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The Joint Role of Staging and Syndication as control mechanisms in Venture Capital Financing

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Guenter Franke

Universität Konstanz

Thomas Weber

Universität Konstanz

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In this paper I analyze role of staging and syndication in Venture Capital financing. I test whether the uncertainty associated with the investment target leads VC providers to limit downside risk through staging and monitoring. The results suggest that staging is more pronounced for younger firms with more uncertain growth prospects. The data reveals that in the earlier years of collaboration between the Venture Capital provider and the funded firm the need for monitoring and staging is significantly more pronounced. Moreover, the results support the view that VC providers more acquainted with country and/or industry particularities are less prone to limit downside exposure through staging. Based on the argumentation that the commitment to syndicate can mitigate the hold-up problem induced through staging mechanisms I test for the effect of staging on the VC provider's propensity to syndicate. The data suggests that VC providers that make more intensive use of staging are also more open to syndication. On the funded firm level syndication appears to improve decision-making and increases the duration of funding rounds.

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The Joint Role of Staging and Syndication as control mechanisms in Venture

Capital Financing1

Christian Hopp*

Abstract:

In this paper I analyze role of staging and syndication in Venture Capital financing. I test whether

the uncertainty associated with the investment target leads VC providers to limit downside risk

through staging and monitoring. The results suggest that staging is more pronounced for younger

firms with more uncertain growth prospects. The data reveals that in the earlier years of

collaboration between the Venture Capital provider and the funded firm the need for monitoring

and staging is significantly more pronounced. Moreover, the results support the view that VC

providers more acquainted with country and/or industry particularities are less prone to limit

downside exposure through staging. Based on the argumentation that the commitment to

syndicate can mitigate the hold-up problem induced through staging mechanisms I test for the

effect of staging on the VC provider’s propensity to syndicate. The data suggests that VC

providers that make more intensive use of staging are also more open to syndication. On the

funded firm level syndication appears to improve decision-making and increases the duration of

funding rounds.

JEL classification: G24; G31

Keywords: Venture Capital, Syndication, Staging, Monitoring

Address correspondence:

Christian Hopp

Department of Economics, University of Konstanz

Room F 256, Box D 147, D-78457, Konstanz, Germany.

Tel.: +49-7531-88-3163, Fax: +49-7531- 8-3559. 8

Email: Christian.Hopp@uni-konstanz.de

1 I am grateful to Günter Franke, Thomas Weber, Julia Hein, Jenny Roth, Rene Stulz, Bogdan Stacescu, Tereza

Tykvova, Utz Weitzel, Seminar Participants at the University of Konstanz, the Study Centre Gerzensee and

participants of the 2006 Geaba conference and the annual meeting of the German Finance Association for helpful

comments and suggestions on an earlier draft of this paper. Hospitality of the Ohio State University during the

preparation of this paper is gratefully acknowledged.

* Christian Hopp, Department of Economics, University of Konstanz, Room F 256, Box D 147, D-78457, Konstanz,

Germany. Tel.: +49-7531-88-3163, Fax: +49-7531-88-3559. Christian.Hopp@uni-konstanz.de

1. Introduction

Among the various techniques employed in Venture Capital financing the successive infusion of

capital (staging) is one of the strongest control mechanisms to help Venture Capital providers to

overcome the asymmetric information problem faced (Sahlman (1990)). Moreover, recent

evidence shows an extensive reliance on syndication when financing new ventures (Manigart et

al. (2005) and Wright and Lockett (2003)). Using German VC market data I investigate the

determinants of staging and syndication in Venture Capital financing. The German VC data does

not only allow meaningful conclusions about the need to stage investments from the side of the

funded firm but, given the great diversity of actors (including especially a large number of

foreign VC providers) also yields insights into the motives of VC providers to make use of

staging and syndication.

As shown in Cumming (2006) the heterogeneity of VC providers plays a significantly more

important role in providing growth capital to entrepreneurial firms in other countries than the US.

The German VC market is the second largest VC market in Europe in terms of the number of

funded firms (ranking only behind the UK) and, more importantly, it is, unlike various other

countries including the US and the UK, characterized by a vast number of outside investors

playing a significant role in providing growth capital (Achleitner and Klöckner; 2005). The data

does not only allow meaningful conclusions about the need to stage investments from the side of

the funded firm but also, given the great diversity of actors, including especially a large number

of foreign VC providers, sheds light on the general use of staging and syndication to mitigate

agency conflicts for VC providers more unfamiliar with the market and cultural environment they

are acting in. Here the distance to the funded firms might even aggravate the informational

asymmetries faced.

One would expect the degree of asymmetric information that investors face to rise with higher

uncertainty about the prospects of the potential venture. In order to control the entrepreneur’s

effort and to cope with the more pronounced information asymmetries the VC provider should

put mechanisms of staging and monitoring in place. The data reveals that the riskiness of the

venture and the possible lack of a corporate history make it more difficult for the VC provider to

judge upon future prospects. If an investment is made in an earlier stage of the financing life

cycle, the risk of failing is much higher than in later stages and therefore VC providers tend to

portion their payments into successive stages rather providing a lump sum payment upfront. With

respect to the intensity of staging activities (measured as the total number of financing rounds

employed) I find evidence that foreign VC providers differ in their patterns of staging in

comparison with their local counterparts. I find that firms more active in the German market are

less prone to make use of staging. For VC providers more acquainted with the German market,

monitoring activities can potentially not add as much value and eventually, the extent to which

staging is used as a mechanism for downside protection is limited.

In a next step, I augment the analysis of staging mechanisms by looking at the interplay of

staging and joint investment activity. With respect to the influence of staging on syndication

activities I find that a VC provider employing a higher average number of financing rounds is

also more likely to syndicate an investment with a partner. My analysis finds support for the

hypothesis that syndication of Venture Capital investments can alleviate the agency problems

between the Venture Capitalist and the entrepreneur. VC providers that make use of staging are

also more open to syndication and on the funded firm level the results suggest that syndication

improves decision-making and prolongs the duration of financing rounds indicating that

syndication can improve decision making when VC providers are faced with subsequent

continuation/abandonment decisions.

In the following chapter I will summarize the related literature. Chapter 3 discusses the factors

impacting the staging and syndication decision and develops the relevant hypothesis to be tested.

Chapter 4 introduces the data set used in this paper. Chapter 5 presents the regression results and

corresponding implications. Chapter 6 concludes.

2. Related Literature: The Role of Staging and Monitoring

There are numerous studies dealing with theoretical arguments on when and how Venture

Capitalists should employ staging mechanisms. The structure of Venture Capital financing has

been described in Sahlman (1990) and Gompers (1995) further shows the importance of the

staged structure of financing in the presence of agency and monitoring costs. Sahlman (1990)

points out that when the investment is staged and the capital infusions take place in small

increments, rather than involving a large upfront payment, the prospects of the firm are re-

evaluated on an ongoing basis. Staging the capital infusion enables the VC provider to keep the

entrepreneur on a tight leash. The more likely conflicts with the entrepreneur are and the more

pronounced the effect of uncertainty over the outcome is, the higher will the value added through

oversight be.

Fluck et al. (2005) point out that staged financing benefits the Venture Capitalist in two ways.

First, it can block the entrepreneur's incentive to continue investing in bad projects to generate

private benefits and secondly, it allows the VC provider to exploit the venture's real option value

by being able to stop investing once the venture turns out to be less successful than expected.

Wang and Zhou (2002) also argue that staging reduces the cost associated with bad information

(the real option value of abandonment) and decreases the costs of moral hazard. Bergemann and

Hege (1998) present a model of VC staging using a real option argument, where the wealth

constrained entrepreneur posses' private information while being funded from an external capital

provider. They show that staging can reduce the imposed moral hazard problem and align the

interests of both parties. Unfortunately, the model exhibits the unpleasant characteristic that

unsuccessful projects receive the most funding. In practice, however, the opposite can be found

(see for example Gompers (1995)). Kaplan and Stroemberg (2004) elaborate on the role of

convertible securities in managing the double moral hazard problem and Neher (1999) compares

staged funding to an arm's length transaction (upfront payment) and finds that once the initial

investment is made (and the relationship exhibits a hold up problem) staging can mitigate the

agency conflict and stimulate the entrepreneur to put in more effort in order to increase the value

of the venture.

The two commonly employed mechanisms of staged financing are milestones and round

financing. In round financing, each new capital infusion is negotiated separately, whereas in

milestone financing the decision whether to inject new capital is made contingent on the portfolio

company meeting predefined targets in terms of product development or financial figures. Talmor

and Cuny (2005) analyze various factors impacting the choice between round financing and

milestones. They find that if the role of the Venture Capital provider is more important than the

entrepreneur, milestone financing is more efficient than round financing and vice versa. Bienz

and Hirsch (2005) analyze the role of milestones versus round financing in the context of German

Venture Capital agreements. The form of staging is determined by the predictability of the

development process. They find that milestone financing is used more often with advanced firms,

where adequate milestones can be implemented. For younger and inexperienced firms round

financing with successive renegotiation is implemented.

3. Determinants of Staging and Monitoring

3.1 The Decision to Invest in Stages

Early stage investments are characterized by uncertainty over the future outcome and VC

providers can limit their downside exposure by not committing all capital upfront and keeping

their option to abandon alive. Once the portfolio firm makes progress in developing products and

management structures, the VC can permanently update its information and commit further

capital to promote growth. One would therefore expect that the higher the uncertainty associated

with a deal the higher would be the degree of asymmetric information so that monitoring and

staging mechanisms are more likely to be put in place by the VC provider to control the

entrepreneurs effort. Gompers (2004) points out that early stage companies have short or no

corporate histories and the evaluation of growth prospects becomes even more difficult in these

phases. He finds that for younger firms the staging effect is more pronounced and less capital is

provided. In contrast, for older firms more information can be gathered and the potential of the

venture can be more easily judged upon. As a consequence staging becomes less valuable to the

VC provider. In order to test the relationship between the characteristics of the funded companies

and the decision to stage and monitor I formulate the following hypotheses:

Hypothesis 1: The younger the funded firm is, the higher will the uncertainty with investing be

and the more likely will the capital infusion be made in stages.

Hypothesis 2: Larger investment targets have shown their ability to generate money successfully

and agency conflicts should therefore be less pronounced resulting in less staging.

3.2 VC Providers and the Need for Staging

Analysing the need for staging solely from the side of the investment targets falls short of taking

into consideration the characteristics of the VC providers. Tykvova (2004) points out the

differences in financing investment targets within the German Venture Capital market. She

reports that German VC providers usually invest in later stages and typically also carry out fewer

investment rounds. According to Gompers (1995) Venture Capital providers can acquire skills

and expertise in industry segments or stages in order to reduce the potential conflicts with the

entrepreneur and might therefore differ in their propensity to stage. Sorensen (2006) finds that

more experienced VC providers invest in better firms and as a consequence, one could conjecture

that staging in order to limit the risk of adverse selection should be less associated with more

experienced VC providers. Gompers (2004) points out that the value from continuous monitoring

is higher for firms not acquainted with the market in general or for firms that invest in a broader

scope across industry segments and which do not acquire specific industry and general market

knowledge. Hence, I formulate the following hypotheses:

Hypothesis 3: Foreign VC providers are more unfamiliar with investing in the German market

and therefore rely more heavily on staging to limit downside exposure.

Hypothesis 4: VC providers that acquired experience through previous deals and by focusing on

specific industry segments benefit less from monitoring and are therefore less inclined to stage

their investments.

3.3 How Syndication might help to overcome the downside of staging

One of the inherent problems with staging is the inability to write complete contracts that cover

all possible contingencies. As a matter of fact, renegotiations in later rounds are observed and

bargaining over future financing takes place for each successive stage. Admati and Pfleiderer

(1994) argue that staged financing can give the incumbent VC provider substantial control over

the financing process as their refusal to finance later rounds can act as a negative signal to other

potential investors. Staged financing can have adverse effects on the value of a venture when an

incumbent VC provider exploits his bargaining power and dilutes the effort of the entrepreneur.

In this respect, Fluck, Garrison and Myers (2005) show that syndicate financing can act as a tool

to mitigate the potential value loss from a powerful monopoly VC provider. Moreover, they also

point out that the strongest signal is observed when the incumbent VC provider also commits to

further participate. Kaplan and Stromberg (2004) provide evidence on the extent of renegotiations

in venture financing and show that these even increase the reliance on the commitment to

syndicate to mitigate the problem of hold-up.

If the commitment to syndicate can mitigate the hold-up problem and align the interests of the

VC and the entrepreneur the VC providers that stage and monitor more extensively should also

be more inclined to involve partners in the financing process. Therefore I formulate the following

Hypothesis:

Hypothesis 5: Partner involvement in the financing process can alleviate agency problems in the

VC provider – entrepreneur relationship and VC providers that make more use of staging should

also be more open to syndication

3.4 The funded firm level impact of syndication

Staging involves several decision-making steps along the process of submitting capital to the

funded firm. The incentive for the entrepreneur to create private benefits and gamble on the

upside of the venture creates an adverse selection problem every time a VC provider faces a

continuation/abandonment decision has to be made. Give the fact that, there is no incentive for

the entrepreneur to report information truthfully, Admati and Pfleiderer (1994) argue that outside

investors should necessarily be involved in later rounds in order to induce optimal continuation

decisions. Lerner (1994) suggests that the evaluation of the same venture proposal by different

VC providers operating in a syndicate reduces the potential danger of adverse selection.2

Cumming (2006) finds that the syndication of Venture Capital can attenuate the problems of

adverse selection and argues that syndication can reduce informational asymmetries, as VC

providers investing jointly are able to combine resources in order to share information and

improve screening abilities. 3

2 Underlying Lerner’s selection hypothesis is the notation that decisions within the syndicate are formed in

hierarchies. One VC provider passes information over a deal on for review to a partner VC provider. Brander et al.

(2005) question the applicability of hierarchies in VC decision making and Wright and Lockett (2003) find evidence

that supports the view that decision making within the syndicate is generally characterized by discussion and

collective agreement. However, if the investment (or abandonment/continuation) decision is more likely to be made

in teams, the underlying argument of improvements in the selection ability remains valid. Among others, Sutter

(2005) finds experimental evidence for the effectiveness of decision making in groups and Rockenbach et al. (2001)

find that teams exhibit better risk/return profiles when investing. According to information load theory, larger teams

might have higher decision consistency and are better able to process high information loads (Chalos and Pickard

(1985).

3 While his analysis focuses only on first rounds investments this paper also considers syndication in later rounds and

can therefore elaborate in more detail on the effect of syndication not only in mitigating adverse selection with

investments into new ventures but also on the need for syndication in later rounds.

Gompers (1995) reasons that the duration of financing rounds (the time in-between subsequent

injections of fresh capital) can be regarded as a measure of the intensity of monitoring. The

shorter the duration of the financing rounds the more frequent the monitoring activities of the VC

provider and the greater is the need to gather additional information. Kaplan and Stromberg

(2004) point out that by providing less funding in a given round, and hence shortening the time

until the next financing round, the VC provider increases the ability to liquidate the venture if

performance is unsatisfactory. The more likely conflicts with the entrepreneur are and the more

pronounced the effect of uncertainty over the outcome is, the shorter should the duration of

financing rounds be. VC providers investing in syndicates might benefit from processing

information more efficient when making abandonment/continuation decisions and should

therefore work more effectively in reducing information asymmetries than single investors.

Consequently I formulate the following hypothesis:

Hypothesis 6: Joint decision-making effort by the VC providers improves the quality of the

investment decision and increases the duration of financing rounds.

4. Data Description and Descriptive Statistics

4.1 The Investment Sample

The sample consists of 2,373 Venture Capital transactions in Germany within the period 1995 -

2005. The transactions have been compiled by using public sources and the Thomson Venture

Economics (TVE) Database. I have identified the involved parties in each transaction and the

corresponding information on the VC provider along with the funded firms. The result is a deal

survey exhibiting who has funded a new company and was joined by which partner. Moreover, I

collected information about each financing round. As such I can identify which VC provider has

made an investment into a target firm at which point in time. In addition I supplemented the

database with information regarding the VC providers and the funded firms in terms of size, age,

industry, along with information specific on the actual deal. Therefore it is possible to make more

distinct inferences about the driving characteristics of staging patterns at the point of investment.

The analysis carried out is made on the basis of investments rounds as indicated through

Thomson Venture Economics. A distinction between milestone and round financing cannot be

observed. 4

I used the information from TVE to identify the industry of a particular venture; I make use of the

Venture Economics Industry Classification (VEIC) - a Venture Economics proprietary industry

classification scheme. Moreover, I reviewed relevant information about the Company Business

Description from the TVE database and from the Balance Sheet databases (Markus and

Amadeus). In order to draw more distinct conclusions I further split the industries in the sample,

which results in finer industry clusters. I divided the Medical/Health classification in two separate

categories. Moreover, I split the Industrial Sector into Industrial Products (such as Chemicals and

Industrial Equipment) and Industrial Services (such as Transportation, Logistics and

Manufacturing Services). I created categories for Software and Internet Firms to cope with the

particularities of investments into "New Economy" Firms over the period. In addition, I collected

information about the different stages of company development when an investment has been

made. TVE gives information about five different categories: Start Up/Seed, Early Stage,

Expansion, Later Stage and Other. Similar to Gompers (1995) who labels the categories for

4 Gompers and Lerner (2002) study the completeness of the TVE database and argue that most VC investments are

contained and that those missing are among the less significant ones. The studied sample therefore is unlikely to

suffer from a sample selection bias by focusing on TVE data.

bridge, second and third stage financing as "Late Stage" financing, I combined the TVE

categories Expansion, Later Stage and Other to form a new category, that I also label "Late

Stage". As there is no clear distinction between Expansion financing that almost always occur in

later phases and other financing activities, namely bridge financing or special purpose financing,

from the "Later Stage" category, this combination appears to be the most reasonable

classification scheme.

[Insert table 1 about here]

Table 1 summarizes the distribution of investments across industries and stages of development.

It reveals, that the total number of round financing peaked in the years 2000 and 2001 and

sharply declined thereafter. The focus on High Technology industries is evident, despite of

changes in the overall investment activity level. The importance of the Software and Internet

industries, however, changed over the years. After the bursting of the dot.com bubble VC

providers had to overcome a tremendous decrease in fund inflow (BVK (2004)). Especially

Internet and Software investment were cut down after the year 2001. In contrast, the relative

weight of investments in Biotech and Pharmaceutical firms increased. Furthermore, table 1 also

provides information about the distribution of yearly rounds across stages of development for the

funded firms. There has been an increasing focus on Late stage financing over the recent years.

4.2 Characteristics of the Funded Firms

Unfortunately, only crude proxy variables with exogenous characteristics are available for the

funded firms, which, however, is a general problem for studies on entrepreneurial companies that

inherently have a short history of operating and financial performance. Based on the information

identified in the TVE database and the industry classifications I have collected information on the

funded companies to investigate which factors play a decisive role in explaining the need for

staging. As pointed out in Bygrave (1987) younger firms are more likely to fail and consequently

firm age at investment can serve as a proxy for the riskiness of a venture. I have gathered data

about the firms founding date and combined that information with the investment date to arrive at

the age of the funded firm at the date of each capital infusion. In addition, I supplemented the

data file with information about the size of the investment target at the time of each investment

made. I included the sales along with the number of employees at the time of the first investment

to proxy for the actual size of the firm at the date of the first capital infusion. Information about

the size of the funded firm, measured in terms of sales and employees, have been collected from

the Markus and Amadeus Balance Sheet Databases and have been combined with publicly

available information from corporate websites. Table 2 summarizes the characteristics of the

funded firm.

[Insert table 2 about here]

When looking at the differences between the various categories one can infer that among Biotech

firms there is a much higher level of staging, with roughly 50% of the deals being financed by

multiple rounds, as opposed to, for example, Industrial Products and Services were only 13% and

11% respectively, have been financed sequentially. In addition, the table provides information

about the characteristics of the firm at the time of the first investment. The age of Biotech firms

that have been subject to stage financing is significantly lower at the date of the first investment

than for firms where a lump sum payment has occurred. The same effect can be found for firms

in the Industrial sector, where firms subject to staging are on average 6 years old, whereas their

non-staged counterparts are on average more than 20 years older. Differences in terms of the size

of the firms at their first investment date can also be verified for Biotech and Industrial Products

firms. Biotech firms that are subject to staging are roughly twice as large in terms of employees

than the firms that are financed by a single payment. Furthermore, table 2 reveals that the average

number of investors differs widely across the industry categories. Biotech and Pharma firms rank

top with 5 and 6 investors on average, whereas firms in the Consulting and Industrial Products

industry only have 1.6 investors on average when it comes to an investment.

4.3 VC Provider Characteristics

I have also included the characteristics of the VC providers to see how those factors impact the

decision to stage an investment. I classify the companies as being an independent VC provider if

there are no strings to other firms or banks attached. Secondly, I classify VC providers as bank-

dependent when a private bank has founded them or a private bank holds more than 50% of the

shares. Thirdly, I classify a VC provider as public if the shares are hold by either the German

government or one of the German public banking associations, i.e. Sparkassen or Landesbanken.

Co-Operative VC providers are associated with one of the so-called Volksbanken in Germany.

Additionally, I have separated Business Angels and Corporate VC providers, with Corporate VC

providers having strings to a large Corporation or when a larger corporation has founded or spun

off the investee company. In terms of the total number of firms the sample comprises 252

Independent VC providers and 67 (private) bank-dependent VC providers. An investor is

recorded as "Foreign" if the VC provider comes from a foreign origin and did not operate from a

German branch. In the sample 117 investors within the category "Independent" are from a foreign

origin. Overall, there are 170 VC providers with a foreign origin across all categories.

[Insert table 3 about here]

Table 3 reports the characteristics of the VC providers in the sample. One can infer that among

the firms that provided capital to young and innovative firms Independent VC providers

outnumber the other VC providers active in the German market. Moreover, those VC providers

turn also out to be the ones that focus more on specific industries. TVE distinguishes between VC

providers that have a focus on Medical/Health and Pharmaceutical companies, Information

Technology or Non-High firms. The TVE information on focus industries reveals that among

Independent Investors, about a third of the firms focus on investments into Information

Technology and roughly five percent focus on Non-High Technology and Medical firms

respectively.

For further analysis I also describe the investor companies by their Syndication Ratio. The

syndication ratio measures the number of syndicated investments to the total number of deals

undertaken. The higher the Syndication Ratio of an investor, the more he tends to invest together

with partners. In addition, TVE provides information about Capital under Management

("Capital") for the VC providers, along with information on the overall sum invested in Germany

("Sum") and the German investment size as a percentage of the overall investment activity world-

wide ("Percentage"). From table 3 one can infer that Independent and (private) bank-dependent

VC providers have the largest amount of Capital under Management measured in Million Euro.

Moreover, they also invested the largest amount of capital in Germany, and also invested (along

with Public investors) the largest percentage of their funds into the German market.

5. Empirical Results

5.1 Funded Firm Characteristics and Staging

In order to analyse which factors impact the decision to stage an investment I estimate a logit

regression with the 0/1 staging decision outcome as the dependent variable. The independent

variables include the industry dummies (i.e. whether the investment target belongs to one of the

industries described in chapter 4.2), the age of the investment target at the date of the first

investment to proxy for the riskiness of the investment, a dummy variable taking on the value 1 if

the first round of investment has been made in a certain stage (I distinguish here between Start

Up/Seed Financing, Early Stage and Late Stage Financing) and zero otherwise. Moreover, I

included variables measuring the size of the investment target in terms of sales and employees at

the date of investment along with information on the size in terms of sales and employees over

the period 1999-2004 to proxy for embedded growth potentials not covered by the single estimate

at the investment date. The results are reported in table 4. 5

[Insert Table 4 about here]

The results presented in table 4 indicate that none of the industry dummies has a significant

influence on the decision to stage an investment. An industry per se is not associated with a

higher level of staging. Thus, I cannot find evidence for the claim of Gompers (1995) that

5 The empirical method chosen in this paper analyzes staging from the side of the involved parties separately. It is,

however, still possible that there is endogenous matching. The classical way to circumvent the endogeneity problem,

would be to make use of an instrumental variable that would be independent of the outcome but related to VC

provider characteristics (such as experience) in order to explain which VC providers choose which ventures.

However, the complexity of the underlying decisions makes it difficult to find a suitable instrument. In order to

mitigate potential problems I have included various variables in my analyses that should alleviate a potential bias.

Sorenson (2006) for example suggests the use of two-sided matching model in order to overcome the problem of

finding a suitable instrument. However, the computational tractability of the structural equations only allow for an

analysis of initial investments rather then subsequent rounds. Moreover, the assumption of complete information

with respect to the ability of VC providers to generate superior deal flow rather than gaining from better screening

abilities is somewhat limiting with respect to the focus of this analysis.

research-intensive industries are likely to exhibit a higher level of staging activities. At least, the

industry itself does not represent a factor explaining why firms are subject to sequential capital

infusions rather than lump-sum payments upfront. The coefficient associated with the variable

"Age at Investment" is negative and significant in all five regression specifications. The

significance varies between the 1% and the 10% level. Additionally, the dummy variables "Start

Up" and "Early Stage" are positive and significant in all five regressions. This lends strong

support for Hypothesis 1 that with an increasing uncertainty over the investment there is a

stronger incentive for monitoring and staging. If an investment is made at an earlier stage of the

financing life cycle, the risk of failing is much higher than in later stages. Therefore, VC

providers tend to portion their payments into successive stages rather than providing a lump sum

payment upfront.

Turning to Hypothesis 2 and the impact of size characteristics on the decision to stage I have

included information about the sales and employees at the date of investment into the analysis.

The variable "Employees at Investment" is positive and significant in regression specification 2 at

the 1% level indicating that a higher number of employees is associated with a higher probability

of staging. The average number of employees is significant at the 1% level and exhibits a positive

coefficient confirming this effect. Moreover, the coefficient associated with the sales variable is

positive and significant for the volume of sales at the investment date, but not for the average

sales volume. I find evidence that clearly contradicts Hypothesis 2. I find that with an increasing

size of the investment target one observes a higher level of staging activity.

Studies have revealed the difficulty of VC providers to supply capital to larger firms. Lockett and

Wright (1999) emphasized that size variables play an important role for the decision to syndicate

an investment, when VC providers want to avoid clustering risks or when the firm is simply to

large for the corresponding VC provider. Syndication of Venture Capital involves the investment

of different partners as opposed to a deal where only a single partner is involved. As a

consequence, a larger size of the funded firm as proxied by the number of employees and sales

might involve a new partner and therefore a new round of financing that is recorded.

In order to test for the robustness of the results I have also estimated the regressions using the

total number of investors as the dependent variable. I estimate a Poisson regression testing the

impact of firm characteristics on the number of financing rounds per funded company. The

number of stages the financed firm went through is the dependent variable and the industry

dummies, the age of the firm at the first investment date, the stage dummies alongside with the

size variables are used again as the explanatory variables. The results are presented in table 5.

[Insert Table 5 about here]

Among the various industry categories the dummy variable associated with the Biotech industry

is positive and significant at the 1% and 5% level indicating that a larger number of financing

rounds is associated with firms in this industry (relative to the variable Consumer Products that

has been dropped). This indicates that staging is not more common for Biotech firms (as the

results from the previous analysis show), but when it comes to staging, the monitoring activities

(as measured by the number of rounds employed) in this industry are more intense. The same

effect can be found for firms in the Pharmaceutical industry (in regressions (1), (2) and (3)) and

Medical (in regressions (3) and (5)).6 As in the previous analysis the coefficient for the age

variable is negative and statistically significant. On average a one standard deviation increase in

the age of the firm leads to a reduction in the number of stages by 0.34. Moreover, a one standard

deviation increase in the number of employees and sales at the investment date leads to a 0.55

and 0.35 increase in the number of financing rounds, respectively. 7

This again supports the arguments brought forward in Hypothesis 1, that more uncertainty calls

for more intensified monitoring and staging. Furthermore, I find that the size variables included

show the same impact as on the propensity to stage an investment. The number of employees at

the investment dates as well as the average over the investigation period influences the number of

financing rounds positively. Additionally, the "Sales at Investment" coefficient is again positive

and significant. Larger investment targets are more likely to be financed through an increased

number of stages. The coefficients associated with the dummy variables indicating at which

stage the first investment has been made are positive and significant (Early Stage at the 1% level

across all specifications and Start Up at the 5% level in 2 out of 5 regressions and at the 10%

level in 3 out of 5 regressions). So in the earlier years of collaboration between the Venture

Capital provider and the financed firm monitoring and staging is significantly more pronounced.8

6 Metrick (2006) and DiMasi et al. (2003) provide insights into the product development process in classical R&D

investments. Given the multitude of clinical phases and research trials, medical firms might by nature be subject to

staging. The successive provision of capital could be a natural consequence given the dynamics of the underlying

research process.

7 The odds ratio is defined as the probability of staging over the probability of not staging the investment following

the usual nomenclature in a logistic maximum likelihood estimation.

8 In order to rule out censoring effects that could stem from investments into portfolio firms at the end of the analysis

horizon (deals in 2004/05 could be subject to censoring as the next financing could not have been observed yet) or

due to bankruptcy (firms were not long enough solvent to obtain a new round of financing or VC providers where

reluctant to finance an additional round) I re-estimate the regressions shown in table 4 and 5 using a year dummy for

the year of the first investment and focusing on active firms in the sample, respectively. Bankruptcy data has been

obtained through the German commercial register. It turns out that the dummy variable for the year 2004 is negative

and statistically significant indicating that deals that were undertaken in this year are subject to less staging, possibly

due to non-observability of subsequent rounds. However, when estimating the regressions in table 4 and 5 excluding

deals made in 2004 the coefficients (and signs) remain about the same, indicating that the results are robust to

5.2 VC Providers Characteristics and Staging

To test which VC providers are more likely to engage in staging I estimate an OLS regression

with VC provider characteristics as explanatory variables and the average number of financing

rounds per VC provider as the dependent variable. The explanatory variables include the VC

provider categories introduced in chapter 4.3 (i.e. Independent VCs, etc.), the dummy variable

whether the VC provider operates from a foreign office, and a variable indicating the focus of the

VC providers (i.e. whether the firm concentrates the investments into Non-High Tech firms,

Information Technology firms, or Medical firms). In addition, I included variables indicating the

Capital under Management, the size of previous deals done in Germany, and the volume of total

transactions done in Germany as percentage of the worldwide volume of transactions. Moreover,

I calculated the concentration on industries for the VC providers to see how the portfolio

influences the need for staging. The concentration measure is calculated as a Herfindahl Index on

the relative share of the industries in the portfolio. Therefore the closer the concentration measure

is to one, the more the VC provider deals are concentrated within a few industries. The results are

presented in table 6.

[Insert table 6 about here]

From the results reported in table 6 one can infer that despite the first regression specification

(and for Public VC providers in the 3rd regression specification) the coefficients associated with

the VC provider category dummies are not significant across all specifications. I do not find

possible affection by censoring. This holds also true when focusing on still active firms only. The results are

available upon request from the author.

evidence that certain groups of VC providers differ with respect to the general use of staging

mechanisms when financing young start-ups. Moreover, I find that the variables "Capital under

Management" and "Sum invested in Germany" are significant at the 1% level in regression (2)

and (3). The Capital under Management and the overall Sum invested in Germany have a positive

coefficient indicating that firms with larger fund volume and more funds invested in Germany are

more likely to make use of staging. 9 The coefficient associated with the dummy variable

"Foreign" is significant in the first regression specification and the coefficient associated with the

variable "Percentage invested in Germany" is negative and significant (at the 1% level) lending

support to Hypothesis 3 that firms investing relatively more in Germany make less use of staging.

Foreign VC providers are not only likely to be more uncertain about the prospects of the Venture,

but might also be more unfamiliar with the particularities of the German market itself and staging

can therefore be more valuable as a downside protection.

Turning to Hypothesis 4, the coefficient associated with the variable "Number of Investments" is

statistically significant in three out of four regressions (at least at the 5% level). There is evidence

that more experienced VC providers, as proxied by the number of deals they already have

undertaken, make less use of staging. The coefficients for the "Non-High Tech" variable are

found to be negative and highly statistically significant. I find evidence that those firms that

acquired more specific knowledge within a market segment are less prone to finance in rounds, as

the benefit from staging might be lower. However, for the concentration measure included I do

9 This result is not driven by a possible correlation between the dummy variable “Foreign” and the variables “Capital

under Management” or “Sum of Investment in Germany”. The correlation coefficient is only 0.097 and –0.1175,

respectively. However, it remains unclear whether TVE reports the Capital under Management as an average over all

funds managed or simply as the size of the last fund managed. Thus, it could well be that the results are somewhat

affected by reverse causality if the measure only includes the size of the last fund. Then staging might have affected

the success of previous funds leading to larger capital inflows and finally resulting in a spurious relationship.

not find that firms focusing more general on industry segments stage to a lesser extent. The

coefficient is not significant in all four regression specifications.

5.3 The Relationship of Staging and Syndication

To make inferences about the relationship between staging and syndication, I calculated the

syndication ratio for all VC providers. The syndication ratio gives the number of the total

syndicated deals to the overall number of transactions of a VC provider (It indicates which

percentage of deals a VC provider has undertaken jointly with a partner). I only consider an

investment as syndicated when two or more investors invest simultaneously. In the following I

estimate a tobit regression using the syndication ratio of a VC provider as the dependent variable.

The explanatory variables are: The VC provider category as introduced in chapter 4.3. , the total

number of investments by a VC provider, the portfolio concentration over industries and

information on the investment focus and the overall capacity to invest. The results are reported in

table 7.

[Insert table 7 about here]

First of all, table 7 reports that none of the VC provider category dummies is significant (except

the dummy for Independent in the first regression at the 5% level). Therefore, the VC providers

do not differ in their corresponding use of syndication, which again displays that none of the

categories is more or less experienced in structuring deals and differences in neither the level of

staging nor the level of syndication can be attributed to one of the VC provider categories.

Moreover, the coefficient for the investors focusing on Non High Tech firms is negative and

significant indicating that those VC providers focusing on industries that generate less specific

knowledge and possibly show a lower level of asymmetric information are less inclined to co-

invest their deals (compared to firms without a stated investment focus). For the Medical VC

providers I find that the reliance on partners in the investment process is more pronounced. The

coefficient for the focus variable "Medical" is positive and significant in all three regressions (at

the 5% level). The coefficient for industry concentration is insignificant.

I find that the coefficient for the foreign VC dummy is positive and significant in two out of three

regressions (at the 1% level). One can see that foreign investors, who presumably are less

acquainted with the German market, make more use of syndication in a narrower sense and are

more inclined to involve a partner into the decision making process in order to reduce the level of

asymmetric information associated with the transaction under consideration. In addition, I find

that the variables representing capital in- and outflow are not significant. Neither the amount of

percentage of investments made in Germany nor the overall amount of capital under management

has an impact on the level of syndication activity. With respect to the influence of staging on

syndication activities table 7 reports that the coefficient for the average number of rounds for the

VC providers is positive and significant meaning that a VC provider with a higher average

number of financing rounds is also more likely to syndicate an investment with a partner VC

provider. There is evidence that those VC providers that finance more intensively through stages

are also more open to syndication supporting hypothesis 5.

5.4 The influence of syndication on the duration of financing rounds

With respect to the influence of syndication as a control mechanism on the funded firm level I

estimate a Weibull Duration model using the time between successive financing rounds as the

dependent variable. The sample under consideration uses only firms that have been subject to at

least two rounds of Venture Capital financing in order to test the influence of syndication on the

duration of financing rounds, thereby controlling for funded firm characteristics. As the

explanatory variables I use the industry dummies for the financed firms, along with the age and

size variables (sales and employees) of the firm at each successive investment date.

I also included several measures estimating the impact of partner involvement on the financing

duration. First of all, I added a dummy variable equaling one when the investment in a specifc

round is provided by more than a single VC provider ("Syndication"). In addition, I included the

cumulative number of syndicated rounds ("Cum. Synd. Rounds") indicating how many previous

rounds (including the current) have been subject to joint investment decisions. Additionally, I

also control for possible selection effects in the first round by including a dummy equaling one

when a syndicate has financed the first round. The results are reported in table 8.

[Insert table 8 about here]

The results indicate that among the industry dummies solely the coefficient for the firms in the

Energy and Utilities sector (relative to Consumer Products) is found to be positive and significant

at the 5% level in all regression specifications, indicating that here the financing rounds last

longer and less monitoring is required (due to the large number of insignificant coefficients the

industry dummies are not reported in the table). The coefficient for the "Start Up" dummy is

negative and significant in three out of four regressions. When an investment is made in the Start

Up stage money is provided for a shorter period of time. The coefficient for the age variable is,

however, not significant at conventional levels.

Additionally, I find that the variables displaying whether an investment has been subject to

syndication has an influence on the financing duration. The coefficient associated with the first

round syndication dummy is not found to be significant. This indicates that the initial screening

effort of VC providers does not affect the provision of capital in later rounds. I do, however, find

that syndication has a positive effect on the duration of financing rounds. The coefficient for this

dummy variable is positive and significant at the 5% level. When financed through a syndicate

the duration of the corresponding round increases and VC providers let the entrepreneurs work

for a longer period of time with the money provided. This effect is confirmed by the positive and

significant coefficient for the cumulative number of syndicated rounds, including the joint effort

made previously. The results suggest that joint decision-making might improve the continuation

decision and VC providers investing jointly invest money for a longer period of time. Hence,

syndication appears to reduce the problem of asymmetric information and consequently, less

intensive monitoring is required. 10

The results show support for hypothesis 6 indicating that when the evaluation process before the

selection of an investment opportunity is undertaken by more than one Venture Capitalist, the

duration of the financing round increases. The involvement of a partner into a round of financing

increases the duration of financing provided and likewise reduces the incentive for monitoring the

10 The results found are robust when controlling for the amount provided per round. The coefficient for the log

amount provided is insignificant, whereas the coefficients for syndication and the cumulative number of syndicated

rounds remain positive and significant (at the 10% and 1% level respectively). The number of observations used

drops, however, to 209. Results are not reported here but are available upon request from the author. By taking the

amount of capital provided is exogenous I follow the typically procedure in theoretical and empirical work (see

among others Keuschnigg (2003) or Manigart et al. (2005).

firm's prospects. However, when financing subsequent rounds VC providers do not simply rely

on the screening effort made when selecting the investment but it seems that they rather gather

additional information and consider a second opinion on the abandonment/continuation decision

as more valuable in reducing the problem of asymmetric information.

6. Conclusions

In this paper I analyze the determinants of staging and syndication activities of VC providers

using a unique data set of Venture Capital deals in Germany over the period 1995 - 2005. The

analysis reveals that the need to make more intensive use of staging is more pronounced in earlier

stages. The higher the uncertainty associated with a deal the higher is the degree of asymmetric

information and as such monitoring and staging mechanisms are more likely to be put in place by

the VC provider to control the entrepreneurs effort. Moreover, I find that familiarity with the

German VC market affects the extent to which firms make use of staging negatively. Hence, VC

providers less active in the German market are more amenable to staging.

In addition, I analyze the relationship of staging and syndication activities and find that VC

providers that make more extensive use of staging are more inclined to work with a partner.

There is evidence that syndication of Venture Capital investments might help to alleviate agency

problems between the VC provider and the entrepreneur and that VC providers that make use of

staging are also more open to syndication. For foreign VC providers (as compared to their more

locally embedded counterparts) it seems to be more worthwhile to engage in joint investing when

deciding on whether to fund a subsequent round.

On the funded firm level I find evidence for an effect of joint decision making on mitigating

agency conflicts between the VC provider and the entrepreneur. Involving partners into the

decision-making process before the decision to fund a new investment proposal or an additional

round of financing increases the duration of the financing round and therefore reduces the

incentive to monitor the firm more closely. The results found support the view of Lerner (1994)

who suggests that the evaluation process in a syndicate reduces the potential danger of adverse

selection.

Given the results, it might be worthwhile for future research to further investigate the

determinants of control mechanisms in venture financing by paying attention to the role of joint

investment activities. Keeping in mind that the sharing of formal decision-making powers among

the syndicate members might create additional agency problems among the involved VC

providers, gaining a deeper understanding about the role of trust and social competence in

syndicate formation might further enlighten our understanding on the mechanics behind VC

partner selection and the corresponding impact on the success of funded firms. If VC providers

form syndicates to benefit from a second opinion in the investment selection process and for

subsequent investment decisions, it could be an interesting avenue for further research to analyze

with whom VC providers form alliances in order to better understand the mechanics behind

syndicate formation. Moreover, this paper has shown how staging and syndication as

mechanisms of venture governance are interacting when financing is provided. Given the results

in Kaplan and Stromberg (2004) it might be a rewarding task to further analyze the way

contractual forms of governance mechanisms might interact with the joint investment behavior of

VC providers. In this light, one might also be able to analyze how joint decision-making could

help to overcome information asymmetries and replace explicit contractual agreements between

the VC providers and the entrepreneur.

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Table 1:

Round Financing by Industry and Stage of Development 1995 - 2005

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 1995 - 2005

Panel A: Percentage of Yearly Rounds by Industry Average Observations

Biotech 0.0 0.0 48.9 39.1 22.2 22.8 25.1 39.9 34.5 40.6 36.6 29.1 691

Consulting 40.0 3.2 2.2 4.3 2.7 6.1 6.6 3.9 4.1 0.9 2.4 4.7 112

Consumer 0.0 9.7 2.2 2.2 0.4 3.0 1.3 1.6 1.0 0.9 0.0 1.8 42

Electronics 0.0 3.2 11.1 12.0 10.2 6.5 12.6 15.9 11.9 6.8 13.4 10.2 243

Utilities 0.0 0.0 0.0 0.0 0.4 0.3 0.9 1.2 3.1 5.6 2.4 1.3 32

Financial 0.0 0.0 0.0 0.0 1.3 1.4 0.5 0.8 1.0 0.0 0.0 0.8 19

Ind. Products 20.0 9.7 6.7 5.4 5.8 6.1 5.3 6.2 8.2 8.5 0.0 6.2 146

Ind. Services 20.0 9.7 2.2 0.0 0.9 2.0 1.3 1.6 1.0 1.7 3.7 1.7 40

Internet 20.0 3.2 2.2 4.3 16.4 17.2 11.7 5.0 5.7 2.1 1.2 10.6 252

Pharma 0.0 6.5 2.2 7.6 3.6 3.8 4.0 3.1 7.2 10.7 12.2 5.1 122

Media 0.0 0.0 4.4 1.1 5.3 5.4 3.8 1.9 3.6 3.0 9.8 4.2 99

Medical 0.0 16.1 4.4 4.3 4.4 5.1 4.4 3.9 4.6 5.1 3.7 4.8 113

Software 0.0 38.7 13.3 19.6 26.2 20.3 22.3 15.1 13.9 14.1 14.6 19.5 462

Total 5 31 45 92 225 661 546 258 194 234 82 2373

Panel B: Percentage of Yearly Rounds by Stage of Development

Start Up 0.0 3.2 40.0 33.7 24.0 17.1 12.6 8.5 7.2 7.7 11.0 14.7 349

Early Stage 0.0 9.7 17.8 41.3 28.4 27.4 20.0 19.4 19.6 22.2 15.9 23.4 556

Late Stage 100.0 87.1 42.2 25.0 47.6 55.5 67.4 72.1 73.2 70.1 73.2 61.9 1468

The table reports the yearly distribution of 2.373 Financing events over the years 1995-2005. The deals have been collected until the 31st of August 2005. The Financing

Rounds have been made into 964 ﬁrms across the industries indicated. Panel A shows the industry composition of the VC investments in the sample over time. Panel B shows

the variations in the the stage of development for the investments in the sample. Industry classiﬁcations are based on TVE VEIC codes. Further industry splitting has been

made in Industrial Products and Industrial Services as well as for Medical, Pharma and Biotech. Moreover, the category Internet has been introduced to cope with ”New

Economy” ﬁrms. Stages include TVE indicated Start Up/Seed and Early Stage. The TVE categories ”Expansion”, ”Later Stage” and ”Other” have been grouped in the new

category ”Late Stage”. All values are shown in percent, despite for the the last row and the last column that aggregates numbers over time and industry/stage respectively.

Table 2:

Summary Statistics for Funded Companies

Observations Age at Investment Av. # Employees Av. Sales

Staged? Staged? Staged? Staged?

Yes No Yes No Diﬀerence Investors Yes No Diﬀerence Yes No Diﬀerence

Biotech 67 78 1.25 2.24 0.99∗∗ 4.86 38.00 14.25 -23.75∗∗ 1,400 640 -760∗∗

Consulting 19 48 4 4.21 0.21 1.65 26 25.00 -1.00 2,017 2,284 267

Consumer 6 19 18.5 13.2 -5.3 1.86

Electronics 30 75 2.76 4.04 1.29 2.68 12.00 28.00 16.00∗∗ 667 5,000 4,333∗∗

Utilities 4 7 2.5 6 3.5 3.46

Financial 1 15 1.11

Ind. Products 13 86 5.8 26.7 20.9∗∗ 1.67 72.00 191.00 119.00∗∗ 2,032 36,250 34,218∗

Ind. Services 3 23 0.67 13.3 12.33 2.17 15.00 9.00 -6.00∗1,466 500 -967

Internet 36 71 1.27 3.04 1.77 2.84 45.00 19.00 -26.00 1,723 1,500 -223

Pharma 13 18 4.15 6.88 2.73 6.22 27.00 10.50 -16.50

Media 13 47 3.92 6.19 2.27 1.92 151.00 12.50 -138.50∗131,834 32,339 -99,495∗∗

Medical 13 37 4.31 6.22 1.91 2.63 92.28 3.00 -89.28 51.888 19,353 -32,535

Software 64 158 3.51 4.35 0.84 2.30 50.00 30.00 -20.00∗13,340 7,576 -5,764

The table reports the Summary Statistics for the funded companies in the sample. The data has been obtained through the use of the Thomson Venture Economics

Database and public sources for identifying transactions and the involved parties. Data concerning the size in terms of sales and employees have been collected from

Markus and Amadeus Balance Sheet databases. The sample has been partly into staged and non-staged deals. The Age Variable is measured as the Age of the Funded

company at the ﬁrst date of investment. Employees gives the average number of employees at the ﬁrst date of investment for the ﬁrms within the corresponding

industry segment. Sales is measured as the average volume of sales (in 1.000 Euro) at the ﬁrst date of investment for all the ﬁrms within the corresponding industry

segment. The subsample contains all venture capital ﬁrms for which data concerning sales and employees were obtainable. A t-test for equal means has been carried

out for the Age variable. A C hi2test for equal median values has been undertaken for the Employees and Sales variable. The column investors reports the average

number of investors for the corresponding industry. The number of employees is recorded at the date of the ﬁrst investment by any VC provider. The sales are

measured in ´000 Euros at the date of the ﬁrst investment by any VC provider.

*, **, *** denotes signiﬁcance at the 1%, 5% and 10% level (two-sided) respectively.

Table 3:

Summary Statistics for Venture Capital Providers: Aggregated Numbers for VC categories

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

Total

Firms

Foreign

Firms

Non-High

Tech

Information Medical Portfolio

Firms

Average

Rounds

Syndication

Ratio

Capital Sum Percentage

Private Bank 67 29 5 15 8 275 1.30 0.70 1,093 42.5 35

Corporate 52 21 0 25 3 143 1.22 0.81 170 15.2 28

Co-Operative

Banks

4 0 0 2 1 14 1.07 0.57 39 19.1 100

Independent 252 117 19 78 17 1,003 1.45 0.62 824 44.5 39

Public 43 2 0 8 1 309 1.14 0.69 161 20.9 80

Business Angel 13 1 0 0 0 13 1.08 1.00

The table reports the Summary Statistics for the VC providers in the sample. The data has been obtained through the use of the Thomson Venture Economics Database

and public sources for identifying transactions and the involved parties. Column (1) gives information about the Total ﬁrms belonging to each category. Moreover, column

(2) indicates how many ﬁrms in each category operate from a foreign country. A ﬁrm is accounted as ”Foreign” if the ﬁrm does not have a German branch. Additionally,

columns (3) till (5) give information about the number of VC ﬁrms from each category that have an investment focus as indicated by TVE in Non-High Tech ﬁrms,

Information Technology ﬁrms or Medical and Life Science ﬁrms respectively. Column (6) reports the total number of ﬁrms in which the VC providers have invested in.

Column (7) provides information about the Average Number of Financing Rounds per investment undertaken. Column (8) contains information about the Syndication

Ratio for each VC category. The Syndication Ratio measures to which extent VC ﬁrms make use of joint investment actions and is calculated as the ratio of syndicated

investments to the number of total transactions by the respective VC. Column (9) presents the Capital under Management for the VC categories and is measured in Mio.

Euro. Column (10) gives the sum of total investments made in Germany as indicated by TVE and is measured in Mio. Euro. Column (11) indicates which percentage of

overall investment volume has been undertaken in Germany.

Table 4:

Funded Firm Characteristics and the Decision to Stage Funding

Dependent Variable: Number of Financing Rounds per Firm

(1) (2) (3) (4) (5)

Biotech 0.7336 0.6112 0.9303 0.8089 1.8170

(0.155) (0.645) (0.340) (0.477) (0.114)

Consulting 0.0787 -0.5555 -0.2050 0.5450 1.0879

(0.887) (0.679) (0.836) (0.641) (0.348)

Electronics 0.0388 0.0649 -0.1600 -0.0235 1.0650

(0.942) (0.761) (0.873) (0.984) (0.354)

Utilities 0.6062 -1.4324 -1.1918 -0.9250 1.0615

(0.431) (0.416) (0.404) (0.564) (0.418)

Financial -1.8100

(0.114)

Ind. Products -0.5870 -1.7651 -1.2149 -1.7331 -0.3432

(0.302) (0.215) (0.261) (0.240) (0.783)

Ind. Services -1.0243 0.2206 -0.0618 -0.6428 0.1883

(0.203) (0.382) (0.963) (0.644) (0.891)

Internet 0.1877 0.1222 0.9597 0.7914 1.8231

(0.723) (0.927) (0.328) (0.493) (0.111)

Pharma 0.5998 0.7801 1.4765 2.1718

(0.323) (0.638) (0.261) (0.094)∗

Media -0.1434 -0.2852 -0.4223 -1.337 0.4113

(0.804) (0.843) (0.696) (0.344) (0.740)

Medical 0.0599 0.7633 1.1514 0.2169 1.9815

(0.917) (0.612) (0.296) (0.880) (0.098)∗

Software 0.0581 -0.3925 -0.1379 -0.1113 0.9824

(0.909) (0.763) (0.883) (0.921) (0.380)

Age -0.0211 -0.0797 -0.0572 -0.0635 -0.0480

(0.055)∗(0.008)∗∗∗ (0.039)∗∗ (0.073)∗(0.027)∗∗

Start Up 0.5381 1.0007 0.9706 1.1075 0.8672

(0.027)∗∗ (0.054)∗(0.054)∗(0.006)∗∗∗ (0.028)∗∗

Early Stage 0.8557 0.9376 1.0499 1.0677 1.0076

(0.000)∗∗∗ (0.009)∗∗∗ (0.005)∗∗∗ (0.000)∗∗∗ (0.001)∗∗∗

LN(Emp at Inv.) 0.5496

(0.000)∗∗∗

LN(Sales at Inv.) 0.1785

(0.048)∗∗

LN(Av. Emp) 0.5170

(0.000)∗∗∗

LN(Av. Sales) 0.0940

(0.186)

Number of obs 964 264 265 338 397

χ2

−T est 87.65 74.82 64.15 75.41 82.81

Pseudo R20.0752 0.2090 0.1826 0.1675 0.1627

The table reports a logit regression model estimating the likelihood of ﬁnancing rounds for the invest-

ment targets being subject to staging. The sample for the ﬁrst regression includes 964 venture capital

deals that have been subject to one or more capital infusions in the period 1995-2005. I additionally

included the Age of the investment target at the time of the ﬁrst investment along with thy type of

stage at which the investment took place. For the second regression the sample has been reduced to

264 deals for which I can also calculate the size (measured in terms of the number of employees) of

the investment target at the date of investment. Column 3, 4 and 5 use a diﬀerent sample of deals for

which I have further information on the size of the investment target (measured in terms of sales) at

the date of the investment and average values for employees and sales respectively. The table reports

the coeﬃcient estimate along with the p-values in parentheses. Intercepts are not shown. The variable

Consumer Products has been dropped. The Variables Financial and Pharma have been deleted from

part of the regressions due to the insuﬃcient number of observations available.

*, **, *** denotes signiﬁcance at the 10%, 5% or 1% level respectively.

Table 5:

Funded Firm Characteristics and the Number of Financing Rounds

Dependent Variable: Number of Financing Rounds per Firm

(1) (2) (3) (4) (5)

Biotech 0.4699 0.5882 0.5618 0.8404 0.6907

(0.000)∗∗∗ (0.003)∗∗∗ (0.012)∗∗ (0.011)∗∗ (0.000)∗∗∗

Consulting 0.0428 -0.0064 -0.0709 0.4384 0.1617

(0.621) (0.975) (0.742) (0.185) (0.196)

Electronics 0.0865 0.0649 -0.0003 0.3741 0.1777

(0.307) (0.761) (0.999) (0.253) (0.182)

Utilities 0.2274 0.1946 0.0450 0.5009 0.3012

(0.245) (0.575) (0.899) (0.244) (0.218)

Financial -0.1936

(0.019)∗∗

Ind. Products -0.0124 -0.2208 -0.2356 -0.0088 -0.0088

(0.881) (0.195)∗(0.216) (0.654) (0.927)

Ind. Services -0.0702 0.2850 0.0638 0.0981 0.0981

(0.515) (0.279) (0.815) (0.353) (0.519)

Internet 0.1440 0.2490 0.2504 0.5480 0.3899

(0.108) (0.224) (0.246) (0.941) (0.003)∗∗∗

Pharma 0.2875 0.4044 0.8122 0.5375

(0.023)∗∗ (0.156) (0.027)∗∗ (0.018)∗∗

Media 0.0327 0.1523 -0.0099 0.1440 0.1441

(0.713) (0.509) (0.966) (0.394) (0.347)

Medical 0.1362 0.3264 0.2587 0.4423 0.4491

(0.233) (0.261) (0.006)∗∗∗ (0.220) (0.020)∗∗

Software 0.1399 0.1792 0.0638 0.4179 0.2789

(0.074)∗(0.358) (0.759) (0.199) (0.019)∗∗

Age -0.0021 -0.0062 -0.0034 -0.0080 -0.0025

(0.010)∗∗∗ (0.001)∗∗∗ (0.014)∗∗ (0.084)∗(0.033)∗∗

Start Up 0.1792 0.2303 0.2692 0.2560 0.2487

(0.019)∗∗ (0.099)∗(0.093)∗(0.021)∗∗ (0.057)∗

Early Stage 0.2623 0.3302 0.4095 0.3474 0.3578

(0.000)∗∗∗ (0.000)∗∗∗ (0.000)∗∗∗ (0.000)∗∗∗ (0.000)∗∗∗

LN(Emp at Inv.) 0.1144

(0.000)∗∗∗

LN(Sales at Inv.) 0.0466

(0.041)∗∗

LN(Av. Emp) 0.1773

(0.000)∗∗∗

LN(Av. Sales) 0.0197

(0.271)

Number of obs 964 264 265 338 397

χ2

−T est 172.89 155.64 148.23 114.72 171.09

Pseudo R20.0281 0.0708 0.0677 0.0688 0.0550

The table reports a Poisson regression model using robust Standard Errors (Huber/White/sandwich)

estimating the number of ﬁnancing rounds for the investment targets. The sample for the ﬁrst regression

includes 964 venture capital deals that have been subject to one or more capital infusions in the period

1995-2005. I additionally included the Age of the investment target at the time of the ﬁrst investment

along with thy type of stage at which the investment took place. For the second regression the sample

has been reduced to 264 deals for which I can also calculate the size (measured in terms of the number

of employees) of the investment target at the date of investment. Column 3, 4 and 5 use a diﬀerent

sample of deals for which I have further information on the size of the investment target (measured in

terms of sales) at the date of the investment and average values for employees and sales respectively.

The table reports the coeﬃcient estimate along with the p-values in parentheses. Intercepts are not

shown. The variable Consumer Products has been dropped. The Variables Financial and Pharma have

been deleted from part of the regressions due to the insuﬃcient number of observations available.

*, **, *** denotes signiﬁcance at the 10%, 5% or 1% level respectively.

Table 6:

VC Characteristics and Average Number of Financing Rounds

Dependent Variable: Average Number of Financing Rounds

(1) (2) (3) (4)

Foreign 0.2334 0.1334 0.1627

(0.001)∗∗∗ (0.127) (0.092)∗

Banking 0.1380 -0.0427 -0.0434 -0..0221

(0.068)∗(0.665) (0.692) (0.834)

Business Angel 0.3985

(0.089)∗

Corporate 0.2037

(0.018)∗∗

Independent 0.2504 0.1237 0.1424 0.1670

(0.000)∗∗∗ (0.154) (0.142) (0.083)∗

Public 0.1290 -0.0527 -0.1571 -0.1086

(0.100) (0.649) (0.093)∗(0.256)

Investments 0.1482 0.0842 0.0631 0.0863

(0.001)∗∗∗ (0.040)∗∗ (0.140) (0.038)∗∗

Industry Concentration 0.1565 0.1044 0.1725 0.0580

(0.255) (0.478) (0.280) (0.751)

Capital 0.0514

(0.002)∗∗∗

Sum 0.0684

(0.000)∗∗∗

Percentage -0.3476

(0.000)∗∗∗

Non High-Tech -0.4699 -0.5316 -0.3927

(0.000)∗∗∗ (0.000)∗∗∗ (0.000)∗∗∗

Information Tech -0.0963 -0.0965 -0.1175

(0.156) (0.207) (0.121)

Medical 0.1341 0.0904 0.1065

(0.239) (0.453) (0.398)

Number of obs 432 312 263 264

F-Test 5.74 8.25 7.78 7.46

R20.0699 0.1343 0.1377 0.1293

The table reports an OLS regression model using robust Standard Errors (Huber/White/sandwich)

estimating the impact of Venture Capital provider characteristics on the average number of ﬁnancing

rounds in which capital is provided to the investment target. The sample for the ﬁrst regression

includes 432 Venture Capital providers that have made at least one investment in the period 1995-2005.

The number of investments made enters the regression as the log. Moreover the regressions include

a Herﬁndahl measure of concentration for the industries invested in. For regression (2) I additionally

included the Capital under Management for the VC ﬁrm. This variable enters the regression as the log.

For regression (3) the sample has been reduced to 312 VC ﬁrms. Moreover, I have included a dummy

variable indicating the investment focus of the VC ﬁrm, i.e. whether the focus is on Non High-Tech,

Information Technology, Medical and Life Sciences Products, or no speciﬁed focus at all. Regression (3)

uses the total Sum of investments made in Germany by the respective VC provider (enters the regression

as the log), whereas regression (4) includes the investments made in Germany as a percentage of the

overall sum of investments made. The table reports the coeﬃcient estimate along with the p-values

in parentheses. Intercepts are not shown. The variable Co-Operative VC has been dropped. The

variable Foreign has been dropped from regression (4) to avoid collinearity problems with the variable

Percentage. The Variables Corporate and Business Angel have been deleted from part of the regressions

due to the insuﬃcient number of observations available.

*, **, *** denotes signiﬁcance at the 10%, 5% or 1% level respectively.

Table 7:

VC Characteristics, Staging and the Impact on Syndication Activity

Dependent Variable: Syndication Ratio

(1) (2) (3) (4)

Foreign 0.5713 0.5487 0.4981

(0.000)∗∗∗ (0.024) (0.010)∗∗∗

Banking -0.4856 0.6743 0.5233 0.2262

(0.237) (0.022) (0.023)∗∗ (0.250)

Corporate -0.4997

(0.231)

Independent -0.9735 0.1148 0.0677 -0.2032

(0.011)∗∗ (0.609) (0.704) (0.161)

Public -0.5408 0.6055 0.4911 0.2688

(0.204) (0.045)∗∗ (0.037)∗∗ (0.237)

Investments -0.2004 -0.2140 -0.14288 -0.1734

(0.071) (0.029) (0.065) (0.026)

Average Rounds 0.5606 0.4637 0.4184 0.3838

(0.000)∗∗∗ (0.001)∗∗∗ (0.001)∗∗∗ (0.001)∗∗∗

Industry Concentration 0.2225 0.1380 0.3815 0.4387

(0.525) (0.673) (0.147) (0.105)

Capital -0.0120

(0.686)

Sum -0.0152

(0.628)

Percentage -0.14193

(0.372)

Non High-Tech 1.2860 1.159 1.145

(0.000)∗∗∗ (0.000)∗∗∗ (0.000)∗∗∗

Information Tech 0.2013 0.1751 0.1663

(0.119) (0.103) (0.126)

Medical 0.6619 0.4431 0.4874

(0.010)∗∗ (0.026)∗∗ (0.018)∗∗

Number of obs 432 312 263 264

χ2

−T est 72.11 107.32 121.10 109.43

Pseudo R20.0848 0.1735 0.2433 0.2176

The table reports a Tobit regression model estimating the impact of Venture Capital provider charac-

teristics on the narrower version of the syndication ratio. The syndication ratio measures the number

investments with joint investment activity by two or more partners in one of the ﬁnancing rounds as a

percentage of the total number of transactions undertaken. The sample for the ﬁrst regression includes

432 Venture Capital providers that have made at least one investment in the period 1995-2005. The

number of investments made enters the regression as the log. Additionally, the regressions include in-

formation on the average number of round ﬁnancing used for the investments made and a Herﬁndahl

measure of concentration for the industries invested in. For regression (2) I additionally included the

Capital under Management for the VC ﬁrm which enters the regression as the log. Moreover, I have

included a dummy variable indicating the investment focus of the VC ﬁrm, i.e. whether the focus is

on Non High-Tech, Information Technology, Medical and Life Sciences Products, or no speciﬁed focus

at all. For regression (3) the sample has been reduced to 312 VC ﬁrms. Regression (3) uses the total

Sum of investments (enters the regression as the log) made in Germany by the respective VC provider,

whereas regression (4) includes the investments made in Germany as a percentage of the overall sum

of investments made. The table reports the coeﬃcient estimate along with the p-values in parentheses.

Intercepts are not shown. The variable Co-Operative VC has been dropped. The variable Business

Angel has not been included. The variable Foreign has been dropped from regression (4) to avoid

collinearity problems with the variable Percentage. The Variable Corporate has been deleted from part

of the regressions due to the insuﬃcient number of observations available.

*, **, *** denotes signiﬁcance at the 10%, 5% or 1% level respectively.

Table 8:

Firm Characteristics and Funding Duration

Dependent Variable: Duration of Financing Round

(1) (2) (3) (4)

Age -0.0001 -0.0001 -0.0001 -0.0001

(0.606 ) (0.499) (0.615) (0.802)

Start Up -0.2813 -0.2643 -0.2360 -0.0651

(0.023)∗∗ (0.032)∗∗ (0.051)∗(0.692)∗

Early Stage -0.0329 -0.0417 -0.0275 -0.1045

(0.760) (0.751) (0.694) (0.371)

First Round 0.1224

(0.187)

Syndication 0.2643

(0.039)∗∗

Cum. Synd. Rounds 0.1668

(0.002)∗∗

Number of obs 493 493 493 493

χ2

−T est 136.73 135.78 140.32 143.19

The table reports a Weibull Duration model using robust Standard Errors estimating

the impact of ﬁnanced ﬁrms characteristics on the duration of the individual ﬁnancing

rounds. The sample for the regressions includes 493 Venture Capital transactions in the

period 1995-2005. For regression (2) I additionally included a variable that indicates

whether the investment has been syndicated in the ﬁrst round. Regression (3) includes

a variable indicating whether a particular round has been subject to joint investment

activity. Regression (4) includes a term measuring the cumulative number of total

rounds that have been syndicated. The table reports the coeﬃcient estimate along with

the p-values in parentheses. Intercepts are not shown. The variable Consumer Products

has been dropped. The industry variables were included in all regression speciﬁcations,

but none of the coeﬃcients was signiﬁcant, despite Utilities in regression at the 5%

level with a positive coeﬃcient. For reasons of brevity the coeﬃcient estimates and the

corresponding p-values of the insigniﬁcant industry dummies are not shown here.

*, **, *** denotes signiﬁcance at the 10%, 5% or 1% level respectively.

ResearchGate has not been able to resolve any citations for this publication.

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