year a VC firm made the first investment in a portfolio company to the year of exit. For unsuccessful exits, it is
the average number of patents granted per thousand people in a domestic country over the entire time period starting
from the year a VC firm made the first investment to year 2009.
VC Deals
Patents
Portfolio company-related factors
Total VC Funding**
Valuation Disclosure Indicator*
Early Investment Indicator*
Distance**
VC firm-related factors
Local VC Ratio
VC Experience**
VC Size**
VC Size Squared
VC Portfolio Size**
Institutional VC Indicator*
Corporate VC Indicator*
Asia VC Indicator*
Europe VC Indicator*
Global VC market-related factors
VC Industry Competition**
IPO Market Conditions**
M&A Market Conditions**
La Porta et al.
(1997, 1998)
La Porta et al.
(1997, 1998)
VentureXpert
WIPO
The logarithm of total VC funding across all financing rounds (US$mil).
A dummy variable indicating whether a portfolio company discloses its market valuation after a VC firm’s first investment.
A dummy variable indicating whether the first investment by a VC firm in a portfolio company occurred at the seed or
early stage of development.
The logarithm of the geographical distance, in kilometers, between the state (or province) of a portfolio company and
that of a VC firm.
VentureXpert
VentureXpert
The ratio of domestic VCs participating in the syndicate.
The logarithm of a VC firm’s age at the time of its first investment in a portfolio company.
The logarithm of a VC firm’s aggregate amount of funds raised (US$mil) over the ten years prior to its first investment
in a portfolio company.
The squared term of VC Size.
The logarithm of the number of companies in a VC firm’s portfolio.
A dummy variable indicating whether a VC firm is affiliated with a financial institution.
A dummy variable indicating whether a VC firm is affiliated with a corporation.
A dummy variable indicating a VC firm comes from Asia.
A dummy variable indicating a VC firm comes from Europe.
VentureXpert
VentureXpert
VentureXpert
The logarithm of the lagged yearly aggregate worldwide VC fund inflows (US$mil) at the time a VC firm made the first
investment in a portfolio company.
The logarithm of the lagged quarterly number of worldwide VC-backed IPOs prior to a VC firm’s exit from a portfolio
company. For unsuccessful exits, it is the logarithm of the average lagged quarterly number of worldwide VC-backed
IPOs over the entire time period starting from the date when a portfolio company received the initial VC funding to the
end of 2009.
The logarithm of the lagged quarterly number of worldwide M&A transactions with VC-backed portfolio companies as
the targets prior to a VC firm’s exit from a portfolio company. For unsuccessful exits, it is the logarithm of the average
lagged quarterly number of worldwide M&A transactions with VC-backed portfolio companies as the targets over the
entire time period starting from the date when a portfolio company received the initial VC funding to the end of 2009.
VentureXpert
Google Maps
VentureXpert
VentureXpert
VentureXpert
VentureXpert
VentureXpert
VentureXpert
VentureXpert
VentureXpert
VentureXpert
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Appendix Table 3. Correlation Matrix
This table presents the Spearman correlation matrix among the dependent variables, explanatory variables and control variables. The sample consists of 10,205 crossborder VC investments during 1995–2005 in those portfolio companies that received their initial VC funding during 1995–2005. The definitions, measures and data sources of
the variables are described in Appendix Table 2. The significance levels at 1%, 5% and 10% are indicated by a, b and c, respectively.
Variable
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(1)
Investment Success
1
(2)
Investment Duration
-0.549a
1
(3)
Index of Economic Freedom
0.088a -0.119a 1
(4)
Economic Freedom Quartile
0.079a -0.146a 0.968 a 1
(5)
Ranking in Economic Freedom 0.247a -0.133a 0.988a 0.912 a 1
(6)
GDP per Capita
-0.048a 0.162a 0.524a 0.457a 0.528a 1
(7)
Market Capitalization
0.312a -0.037a 0.428a 0.542a 0.493a 0.319a 1
(8)
Market Return
-0.02b -0.056a -0.206a -0.159a -0.200a -0.203a -0.092a 1
(9)
Legality
0.216a -0.104a 0.544a -0.193a 0.574a 0.462a 0.462a -0.223a 1
(10)
Foreign Country Legality
-0.135a 0.057a -0.161a 0.185a -0.174a -0.065a -0.221a 0.022b -0.101a 1
0.398a 0.210a 0.016
0.182a -0.018c 0.039a -0.088a 1
(11)
Same Legal Origin Indicator
-0.013 0.038a 0.215a
a
a
a
a
a
a
-0.042 0.506
0.274
0.561
0.459
0.499a -0.067a 0.488a -0.191a 0.132a 1
(12)
VC Deals
0.369
1
(13)
Patents
0.108a -0.076a 0.186a 0.237a 0.208a 0.429a 0.097a -0.098a 0.370a -0.007a -0.242a 0.099a
(14)
Total VC Funding
0.259a -0.182a 0.158a 0.185a 0.185a 0.074a 0.270a 0.009a 0.120a -0.039a 0.064a 0.312a -0.04a 1
0.255a 0.146a 0.282a 0.014a 0.213a -0.076a 0.024a 0.430a 0.032a 0.320a
(15)
Valuation Disclosure Indicator 0.292a -0.066a 0.212a 0.005
a
a
b
a
0.030
0.308
0.029b 0.055a 0.036a 0.017a 0.052a -0.041a 0.051a 0.068a -0.022c 0.042a
(16)
Early Investment Indicator
-0.038 0.080
a
c
a
a
-0.019 0.087
0.043
0.107a -0.101a 0.192a 0.007
-0.059a -0.138a -0.032a 0.234a -0.043a 0.227a
(17)
Distance
0.127
a
a
a
a
a
a
a
a
-0.208 0.312
-0.108 0.344
0.297
0.309
-0.067 0.343a -0.075a 0.034a 0.491a 0.085a 0.464a
(18)
Local VC Ratio
0.293
-0.061a 0.161a
-0.045a -0.131a -0.007a -0.042a
(19)
VC Experience
-0.055a -0.058a -0.101a -0.119a -0.112a -0.038a -0.131a 0.012
-0.125a -0.153a -0.137a -0.091a -0.152a 0.020b -0.095a 0.057a -0.005a -0.168a 0.010
-0.003
(20) VC Size
-0.073a 0.004
(21)
VC Portfolio Size
-0.071a 0.088a -0.120a 0.171a
-0.128a -0.046a -0.121a 0.016
-0.072a 0.217a
-0.010 -0.142a 0.003
0.003
-0.041a 0.025b 0.010
-0.027b -0.150a 0.043a -0.016 -0.049a -0.003
(22) Institutional VC Indicator
0.024b -0.004 0.020c 0.069a 0.019
a
a
a
a
a
b
a
-0.047 0.072
0.126
0.084
0.031
0.091
0.005
0.043a 0.044a -0.022c 0.131a
0.024b 0.066a
(23) Corporate VC Indicator
0.076
a
a
a
a
a
b
a
a
a
a
a
-0.064 0.093
0.073
0.112
0.030
0.138
0.004
0.095
-0.530 0.089
0.201
0.032a 0.075a
(24) Asia VC Indicator
0.131
-0.046a 0.162a 0.176a
-0.320a 0.084a 0.055a -0.047a
(25) Europe VC Indicator
0.005
-0.079a 0.093a 0.108a 0.090a 0.170a 0.08a
0.097a -0.145a 0.084a 0.091a -0.011
-0.015 0.078a -0.022c 0.036a 0.084a -0.007 0.103a
(26) VC Industry Competition
-0.018c 0.172a
0.002
-0.04a 0.169a 0.238a -0.003 0.027b -0.003 0.136a 0.018
0.021c
(27)
IPO Market Conditions
0.122a 0.453a -0.031b 0.173a
a
a
a
a
a
a
a
a
b
-0.043 0.006
-0.144 -0.054 -0.037 0.000
0.002
-0.114
-0.031 -0.005
(28) M&A Market Conditions
-0.102 -0.539 -0.006 0.177
Appendix Table 3. Correlation Matrix (Con’t)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
(25)
(26)
(27)
(28)
Variable
Valuation Disclosure Indicator
Early Investment Indicator
Distance
Local VC Ratio
VC Experience
VC Size
VC Portfolio Size
Institutional VC Indicator
Corporate VC Indicator
Asia VC Indicator
Europe VC Indicator
VC Industry Competition
IPO Market Conditions
M&A Market Conditions
(15)
1
0.028b
0.195a
0.375a
-0.028b
-0.048a
-0.033a
-0.039a
0.083a
0.122a
-0.008
0.111a
0.091a
-0.086a
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
(25)
(26)
(27)
1
-0.006
0.003
-0.060a
-0.018
0.027b
-0.065a
-0.044a
-0.033a
0.018
0.064a
-0.029b
0.018
1
0.150a
0.031b
-0.009
0.122a
0.001
0.109a
0.118a
-0.343a
-0.010
0.040a
-0.043a
1
-0.040a
-0.168a
-0.046a
-0.056a
0.165a
0.124a
0.080a
0.073a
-0.021c
0.010
1
0.206a
0.526a
-0.066a
-0.059a
0.071a
-0.209a
-0.140a
-0.001
0.044a
1
0.433a
-0.094a
-0.194a
0.116a
-0.151a
-0.031b
-0.019
0.042a
1
-0.144a
-0.086a
-0.120a
-0.166a
-0.069a
0.053a
-0.019
1
-0.171a
0.048a
-0.011
0.037a
-0.024c
0.002
1
0.020
-0.049a
0.068a
0.019
-0.006
1
-0.470a
-0.004
0.013
-0.019
1
0.016
-0.057a
0.051a
1
-0.236a
0.022c
1
-0.111a
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