Asia-Pacific Journal of Financial Studies (2018) 47, 306–328
doi:10.1111/ajfs.12212
Ethnic Ties in US Venture Capital Stage
Financing*
Na Ding**
PBC School of Finance, Tsinghua University, China
Received 23 June 2017; Accepted 16 December 2017
Abstract
This paper examines the effect of ethnic ties on venture capital (VC) stage financing in
the US market. After dealing with the endogeneity problem, the paper shows that VC
investors who share ethnicity with an entrepreneur tend to finance the company using a
smaller number of rounds, longer durations between successive rounds, and a larger
amount in each round. The effect is more pronounced when the startup company and
the VC firm are not located in the same city or the VCs lack industry-specific expertise.
My findings also suggest that such trust due to co-ethnicity leads to bad investment performance.
Keywords Ethnic ties; Venture capitalist; Entrepreneur; Staging
JEL Classification: G24, L26
1. Introduction
Ethnic minority immigrant entrepreneurs make up a large number of all US companies’ founders (Saxenian, 2000, 2007). There is also a large number of venture
capitalists who are minority immigrants (Bengtsson and Hsu, 2015). It is less likely
for a traditional venture capitalist to invest in companies owned by ethnic-minority
entrepreneurs than those owned by white entrepreneurs (Bates and Bradford, 1992).
The reasons for this include discrimination, a mismatch between the types of
minority-owned businesses and those the traditional venture capital (VC) industry
fund; and the fact that ethnic-minority entrepreneurs lack access to VC networks,
*I acknowledge financial support from the National Natural Science Foundation of China
(Grant No. 71790591) and Tsinghua University Research Grant (Grant No. 20151080451). I
remain responsible for any remaining errors or omissions.
**Corresponding author: PBC School of Finance, Tsinghua University, 43 Chengfu Road,
Beijing, 100083, China. Tel: +86-188-1091-5594, Fax: +86-10-6279-8655, email: dingn.13@
pbcsf.tsinghua.edu.cn.
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which are populated mainly by white people (Rubin, 2004). Ethnic ties1 between
entrepreneurs and venture capitalists increase the likelihood of a venture capitalist
investing in an entrepreneur (Bengtsson and Hsu, 2015).
Stage financing refers to the stepwise infusion of funds from VC firms to ventures.
Instead of investing a lump sum, VC investors split finance into multiple rounds,
where the next round disbursement depends on whether the startup company meets
the current round’s performance target set by the VC. By allowing VC investors to
retain the option to abandon the entrepreneur’s project if it fails to meet stage goals,
staging mitigates the problem of agency costs and information asymmetry (Sahlman,
1990; Admati and Pfleiderer, 1994; Gompers, 1995). Moreover, staging mitigates the
hold-up problem because it reduces the amount the VC firm invests in the startup
company at any given time, and therefore, increases the founder’s human capital in
the company’s physical capital, which reduces the founder’s incentives to leave the
company (Neher, 1999; Da Rin et al., 2011; Tian, 2011).
However, as Tian (2011) argues, stage financing is costly. Potential costs arising
from VC staging include negotiation and contracting costs in each round of financing, forgone economies of scale due to divided capital infusions, induced short-termist behavior on the part of an entrepreneur, and underinvestment in early-stage
ventures. Hence, if it is possible to reduce agency costs and mitigate the risk of
information asymmetry due to less information uncertainty in a startup company’s
future development, the venture capitalist may use less staging. For example, when
public market prices are more informative, such that VC fund managers can more
effectively learn to guide their investment decisions, they will stage less to save the
cost of staging (Liu and Tian, 2016).
The effect of ethnic ties on stage financing is not clear. On the one hand, individuals from the same ethnic minority may have frequent exchanges and corporations on account of having the same language and culture; thus, the existence of
ethnic ties makes the venture capitalist trust the entrepreneur more and understand
the value of the project much better. As a result, VCs can expect to work better
with co-ethnic startups (Hegde and Tumlinson, 2014). Thus, there is less uncertainty about the company’s future performance in this situation, which leads to
fewer rounds of investment. On the other hand, in response to the limitations of
the traditional VC industry, some minority-focused venture capitalists primarily
make investments in businesses owned by ethnic-minority entrepreneurs (Rubin,
2004). Bengtsson and Hsu (2015) find that co-ethnicity increases the likelihood of a
VC firm investing in a company. Hegde and Tumlinson (2014) also argue that venture capitalists select co-ethnic startups even when they appear to be of lower
1
Ethnic ties, co-ethnicity, and same ethnicity refer to the same dummy variable. The variable
equals to one only if the venture capitalist and entrepreneur are of the same minority. There
are two situations in which the variable equals zero: one where both the venture capitalist
and the entrepreneur are of the same ethnicity but not the minority (e.g., two Caucasians);
the other where they are from different ethnicities.
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N. Ding
quality than non-ethnic ventures. Therefore, VCs may prefer to stage more, due to
a higher likelihood of being held-up by an entrepreneur who shares the same ethnicity, and who can relatively easily find another minority-focused venture capitalist
with ethnic ties. Thus, it is worth exploring the real effects of ethnic ties on staging.
In this paper, two competing hypotheses are developed relating to the above
analysis, namely trust hypothesis and hold-up hypothesis. Then, the relationship
between co-ethnicity and staging is empirically tested. A standard and na€ıve ordinary least squares (OLS) regression suggests that ethnic ties do not appear to have
an effect on the number of financing rounds and duration between successive
rounds. However, this finding is likely driven by the fact that the Same ethnicity is
endogenously determined. Unobservable VC investor or startup company heterogeneity correlated with both co-ethnicity and staging remains in the residual term
of the regressions, which makes it difficult to draw correct statistical inferences.
To establish causality, an instrumental variable (IV) for Same ethnicity was constructed and two-stage least squares (2SLS) analysis was undertaken. For a pair
comprising a startup company and VC (located in city C) sharing the ethnicity E,
the IV is the ratio of venture capitalists of ethnicity E to all the venture capitalists
in city C when the VC firm is found. For example, a San Francisco-based VC firm
founded in 1980 invests in a company in 1990, and they share the same ethnicity,
Chinese. In 1980, suppose there are 100 venture capitalists in San Francisco and 10
of them are Chinese, then the IV here is 0.1. The rationale behind this instrument
is that the larger the ratio of capitalists from one ethnicity, the more likely a founder can find a capitalist who shares the same ethnicity with himself to invest in his
company. However, it is reasonable to believe that such a ratio is not correlated
with the VC firm’s staging strategy for one particular company.
The 2SLS analysis suggests a negative effect of Same ethnicity on the number of
financing rounds a VC firm participates in, and a positive effect on the duration
between two rounds and the average amount per round. In other words, if the startup company and VC firm share the same ethnicity, the VC tends to stage less and
invest more money in each round.
Additional tests were conducted to explore the heterogeneous effects of co-ethnicity on staging. The first test involved how the company’s location and the VC
firm’s industry expertise alter the effect of co-ethnicity on staging. The results suggest that when the geographic distance between the company and the VC firm is
great, or if the VC firm lacks expertise in the corresponding industry, the effect of
co-ethnicity on staging is much more significant. This is because monitoring the
company face-to-face or estimating the company’s future value precisely becomes
harder for investors. Thus the staging decision relies much more on ethnic ties in
such an uncertain situation. Additionally, this finding supports the trust hypothesis.
In the last part of the paper, the effect of co-ethnicity on investment outcome is
explored. The findings reveal that co-ethnicity between a venture capitalist and an
entrepreneur decreases the company’s probability of achieving success. Furthermore,
such a negative effect is due to the VC’s decreased monitoring of the entrepreneur
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(i.e., less staging). Therefore, trust lead by co-ethnicity is costly. Although counterintuitive, this finding suggests that ethnic ties appear to establish more trust than is
reasonable between the startup company and the VC, leading to too little staging
and thus, a less successful outcome. This finding may provide a useful tip to those
VCs: be more cautious and use more staging when you decide to invest in startups
whose founders share the same ethnicity with you.
The paper contributes to two streams in the existing literature. One is the literature on stage financing. Gompers (1995) uses industry average ratios as proxies for
agency costs and shows evidence of the determinants of VC staging. Tian (2011)
empirically tests how the geographic distance between the VC firm and the startup
company affect staging. Liu and Tian (2016) find that the more informative public
market prices are, the less financing rounds a startup company may receive. The
present paper pushes this line of inquiry forward by empirically exploring the relationship between social network and stage financing.
The other stream of literature this paper contributes to is that regarding the role
of ethnic ties in corporate finance. Freeman and Huang (2015), Bengtsson and Hsu
(2015), and Gompers et al. (2016) find a negative association between co-ethnicity
and performance outcomes in their respective empirical settings. For a better understanding, this paper focuses on the relationship between ethnic ties and venture
capital staging and proves that such a negative effect is, to some extent, a result of
the trust caused by co-ethnicity.
The rest of the paper is organized as follows. Section 2 develops two competing
hypotheses. Section 3 discusses the sample selection procedures and presents
descriptive statistics. Section 4 reports the empirical results. Section 5 presents additional tests. Section 6 concludes the paper.
2. Hypothesis
Staging is an effective instrument used by VCs to mitigate information asymmetry
and agency problems because it can keep entrepreneurs “on a tight leash” (Sahlman, 1990; Gompers, 1995) and can give the VC firm bargaining power to mitigate
any hold-up problem. Neher (1999) argues that upfront financing leads to a different problem, namely, giving the entrepreneur a large hold-up opportunity because
he is assumed to be the only one who can implement the idea. Staging helps the
investor to build collateral that limits the entrepreneur’s hold-up power. When the
venture capitalist and the founder share the same ethnicity, it is more likely that
they belong to the same networking association (Saxenian, 2000, 2007) and thus,
increase the entrepreneur’s hold-up power. First, there are many chances for the
entrepreneur to cooperate with other venture capitalists who also belong to the
same association (Bengtsson and Hsu, 2015), and it is much easier for the founder
to receive investment from minority-focused VCs especially when ethnic ties exist
(Hegde and Tumlinson, 2014). Second, the venture capitalist may be motivated to
establish a good reputation for their association in the US and prefer not to punish
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the entrepreneur on leaving the company after receiving investments, which leads
to the VCs bearing the loss. Therefore, the venture capitalist may use more staging
to limit the entrepreneur due to the fact that they share the same ethnicity.
However, as Tian (2011) argues, stage financing is costly. VC staging induces
negotiation and contracting costs in each round of financing, forgone economies of
scale due to divided capital infusions, delays in market entry, short-termist behavior
on the part of entrepreneurs, and underinvestment in early-stage ventures. Therefore, if the venture capitalist and the founder belong to the same ethnicity, they can
communicate with each other using the same language, and gain a better understanding because they share the same culture. Such effective exchanges and interactions make the venture capitalist trust the founder much more and reduce the
uncertainty of the startup company’s future outcome. Then, the VC firm could
stage finance less to save the cost of staging. In other words, the venture capitalist
prefers less staging when co-ethnicity exists.
This paper follows Tian’s (2011) three measures to capture venture capital staging: the number of rounds, which is the total number of financing rounds a startup
company receives from one particular VC firm; the duration, which is the number
of months between two financing rounds from one particular VC firm; and the
average amount per round, which is the total investment amount a startup company receives from one particular VC firm divided by the number of rounds.
The predictions of the relationship between the ethnic ties and staged financing
can, therefore, be summarized as follows:
The hold-up hypothesis: Co-ethnicity between the founder of the company and the
VC partner leads to more financing rounds, a shorter duration between two successive rounds, and a smaller average amount per round.
The trust hypothesis: Co-ethnicity between the founder of the company and the
VC partner leads to fewer financing rounds, a longer duration between two successive rounds, and a larger average amount per round.
3. Data and Sample Characteristics
3.1. Data Sources and Sample Selections
Data of round-by-round investments from VC investors to startup companies who
received their first VC financing between January 1, 1990 and December 31, 2011
were obtained using the Thomson VentureXpert database. Considering that
VentureXpert reports nearly 30% more financing rounds than actually occurred
(Gompers and Lerner 2004), repeated rounds within 90 days were eliminated if they
had the same amount of financing in one round. VC-company pairs in which the
startup company is non-U.S. were excluded, as were those where the startup company founders’ names or the VC firm partners’ names were missing. Additionally,
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companies that only received one round from one VC firm due to inferior quality
channel were excluded, which left a final total of 11 773 pair-year observations.
Following Bengtsson and Hsu (2015), each individual’s surname was used to determine his or her ethnicity. The mapping from surnames to ethnicity was conducted
using information obtained through two sources: (i) the website https://www.familyed
ucation.com/baby-names/browse-origin/surname, which provides lists of the most
common Chinese, Indian, Japanese, Korean, Russian, Hispanic, and Vietnamese surnames; and (ii) a list from Wikipedia of the most common Jewish surnames. This
paper focuses on these eight ethnic groups because (a) they represent important subgroups that are active in the US VC industry, and (b) in other ethnic groups, there is
considerable overlap in surnames that makes it difficult to identify where they come
from. Within these groups, there are also overlaps between different groups like Chinese and Korean. For these cases, the particular person was identified using his/her
whole name and from ethnicity information contained in public data sources. “Broad”
ethnic groups, such as Anglo-Saxon/British, are beyond the scope of this work because
those groups are primarily formed by individuals with little or no connection to each
other, particularly shared ethnicity. Strictly speaking, labeling Jewish people as an
ethnic group is incorrect. Rather, Jewish people are united by their common religion,
culture, and heritage. However, because there is a large number of Jewish VC partners
and founders, Jewish affiliation is included in the analysis. Thus, “Jewish” is an
ethnicity in the context of this work.
Furthermore, following Bengtsson and Hsu (2015), the variable Same ethnicity,
which is the main independent variable of the study, was created. The variable compares the ethnicity between the company’s founders and the VC firm’s partners.
The variable equals one if there is one founder and one VC partner that are in the
same ethnic group (i.e., Chinese–Chinese), and zero if either there is no such ethnic
match between any founders and any VC partners or if there are ethnic ties but not
the ethnic minority mentioned above.
To construct the control variables, Gompers’s (1995) method of capturing
industry information about each company in the round year was followed. This
method matches the company with all public firms using the SIC code and the
round date and calculates the industry average market-to-book ratio, the industry
average R&D intensity, and the industry average tangibility of assets for each company in each investment round year. The public firms’ information is from Compustat.
Regarding the control variable Write-off dummy, Tian’s (2011) procedure of
adjusting some companies’ eventual outcomes to “write off” if they had not
received any investment for more than 10 years after the last financing round was
adopted.
3.2. Summary Statistics
Panel A of Table 1 reports the sample overview of all observations. Panel B of
Table 1 provides summary statistics of ethnicity, VC firm staging characteristics,
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Table 1 Summary statistics
Panel A provides the sample overview of all observations. Panel B reports the summary statistics for Same
ethnicity, VC stage financing characteristics, and VC investment characteristics. The sample consists of
11 773 pair-year observations for companies that received their first round of investment between January 1, 1990 and December 31, 2011.
Panel A: Sample overview
VC firm-startup company
Unique company founders
Unique startup companies
Unique VC partners
Unique VC firms
4407
3173
1867
2285
914
Panel B: Summary statistics of key variables
Variable
N
25%
Median
Mean
75%
SD
Same ethnicity
Number of rounds
Duration (months)
Average amount per round (mil)
Total investment amount (mil)
Firm age at round one
Number of VC investors
Early stage at round one
Industry market–book ratio
Industry R&D–assets
Industry asset tangibility
11 773
11 773
9003
11 773
11 518
11 773
11 773
11 773
8782
8782
8782
0
2
7.33
1.00
3.49
0.68
5
0
0.46
0.08
0.07
0
4
14.53
2.02
7.76
1.87
7
0
1.72
0.17
0.10
0.07
4.23
44.98
3.08
12.29
2.85
8.22
0.45
1.26
0.26
0.13
0
6
29.93
3.54
14.77
3.89
11
1
3.84
0.23
0.14
0.26
2.72
65.94
5.23
22.36
3.32
4.78
0.50
14.21
0.55
0.10
and company characteristics. For the full sample, there are 4407 company–VC pairs,
1867 unique companies, and 914 unique VC firms, which include 3173 entrepreneurs and 2285 VC investors. Seven percent of pairs have the same ethnicity. On
average, a startup company receives 4.2 rounds of financing from eight VC investors, with an inter-round duration of 45 months, and an average VC investment
amount per round of 3.1 million dollars. The date on which the company receives
the first round of VC financing, about half are <1.9 years old, and they are at the
early development stage of their life cycles. Each company conducts business in the
industry with an average market-to-book ratio of 1.3, an average R&D/assets ratio
of 25.8%, and an average asset tangibility ratio of 12.7%. For a VC firm, the average
ratio of the companies it invests in that eventually go public is 15.6%.
Table 2 reports the ethnicity for the VC partner, company founder, and co-ethnicity between the partner and the founder. Jewish is the largest ethnicity in the VC
market. The percentage of Jewish venture capitalists and Jewish entrepreneurs is
27.3% and 10.6%, respectively. In this sample, there are 4.2% pairs where the venture capitalist and the entrepreneur are both Jewish.
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Ethnic Ties and Stage Financing
Table 2 Summary statistics
This table reports the ethnicity of the VC partner, the company founder, and the co-ethnicity between
the partner and founder.
Jewish
Indian
Chinese
Korean
Hispanic
Russian
Japanese
Vietnamese
Average %
of ethnic
minority
founders
Average %
of ethnic
minority
VC partners
VC firms where
at list one
partner is
from minority,
%
Companies
where at list
one founder
is from minority,
%
Companies and
VC firms that
have ethnic
ties, %
5.6
0.3
4.8
1.3
2.4
0.3
0.1
0.6
4.3
2.6
6.5
2.9
1.8
0.3
0.9
0.6
27.3
10.3
19.6
8.9
5.9
0.9
2.8
1.4
10.6
6.8
9.1
3.5
4.1
0.4
0.3
1.1
4.2
1.8
2.4
0.6
0.9
0.0
0.02
0.0
4. Empirical Analysis
This section reports the empirical results of three major areas of the analysis. Specifically, it explores how ethnic ties between the founder of a company and the capitalist of the VC firm that invests in the company affect the number of financing
rounds, the duration between successive financing rounds, and the average investment amount per round.
4.1. OLS Results
The trust hypothesis suggests that the VC investor tends to reduce the number of
financing rounds to avoid the cost of staging if the founder of the company and one
of the capitalists of the VC firm share the same ethnicity. Thus, the duration between
two rounds will be longer, and the average amount per round will be larger.
Table 3 reports the OLS results in which the dependent variable is the natural
logarithm of the number of financing rounds. The observation unit in this exercise
is a startup company–VC firm pair. The dummy variable, Same ethnicity, is the
main independent variable. The number of investing VC firms, startup company
characteristics, investment characteristics, and VC firm’s IPO experience are controlled. Following Gompers (1995) and Tian (2011), three dummy variables are
included to represent the outcomes of venture financing in the regressions: the IPO
dummy, the acquisition dummy, and the write-off dummy. Finally, dummies for
the first round investment year, the company’s industry, and the company’s state
are included to absorb any variables that vary only by year, industry, or state. In
column 1, these three variables are controlled. In column 2, company industry is
not controlled. In column 3, company state is not controlled. Heteroskedasticityrobust standard errors are reported in parentheses.
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Table 3 The relationship between financing rounds and co-ethnicity: OLS regressions
This table reports the OLS analyses examining the effect of same ethnicity on the number of financing
rounds. The dependent variable is the natural logarithm of the number of financing rounds the VC firm
participated in. The independent variables are the same ethnicity dummy, the number of VC investors,
the natural logarithm of the total amount the company receives, the natural logarithm of the amount the
VC firm invested, the earlier-stage dummy, the natural logarithm of the company’s age when it received
its first round of VC financing, the industry average market-to-book ratio, the industry average R&D
intensity, he industry average tangibility of assets, the IPO dummy, the acquisition dummy, the write-off
dummy, and the VC IPO experience. Data regarding the VC-company pairs are obtained from the VentureXpert database. Heteroskedasticity-robust standard errors are reported in parentheses. ***, **, and *
indicate significance at the 1%, 5%, and 10% levels, respectively.
Dependent variable: Ln (Number of financing rounds)
(1)
Same ethnicity
0.026
Number of VC investors
0.024
Ln (Total amount)
0.060
Ln (Amount)
0.292
Earlier-stage dummy
0.061
Ln (Firm age at round one)
0.083
Industry market–book ratio
0.001
Industry R&D–assets ratio
0.023
Industry asset tangibility
0.235
IPO dummy
0.239
Acquisition dummy
0.620
Write-off dummy
0.617
IPO experience
0.322
Constant
0.213
Company state fixed effects
Yes
Industry fixed effects
Yes
Year fixed effects
Yes
N
2987
R2
0.502
(2)
(0.034)
(0.003)***
(0.013)***
(0.012)***
(0.020)***
(0.015)***
(0.001)*
(0.039)
(0.119)**
(0.054)***
(0.048)***
(0.045)***
(0.079)***
(0.247)
0.038
0.026
0.061
0.291
0.067
0.084
0.001
0.011
0.086
0.212
0.619
0.625
0.309
0.375
Yes
No
Yes
2987
0.495
(3)
(0.034)
(0.003)***
(0.013)***
(0.012)***
(0.020)***
(0.015)***
(0.001)*
(0.037)
(0.105)
(0.053)***
(0.048)***
(0.045)***
(0.077)***
(0.241)
0.034
0.024
0.075
0.293
0.059
0.073
0.001
0.031
0.208
0.259
0.623
0.636
0.404
0.251
No
Yes
Yes
2987
0.479
(0.035)
(0.003)***
(0.013)***
(0.011)***
(0.020)***
(0.015)***
(0.001)*
(0.038)
(0.117)*
(0.054)***
(0.048)***
(0.044)***
(0.078)***
(0.112)**
Table 4 shows how ethnic ties affect the investment duration between successive
rounds. The dependent variable is the natural logarithm of the duration in months
of a particular venture financing round for each VC firm invested in the company.
Unlike the analysis conducted in the previous table, in which the observation unit
is a startup company–VC firm pair, the observation unit in this table is a financing
round of each pair. If there is more than one round of financing in a certain pair,
it appears in the same multiple times. Then, standard errors are clustered by pair,
since the residuals could be correlated across observations of the same pair.
Table 5 shows how ethnic ties affect the VC financing round size. The dependent variable is the natural logarithm of the average amount per round in thousands of dollars. The observation unit in this exercise is also a pair.
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Table 4 The relationship between inter-round duration and co-ethnicity: OLS regressions
This table reports the OLS analyses examining the effect of same ethnicity on inter-round duration. The
dependent variable is the natural logarithm of the duration between the two rounds. The independent
variable and other control variables are the same as those in Table 3. Data regarding the VC–company
pairs are obtained from the VentureXpert database. Heteroskedasticity-robust standard errors clustered
by pair are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels,
respectively.
Dependent variable: Ln (Duration of financing rounds)
(1)
Same ethnicity
0.087
Number of VC investors
0.017
Ln (Total amount)
0.113
Ln (Amount)
0.466
Earlier-stage dummy
0.016
Ln (Firm age at round one)
0.086
Industry market–book ratio
0.003
Industry R&D–assets ratio
0.075
Industry asset tangibility
0.510
IPO dummy
0.100
Acquisition dummy
0.126
Write-off dummy
0.188
IPO experience
0.305
Constant
5.649
Company state fixed effects
Yes
Industry fixed effects
Yes
Year fixed effects
Yes
N
6581
R2
0.268
(2)
(0.061)
(0.005)***
(0.032)***
(0.027)***
(0.034)
(0.027)***
(0.001)***
(0.024)***
(0.221)**
(0.067)
(0.087)
(0.092)**
(0.139)**
(0.479)***
0.102
0.017
0.114
0.467
0.017
0.077
0.002
0.070
0.438
0.107
0.129
0.190
0.328
5.584
Yes
No
Yes
6581
0.264
(3)
(0.060)*
(0.005)***
(0.032)***
(0.027)***
(0.034)
(0.027)***
(0.001)**
(0.023)***
(0.183)**
(0.067)
(0.088)
(0.091)**
(0.137)**
(0.469)***
0.095
0.020
0.152
0.469
0.018
0.067
0.003
0.073
0.471
0.117
0.133
0.249
0.468
4.584
No
Yes
Yes
6581
0.249
(0.060)
(0.005)***
(0.032)***
(0.027)***
(0.034)
(0.027)**
(0.001)***
(0.024)***
(0.209)**
(0.067)*
(0.087)
(0.091)***
(0.136)***
(0.213)***
In Tables 3 and 4, the coefficient estimate of Same ethnicity is negative and positive, respectively, while they are both statistically insignificant in five columns.
These findings suggest that the ethnic ties between the founder and the capitalist do
not appear to be related to the VC stage financing. However, in Table 5, the coefficient estimate of Same ethnicity is positive and significant at the 1% or 5% level
across all three regressions; if the founder of a startup company and one of the
partners of the VC firm come from the same ethnic minority, the average amount
per round the company receives increases by 13.7%.
The regression results reported in Table 5 support the implications of the trust
hypothesis. Specifically, ethnic ties between founder and capitalist leads to a larger
amount per round. This finding suggests that VC investors tend to trust an entrepreneur more when they share the same ethnicity, and thus, they prefer to invest
more in each round to reduce staging costs.
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Table 5 The relationship between funding amount per round and co-ethnicity: OLS
regressions
This table reports the OLS analyses examining the effect of same ethnicity on funding amount per round.
The dependent variable is the natural logarithm of the average amount the VC invests per round. The
independent variable and other control variables are the same as those in Table 3. Data regarding the VC
–company pairs are obtained from the VentureXpert database. Heteroskedasticity-robust standard errors
are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Dependent variable: Ln (Average amount per round)
(1)
Same ethnicity
0.137
Number of VC investors
0.101
Ln (Total amount)
0.650
Earlier-stage dummy
0.044
Ln (Firm age at round one)
0.104
Industry market–book ratio
0.002
Industry R&D–assets ratio
0.074
Industry asset tangibility
0.108
IPO dummy
0.002
Acquisition dummy
0.245
Write-off dummy
0.197
IPO experience
1.217
Constant
0.281
Company state fixed effects
Yes
Industry fixed effects
Yes
Year fixed effects
Yes
N
2987
R2
0.554
(2)
(0.052)***
(0.005)***
(0.015)***
(0.032)
(0.025)***
(0.001)
(0.064)
(0.168)
(0.078)
(0.076)***
(0.078)**
(0.143)***
(0.508)
0.152
0.102
0.654
0.053
0.108
0.001
0.098
0.163
0.025
0.240
0.207
1.215
0.097
Yes
No
Yes
2987
0.550
(3)
(0.052)***
(0.005)***
(0.015)***
(0.032)*
(0.025)***
(0.001)
(0.060)
(0.157)
(0.078)
(0.076)***
(0.078)***
(0.140)***
(0.511)
0.132
0.101
0.665
0.045
0.095
0.002
0.052
0.059
0.024
0.249
0.218
1.259
0.445
No
Yes
Yes
2987
0.544
(0.052)**
(0.005)***
(0.014)***
(0.032)
(0.025)***
(0.001)
(0.061)
(0.163)
(0.078)
(0.076)***
(0.077)***
(0.141)***
(0.166)***
4.2. Identification
The results in Tables 3 and 4 are not surprising given that there are some endogeneity concerns about the variable Same ethnicity. Specifically, some omitted variables (i.e., the variables cannot be observed by researchers) that drive both ethnic
ties between the founder and the capitalist and VC staging decisions simultaneously
may exist. This could bias the coefficient estimate of Same ethnicity and make the
correlations spurious. In this section, the endogeneity problem using the Instrumental Variable (IV) strategy is addressed. For a company–VC pair (located in city C)
sharing the ethnicity E, the instrumental variable for the variable Same ethnicity is
the ratio of venture capitalists of ethnicity E to all the venture capitalists in city C
at the time the VC firm is founded. For example, a San Francisco-based VC firm
founded in 1980 invested in a startup company in 1990, and they share the same
ethnicity, Chinese. In 1980, suppose there are 100 venture capitalists in San Francisco and 10 of them are Chinese, then the corresponding IV here is 0.1.
316
© 2018 Korean Securities Association
Ethnic Ties and Stage Financing
The rationale behind this instrument is that the larger the ratio of venture capitalists from one ethnicity, the more likely a founder can find a capitalist who shares
the same ethnicity with himself to invest in his company. Therefore, the proposed
instrument variable satisfies the relevance requirement. It is also reasonable to
believe that the ratio of one specific is founded is not correlated with the VC investor’s investment decision for a certain startup company, which is founded several
years later, and therefore, the instrument satisfies the exclusion restriction. There is
a concern that when the proportion of venture capitalists of ethnicity E is high,
people of ethnicity E are more capable and powerful. Thus, the venture capitalists
will possibly stage less and invest more in each round. However, this paper only
focuses on the ethnic minority venture capitalist, and it is unlikely that, because
they are small in number, they do not have enough power to affect VC firms’
financing decisions.
In Table 6, the regression estimates for the 2SLS analysis, in which the instrumental variable, Ethnicity ratio, is constructed for the full sample, is presented. Panel A
reports the first-stage regression estimates with Same ethnicity as the dependent variable. The main independent variable is the constructed instrument, Ethnicity ratio.
Control variables are the same as those in the OLS regressions reported in Table 2.
Consistent with the intuition for the instrument construction, the coefficient
estimates for the ethnicity ratio, when one VC firm is founded, are positive and significant at the 1% level across all regressions when Same ethnicity is the dependent
variable, as shown in column 1 through column 3 of Panel A. The results suggest
that if the ratio of one certain ethnicity among all venture capitalists is large, then
it is more likely for the entrepreneur who shares the same ethnicity to receive
investment from a venture capitalist from the same ethnicity.
Although the first-stage regressions suggest that the instruments are relevant, it
is still possible to have biased estimates due to weak instruments. The F-statistics of
the first-stage in IV estimation are reported, and the value is 35.04, which suggests
that the instrument is highly correlated with the endogenous variable in the second
stage, and it does not appear to suffer from a weak instrument problem.
Panel B of Table 6 reports the second-stage regression results, with the natural
logarithm of the number of VC financing rounds as the dependent variable and the
predicted values of Same ethnicity as the independent variable. The coefficient estimates of Same ethnicity across all columns are negative and statistically significant
at the 5% level, supporting the trust hypothesis. According to the estimate in column 1 of Table 6, Panel B, if the founder of a company shares the same ethnicity
with the venture capitalist who invests in the firm, the financing rounds he receives
from this capitalist decreases by 11.7%, as compared with the counterpart who does
not have ethnic ties with the venture capitalist.
Table 7 shows the 2SLS regression for the inner-round duration of financing
rounds. The estimate of same ethnicity is positive and significant at the 5% level
across all three regressions. According to the coefficient estimate reported in column 1 of Table 7, if a founder of a company shares the same ethnicity with the
© 2018 Korean Securities Association
317
N. Ding
Table 6 Relationship between number of financing rounds and co-ethnicity: 2SLS analysis
This table reports the 2SLS regressions for the determinants of the number of financing rounds. Panel A
reports the first-stage regression results. The dependent variable is Same ethnicity. Panel B reports the second-stage regression results. The dependent variable is the natural logarithm of the number of financing
rounds. The instrumental variable is the ratio of venture capitalists of ethnicity E to all the venture capitalists in city C when the VC firm is founded. The independent variable and other control variables are
the same as those in Table 3. Data regarding the VC–company pairs are obtained from the VentureXpert
database. Heteroskedasticity-robust standard errors are reported in parentheses. ***, **, and * indicate
significance at the 1%, 5%, and 10% levels, respectively.
Panel A: First-stage regressions
Dependent variable: Same ethnicity
(1)
Ethnic ratio
2.327
Number of VC investors
0.000
Ln (Total amount)
0.009
Ln (Amount)
0.005
Earlier-stage dummy
0.009
Ln (Firm age at round one)
0.024
Industry market–book ratio
0.001
Industry R&D–assets ratio
0.027
Industry asset tangibility
0.070
IPO dummy
0.058
Acquisition dummy
0.013
Write-off dummy
0.012
IPO experience
0.076
Constant
0.255
F statistics
35.04
Company state fixed effects
Yes
Industry fixed effects
Yes
Year fixed effects
Yes
N
2987
R2
0.321
(2)
(0.393)***
(0.001)
(0.005)*
(0.004)
(0.008)
(0.006)***
(0.000)*
(0.013)**
(0.039)*
(0.023)**
(0.022)
(0.016)
(0.033)**
(0.052)***
2.347
0.000
0.009
0.005
0.011
0.025
0.001
0.044
0.098
0.052
0.012
0.014
0.066
0.228
35.30
Yes
No
Yes
2987
0.313
(3)
(0.395)***
(0.001)
(0.005)*
(0.004)
(0.008)
(0.006)***
(0.000)*
(0.013)***
(0.036)***
(0.023)**
(0.022)
(0.016)
(0.033)**
(0.045)***
2.337
0.000
0.010
0.005
0.008
0.024
0.001
0.029
0.070
0.058
0.013
0.011
0.090
0.236
35.13
No
Yes
Yes
2987
0.315
(0.394)***
(0.001)
(0.005)**
(0.004)
(0.008)
(0.006)***
(0.000)*
(0.012)**
(0.039)*
(0.022)***
(0.021)
(0.016)
(0.033)***
(0.044)***
Panel B: Second-stage regressions
Dependent variable: Ln (Number of financing rounds)
(1)
Same ethnicity
Number of VC investors
Ln (Total amount)
Ln (Amount)
Earlier-stage dummy
Ln (Firm age at round one)
318
0.117
0.024
0.060
0.293
0.061
0.085
(2)
(0.061)*
(0.003)***
(0.013)***
(0.011)***
(0.020)***
(0.015)***
0.134
0.026
0.061
0.292
0.066
0.086
(3)
(0.064)**
(0.003)***
(0.013)***
(0.011)***
(0.019)***
(0.015)***
0.133
0.024
0.075
0.294
0.058
0.075
(0.065)**
(0.003)***
(0.013)***
(0.011)***
(0.020)***
(0.015)***
© 2018 Korean Securities Association
Ethnic Ties and Stage Financing
Table 6 (Continued)
Panel B: Second-stage regressions
Dependent variable: Ln (Number of financing rounds)
(1)
Industry market–book ratio
Industry R&D–assets ratio
Industry asset tangibility
IPO dummy
Acquisition dummy
Write-off dummy
IPO experience
Constant
Company state fixed effects
Industry fixed effects
Year fixed effects
N
R2
Hausman test v2ð1Þ
Hausman test Prob> v2
0.001
0.026
0.224
0.231
0.620
0.617
0.312
0.238
Yes
Yes
Yes
2987
0.501
2.84
0.092
(2)
(0.001)
(0.039)
(0.117)*
(0.054)***
(0.047)***
(0.044)***
(0.078)***
(0.244)
0.001
0.005
0.074
0.205
0.619
0.625
0.299
0.397
Yes
No
Yes
2987
0.494
(3)
(0.001)
(0.037)
(0.104)
(0.053)***
(0.047)***
(0.044)***
(0.077)***
(0.239)*
0.001
0.035
0.196
0.252
0.623
0.636
0.390
0.225
No
Yes
Yes
2987
0.478
(0.001)
(0.038)
(0.116)*
(0.054)***
(0.047)***
(0.044)***
(0.078)***
(0.112)**
venture capitalist, the number of months the firm needs to wait is 31.5% longer to
receive successive financing rounds than its counterpart who does not have ethnic
ties with the venture capitalist. This result supports the implications of the trust
hypothesis. Specifically, a founder–capitalist pair who have different ethnicities indicates a shorter financing duration between successive rounds, because the capitalist
tends to “keep a tight leash” by shortening the duration between successive rounds
in order to monitor the startup company when there are no ethnic ties.
Comparing the results based on the OLS (Tables 3 and 4) and IV (Tables 6 and 7)
analyses, the effect of Same ethnicity on staging (number of financing rounds and
duration between financing rounds) are both not significant, and they become marginally significant after including the IV. It appears that OLS biases the effect of Same
ethnicity on VC staging due to endogeneity. According to Jiang (2017) and Mao et al.
(2014), there are some omitted variables in the model of Table 3 that are positively
correlated with the dummy of Same ethnicity, and such omitted variables result in the
company receiving more stage financing rounds. The complexity of the startup company’s core technology could be an example of such an omitted variable. If an earlystage company’s core technology is extremely complicated, it will be better understood
by a VC investor who shares the same ethnicity with the entrepreneur than others,
because of more efficient communication and more effective comprehension. Meanwhile, the more difficult an early-stage company’s core technology, the more rounds it
will receive (more rounds also means a shorter duration between financing rounds)
© 2018 Korean Securities Association
319
N. Ding
Table 7 Relationship between inter-round duration and co-ethnicity: 2SLS analysis
This table reports the 2SLS regressions for the determinants of the inter-round duration. The dependent
variable is the natural logarithm of the duration between two rounds. The instrumental variable is the
ratio of venture capitalists of ethnicity E to all the venture capitalists in city C when the VC firm is
founded. The independent variable and other control variables are the same as those in Table 3. Data
regarding the VC–company pairs are obtained from the VentureXpert database. Heteroskedasticity-robust
standard errors clustered by pair are reported in parentheses. ***, **, and * indicate significance at the
1%, 5%, and 10% levels, respectively.
Dependent variable: Ln (Duration of financing rounds)
(1)
Same ethnicity
0.315
Number of VC investors
0.017
Ln (Total amount)
0.114
Ln (Amount)
0.470
Earlier-stage dummy
0.016
Ln (Firm age at round one)
0.092
Industry market–book ratio
0.003
Industry R&D–assets ratio
0.074
Industry asset tangibility
0.487
IPO dummy
0.082
Acquisition dummy
0.124
Write-off dummy
0.183
IPO experience
0.275
Constant
5.697
Company state fixed effects
Yes
Industry fixed effects
Yes
Year fixed effects
Yes
N
6581
R2
0.266
Hausman test v2ð1Þ
8.64
Hausman test Prob>v2
0.003
(2)
(0.132)**
(0.005)***
(0.031)***
(0.027)***
(0.034)
(0.028)***
(0.001)***
(0.024)***
(0.220)**
(0.069)
(0.087)
(0.092)**
(0.139)**
(0.478)***
0.318
0.017
0.115
0.470
0.016
0.082
0.002
0.067
0.411
0.093
0.126
0.186
0.303
5.628
Yes
No
Yes
6581
0.262
(3)
(0.129)**
(0.005)***
(0.032)***
(0.027)***
(0.034)
(0.027)***
(0.001)**
(0.023)***
(0.183)**
(0.068)
(0.087)
(0.091)**
(0.137)**
(0.469)***
0.317
0.020
0.153
0.473
0.018
0.072
0.003
0.071
0.451
0.100
0.131
0.246
0.435
4.633
No
Ye
Yes
6581
0.247
(0.132)**
(0.005)***
(0.032)***
(0.027)***
(0.034)
(0.027)***
(0.001)***
(0.024)***
(0.209)**
(0.068)
(0.087)
(0.091)***
(0.137)***
(0.214)***
due to much uncertainty over its future development. Assuming that the number of
financing rounds is negatively correlated with Same ethnicity, the positive correlation
between Same ethnicity and financing rounds arising from such omitted variables, thus
biases the coefficient estimate upward and renders it insignificant. In the same way,
the complexity of an early-stage company’s core technology has a positive effect on
Same ethnicity and a negative effect on the duration between financing rounds (as analyzed above) in the model of Table 4. Assuming that the duration between financing
rounds is positively correlated with Same ethnicity, the negative correlation between
Same ethnicity and duration arising from such omitted variables also biases the coefficient estimate downward and renders it insignificant. In addition, Hausman tests are
performed to confirm the necessity of conducting IV tests. The results imply that there
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© 2018 Korean Securities Association
Ethnic Ties and Stage Financing
are endogeneity problems in the OLS model of Tables 3 and 4, and it is therefore necessary to use IV.
Table 8 shows the 2SLS regressions for the average amount per round. The estimate of Same ethnicity is positive and significant at the 1% level across all three
regressions. If the founder of a startup company and one of the partners of the VC
firm come from the same ethnic minority, the average amount per round the company receives increases by 33%. Comparing results obtained from the OLS regressions (Table 5) with those obtained from the 2SLS regressions (Table 8), it is
interesting to observe that the magnitude of the 2SLS coefficient estimates is larger
than those of the OLS estimates (even though the coefficient estimates from both
approaches are positive and statically significant), indicating that OLS regressions
bias the coefficient estimates downward due to endogeneity in Same ethnicity. This
finding suggests that the omitted variables simultaneously make the amount per
round larger and the likelihood of Same ethnicity lower. Again, the complexity of a
startup company’s core technology could be an example of an omitted variable.
The negative correlation caused by the omitted variable is the major driving force
that biases the coefficient estimates of Same ethnicity downward. Once I the instruments are used to clean up the correlation between the co-ethnicity and the residuals (the firm’s unobservable characteristics) in the structural equations, the
endogeneity of the co-ethnicity is removed, and the coefficient estimates increase,
that is, become more positive.
5. Additional Tests
In this section, additional tests are performed to further understand the mechanisms through which ethnic ties affect VC staging. The section investigates how the
effect of ethnic ties varies according to the distance between a VC firm and a company it invests in, and how the effect of ethnic ties varies according to the extent of
industry expertise of VC investors. It also examines how co-ethnicity and stage
financing jointly affect the final outcome of startup companies.
5.1. Geographic Distance and the Effect of Ethnic Ties on VC Staging
Table 9 shows how the effect of ethnic ties on VC staging varies according to the
location of the startup company and the VC firm. Tian (2011) argues that VC staging and monitoring are substitutes and finds that VCs located farther away from
the startup companies tend to rely more heavily on staging because it is costlier for
them to effectively monitor the ventures. Since ethnic ties enable the entrepreneur
and the venture capitalist to gather information more efficiently and enhance trust
between them, the venture capitalist tends to use less staging even if the VC firm is
located far away. Therefore, less monitoring (i.e., less staging) induced by ethnic ties
has a more pronounced effect on the rounds, duration, and amount per round if
the VC firm and startup company are not located in the same city.
© 2018 Korean Securities Association
321
N. Ding
Table 8 Relationship between funding amount per round and co-ethnicity: 2SLS analysis
This table reports the 2SLS regressions for the determinants of the funding amount per round. The
dependent variable is the natural logarithm of the average amount the VC invests per round. The instrumental variable is the ratio of venture capitalists of ethnicity E to all the venture capitalists in city C
when the VC firm is founded. The independent variable and other control variables are the same as those
in Table 3. Data regarding the VC–company pairs are obtained from the VentureXpert database.
Heteroskedasticity-robust standard errors are reported in parentheses. ***, **, and * indicate significance
at the 1%, 5%, and 10% levels, respectively.
Dependent variable: Ln (Average amount per round)
(1)
Same ethnicity
0.330
Number of VC investors
0.100
Ln (Total amount)
0.648
Earlier-stage dummy
0.043
Ln (Firm age at round one)
0.109
Industry market–book ratio
0.001
Industry R&D–assets ratio
0.066
Industry asset tangibility
0.084
IPO dummy
0.013
Acquisition dummy
0.247
Write-off dummy
0.199
IPO experience
1.193
Constant
0.227
Company state fixed effects
Yes
Industry fixed effects
Yes
Year fixed effects
Yes
N
2987
R2
0.552
(2)
(0.095)***
(0.005)***
(0.014)***
(0.032)
(0.025)***
(0.001)
(0.064)
(0.166)
(0.078)
(0.074)***
(0.077)***
(0.141)***
(0.501)
0.359
0.102
0.651
0.051
0.113
0.001
0.085
0.189
0.038
0.241
0.210
1.190
0.047
Yes
No
Yes
2987
0.548
(3)
(0.102)***
(0.005)***
(0.014)***
(0.032)
(0.025)***
(0.001)
(0.060)
(0.156)
(0.078)
(0.075)***
(0.077)***
(0.139)***
(0.504)
0.325
0.100
0.662
0.044
0.099
0.001
0.044
0.036
0.010
0.250
0.219
1.230
0.393
No
Yes
Yes
2987
0.542
(0.096)***
(0.005)***
(0.014)***
(0.031)
(0.024)***
(0.001)
(0.061)
(0.163)
(0.078)
(0.075)***
(0.076)***
(0.140)***
(0.166)**
To test this empirically, the samples are split into two groups based on location of firm and company. Table 9 reports the second-stage of the 2SLS estimates
for the two subsamples. In column 1, column 3, and column 5, the VC firm and
startup company are located in the same city, while in column 2, column 4, and
column 6, they are located in different cities. If the two firms are located in different cities, the coefficient estimates of Same ethnicity—instrumented by Ethnic
ratio—on financing rounds, duration, and amount per round is negative, positive,
and positive, respectively, and all are statistically significant. By contrast, Same
ethnicity does not seem to significantly affect the number of rounds, duration,
and amount per round if the two firms are located in the same city. The differential effect of ethnic ties in the same city versus different cities is consistent with
the trust hypothesis.
322
© 2018 Korean Securities Association
© 2018 Korean Securities Association
Same ethnicity
Number of VC investors
Ln (Total amount)
Ln (Amount)
Earlier-stage dummy
Ln (Firm age at round one)
Industry marke–/book ratio
Industry R&D–assets ratio
Industry asset tangibility
IPO dummy
Acquisition dummy
Write-off dummy
IPO experience
Constant
Company state fixed effects
Industry fixed effects
Year fixed effects
N
R2
(0.063)*
(0.003)***
(0.014)***
(0.012)***
(0.021)***
(0.015)***
(0.001)**
(0.040)
(0.126)**
(0.055)***
(0.049)***
(0.046)***
(0.082)***
(0.244)
0.120
0.024
0.059
0.290
0.061
0.081
0.002
0.017
0.280
0.244
0.635
0.624
0.309
0.255
Yes
Yes
Yes
2711
0.498
0.268
0.027
0.044
0.333
0.091
0.062
0.001
0.035
0.451
0.057
0.706
0.703
0.140
2.220
Yes
Yes
Yes
276
0.646
Dependent variable
(0.199)
(0.009)***
(0.031)
(0.033)***
(0.064)
(0.052)
(0.001)
(0.172)
(0.349)
(0.245)
(0.231)***
(0.218)***
(0.258)
(0.329)***
Ln (number of
rounds)
Same city = 0
(2)
Ln (number of
rounds)
Same city = 1
(1)
0.693
0.067
0.231
0.681
0.164
0.091
0.004
0.139
0.978
0.286
0.277
0.540
0.321
9.508
Yes
Yes
Yes
587
0.445
(0.437)
(0.020)***
(0.071)***
(0.066)***
(0.142)
(0.104)
(0.002)
(0.075)*
(0.755)
(0.319)
(0.484)
(0.475)
(0.513)
(0.622)***
Ln (duration)
Same city = 1
(3)
0.326
0.015
0.101
0.454
0.006
0.096
0.003
0.068
0.544
0.090
0.140
0.188
0.240
5.733
Yes
Yes
Yes
5994
0.263
(0.138)**
(0.005)***
(0.034)***
(0.029)***
(0.035)
(0.029)***
(0.001)**
(0.026)***
(0.230)**
(0.072)
(0.089)
(0.094)**
(0.146)
(0.473)***
Ln (duration)
Same city = 0
(4)
0.121
0.266
0.001
0.397
0.815
0.227
0.509
0.266
1.725
3.397
Yes
Yes
Yes
276
0.667
(0.119)
(0.088)***
(0.003)
(0.433)
(0.724)
(0.191)
(0.308)*
(0.368)
(0.385)***
(0.600)***
0.263 (0.242)
0.118 (0.017)***
0.531 (0.046)***
Ln(average
amount)
Same city = 1
(5)
0.043
0.084
0.002
0.076
0.218
0.006
0.258
0.216
1.121
0.268
Yes
Yes
Yes
2711
0.546
(0.033)
(0.026)***
(0.001)
(0.064)
(0.172)
(0.081)
(0.077)***
(0.079)***
(0.148)***
(0.498)
0.339 (0.097)***
0.098 (0.005)***
0.656 (0.015)***
Ln(average
amount)
Same city = 0
(6)
This table reports the 2SLS regression analyses examining how the effect of co-ethnicity on staging varies with the geographical distance between the VC firm and the
startup company. Geographical distance is measured by a dummy variable that equals one if a company and VC firm are located in the same city. The independent
variable and other control variables are the same as those in Table 3. Data regarding the VC–company pairs are obtained from the VentureXpert database.
Heteroskedasticity-robust standard errors are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Table 9 Geographical distance and the effects of ethnic ties on VC staging
Ethnic Ties and Stage Financing
323
N. Ding
5.2 Industry Expertise and the Effect of Ethnic Ties on VC Staging
Table 10 shows how the effect of ethnic ties on VC staging varies with the VC’s
industry expertise. If the VC firm lacks experience in the industry to which the
venture belongs, the outcome of the venture is quite uncertain, and the VC may
use more staging to limit the company. Since ethnic ties enable the entrepreneur
and the venture capitalist to gather information more effectively and enhance trust
between them, the effect of ethnic ties is quite important in this situation.
To test this, a VC’s industry-specific experience is captured by constructing a
dummy variable, Industry expertise, which equals one if the venture’s lead VC firm
is ranked in the top quintile, in terms of the number of IPOs in the same industry
of the venture since 1982, and zero otherwise. The samples are split into two
groups based on the VC’s industry expertise. Table 10 reports the second-stage of
the 2SLS estimates for the two subsamples. In column 1, column 3, and column 5,
the VC Industry expertise is equal to one, while in column 2, column 4, and column 6, it is equal to zero. The coefficient estimates of the instrumented Same ethnicity on the number of financing rounds, duration, and average amount per
round are negative, positive, and positive, respectively. Additionally, all the coefficients are significant for the “industry expertise equal to zero” subsample, but are
insignificant for the “industry expertise equal to one” subsample. The evidence suggests that co-ethnicity negatively affects staging in companies when VCs are less
experienced in the industries in which their portfolio companies operate, but this
effect is absent if the VC investors have expertise in the company’s industry. The
evidence is consistent with the trust hypothesis because when the VCs lack experience, trust in the entrepreneur is relatively more important when making staging
decisions.
5.3 Evidence on the Investment Outcome
This last set of tests attempts to answer the following question: Do ethnic ties on
staging impede or promote the company’s successful performance? Following previous studies (e.g., Gompers and Lerner, 2000; Brander et al., 2002; Sørensen, 2007;
Bottazzi et al., 2008; Nahata, 2008), investment outcomes are measured by constructing a successful exit dummy that equals one if the company either goes public
(IPO) or is acquired by another company, and zero otherwise.
Table 11 reports the probit regression results for the outcomes of the startup
company. The main independent variable is Same ethnicity. The marginal effects
of independent variables are reported because the coefficients of probit models
are difficult to understand. In addition, to ascertain the impact of stage financing
on the effect of ethnic ties on the company’s performance, two additional variables are included in column 1: Ln (Number of financing rounds) and an interaction term between Same ethnicity and Ln (Number of financing rounds). In
column 2, the natural logarithm of the average amount per round used in the
previous analysis replaces Ln (Number of financing rounds). The coefficient estimates of Same ethnicity are all negative and statistically significant, which is
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© 2018 Korean Securities Association
© 2018 Korean Securities Association
Same ethnicity
Number of VC investors
Ln (Total amount)
Ln (amount)
Earlier-stage dummy
Ln (Firm age at round one)
Industry market–book ratio
Industry R&D–assets ratio
Industry asset tangibility
IPO dummy
Acquisition dummy
Write-off dummy
IPO experience
Constant
Company state fixed effects
Industry fixed effects
Year fixed effects
N
R2
Dependent
variable
(0.086)**
(0.004)***
(0.014)***
(0.012)***
(0.025)**
(0.018)***
(0.001)
(0.046)
(0.146)
(0.073)***
(0.063)***
(0.057)***
(0.115)**
(0.344)***
0.177
0.028
0.080
0.289
0.054
0.090
0.001
0.041
0.187
0.377
0.646
0.634
0.275
1.865
Yes
Yes
Yes
1916
0.499
0.005
0.016
0.000
0.287
0.069
0.062
0.002
0.050
0.238
0.122
0.629
0.672
0.496
0.590
Yes
Yes
Yes
1071
0.527
(0.058)
(0.005)***
(0.028)
(0.025)***
(0.031)**
(0.025)**
(0.001)
(0.074)
(0.205)
(0.072)*
(0.069)***
(0.067)***
(0.162)***
(0.314)*
Ln (number
of rounds)
Industry
expertise = 0
(2)
Ln (number
of rounds)
Industry
expertise = 1
(1)
0.131
0.001
0.039
0.411
0.064
0.015
0.001
0.027
0.753
0.035
0.037
0.131
0.186
5.937
Yes
Yes
Yes
2757
0.240
(0.095)
(0.007)
(0.055)
(0.050)***
(0.046)
(0.039)
(0.001)
(0.035)
(0.307)**
(0.081)
(0.110)
(0.125)
(0.229)
(0.553)***
Ln (duration)
Industry
expertise = 1
(3)
0.436
0.031
0.149
0.500
0.006
0.150
0.004
0.111
0.363
0.356
0.236
0.335
0.204
8.204
Yes
Yes
Yes
3824
0.300
(0.210)**
(0.007)***
(0.035)***
(0.030)***
(0.047)
(0.035)***
(0.001)***
(0.028)***
(0.293)
(0.104)***
(0.131)*
(0.127)***
(0.225)
(0.816)***
Ln (duration)
Industry
expertise = 0
(4)
0.046
0.069
0.001
0.046
0.285
0.025
0.345
0.262
0.676
0.160
Yes
Yes
Yes
1071
0.495
(0.049)
(0.044)
(0.002)
(0.118)
(0.282)
(0.116)
(0.112)***
(0.116)**
(0.271)**
(0.652)
0.068 (0.077)
0.084 (0.008)***
0.620 (0.029)***
Ln (average
amount)
Industry
expertise = 1
(5)
0.097
0.137
0.002
0.069
0.037
0.108
0.224
0.174
0.936
2.135
Yes
Yes
Yes
1916
0.557
(0.042)**
(0.030)***
(0.001)
(0.076)
(0.202)
(0.102)
(0.099)**
(0.100)*
(0.219)***
(0.677)***
0.482 (0.127)***
0.107 (0.006)***
0.649 (0.017)***
Ln (average
amount)
Industry
expertise = 0
(6)
This table reports the 2SLS regression analyses examining how the effect of co-ethnicity on staging varies with the VC’s industry expertise. Industry expertise is measured by a dummy variable that equals one if the venture’s lead VC firm is ranked in the top quintile, in terms of the number of IPOs in the same industry of the venture since 1982, and zero otherwise. The independent variable and other control variables are the same as those in Table 3. Data regarding the VC–company pairs are
obtained from the VentureXpert database. Heteroskedasticity-robust standard errors are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and
10% levels, respectively.
Table 10 VC industry expertise and the effects of ethnic ties on VC staging
Ethnic Ties and Stage Financing
325
N. Ding
Table 11 Evidence on the investment outcome
This table reports the Probit regression results on VC investment performance. The dependent variable is
the successful exit dummy that equals one if investing VCs exit the company either by IPO or acquisition
and zero otherwise. The independent variables are the same ethnicity, the natural logarithm of the number of financing rounds, the interaction term between same ethnicity and the natural logarithm of the
number of financing rounds, the natural logarithm of the average amount per round, and the interaction
term between same ethnicity and the natural logarithm of the average amount per round. All other control variables are the same as those in Table 3. Data about the VC-company pairs are obtained from the
VentureXpert database. Heteroskedasticity-robust standard errors are reported in parentheses. ***, ** and
* indicate significance at the 1%, 5%, and 10% levels, respectively.
Dependent variable: Success
(1)
(2)
Same ethnicity
Ln (Number of financing rounds)
Same ethnicity * Ln (Number of financing rounds)
Ln (Average amount per round)
Same ethnicity * Ln (Average amount per round)
Number of VC investors
Ln (Total amount)
Earlier-stage dummy
Ln (Firm age at round one)
Industry market–book ratio
Industry R&D–assets ratio
Industry asset tangibility
IPO experience
Company state fixed effects
Industry fixed effects
Year fixed effects
Lead VC fixed effects
N
Pseudo-R2
0.034 (0.019)*
0.062 (0.025)**
0.079 (0.039)**
0.246 (0.128)*
0.011
0.070
0.052
0.007
0.001
0.000
0.069
0.282
Yes
Yes
Yes
Yes
480
0.631
(0.004)***
(0.021)***
(0.022)**
(0.014)
(0.001)
(0.052)
(0.108)
(0.323)
0.040
0.086
0.007
0.027
0.045
0.001
0.001
0.006
0.119
0.426
Yes
Yes
Yes
Yes
477
0.573
(0.020)**
(0.040)**
(0.004)*
(0.015)*
(0.022)**
(0.013)
(0.001)
(0.050)
(0.100)
(0.355)
consistent with Bengtsson and Hsu (2015). The coefficient estimates of the interaction terms in column 1 and column 2 are positive and negative, respectively.
Moreover, both are significant at the 5% level. The evidence implies that when
there are ethnic ties between the startup company’s founder and the venture capitalists, VC stage financing increases the company’s probability of going public or
being acquired. However, if the VC uses fewer rounds due to ethnic ties, the likelihood of a successful outcome is lower. To be more specific, for example, the
coefficient estimates reported in column 1 indicate that without consideration of
VC staging, ethnic ties between a startup company’s founder and one of the lead
VC firm’s partner reduces the probability of the company going public or being
acquired by 3.4%. However, when stage financing is taken into account, when
ethnic ties exist, one more percent of venture capital financing increases the company’s success probability by 4.5% (0.045 = 0.034 + 0.079). Combined with the
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© 2018 Korean Securities Association
Ethnic Ties and Stage Financing
previous analysis on the trusted hypothesis that ethnic ties lead to less venture
capital staging, the investment outcome suggests that trust induced by co-ethnicity
is costly.
6. Conclusion
This paper has examined the real effects of ethnic ties on VC staging. Using information about the last name of an entrepreneur and venture capitalist, I distinguished between two competing hypothesis: the trust hypothesis, which argues that
if the entrepreneur and venture capitalist share the same ethnicity, they tend to
trust each other more and thus, fewer staging will be used for monitoring; and the
hold-up hypothesis, which argues that because venture capitalist is more likely to
invest in those who have ethnic ties with himself, he is also more likely to be heldup by such entrepreneur. In order to avoid such problem, the capitalist will use
more staging to restrain the founder of the company.
My findings are as follows: after dealing with endogeneity problems by instrument, a venture capitalist who shares the same ethnicity with the founder tends to
finance the firm with a smaller number of rounds, a longer duration between successive rounds, and with a larger amount in each round. When the VC firm is located
far away from the startup company or when the VC firm lacks industry expertise,
such effect is more significant. Overall, the evidence supports the trust hypothesis.
However, such trust may decrease the VC’s probability of successful exit.
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