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Institutional Reinvestments in Private Equity Funds as aDouble-Edged Sword: The Role of the Status Quo BiasMarkus Freiburg a & Dietmar Grichnik ba WHU–Otto Beisheim School of Managementb University of St. Gallen
To cite this article: Markus Freiburg & Dietmar Grichnik (2013) Institutional Reinvestments in Private Equity Fundsas a Double-Edged Sword: The Role of the Status Quo Bias, Journal of Behavioral Finance, 14:2, 134-148, DOI:10.1080/15427560.2013.791295
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THE JOURNAL OF BEHAVIORAL FINANCE, 14: 134–148, 2013Copyright C© The Institute of Behavioral FinanceISSN: 1542-7560 / 1542-7579 onlineDOI: 10.1080/15427560.2013.791295
Institutional Reinvestments in Private Equity Fundsas a Double-Edged Sword: The Role of the Status
Quo Bias
Markus FreiburgWHU–Otto Beisheim School of Management
Dietmar GrichnikUniversity of St. Gallen
Reinvestments can be a rational investment strategy for institutional investors in private equityfunds to make use of inside information from previous funds or to get preferred access torestricted funds. However, reinvestments could also be motivated by a status quo bias, whichdescribes a behavioral tendency to pursue the status quo option disproportionately often. Thispaper investigates the role of the status quo bias for institutional investments in private equityfunds. Based on our fieldwork and a unique data set of 136 institutional investors and privateequity firms in Germany, we show that institutional investors strongly prefer private equityfirms in which they have invested before. The magnitude of the status quo bias depends on thenature of the investment opportunity and certain investor characteristics. Our results suggestthat reinvestments are a double-edged sword for institutional investors that can result from arational investment strategy, but also from an irrational investment behavior.
Keywords: Private equity, Investment decisions, Institutional investors, Status quo bias,Decision biases
INTRODUCTION
Reinvestment decisions can be a rational investment strategyfor institutional investors (limited partners, or LPs) in privateequity (PE) funds to make use of inside information fromprevious funds or to get preferred access to restricted funds(Lerner and Schoar [2004], Lerner, Schoar and Wongsunwai[2007], Ljungvist, Hochberg and Vissing-Jorgensen [2009],Glode and Green [2009], Phalippou [2010]). However, rein-vestment decisions of LPs for PE firms (general partners, orGPs) could also be motivated by a status quo bias, whichdescribes a behavioral tendency to pursue the status quooption disproportionately often (Samuelson and Zeckhauser[1988], Burmeister and Schade [2007]). LPs could prefer aninvestment alternative just because it was a previously cho-sen option (Patel, Zeckhauser and Hendricks [1991], Kempf
Address correspondence to Markus Freiburg, Ph.D. Student, WHU –Otto Beisheim School of Management, Burgplatz 2, 56179 Vallendar,Germany. E-mail: [email protected]
and Ruenzi [2006]). As current investment decisions formthe basis for future performance (Sahlman [1990], Gompersand Lerner [2001], Lerner et al. [2007]), it is important toidentify structural obstacles that might impede these invest-ment decisions. A status quo bias resulting from cognitivemisperceptions and psychological commitment (Samuelsonand Zeckhauser [1988]) is likely to inhibit rational decisionmaking of LPs.
Although decision biases have gained increasing atten-tion among entrepreneurship researchers (Zacharakis andMeyer [2000], Zacharakis and Shepherd [2001], Shepherd,Zacharakis and Baron [2003], Franke, Grubar, Harhoff andHenkel [2006], Burmeister and Schade [2007], Cummingand Dai [2010]), our study is the first to examine the roleof the status quo bias for investment decisions in PE funds.Therefore, the aim of this paper is to investigate the role of thestatus quo bias for investment decisions of LPs for GPs. Thecentral research questions are as follows: (1) Does the statusquo bias influence investment decisions of LPs for GPs? (2)To what extent do certain context factors affect the magni-tude of the status quo bias? Drawing on our fieldwork, we
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tested our hypotheses using a unique data set of 136 GermanLPs and GPs covering 121 positive and negative investmentdecisions.
Our results show that the status quo bias influences the in-vestment decisions of LPs. LPs strongly prefer GPs in whichthey have invested before, even when potential informationand access benefits of these reinvestments are controlled for.Both the nature of the investment opportunity and certaininvestor characteristics moderate the influence of the statusquo bias. The status quo bias is stronger for investments inyounger GPs and for larger investments, whereas investor-specific and industry-wide learning effects reduce it. Our re-sults suggest that reinvestment decisions are a double-edgedsword for LPs that can result not only from a rational invest-ment strategy but also from an irrational investment behavior.
The contribution of this paper is threefold. First, we com-bine a principal-agent perspective on information asymmetryand social embeddedness (Venkataraman [1997], Shane andCable [2002]) with a behavioral finance perspective on deci-sion biases (Samuelson and Zeckhauser [1988]) to theorizeabout the ambivalence of reinvestment decisions and to de-rive contingencies under which a status quo bias might existand vary in magnitude. Second, we present the first empir-ical study of the status quo bias for investment decisions inentrepreneurial finance suggesting that institutional investorsare not perfectly rational when they decide which PE fund toinvest in. Third, we contribute to a recent line of research in-vestigating the interaction effects of decision biases (Frankeet al. [2006], Cumming and Dai [2010]) by showing the de-pendence of the status quo bias on the nature of the investmentopportunity and certain investor characteristics.
The remainder of this paper is structured as follows: Sec-tion B develops the theoretical background and derives ourhypotheses. Section C explains our data and methodology,followed by the presentation of our results in section D. Sec-tion E discusses our findings and concludes.
THEORETICAL BACKGROUNDAND HYPOTHESES
Information Asymmetry and Agency Problems
The relation between LPs and GPs constitutes a typicalprincipal-agent relationship (Ross [1973], Jensen and Meck-ling [1976], Fama [1980]). GPs act as an agent for LPs,which invest through an intermediary in PE rather than di-rectly in portfolio companies (Sahlman [1990], Gompers andLerner [2000]). PE describes an asset class that provides eq-uity capital to enterprises not quoted on the stock marketand includes both venture capital (VC) and buyouts (Kaplanand Schoar [2005], Lerner et al. [2007]). The relationshipbetween LPs and GPs is characterized by information asym-metry (Sahlman [1990], Norton [1995], Balboa and Marti[2007]), which is especially severe when LPs decide whichGP to invest in (Fried and Hisrich [1989], Burton and Sch-
erschmidt [2004], Barnes and Menzies [2005]). LPs can exante neither fully assess the quality of GPs nor fully assessthe intrinsic motivation of their management teams.
As a consequence of this information asymmetry, twokinds of agency conflicts between LPs and GPs can arise(Fama and Jensen [1983], Sahlman [1990], Norton [1995],Balboa and Marti [2007]). First, LPs can experience adverseselection (Akerlof [1970], Amit, Glosten and Muller [1990])and select GPs without the capabilities necessary to managea successful fund. Second, LPs can suffer from moral hazard(Eisenhardt [1989], Cable and Shane [1997]) when GPs try tomaximize their own benefit at the expense of LPs. Althoughmoral hazard can occur over the entire lifetime of a fund, LPswill initially try to select GPs that minimize this risk.
Previous research has suggested different remedies toreduce the information asymmetry between LPs and GPs(Eisenhardt [1989], Norton [1995]). While LPs can screenGPs prior to the investment decision, GPs can signal theirquality or self-select into contractual agreements beneficialfor LPs (Sahlman [1990], Norton [1995], Gompers [1996]).However, the underlying assumption of these suggestionsis that LPs can process the additional information correctlyand evaluate the success potential of GPs objectively (Frankeet al. [2006]). If the assessments of LPs are systematicallybiased, the added value of these remedies will be mitigated,and information asymmetry and agency conflicts will prevail.
One of the most important classes of factors that in-hibit perfectly rational decision making consists of biasesand heuristics (Tversky and Kahneman [1974], Hogarth andMakridakis [1981], Kahneman, Slovic and Tversky [1982],Schwenk [1988]). Biases and heuristics comprise decisionrules, cognitive mechanisms, and subjective opinions thatpeople use to simplify their decision making (Busenitz andBarney [1997]). These decision biases cause decision mak-ers to process information incorrectly, which can lead toinaccurate judgments and decisions (Tversky and Kahneman[1974]). Investment decisions in entrepreneurial finance areespecially susceptible to these decision biases due to theircontext of high uncertainty (Zacharakis and Meyer [2000]).
Main Effect of the Status Quo Bias
The status quo bias describes a behavioral tendency to decidefor the status quo option disproportionately often (Samuelsonand Zeckhauser [1988], Burmeister and Schade [2007]). In-stead of considering all available information in the decision-making process, people tend to rely on what they have chosenbefore and on what represents their current state (Burmeisterand Schade [2007]). Previous research has identified threedifferent explanations for the status quo bias: (1) cogni-tive misperceptions, (2) psychological commitment, and (3)rational decision making. To develop our hypotheses, webriefly elaborate the relevance of each explanation for insti-tutional investments in PE funds.
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Cognitive Misperceptions
Cognitive misperceptions due to loss aversion and an-choring can lead to a status quo bias of LPs (Samuelsonand Zeckhauser [1988]). Loss aversion describes people’stendency to prefer avoiding losses to acquiring gains (Kah-neman and Tversky [1979], Kahneman, Knetsch and Thaler[1991]). LPs might weigh potential losses from changing aGP larger than potential gains and thus stay with their statusquo (Thaler [1980], Kahneman and Tversky [1984]). Anchor-ing describes people’s tendency to take a known referencevalue as a starting point for their decision making. AlthoughLPs can adjust their initial assessments of GPs in responseto economic changes, these adjustments are typically insuf-ficient (Tversky and Kahneman [1974]) so that LPs tend tokeep the status quo.
Psychological Commitment
Psychological commitment due to misperceived sunkcosts, regret avoidance, or drive for consistency can lead to astatus quo bias of LPs (Samuelson and Zeckhauser [1988]).LPs might base their decisions on sunk costs. A greater in-vestment in a status quo option leads to a greater degreeto which it will be retained in subsequent decisions (Thaler[1980]). LPs also might try to avoid the impression that theyinitially made the wrong choice and stick to a previous GPchoice in a subsequent decision (Bell [1982], Kahnemanand Tversky [1982]). LPs also might try to reduce cognitivedissonance and mentally suppress information that indicatesthat a past decision was wrong (Akerlof and Dickens [1982]).Similarly, LPs might infer their own preferences from pastactions to present choices and tend to persist with their statusquo option (Bem [1972]).
Rational Decision Making
In the presence of transaction costs or uncertainty, ad-herence to the status quo can be consistent with rationaldecision making. Transaction costs can result in a status quobias whenever the cost of changing the status quo option ex-ceeds the efficiency gain of a superior alternative (Samuelsonand Zeckhauser [1988], Burmeister and Schade [2007]). Al-though there could be some transaction cost savings fromreduced due diligence efforts when LPs reinvest in GPs,the major transaction costs in the form of management feesand carried interest are unlikely to systematically differ be-tween GPs (Gompers and Lerner [1999], Cumming and Jo-han [2009]). Uncertainty can lead to a status quo bias whenthe utility of the status quo option is sufficiently high andthe utility of alternatives is uncertain (Schmalensee [1982],Samuelson and Zeckhauser [1988]). However, when LPs in-vest in a new fund, the future return potential is uncertainfor all GPs. Therefore, transaction costs and uncertainty areunlikely to play a major role in explaining a potential statusquo bias of LPs.
Taken together, reinvestment decisions of LPs could bedriven by a status quo bias resulting from cognitive mis-perceptions and psychological commitment so that LPs tendto reinvest in GPs. As Samuelson and Zeckhauser [1988]suggested, the status quo could act as a psychological an-chor for subsequent investment decisions of LPs. Addition-ally, prior empirical research in behavioral finance has pro-vided rich evidence that individuals are subject to the statusquo bias when making financial decisions (Samuelson andZeckhauser [1988], Patel et al. [1991], Agnew, Balduzzi andSunden [2003], Rubaltelli, Rubichi, Savadori, Tedeschi andFerretti [2005], Kempf and Ruenzi [2006], Barber, Odeanand Zhu [2009]).
Although the status quo bias has been confirmed in dif-ferent contexts, it is not obvious that significant and sizeableeffects can be found for LPs when selecting a GP. The in-vestment decisions of LPs should be more rational than thoseof private individuals because LPs make substantial effortsto strive for rationality in their decision processes (Fried andHisrich [1989], Barnes and Menzies [2005]). Additionally,the concept of social embeddedness (Granovetter [1985],Burt [1992], Granovetter [2005]) provides two explanationsfor the potential consistency of reinvestment decisions of LPswith rational decision making: inside information from previ-ous funds and preferred access to restricted funds. We brieflydiscuss each explanation for institutional investments in PEfunds and show how these effects can be disentangled from astatus quo bias resulting from cognitive misperceptions andpsychological commitment.
Inside Information
Under conditions of information asymmetry, social rela-tionships can transfer information about the quality of trans-action partners and their tendency to behave opportunistically(Coleman [1988], Uzzi [1996], Gulati and Gargiulo [1999],Burt [2000], Shane and Cable [2002]). Therefore, LPs couldobtain inside information about the quality of GPs from theirinvestments in previous funds (Lerner and Schoar [2004],Lerner et al. [2007], Ljungqvist et al. [2009]). This infor-mation advantage could lead LPs to reinvest into GPs, whenthey have learned that a GP is successful. These reinvestmentscan be a rational investment strategy because there seems tobe a performance persistence across subsequent funds of aGP (Kaplan and Schoar [2005], Phalippou [2010]). Conse-quently, LPs tend to reinvest in GPs when the performanceof the previous fund was high (Lerner and Schoar [2004],Lerner et al. [2007]). Sophisticated LPs can also anticipatewhich funds will have poor subsequent performance so thaton average the returns of funds, in which these LPs reinvest,are higher than those of funds, in which they decide not toreinvest (Lerner and Schoar [2004], Lerner et al. [2007]).Additionally, inside information about the capabilities or in-vestment strategy of GPs could give current LPs hold-uppower when GPs raise new funds (Ljungqvist et al. [2009],
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Glode and Green [2009]). This hold-up power could provideLPs with an additional profit margin so that they tend toreinvest in GPs (Phalippou [2010]).
To disentangle this reinvestment effect based on insideinformation from a status quo bias, we used the previousperformance of a GP. The previous performance of a GP isdefined as the performance of its previous funds comparedto competitor funds (Lerner and Schoar [2004], Kaplan andSchoar [2005], Lerner et al. [2007]). As the previous perfor-mance of a GP provides information about its quality, LPsare more likely to invest in GPs with better previous perfor-mance (Gompers, Lerner, Blair, and Hellmann [1998], Lernerand Schoar [2004], Kaplan and Schoar [2005], Lerner et al.[2007]). If information about the quality of a GP is alreadypublicly available from its performance track record, insideinformation obtained from previous funds will provide littleadditional value to LPs (Podolny [1994], Shane and Cable[2002]). Therefore, to the extent that reinvestment decisionsof LPs are driven by inside information from previous funds,the effect of reinvestments should be mitigated when theprevious performance of a GP is accounted for. However,if reinvestment decisions of LPs are also driven by a statusquo bias, reinvestments should affect the probability to in-vest in a GP even after information benefits are controlledfor. Therefore, to the extent that reinvestments continue topredict investment decisions of LPs after the previous per-formance of a GP is accounted for, evidence for a status quobias would exist.
Previous Access
Social relationships can also provide access to poten-tial transaction partners and support the resource acquisi-tion from others (Burt [1992], Podolny [1994], Uzzi [1999],Shane and Stuart [2002], Hsu [2007]). Therefore, LPs couldget preferred access to restricted funds due to their existingrelationship with GPs. If GPs limit the access to their funds,LPs are no longer free to invest in any fund they choose(Lerner et al. [2007], Ljungqvist et al. [2009], Hochberg,Ljungqvist and Lu [2010]). Especially, the top performingGPs voluntarily limit their fund size to avoid unprofitablegrowth, even if they could raise far more capital from LPs(Kaplan and Schoar [2005]). Typically, these limitations areimplemented by restricting access to existing LPs, who are al-lowed to reinvest, and by not accepting new investors (Lerneret al. [2007]). Consequently, LPs could benefit from their in-vestments in previous funds of a GP and tend to reinvest inGPs.
We used the access restriction of a GP to disentangle thisreinvestment effect based on preferred access from a statusquo bias. An access restriction of a GP is defined as an in-vestment limitation for LPs due to an oversubscription of afund (Lerner et al. [2007], Ljungqvist et al. [2009]). As anoversubscription indicates a larger demand of LPs comparedto the available fund size, the GP can be selective in terms
of which LPs are allowed to invest (Lerner et al. [2007]). IfLPs only reinvest into a GP, because they have access to afund they would not have otherwise, the access restriction ofa GP should capture this preferred access effect on invest-ment decisions of LPs (Lerner et al. [2007]). Therefore, tothe extent that reinvestment decisions of LPs are driven bypreferred access to restricted funds, the effect of reinvest-ments should be mitigated when the access restriction of aGP is accounted for. However, if reinvestment decisions ofLPs are also driven by a status quo bias, reinvestments shouldaffect the probability to invest in a GP even after potentialaccess benefits are controlled for. Therefore, to the extentthat reinvestments continue to predict investment decisionsof LPs after the access restriction of a GP is accounted for,evidence for a status quo bias would exist.
Because reinvestments can be a rational investment strat-egy for LPs to make use of inside information from previousfunds or to get preferred access to restricted funds, there arehigh reinvestment rates of LPs across follow-on funds of GPs(Lerner and Schoar [2004], Lerner et al. [2007], Ljungqvistet al. [2009]). However, if reinvestments are also driven bya status quo bias resulting from cognitive misperceptionsand psychological commitment, reinvestments could be anirrational investment behavior. As current investment deci-sions form the basis for future performance (Sahlman [1990],Gompers and Lerner [2001], Lerner et al. [2007]), it is impor-tant to analyze the existence and context factors of the statusquo bias, which could inhibit optimal investment decisionsof LPs. For our analysis, we define the status quo as the GPthat a LP already chose in a previous investment decision(Patel et al. [1991], Kempf and Ruenzi [2006]). Thus, wehypothesize for the main effect of the status quo bias:
Hypothesis 1: LPs are more likely to invest in a GP whenthey have previously invested in another fund of the same GPthan when they have not, even after controlling for potentialinformation and access benefits of these reinvestments.
Moderating Effect of the Investment Opportunity
Previous research has argued that the presence, magnitudeand consequences of decision biases are likely to depend onthe nature of the investment decision (Zacharakis and Shep-herd [2001], Franke et al. [2006]). Therefore, it is importantto analyze the status quo bias across different investmentcontexts and to examine the conditions that influence itspresence and magnitude. These context factors also providean additional possibility to compare the rational and irra-tional motivations for reinvestment decisions, as we couldidentify various contingencies that help to disentangle a ra-tional investment strategy driven by inside information froma status quo bias driven by cognitive misperceptions and psy-chological commitment. We have explored the dependenceof the status quo bias on the nature of the investment oppor-tunity and on certain investor characteristics. Regarding the
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investment opportunity, we examine the moderating effectsof reputation and investment size on the status quo bias, asattractiveness and size are two key dimensions of any invest-ment opportunity.
Reputation
The reputation of a GP is defined as information about itspast performance (Podolny [1994], Shane and Cable [2002],Dimov, Shepherd and Sutcliffe [2007]). In contexts of in-formation asymmetry, reputation can serve as a signal fu-ture performance based on perceptions of past performance(Fombrun and Shanley [1990], Podolny [1994], Roberts andDowling [2002]). Therefore, GPs can use their reputationto signal their quality to LPs (Sahlman [1990], Gompers[1996]). Because the reputation of a GP is associated withthe success of its previous investments, it affects the abilityto raise new funds (Gompers [1996], Gompers et al. [1998]).
As the reputation of a GP provides public informationabout its quality, LPs are more likely to invest in GPs witha positive reputation (Gompers et al. [1998], Balboa andMarti [2007]). If information about the quality of a GPis already publicly available from its reputation, inside in-formation from previous funds will provide relatively littleadditional value to LPs (Podolny [1994], Shane and Cable[2002]). These private information advantages are likely todecrease when LPs invest in GPs with higher reputation.While this argumentation based on inside information wouldsuggest that the importance of reinvestments decreases forhigher levels of GP reputation, cognitive misperceptions andpsychological commitment suggest the opposite effect forthe magnitude of the status quo bias.
The concept of perceived quality plays an important role indetermining a GP’s reputation (Dimov et al. [2007]) becausethe reputation of a GP can be seen as perceptual representa-tion of its past actions and future prospects that describe itsoverall appeal to LPs (Fombrun [1996]). Because the psycho-logical commitment to a formerly chosen alternative is likelyto increase for higher levels of perceived quality (Samuelsonand Zeckhauser [1988], Ackert and Church [2006]), the psy-chological anchoring effect of a GP should increase with itsreputation. If reinvestments are driven by a status quo biasresulting from cognitive misperceptions and psychologicalcommitment, the magnitude of the status quo bias shouldbe greater when LPs invest in GPs with higher reputation.Therefore, we hypothesize the following:
Hypothesis 2: The effect of the status quo bias on investmentdecisions is moderated by the reputation of a GP, such that ahigher reputation leads to a stronger status quo bias.
Investment Size
If LPs obtain inside information about the quality of GPsfrom previous funds, LPs are likely to have these informationadvantages regardless of the size of their current or formercommitment (Lerner et al. [2007], Ljungvist et al. [2009],
Glode and Green [2009], Phalippou [2010]). While this rea-soning based on inside information would suggest that theimportance of reinvestments is independent of the investmentsize, cognitive misperceptions and psychological commit-ment suggest a positive relationship between the magnitudeof the status quo bias and the investment size.
Individuals have a tendency to remain the status quo be-cause the loss of the status quo option looms larger thanpotential gains of alternative options (Kahneman and Tver-sky [1979], Kahneman et al. [1991]). Some studies have evensuggested that psychological losses weigh twice as much asgains (Tversky and Kahneman [1991]). Therefore, psycho-logically, the incremental loss potential of an increase in theinvestment size should be larger than its incremental gainpotential and thereby increase the status quo bias of LPs.Additionally, Samuelson and Zeckhauser [1988] suggestedthat the stronger an individual’s previous commitment, thestronger the psychological anchoring effect in a subsequentdecision. Because the investment size of LPs for a new fundis likely to correlate with their investment size in a previousfund (Gompers et al. [1998], Balboa and Marti [2007]), thepsychological anchoring effect of a GP should be stronger forlarger investments. If reinvestments are driven by a status quobias resulting from cognitive misperceptions and psycholog-ical commitment, the magnitude of the status quo bias shouldincrease with the current investment size of LPs. Therefore,we hypothesize the following:
Hypothesis 3: The effect of the status quo bias on investmentdecisions is moderated by the investment size of a LP, suchthat a larger investment leads to a stronger status quo bias.
Moderating Effect of Investor Characteristics
We have explored the dependence of the status quo biason certain investor characteristics to investigate the impactof learning effects on the status quo bias. Specifically, weexamine the moderating effects of investor experience andindustry maturity on the status quo bias, as investor-specificand industry-wide learning effects describe two complemen-tary but conceptually different dimensions of learning effects.While investor experience allows a cross-sectional analysisof differences between LPs due to different levels of investor-specific experience, industry maturity allows a longitudinalanalysis of differences across all LPs due to industry-widelearning effects.
Investor Experience
Previous research has suggested that individuals becomeincreasingly efficient as they gain experience, for example,by focusing their attention on key dimensions of a problem(Gobbo and Chi [1986], Choo and Trotman [1991]). Expe-rienced decision makers can use superior decision processescompared with those with less experience (Shepherd et al.[2003], Franke, Gruber, Harhoff and Henkel [2008]). How-ever, previous research has also suggested that increasing
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Investment Decision
Status Quo Bias
Investment Opportunity
ReputationInvestment Size
Investor Characteristics
Investor ExperienceIndustry Maturity
FIGURE 1 Overview of Conceptual Model.
experience does not always lead to better decisions (Camererand Johnson [1991], Einhorn [1974]). Experienced decisionmakers can become trapped in current modes of thought(Shepherd et al. [2003], Burmeister and Schade [2007]).However, Shepherd et al. [2003] showed that the increasingexperience of venture capitalists is initially associated withimprovements in reliability and performance of investmentdecisions.
If LPs obtain inside information about the quality of GPsfrom previous funds, LPs should become increasingly effi-cient in exploiting these information advantages. For exam-ple, LPs should become increasingly knowledgeable aboutthose characteristics of a GP that are crucial for high per-formance (Franke et al. [2008]). Therefore, information ad-vantages from previous funds are likely to increase with theexperience of LPs. While this argumentation based on insideinformation would suggest that the importance of reinvest-ments increases for higher levels of investor experience, cog-nitive misperceptions and psychological commitment sug-gest the opposite effect for the magnitude of the status quobias.
As increasing experience improves the quality of invest-ment decisions (Shepherd et al. [2003]), LPs should learn toreduce their susceptibility to decision biases. Therefore, ex-perienced LPs should use superior decision processes and beless susceptible to the status quo bias compared to LPs withless experience. If reinvestments are driven by a status quobias resulting from cognitive misperceptions and psycholog-ical commitment, the magnitude of the status quo bias shoulddecrease for higher levels of investor experience. Therefore,we hypothesize the following:
Hypothesis 4: The effect of the status quo bias on investmentdecisions is moderated by the experience of a LP, such that agreater level of experience leads to a weaker status quo bias.
Industry Maturity
If LPs obtain inside information about the quality of GPsfrom previous funds, LPs should increasingly learn to exploit
these information advantages during a maturation process(Shepherd et al. [2003], Franke et al. [2008]), as the entirePE industry becomes increasingly professional. Therefore,information advantages from previous funds are likely toincrease with the maturity of the PE industry. While this rea-soning based on inside information would suggest that theimportance of reinvestments increases for higher levels of in-dustry maturity, cognitive misperceptions and psychologicalcommitment suggest the opposite effect for the magnitude ofthe status quo bias.
As increasing experience improves the reliability and per-formance of investment decisions in entrepreneurial finance(Shepherd et al. [2003]), LPs should be especially vulnerableto decision biases in the early years of a maturation process,when the entire PE industry is still immature. The profes-sionalization of the industry should improve the quality oftheir investment decisions and therefore reduce their suscep-tibility to decision biases. If reinvestments are driven by astatus quo bias resulting from cognitive misperceptions andpsychological commitment, the magnitude of the status quobias should decrease for higher levels of industry maturity.Therefore, we hypothesize the following:
Hypothesis 5: The effect of the status quo bias on investmentdecisions is moderated by the maturity of the PE industry,such that a more mature industry leads to a weaker status quobias.
Figure 1 summarizes the conceptual model, which formedthe basis for our empirical analysis.
DATA AND METHODOLOGY
Data Set
Prior to starting a quantitative analysis, we conducted semi-structured interviews with 13 LPs and GPs to increase ourunderstanding of the issues that LPs face when they decidewhich GP to invest in. In particular, we explored the role of
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140 FREIBURG AND GRICHNIK
the status quo bias for investment decisions of LPs and col-lected qualitative evidence for the existence of such a bias. Weinterviewed 13 German LPs and GPs about their last invest-ment decisions. To create the sample, we randomly selected 9LPs with different institutional backgrounds and 4 GPs withdifferent investment strategies from our survey database. Intotal, we conducted more than 14 hours of interviews.
The data set for our study was collected via survey fromLPs and GPs in Germany in 2009. We asked the participantsabout the last positive and negative investment decision inwhich they were involved (Shane and Cable [2002]). A pos-itive investment decision was defined as an investment situ-ation when a LP evaluated a GP and decided to invest in itsPE fund. A negative investment decision was defined as aninvestment situation when a LP evaluated a GP and finallydecided not to invest in its PE fund. An alternative empiricalstrategy would have been to limit our analysis of the statusquo bias to positive investment decisions. However, the in-clusion of negative investment decisions offers an additionalpossibility to test the logic of the status quo bias empirically.The analysis of negative investment decisions can falsify theexistence of a status quo bias, if LPs do not invest in a GP,although they have previously invested in another fund of thesame GP. Negative investment decisions without previousinvestments in a GP are also consistent with the logic of thestatus quo bias, because in these cases LPs would not investin a GP, if they have not previously invested in another fundof the same GP.
Our data set contained all types of investment decisionsof LPs for GPs (i.e., not only reinvestments) because weneeded an appropriate control group of nonreinvestment de-cisions to analyze the effects of reinvestments on investmentdecisions of LPs. We surveyed both LPs and GPs to gen-erate a sufficiently large sample of investment decisions inGermany. Such a sampling process also allowed us to cap-ture the perspectives of both parties that are involved in theinvestment decision. As a result of our sampling, our dataset is likely to contain a small fraction of cross-national in-vestment decisions, because both German LPs can invest ininternational GPs and international LPs can invest in GermanGPs.
As there are no comprehensive databases about GermanLPs and GPs, we developed our own database. First, weidentified potential LPs covering all major groups of institu-tional investors in PE funds: fund-of-funds, insurances, pen-sion funds, asset managers, banks, and endowments. Thesegroups covered together almost 80% of the PE fundraising inGermany in 2009 (BVK [2010]). An initial list was obtainedfrom the regulator (BaFin) and different industry associations(e.g., German Insurance Association, Association of GermanBanks) and then consolidated to 710 institutional investors.We contacted these potential LPs by phone and identified 111actual investors in PE funds. Second, we identified 301 GPsin Germany from industry associations (e.g., German PE As-sociation) and PE directories (FYB [2009], AltAssets [2009],
Handelsblatt [2007]). From these GPs, 216 were classifiedas independent and potentially fundraising. Our final data setconsisted of 111 LPs and 216 GPs.
Together with a personalized cover letter, the question-naires were sent by email to the responsible LP invest-ment director or GP management partner. Following threereminders, 136 questionnaires were received back, resultingin a response rate of 41.6%, which is relatively high com-pared with other studies with PE focus in Germany (Brettel[2002]). The questionnaires were received from 66 LPs and70 GPs. On average, the respondents had eight years of PEinvestment experience and were 42 years old. A total of 55%of the respondents were Managing or Investment Directors(LPs) or General or Managing Partners (GPs), while 24% ofthe respondents were Investment Managers (LPs and GPs).The remaining 21% mainly had job titles describing com-parable positions. The returned questionnaires covered 244investment decisions, 136 positive and 108 negative deci-sions. However, due to missing data, our final sample wasreduced to 121 investment decisions.
To examine the representativeness of our sample, wetested for potential nonresponse bias. As late respondents areconsidered to be more similar to nonrespondents (Kanuk andBerenson [1975]), we divided our data into early and late re-spondents. We found statistically significant differences onlyfor some control variables. Early respondents screened morefunds with investment focus outside Germany (p = 0.004)and more funds from GPs with better previous performance(p = 0.048) than late respondents. LPs also returned theirquestionnaires faster than GPs (p = 0.037). However, re-garding the main variables of our study, we did not observeany statistically significant differences between early and laterespondents. In combination with the overall high responserate, we conclude that nonresponse bias is not a significantproblem in our study.
Because our variables came from answers provided bya single respondent, common method variance might af-fect our empirical results, which describes variance that isattributable to the measurement method rather than to themeasured constructs (Podsakoff and Organ [1986]). As rec-ommended by Podsakoff, MacKenzie, Lee and Podsakoff[2003], we assured respondents of the confidentiality of theiranswers and used an objective dependent variable to reduceevaluation ambiguity. Additionally, we conducted Harman’sone-factor test to examine the existence of a common methodbias (Podsakoff and Organ [1986]). The results indicated thatno single factor accounts for the majority of the variance. Thefirst, second, and third factor accounted for only 23%, 13%,and 13%, respectively, of total variance. Therefore, commonmethod bias is not a significant problem in our study.
Due to the design of our study, our sample is choice-based,because we asked each participant about one positive and onenegative investment decision. In linear regression models,choice-based sampling can result in biased coefficients, ifthe sample frequencies of the groups differ from their true
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INSTITUTIONAL REINVESTMENTS IN PRIVATE EQUITY 141
frequencies. However, in binary logistic regression models,which we used to analyze our data, the coefficients and oddsratios are not affected by unequal sampling rates; only theconstant is affected (Prentice and Pyke [1979], Scott and Wild[1986], Madalla [1991]). As our hypotheses focus on the sizeof individual coefficients, an adjustment of our estimates wasnot necessary.
Measures
All variables in our study were measured by adapted versionsof previously used measures. We assessed the face validityand content validity of our measures in a pretest with sixexperts (Churchill [1979]). To ensure the appropriateness ofour measures for the use for both LPs and GPs, we onlyused measures that LPs and GPs could evaluate likewise andtested this in the pretest. LPs and GPs should be able toprovide comparable information about an investment situa-tion, because both are closely involved in the fund selectionprocess and usually meet each other personally prior to theinvestment decision (Barnes and Menzies [2005]). We alsoincluded a control variable to account for potential differ-ences between answers from LPs and GPs. Even if there issome degree of variation between LPs and GPs, this shouldnot introduce a systematic bias in our analysis because posi-tive and negative investment decisions should be affected inthe same way.
Dependent Variable
Investment decision: The dependent variable was a binaryvariable of one if the investment was made and zero if theinvestment was not made (Shane and Cable [2002]).
Independent Variables
Reinvestment: An investment decision was defined asreinvestment if a LP had already invested in earlier fundsof a GP (Lerner et al. [2007]). The binary variable was codedone if an earlier investment in the same GP had been madeand zero otherwise. If the reinvestment variable predicts theinvestment decision after controlling for potential informa-tion and access benefits of these reinvestments, evidence forthe status quo bias would exist.
Previous performance: The previous performance of a GPwas measured by the proportion of its top quartile fundscompared to all previous funds (Kaplan and Schoar [2005],Lerner et al. [2007]). A top quartile fund was defined as afund with an internal rate of return (IRR) that belonged tothe best 25% of PE funds with comparable investment focusin a certain vintage year.
Access restriction: The access restriction of a GP wasmeasured by the oversubscription of a fund (Lerner et al.[2007], Ljungqvist et al. [2009]). The binary variable wascoded one if the fund was oversubscribed and zero if thefund was not oversubscribed.
Moderators
Reputation: Following previous research (Gompers[1996], Gompers et al. [1998]), we used firm age as proxyfor the reputation of a GP. The age of a GP was measuredby the number of years between its founding year and thevintage year of the fund under study.
Investment size: The investment size of a LP was mea-sured by the size of the commitment to the new fund inmillions of euros (EUR) (Lerner et al. [2007]).
Investor experience: The experience of a LP was measuredby the number of years a LP had already invested in PE atthe time of the investment decision (Shepherd et al. [2003]).
Industry maturity: As the vintage year describes the year,when a fund was created, it can account for changes in theinvestment behavior over time (Lerner et al. [2007], Walskeand Zacharakis [2009]). Therefore, we used the vintage yearas a proxy for industry maturity, because the whole PE in-dustry should become more mature over time (Gompers andLerner [2001], Fiedler and Hellmann [2001]). We measuredthe vintage year relative to the median in our sample, whichwas 2007.
Control Variables
Investment strategy of fund: We controlled for fund strat-egy by the financing stage and geography (Walske andZacharakis [2009], Lerner et al. [2007]). For the financingstages, we referred to the categorization of the German PEAssociation: early-stage VC, later-stage VC, buyouts andother (BVK [2010]). We likewise classified the geographicfocus of a fund (BVK [2010]): Germany, Europe, and other.A focus on other stages and regions served as our referencecategory.
Fund size: We also controlled for fund size (Lerner et al.[2007], Walske and Zacharakis [2009]). We measured theinitially planned fund volume in EUR millions. Followingprevious research, we used the natural logarithm of the fundvolume for our analysis.
Limited partner: We also used a binary variable to controlfor potential differences between answers from LPs and GPs.The variable was coded one if the questionnaire was filledout by a LP and zero if the questionnaire was filled out by aGP (Shane and Cable [2002]).
RESULTS
Given that our dependent variable is dichotomous, we usedbinary logistic regressions to analyze our data (Shane andCable [2002], Hair, Black, Babin and Anderson [2008]). Thedata analysis was performed using PASW Statistics 17. Ta-ble 1 presents the descriptive statistics and Pearson corre-lation matrix for our variables. The correlation coefficientsshow that of our independent variables, only the reinvest-ment variable (r = 0.40, p < 0.001) and the previous GP
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142 FREIBURG AND GRICHNIK
TABLE 1Descriptive Statistics and Pearson Correlation Matrix
Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1. Investment Decision 1.002. Early-Stage VC −.03 1.003. Later-Stage VC .03 −.23∗∗ 1.004. Buyouts −.04 −.55∗∗ −.49∗∗ 1.005. Germany −.07 .14 −.04 −.12 1.006. Europe .08 −.25∗∗ −.02 .33∗∗ −.52∗∗ 1.007. Fund Volume (ln) .12 −.35∗∗ −.17 .45∗∗ −.50∗∗ .30∗∗ 1.008. Limited Partner .09 −.36∗∗ .01 .19∗ −.42∗∗ .20∗ .42∗∗ 1.009. Reinvestment .40∗∗ −.12 .04 .08 −.03 .09 .24∗∗ .09 1.00
10. Previous Performance .24∗∗ −.10 −.09 .08 −.07 .04 .31∗∗ .09 .04 1.0011. Access Restriction .06 −.06 −.16 .21∗ .00 −.01 .25∗∗ −.09 .17 .12 1.0012. Reputation .07 −.05 −.12 .11 −.25∗∗ .23∗ .49∗∗ .25∗∗ .27∗∗ .19∗ .08 1.0013. Investment Size .15 −.12 −.09 .24∗∗ −.18∗ .20∗ .31∗∗ −.20∗ .12 −.09 .10 −.08 1.0014. Investor Experience −.07 −.02 −.21∗ .15 −.03 .13 .11 .11 −.05 .14 −.13 .37∗∗ .09 1.0015. Industry Maturity −.05 −.08 −.09 .08 −.19∗ .09 .14 .33∗∗ −.04 .21∗ −.23∗ .30∗∗ −.15 .27∗∗ 1.00
Mean .57 .21 .17 .54 .28 .40 5.62 .51 .44 .59 .41 10.41 28.17 9.03 −.55Std. deviation .50 .41 .38 .50 .45 .49 1.55 .50 .50 .37 .49 7.85 45.66 7.83 2.21Minimum .00 .00 .00 .00 .00 .00 1.10 .00 .00 .00 .00 .00 .10 .00 −7.00Maximum 1.00 1.00 1.00 1.00 1.00 1.00 9.31 1.00 1.00 1.00 1.00 43.00 300.00 47.00 2.00
N = 121; Significance levels: ∗p < 0.05, ∗∗p < 0.01 (two-tailed test).
performance (r = 0.24, p = 0.008) correlated significantlywith the investment decision. All correlation coefficientswere below 0.6, indicating that multicollinearity is not asignificant problem in our study (Hair et al. [2008]). Nev-ertheless, we tested for potential multicollinearity concernsby calculating variance inflation factors (VIFs). All valueswere below 4.1 and therefore well below the threshold of 10that would indicate serious multicollinearity problems (Hairet al. [2008]). However, the interpretability of VIFs is lim-ited in our case, because their calculation is based on linearregressions, whereas we used logistic regressions to analyzeour data.
The results of our analysis are presented in Table 2, whichshows the binary logistic regression models predicting theinvestment decision. Model 1 is the base model, includingall control variables. Model 2 shows the effect of the statusquo bias on the investment decision. The reinvestment vari-able had a statistically significant effect on the investmentdecision, even after controlling for potential information andaccess benefits of these reinvestments. The reinvestment vari-able was strongly and positively related to the probability ofa positive investment decision (Exp(B) = 7.48, p < 0.001).An increase of the odds ratio by a factor of 7.48 indicates thatthe probability of investing in a GP compared to the prob-ability of not-investing in a GP was more than seven timeshigher when a LP had previously invested in the same GP.The previous performance also had a positive and statisti-cally significant effect on the investment decision (Exp(B) =5.24, p = 0.009), whereas the access restriction showed nostatistically significant effect (Exp(B) = 0.98, p = 0.970).The addition of these variables significantly improved the
model fit (change in chi-square = 26.04, p < 0.001). There-fore, our results supported Hypothesis 1, which concerns theexistence of the status quo bias.
To determine the presence of a moderation effect in linearregression models, a statistically significant interaction termand an increase in R2 are necessary (Baron and Kenny [1986],Hair et al. [2008]). However, in logistic regression models amore detailed analysis of the moderation effect is necessary,because the signs and significance levels of interaction termsmight be misleading (Ai and Norton [2003], Hoetker [2007]).Therefore, we used STATA 10 to analyze the interaction ef-fect for each observation in our sample (Norton, Wang andAi [2004], Wiersema and Bowen [2009]). Additionally, wecalculated the conditional effects of reinvestments on invest-ment decisions for representative values of the moderator,with the other variables set at their sample means (Hayes andMatthes [2009]). Model 3 is the base model for our moder-ation analysis. The reinvestment variable was strongly andpositively related to the probability of a positive investmentdecision (Exp(B) = 8.50, p < 0.001). From the moderatorvariables, only the investment size had a statistically signif-icant effect on the investment decision (Exp(B) = 1.02, p =0.014).
Model 4 shows the moderation effect of reputation on thestatus quo bias. The interaction effect between reinvestmentand reputation had a statistically significant negative effecton the investment decision (Exp(B) = 0.86, p = 0.026). Theinteraction effect also significantly improved the model fit(change in chi-square = 5.27, p = 0.022), while Nagelk-erkes R2 increased from 39% in Model 3 to 43% in Model4. A calculation of the interaction effect for each sample
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TAB
LE2
Logi
stic
Reg
ress
ions
Pre
dict
ing
Inve
stm
entD
ecis
ion
Mod
el1
Mod
el2
Mod
el3
Mod
el4
Mod
el5
Mod
el6
Mod
el7
Var
iabl
esB
S.E
.E
xp(B
)B
S.E
.E
xp(B
)B
S.E
.E
xp(B
)B
S.E
.E
xp(B
)B
S.E
.E
xp(B
)B
S.E
.E
xp(B
)B
S.E
.E
xp(B
)
Con
stan
t−0
.60
1.23
0.55
−0.4
21.
420.
66−0
.62
1.58
0.53
8−1
.62
1.74
0.20
−0.2
61.
570.
77−0
.70
1.72
0.50
−0.7
31.
570.
482
Con
trol
sE
arly
-Sta
geV
C−0
.41
0.86
0.66
−0.2
20.
970.
800.
161.
041.
18−0
.06
1.09
0.94
0.06
1.01
1.06
−0.5
41.
140.
580.
291.
051.
33L
ater
-Sta
geV
C−0
.37
0.87
0.69
−0.3
80.
950.
68−0
.33
1.03
0.72
−0.2
61.
060.
77−0
.30
0.99
0.74
−0.9
91.
130.
37−0
.19
1.02
0.83
Buy
outs
−0.9
50.
810.
39−0
.75
0.87
0.47
−0.7
10.
950.
49−0
.98
0.98
0.38
−0.8
60.
930.
42−1
.56
1.06
0.21
−0.7
30.
930.
48G
erm
any
0.28
0.58
1.33
−0.2
30.
670.
79−0
.02
0.72
0.98
0.24
0.77
1.27
0.10
0.73
1.11
0.34
0.77
1.41
−0.2
00.
740.
82E
urop
e0.
430.
481.
530.
290.
531.
330.
210.
571.
240.
090.
591.
100.
230.
591.
260.
020.
601.
020.
210.
581.
24Fu
ndV
olum
e(l
n)0.
230.
171.
26−0
.11
0.21
0.90
−0.3
60.
280.
70−0
.35
0.29
0.70
−0.4
10.
280.
66−0
.39
0.30
0.67
−0.2
90.
290.
75L
imite
dPa
rtne
r0.
160.
461.
170.
190.
531.
211.
240.
673.
47†
1.49
0.71
4.42
∗1.
270.
683.
55†
1.36
0.72
3.90
†1.
200.
713.
32†
Mai
nE
ffec
tR
einv
estm
ent
2.01
0.48
7.48
∗∗∗
2.14
0.55
8.50
∗∗∗
3.92
1.03
50.2
8∗∗∗
0.82
0.80
2.28
4.00
0.98
54.3
9∗∗∗
2.15
0.58
8.57
7∗∗∗
Prev
ious
Perf
orm
ance
1.66
0.64
5.24
∗∗2.
690.
7814
.74∗∗
∗2.
860.
8317
.47∗∗
∗2.
580.
7913
.14∗∗
2.86
0.82
17.4
7∗∗∗
2.62
0.79
13.6
9∗∗∗
Acc
ess
Res
tric
tion
−0.0
20.
470.
98−0
.11
0.50
0.90
−0.2
20.
520.
81−0
.19
0.51
0.83
−0.3
40.
530.
71−0
.34
0.52
0.71
4M
oder
ator
sR
eput
atio
n0.
010.
041.
010.
110.
061.
12†
0.03
0.05
1.03
0.05
0.05
1.05
0.01
0.05
1.01
Inve
stm
entS
ize
0.02
0.01
1.02
∗0.
020.
011.
02∗∗
0.02
0.01
1.02
∗0.
020.
011.
02∗∗
0.02
0.01
1.02
∗
Inve
stor
Exp
erie
nce
−0.0
40.
030.
96−0
.05
0.03
0.96
−0.0
40.
030.
960.
000.
041.
00−0
.04
0.03
0.96
Indu
stry
Mat
urity
−0.1
50.
130.
86−0
.22
0.14
0.80
−0.1
30.
130.
88−0
.22
0.14
0.80
−0.0
60.
140.
94In
tera
ctiv
eE
ffec
tsR
einv
estm
ent×
Rep
utat
ion
−0.1
60.
070.
86∗
Rei
nves
tmen
t×In
vest
men
tSiz
e0.
070.
041.
07†
Rei
nves
tmen
t×In
vest
orE
xper
ienc
e−0
.20
0.08
0.82
∗
Rei
nves
tmen
t×In
dust
ryM
atur
ity−0
.76
0.40
0.47
†
Chi
-squ
are
4.41
30.4
5∗∗∗
41.2
5∗∗∗
46.2
5∗∗∗
46.1
8∗∗∗
48.5
7∗∗∗
46.0
6∗∗∗
Cha
nge
inC
hi-s
quar
e26
.04∗∗
∗10
.81∗
5.27
∗4.
93∗a
7.31
∗∗a
4.80
∗a
-2L
ogL
ikel
ihoo
d15
9.67
133.
6312
2.83
117.
5611
7.90
115.
5111
8.02
Hos
mer
&L
emes
how
Test
0.98
0.69
0.95
0.64
0.51
0.76
0.99
Ove
rall
Pred
ictiv
eA
ccur
acy
60.3
%71
.9%
74.4
%76
.0%
75.2
%74
.4%
77.7
%
Cox
&Sn
ellR
20.
040.
220.
290.
320.
320.
330.
32C
hang
ein
R2
0.19
0.07
0.03
0.03
a0.
04a
0.03
a
Nag
elke
rkes
R2
0.05
0.30
0.39
0.43
0.43
0.45
0.43
Cha
nge
inR
20.
250.
090.
040.
04a
0.06
a0.
04a
N12
112
112
112
112
112
112
1
Sign
ifica
nce
leve
ls:†
p<
0.10
,∗p
<0.
05,∗
∗ p<
0.01
,∗∗∗
p<
0.00
1.a.
Rel
ativ
eto
Mod
el3.
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144 FREIBURG AND GRICHNIK
TABLE 3Conditional Effects of Reinvestments on Investment Decisions
Conditional Effect of Reinvestment on Investment Decisiona
Moderator Reputation Investment Size Investor Experience Industry Maturity
Value of Moderatorb, c B S.E. Z B S.E. Z B S.E. Z B S.E. Z
Low 3.52 0.89 3.94∗∗∗ 0.83 0.80 1.04 3.76 0.91 4.14∗∗∗ 4.27 1.35 3.15∗∗Mean 2.30 0.61 3.78∗∗∗ 2.76 0.78 3.54∗∗∗ 2.23 0.58 3.86∗∗∗ 2.57 0.66 3.89∗∗∗High 1.08 0.74 1.47 5.89 2.38 2.48∗ 0.70 0.80 0.88 0.88 0.79 1.12
N = 121; Significance levels: †p < 0.10, ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.a. Calculated at sample means of other variables.b. Moderator values are its sample mean and plus/minus one standard deviation from mean.c. Low value for investment size at sample minimum, as one standard deviation below mean beyond available data.
observation revealed that the interaction effect was negativefor all observations (mean effect on investment probability =-0.026, std. dev. = 0.007). Table 3 shows the conditional ef-fects of reinvestments on the investment decision for differentlevels of reputation, which are by convention the mean andplus/minus one standard deviation from mean. While the ef-fect of reinvestment was positive and statistically significantat a low reputation (B = 3.52, p < 0.001), the effect becamesmaller at the mean reputation (B = 2.30, p < 0.001) andfinally turned statistically insignificant at a high reputation(B = 1.08, p = 0.142). Therefore, the reputation of a GPmoderated the effect of the status quo bias on the investmentdecision, such that a higher reputation led to a lower statusquo bias. Because we expected the opposite effect (that thestrength of the status quo bias increases with reputation),Hypothesis 2 was not supported.
Model 5 shows the moderation effect of the investmentsize on the status quo bias. The interaction effect betweenreinvestment and investment size had a statistically signif-icant positive effect on the investment decision (Exp(B) =1.07, p = 0.078). The interaction effect significantly im-proved the model fit (change in chi-square = 4.93, p =0.026), and Nagelkerkes R2 increased from 39% in Model 3to 43% in Model 5. A calculation of the interaction effect foreach sample observation revealed that the interaction effectwas positive for most observations and only slightly nega-tive for some of them (mean effect on investment probability= 0.008, std. dev. = 0.008). Table 3 shows the conditionaleffects of reinvestments on the investment decision for differ-ent levels of investment size. While the effect of reinvestmentwas small and not statistically significant at a low investmentsize (B = 0.83, p = 0.300), the effect became larger and sta-tistically significant at the mean investment size (B = 2.76, p< 0.001) and increased even further at a high investment size(B = 5.89, p = 0.013). Therefore, the investment size of a LPmoderated the effect of the status quo bias on the investmentdecision, such that a larger investment led to a stronger statusquo bias. Therefore, Hypothesis 3 was supported.
Model 6 shows the moderation effect of investor experi-ence on the status quo bias. The interaction effect betweenreinvestment and investor experience had a statistically sig-
nificant negative effect on the investment decision (Exp(B) =0.82, p = 0.015). The interaction effect also significantlyimproved the model fit (change in chi-square = 7.31, p =0.007), while Nagelkerkes R2 increased from 39% in Model 3to 45% in Model 6. A calculation of the interaction effect foreach sample observation revealed that the interaction effectwas negative for all observations (mean effect on investmentprobability = -0.023, std. dev. = 0.015). Table 3 shows theconditional effects of reinvestments on the investment de-cision for different levels of investor experience. While theeffect of reinvestment was positive and statistically signif-icant at a low investor experience (B = 3.76, p < 0.001),the effect became smaller at the mean investor experience(B = 2.23, p < 0.001) and finally turned statistically insignif-icant at a high investor experience (B = 0.70, p = 0.380).Therefore, the experience of a LP moderated the effect ofthe status quo bias on the investment decision, such that agreater level of experience led to a weaker status quo bias.Therefore, Hypothesis 4 was supported.
Model 7 shows the moderation effect of industry maturityon the status quo bias. The interaction effect between rein-vestment and industry maturity had a statistically significantnegative effect on the investment decision (Exp(B) = 0.47,p = 0.057). The interaction effect also significantly improvedthe model fit (change in chi-square = 4.80, p = 0.028), whileNagelkerkes R2 increased from 39% in Model 3 to 43% inModel 7. A calculation of the interaction effect for each sam-ple observation revealed that the interaction effect was nega-tive for most observations and only slightly positive for someof them (mean effect on investment probability = -0.091, std.dev. = 0.074). Table 3 shows the conditional effects of rein-vestments on the investment decision for different levels ofindustry maturity. While the effect of reinvestment was pos-itive and statistically significant at a low industry maturity(B = 4.27, p = 0.002), the effect became smaller at the meanindustry maturity (B = 2.57, p < 0.001) and finally turnedstatistically insignificant at a high industry maturity (B =0.88, p = 0.263). Therefore, the maturity of the PE industrymoderated the effect of the status quo bias on the investmentdecision, such that a more mature industry led to a weakerstatus quo bias. Therefore, Hypothesis 5 was supported.
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DISCUSSION
Our study suggests that LPs are not perfectly rational whenthey decide which GP to invest in. The status quo biasinfluences their investment decisions. LPs strongly preferGPs in which they have invested before, even when control-ling for potential information and access benefits of thesereinvestments. Both the nature of the investment opportunityand certain investor characteristics moderate the influenceof the status quo bias on investment decisions. The statusquo bias is stronger for investments in younger GPs and forlarger investments, whereas investor-specific and industry-wide learning effects reduce it.
Our results indicate that the status quo bias of LPs is drivenby a psychological anchoring effect of the status quo optionfor subsequent decisions. For example, one LP explainedduring our interviews: “Re-up investments are preferred byus. When a GP in our portfolio . . . raises the next fund, we . . .
have the aim to continue our investment relationship. Thiskind of existing professional relationship . . . is an anchorpoint of our portfolio development.” The increase of the sta-tus quo bias with the investment size and investor-specificand industry-wide learning effects support this interpreta-tion and indicate the relevance of cognitive misperceptionsand psychological commitment for reinvestment decisions ofLPs. These findings suggest that the psychological anchoringeffect of a GP increases with the investment size, whereasincreasing experience of LPs improves the quality of theirinvestment decisions and reduces cognitive misperceptionsand psychological commitment. The difference between ourresults and those of Burmeister and Schade [2007], who pro-posed that more experience should increase the susceptibilityto the status quo bias, might be explained by the different re-search context and a positive effect of increased knowledgeon the decision quality of LPs (Shepherd et al. [2003]).
However, our results also suggest that reinvestment deci-sions of LPs are reinforced by information advantages fromprevious funds. The decreasing importance of reinvestmentsfor higher levels of GP reputation indicates that LPs useinside information when reinvesting in GPs, as these infor-mation advantages are more valuable for LPs when investingin younger GPs, which have not yet established a reputa-tion. An alternative explanation for this investment patterncould be a grandstanding phenomenon among institutionalinvestors (Gompers [1996]). LPs could continue to reinvestin young GPs to establish a reputation as reliable investorsand to remain invested in GPs that could become future stars.
Taken together, our results suggest that reinvestment de-cisions are a double-edged sword for institutional investorsthat can result from a rational investment strategy, but alsofrom an irrational investment behavior. On the one side, rein-vestment decisions based on inside information from pre-vious funds or preferred access to restricted funds (Lernerand Schoar [2004], Lerner et al. [2007], Ljungvist et al.[2009], Glode and Green [2009], Phalippou [2010]) couldlead to superior investment decisions, as the rational use of
inside information or the access to superior investment alter-natives should improve the quality of investment decisions.On the other side, reinvestment decisions based on a statusquo bias resulting from cognitive misperceptions and psy-chological commitment (Samuelson and Zeckhauser [1988])are likely to lead to inferior investment decisions, as deci-sion biases inhibit optimal decision making (Tversky andKhaneman [1974], Hogarth and Makridakis [1981], Kahne-man et al. [1982]). As current investment decisions form thebasis for future performance (Sahlman [1990], Gompers andLerner [2001], Lerner et al. [2007]), the quality of these in-vestment decisions should be one of the main drivers of LPperformance. Whether the final performance effect of rein-vestments is positive or negative for LPs could depend ontheir individual susceptibility to the status quo bias.
In addition to the status quo bias, the effects of familiar-ity and fluency (Whittlesea [1993], Lee and Labroo [2004])could provide an alternative psychological explanation forreinvestment decisions of LPs for GPs. Whereas the statusquo bias can be defined by a tendency to inaction that rep-resents a passive investment decision without searching forbetter alternatives (Samuelson and Zeckhauser [1988]), rein-vestment decisions of LPs are usually active investment de-cisions. By contrast, familiarity makes a decision alternativelook more valuable and less risky, even if the decision is madeafter an extensive search for better alternatives (Whittlesea[1993]). Therefore, familiarity could have the same effect asthe status quo bias but without implying that the decision ispassive. With the status quo bias, LPs tend to reinvest intoGPs because it is easier to keep the status quo option ratherthan making an extensive search for better investment alter-natives. With familiarity, LPs search for alternative GPs butthen reinvest in a familiar one because fluency results in anoverestimation of how risky other solutions could be. Con-sequently, the effects of familiarity and fluency could havereinforced the strength of the status quo bias in our study, aswe could not disentangle these two effects empirically.
As with all research, our study is not without its limi-tations. First, our study examined PE fund investments byinstitutional investors. Consequently, we can generalize ourfindings about the status quo bias only for this asset class.Second, we investigated the role of the status quo bias forinvestment decisions of LPs. However, we could not explorethe performance implications of the status quo bias withour data set. Third, there could be a selection bias in oursample. A LP could repeatedly choose a GP, because theGP invests in a certain field that corresponds to the LP’sinvestment strategy. The reason for the reinvestment couldthen be unrelated to the status quo bias. However, such aselection bias appears unlikely, as LPs can usually choosefrom different GPs in any investment segment so that theinvestment strategy alone should not determine the selec-tion of a specific GP (Lerner et al. [2007]). Additionally, wecontrolled for the investment strategy of a PE fund in ouranalysis. Fourth, due to the retrospective design of our study,our results might be affected by recall biases and post-hoc
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146 FREIBURG AND GRICHNIK
justification (Golden [1992], Zacharakis and Meyer [1998],Shepherd and Zacharakis [1999]). However, retrospectivereporting is a viable research methodology if the measuresused are adequately reliable and valid (Miller et al. [1997]).To ensure the reliability and validity of our measures, weused only established measures and conducted a pretest ofour questionnaire with six experts. Fifth, our study reliedon self-reported data. Due to significant confidentiality con-cerns from both LPs and GPs regarding the identity of theirinvestment partners, we could not obtain objective measures.Furthermore, as all information in our study was obtainedfrom a single survey, our study is exposed to the risk of com-mon method bias. However, after conducting a posthoc testfor common method bias (Podsakoff and Organ [1986]), ouranalysis did not indicate any significant concerns.
Our research highlights several avenues for future re-search. First, the status quo bias offers a field for poten-tial performance differences between LPs. While recent re-search has identified structural performance differences be-tween different LP groups (Lerner et al. [2007], Hobohm[2010], Bates and Bradford [2009], Phalippou [2010]), thedrivers behind these differences are still unclear. Lerner et al.[2007] argued that some LPs are better than other investorsat predicting whether follow-on funds will have high returns.It could be that LPs differ in their susceptibility to the statusquo bias, thereby driving performance differences. Futureresearch should explore the performance consequences ofthe status quo bias in detail and analyze whether the perfor-mance of LPs would have been better had they invested indifferent GPs (Hamilton and Nickerson [2003]). Second, itwould be interesting to investigate whether the status quo biasequally applies to investment decisions of venture capitalistsfor entrepreneurs (MacMillan, Siegel and Narasimha [1985],Franke et al. [2008], Zhang [2011]). Previous research hasshown that the status quo bias increases with the numberof alternatives to choose from (Samuelson and Zeckhauser[1988], Kempf and Ruenzi [2006]). As the number of en-trepreneurs should by far exceed the number of PE funds, wewould expect a stronger status quo bias of venture capitalists.Third, future research should try to disentangle the effects offamiliarity and fluency (Whittlesea [1993], Lee and Labroo[2004]) from the status quo bias for investment decisions inentrepreneurial finance, as it is important to fully understandthe psychological drivers of reinvestment decisions.
Our study also has important practical implications forLPs and GPs. Our findings should warn LPs of the ambiva-lence associated with reinvestments. Although reinvestmentdecisions can be based on valuable inside information orpreferred access to restricted funds, LPs should be aware ofpotential downsides of cognitive misperceptions and psycho-logical commitment. In our field work, many of our interviewpartners expressed their fears of suboptimal investment deci-sions due to too-strong adherence to GPs in which the LP hadpreviously invested. LPs should find out to what degree theyare susceptible to the status quo bias. Therefore, LPs shouldsystematically analyze and discuss their past reinvestment
decisions as well as their overall reinvestment rate. To reducethe status quo bias, LPs should rotate due diligence respon-sibilities so that someone who was involved in a previousinvestment does not evaluate another fund of the same GP.LPs could also require an explicit rationale for reinvestmentsto increase their awareness of the status quo bias. Our find-ings should encourage GPs to proactively address potentialstatus quo concerns of their LPs by offering them convincinginvestment stories for follow-on funds. Our results shouldalso provide GPs with additional fundraising arguments toattract new investors that were previously firmly attached toother GPs.
In conclusion, our paper indicates that institutional in-vestors in PE funds are not perfectly rational decision mak-ers. The status quo bias influences their investment decisionsso that they strongly prefer PE firms in which they have in-vested before, even when potential information and accessbenefits are controlled for. The magnitude of the status quobias depends on the nature of the investment opportunityand certain investor characteristics. Our results suggest thatreinvestment decisions are a double-edged sword that can bedriven not only by a rational investment strategy but also byan irrational investment behavior.
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