The investment committee’s decision rule
Evidence from the private equity market
Master Thesis Finance
Femke Helgers
323334
[email protected]
+31(0)6 50 550 324
July, 2011
Supervisor: Associate Professor M. Da Rin
Second reader: Assistant Professor R.G.P. Frehen
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Abstract
The optimal decision rule in committees and the effect this rule has on the activities
performed and decisions made has been widely researched and discussed in different
contexts. Due to the lack of data, however, it has not yet been investigated for the
private equity market. This thesis contributes to the private equity literature in two
important dimensions. First, it shows that the heterogeneity among Limited Partners
seems to have a significant effect on their effort levels and activism. Second, it supports
the interesting hypothesis that the decision rule of the investment committee is an
important determinant of the due diligence hours and monitoring activities performed
and the rejection of co-investments.
I would like to take this opportunity to thank Associate Professor Marco Da Rin and
Associate Professor Ludovic Phalippou for giving me the chance to become involved in
their interesting research project. Contributing to such a large research study in terms of
number of respondents, geographical coverage and scope was an honor. I learnt how
difficult and important it is to gather data and that it is sometimes necessary to
(literally) cross the border to get the right data. It was a valuable experience. I would
also like to thank Yves Kessels for his helpful comments and all the other research
assistants for their help and support in gathering the data.
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Table of contents
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1. Introduction
1.1 Introduction
1.2 Relevance of the Topic
1.3 Research Questions
1.4 Methodology
1.5 Outline
2. Literature Review
2.1 The Private Equity Asset Class
2.1.1 Private Equity
2.1.2 Limited Partnerships
2.2 Investor’s effort levels
2.2.1 Due Diligence
2.2.2 Monitoring
2.3 Investor’s activism
2.3.1. Refusal to re-invest
2.3.2. Refusal to co-invest
2.4 The Investment Committee
2.4.1 Existence of the Investment Committee
2.4.2 The Investment Committee Decision Rule
3. Data
3.1 Sources
3.2 Content of the Survey
3.3. Hypotheses
3.4 Variable Definitions
3.3.1 Dependent Variables
3.3.2 Independent Variables
3.3.3 Control Variables
3.5 Descriptive Statistics
3.5.1 The Limited Partners
3.5.2 Limited Partner’s effort levels
3.5.3Limited Partner’s activism
3.5.4 The Investment Committee
3.6 Methodology
3.6.1. OLS and Logit Regressions
3.6.2 Two-step Heckman Procedure
4. Regression Analysis Results
4.1 Hypothesis 1
4.1.1. Hypothesis 1.1
4.1.2 Hypothesis 1.2
4.1.3. Hypothesis 1.3
4.2 Hypothesis 2
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4.2.1. Hypothesis 2.1
4.2.2. Hypothesis 2.2
4.3 Two-step Heckman Procedure
5. Conclusion and Recommendations
5.1 Conclusion
5.2 Limitation and Future Research
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References
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Appendix A: The Survey
Appendix B: Correlation Matrices
Appendix C: Sample Representativeness
Appendix D: Two-step Heckman Procedure
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List of Tables and Figures
Tables
Table 1: Correlation matrix control variables
Table 2: Correlation matrix investment committee specific variables
Table 3: LP sample coverage and mean (median) AUM per country
Table 4: LP mean (median) PE allocation and experience per country
Table 5: LP sample coverage and mean (median) AUM per category
Table 6: LP mean (median) PE allocation and experience per category
Table 7: The due diligence process
Table 8: The monitoring process
Table 9: The investment committee
Table 10: Differences in LP’s investment committees
Table 11: Eta square values for t-tests
Table 12: OLS regression results for the due diligence hours index
Table 13: OLS regression results for the due diligence activity index
Table 14: OLS regression results for the monitoring index
Table 15: Logit regression results for refusal to re-invest
Table 16: Logit regression results for refusal to co-invest
Table 17: Probit regression results for the decision rule
Table 18: OLS regression results for investor’s effort levels, including Lambda
Table 19: Logit regression results for investor’s activism, including Lambda
Table 20: Hypotheses results
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Figures
Figure 1: Investment committee decision rule
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1. Introduction
1.1 Introduction
Start-up firms, private middle market firms, firms facing financial distress or public firms
that want to go private and cannot raise money from the debt or public equity market
often turn to the private equity (PE) market (Fenn, Liang & Prowse, 1997). In the PE
market, at least until the late 1970s, investors mainly invested directly in the PE
securities issuing firm. However, due to information asymmetries and incentive
problems that arise between the different participants, funds are now mainly organized
as Limited Partnerships. Fund investors (also known as Limited Partners (LPs)) provide
the mass of capital and include, among others, wealthy individuals and large
institutional investors. Private equity funds are subsequently managed by private equity
firms, also known as General Partners (GPs) (Metrick & Yasuda, 2010). Limited
Partnerships offer several organizational and contractual mechanisms to avoid the
information asymmetries and incentive problems including pre-investment due
diligence and post-investment monitoring (Fenn et al., 1997). Due to this Limited
Partnership, LPs have to follow a hands-off approach during the lifetime of the fund
(generally ten years). In the first three to five years capital is invested in issuing firms,
after which it is managed by the GPs and eventually liquidated (Fenn et al., 1997). The
two most important sectors in the PE market include venture capital funds and buyout
funds (Metrick & Yasuda, 2010).
1.2 Relevance of the topic
The registration of PE securities is not obligated by the Securities and Exchange
Commission (SEC), and therefore few data is available on the PE market (Fenn et al.,
1997). Due to this lack of data, analyzing developments in the market is very difficult
and thereby limited. There has been some research on the general characteristics of
the PE market (Fenn et al., 1997), on the returns on PE (Kaplan & Schoar (2005) and
Lerner, Schoar & Wongsunwai (2007)) and on the relationship and contracts between
LPs and GPs (Gompers & Lerner (1996) and Phalippou (2009)). To this date, however,
there has not been any extensive research on the relation between LP characteristics
and the efforts and activities of LPs. There is, for example, no extensive empirical
evidence on the characteristics of the investment committee of the LP, the body
responsible for investment decisions, and investor’s effort levels and activism,
particularly in an international context. The existing literature on decision making in
other sectors suggests that it might be interesting to investigate what explains the
existence of an investment committee and if decisions are taken by majority or by
consensus. According to Harvey and Lusch (1995) the due diligence process is influenced
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by time restrictions, cost constraints and situational factors, however, there may be
more factors influencing the investor’s efforts including the decision rule of the
investment committee. Moreover, it would be interesting to investigate the effect the
decision rule has on investor’s effort and activities with respect to monitoring, coinvestment opportunities and the rejection of re-investments.
1.3 Research Questions
The main research question in this thesis is:
What is the effect of the investment committee’s decision rule on investor’s effort levels
and activism?
Investor’s effort levels and activism can be divided into several activities and therefore
the following sub questions are defined:
1. What is the effect of the investment committee’s decision rule on the intensity of the
due diligence efforts?
2. What is the effect of the investment committee’s decision rule on the intensity of the
monitoring efforts?
3. What is the effect of the investment committee’s decision rule on the rejection of reinvestments?
4. What is the effect of the investment committee’s decision rule on the rejection of coinvestment opportunities?
In chapter 3.3 of this thesis, different hypotheses are formulated based on these
research questions.
1.4 Methodology
To answer the main research question, a literature review as well as an empirical
analysis is performed. The literature review in chapter two, three and four is the basis
for the empirical analysis. In the empirical sector, first correlations are performed to
control for linear dependence. Next, descriptive statistics are provided to get a feeling
for the data. After this, a number of Ordinary Least Squares (OLS) and Logit regressions
are performed. At last, as a check for selection bias, a two-step Heckman procedure is
performed.
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1.5 Outline
The remainder of this thesis is organized as follows. In the first part of the literature
review, the characteristics of the private equity market are described and it is explained
how and why it is organized mainly as a limited partnership. In the second part of the
literature review, both investor’s efforts and activism are discussed. In the third section,
existing literature on the existence of investment committees and the theory of
committee decision making are discussed. Chapter 3 then describes the data used, the
variables, the hypotheses, the descriptive statistics and the methodology. In chapter 4
regressions are performed, which are checked for selection bias in chapter 4.3. Chapter
5.1 provides some conclusions and recommendations after which, in chapter 5.2, the
limitations of this research are discussed and some points for future research are given.
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2. Literature Review
In this chapter the existing literature on the PE asset class, investor’s effort levels and
activism and the investment committee is reviewed. This literature review is necessary
to understand the importance of the empirical analysis.
2.1 The Private Equity Asset Class
Chapter 2.1 provides the necessary background information on the PE asset class. First,
some general information on PE is provided after which the limited partnership
structure is reviewed.
2.1.1. Private Equity
As said, start-up firms, private middle market firms, firms facing financial distress or
public firms that want to go private and cannot raise money from the debt or public
equity market often turn to the PE market (Fenn et al., 1997). PE funds are closed-end
funds from which the finishing date is known (Fraser-Sampson, 2010). While the
majority of PE transactions can be defined as ‘any equity investment in a company
which is not listed on a stock exchange (Fraser-Sampson, 2010)’, this definition, is for
example not suitable for a buyout. Since it is very difficult to find an all-encompassing
definition for all PE investments, rather than to search for this definition FraserSampson (2010) divides the PE class in several subdivisions. He does this by looking at
the different levels at which PE investments operate and by making a distinction
between fund and direct investing.
Those who invest in funds are called Limited Partners (LPs) or simply passive investors
since private equity funds are mainly organized as Limited Partnerships. Several types of
LPs exist where the Fund of Funds managers are the ones who invest everything in
private equity and are at the top of the scale (Fraser-Sampson, 2010). Other type of
investors include corporate pension funds, public pension funds, endowments,
foundations, bank holding companies, wealthy families and individuals, insurance
companies, investment banks, non financial corporations and other investors (Prowse,
1998). For most LPs, the Fund of Funds approach is a preferred way of entering the PE
asset class, so as to ensure their investment is diversified and they can sit back and
relax. However, some LPs may choose to directly invest their money into some buyout
funds but to also use aspects of the Fund of Funds approach. These investors are
between direct and fund investing.
At last, direct investing is the final layer where the GPs (including Limited Partnerships,
Small Business Investment Companies and Publicly Traded Investment Companies
(Prowse, 1998)) directly invest the money of the LPs into an investee company (Fraser-
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Sampson, 2010). Issuing companies include new ventures, middle-market private
companies and public companies (Prowse, 1998). In the case of direct investing, LPs
sometimes get the opportunity to co-invest (Fraser-Sampson, 2010).
Fundraising
For a GP to invest in an investee company it first has to raise the necessary money. It
begins this process by first identifying where it could get the highest returns, by deciding
which investments to make and then by calculating how much money is needed.
In the second step it will prepare a Private Placement Memorandum (or Offering
Memorandum) which it will send to the LPs. If LPs are interested in the investment, the
next step is often the due diligence process, which is discussed later.
The last step is the one where the lawyers get in and discuss the terms of the Limited
Partnership Agreement (LPA) (Fraser-Sampson, 2010). After the LPA is signed the
commitment is legally binding and the investments can be made.
The Working of Private Equity Funds
When a PE fund is started investors do not give the, for example, new venture all their
money right away. When the entrepreneur of the new venture wants to make an
investment and needs money, he issues a capital call. LPs then pay this amount of
capital, pro rata to their share of the fund’s committed capital, into the fund (drawdown
of capital) after which the GP makes the investment. When the new venture goes
public, on average 80% of the gains plus the original cost of the investment go back to
the LP, and the GP receives the remaining 20% of the gains.
Allocated, committed, drawdown and invested capital need not to be the same. Where
allocated capital is the amount the investors wants to allocate to the PE asset class,
committed capital is the capital he/she actually promised to pay to a PE fund. This
promise is legally binding when the LP signed a LPA. Drawdown capital is then the
amount of capital for which a capital call has been issued and is paid to the PE fund.
Besides the amount which is going to be invested in companies, it also includes fees and
expenses. Finally, invested capital is the amount which is actually invested in the new
venture. Especially in the first years of investing in PE the difference between allocated
and invested capital can be quite large and it will probably take several years for these
numbers to converge (Fraser-Sampson, 2010).
2.1.2. Limited Partnerships
The PE asset class grew explosively after 1980 due to organizational innovation. Before
1980 it consisted mostly of individuals and wealthy families, industrial corporations and
financial institutions who were directly investing in PE. After 1980, however, mostly
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intermediaries acting on behalf of institutional investors were dominating the PE
market. This change was due to the emergence of the Limited Partnership as the
dominant form of intermediary (Prowse, 1998). Around 80% of the PE investments are
now organized as Limited Partnerships. The Limited Partnership structure involves
intermediaries (GPs) making investments on behalf of LPs in an investee company.
During the lifetime of the investment, which could be more than 10 years, the
investment is illiquid and the investors have to follow a hands-off approach (Prowse,
1998). This hands-off approach ensures LPs keep their limited liability and are only
limited to the amount of their committed capital (Fraser-Sampson, 2010). However,
because of this structure the LPs cannot discipline the GPs using methods as dismissal
and active director board involvement (Gompers & Lerner, 1999). Although this seems
paradoxical, the structure benefits both parties (Prowse, 1998).
Limited Partnerships mainly exists due to the information asymmetries (adverse
selection problems) and incentive problems arising in the private equity asset class
(Prowse, 1998). Adverse selection problems refer to the famous lemon problem where
the seller (firm owner and/or manager) has more information than the buyer (outside
investor) (Akerlof, 1970). In selecting investments insiders often have more information
than outsiders and may benefit from exaggerating beneficial activities (Leland & Pyle,
1977), leading to adverse selection problems. Incentive problems arise when insiders
perform activities that benefit themselves at the expense of the outside investors
(Prowse, 1998). These problems are especially severe in situation were PE is issued
since, for example, young start-up firms do not yet have any track record or in the case
of buyouts were owners have little incentives to act in the interest of the outside
investors (Prowse, 1998).
The Limited Partnership structure employs several instruments to control the
information asymmetries and incentive problems, including pre-investment due
diligence and post-investment monitoring. To avoid over or under monitoring of an
investee company these mechanisms can best be employed by one intermediate party,
the GP (Prowse, 1998). Chapter 2.2 provides some necessary background information
on due diligence and monitoring.
2.2 Investor’s effort levels
According to Da Rin and Phalippou (2010), the PE investment process can be divided
into three dimensions, including investor’s effort level, investor activism and investor
favoritism by PE fund managers. Investor’s effort levels is measured by the intensity of
the due diligence and monitoring efforts, while investor’s activism relates to the refusal
to re-invest and the tendency to take into account other investors’ investment
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decisions. For this thesis, the refusal to re-invest but also to co-invest is classified as
investor’s activism. First, a description of the investor’s effort levels is given.
2.2.1. Due Diligence
To make sure the GP effectively invests the money of the LPs and the interests of both
parties are aligned, the LPs have to exert considerable efforts including pre-investment
due diligence. While the pre-investment due diligence process of a company investment
should include, according to Harvey & Lusch (1995), the investigation of seven fields
including a macro-environment, legal/environment, marketing, production,
management, information/system, and financial audit, the pre-investment due diligence
process of a LP often includes investigating the quality of the GP’s management team,
its track record, investment strategy and fund structure (Weidig & Mathonet, 2004).
According to Harvey & lusch (1995) there are several factors influencing the due
diligence level of investors including time restrictions, cost constraints and situational
factors.
The due diligence process starts with the Private Placement Memorandum (PPM). In the
PPM, which are sent to the LPs by the GPs, the firm and investment philosophy is stated,
as well as information on investment professionals and advisory committees, GP/LP
terms and agreements, the track record of previously raised funds and performance,
legal and tax matters, information on inherent related investment risks and accounting
and reporting standards (Sorrentino, 2003). If the PPM is positively evaluated and if the
investment opportunity fits the investor’s profile, meetings will be scheduled. PE
partnerships are typically categorized by geographic location, industry and type of
investment (Prowse, 1998). In the due diligence process, LPs want to perform as many
analyses and background checks on the GP as possible (Fraser-Sampson, 2010).
2.2.2. Monitoring
Post-investment monitoring includes, among others, making sure the firm is investing
according to its agreed model and is investing efficiently (Fraser-Sampson, 2010). LPs
could keep track of the composition of their PE portfolio in terms of industry, size and
country but also of the cash flows realized on each fund. If LPs invest in buyout portfolio
companies they could obtain information on the portfolio company fees (monitoring
and transaction) and on leverage. At last LPs could visit portfolio companies and attend
annual meetings to make sure the GPs are investing efficiently (Gompers & Lerner,
1996). One of the most important reasons to monitor the GP is to make an informed
decision about re-investing in the GP’s next fund (Lerner & Schoar, 2004).
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2.3 Investor’s activism
Investor’s activism includes the refusal to re-invest and the refusal to invest in coinvestment opportunities. First, the decision of a LP not to re-invest in the GP’s next
fund is explained.
2.3.1 Refusal to re-invest
According to Da Rin and Phalippou (2010), an investor with a passive approach may
always re-invest, while an active investor evaluates the performance of the fund they
invested in and may decide to refuse to re-invest. In the sample of Lerner & Schoar
(2004), 55% of the investors re-invested in the GP’s next fund. This means that 45% of
the investors had a reason to refuse to re-invest.
Investors choose exit over voice and thus refuse to re-invest when fund performance is
poor. However, it can also be that the investor faced a liquidity shock (Lerner & Schoar,
2004). While the incumbent investor has soft information about the company, the
outside investors do not know if the incumbent investor is leaving because of poor
performance or because they faced a liquidity shock. This lemon problem is the most
severe when GPs want to raise a new fund and outside investors charge a lemons
premium. When incumbent investors refuse to re-invest, the GP not only has to pay a
premium to raise new funds, their fundraising ability is also lower. According to Lerner
and Schoar (2004) transfer constraints de facto allow the GP to minimize this lemon
problem with respect to outside investors.
Investors may not only refuse to re-invest but can also threaten not to re-invest for two
reasons. The first is that investors may be concerned with conflicts of interest which are
very common when a minority shareholder controls the company (which is most often
the case with buyout funds). Another reason to threaten not to re-invest is to earn a
superior return on their investment. Funds sometimes give in to these threats, because
they are afraid that when the investor leaves this will damage their reputation
(Phallipou, 2011). The re-investment decision of some type of investors, such as
endowments, predicts the future performance of funds. When endowments decide to
re-invest this fund performs well, while if they decide not to re-invest future
performance is poor (Phalippou, 2011).
2.3.2 Refusal to co-invest
Investors cannot only refuse to invest in the GP’s next fund but can also refuse to invest
in a co-investment opportunity. The offering of co-investment opportunities by GPs is
one of the dimensions in the PE investment process that captures investor favoritism by
GPs (Da Rin & Phalippou, 2010). Some investors are favored because they indicate
investment opportunities, provide services, have access to important parties or have a
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good reputation. Reputation can be important when the list of investors in the previous
fund-raising rounds is revealed, since this will attract new investors. Offering coinvestments is the main way to express this favoritism since it is not accepted to
propose very different contract to different investors (Phalippou, 2011). Large coinvestors include corporate pension funds and endowments (Fenn et al., 1997).
Co-investments are attractive in the way that the LP does not pay any fees besides
portfolio company fees (Phalippou, 2011). Large investors are sometimes favored by
GPs because it is less costly to have a few large investors. It is therefore that the total
investments of large investors exist for 25%-33% out of co-investments. It has, however,
not been empirically proven that co-investment offer better returns than average. It
could be that they are simply larger or riskier investments leading to a higher return
(Phalippou, 2011).
2.4 The Investment Committee
The investment committee of the LP is the committee responsible for reviewing and
approving the long-term investment strategy of the firm and for approving individual
investment projects (Klein, 1998). Members of the investment committee do not need
to be investment professionals per se, which is often the case for endowment
investment committees of large universities, but could also be members of the
sponsoring organization (Olson, 2005). According to Olson (2005) investment committee
members should have, among others, a high moral character, knowledge of the relation
between the fund and the financial situation of the plan sponsor and a willingness to
take some risk in order to earn a return higher than the risk-free rate over the long
horizon. But why is it that these investment decisions are taken by a committee and not
by an individual?
2.4.1. Existence of the Investment Committee
In recent years LPs are making more use of investment committees, following one of the
four trends in central banking practices according to Blinder and Wyplosz (2004). In the
last two decades decision making in the banking sector by one individual has more and
more been replaced by monetary policy committees (MPCs) decision making. Blinder
(2004) lists four reasons why committee decision making is preferred to individual
decision making, including:
- Committee decisions are less volatile
- Extreme positions are less likely
- Knowledge is pooled
- Different heuristics are considered in case of a very difficult decision problem
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It is believed that diversification pays and that the whole is somewhat greater than the
sum of its parts (Blinder, 2009). Blinder and Morgan (2005) argue that group decision
making is preferred to individual decision making as long as group interaction makes
sure group decisions taken not simply reflect the average or median opinion nor its
most skillful member’s decision.
Assuming investment committee decisions are better than the decision made by an
individual, what is then the optimal size of the investment committee? Increasing the
size of a committee induces a trade-off since on the one hand more members have
access to more resources and information. On the other hand, more members also
increases the chance of coordination and motivation losses due to social loafing and
free riding (Levine & Moreland, 1998). According to Hackman (2002), committees
should not exceed nine members and the preferred number is six.
To optimize committee decision making, not only the size is important, but also the
composition, the appointment rule, the member’s contracts, the protocol before and
during meetings, the communication strategy, the frequency of meetings and the
decision rule. Although the decision rule is widely discussed and researched in central
banking and other industries, it has not been researched for the PE sector. However,
some of the beliefs regarding the decision mechanism discussed in social behavior
literature extend to other settings like criminal convictions and probably also to PE
investment decisions.
2.4.2 The Investment Committee Decisions Rule
In general, three thresholds can be considered to make a decision. These thresholds
include a quorum, a majority or a consensus. In this thesis only majority and consensus
are discussed since a quorum is often used for relatively quick decisions which, most
likely, do not include investment decisions. Investment committees using the second
threshold, majority, reject an investment when more than half of the members vote
against. The third threshold, consensus, is the greatest threshold in which case a
decision is only rejected or accept if all the members vote against or in favor
(Maboussin, 2009).
Research on the merits of these two decision rules goes back way long and one of the
first theories in this strand of literature is the Condorcet Jury Theorem by Marie Jean
Antoine Nicolas de Caritat Marquis de Condorcet’s in 1785. The Condorcet Jury Theorem
states that groups using the majority decision rule are more likely than any individual to
select the better option of a binary choice problem when there is actually uncertainty
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about which one of the two options is preferred (Austen-Smith & Banks, 1996). This
would lead to the conclusion that decision making by the investment committee using
the majority rule would be preferred since they would probably choose the better
investment alternative out of two options when there is uncertainty about which
investment opportunity offers the higher return. The Condorcet Jury Theorem,
however, assumes individuals take the same decision when they take a decision
individually then when they take it in a committee (it is assumed that individuals vote
sincerely) and more recent research has shown that this is not always the case.
According to Austen-Smith and Banks (1996) voters do not vote sincerely when a gametheoretic view of collective behavior is taken into account. Due to personal information
and common interests members of an investment committee often vote strategically
(Ladha, Miller & Oppenheimer, 1996). Austen-Smith and Banks (1996) discuss that an
individual who has information about the private information of another individual only
takes this into account (and thus votes strategically) when his or her choice is pivotal
and actually makes a difference in the option chosen. When an investment committee
decides not simultaneously and by consensus a vote is pivotal when all the other
members already voted in favor or against. In this case the pivotal decision member will
take into account the private information of the other members. If all the other
members voted against this probably reveals additional negative private information
about the investment and thus influences the decision of the pivotal decision member
(Ladha, Miller & Oppenheimer, 1996). When voting simultaneously, Feddersen and
Pesendorfer (1998) assume jurors always vote as if their vote is pivotal and thus vote
strategically. If the juror’s vote is actually not pivotal, his or her assumption has no
impact on the verdict (Neilsen & Winter, 2005). Guarnaschelli, McKelvey and Palfrey
(2000) also show that the consensus decision rule (without communication) induces
strategic voting. Moreover, investment committee members using the consensus
decision rule are less likely to stick to their own opinion if this opinion is less popular
(Vandenbussche, 2006). Scharfstein and Stein (1990) try to explain this herd behavior in
corporate investments, the stock market and decision making within firms. They actually
try to explain why in making corporate investment decisions managers sometimes
ignore private information and just follow the investment decisions of other managers.
In their explanation they use the theory of Keynes (1936) which suggests that managers
follow the herd when they are concerned with their reputation. If managers are
concerned with others and how they asses the manager’s ability to make sound
judgments, they are more inclined to make the same decisions as other managers and
to ignore their own private information. An example of this is in case of a bubble in the
stock market. Although some investors may expect that stocks are overpriced most of
them do not act on this information because they are afraid their reputation gets
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damaged when the stock prices go up even further (Scharfstein & Stein, 1990).
Feddersen and Pesendorfer (1998) found that besides reputational concerns also the
outside job opportunities of managers and their compensation structure influences
herding behavior. Another explanation of group behavior is given by Solomon (1955)
and relates to the effects of group pressure. In his experiment he found that ‘individuals
have an inherent psychological desire to conform to group norms’ and where thus
reluctant to disagree with statement expressed by others but that were clearly wrong.
In case of herd behavior the benefit of having an investment committee disappears
when members simply mimic the choice of the first voting member. The effect of
diversification is totally lost in this case and a falls belief of consensus is achieved.
If a committee decides by consensus or majority thus influences the decision eventually
taken. Feddersen and Pesendorfer (1998), for example, show the effect of the decision
rule in the context of juries for criminal trials. Although it is commonly thought that
consensus voting for conviction protects the innocents but increases the chance of a
guilty defendant not getting convicted, Feddersen and Pesendorfer (1998) show in their
article that consensus voting actually increases the chance of convicting an innocent
person and acquitting a guilty defendant. According to Feddersen and Pesendorfer
(1998) consensus voting increases both type I and type II errors. In civil cases, convicting
an innocent person is a Type I error, while acquitting a guilty defendant is a Type II error
(King & Nesbit, 2009). For the investment sector, a Type I error occurs when the firm
rejects a value increasing investment, while a Type II error occurs when the firm accepts
a value decreasing investment (Okuyama & Francis, 2010). Feddersen and Pesendorfer,
just like Condorcet, are in favor of the majority decision rule. Coughlan (2000),
however, includes the possibility of mistrial and communication among jurors in the
model and finds that the consensus decision rule is just as effective in minimizing the
probability of errors occurring. Moreover, Guarnaschelli, McKelvey and Palfrey (2000)
also show that after adjusting the model, with e.g. straw polls, fewer incorrect
convictions take place when the consensus rule is used instead of the majority rule. At
last, Wallach and Kogan (1965) show that the discussion induced by reaching a
consensus leads to riskier decision making. The evidence of the impact of the decision
rule on the quality of group decisions is, unfortunately, still unclear.
The reason different decision rules lead to different decisions is not the voting rule per
se but it is often the interaction among the group members needed to reach a
consensus or majority (Holloman & Hendrick, 1972). While reaching a consensus often
promotes extensive discussions (were more alternatives are explored), compromise
decisions and positive feelings of the committee members toward one another, deciding
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by majority helps maintaining a diversity of beliefs over time within the committee
(Vandenbussche, 2006). Majority voting often needs less discussion and alternatives
explored simply because differences of opinion can be ignored (Holloman & Hendrick,
1972). Also, Holloman & Hendrick (1972) and Bower (1965) argue that committee
members exchange more information and search intensively for the better solution
when they have to reach a consensus. Besides, in the context of criminal convictions,
Hastie, Penrod and Pennington (2002) found that jurors were more thorough with the
evidence under consensus voting. On the other side, however, Persico (2002) adjusts
the model by including costly information and inaccurate information and the consensus
decision rule actually reduces the amount of information gathering. If information is
noisy, committees deciding by consensus are less motivated to acquire expensive
information because they do not expect their vote to be pivotal since committee
members will probably have different opinions about the best investment. The decision
rule thus not only influences the decisions made but also the amount of research done
to make a decision.
In conclusion, it seems that the decision rule influences investor’s effort levels and
activism and thus eventually performance. Although the relation between these
variables has only been researched for industries other then the PE industry, some of
these beliefs probably extend to the PE sector. If, for example, the decision rule of the
investment committee turns out to be an important determinant of the due diligence
performed, this has consequences for PE investors as they then may want to rethink the
working of their investment committees.
In the empirical part of this thesis, the theoretical discussion that the decision rule
influences the decision made is tested in the PE market by using the investment
committee’s refusal to re-invest and the refusal to invest in co-investments. It is thus
tested if the consensus decision rule influences the investor’s activism. Moreover, to
investigate if the consensus decision rule influences the intensity of the due diligence
and monitoring performed, the investor’s effort levels are regressed on the decision
rule. Before these regressions can be performed, the data needs to be described to
eventually find some answers to these, until now, unexplored questions.
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3. Data
This chapter describes the data source, the survey, the hypotheses, the variable
definitions and the descriptive statistics. Finally, this chapter describes the methodology
used to test the hypotheses.
3.1 Sources
Hand-collected data on the PE market is used to conduct this study. Since 2008,
Associate Professor Marco Da Rin, Associate Professor Ludovic Phalippou and several
research assistants from Tilburg University and University of Amsterdam Business
School are collecting data by contacting LPs all over the world. The sample was
constructed by using the 2008 Private Equity International (PEI) directory of LPs
(purposive sampling). 700 of the 1,100 non-US investors listed in the PEI directory were
emailed to introduce the survey. Besides, 150 of the 1,200 US investors were also
emailed. After sending the email, each investor was contacted by phone to ask whether
they received the email, intended to participate and had any questions. In addition, the
survey was advertised in the United States by Burgiss LLC, a professional services
company for PE investors. Currently, remaining investors all over the world are
contacted from the PEI directory. Investors who respond to the survey can leave their
contact details and more than half of them do so. Investors who left their contact details
and did not answer some of the questions, were followed up by phone or email. Half of
these investors completed the whole survey following this follow-up requests.
3.2 Content of the Survey
The survey contains ten parts and focuses on investors’ size, type (pension fund, fundof-funds, asset manager etc.), location, experience, and compensation structure
influencing their actions. The actions considered are monitoring intensity, the depth of
due diligence, the way funds are chosen, the access to co-investment opportunities, the
negotiation of contract terms, and the presence on advisory boards. The complete
survey can be found in appendix A.
3.3 Hypotheses
In this section the two main hypotheses to test the effect of the investment committee’s
decision rule on investor’s effort levels and investor’s activism are formulated. For
investor’s effort levels multiple proxies are included in the survey for due diligence and
monitoring. In this thesis, however, indices are calculated for both effort levels. Besides
the activity index also an hours index can be calculated for the intensity of the due
diligence performed (see chapter 3.4). The hypotheses are as follows:
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Hypothesis 1: Investor’s effort levels are higher when the investment committee
decides by consensus.
Due diligence
Hypothesis 1.1: The investor’s due diligence hours index is higher when the
investment committee decides by consensus.
Hypothesis 1.2: The investor’s due diligence activity index is higher when the
investment committee decides by consensus.
Monitoring
Hypothesis 1.3: The investor’s monitoring index is higher when the investment
committee decides by consensus.
In the literature it is explained that committee members search more intensively for the
better solution and exchange more information when they decide by consensus. Also,
deciding by consensus often promotes extensive discussions. For this reason it is
hypothesized that investment committees deciding by consensus put more effort into
pre-investment due diligence and post-investment monitoring to make the right
investment decision.
Hypothesis 2: Investor’s activism is higher when the investment committee decides by
consensus
Fund rejection
Hypothesis 2.1: Investors more often reject to re-invest when the investment
committee decides by consensus
Co-investment opportunities
Hypothesis 2.2: Investors less often reject to co-invest when the investment
committee decides by consensus
Feddersen and Pesendorfer (1998) explain that both type I and type II errors increase
when jurors decide by consensus. It is therefore tested if more investments are rejected
if the investment committee decides by consensus (hypothesis 2.1). Investor’s activism
does, however, not only include the rejection of re-investments but also the rejection of
co-investments. For hypothesis 2.2 it is explained in the literature that the discussion
induced by reaching a consensus leads to riskier decision making. As co-investments are
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often perceived as riskier investments by LPs, it is hypothesized that investment
committees deciding by consensus reject fewer co-investments.
3.4 Variable Definitions
To be able to proceed with this empirical study, the most important variables to test the
hypotheses need to be defined. In this section, first the definitions of the dependent
variables are presented, after which the definitions of the independent variables and
the control variables are given.
3.4.1. Dependent Variables
Dependent variables can be divided into variables describing investor’s effort levels and
variables describing investor’s activism. Variables describing investor’s effort levels
include due diligence and monitoring variables, while variables describing investor’s
activism include co-investments and re-investment variables. These variables are
defined as follows:
a) Due Diligence:
a. Due diligence activity indexi: Index of three due diligence activities
including calculating an own performance measure, benchmarking GP’s
track records and interviewing executives of the GP’s portfolio companies
(Question 8.2, 8.3 and 8.5). For every activity a dummy was created with a
value of one if the respondents answered ‘always’ or ‘sometimes’ to the
question if they performed a specific activity and a value of 0 if they
answered ‘never’ or ‘do not know’. These dummies were then summed.
To fill the missing answers, dummies were created with a value of one if
the respondent did fill in an answer, zero otherwise. Again, these
dummies were summed. To create the due diligence activity index, the
sum of activities performed was divided by the sum of the activities filled
in. The index is the average score across all the questions answered.
b. Due diligence hours indexi: Index of due diligence hours being above
median for first, seasoned and reinvest funds (Question 9.1.9, 9.2.3 and
9.3.3). First, a dummy was created with the value of one if the hours used
for performing due diligence for the different funds was above median.
These dummies were then summed. To fill the missing answers, dummies
were created with a value of one if the respondents did fill in an answer.
Again, these dummies were summed. To create the due diligence hours
index, the sum of dummies being above median hours was divided by the
answers filled in.
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b) Monitoring:
a. Monitoring indexi: Index of three monitoring activities including keeping
track of the composition of the LP’s portfolio in terms of
industry/size/country, providing services (or support) to the GP and
visiting portfolio companies (Question 10.2, 10.5 and 10.6). For every
activity a dummy was created with a value of one if the respondents
answered ‘always’ or ‘sometimes’ to the question if they performed a
specific activity and a value of 0 if they answered ‘never’ or ‘do not know’.
These dummies were then summed. To fill the missing answers, dummies
were created with a value of one if the respondent did fill in an answer.
Again, these dummies were summed. To create the monitoring index, the
sum of activities performed was divided by the sum of the activities filled
in. The index is the average score across all the questions answered.
c) Re-investments:
a. Re-investment rejection dummy variablei: Value is one if the fraction of reinvestments rejected is larger than 25%, zero otherwise (Question 9.3.5).
25% threshold is chosen to make the percentage of respondents
belonging to each category more or less equal (63/37%).
d) Co-investments:
a. Co-investment rejection dummy variablei: Value is one if the fraction of
co-investment rejected is larger than 85%, zero otherwise (Question
4.1.1.). 85% threshold is chosen to make the percentage of respondents
belonging to each category more or less equal (53/47%).
3.4.2. Independent Variables
The most important independent variable included in the regressions in the next
chapter is the decision rule variable to see if this variable has an effect on the decisions
made and the efforts and activities performed. Other important explanatory variables
are the other investment committee specific variables. After listing all the variables, it is
explained why they are included in the regressions (except for the decision rule variable,
for which it is already explained). The investment committee (IC) specific variables are
defined as follows:
a) Decision by consensus dummy variablei : By what rule the investment committee
decides (consensus or majority). Value is one if the decision rule is consensus,
zero otherwise (Question 5.1.1.1).
b) Independence dummy variablei: Independence of the IC. Value is 1 if decisions
can be influenced or vetoed by people outside the IC, zero otherwise (Question
5.1).
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c) Hierarchyi: Number of people on the IC with voting rights in 2008 in proportion
to the number of people on the IC in 2008 (Question 5.1.1.2 and 5.1.1.3).
d) IC sizei: Number of IC members in 2008 (Question 5.1.1.2).
e) IC preparing due diligencei: Number of people in the IC preparing due diligence
in 2008 in proportion to the number of people on the IC in 2008 (Question
5.1.1.2 and 5.1.1.7).
f) IC turnoveri: Number of people who left the IC between 2003 and 2008 in
proportion to the number of people on the IC in 2008 (Question 5.1.1.2 and
5.1.1.5).
g) IC agei: Average age of the IC members (Question 5.1.1.8)
h) IC PE experiencei: Average PE experience of the IC members (Question 5.1.1.8)
i) IC tenurei: Average number of years IC members are with the IC (Question
5.1.1.8)
j) IC LP tenurei: Average number of years IC members are with the LP (Question
5.1.1.8)
k) Consultancy backgroundi: Fraction of people within the IC (with voting rights)
with a consultancy background (Question 5.1.1.8).
l) Finance backgroundi: Fraction of people within the IC (with voting rights) with a
Finance background (Question 5.1.1.8).
m) Entrepreneurial backgroundi: Fraction of people within the IC (with voting rights)
with a Entrepreneurial background (Question 5.1.1.8).
Independence is included since investment committee members are not only influenced
by public and private information but also by information from people outside the
committee (Vandenbussche, 2006). These non-voting experts or staff members outside
the investment committee sometimes even veto the decision made by the committee. It
is empirically tested if investment committee members’ effort levels and activism is
lower when they are less independent due to social loafing and free-riding but also due
to a loss of motivation because they are not the ones who make the final decision
(Yaniv, 2004). Hierarchy is included since not only the decision rule is important in
decision making but also the organizational structure. The proportion of investment
committee members that is allowed to vote can be used as a proxy measure of the
degree of hierarchy within the LP which can explain how information is generated and
processed (Da Rin & Phalippou, 2010). IC size is included since Feddersen and
Pesendorfer (1998) show that increasing the size of the jury increases the probability of
convicting an innocent person (Type I error), while Guarnaschelli, McKelvey and Palfrey
(2000) show that (under the consensus decision rule and including straw polls) fewer
innocent defendants were convicted when the jury was larger. This theoretical result
can be used to investigate if more investment opportunities are rejected when the size
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of the investment committee increases. Increasing the size of the investment
committee, however, also has a trade-off due to social loafing and free-riding and it is
tested if this leads to less due diligence and monitoring activities (Levine & Moreland,
1998). IC preparing due diligence is included since this is a measure for the knowledge
available in the investment committee which also influences performance and the
decisions made. IC turnover is included since a high turnover often means a modest
average level of experience and this influences, again, performance (Lerner et al, 2007).
Also IC PE experience, IC LP tenure and IC age are included as a measure of the
experience available within the investment committee. IC tenure is then included as a
measure of experience the members have with being in an investment committee. At
last, the background of the investment committee members is included as a measure
for the knowledge and skills available within the investment committee, which again
influences performance (Da Rin & Phalippou, 2010).
At last, to test the findings of Persico (2004), a measure of accurate information is
included which is interacted with the decision rule. This measure is defined as follows:
n)
Quantitative Due Diligencei: The proportion of time the LP spends on quantitative
(vs. qualitative) due diligence for a first-time GP’s fund (Question 9.1.10).
3.4.3. Control Variables
The base regression includes several variables which are used as control variables in
additional regressions. General control variables include investor type, investor size,
investor experience, continent, financial center location and workload. These control
variables are included to explain the heterogeneity among investors and are explained
individually after listing them.
a) Investor typei: Investor types include pension fund, corporate investor (nonpension), endowment, insurance company, bank, government-owned investor
(excluding pension funds), family office, fund-of-funds (excluding asset
managers), asset managers and other (Question 1.2). For the pension funds,
foundations & endowments, fund-of-funds, insurance companies and banks an
individual dummy is created. Other LP’s is the omitted variable.
b) Firm Size:
a. LP Sizei: Asset under Management (AUM) for the firm. Expressed in
million of US dollar. Investors may provide it in another currency; in
which case, it is manually converted by using the average exchange rate
of 2008 (Question 2.1).
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b. LP PE Allocationi: Amount invested in PE, not restricted to venture capital
and buyout. Expressed in million of US dollar (Question 2.2).
c) Firm Experience:
a. LP PE Experiencei: Number of years the organization has been investing in
venture or buyout funds; it is equal to 2008 minus the lower of the
answers to questions 1.4.1 and 1.5.1.
b. LP Experiencei : Number of years the firm exists; it is equal to 2008 minus
the answer to question 1.1.
d) Continent:
a. North American LP dummy variablei: Value is one if the LP’s investment
committee is located in North America (USA , Canada or Mexico), zero
otherwise (Queston 1.3).
b. European LP dummy variablei: Value is one if the LP’s investment
committee is located in Western Europe, including the UK and
Scandinavia, zero otherwise (Question 1.3).
c. Other continent dummy variablei: Value is one if the LP’s investment
committee is located in Asia, Australia (including New Zealand) or Africa,
zero otherwise (Question 1.3).
e) Financial center dummy variablei: Value is one if the LP is located in a country’s
main financial city, where also the stock exchange is located, zero otherwise
(Question 1.3).
f) Workloadi: Number of funds managed by each PE professional in 2008 (Question
2).
Investor type is included as a control variable since Lerner, Schoar and Wongsunwai
(2007) show that different types of investors have different returns on their PE
investments. Da Rin and Phalippou (2010) discuss that these different returns are
eventually explained by the investor’s effort levels, activism and favoritism. The
intensity of the due diligence and monitoring activities performed and the amount of reand co-investments refused are probably different for different type of investors and it
is important to control for this effect in the regressions. Firm size is included since Da
Rin and Phalippou (2010) found that size is one of the most important factors
influencing among others investor’s effort levels and investor’s refusal to re-invest.
Again it is important to control for this effect. Firm experience is included since more
experienced members have a deeper understanding of the PE market which can
increase performance. Besides, due to their long existence, experienced firms
sometimes have the right to re-invest in funds that are not accessible to new investors
(Lerner et al, 2007). The continent the LP is located is included since Hobohm (2008)
finds some limited return differences for global LPs. Although the return difference is
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little, there is a small difference in the global LPs ability to select the funds with the
highest return. This means that the global LPs probably also differ in their effort levels
and activism, if it assumed that these activities lead to better performance, and the
refusal of re- and co-investments. Besides, Europe and North-America have the oldest
PE markets and their experience could also influence performance. Financial center
location is included to control for preferential access to information or networks when a
LP is located in a financial center (Da Rin & Phalippou, 2010). Workload is the last
variable included to explain the heterogeneity in performance among LPs (Da Rin &
Phalippou, 2010). If the workload is high this could lead to less due diligence and
monitoring activities simply because there is not enough time to perform these
activities. Workload, in this way, could also influence the rejection of re- and coinvestments.
Before proceeding with the descriptive statistics of the dependent, independent and
control variables, a pairwise correlation is performed to control for linear dependence.
To reduce the influence of outliers on the correlation results, the natural logarithms of
LP size, LP PE allocation, LP experience, LP PE experience, IC size, IC average age, IC PE
experience, IC tenure, IC LP tenure and workload are used. It is tested if any correlation
exists among the investment committee specific variables themselves and among the
control variables themselves (see appendix B).
For the control variables it seems that there is a significant (p-value = 0.0000)
correlation of 0.45 between LP PE experience and LP experience and a significant (pvalue = 0.0445) correlation of 0.63 between LP size and LP PE allocation. Due to their
significance in the following regressions, LP size and LP experience are used.
For the investment committee specific variables there is a significant (p-value = 0.0000)
correlation of 0.68 between IC LP tenure and IC tenure. For this reason only the variable
IC tenure will be used to measure the experience investment committee members have
with working in a committee.
Although there is a significant (p-value = 0.0000) correlation of -0.67 between the
dummy variable for Europe and the dummy variable for North-America, it is decided to
leave both dummies in the regressions due to their significance in other literature (Da
Rin & Phalippou, 2010).
3.5 Descriptive statistics
Until now, 332 results are received from all over the world, where only 171 results were
filled in for more than 50%. These 171 results will be used for the data description and
regressions. To make the interpretation of the variables clear, no natural logarithms are
used for the descriptive statistics.
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3.5.1. The Limited Partner
Of the 171 results, 53 results come from the US and only one result comes from Italy,
India, South-Africa, Mexico and Iceland. Appendix C, table 3, 4, 5 and 6 show the sample
coverage of LPs for each country and category. Compared to the PEI directory there is
an overrepresentation of Taiwanese respondents and an under-representation of Italian
and Indian respondents but also of American respondents. 36 respondents specified the
nature of their organization as Fund-of-Fund, while only 6 respondents classified
themselves as corporate investor. The high number of Fund-of-Fund LPs filling out the
survey can be explained by the fact that performing due diligence is at the core of their
business and the results of the survey are probably the most interesting to them (Lerner
et al., 2007). Another dimension to assess the sample representativeness is the size of
the investors. The median PE allocation of the sample is not significantly different from
the PE allocation of the investor population. However, the mean is significantly smaller,
which means there are few large investors included in the sample. This is also the case
for AUM. At last, respondents are slightly more experienced than the population of
investors. See table 3, 4, 5 and 6 (appendix C) for a complete oversight of the AUM,
mean (median) PE allocation and experience of the LPs per country and category.
3.5.2 Limited Partner’s effort levels
Due Diligence
The survey first asks some general information on the due diligence process. The most
important findings are described below (all non-tabulated).
The most important piece of information 71% of the LP wants to obtain from the GP
during the due diligence process is the track record. Besides, a lot of LPs want to receive
information on the team (31%) and on the strategy (27%). Other important pieces of
information include, among others, references, information on carry split and value
added. 58.1% of the LPs then calculate their own aggregate performance measure
based on the information they are provided with. LPs do this because they distrust the
numbers given by the GPs. Phalippou (2009) showed that GPs window dress their track
record by not recording their low Internal Rate of Return or even by firing team
members so that they can show a track record of the current team (not including the
investments who performed less). Although some GPs window dress, 9.01% of the LPs
say they never try to undress these performance measures. After receiving or
computing the performance measures, 88% of the respondents indicate that they
benchmark GP’s track record. Besides, 46.43% of the respondents indicated that they
always interview the executives of the GP’s portfolio companies, while 42.86% indicated
they did this sometimes. At last, the LPs indicated that 27.24% of the PPM’s they
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received went through due diligence (by the LP or by the consultant) and that they
committed to on average 12.03% of these funds.
The survey then proceeds by asking some more quantitative questions about the LPs
due diligence. The results are as follows: LPs spent most days on the due diligence
process for a first-time fund (27.11). Reasons for this is that first-time funds often have
high information asymmetries and do not yet have a track record (Lerner, 2007). For
seasoned funds, the LPs already spent fewer days on average on the due diligence
process (19.80) and for re-investing in a seasoned GP’s fund it is the least (16.61).
Besides the days spent on due diligence, it is also important to know the proportion of
quantitative versus qualitative due diligence. For first-time funds the percentage of
quantitative versus qualitative due diligence is on average 31.51%, for seasoned GP’s
funds it is 38.68% and for re-investing in GP’s seasoned funds it is 37.24%. The most
important descriptive statistics are reported in table 7.
Table 7: The due diligence process
Calculating own performance measure
Benchmarking the GP's track record
Interviewing the executives of the GP's portfolio
companies
Yes (%)
89.9
87.0
88.8
N
169
169
170
Column5
Column6
Column7
Due diligence activity index
Mean
0.885
Median
1
Min
0
Max
1
St. dev
0.207
N
170
Due diligence hours index
0.437
0
0
1
0.471
156
Monitoring
From the 166 respondents who answered the question if they keep track of the
composition of their PE portfolio in terms of industry/size/country, 141 answered this
question with a yes. Moreover, only 28 respondents (of the 162 respondents) indicated
that they provide services (or support) to the GPs whose funds they invest in. To the
question ‘do you visit portfolio companies’, 9.88% responded ‘always’, 54.94%
responded ‘sometimes’ and 35.19% responded ‘never’ (non-tabulated). Investors that
visit portfolio companies are most of the times also very active in other monitoring
activities. On average, LPs obtained information from GPs on portfolio company fees for
60.86% of their buyout portfolio companies. For leverage this percentage was 75.63%.
On average 16.19 days were spent to keep track of the cash flows realized on each fund.
The most important descriptive statistics are tabulated in table 8.
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Table 8: The monitoring process
Tracking the composition of the LP's PE portfolio
Providing services (or support) to the GP
Visiting portfolio companies
Monitoring activity index
Yes(%)
84.9
17.3
64.8
Mean
0.566
N
166
162
162
Median
0.667
Column5
Min
0
Column6
Max
1
Column7
St. dev
0.281
3.5.3. Limited Partner’s activism
Refusal to re-invest
In the sample, on average, 63% of the respondents indicated that they refused to reinvest with a GP up to 25% of the times over the last five years. This figure is
considerably lower than the percentage Lerner and Schoar found in 2004. Important
reasons for refusing to re-invest include allocation limits, increasing fund size, GP’s
deviation from the original strategy, excess company fees, disappointing performance
and the turnover of key professional/partners within the GP. LPs indicated that
disappointing GP performance is the main reason for not re-investing.
Refusal to co-invest
75% of the respondents have been offered a co-investment opportunity by a GP.
Typically 79% of these co-investment opportunities are, however, rejected by the LP.
32% of the respondents indicated that adverse selection is the most frequent
motivation for rejecting co-investment opportunities. Another important reason for
rejecting is because of their policy and the risk included in co-investment opportunities.
Less important reasons include the lack of skills and their allocation. When LPs do
decide to co-invest, 41% of the respondents indicated that they do this to improve their
performance before fees. Other reasons include the reduction of total fees (34%), to
customize the portfolio (adjust to country, industry..)(33%), to free ride on the GP’s due
diligence (16%) or another reason (10%). 45% indicated that improving performance
before fees is the main motivation for co-investing. LPs indicated that on average 5.9%
of their PE portfolio consisted out of co-investments in 2008.
3.5.4 The Investment Committee
79.88% of the respondents answered ´yes´ to the question if they had an investment
committee. Of this percentage it were most likely the corporate investors and asset
managers who answered the question with a ‘yes’ and the LPs from Sweden who
answered the question with a ‘no’ (non-tabulated). The average number of investment
committee members was seven in 2008. Of this number, on average, six members had
voting rights in 2008. The turnover (number of persons with voting rights who left the
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N
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investment committee) between 2005 and 2008 was one. On average two investment
committee members prepared due diligence. For the average age, experience with
private equity, tenure with the investment committee and the professional background
of investment committee members, see table 9.
Table 9: The investment committee
The investment committee
Independence
Hierarchy (%)
IC Size
IC Preparing Due Diligence (%)
IC Turnover (%)
IC Average Age (Years)
IC PE Experience (Years)
IC Tenure (Years)
IC Consultancy Background (%)
IC Finance Background (%)
IC Entrepreneurial Background (%)
Mean
1.55
0.3
7.26
0.4
0.3
49.10
11.94
6.91
24.6
62.2
4.1
Median
1
0
6
0.3
0.2
48.95
11.30
6.50
13
70
0
Min
1
0
1
0
0
32.3
1
1
0
0
0
Max
3
1
72
1.5
1
67.5
27.7
20
100
100
57
St.dev
0.73
0.4
7.46
0.4
0.4
7.25
5.79
3.54
31.2
35.7
10.8
N
126
163
134
129
171
94
91
91
99
99
99
Of the organizations who had an investment committee, the majority takes their
decisions by consensus (60.31%, see figure 1). This is surprising as Maboussin (2009)
argued that the consensus treshold is probably too high for dealing with future
unknowns, like investment returns. Six respondents indicated that they did not take
their decisions by consensus or majority but used another decision rule (e.g.
supermajority).
Figure 1: Investment committee decision rule
To demonstrate the importance of including the investment committee specific
variables in the regressions it is investigated if there are significant differences among
the variables for different LPs by performing independent sample t-tests (table 10).
Assumptions for the independent sample t-test include the level of measurement
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(interval or ratio level), the randomness of the sample, the independence of the
observations, a normal distribution of the scores within each group and homogeneity of
variance. Due to the measurement assumption, the decision rule and the independence
of the investment committee are excluded. To test if the variances between the groups
are equal for the different investment committee specific variables Levene tests are
performed. In case equal variances are not assumed, an alternative t-value is provided
which compensates for the fact that the variances are not the same.
Table 10: Differences in LP's investment committees. Independent sample t-test for different groups of LPs. In case variances are not the
same, alternative p-values are used. Asterisks denote statistical significance for mean differences at 1% (***), 5% (**), and 10% (*).
NEurope
Difference
Small
Big
Difference Young
Old
Difference
Limited Partner
America
Hierarchy
0.25
0.26
-0.01
0.18
0.37
-0.19***
0.20
0.35
-0.15**
IC Size
5.62
8.47
-2.85***
6.89
6.88
0.01
6.18
8.56
-2.38*
IC Preparing Due Diligence (%)
IC Turnover (%)
0.38
0.31
0.35
0.30
0.03
0.01
0.32
0.23
0.44
0.39
-0.12*
-0.16***
0.44
0.27
0.25
0.39
0.19***
-0.12**
IC Average Age (Years)
47.03
51.76
-4.73***
50.58
46.27
4.31***
49.37
48.71
0.66
IC PE Experience (Years)
IC Tenure (Years)
11.64
6.23
13.71
7.58
-2.07
-1.35
12.18
6.79
10.68
6.54
1.5
0.25
13.35
6.51
9.88
7.49
3.47***
-0.98
IC Consultancy Background (%)
24.83
22.86
1.97
26.84
19.28
7.56
26.90
21.37
5.53
IC Finance Background (%)
66.46
60.69
5.77
56.57
72.31
-15.74**
56.30
70.56
-14.26**
IC Entrepreneurial Background (%)
4.84
4.80
0.04
5.43
0.54
4.89***
4.80
3.10
1.70
For the t-tests, different LPs are categorized in different continents (Europe – NorthAmerica), different sizes (Big – Small) and different ages (Young – Old).
For the different contintents, it can be seen that the size of the European investment
committee is significantly different from the size of the American investment
committees. The American committees are on average larger which can have an effect
on the effort levels performed, due to free-riding, and on the decisions taken.
Moreover, the average age of the American investment committee members is higher
than the average age of the European investment committee members, which could
mean the American members have more experience and knowledge and also this can
influence the effort levels performed and the decisions taken.
There is a significant difference between small and large LPs for hierachy, the proportion
of the investment committee preparing due diligence, turnover, the average age of the
investment committee members and the % of investment committee members with an
entrepreneurial background. These variables are all expected to have an effect on the
effort levels and activism performed, as was explained in chapter 3.4.2.
Both size and experience seem to have an impact on the characteristics of the
investment committee. For the young/old LPs there are again significant differences
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between almost all investment committee specific variables except for the average age
of investment committee members, IC tenure and the % of investment committee
members with a finance and entrepreneurial background. These significant differences
between young and old LPs indicate that it is important to include these investment
committee specific variables to see if they have a significant impact on the effort levels
performed and the decisions taken.
To not only test the statistical significance of the t-tests but also the practical
significance, the effect size is calculated (strengt of association). This will be done by
calculating the partial η² which represents ‘the proportion of variance of the dependent
variable that is explained by the independent variable (Pallant, 2006)’. The η² can be
calculated as:
t2
t 2 + ( N1 + N 2 − 2)
Where, t is the calculated t-value and N are the number of observations. From Cohen
(1988), it can be read that 0.01 is interpreted as a small effect, 0.06 as a moderate effect
and 0.14 as a large effect. In table 11 it can be seen that none of the differences has a
large effect and is thus of practical significance. This result is used in explaining the
results of the regressions.
Table 11: Eta squared values for t-tests
Limited Partner
Europe - N-America
Hierarchy
0.000
IC Size
0.064
IC Preparing Due Diligence (%)
0.002
IC Turnover (%)
0.001
IC Average Age (Years)
0.103
IC PE Experience (Years)
0.033
IC Tenure (Years)
0.035
IC Consultancy Background (%)
0.001
IC Finance Background (%)
0.007
IC Entrepreneurial Background (%)
0.000
Small - Big
0.058
0.000
0.027
0.049
0.086
0.018
0.001
0.017
0.054
0.085
Young - Old
0.034
0.023
0.068
0.025
0.002
0.088
0.019
0.008
0.039
0.006
3.6 Methodology
For this study cross-sectional (with regard to the study population) data is used, since
many firms are observed within a small time span (three years) and without regard to
time. Respondents with a completion rate below 50% are dropped.
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3.6.1. OLS and Logit Regressions
For the models were the dependent variable is continuous (due diligence hours and
activity index and monitoring activity index), OLS regressions are performed. Tabachnick
and Fidell (2001) and Stock and Watson (2007) provide the following major assumptions
for OLS regressions about the data: no linear dependence, no autocorrelation, the error
term ui has conditional mean zero given Xi, no outliers, (Xi,Yi), i=1,…,n are independently
and identically distributed, and homoscedasticity. These assumptions are necessary to
create unbiased and consistent results. The presence of these (and some other less
important) assumptions is checked and if necessary, controlled for.
Already in chapter 4.1 there is controlled for the linear dependence assumption by
performing correlation matrices (see appendix B, table 1 and 2) and excluding certain
variables in the regressions. Second, to reduce the influence of outliers, the natural
logarithms of AUM, firm age, investment committee size, investment committee
average age, investment committee PE experience and workload (number of funds per
PE professional) are used in the regressions. Although Tabachnick and Fidell (2001)
believe that multiple regressions require normality, this is not the case (Stata web book,
n.d.). To obtain unbiased estimates of the regressions coefficient, normality of residuals
is not required. Hence, it is not necessary to check for this assumption. To check for
linearity of residuals the standardized residuals are plotted against each of the
independent variables in the base regression model. Again the natural logarithms are
used to control for linearity. Furthermore, the homoscedasticity assumption assumes
that the variance of the error term is constant. If, however, the variance of the error
term varies with the independent variable, heteroscedasticity is present. To check for
heteroscedasticity a White’s test is performed. This test tests the null hypothesis that
the variance of the residuals is homogenous. The p-value for this test is 0.4371, and
hence it is assumed that the variance of the error term is constant. To make sure the
model is specified correctly and no relevant variables are omitted or irrelevant variables
are included in the model, linktests are performed. A linktest creates two new variables,
_hat and _hatsq (the variable of squared prediction) and uses these variables as
predictors. _hatsq should not be significant, since this would indicate that there are
additional predictors that are statistically significant for the dependent variable. For
none of the models tested in this thesis this was the case except for the model
regressing the due diligence activity index on only the control variables. At last, robust
regressions are performed to deal with cases that have a high leverage (observation
with an extreme value on a predictor variable) or that are outliers (observation with a
large residual). Performing a robust regression makes sure these observations do not
have to be dropped, while not violating the assumptions of the OLS regression. Robust
regressions avoid biased estimates of coefficients and standard errors.
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When the dependent variable is a binary variable and can only take the value 0 or 1
(accept or reject a re-investment or co-investment opportunity) the Logit or Probit
regression model can be used. Logit and Probit regressions also make some assumptions
about the data including no linear dependence and no outliers. Already has there been
controlled for these assumptions. Since Logit and Probit regressions model the
probability that Y=1, standard normal cumulative probability distributions functions
(c.d.f.’s) and logistic c.d.f.’s are used since they produce probabilities between 0 and 1.
Since the differences between the two functions are small and historically the Logit
function is used more often, the Logit function is used in this thesis (Stock & Watson,
2007). To give the economic significance and interpret the values of coefficients in Logit
regressions, marginal effects are provided in the regressions tables.
3.6.2 Two-step Heckman Procedure
In chapter 4.3, a two-step Heckman procedure is performed to control for selection bias,
which is a central concern in much of the corporate finance literature (Bottazzi, Da Rin &
Hellmann, 2008). If selection bias is present, biased parameter estimates and incorrect
standard errors would be the result, leading to incorrect conclusions. There are several
remedies to control for selection bias and each model has its own economic and
statistical assumptions (Li & Prabhala, 2007). The baseline model for selection bias is the
Heckman selection model, which is often used in corporate finance (Heckman, 1979).
Other selection models are often extensions of the Heckman model, allowing some
flexibility in specification. This flexibility, however, comes at a cost and places additional
demands on the data (Li & Prabhala, 2007). For this thesis the two-step Heckman
procedure is used.
The form of selection bias addressed is sometimes called heterogeneity bias. If, for
example, the due diligence hours index is regressed on the decision rule dummy
variable, a biased estimate of the decision rule effect can be the result because the
distribution of respondents over the categories of consensus and majority was not
random. LPs who choose to decide by consensus may differ in many (measured and
unmeasured) characteristics from LPs who do not. If these characteristics are related to
the due diligence hours index, the coefficient of the decision rule dummy may catch up
these effects and may be biased because of this (Smits, 2003).
An important condition for the use of the two-step Heckman procedure is the inclusion
of a variable in the selection equation of the first step, which is not related to the
dependent variable in the second step and hence, is excluded from that step. When
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such a variable is included in the second step, linear dependence problems could arise
and biased parameter estimates would be the result (Smits, 2003).
First, a Heckman selection bias control factor is constructed which is added to the OLS
regressions to produce unbiased parameter estimates in the second step. In the first
step, the LPs deciding by consensus and the LPs deciding by majority are compared by
performing a Probit regression. The dependent variable in the Probit regression is a
dummy variable indicating whether or not the investment committee decides by
consensus. Independent variables in the model are the (relevant) characteristics of the
LPs. Of interest, however, is the effect of the unmeasured characteristics of the LPs on
the decision rule. Information on the effect of the unmeasured characteristics is
available in the residuals of the Probit analysis. These residuals are then used to
construct the selection bias control factors, also called lambda (λ), which are added to
the data file as an additional variable. The selection bias control factor summarizes the
effect of all unmeasured characteristics, which are related to the decision rule.
In the second step of the Heckman procedure, regressions of the decision rule on the
control variables and the selection bias control factor are performed. Because the
selection bias control factor is added as an extra variable, the regression analysis should
produce unbiased estimates for the decision rule and other variables.
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4. Regression Analysis Results
To test if the decision rule has an impact on the effort levels and activism of LPs, several
OLS or Logit regressions are performed for every hypothesis.
4.1 Hypothesis 1
Investor’s effort levels are higher when the investment committee decides by consensus
To test the relation between the intensity of the effort levels and the decision rule,
multiple cross-sectional OLS regressions of the due diligence activity and hours index
and the monitoring index on the decision rule, investment committee specific and
control variables are performed.
4.1.1. Hypothesis 1.1
The investor’s due diligence hours index will be higher when the investment
committee decides by consensus.
The model for testing hypothesis 1.1 is the following:
Due Diligence Hours Index = β0 + Decision Rulei*β1 + Other Investment Committee
Variables + Control Variables + Error term
For this model, four specifications are estimated starting with the first equation
including only the control variables and the last equation including the decision rule
variable, the investment committee specific variables and the control variables.
In the end only for three of the four specifications the estimation results are tabulated.
For the fourth specification, were all the investment committee specific variables were
included, none of the variables was statistically significant anymore. Although the
number of observations is already too small for the first specification according to
Tabachnick and Fidell (2007) it is definitely too small to include all the investment
committee specific variables. According to Tabachnick and Fidell (2007) the number of
observations required can be calculated by using the formula:
Ν > 50 + 8 * m
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Where, m = number of independent variables. If all the investment committee specific
variables are included, 23 independent variables are included meaning 234 observations
would be needed to generalize the regressions results. This could be an explanation why
none of the variables is significant anymore for the fourth regression. Another
explanation could be the practical insignificance of the mean differences of investment
committee characteristics for different LPs. Table 12 summarizes the results of these
estimations.
Table 12: OLS Regression results for the due diligence hours index
Due Diligence Hours Index
Coefficient Coefficient Coefficient
(std error) (std error) (std error)
Decision by Consensus
Europe
North-America
Pension fund
Insurance Company
Fund-of-Fund
Foundation & Endowment
Bank
LP Experience
LP Size
Financial Center
Workload
-0.220*
(0.127)
-0.267*
(0.152)
-0.158
(0.128)
-0.067
(0.185)
0.136
(0.129)
-0.014
(0.136)
0.221
(0.136)
-0.028
(0.044)
0.086***
(0.022)
-0.181**
(0.086)
-0.044
(0.034)
Independence
-0.185*
(0.104)
-0.193
(0.171)
-0.232
(0.197)
-0.223
(0.158)
0.069
(0.238)
0.133
(0.141)
0.035
(0.161)
0.263
(0.160)
-0.051
(0.053)
0.071***
(0.026)
-0.105
(0.100)
-0.025
(0.043)
-0.195*
(0.114)
-0.314
(0.190)
-0.393*
(0.211)
-0.231
(0.166)
0.001
(0.248)
0.127
(0.168)
0.083
(0.164)
0.234
(0.173)
-0.019
(0.053)
0.062**
(0.026)
-0.058
(0.107)
-0.067
(0.046)
-0.100
(0.129)
92
Number of observations
120
77
2.41
F
4.56
3.5
0.0102
Prob > F
0.0000
0.0004
0.2032
R-squared
0.2114
0.2660
Asterisks denote statistical significance at 1% (***), 5% (**), and 10%
(*).
From table 12 it can be inferred that for the first specification, were only the control
variables are included, the variables Europe, North-America, LP size and Financial center
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are significant at the respectively 1%, 5% and 10% level. The most important control
variable seems to be LP size, as this variable is statistically significantly positive for every
regression. This is as expected since Da Rin and Phalippou (2010) found it was the most
important variable influencing, among others, investor’s effort levels. The bigger the LP,
the higher the incentive probably is to spend time and resources on due diligence. If the
LP is located in Europe or America this seems to have a negative impact on the due
diligence performed. The PE markets in Europe and North-America are the oldest ones
and are probably better developed than the PE markets in other continents (Firm PE
experience for North-America is on average 12 years, 10 years for Europe and 9 years
for other continents). This could explain the negative effect since they are more
experienced and probably need less time to perform their due diligence. For the last
control variable, location in a financial center, it seems that the preferential access to
information or networks seems to have a negative effect on the due diligence hours
performed. This is as expected since it costs them less time to find the right information.
For investment committee specific variables, the consensus decision rule seems to have
a statistically significant negative effect on the due diligence performed, in contrast to
what was predicted in the theory. This contradicting result is difficult to explain, but it
can be that reaching a consensus leads to less discussion due to strategic voting and
herding behavior. Investment committee members assume that their vote is pivotal
when voting simultaneously and by consensus, so it could be that they rely on the
information gathered by other members and not on their own gathered private
information, when communication is allowed. Or when voting is not simultaneously,
pivotal members try to deduce information from others members’ votes as predicted by
Austen-Smith and Banks (1996) and thus perform less due diligence. The more
investment committee specific variables are included, the less statistically significant the
decision rule becomes.
4.1.2 Hypothesis 1.2
The investor’s due diligence activity index will be higher when the investment
committee decides by consensus.
The model for testing hypothesis 1.2 is the following:
Due Diligence Activity Index = β0 + Decision Rulei*β1 + Other Investment Committee
Specific Variables + Control Variables + Error term
For this model five specifications are estimated starting with the first equation including
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only the control variables and the last equation including the decision rule variable,
investment committee specific variables and the control variables. The extra
specification added is the one with the interaction term, testing the results of Persico
(2004). In the end only for four of the five specifications the estimation results are
tabulated. For the fifth specification, were all the investment committee specific
variables were included, none of the variables was statistically significant anymore.
Independence is reported because of its effect on the significance of other variables.
Table 13 summarizes the estimation results for this model.
Table 13: OLS Regression results for the due diligence activity index
Due Diligence Activity Index
Coefficient Coefficient Coefficient
(std error) (std error) (std error)
Decision by Consensus
0.000
-0.002
(0.040)
(0.047)
Quantitative Due Diligence
Decision by Consensus *
Quantitative Due Diligence
Europe
North-America
Pension fund
Insurance Company
Fund-of-Fund
Foundation & Endowment
Bank
LP Experience
LP Size
-0.108**
(0.051)
-0.090
(0.065)
-0.096
(0.062)
-0.007
(0.066)
-0.129**
(0.064)
-0.046
(0.073)
Coefficient
(std error)
-0.319***
(0.109)
-0.001
(0.002)
0.010***
(0.003)
-0.162**
(0.070)
-0.079
(0.093)
0.015
(0.049)
-0.218*
(0.121)
0.113***
(0.038)
0.008
(0.078)
-0.0119
(0.060)
-0.008
(0.019)
-0.001
(0.058)
-0.063
(0.127)
0.103**
(0.046)
0.013
(0.083)
0.033
(0.071)
-0.033
(0.021)
0.048
(0.073)
-0.030
(0.127)
0.106*
(0.054)
0.055
(0.093)
0.021
(0.076)
-0.036
(0.023)
0.027
(0.100)
-0.141
(0.134)
0.113
(0.071)
0.104
(0.082)
0.150
(0.130)
-0.071**
(0.033)
0.012
(0.009)
0.013
(0.035)
0.004
(0.012)
0.012
(0.010)
-0.009
(0.038)
0.007
(0.015)
0.005
(0.011)
Financial Center
-0.008
(0.043)
Workload
0.009
(0.017)
Independence
-0.064
(0.057)
98
Number of observations
125
82
2.05
F
2.78
1.65
Prob > F
0.0031
0.0289
0.0928
0.1781
R-squared
0.168
0.2196
Asterisks denote statistical significance at 1% (***), 5% (**), and 10% (*).
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0.026
(0.018)
-0.061
(0.054)
0.036
(0.026)
0.000
(0.069)
47
2.48
0.0158
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Master Thesis Finance
For this model, remarkably, the size of the LP does not have an effect on the due
diligence activities performed. The European dummy, however, has and again it has a
negative effect on the intensity of the effort level. Also the investor categories
sometimes have a statistically significant effect on the due diligence activities
performed. While being an insurance company seems to have a negative effect on the
due diligence activities performed, being a Fund-of-Fund seems to have a positive
effect. Since Fund-of-Funds invest the aggregate capital of LPs in funds managed by GPs,
they are sometimes called investment advisors and performing due diligence is at the
core of their business (Lerner et al., 2007). The positive effect is thus as expected. As
insurance companies generally invest less of their total investment in PE, their focus on
performing due diligence is probably also less (Table 5 and table 6).
The decision rule does not seem to have a statistically significant effect on the due
diligence activity index. The linktest indicated the model is not specified correctly, which
could lead to this insignificant effect. If, however, the interaction term is added, both
the decision rule and the interaction term are significant. This confirms the results of
Persico (2004) that committees deciding by consensus spent less effort on gathering
information when information is inaccurate and thus more effort when information is
accurate (as measured by the proportion of quantitative due diligence). The interaction
term is, however, only significant for the due diligence activity index and is not reported
for the other effort levels.
4.1.3 Hypothesis 1.3
The investor’s monitoring index will be higher when the investment committee
decides by consensus.
To test the relation between the intensity of the monitoring performed and the decision
rule, multiple cross-sectional regressions of the monitoring activity index on the decision
rule, investment committee specific and control variables are performed.
The model for testing hypothesis 1.3 is the following:
Monitoring Activity Index = β0 + Decision Rulei*β1 + Other Investment Committee
Specific Variables + Control Variables + Error term
For this model again four specifications are estimated starting with the first equation
including only the control variables and the last equation including the decision rule
variable, investment committee specific variables and the control variables. In the end
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only for three of the four specifications the estimation results are tabulated. For the
fourth specification, were all the investment committee specific variables were
included, none of the variables was statistically significant anymore. Table 14
summarizes the estimation results for this model.
Table 14: OLS Regression results for the monitoring index
Monitoring Index
Coefficient Coefficient
Coefficient
(std error) (std error)
(std error)
Decision by Consensus
Europe
North-America
Pension fund
0.139**
(0.061)
0.117*
(0.070)
-0.114*
(0.064)
0.120*
(0.072)
0.045
(0.085)
-0.009
(0.087)
0.043
(0.105)
0.055
(0.072)
-0.023
(0.091)
0.072
(0.090)
-0.015
(0.026)
0.029**
(0.014)
0.003
(0.052)
-0.043*
(0.023)
-0.043
(0.110)
0.079
(0.114)
0.052
(0.070)
0.017
(0.111)
0.050
(0.092)
-0.017
(0.030)
0.044***
(0.016)
0.031
(0.062)
-0.041
(0.029)
-0.139**
(0.068)
0.090
(0.080)
0.020
(0.090)
-0.081
(0.125)
Insurance Company
0.124
(0.113)
Fund-of-Fund
0.089
(0.073)
Foundation & Endowment
0.025
(0.122)
Bank
0.064
(0.093)
LP Experience
-0.023
(0.031)
LP Size
0.033**
(0.013)
Financial Center
0.040
(0.062)
Workload
-0.018
(0.029)
Independence
0.016
(0.079)
96
Number of observations
124
81
3.6
F
2.02
2.89
0.0002
Prob > F
0.0326
0.0023
0.2080
R-squared
0.1129
0.2243
Asterisks denote statistical significance at 1% (***), 5% (**), and 10% (*).
For the monitoring index again size does seem to have an important positive effect on
the monitoring performed. Just as the continent the LP is located. However, this time
being located in Europe of North-America has a statistically significant positive effect on
the monitoring activities performed. Although this is difficult to reconcile with the lower
due diligence efforts, maybe because of their experience they know how important it is
to monitor your investments intensively to make an informed decision about re- 38 -
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investments. When only the control variables are included, also the workload seems to
have a statistically significant negative effect on the monitoring activities performed.
This effect is as expected, since a higher workload means less time to perform any
monitoring activities.
The consensus decision rule has a statistically significant negative effect on the
monitoring activities performed. This effect can, again, be explained by the herding and
strategic behavior induced by consensus voting
4.2 Hypothesis 2
Investor’s activism is higher when the investment committee decides by consensus
To test the relation between the intensity of the investor’s activism and the decision
rule, multiple cross-sectional regressions of the refusal to re-invest and co-invest on the
decision rule, investment committee specific variables and control variables are
performed.
4.2.1 Hypothesis 2.1
Investors more often reject to re-invest when the investment committee decides by
consensus
To test the relation between the rejection to re-invest and the decision rule, multiple
cross-sectional regressions of the rejection of re-investments on the decision rule,
investment committee specific and control variables are performed.
The model for testing hypothesis 2.1 is the following:
Re-investment rejection dummy variable = β0 + Decision Rulei *β1 + Other Investment
Committee Specific Variables + Control Variables + Error term
For this model, again, four specifications are estimated starting with the first equation
including only the control variables and the last equation including the decision rule
variable, investment committee specific variables and the control variables. Table 15
summarizes the estimation results for this model.
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Table 15: Logit Regression results for refusal to re-invest
Refusal to re-invest
dy/dx
dy/dx
(std error)
(std error)
Decision by Consensus
-0.062
(0.168)
Europe
-0.058
-0.224
(0.162)
(0.196)
North-America
-0.418***
-0.495**
(0.161)
(0.215)
Pension fund
-0.063
-0.233*
(0.172)
(0.133)
Insurance Company
0.105
0.251
(0.298)
(0.427)
Fund-of-Fund
-0.057
0.090
(0.170)
(0.182)
Foundation & Endowment
0.048
0.012
(0.219)
(0.215)
Bank
-0.060
-0.010
(0.187)
(0.256)
LP Experience
-0.147**
-0.177***
(0.061)
(0.066)
LP Size
-0.134***
-0.113**
(0.041)
(0.051)
Financial Center
0.066
-0.083
(0.125)
(0.141)
Workload
0.107*
(0.056)
Independence
0.040
(0.079)
dy/dx
(std error)
dy/dx
(std error)
0.035
(0.175)
-0.198
(0.212)
-0.462*
(0.243)
-0.169
(0.181)
0.355
(0.401)
-0.061
(0.203)
0.077
(0.254)
0.351
(0.256)
-0.194***
(0.074)
-0.120**
(0.048)
-0.093
(0.146)
-0.055
(0.307)
-0.006
(0.336)
-0.165
(0.117)
-0.158***
(0.055)
0.263
(0.224)
0.045
(0.087)
-0.081
(0.170)
Proportion of the IC
preparing due diligence
-0.078
(0.425)
IC Average Age
IC Consultancy Background
IC Finance Background
IC Entrepreneurial
Background
60
Number of observations
81
58
16.77
Wald chi
18.36
19.09
0.1585
Prob > chi
0.0736
0.1204
0.2109
Pseudo R-squared
0.2164
0.2502
Asterisks denote statistical significance at 1% (***), 5% (**), and 10% (*).
dy/dx is for discrete change of dummy variable from 0 to 1
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-2.004***
(0.630)
-0.002
(0.003)
0.001
(0.004)
-0.009
(0.017)
40
22.29
0.0222
0.3418
Master Thesis Finance
Both the size and the experience of the LP seem to have a statistically significant
negative effect on the probability that the fraction of re-investments rejected is larger
than 25%. The lower probability of large investors facing a liquidity shock is probably the
reason large investors refuse fewer re-investments as allocation limits was named as
one of the important reasons for refusing to re-invest. Besides, the more experienced
the LP, the lower the probability the investor will reject more than 25% of the reinvestments. One extra year of experience, decreases the probability of rejecting more
than 25% of the re-investments with 14.7 percentage points. Since more experienced
investors sometimes have the right to re-invest in funds that are not accessible to new
investors (Lerner et al., 2007), this can explain their reluctance to deny to re-invest in
these surviving (superior) funds.
The North-America dummy again has a statistically significant negative effect, this time
on the probability to refuse to re-invest. North-American LPs are the oldest investors in
PE which means that, again, there lower probability to refuse to re-invest can be
explained by their access to superior re-investments.
For the investment committee specific variables the decision rule does not seem to have
a statistically significant effect on the refusal to re-invest probability. The average age of
the investment committee members, however, has. Experience (older age), again, has a
negative effect on the refusal to re-invest probability. Bottazzi, Da Rin & Hellmann
(2008) found that more experienced human capital within venture capitalist perform
more activities for the firm they invest in, including providing assistance with obtaining
additional financing. This could explain the lower probability of rejecting to re-invest. All
the variables not included in the fourth specification were excluded because they
predicted failure perfectly or robust standard errors could not be calculated.
4.2.2 Hypothesis 2.2
Investors less often reject to co-invest when the investment committee decides by
consensus
To test the relation between the rejection of co-investment and the decision rule,
multiple cross-sectional regressions of the refusal to co-invest on the decision rule,
investment committee specific variables and control variables are performed.
The model for testing hypothesis 2.2 is the following:
Co-investment rejection dummy variable = β0 + Decision Rulei*β1 + Other Investment
Committee Specific Variables + Control Variables + Error term
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For this model four specifications are estimated, starting with the first equation
including only the control variables and the last equation including the decision rule
variable, investment committee specific variables and the control variables. Table 16
summarizes the estimation results for this model.
Table 16: Logit regression results for refusal to co-invest
Refusal to co-invest
dy/dx
dy/dx
(std error)
(std error)
Decision by Consensus
-0.146
(0.144)
Europe
0.098
0.110
(0.188)
(0.224)
North-America
-0.097
-0.177
(0.195)
(0.230)
Pension fund
0.342**
0.437**
(0.169)
(0.185)
Insurance Company
0.308
0.581***
(0.216)
(0.101)
Fund-of-Fund
0.138
0.126
(0.158)
(0.188)
Foundation & Endowment
0.315*
0.312
(0.171)
(0.217)
Bank
-0.305*
-0.292
(0.161)
(0.181)
LP Experience
-0.014
-0.028
(0.052)
(0.060)
LP Size
-0.022
-0.051
(0.033)
(0.035)
Financial Center
-0.178
-0.253*
(0.112)
(0.135)
Workload
-0.004
(0.049)
Independence
Hierarchy
IC Size
0.005
(0.062)
dy/dx
(std error)
dy/dx
(std error)
-0.280*
(0.160)
-0.008
(0.226)
-0.328
(0.202)
0.356*
(0.215)
0.597***
(0.093)
0.144
(0.222)
0.335
(0.244)
-0.294
(0.209)
0.004
(0.062)
-0.091**
(0.042)
-0.249
(0.160)
-0.137
(0.205)
-0.362
(0.386)
-0.637**
(0.284)
0.945***
(0.059)
-0.033
(0.076)
0.010
(0.189)
-0.074
(0.071)
-0.214
(0.169)
1.674
(1.162)
-1.017**
(0.515)
0.936***
(0.100)
0.972***
(0.031)
0.132
(0.104)
-0.079
(0.092)
-0.558**
(0.242)
Proportion of the IC preparing
due diligence
-0.017
(0.340)
IC Average Age
-1.884
(1.181)
-0.682
(0.469)
-0.065
(0.238)
IC PE Experience
IC Tenure
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IC Consultancy Background
-0.030**
(0.015)
IC Finance Background
-0.026*
(0.014)
0.029**
(0.014)
IC Entrepreneurial
Background
75
Number of observations
95
63
17.52
Wald chi
11.71
21.92
0.1312
Prob > chi
0.3861
0.0566
0.1803
Pseudo R-squared
0.0951
0.2308
Asterisks denote statistical significance at 1% (***), 5% (**), and 10% (*).
dy/dx is for discrete change of dummy variable from 0 to 1
46
54.89
0.0000
0.6134
For the control variables, all investor category dummies are statistically significant for
one or more specification, however, the pension fund dummy is significant for every
specification. If the investor is a pension fund, the probability increases that the LP
rejects more than 85% of their co-investment opportunities. Corporate pension funds
are one of the largest co-investors and thus have a lot of experience with coinvestments (Fenn et al., 1997). Public pension funds, however, probably reject more
investments because their co-investment decisions are subject to a slow and
cumbersome approval process (Fenn et al., 1997). For the pension fund dummy, 79% of
the observations are public pension funds which can explain the higher refusal to coinvest. \All categories have a positive effect on the refusal to co-invest probability except
for the bank dummy. If the LP is a bank, the probability that it will reject more than 85%
of the co-investment decreases with 30.5 percentage points. Banks probably have
enough resources to do co-investments and are probably knowledgeable enough
considering their PE allocation. Size seems to have a statistically significant negative
effect on the refusal to co-invest probability, just as the location in a financial center.
More resources and a preferential access to information, both indicating they are more
knowledgeable, thus again seems to reduce the probability of refusing a co-investment.
For the investment committee specific variables the consensus decision rule only has a
statistically significant negative effect on the probability that more than 85% of the coinvestments are rejected when also the independence variable is included. Rejecting
fewer co-investments when deciding by consensus is as expected since co-investment
can be risky investments and investment committees deciding by consensus are more
likely to accept riskier investments. Also, IC size seems to have a statistically significant
negative effect on the refusal to co-invest probability. Feddersen and Pesendorfer
(1998) found that increasing the size of the jury increases the probability of convicting
an innocent person (Type I error). In the private equity sector, increasing the size of the
investment committee, and thus increasing the knowledge available, decreases the
probability of rejecting more than 85% of the co-investments. This could, however, by a
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Master Thesis Finance
success or a Type II error. Moreover, the proportion of investment committee members
with a consultancy/finance background decreases the refusal to co-invest probability.
The proportion of members with an entrepreneurial background, however, seems to
have a positive statistically significant effect on the refusal to co-invest probability.
It seems that more experience and knowledge both have a negative effect on the
probability of refusing to co-invest, just as it has a negative effect on the probability of
refusing to re-invest. It could be that more experienced LPs are more confident in
accepting co-investments because risk-aversion might decrease with experience.
4.3 Two-step Heckman Procedure
A two-step Heckman procedure can actually be performed by one single command
within STATA. However, to better understand the process and implications for the
regression results the two steps are performed separately.
First, the selection equation is estimated in which the decision rule is the dependent
variable and the control variables, as specified in section 3.3.2, are the independent
variables (see appendix D for estimation results).
To construct the λ (also called inverse mills ratio), the sum of each variable evaluated at
its mean value multiplied by its Probit estimate is obtained by using the formula:
Zy = z1 yˆ1 + z2 yˆ 2 + ... + zk yˆ k
Second, the standard normal pdf and standard normal cdf of Zy are calculated. To
eventually calculate the λ, the standard normal pdf is divided by the standard normal
cdf. The second step of the two-step Heckman procedure is to include this λ term as an
additional variable in the original base model. As mentioned in section 3.6.2 it is
important to include at least one variable in the selection equation which has no effect
on the dependent variables of the second step. Hence, excluded variables need to be
defined. The variables Europe and North-America are significant for the selection
equation and, based on the estimation results for specifications including the decision
rule and the control variables in this chapter, it is assumed that these two variables have
no effect on the dependent variables in step two. Hence, Europe and North-America are
chosen as the excluded variables and step two can be carried out. Table 18 and 19
summarize the estimation results.
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Master Thesis Finance
Table 18: OLS regression results for investor's effort levels,
including λ
Due
Due
Monitoring
diligence
diligence
index
hours
activity
index
index
Decision by Consensus
Coefficient
(std error)
Coefficient
(std error)
Coefficient
(std error)
-0.198*
(0.103)
0.008
(0.039)
-0.118*
(0.065)
Europe
Pension fund
0.084
(0.065)
-0.054
-0.120
(0.166)
0.027
(0.059)
(0.101)
0.181
(0.143)
0.096**
(0.045)
(0.072)
0.131
(0.183)
0.196
(0.165)
-0.073
(0.053)
0.081***
(0.026)
-0.175*
(0.105)
-0.047
(0.049)
0.020
(0.086)
0.023
(0.073)
-0.039**
(0.018)
0.002
(0.011)
-0.013
(0.041)
0.016
(0.0154)
-0.403
(0.287)
0.073
(0.086)
Insurance Company
Fund-of-Fund
Foundation &
Endowment
Bank
LP Experience
LP Size
Financial Center
Workload
λ
Number of observations
F
Prob > F
0.050
0.021
(0.118)
0.055
(0.096)
-0.018
(0.031)
0.046***
(0.017)
0.036
(0.062)
-0.042
(0.032)
0.022
(0.135)
86
92
90
2.4
2.27
3.73
0.0155
0.0215
0.0003
0.2037
0.1614
0.2074
R-squared
Asterisks denote statistical significance at 1% (***), 5% (**), and
10% (*).
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Table 19: Logit regression results for investor's
activism, including λ
Refusal to
re-invest
dy/dx
(std error)
Refusal to
co-invest
dy/dx
(std error)
Decision by Consensus
-0.083
(0.157)
-0.140
(0.137)
North-America
0.282
(0.513)
Pension fund
-0.357***
(0.129)
0.387*
(0.189)
0.309
(0.520)
0.850**
(0.362)
-0.252***
(0.085)
-0.255***
(0.082)
0.123
(0.186)
0.251
(0.248)
-0.260
(0.187)
-0.032
(0.055)
LP Size
0.056
(0.162)
-0.027
(0.040)
Financial Center
-0.375
(0.310)
-0.246*
(0.132)
Workload
-0.234
(0.221)
-0.023
(0.067)
λ
-3.004
(2.363)
-0.196
(0.343)
54
71
Wald chi
13.02
13.6
Prob > chi
0.2923
0.1922
Insurance Company
Fund-of-Fund
Foundation &
Endowment
Bank
LP Experience
Number of observations
0.3092
0.1357
Pseudo R-squared
Asterisks denote statistical significance at 1% (***), 5%
(**), and 10% (*).
For the due diligence hours and monitoring index, the decision rule variable still has a
statistically significant negative impact on the intensity of the effort levels. The λ
coefficient, however, is not statistically significant for all three regressions, indicating
there is no heterogeneity bias present in the data sample. There are no significant
unmeasured characteristics present that are related to the investment committee
decision rule and that have an effect on the effort levels.
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For the refusal to re- and co-invest, the decision rule has no statistically significant effect
for the base regression. The λ coefficient is, again, insignificant, indicating there is no
heterogeneity bias present in the data sample. For this reason the results from section
4.1 and 4.2 are used for the conclusion.
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5. Conclusion and Recommendations
In this chapter the overall results of the regressions will be discussed in order to answer
the main research question:
What is the effect of the investment committee’s decision rule on investor’s effort levels
and activism?
5.1 Conclusion
The decision rule
Although not all the results are statistically significant, there is an indication that the
consensus decision rule has a negative effect on the effort levels performed by the LP
and the probability of rejecting co-investments. This means the following for the
hypotheses:
Table 20: Hypotheses results
Hypothesis
Hypothesis 1: Investor’s effort levels are higher when the
investment committee decides by consensus.
Hypothesis 1.1: The investor’s due diligence hours index is
higher when the investment committee decides by consensus.
not confirmed
Hypothesis 1.2: The investor’s due diligence activity index is
higher when the investment committee decides by consensus.
not confirmed
Hypothesis 1.3: The investor’s monitoring index is higher when
the investment committee decides by consensus.
Hypothesis 2: Investor’s activism is higher when the
investment committee decides by consensus
Hypothesis 2.1: Investors more often reject to re-invest when
the investment committee decides by consensus
Hypothesis 2.2: Investors less often reject to co-invest when
the investment committee decides by consensus
Result
not confirmed
not confirmed
partially confirmed
not confirmed
partially confirmed
If investment committees decide by consensus, contradicting the theory, less due
diligence hours and monitoring activities are performed. Besides, fewer co-investments
are rejected. The theory stated that committee members exchange more information
and search intensively for the better solution when they have to reach a consensus. This
contradicting result is difficult to explain, but it might be contributed to the fact that
reaching a consensus leads to less discussion due to herding behavior and strategic
voting. Investment committee members assume that their vote is pivotal when voting
simultaneously and by consensus. Hence, they might rely on the information gathered
by other members and not on their own gathered private information, when
communication is allowed. Another explanation could be that when voting is not
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Master Thesis Finance
simultaneously, members try to deduce information from others members’ votes as
predicted by Austen-Smith and Banks (1996) and this would actually lead to lower effort
levels. The rejection of fewer co-investments when deciding by consensus could be
explained by the fact that co-investments can be perceived as more risky investments
and investment committees deciding by consensus (inducing a discussion) take higher
risks.
Control variables
Besides the decision rule, the heterogeneity among investors seems to have a
statistically significant effect on the effort levels and activism of LPs. For the different
investor categories, fund-of-funds have a statistically significant positive effect for all
specifications for the due diligence activity index. This is as expected since fund-of-funds
have due diligence at the core of their activities. Pension funds seem to be another
important category, especially for the refusal of co-investments. The pension fund
dummy has a significant positive effect on the probability of rejecting more than 85% of
the co-investments. Because of the stricter rules pension funds have to adhere to and
because of their low risk tolerance it is expected pension funds are more selective in
selecting their co-investments and thereby reject more co-investments.
The continent the LP is located is statistically significant for one or more specifications
for every dependent variable. While the European and North-American dummies have a
negative effect on the due diligence (both for the activities and the hours) effort levels,
it has a positive effect on monitoring effort levels. Besides, the North-American dummy
has a negative effect on investor’s activism. Hobohm (2008) finds positive funds returns
for European investors which could explain the higher effort levels for monitoring (if it
assumed higher effort levels lead to better fund selection). More difficult is it to explain
the negative effect on the due diligence effort levels. However, it could be that because
of the higher level of experience of American and European LPs, they need less time and
activities for their due diligence. The lower probability of rejecting re- and coinvestments by American LPs can be explained by the preferential access to these
superior investments by the, on average, older American LPs.
The LP size coefficient has a positive and almost always statistically significant effect on
the effort levels performed by the LP. This is as expected since larger LPs have more
resources to invest and to make sure they make the right investments. Size, however,
has a (significant) negative effect on the rejection of re- and co-investments. The more
AUM the LP has, the fewer re- and co-investments are rejected. This is as expected,
since LPs with more AUM have less chance to face a liquidity shock and are thus better
able to re- and co-invest.
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Master Thesis Finance
LP experience has a significant negative effect on the rejection of re-investments. The
more experienced the Limited Partner, the fewer investments are rejected. More
experienced investors sometimes have the right to re-invest in funds that are not
accessible to new investors (Lerner et al., 2007), which could explain their reluctance to
deny those re-investments.
The financial center dummy seems only to be statistically significant for the due
diligence hours index and the refusal to co-invest. Being located in a financial center
reduces the hours LPs spent on due diligence. This is as expected since it takes LPs less
time to find the necessary information because of the preferential access to information
or networks. The probability of refusing a co-investment also decreases when the LP is
located in a financial center. Due to the easier access to information, LPs are probably
better informed and more confident in accepting a co-investment.
Only for the base regressions, workload is statistically significant for the monitoring
index and the refusal to re-invest. As soon as one of the investment committee specific
variables is included, workload is no longer statistically significant. As expected, it has a
negative effect on the monitoring efforts; the higher the workload, the less time LPs
have to spent on monitoring activities. A higher workload also increases the chance of
refusing more than 25% of the re-investments. Since LPs with a higher workload are
probably less informed, they could be less confident in accepting a re-investment.
All the control variables are statistically significant for one or more dependent variables
and one or more specifications. It thus seems that the heterogeneity among investors is
an important factor determining the intensity of LP’s effort levels and activism.
Conclusion
This thesis contributes to the PE literature in two important dimensions. First, it seems
that the heterogeneity among LPs has a significant effect on their effort levels and
activism. Second, the interesting hypothesis that the decision rule of the investment
committee is an important determinant of the due diligence hours and monitoring
activities performed and the rejection of co-investments is supported.
Size seems to be one of the most important characteristics of the LPs influencing their
effort levels and activism. Larger LPs perform more activities to make sure they make
the right investments and it could be that there are important economies of scale when
investing in PE. This would have important implications for small and starting LPs.
Besides, experience (measured by firm age) seems to have a statistically significant
effect on the probability of rejecting re-investments. Older LPs have access to surviving
(superior) funds to which younger LPs have no access. The continent the LP is located,
which could also be a measure for experience, is also often significant. This again has
important implications for starting LPs. At last, the effect the consensus decision rule
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Master Thesis Finance
has on LP’s effort levels and activism is an important finding as PE investors now may
want to rethink the working of their investment committee. Since there was no data
available on the performance of LPs, it cannot be said if rejecting fewer co-investments
is positive. Co-investment reduces the total fee amount, so it is important for LPs to
assess the performance of their co-investments and their total portfolio and to decide if
they want to reject more or less co-investments. Also, if the consensus decision rule
decreases the amount of due diligence hours and monitoring activities, LPs may want to
change their decision threshold to majority as this increases their due diligence hours
and monitoring activities. Increasing the LPs effort levels may lead to better decision
making and increased performance.
5.2 Limitations and Future Research
This thesis is subject to some limitations, which might impact the coefficients of the
different variables and thereby the conclusions of this research. A first drawback is the
relatively small sample size which makes the results non-representative for the LPs
population. Also, none of the investment committee specific variables (when all were
included) was statistically significant, probably due to the small sample size.
For future research an important extension would be to have the ex post performance
of the re- and co-investments to investigate if the consensus decision rule increases the
chance of a Type I error (give up gains) or success (avoid losses). With such an extension
it can be tested if the consensus decision rule increases or decreases the quality of
investment committee decision making. Another interesting extension would be to
include the communication among investment committee members, as research shows
that this is also an important determinant in the decision making process.
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Master Thesis Finance
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Appendix A: The Survey
Dear Investor,
We thank you for your participation in a survey that aims at learning how Limited Partners conduct their
due diligence of Private Equity investments, therefore providing new insights useful for your investing
activities.
The data we collect will be stored on a secure server at Tilburg University. For the protection of your
privacy responses will be used only in aggregate form for the purpose of statistical analysis and will not be
made available to anybody else.
Answering the Survey will take about 20 minutes once you have the necessary information at hand.
All respondents who provide substantially complete answers are eligible to receive our White Paper based
on the collected information, and to be invited with a discount to the conference where the results will be
presented and discussed.
You can reach us anytime for any questions by e-mail:
[email protected], and [email protected], or by phone:
+39 347 157-5972 and +31 20 525-4153
With many thanks for your time,
Prof. Marco Da Rin
Tilburg University, IGIER-Bocconi University,
and European Corporate Governance Institute
http://center.uvt.nl/staff/darin/
Prof. Ludovic Phalippou
University of Amsterdam Business School
http://www1.fee.uva.nl/pp/lphalippou/
1. Organization characteristics
1.1.
When was your firm created? If you feel uncomfortable with providing the exact year,
please indicate a date range.
1.2 What is the nature of your organization?
[Public Pension Fund, Corporate Pension Fund, Corporate Investor, Family Office, Public
Endowment, Private Endowment, Foundation, Insurance company, Bank, Governmentowned bank, Asset manager, Fund-of-funds, Other]
1.3 In which city and country is the investment committee (or person making investment decisions)
located? Country:_____
City:_____
1.4 Have you ever invested in buyout funds?
YES
NO
If YES:
1.4.1 In which year did your organization start investing in buyout funds?
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1.4.2 How many advisory boards of buyout funds does your organization currently sit on?
1.4.3 What proportion of the buyout funds’ advisory board meetings do you
attend each year? ______%
1.5 Have you ever invested in venture capital funds?
YES
NO
If YES:
1.5.1 In which year did your organization start investing in venture capital funds?
1.5.2 How many advisory boards of venture capital funds does your organization
currently sit on?
1.5.3 What proportion of the venture capital funds’ advisory board meetings do
you attend each year? YES
NO
1.6 Does the team that manages buyout and venture capital investments also manage:
. Real estate funds
YES
NO
. Hedge funds
YES
NO
. Debt instruments
YES
NO
. Direct co-investments
YES
NO
1.7 Please indicate the relative role played by external factors (vs. internal factors) in
determining your organization’s PE allocation, where:
(a) External factors: perception of the PE asset class (e.g. publicized returns of PE and of other
asset classes, diversification benefits);
(b) Internal factors: your past performance in PE, your access to funds, your access to coinvestment opportunities, the concessions you obtained on funds’ terms and conditions etc.:
EXTERNAL:
INTERNAL:
Our survey is anonymous. If you are not concerned about anonymity please provide us with a contact
email or phone number; if so, we will send you our final Report and working paper, and invite you at the
conference where these results will be discussed. You can also use the space below to give us any
comments. Thank you!
Name:
E-mail/phone:
2. Allocation
Please fill in the following table. Please specify the currency you use [USD, Yen, Euro, GBP].
Currency:_____
2000
(or year you started to invest,
if more recent)
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Master Thesis Finance
What was the amount (or range) of your organization Asset
Under Management (not just PE) (in millions)?
What was the amount (or range) of private equity funds under
management at your organization?
What was the amount (or range) of buyout investments under
management at your organization (in millions)?
What was the amount (or range) of venture capital
investments under management at your organization (in
millions)?
What was the number of buyout funds directly held by your
organization?
What was the number of venture capital funds directly held by
your organization?
How many (full-time equivalent) investment professionals were
working on these buyout and venture capital investments?
What was the number of buyout and venture capital fund-offunds directly held by your organization?
3. Access to Funds
3.1 Over the last five years, what is the fraction of buyout funds for which you faced a full refusal of your
capital commitment? _____%
3.2 Over the last five years, what is the fraction of buyout funds for which you faced a partial refusal of
your capital commitment? _____%
3.3 Over the last five years, what is the fraction of venture capital funds for which you faced a full refusal
of your capital commitment? _____%
3.4 Over the last five years, what is the fraction of venture capital funds for which you faced a partial
refusal of your capital commitment? _____%
3.5 Over the last five years, have you refrained from offering a commitment to a buyout fund in which you
would have liked to invest, because you expected a refusal?
YES NO
If YES, for how many funds?
3.6 Over the last five years, have you refrained from offering a commitment to a buyout fund in which you
would have liked to invest, because you expected a refusal?
YES NO
If YES, for how many funds?
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Master Thesis Finance
3.7 In your experience, does investing in a fund give you priority over other investors when the GP raises
subsequent funds? ALWAYS SOMETIMES NEVER
If SOMETIMES or ALWAYS:
Why do you think you get priority?
a. If I would not re-invest, other investors would be suspicious and would not invest.
YES DEFINITELY
YES POSSIBLY
NO
b. If the GP would not keep me in, I could replicate their strategy (myself or in
cooperation with another GP).
YES DEFINITELY
YES POSSIBLY
NO
c. Other reasons (please specify):
3.8 If you think GPs constrain the size of their fund, why do you think they do so?
4.
Co-investments
4.1 Have you ever been offered a co-investment opportunity by a GP? YES
NO
If yes:
1. Which fraction of co-investment opportunities do you typically reject? ____%
2. What fraction of your PE portfolio was made of co-investment in 2008? ____%
3. What are your firm’s motivations for co-investing? (multiple choices possible):
1. Improve performance before fees
YES
NO
2. Reduce total fees
YES
NO
3. Customize portfolio (adjust exposure to country, industry…)
YES
NO
4. Free-ride on GP due diligence
YES
NO
5. Other (please specify):________________________________ YES
NO
4. Which of the above criteria (1 to 5) is your firm’s main motivation for co-investing? Criterion
number:_____
5.
Investment Committee
5.1 Do you have an Investment Committee (IC)?
YES
The investment decisions of the IC:
ARE MADE IN A COMPLETELY INDEPENDENT WAY
CAN BE INFLUENCED BY PEOPLE OUTSIDE THE IC
CAN BE VETOED BY PEOPLE OUTSIDE THE IC
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Master Thesis Finance
If YES:
1. IC decisions are taken by:
MAJORITY
CONSENSUS
OTHER
2. How many members did the IC have in 2008?
3. How many members with voting rights did the IC have in 2008?
4. How many members with voting rights did the IC have in 2003?
5. How many IC members with voting rights left your organization between 2003 and 2008?
6. How many professionals prepared due diligence in 2008?
7. How many of these professionals also sat on the IC in 2008?
8. Please fill in the following table for each IC member with voting rights:
(you can add lines if you have more IC members with voting rights)
Age
Year started
working in PE
Year joined
your
organization
Year joined the
IC
Professional Background
(Yes or No)
Corporate
Finance
Entrepreneur
If NO:
1. What is the age of the person taking the investment decision?
2. In what year did this person start working in PE?
3. In what year did this person start working for your firm?
4. How many people performed this job over the last 5 years?
5. What is the professional background of this person?
6.
HR Policy
6.1 Is part of the compensation of investment committee members directly related to financial
performance?
YES
NO
If YES:
6.1.1 Is such performance bonus larger than the fixed part of their compensation in a typical year?
(This applies also to the person who makes investment decisions when there is not an
investment committee).
YES
NO
6.2 Do (some of) the investment professionals that do not sit on the investment committee receive a
performance-related bonus?
YES
NO
If YES:
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Other
(specify)
Master Thesis Finance
6.2.1 Is such a performance bonus larger than the fixed part of their compensation in a typical year?
YES
NO
6.3 Has the compensation policy changed over the last ten years?
If YES:
6.3.1. Can you please briefly describe how?
7.
YES
NO
Contracting
7.1. Do you benchmark the contract between you and the GP?
YES
NO
7.2. How much time do you spend on benchmarking the contract? (please provide a full-time employee
equivalent, expressed in number of days)
Internally:
Externally (consultants):
7.3. Do you typically negotiate the contract terms?
ALWAYS
SOMETIMES
NEVER
If ALWAYS or SOMETIMES:
7.3.1 Which terms do you negotiate?
7.4 Do you obtain side letters?
ALWAYS
SOMETIMES
NEVER
If ALWAYS or SOMETIMES:
7.4.1
For which fraction of the funds do you obtain side letters? _____%
7.5 Do you obtain ‘most preferred nation’ clauses?
ALWAYS
SOMETIMES
NEVER
If ALWAYS or SOMETIMES:
7.5.1 For which fraction of the funds do you invest in? _____%
8. Due diligence – General
8.1 Which pieces of information from GPs are crucial to your decision, i.e., you do not invest if you are not
satisfied with them or do not obtain them?
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Master Thesis Finance
8.2 Do you (or your consultant) calculate your own aggregate performance measure based on the
information you are provided with?
ALWAYS
SOMETIMES
NEVER
8.3 Do you (or your consultant) benchmark GPs' track records?
ALWAYS
SOMETIMES
NEVER
8.4. What do you (or your consultant) do to measure GP’s past performance?
. Use the NAV provided by the GP:
. Compute your own fair value estimation of non-liquidated investments: YES
. Look only at the performance of liquidated investments:
YES
NO
YES
NO
NO
8.5 Do you (or your consultant) interview the executives of the GP's portfolio companies?
ALWAYS
SOMETIMES
NEVER
8.6 Consider the set of funds for which you have received or requested a Private Placement
Memorandum (PPM) over the last 5 years.
What fraction went through due diligence? _____%
What fraction did you financially commit to?
_____%
9.
Due diligence – Investing in funds
9.1 Do you invest in first-time funds?
YES
NO
If NO:
9.1.1 Why not?
9.1.2 Would you invest if their fees were lower?
YES
NO
If yes:
9.1.3 What are your firm’s reasons for investing in first-time funds? (multiple choices possible)
1. Because there was a credible special LP
YES
NO
2. Given our size we cannot discard all the first-time funds
YES
NO
3. Because we do not always invest for performance reasons
YES
NO
4. Because we expect these first time funds to outperform
YES
NO
5. Because we get priority access to follow-up funds if that
team is successful
YES
NO
6. Because the GP’s partners made a sizeable financial
commitment to the fund
YES
NO
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Master Thesis Finance
7. Because with these funds we can obtain stricter covenants
YES
8. Because we do not have access to seasoned funds
YES
9. Because of the presence of a credible strategic partner
10.Other reasons (please specify):________________________ YES
NO
NO
YES
NO
NO
9.1.4 Which of the above reasons (1 to 10) is your firm’s main one for investing in first-time funds?
Reason number:
9.1.5 How many first-time funds have you invested in since 1998?
9.1.6 Consider the criteria that your firm typically uses to select a first-time fund. Please tell us
about their importance (for each criterion check one answer only).
Criterion
Crucial
1
Partners' previous successes in PE
2
Partners’ previous successes in non-PE jobs
3
Partners’ previous experience in working together
4
Partners’ quality of education
5
Quality of the partners’ network of contacts
6
The advisor/gatekeeper’s opinion
7
The proposed investment strategy
8
Commitments to this fund by top LPs
9
The fund’s size
10
The level and structure of fees
11
Whether the fund provides exposure to a certain
industry/geography/stage
12
The opportunity to access follow-on funds
13
Whether the fund may generate business for other
divisions of my organization
14
Co-investment opportunities
15
Other (specify):
Very
Important
Somewhat
Important
Irrelevant
9.1.7 Which of the above criteria is the most important? Criterion number:
9.1.8 Please indicate any other criteria that are crucial for your decision to invest in a first-time fund.
9.1.9 How much time is spent on the typical due diligence for a first-time fund (full time employee
equivalent, number of days)? (NB: Not how long the due diligence process)
Internally:
Externally (consultants):
9.1.10 What proportion of your time is spent on quantitative (vs. qualitative) due
diligence for a first-time GP’s fund? _____% quantitative
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Master Thesis Finance
9.2 Consider funds raised by seasoned GPs in which your firm had not previously invested. How
many such funds have you invested in over the last ten years?
If more than zero:
9.2.1 Consider the criteria that your firm typically uses to select these funds. Please tell us about
their importance (for each criterion check one answer only).
Criterion
Crucial
1
The GP’s reported aggregate IRR on previous funds
2
3
The GP’s reported aggregate multiples on previous
funds
Partners’ quality of education
4
Quality of the partners’ network of contacts
5
The GP’s reputation
6
Stability of the team at the partner level
7
The advisor/gatekeeper’s opinion
8
The proposed investment strategy
9
Commitments to this fund by top LPs
10
The fund’s size
11
12
The change in fund size from previous funds
The level and structure of fees
13
Whether the fund provides exposure to a certain
industry, geography, or stage
14
The valuation of unrealized investments (NAVs) in
previous funds
Whether the fund may generate business for other
divisions of my organization
15
16
Co-investment opportunities
17
Renewed commitment to this fund by its existing LPs
18
Other (please specify):
Very
Important
Somewhat
Important
Irrelevant
9.2.2 Which of the above criteria is the most important? Criterion number:
9.2.3 How much time is spent on the typical due diligence for a seasoned GP’s fund (full time
employee equivalent, number of days)? (NB: Not how long the due diligence process)
Internally:
Externally (consultants):
9.2.4
What proportion of your time is spent on quantitative (vs. qualitative) due diligence for a
seasoned GP’s fund? _____% quantitative.
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Master Thesis Finance
9.3 Consider re-investing in a seasoned GP. How many such funds have you invested in over the last ten
years?
If more than zero:
9.3.1 Consider the criteria your firm typically uses for this decision. Please tell us about their
importance (for each criterion check one answer only).
Criterion
Crucial
1
The GP’s reported aggregate IRR on previous funds
2
3
The GP’s reported aggregate multiples on previous
funds
Partners’ quality of education
4
Quality of the partners’ network of contacts
5
The GP’s reputation
6
Stability of the team at the partner level
7
The advisor/gatekeeper’s opinion
8
The proposed investment strategy
9
Commitments to this fund by top LPs
10
The fund’s size
11
12
The change in fund size from previous funds
The level and structure of fees
13
The syndicate/club co-investors in previous funds
14
The quality of the GP’s reporting on our previous
investments
Whether the fund provides exposure to a certain
industry, geography, or stage
15
16
18
The valuation of unrealized investments (NAVs) in
previous funds
Whether the fund may generate business for other
divisions of my organization
Co-investment opportunities
19
Renewed commitment to this fund by its existing LPs
20
Other (please specify):
17
Very
Important
Somewhat
Important
Irrelevant
9.3.2 Which of the above criteria is the most important? Criterion number:
9.3.3 How much time is spent on the typical due diligence for re-investing in a seasoned GP’s fund
(full time employee equivalent, number of days)? (NB: Not how long the due diligence
process)
Internally:
Externally (consultants):
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Master Thesis Finance
9.3.4 What proportion of your time is spent on quantitative (vs. qualitative) due diligence for a reinvesting in a seasoned GP’s fund? _____% quantitative.
9.3.5 How often did you refuse to re-invest with a GP over the last 5 years? ____%
If more than zero: please evaluate the following reasons for not re-investing and indicate their
frequency.
Reason
Always
1
The fund’s size increased too much
2
The GP deviated from their original strategy
3
The GP’s fees increased too much
4
The GP had charged excessive company fees
5
The GP’s performance had been disappointing
6
Some key professionals/partners left the GP
7
Other reasons:
Sometimes
Never
9.5 If you outsource some of the due diligence process, could you please evaluate the following reasons:
1. Lack of experience in PE investing
ALWAYS
SOMETIMES
NEVER
2. Gives access to unique information
ALWAYS
SOMETIMES
NEVER
3. Other reasons (please specify)
ALWAYS
SOMETIMES
NEVER
9.6 Has your firm changed its main investment criterion for investing in first-time or seasoned funds over
the last ten years? YES
NO
If YES:
9.7 Can you please describe how?
10. MONITORING
10.1 For which % of buyout portfolio companies do you obtain from GPs information on:
1. Portfolio company fees:
_____%
2. Leverage
_____%
10.2 Do you keep track of the composition of your PE portfolio in terms of industry/size/country?
YES
NO
10.4 How much time is spent to keep track of the cash flows realized on each fund? (in days, full-time
employee equivalent)
Internally:
Externally (consultants):
10.5 Do you provide any services (or support) to the GPs whose funds you invest in?
NO
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YES
Master Thesis Finance
If YES:
10.5.1 Can you specify which ones?
10.6 Do you visit portfolio companies?
ALWAYS
SOMETIMES
NEVER
Thank you for your cooperation. Please do not forget to save your answers.
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Master Thesis Finance
Appendix B: Correlation Matrices
1.00
0.17
0.25
0.35
-0.20
1.00
0.63
0.21
0.15
1.00
0.14
0.13
1.00
-0.15
Financial Center
Workload
1.00
0.45
0.18
-0.09
0.20
-0.08
LP PE Allocation
1.00
0.17
0.02
-0.22
-0.32
0.11
-0.20
LP Size
1.00
-0.22
-0.40
-0.18
-0.09
0.35
-0.09
0.11
LP PE
Experience
LP Experience
1.00
-0.15
-0.13
0.03
0.04
-0.15
-0.07
0.00
-0.02
Endowment
1.00
-0.10
-0.17
-0.14
0.22
-0.02
0.41
-0.01
0.01
0.20
Bank
1.00
-0.17
-0.15
-0.26
-0.21
0.19
0.11
0.18
-0.03
0.20
-0.12
Insurance
Company
1.00
0.02
-0.13
-0.14
0.00
0.32
0.17
0.12
-0.20
-0.08
0.05
-0.36
Fund-of-Fund
1.00
-0.67
-0.12
-0.10
0.16
0.12
-0.21
-0.16
-0.06
0.08
0.20
0.06
0.25
Pension Fund
Europe
North-America
Pension Fund
Insurance Company
Bank
Fund-of-Fund
Endowment
LP Experience
LP PE Experience
LP Size
LP PE Allocation
Workload
Financial Center
North-America
Europe
Table 1: Correlation matrix control variables. The table shows the correlation between the control variables. The variables excluded from
the regressions due to multicollinearity are Firm PE experience and PE allocation. Although Dummy Europe and Dummy North-America have
a high correlation of -0.67, both dummies are still included in the regressions because of their significance in other studies (Da rin &
Phalippou, 2010).
Control Variables
1.00
IC LP Tenure
Consultancy
Background
1.00
0.04
-0.35
-0.19
-0.05
-0.24
0.19
1.00
0.13
0.27
0.16
0.06
-0.12
1.00
0.21
0.32
0.05
-0.10
1.00
0.68
0.07
-0.10
1.00
0.01
0.09
1.00
-0.39
1.00
Entrepreneurial Background
0.02
0.01
-0.01
0.01
0.11
-0.09
0.04
0.05
-0.03
-0.09
0.21
-0.15
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Entrepreneurial
Background
IC Tenure
1.00
0.00
-0.19
0.33
-0.12
-0.05
-0.08
-0.05
Finance
Background
IC PE
Experience
1.00
-0.26
0.08
-0.03
-0.11
0.07
-0.08
0.22
0.07
IC Age
1.00
0.35
0.05
0.05
-0.01
0.16
0.07
0.07
0.23
0.08
IC Turnover
1.00
0.32
-0.17
-0.18
0.07
0.04
-0.33
0.05
0.24
0.06
-0.04
IC Preparing
Due Diligence
1.00
-0.11
-0.03
0.15
0.03
-0.02
0.10
0.07
-0.03
-0.01
0.06
-0.22
IC Size
Independence
Decision Rule
Independence
Hierarchy
IC Size
IC Preparing Due Diligence
IC Turnover
IC Age
IC PE experience
IC Tenure
IC LP Tenure
Consultancy Background
Finance Background
Hierarchy
Decision Rule
Table 2: Correlation matrix investment committee specific variables. The table shows the correlation between the investment committee
specific variables. The only variable excluded from the the regressions due to multicollinearity is LP tenure.
Investment Committee
1.00
Master Thesis Finance
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Master Thesis Finance
Appendix C: Sample Representativeness
Table 3: LP sample coverage and mean (median) AUM per country
Number of observations
Mean AUM ($ bn)
Sample
PEI
Coverage
Sample
PEI
Spread
US
53
1,162
5%
6.0
42.8
-36.8
UK
18
201
9%
9.2
39.8
-30.6
Germany
14
85
16%
31.0
72.4
-41.5
12
Austria
4
33%
5.0
43.7
-38.7
Switzerland
5
66
8%
4.7
18.2
-13.4
51
France
4
8%
14.3
72.6
-58.3
Belgium
2
21
10%
5.9
40.7
-34.9
Netherlands
4
40
10%
11.9
35.5
-23.6
Italy
1
37
3%
59.8
Spain
3
32
9%
0.6
111.0
-110.3
Finland
2
37
5%
5.8
10.2
-4.4
Sweden
3
28
11%
10.1
14.2
-4.1
35
Denmark
8
23%
13.5
20.8
-7.3
Norway
3
23
13%
12.8
39.3
-26.4
Ireland
2
12
17%
12.4
79.5
-67.0
Luxembourg
2
5
40%
5.0
Canada
8
70
11%
14.4
60.5
-46.1
Australia
12
99
12%
6.1
12.0
-5.9
New Zealand
2
8
25%
15.0
Japan
10
90
11%
67.9
88.0
-20.1
India
Taiwan
South Korea
South Africa
Mexico
Iceland
Total
Median AUM ($ bn)
Sample
PEI
Spread
1.2
2.4
-1.2
1.4
3.1
-1.7
1.6
4.0
-2.4
1.8
20.5
-18.7
5.0
5.5
-0.5
14.3
2.9
11.4
5.9
6.3
-0.4
1.8
5.0
-3.1
5.0
0.6
18.5
-17.9
-5.8
2.9
-8.7
2.6
8.0
-5.4
3.4
7.0
-3.7
2.3
2.7
-0.4
12.4
30.9
-18.4
5.0
2.7
10.2
-7.5
3.5
2.3
1.2
15.0
6.0
14.5
-8.5
1
4
38
10
3%
40%
2.8
-
-
2.8
-
-
2
1
1
1
170
40
21
4
12
2,239
5%
5%
25%
8%
8%
6.0
0.4
2.5
11.5
56.7
24.0
4.2
45.0
-18.0
-3.9
-33.6
6.0
0.4
2.5
4.0
6.8
7.5
2.2
8.0
-1.4
-1.9
-4.0
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Master Thesis Finance
Table 4: LP mean (median) PE allocation and experience per country
Mean PE allocation
Median PE allocation
($ bn)
($ bn)
Sample
PEI
Spread
Sample
PEI
Spread
0.5
US
1.8
1.3
0.4
0.2
0.1
-1.9
UK
1.4
3.3
0.6
0.3
0.3
-1.1
Germany
0.7
1.8
0.3
0.9
-0.6
-0.2
Austria
0.2
0.4
0.2
0.3
-0.1
-0.8
Switzerland
1.7
2.4
0.7
0.4
0.3
-3.3
France
5.4
8.6
3.7
2.5
1.2
-37.9
Belgium
0.4
38.2
0.4
0.4
0.0
8.7
Netherlands
11.2
2.5
0.4
0.3
0.1
1.8
Italy
2.2
0.4
2.2
0.1
2.0
-0.1
Spain
0.5
0.6
0.3
0.6
-0.3
0.3
Finland
0.8
0.5
0.8
0.2
0.6
-0.1
Sweden
0.7
0.8
0.3
0.3
0.0
0.8
Denmark
1.5
0.7
0.9
0.5
0.4
-0.1
Norway
0.2
0.3
0.2
0.1
0.0
0.2
Ireland
0.8
0.6
0.8
0.5
0.3
Luxembourg
4.4
4.4
0.8
Canada
1.5
0.6
0.5
2.8
-2.3
-0.3
Australia
0.7
1.0
0.3
0.1
0.2
New Zealand
0.4
0.4
-8.5
Japan
0.8
9.4
0.5
0.4
0.1
India
0.0
0.0
Taiwan
0.1
0.0
-1.4
South Korea
0.1
1.4
0.1
0.1
0.0
-0.8
South Africa
0.1
0.9
0.1
0.2
-0.1
0.0
Mexico
0.2
0.2
0.2
Iceland
0.1
0.1
Total
37.9
76.0
-38.1
0.7
0.6
0.2
- 72 -
Median year start PE
investing
Sample
PEI
Spread
1998
1998
0
1995
1994
1
2000
2001
-1
2001
2001
-1
2000
1997
3
1997
1998
-2
1991
1994
-3
1999
1997
2
1998
2001
-3
2000
2002
-2
1996
1995
1
1992
2000
-8
2000
2000
0
1998
1997
1
2005
1996
9
2002
1999
2000
-1
1998
2000
-2
2002
2004
-2
2001
1999
2
2008
1998
2002
2004
-3
1985
1996
-11
2006
2000
1999
1999
0
Master Thesis Finance
Table 5: LP sample coverage per LP category and mean (median) AUM
Number of observations
Mean AUM ($ bn)
Sample
PEI
Coverage Sample
PEI
Spread
Pension Fund
34
392
9%
11.4
20.4
-9.0
Median AUM ($ bn)
Sample
PEI
Spread
3.2
4.2
-1.0
Corporate Investor
(non-pension)
6
410
1%
29.5
19.1
10.4
0.3
5.7
-5.4
Foundation & Endowment
26
370
7%
2.5
20.9
-18.4
0.6
0.8
-0.2
Insurance Company
17
155
11%
70.2
72.7
-2.5
36.7
18.7
18.0
Bank
14
289
5%
23.7
135.5
-111.8
0.4
19.4
-19.0
Government-owned investor
(excluding pension funds)
14
93
15%
11.0
74.6
-63.6
4.0
4.6
-0.6
Family Office
9
68
13%
2.3
3.9
-1.6
1.5
2.0
-0.5
Fund-of-funds
(excluding asset managers)
Asset Managers
36
244
15%
5.0
14.1
-9.1
2.0
2.0
0.0
8
394
2%
7.3
63.8
-56.5
1.3
2.4
-1.1
Other
6
14
43%
1.2
11.9
-10.7
1.2
1.3
-0.1
Total
170
2,429
7%
16.4
43.7
-27.3
5.1
6.1
-1.0
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Master Thesis Finance
Table 6: LP sample coverage per category, PE allocation and experience
Mean PE allocation
Median PE allocation
($ bn)
($ bn)
Sample
PEI
Spread
Sample
PEI
Spread
Pension Fund
1.3
1.1
0.2
0.3
0.2
0.1
Median year start PE
investing
Sample
PEI
Spread
1998
1999
-1
Corporate Investor
(non-pension)
Foundation & Endowment
0.2
0.6
-0.4
0.1
0.2
-0.1
2001
1997
4
0.4
0.8
-0.5
0.1
0.1
0.0
1998
1995
3
Insurance Company
0.9
1.9
-1.0
0.5
0.6
-0.1
2000
1996
4
Bank
0.6
9.0
-8.3
0.3
0.6
-0.3
1998
1998
0
Government-owned investor
(excluding pension funds)
0.8
11.2
-10.4
0.3
0.2
0.1
1998
1996
2
Family Office
0.4
0.3
0.1
0.3
0.2
0.1
1997
1994
3
Fund-of-funds
(excluding asset managers)
3.3
9.7
-6.4
1.3
1.1
0.2
2000
1999
1
Asset Managers
6.3
6.4
-0.1
0.9
0.7
0.2
1999
1997
2
Other
0.4
3.2
-2.8
0.4
0.7
-0.3
1998
1995
3
Total
1.4
4.4
-3.0
0.4
0.5
0.0
1999
1997
2
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Master Thesis Finance
Appendix D: Two-step Heckman Procedure
Table 17: Probit regression results for
the Decision by Consensus variable
Decision by Consensus
dy/dx
(std error)
Europe
-0.442**
(0.177)
North-America
-0.542***
(0.175)
Pension fund
-0.161
(0.191)
Insurance Company
Fund-of-Fund
-0.089
(0.143)
Foundation &
Endowment
-0.175
(0.183)
Bank
0.125
(0.191)
LP Experience
0.019
(0.056)
LP Size
-0.033
(0.035)
Financial Center
0.097
(0.112)
Workload
0.050
(0.049)
92
Number of observations
12.40
LR chi
0.2593
Prob > chi
0.0982
Pseudo R-squared
dy/dx is for discrete change of dummy
variable from 0 to 1. Asterisks denote
statistical significance at 1% (***), 5%
(**), and 10% (*).
- 75 -