The effect of patents, human capital and alliances on VC funding and startup success

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The effect of patents, human capital and alliances on VC

funding and startup success

31/01/2018

Sophia Zurli

11119918

Drs. P.V.Trietsch, M.Phil.

Economics and Business, Finance and Organization

12 credits

ABSTRACT

This paper analyzes the importance of patents and human capital in attracting venture capital financing (signaling effect) and in determining the success of startups (productive effect). In an empirical study of 160 US biotechnology startups in the period 2015-2017, multiple regressions are analyzed to isolate the double role of these resources in the context of venture capital financing. The contributions of this paper are twofold. First, it determines whether the amount of VC financing and the number of VC rounds are associated with two resources related to biotechnological portfolios: number of patents and human capital. Second, it examines whether these resources are relevant in determining startup success, measured by the startup’s ability to go public. The results show that VCs tend to overemphasize patents when making their investment decisions, even though patents are not relevant in determining the success of startups. Instead, human capital seems to affect startup success but not VCs’ financing decision.

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Statement of Originality

This document is written by Sophia Zurli who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and in its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision od completion of the work, not for the contents.

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1. Introduction

1.1 Motivation

Alliances, patents and human capital have long been recognized as impacting a startup’s ability to attract venture capital financing (Hoenig and Henkel, 2015). In this context they potentially fulfill two roles: that of productive resources, influencing startup success, and that of signaling resources, impacting venture capital funding (Hoenig and Henkel, 2015).

The choice to focus on this particular topic comes from the problem recognized in the context of venture capital finance of disentangling the signaling from the productive effect of these resources (e.g. Baum and Silverman, 2004; Hoenig and Henkel, 2015; etc.). While past studies have described in detail the resources VCs look at when choosing which firms to fund, there are not many who have explored whether these actually influence startup success (Baum and Silverman, 2004).

What this paper adds to this limited existing literature is then an analysis of the effect that patents and human capital have on startup success, on top of their effect on VC funding, for a sample of startups in the biotechnology industry. This paper is also significant from a practical perspective because the distinction of the double role of patents and human capital could be useful for future venture capitalists, the “stakeholders” of this problem, allowing them to decide if they should consider these particular resources in their future financing decisions.

1.2 Central question

The research question this thesis asks is then: “To what extent do patents, human capital and alliances affect VC funding and startup success?”

1.3 Summary existing literature

Human capital characteristics are shown to be both associated with receiving VC funding and with the likelihood that an entrepreneurial firm will succeed (Beckam et al., 2007). As for patents, significant and robust positive correlations with several variables measuring the firm’s performance are found, together with a positive link with total VC investment and number of financing rounds (Mann and Sager, 2007; Cao and Hsu, 2011). Finally, alliances are found to both help startups attract financing and impact their performance (Baum and Silverman, 2004).

1.4 Research sub-questions

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i. What is the role of resources in assessing the quality of a startup and what resources do VCs tend to use in their financing decision?

ii. How do alliances affect VC and startup success? iii. How do patents affect VC and startup success?

iv. How does human capital affect VC and startup success?

1.5 Data and sample

The sample consists of 160 biotechnology startups in the US which received their first round of VC funding between 2015 and 2017. The variables of interest are: patents, human capital, alliances, venture capital funding and startup success. Due to the fact that a database that cumulates startup data on alliances is not available, this research restricts the study on patents and human capital, excluding alliances from the empirical analysis, but nonetheless dealing with them extensively in the literature chapter so as to not neglect their significance in their signaling and productive functions. Given that the sample consist of only startups that received VC, this study does not analyze whether the resources affect the chances of a startup of being funded, but rather the effect these resources have on the VC investment dollar amount and number of rounds.

1.6 Method

This research analyzes the aforementioned roles played by patents and human capital, that of productive resources, influencing startup success, and that of signaling resources, influencing VC funding. Drawing from previous studies on the subject (e.g. Baum and Silverman, 2004; Mann and Sager, 2007; Beckam et al., 2007; Cao and Hsu, 2011; etc.), what is expected is for human capital and patents to have a positive effect on both VC funding and startup success. The regression models used to analyze the effect of patents and of human capital on VC funding are log-linear OLS regressions, while to analyze the effect of these resources on startup success a PROBIT regression model is used.

1.7 Structure

Chapter 2 introduces the role played by resources in the entrepreneurial setting and describes what are the resources VCs typically look at in their financing decision, with a clear distinction between their productive and signaling effects. Chapter 3 explains in more detail the methodology used to answer the research question and presents the regression results from the empirical analysis. Chapter 4 provides the discussion of the results and the conclusion.

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2. Literature

This chapter begins by introducing the role of resources in the venture capitalists’ assessment of startups and goes on to explain what resources VCs usually consider in their investment decision, with a clear distinction between their signaling and productive roles. Further, hypotheses concerning the expected effects of patents and human capital on VC and startup success are formulated.

2.1 VCs’ startup assessment: the role of resources

Confronted with limited information when assessing a new venture, venture capitalists have to rely on those resources owned by the startup that are observable at the time of their assessment and use them in their financing decision as signals of unobservable quality measures (Hoenig and Henkel, 2015). In this context, information asymmetries are present: the entrepreneurial team possesses more information about the quality of the startup than the investor (Hoenig and Henkel, 2015) and this lowers the ability of the capitalist to assess the legitimacy of new ventures and of the startup to attract VC investments (Hoenig and Henkel, 2015). One solution recognized by Spence’s (1973) signaling theory is for the better informed party (entrepreneur) to send signals about the startup’s quality to the less informed party (investor), which are then used in the screening phase by VCs.

VCs consider patents, alliances and human capital when assessing biotechnology startups for their funding decision because biotechnology startups are known to require access to these three resources in order to progress (Baum et al., 2000; Hoenig and Henkel, 2015) and presumably because these particular resources are believed to materially affect subsequent firm outcomes (Hoenig and Henkel, 2015). Entrepreneurs and VCs can and have used them as signals because they have different costs for different quality startups, and so are useful in the screening process.

The selection process of venture capitalists is typically based on patents, alliances and human capital and it is unlikely that different VCs will apply vastly different selection criteria (Fitza et al, 2008). Moreover, Baum and Silverman (2004) find that VCs select based on these same three resources, regardless of whether they are actually tied to the ultimate performance of the startup, which is what this paper researches.

To give a clearer picture of what has been dealt till now in the academic literature, Table 1 provides some of the existing studies that have, to some extent, discussed the role of the resources under question in the context of venture capital financing, together with information on the sample, data years, methodology, variables and key results.

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TABLE 1

Existing studies that discuss resources in the context of VC financing

Key

resource/s Research Sample

Year/s of

data sample Methodology Key findings and results

Alliances Hoenig and Henkel (2015) 187 European and U.S. venture capitalists 2011 Conjoint-based survey

Alliances help startups attract financing. Alliances are valued both as productive assets and as quality signals. Baum and Silverman (2004) 204 biotech start-ups (Canada) 1991-2000 Database search

Alliances are positively related to the amount of VC financing and to startup performance.

Patents

Maan and

Sager (2007) 1089 start-ups (US) 1997-1999

Database search

Positive link between patents and total VC investment, the number of financing rounds and startup performance.

Cao and Hsu (2011)

>20,000 VC-backed

firms (US) 1976-2005

Database search

Patents filed before the first VC round are correlated with larger VC funding and higher success.

Hoenig and Henkel (2015) 187 European and U.S. venture capitalists 2011 Conjoint-based survey

Patents help startups attract financing. They are valued both as productive assets and as quality signals.

Hsu and Ziedonis (2013)

370 start-ups (US) 1975-1999 Survey

Positive link between patents and VC financing and IPO pricing. Stronger effect of patents in early stage. Human capital Gompers et al. (2010) 9932 entrepreneurs (US) 1986-2000 Database search

Startup human capital is positively linked to start-up success (IPO) and VC funding. Beckman et al. (2007) 161 high-tech start-ups 1994-95 Interviews and survey

Startup human capital is positively correlated with the ability to attract VC financing and to startup success (IPO).

Hsu (2007) 149 technology

start-ups (US) 1995-2000 Survey

Startup human capital is positively linked to the chance of receiving VC funding.

Burton et al. (2002)

173 high-tech

start-ups (US) 1994-1995 Interviews

Startup human capital is positively linked to venture financing.

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2.2 Productive and signaling effects: prior literature and hypothesis development

Further, this paper now turns to the three observable resources of interest, alliances, patents and human capital, and discusses their importance in attracting VC investment and in affecting startups success, for a better understanding of their double function in venture capital financing (Hoenig and Henkel, 2015). First, Figure 1 presents a conceptual framework that summarizes past literature in terms of the relations between the variables and the corresponding hypotheses.

FIGURE 1

The effect of patents, human capital and alliances on VC and IPO

2.2.1 Alliances

Alliances both impact startup success (productive effect), as well as influence the funding decision of venture capitalists (signaling effect).

H4 VC investment & VC rounds Resources IPO Patents Human capital Alliances + Include Include Include + + + + + H1 H3 H2

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Signaling effect

As for the signaling role, alliance agreements serve as a fundamental investment criterion for VCs (Hoenig and Henkel, 2015). An alliance may make the new startup legitimate in the eyes of VCs, and as a result make it easier for the new venture to receive capital such as venture funding by both signaling the startup’s access to valuable resources and knowledge critical to its performance and by providing an external endorsement with its positive assessment by other parties (Baum and Silverman, 2004).

Alliances are valid signals according to the signaling theory because they are less costly to obtain for a high quality startup than for a low quality startup. As a consequence, outsiders should base their judgment about the quality of a startup by looking at the business relationships it holds (Stuart et al., 1999). Previous studies that analyze the relationship between venture capital and the number of alliances possessed by a startup show that the latter is positively related to the amount of financing the venture receives (Baum and Silverman, 2004).

Productive effect

With respect to the productive effect, prior research shows that ventures that have a network of alliances to back them are likely to benefit from the relation by outperforming other venture backed comparable firms that do not possess a similar alliance network (Hoenig and Henkel, 2015). Moreover, Baum et al. (2000) found that biotechnology startups that are capable of establishing both upstream and downstream alliances manage to obtain substantial performance improvements during their early years.

What follows are the multiple ways a collaboration of such a kind may help increase the startup’s success. Firstly, alliances, and more specifically upstream agreements, help secure access to resources and valuable technological knowledge and information, which the startup would not have had had it not made the agreement (Hoenig and Henkel, 2015). Secondly, a sales alliance can help a start-up bring its product to market. Thirdly, alliances often help a startup expand its business opportunities (Hoenig and Henkel, 2015).

In conclusion, literature has found a positive relationship between alliances on one hand and both VC and startup success on the other (Baum and Silverman, 2004; Hoenig and Henkel, 2015).

2.2.2 Patents

Just like for the case of alliances, patents may both influence a startup’s success and the amount of venture capital financing it receives. These two effects reflect their dual role of productive and signaling resources (Hoenig and Henkel, 2015).

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Signaling effect

According to Spence’s (1973) signaling theory, a patent is a relevant signal of a startup’s quality since it is easier (less costly) to get for high quality startups than for low quality ones and can be observed by external parties. Biotechnology startups with a patent have more chance of receiving venture capital financing than ones without (Lerner, 1994). This because possessing patents, or even patents pending, is a relevant signal of a startups future potential success and innovative abilities, and thus makes it more likely for them to obtain the financing (Hoenig and Henkel, 2015). Biotechnology entrepreneurs know the value of a patent to VCs, and as such are always trying to increase the dimensions of their patent portfolio to attract investors (Hoenig and Henkel, 2015).

Some examples of the most significant findings on the signaling role of patents in past literature are reported next. Mann and Sager (2007) find a positive link between patents and the number of rounds of venture capital funding and the aggregate amount of capital received in a sample of software startups. Hsu and Ziedonis (2013) show that patents do influence venture capital financing, but only when signals are highly needed, like in early rounds of financing. Similar results are found by Hoenen et al. (2014), who show that patents have a positive signaling effect only in the first round, while the productive effect should be present in later rounds. Lastly, Audretsch et al. (2012) finds that startups who are granted more patents are more likely to receive capital from venture investors, but only if they own a prototype. Contrary to this line of findings, Heeley et al. (2007) find that patents in complex product industries do not give a positive signal to investors.

Productive effect

Patents may influence the success of a startup because they give the new venture the legal right to an invention. By law, the party with the patent and owner of the invention, by excluding other parties from its future returns, is more likely to outperform startups without a similar patent (Hoenig and Henkel, 2015). Cao and Hsu (2011) base their research on a sample of US startups in different industries and show how startups that filed patents before the first round of investment not only received a higher amount of venture capital financing but were also more likely to succeed, measured by their ability to go public.

Hypotheses

Drawing from existing literature, the resulting hypotheses concerning patents tested in the empirical analysis are:

Hypothesis 1. The number of patents owned by a startup positively impacts its success, measured

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Hypothesis 2. The number of patents owned by a startup positively impacts VC funding, measured

by the total VC investment amount and by the number of VC rounds. 2.2.3 Human capital

As for the case of alliances and patents, human capital has both a productive and a signaling function (Hoenig and Henkel, 2015).

Signaling effect

Researchers believe human capital signals the strength of the management team to investors (Baum and Silverman, 2004). This results from the fact that its less costly for a high quality startup to hire experienced employees than it is for low quality startups (Hoenig and Henkel, 2015). Human capital is the selection criteria VCs look at the most when deciding how much to invest (Zacharakis and Meyer, 2000). More specifically, studies find team characteristics to be in the top three criteria VCs use in their financing decision (Zacharakis and Meyer, 2000), and are considered by VCs as more important than business related characteristics (Gompers, Garnall et al., 2010). As a result, the resource is commonly considered as a relevant signal of a startup’s future success, and thus positively impacts the likelihood of receiving venture capital (Hoenig and Henkel, 2015).

Human capital includes a wide variety of factors, and depending on the research, authors have identified management experience, sector-specific experience, leadership experience, experience with (successful) prior ventures and familiarity with the target market as relevant and further confirming the startup’s potential to VCs (Hoenig and Henkel, 2015). In particular, researchers include the number of people in the top management team in their empirical analysis since a bigger group is thought to signal a bigger human capital (e.g. Burton et al.; 2002, Baum and Silverman, 2004). Other examples include Hsu (2007) and Robinson and Sexton (1994), who find that educational attainment is related to the amount of financial help received. In addition, Hsu (2007) argues that experience may also play a signaling role, and in particular having experience with founding startups repays in terms of returns from greater venture investments.

Productive effect

Human capital is considered to have a productive value since a more experienced team has more knowledge and is better at solving challenges than a lower quality team. Similarly a better skilled team could give the startup access to advantageous network connections for the startup (Hsu, 2007). Moreover, industry specific experience may make the team more adept at grasping industry specific opportunities and

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dealing with industry specific challenges that may arise during the venture’s life (Hoenig and Henkel, 2015).

Although it would seem obvious that the human capital of a startup would determine, at least in part, its future success, researchers have shown there is no clear answer as to whether it actually does. While it is widely reported by scholars that human capital is positively linked to startup superior performance, and that the team’s background influences the likelihood of survival for a startup (Burton et al., 2002), Baum and Silverman (2004) find that management team characteristics have little impact on future startup success. Given the huge effect of human capital on VCs’ financing decision, the weak relationship between team characteristics and startup success is found surprising, and is explained by the authors as the tendency of VCs to overestimate the influence of entrepreneurs’ abilities on the performance of a new startup.

Hypotheses

The hypotheses concerning human capital tested in the empirical analysis are:

Hypothesis 3. The human capital owned by a startup positively impacts its success, measured by

its ability to successfully complete an IPO.

Hypothesis 4. The human capital owned by a startup positively impacts VC funding, measured by

the total VC investment amount and by the number of VC rounds.

To conclude, alliances, patents and human capital have not only been found influencing VC funding but also to be determinants of startup growth and to influence the startup’s chances of staying in business and going public (Hsu, 2007).

3. Empirical analysis

The objective of this study’s empirical analysis is to analyze the effect of patents and of startup human capital on the two outcomes of interest, VC investment and startup success. Firstly, the sample and data used are explained. Secondly, the variables included in the analysis are listed and described. Thirdly, the regression models and corresponding hypotheses are presented, which are used as a premise to answer the research question. Finally, the summary statistics, variable correlations and results from the STATA analysis are provided.

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3.1 Sample and data

Sample data on the 160 biotechnology startups is obtained from the VentureXpert database. VentureXpert is a proprietary database of Venture Economics, which is a division of Thomson Financial. The search is restricted to “venture capital” deals, which reports firms that received at least one venture capital investment. To get data on only startups the search is restricted to seed and early stage deals. The search is limited to United States investments, country where the majority of venture capital deals take place. The biotechnology industry is chosen since it is considered to be the largest sector in the context of VC investments (Hoenig and Henkel, 2015), by selecting “biotechnology” as the firm’s industry of choice. To be able to analyze firms of similar ages and sizes, the search is restricted to startups that have received their first round of venture financing within a relatively narrow frame of time, and also a time recent enough that the experience of the startups and their patenting would be representative of current conditions in the industry. The period selected is that of the years 2015-2017.

After excluding startups with only limited data available the selection criteria reported results in a dataset including 160 startups. Compared with other researchers in the field (see Table 1), the dataset and number of observations is appropriate and in line with their samples.

Several different pieces of information are also provided from VentureXpert: details about the investment decisions, including the total amount of financing obtained by the firm and the number of financing rounds, both used to measure VC investment. Also, information about the performance of the investment is provided, including a designation of the firm’s status as public, which is used to measure startup success. Information on the management team and the leader’s background and patent portfolios of the startups were obtained from Orbis database by doing a batch search using the VentureXpert sample. Data on the startup’s alliances is not present in any available database, so this research restricts the study to patents and human capital, excluding alliances from the empirical analysis.

3.2 Variables

As discussed in the preceding chapter, prior research suggests that VCs rely on three types of startup resources in the screening process: human, alliance, and intellectual capital. Alliances are not studied in the empirical analysis, only human capital and patents are included, due to the aforementioned database problem. Human capital and patents have multiple ways they can be measured. To make sure to only use variables that are relevant in practice, an analysis of the relevant academic literature and corresponding variables used is provided in the literature chapter.

As for the dependent variables, the literature uses firm success as a measure of firm performance, and many researchers suggest that the ability of a startup to go public is an appropriate measure of startup

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success (e.g. Cao and Hsu, 2011, Mann and Sager, 2007, Beckman et al., 2007, etc.). These studies also measure VC investment by including both the total amount of VC financing received and the number of VC rounds. Obtaining VC funding and going public represent the two fundamental turning points in the life of a startup (Beckman et al., 2007), and are thus the two outcomes of interest in this research.

Control variables included are: the startup’s age, which potentially affects the amount of VC it obtains and its success since an older venture tends to have more experience than a younger venture; similarly, the startup’s size is added, since the bigger the venture the more creditworthy it tends to be in the eyes of VCs and the more likely it is going to succeed; lastly, a variable measuring whether the venture has a subsidiary/parent firm is included since this can give the startup access to knowledge which can help it succeed and obtain more VC than had it not made the connection (Baum and Silverman, 2004).

After thorough discussions around the key variables most used by past researchers, the variables chosen for the empirical analysis are presented and defined in Table 2.

TABLE 2

Variables included in the empirical analysis

Variables Type Definition

IPO (IPO) Dependent Dummy measuring startup success equal to 1 if the startup went public, 0 otherwise

VC investment (VCi) Dependent Continuous variable measuring VC investment equal to the total VC funding dollar amount (in millions) received by the startup

VC rounds (VCr) Dependent Continuous variable measuring VC investment equal to the number of VC deals between the startup and a venture capital investor

Patents (PAT) Independent Continuous variable equal to the number of patents granted to the startup at the time of financing

Team size (TS) Independent Continuous variable equal to the number of people in the top management team

Ivy League (IL) Independent Dummy equal to 1 if the startup leader went to an Ivy League school, 0 otherwise

Age (A) Control Continuous variable equal to the age (in months) of the startup at the time of financing

Subsidiary (SUB) Control Dummy equal to 1 if the startup has a parent/ subsidiary firm, 0 otherwise

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3.3 Method and hypotheses

What follows is the methodology used in this study and the corresponding hypotheses. Multiple model specifications are analyzed and regressed to check for robustness. The ability to attract VC financing is measured with both the total amount of VC investment and the number of VC rounds. Also, the effect of human capital is measured by looking at the number of employees in the top management team and at whether the team leader graduated from an Ivy League school.

The regression model proposed to analyze the effect of patents and of human capital on startup success, measured by the startup going public, is the following PROBIT regression:

Model 1:

𝐼𝑃𝑂 = 𝛿0+ 𝛿1𝑃𝐴𝑇 + 𝛿2𝑇𝑆 + 𝛿3𝐼𝐿 + 𝛿4𝐴 + 𝛿5𝑆 + 𝛿6𝑆𝑈𝐵 + 𝛿7𝑉𝐶𝑖 + 𝛿8𝑉𝐶𝑟

The regression models proposed to analyze the effect of patents and of human capital on VC investment, measured by the number of VC rounds and the total dollar amount of VC investment, are the following log-linear OLS regressions:

Model 2a:

𝐿𝑛(𝑉𝐶𝑟) = 𝜃0+ 𝜃1𝑃𝐴𝑇 + 𝜃2𝑇𝑆 + 𝜃3𝐼𝐿 + 𝜃4𝐴 + 𝜃5𝑆 + 𝜃6𝑆𝑈𝐵 + 𝜃7𝐼𝑃𝑂

Model 2b:

𝐿𝑛(𝑉𝐶𝑖) = 𝛼0+ 𝛼1𝑃𝐴𝑇 + 𝛼2𝑇𝑆 + 𝛼3𝐼𝐿 + 𝛼4𝐴 + 𝛼5𝑆 + 𝛼6𝑆𝑈𝐵 + 𝛼7𝐼𝑃𝑂

Further, in Figure 2, the hypotheses concerning patents and human capital mentioned in the literature chapter are translated into the expected signs of the regression coefficients.

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FIGURE 2

Correspondence between models and hypotheses

Model 1

H1: The number of patents owned by a startup positively impacts its success, measured with successfully completing an

IPO.

H3: The human capital owned by a startup positively impacts its success, measured by its ability to successfully complete an

IPO.

𝛿1 > 0

𝛿2 > 0

𝛿3 > 0

Model 2a

H2a: The number of patents owned by a startup positively impacts VC funding, measured by the number of VC rounds.

H4a: The human capital owned by a startup positively impacts VC funding, measured by the number of VC rounds.

𝜃1 > 0

𝜃2 > 0

𝜃3 > 0

Model 2b

H2b: The number of patents owned by a startup positively impacts VC funding, measured by the total VC investment amount

H4b : The human capital owned by a startup positively impacts VC funding, measured by the total VC investment amount

𝛼1 > 0

𝛼2 > 0

𝛼3 > 0

3.4 Results

Table 3 reports the descriptive statistics for the sample of biotechnology startups; for each measure, the across-startup averages, standard deviations, minimum and maximum are provided. Table 4 provides the correlation matrix for the entire set of variables. Finally, Table 5 presents the results of the different STATA regression analyses.

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TABLE 3 Summary statistics

Variable Sample N=160

Mean Std. Dev. Min Max

IPO 0.21 0.41 0.00 1.00 VC rounds 1.33 0.73 0.00 2.64 VC investment 3.58 1.40 -2.30 6.29 Patents 6.25 8.98 0.00 45.00 Team size 11.56 15.75 0.00 96.00 Ivy League 0.05 0.22 0.00 1.00 Subsidiary 0.28 0.45 0.00 1.00 Size 25.90 45.24 1.00 175.00 Age 24.48 23.71 0.00 139.00 TABLE 4 Correlation matrix

Age IPO Patents Ivy League Subsidiary Team size Size VC rounds VC investment Age 1.0000 IPO -0.2471 1.0000 Patents -0.0595 0.2330 1.0000 Ivy League -0.0568 0.3014 0.0448 1.0000 Subsidiary -0.0316 0.2867 0.1269 0.1116 1.0000 Team size -0.1563 0.6190 0.3706 0.0923 0.2736 1.0000 Size -0.1872 0.6598 0.2305 0.1938 0.3722 0.6536 1.0000 VC rounds -0.3774 0.2480 0.3524 0.0202 0.0250 0.2676 0.1815 1.0000 0.4675 VC investment -0.3567 0.3910 0.3532 0.1423 0.2617 0.4014 0.4240 0.4675 1.0000

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TABLE 5

Regression results: the effect of patents and human capital on IPO and VC

Model Model Model

1 2a 2b

IPOᵇ VC roundsᵃ VC investmentᵃ

Main variables and interactions

Patent -0.010 0.024*** 0.037** (-0.54) -3.93 -3.33 Ivy league 1.473* -0.113 0.295 -2.15 (-0.46) -0.66 Team size 0.037** 0.005 0.008 -3.19 -0.96 -0.94 Controls Subsidiaryᵇ 0.157 -0.089 0.354 -0.42 (-0.73) -1.61 Size 0.010* -0.001 0.005 -2.44 (-0.45) -1.77 Age -0.019 -0.010*** -0.016*** (-1.40) (-4.66) (-4.09) IPOᵇ 0.167 0.171 -0.91 -0.52 VC rounds 0.273 -0.92 VC investment 0.186 -0.86 Constant -2.716** 1.394*** 3.364*** (-2.78) -15.08 -20.070 R squared 0.268 0.348 Number of observations (startups) 160 160 160

This table presents multivariate regression analyses to study the effect of patents and of human capital on IPO and VC figures. The regressions are based on a sample of 160 US biotechnology startups which received VC funding over the period 2015-2017, and extracted from Thomson One database. The extraction criteria used are presented in Section 3.1. In Model 1, the dummy variable IPO is the dependent variable. In Model 2a, the natural logarithm of the number of VC rounds is the dependent variable. In Model 2b, the natural logarithm of the total dollar amount of VC received by a startup is the dependent variable. Patent, Ivy League and Team size are the main variables of interest in all 3 models and are extracted from Thompson one and Orbis Databases. All variables are defined in Table 2. VC investment is in USD Mil. Age is in months. ᵃDenotes a variable measured in natural logarithm. ᵇDenotes a dummy variable. The numbers in parentheses are t-statistics. p<0.05, **p<0.01, ***p<0.001.

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4. Discussion and conclusion

Little research has attempted to disentangle the signaling from the productive function of patents and human capital. This paper was motivated by this lacuna. In a study of 160 US biotechnology startups, this research implements multiple regression analyses that isolate the double role of patents and human capital in the context of venture capital financing.

As expected, the number of patents owned by a startup has a positive and significative effect on the total VC funding received and on the number of VC rounds. These results support previous research in which patents have been shown to drive VCs’ evaluation of startups (e.g. Baum and Silverman, 2004; Mann and Sager, 2007). Surprisingly, no significant signaling effects for the team’s size and leader’s education are observed. The analysis shows that when presented with several resources as quality indicators, VCs rely on patents but not on the startup’s human capital. The high relative importance of patent protection compared to other startup attributes is unanticipated, but might be explained by the fact that this study is targeted at biotech startups, a setting where patents are known to be most effective at attracting VC (Hoenig and Henkel, 2015).

Further, patents do not seem to have any significant influence in determining the success of a startup, while the leader’s education and team size do have a significant and positive effect on the startup’s ability to successfully complete an IPO. The connection found between human capital and startup performance is in line with other studies, which find the same positive relationship: a bigger group of talented people increases the success of the startup, compared to cases where the starting group is small and has a lower aggregate educational background (e.g. Hsu and Ziedonis, 2013; Hoenig and Henkel, 2015).

From a theoretical and practical perspective, this study makes several contributions. First, and most importantly, this study provides interesting insights for entrepreneurs and VCs alike on the importance of the investigated startup resources. On one hand, patents embody a very important advertising mechanism for biotech startups when applying for VC. Hence, it is advisable for new ventures to invest time and money into patenting before approaching VC investors. On the other hand, VCs may take these results as a benchmark in choosing whether to concentrate on the human capital or patent side of startups in their financing decision.

Although there is much to learn from this study, when interpreting the results of this paper certain limitations need to be kept in mind.

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First, a regression model is always a simplified model of real-world decision making: only a limited number of resources can be included and so the choice was to focus on human capital and patents. In the real investment decision, financial criteria such as expected growth rates or estimated market size may play a role. Thus, focusing on only three startup resources may cause the analysis to suffer from omitted variable bias. Related to this problem, another limitation is that it is possible that some VCs put more weight on a startup’s actual performance, as opposed to its (more distant) ability to go public.

Also with respect to the VC’s relationship with the startup team this study’s method implies a simplification. In this analysis, the size of the top management team and whether the team leader graduated from an Ivy League school is all the VC knows about the team. Thus, these results may not apply to situations in which team and VC had prior communication, or in which other information about the team is available, like for example about successful earlier ventures.

Another potential criticism of this study is that VCs are not interested in the performance variable measured, the ability to complete an IPO. For example VCs may earn returns on their investments when another firm acquires the venture and not only when the venture’s shares are sold on in an initial public offering. Their task is to maximize returns from acquisitions and public offerings. This study concentrates only on the extent to which IPOs, and not acquisitions, occur for startups with different amounts of alliances, intellectual, and human capital.

Lastly, this study focuses on the biotechnology industry. Although the results appear consistent with some past research in the electronics industry (e.g., Burton et al., 2001; Hellmann and Puri, 2002), application of this study’s methodology to other industries would give evidence on the generalizability or lack thereof of these results. Different sources of uncertainty in different industries may, for example, generate different VC and startup performance figures.

In conclusion, VCs’ financing decisions appear to be affected by tendencies that lead them to overemphasize patents, because thought to lead to strong performance, even though shown not be relevant in determining the success of the startup. Instead, human capital characteristics seem to affect startup success but to not influence VCs decision. Given the empirical results, future studies clarifying the role of patents and human capital can add greatly to prior VC studies and more generally to the field of entrepreneurship.

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The effect of patents, human capital and alliances on VC funding and startup success