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Venture Capital Enters Academia: An Analysis of University-

Managed Funds

Annalisa Croce, Luca Grilli and Samuele Murtinu*

Politecnico di Milano

Department of Management, Economics and Industrial Engineering

Via R. Lambruschini, 4b - 20156, Milan, Italy

*Corresponding author. Email: samuele.murtinu@polimi.it. Ph.: +39 02 23992807. Fax: +39 02

23992710.

This work has been accepted for publication by the Journal of Technology Transfer.

The final publication is available at link.springer.com.

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Abstract

In recent years, a “third mission” pursued by universities, i.e. knowledge transfer to industry and

society, has become more important as a determinant of enhancements in economic growth and social

welfare. In the vast world of technology transfer practices implemented by universities, the

establishment and management of university venture capital and private equity funds (UFs) is largely

unknown and under-researched. The focus of this work is to provide a detailed description of this

phenomenon from 1973 to 2010, in terms of which universities set-up UFs, their target industries and

the investment stages of portfolio companies, which types of co-investors are involved in the deals,

and which are the determinants of UFs’ ultimate performances. The picture offers us the opportunity to

draw some implications about the relevance of UFs in different contexts (i.e. Europe and the United

States) and provide to interested stakeholders with some useful guidelines for future development.

Keywords: university-managed funds, university, venture capital and private equity, technology

transfer, third mission, fund performance

JEL classification: G24, I23

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Venture Capital Enters Academia: An analysis of University-

Managed Funds

1. Introduction

In recent decades, universities have significantly increased their interaction with entrepreneurs

and corporations. Through the pursuit of a “third mission”, university industry collaborations have

generated a process of technology and knowledge transfer (Florida and Cohen 1999; Etzkowitz et al.

2000), which has engendered social and economic benefits for society (Lee 1996, 2000). This

collaborative process has assumed several institutional forms, ranging from patent licensing to

academic spin-outs passing through academic incubator facilities. In this respect, one (often neglected)

array in the quiver of an academic institution is represented by the establishment and management of

venture capital and private equity funds (university-managed funds-henceforth, UFs).

Despite the fact that the first ever modern private venture capital (VC) firm was founded by

academics from the Massachusetts Institute of Technology (MIT) and the Harvard Business School

(Lerner 2005),1 UFs are still a limited (and consequently under-researched) phenomenon. According to

the Thomson One database, from 1973 to 2010, only 26 UFs were active, 15 in Europe and 11 in the

United States (see Section 4). However, UFs are definitely worth of being analyzed. In fact, important

academic institutions have set-up such funds, and it is not unlikely that many other universities will do

the same in the next future. The question is: ‘why did they do it?’. The starting point is that VC is

increasingly viewed has a sine qua non condition to spur innovation (Kortum and Lerner 2000),

entrepreneurship (Da Rin et al. 2006; see Knockaert et al. 2010 for a specific reference to academic

spin-outs) and economic growth (Samila and Sorenson 2011). Even though universities have played in

the past and still continue to play today a marginal role both in mature VC markets (e.g. the United

States) and in the thin ones (e.g. the fragmented European market), the increasing emphasis of

policymakers on the need to sustain the development of VC, especially during the current economic

1 As explained by Lerner (2005): ‘The first modern venture capital firm, American Research and Development (ARD), was designed to focus on technology-based spinouts from the Massachusetts Institute of Technology. As envisioned by its founders, who included MIT President Karl Compton, Harvard Business School Professor Georges F. Doriot, and Boston-area business leaders, this novel structure would be best suited to commercialize the wealth of military technologies developed during World War II’.

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and financial crisis (European Commission 2010, p. 20; Bertoni and Croce 2011), makes potentially

envisage a growing relevance of the academic actor in the next future.

Unfortunately, we know very little about UFs. In the extant literature, the very few scientific

works on the topic are of argumentative nature (e.g. Atkinson 1994; Lerner 2005). Until now, there has

been no systematic empirical analysis of this phenomenon. We propose to fill this gap. Specifically,

we provide a detailed description of UFs in terms of which universities set-up them, which are the

target industries and the investment stages of portfolio companies, which types of co-investors are

involved in the deals, and which are the determinants of UFs’ ultimate performances. In doing so, our

work will take into account that VC markets are influenced by the institutional characteristics of the

environment in which investors and companies operate (Groh et al. 2011). Hence, much of the effort

will be devoted to analyze the characteristics and performances of UFs operating in the United States

(US) and compare them with those active in Europe (EU).

Our work lies on an under-researched area at the crossroads of entrepreneurial finance and

technology transfer and provides relevant implications for academics, practitioners and policymakers.

The picture we offer on the specific role played by UFs in different geographical contexts and how

university initiatives interact with more traditional types of VC investors represents an update of the

multi-faceted landscape of technology transfer mechanisms. This effort enlarges the information set of

policymakers at national and supranational levels so to enable them to improve technology transfer

mechanisms, taking into account the peculiarities of the local institutional context in which policies are

applied.

The rest of the paper is organized as follows. Section 2 shows the organizational structure of

UFs and their distinctive characteristics in comparison with other (more traditional) technology

transfer mechanisms. Section 3 provides a general description of VC markets in EU and in the US.

Section 4 describes the data. Section 5 shows detailed descriptive statistics and presents the results.

Section 6 discusses the main findings and draws some reflections and relevant implications. Section 7

is devoted to concluding remarks.

2. The role and the organization of UFs

2.1 UFs as a technology transfer mechanism

In the last twenty years, universities have become more “entrepreneurial” (Slaughter and Leslie

1997; Clark 1998; Etzkowitz 2003; Bercovitz and Feldman 2006; Martinelli et al. 2008). In fact, in

addition to their traditional two missions of teaching and research (through which knowledge is

preserved, created, applied and disseminated), universities have embraced initiatives that facilitate

their interaction with industry and society as a whole, i.e. the so called “third mission” of knowledge

transfer.

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The “push to interact” might come from either the academia or industry players (e.g. an

entrepreneur, a company, a corporation). Academics aim at bridging science and technology in a pro-

active way and often need an industry player to commercialize the technologies and the innovative

products that emerge from their research activity (Shane 2004; D’Este and Perkmann 2011). Industry

players often require the complementary assets of universities in order to develop technologies (e.g.

laboratories and other research facilities, high-skilled researchers) and/or business ideas (e.g. science

parks, incubators) and thus commercialize their products and/or services.

Whatever the “source of the push”, there are several technology transfer mechanisms put in use

by universities, such as science parks (Löfsten and Lindelöf 2002; Siegel et al. 2003; Phan et al. 2005;

Fukugawa 2006), incubators (Mian 1996; Colombo and Delmastro 2002; Aernoudt 2004; Phan et al.

2005; Soetanto and Jack 2011), university-industry joint projects (Morandi 2013), and technology

licensing (Markman et al. 2005; Bercovitz and Feldman 2006; Algieri et al. 2011).2

These technology transfer mechanisms are always evolving, they interact each other, and

different mixed governance forms unfold very rapidly.3 In this vast range of technology transfer

initiatives, here we focus on “pure” UFs, which represent the most direct and pro-active initiative at

disposal of academic institutions. UFs are funds directly affiliated to the parent universities that invest

(or co-invest with other investors) in the equity capital of portfolio companies.4 In general terms, the

objective of the universities involved in this type of initiative is twofold: i) invest through equity

capital in promising companies whose technologies are possibly close to the scientific fields in which

the faculty is specialized; ii) use the additional funding and revenues generated by the UF’s activity to

speed-up the commercialization process of technologies developed by university scientists through

more conventional technology transfer mechanisms (e.g. technology licensing).

2.2 The organization of UFs

Universities usually enter in VC and private equity markets through their existing technology

transfer offices (TTOs) that manage the UFs. Typically, these UFs mirror traditional TTOs and convey

the alleged mission of easing technology transfer from the parent university to the markets through the

2 Universities can also participate in publicly-funded research joint ventures with entrepreneurial firms and/or corporations (Colombo et al. 2009; Barajas et al. 2012). For a comprehensive review of technology transfer mechanisms, see Autio and Laamanen (1995), Wright et al. (2004), Siegel (2006), and Phan and Siegel (2006).

3 For instance, in April 2012, in Washington DC, Acceleprise was created. Acceleprise is a newly formed technology accelerator targeting young high-tech entrepreneurial companies, whose three founders avail themselves of several mentors, including VC investors and industry experts. Each selected company receives a seed investment ($ 30,000 in exchange of 5% equity), an office space, coaching, business and managerial consultancy services and participates to dedicated workshops with potential investors and interested partners.

4 More specifically, we refer to the financial vehicles put in place by the parent universities. In some cases, these financial vehicles invest in portfolio companies through more than one fund. For the sake of simplicity, we refer to the financial vehicles as UFs, whichever the number of funds used by the vehicle.

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creation of university spin-outs. Therefore, their investment focus is usually internal through the

financing of business projects and technologies developed inside the parent university (potentially in

conjunction with other research organizations). Alternatively (or in addition), they have a specific

geographic mandate related to the support of local development. The Innovation Transfer Center at the

Carnegie Mellon University and the University of Michigan in the US, and the University of Cardiff in

EU are three paradigmatic examples of this approach.

Most UFs evolve from the traditional organizational form of a TTO to more professionalized

structures. Such UFs resemble VC companies, being organized as limited partnerships, where the

university acts as the sole limited partner or one of the most relevant ones. Generally, these UFs are

managed by executive teams of professionals with a consolidated experience in the VC and private

sectors5 and are often assisted by formal advisory boards.6 This typology of UFs does not necessarily

abandon the internal or geographically-limited focus. Most UFs in Europe can be ascribed to this

typology (e.g. Imperial Innovations at the Imperial College in London; Manchester Technology Fund

Ltd, Thele at the University of Manchester; Sopartec SA from the Université Catholique de Louvain).

Similarly, ARCH Development Partners LLC at the University of Chicago represented an archetypal

example in the US.7

Finally, few other UFs (even since their inception) do not seem to comply neither with specific

requirements in terms of investment targets nor with any specific geographic limitation. For example,

Qubis Ltd from the Queen’s University of Belfast states in its institutional website “we don’t just work

within Queen’s University, we have successfully worked with other ambitious companies through the

transition to full commercialization” (http://www.qubis.co.uk/the-business-clinic, accessed on May

15th, 2013). The fund affiliated to the University of Rochester in the US, created in 1973 and initially

capitalized from the parent university with $ 67 million (Shane 2004), channeled most of the

investments in the 1980s to the Silicon Valley - far more than 4,000 kilometers from its headquarter.8

5 As notable exceptions, in US, there are UFs which are managed by teams formed by graduate (MBA) students. The University Venture Fund of University of Utah was the first one (see the article “Students running a VC fund? The University Venture Fund says yes” by Cheryl Conner appeared in Forbes on January 19th, 2013). Also the UF BR Ventures at Cornell University adopted the same model.

6 The establishment of a formal advisory committee is a typical trait of professional VC funds (e.g. Sahlman 1990).

7 When active, ARCH Development Partners LLC always maintained its geographical interest on the US Midwest States with a primary focus in Illinois, Ohio, Indiana, and Michigan. Until 2011, Imperial Innovations only invested in businesses built on intellectual property developed at the Imperial College. Since then, it enlarged its operations to the University of Cambridge, the University of Oxford and the University College London. The UFs created by the University of Manchester and the Université Catholique de Louvain have always maintained the exclusive aim of increasing the spin-out rate from the corresponding parent universities (see the institutional websites for further references).

8 Incidentally note that this UF contributed to create some legendary companies: e.g., the storage media manufacturing corporation Dysan founded by C. Norman Dion in 1973 in a garage that during 1980s became a 500 Fortune company with more than 1,200 employees.

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3. A brief sketch on VC markets in EU and in the US

The stage of development and maturity of VC markets is substantially different between EU

and the US. Our description of the phenomenon of UFs at global level will take into account this and

other institutional differences between the two geographical areas.

First of all, a relevant difference exists in the size of VC markets as a whole, both in terms of

fundraising and investments (either measured in absolute values or relative to GDP), where EU is

significantly lagging behind the US. For example, according to the data reported by the European

Parliament (2012), the fundraising of US funds reached € 10.1 billion in 2010 (0.09% of GDP)

compared to only € 3.3 billion for European funds (0.03% of GDP). Similarly, in the same calendar

year, the amount of VC investments reached a value of € 11.8 billion in the US (0.11% of GDP)

compared to € 3.5 billion (0.03% of GDP) in EU. The gap has further increased in the last two years.

Quarterly data provided by the European Venture Capital Association show that the European VC

fundraising decreased by 39% in the first three quarters of 2012 compared to the same period of 2011

and amounted to only € 1.6 billion. Conversely, recent statistics provided by CB Insights reveal that in

the US in the second quarter of 2012, VC fundraising reached the peak of $ 8.1 billion (37% more than

the prior quarter and 5% more than the second quarter of 2011), the highest VC quarterly amount

invested since 2000.

Second, relevant differences also exist in the typology of investments according to the stage of

portfolio companies. As reported by the European Parliament (2012), looking at the investment

volumes in 2010, only € 3 out of € 100 were invested in companies at the seed stage in EU, compared

to nearly 11 out of 100 in the US. Fairly less dramatic differences emerge as long as one considers the

number of portfolio companies: 14.3% of invested companies were in the seed stage in the US

compared to 12.4% in EU (source: European Parliament 2012; see also Bottazzi and Da Rin 2002 and

Bertoni et al. 2012 on the number of seed VC investments in EU).

Differences between European and US VC markets are certainly not only confined to their size

and stage of investments. First, as documented by several authors (Bottazzi et al. 2004, 2008), the

European VC market is characterized by a higher degree of heterogeneity in terms of the typology of

VC investors than the US one. Bank-based and governmental-sponsored VC investors are rare in the

US, while they abound in the European context. The opposite is true for independent VC funds

(Bottazzi and Da Rin 2002; Bertoni et al. 2012). Second, independent VC funds appear much less

interested in the seed stage in EU than in the US (Leleux and Surlemont 2003, Bertoni et al. 2012).

Finally, initial public offerings (IPOs), a typical (successful) exit mode for VC investors, is much less

viable in EU than in the US (Ritter 2003).

4. Data

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We extract information on UFs in EU and the US over the period 1973-2010 from the Thomson

One database (formerly known as VentureXpert).9 The Thomson One database has been used in

several previous studies on VC and private equity (e.g. Lerner 1995; Kaplan and Schoar 2005;

Sørensen 2007; Phalippou and Gottschalg 2009). The reliability of the Thomson One database has

been investigated by Gompers and Lerner (1999) and Kaplan et al. (2002), who found that missing

investments in the Thomson One database tend to be among the least significant ones in the scenario

of commercial datasets.10 According to the definition of the Thomson One database, an UF is an

“University Affiliated Program” (i.e. “a program funded by a university/college to make private equity

investments”). In other words, our definition of UFs refers to vehicles for direct equity investments in

which the parent university (often through its TTO) acts as general partner at foundation with an

investment process which often resembles that of independent VC funds.11 In particular, we consider

an UF as an “European” (“US”) fund if the investor is registered in EU (the US).

The extraction from the Thomson One database includes 26 investors, 15 of which registered in

EU. We cross-checked data on each fund on the website of the affiliated university in order to verify

that it could be classified as an UF. The full list of UFs (together with their university affiliation)

included in our sample is provided in Table 1. Out of 15 European UFs, 7 are affiliated to UK

universities, 2 to Swedish universities, 2 to Spanish universities and the rest is spread out among

Denmark, Belgium, Germany and Ireland. Out of 11 US funds, 2 are in Massachusetts, 2 in the State

of New York and the remaining are spread out among Minnesota, Illinois, Utah, Kentucky,

Pennsylvania, California and Ohio.

[Insert Table 1 here]

5. Empirical evidence

5.1. Basic descriptive statistics

Table 2a provides information on the importance of the phenomenon of UFs in EU and in the

US. If we consider the total number of portfolio companies, 112 companies out of a total of 370

(30.27%) are backed by EU UFs. While, in terms of amount, EU UFs invested k€ 122,818 (28.35%)

out of k€ 433,176.

9 The extraction refers to February 2010.

10 As said in the main text, the Thompson One database is considered one of the most reputed source of information for VC activity at global level. Needless to say, this does not imply that the database is able to trace every single VC (and possibly UF) investment all over the world (see Ivanov and Xie, 2010: p. 135).

11 We exclude investors classified by the Thomson One database as “Incubators”. The Thomson One database defines an Incubator as “an entity designed to nurture business concepts or new technologies to the point that they become attractive to VC investors. An incubator usually provides both physical space, and some or all of the other services needed for a business concept to be developed”.

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[Insert Table 2a here]

Comparative descriptive statistics on the characteristics of UFs in EU and in the US are shown

in Table 2b. More specifically, we perform parametric t tests when comparing mean values and non-

parametric Wilcoxon tests when comparing median values.

[Insert Table 2b here]

Globally, the average (median) UF is 14.50 (11) years old, is run by 4.88 (4) executives, and

has channeled towards 14.23 (6) portfolio companies an amount of financial resources equal to k€

16,660.62 (k€ 4,571.50). The average (median) amount invested per company is k€ 1,407.67 (k€

1,010.79). On average, UFs targeted 4.04 different industries and co-invested 12.69 investments with

4.28 partners.

Interesting insights can be drawn through the comparison between EU and the US. First, it is

important to observe that the first two registered UFs in the US (University of Rochester and

Massachusetts Institute of Technology) date back to 1973 while the first one in EU (Qubis Ltd

affiliated to the Queen's University of Belfast) was founded in 1984. Nonetheless, despite the US older

genesis of the UFs’ phenomenon, overall there is not a statistically significant difference in the age of

UFs between EU and the US.12

There is a noticeable difference between EU and the US in terms of portfolio companies: the

mean value of the number of portfolio companies backed by US funds (23.45) is significantly higher

than that of European funds-backed companies (7.47). However, if one looks at the median values, the

difference appears less dramatic (7 companies for the US UFs and 5 for the EU ones). The higher

mean value in the US appears to be driven by the existence of outliers: two of the oldest US funds

(Boston University and University of Rochester) have 119 and 65 companies in their portfolio,

respectively. These numbers are well above the national corresponding average level.

US funds also appear to invest significantly more money than EU UFs (on average k€

28,214.45 and k€ 8,187.80, respectively). This is clearly related to the higher number of companies in

US funds’ portfolio than that of European funds. But it is also due to the greater financial resources

invested per portfolio company. The low number of observations at our disposal and skewness hamper

to achieve a statistically significant difference, but as a matter of fact, the average amount invested per

portfolio company by US UFs doubled that of EU funds (k€ 2,045.62 vs. k€ 939.83). Equally

interesting, if the number of funds per university is greater in the US than in Europe (on average, 3.09

vs. 1.60), for other measures, like the number of targeted industries, the number of executives that

manage the fund and the number of co-investors, there are much less neat differences between the two

areas.

12 It is worth noting that the Thomson One database does not provide panel data on UF investments. As a consequence, descriptive statistics and univariate tests we show in Tables 2-7 are based on cumulated investments over time.

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Lastly, similarly to the case of the number of portfolio companies, we see a statistically

significant difference between EU and the US in the average number of co-invested investments. The

mean value of the number of co-invested investments of US funds (22.09) is significantly higher than

that of European funds (5.80). However, the median values (5 for EU funds and 7 for US UFs) are

again much closer.

5.2. Stage of portfolio companies

The Thomson One database provides different dimensions of classification of UF investments.

We first look at the dimension through which UF investments are disentangled according to the stage

of the portfolio company (i.e. Start-up/Early-stage, Later-stage, Buyouts). Results are reported in Table

3. In global terms, UFs invest more in the Later-stage (267 companies for a total amount invested of k€

285,855) than in the Start-up/Early-stage (210 companies for a total amount invested of k€ 140,089)

and particularly in Buyouts (11 companies and k€ 7,232). But also in this case, dramatic differences

emerge between the US and EU. On average, European funds are more likely to invest in Start-

up/Early-stage (61.24%) than US funds do (36.49%). Such differences are confirmed by a χ2 test on

the number of portfolio companies (χ2[2] = 36.58). The same pattern applies to the amount invested.

Even though US funds invest more financial resources on Start-up/Early-stage companies, the

percentage of the total amount invested directed towards Start-up/Early-stage companies is higher in

EU (44.14%) than in the US (27.67%) (χ2[2] =16,726.52).

[Insert Table 3 here]

5.3. Industry of portfolio companies

Table 4 provides a classification of the industries in which UFs invest.13 Overall, the ICT

industry has attracted much of the resources (40.54% of the portfolio companies and 37.14% of the

total amount invested). Also in this case, important differences emerge between the US and EU. χ2

tests on the number of portfolio companies (χ2[6] = 42.81) and on the amount invested (χ2[6] =

103,645.64) reject the null hypothesis of no differences in the distributions of UF investments between

EU and the US across the seven macro-industries. In particular, European funds are more likely to

focus on biotechnology (29.46% of the total number of portfolio companies and 38.24% of the total

amount invested) and medical/health industries (21.43% of the total number of portfolio companies

and 29.20% of the total amount invested). While, US funds invest more in ICT (47.67% of the total

number of portfolio companies and 44.73% of the total amount invested) and

13 We follow the industry classification provided by the Thomson One database. The only notable exception is represented by the grouping of some specific industries into two macro categories. More specifically, we define: i) a “Non high-tech” industry that includes Business Services, Construction, Consumer Related and Other; and ii) an “ICT” industry that includes Communications, Computer Hardware, Computer Software, Computer Other and Internet Specific.

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semiconductors/electronics (11.24% of the total number of portfolio companies and 15.67% of the

total amount invested).

[Insert Table 4 here]

5.4 Status of portfolio companies

In Table 5, UFs are classified according to the status of the investments. In particular, we

consider IPOs and acquisitions as “successful exits” and failures of portfolio companies as

“unsuccessful exits”. The residual category is represented by those companies that are still in portfolio.

In the absence of available data on fund returns, as typical in the finance literature (e.g. Hochberg et al.

2007), we adopt exit rates as proxies of the performance of UFs in the period 1973-2010.14 Exit rates

are a good proxy of fund performance because portfolio companies, when exiting via IPO or

acquisition, generate capital inflows that are distributed to the UF’s limited partners. More specifically,

as suggested by Hochberg et al. (2007), we use the percentage of portfolio companies at UF-level that

were successfully exited and the percentage of portfolio companies that failed as proxies of UFs’

performance.

[Insert Table 5 here]

Overall, 160 companies (43.24%) are still in portfolio, 53 were failed (14.32%), 47 have gone

public (12.70%) and 110 were targets of an acquisition (29.73%). Again, important differences emerge

between the US and EU. The distribution of the number of portfolio companies according to the four

potential statuses (IPO, acquisition, failure and still in portfolio) between EU and the US are

statistically different, as testified by the χ2 test (χ2[3] = 350.34). Notably, 19.77% of portfolio

companies in the US failed while in EU this percentage drops to 1.79%. This evidence can be

interpreted as a signal that US investors are less risk adverse than the European ones. Conversely, the

proportion of “still in portfolio” companies is higher in EU (93.75%) than in the US (21.32%). A

number of reasons may be invoked to explain such difference. First, exits from the VC investments are

more difficult in EU than in the US. As a matter of fact, IPOs are less viable in EU due to the

slenderness of financial markets in the old continent. This is perfectly reflected in our data on UFs:

there are 46 investments exited through an IPO in the US while only one in EU. Similar results hold

for the alternative successful exit mode - Acquisitions - with 106 cases in the US and only 4 in EU.

Again, this reflects an European mergers and acquisitions (M&A) market far less liquid than the US

14 As suggested by Da Rin et al. (2011): ‘obtaining data for computing returns turns out to be a difficult task. VC firms are not required by regulations to disclose their investments, distributions or returns [...]. As a consequence there are no comprehensive databases for valuation and returns data. The main data sources are those VCs or LPs who voluntarily provide information, either to the commercial data providers, or directly to researchers. LPs and VCs may thus choose whether to report, and if so, what data to report. The main problem is reporting bias, i.e., that fact that reporting is likely to be (positively) correlated with performance’. This problem is extremely exacerbated with regard to UFs. Even through the use of the Thomson One database, we have to face a strong reporting bias, which does not allow us (like other scholars in the field) to estimate UFs’ returns.

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one. Second, one can assert that European funds may be more patient investors, i.e. their holding

period is longer than that of their US counterparts. Industry specialization, with a relatively stronger

tendency of European UFs in investing in biotechnology and medical/health industries, certainly plays

a role in this respect. Third and equally important, even though no strongly significant differences

emerge in the age distribution of UFs between EU and the US, undoubtedly UF investments are a

newer phenomenon in EU than in the US. As a consequence, a relatively high portion of companies

backed by European UFs may be still in the holding investment period.

With regard to this third reason, we report in Table 6a the distributions of the total number of

active UF investors, the total number of UF investments and the UFs’ amount invested in EU and in

the US by the investment period, i.e. “2000 and before” and “after 2000”: the year 2000 represents a

crucial turning point for the VC industry because of the Internet bubble burst. While in the US more

than 70% of the investments occurred before 2000, in EU the percentage in the same time window

drops to less than 6%. As to the amount invested, European UFs channeled 93.32% of the financial

resources after the year 2000, while the percentages related to US funds were 56.15% over the period

1973-2000 and 43.85% “after 2000”. Allegedly, the χ2 tests on the total number of investments (χ2[1] =

338.50) and on the amount invested (χ2[1] = 122,082.53) reject the null hypothesis of no differences in

the distributions of UF investment patterns between EU and the US.

[Insert Table 6a here]

If the comparison across periods for EU is not informative (since before the year 2001 UFs

were almost absent in the old continent), the difference in the US in terms of dynamics of investments,

amount invested and number of investors across the two macro-periods (“1973-2000” and “2001-

2010”) is worth of some reflections.

On the one hand, this abrupt change could simply be due to a general trend in the VC industry.

Based on aggregate data from the European and the US venture capital associations, Kelly (2011)

clearly shows that the average VC investment per portfolio company in the US sharply decreased after

the year 2000, while the decline after the burst of the Internet bubble was less intense for EU VC

funds. This general tendency of a lower US VC activity rate in the post-2000 period, is also

corroborated by the statistics on the US VC funds’ internal rate of return (IRR). As reported by Kaplan

and Schoar (2005), US VC funds show an average (median) IRR of 17% (13%) in the period 1980-

2001, while the average (median) IRR for the whole period 1980-2011 is only 9% (2%).15

15 As highlighted by Samila and Sorenson (2011), the returns earned by the limited partners (cash inflows distributed by the general partner of the VC fund after the exit from the portfolio companies) predict well the supply of VC. Given that the IRR is highly correlated with the return earned by the limited partners, the slowdown in the returns of US VC funds in the post-2000 period is associated to a decrease in US VC investment activity. For more details on (EU and US) VC funds’ returns, see also Machado Rosa and Raade (2006).

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But on the other hand, it could also be prompted by a more specific trend of the US UFs’

performance in the period 1973-2000. As reported in Table 6b (where we include EU data only for the

sake of completeness), if we split the status of the portfolio companies according to the two sub-

periods (“2000 and before” and “after 2000”) and consider only positive and negative exits (i.e.,

exclude the “still in portfolio” companies), the percentage of failed investments in the US in the period

“2000 and before” is 28.41%, while after the year 2000 the percentage drops to 3.70%.

[Insert Table 6b here]

Of course, one cannot argue in favor of a direct relationship between the high number of failed

investments before the year 2001 and the lower investment activity by US UFs after the year 2000. Or,

in other words, one should not invoke the non-negligible percentage of failures as the sole reason for

the low rate of activity of US UFs in the post-2000 period. By the same token, it would be also

difficult to sustain an opposite thesis, i.e. that the investments’ failure rate in the period “2000 and

before” has not influenced to any extent the UF’s behavior after the year 2000.

To sum up, both the general and the specific motives could have a role in explaining the US

UFs’ investment attitude after the burst of the Internet bubble in the year 2000. This attitude has also

probably led to a more cautious and risk-adverse approach in the selection of relatively (few

investments) after 2000. These investments proved to bring some benefits, as testified by the higher

percentage of positive exits experienced by US UFs in the period “after 2000”: the percentage of

acquisitions (IPOs) out of the total number of exited investments passed from 50% (21.59%) in the

period “2000 and before” to 66.67% (29.63%).

5.5. Performance of UF investments: univariate analysis

In order to gain further insights on the characteristics associated to UFs’ performance, we

collected data on the quality of the universities included in our analysis to verify whether a correlation

exists among fund performance and the quality of its academic institution. In particular, we resorted to

“The Times Higher Education World University Rankings 2012-2013”, powered by Thomson Reuters,

that collects inputs from more than 50 leading figures in the sector from 15 countries in every

Continent.16 More specifically, using the overall world top 100 ranking, we registered the rank position

for each university included in our dataset. Out of 26 universities, 12 of them are included in the

ranking and 14 are not. Table 7 reports the percentage of failures and successful exits for the UFs

according to the whether their parent university is ranked or not in the top 100 universities.

[Insert Table 7 here]

16 These rankings of the top universities across the globe employ 13 separate performance indicators designed to capture the full range of university activities, i.e. teaching, research, knowledge transfer. These 13 elements are brought together into separate categories (i.e. Teaching, Research, Citations, Industry income, Innovation, International outlook and Staff’s internationalization. For more details, please refer to: http://www.timeshighereducation.co.uk/world-university-rankings/).

14

Results show that the percentages of positive exits (both IPOs and acquisitions) are positively

correlated with the inclusion of the UFs in the top 100 ranked universities. When performing two

parametric t tests on the differences in the percentages of positive exits between the UFs affiliated to a

“top 100” university and the UFs affiliated to a “non-top 100” university, we see a statistically

significant difference at 5% (10%) for IPOs (acquisitions). This suggests that the quality of the

academic institution is positively associated with the performance of its fund. The finding is also

corroborated when we consider the specific position of the focal university in the ranking and not

merely its inclusion. Considering the only 12 UFs affiliated to the top-ranked universities, we find a

negative correlation for both the percentages of IPOs (r = -0.19) and the percentages of acquisitions (r

= -0.05) of the focal UF and the position of its parent university (equal to 1 for the best university and

100 for the lowest ranked university). Coherently, the percentage of failures increases as long as the

parent university is ranked in a low position (r = 0.15).

5.6. Performance of UF investments: multivariate analysis

In addition to the abovementioned descriptive statistics, we further explore the characteristics

associated to the performance of UFs, through an explorative multivariate analysis.

In particular, we estimate a series of regression models where the dependent variable is Exiti,

which is the percentage of fund i’s portfolio companies that have been exited via an IPO, an

acquisition or that failed, alternatively. For every exit mode, we estimate different models in order to

give a description of the performance of UFs, by including the following groups of independent

variables. First, in Model (I), we introduce as unique regressor USi, a dummy variable that equals 1 if

the UF i is US-based. This way, we control for systematic differences characterizing the EU and the

US VC markets with respect to funds’ performance. In Model (II), in addition to the variable USi, we

insert the average amount invested per portfolio company (Amounti) to test whether the financial

resources provided by the focal UF decrease the likelihood to fail and increase the likelihood to have a

successful exit. In Model (III), we control for the structure of the UF by inserting the number of

executives managing the fund (Executivesi). On the one hand, we expect that a high number of

executives is likely to provide more value-added to portfolio companies. On the other hand, such

number of executives may engender coordination costs among the fund managers that are detrimental

to fund performance. In Model (IV), we test if the number of co-investors (Coinvestorsi) has any

impact on fund performance, due to better screening through a “second opinion” or to the augmented

set of financial and intangible resources at disposal of the syndicate (Brander et al. 2002). The idea is

that, even though a sole UF would be not effective, it could still be successful if it brings on board

15

other capable investors.17 It is important to observe that, in this model, we only consider the number of

co-investors without taking into account their typology.18 In Model (V), in order to control for the

degree of industry diversification of the UF, we include an industry diversification index

(Industry_diversificationi) defined as the ratio between the number of industries in which the UF i

invested and the average number of industries targeted by all the UFs in our sample. By including this

variable, we investigate if UFs’ industry diversification strategies exert any effect on exit modes. In

this respect, the finance literature offers two opposite theories. According to Gompers et al. (2009),

specialized funds obtain a higher performance because of a more efficient allocation of equity capital

and a better selection of investments. Specialized funds should make better investments because of

their industry expertise in terms of technology, human capital and market knowledge. Conversely,

Stein (1997) argues that managers of narrowly focused funds are slower than managers of diversified

funds in investing their capital in other industries, when they face poor investment opportunities in

their core industries. Therefore, specialized funds might end up investing in not profitable companies.

In other words, diversified funds are more prone than narrowly focused ones to scale back investments

in industries with gloomy prospects and scale up their investments in promising industries. Finally, in

Model (VI), in order to control for the experience of the UF, we include a dummy variable

(Experiencei) that equals 1 if the period of activity of the specific UF i is longer than the median value

estimated separately for the US and EU funds.19 The period of activity is measured by the time elapsed

from the first to the last investment of the UF i in our sample.20 Finally, εi is an i.i.d. error term.

The estimated results of these OLS regression models are shown in Table 8, Table 9 and Table

10. More specifically, as dependent variables, we use the percentage of a fund’s portfolio companies

that failed in Table 8, the percentage of a fund’s portfolio companies that exited via an IPO in Table 9,

and the percentage of a fund’s portfolio companies that exited via an acquisition in Table 10. In each

table, we report estimates of Model (I)-Model (VI). Because of the low number of observations at our

disposal, we perform six different specifications by adding separately the independent variables of

17 The extant literature has justified co-invested deals through a reduction in information asymmetries (Casamatta and Haritchabalet 2007; Lerner 1994), an exploitation of complementary value-adding resources, skills, networks and industry expertise (Bygrave 1987), a reduction in overall portfolio risk (Lerner 1994), a higher influence in control rights in co-financed portfolio companies (Kaplan and Strömberg 2003), a reduction in agency problems with portfolio companies’ top management team (Admati and Pfleiderer 1994), and a signal to capital markets of the quality of the focal portfolio company. Especially with regard to the latter reason, Tian (2012) finds that co-financing increases the likelihood to have a successful exit.

18 We provide additional evidence on the specific typology of co-investors in Section 5.7.1.

19 The median value is 8 years for the US funds and 5 years for the EU ones, respectively. Results do not change if we use the mean instead of the median as threshold values.

20 In order to measure UF's experience we resort to other different proxies as the number of years from the first investment in our dataset, the number of years from UF's vintage year and the age of UF fund. Results obtained by the use of these different proxies are qualitatively similar to those reported in Tables 8-10 and are omitted from the authors for the sake of synthesis. They are available from the authors upon request.

16

interest. This way, albeit caution is always warranted in interpreting the results, regressions can meet

the rule of thumb of Hair et al. (1998).21

Starting from Table 8, a larger amount of financial resources provided by an UF lowers the

percentage of portfolio companies that failed (the statistical significance is at 10% confidence level).

The only other statistically significant result (still at 10%) is that the higher is the number of executives

managing the fund, the lower appears the percentage of failures. It seems that, as to the exit mode

represented by the percentage of failed companies in the fund’s portfolio, the higher value-added

provided by a large number of executives is not offset by the coordination costs among fund managers.

We do not find any statistically significant impact of the US dummy variable, the number of co-

investors, the industry diversification index, and the experience of the fund.

[Insert Table 8 here]

Turning to the percentage of IPO companies (Table 9), we find a positive and statistically

significant effect (at 1%) played by the amount invested per portfolio company. Conversely, we do not

find any systematic difference in the percentage of portfolio companies that exited through an IPO

between EU and the US. Again, both the number of co-investors, the industry diversification index,

and the experience of the fund do not seem to have any significant impact on the percentage of IPOs in

the UF’s portfolio.

[Insert Table 9 here]

Finally, Table 10 shows a positive and significant impact of the dummy USi on the percentage

of UF’s portfolio companies that exited through an acquisition (at 1% level in all models, except in

Model II where it is significant at 5%). This result suggests that institutional differences in the VC

market between EU and the US do play a role on this specific exit mode of UF-backed companies.

Moreover, the percentage of acquired UF-backed companies seems to be positively and significantly

influenced by the industry diversification index (at 1% level). This result corroborates the view on the

beneficial role of funds’ diversification strategies expressed by Stein (1997). We also find that a higher

number of co-investors positively affects the percentage of portfolio companies that exited through an

acquisition (the result is statistically significant at 5% level). Finally, as to the experience of UFs,

results indicate that it positively and significantly (at 1% level) increases the percentage of acquired

UF-backed companies.

[Insert Table 10 here]

21 In order to have minimally reliable statistics, the ratio between the number of observations and the number of variables must be always greater than 5.

17

5.7. Additional evidence

5.7.1. Focus on the type of co-investors

The VC literature suggests that it is important to consider not only the number of co-investors

but also their identity and type in ensuring the odds of success of the investment. With regard to UFs,

co-investments with ”more traditional” VC investors might be a very successful strategy to improve

fund performance for different reasons. First, UF managers might lack the managerial experience and

the value-adding skills necessary to advice portfolio companies in comparison with managers of

traditional VC funds. Second, due to an alleged different incentive structure from the one of traditional

VC funds (where managers benefit from a performance-related bonus), UF managers might not be

sufficiently incentivized to increase their skills and capabilities, given the (at least partial)

unresponsiveness of their wages to realized performances. Third, the fact that, especially in EU, UFs

have a tendency to focus on Start-up/Early-stage companies, which are typically characterized by great

uncertainty and large information gaps, might exacerbate the UFs’ need to syndicate with experienced

investors. In doing so, when co-investing with reputed VC investors, UFs can also take advantage of

important certification and reputation effects (Barry et al. 1990).

In order to include the type of co-investors in our analysis, we collected data on the number and

the names of co-investors for each UF from the Thomson One database. Then, looking at their website,

we classify co-investors in business angels (BA), independent VC funds (IVC) or captive VC

investors. These latter are funds structured as investment vehicles or business units of a parent

company. Specifically, the parent company is a non-financial company in the case of corporate VC

(CVC), a financial intermediary in the case of bank-affiliated VC (BVC), or a governmental body in

the case of governmental VC (GVC).

Then, we analyze if the percentage of portfolio companies that exited through an acquisition

was influenced not only by the number of co-investors, as shown in Table 10, but also by the specific

typology of co-investors.22 Interestingly, we find a negative and statistically significant effect (at 1%

level) of co-financing with business angels. This result is in line with the view of a famous US

business angel, David Rose, who claimed: ‘I prefer to invest in highly-scalable, technology-based

ventures [...] at a very, very early stage. [...] Because I'm a poor little angel [...] I typically start with

investments in the $25K-$50K range, and [...] I rarely invest at pre-money valuations north of $2-3

22 In particular, we estimated the Model (IV) in Table 10 by replacing the variable Coinvestorsi with a series of dummy variables indicating the type of co-investors (i.e. BVCi, CVCi, GVCi, and BAi). We do not include a dummy variable IVCi because all UFs co-invested with (at least) an IVC investor in their life. In an alternative specification, we substituted these dummy variables with the number of co-investors in each category (nBVCi, nCVCi, nGVCi, and nBAi, respectively). For the sake of brevity, results are only discussed in the text and not reported in tables (they are available upon request from the authors). Needless to say, note also that this further explorative exercise has to be viewed as purely ancillary to the previous analysis about the effects of the number of co-investors on the different exit modes of UFs’ portfolio companies. For this reason, it has been confined to the only dependent variable (i.e. “Acquisitions”) for which we detect a significant impact of the variable Coinvestorsi.

18

million, and usually well below that’.23 From David Rose’s words, it seems that most business angels

are financially unable to target business prospects, which are close to an acquisition by a large

acquirer. Conversely, co-investing with CVC investors and GVC investors is found to have a positive

and significant impact on the percentage of acquired portfolio companies (at 1% and 5% levels,

respectively). In the case of CVC investors, this result is coherent with their strategic objectives,

among which a potential acquisition represents one of the most important ones (Benson and Ziedonis

2008). Through the acquisition of a promising company, a CVC fund’s parent corporation might

exploit a list of benefits, such as the access to complementary products/services, the leverage of

external sources of innovation and the possibility to take real options on promising new ideas and

technologies. As claimed by Ivanov and Xie (2010): ‘Through strategic investments in entrepreneurial

companies, CVCs can serve as their parent corporations’ eyes and ears for promising technologies and

innovations’. In the case of GVC investors, Jeng and Wells (2000) claimed that these public funds

‘appear to be willing to finance early stage projects that would not be funded privately’. In line with

this view, GVC investors might co-invest with UFs in those promising companies, which are privately

under-funded, exerting a certification or “stamp of approval” effect towards third parties (Lerner

2002), and thus increase their likelihood of acquisition by large firms. The other types of co-investors

(i.e. BVC investors) do not exert any statistically significant effect on the percentage of acquisitions in

the UF’s portfolio.

5.7.2. Focus on the performance of UF-backed companies

In order to better analyze UFs’ performance, in addition to focus on fund-level successful exits,

as commonly measured in the VC literature, we further examine the ability of UFs to speed-up the

commercialization process of technologies developed by their portfolio companies by looking at the

current size of UF-backed companies.

To perform this analysis, we started from the list of the names of those 317 UF-backed

companies in our sample that were not classified as “Failures” by the Thompson One database.24 For

each of these companies, we hand-collected information on the size (in terms of both the number of

employees and revenues), the foundation year, the IPO year for companies that went public, and the

year of acquisition for companies that were acquired. Data were gathered through the use of popular

and well-reputed business on-line directories/search engines (e.g. ZoomInfo, Businessweek, Lexis

23 Andrea Huspeni “How To Score A Meeting With An Angel Investor”, Business Insider, 7/20/2012.

24 In other words, we consider only companies included in the categories: “Still in portfolio”, “IPOs” and “Acquisitions” (see again Table 5). Companies exited in earlier periods (e.g., 1970s, 1980s) show a higher probability to have missing information at both founder- and firm-level. This may be also due to the fact that some companies could not be longer in operation. Obviously, it is very hard to collect data on companies classified as “Failures” by the Thompson One database. For this reason, we excluded these latter from the beginning of the hand-collection data search on which the analysis of this section is based.

19

Nexis, websites of the companies; for a similar approach see Zarutskie, 2010). We were able to

provide figures for more than 55% of these 317 UF-backed companies (information on revenues are

available for a percentage that is slightly less than 50%).

Then, we identified the founders of these companies and we further hand-collected information

on the universities they attended and the degrees they attained. For faculty members/academic

personnel, we further hand-collected information on their academic affiliation and the position held at

the university. Through this founder-level information, we were able to understand if founders held a

faculty position or gained a PhD in the same (parent) university of the UF that provided equity capital

to their company. More specifically, to collect this data, we first visited the websites (if available) of

the 317 UF-backed companies. For the founders that still work in the UF-backed companies, we hand-

collected information from the biographies listed on these websites (if available). Then, we eventually

complemented our collection process through a biographical search engine - ZoomInfo - and other

websites including Wikipedia, entrepreneurs’ personal websites, Lexis Nexis and Linkedin. With this

two-step search process, we were able to obtain information on the background of (at least one of the)

founders for about the 56% of the 317 UF-backed companies. By analyzing these data and those on

portfolio companies’ size, we tried to infer the role played by the UFs in the commercialization

process of technologies developed by their portfolio companies.

Specifically, Table 11 reports the current (i.e., the last available yearly data) size of still active

UF-backed companies (including companies that went public or were acquired). In particular, Panel A

refers to the number of employees, while Panel B shows data on annual revenues. Considering the

sample of 317 firms, we were able to collect data for nearly 60% of the US UF-backed companies (132

in terms of employees and 117 in terms of revenues out of a total of 207 companies) and for slightly

less than 40% of the EU UF-backed companies (43 in terms of employees and 39 in terms of revenues

out of a total of 110 companies). As the table highlights, UFs seem to have achieved remarkable

results in the commercialization process of technologies: only 7.43% of the portfolio companies are

micro-firms (i.e. have less than 10 employees),25 32% of portfolio companies are small firms (i.e.,

have from 11 to 50 employees), while 60.57% of the portfolio companies have more than 50

employees (40.57% are medium-sized firms with less than 250 employees and the remaining 20% are

large firms).26

[Insert Table 11 here]

Again, significant differences emerge between EU and the US. On average, EU UF-backed

companies are smaller than US UF-backed ones, both in terms of employees and revenues. In fact,

while more than a half (53.49%) of the EU UF-backed companies are micro-small firms (i.e., have less

25 Both Europe and the US use the same threshold of less than 10 employees to define micro-firms.

26 The threshold of 50 (250) employees is commonly used by the European Union to define a small (medium-sized) firm.

20

than 50 employees), US UF-backed companies have a more heterogeneous distribution showing a

34.85% of micro-small firms, a 38.64% of medium-sized firms (i.e., have from 51 to 250 employees)

and 26.51% of large firms (i.e., have more than 250 employees). The same picture emerges if one

looks at revenues: all EU UF-backed companies show a level of revenues which is lower than $ 50

million (SMEs according to the definition provided by the European Commission), while the sample

of US UF-backed companies is composed by firms showing up to $ 5 billion of revenues. Such

differences are confirmed by a χ2 test on the number of companies in the different size ranges (χ2[8] =

20.17 for employees and χ2[9] = 16.42 for revenues).

In Table 12, we classify UF-backed companies according to the following classification:

“Faculty” whether at least one of the founders held an academic position or she/he is still a faculty

member; ”PhD” whether at least one of the founders got a PhD. For both categories, we indicate

whether founders held a faculty position or gained a PhD in the same (parent) university of the UF that

provided equity capital to their company. Finally, the category “No university related” refers to the

companies whose founders neither got a PhD nor had an academic position.

We were able to find biographical information on the founding management teams for 101

companies backed by US UFs (39.15% of the total sample of 258 US UF-backed companies) and for

77 EU UF-backed companies (68.75% of the total sample of 112 EU UF-backed companies). Quite

interestingly, 61.80% (44.38%) of the UF-backed companies were founded by at least a faculty

member (from the same parent university of the UF that provided equity capital to the company) and

17.42% (5.62%) by at least an entrepreneur with a PhD (received from the same parent university of

the UF that provided equity capital to the company). These data are useful to understand how UFs

accomplish the third mission of the parent university. Table 12 seems to suggest that UFs play a

considerable role in favoring the entrepreneurial activity of university scientists.

[Insert Table 12 here]

Again, significant differences emerge between EU and the US. On average, while EU UFs

invest almost exclusively in companies where the founders have relationships with the academic

world, this is not the case for US UFs. In fact, 84.42% of EU UF-backed companies were founded by

(at least) a faculty member, while in US this percentage drops to 44.55%. This difference is

remarkable also if we consider the affiliation of faculty members: 72.73% of EU UF-backed

companies are founded by (at least) an entrepreneur who held an academic position in the same

university of the fund while in US this percentage is equal to 22.77%. In EU only 7.79% of founding

teams have no relationships with the academic world, while, in the US the same percentage increases

to 30.69%. Such differences are confirmed by a χ2 test on the number of portfolio companies in the

different categories (χ2[2] = 49.56).

21

6. Discussion

The picture emerging from our analysis offers the opportunity for some considerations on the

state of the art and possible future trajectories in the development of UFs.

First of all, some important structural differences seem to emerge between the US and EU

funds. First, the former ones invest in more companies using more rounds of investment and

channeling more financial resources towards portfolio companies than the latter ones. However, this is

very likely driven by the different stages of development of the VC markets in the two geographical

contexts. Second, EU UFs seem to be more focused in financing companies in the Start-up/Early-stage

phase than the US counterparts. In this respect, UFs in EU could complement the supply of equity

capital provided by traditional VC funds, due to the scarce structural tendency of EU independent VC

funds in channeling resources towards start-up and early-stage companies (see Section 3). Third, there

is a different industry specialization pattern between EU and US UFs, i.e. EU UFs are focused on

biotechnology and medical/health industries and US UFs on ICT and related industries.

In general, US UFs have seemed to resemble much more to independent venture capitalists

than EU UFs. This evidence is strengthened by looking at the different exit performances between EU

and US UFs. Overall, but particularly before the year 2000, US UFs adopted a riskier attitude (e.g.

from 1973 to 2010, 19.77% of companies invested in the US failed vs. 1.79% in EU), and obtain more

successful exits through IPOs (17.83% in the US vs. 0.89% in EU) and acquisitions (41.09% in the US

vs. 3.57% in EU) than their European counterparts. UFs’ exit performances are certainly related to the

difference between EU and the US in terms of idiosyncratic VC market characteristics, different age of

the funds and their industry specialization. However, one cannot say that EU UFs rapidly exit from

portfolio companies: 93.75% of companied invested in EU are still in portfolio versus 21.32% in the

US. This inertia differentiates European UFs from the traditional behavior of an independent venture

capitalist, which aims at realizing the largest possible gain in the shortest possible time period (Bertoni

et al. 2011; Croce et al. 2013).

Moreover, adopting a more global perspective, it seems that the success of UFs cannot be

disjointed by the quality of their affiliated academic institutions. In other words, our analysis show that

better universities (also in terms of the first two missions, i.e., teaching and research) are more likely to

have successful UFs. In this respect, our explorative econometric analysis also suggests that UFs are

successful not only as providers of financial resources but also through their ability to find tailored co-

investors, such as CVC operators, and their ability to adopt diversification strategies that enable them

to spread out risk across different industries.

Finally, when looking at the current size of active portfolio companies, US UFs appear more

able than EU funds to speed-up the commercialization process of technologies developed by their

portfolio companies: more than 50% of the EU UF-backed companies have less than 50 employees,

22

while almost two thirds of the US UF-backed companies have more than 50 employees. The same

picture applies to revenues. This important discrepancy between US and EU very much reflects the

historical weakness experienced by the old continent in the capacity to generate high-tech rapid-

growth firms (see European Commission 2010). How to solve what has been defined in the early

1990s (see European Commission 1995) as the “European paradox”, i.e. the production of high-level

research that does not translate into commercially valuable innovations possibly marketed by rapid-

growth start-ups, remains an open question. In this respect, our analysis puts in evidence that the

nascent segment of EU UFs is still far for representing a satisfactory answer, even probably in the

medium-term.

7. Concluding remarks

There is an important policy debate regarding technology transfer policies. As suggested by the

European Commission (2007), technology transfer schemes are reputed to be ‘the processes for

capturing, collecting and sharing explicit and tacit knowledge, including skills and competence’ (p. 2)

and so constitute a fundamental engine for enhancing economic and social welfare. In this work, we

have analyzed a “relatively new university practice”: the establishment and management of venture

capital and private equity funds at the university level (UFs). Despite the “academic imprinting” of the

first modern private VC firm (Lerner 2005), very few academic papers have been published on this

topic.

In this study, we provide a detailed analysis from 1973 to 2010 of this (still small but probably

increasingly relevant) technology transfer mechanism already embraced by some of the most important

universities in the world. This descriptive effort provides some useful benchmarking data for

stakeholders interested to the phenomenon at different levels, e.g. academic entrepreneurs, UFs’

managers, policymakers. Our analysis also led us to individuate preliminary but significant

determinants of UFs’ performances, thus delivering to present and prospective actors some useful

guidelines for the next future.

Of course, we are aware that this study presents more than a limitation which, nevertheless,

may open up interesting avenues for future research, especially if the phenomenon will eventually

grow in size and in geographical relevance and new data sources will be available. First, we have not

been able to take into account at a deeper level the heterogeneity of UF programs both in EU and in the

US. In particular, future research should identify the main features of UFs in terms of: i) sources of

financing; ii) objectives; and iii) complementary assets and value-added provided to portfolio

companies (competences, network). Second, the analysis on the performance of UFs might be enlarged

by considering important moderating factors. We have not been able to consider them due to the small

“universe of UFs” and the few observations we dealt with. For example, one may wish to take into

23

account the different stages of development of the VC industry or the different phases of the economic

cycle. Then, with regard to co-financing and syndication patterns, it would be useful to have more

information on co-investors, such as their experience, their investment strategy or their industry of

operation (especially in the case of CVC investors), so to better investigate the complementarities and

synergies arising between UFs and traditional VC investors. All these areas of inquiry figure high in

our research agenda. With this information available, we and other researchers could provide further

detailed recommendations to academic entrepreneurs, UF managers and policymakers on how UFs

may effectively play a major role in transferring technologies and innovations from universities to the

economic system.

ACKNOLWEDGMENTS

We are indebted to Massimiliano Guerini for valuable assistance in the data collection process. We are thankful to the Editor Donald Siegel and anonymous reviewers for helpful comments and suggestions. Responsibility for any possible errors and deficiencies lies solely with the authors.

24

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30

Tables

Table 1. UFs

UF University EU/US State/Country

University of Michigan University of Michigan US MI

ARCH Development Partners LLC University of Chicago US IL

University Venture Fund, The University of Utah US UT

BR Ventures Cornell University US NY

Bluegrass Angels University of Kentucky US KY

Innovation Transfer Center - Carnegie Mellon University

Carnegie Mellon University US PA

University of Rochester University of Rochester US NY

UCLA Anderson School of Management

University of California Los Angeles US CA

Boston University Technology Development Fund

Boston University US MA

Case Technology Ventures Case Western Reserve University US OH

Massachusetts Institute of Technology Massachusetts Institute of Technology US MA

Cardiff University Cardiff University EU UK

DTU Innovation A/S Technical University of Denmark EU Denmark

EMBL Venture Capital Partners GmbH

EMBL Heidelberg (European Molecular Biology Laboratory)

EU Germany

Imperial Innovations (AKA: Imperial College Innovations)

Imperial College London EU UK

Innova 31 Universitat Politècnica de Catalunya EU Spain

KTH-Chalmers Capital KB Royal Institute of Technology EU Sweden

Lund University Bioscience AB Lund University Bioscience EU Sweden

Manchester Technology Fund Ltd, Thele

University of Manchester EU UK

Millennium Capital Limited University of Dublin (Trinity College) EU Ireland-Rep

Qubis Ltd Queen's University Belfast EU UK

Sopartec SA (AKA: Vives) Université catholique de Louvain EU Belgium

Sussex Place Investment Management (SPIM), Ltd.

London Business School EU UK

UNIRISCO Galicia SCR SA Universidad de Santiago de Compostela, Universidad de Vigo, Universidad de La Coruña

EU Spain

University of Cambridge Challenge Fund (AKA: UCF)

University of Cambridge EU UK

UUTech Limited University of Ulster EU UK

31

Table 2a. Portfolio companies and amount invested

Total EU US

N° portfolio companies 370 112 258

100.00% 30.27% 69.73%

Amount invested (th €) 433,176 122,818 310,358

100.00% 28.35% 71.65%

Table 2b. Descriptive statistics on UFs

Total EU US EU vs US

N° portfolio companies Mean 14.23 7.47 23.45 -15.99 *

Median 6 5 7 -2

Age Mean 14.50 12.40 17.36 -4.96

Median 11 11 9 -2

Amount invested (th €) Mean 16,660.62 8,187.80 28,214.45 - 20,026.65 *

Median 4,571.50 3,518 11,154.75 - 7,636.75

Amount invested per portfolio company (th €)

Mean 1,407.67 939.83 2,045.62 - 1,105.79

Median 1,010.79 747.40 1,145.18 - 397.78

N° funds Mean 2.23 1.60 3.09 -1.491 ***

Median 2 1 3 -2 **

N° targeted industries Mean 4.04 3.60 4.64 -1.04

Median 3 3 4 -1

N° executives mean 4.88 4.93 4.82 0.115

median 4 4 3 1

N° co-investors mean 4.28 4.14 4.45 -0.31

median 5 5 5 0

N° co-invested investments mean 12.69 5.80 22.09 -16.29 *

median 6 5 7 -2 *

Legend. ***, ** and * indicate significance levels of <1%, <5% and <10%, respectively. The variable “N° funds” refers to the number of funds used by UFs (see footnote n. 4).

32

Table 3. Classification of UFs by the stage of the portfolio company

Total EU US

n° companies

a Amount

invested (th€) n° companies

a Amount

invested (th€) n° companies

a Amount

invested (th€)

Start-up/Early stage 210 140,089 79 54,217 131 85,872

43.03% 32.34% 61.24% 44.14% 36.49% 27.67%

Later stage 267 285,855 46 67,209 221 218,646

54.71% 65.99% 35.66% 54.72% 61.56% 70.45%

Buyouts 11 7,232 4 1,392 7 5,840

2.25% 1.67% 3.10% 1.13% 1.95% 1.88%

Total 488 433,176 129 122,818 359 310,358

100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Legend. a Note that the number of portfolio companies is higher than that shown in Table 2a as a portfolio company may receive VC

investments in different stages of its life (and so it is counted more than once in the table).

33

Table 4. Classification of UFs by industry

Total EU US

n° companies

Amount

invested (th€) n° companies

Amount

invested (th€) n° companies

Amount

invested (th€)

Biotechnology 65 85,626 33 46,969 32 38,657

17.57% 19.77% 29.46% 38.24% 12.40% 12.46%

Industrial/Energy 31 13,633 10 2,599 21 11,034

8.38% 3.15% 8.93% 2.12% 8.14% 3.56%

Manufacturing 2 3,627 1 970 1 2,657

0.54% 0.84% 0.89% 0.79% 0.39% 0.86%

Medical/Health 66 87,578 24 35,862 42 51,716

17.84% 20.22% 21.43% 29.20% 16.28% 16.66%

Seminconductors/ Electronics

40 60,425 11 11,798 29 48,627

10.81% 13.95% 9.82% 9.61% 11.24% 15.67%

Non-high tech 16 21,390 6 2,558 10 18,832

4.32% 4.94% 5.36% 2.08% 3.88% 6.07%

ICT 150 160,897 27 22,062 123 138,835

40.54% 37.14% 24.11% 17.96% 47.67% 44.73%

Total 370 433,176 112 122,818 258 310,358

100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

34

Table 5. Classification of UFs by the status of the portfolio companies

Total EU US

n° companies

Amount

invested (th€) n° companies

Amount

invested (th€) n° companies

Amount

invested (th€)

Still in portfolio 160 202,129 105 111,678 55 90,451

43.24% 46.66% 93.75% 90.93% 21.32% 29.14%

Failures 53 29,243 2 312 51 28,931

14.32% 6.75% 1.79% 0.25% 19.77% 9.32%

IPOs 47 79,491 1 4,569 46 74,922

12.70% 18.36% 0.89% 3.72% 17.83% 24.14%

Acquisitions 110 122,313 4 6,259 106 116,054

29.73% 28.24% 3.57% 5.10% 41.09% 37.39%

Total 370 433,176 112 122,818 258 310,358

100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

35

Table 6a. Classification of UFs by investment period

Total EU US

Period n° active

investors

n°

investments

Amount

invested

(th€)

n° active

investors

n°

investments

Amount

invested

(th€)

n° active

investors

n°

investments

Amount

invested

(th€)

1973-2000 7 322 182,465 1 8 8,201 6 314 174,264

26.92% 56.10% 42.12% 6.67% 5.52% 6.68% 54.55% 73.19% 56.15%

2001-2010 19 252 250,711 14 137 114,617 5 115 136,094

73.08% 43.90% 57.88% 93.33% 94.48% 93.32% 45.45% 26.81% 43.85%

Total 26 574 433,176 15 145 122,818 11 429 310,358

100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Table 6b. UFs’ performance before and after 2000

Total EU US

2000 and

before After 2000

2000 and

before After 2000

2000 and

before After 2000

Acquisitions 88 22 0 4 88 18

49.72% 66.67% 0.00% 66.67% 50.00% 66.67%

Failures 50 3 0 2 50 1

28.25% 9.09% 0.00% 33.33% 28.41% 3.70%

IPOs 39 8 1 0 38 8

22.03% 24.24% 100.00% 0.00% 21.59% 29.63%

Total 177 33 1 6 176 27

Legend. “Still in portfolio” companies are not considered. Numbers in the cells refer to the number of portfolio companies. Portfolio

companies are categorized in the periods “2000 and before” or “after 2000” according to the last year in which they receive an UF

investment.

36

Table 7. Reputation of universities and performance of UFs - descriptive statistics

Total Top 100 universities Non - Top 100 universities Top 100 vs Non - Top 100

% Failures 6.03% 5.03% 6.90% -1.87%

%IPOs 8.12% 15.80% 1.54% 14.26% **

%Acquisitions 10.30% 14.91% 6.35% 8.56% *

Legend.***, ** and * indicate significance levels of <1%, <5% and <10%, respectively.

37

Table 8. UFs’ performance: Failures

Model (I) Model (II) Model (III) Model (IV) Model (V) Model (VI)

USi 0.024

0.034

0.024

0.027

0.024

0.027

(0.048)

(0.051)

(0.048)

(0.047)

(0.045)

(0.054)

Amounti -0.008 *

(0.005)

Executivesi -0.006 *

(0.003)

Coinvestorsi -0.019

(0.031)

Industry_diversificationi 0.053

(0.039)

Experiencei 0.020

(0.063)

Constant 0.050

0.058

0.078 * 0.131

-0.003

0.037

(0.036)

(0.039)

(0.048)

(0.156)

(0.066)

(0.072)

N 26

26

26

25

26

26

Legend.***, ** and * indicate significance levels of <1%, <5% and <10%, respectively.

38

Table 9. UFs’ performance: IPOs

Model (I) Model (II) Model (III) Model (IV) Model (V) Model (VI)

USi 0.139

0.041

0.138

0.149

0.139

0.1378

(0.091)

(0.042)

(0.090)

(0.098)

(0.093)

(0.084)

Amounti 0.089 ***

(0.005)

Executivesi -0.010

(0.007)

Coinvestorsi -0.036

(0.038)

Industry_diversificationi -0.027

(0.086)

Experiencei -0.013

(0.091)

Constant 0.022

-0.061 *** 0.070 * 0.173

0.049

0.0311

(0.022)

(0.021)

(0.043)

(0.168)

(0.091)

(0.061)

N 26

26

26

25

26

26

Legend.***, ** and * indicate significance levels of <1%, <5% and <10%, respectively.

39

Table 10. UFs’ performance: Acquisitions

Model (I) Model (II) Model (III) Model (IV) Model (V) Model (VI)

USi 0.178 *** 0.194 ** 0.177 *** 0.165 *** 0.178 *** 0.201 ***

(0.065)

(0.072)

(0.064)

(0.061)

(0.046)

(0.045)

Amounti -0.015

(0.011)

Executivesi -0.006

(0.005)

Coinvestorsi 0.033 **

(0.014)

Industry_diversificationi 0.157 ***

(0.035)

Experiencei 0.192 ***

(0.045)

Constant 0.028

0.041 * 0.057 * -0.106 * -0.130 *** -0.101 **

(0.018)

(0.022)

(0.034)

(0.060)

(0.042)

(0.041)

N 26

26

26

25

26

26

Legend.***, ** and * indicate significance levels of <1%, <5% and <10%, respectively.

40

Table 11. UFs’ performance: current size of UF-backed companies

Panel A: UF-backed companies’ number of employees

Number of employees Total % US % EU %

[0,10] 13 7.43% 9 6.82% 4 9.30%

[11,50] 56 32.00% 37 28.03% 19 44.19%

[51,100] 55 31.43% 37 28.03% 18 41.86%

[101,250] 16 9.14% 14 10.61% 2 4.65%

[251,500] 10 5.71% 10 7.58% 0 0.00%

[501,1000] 9 5.14% 9 6.82% 0 0.00%

[1001,5000] 9 5.14% 9 6.82% 0 0.00%

[5001,10000] 3 1.71% 3 2.27% 0 0.00%

Over 10000 4 2.29% 4 3.02% 0 0.00%

Total 175 100.00% 132 100.00% 43 100.00%

Panel B: UF-backed companies’ revenues

Revenues Total % US % EU %

Under $1 mil. 4 2.29% 4 3.42% 0 0.00%

$1 mil. - $5 mil. 38 21.71% 27 23.08% 11 28.21%

$5 mil. - $10 mil. 28 16.00% 20 17.09% 8 20.51%

$10 mil. - $25 mil. 50 28.57% 32 27.35% 18 46.15%

$25 mil. - $50 mil 8 4.57% 6 5.13% 2 5.13%

$50 mil. - $100 mil. 10 5.71% 10 8.55% 0 0.00%

$100 mil. - $250 mil. 7 4.00% 7 5.98% 0 0.00%

$500 mil. - $1 bil. 4 2.29% 4 3.42% 0 0.00%

$1 bil. - $5 bil. 6 3.43% 6 5.13% 0 0.00%

Over $5 bil. 1 0.57% 1 0.85% 0 0.00%

Total 156 100.00% 117 100.00% 39 100.00%

41

Table 12. The relationship between UFs and UF-backed companies’ founders

Total US EU

n°

companies %

n°

companies %

n°

companies %

Faculty 110 61.80% 45

44.55% 65

84.42%

Faculty (same university as UF) 79 44.38%

23 22.77%

56 72.73%

PhD 31 17.42% 25

24.75% 6

7.79%

PhD (same university as UF) 10 5.62%

6 5.94%

4 5.19%

No university related 37 20.79% 31

30.69% 6

7.79%

Total 178 100% 101

100.00

% 77

100.00

%