The benefits of size for private equity investors
Marco Da Rin* and Ludovic Phalippou**
November 2011
Preliminary
Abstract
We show that institutional investors exhibit considerable heterogeneity in their approach to private
equity investments. Size (the absolute amount allocated to private equity) is the foremost driver of
such heterogeneity. Other characteristics that broadly capture prestige, experience, and long-term
relationship play virtually no role. Investors with larger allocations to private equity receive more
favorable contractual terms, pay lower fees, and engage in more specialized screening and
monitoring activities. Further analysis points towards larger investors having larger teams of
specialized in-house professionals as a likely explanation. Our findings have implications for the
organizational design of institutional investors and contribute to opening the black-box of the
investment process in alternative asset classes.
*Tilburg university, [email protected]
**University of Oxford, Said Business School and Oxford-Man Institute of Quantitative Finance.
[email protected]
We are grateful to the many investors who provided us with the data. We especially thank James Bachman and Peter
Cornelius for their help in designing the survey. We thank Ulrich Hege, José Liberti, Josh Rauh, David Robinson and
seminar participants at Nottingham U., and Tilburg U. for valuable comments on the paper. Our research assistants
provided invaluable help in collecting the data: Jan Peter Gabrielse, Marlon de Haas, Femke Helgers, Joris
Hoendervangers, Edvardas Moselka, Robin Rijnders, Jacco Vogels, and especially Yves Kessels. We gratefully
acknowledge funding from an EIBURS grant.
The asset management industry is increasingly concentrated. There are now nine pension
funds and eleven sovereign wealth funds in the world with more than $100 billion under
management. More than half of these have been created less than two decades ago. Another
noticeable trend is that these large investors are increasingly investing in alternative asset classes,
such as private equity. For example, both the Canadian CPP and the Dutch AlpinInvest, have built
private equity portfolios that are among the largest worldwide in just a decade (both started in
2011). These private equity portfolios are each worth over $35 billion, compared to less than $5
billion for those of both Yale and Harvard Endowment, who are famous pioneer investors in that
asset class. Some of these giants, however, have not invested yet in private equity (e.g. The $1
trillion Japanese GPIF) and ponder whether they should begin to invest in that asset class (e.g. the
$0.5 trillion Norwegian GPFG).
A question that is often raised in this context in whether size provides these investors with
an advantage and thus put at a disadvantage other investors (e.g. University Endowments which
have long-standing relationships with funds but are smaller). While large positions in publiclylisted equity are usually thought to be detrimental to returns, the situation in private equity is often
said to be less clear-cut. On the one hand, it is thought that there is a relatively fixed pool of alphagenerating funds, hence more money may mean lower returns. On the other hand, more complex
asset classes such as private equity may offer potentially significant economies of scale. In a
nutshell, one needs a critical size to have enough different specialized employees to screen and
monitor a complex asset class. In addition, being large can lead to more favorable terms and
conditions.
In fact, recent research analyzes detailed pension fund return data (mainly US and Canada)
and finds that it is precisely in alternative asset classes, and particularly so in private equity, that
there are marked out-performance of larger investors (Dyck and Pomorski, 2011, and Andonov,
Bauer, and Cremers et al., 2011). In other asset classes (fixed income, listed equity) there are
marked diseconomies of scale (Andonov, Bauer, and Cremers et al., 2011). What is not yet
documented is the source of this out-performance. In other words, what is it that larger investors do
that smaller investors do not do when it comes to asset classes like private equity?
To answer that question we need to open the “black-box” of private equity investors’
activities (screening, monitoring, favors obtained). It also seems important to control for investor
characteristics that are related to size and are commonly believed to play an important role in
private equity. These are characteristics such as how long the investor has been investing in private
equity, its type (e.g. Endowment, insurance company), and its country of location.
2 To achieve this objective, the only empirical route is that of a survey. We thus conduct an
ambitious worldwide survey of private equity investors from 2008 to 2010. We present the survey
to investors as a unique opportunity to benchmark their due diligence practices (anonymously) to
that of a large set of other investors (for free).1 Respondents do not, therefore, have clear incentives
to lie. In addition, and probably reflecting a high demand for such information, we obtain a
response rate of over 10%, which is reasonably high for academic surveys (e.g. the famous survey
of CFOs of Harvey and Graham has slightly less than a 10% response rate). Finally, we are careful
to only ask questions which are quite specific and should lead to an objective answer.
We contact about 2,000 “Limited Partners” – the name given to private equity investors –
from the Limited Partners Directory published by Private Equity International, a consultancy. We
obtain 230 sufficiently complete responses. To our knowledge, it is the largest survey of private
equity investors to date in terms of number of respondents, geographical coverage and scope.
To facilitate exposition, we pool together in an index variables that are expected to influence
returns. There are twelve such variables and they relate to the degree of screening, monitoring and
favors obtained. This index is called the “Limited Partner Efficiency” (LPE) index, and simply
averages the answers to these twelve questions very much in the spirit of the corporate governance
index of Gompers, Ishii and Metrick (2005). The LPE index is then first related to key investor
characteristics, which include the amount committed to private equity (size), experience, country,
and type (e.g. pension fund, endowment).
We find that the LPE Index, and in fact each of its twelve underlying components, is
strongly related to size but is not significantly related to any other characteristic of investors. For
example, having old ties to the private equity industry does not affect the LPE index once size is
controlled for, nor does it matter to be an Endowment or a financial institution.
Next, we try to understand why size is so important. We investigate four potential
explanations. First is an omitted variable issue. Larger investors differ along many dimensions and
we cannot obviously capture all of them but we can look at the most relevant candidates. Investors
with more cash in private equity may find it easier to retain employees, may be more likely to have
incentive-pay schemes, may enjoy more freedom/autonomy, attract more experienced employees,
or be better located (e.g. in a financial center). We ask these questions in our survey and find that
none of these variables are significantly related to the LPE index once we control for size.
1
The survey is anonymous in that we do not reveal the identity of the respondents to anyone, nor is it possible to infer
from any of our statistics the identity of any of the respondents. The respondent can leave its email address if it wishes
to receive a report. The majority of the respondents do so.
3 A second potential explanation is that larger investors have greater bargaining power and
this is why they obtain better terms and conditions. While this probably plays a role in some of our
results, it does not explain the bulk of the evidence. We find that each component of the index is
significantly related to size, not just those variables related to terms and conditions. In addition, if
we add the total asset under management of the parent company, it is not significantly related to the
LPE index. This means that what matters is not to have deep pocket and large amount of ‘potential’
money to be invested, but rather how much is actually invested in private equity.
A third potential explanation is that large investors may be better staffed and what we
capture is just their staff doing something with their extra time. In other words, because they have
more time to spend, they simply do more things and thus score higher on our index. To test for this
explanation we construct the ratio of the amount invested in private equity to the number of
professionals managing this asset class (“dollars per professional”). If this explanation holds true
then we should have a positive relationship between the LPE index and relative staffing. But we do
not find this to be the case.2
The fourth potential explanation is that larger investors can hire a large team of specialized
professionals. Having a large team in absolute terms means that there can be one or two people that
are specialized in contracts, one or two people specialized in corporate valuations etc. This, in turn,
means that larger investors can more efficiently tackle the multi-faceted and difficult task of
investing in an asset class like private equity (characterized by high information asymmetry and
little or no regulatory protection).
We find strong supportive evidence for this. Investors with a PE team that also manages
hedge funds, a very different asset class, are associated with a lower LPE Index, even after
controlling for size. We also find that absolute staffing (“number of professionals”) is highly
statistically significant and positively related to size (and not relative staffing as shown above). In
fact, the number of professionals is the only variable which significantly reduces the coefficient on
size.3
We go through a battery of robustness tests that show that the various choices we make do
not have any material effect on the results. Results hold when we change the composition of the
index and for a number of sub-samples like the one that contains investors who are focusing most in
buyout (or venture capital), investors that are based in the US, etc. We also find that our results hold
2
Results hold true for similar ratios like the number of funds per professionals, and professionals per dollar invested.
One may also argue that investors with little money in private equity spend less effort on private equity investing
because it is not an important part of their portfolio. We thus also looked at the effect of the allocation to private equity
(fraction of total portfolio invested in private equity) but this is not statistically significant.
3
4 when excluding pension funds, which indicate that the outperformance of larger private equity
investors found in the literature (mentioned above) is unlikely to be restricted to pension funds.
It could be argued that size is partly endogenous and, in particular, that there is a risk of
reverse causality. Namely, it can be argued that current size is caused by good past performance and
thus by the sort of past behavior captured in our LPE index. To address this concern, we replace
current size with size in year 2000 – which we ask for in the survey. Despite a sharp decrease in
number of observations, the economic magnitude of the effect is similar and statistical significance
remains at the 1% level. In addition, we ask investors how much their size depends on their own
past performance (versus the overall industry performance).4 Investors on average say that it is
evenly split. We therefore run the analysis separately for those investors that say that their size
depends most on their own past returns and for those that say that their size depends least on their
own past returns. In both sub-samples the results are similar to those of the full sample. We also
exclude fund-of-funds because their size depends most on their past returns. Again, we obtain
similar results. We thus believe that our results are unlikely to be explained by reverse causality.
Finally, our results help opening the black-box of the investor decision process in alternative
asset classes. We show that as much as half of the investors have performance-sensitive pay, and
co-invest directly in some portfolio companies alongside the fund they hold. We show that our
median investor holds 25 funds with a team of only three full time investment professionals who
have an average private equity investing experience of ten years. As many as 25% of the investors
use the same team/person to invest in both private equity funds and hedge funds, two asset classes
that seem to have little in common. It is 37% for it comes to supervise both private equity and real
estate.
When screening fund investment opportunities, only 58% of investors compute some past
performance statistics (themselves or their consultants). An even lower proportion (29%) reassesses the valuations of non-liquidated portfolio companies provided by fund managers. It is even
the case that 15% of the investors simply ignore the performance of non-liquidated investments.
However, 44% of the investors (themselves or their consultants) interview executives of the
portfolio companies of the private equity firms they consider backing. As many as 21% of investors
say that “investing for reasons other than performance” is not an irrelevant criterion. For banks that
percentage shoots up to 57%. For both government-owned investors and public pension funds, that
4
In practice the asset allocation decision across teams (fixed income, domestic equity, foreign equity, private equity
etc.) is decided using a model, which takes as input the expected return of the teams’ benchmark. Hence allocation to
private equity is primarily driven by the past and expected return of the private equity asset class and not of the team in
place. Evidence is provided in numerous practitioner articles and in case studies such as the “Harvard Management
Company” and “Yale Endowment” HBS case studies.
5 percentage is around 25%. For US public pension funds, it is 30%, which nicely complement the
finding of Hochberg and Rauh (2011) showing that US public pension funds exhibit a severe and
costly local bias.
Our results have a number of important implications for private equity investment decisions
and for the design of their private equity program. Potentially, the most important implication is
that large but novice investors in private equity (e.g. sovereign wealth funds) need not be at a
disadvantage compared to historical investors (e.g. endowments), but if anything, they would be at
an advantage. Their large size means that they can carry more detailed screening and monitoring
and obtain better terms and conditions. These arguments square well with recent evidence cited
above showing that larger pension plans outperform smaller pension plans in more complex asset
cases like private equity. We detail this implication and offer some additional implications in the
conclusion section.
A closely related study is that of Lerner, Schoar and Wong (2007) who are the first to point
out that institutional investors are quite heterogeneous. They use venture capital as a laboratory and
show how returns and the quality of re-investment decisions differ across institutional investors.
Endowments show the highest abilities and Banks show the lowest abilities. Our results indicate
that this finding is unlikely to be related to some organizational differences between investor types.
On the practitioner side, the book by Fraser-Sampson (2007) is probably the most
comprehensive coverage of investors’ due diligence practices but it does not quantify differences
across investors like we do here. There are also two contemporary surveys of private equity
investors we are aware of. Groh and Liechtenstein (2009) study how investors select Venture
Capital funds.5 Groh, Liechtenstein and Canela (2010) gauge the willingness of investors to get
private equity exposure in certain countries. They thus ask investors about the importance they
attribute to the legal environment or the financial market development.
The rest of the paper is organized as follows. Section 2 describes the data and the
construction of our variables. Section 3 provides empirical evidence. Section 4 provides some
robustness checks. Section 5 concludes and discusses the implications of our results.
5
They have 75 respondents. Consistent with our results, they find that the following criteria are important: the expected
deal flow and access to transactions, a venture capital fund's historic track record, its local market experience, the match
of the experience of team members with the proposed investment strategy, the team's reputation, and the mechanisms
proposed to align interest between the institutional investors and the venture capital funds. The level of fees payable to
the funds and commitments of other reputed LPs, however, are not important selection criteria.
6 2. Data
We conduct an online survey of private equity (PE) investors, i.e., Limited Partners (LPs). These
LPs may belong to what we call a “parent organization,” like a bank, insurance company, or
corporation. Our questions relate mostly to how investors carry their due diligence for their
investments in PE funds, which are run by PE firms, a.k.a General Partners (GPs). In this section,
we first describe the survey and the sample construction. Section 2.2 details the construction of our
“LPE” index. Section 2.3 provides descriptive statistics on key investor characteristics and uses
them to assess sample representativeness.
2.1. Survey design
We designed the survey with a senior LP executive. The survey contains eight parts and is
described in the appendix. We present the survey to investors as a unique opportunity to benchmark
their due diligence practices (anonymously) to that of a large set of other investors (for free).6
Respondents do not, therefore, have clear incentives to lie. We are also careful to ask only questions
which are quite specific and should lead to an objective answer.
To construct our sample of respondents, we use the 2008 Directory of Limited Partners
published by Private Equity International (PEI). We emailed all of the 2,000 LPs listed in the
directory to introduce the survey and provide the website address for the survey.7 After sending the
email, we contacted each investor by phone to ask whether they received the email, intended to
participate and had any questions.8 Investors who respond to the survey can leave their contact
details and two thirds have done so. For investors who left their contact details and did not answer
some of the questions, we follow up by phone.
We have received 230 sufficiently complete responses (see below for details), implying a
response rate that is slightly above 10%.9 This response rate is similar to other academic large-scale
surveys. For example, the famous survey of CFOs of Harvey and Graham (2001) has slightly less
than a 10% response rate. We believe it reflects a significant interest in the investor community
about learning how others perform due diligence.
6
The survey is anonymous in that we do not reveal the identity of the respondents to anyone, nor is it possible to infer
from any of our statistics the identity of any of the respondents. The respondent can leave its email address if it wishes
to receive a report. The majority of the respondents do so.
7
The Directory contains several organizations that are not LPs, like GPs or investors that do not invest (or have stopped
investing) in private equity. We do not consider them as part of the population.
8
This could not be done with a number of countries in which English is not well-spoken and for which we had no
research assistant speaking the language (e.g. Arabic countries). However, these countries have very few investors.
9
The median respondent files 72% of the questionnaire.
7 2.2 Limited Partner Efficiency index
We identify twelve variables that should in fine relate to returns. These variables broadly pertain to
contractual favors, monitoring intensity and screening intensity. They are described in this subsection and a summary is shown in table 1. The Limited Partner Efficiency (LPE) index combines
these twelve variables.
2.2.1 Favoritism
2.2.1.a Favorable terms
The contractual rights of an LP in a private equity fund are governed by the Limited Partnership
Agreement (LPA). Starting in the late 1990s, however, many investors have obtained special rights
that are granted via separate "side letters". Common reasons invoked for these are that some
investors are considered as strategic, and/or a large client, and/or because the investor is subject to
government regulation (e.g., ERISA, the Bank Holding Company Act, or public records laws). In
addition, because side letter terms vary from fund to fund, and from investor to investor within a
given fund, LPs may negotiate for a “Most Favored Nation” (MFN) provision that permits the
election of certain benefits granted to other LPs via side letters. In general, MFN guarantees that the
investor has the best terms granted to any other investor.
Side letters are therefore the prime way for a GP to favor some investors. In our survey 48%
of the LPs “always” negotiate the LPA (37% “sometimes”, 15% “never” negotiate it). When
negotiating the LPA, one may obtain side letters. 44% of the investors report that they “always”
obtain side letters (30% “sometimes”, 26% “never” obtain side letters). 38% of the investors report
that they “always” obtain MFN (29% “sometimes”, 33% “never” obtain MFN).10 Obtaining side
letters or MFN can be seen as a form of favoritism, which may lead to better performance for
investors. We include in the LPE index both side letters and MFN by counting the LPs which
“always” obtain them.
From a descriptive point of view, it is interesting to note how widespread side letters are.
Three quarter of the investors have received some. It is almost the same proportion of investors who
have received MFN at least once. It seems that there are two main LPAs: the default one and a
special one, both of which are granted to a large group of investors. There may be some
intermediary ones too but given how widespread the use of MFN is, it is probably not much. This
result implies that empirical research on GP-LP contracts may be hampered by the fact that the
LPAs that may be observed by researchers are just a default template that applies only to a sub-set
10
The correlation between always negotiating and always getting side letters is 63%.
8 of investors. In addition, the fact that some investors systematically get MFN or side letters is a first
indication that some investors are perceived as special and given special treatment, and thus that
investors are not all alike.
2.2.1.b Co-investment opportunities
A second element that captures favoritism is the ability to co-invest.11 Co-investing means that a GP
may invite an LP to co-invest with the fund in a specific company, without charging additional fees
or charging much less. This can have a dramatic impact on the overall fee bill but also on gross-offees performance because the GP may overweight the selected LPs in the best investments (and
therefore squeeze out the non-participating LPs).
Anecdotally, a large investor recently told one of the authors that the reason why they
invested in the buyout funds raised at the pick of the buyout boom (2005-2007) was because if they
would not have participated, the large private equity firms would not have invited them to co-invest
anymore. So it looks like co-investment is a sizeable carrot used by PE firms to reward or retaliate
some of their investors.
Our survey gives the first evidence on how widespread co-investing is. First, we find that
78% of the investors have been invited at least once to co-invest. This is our third index component.
However, we find that a staggering 70% of the invitations get rejected on average and overall it is
only half of the investors who ever have co-invested. It means that although most investors get
invited, much fewer actually make co-investments. When we ask the fraction of the portfolio that is
made of co-investments for the investors who do co-invest, the median is thus 0% (but the average
is 5%). Hence we add a fourth component to the favoritism index and that is that the investor has
some co-investments in its portfolio.
A natural question is why investors refuse co-investments. We ask this in an open question
and find that small investors say that they do not have the capacity to evaluate direct investments of
this sort. Large investors also boost some high rejection rates but typically mention risk as their
main reason to reject.
< Table 1 >
11
The PE literature has studied compensation contracts between LPs and GPs (Gompers and Lerner, 1996, and Metrick
and Yasuda, 2009, Phalippou, 2009, Phalippou and Gottschalg, 2009). A number of puzzles have emerged from this
literature. First, fees are surprisingly large especially when performance is relatively low. Second, most of the fees seem
fixed and not performance related. Third, the headline fees (management fees, carried interest and hurdle rate) seem
remarkably stable across time and fund-raising cycles. However, besides Phalippou (2009), the literature has not
mentioned existence of co-investments by LPs.
9 2.2.2 Screening intensity
To capture screening intensity we use four proxies. First, one of the key elements LPs consider
when deciding to invest is past performance. Past performance is provided by the GP in the fund
raising prospectus. It is however usually the case that performance figures that are provided are
“too” aggregated. For example, a GP may have pooled together investments in venture capital and
buyout and is now raising a buyout fund. The investor may want to separate these two track records
and re-compute returns. Another situation that arises is that a GP with high returns in its early funds
and not in its later funds have an incentive to pool all the funds together and give only one
aggregate performance number.12 In that case, one probably wants to know the performance of each
fund separately. Another example is that some GPs may include in the past performance figure the
returns of the investments that some of the partners supervised when working for their previous
employer; something the investor may want to take out of the track record. An investor also told us
that the dates of cash flows need to be checked because it is not always the dates at which actual
cash flows occur that are used (e.g. announcement dates may be used in some cases). For all these
reasons, an LP may want to re-compute past performance. We thus ask whether investors use the
underlying data given by the GP (or could be requested to the GP) to compute performance
statistics themselves. We find that 58% of the LPs do. This variable is the fifth component of the
index.
Second, an important hurdle to evaluate a track record is the valuation of Net Asset Values
(NAVs). That is the valuation of unrealized investments. For example, Kohlberg, Kravis and
Roberts (KKR), one eponymous buyout firm, shows in its annual report that 80% of all of their
investments in dollar terms are un-exited (source: SEC filings). So it means that the past return that
LPs see is made up for 80% of unrealized investments. Importantly, these valuations are subjective.
KKR for example explicitly state that they use an internal model. Hence prospective investors may
want to re-evaluate that NAV. We find that 29% of investors do so; 56% of investors just use the
NAV given by the GP and 15% simply ignore unrealized investments. The re-evaluation of NAVs
is the sixth component of the index.
Another common due-diligence item for investors is to interview executives of portfolio
companies. The motivation for investors is to gain insight on whether the GP adds value to the
portfolio companies. Also, they can assess whether the GPs has a good reputation with
entrepreneurs and executives in general, which is important to assess the quality of future
12
This is because when using IRR, this pooling generates a high overall return.
10 investments by the PE fund. The seventh component of the index is whether investors “always”
interview portfolio company executives (44% do; 45% say they sometimes do it, and 9% say they
never do it).
The eight component of the index is whether the investor benchmarks the Limited
Partnership Agreement (LPA). As mentioned above, the LPA is an important document, which
details the fees and the covenants. Because the fees can have a large impact on performance and
because the inclusion and exclusion of certain covenants can significantly impact potential conflicts
of interest, comparing the LPAs of different funds is an important exercise. It is also a costly
exercise given that LPAs are quite technical and lengthy documents (over 50 pages). Two thirds of
the investors report that they benchmark LPAs; one third of the investors do not.
The last index component related to screening is simply the amount of time spent on due
diligence (in numbers of days; full time equivalent). We ask this question separately for an
investment in first time fund, for a re-investment (called “re-up”) and a first investment in a
seasoned fund. The three figures are highly correlated and we choose to use the latter because it has
more observations. Next, we compute the median and create a binary variable (0/1) depending on
whether the investor spends more or less than 15 days (the median).13
2.2.3 Monitoring intensity
The main way investors can monitor fund managers is by seating on advisory boards.14
These advisory boards include limited partners and are often designed to provide access to deals or
technical expertise. Advisory boards are less formal (and have no legal obligations) than traditional
boards of directors, but sometimes provide guidance and oversight for the operation of the fund.
Sometimes the board may be involved in portfolio company valuations. GPs are those inviting LPs
to seats on the advisory board.15
We thus ask investors for the number of board seats they have. Obviously, we need to scale
this by the number of funds they have and we need to require investors to have a minimum number
of funds (which we set to 5). The median number of board seats per fund is 0.21. We then create a
13
Note: for first time funds the median is 20 days, and for a re-up it is 10 days.
Quite some research efforts have been devoted to studying the boards of public companies (see Adams, Hermalin,
Weisbach (2011)). We are not aware of work on advisory boards of PE funds.
15
Being granted a board seat could also be seen as a favor. Doing so would not change results.
14
11 dummy variable that is one if an LP has more board seats per fund than 0.21 and zero if it has less.
This is the tenth component of the index.
Next, we use visits to portfolio companies as another proxy for the monitoring intensity.
This refers to visiting companies that are held by the fund in which the LP is invested, or in other
words visiting what the LP owns in-fine. Not surprisingly it is not commonplace and relatively few
LPs say they always do so (9%) but interestingly 32% report that they never do so. As a result we
create a binary variable for the index, which is one if an LP may visit a portfolio company (i.e., the
LP replies “sometimes” or “always”) and is zero if it never does so.
Finally, the last component that pertains to monitoring relates to whether the LP keeps track
of portfolio company fees. These fees are those charged by the fund manager directly on the
portfolio company.16 For example, KKR may refinance the debt of company ABC and charge that
company for this operation.17 Part of these fees is therefore borne by investors but not directly
visible. We ask LPs for the fraction of portfolio companies for which they obtain information on
portfolio company fees. The median is 75% and the twelfth component of the LPE index is a binary
variable that is one if the LP has PC fees information for more than 75% of the portfolio companies,
and zero if it has less.
< Table 2 >
2.2.4 The Limited Partner Efficiency Index
In a context similar to ours, the literature has opted for the building of a simple index (e.g.
Gompers, Ishii and Metrick, 2005). It is a simple way to reduce the dimensionality of the problem
and to improve on the robustness of the findings. We follow that approach.
To construct our Limited Partner Efficiency (LPE) index, we simply take the average across
the twelve binary variables we described above and which are labeled as follows: “Side-letters”,
“Most Favored Nation”, “Invitation to co-invest”, and “Has co-investments”, “Own return
calculation”, “Own NAV calculation”, “Interview executives”, “Benchmark LPA”, “Time spent
screening”, “Board seats”, “Company visits”, and “Track PC fees.”
We take an equally weighted average of these twelve binary variables. Although equally
weighting each component does not accurately reflect the relative impact of the different
16
See Metrick and Yasuda (2009) for a description of portfolio company fees.
These fees concerns only buyout funds and we thus exclude respondents with less than five buyout funds in their
portfolio.
17
12 components, it has the advantage of being transparent, simple and agnostic about the relative
efficacy of any of these twelve actions. In addition, to avoid losing observations we take the
average across the variables for which we have at least one valid answer.
Table 2 shows the correlation matrix and descriptive statistics for the LPE index and its
twelve components. The correlation between each component is usually relatively low. Some
interesting exceptions are the relatively high correlation between board seats and each contractrelated variable (side letters, most favored nation and benchmarking the LPA). However, each
component is quite well correlated with the LPE index, with a minimum of 36% for “tracking PC
fees” and a maximum of 66% for “side letters.” The LPE index varies between 0 and 1, by
construction, and averages 0.545, with a standard deviation of 0.254.
The number of observations is the lowest for “Board seats” and “Track PC fees”. This is
because we restricted the sample to investors with at least five buyout funds in their portfolio to
compute these two variables. The number of observations is also low for the time spent on
screening, mainly because of the original lack of responses to that question.
2.3. Main investor characteristics and sample representativeness
From the survey, we obtain key LP characteristics. The first characteristic we collect is type. This is
motivated mainly by the study of Lerner et al. (2007) who point out that an important source of
heterogeneity across institutional investors is their organizational type. They argue, for example,
that endowments generally benefit from more flexibility when investing and that shows up mainly
in asset classes such as private equity. We ask for the organization type by offering eleven choices
plus an open field for “other”. From these entries we create five types: i) Pension funds, ii) Fund-offunds, iii) endowments (which include foundations), iv) Financial institutions (banks, insurance
companies, asset managers), v) Other (sovereign wealth fund, family desks, corporate, other…).
The sample splits about equally across these five types as shown in Table 3 – Panel A. The largest
type, financial institutions, represents 24% of the sample. The smallest type, endowments,
represents 14% of the sample.
Compared to the PEI universe we have clearly more fund-of-funds (20% of our sample
versus 11% of the universe), slightly more financial institutions (24% versus 18%) and less of other
investor types. In this “other investor types” category, we are mainly lagging behind among
“corporate investors.” We conjecture that funds-of-funds may find our survey more important
because due diligence is at the core of their business. For corporate investors, it is often difficult to
locate the people investing in PE within the organization. Hence, we had difficulties contacting the
13 right person. Nonetheless, overall, all categories are well represented; both investors that have a
long tradition of PE investing (like pension funds and endowments) and investors that are thought
to be less familiar with this asset class (like family offices and banks).
The second characteristic is the country where the PE investment committee (or the person
taking the PE investment decisions) is located. Investors located in North America (USA and
Canada) may have access to a more skilled labor market, may be seen as more prestigious etc. It is
therefore a potential source of heterogeneity across investors. It is also the case that the majority of
the PE funds are based in North America (even more so on a value-weighted basis). This
geographical (and cultural) proximity may affect investor’s behavior.
An outstanding feature of our study is that it provides a global perspective as it spans 27
different countries. Because we cannot include each country separately, we pool countries together
to form regions. We distinguish between North America (USA and Canada), continental Europe
(i.e. Europe excluding the UK and Scandinavia), Scandinavia and the UK. North American
investors are the largest group, being 34% of the respondents; continental Europe follows with 27%
of the respondents; then Scandinavia, and the UK come with a weight of 13% and 10%,
respectively. Next come two countries with a weight of 6% and 4% (Australia and Japan). The rest
of the world weights 6% of the sample. It includes Iceland, Israel, Mexico, New Zealand,
Singapore, South Africa, South Korea, and Taiwan.
Compared to the universe (the PEI directory) we have less North American investors and
more European investors (continental Europe and Scandinavia). For the UK, Australia and Japan,
our coverage is close to that of the PEI directory (Table 3 – Panel B). For the rest of the world we
have less than the PEI directory, which is mainly explained by the fact that we did not have
qualified research assistants who could talk the local language to call Arabic and Asian countries.
Our high response rate in Scandinavia and Australia is mainly due to a higher willingness of
investors in these countries to answer the survey. The higher response rate in continental Europe is
also due to a generally higher willingness to be transparent, especially in countries like Germany for
which we have a particularly high response rate. In addition, our personal network in Europe
enabled us to increase response rate. In North America we had difficulties reaching out investors
despite us knowing some investors and consultants who helped us reaching out investors.
Another key characteristic, motivated by the studies of Dyck and Pomorski (2011) and
Andonov, Bauer, and Cremers (2011) mentioned in the introduction, is LP size (as of 2008). We
split the sample in about five equal groups: those with size below $100 million, between $100
14 million and $250 million, between $250 million and $600 million, between $600 million and $2
billion, and above $2 billion. This shows the wide dispersion in total capital allocated to private
equity (Table 3 – Panel C). One investor in five has less than $100 million invested in private
equity while another one in five investors have more than $2 billion invested in private equity.
Compared to the universe (the PEI directory), we have fewer very small investors (those
with less than $100 million in PE). This is consistent with our findings below that smaller investors
are less well staffed and spend much less resources in their investment process. Hence filing such a
survey is more costly for them and they may see little added benefits since benchmarking their due
diligence process may not be an important concern for them (they do not have much of it). In the
other size categories, we match the proportions of the PEI directory quite well (Table 3 – Panel D).
The fourth key characteristic we retain is the parent’s age. That is the age of the organization
to which the LP belongs to. This may broadly capture prestige. Table 3 – panel D shows that our
sample is about equally divided between parents who are less than ten years old, those between 10
and 25 years old, those between 25 and 50 years old and those that are more than 50 years old.
The fifth key characteristic we chose is the number of years of PE investing. This is a
natural explanatory variable because it is often thought that in private equity having long-standing
relationships is important. It could also be that more experienced investors behave differently as
they know what is more important and what is less important, or that they do less effort because
they are more productive. We note that on that dimension, our sample and the PEI universe are very
close (Table 3 – Panel E).
Our sample thus differs on some dimensions from the universe according to the PEI
directory. We think this is inherent to the survey approach. In the robustness section, we will assess
whether this feature may influence our results by running our analysis on sub-samples which
exclude some under-represented or over-represented categories of investors.
< Table 3 >
15 3. Empirical analysis
3.1. The LPE index and key investor characteristics
We begin by regressing the LPE index on the key investor characteristics we have just
described. The first specification shows that LP size is highly significantly related to the index and
that it alone leads to an R-square of 21%. Adding investor type brings the R-square to close to 30%.
Pension funds score significantly less and fund-of-funds score significantly more on our LPE index.
Specification 3 shows that investors’ regions are not related to the LPE index. LPs that are part of
older institutions and/or have been investing for longer are significantly negatively associated to the
LPE index (specifications 4 and 5). However, their statistical significance depends on which control
variables are added.
When LP size is omitted (specification 6) we see that the coefficient on pension funds is not
significant and that of fund-of-funds remains significantly positively. This means that pension funds
score less because given that they tend to be large investors in PE they do not achieve as high a
score as similarly large investors do. For fund-of-funds, it means that a large part of their high score
is due to the fact they are large PE investors.
The last specification shows that LP size is strongly positively related to our index when all
the control variables are included at the same time. This specification is our base specification for
the analysis that follows.
< Table 4 >
3.2. What makes larger investors special?
In the previous sub-section we have regressed our index on a set of explanatory variables, which we
can reasonably think of as exogenous. The conclusion from this analysis is that what drives investor
heterogeneity is the amount they have invested in PE. It is naturally interesting to ask what is
different about these larger investors. We investigate four potential explanations in this sub-section.
3.2.1. Omitted variable
The first explanation we investigate is an omitted variable issue. Larger investors differ along many
dimensions and we cannot obviously capture all of them but we can look at the most relevant
candidates. Investors with more money invested in private equity may find it easier to retain
employees (low turnover), may have less conflicting objectives, may be more likely to have
incentive-pay schemes, may enjoy more freedom/autonomy, may vote differently (majority versus
unanimity), may attract more experienced employees, may rely more on soft information, or may be
16 located in a financial center18. This list is mainly drawn from that of Lerner et al. (2007) who list
potential explanations for why some investors may perform better in venture capital. We ask these
questions in our survey and find that none of these variables are significantly related to the LPE
index once we control for LP size.
Table 5 lists the above-mentioned variables, describes how we construct them, and shows
some descriptive statistics. These descriptive statistics are interesting by themselves because they
document a number of stylized facts about the investment process, of which there is little existing
knowledge. For example, we see that as much as one third of investment committees are not
autonomous (their decisions can be partially or fully vetoed), half of the investors are located in
financial centers, have performance-sensitive pay, and co-invest directly in some portfolio
companies alongside the fund they hold. Our median investor holds 25 funds with a team of only
three full time investment professionals who have an average PE investing experience of ten years.
As many as 25% of the LPs use the same team to invest in both private equity funds and hedge
funds, two asset classes that seem to have little in common. It is 37% for it comes to supervise both
private equity funds and real estate funds.
To control for the potential channels outlined above, we add the above-mentioned variables
to our base specification, one at a time. These variables are either unrelated to the index (once we
control for LP size) or weakly so. In table 6, we show the results for the four variables that are
weakly related to the index (specifications 1 to 4).
< Table 5 >
< Table 6 >
3.2.2. Bargaining power
A second explanation is that larger investors have greater bargaining power and this is why
they obtain better terms and conditions. While this probably plays a role in some of our results, it
does not explain the bulk of the evidence. We find that each component of the index is significantly
related to LP size, not just those variables related to terms and conditions (see appendix table). In
addition, if we add the total asset under management of the parent company, it is not significantly
related to the LPE index in the regression setup used above (non-tabulated).
To illustrate this result, we double sort the observations based on AUM and LP size terciles.
Table 7 – Panel A shows results when we first form AUM terciles and within each of these, we
form LP size terciles (i.e. PE allocation terciles). Within each AUM terciles, the LPE index
18
Could matter if location in a financial center gives preferential access to information or networks.
17 increases sharply with PE allocation. In Table 7 – Panel B, we do the opposite and observe that
within each PE allocation terciles there is no link between AUM terciles and LPE index. Finally,
Table 7 – Panel C shows results from an independent sort. Because the two variables are highly
correlated (60%) some combinations have little observations. Here too we see that the LPE index
increases with the PE allocation but not with AUM.
These results show that it is more important to have a large allocation to private equity than
to have deep pockets (i.e. having a large amount of ‘potential’ money to be invested in private
equity). Higher bargaining power, therefore, is unlikely to explain our results.
3.2.3. Doing something
A third explanation is that large investors may be better staffed and what we capture is just
staff doing something with their time. In other words, because they have more time to spend, they
simply do more things and thus score higher on our index. To test for this explanation we construct
the ratio of the amount invested in private equity to the number of professionals working (labeled
“dollars per professionals”). If this third explanation holds true then we should have a strong
positive relationship between the LPE index and this measure of relative staffing. We actually find
the opposite (see specification 5 in table 6). When controlling for LP size, relative staffing is
strongly negatively related to the LPE index. Absent LP size as a control, the relationship is not
significantly different from zero (non-tabulated).
We also note that similar results are obtained when alternative ratios are used (e.g. number
of funds per professionals, and professionals per dollar invested; results non-tabulated).
One may also argue that investors with little money in private equity spend less effort on
private equity investing because it is not an important part of their portfolio. We thus also looked at
the effect of the allocation to private equity (fraction of total portfolio invested in private equity) but
this is not statistically significant (non-tabulated).
< Table 7 >
18 3.2.4. Specialization
The fourth explanation is that larger investors can hire a large team of specialized
professionals. Having a large team in absolute terms means that there can be one or two people that
are specialized in contracts, one or two people specialized in corporate valuations etc. This, in turn,
means that larger investors can more efficiently tackle the multi-faceted and difficult task of
investing in an asset class like private equity, characterized by high information asymmetry and
little or no regulatory protection.
We find strong supportive evidence. We find that the “number of professionals” is highly
statistically significant and positively related to LP size (specification 6 in table 6). In fact, it is the
only variable which significantly reduces the coefficient on LP size.
We also find that investment scope (“team also manages hedge funds” or “team also
manages real estate funds”) is negatively related to the LPE index. With managing hedge funds
having a stronger effect, consistent with the fact that real estate funds share more common
characteristics with private equity funds (e.g. the contracts) than do hedge funds.
We interpret these results as showing that large investors have larger and more specialized
teams and this is likely to drive the strong relationship between size and the LPE index. When more
people are present they can each specialize on something (e.g. NAV evaluate, computation of
performance, contracts etc.), they do not spread in unrelated activities like hedge fund investing.
To further illustrate, compare investor A and investor B. A has a team of two overlooking
two private equity funds and one hedge fund. B has a team of fifty overlooking one hundred private
equity funds. B is not as well staffed as A, but B is more likely to have staff specialized in
contracts, co-investments, valuation of non-traded investments, risk and return measurements for a
non-traded asset, assessment of corporate strategies etc. This enables investor B to engage in a
number of negotiations, and screening and monitoring activities, which are captured by our index;
while such a move is very difficult for investor A. This aspect is what drives the bulk of
heterogeneity across PE investors according to our data.
19 4. Robustness
In this section, we undertake a number of robustness tests. They enable us to gauge the robustness
of our empirical choices, the likelihood of reverse causality and generate further evidence to
understand better what drives the effect of LP size.
4.1. Robustness to empirical design
Table 8 shows first the results of the base specification of table 4. They serve as benchmark
figures. Note that all specifications of table 8 are the same as our base specification. They include
all the control variables that are in the base specification but only show the coefficients on LP size.
We begin by selecting a subset of the index components. To keep it simple we select either
all the even numbered components or the odd-numbered components. Results are very similar
either way. This shows that the components that are chosen do not affect results much. This is good
because these components (and the questions they relate to) are relatively arbitrary.
Next, we look if our results are different for investors that are more buyout oriented than
venture capital oriented. We find it is significant in both sub-samples but stronger for those who
hold more buyout funds.
Third, we split investors by experience. Those with more than 10 years of PE investing have
a slightly stronger effect, but the effect is significant in both sub-samples.
Fourth, we find that the effect is stronger for investors who hold more fund-of-funds, but
again it is significant in both sub-samples.
Fifth, some investor size comes from public directories (only 16 of them) but excluding
those does not affect results.
Sixth, we note that the significance of LP size on the LPE index is slightly stronger among
North American investors than on non North American investors.
Seventh, the effect is also unaffected is we exclude observations with valid answers for less
than half of the index component questions, although we lose 23 observations.
Eight, LP size has a similar coefficient if we exclude pension funds. All these result show
that our main finding is robust to the empirical design.
Finally, we also run a Probit regression for each component of the LPE index using the Base
specification of table 4. Results are shown in table A.1. For each component of the index, LP size is
significantly statistically related to the component.
< Table 8 >
20 4.2. Reverse causality
It could be argued that investor size is partly endogenous and that there is reverse causality.
Specifically, it can be argued that current investor size (which is what we use) is caused by good
past performance and thus past favors and efforts. To address this concern, we do three things, the
result of which is shown in table 7.
First, exclude fund-of-funds because their size depends most on their past returns. Results
are similar. Second, we ask investors how much their size depends on their own past performance
(versus the overall industry performance). Investors on average say that it is 50-50. We run the
analysis for the investors that say that their size depends most on their own past returns and those
that say that their size depends least on their own past returns. Results are similar in both subsamples.
Third, and finally, we replace current investor size with the one in year 2000 – which we ask
investors in the survey. Despite a decrease in number of observations, the economic magnitude of
the effect is similar and statistical significance remains at 1%-level. We thus believe that our results
are unlikely to be explained by reverse causality.
5. Conclusion
We show that institutional investors exhibit considerable heterogeneity and cash (the amount
allocated to private equity) is its foremost driver. Other characteristics that broadly capture prestige
and long-term relationships (e.g. investor type, tenure, total asset under management, and location)
play virtually no role. Investors with more cash receive more favorable contractual terms, pay less
fees, and engage in more specialized screening and monitoring activities. It seems that having a
large team enables larger investors to have more specialized professionals and this may explain our
results.
These findings have implications for the organizational design of institutional investors. For
example, let us evaluate the potential benefit from investing in private equity for the Norwegian or
Chinese Sovereign Wealth funds, two of the largest investors in the world, yet private equity
novices. On the one hand, one could think that it will be difficult for these investors to enter that
asset class because they have little experience and lack long standing relationship with funds. On
the other hand, one could think that all you need is cash. If you have cash, you can assemble a large
team of specialized and complementary people. This will translate into more effective investing
(better negotiation, screening and monitoring). As we find evidence that it is size rather than being
an early investor in private equity, investor’s country, or type that is most related to the LPE index,
21 then the latter may be more likely to hold true. That is, the case for private equity investing is
stronger for these large but new investors, all else equal.
Let us consider a second example. Following the perceived success of large endowments
such as that of Yale in venture capital, and further comforted by the finding that endowments
outperform in venture capital, a large number of small endowments have targeted aggressive
allocation to private equity.19 If what explains investor heterogeneity is investors’ type (such as
endowment versus pension fund) this move is warranted, but if the source of heterogeneity is size,
as we find here, this move cannot be justified on the grounds of being an endowment, just like Yale.
Our findings also suggest significant benefits for investors pooling resources either directly
or via fund-of-funds.20 They may thus provide a rationale for the current trend in the consolidation
of money management in the pension fund industry. For example, larger pension funds in the
Netherlands (e.g. APG and PGGM) obtain mandates from smaller pension funds to invest in certain
asset classes, predominantly alternative assets. In Canada, large Canadian pension plans such as
Ontario Teachers Pension Plan and OMERS are discussing similar moves. Oxford University
created a university endowment, which is the collection of a number of smaller Oxford colleges’
endowments. The university wide endowment is targeting a much larger private equity allocation
than what the merged colleges were before the merge.
This trend may be contrasted with the Swedish situation when the main pension fund was
split in five independent funds (AP1, AP2, AP3, AP4 and AP6) because the government did not
want most of the country’s equity to be concentrated in one hand. Each of these pension funds
invests in private equity independently, they carry their due diligence, negotiate terms and
conditions, monitor independently. There are talks in Sweden about merging them back together.
Evidence in this paper shows the potentially large benefits of doing so in an asset class like private
equity.21
A further example of our results’ implications is that investors often decide to start small in
private equity, see how it goes and then increase their allocation if it goes well. Our results point out
the shortcoming of such a strategy. We find that the parent’s organization asset under management
has no marginal effect. It means that being a large organization is not enough, what is needed is to
have cash into private equity. A large organization starting with a small allocation to private equity
19
See OEUM case study available at the Oxford Private Equity Institute and anecdotal evidence such as this: “The
success of Harvard and Yale attracted imitators. After suffering endowment losses in 2001 and 2002, smaller schools
looked to their Ivy League idols for guidance on bulletproofing their portfolios. “Alumni called me up and said, ‘We’re
going to be just like Yale, right?’” recalls the CIO of one midsize endowment fund. As a result, many small schools
crowded into hedge funds and private equity (…)” Institutional Investor, November 4th, 2009.
20
Obviously, this means an additional layer of fees and potential conflicts of interest, which we do not quantify here.
21
These benefits should however be evaluated against potential costs such as the decrease in competition.
22 is not expected to rip out the same benefits as a smaller organization with more money in private
equity. This means two things for the large sovereign wealth funds we took as an example above.
First, their large overall size will not bring them direct benefits if they do not allocate a large
(absolute) amount to private equity. So starting small to see how it is going is unlikely to bring
significant benefits. Second, the fact they are novice to private equity should not be a handicap. If
they have the cash they can invest efficiently.
Our results also indicate that the source of out-performance of larger pension funds in
private equity documented by Dyck and Pomorski (2011) is likely to be due to obtaining better
terms and conditions, co-investing (thereby reducing fees and maybe overweighting better
investments), spending more effort, and especially undertaking specialized tasks when screening
and monitoring.
Finally, our findings call into question the idea that institutional investors are a homogenous
lot, with equal sophistication, and as such the regulators should leave them outside of its radar
screen. Smaller institutional investors seem very different than the large ones.
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25 Appendix 1: The survey (this section is to be updated and exposition should be improved)
Survey description
In Part 1, we ask general questions about the investor's organization. We ask for the organization’s
type (e.g. pension fund), location of the main office, and foundation year. Next, we ask questions
related to PE activity such as when they started investing in buyout and venture capital, how many
fund advisory board seats they have and the fraction of board meeting attended. We ask whether the
team also manages other alternative investments (real estate, hedge funds) and the relevance of
external factors for the allocation to PE.
Part 2 is dedicated to asset allocation (parent organization’s assets under management, or
AUM, and amount invested in buyout and venture capital) and the number of investment
professionals. Part 3 inquires about co-investment opportunities. We ask whether investors are
offered such opportunities, how much they invested in them, and what are the reasons for rejecting
or accepting them. Part 4 asks about the investment committee. We ask whether it can take
decisions autonomously, the type of voting system in place, how many members left over the last
five years (measure of turnover) and the age and experience of the members.
Part 5 is dedicated to compensation policy. We ask whether part of the compensation is
linked to performance, whether this part can be higher than the fixed salary; and whether staff
members who are not sitting on the investment committee may receive such a performance-related
compensation and whether this part can be higher than the fixed salary.
Part 6 asks about the contract between LPs and GPs, which is called the Limited Partnership
Agreement (LPA). We ask whether investors benchmark the LPA, and if so how much time they
spend on it. We ask how frequently they negotiate these contracts and what terms they find
essential (open field). Finally, we ask how frequently they obtain side letters (i.e., favorable terms
and conditions) and the Most Favored Nation clause (which ensures they obtain the best terms and
conditions given to any investor).
Part 7 covers Due Diligence (DD) issues. We start by trying to capture effort level by
asking: i) whether they compute their own performance measure, ii) how do they treat nonliquidated investments, iii) how frequently they interview portfolio company executives. Next, we
ask about the fraction of funds that get selected for due diligence analysis and then committed to.
Next, we ask investors to rate the different criteria they use when choosing funds, and which is the
most important one. We also ask how much time is spent on due diligence. We ask these questions
separately for their investments in first-time funds, for first investments in a seasoned firm, and for
26 re-investments. We also ask for the main motivation to outsource part of due diligence. Finally, we
ask how often and why they have decided not to re-invest with some PE firm (if ever) and their
motivation for investing in first time funds (when they do).
The last section, Part 8, tries to quantify the intensity of monitoring. We ask the fraction of
portfolio companies for which they collect information on portfolio company fees, whether they
track portfolio composition (in terms of industry, size and country) and whether they visit portfolio
companies.
Filtering respondents
From our original sample of 368 respondents, requiring at least one of the twelve questions
composing the index to be answered reduces the sample size to 277 respondents.22 In addition, as
explained below, we want to link the index to five key investor characteristics (type, size, country
of location, parent age and years of PE investing). Requiring these five characteristics further
reduces the sample size to 244 respondents. Finally, we filter out an additional 14 respondents who
invest only in fund-of-funds, reaching our final sample size of 230 respondents.
Survey content
There are 7 fixed menus that are offered with a drop down button:
Menu 1: [Select, 0, 1, 2, …, 50, More than 50]
Menu 2: [Select, 0%, 1-10%, 10-20%,…, 90-99%, 100%]
Menu 3: [Select, 1980 and before, 1981, 1982,…,2008]
Menu 4: [Select, yes definitely, yes possibly, no, I do not know]
Menu 5: [Select, always, sometimes, never, I do not know]
Menu 6: [Select, largely irrelevant, somewhat important, very important, crucial]
Menu 7: [Select, use the NAV provided by the GP, compute your own fair value estimation of nonliquidated investments, look only at performance of liquidated investments]
Menu 8: radio button [yes, no]
If the “Other (please specify)” option is selected, then it opens a blank box to write in.
22
Virtually all of these respondents that are dropped here are people who just answered the first one or two questions
and then left the survey.
27 Questions asked
1. Organization characteristics
1.1 When was your firm created? If you feel uncomfortable with providing the exact year, please indicate a
date range. [Open question]
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,
Government-owned bank, Asset manager, Fund-of-funds, Other]
1.3 In which city and country is the investment committee (or person making investment decisions) located?
[Open question]
1.4 Have you ever invested in buyout funds? [Menu 8]
If yes:
1.4.1 In which year did your organization start investing in buyout funds? [Menu 3]
1.4.2 How many advisory boards of buyout funds does your organization currently sit on? [Menu 1]
If positive:
1.4.2.1 What proportion of the buyout funds’ advisory board meetings do you attend each year? [Menu 2]
1.5 Have you ever invested in venture capital funds? [Menu 8]
If yes:
1.5.1 In which year did your organization start investing in venture capital funds? [Menu 3]
1.5.2 How many advisory boards of venture capital funds does your organization currently sit on? [Menu 1]
If positive:
1.5.2.1 What proportion of the venture capital funds’ advisory board meetings do you attend each year?
[Menu 2]
1.6 Does the team that manages buyout and venture capital investments also manages (multiple choices
possible):
. Real estate funds
. Hedge funds
. Debt instruments (e.g. mezzanine)
. Direct co-investments
2. Allocation
Please fill in the following table. Please specify the currency you use [USD, Yen, Euro, GBP]
2000(or year you started 2008
to invest, if more recent)
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
28 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-of-funds 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? [Menu 2]
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? [Menu 2]
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? [Menu 2]
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? [Menu 2]
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? [Menu8]
If yes : 3.5.1 For how many funds? [Menu 1]
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? [Menu 8]
If yes : 3.6.1 For how many funds? [Menu 1]
3.7 In your experience, does investing in a fund give you priority over other investors when the GP raises
subsequent funds? [Menu 5]
If yes:
Why do you think you get priority? [Open question]
3.8 If you think GPs constrain the size of their fund, why do you think they do so? [Open question]
4. Co-investments
4.1 Have you ever been offered a co-investment opportunity by a GP? [Menu 8]
If yes:
4.1.1 Which fraction of co-investment opportunities do you typically reject? [Menu 2]
4.1.2 What fraction of your PE portfolio was made of co-investment in 2008 (in value)? [Menu 2]
4.1.3 What are your firm’s motivations for co-investing? (multiple choices possible)
. Improve performance before fees (we get invited to the best deals)
. Reduce total fees
. Customize portfolio (adjust exposure to country, industry…)
. Free-ride on GP due diligence
. Other (please specify)
4.1.4 Which of the above criteria is your firm’s main motivation for co-investing?
29 5. Investment committee
5.1 Do you have an Investment Committee (IC)? [Menu 8]
If yes:
5.1.1.1 IC decisions are taken by [majority, consensus, other]
5.1.1.2 How many members did the IC have in 2008? [Menu 1]
5.1.1.3 How many members with voting rights did the IC have in 2008? [Menu 1]
5.1.1.4 How many members with voting rights did the IC have in 2003? [Menu 1]
5.1.1.5 How many IC members with voting rights left your organization between 2003 and 2008? [Menu 1]
5.1.1.6 How many professionals prepared due diligence in 2008? [Menu 1]
5.1.1.7 How many of these professionals also sat on the IC in 2008? [Menu 1]
5.1.1.8 Please fill in the following table for each IC member with voting rights: [age, year started working
in PE, year started working in your organization, year joined the IC, professional background (consulting or
corporate, finance, entrepreneur, other (please specify))].
If no:
5.1.2.1 What is the age of the person taking the investment decision? [Open question]
5.1.2.2 In what year did this person start working in PE? [Menu 3]
5.1.2.3 In what year did this person start working for your firm? [Menu 3]
5.1.2.4 How many people performed this job over the last 5 years? [Menu 1]
6. Human resources policy
6.1 Is part of the compensation of investment committee members directly related to financial performance?
[Menu 8]
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)
[Menu 8]
6.2 Do (some of) the investment professionals that do not sit on the investment committee receive a
performance-related bonus? [Menu 8]
If yes:
6.2.1 Is such a performance bonus larger than the fixed part of their compensation in a typical year? [Menu
8]
6.3 Has the compensation policy changed over the last ten years? [Menu 8]
If yes: 6.3.1 Can you please briefly describe how? [Open question]
7. Contracting
7.1. Do you benchmark the contract between you and the GP? [Menu 8]
7.2. How much time do you spend on benchmarking the contract?
(full-time employee equivalent, number of days)
7.2.1 Internally [Menu 1]
7.2.2 Externally ( consultants) [Menu 1]
7.3. Do you typically negotiate the contract terms? [Menu 5]
If always or sometimes:
7.3.1 Which terms do you negotiate? [Open question]
7.4 Do you obtain side letters? [Menu 5]
If yes:
7.4.1 For which fraction of the funds do you obtain side letters? [Menu 2]
7.5 Do you obtain ‘most preferred nation’ clauses? [Menu 5]
If yes:
30 7.5.1 For which fraction of the funds do you invest in? [Menu 2]
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? [Open question]
8.2 Do you (or your consultant) calculate your own aggregate performance measure based on the information
you are provided with? [Menu 5]
8.3 Do you (or your consultant) benchmark GPs' track records? [Menu 5]
8.4. What do you (or your consultant) do to measure GP’s past performance? [Menu 7]
8.5 Do you (or your consultant) interview the executives of the GP's portfolio companies?
[Menu 5]
Consider the set of funds for which you have received or requested a Private Placement Memorandum
(PPM) over the last 5 years.
8.6.1. What fraction went through due diligence? [Menu 2]
8.6.2. What fraction did you financially commit to? [Menu 2]
9. Due diligence – Investing in funds
9.1 Do you invest in first-time funds? [Menu 4]
If no:
9.1.1 Why? [Open question]
9.1.2 Would you invest if their fees were lower? [Menu 8]
If yes:
9.1.3 What are your firm’s reasons for investing in first-time funds? (multiple choices possible)
. Because there was a credible special LP
. Given our size we cannot discard all the first-time funds
. Because we do not always invest for performance reasons
. Because we expect these first time funds to outperform
. Because we get priority access to follow-up funds if that team is successful
. Because the GP’s partners made a sizeable financial commitment to the fund
. Because with these funds we can obtain stricter covenants
. Because we do not have access to seasoned funds
. Because of the presence of a credible strategic partner
. Other reasons [please specify]
9.1.4 Which of the above reasons is your firm’s main one for investing in first-time funds?
9.1.5 How many such funds have you invested in since 1998? [Menu 1]
9.1.6 Consider the criteria that your firm typically uses to select a first-time fund. Please tell us about their
importance. [Menu 6 for all the options below]
9.1.6.01 Partners' previous successes in PE
9.1.6.02 Partners’ previous successes in non-PE jobs (e.g., consulting, finance,
industry, entrepreneurs)
9.1.6.03 Partners’ previous experience in working together
9.1.6.04 Partners’ quality of education
9.1.6.05 Quality of the partners’ network of contacts
9.1.6.06 The advisor/gatekeeper’s opinion
9.1.6.07 The proposed investment strategy
9.1.6.08 Commitments to this fund by top LPs
9.1.6.09 The fund’s size
9.1.6.10 The level and structure of fees
9.1.6.11 Whether the fund provides exposure to a certain industry/geography/stage
31 9.1.6.12 The opportunity to access follow-on funds
9.1.6.13 Whether the fund may generate business (profits) for other divisions of my organization
9.1.6.14 Co-investment opportunities
9.1.6.15 Other (please specify)
9.1.7 Which of the above criteria is the most important?
9.1.8 Please indicate any other criteria that are crucial for your decision to invest in a first-time fund. [Open
question]
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)
9.1.9.1 Internally [Menu 1]
9.1.9.2 Externally (consultants) [Menu 1]
9.1.10 What proportion of your time is spent on quantitative (vs. qualitative) due diligence for a first-time
GP’s fund? [Menu 2]
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? [Menu 1]
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. Menu 6 for all the options below
9.2.1.01 The GP’s reported aggregate IRR on previous funds
9.2.1.02 The GP’s reported aggregate multiples on previous funds
9.2.1.03 Partners’ quality of education
9.2.1.04 Quality of the partners’ network of contacts
9.2.1.05 The GP’s reputation
9.2.1.06 Stability of the team at the partner level
9.2.1.07 The advisor/gatekeeper’s opinion
9.2.1.08 The proposed investment strategy
9.2.1.09 Commitments to this fund by top LPs
9.2.1.10 The fund’s size
9.2.1.11 The change in fund size from previous funds
9.2.1.12 The level and structure of fees
9.2.1.13 Whether the fund provides exposure to a certain industry, geography, or stage
9.2.1.14 The valuation of unrealized investments (NAVs) in previous funds
9.2.1.15 Whether the fund may generate business (profits) for other divisions of my organization
9.2.1.16 Co-investment opportunities
9.2.1.17 Renewed commitment to this fund by its existing LPs
9.2.1.18 Other
9.2.2 Which of the above criteria is the most important?
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)
9.2.3.1 Internally [Menu 1]
9.2.3.2 Externally (consultants) [Menu 1]
9.2.4 What proportion of your time is spent on quantitative (vs. qualitative) due diligence for a seasoned
GP’s fund? [Menu 2]
9.3 Consider re-investing in a seasoned GP. How many such funds have you invested in over the last ten
years? [Menu 1] If more than zero:
9.3.1 Consider the criteria your firm typically uses for this decision. Please tell us about their importance.
[Menu 6 for all the options below]
Same criteria as in 9.2.1, with three additional ones:
32 9.3.1.1 The syndicate/club co-investors in previous funds
9.3.1.2 The quality of the GP’s reporting on our previous investments
9.3.1.3 Renewed commitment to this fund by its existing LPs
9.3.2 Which of the above criteria is the most important?
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)
9.3.3.1 Internally [Menu 1]
9.3.3.2 Externally (consultants) [Menu 1]
9.3.4 What proportion of your time is spent on quantitative (vs. qualitative) due diligence for a re-investing
in a seasoned GP’s fund? [Menu 2]
9.3.5 How often did you refuse to re-invest with a GP over the last 5 years? [Menu 2]
If more than zero:
9.3.5.1 Please evaluate the following reasons for not re-investing and indicate their frequency. [Menu 5 for
all options]
9.3.5.1.1 The fund’s size increased too much
9.3.5.1.2 The GP deviated from their original strategy
9.3.5.1.3 The GP’s fees increased too much
9.3.5.1.4 The GP had charged excessive company fees
9.3.5.1.5 The GP’s performance had been disappointing
9.3.5.1.6 Some key professionals/partners left the GP
9.3.5.1.7 Other reasons (please indicate)
9.4 Do you invest in foreign GPs? [Menu 8]
If yes:
9.4.1 How many directly held foreign GP funds did you have running at the end of 2008? [Menu 1]
9.4.2 Do you use local sources of intelligence when investing abroad? [Menu 8]
If yes:
9.4.2.1 Please describe these sources. [Open question]
9.5 If you outsource some of the due diligence process, could you please evaluate the following reasons:
. Lack of experience in PE investing [Menu 5]
. Outsourcing gives access to unique information [Menu 5]
. Other reasons (please specify) [Menu 5]
9.6 Has your firm changed its main investment criterion over the last ten years? [Menu 8]
10. Monitoring
10.1 Do you actively monitor performance in the PE Funds you invest in? [Menu 8]
If yes:
10.1.1 Can you please briefly describe how? [Open question]
10.2 Has your fund monitoring policy changed over the last ten years? [Menu 8]
If yes:
10.2.1 Can you please briefly describe how? [Open question]
10.3 Do you keep track of the composition of your PE portfolio in terms of industry/size/country? [Menu 8]
10.4 How much time is spent to keep track of the cash flows realized on each fund? (in days, full-time
employee equivalent)
10.4.1 Internally [Menu 1]
10.4.2 Externally (consultants) [Menu 1]
10.5 Do you provide any services (or support) to the GPs whose funds you invest in? [Menu 8]
If yes: 10.5.1 Can you specify which ones? [Open question]
10.6 Do you visit portfolio companies? [Menu 5]
33