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Groups, networks and business angels’ investment process.
Stefano Bonini
Stevens Institute of Technology
School of Business
1 Castle Point Terrace, Hoboken, NJ 07030, USA
Vincenzo Capizzi *
Department of Economics and Business Studies
Università del Piemonte Orientale
Via E. Perrone, 18, 28100, Novara, Italy
Mario Valletta
Department of Economics and Business Studies
Università del Piemonte Orientale
Via E. Perrone, 18, 28100, Novara, Italy
Paola Zocchi
Department of Economics and Business Studies
Università del Piemonte Orientale
Via E. Perrone, 18, 28100, Novara, Italy
This draft: July 6th 2016
JEL Codes: G24, G32, M13
Keywords: business angels, business angel networks, investment process, experience, monitoring,
networking, co-investment
____________________________________________________________________________________
* Corresponding Author. Tel.: +39 0321 375.438. Email: [email protected]
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Abstract
This paper provides first-time evidence on the effects of angel groups and networks (AG/BAN)
membership on the investment choices of business angels. Looking at a proprietary dataset that collects
qualitative and quantitative information on over 800 investments by 625 business ange l s f rom 2008 to
2014, we show that AG/BAN membership generates valuable information, networking, monitoring and
risk reduction effects which, ultimately, affects the amount of financial resources available to invest as well as
the equity stake in the investee company. These results extend our knowledge of the behavior and
characteristics of business angels investing that are rapidly gaining prominence in the support of new ventures
and the development of global economies.
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1. Introduction
In the last few years both academic and practitioners have devoted growing attention to
understanding the dynamics of business angels investment. Market data at both the US and
European level (US ACA, 2015; EVCA, 2014; EBAN, 2015; Kraemer-Eis et al., 2015;
OECD, 2016) show that business angels have become a major segment of the capital
market industry, capable of allocating financial resources to one of the riskiest asset class –
startup companies. As such, they are invaluable enablers of the development of economic
and social systems. Regulatory Authorities all around the world have been paying attention
to this investor class1 who nowadays is officially recognized as one component of the
shadow banking industry. Despite this recent attention our understanding of the features
of business angel investments is still very limited. In particular, little is known of the
differential investment behavior of business angels when they join semi-formal
organizations such as Angel Groups or Business Angel Networks. In recent contribution
Kerr et al. (2014) provide case-study based evidence of the impact of angel groups on later
round financing. Yet, their sample doesn’t allow to shed light on the differential (if any)
investment behavior of BAs within and outside a semi-formal organization. This paper
aims at filling this gap.
Business angels are high net worth individuals who invest their own money in small
unlisted companies, with no family connection, assuming typically a minority equity stake
as well as an active involvement in the portfolio companies (Mason, 2006). Furthermore, i t
is now widely accepted that business angels are amongst the most suitable actors of the
ecosystem for entrepreneurial businesses, considering their capability to fill the so-called
“funding gap” existing between demand and supply of early stage equity capital (Mason
and Harrison, 2000; Johnson and Sohl, 2012; Capizzi, 2015). First, business angels satisfy a
size of investment need (usually falling in the range 100k – 300k euros) which is not
typically considered interesting as well as profitable for venture capitalists, due to both its
relatively low cash flows’ generation potential and the relatively high costs for due
diligence, contracting and monitoring given the relevant adverse selection and moral hazard
issues affecting small-scale young businesses (Jeng and Wells, 2000; Carpenter and
Peterson, 2002; Mason, 2009). Second, alongside the capital injection, business angels
1 Also called “informal investors”, in order to differentiate them from venture capitalists and other financial
intermediaries investing on a professional and institutionalized basis capital raised from third parties (Wetzel,
1986; Freear et al., 1993; Landstrom, 1993; Mason, 1996; Van Osnabrugge, 2000)
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provide non-monetary resources deemed highly valuable for entrepreneurs like industrial
knowledge, management experience, advice and mentoring, standing and personal
relationship networks (Harrison and Mason, 1992; Landstrom, 1993, Politis, 2008 ).
Correspondently, among the major expected outcomes from angels’ investments, a part
from monetary returns there are non-monetary benefits as well, such as such as playing an
entrepreneurial role, working with and mentoring highly-talented and creative people,
discovering new technologies, being invited to pitch presentations, interacting with other
angels and actors in the financial community, and the status from being perceived as a
sophisticated investor (Capizzi, 2015).
Over time, a growing number of angel investors started organizing themselves into groups
(also referred to as syndicates or networks or clubs, depending on their level of internal
structure), usually on a territorial or industrial basis, sharing presentation pitches from
potential entrepreneurs, due diligence over the potential investment opportunities,
transaction costs and investment deals to be implemented by group members (Mason,
2006; Sohl, 2007; Paul and Whittam, 2010; Gregson et al., 2013). These associations, called
Business Angels Networks (BANs), have grown to regional, national (for instance, ACA in
the US, BBAA in the UK, IBAN in Italy) and even continental proportions (among them,
EBAN and BAE in Europe) increasing also their internal structure and coordination
among the members as well as the quality and variety of the service provided (deal flow,
education and coaching, legal and advisory services). Thanks to BANs and angel groups
the informal venture capital market is nowadays much more visible and, hence, easier to
get access to by both demand and supply side (Mason, Botelho and Harrison, 2013).
However, there is still little evidence on the impact of BAN on the investment process of
business angels, most of all based on anecdotal evidence or case studies (May, 2002; Payne
et al., 2002; Mason, 2006; Sohl, 2012; Ibrahim, 2008; Brush et al., 2012; Kerr et a l , 2014 ;
Collewaert and Manigart, 2015; Croce et al. 2016). As such, the aim of this paper is to shed
some light over the major determinants of business angels’ investment decisions, focusing
on the differential role of investors’ membership to business angel groups and networks.
By comparing investment practices of business angels associated with a BAN to those of
business angels operating as individual investors, we investigate whether and how being
member of a semi-formal organization matters by either affecting the share of angels’
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personal wealth invested in a given deal or affecting the amount of the equity stake in the
participated company.
Looking at a unique dataset that collects qualitative and quantitative information on over
800 investments by 625 business angels from 2008 to 2014, our paper for the first time
find evidence of different causal relationships for angels joining BANs when compared to
angels not members of any single group or network of business angels. In particular we
show that being part of an angel group does generate valuable information, monitoring,
networking and risk reduction effects that, ultimately, affect the amount of financial
resources available to invest as well as the equity stake to assume in the investee company.
The remainder of the paper is structured as follows: the second paragraph will derive the
research hypothesis to be tested from the literature dealing with business angels and the
informal venture capital. In the third paragraph will be presented the dataset and specified
the variables used to perform the empirical analysis, whose results are shown and
commented in the fourth paragraph. In the final paragraph will be presented authors’
conclusive remarks and suggestions for future research.
2. Hypotheses development and related literature
In this paper we focus on business angels investment choices trying to isolate the
differential role played by BANs when compared to the investment process of individual
informal investors. By shedding light on the impact of BAN on the asset allocation
decisions of business angels, it is possible to suggest policymakers new and more effective
measures aimed at stimulating entrepreneurship and, therefore, contributing to the
development and growth of economic and social systems (Baldock and Mason, 2015;
Kraemer-Eis et al., 2016).
The starting point of the research program is the identification of the phenomenon under
investigation, the amount of own risk capital invested by business angels. In this study the
authors investigate and measure business angels’ capital allocation decisions according to a
double perspective: the amount of capital invested as a share of a single business angel’s
personal wealth (“WEALTH%”), and the amount of capital invested as a share of the equity
capital of the investee company (“PARTICIPATION%”). The first measure is a commonly
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used one in the literature dealing with both venture capitalists (Jeng and Wells, 2000;
Lerner, 1998) and informal investors (Harrison and Mason, 2002; Maula et al., 2005;
Wiltbank and Boecker, 2007), allowing to relate the investment decision making process to
the involvement of the business angels in the participated companies, to the value added
provided and to the nature of the negotiating process developed with the entrepreneurs. 2
The second measure, on the other hand, is useful in order to identify the perceived risk
drivers and their impact on the asset allocation decisions of informal investors.
We model the expected effects on these two measures as follows.
The role of business angel networks
One major evolutionary trend observed in the informal venture capital market over the last
two decades deals with the growing relevance of associations of business angels, either
structured or semi-structured, ranging from loose network of individual investors to formal
angel syndicates (Ibrahim, 2008; Mason, 2009; Paul and Whittam, 2010; Sohl, 2012;
Gregson et al., 2013; Mason, Botelho and Harrison, 2013). Many authors investigated the
black box of such associations, mainly according to a case study research methodology,
studying their internal structures and investment practices (Kerr et al., 2014) as well as the
kind of services and contribution provided to either the investee companies (Collewaert
and Manigart, 2016; Croce et al., 2016) or their members (Shane, 2000; Mason, 2007; Paul
and Whittam, 2010).
For the aim of this study the term Business Angel Network (BAN) is used to indicate an
association of business angels, whose member can join on a solicited or unsolicited basis,
that uses its resources to organize meetings for pitching events, training and mentoring,
lobbying purposes. Entrepreneurs are solicited to join the BAN through websites and
other networking activities taking place inside the community. There is no or limited
organized deal group processing and the association does not make investments or
recommend investments o members, rather each member decides whether or not to invest
on a deal-by-deal basis, typically finding co-investors (within or outside the BAN), sharing
due diligence, negotiations and term sheets.
2 As common in literature dealing with formal and informal venture capital, one major assumption in the
asset allocation process is the impossibility to rely upon the existing asset allocation techniques
developed by theory of finance and presuming to have traded securities as possible asset classes.
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Consistently with Ibrahim (2008), Brush et al. (2012) and Mason, Botelho and Harrison
(2013), we argue that the membership to a BAN benefits the angel investors mainly
through the information and knowledge sharing effect taking place inside the community.
The possibility for unexperienced angels to get in touch with experienced angels is
particularly important inside the BAN, thus improving new investors human capital and
knowledge of how to implement effective value creating investment decision (Shane,
2000). Also the role of the so-called “gatekeepers”, the individuals who control access to
and manage much of the day-to-day operations of BANs (Paul and Whittam, 2010), is
crucial in favoring the sharing of information among BAN members.
Therefore, the investment made by BAN members, even if not in syndication with other
co-investors, should be a more informed and efficient one, leading to a wider capital
allocation investment choice. In other words, because of the services and contributions
provided by BANs to their members, it is reasonable to assume that BAN members, once
selected an investment opportunity and undertaken the investment decision making
process, will invest more of their personal wealth than non-BAN members. When
considering, on the other side, the participation in the investee company, BAN members
should benefit from the investment opportunities provided by the network inside the angel
communities, leading to invest in more companies, remaining unchanged the share of their
personal wealth allocated to this asset class. This would lead to a decrease in the equity
stake acquired in a given deal.
As such, our first research hypothesis is:
H1: The business angels’ capital allocation investment decision is affected by the membership to a giv en
BAN. The amount of angels’ invested capital is positively affected by BAN membership, whi le
the size of the equity stake is negatively affected by BAN membership.
BAN, groups and risk sharing
Among the many options available to business angels when valuing a given investment
opportunity, there is the possibility to make the deal either as an individual investor – the
“solo angel” – or co-investing with other angel investors, though the latter can be
implemented through different degrees of formal structures ranging from formal angel
syndicates to informal so called “club deals”.
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From the one hand, each single investor, by co-investing in a given deal, can obtain a lower
equity stake in the target companies, though without giving up to the willingness to gain
active involvement in the participated companies themselves and, therefore, providing
value added contributions. The sum of the single equity positions of all the co-investors in
a given deal increase the possibility to play an active role in the investee companies, which
can also be bigger-sized than those affordable by solo angels (Paul and Whittam, 2010).
From the other hand, consistently with modern portfolio theory (Elton and Gruber, 1995),
the co-investment option is a completely rational diversification strategy aimed at reducing
the risk from a given equity investment opportunity. As a direct implication, business
angels choosing to share the risk of a given deal by co-investing with other ones, also
assuming to keep constant the share of their personal wealth devoted to the asset class of
private equity investments in early stage companies, can leverage on wider and better
diversified investment portfolios (Mason, Botelho and Harrison, 2013) as well as on the
possibility to get access to risk-reducing information (Aernoudt, 2005).
This leads to the following research hypothesis
H2: The business angels’ capital allocation investment decision is negatively affected by the possibility to
co-invest in a given deal.
As above mentioned, another yet agreed upon common features of business angels is their
active role played inside the investee company, in order to support the entrepreneur in the
value creation path through an hands on approach. Politis (2008) identifies four different
kind of value added contributions coming from angel investors, identifying a “sounding
board” role, a “monitoring” role a “resource acquisition” role and a “mentoring” role.
However, a number of surveys disclosed on a yearly basis by research centers (EIF,
OECD, IBAN, EBAN) and country federation of angels associations report the existence
of investors not interested or willing to play such an active role in the investee companies,
rather interested to capital gains and portfolio diversification benefits. For the purpose of
this study we call “passive” investors this latter category of business angels. We expect a
negative relationship between passive investors and the amount invested at least for non-
BAN members. However, for BAN-members the possibility to co-invest could generate
the opposite outcome. This leads to the following research hypothesis:
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H3: The business angels’ capital allocation investment decision is affected by the expected active/passive
role to be played in a given deal.
The role of monitoring
Finance literature extensively investigated the role of monitoring as a way to reduce
asymmetric information and moral hazard problems stemming from any kind of securities
investment (Jensen and Meckling, 1976; Diamond, 1984; Aghion and Bolton, 1992).
As far as private equity investments are concerned, many authors evidenced how venture
capital organizations monitor investee companies and what are the major contingent
contracts, clauses and mechanisms used to reduce potential conflicts and incentives to
opportunistic behavior by entrepreneurs (Sahlman, 1990; Gompers and Lerner, 2000;
Triantis, 2001; Kaplan and Stromberg, 2003; Chemmanur et al., 2006; Cumming, 2008;
Wong et al., 2009; Cumming and Johan, 2013).
Dealing with business angels, specific contributions showed that they seldom adopt the
typical control and governance provisions of venture capital investors (Van Osnabrugge,
2000; Wiltbank and Boecker, 2007; Goldfarb et al., 2012; Bonini and Capizzi, 2016),
implementing monitoring mechanisms “non aggressive and striking in their informality” (Ibrahim,
2008). The major substitutes for contractual monitoring are represented by the angels’
knowledge of the industry coming from previous investment or managerial experience, the
existing interactions with entrepreneurs and the geographical proximity of the investee
company (Wong et al. 2009).
Consistently with the above mentioned arguments, we think the kind of monitoring taking
place in the informal venture capital market is a “soft” one, not based mainly on
contractual mechanisms, but rather on high involvement in the participated company
through company visits, interactions with entrepreneurs and other control techniques
based upon trust. We argue the higher such a soft monitoring effort, the lower the
investment risk perception by business angels in their investment decision making process.
Given the possibility to investigate the impact of the soft monitoring for both our sub-
samples of business angels – BAN members and non-BAN members – we expect different
causal relationships between monitoring and angel investments. In fact, BAN members
benefit from the screening support provided by BAN to their members as well as from the
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information and knowledge sharing effect stemming inside the BAN, leaving the need for
higher monitoring effort to the investments perceived as riskier ones. This leads to less
informationally opaque investments when compared to those realized by non-BAN
member business angels, who don’t benefit from the soft information produced inside the
angel community and are supposed to compensate so greater an information asymmetry by
imposing higher level of monitoring. In this case, higher monitoring should not necessarily
be associated to higher investment risk.
We therefore hypothesize that:
H4: The business angels’ capital allocation investment decision is positively affected by the monitoring
effort put by non-BAN members and negatively affected by the monitoring effort put by BAN
members.
Controls
Following the extant literature we will test our hypotheses introducing a set of control
variables that are known to have a causal effect on the investment decision of business
angels. Mason and Harrison, 1996, Van Osnabrugge, 2000 Macht, 2011 explained the role
of experience, while Shane, 2000, Paul et al. 2007 showed the effect of age and education,
as well as the previous background, which could be a managerial one, an entrepreneurial
one or a financial one (Maula et al., 2005; Sudek, 2006; Morrissette, 2007; Collewaert and
Manigart, 2016). As far as age is concerned, consistently with studies dealing with investors’
utility functions in intertemporal portfolio choice model (Samuelson, 1997; Forsfalt, 1999),
it is possible to assume an increase in the risk aversion profile of business angels, leading to
a decrease in the share of their wealth available to riskiest asset classes, like entrepreneuria l
ventures. On the other hand, experience gained through past investments, high degree of
education and the amount of business angels’ personal wealth could act as counteracting
factors on their capital allocation investment decisions.
Additionally, we expect that the equity stake in the target company acquired by a business
angel is negatively affected by the size of the company itself (Mason and Harrison, 2000;
Van Osnabrugge, 2000), as well as by its stage in the life cycle (Wiltbank et al., 2006) and its
location (Sudek, 2006).
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Finally, consistently with the above mentioned contributions investigating the determinants
of investments taking place both in the formal and informal venture capital markets, we do
consider in our model to be tested some industry (market capitalization, performance and
capital intensity) and macroeconomic (market interest rates) variables expected to play a
statistically significant role in the investment decision making process. In fact, it is
reasonable to assume the amount of capital to invest depends, among the others, also on
industry specific drivers as well as on the expected return from alternative asset class
investments.
Table 1 summarizes our research hypotheses and the predicted signs of both the
explanatory and control variables for each one of the proxies used as a measure of business
angels’ capital allocation process.
INSERT TABLE 1 HERE
3. Sample data and variables
Our data are obtained from sequential surveys administered by IBAN to its associates and
other unaffiliated BAs. Surveys are conducted during the period January – March of each
year beginning in 2008. Each survey was designed in order to collect information on
previous year’s operations. Each survey is completed in a four-step process: at the
beginning of January IBAN forwards the survey’s website link to its associates and other
known BAs.3 By the first week of March data are collected (step 1). Non-responding BAs
are contacted by email and phone to solicit the survey completion (step 2) while an IBAN
team reviews the data to identify incomplete, wrong or unverifiable answers (step 3) which
is further checked through direct follow-up calls (step 4). This process is a fairly common
survey technique called sequential mixed mode, which is a particular methodology that
employs a different survey mode in a sequential way (e.g. the non-respondents to a mail
survey are subsequently contacted and requested to answer the questionnaire through a
different mode, such as a phone interview). Evidence shows that a mixed mode survey
3 See IBAN website (www.iban.it) for the survey questionnaire.
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approach significantly improves the response rate (De Leeuw, 2005 and Dillman et al.,
2009).
After the completion of the surveys, IBAN received 625 full responses by investors who
performed at least one investment, for a total 808 investments during the 2008 – 2014 time
period. The overall number of angel unique investors is 490, of which the 35% are BAN
members. When considering the overall number of investments, the 54% of them refers to
BAN members, thus implying the membership to an angel community should presumably
affect angels’ capital allocation choices.
The authors are aware, as a major and common issues affecting literature dealing with
informal venture capital, that despite the high response rate sample representativeness can
still be an issue. Research on BAs shares the key difficulty of finding reliable data given that
investments are not publicly recorded and most investors strive for anonymity , while
others don’t even know to be business angels (Mason and Harrison, 2008; Capizzi , 2015).
Therefore, it is important to emphasize that the data used for the analysis do not represent
the full extent of the business angel activity taking place in the investigated arena, rather
produce a reliable estimate of the visible market of the informal venture capital in Ita ly, as
confirmed by the legitimation of IBAN itself, which is the only recognized and authorized
source of information by research centers both at the national level (Bank of Italy, Bocconi
University, Politecnico Milan) and at the international level (EIB, BAE, OECD).
In the following Table 2 we present the temporal and industry distribution of the final
sample data.
INSERT TABLE 2 HERE
The target ventures belong to various industries, among which the most represented ones
are: Manufacturing of Food and Beverage products (20%), Biotech (17.1%) and Cleantech
(13.1%). The geographical distribution of ventures is quite concentrated, since
approximately the 88% of them is located in Italy.
Table 3 reports summary statistics on the participation to groups and networks and the
conditional distribution of the 2 dependent variables built in order to measure the
investigated phenomenon which is the business angels’ capital allocation decision:
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PARTICIPATION%, which is computed as the amount invested in a venture as a share of its
net-asset-value, and WEALTH%, which is the share of a BA’s financial wealth invested in a
venture.
INSERT TABLE 3 HERE
The business angels joining an angel community (herein after “BAN members”) are almost
54% of the sample, giving the possibility to empirically investigate the role of the business
angel networks in the investment decision making process.
The descriptive statistics related to the dependent variables show that the relative incidence
of BAs’ investments widely varies in the sample, both in terms of participation in the
venture and in terms of personal wealth of the BAs. Nevertheless, the majority of the
investments are relative small. In fact, the participation in a venture is lower than 10% in
half of the cases and lower than 20% in three quarters of them and the share of wealth
invested in a venture is lower than 15% in the 50% of the investments and lower than 17%
in three quarters of them.
Being member of an angel community significantly indeed affects the amount of wealth
invested in a venture. This evidence emerges by performing a two-group-mean comparison
test on the variables PARTICIPATION% and WEALTH%, between those BAs which are
members of a Business Angel Network (BAN) and those which are not members of any
BAN. As presented in Table 1, the test shows that being a member of an angel community
positively affect the share of wealth invested in the investee company, while it does not
influence the size of the participation in the investee company.
The present empirical analysis aims at investigating the existence of a causal relationship
between the two above mentioned dependent variables and some explanatory variables
related to the research hypotheses developed in the previous section. We also introduce in
our model a series of independent variables related to the business angels’ features, the
investee firms and to industry and macroeconomic factors. Table 4 provides a description
of the proxies built for each variable as well as summary statistics. It is worth noting that,
in order to manage the volatility observed for the variables Net_Asset_Value and Co-
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Investors, largely depending on the nature of the sample, we winsorized them at the 95 th
percentile of their respective distributions.
INSERT TABLE 4 HERE
Our first research hypothesis (H1) deals with the role of angel communities, who propose
investment opportunities and also support their members in different stages of the decision
making process, arguably making their members feel more confident in deciding where and
how much to invest. Therefore, we rely on a dummy variable (BAN_MEMBERSHIP).
discriminating BAN members from non-BAN members, expected to be positively related
to the share of personal wealth invested in a given deal by each single angel.
As for the hypothesis dealing with co-investing (H2), the survey directly provides the
required metric. The data presented in Table 4 show that co-investments are very frequent.
More specifically, the 70% of the investments are characterized by the presence of at least
two co-investors. Since co-investing favors risk-sharing, the expected relationship between
both the dependent variables and the explanatory variable CO-INVESTORS is negative.
To test our third research hypothesis (H3), we use a dummy variable (PASSIVE INVESTOR)
directly related to the answer given by the surveyed angels to a specific question: the va lue
1 signs the investment decision was exclusively driven by capital gain motivation. This
variable should be negatively related to both the dependent variables.
The survey also offers interesting evidences regarding the role played by BAs in the
monitoring of the participated firms, allowing us to test the last research hypothesis (H4).
We built the ordinal variable MONITORING, which graduates the frequency of the visits that
a BA made in a participated venture, from 1 to 5, where 1 means a very limited
involvement (no or very few visits) and 5 means a very high involvement (a constant
presence in the firm). Though the survey collects this information ex-post, asking the
effective involvement in the participated firms by BAs, we believe that they a lready know
the future degree of involvement in a venture also at the time when the investment
decision is taken. Moreover, it is likely that it influences the choice concerning the amount
to invest. In particular, a higher degree of monitoring is expected to decrease the
investment risk perceived by a BA. Therefore, we assume the variable MONITORING as a
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proxy of the agreed degree of monitoring at the time when the investment decision was
taken. The expected sign for the variable MONITORING is positive, when related to both the
dependent variables.
As far as the angel specific control variables are concerned, for AGE, EDUCATION and
BACKGROUND (which could be a managerial one or an entrepreneurial one) we again rely
on specific dummies making reference to the answer given by the surveyed angels in the
questionnaire. Furthermore, we measure the EXPERIENCE considering the number of
investments made in the past, consistently, consistently with Hsu (2004) and Capizzi
(2011). We believe that more experienced BAs find easier to identify worthy investment
opportunities. Moreover, a greater experience could also increase self-confidence. For both
of the reasons, more experienced BAs are likely to invest greater amounts than less
experienced BAs. Thus, we expect a positive relationship between the dependent variables
PARTICIPATION% and WEALTH% and the variable EXPERIENCE.
Moving to the firm specific control variables, as far as the size of the investee venture is
concerned, we expect the share of participation to decrease as the size of a firm increases.
In contrast, since it is likely that larger-sized firms are perceived as less risky investments, it
is possible that the relationship between the dependent variable WEALTH% and the
NET_ASSET_VALUE variable is positive. As one can see from Table 3, the net-asset-value
of the ventures varies from 1.000 euro to more than 160 millions of euro. However, 75%
of the ventures show a net-asset-value lower than 1,400,000 euro. It is possible to
argument the such a high variability depends on the great heterogeneity of ventures
belonging to the sample in terms of industry and stage of the life cycle.
Approximately the 36% of the investments mapped in dataset are addressed to seed
projects, while in the other cases the target firms are start-up, early growth enterprises,
turn-around and buy-out projects. Since, investing in a seed enterprise is likely to be riskier
than investing in a well-established entrepreneurial project, the expected relationship
between the dummy SEED and the dependent variables PARTICIPATION% and WEALTH% is
negative.
Dealing with the geographical location of the investee companies, foreign ventures
represent only the 12% of the projects financed. We presume, consistently with previous
mentioned literature, that the hogher the geographical, cultural and juridical distance
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between the residence of the BA and the location of the venture, the lower the amount
invested in a single venture. Thus, the expected sign for the dummy FOREIGN is negative.
Looking at the financial wealth of Bas, we observe a wide range of values (from 250,000 to
7,500,000 euro), however, for the three quarters of them it is lower than 1,450,000 euro.
The wealthier BAs could both choose to invest higher amounts in single ventures, in order
to detain higher participations, and to select a higher number of investment opportunities ,
in order to achieve a better portfolio diversification. Thus, the sign of the relationship
between the dependent variables PARTICIPATION% and WEALTH% and the WEALTH
variable is not plain.
Finally, as proxies of the control variables, consistently with extant literature, we selected
the average 5-years Eurirs (5Y_EURIRS), the industry market capitalization as measured by
the MSCI (MSCI_YEAR_INV), the industry price-to-book value ratio (INDUSTRYPBV) and
the industry net capital assets-to-sales ratio (CAPITAL INTENSITY).
4. Methodology and Results
Determinants of business angels’ capital allocation choices
The first analysis investigates the determinants of the share of personal wealth invested in a
venture by a BA. To this end, we run a OLS regression between the dependent variable
WEALTH% and a set of explanatory variables related to the venture, the investor and the
investment decision. We also add to some model specification time and industry fixed-
effects. We manage heteroskedasticity by computing the natural logarithm of the
dependent variable and of the explanatory variables NET_ASSET_VALUE, WEALTH and
EXPERIENCE and by using White heteroskedasticity-consistent standard errors and
covariances.
Equation (1) comprises a fully balanced model with time fixed-effect.
WEALTH% = f (BAN_MEMBERSHIP, CO-INVESTORS, NET_ASSET_VALUE, SEED,
MSCI_YEAR_INV, EURIRS5Y, YEARt;) (1)
Equation (2) adds to the previous model fixed industry-effects.
WEALTH% = f (BAN_MEMBERSHIP, CO-INVESTORS, NET_ASSET_VALUE, SEED,
MSCI_YEAR_INV, EURIRS5Y, YEARt; INDUSTRYi) (2)
17
Equation (3) comprises all the explanatory variables described in Table 2 and controls:
WEALTH% = f (BAN_MEMBERSHIP, CO-INVESTORS, PASSIVE_INVESTOR, SOFT-MONITORING,
AGE, EDUCATION, WEALTH, ENTREPRENEUR, MANAGER, EXPERIENCE, NET_ASSET_VALUE,
SEED, FOREIGN, MSCI_YEAR_INV, EURIRS5Y, YEARt; INDUSTRYi) (3)
Finally, since the two-group-mean comparison test on the dependent variable WEALTH%,
presented in Table 3, shows that being member of an angel community affect the share of
wealth invested in a venture, we also separately run equation (3) for the sub-samples of
BAN members and non-BAN members.
Table 5 presents the results of the analysis. The model is significant in all the specifications
and shows a R-squared of 25% for the base model in column (3) and above the 30% for
the sub-samples of BAN members and non-BAN members, in column (4-5).
INSERT TABLE 5 HERE
First, it emerges that being member of an angel community increases the share on wealth
invested by approximately 14%, consistently with our H1 research hypothesis.
Second, as far as H2 is concerned, other conditions being equal, one-unit increase in the
number of co-investors reduces by 2% the amount of money invested in a venture.
However, by comparing BAN members with non-BAN members, we observe some
interesting differences highlighting the differential role played by co-investing in their
investment decisions, as it comes out that the decision about the amount to invest is
affected by the presence of co-investors only for the sub-sample of the BAN members,
implying that there could be a positive effect played by trust inside a given angel
community, whereas non BAN member could be prevented from co-investing maybe
because fearing potential issues of free riding and opportunistic behavior. Thus, H2 is
confirmed only for angel member of an angel community.
Third, as expected, for business angels acting as passive investors we find a negative
relationship with the capital invested, confirming H3 hypothesis; such a relationship,
however, is statistically significant only for non-BAN member angels. Maybe, in the case of
BAN members, the possibility to benefit from co-investing, other angels’ experience,
18
mentoring and information provided by the BAN gatekeepers give incentives which
ultimately positively affect asset allocation investment decisions of those angels driven
mainly by capital gain motivations.
Fourth, the SOFT MONITORING variable shows a positive significant sign for the group of
BAs not affiliated to an angel community and a negative sign for the BAN members ,
though not significant. This evidence is consistent with our H4 hypothesis, and seems a
further proof of the quality of the contribution in terms of deal flow and screening
provided by the BA networks to their members. In fact, it is likely that BAN members
impose higher level of monitoring only to ventures that are more opaque. If this is true, the
negative sign is related to the perceived investment risk (which require more monitoring).
In contrast, since non-BAN members do not benefit from the soft-information given by
angel communities, they probably compensate this greater information asymmetry by
imposing more extensively high level of monitoring. In this case, higher monitoring is not
necessarily associated to higher risk.
In order to be sure that the above results do not depend on differences related to personal
characteristics between the two groups of investors, we also run, as a robustness check, a
logistic equation with the dummy BAN_MEMBERSHIP as dependent variable and a set of
personal characteristics as independent variables. As displayed in Table 6, only the fact of
being a manager shows a significant relationship with the affiliation to an angel community.
BAN_MEMBERSHIP = f (AGE, EDUCATION, GENDER, WEALTH, EXPERIENCE,
ENTREPRENEUR, MANAGER) (4)
BAN_MEMBERSHIP = f (AGE, EDUCATION, GENDER, WEALTH, EXPERIENCE,
ENTREPRENEUR, MANAGER, CENTER OF ITALY, SOUTH OF ITALY (5)
INSERT TABLE 6 HERE
Furthermore, results show that BAs not member of an angel community, and thus not
benefitting from the prescreening analyses that BANs make for their members as well as
from the knowledge sharing effect taking place inside the BAN themselves, consider a
wider set of information. In fact, the variables related to the net-asset value of the firm and
the level of the medium-term interest rates are significant for this group of investors, while
19
they are not for the other sub-sample. In particular, the amount invested by non-BAN
members decrease as the net-asset-value of the venture increases. This evidence, however,
does not subtend that they invest in ventures characterized by higher dimensions. In fact,
the regression (equations 6 and 7) presented in Table 7, that we run as robustness check,
shows that the firms proposed by BANs have, on average, a larger size that those identified
by single investors.
PROPOSAL = f (NET_ASSET_VALUE, SEED, INDUSTRY PBV, NET CAPEX/SALES,
MSCI_YEAR_INV) (6)
PROPOSAL = f (NET_ASSET_VALUE, SEED, FOREIGN, INDUSTRY PBV, NET
CAPEX/SALES, MSCI_YEAR_INV) (7)
INSERT TABLE 7 HERE
Finally, dealing with the control variables, it emerges that the amount of wealth invested in
a venture depends on the personal characteristics of BAs, while it is not influenced by the
firms’ characteristics. In particular, the dependent variable increases as the experience of
the BA grows and diminishes as her/his age and her/his financial wealth decrease. In
addition, managers and entrepreneurs invest 10% more than other categories of workers.
Once again, we observe different investment behaviors between BAN members and non-
BAN members as far as the education of the investor is considered. Even without
developing a specific hypothesis about that, it is reasonable to deduct that the information
and knowledge sharing effect taking place inside a community allow to compensate for the
limited education of a given angel investors, who, otherwise, would be prevented from
investing more capital if asked to act as a solo angel.
Determinants of business angels’ investments in the investee firms
The second part of the empirical analysis explores the factors affecting the amount
invested in a venture by a BAs. For this purpose, we estimate the relationship between the
dependent variable PARTICIPATION% and the same set of explanatory variables previously
used, by running a new standard OLS regression.
20
Similarly to the approach used for the dependent variable WEALTH%, we manage
heteroskedasticity by computing the natural logarithm of the dependent variable and of the
explanatory variables PARTICIPATION%, NET_ASSET_VALUE, WEALTH and EXPERIENCE.
We manage heteroskedasticity by considering the natural logarithm of the variables
PARTICIPATION%, NET_ASSET_VALUE, WEALTH and EXPERIENCE, and we also estimate
White heteroskedasticity-consistent standard errors and covariances.
Equation (8) represents a fully balanced model with fixed time-effects.
PARTICIPATION% = f (NET_ASSET_VALUE, SEED, BAN_MEMBERSHIP, CO-INVESTORS,
MSCI_YEAR_INV, INDUSTRY PBV, NET CAPEX/SALES, TIMEt) (8)
Equation (9) comprises the whole set of explanatory variables described in Table 4.
PARTICIPATION% = f (NET_ASSET_VALUE, SEED, FOREIGN, BAN_MEMBERSHIP, AGE,
EDUCATION, WEALTH, ENTREPRENEUR, MANAGER, EXPERIENCE, PASSIVE_INVESTOR, CO-
INVESTORS, MONITORING, MSCI_YEAR_INV, INDUSTRY PBV, NET CAPEX/SALES, TIMEt) (9)
Equation (10) adds to the previous model Industry fixed-effects
PARTICIPATION% = f (NET_ASSET_VALUE, SEED, FOREIGN, BAN_MEMBERSHIP, AGE,
EDUCATION, WEALTH, ENTREPRENEUR, MANAGER, EXPERIENCE, PASSIVE_INVESTOR, CO-
INVESTORS, MONITORING, MSCI_YEAR_INV, INDUSTRY PBV, NET CAPEX/SALES, TIMEt,
INDUSTRYt) (10)
Table 8 presents the results of the model. The analysis shows a high explanatory power: the
adjusted R2 is greater than 50% in the model specification (2) and (3) and the majority of
the independent variables are significant.
INSERT TABLE 8 HERE
In general, the outcomes suggest that BAs are very well conscious about the equity risks of
their investments and, because of that, they rationally manage their risk exposures, a lso by
taking benefit of the risk-reduction effect coming from the membership to angel
community, the co-investments and the monitoring effort.
21
More in detail, BAN_MEMBERSHIP (H1) does negatively affects business angels’ investment
decisions, because, as we argue, a given community provide the possibility to better
diversify the risk by providing more investment opportunities, each one after a deal flow
and screening process more efficient when compared to that undertaken by solo investors.
Furthermore, we find positive evidence for H2 hypothesis, in that as the number of co-
investors increases the individual participation shows a 7% decrease: co-investing appears
an effective way for pursuing risk-minimizing investment decisions, also benefitting from
portfolio diversification upsides.
On the other side, when the main motivation is just and explicitly the search for capital
gain, (when the dummy PASSIVE_INVESTOR is equal to 1) the dependent variable shows a
18% reduction, consistently with our H3 hypothesis.
Dealing with H4 hypothesis, data show that the share of participation in a given investee
company increases by more than 20% as the degree of soft monitoring increases, once
again confirming the relevance of monitoring mechanisms, even if non-contractual based,
as usually agreed upon between entrepreneurs and business angels (Ibrahim, 2008).
As far as the angel specific control variables, the model results display a progressive
reduction of the amount invested in a venture as the age of the investor increases. It also
emerges that, less educated BAs show a greater risk exposure. The degree of experience in
BAs’ investments positively affects the amount invested. The same evidence emerges if the
BA is a manager or an entrepreneur. In contrast, no significant evidences emerge for the
level of financial wealth.
Dealing with the firm specific control variables, the analysis shows that the share of
participation in a venture decreases as the net-asset value of the firm increases. Moreover,
the participation diminishes by more than 30% if the target company is located abroad.
Finally, regarding the market conditions and the sector specific indexes, the outcomes
highlight that BAs invest growing amounts in presence of positive market trends and
prefer sectors characterized by a lower price-to-book value and a higher capital intensity.
5. Conclusive remarks and suggestions for future research
22
In this paper we presented evidence of the existence of a number of factors producing a
significant impact on the business angels’ investment. More in detail, by making reference
to an unique dataset which provides data on the Italian informal venture capital market
over a seven-year time period, we contributed to extant finance literature by exploring for
the first time the differential investment behavior of business angels when they join semi-
formal organizations such as Angel Groups or Business Angel Networks, compared to the
investment decisions of business angels investing alone outside a given angel community.
The main results of the empirical analysis allow us to understand the relevant role played
by angel communities who indeed affect the business angels’ investment decisions. This
means that angel investors do recognize and appreciate the contribution provided by BAN
in terms of deal flows, screening, mentoring, information and knowledge sharing.
Furthermore, only for BAN members the possibility to co-invest appear to be a factor
significantly affecting their investment decisions, giving them the possibility, on the one
hand, to benefit from risk-reduction effects and, on the other hand, to still continue playing
an active role in the investee company. This means that there is a role for angel
communities in spreading information, reducing incentives to opportunistic behaviors and
enhancing trust among investors.
For non-BAN members, instead, one major significant driver of the investment decision is
the capital gain motivation or, better, the unwillingness to play an active role in the investee
company (passive investor behaviour), which is negatively related to the amount of capita l
invested, even due to the lower probability to effective give rise to club deals with other
solo investors.
Finally, the soft monitoring – not based on corporate governance and contractual
provisions typically adopted by venture capitalists – plays a different role in the business
angels’ capital allocation decisions depending on the membership to an angel community.
Non-BAN members must rely on monitoring in order to reduce information asymmetries
and incentives to opportunistic behaviours of entrepreneurs: the higher the possibility to
monitor the investment with non-contractual mechanisms, the higher the amount of
capital they are available to invest. BAN members, on the other side, can compensate
monitoring with other benefits provided by the angel community aimed at managing and
minimizing the investment risk perception, like the screening support provided by BAN to
23
their members, the knowledge sharing effect stemming inside the BAN, the probability to
gain more active involvement in the participated companies thanks also to co-investments.
Angel communities, thus, can decrease the need for individual monitoring effort, increasing
at the same time the members’ confidence in the investments made.
Our findings have relevant implications for academicians, policy makers and entrepreneurs
seeking finance, given the insight on business angels’ investment decisions. Stimulating and
strengthening the networks could be a major goal in the future for the growth of
entrepreneurship and the recovery of employment level of a given economic and social
system.
However, in order to both continue shedding light over the differential investment
behaviors of angels joining a given business community when compared to solo angels,
and further investigate the beneficial effects coming from the BANs, the academic
community needs to get a deeper access to the black box of the angel communities
themselves, sharing at a national and international level common and homogeneous data
about their operations and decision making processes.
24
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29
Table 1
Causal relationships to be tested: predicted signs
Explanatory Variables
Expected sign
Dependent variable =
PARTICIPATION%
Dependent variable = WEALTH% Whole sample BAN members Non BAN
members BAN_MEMBERSHIP + -
CO-INVESTORS - - - - PASSIVE_INVESTOR - - - - SOFT-MONITORING + + - + Firm Specific Controls
SIZE - + LIFE CYCLE - -
ARENA - -
Angel Specific Controls AGE - - PERSONAL WEALTH +/- +/- EDUCATION +/- +/- ENTREPRENEURIAL
BACKGROUND +/- +/-
MANAGERIAL
BACKGROUND +/- +/-
EXPERIENCE + +
Market and Industry Specific Controls MARKET INDUSTRY
CAPITALIZATION +/- +/-
MARKET INTEREST
RATES +/-
INDUSTRY
PERFORMANCE -
INDUSTRY CAPITAL
INTENSITY +
30
Table 2
Sample distribution
PANEL A – YEAR DISTRIBUTION
Year Investments
2008 95 2009 142 2010 137 2011 159 2012 162 2013 58
2014 57 Total 810
PANEL B – INDUSTRY DISTRIBUTION
Industry
Investments
BIOTECH
137
CLEANTECH
105
COMMERCE AND DISTRIBUTION
81
ELECTRONICS
75
FINANCIAL SERVICES
27
FOOD & BEVERAGE
23
ICT (SW AND HW, APP WEB AND MOBILE)
167
MECHANICAL ENGINEERING
60
MEDIA & ENTERTAINMENT
80
TELECOMMUNICATIONS & SIMILAR SERVICES
23
TEXTILE & APPAREL
25
Total
803
31
Table 3
Dependent variables: descriptive statistics.
Total sample BAN members Non-BAN members
Dependent variable = PARTICIPATION% Mean 14.74 14.87 14.59 Maximum 100 100 100 Minimum 1 1 1 Standard deviation 19.54 18.30 20.93 No. observation 808 436 372
Two-group mean-comparison test It-statI 0.197 Dependent variable = WEALTH% Mean 15.48 17.09 13.67 Maximum 60 60 60 Minimum 5 5 5 Standard deviation 11.80 13.13 9.80
No. observation 669 354 315 Two-group mean-comparison test It-statI 3.480***
Table 4
Summary statistics
Description Obs. Mean Std.Dev
.
Min Max Dummy=1
percentage
BAN_MEMBERSHIP Dummy =1 if the BA is a BAN member 810 54.1
CO-INVESTORS Number of co-investors 809 4.30 4.99 0 15
PASSIVE INVESTOR Dummy =1 if the investment is exclusively driven by capital gain
motivations
668 22.0
SOFT-MONITORING Ordinal variable ranging from 1 to 5, where 1 means monitoring
very low or absent and 5 means monitoring very high, with a
constant presence in the firm
668 2.75 1.25 1 5
Angel specific controls
AGE Age of the BA 668 48.32 9.40 28 71
LOW-EDUCATION Dummy = 1 if the BA holds a high school diploma or a lower
educational qualification
668 6.7
WEALTH (in euro) BAs’ financial wealth in the year of the investment 669 1.480.682 1515.29 250.000 7.500.000
ENTREPRENEUR Dummy =1 in case of prevalent working occupation as
entrepreneur
668 37.7
MANAGER Dummy =1 in case of prevalent working occupation as manager 668 16.8
EXPERIENCE Number of BA’ investments in lifetime 668 6.36 4.01 0 26
Firm specific controls
NET_ASSET_VALUE
(in euro)
Enterprises’ net asset value in the year of the BA’s investment 806 1389.67 2281.66 1,430 8,928,570
SEED Dummy = 1 if the BA has invested in a seed enterprise 810 35.7
FOREIGN Dummy = 1 if the BA has invested in a foreign enterprise 711 12.1
Market and industry controls MSCI_YEAR_INV Industry market capitalization as measured by the MSCI, in the
investment year
810 58.93 10.01 45.78 81.81
5Y_EURIRS Average 5y Eurirs, in the investment year 810 2.251 1.01 0.73 4.31
INDUSTRY PBV Industry price-to-book value, in the investment year 810 3.05 1.36 0.71 8.62
NET CAPEX/SALES Industry net capital assets to sales, in the investment year 810 0.80 3.18 -4.47 22.96
Table 5
Regression Results (dependent variable: WEALTH%)
The regressions are all conducted with the ordinary least square method (OLS), using White heteroskedasticity -consistent
standard errors and covariances. The dependent variable, WEALTH%, is the share of Angels’ Wealth invested in each BA
enterprise. Equation (1) comprises a fully balanced model with time f ixed-ef f ect. Equation (2) adds to the previous model f exed
industry-ef f ects. Equation (3) comprises all the explanatory variables described in Table 2. We also run equation (3) for the two
sub-samples originated by grouping BAs on the basis of the BAN_MEMBERSHIP dummy. The t-stat are reported in brackets
under each coef f icient.
Indipendent Variables
Whole Sample BAN Member
Non-BAN Member
(1) (2) (3) (4) (5)
BAN_MEMBERSHIP 0.109 0.106 0.139
(2.32)** (2.23)** (2.93)*** CO-INVESTORS -0.026 -0.026 -0.016 -0.037 -0.005 (6.38)*** (6.25)*** (2.98)*** (4.83)*** (0.85) PASSIVE INVESTOR -0.042 0.024 -0.176 (0.72) (0.27) (2.29)** SOFT-MONITORING 0.057 -0.050 0.154
(2.01)** (1.52) (4.58)*** AGE -0.015 -0.010 -0.017
(5.58)*** (2.70)*** (3.81)***
LOW-EDUCATION 0.038 0.208 -0.190 (0.49) (1.78)* (1.83)*
WEALTH -0.049 -0.039 -0.094 (1.64) (0.93) (2.73)***
ENTREPRENEUR 0.081 0.026 0.152 (1.56) (0.36) (2.31)** MANAGER 0.064 0.289 -0.103 (1.03) (2.64)*** (1.40) EXPERIENCE 0.198 0.280 0.160 (4.79)*** (4.36)*** (3.32)*** NET_ASSET_VALUE -0.009 -0.003 0.009 -0.011 0.039 (0.47) (0.15) (0.45) (0.39) (1.55) SEED 0.006 -0.007 -0.050 -0.022 -0.143 (0.11) (0.12) (0.91) (0.31) (1.78)* FOREIGN -0.006 -0.007 0.030 (0.09) (0.06) (0.34) MSCI_YEAR_INV -0.013 -0.013 -0.018 -0.029 -0.022 (3.10)*** (3.23)*** (4.33)*** (4.79)*** (3.57)*** 5Y_EURIRS 0.116 0.118 0.096 0.146 0.162 (2.60)*** (2.66)*** (1.68)* (1.87)* (2.34)** _CONS 3.138 3.026 3.815 4.496 4.045 (14.68)*** (14.16)*** (12.15)*** (10.81)*** (7.75)*** YEAR YES YES YES YES YES INDUSTRY NO YES YES YES YES
R2 0.08 0.11 0.25 0.32 0.36 Observation 668 668 569 292 277 * = significant at 10% level; ** = significant at 5% level; ***= significant at 1% level
34
Table 6
BA’s investments: probability of being proposed by a BA network
Logistic regression with the dummy PROPOSAL as dependent variable, which corresponds to 1 if the project f inanced by an Angel
has been proposed by a BA network or an Investors’ club. The table reports results of equation (4). The z-scores are reported in
brackets under each odds-ratio.
Indipendent Variables
Odds-ratio (z-score)
(1) (2) NET_ASSET_VALUE 1.14
(2.40) ** 1.16
(2.43) ** SEED -1.95
(-0.30) 1.07 (0.36)
FOREIGN 0.004 (0.02)
INDUSTRY PBV 1.07 (1.09)
1.01 (0.23)
NET CAPEX/SALES -0.97 (-1.14)
-0.96 (-1.34)
Prob chi2 0.0639 0.7529
Pseudo R2 0.0106 0.0037
Observations 668 569 * = significant at 10% level; ** = significant at 5% level; ***= significant at 1% level
35
Table 7
Probability of being a BAN member
Logistic regression with the dummy BAN_MEMBERSHIP as dependent variable, which assumes the value 1 in case of BAN
membership. The table reports results of equation (5). The z-scores are reported in brackets under each odds-ratio.
Indipendent Variables
Odds-ratio (z-score)
(1) (2) AGE 1.005
(0.60) -0.998 (-0.22)
EDUCATION -0.959 (-0.13)
-0.846 (-0.52)
GENDER -0.867 (-0.45)
-0.929 (-0.23)
WEALTH -0.962 (-0.45)
-0.977 (-0.27)
EXPERIENCE -0.979 (-1.03)
-0.982 (-0.89)
ENTREPRENEUR 1.112 (0.50)
1.202 (0.85)
MANAGER -0.581 (-2.05) **
-0.593 (-1.94) *
CENTER OF ITALY - 1.044 (0.24)
SOUTH OF ITALY - -0.987 (-0.04)
Prob chi2 0.2664 0.3342
Pseudo R2 0.0108 0.0125 Observations 668 655 * = significant at 10% level; ** = significant at 5% level; ***= significant at 1% level
36
Table 8
Regression Results (dependent variable: PARTICIPATION%)
The table reports results of equation 4. The regressions are all conducted with the ordinary least square method (OLS), using
White heteroskedasticity-consistent standard errors and covariances. The dependent variable, PARTICIPATION%, is the amoun t
invested in a venture by an Angel as a share of its net -asset-value. The explanatory variables are described in Table 2. In column
(1) we show a fully balanced model with time f ixed-ef f ect. In column (2) we add the full set of explanatory variables. In column
(3) we add the interaction term between BAN_MEMBERSHIP and SOFT-MONITORING. In column (4) we run equation
(8) with also industry f ixed-ef f ects, which represents the base model The t-stat are reported in brackets under each coef f icient.
Independent Variables (1) (2) (3) (4)
BAN_MEMBERSHIP -0.114 -0.157 0.317 -0.147
(1.83)* (2.20)** (1.74)* (2.05)**
CO-INVESTORS -0.093 -0.069 -0.069 -0.067
(16.47)*** (9.33)*** (9.62)*** (8.96)***
PASSIVE INVESTOR -0.176 -0.173 -0.176
(2.36)** (2.33)** (2.33)**
SOFT-MONITORING 0.213 0.295 0.215
(5.87)*** (6.17)*** (5.67)***
AGE -0.009 -0.009 -0.009
(2.37)** (2.23)** (2.12)**
LOW-EDUCATION 0.310 0.253 0.340
(2.38)** (1.89)* (2.56)**
WEALTH 0.044 0.038 0.046
(1.12) (0.99) (1.20)
ENTREPRENEUR 0.345 0.352 0.351
(4.64)*** (4.76)*** (4.63)***
MANAGER 0.333 0.351 0.332
(3.47)*** (3.60)*** (3.38)***
EXPERIENCE 0.100 0.115 0.092
(1.72)* (2.01)** (1.54)
NET_ASSET_VALUE -0.244 -0.251 -0.252 -0.249
(10.08)*** (10.47)*** (10.72)*** (10.23)***
SEED -0.048 -0.117 -0.124 -0.128
(0.71) (1.49) (1.59) (1.60)
FOREIGN -0.315 -0.302 -0.325
(3.06)*** (2.89)*** (3.06)***
MSCI_YEAR_INV 0.012 0.011 0.010 0.011
(3.96)*** (2.08)** (2.02)** (2.08)**
INDUSTRY PBV -0.040 -0.065 -0.054 -0.049
(1.52) (2.32)** (1.90)* (0.83)
NET CAPEX/SALES 0.029 0.035 0.036 0.024
(2.41)** (2.75)*** (2.87)*** (1.35)
BANMEMB*MONIT -0.169
(2.70)***
_CONS 3.476 2.870 2.651 2.709
(12.29)*** (6.05)*** (5.60)*** (4.95)***
YEAR
INDUSTRY
YES
NO
YES
NO
YES
NO
YES
YES
R2 0.47 0.55 0.56 0.56
N 798 569 569 569