DBJ Discussion Paper Series, No. 1801

Skin in the game, wealth and risk-taking:
Evidence from private equity funds

Carsten Bienz(Norwegian School of Economics)
Karin S. Thorburn(Norwegian School of Economics, CEPR and ECGI)Uwe Walz(Goethe University Frankfurt, SAFE/CFS)
October 2018

Discussion Papers are a series of preliminary materials in their draft form. No quotations,reproductions or circulations should be made without the written consent of the authors in orderto protect the tentative characters of these papers. Any opinions, findings, conclusions or
recommendations expressed in these papers are those of the authors and do not reflect the viewsof the Institute.

Skin in the game, wealth and risk-taking:
Evidence from private equity funds∗
Carsten Bienz
Norwegian School of Economics
Karin S. Thorburn
Norwegian School of Economics, CEPR and ECGIUwe Walz
Goethe University Frankfurt, SAFE/CFS
October 17, 2018

Abstract
We investigate the incentive effects of ”skin-in-the-game” compensation in particular on
risk-taking. We use the private equity industry where fund managers are typically required
to co-invest their own money alongside the fund as our testing ground. We check thepredictions of our model, namely that more skin in the game will lead to the acquisitionof less risky, but more levered firms,in a unique sample of private equity investments in
Norway. This data set allows us to compute the fund managers’ skin in the game as wehave information on their personal wealth. Consistent with the model, portfolio company
risk decreases and leverage ratios increase with the co-investment fraction of the manager’s
wealth. Hence, our results clearly show that wealth effects are of first order importancewhen designing incentive compensation.

Keywords: Private equity, leveraged buyouts, incentives, co-investment, risk taking, wealth
JEL Classification: D86, G12, G31, G32, G34.
∗

We are grateful for comments by Ulf Axelson, Francesca Cornelli, Dirk Jenter, Alexander Ljunqvist, Tiago
Pinheiro, Morten Sorensen, Joacim Tag, Lucy White and seminar participants at BI, DBJ, Hitotusbashi, NHH,
Nagoya, Conference on Entrepreneurship and Finance in Lund 2013, EFA 2017, HKUST Conference on Entrepreneurship and Finance 2014, and the 8th Private Equity Findings Symposium at LBS. Parts of this paper
were written when Carsten Bienz was a Shimonura Fellow at Development Bank of Japan. We would also liketo thank Marit Hofset Stamnes for research support. Emails: carsten.bienz@nhh.no; karin.thorburn@nhh.no;
and uwalz@econ.uni-frankfurt.de.

1Introduction

Looking back at the financial crises of 2008 it is not difficult to find a large number of voicesthat claim that compensation schemes in the financial industry played their part in causing
the crises. Two types of criticsm were lobbed at boni schemes - a fundamental one that simply
thinks that boni are inappropriate for employees in financial institutions (Taleb (2011)). A
less extreme case of thinking takes aim at the structure of the bonus scheme, namley the large
reliance on short term cash payments, the absence of long term incentives and the lack of “skinin the game” (Quinn (2009)). Politicians, particularly in the EU, have reacted and introduced
limits to boni payments in 2013 in many types of financial instituions (European Commission(2013)). Currently large cash boni are forbidden and boni are paid out over a number of years.
This deferrement of boni leads to a build up of equity (and wealth) in the firm and implictly
relaxes the limit-liability constraint of the bonus recipient by accumulating “skin in the game”.
Unfortunately we know very little about the effect of “skin in the game” on incentives
as data on such contracts is very hard to come by. In this paper we attempt to start toinvestigate “skin in the game”, or the link between risk taking, incentives and wealth. We doso by looking at particular setting in finance, namely private equity funds. Compensation in
Private equity funds is on the one hand quite similar to contracts in other areas of financial
intermediation. Private equity funds are raised and managed by a general partner (GP),
who makes the investment decisions for the fund. GPs are compensated with a mix of fixed
fees and a bonus . A typical compensation structure is a two percent annual management
fee on the fund’s capital and a 20% carried interest on the profits above a certain threshold(Metrick and Yasuda, 2010).1 On the other hand are GPs typically required to co-invest theirown money in the portfolio companies alongside the private equity fund. This “skin in thegame” forces the GP to participate in any losses incurred by the fund.2 A typical GP in the
US is required to invest 1% of the fund’s capital, corresponding to a $3.6 million investment(Robinson and Sensoy, 2015).3 This setup makes provides us with an ideal testing ground1

The largest differences is a sense the fact that funds last for ten years, so compensation is set for a much
longer period than in other areas.
2
Edmans and Liu (2011) argue that inside debt provides an efficient solution to agency problems, since its
payoff depends not only on the incidence of bankruptcy but also on firm value in bankruptcy.
3
In the US, GPs must invest at least one percent of the fund’s capital in order for the carry to be taxed ascapital gains (Gompers and Lerner, 2001).

1

to examine the impact of “skin in the game” on incentives for risk taking: while a large coinvestment mitigates incentives for excessive risk taking, it may also make a risk-averse manager
too conservative, foregoing valuable investment opportunities with high risk. In addition to
exploring the relationship between “skin in the game” and risk taking incentives, our paper
aims to contribute to the existing body of knowledge in two dimensions.
First, due to our data set we are able to control for the GP’s wealth . Thereby, we canrelate the co-investment of the GP to his own wealth. Hence, we can control for the welldocumented fact of risk-preference being dependent of wealth (see e.g. Holt et al. (2002)).
We find that controlling for wealth-effects matters a lot. While the absolute co-investment
levels are unrelated to risk-taking of the GP, the relative, wealth-adjusted co-investment levels
significantly affect risk-taking of the private equity fund. Our approach may hence also explain
the absence of any effect of co-investment on fund performance (see e.g. (Robinson and Sensoy,2015)).
Second, since we are using data on ex-ante designed contracts which are applicable to theinvestment in not only one, but many firms we are able to cope with the endogeneity concerns inherent in the relationship between incentive contracts and risk-taking. There is ample
evidence on the relation between incentive compensation and risk-taking, e.g. on the relationship between compensation of corporate managers and corporate investments (see e.g. Guay
(1999), Knopf et al. (2002) and Rajgopal and Shevlin (2002)) as well as between compensationand financial risk or leverage (see e. g. Tchistyi et al. (2011) ) These studies suffer, however,potentially from endogeneity concerns. Value-maximizing shareholders may have an incentive
to jointly determine the risk of the firm and the incentives schemes. This implies, for example,
that shareholders aiming for more risk also aim to incentivize risk-averse corporate managers
with steeper contracts to join the company. While there are some efforts to overcome theseendogeneity concerns (see Chava and Purnanandam (2010) and Shue and Townsend (2013),
but also Coles et al. (2012) on the difficulties with such approaches) we provide a new route toaddress this issue by investigating the impact of incentive contracts which are ex-ante designed
and applicable to new investments into a number of portfolio firms.4
4

To understand this point better, consider the following setup: GPs commit to a certain co-investment level
during fundraising. The fund closes six months to a year later and then proceeds to acquire on average ten firms
over the next five years. Many of these firms are bought through auctions. If one were for example concerned2

We start by developing a simple theoretical model, in which the selection of a target firm
and the decision on deal financing are made simultaneously. Fund managers can chose between
firms with different risk and have to decide how much debt to use in the acquisition, the restof the consideration being equity contributed from the fund. Firms with relatively high risk
have higher expected cash flows, but also have a higher probability of default. For tractability,
we assume that firm value is independent of the capital structure, ignoring potential benefits
from debt tax shields and reduced agency costs (Jensen, 1986; Modigliani and Miller, 1958).
The fund manager is required to co-invest a fraction β of the equity in the firm and receives
a performance based carried interest α on the cash flows above a certain threshold. Since firm
value is independent of the capital structure, β has no direct effect on the leverage decision.
However, because the GP is risk averse and derives negative utility from downside risk, β has
direct implications for the choice of project risk. The fund manager selects investments bytrading off the project’s expected cash flows against the negative utility associated with higher
risk. Ceteris paribus, managers with a higher co-investment will invest in less risky firms.
The incentive effect of α is more straightforward. Since leverage increases the payoff toequity in the good states, managers will chose more debt the higher is α. The optimal leveragedepends, among other things, on the firm’s debt capacity. High-risk firms have greater default
risk and therefore higher expected bankruptcy costs. Because managers with a relatively
high co-investment share prefer to invest in projects with less risk, the debt capacity and themarginal value of additional debt will be increasing in β. As a result, for a given α, funds witha higher co-investment share will finance their portfolio companies with more debt.
We then take the model predictions to the data, using a unique sample of 62 portfolio
company investments made by 20 Nordic leveraged buyout funds between 2000 and 2010. We
limit the analysis to firms in Norway, where the manager’s taxable wealth is public information,
as are the financial statements of firms after going private.5 The wealth data allows us toestimate the incentives provided by the co-investment, not only in percent and dollar amount,but also as a fraction of the manager’s total wealth. This is an important empirical contributionof this paper. As shown below, and consistent with a declining risk aversion in wealth, the effectabout reverse causality then the principal would need to anticpate all these decisions.
5
Norway’s tax system makes it attractive to have holding companies to be located in Norway, in contrast to
Sweden or Denmark3

of the co-investment becomes evident only after controlling for the fund manager’s wealth.6
The required co-investment proportion varies substantially across the 20 funds, ranging
from zero to 15% of the fund’s equity investment, and with an average of 3.7% (median 1.5%).
When measured as a fraction of the wealth at the time of the investment, the average GP
personally invests 93% (median 48%) of his total wealth in the fund.
Our empirical tests confirm the model predictions. Funds with a higher co-investment
requirement tend to acquire target firms with lower asset beta and use more leverage. That is,
firms with stable cash flow that can safely operate with higher leverage without jeopardizing
their ability to service the debt. Axelson et al. (2013) show that buyout leverage is determinedprimarily by economy-wide credit conditions. We add to their evidence by showing that thefund manager’s personal co-investment also helps explain portfolio company leverage in the
cross-section.7
We further each look at relationship between a target firm’s equity beta and the GP’s
co-investment. The higher the equity beta, the higher overall risk as it corrects firm risk forleverage. We find a negative correlation between the equity beta and the co-investment, again
suggesting that the overall effect of project risk dominates the leverage effect, showing thatthe manager’s risk appetite is lower the more he has to invest of his own funds.
Finally, we investigate whether we can find effects at the portfolio level as well. We lookat the relative size of deals and find that the higher the relative co-investment fraction, thesmaller the relative size each individual deal. This finding suggests that the incentive effect ofa higher co-investment is not limited to the deals itself but has a broader effect on the GP’s
decision-making process.
This finding may also shed light on one curious aspect of our analysis. We do not find thatGPs with a high co-investment select firms with lower absolute risk. Rather these GPs seem toselect firms with lower systematic risk. However, given that we find an diversification effect at
the portfolio level, GPs still opt for more diversification, but the pattern is somewhat different
from what we may suspect initially.
6
Robinson and Sensoy (2015) fail to find any relationship between the fund-level net-of-fee performance and
the GP co-investment, perhaps because they lack data on GP wealth. Becker (2006) shows that corporateboards in Sweden tend to provide higher variable incentives to wealthy CEOs.
7
See also Colla and Wagner (2012), who find that buyout leverage increases with firm profitability anddecreases with cash flow volatility.

4Overall, our evidence suggests that limited partners effectively reduce fund managers’ incentives to take risk by requiring them to co-invest in the portfolio companies. Whether thisreduction in GP’s risk appetite is optimal or not goes beyond the scope of this paper. Limited
partners ultimately care for the risk-adjusted net-of-fee returns, something which we do not
examine here.8
In our framework, we treat the co-investment fraction as exogenous. Obviously, fundmanagers may design a compensation structure at the outset—when raising the fund—that
fits their own risk preferences. In such case, the co-investment fraction and the investment
risk may simply both be a result of the fund manager’s risk preferences. Thus, an alternativeinterpretation of our evidence is that limited partners could infer the GP’s risk preferences fromthe co-investment fraction and pick funds with risk profiles that fit their investment strategy.
The paper proceeds as follows. Section 2 sets up and discusses our theoretical model andits predictions. Section 3 describes the data, while Section 4 presents the empirical results.
Section 5 concludes.

2

Model

2.1

Model set-up

To analyze the incentive effects of the private equity manager’s co investment, we propose
a model that combines a project choice with a capital-structure decision. Specifically, we
consider a buyout fund’s selection of a target company and the amount of debt with which
the acquisition is financed. In our model we take the structure of the incentive contract as agivens in an attempt to understand the implications of this type of arrangement.
There are three players in this model. The principal is the LP (which we also call investor),with the GP (or fund manager) being the agent. Banks constitute the third group. An implicit
assumption is that investors and banks are diversified, while the GP is not.
The private equity fund manager can choose among a set of firms that vary in their degreeof risk. Investing in a firm leads to three potential outcomes: high, medium and low. The cash
8
For evidence on private equity fund returns, see, e.g., Kaplan and Schoar (2005), Phallipou and Gottschalg(2009), Groh and Gottschalg (2011), Driessen et al. (2012), Harris et al. (2014), Higson and Stucke (2012) andPhalippou (2012).

5

flows x in each outcome are, respectively, R + ∆, R and R − ρ. The high and low outcomesarise with probability 0.5q, while the probability of the medium outcome is (1 − q). A higher
q increases the likelihood of the high and the low outcome. Hence, q can be interpreted as ameasure of firm risk. We assume that ∆ > ρ, a zero discount rate, and risk neutral investors,
so the expected value of the firm V (q) = R + 0.5q(∆ − ρ) is increasing in the risk measure q.
Concurrent with the selection of a target firm, the fund manager has to decide on how tofinance the investment I. This is tantamount to choosing a capital structure for the newly
acquired firm. Specifically, the GP has to choose the amount of debt D, with the remainderof the purchase price (I − D) being equity from the buyout fund.
Creditors receive the principal D plus a credit spread D(1 + r) as long as the firm’s cash
flows exceed this amount. We let R > D(1 + r) > R − ρ, so the firm defaults on its debt in the
low state. In default, creditors receive R − ρ and the equity is worth zero. For tractability, we
ignore potential benefits from the tax shield of debt and reduced agency costs, so firm value
V is independent of leverage.

2.2

The incentive scheme of the fund manager

In our model, the GP is compensated with the components typically observed for private equity
funds. First, he receives a fixed management fee M from the limited partners. Since we ignore
future fund raising efforts, this fixed fee has no impact on his investment decisions, as shown
below.
Second, the GP receives a performance based payment equal to a fraction α ∈ (0, 1) of the
cash flows to equity exceeding a normal return e. We assume that e is a non-risk adjusted
return, with e > r. Letting e be exogenous maps industry practice, where the hurdle rate
typically is set when the fund is raised, well before the fund manager starts selecting portfolio
companies.
The carried interest thus pays the fund manager α(x − C) > 0, where x − C is the cash
flow in excess of C, the payments to creditors and the hurdle return paid to limited partners:

C(D) = D(1 + r) + (I − D)(1 + e) = I(1 + e) − D(e − r).

6(1)

For C ≤ x, the carried interest is zero.
To make debt financing attractive, we assume that ∆ + ρ − R > D(1 + e). That is, thesum of the cash flow upside and downside ∆ + ρ exceeding the mean return is larger than thehurdle rate reduction due to debt financing. For simplicity, we also set the cash flow in themedium outcome equal to the hurdle equity return, R = I(1 + e).9 These assumptions ensurethat the all-equity financed firm has a positive net present value (NPV).10 They further imply
that, in the medium state and with debt financing, x − C = D(e − r) > 0 and the GP receives
a carry.
Third, in addition to the management fee and the carry, the GP is required to co-invest
his own money alongside the fund. This co-investment relaxes the limited-liability constraintof the fund manager and forces him participate in the downside risk. Specifically, the GP
contributes the fraction β ∈ (0, 1) of the fund’s equity investment and receives a fraction β ofthe realized equity value, where the value of the leveraged firm is:V D (q, D) = 0.5q[R + ∆ − D(1 + r)] + (1 − q)[R − D(1 + r)](2)

We allow creditors to observe firm risk q, depicting the notion that the demand for credit
occurs after the target has been selected. The creditor charges a credit spread r that allows
him to at least break even:

0.5qD(1 + r) + (1 − q)D(1 + r) + 0.5q(R − ρ) ≥ D(3)With a competitive market for loans, the creditor’s participation constraint in Eq. (3) is strictly
binding. In our model, project risk and capital structure are decided simultaneously. Since
creditors can observe the GP’s selection of target firm, we assume that q is contractible andlet creditors account for q in setting the loan contract terms. Using the binding participation
constraint (Eq. (3)) of the creditor allows us to rewrite (Eq. (1 )) toC(D) = I(1 + e) − De +9100.5q(R − ρ)
0.5qD
−
.
1 − 0.5q
1 − 0.5q(4)This assumption could be relaxed without changing the implications of the model.
The NPV of the all-equity firm is V (q) − I. With R = I(1 + e), V − I = Ie + 0.5q(∆ − ρ) > 0 since ∆ > ρ.

7

While debt funding increases the equity returns in the high and medium states, it does
not come without a cost to the GP. In case of default, the manager incurs a reputational loss.
We let the personal bankruptcy costs B be increasing in the creditor losses and convex in
the face value of debt. Furthermore, we rely on the notion that the failure of a risky firmcauses less reputational losses than that of a more mature and stable firm. Hence, we let
B(q, D) = λD2 /q, where λ ∈ (0, 1) is an exogenous liquidation cost.
We further assume that the private equity manager shows some degree of risk aversion
and derives negative utility from downside risk. We depict this negative utility k(q) = 0.5cq 2 ,where c ∈ (0, 1) captures the fund manager’s sensitivity to risk or his degree of risk aversion.
In our setting, k is more pronounced the higher the risk of the venture. Since the GP realizes
downside risk only from his co-investment, this cost is assumed to be proportional to β (seeBolton et al. (2011) for a related approach). Moreover, in our empirical analysis below—andin line with much of the extant literature—we assume c to be decreasing in wealth w (i.e. c(w)with ∂c/∂w < 0), implying that wealthier fund managers are less risk averse.11

2.3

The analysis

Having outlined the incentive structure of the GP, we now derive the implications for his choiceof project risk and leverage. The objective function of the fund manager is:12

V GP (q, D) = β(V D (q, D) − (I − D)) + α(V D (q, D) − C(D)|x > C)
−0.5qB(q, D) − βk(q) + M.

(5)

Inserting the binding creditor constraint from Eq.(3) into the function for the value of theleveraged firm and substituting for the functions of C, B and k, the GP’s objective function
11

See, for example, Rabin (2000).
For tractability, we ignore the portion of the carry that the GP has to pay from his ownership stake β in thetarget firm. With α = 0.20 and β = 0.01, this portion will be small in comparison with the other componentsof the GP’s payoff and could safely be ignored without altering the results.
128

can be rewritten as:

V GP (q, D) = β(0.5q(R + ∆) + (1 − q)R + 0.5q(R − ρ) − 0.5cq 2 − I))+α[0.5q(R + ∆ − C + (1 − q)(R − C)] − 0.5λD2 + M(6)Furthermore, when choosing the level of project risk and debt financing, the GP faces two
opposing effects that he has to trade off against each other. Higher q is associated with, on the
one hand, larger expected cash flows and, on the other hand, greater negative utility k related
to risk aversion. Similarly, higher leverage D is accompanied by higher expected carry as
cheaper debt replaces more expensive equity, but also by greater expected costs of bankruptcy
B.
Since, from Eq. (1), ∂C/∂D = −(e − r), the first-order condition of the GP’s choice ofdebt is:dV GP= −λD + α((1 − 0.5q)e − 0.5q) = 0
dD(7)

and the first-order condition for his choice of risk is:dV GP= β(0.5(∆ − ρ) − cq) + 0.5α(∆ + ρ − D(1 + e) − R) = 0.
dq

(8)Solving these two equations yields:α((1 − 0.5q)e − 0.5q)
λ(9)(∆ − ρ) α(∆ + ρ − D(1 + e) − R)
+
.
2c
2cβ

(10)

D(q, α) =andq(D, β, α) =Note that leverage and project risk are complements to each other. That is, D is a functionof q in Eq. (9) and q is a function of D in Eq. (10). Notice also that the two dimensionsof risk, D and q, operate in opposite direction. Higher project risk leads the private equity
manager to choose lower leverage and vice versa.13 Our two choice variables are in this sense
risk-substitutes. This tradeoff between project risk and leverage which can be already be seen13dq
= − αe−1< 0 and dD= − α(1+e)< 0.
This follows from dD
dq
2λ
2cβ

9

in the first-order conditions is a key mechanism in our model.
An important consequence of this complementarity is that exogenous parameters may affectthe choice of risk and leverage directly, via the respective first-order condition, as well as
indirectly, through the other choice variable. For example, the carry α affects both D and q
directly, and therefore also indirectly. In contrast, the co-investment share β has a direct effectsolely on q and hence only an indirect effect on the leverage choice.
We derive the comparative static effects of the co-investment share by totally differentiating
the first-order conditions. From Eqs. (7) and (8), we get:dD(cq − 0.5(∆ − ρ))(0.5(1 + e))
=
>0
dβ
Γ

(11)

−λ(cq − 0.5(∆ − ρ))
dq
=
< 0,
dβ
Γ

(12)

and

where Γ > 0 is the determinant of the Hessian matrix of the two endogenous variables.14
Recall from above that we let ∆ + ρ − R > D(1 + e), so that debt financing increases
the cash flow to equity in the good states. For the first-order condition with respect to q in
Eq. (8) to be satisfied, it follows that cq > 0.5(∆ − ρ). Thus, at the optimum, the cost of amarginal increase in project risk is higher than the marginal benefit from the point of view of
the risk-averse GP. Consequently, an increase in β has a negative effect on q and a positiveeffect on leverage. The economic intuition hence, is that co-investment makes the GP to own
a higher fraction of the portfolio firm. Given the risk-aversion of the GP, this higher degree ofownership in the firm induces the GP to choose a lower risk firm, i.e. to reduce q. Since, ourtwo choice variables are substitutes, the GP, in turn, decides to lever up the firm more.
In a second step, we analyze the wealth effects on our two risk dimensions. By taking thenegative relation between c and w into account we find by totally differentiating Eqs. (7) and(8):(0.5βq(1 + e)(∂c/∂w)
dD
=
<0
dw
Γ

(13)

14
Γ is the determinant of the D-q matrix of the second derivatives stemming from Eqs. (11) and (12). SinceΓ is the product of two second-order conditions that are negative, it must be positive. In our case, the nonzero cross derivatives imply that the direct effects dominate the indirect effects, which is a relatively standard
assumption.

10

and
dq
−βqλ(∂c/∂w)
=
> 0,
dw
Γ

(14)

Hence, an increase in wealth has just the opposite effect on the two risk measure. WealthierGPs are less risk reverse and hence invest in riskier projects which they, however, lever up less.
To sum up, there are three main results of our model that will guide our empirical testing
strategies below. First, the GP’s incentives to invest in risky projects are declining in his
required co-investment share. That is, a higher β induces the GP to be more conservative in his
project choice. Second, having chosen a less risky project, a higher co-investment share induces
the GP to use more debt financing. Third, wealth reduces the negative utility associated withrisk, these effects are more pronounced the less wealthy the GP. A measure which relates theco-investment level to the wealth of the GP takes up both effects. We now turn to an empiricalexamination of these implications.

3

Sample selection and description

3.1

Sample selection

We start by manually assembling a list of all leveraged buyout transactions in Norway between
1991 and 2010. This list, provided by the Argentum Centre for Private Equity at NHH, iscreated by combining information from two sources: (i) the public websites of Nordic buyout
funds, and (ii) the Argentum private equity market database.15 We are able to identify a totalof 142 buyout transactions targeting 134 unique Norwegian firms.
In Norway, all firms—public and private—are required to file their financial statementswith the Norwegian corporate registry (”Brønnøysundregistrene”).16 By manually matching
the target firm names to the corporate registry, we are able to identify the record in theyear of the buyout transaction for 117 firms. We retrieve the annual financial statements andownership information during the period 1997 to 2012 for these firms.
The fee structure of the private equity fund is generally confidential information, foundin the fund’s Investment Memorandum. We are able to get privileged fee information from a
15
16

The Argentum market database can be accessed at http://www.argentum.no/en/Market-Database/.
See Mjøs and Øksnes (2012) for information about this data.

11

large limited partner for 68 of the transactions. We are able to match 62 of these 68 firms
with public firms and obtain an asset beta for each of these 62 firms. The appendix contains acomparison of the characteristics of the 62 firms we ultimately include in the sample with the
51 firms with missing fee data.17
As shown in the appendix, the average firm included in the sample has slightly larger total
assets and is acquired by a fund of higher sequence number managed by a somewhat older
private equity firm. However, other characteristics such as fund size, firm profitability, asset
tangibility, industry, and market conditions, are not significantly different across the two groups
so we are not concerned that our sample differs substantially from the firms we were unable
to include. The information necessary for a transaction to be included in our sample is that
we know the GP’s co-investment fraction, fund age, and fund size. We have this information
for twenty funds. We also receive information about the management fee, the percent carry,
the hurdle rate and any clawbacks for fourteen funds.18 The difference can be attributed to
the fact that the limited partner in question declined to invest into some of the funds in our
sample but retained the fund-raising prospectuses.
Norwegian corporate law prevents an acquiring firm from servicing the acquisition debt
with the target firm’s cash flows.19 For this reason, buyout transactions are typically structured in two steps. First, the buyout fund levers up an empty holding company used as an
acquisition vehicle. Second, as a generally accepted practice, the holding company merges withthe portfolio company about 12 months after the acquisition.20 To account for this practice,
we consider the transaction leverage to be the total debt across the portfolio company andits holding company. We therefore track the ownership for each firm to the point where theultimate parent is the buyout fund itself. In our sample, 32% of the firms are owned directlyby the private equity fund, 31% of the firms have one holding company above them, while
the remaining 27% have two or more levels of holding companies. We retrieve the balance17

As explained further down below, we have to exclude eight firms for which we have the GP’s co-investment.
Nordic funds often pay carry on a deal-by-deal basis as the fund exits its investments. If a fund that paid
carry to its GP subsequently underperforms, the clawback requires the GP to return the excess carry paid out.
Also, in contrast to the US, Nordic funds do not charge transaction and management fees from their portfolio
companies.
19“Aksjeloven §8-10. Kreditt til erverv av aksjer mv”.
20
We are grateful to Tore Rynning-Nielsen at Herkules Capital for helping us understand the intricacies ofNorwegian buyouts.
18

12

sheets for all holding companies registered in Norway to compute the total debt used in thetransaction.
Finally, to retrieve data on the wealth of the general partners, we first identify all relevant
partners and associates from the buyout funds’ websites. We drop professionals that join thefirm after the fund’s investment phase and do Google searches for professionals that have left.
For private equity firms with part of its deal team located outside Norway, we limit our analysis
to the professionals living in Norway, for a total of 120 (out of 243 world-wide) individuals.21
We then obtain the historical tax records for all the professionals from the Norwegian tax
authorities. The tax records disclose their taxable wealth. This information is used below
to compute the required co-investment as a fraction of the GPs’ total wealth. There are
two caveats with this data. First, while most assets are marked-to-market, real estate is an
exception and generally has a tax assessment below 30% of its market value. This prevents
us from identifying the exact level effect of wealth on risk taking, but rather we examine
differences in the cross-section. Second, since we are unable to identify the exact deal team, we
assume that all professionals or partners in a private equity firm have equal responsibility forthe fund’s investments.22 This assumption introduces noise in the wealth estimate that should
work against us. Also, we winsorize the relative GP at five times wealth.

3.2

Sample description

The 62 portfolio companies in the sample are acquired by 20 different buyout funds raised
between 2000 and 2010 by 11 unique Nordic private equity firms. All variables are defined inTables 11 based on Table 12.
Insert Table 11 about here
Table 1 presents summary statistics for the 20 buyout funds. The fee structure (see PanelB) is quite standard with an average carry of 18% (median 20%), management fee of 2%(median 2.0%) and equity hurdle rate of 8% (median 8.0%). Data on these fees are missing for21
Private discussions with a limited partner suggest that the professionals residing in Norway are responsible
for the local deals.
22
Since the professionals’ wealth largely depends on the success of earlier funds raised by the private equity
firm, there is likely a relatively large correlation in the wealth of professionals within the same firm.

13almost one-third of the funds. However, because there is virtually no cross-sectional variation,
we ignore these fees in our empirical analysis below.
Insert Table 1 about here
The variable of particular interest to our study, the absolute (i.e. independent from wealthconsiderations) co-investment β, averages 3.1% of total fund size with a median of 1.5% of theconsideration, ranging from zero to 15% across the different funds (see Panel B) . We assume
that the proportion of the fund invested in Norway equals the fraction of the private equity
firm’s professionals that reside in Norway. With this assumption, the average absolute coinvestment in Norway is $17.83 million (median $5.45 million) per fund. The average fund in
the sample is managed by a private equity firm with 8 partners or 17 professionals. The mean
wealth of these partners is $3.2 million (median $1.53 million) in the year of the investment(see Panel A). The corresponding number for all professionals is $1.92 million (median $1.31
million).
Compared to Robinson and Sensoy (2015) there is more dispersion in Norwegian GPs’
co-investment. They find that on average buyout funds own 2.38% of their fund. This is
somewhat lower than the 3.1% we report.
We then compute several measures for the co-investment relative to wealth. The firstmeasure, Relative co-investment all, is the ratio of the dollar total co-investment for the fundand the combined wealth of all the fund’s professionals averaged over three years prior to theinvestment.23 We use the co-investment in the fund and not the individual target firm because
the fund manager’s risk aversion will be determined by his total co-investment amount. For the
average firm, the professional has to invest 117% of his wealth (median 43%). We repeat thisexercise using only partners who are responsible for the co-investment (”Relative co-investment
partners”) with the mean at 113% and the median at 43%. The variable Relative co-investment
partners is our main measure for the co-investment fraction of the GP’s wealth, used in mostof the empirical analysis below. Due to our winsorization procedure, the relative co-investment
share range from zero to five.
The table also provides general information about the funds (see Panel A). Our sample
23

We use the three-year average to smooth large variations in the taxable wealth.

14

includes on average 3.65 firms per fund. The funds in the sample are relatively large, withan average (median) size of $942 ($325) million. The typical fund is a follow-up fund and on
average the fourth fund raised by the private equity firm.
Table 2 shows characteristics of the 62 sample firms. The portfolio company investments
are from the period 2000 to 2010. At year-end of the transaction, leverage was on average
62% (median 64%). Total assets size is $120 million. The sample firms have relatively low
profitability, with a return on assets of 3%. A substantial fraction of the firms (42%) arein the technology industry. We also display the macroeconomic conditions we use in our
analysis. This reveals quite some variation in credit spread as well as of Nibor (the Norwegiancorrespondence to LIBOR) which ranges from slightly above two percent up to more thanseven percent.
Insert Table 2 about here
Table 13 in the Appendix provides a comparison between the firms in our sample andthose buyout deals for which we lack information about the GP’s investment. The deals in our
sample are somewhat more recent and the firms in our sample are somewhat larger (regressing
time on size shows a trend towards larger deals in recent years).
As a measure of project risk, we estimate asset beta for the portfolio companies. To
estimate this asset beta, we run a propensity score estimator that looks at all listed firms
on the Oslo Stock Exchange in a particular year and finds the best fit to our buyout target.
There are about 250 listed firms on the Oslo Stock Exchange in any given year.24 We run
yearly a regression were we match on the firms’ profitability, return on assets, (log) size, fixed
asset ratio and industry (at the one-digit level). We allow each control firm to be used acrossmultiple deals. Including sales growth does not change our results.
We use nearest neighbor matching with replacement and assign five matches to each firm.
For each matching firm, we estimate equity beta using monthly returns over an 24 monthrolling window against the Oslo Main Index. We then delever these equity betas and computethe average asset beta by averaging over the individual asset betas of the five matching firms.
Two treatments are not on common support but our results below do not change if we include
24

Our return data are from NHH’s Børsprosject.

15

these deals and hence we keep them in the sample. The average asset beta of our portfolio
firms is 0.47 (median 0.46). This is consistent with the relatively low asset beta of 0.33 ofbuyout portfolio companies in the US found by Driessen et al. (2012).
Table 4 displays the correlation matrix of our main variables. Most importantly it reveals
that while there is a very close relationship between our two relative co-investment measure,the correlation between the relative co-investment measure and the absolute co-investment
percentage is almost zero. This already indicates that the absolute and relative co-investment
measure capture something quite different.
Insert Table 4 about here

4

Empirical Analysis

We next set out to test our model. The two main implications to be tested are the effect of theco-investment fraction on leverage and project risk. We will then test what the combined effectof project risk and leverage risk predicts. Finally, we check the effect of the GP’s co-investment
on the deal size in order to see if GP’s with a higher co-investment percentage reduce deal size.
In order to take potentially enogeneity and omitted variable concerns into account we accountin our multivariarate analysis for a number of factors which may determine our measuresof risk-taking into account. More importantly, however, we argue that our research-design
makes endogeneity concerns less likely. Since our contracts including the co-investment are
determined ex-ante and are applicable to many firms, it is much less likely that unobserved
variables (such as risk factors or preferences) may jointly determine the co-investment level
as well as the risk of the investments. Even if risk preferences of GPs would determine theco-investment levels (which is rather unlikely this they are influenced heavily by LPs as well)this would even strengthen our hypotheses. This would call for more co-investment levels andhigher risk of investment just running contrary opposite to our result. Hence, when we findevidence for our hypotheses these arguments would even strengthen our findings on the causal
effects.
Table 3 gives a first impression by showing a univariate comparison of the co-investment-towealth measures across groups of firms double-sorted on asset beta and leverage. Our theory16

predicts that the co-investment fraction for the low beta/high leverage group should be higher
than that of the high beta/low leverage group. The table supports this prediction. In the top
panel, for example, using Relative GP co-investment all, β=0.444 for high beta/low leveragegroup vs. β=1.407 for the low beta/high leverage group, the difference being significant at
the five percent level. The same pattern appears for the partners’ co-investment-to-wealthmeasures in the table. There, the relative co-investment level differs almost to the same extent,
namely between β=0.480 for high beta/low leverage group vs. β=1.431 for the low beta/highleverage group, the difference being significant at the five percent level. Given the dispersion of
the relative co-investment variables (see Table 1), both differences are also economically quite
pronounced.
Insert Table 3 about here

4.1

Project risk

Our model predicts that GP’s with a higher co-investment fraction choose to invest in projects
with less risk. We test this notion by regressing the GP co-investment on the portfolio company
asset beta.
In table 5, we regress the firm’s asset beta on the different definitions of the GP’s relative co-investment fraction. We use Relative co-investment partners as a measure for theco-investment-to-wealth ratio. Standard errors are clustered by private equity fund. We also
use robust standard errors but do not find any changes. The regression model contains additional variables that may affect the firm’s risk. These variables include three broad categories
of control variables: macro-economic, firm and GP specific characteristics. The firm-specificcharacteristics are firm size (log of total assets,), firm profitability measured by the firm’s return on assets and fixed asset ratio. We also add a control for the firm’s industry by including
a dummy variable indicating NACE category 7. To control for the macro environment, weeither include a dummy variable for the deal year or we directly control for Nibor and Credit
Spread. We further control for fund characteristics, namely the fund size, the fund’s sequencenumber (in our sample) and the private equity firm’s age (GP Age).
Insert Table 5 about here
17

The variable Relative co-investment partners is negative and significant in all specifications.
Consistent with our model, a higher co-investment fraction is associated with lower projectrisk, here measured as the firm’s asset beta. In contrast, the absolute co-investment percent
is unable to explain the cross-sectional variation in asset beta. Hence, it is essential to control
for wealth in order for the GP’s co-investment to explain his choice of project risk. That is,
without controlling for the level of wealth, there is little information in the GP percentage
itself regarding the fund manager’s risk appetite.
To gauge the economic impact of this results we note that the average asset beta is 0.47,
while the coefficient estimate is -0.049. A one standard deviation increase (1.73) in the relative
co-investment fraction reduces asset beta from 0.47 to 0.39.
Overall, the regression suggests that the GP’s co-investment relative to his wealth is asignificant determinant for the choice of asset beta, while the absolute co-investment lacks any
explanatory power.

4.2

Leverage

Our model predicts a positive relationship between leverage and the GP’s co-investment percentage. In Table 6 we next regress the full model on leverage, using the different measures for
the GP’s co-investment-to-wealth ratio. Standard errors are clustered by private equity fund.
We use the same set of controls as above. As shown in the table, all the various specifications (columns (1)-(4)) of the co-investment relative to wealth produce positive and significant
coefficients.
That is, the higher the GP co-investment, the higher the portfolio company’s leverage, as
implied by our model. The debt ratio further tends to be higher for older private equity firms.
Consistent with much of the extant literature, firm leverage is increasing in the proportion of
tangible assets. Furthermore, firms of larger funds are more levered.
To gauge the economic impact of this results we note that the average leverage ratio is0.62. Given our estimated coefficient of 0.088 one standard deviation increase (1.73) in therelative co-investment fraction would increase the leverage ratio from 0.62 to about 0.77.
In columns (5) to (8), we replace the relative GP co-investment with the absolute coinvestment and the percentage co-investment, i.e. the fraction of the fund’s investment that18

the general partner have to contribute. As shown in the table, both measures are consistently
insignificant. That is, once again, without controlling for the level of wealth, there is little
information in the GP percentage itself regarding the fund manager’s risk appetite.
Overall, table 6 is consistent with the notion that that the GP co-investment relative to his
wealth is a significant determinant for the choice of leverage, while the absolute co-investment
lacks explanatory power.
Insert Table 6 about here

4.3

Total risk

Our analysis has so far shown that on the one hand, GPs with a higher relative co-investment
select less risky firms. But on the other hand, these firms tend to be higher levered. Whatabout the overall effect? We use each deal’s equity beta to measure total risk for the deal.
This measure comprises both of our risk factors and allows us to ask us about the overall effectof leverage on risk given that our findings so far point in opposite direction. While on the one
hand a higher relative co-investment share reduces asset beta (and hence the overall firm risk),
the opposite is true for leverage. Since these two effects have opposing impact on equity beta,
investigating the effect of co-investment on equity beta allows us to look into the net effect.
Table 7 shows coefficient estimates for a regression of equity beta on the GP’s relative
co-investment. Standard error are clustered by private equity fund. We use once again thesame controls as in our previous regression tables. As shown in the table, all the various
specifications of the co-investment relative to wealth produce significant negative coefficients.
The various estimates are not only robust with respect to the precise co-investment measureused but also with regard to the precise specification of the regression. It appears that overallrisk is lower the higher the GP’s relative co-investment in his fund. We consider this to be an
important finding of our analysis: more “skin in the game” leads to a reduction in overall risk.
In columns (7) and (8), neither the absolute co-investment percent, nor the dollar amount
has any significant impact on the leverage choice, consistent with the results on asset beta andleverage.
To allow for a better understanding of the economic impact of these results we note that the19average leverage ratio is 0.69, while the coefficient estimate is -0.15. A one standard deviation
increase (1.73) in the relative co-investment fraction would decrease the equity beta from 0.69
to about 0.43 implying quite a pronounced effect.
Insert Table 7 about here

4.4

Project Size

We now turn to a further aspect of the impact of co-investment shares on risk-taking by asking:
Does a higher co-investment also lead to more diversification of the GP’s wealth? GP’s havevarious ways in order to achieve this aim. A simple way would be to reduce non-systematic risk
by investing into projects with lower absolute volatility. As discussed in the introduction we do
not find such an effect (as measured by the comparables’ daily standard deviations).25 There ishowever a second way to diversify the portfolio. Instead of selecting more projects with lower
absolute volatility, GPs might simply invest into more but smaller deals. Why might GP’sbe reluctant to undertake the former type of diversification and prefer the latter type ? GP’s
generally are not passive investors like a mutual fund manager but are expected to actively
influence the firms they are invested into. If there is a link between GP ability and a certain
type of industry or firm type then GP’s might be reluctant to invest into projects where theywould have a reduced ability to influence the firm. Hence, due to the role of GPs there arereasons not to diversify optimally by increasing the number of investment and reducing thesize of the individual investment, However, we would expect that “skin in the game” tilts theobjectives of the GP in the direction of more and smaller investments.
If our conjecture is correct, we would expect to see that higher personal risk leads tomore diversification in the form of more but smaller investments. In table 8 we investigate
this relationship. We regress the relative co-investment percentage on each firm’s total assets
divided by fund size. We call this variable ”Ticket Size”. We find that there is a negative
correlation between co-investment amount and ticket size, consistent with our conjecture. More“skin in the game” seems to be an incentive for the GP to reduce the size of the individual
investment allowing him – given the size of the fund (for which we control) to invest in more25

To conserve space we omit the relevant tables.

20

portfolio firms.
Insert Table 8 about here
There is an additional tradeoff here: if GP’s are constraint in how large a fund they canraise then an increase in leverage could lead to an increase in the number of deals to be financed.
In a sense this is a trade-off between firm risk and portfolio risk, in the sense that an increasein leverage may lead to an increase in firm risk but could lead to a decrease in portfolio risk.
This finding suggests that GPs prefer to correct for lower personal diversification caused bya higher relative-co-investment percentage by investing into more deals rather than by lowering
total risk. This result may also suggest that a pure portfolio-theoretic approach to portfolio
risk may neglect the fact that GPs tend to be specialized in their skills and hence there may
be limits to their desire to diversify away certain types of risk.

4.5

Returns

We also look at the returns delivered by the firms in our sample. Unfortunately we only havereturn (or valuation) data available for 26 firms. We have quarterly valuations and divestments or additional investments. Valuations are a mixture of market based and model based
valuations as some of them are based on partial sales while others are just NAVs reported by
the GP. We also keep track of intermediate cash-flows as there are frequent add-on investments
and recapitalizations. We transform this two data sources into a single cash flow for each firmby treating the last valuation in our data set as the firm’s ultimate value and discount thisfinal value and each intermediate cash-flow back to the initial investment date. We computea risk adjusted discount rate for each firm by using the leverage ratio in the first year of thebuyout deal and we use the equity beta that corresponds to this rate.26 We then sum up all the
cash flows to get the deal’s NPV. We do get a negative relationship between the GP’s relative
co-investment and the risk adjusted returns, however the relationship is not even remotely
significant. In fact, none of the GP investment related variables turn out to be significant.
Controlling for other GP characteristics reduces the sample to 17 observations but does notchange the findings.
26

We use the Nibor in the deal year and a five percent equity market premium.

21

What should we expect to find? We beleive we should not expect to find a relationshipbetween the GP’s co-investment level and the NPV of the investment. If risk is properly
accounted for and the GPs’ get the right set of incentives, then in equilibrium all should deliver
the same risk-adjusted rate of return. This is exactly what we find here. Our conjecture isbased on the assumption that we can properly capture the project’s risk through the risk
adjustment.
The other alternative is of course that, given the small sample size, we simply do not havethe power to detect statistical significance.

4.6

Wealth effects

In this subsection we explore whether additional aspects of the GP’s wealth affect the GP’s
decisions to take on risk. We add to variables to our analysis, the GP’s absolute level of
wealth and wealth changes. Table 9 shows that the coefficient for Partner Average Wealth isinsignificant in almost every specification and hence, it is not the absolute level of wealth that
influences risk taking. What about wealth changes? We compute the change in wealth fromthe year prior to the year of the deal and include it in the regression. We interpret this asevidence how changes to the GP’s wealth portfolio influence his risk choice. Given that on
average a large fraction of the GP’s wealth is invested in his funds this variable should also
be a reasonable proxy for previous fund returns (especially distributions). Partner Year on
Year Wealth Change in table 9 is never significant. We interpret this as evidence that there isno influence of wealth changes in the GP’s portfolio on risk taking. Moreover the estimatedcoefficient is positive, not negative. This result is interesting as it also indicates that past losses
do not seem to lead to an increase in current risk taking. This result essentially rules out arisk shifting channel that would run from wealth losses to increased portfolio risk.
Insert Table 9 about here
Finally does our standard relative GP percentage measures survive the inclusion of these
additional controls? We find that (apart from asset beta where the coefficient is slightly below
the 10 percent significance level) they do not seem to be affected by these additional variables.

22

A final note is due on the number of observations. As we include wealth changes we lose
one observation where we do not have information about the GP’s wealth in the year prior to
the deal.

5Conclusion

In this paper, we examine how the requirement for a co-investment by a private equity fundmanager affect his incentives to make risky investments for the fund. We first develop a model,
which predicts that a higher co-investment reduces the appetite for project risk while at thesame time increasing the appetite for leverage. We then take the model predictions to thedata, using a unique sample of Norwegian private equity transactions. The Norwegian institutional setting allows us to control for the fund managers’ wealth, which may have important
implications for risk aversion.
The predictions of our model are borne out in the data. Cross-sectional regressions showthat a higher co-investment fraction is associated with less risky portfolio companies (lower
assets beta) and a higher degree of debt financing. Importantly, the co-investment fraction isa significant determinant of investment risk only when adjusted for the fund manger’s wealth.
This emphasizes the importance of wealth data in research examining the effect of variable
compensation on the incentives to take risk.

23

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25

Table 1: Summary Statistics
The table shows summary statistics for the sample of 20 buyout funds that have partners based in Norway. The
GP characteristics are measured in the year of fund’s inception - with the exception of number of portfolio firms.
The firm characteristics are from the fiscal year following the buyout. All variables are defined in Appendix
Table 2. Relative coinvestment is the total required coinvestment for the fund as a fraction of the professionals’
or partners’ total wealth averaged over the three prior to the buyout transaction. We assume that one doller isequal to six kroner.
Variable
Panel A: Fund Characteristics

Obs

Mean

MedianStd. Dev.

Min

Max

Total Number of employees
Total Number of Partners
Total Average Wealth All Employees (in $m)
Total Average Wealth Partners (in $m)
Number of Portfolio Firms in Sample
Panel B: Dependent Variables

20
20
20
20
20

16.6
8.45
1.92
3.22
3.1

10
7
1.31
1.53
3

17.95
4.25
2.05
4.27
1.619

4
3
0.02
0.03
1

83
21
6.82
17.33
7

Rel. coinv. all
Rel. coinv. partners
Absolute GP Investment (in $m)
Absolute GP Percentage
Panel C: Fund Characteristics

20
20
20
20

1.177
1.138
17.83
0.031

0.426
0.425
5.45
0.015

1.740
1.730
26.17
0.043

0
0
0.00
0

5
5
88.33
0.15

GP AgeFund Size (in $m)log(Fund Size)Fund Sequence Number
Fund Inception Year
Carry
Management Fee
Hurdle Rate
Fund Duration

20
20
20
20
20
11
14
12
14

9.65
942
21.491
3.65
2004
0.18
0.02
0.08
9.64

8.5
325
21.389
3
2004
0.2
0.2
0.08
10

6.53
1700
1.314
2.35
4.30
0.05
0.00
0.00
0.93

1
53
19.57
1
1989
0.02
0.013
0.07
7

20
5883
24.29
8
2008
0.2
0.023
0.08
10

26

Table 2: Summary statistics of firm characteristics and required coinvestment
The table shows summary statistics for the sample of 62 Norwegian portfolio companies, acquired by Nordic
buyout funds between 2000 and 2010. The GP characteristics are measured in the year of the buyout. The
firm characteristics are from the fiscal year following the buyout. All variables are defined in Appendix Table
2. Relative coinvestment is the total required coinvestment for the fund as a fraction of the professionals’ or
partners’ total wealth averaged over the three prior to the buyout transaction.

N

Mean

MedianStd.dev.

Min

Max

Panel A: Dependent Variables
Asset Beta
Leverage Ratio
Equity Beta
Ticket Size

62
62
62
62

0.473
0.618
0.691
0.410

0.459
0.641
0.586
0.586

0.298
0.276
0.538
0.487

-0.29
0.02
-0.47
0.01

1.237
1.325
2.747
2.431

Panel B: Fund characteristics
Rel. coinv. all
Rel. coinv. partners
Absolute GP Investment Amount (in $m)
Absolute GP Investment Fraction
GP Age

62
62
62
62
62

0.893
0.932
13.02
0.037
9.903

0.427
0.476
5.90
0.015
8

1.32
1.33
20.67
0.049
5.955

0
0
0
0
1

5
5
88.33
0.15
21

Panel C: Firm characteristics
Fund Size (in $m)log(Fund Size)Fund Sequence Number
Total Assets (in $m)log(TA)
Fixed Asset Ratio
RoA
Firm Volatility
Industry Control

62
62
62
62
62
62
62
62
62

652
21.3
3.6
119.7
12.86
0.08
0.03
0.03
0.42

315
21.4
3
67
12.91
0.004
0.072
0.031
0

1232
1.152
2.129
223
1.157
0.149
0.243
0.016
0.497

53
19.6
1
2.10
9.44
0
-1.66
0.011
0

5883
24.3
8
1717
16.15
0.554
0.315
0.078
1

Panel D: Macro characteristics
Credit Spread
Nibor
Year

62
62
62

5.65
4.23
2007

4.742
3.58
2007

3.042
1.507
2.285

2.702
2.21
2000

10.27
7.19
2010

27

Table 3: Univariate comparison of the required coinvestment
The table shows the average coinvestment for firms in a double sort on asset beta (vertical) and leverage(horizontal). The sample is 62 Norwegian portfolio companies acquired by Nordic buyout funds between 2000
and 2010. Panel A shows the Relative coinvestment, defined as the total required coinvestment for the fund asa fraction of the professionals’ (columns (1) to (3)) or partners’ (columns (4) to (6)) total wealth averaged over
the three years prior to the buyout transaction. All variables are defined in Appendix Table 2. At the bottomof each panel is a one-sided t-test that the difference in mean between firms with high asset beta/low leverageand firms with low asset beta/high leverage is postive. The p-value for the t-test is in parenthesis. The numberof observations for each subsample is shown in square brackets.

All professionals
High leverage(1)

Low leverage(2)Partners only
Total
(3)

High leverage(4)

Low leverage
(5)

Total
(6)

Panel A: Relative coinvestment
High beta

0.991
[15]

0.444
[17]

0.701
[32]

1.069
[15]

0.480
[17]

0.756
[32]

Low beta

1.407
[16]

0.745
[14]

1.098
[30]

1.431
[16]

0.765
[14]

1.120
[30]

Total

1.206
[31]

0.580
[31]

0.893
[62]

1.256
[31]

0.609
[31]

0.932
[62]

Difference in mean
p-value-0.963(0.045)28-0.951(0.048)

Table 4: Correlations

Rel. Coinv. Partners
Rel. Coinv. All
Abs. GP Investment AmountAbs. GP Investment in %GP Agelog(Fund Size)
Actual Sequencelog(TA)
Fixed Asset Ratio
RoA
Firm Volatility
Industry Control
Credit Spread
Nibor

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)

(1)
1
0.99760.30070.04040.28780.20670.35630.30320.0644-0.0057-0.18730.2017-0.1873-0.21471
0.31830.031
0.29120.2231
0.3616
0.31130.05720.0001-0.1908
0.2129-0.192-0.2231(2)

1-0.0063
0.24280.66810.26320.3099-0.0470.0316-0.1602
0.15690.0438-0.0724(3)

10.0171-0.55220.0677-0.20270.0501-0.3478-0.0149-0.078-0.1353
0.1774(4)10.1706
0.78330.02490.139
0.09190.13770.11910.1113-0.1424(5)

10.20640.4378-0.0009
0.2289-0.0483
0.06330.1213-0.1957(6)

10.07580.1
0.08750.02710.07190.0985-0.1574(7)10.19750.3802-0.3432-0.0162-0.1899-0.1337(8)

10.10510.1646-0.11650.0294-0.2714(9)1-0.1804
0.00020.025-0.1503(10)

10.0414
0.6766
0.3633(11)

10.0449-0.0271(12)

10.5464(13)(14)

The table shows correlations for the independent variables in our sample. The sample is 62 Norwegian portfolio companies acquired by Nordic buyout funds between 2000 and 2010. Rel. coinvestment
is the total required coinvestment for the fund as a fraction of the professionals’ or partners’ total wealth averaged over the three prior to the buyout transaction. All variables are defined inAppendix Table 2. Standard errors are clustered by private equity firm and shown in parenthesis. ***, ** and * denote that the coefficient is significantly different from zero at the 1%, 5% and
10% level, respectively.

Table 5: Coinvestment and project choice
The table shows the coefficient estimates from cross-sectional ordinary least squares regressions of asset beta. The sample
is 62 Norwegian portfolio companies acquired by Nordic buyout funds between 2000 and 2010. Rel. coinvestment is thetotal required coinvestment for the fund as a fraction of the professionals’ or partners’ total wealth averaged over thethree prior to the buyout transaction. All variables are defined in Appendix Table 2. Standard errors are clustered byprivate equity firm and shown in parenthesis. ***, ** and * denote that the coefficient is significantly different from zeroat the 1%, 5% and 10% level, respectively.

VARIABLES
Rel. coinv. partners

(1)

(2)

-0.0490*(0.0235)

Rel. coinv. all

Dependent Variable: Asset Beta
(3)
(4)
(5)
(6)

-0.0539**(0.0225)-0.0497**(0.0205)-2.80e-10(3.32e-10)

Absolute GP Investment in %log(Fund Size)Fund Sequence Number
Firm characteristics:log(TA)
Fixed Asset Ratio
RoA
Macro Controls:
Credit Spread
Nibor
Constant

Industry Control
Year Dummies
Observations
R-squared(8)-0.0463**(0.0212)Absolute GP Investment AmountFund characteristics:
GP Age(7)-0(4.61e-10)0.108(0.867)-0.0881(1.076)-0.0111 -0.0111 -0.0241** -0.0238** -0.0106 -0.0115 -0.0252*** -0.0252**(0.00995) (0.00993) (0.00911) (0.00904) (0.0102) (0.00992) (0.00877) (0.00899)-0.0120 -0.0127 -0.0131 -0.0141 0.00607 -0.0143 -0.0225-0.0259(0.0317) (0.0319) (0.0351) (0.0352) (0.0350) (0.0429) (0.0633) (0.0388)
0.0533* 0.0540* 0.0794** 0.0794** 0.0452
0.0430 0.0720** 0.0724*(0.0271) (0.0268) (0.0337) (0.0332) (0.0291) (0.0312) (0.0335) (0.0355)-0.0398(0.0435)
0.182(0.348)-0.0810(0.0901)-0.0382 -0.00887 -0.00759 -0.0566 -0.0552(0.0432) (0.0351) (0.0351) (0.0433) (0.0449)
0.185
0.295
0.294
0.182
0.173
(0.348) (0.398) (0.398) (0.342) (0.346)-0.0855-0.238-0.240-0.0628 -0.0350(0.0881) (0.195) (0.195) (0.106) (0.0996)-0.0199(0.0362)0.271(0.393)-0.205(0.200)-0.0194(0.0340)
0.271(0.397)-0.208(0.182)0.00635(0.0126)-0.0242(0.0325)
1.224**(0.492)0.00618(0.0127)-0.0246(0.0323)1.221**(0.498)0.633(0.534)0.634(0.531)0.00954 0.0106(0.0118) (0.0126)-0.0168 -0.0254(0.0388) (0.0336)
1.015
1.454*(0.658) (0.702)

0.993(1.024)1.065(0.673)

Yes
No
62
0.183

Yes
No
62
0.190

Yes
Yes
62
0.347

Yes
Yes
62
0.351

Yes
No
62
0.155

Yes
Yes
62
0.319

Yes
Yes
62
0.320

30

Yes
No
62
0.150

Table 6: Coinvestment and gearing
The table shows the coefficient estimates from cross-sectional ordinary least squares regressions of leverage. The sample
is 62 Norwegian portfolio companies acquired by Nordic buyout funds between 2000 and 2010. Leverage is defineds as
Liabilities/Total Assets. Rel. coinvestment is the total required coinvestment for the fund as a fraction of the professionals’
or partners’ total wealth averaged over the three prior to the buyout transaction. All variables are defined in Appendix
Table 2. Standard errors are clustered by private equity firm and shown in parenthesis. ***, ** and * denote that thecoefficient is significantly different from zero at the 1%, 5% and 10% level, respectively.

VARIABLES
Rel. coinv. partners(1)
0.0700*(0.0357)

Rel. coinv. all
Absolute GP Investment AmountAbsolute GP Investment in %Fund characteristics:
GP Agelog(Fund Size)Fund Sequence Number
Firm characteristics:log(TA)
Fixed Asset Ratio
RoA
Macro Controls:
Credit Spread
Nibor
Constant

Industry Control
Year Dummies
Observations
R-squared

Dependent Variable: Leverage Ratio
(3)
(4)
(5)
(6)
(7)
(8)
0.0876**(0.0348)0.0669*0.0875**(0.0363)(0.0348)-8.62e-10-7.72e-10(5.74e-10)(6.03e-10)-1.895-1.339(1.225)(1.164)(2)0.00561 0.00563 -0.00267 -0.00292 0.00907 0.00592 0.00361 0.000193(0.00794) (0.00797) (0.00616) (0.00620) (0.00660) (0.00641) (0.00832) (0.00812)
0.06480.06660.04240.04560.141*
0.02950.140*
0.0400(0.0610) (0.0609) (0.0608) (0.0600) (0.0697) (0.0601) (0.0744) (0.0738)-0.0483** -0.0474** -0.0416** -0.0405** -0.0297* -0.0235 -0.0232 -0.0195(0.0201) (0.0195) (0.0180) (0.0178) (0.0162) (0.0217) (0.0212) (0.0247)-0.00440 -0.00354 -0.0191 -0.0199
0.0121
0.0221 -0.00992 0.00469(0.0341) (0.0334) (0.0451) (0.0445) (0.0291) (0.0346) (0.0439) (0.0484)
0.257
0.255
0.324*
0.329*
0.286
0.308
0.417** 0.398**(0.209) (0.211) (0.183) (0.181) (0.230) (0.238) (0.167) (0.179)-0.182-0.183-0.259-0.260-0.305-0.345-0.358-0.386(0.214) (0.214) (0.239) (0.238) (0.210) (0.229) (0.225) (0.255)
0.00132 0.000759(0.0110) (0.0111)
0.01350.0140(0.0254) (0.0254)-0.686-0.7340.146(1.415) (1.409) (1.093)
Yes
No
62
0.243

Yes
No
62
0.236

Yes
Yes
62
0.463

31

0.0933(1.076)-0.00743 -0.00681(0.0118) (0.0126)
0.03940.0231(0.0240) (0.0256)-2.580*-0.239(1.446) (1.212)-1.971(1.224)-0.00482(1.172)

Yes
Yes
62
0.463

Yes
No
62
0.224

Yes
Yes
62
0.387

Yes
Yes
62
0.376

Yes
No
62
0.227

Table 7: Coinvestment and total risk
The table shows the coefficient estimates from cross-sectional ordinary least squares regressions of equity beta. The sample
is 62 Norwegian portfolio companies acquired by Nordic buyout funds between 2000 and 2010. Rel. coinvestment is thetotal required coinvestment for the fund as a fraction of the professionals’ or partners’ total wealth averaged over thethree prior to the buyout transaction. All variables are defined in Appendix Table 2. Standard errors are clustered byprivate equity firm and shown in parenthesis. ***, ** and * denote that the coefficient is significantly different from zeroat the 1%, 5% and 10% level, respectively.

VARIABLES
Rel. coinv. partners

(1)

(2)

-0.151***(0.0281)

Rel. coinv. all

Dependent Variable: Equity Beta
(3)
(4)
(5)
(6)

-0.157***(0.0266)-0.174***(0.0394)
4.81e-10(7.77e-10)

Abolsute GP Investment in %log(Fund Size)Fund Sequence Number
Firm characteristics:log(TA)
Fixed Asset Ratio
RoA
Macro Controls:
Credit Spread
Nibor
Constant

Industry Control
Year Dummies
Observations
R-squared

9.54e-10(9.01e-10)
1.178(1.905)-0.0194(0.0201)-0.0396(0.0568)
0.136**(0.0545)(8)-0.169***(0.0410)

Absolute GP Investment AmountFund characteristics:
GP Age(7)

0.817(2.328)-0.0193 -0.0320* -0.0314* -0.0223 -0.0205 -0.0414** -0.0368**(0.0201) (0.0169) (0.0168) (0.0184) (0.0196) (0.0158) (0.0174)-0.0425 -0.00771 -0.0127 -0.0930 -0.0279 -0.143-0.0343(0.0556) (0.0739) (0.0717) (0.0582) (0.0857) (0.0995) (0.107)
0.137** 0.167*** 0.165*** 0.104* 0.0995 0.134** 0.134*(0.0539) (0.0540) (0.0526) (0.0599) (0.0659) (0.0618) (0.0664)

0.00746 0.00966 0.0628
0.0657-0.0363 -0.0422 0.0374
0.0215(0.0522) (0.0514) (0.0799) (0.0790) (0.0605) (0.0572) (0.0791) (0.0780)
0.05590.06230.233
0.224
0.0250 0.0103 0.0843
0.124(0.586) (0.586) (0.665) (0.665) (0.571) (0.590) (0.660) (0.666)-0.218-0.225-0.413-0.415-0.0580 -0.0291 -0.247-0.251(0.133) (0.131) (0.254) (0.254) (0.148) (0.151) (0.264) (0.236)
0.02260.0228(0.0243) (0.0242)-0.0249 -0.0261(0.0600) (0.0600)
1.131
1.166
(0.925) (0.902)-0.482(1.286)

Yes
No
62
0.295

Yes
Yes
62
0.452

Yes
No
62
0.304

32

-0.422(1.239)0.0369 0.0367(0.0249) (0.0257)-0.0410 -0.0325(0.0726) (0.0609)
2.821** 1.461
2.650(1.079) (1.682) (1.542)

0.590(1.898)

Yes
Yes
62
0.459

Yes
No
62
0.203

Yes
Yes
62
0.343

Yes
No
62
0.204

Yes
Yes
62
0.356

Table 8: Coinvestment and Relative Investment Size
The table shows the coefficient estimates from cross-sectional ordinary least squares regressions of ticket size. Ticketsize
is defined as balance sheet size of the firm divided by fund size. The sample is 62 Norwegian portfolio companies acquiredby Nordic buyout funds between 2000 and 2010. Rel. coinvestment is the total required coinvestment for the fund asa fraction of the professionals’ or partners’ total wealth averaged over the three prior to the buyout transaction. Allvariables are defined in Appendix Table 2. Standard errors are clustered by private equity firm and shown in parenthesis.
***, ** and * denote that the coefficient is significantly different from zero at the 1%, 5% and 10% level, respectively.

VARIABLES
Rel. coinv. partners

(1)

(2)

-0.0701**(0.0278)

Rel. coinv. all

Dependent Variable: Ticket Size
(3)
(4)
(5)
(6)

GP Agelog(Fund Size)Fund Sequence Number
log(TA)
Fixed Asset Ratio
RoA
Credit Spread
Nibor
Constant

Industry Control
Year Dummies
Observations
R-squared(8)-0.0681**(0.0284)-0.0677**(0.0270)-0.0666**(0.0275)

Absolute GP Investment Amount
Abolsute GP Investment in %(7)

2.50e-10(6.50e-10)

4.27e-10(6.38e-10)

2.072**
1.361(0.957)(0.856)-0.00971 -0.00973 -0.00368 -0.00354 -0.0111 -0.0100 -0.00768 -0.00604(0.00870) (0.00874) (0.0101) (0.0101) (0.00768) (0.00598) (0.00796) (0.00846)-0.288*** -0.290*** -0.262*** -0.265*** -0.315*** -0.249*** -0.321*** -0.254***(0.0620) (0.0628) (0.0461) (0.0472) (0.0848) (0.0592) (0.0695) (0.0668)
0.03520.03440.01990.01880.0200 0.00938 0.00650 0.000808(0.0277) (0.0275) (0.0259) (0.0255) (0.0179) (0.0154) (0.0208) (0.0239)
0.371*** 0.370*** 0.376*** 0.377*** 0.351*** 0.344*** 0.367*** 0.357***(0.0941) (0.0946) (0.0984) (0.0989) (0.0951) (0.0946) (0.0985) (0.100)-0.0354 -0.0333-0.140-0.145-0.0504 -0.0903-0.203-0.205(0.239) (0.240) (0.229) (0.230) (0.241) (0.217) (0.218) (0.214)-0.322* -0.321*-0.295-0.293-0.245-0.148-0.226-0.182(0.174) (0.175) (0.225) (0.225) (0.194) (0.190) (0.226) (0.232)
0.00479 0.00529
0.01150.0131(0.0129) (0.0129)(0.0138) (0.0118)
0.02940.02880.02120.0189(0.0224) (0.0228)(0.0213) (0.0227)
1.651
1.696
2.162* 2.215**
2.482
1.126
3.500** 2.138*(1.064) (1.076) (1.041) (1.040) (1.663) (1.107) (1.575) (1.133)

62
0.716

62
0.714

62
0.790

33

62
0.789

62
0.692

62
0.714

62
0.772

62
0.777

Firm Controls
Industry Control
Year Dummies
Observations
R-squared

Rel. coinv. all

All Emp. YoY Wealth Change (in %)All Emp. Average Total WealthFirm Controls
Industry Control
Year Dummies
Observations
R-squared

Rel. coinv. partners-2.64e-09(2.45e-09)
0.0221(0.0181)-0.0441(0.0262)
Yes
Yes
No
59
0.179

0
(2.35e-09)
0.0150**(0.00695)-0.0357(0.0223)
Yes
Yes
No
59
0.179

Partner Average Total WealthPartner YoY Wealth Change (in %)Asset Beta

VARIABLES-4.56e-09(2.79e-09)
0.00577(0.0112)0.0792**(0.0312)
Yes
Yes
No
59
0.349

-2.56e-09(1.98e-09)-0.000276(0.00482)
0.0807**(0.0349)
Yes
Yes
No
59
0.340

(1)
Leverage Beta

3.17e-10(5.08e-09)
0.00471(0.0386)-0.168***(0.0442)
Yes
Yes
No
59
0.271

8.05e-10(3.51e-09)
0.0113(0.0161)-0.162***(0.0509)
Yes
Yes
No
59
0.267

(2)
Equity Beta

0.00546**(0.00199)-24,702(16,929)-51,411**(24,391)
Yes
Yes
No
59
0.737

0.00400***(0.00133)-9,528(9,714)-41,262(30,877)
Yes
Yes
No
59
0.735

(3)
Ticketsize-1.65e-09(2.25e-09)
0.0160(0.0192)-0.0437(0.0295)
Yes
Yes
Yes
59
0.318

6.30e-10(2.46e-09)
0.0123(0.00766)-0.0373(0.0288)
Yes
Yes
Yes
59
0.324

(4)
Asset Beta

-5.96e-09**(2.56e-09)
0.00643(0.0134)
0.103***(0.0200)
Yes
Yes
Yes
59
0.597

-3.14e-09(2.49e-09)
0.000649(0.00591)
0.0981***(0.0232)
Yes
Yes
Yes
59
0.566

(9)
Leverage Beta

4.72e-09(5.49e-09)-0.00169(0.0377)-0.187***(0.0503)
Yes
Yes
Yes
59
0.443

3.36e-09(3.75e-09)
0.00780(0.0162)-0.177***(0.0562)
Yes
Yes
Yes
59
0.435

(10)
Equity Beta

0.00720***(0.00192)-23,944(17,691)-50,257*(24,551)
Yes
Yes
Yes
59
0.820

0.00490**(0.00171)-10,974(9,374)-37,690(32,306)
Yes
Yes
Yes
59
0.814

(11)
Ticketsize(12)

The table shows the coefficient estimates from cross-sectional ordinary least squares regressions for partner’s (all professional’s) total wealth, their change in wealth from last year and their skin on
the game on asset beta, the deal’s leverage ratio, equity beta, and ticket size. The sample is 59 Norwegian portfolio companies acquired by Nordic buyout funds between 2000 and 2010. Partner (Allemployee’s) Average Total Wealth is the average wealth per partner per fund for the year prior to the deal. Partner (All employee’s) YoY Wealth Change (in %) is the change in wealth between
t-2 and t-1. Rel. coinvestment is the total required coinvestment for the fund as a fraction of the professionals’ or partners’ total wealth averaged over the three prior to the buyout transaction. Allvariables are defined in Appendix Table 2. Standard errors are clustered by private equity firm and shown in parenthesis. ***, ** and * denote that the coefficient is significantly different from zeroat the 1%, 5% and 10% level, respectively.

Table 9: Wealth and project choiceTable 10: Fund Terms

Observations
R-squared

Constant

GP AgeAverage Wealth Partners (in $m)

Fund Sequence Number

VARIABLES
log(Fund Size)(1)
Abs. GP %-0.0215(0.0126)
0.00873(0.00585)
4.51e-10(4.24e-10)-0.00114(0.00199)
0.464(0.266)20
0.309
0.364*(0.189)
20
0.253

(2)
Abs. GP %-0.0165*(0.00867)
0.00611**(0.00253)(3)
Abs. GP Am. (in $m)
8.004e+07***(2.160e+07)-2.160e+06(9.988e+06)
1.361(0.830)3.660e+06(4.032e+06)-1.667e+09***(4.347e+08)20
0.743-1.697e+09***(3.896e+08)
20
0.727

1.447*(0.774)(4)Abs. GP Am. (in $m)
8.264e+07***(1.886e+07)(5)Rel. coinv. Partners
0.892**(0.330)-0.356(0.295)-5.52e-08***(1.79e-08)
0.179(0.120)-16.86**(6.538)20
0.465-15.99**(6.632)20
0.341-5.06e-08***(1.73e-08)(6)Rel. coinv. Partners
0.868**(0.320)

The table shows regression explaning fund contract details for the sample of 20 buyout funds that have partners based in Norway. The GP characteristics are measured in the yearof fund’s inception - with the exception of number of portfolio firms. All variables are defined in Appendix Table 2. Relative coinvestment is the total required coinvestment for thefund as a fraction of the professionals’ or partners’ total wealth averaged over the three prior to the buyout fund inception . We assume that one doller is equal to six kroner.

= (sumeiend - ek)/sumeiend= (capital employed - ek)/capital employedrgjeldmax /(rgjeldmax + ek)= Equally weighted average of the five closest firms, using Leverage 1,
marketcap
where the individual βa = βe ∗ (marketcap+sumeiend∗1000−ek)= Equally weighted average of the five closest firms, using Leverage 2,marketcap
where the individual βa = qβe ∗ (marketcap+(capitalemployed)∗1000)
Equally weighted average of the five closest firms, using Leverage 3.
marketcap
where the individual βa = qβe ∗ (marketcap+rgjeld
m ax∗1000)= sumeiend*1000/Fund Size in NOK= GP Co-investment current fund in fund per person/Average wealth over the last three years= GP Co-investment current fund in fund per partner/Average wealth per partner over the last three years= Absolute GP’s co-investment amount= Absolute GP’s co-investment in percentage points= Absolute Amount of Partner Wealth= Year Change in Absolute Amount of Partner Wealth= Absolute Amount of otal Employee Wealth= Year Change in Absolute Amount of Total Employee Wealth= Year the GP was founded= Sequence number for the funds in the sample= Credit Spread for European Bond issues from Bloomberg= Norwegian Interbank Interest Rate at the year end from the Norwegian Central Bank= log (Fund Size in NOK)== (eiend+ maskanl)/sumgjek= log(sumeiend)= salgsinn/sumgjek= ebitda/sumgjek

Leverage 1
Leverage 2
Leverage 3
Asset Beta 1

Rel. coinvestment all
Rel. coinvestment all Partners Only
Absolute GP Investment AmountAbsolute GP Percentage
Partner Average WealthPartner YoY Wealth Change
All Employee Average Total Wealth
All Employee YoY Wealth Change

PE Firm Founding Year
Fund Sequence Number
Credit Spread
NIBOR
log(Fund Size)
Fixed Asset Ratiolog(TA)
Sales
RoA

Ticket Size

Asset Beta 3

Asset Beta 2

Definition

Variable

Variables used in the paper

Table 11: Variable Definitions

Table 12: Coinvestment and total risk
The table shows the variable definitions for our sample based on the definitions used by ?.

Variable
Market Cap
Capital Employedrgjeld avg
ebitdarentekost
sumeiend
ek
gjeld
cash
investvarer
kundefpforplrgjeldmin
rgjeldmax
rlgjeld
sumgjek

Definition
From NHH’s Børsprosjekt
sumeiend -cash -invest -varer -kundef -pforpl(rgjeldmax + rgjeldmin )/2’Earnings before interest, tax, depreciation, and amortisation’’Interest expenses’’Total assets’’Shareholder’s equity’’Total Liabilities’’Bank deposits, cash etc.’’Investments’’Stocks’’Trade debtors’’Pension commitment’’Total interest-bearing liabilities, minimum’’Total interest-bearing liabilities, maximum’’Interest-bearing long-term liabilities’’Total equity and liabilities’
All years. Total all liabilities and equity. Total gjeld + ek
’Sales revenues’’Real property’’Machinery and plant’’Operating equipment, fixtures and fittings’’Ships, rigs, planes etc.’

salgsinneiendmaskanl
drlosoreskiprigfl

Table 13: Sample Comparison
The table compares the 60 firms included in the sample relative to the 51 firms for which we do not have information
about the GP’s co-investment. The first part of the sample has at maximum 51 firms from between 1996 and 2010
whereas the other contains 60 Norwegian portfolio companies acquired by Nordic buyout funds between 2000 and 2010.
We report the difference in means between the two samples and run a t-test on the difference. All variables are definedin Appendix Table 2. ***, ** and * denote that the coefficient is significantly different from zero at the 1%, 5% and 10%
level, respectively.

VariableFund characteristics:
GP Agelog(Fund Size)Fund Sequnce Number
Firm characteristics:log(TA)
Fixed Asset Ratio
RoA
Industry Control
Macro characteristics:
Credit Spread
Nibor
Year

N

Mean

Std. Dev.

N

Mean

Std. Dev.

Diff

28
28
32

7.607
20.979
2.688

6.136
1.267
1.839

62
62
62

9.90
21.29
3.63

5.96
1.15
2.13

-2.30
-0.32
-0.94

51
51
51
51

11.94
0.142
-0.155
0.490

2.009
0.250
1.040
0.505

62
62
62
62

12.86
0.08
0.03
0.42

1.16
0.15
0.24
0.50

-0.93
0.06
-0.19
0.07

***

40
51
51

4.757
4.183
2004

2.326
1.606
4.043

62
62
62

5.65
4.23
2007

3.04
1.51
2.28

-0.89
-0.05
-3.49

*

37

**

***