1 Introduction

University endowments actively invest in private equity (PE) and are known to be highly successful in the segment (Lerner et al. 2007; Sensoy et al. 2014). We argue that they are in a unique position compared to other investor types due to a network advantage. As universities educate students who may eventually work as fund managers within the private markets asset class, their endowments can have an exclusive access to a specific network within the PE industry, namely its own graduates. During the investment process, endowments may benefit from such a social tie, hereafter also referred to as “alumni tie”. First, it may serve as a channel of access granting endowments the opportunity to invest into PE funds otherwise not open and/or not known to them. Second, it may act as a channel of information in an opaque asset class such as PE, helping endowments to better assess the quality of an investment. The first channel would result in a higher probability to invest, while the second one would correlate with a superior investment performance. The conjecture that such ties may impact the investment choices of endowments is supported by anecdotal evidence. Dolan and Jesse (2018), for example, show that a substantial amount of a university’s investments goes into alumni-managed funds.

Through a unique dataset consisting of U.S. endowment commitments into PE fundsFootnote 1 and the biographies of involved fund managers, we study the impact that an alumni tie, defined as an existing social tie between an university endowment and a fund manager deriving through an alumni networkFootnote 2, has on an endowment’s investment decision and subsequent fund performance. Our dataset comprises 1,590 commitments of PE investments made by 189 different U.S. university endowments into 613 PE funds along with fund manager biographies. A total of 2,351 individual fund managers are connected to these funds. We find that, with an average of 15% of fund commitments (i.e., the absolute number of fund commitments), endowments trust a substantial amount of their capital to their own alumni (given that the average size of individual endowment commitments is USD 1,383 million among all PE funds).

We examine our main research question of whether university endowments are more likely to be invested in alumni managed funds by comparing investment rates in funds managed by alumni to counterfactual funds with similar characteristics. We further control for characteristics such as the degree of alumni presence among fund managers within a fund, as well as university rankings. We find that endowments are 70% more likely to invest into PE funds that are managed by alumni compared to similar funds with no former graduate among the fund management team. The direction and significance of this finding holds regardless of universities’ reputations, which are proxied by university rankings. For the less prominent and lower ranked institutions, alumni ties appear to be (even) more important, increasing the odds of an investment into an alumni-linked fund by fivefold. The odds of an investment into an alumni-managed fund are higher and more significant in the case of oversubscribed funds. This finding supports our argumentation that an alumni tie serves as a channel of access for endowments.

Separately, we also analyze the performance of alumni-connected investments compared to other investment opportunities where similar ties do not exist in order to evaluate whether the presence of alumni ties benefits or actually hinders the performance of endowments’ investment decisions. We find no consistent evidence that the presence of alumni ties is associated with over- or underperformance. However, some benefits of investing in alumni funds compared to other endowment investments may be reflected in lower search costs rather than directly manifesting in investment outperformance. In addition, we note that the role of alumni ties has diminished over time with having been of greater relevance during the period of the 1990s to the early 2000s compared to the more recent period, which is conducive to the increased level of professionalization and transparency seen in the PE industry over the last decades.Footnote 3

To the best of our knowledge, this study is the first to explore the role of alumni ties in the context of endowment PE investments. We contribute to the academic literature on the role of social ties in the investment decision process and shed light into another way how alumni connections may be of importance for universities - beyond the typical specifics of alumni relationships (such as gifting or governance). Cohen et al. (2010) find against the background of public markets that educational ties appear relevant for the flow of information. Ishii and Xuan (2014) verify that while such ties may lead to more merger & acquisition (M &A) activities, they can also result in poor decision making. Fuchs et al. (2021) document that educational ties between fund and target company officers are an important predictor for PE deals. Our study complements existing work on the PE investment patterns of endowments (e.g., Lerner et al. 2007) and suggests a potential channel through which endowments tap into PE funds. The closest study to ours is that of Binfarè et al. (2021), who explore the impact of expertise and general network sizes of endowment board members on investmentsFootnote 4. In contrast, our paper focuses on the educational background of fund managers and their connections to university endowments (and not directly to the staff or board members). In our study, we provide evidence that such alumni ties play an important role in the fund manager selection process.

The remainder of the paper is as follows: In the next section, we review the related literature and provide the theoretical motivation for our testable hypotheses. Section 3 describes the data and the matching procedure of our broad set of data. Section 4 presents our empirical results along with extensive robustness tests. Section 5 concludes.

2 Social Ties, Investment Decisions, and Performance

Several studies have previously addressed the impact of social ties on investment decisions. Cohen et al. (2008) identify ties through higher education connections and find that mutual fund managers tend to invest more and earn higher investment returns in companies where managers share a similar background. The closer such similarities, e.g., due to similar majors or overlapping study periods in addition to common alma mater, the stronger the results are. The authors attribute their findings to the existent information channel where investors can obtain direct information, have facilitated access to it, and/or obtain a better grasp of management’s capabilities. Their study furthermore highlights that this information premium is not only restricted to certain universities. Cohen et al. (2010) confirm that connected sell-side analysts also outperform their peers without the relevant ties before stricter regulations were implemented, which may imply that they were benefitting from direct information. Within PE, the interest in the role of social ties is increasing. Hochberg et al. (2007) identify different measures related to the concept of network centrality and, based on co-investment data, they find that venture capital (VC) funds with larger networks perform better. Fuchs et al. (2021) find evidence that buyout fund managers who share the same educational background with chief executive officers (CEOs) of target companies are more likely to win deals. This effect is particularly stronger for more exclusive ties where connections are not as abundant, such as the group outside of the top universities. Binfarè et al. (2021) focus on endowment investments into alternatives (such as PE and hedge funds) and highlight the influence of well experienced and connected endowment managers in determining allocations, as well as the impact of experience on returns.

While the impact of social ties is apparently confirmed in recent literature, empirical evidence on the effects that social ties have on performance is mixed. Kuhnen (2009) finds no significant impact on expenses and returns in favored hiring choices of mutual fund directors and advisory firms for which previous business relationships exist. With regard to M &A transactions, for example, Ishii and Xuan (2014) find that acquisitions are more likely to take place between firms with connected individuals, either from previous educational or employment experience, and that there is a negative relationship between connectedness and performance. The authors argue that network proximity may hinder decision making due to a heightened sense of trust and less due diligence, a familiarity bias, or groupthink. Meanwhile, Hochberg et al. (2007) show that well-connected VC funds perform better, while Fuchs et al. (2021) find no clear pattern on private equity deals when fund managers and target company CEOs share an educational tie. Binfarè et al. (2021) find that endowments managed by individuals with expertise in VC demonstrate superior performance, but do not show conclusive evidence arising from network sizes.

Due to their strong reputation as PE investors, there is widespread interest in understanding how university endowments invest and what their drivers of success are. In this paper, we explore how alumni relationships may play a role in their investment choices and test two hypotheses: (i) whether alumni ties increase the odds of an endowment investment into a PE fund and (ii) whether this correlates with performance. While the close connection to alumni networks is a unique feature of endowments compared to other investors, the rationale for why it could significantly influence decisions is supported by previous studies, as mentioned above. Anecdotally, evidence that this is a relevant channel is even highlighted by endowments themselves. Yale’s 2015 endowment report, for example, emphasizes the value of their alumni ties as an edge supporting its success. It lists more than 20 alumni venture capitalists and entrepreneurs, while highlighting the importance of relationships and networks, stating that the endowment’s “vast experience in VC provides an unparalleled set of manager relationships, significant market knowledge and an extensive network” (Yale Investments Office 2015, p.16). The existing literature also supports such an argument as it points out that endowments have benefitted from being able to access successful funds where entry was restricted or the funds were oversubscribed (Lerner et al. 2007; Sensoy et al. 2014). We argue that one channel to get access to such funds could be via those alumni ties. The increased network proximity to alumni fund managers, who are likely to welcome investments from their own alma mater more than that of other investors, may lead to more investment opportunities through ease of access to sought-after funds. We therefore hypothesize that the existence of an alumni tie increases the odds of an endowment’s investment into a PE fund.

Alumni ties could also benefit endowments as an information channel. As highlighted by Preda (2007), “a social tie is not only a pipe through which information flows, but, when viewed by a third-party observer, information in itself.” While the evidence on the impact of social ties on investment performance is mixed, we argue that in the context of PE funds they could be advantageous given the opaque nature of private markets. Within PE, it is common for investors to actively tap into their networks to acquire information. As emphasized by Swensen (2009), network connections “facilitate reference checking and increase the quality of decision making” (p. 229). Importantly, this is not restricted to close relationships but also to “weak ties”Footnote 5, as acquaintances or even individuals who are simply part of the same network may provide investment decision makers with valuable insights. Johan and Zhang (2016) exemplify the way reduced information asymmetries can benefit endowments. For a U.S. sample, they find that endowments receive more frequent and less inflated performance reports compared to other limited partner (LP) types, arguing that this improved monitoring positively impacts performance. Thus, we propose that the existence of an alumni tie correlates with a higher PE fund return achieved by the endowment.

Other possible factors driving those investments could lead to the opposite effect, however, such as homophily - the tendency for individuals of similar backgrounds to choose each other. This was suggested in Kuhnen (2009), but since the author finds inconclusive performance results, her conclusion is that different effects may balance out. For the particular case of this paper, another possible avenue relates to the importance of donation relationships universities maintain with alumni. Just as endowment returns, they are an important revenue stream and therefore universities do have a strong incentive to keep alumni close. One could argue that investments into alumni-managed PE fund could be a form of keeping relationships strong. Due to the reputational risks associated with those, we do not believe this could be a major effect across institutions, and initial analysis, albeit with limited data availability, confirms this assumptionFootnote 6.

3 Data

We build a comprehensive dataset based on PE fund and LP commitment observations from four different data providers: PitchBook, Preqin, Dow Jones, and FactSet.Footnote 7 While LP fund commitments are available through all these providers, merging them and cleaning for potential duplicates results in additional observations. For instance, the largest number of endowment commitments in our main sample is derived through Preqin (1,050, as shown in Table 12 of the Internet Appendix), but using other sources allows us to increase the sample size by 540 commitments or over 50%. Another benefit of considering various data sources is that we are able to extend the set of variables, and thus, include additional information otherwise not available through an individual provider. For instance, it is through PitchBook only that we are able to source fund managers’ educational backgrounds, which allows us to identify potential alumni ties. Performance data is added from Preqin and Dow Jones.

Our study focuses on university endowments and PE funds based in the U.S., which is not only the largest and most mature PE market, but also hosts the largest number of active endowment investors.Footnote 8 Our final dataset is comprised of funds that are managed by asset managers focusing on buyout funds. The manager biographies for those are provided by PitchBook. However, in case these GPs also manage funds focusing on VC and growth strategies, we also have data on these fund managers biographies. As those are not funds managed by pure-play VC and growth firms, however, we do note that they are not representative of the entire VC and growth segments. As a result, and as reported in Table 1 and Table 13 of the Internet Appendix, the VC commitments we analyze in this study (roughly 15% of all available VC commitments) tend to be bigger and perform more poorly than the entire VC segment on average.Footnote 9 In contrast, the performance of buyout funds for which we have manager data (representing over 80% of all commitments) is largely in line with the overall segment sample.Footnote 10

Table 1 Number of commitments by fund type
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In total, we are able to identify 3,425 commitments into 1,522 PE funds undertaken by 227 U.S. based endowments between 1995 and 2017. Of those commitments, we are able to track the fund manager biographies for 613 funds (with no missing fund size values) managed by 295 general partners (GPs) and connected to 1,590 commitments made by 189 endowments. For each of these 1,590 commitments, we have at least one individual linked at the fund level for a total of 2,351 different biographies.Footnote 11 The average (median) reported number of managers for each fund amounts to 7 (6). Table 1 provides a breakdown of our final dataset, of which 78% are classified as buyout funds, 5% as growth, and 17% as VC. Table 14 of the Internet Appendix shows the funds that received the most endowment commitments.

Our sample comprises commitments made into funds with vintage years ranging from 1995 until 2017. The average fund size amounts to approximately USD 2.3 billion, whereas buyout funds are larger in size (USD 2.7 billion) compared to VC (USD 0.6 billion) and growth funds (USD 0.9 billion). The number of commitments per vintage year and main performance statistics are shown in Table 2. Net internal rates of return (IRR), i.e. after fund fees and expenses, are added from both sources and are available for 1,312 endowment commitments or 76% of our funds sample. The total value to paid-in (TVPI) multiple obtained from Preqin is available for 1,349 endowment commitments or 79% of funds that received an investment from an endowment. The average fund performance amounts to an IRR (TVPI) of 14.02% (1.73). Similar to previous studies (see, e.g., Lerner et al. (2007)), commitment observations with available performance data tend to be those from larger funds. Most of the commitments in our sample are made in the 2000s, while performance shows a cyclical pattern with peaks for vintages in the mid- to late nineties as well as between 2002 to 2003 and 2009 to 2010.

Table 2 Endowment commitments by vintage year and performance summary
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We also gather information on additional 960 funds with no underlying endowment commitment but for which PitchBook also provides fund manager biographies. These are funds in which endowments theoretically could have also invested. We use this information to build a counterfactual sample that is later applied to the odds analysis of endowment investments into funds managed by alumni. Table 15 of the Internet Appendix describes the basic characteristics of these funds compared to the endowment commitment sample as presented in Table 1. Table 3 presents the number of fund commitments and average performance of selected funds for each endowment with at least one investment into a PE fund, managed by at least one alumni fund manager. Out of the total sample of 1,590 commitments, 238 are into funds with alumni fund managers and those relate to 41 different endowments. The descriptive statistics highlight that some of the larger endowments are overrepresented in our data sample, with the University of California (124), the University of Michigan (114), and the University of Texas (100), all public institutions, being among the group with the highest number of known commitments in our sample.

Table 3 Endowments and universities invested in alumni funds
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Some universities have a strong tradition of educating future business leaders that end up working in certain industries such as finance and including PE. This might be due to renowned (under)graduate programs or the preference of (big) financial institutions to recruit from “target schools” such as Ivy League universities. Another aspect to note is that university reputation tends to be correlated with endowment size (Lerner et al. (2008)). It is therefore not surprising that the most commonly cited schools in fund managers’ educational backgrounds also tend to be among the endowments with most commitments into funds managed by alumni connections according to our data (see Table 16 of the Internet Appendix). In this context, Harvard University is the institution at the top with 43 (77%) of 56 commitments into PE funds being managed by its own alumni, as seen in Table 3. Based on an initial univariate comparison, we observe that alumni-matched funds only slightly outperform the overall sample of commitments (14.64% versus 14.01%).

In addition to the fund managers’ alma mater, their degree types (e.g., Bachelor of Arts, MBA, etc.) are often listed as well. Among the 2,272 fund managers of invested funds who disclose educational backgroundsFootnote 12, 1,295 or 57% of them have MBA degrees, and thus, hold at least two degrees. However, not all fund managers disclose their conferred degree type. In total, we identify the exact types of academic degrees for 1,948 managers or 86% of those with disclosed educational credentials.

For the creation of our counterfactual sample, used as part of our empirical analysis in Section 4.1, we retrieve information on 960 additional funds that endowments could have potentially invested in, but eventually did not commit capital to (see Table 14 in the Internet Appendix). The addition of these 960 funds results in an expansion of another 1,995 different individual fund managers whose educational background is available.Footnote 13 As seen in Table 15 of the Internet Appendix, these additional observations share similar characteristics with the main fund manager sample, with Harvard still being the most represented school (with a slightly lower percentage of 18%) and 57% of managers being MBA graduates.

Equipped with the educational background information of fund managers, we create a dummy variable that identifies the (actual or counterfactual) commitments managed by alumni. It takes the value of one if at least one fund manager attended the endowment’s university. For instance, when the endowment fund of Harvard University invests into a PE fund managed by a Harvard graduate the created dummy variable equals one, or zero otherwise. In addition, we also generate variables that count the number of alumni per PE fund and the prevalence (percentage) of alumni out of total managers per fund as a way to measure the degree of connectedness between fund management and their alma mater. Funds chosen by endowments have an average of 6 (median of 5) listed individuals as part of their management teams. For the subsample of funds where there is at least one alumni tie, this number rises to an average of 8 (median of 7) of which on average 1.58 (median is 1) managers graduated from the respective university of the invested endowment fund. Funds with only one listed university endowment as an LP (as opposed to funds with multiple endowments being part of its LP base) accounts for less than 20% of all endowment commitments (see Table 4).

4 Empirical analysis

4.1 Investment choices

We start our analysis by focusing on the question of whether endowments are more likely to invest in alumni-matched funds compared to other funds. Ideally, we would know the specific fund criteria that endowments were considering before they made a decision to commit capital. As this information is not accessible, we create alternative fund pools for each actual fund investment based on general criteria such as same fund vintage year, strategy type, and size (within a range of 50% to 150% of actual fund size). For example, alternatives to commitments into a USD 1.0 billion buyout fund of vintage year 2010 would include buyout funds with the same vintage year and fund sizes between USD 500 million and USD 1.5 billion. Similar to the approach proposed by Kuhnen (2009), Siming (2014) and Bengtsson and Hsu (2015), the groups of alternative investments determine our counterfactual sample. We delete commitments for which we do not find counterfactual alternatives according to our criteria, so that the number of actual investments used for this identification strategy lowers slightly from 1,590 to 1,523. The number of counterfactual commitments amounts to 15,553 observations. While we match fund managers in the counterfactual sample with potential endowment investors, the number of funds managed by alumni reach approximately 8%, which is notably smaller than the 15% seen in the actual investment sample.

Table 4 Investments and educational ties: actual and counterfactual
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We recognize that not only more investment criteria may have been used by endowments to decide on an investment but also the presence of networks itself may lead to some investments not necessarily following our strict selection rule. For instance, an endowment could potentially not have been planning to allocate capital to a certain type of fund strategy until it became aware of a specific initiative. However, this would actually mean that we are underestimating the importance of alumni ties, and thus our estimates are rather conservative. While it is possible that our broad set of criteria overestimates the amount of funds that would be considered as close alternatives by endowments, there is also a possibility that our counterfactual approach does not include all potential alternatives. The average and median number of selected fund alternatives for each commitment, counting both actual and counterfactual investments, is at 24 and 17 respectively, and the maximum reaches 104.Footnote 14 We do not claim to be able to reproduce the full range of potential fund alternatives, however, we do control for preferences for similar geographies, later fund sequences, existing relationships, and background of fund partners.Footnote 15 One can also argue that different finance teams at the endowment level may follow different investment styles, and this heterogeneity among endowments might systematically affect our results. Moreover, investment behavior, or simply the number of investment options available (i.e., competition among investors to access funds), may also change depending on the investment environment of each year and it may be different across fund types. For example, the options to invest into smaller VC funds may be more limited compared to larger buyout funds, which could impact the effect that we see for alumni ties. We address these concerns in our identification strategy by including multi-way fixed effects to control for specific endowment, vintage years, and fund strategy types. The main model specification is as follows:

$$\begin{aligned} \begin{aligned} ln(\frac{p_{i,j}}{1-p_{i,j}})= a+ \beta _1 \textit{Alumni}_{i,j}+\beta _2 \textit{Fund Size}_i+\beta _3 \textit{Fund Sequence}_i \\ + \beta _4 \textit{Same State}_{i,j} +\beta _5 \textit{GP Relationship}_{i,j} +\beta _6 \textit{Experience}_i+ \textit{Fixed Effects}+\epsilon _i. \end{aligned} \end{aligned}$$
(1)
Table 5 The odds of investment
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Table 6  [XMLCONT] 
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Our binary dependent variable Y\(^{i,j}\) equals one when a commitment in fund i is made by an endowment j, and zero when an alternative fund could have been considered as a potential investment according to our criteria but was actually not chosen. We use a logistic regression model, where the left hand-side of the equation represents the log of the odds ofY\(^{i,j}\), with p\(^{i,j}\) being the probability of Y\(^{i,j}\) being equal to one. Our main variable of interest is Alumni\(^{i,j}\), which takes the value of one for funds where the educational background of managers matches the endowments’ universities and zero where there is no such link. We also show results for variations of our independent variable in Table 5, breaking it down by the degree of commonality (i.e., the number or percentage of individuals with the same background within a fund), degree types (although not available for all alumni ties), and university rankings. Fund Size\(^{i}\) and Fund Sequence\(^{i}\) are the natural logarithm of final fund sizes (in USD million) and the sequences of funds managed within fund families (managed by the same GP). Same State\(^{i,j}\) is a dummy variable that equals to one when endowments and fund headquarters are located within the same U.S. state and controls for a potential home bias, as suggested by Hochberg and Rauh (2013). Over 11% of endowment investments in our sample are within the same state, which compares to just below 6% in the counterfactual sample. GP Relationship\(^{i,j}\) is another dummy that equals one when it indicates that an endowment has prior history in investing with a manager and zero otherwise.Footnote 16 Table 17 of the Internet Appendix also shows results where we control for previous GP performance in a subsample for which such information is available. The estimates are in line with our main results of Table 5. Experience\(^{i,j}\) represents a set of three variables related to the percentage of fund managers that have backgrounds in consulting, banking, and finance industry, similarly to the controls applied in Fuchs et al. (2021).

Table 5 shows the results derived from a logistic regression with coefficients shown in log odds. We confirm our first hypothesis that endowments are more likely to invest into funds with an alumni tie. After exponentiation of the coefficients, we see that such tie increases the odds of an investment by a factor of 1.70, i.e. ceteris paribus, the odds of an endowment investment into an alumni-linked fund are 70% higher than in other funds. By breaking down the ties by degree types, our results remain significant across different degrees, while appearing to be stronger for post-graduate ties and, particularly, for MBA ties.

As previously noted, we observe in our educational background data sample (Table 14 of the Internet Appendix) that certain universities, particularly the higher-ranked institutions with the biggest endowments, have a more abundant alumni presence in PE fund management than others. To test whether the alumni connection matters for different types of institutions, we further categorize our alumni tie variable according to school rankings. We classify American universities according to the QS World University Rankings list for 2010. Therefore, a university is defined as a top-20 school if it is among the top-20 institutions in the worldwide ranking. We also divide MBA ties according to the Financial Times 2010 Global MBA ranking into top-10 (in the United States) and others. As there is a lower number of universities that offer MBA programmes, top universities represent an even larger portion of the sample for this type of degree.Footnote 17

To further ensure that our main variable is not influenced by the dominance of alumni from high-ranked universities working in the PE industry, we create a new independent variable, which we refer in the following as “scaled” alumni tie. The introduction of this variable reflects on the idea that there may be situations where an alumni tie with an endowment can be an exclusive feature no other competing fund possesses. Thus, it can be a differential that may impact the corresponding investment odds.

$$\begin{aligned} \begin{aligned} \textit{Scaled tie}_{i,j}=\frac{\textit{Actual tie}_{i,j}}{\sum _{i=1}^n\textit{Alumni tie}_{i,j}}. \end{aligned} \end{aligned}$$
(2)

The “scaled” alumni tie variable in Eq. 2 is defined as the number of alumni ties in actual investments divided by the number of total alumni ties in actual and counterfactual investments within the same criteria group (according to fund strategy, vintage, and size). The value of this variable ranges from zero to one, i.e. Eq. 2 transforms a binary variable into a probability. A value of one represents the situation where, among alternative funds, only the chosen fund had one or more alumni managers from the endowment’s university. It therefore reaches the maximum degree of exclusivity. A value of zero in turn represents the scenario where there are no matches. Accordingly, values between zero and one mean that there were other possible funds to invest that were also managed by alumni. For example, in our data we see that, among 45 possible similar buyout funds with vintage 2000, MIT Investment Management Company selected the only fund where we identify an alumni tie. Therefore, its scaled tie equals to 1. Meanwhile, the scaled tie equals 0.0625 for Harvard Management Company for its investment in 2012 buyout fund since, in addition to the matched alumni in the actual investment, there are 15 other funds among 21 counterfactual opportunities that also have at least one alumna among its managers (e.g., 1/16 = 0.0625). Average scaled tie values by rankings are reported in Table 6.

Table 7 The exclusivity of ties
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Results of Table 6 highlight that, on average, the higher the ranking position of the university is, the lower the exclusivity ratio. Under the assumption, and as shown in Table 6, that endowments are indeed more likely to invest into funds managed by their own alumni, this finding is not surprising. Graduates of lower ranked universities are underrepresented in the PE industry and are less likely to appear with an alumni match both in the actual and counterfactual sample. Thus, this leads to higher exclusivity ratios. Table 6 represents a first evidence that universities with a smaller footprint in the PE industry tend to rely more on alumni ties when making PE investments. Table 7 further elaborates on this hypothesis within a multivariate setting.

Table 8 The odds of investment according to ranking and exclusivity
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Columns 1-4 of Table 7 show results for the regressions on the odds of investment for alumni tie variables that were previously reported and explore the possibility that having more than one tie in a fund might have a greater effect than just one. Column 5 reports the results when we re-run our models based on universities’ ranking positions. Panel B reports results when such variables are scaled as defined in Eq. 2. In Panel A, alumni ties connected to the top-20 universities are significant, however, the effects of ties of universities that do not make it to the top-100 list are not only statistically significant but also economically stronger. Using scaled ties, as displayed in Panel B, our results are overall consistent with our initial analysis in Panel A, with ties from top-20 universities remaining significant. More notably, alumni ties on the level of lower-ranked universities continue to appear as more economically and statistically significant. For scaled ties taking the maximum value of one, top-20 alumni ties lead to an increase in the odds of investment of 318% and that of lower-ranked institutions of 929%. The same pattern holds for MBAs as shown in Column 6. Overall, alumni networks seem to matter in general, but some of them appear to be particularly powerful and alumni ties can be even more important for lower-ranked universities. Following the specification of Eq. 2 a high value for our “scaled” alumni tie variable means that the observed tie is rather exclusive and few fund managers of the counterfactual sample share the same alma mater. With the specification of Panel B we are able to explore these situations in more detail and investigate if the overall presence of a university’s alumni community in the PE industry (e.g., again measured via the counterfactual sample) impacts the odds of an alumni tie. Our results in Panel B display a positive correlation relating to the level of exclusivity. The introduction of a “scaled” alumni tie also allows us to control for the size of the underlying alumni community in the PE industry. As outlined in Table 16 of our Internet Appendix, we observe that higher ranked universities maintain a stronger footprint in the PE industry as lower ranked universities leading to lower values relating to the “scaled” alumni tie variable (e.g., it is more likely that you find another Harvard alumni tie in our counterfactual model as compared to a lower ranked university, which in turn leads to a lower value for the “scaled” alumni tie variable). Our results show that alumni ties do matter for lower ranked universities (e.g., with a lower alumni community in the PE industry) and that the significance of alumni ties is not limited to higher ranked universities but holds also for lower ranked universities and increases with the level of exclusivity.

4.2 Performance

In a next step, we test whether investments into funds managed by alumni translates into better return performance. Thereby, we regress the PE fund performance of the endowment commitments on our main independent variable, the alumni tie, and control for a comparable set of variables used in prior analyses.Footnote 18

$$\begin{aligned} \begin{aligned} \textit{Fund Net IRR}_{i,j}=a+\beta _1\textit{Alumni}_{i,j}+\beta _2 \textit{Fund Size}_i+\beta _3 \textit{Fund Sequence}_i \\ +\beta _3 \textit{Same State}_{i,j} + \beta _4 \textit{GP Relationship}_{i,j}+\beta _5\textit{Track Record}_i \\ + \beta _6 \textit{Experience}_i+\textit{Fixed Effects}+\epsilon _i. \end{aligned} \end{aligned}$$
(3)

Compared to Eq. 1, we add a Track Record\(^{i}\) variable to our performance regressions, which is defined as the average net IRR performance a GP has realized across all previous funds prior to the current fund generation. As our goal is to see whether investments into alumni-managed funds are beneficial or detrimental to endowments, we compare their performance to other endowment commitments to PE funds (without alumni ties). Thus, and in contrast to our odds analysis, we do not need to apply a counterfactual approach. We use ordinary least-squares (OLS) estimates including fixed effects for fund vintage years, fund strategies, and endowments. Standard errors are robust and clustered at the endowment level, similarly to previous studies on performance (e.g., Korteweg and Sorensen (2017).

The main results of our performance regressions are shown in Table 8 for net IRR measurements, whereas TVPI results are shown in Table 18 of the Internet Appendix. We note that these measurements are popular in the literature but are not risk adjusted, which is a well-known challenge in private markets. Looking at them, we neither observe significant outperformance nor underperformance of fund commitments with alumni ties, which suggests that funds managed by alumni do not tend to perform differently than other funds in endowment portfolios. Thus, we are not able to find empirical evidence supporting our second hypothesis that alumni ties could be advantageous to endowments and translate into higher performance.

Table 9 The performance of investments into alumni funds
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Table 10  [XMLCONT] 
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An interesting exception, however, is MBA ties. As seen in Column 2 of Table 8, they are associated with statistically significant higher performance. Further analyses, shown in Table 19 of the Internet Appendix, suggest that ties for graduates from highly ranked MBA program, which represent over 70% of ties, affect fund performance significantly. A similar pattern was also documented by Wu (2011), where the performance of non-syndicated leveraged buyout deals is shown to be higher when a team member has an MBA. The author argues that this is evidence for MBAs being better at deal screening and that, when syndication occurs, partnerships involving Harvard MBA social ties seem particularly fruitful. Fund managers with such a background show a strong preference to collaborate and can find a larger number of partners. This highlights the advantages of being part of the alumni network of a highly ranked university. Our findings support such an argumentation. In order to ensure that the positive relationship of MBA ties on performance is not driven by the MBA degrees themselves (see, e.g., Bertrand and Schoar 2003 and Graham and Harvey 2001), we also run regressions as in Eq. 3 with MBA experience reflected by the percentage of fund staff with MBAs as an explanatory variable. Our results, reported in Table 20 of the Internet Appendix, confirm that, although MBA experience is indeed associated with higher performance, MBA alumni ties are still economically and statistically significant.Footnote 19 Overall, as we only observe a significant effect in the case of MBA ties, our findings suggest that general alumni ties do not prove to be a systematic factor driving the performance of endowments’ PE investments.

4.3 Robustness tests

We perform a range of different robustness checks to validate our findings. First, we test whether our main finding that endowments seem more likely to invest in alumni-managed funds is not driven by the design of our counterfactual approach. In doing so, we use random draws similarly to Ishii and Xuan (2014) and propensity score matching as alternative selection methods. The results and procedure details are reported in Tables 21 and 22 of the Internet Appendix. In addition, we also use different criteria for the setup of our counterfactual approach. First, we relax size restrictions when selecting counterfactual funds, resulting in an increasing number of potential options for each actual investment. As reported in Table 23 of the Internet Appendix, this adjustment leads to similar conclusions as derived from our main analysis – alumni ties significantly increase the odds of an investment. Second, in contrast to the main analysis, we restrict our sample to investments into “local” funds only, i.e., within the same state or based within a distance of 100km to the location of the endowment fund. We still find positive, but mostly statistically insignificant, effects stemming from alumni ties, as reported in Table 24 of the Internet Appendix. Even though there is a preference for same-state investments in our data, endowments do not only consider local funds. Moreover, such ties could be particularly key for endowments that are not from the same geography due to the absence of local networks and increased information asymmetries.Footnote 20 We run a series of subsample analyses according to fund and endowment characteristics and confirm that we can draw similar conclusions for both investment odds and performance regressions as specified in the main models. Results are reported in Tables 9 and 10.

Table 11 Investment odds subsample robustness
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Table 12 Performance subsample robustness
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Table 9 shows that alumni ties appear to be particularly important for investments into oversubscribed funds, or for funds being raised by fund managers with a track record of high historic investment returns, which supports the hypothesis that alumni ties may facilitate access to highly demanded funds. Investments into growth funds appear to be big outliers with significantly stronger effects, but we take a cautious approach to avoid overinterpreting it since our growth fund sample is very limited (see Table 1). Our results also show that less experienced university endowments in terms of PE investments (e.g., those with less than 20 fund commitments) are more likely to rely on their alumni ties when they invest into PE funds. This is in line with our previous findings as those endowments also tend to represent lower ranked institutions. Similarly, we see that the effect on investment odds is not being driven by the most matched endowments, which again tend to also be the better ranked universities, while those appear to be the ones that show a positive impact on performance, particularly in the MBA case. This also confirms previous findings.

Another key finding, demonstrated in Table 9, is that any impact stemming from alumni ties has weakened in the more recent years as regression coefficients decrease in magnitude and are no longer statistically significant for post-2005 vintage years. This does not come as a surprise given the maturing or professionalization of the PE industry and of endowments as investors. Once endowments establish relationships with private equity firms, fund managers and other industry specialists, the importance of alumni networks for facilitated access to funds and as an information channel weakens. In our robustness checks, we see that alumni ties are particularly important for funds where previous GP Relationships do not exist and that the impact of previous firm relationships seem higher in later periodsFootnote 21. As endowments became more established in the PE industry over time, the way they approach managers or are approached by them changed. Big endowments now have specialized fund management staff that are often experts in the field of alternative investments, while many smaller endowments are managed by general university financial officers and/or often rely on recommendations given by external investment consultants. Such a higher level of professionalization may have led to an attenuated role of university-related networks over time.

In further regressions, we add an additional category of fixed effects to our main specification to control for variation at the GP level. The rationale for this is that different private equity firms may attract varying levels of endowment investors or show different fundraising strategies. We do not include these fixed effects in our main analysis as many observations would have been dropped in the logistic regressions due to a high number of GPs only being represented with one fund in our data set. This would have resulted in a subsequent selection bias as we would have run our main analysis only for large GPs. However, we still obtain similar results for the odds of investment and performance in Tables 25 and 26 of the Internet Appendix when including GP fixed effects. We also explore using interaction terms and report it in Tables 27 and 28. Table 27 further confirms the relevance of MBA ties and, not surprisingly, the effect of alumni ties differs for endowments representing universities within systems instead of single institutions. In addition to the logit regressions following the main approach of the paper, we report and refer to OLS estimates due to the problems that arise when using interaction terms in non-linear models (see Ai and Norton (2003)). Table 28 reports the results for performance regressions with interaction terms, where we again see that MBA ties are related to better performing investments, although we do not see any statistically significant interaction for university and endowment characteristics. We do see, however, that the MBA alumni effect itself remains strong and that a better ranking and more experience are linked to lower performance. Our results on the impact of MBA alumni ties remain robust when we also control for outliers by winsorizing performance as reported in Table 29.

Since our access to the fund managers’ biographies is restricted to GPs that manage at least one buyout fund, we note that a key limitation of our study is that our data sample does not capture investments into fund managers who focus exclusively on VC investments. While access to top-performing VC funds can be particularly difficult (compared to larger buyout funds), they are seen as a key driver of the endowments’ investment success (e.g., Sensoy et al. (2014)). We can therefore expect the results that we derive to be even more pronounced for managers who exclusively follow a VC investment strategy. Thus, our observed estimates may underestimate the effect of alumni ties. However, the fact that we still find significant results, i.e. funds managed by alumni are preferred, is a strong indicator that this effect is non-trivial and must hold for the PE industry as a whole.

Finally, we understand that what we refer to as “alumni ties” is a broad term to classify the connections with individuals that had some sort of experience in or exposure to an institution. We are able to differentiate between types and intensity of these social ties by means of degree types (such as undergraduate or MBA degrees), how extensive or tight an alumni community is, or through university rankings. This allows us to account for different levels of involvement and potential influence of alumni ties and their effect on investment decisions.

5 Conclusion

In this paper, we argue that alumni ties play an important role in the process of selecting investment opportunities. On the one hand, they can serve as a channel of access for investors in a competitive market for promising investments. On the other hand, they can help to reduce information asymmetries in a highly opaque asset class. Based on a unique dataset consisting of information about U.S. university endowments, its commitments into PE funds, and fund managers’ biographies, we address the research question of whether university endowments are more likely invested in funds managed by their own alumni and whether such alumni ties pay off in terms of superior performance.

Our empirical results confirm a higher incidence of alumni ties in PE fund commitments made by university endowments. The strongest evidence is found for endowments from lower ranked universities and for less experienced endowments, highlighting that the relevance of such ties is not restricted to a certain segment of prestigious universities but applicable to a broad range of university endowments. This main finding, combined with the results in our robustness section, can be seen as an indication that universities benefit from facilitated access to funds managed by their own alumni.

We do not find strong and statistically significant evidence that endowment commitments to funds managed by alumni outperform other endowments’ PE investments overall. We demonstrate that this is the case for investments into funds managed by MBA graduates specifically. We highlight, however, that the fact that we do not find any signs of underperformance is noteworthy. On the one hand, some of the benefits associated with investments within social networks such as lower search and due diligence costs are not reflected in fund performance data. On the other hand, the quality of decisions in a highly professionalized sector like PE is less likely affected by social connections, even if such circles facilitate investments.