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Abstract

Purpose- This study aims to analyze the contribution of business angels (BAs), defined as wealthy individuals who provide risk capital to entrepreneurial firms without family connections, in Estonia, an emerging country in Eastern Europe. Design/methodology/approach- This study compared the data of the financial and non-financial performance of BA-backed firms with that of "twin" non-BA-backed firms, extracted from all Estonian unlisted firms using propensity score matching. Findings- The results of the comparative analysis showed that BAs were patient enough to allow their investees to spend for future growth rather than squeezing profit from increased sales. This is not patience without options for a BA in a situation in which the investee's sales are deteriorating, but rather deliberate patience in the presence of options for a BA where the investee's sales growth is increasing, contrary to conventional investor behavioral principles. It also showed that BAs' post-investment involvement did not make a direct contribution to their investees' sales, although BAs contributed to the sales increase through BA funding itself. Originality/value- This study has two unique research contributions. First, it shows that the patience of BAs was not a by-product but was intentional, and adds to the debate on whether BAs are patient investors. Second, there are only a few studies on the contribution of BAs to their investees in emerging countries; this study aims to help fill this research gap using the case of Estonia.
Contribution of business angel
investments: evidence
from Estonia
Tetsuya Kirihata
College of Business Administration, Ritsumeikan University Osaka Ibaraki Campus,
Ibaraki, Japan
Abstract
Purpose This study aims to analyze the contribution of business angels (BAs), defined as wealthy
individuals who provide risk capital to entrepreneurial firms without family connections, in Estonia, an
emerging country in Eastern Europe.
Design/methodology/approach This study compared the data of the financial and non-financial
performance of BA-backed firms with that of twinnon-BA-backed firms, extracted from all Estonian unlisted
firms using propensity score matching.
Findings The results of the comparative analysis showed that BAs were patient enough to allow their
investees to spend for future growth rather than squeezing profit from increased sales. This is not patience
without options for a BA in a situation in which the investees sales are deteriorating, but rather deliberate
patience in the presence of options for a BA where the investees sales growth is increasing, contrary to
conventional investor behavioral principles. It also showed that BAspost-investment involvement did not
make a direct contribution to their investeessales, although BAs contributed to the sales increase through BA
funding itself.
Originality/value This study has two unique research contributions. First, it shows that the patience of
BAs was not a by-product but was intentional, and adds to the debate on whether BAs are patient investors.
Second, there are only a few studies on the contribution of BAs to their investees in emerging countries; this
study aims to help fill this research gap using the case of Estonia.
Keywords Business angels, Risk capital, Comparative analysis, Propensity score matching, Estonia,
Emerging countries
Paper type Research paper
1. Introduction
Business angels (BAs) are wealthy individuals who provide risk capital to entrepreneurial
firms without family connections (Mason and Harrison, 1995;Duxbury et al., 1996).
According to prior research, BAs have two major characteristics. First, BAs mainly invest in
start-ups that do not have enough money for growth (Wetzel, 1981,1983). The phenomenon
whereby start-ups do not have sufficient growth funds is called the financial gap. It is
considered one of the most critical issues in entrepreneurial finance policies around the world
(Mason and Harrison, 1995;Block and Sandner, 2009;Kirihata, 2018). Venture capital (VC)
Business angel
investments
JEL Classification G23, G32, L26, M13
© Tetsuya Kirihata. Published in Journal of Capital Markets Studies. Published by Emerald
Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0)
license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both
commercial and non-commercial purposes), subject to full attribution to the original publication and
authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/
legalcode
This work was supported by JSPS KAKENHI (Grant Number JP18K01779). The author thanks Dr.
Meelis Kitsing, the rector and professor of political economy at the Estonian Business School, for giving
valuable information and suggestions.
Disclosure statement: No potential competing interest was reported by the authors.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2514-4774.htm
Received 23 August 2022
Revised 3 October 2022
Accepted 3 October 2022
Journal of Capital Markets Studies
Emerald Publishing Limited
2514-4774
DOI 10.1108/JCMS-08-2022-0033
became a source of growth capital for start-ups in 1946, which was the founding year of the
American Research and Development Corporation, the worlds first organized VC (Jacobs,
1969;Bygrave and Timmons, 1992). However, in the early 1990s, VC shifted from start-up
investment to later-stage investment (Wilson, 1995;Sohl, 1999,2003;Hindle and Lee, 2002),
owing to higher risks for start-up investments and longer holding time before their exit
(Sapienza et al., 1996;Kirihata, 2009).
The second characteristic is that BAs adopt a different stance to VC-related investments in
start-ups (Stedler and Peters, 2003;Mason and Stark, 2004;Tenca et al., 2018), as outlined in
detail in the next paragraph. As a result, BAs in developed countries have become the
primary source of funding for start-ups, which leads to the closing of the financial gap
(Landstr
om, 1993;Reitan and Sorheim, 2000;Morrissette, 2007). Studies on BAs in emerging
countries indicate that they play a positive role in closing the financial gap among start-ups
(Jones and Mlambo, 2013). Studies in China (Tingchi and Chang, 2007), Southeast Asia
(Scheela et al., 2015) and Chile (Roman
ıet al., 2018) also show that BAs tend to invest in local
start-ups.
The second characteristic is that BAs are considered investors who do not merely seek
personal financial returns. BAs are usually former entrepreneurs or professionals involved
with their investees based on their experience, networks and expertise. They are eager to
support an investees growth (Van Osnabrugge, 2000;Mason and Harrison, 2002;Mason and
Stark, 2004;Morrissette, 2007). BAs also enjoy the challenges in a new venture (Morrissette,
2007;Harrison et al., 2016). BAs value their compatibility with their investees and position
themselves as co-creators, not merely as investors (Landstr
om, 1998;Mason and Stark, 2004;
Drover et al., 2017). In terms of their pre-investment activities, BAs with a higher level of
human capital, having higher education and a longer entrepreneurial experience, present a
higher firm valuation to their potential investees, contrary to conventional investor
behavioral principles (Collewaert and Manigart, 2016). For their post-investment activities,
many BAs participate in the decision-making process of their investees, usually as members
of the board of directors. They provide hands-on assistance to their investees in areas of
human resource management, finance, sales and marketing (Van Osnabrugge, 2000;Brettel,
2003;Madill et al., 2005).
Even though prior research has shown that BAs invest in start-ups and do not just seek
personal financial returns, the results of previous studies on the contribution of BAs to
investees are not conclusive, as explained in detail in Section 2. Moreover, most previous
studies have focused on developed countries; there are only a few studies on the contribution
of BAs toward their investees in emerging countries.
The lack of empirical research on BAs in emerging countries has been the discussion
subject in several research papers (Landstr
om, 1993;Bruton et al., 2004;Klonowski, 2007;
Ding et al., 2014). Moreover, identifying and studying BAs in emerging countries is difficult
because they tend to rely on personal connections to find investments, making sampling
challenging for researchers. BA research in emerging countries tends to have small and non-
random samples (Avdeitchikova et al., 2008;Mason and Harrison, 2008;Tenca et al., 2018)[1].
In summary, research on the contribution of BAs to their investees is still in the process of
being theorized, even in developed countries, while in emerging countries, empirical research
has only just started.
The following is the research question in this study: how do BAs contribute to their
investees? This study focuses on BA-backed firms in Estonia, an emerging country in
Eastern Europe, and compares them with non-BA-backed firms. By using propensity score
matching, this study extracts twinnon-BA-backed firms demonstrating similar
performance to BA-backed firms at the start of the comparison. The only difference
between the BA-backed firms and the twin non-BA-backed firms is whether or not they
received BA funding in the starting year of the comparison. This study analyzes the
JCMS
contribution of BAs to their investees by focusing on the difference in the subsequent
performance of the BA-backed firms and the twin non-BA-backed firms over a period of
five years from the starting year of the comparison.
The period covered in this study is from 2006 to 2015 because the research focuses on the
beginning stage of the Estonian BA industry, which kicked off around 2006. This study
period may have valuable implications for emerging countries that have not introduced BA
industry promotion measures. Estonia successfully created a number of fast-growing
information and communication technology (ICT) start-ups (Kitsing, 2019;Owen and Mason,
2019) in this period. The reasons for this are that, despite only beginning around 2006, the BA
industry in Estonia became the most active in Northern and Eastern Europe in the late 2010s
(Prohorovs and Fainglozs, 2019;Prohorovs et al., 2019). Furthermore, the aggressive
implementation of ICT, such as through e-government policy and entrepreneurship
promotion measures, contributed (Nauwelaers et al., 2013;Kirihata, 2016a,b,2022;Kitsing,
2019)[2].
2. Theoretical background
Start-ups, which start new businesses with a new technology or idea, are considered to play
an important role not only in the creation of innovative technologies but also in economic
growth, employment and the competitiveness of the economic system (Jacobs, 1969;
Audretsch, 1995). VCs have contributed to the development of start-ups by encouraging
specialization of their investees through their involvement and support (Gorman and
Sahlman, 1989;Bygrave and Timmons, 1992;Gompers and Lerner, 1999;Hellmann and Puri,
2002;Manigart et al., 2002).
Since the 1990s, as VCs have shifted to investing in late-stage firms, academia has been
interested in the function of BAs to foster start-ups. That is because BAs are different from
VCs, in that BAs invest in start-ups and are eager to support the growth of their investees
rather than pursuing personal financial returns (Wetzel, 1981,1983;Van Osnabrugge, 2000;
Mason and Harrison, 2002;Mason and Stark, 2004;Morrissette, 2007). However, the results of
previous studies on the contribution of BAs to their investees are not conclusive.
According to previous studies, there are three main research results on the contribution of
BAs to investees. The first identifies the positive effects of BAs on their investees, the second
shows that the contribution of BAs is inferior to that of VC and the third is that BAs
contribution to investees depends on the individual BAs and cannot be generalized. This
section further discusses these findings in the relevant literature.
The first group of studies highlights the positive effects of BAs on their investees. Kerr
et al. (2014) found that BA-backed firms in the USA had a higher survival rate, more
employees and website traffic than firms rejected by BAs. Roach (2010) showed that the
internal rate of return (IRR) of a BA group in the USA was higher than that of index funds.
Bonini et al. (2019) showed that the performance and probability of survival of BA-backed
firms were positively affected by the presence of hands-on involvement by BAs. According to
Levratto et al. (2018), BA-backed firms tend to benefit from BAssupport when they have
higher employment growth rates.
The second group of studies shows that the contribution of BAs to their investees is not
higher than that of VC. BAs in the USA contributed less to innovation and exit of their
investees than VC (Dutta and Folta, 2016). Choi and Kim (2018) concluded that the survival
rate of BA-backed firms was inferior to that of VC-backed firms based on an international
private equity database.
The third group of studies indicates that the contribution of BAs to their investees cannot
be generalized and depends on the individual competence of a BA. In a study based in the UK,
Mason and Harrison (2002) pointed out that 34% of BA investments were complete losses,
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investments
and 13% were break-even, while 23% of them achieved an IRR of more than 50%, indicating
that the BAsIRR did not follow a normal distribution. In Canada, BAsIRR was higher than
their investors, who mainly invest in firms for friendship and family ties (Riding, 2008). BAs
IRR was correlated with individual BAspast business experience and their pre- and post-
investment activities, such as their due diligence, the rejection rate of investment proposals,
involvement with their investees and period of holding time in the USA (Wiltbank, 2005) and
Italy (Capizzi, 2015). Even in studies in developed countries, where the BA industry and the
sample size is larger than in emerging countries, their theory has not kept up with practice.
In emerging countries, the number of studies on the contribution of BAs to their investees
is itself inadequate. The lack of empirical research on BAs in emerging countries has been the
subject of discussion in several research papers (Landstr
om, 1993;Bruton et al., 2004;
Klonowski, 2007;Ding et al., 2014). There is a certain degree of difficulty in identifying and
studying BAs in emerging countries because they tend to rely on personal connections to find
investments, making sampling challenging for researchers. For these reasons, BA research in
emerging countries tends to have small and non-random samples (Avdeitchikova et al., 2008;
Mason and Harrison, 2008;Tenca et al., 2018).
Previous literature on emerging countries has the following limitations. BAs in China tend
to be more passively involved in their investees than those in developed countries (Li et al.,
2014). BAs in Malaysia and Vietnam have high returns on investment by developing informal
networking and co-investment skills (Scheela and Jittrapanun, 2012;Harrison et al., 2018).
According to prior research, only a few studies have considered the case of emerging
countries.
3. Materials and methods
3.1 Methodology
The research question of this study is as follows: How do BAs contribute to investees? One
method is to compare the pre- and post-investment performance of BA-backed firms widely
used in management research. However, this approach was not adopted in this study because
there is very little performance data of pre-BA investment compared to post-BA investment
data. As mentioned in the introduction, this is because BAs mainly invest in start-ups. For
this reason, this study compared the post-investment financial and non-financial
performance of BA-backed firms with that of non-BA-backed firms.
For a proper comparison, the only difference required between BA-backed firms (the
treatment group) and the extracted non-BA-backed firms (the control group) is whether they
received BAs in the starting year of the comparison. This study used propensity score
matching to extract the control group from 1,099,068 yearly panel data of 186,999 Estonian
unlisted firms and compared the two groups. Then, the treatment and control groups were
compared in terms of profit/loss and sales as financial performance indicators, profit/loss and
sales per employee and the number of employees as a non-financial performance indicator
during five years from the starting year of the comparison. The comparison method is based
on a t-test for the means of the treatment and control groups.
This study analyzed profit/loss, sales, profit/loss and sales per employee as financial
performance indicators and the number of employees as a non-financial performance
indicator. The rationale for analyzing the profit/loss and sales and the profit/loss and sales
per employee is to distinguish the contribution of the increase in firm size through BA
funding from that of BAsinvolvement with and support for investees. The performance of
profit/loss and sales are affected by the BAsinvolvement with and support for investees and
the increase of firm size through BA funding.
Meanwhile, the performance of profit/loss and sales per employee can more appropriately
indicate the contribution of BAsinvolvement and support to the investee by controlling the
JCMS
number of employees as a proxy variable of firm size. The number of employees is not the
only proxy variable for the firm size of investees. A variety of variables is possible, such as
assets, capital and liabilities. However, this study chose the number of employees because it is
a stable variable and reflects the firm size in a timelier manner than assets, capital or
liabilities.
3.2 Data
The dataset used in this study contains information on the performance of all unlisted firms
and BA funding in Estonia. All performance data on unlisted firms in Estonia were obtained
from the business registry of the Estonian Ministry of Economic Affairs and
Communications. The dataset consists of 1,099,068 yearly data from 2006 to 2015 for
186,999 unlisted firms registered in the Estonian business registry. Data on BA funding were
obtained from multiple sources: the Estonian Private Equity and Estonian VC Associations,
the Estonian Business Angels Network and a database made by Start-up Estonia, an affiliate
organization of the Estonian Ministry of Economic Affairs and Communications.
After consolidating all the data gathered on BA financing from these organizations,
individual BAs and their investees were contacted by phone and e-mail to verify the exact
year of BA funding and the investees acceptance.
3.3 Variables
Definitions of all variables are explained in Table 1. In this study, the natural logarithms (ln)
were taken for all continuous variables. That is because of the skewed distribution of the
variables and the appropriateness of this technique for dealing with non-linearity in the
relationship between the dependent and independent variables. It also reduces the effect of
outliers (Armstrong et al., 2006;Collewaert and Manigart, 2016).
The variables converted to the natural logarithm are profit/loss, sales, profit/loss and
sales per employee, number of employees and age. Profit/loss and sales were adjusted for
Continuous variables
ln_profit/loss Natural logarithm of profit/loss before taxation of a firm in a year after adjusting for
inflation (base year 2015)
ln_sales Natural logarithm of sales of a firm in a year after adjusting for inflation (base year
2015)
ln_employees Natural logarithm of the number of employees of a firm in a year
In_age Natural logarithm of the age of a firm in a year
ln_sales per employee Natural logarithm of sales per employee of a firm in a year after adjustment for
inflation (base year 2015)
ln_profit/loss per
employee
Natural logarithm of profit/loss per employee of a firm in a year after adjustment for
inflation (base year 2015)
Dummy variables
Headquarters dummy A dummy variable equal to 1 if a firms headquarters are in Harju County in a year
(and 0 otherwise)
ICT A dummy variable equal to 1 if a firms industry is ICT in a year (and 0 otherwise)
Professional A dummy variable equal to 1 if a firms industry is professional, scientific and
technical activities in a year (and 0 otherwise)
Manufacturing A dummy variable equal to 1 if a firms industry is manufacturing in a year (and
0 otherwise)
Transportation A dummy variable equal to 1 if a firms industry is transportation in a year (and
0 otherwise)
Table 1.
Variable definitions
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inflation using the gross domestic product deflator, with the base year as 2015. Profit/loss
and sales per employee were calculated based on the inflation-adjusted profit/loss and
sales. Then, they were converted to the natural logarithm. The headquarters dummy is a
dummy variable, indicating the headquarters of a firm in a year in Harju County located in
the northern part of Estonia (North); it is the largest county and includes Tallinn, the
capital of Estonia. For the industry dummies, theindustriesinwhichBAsinvestedwere
used (ICT, professional, manufacturing and transportation). The classification is based on
the Estonian Classification of Economic Activities, the Estonian equivalent to the
Nomenclature of Economic Activities codes, Europes statistical classification of economic
activities.
3.4 Treatment group
The treatment group is the group of firms that received funding from BAs. This study
focuses on firms that received BA funding in their first round. Firms that received BA
funding in their second or later rounds were excluded from this study. As a result, the number
of BA-backed firms in the analysis was reduced from 48 to 36. Firms that received BA
funding in their second or later rounds tend to be more influenced by their first- or former-
round investors with a higher percentage of shares. The reason that this study focused on
firms that received BA funding in their first round is to eliminate the influence of these other
lead investors on the performance of the investees. Then, the five BA-backed firms with
missing data of the starting year of the comparison (the year of receiving BA funding) were
excluded. This is because it is impossible to extract the corresponding control group owing to
the missing data. As a result, the treatment group finally included 31 firms. Of the 31
treatment groups, 61% are headquartered in the countrys largest county, Harju County,
which includes the capital of Tallinn. By industry, ICT accounts for 45%, professional for
29%, manufacturing for 21% and transportation for 5% (Table 2).
3.5 Control group
The control group is the group of non-BA-backed firms and is compared to the treatment
group. The only difference between the treatment and the control group is whether the
treatment group received BAs in the starting year of the comparison. In extracting the control
group, this study first removed the 440 yearly panel data of firms that had already raised
funds from other investors, such as VCs, corporate VCs and business accelerators, to
Treatment
group
Control
group
Standardized
difference t-value (p-value)
ln_profit/loss 5.23 5.75 0.066 0.3436 (0.7315)
ln_profit/loss per employee 4.85 5.36 0.072 0.3782 (0.7057)
ln_sales 7.49 7.67 0.036 0.1837 (0.8545)
ln_sales per employee 6.93 7.06 0.029 0.1483 (0.8823)
ln_employees 0.85 0.83 0.016 0.0731 (0.9418)
In_age 0.37 0.32 0.084 0.4413 (0.6596)
North 0.61 0.65 0.070 0.3519 (0.7253)
ICT 0.45 0.48 0.064 0.3275 (0.7437)
Liberal professions 0.29 0.26 0.075 0.3737 (0.7091)
Manufacturing 0.21 0.23 0.042 0.2149 (0.8301)
Transportation 0.05 0.03 0.082 0.3933 (0.6945)
Note(s): This table compares the means of 11 variables of annual data of 31 treatment group firms and 144
control group firms in the starting year
Table 2.
Comparison of means
of variables between
the treatment and
control groups in the
starting year of the
comparison
JCMS
eliminate the firms influenced by investors other than BAs. After completing these steps,
propensity score matching was utilized to extract the control group. Specifically, this study
first estimated the probability of receiving BAs for all the panel data by using a logit model
(Lerner, 2000;Guerini and Quas, 2016).
The independent variables used in the estimation are profit/loss, sales, profit/loss and
sales per employee, number of employees, age, location of headquarters and industry
dummies. Further, the propensity score was calculated based on the predicted probability
estimated by the logit model. Next, the five-year panel data, closest to those of the treatment
group in the year of receiving BA funding, were extracted from the remaining all yearly panel
data (Guerini and Quas, 2016). The Mahalanobis distance was used for the extraction. Thus,
this study finally extracted 144 yearly panel data for the control group.
3.6 Comparison between the treatment and control groups in the starting year of the
comparison
Table 2 compares the 31 yearly panel data of the treatment group in the year of receiving BA
funding and the 144 yearly panel data of the control group in the starting year of the
comparison. This indicates the comparison of the means of profit/loss, sales, profit/loss and
sales per employee, number of employees, age, location of headquarters and industry
dummies between the treatment and control groups. None of the mean differences between
the treatment and control groups was statistically significant. The t-values ranged from
0.3519 to 0.4413 (p-values: from 0.9418 to 0.6596); all the standardized differences were less
than 0.084, which is less than 0.1 of the recommended values in propensity score matching
analysis (Normand et al., 2001;Austin, 2011). In the starting year of the comparison, the
treatment and control groups are not statistically different in terms of profit/loss, sales, profit/
loss and sales per employee, number of employees, age, location of headquarters and industry
dummies. In other words, the only difference between the treatment and control groups is
whether they receive BA funding in the starting year of the comparison.
4. Results
Table 3 and Figure 1 show the movement of profit/loss of the treatment and control group
over the five years since the starting year of the comparison. The mean of profit/loss of the
treatment group is lower than that of the control group in all five years. In the second year, the
mean profit/loss of the treatment group was lower than that of the control group, with a
statistically significant difference at the 1% level. In the first, third, and fourth years from the
starting year of the comparison, the mean of profit/loss of the treatment group was lower than
that of the control group with a statistically significant difference at the 5% level.
Table 4 and Figure 2 compare the movement of profit/loss per employee of the treatment
and control groups. The mean of profit/loss per employee of the treatment group was lower
than that of the control group in all five years. The mean of the treatment group was lower
Treatment
group
Control
group t-value No. of treatment group No. of control group
1st year 4.84 0.68 2.8613*** 21 92
2nd year 6.80 0.71 3.2015*** 15 73
3rd year 4.64 1.02 2.4683** 18 58
4th year 3.25 1.75 1.8236* 11 44
5th year 0.49 3.12 0.8576 9 35
Note(s): *** 1% significance level; ** 5% significance level; * 10% significance level
Table 3.
Annual comparison of
profit/loss per
employee between the
treatment and control
groups
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than that of the control group, with a statistically significant difference at the 1% level in the
first and second years. In the third year, the mean of the treatment group was lower than that
of the control group, with a statistically significant difference at the 5% level. In the fourth
year, the mean of the treatment group was lower than that of the control group, with a
statistically significant difference at the 10% level.
Table 5 and Figure 3 compare the mean sales of the treatment and control groups. The
mean sales of the treatment group were higher than those of the control group for all five
years. The mean of the treatment group was higher than that of the control group, with a
Treatment
group
Control
group t-value No. of treatment group No. of control group
1st year 5.03 0.02 2.4012** 22 120
2nd year 7.72 0.43 3.3324*** 16 104
3rd year 5.28 0.04 2.2861** 18 93
4th year 3.96 1.82 2.0263** 11 73
5th year 0.98 1.82 0.2535 9 62
Note(s): *** 1% significance level; ** 5% significance level; * 10% significance level
Figure 1.
Annual comparison of
profit/loss per
employee between the
treatment and control
groups
Table 4.
Annual comparison of
profit/loss between the
treatment and control
groups
Figure 2.
Annual comparison of
profit/loss between the
treatment and control
groups
JCMS
statistically significant difference at the 5% level in the third and fourth years from the
starting year of the comparison.
Table 6 and Figure 4 compare the means of sales per employee of the treatment and
control groups. The mean sales per employee of the treatment group was higher than that of
the control group in the third and fourth years but was lower than that of the control group in
the first, second and fifth years from the starting year of the comparison. In the second year,
the mean of the treatment group was lower than that of the control group, with a statistically
significant difference at the 5% level.
Finally, Table 7 and Figure 5 compare the number of employees between the treatment
and control groups. The mean number of employees in the treatment group was higher than
that of the control group for all five years. In the first, third and fourth years from the starting
year of the comparison, the mean of the treatment group was higher than those of the control
group, with a statistically significant difference at the 1% level. In the second year, the
treatment group was higher than the control group, with a statistically significant difference
at the 5% level.
Treatment group Control group t-value No. of treatment group No. of control group
1st year 10.30 9.91 0.4848 20 117
2nd year 10.12 9.60 0.5057 15 102
3rd year 11.81 9.10 2.5658** 16 88
4th year 12.39 9.72 2.0469** 9 64
5th year 10.12 9.40 0.4945 10 59
Note(s): *** 1% significance level; ** 5% significance level; * 10% significance level
Treatment group Control group t-value No. of treatment group No. of control group
1st year 8.86 9.49 0.9090 19 91
2nd year 8.48 9.88 2.1860** 14 73
3rd year 10.11 9.76 0.5910 16 58
4th year 10.48 10.00 0.6757 9 43
5th year 8.99 9.57 0.5171 10 36
Note(s): *** 1% significance level; ** 5% significance level; * 10% significance level
Table 5.
Annual comparison of
sales between the
treatment and control
groups
Figure 3.
Annual comparison of
sales per employee
between the treatment
and control groups
Table 6.
Annual comparison of
sales per employee
between the treatment
and control groups
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In summary, in terms of the profit/loss and profit/loss per employee between the treatment
and control groups, the treatment group was consistently lower than the control group with a
statistically significant difference from the first to the fourth year. There was no significant
difference between increases in the factor of firm size when accounted through BA funding
and when it was not. Meanwhile, the number of employees in the treatment group was higher
than that of the control group for all years, indicating that the BA funding increased the firm
size of the treatment group. The mean sales of the treatment group were also higher than that
of the control group for all years, indicating that the sales of the treatment group increased
Treatment group Control group t-value No. of treatment group No. of control group
1st year 1.37 0.76 2.6132*** 21 111
2nd year 1.40 0.70 2.5292** 16 98
3rd year 1.52 0.55 3.5901*** 18 87
4th year 1.56 0.51 3.1086*** 11 76
5th year 1.13 0.55 1.5500 10 62
Note(s): *** 1% significance level; ** 5% significance level; * 10% significance level
Figure 4.
Annual comparison of
sales between the
treatment and control
groups
Table 7.
Annual comparison of
the number of
employees between the
treatment and control
groups
Figure 5.
Annual comparison of
number of employees
between the treatment
and control groups
JCMS
with the expansion in firm size. These results show that BAs displayed patience in tolerating
investeeslosses even when sales increased.
Meanwhile, the mean sales per employee was higher or lower in each year than that in the
control group. From the results of sales per employee, which take into account the number of
employees as a proxy variable for firm size, it is assumed that the contribution of BAs
involvement with and support for their investees is not clearly confirmed. Therefore, the
finding indicates that the increase in sales is due to the expansion of firm size through BA
funding, rather than the contribution of BAsinvolvement with and support for their
investees.
5. Discussion
This study compared the financial and non-financial performance of BA-backed firms with
that of twin non-BA-backed firms extracted from all Estonian unlisted firms by propensity
score matching. The results of the comparative analysis revealed that BAs displayed
patience in tolerating investeeslosses even when sales increased. The results also found that
BA-backed firms increased their sales and the number of employees compared to twin non-
BA-backed firms. However, the results for sales per employee suggest that the increase in
sales of BA-backed firms was due to the expansion of firm size through the provision of BA
funding, rather than their involvement with and support for their investees.
These results indicate that BAs contribute to their investees not only by increasing the
firm size of their investees through providing funds, but also by enabling expenditure for the
future growth of the firm rather than requiring firms to squeeze profit from increased sales.
This is not patience without options for a BA in a situation where the investees sales are
deteriorating, but rather deliberate patience in the presence of BA options where the
investees sales growth is increasing; this finding is contrary to conventional investor
behavioral principles.
In general, investors expect investees to increase profit and maximize a firms wealth.
Deeg and Hardie (2016) defined investorspatience based on the following three questions: (1)
What is the initial intended investment term? (2) Is voice motivated by short-term
performance? (3) Is the likelihood of exit motivated by poor short-term performance? Deeg
and Hardie (2016) suggested that how investors react to the short-term performance of their
investees is one of the indicators of their patience. Regarding the BAspatience, Harrison et al.
(2016) found that the majority of BAs were not intentionally patient concerning their exit
strategies, but rather as a result of the deteriorating performance of their investees.
However, BAs in this study did not require their investees to improve their profit/loss
either in the short-term immediately after the investment or in the medium term of 45 years,
contrary to conventional investor behavioral principles. Notably, BAs intentionally tolerated
the deterioration in profit/loss while firmssales were increasing. This is not patience
without options for a BA in a situation where the investees sales are deteriorating, but
deliberate patience in the presence of options for a BA where the investees sales growth is
increasing.
Interviews with leading Estonian BAs confirmed their patience, as revealed in this study.
According to Heidi Kakko, founding board member of Estonian Business Angels Network in
Estonia, BAs do not have a sell-by date; therefore, they can adopt a long-term investment
strategy; conversely, with VC, one is obliged to sell their investments usually in ten years to
refund their investorsmoney. Jaan Tallinn, co-founder of Ambient Sound Investments and
Skype, in an interview with the author, says that he invests in young entrepreneurs when
their products or services excite him. He is even willing to participate in all-night meetings
with them and continue to provide the necessary funding. He prefers that his investees
re-invest their income for future growth ahead of profit/loss generation. Indrek Jaaska, an
Business angel
investments
entrepreneur and former investment manager of Ambient Sound Investments, says that he
always cares about his investeeslong-term potential but is not concerned about their short-
term profit/loss as some of his investees exited just before they became profitable.
The interview results specifically showed that BAs enjoyed being involved with and
supporting their investees from a long-term perspective. The results of this study showed
that the patience of BAs was not a by-product but was intentional. This study adds to the
debate on whether BAs are patient investors.
6. Limitations and further research
This study has the following three limitations. It could not identify any direct contribution of
BAspost-investment involvement with and support for their investees. Previous research on
VC shows that VC is not just a financial intermediator but also adds non-monetary value to
investees, thus contributing to their growth (Gorman and Sahlman, 1989;Bygrave and
Timmons, 1992;Manigart et al., 2002). The analysis results of this study showed that BAs
have the patience to tolerate spending for the future growth of their investees. They are also
willing to participate in all-night meetings. However, unlike VC, this study could not reveal
that BAs provided more than monetary value to investees. This is in line with the results of
previous studies, showing that the contribution of BAs to their investees has been
controversial. What is the reason for the difference between BAs and VCs? This study
focuses only on BAs, and thus, could not clarify this point. A comparative study of the
differences between BAs and VCs in terms of their contribution to their investees would be a
promising research area.
Second, this study relies on the Business Registry of the Estonian Ministry of Economic
Affairs and Communications for the independent variables, such as profit/loss, sales and
the number of employees of all samples. The government data are reliable. However, there
are some issues, as follows. First, for both the treatment and control groups, this study
could not analyze the subsequent growth of firms that have successfully exited and sold
their businesses to others. It also could not follow the subsequent business growth of firms
that relocated their headquarters overseas. Furthermore, it could not solve the problem of
bankrupted firms, i.e. the problem of survival bias. In this study, the researchers
considered the possibility of conducting follow-up research on the growth of treatment and
control groups post-sale of their businesses, including their performance overseas;
however, a fair and reliable measure to merge data from different sources was not found;
this is an issue for future research.
Lastly, there are issues arising from the BA research. According to previous studies,
BA research in emerging countries tends to have a small research sample with difficulty
ensuring random sampling (Avdeitchikova et al., 2008;Mason and Harrison, 2008;Tenca
et al., 2018). Nonetheless, this study focused only on first-round investments to analyze the
contribution of BAs to investees to exclude the influence of other investors. Second, the
period from 2006 to 2015 was chosen for this study based on the beginning stage of the BA
industry in Estonia, which started around 2006. Focus on only the beginning stage of the
BA industry from the perspective of implications for emerging countries resulted in a
limited sample. Furthermore, exit issues, overseas headquarters relocation, and
bankruptcies also contributed to the small sample size. As a result, the sample size was
finally reduced to 31. In previous studies of BAs in emerging countries with small-sized
economies, case studies have been the mainstream, and even in the few empirical analyses,
the same issues have been found. It is necessary to develop research methods, such as
targeting multiple emerging countries across national borders. In addition, following the
research on the beginning stage of the BA industry, research on BAs in Estonia over a
longer span of time after 2016 should be conducted.
JCMS
7. Conclusion and implications
This study analyzed the contribution of BAs to their investees in Estonia. The results of the
comparative analysis showed that BAs were patient enough to allow their investees to spend
for future growth rather than requiring them to squeeze profit from increased sales, which is
contrary to conventional investor behavioral principles. This is not patience without options
for a BA in a situation where the investees sales are deteriorating, but deliberate patience in
the options available for a BA where the investees sales growth is increasing.
BA patience suggests that this might be an effective function for fostering start-ups in
emerging economies. Start-ups are recognized to play an important role in the creation of
innovation, and also in economic growth, employment and strengthening the
competitiveness of the economic system (Jacobs, 1969;Audretsch, 1995). BAs could
patiently take on the responsibility of providing longer-term funding for startups.
In the analysis of this study, BAspost-investment involvement and support did not make
a direct contribution to their investees sales, although BAs contributed to the sales increase
of their investees through the BA funding itself. That may be a challenge for Estonian BAs or
BAs in emerging economies. In previous studies, VCs have been recognized to contribute to
the start-upsdevelopment through their involvement with and support for their investees,
such as encouraging their investeesprofessionalization (Hellmann and Puri, 2002). For BAs
to take over the function of start-up investors from VCs in emerging economies, it will be
essential to improve their quality of post-investment involvement and support.
Finally, this study focused on the beginning stage of the BA industry in Estonia, from
2006 to 2015. The results of this study have implications for what functions BAs can and
cannot provide in the beginning stage of the BA industry. Policymakers in emerging
economies that have not yet introduced BA industry promotion measures could take
advantage of the patience of BAs and help the BA industry improve the quality of individual
BAs post-investment involvement and support.
Notes
1. This problem would also have arisen in Estonia if not for Start-up Estonia, an affiliate organization of
the Estonian Ministry of Economic Affairs and Communications, which has been given the task of
collecting the data of start-up investments since 2006 through their network of start-ups and
investors.
2. Skype founders Toivo Annus, Priit Kasesalu, Ahti Heinla and Jaan Tallinn began investing in 2006
through Ambient Sound Investment, the first organized and biggest ever BA in Estonia
(Kirihata, 2016b).
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Corresponding author
Tetsuya Kirihata can be contacted at: kiri@fc.ritsumei.ac.jp
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