“Independent and government-sponsored venture capital investments in clean-tech ventures: What is the value-added effect?”

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“Independent and government-sponsored venture

capital investments in clean-tech ventures: What is the

value-added effect?”

Master Thesis

by

Bas Kempen

S2199408

Erasmusweg 62

9602 AG Hoogezand

b.t.kempen@student.rug.nl

University of Groningen

Faculty of Economics and Business

MSc BA SB&E

January 2018

Supervisor: Dr. S. Murtinu

Co-assessor: Dr. A.J. Frederiks

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Abstract

The aim of this Thesis is to investigate how independent venture capital (IVC), government-sponsored venture capital (GVC), and the syndication of IVC and GVC impact the performance of clean-tech companies. The goal is to discover what is the value-added effect of these different investments, and disentangle this “post-investment” effect from “selection” effects. Building on the RBV and horizontal agency cost theory, new insights are provided into how syndicated investments positively or negatively impact the performance of the syndicate-backed clean-tech firm. Using a hand-collected longitudinal dataset including 135 VC investments in multiple countries over the period 2010-2017, econometric analyses are performed in order to test the impact of IVC, GVC, and their syndication on the short-term and long-short-term performance of portfolio companies. The results show that the impact of IVC is significantly positive, yet contingent on the performance measure. Namely, the impact on long-term (short-term) performance is a pure selection (treatment) effect. By contrast, GVC and IVC-GVC syndicated investments are not found to have a significant impact. Nonetheless, the negative coefficient of syndicated investments implies the presence of horizontal agency costs. Findings are robust to controls for selection bias and endogeneity concerns.

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Acknowledgements

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1. Introduction

According to Friedman (1988), the only purpose for businesses is to generate profits for shareholders. This would bring the most benefits to society, since without profits, the firm is not able to conduct business anymore. However, other researchers (Baumol, 2002; Christensen and Raynor, 2003) argued that innovation is a significant benefit to society, because innovation leads to economic growth, employment, and tremendous improvements to people’s lives. Disruptive innovations, which are innovations that create new markets (Baumol, 2002), are in particular responsible for bringing new products and services to consumers who did not have access to these products before. Technological innovation is especially important because this fosters productivity and economic growth. Schumpeter (1934) argued that entrepreneurs are the drivers of innovation, technological change, and are responsible for fostering long-term economic growth.

There may also be a downside to economic growth in terms of energy consumption and carbon emissions. Economic growth is hurting the environment, which leads to the increasing interest in sustainable strategies and innovations (Ahlstrom, 2010). According to the entrepreneurial literature field, entrepreneurship is considered to be one of the most effective solutions towards the implementation of sustainable products and processes (Berle, 1991; Demirel and Parris, 2015). This has led to the development of a new industry called clean-tech, which refers to any product, process, or service that delivers value using limited or zero non-renewable resources and/or creates significantly less waste than conventional offerings (Pernick and Wilder, 2007). Another frequently used term is green entrepreneurship, where the firms are known as green ventures. Examples of green ventures are firms that use green technologies such as renewable energy sources, energy storage, recycling and waste technologies, and technologies for the capture, storage, and treatment of greenhouse gasses (Hall and Helmers, 2010).

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6 Lerner, 2001). VC can be classified as capital that is being invested by a venture capital firm, where the funds are raised from institutional and individual wealthy investors. The funds are invested in young high-growth firms in exchange for equity. Academics and practitioners both consider VC as one of the most important drivers for venture success (Croce et al., 2013).

According to Hellmann and Puri (2002), existing research has shown that VC-backed firms play a key role in commercializing breakthrough technologies. Consequently, clean-tech ventures have attracted the attention of venture capitalists and policy makers (Ghosh and Nanda, 2010). Despite the increased attention, it is still unclear whether VCs are more or less likely to invest in clean-tech ventures, as compared to those operating in other industries. Two contradictory arguments arise. Compared to “normal” high-tech ventures, VCs might have an interest to invest in clean-tech ventures since this is an upcoming and promising industry. On the other hand, VCs might be less likely to invest because the uncertainty about the quality of the product or process is simply too high (even though VCs are specialized in investing in high risk ventures). The reason for this is that green technologies are still very new, and the sector is in its infancy (Mrkajic et al., 2017). Moreover, information asymmetries in the clean-tech sector arise because ventures typically possess a lot of intangible assets, while possessing few tangible assets. This in combination with the lack of a track record leads to high uncertainty for investors (Ghosh and Nanda, 2010). Furthermore, the potential of the sector is unclear, which results from the lack of adequate frameworks to evaluate startups (Petkova et al., 2014). Another important factor which leads to difficulties in attracting funding are the high capital amounts that tech firms require (Knight, 2010). Knight (2010) argues that the development costs of clean-tech products or processes are large to such extent that banks are not willing to take on this risky financing. Research by Mrkajic et al. (2017) investigated if being green is perceived as a signal towards VC investors, either positively or negatively, so that being green increases or decreases the likelihood of getting a VC investment. The authors did not find a significant relationship between being green and the likelihood of getting VC; however, the being green signal can be a reliable signal for investors when entrepreneurs position their business in a green sector while simultaneously offering a truly green product or service. Moreover, in this paper they state that there is a literature gap concerning the interaction of the likelihood of getting VC investment and government policies in green ventures. Also, Croce et al. (2013) found that VC investments significantly add value to VC-backed firms in European high-tech industries. If this is also the case for the uncertain clean-tech industry would be an interesting question which this research tries to answer.

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7 and in particular the energy sector, are known to be impacted by regulations and policies. Policymakers have multiple approaches to promote the innovation of low-carbon technologies and to make them survive the technological “valley of death” (Bürer and Wüstenhagen, 2009). This term refers to the phase of innovation where successful prototypes have been developed, but the product or process is not yet commercially introduced to the market. Government intervention thus might be an important factor to consider for the transition to a more sustainable business environment. For instance, the European Commission has launched the Horizon 2020 program, which is the biggest EU Research and Innovation program ever established with almost €80 billion of funding available. The goal of Horizon 2020 is to drive economic growth and to create jobs in order to tackle environmental and societal challenges (“What is Horizon 2020?,” 2017).

Another type of government involvement is the investment by governmental venture capital funds (GVCs). GVCs typically adopt a “hands-on policy approach” which means that they directly invest in a young (high-tech) firm. The reason for this investment is usually because there is a market failure, which implies that the flow of independent venture capital (IVC) is scarce. This scarcity of investment by IVCs could have multiple reasons, but the most obvious reason is that the uncertainty in terms of risks and returns is simply too high. IVCs and GVCs are very heterogeneous in their goals and objectives (which will be elaborated on in the literature section) and therefore they could have a significantly different impact on the performance of the VC-backed firm. It also happens that IVCs and GVCs co-invest in a firm, which is known as syndication. There could be several reasons, for instance the reduction of information asymmetries, overcoming capital constraints, and the exploitation of complementary resources, skills, networks, experience, and expertise. The syndication of IVC and GVC investments will be explained in detail in the literature section.

It is also possible that conflicts between the IVC and the GVC will arise. The so-called agency problem originates when cooperating parties have different goals and motives (Jensen and Meckling, 1976). There are two types of agency costs: “vertical” agency costs that exist between owners and managers arising from the separation of ownership and control (Jensen and Meckling, 1976), and “horizontal” agency costs between majority shareholders and minority shareholders (Young et al., 2008). In the VC literature, the most researched agency costs are vertical, so between the VC investor and the entrepreneur. Therefore, this Thesis will provide insights about the potential horizontal agency costs, which are also known as principal-principal conflicts, between IVCs and GVCs in the clean-tech industry.

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8 limited. Thus, the impact of the investments by IVCs and GVCs are expected to be different in this uncertain and risky industry. Second, while horizontal agency theory has been used in the context of IVC and GVC syndication (e.g. Cumming et al., 2017), this theory has never been developed specifically to extremely uncertain situations, such as clean-tech investments. This is interesting to research because co-investments of IVC and GVC could be crucial for the development of the clean-tech sector, and thus for the associated positive externalities to the society as a whole. Hence, this research will give insights in what would be the optimal source of finance for clean-tech firms (either only IVC or GVC, or a combination of the two). This latter issue is tricky because, on the one hand, IVCs alone could be reluctant to invest in clean-tech firms, due to their high risk and very uncertain returns (Marcus et al., 2013). On the other hand, GVCs alone typically lack the experience and the knowledge to add value to the GVC-backed clean-tech firm (Wüstenhagen and Teppo, 2006). Hence, syndication of IVC and GVC might remove barriers to innovation, especially since public-private partnerships are strongly fostered by Horizon 2020.

This research will contribute to the entrepreneurial finance literature, with the goal of estimating and quantifying the value-added effect of IVC and GVC investments, separately and in syndication, in clean-tech ventures. In sum, the following research questions are developed:

RQ1: What is the value-added effect of IVC investments in clean-tech ventures? RQ2: What is the value-added effect of GVC investments in clean-tech ventures?

RQ3: What is the value-added effect of the syndication of IVC and GVC investments in clean-tech ventures?

The Thesis is structured as follows. Section 2 discusses the literature review, which describes the existing literature field and the development of the hypotheses. Section 3 describes the process of the data collection as well as the methodology used to empirically test the hypotheses. Section 4 presents the results of the empirical analysis. Section 5 consists of the discussion and the conclusion. Additionally, limitations and implications for future research are given.

2. Literature review

2.1 The RBV

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9 employees in the firm such as experience, intelligence, relationships, and judgment. A firm’s formal and informal planning, controlling, and coordination activities can be grouped in the organizational capital resources. These resources can lead a firm to establish a sustained competitive advantage. This can be defined as a value creating strategy that is not being implemented by a current or potential competitor and these competitors are not able to copy the benefits of this strategy (Barney, 1991).

Barney (1991) argued that in order to hold the potential of sustained competitive advantage, firm resources must be valuable, rare, imperfectly imitable, and non-substitutable. Valuable resources are classified as resources that enable a company to conceive of or implement strategies that enhance its efficiency and effectiveness. In other words, the resource must be able to exploit opportunities and/or eliminates threats in the environment. A resource should also be rare in order to achieve a sustained competitive advantage. This means that competitors should not have access to these resources. Hence, the value creating strategy must not be used by another firm, because then the resource is not unique anymore and does not lead to an edge over competitors. Resources that are imperfectly imitable are also needed for a sustained competitive advantage. This means that firms that do not possess this resource are also not able to obtain them. Lastly, Barney (1991) argues that resources need to be non-substitutable in order to be a source of sustained competitive advantage. This means that there should not exist a strategically equivalent substitute that is valuable, rare, and imperfectly imitable.

2.2 The impact of IVC on firm performance

Independent venture capital firms are ultimately interested in one single goal: making money. They care about getting a return on their investment as high as possible. The preferred strategy is to grow the company aggressively, on average over a period from four to seven years. If the company has reached a sufficient size and credibility, it can either be sold to a corporation or the public equity markets can be entered (Zider, 1998). This is done by an initial public offering (IPO), in which the shares are being sold on the stock exchange. On average, venture capitalists expect a ten times return of capital over the investment period (Zider, 1998).

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10 investment. Factors that are being considered are market size, strategy, competition, customer adoption, the management team, and the contract terms (Kaplan and Stromberg, 2001). There is research that has investigated whether the screening process leads to higher firm performance. In particular, researchers are interested in whether there might be a causal effect on firm performance. It is being argued that IVC-backed firms might be better in terms of performance than non-IVC-backed firms before the actual IVC financing (Croce et al, 2013). This result might be to a certain degree due to the screening effect, for example, the better performance of the IVC-backed firm may be connected to the ability of IVCs to select higher quality firms (Gompers and Lerner, 2001). Croce et al. (2013) found that productivity growth is not significantly higher in IVC-backed firms compared to similar non-IVC-backed firms before the first investment round.

The second stream consists of value-adding mechanisms using corporate governance (e.g. monitoring). After the screening process is finished and the IVC has invested in a firm, the monitoring process starts. Monitoring is a crucial practice for venture capitalist firms since this could help in combatting agency costs, which are the costs that arise because of conflicts of interest between shareholders and management (Alemany and Marti, 2005). For instance, IVCs engage in post-investment activities such as taking a seat on the board of the company. This is more likely to occur in periods when the chief executive officer (CEO) changes (Lerner, 1995). Furthermore, it is also more likely that IVCs hire CEOs from outside the firm. Kaplan and Stromberg (2001) found that IVCs play a substantial role in shaping and recruiting the senior management team. Additionally, they found that in more than one third of their sample the IVC expects to be active in developing business plans, facilitating strategic relationships, and assisting with mergers and acquisitions. All these activities add value to the IVC-backed firm. Lastly, IVCs use their intangible assets such as networks, extensive knowledge, and other close professional relationships to add value to the IVC-backed firm. Alemany and Marti (2005) found that employment, sales, gross margin, and total assets grow faster in IVC-backed firms.

There have been several researchers that studied the impact of IVC on high-tech entrepreneurial firms (Cumming et al., 2017; Grilli and Murtinu, 2014a; Grilli and Murtinu, 2014b). Cumming et al. (2017) found that IVCs have a significant positive impact on the likelihood of an initial public offering (IPO). Specifically, they found that the chance of a positive exit is 87.3 % higher when an IVC investor is involved. Grilli and Murtinu (2014a) found that IVC-backed firms experience a significant increase in the growth of sales. Furthermore, Grilli and Murtinu (2014b) found that the sales value substantially increases after the investment of IVC. They also found that this effect is larger for very young firms.

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H1: IVC investments in clean-tech ventures positively impacts the performance of the IVC-backed firm

2.3 The impact of GVC on firm performance

In order to get VCs to invest in the clean-tech sector, it is crucial that the sector is legitimized. Petkova et al. (2014) found that an industries’ legitimacy could be established in two different ways. Government support and positive media attention are key factors to consider for VCs when contemplating to invest in a young high-tech industry. Bürer and Wüstenhagen (2009) found that VC fund managers perceive certain government policies such as government demonstration grants and public R&D spending as favorable for the development of clean energy technologies, and hence fosters investment of VC in the ventures that develop these technologies. Furthermore, positive media attention in the form of newspaper articles has increased significantly in the last years (Migendt et al., 2013). Both policy tools and media attention have contributed to the legitimacy of the clean-tech sector (Mrkajic et al., 2017).

Private investors such as venture capitalists may see government support (e.g. subsidies) as a signal for good investments (Kleer et al., 2010). However, Grilli and Murtinu (2014) found a negative relation between the government’s ability to support high-tech firms and directly operating (GVC) in the VC market. The policy implication that can be concluded from their analysis is: if the European VC industry ever needed governmental support, public intervention would preferably create a favorable environment for private VC initiatives through indirect forms of support rather than adopt a “hands-on-approach” (Grilli and Murtinu, 2014).

Governmental venture capital funds could be defined as funds that are managed by a firm that is completely possessed by government bodies (Grilli and Murtinu, 2014a). They are meant to add or complement to the low supply of IVC investments. Therefore, direct investment by the government (GVC) is another possible solution to develop the legitimacy of the clean-tech sector. GVCs usually invest in young high-tech firms when the firm is perceived as too risky for an IVC to invest in. In fact, the rationale for government intervention is asymmetric information. Moral hazard and adverse selection can lead to market failures, which in turn leads to a limited supply of IVC.

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12 invest compared to IVCs. Generating good returns for investors is only one objective, but not the most important. In particular, the key objective of the investment by the government is the enhancement of the overall economic value (Brander et al., 2008). One important goal of GVCs is to develop innovative firms that create value in the economy. Another tremendously important goal of GVCs is the creation of innovation and employment. Stimulating innovation might lead to new solutions to existing environmental problems and innovation can also lead to the creation of new jobs. Hence, GVCs are more willing to dedicate resources to innovative activities that could lead to new inventions, compared to IVCs who are more reluctant because this activity might be too risky and too uncertain.

There are also some differences in the value-addition that IVCs and GVCs provide. GVCs usually have a better connection to universities, research centers, and public institutions whereas IVCs are more known to provide the firm with unique contacts to a qualified workforce, potential alliance partners, suppliers and customers (Bertoni and Tykova, 2015). Bertoni and Tykova (2015) argue that GVCs are more able to obtain additional public financing because of these connections with the above mentioned public institutions. In terms of the support provided to the management team, GVCs also substantially differ. IVCs are known to provide a more strategic and technological contribution whereas GVCs put more effort into signaling the quality of the firm to stakeholders to attract additional resources (Lerner, 1999).

For the stimulation and development of the clean-tech industry, there might also be disadvantages present when GVCs decide to invest in a clean-tech company. Wüstenhagen and Teppo (2006) argue that GVCs are less experienced and lack the knowledge to add value to a clean-tech firm. This is because clean-tech is a relatively new industry and therefore highly uncertain. Coping with this uncertainty and high risk is typically easier for IVCs since they generally possess better value-adding and monitoring capabilities. Leleux and Surlemont (2003) argue that the activities of GVCs could be counterproductive for two reasons. First, the managers of a GVC tend to be civil servants and government employees. They might lack the drive and the experience necessary to support high-tech ventures. Second, managers of GVCs face different incentives. Managers in IVCs share in the profit because there are bonuses paid to managers that are linked to performance. These are usually not present in GVCs.

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13 industry. Cumming and Macintosh (2006) found the opposite in a study about Canadian GVCs. They found that GVCs tend to reduce the supply of total VC. So, GVCs displaced more effective IVC investments and also lowered the amount of total VC to Canadian high-tech firms.

There have also been researchers that studied the impact of GVC on the performance of high-tech firms. Cumming et al. (2017) found that GVCs ability to reach a successful exit (IPO) is significantly lower compared to IVCs. Brander et al. (2008) found that Canadian GVCs have a lower performance in terms of successful exits, exit values, and survivorship in comparison with IVCs. Grilli and Murtinu (2014a) found that GVCs do not exert any significant positive effect on the sales growth of entrepreneurial high-tech firms. Moreover, they also found that the impact of GVCs on the growth of employees is not significant. Bertoni and Tykova (2015) found that GVCs do not significantly impact innovation and invention. They compared IVC-backed companies with GVC-backed companies and found that IVC-backed firms patent more than GVC-backed firms.

Taking all the literature about GVC investments into account, as well as the characteristics of the clean-tech industry (highly uncertain and risky), the second hypothesis is the following:

H2: GVC investments in clean-tech ventures do not significantly impact the performance of the

GVC-backed firm

2.4 The impact of the syndication of IVC and GVC on firm performance

Syndication is a common practice in the VC industry and therefore much has been written about the syndication of venture capital investments (Brander et al., 2002; Jääskeläinen, 2012; Lerner, 1994). Syndication simply means that multiple investors invest in the same company. This could either be syndicates between multiple IVCs, multiple GVCs, CVCs (corporate venture capital funds), or a combination. This section will focus on the mixed syndication between IVCs and GVCs, and the impact they have on the performance of the syndicate-backed clean-tech firm. The underlying rationale for this interaction between IVCs and GVCs is the reduction of information asymmetries and the sharing of resources (Brander et al., 2002). Combining the pools of capital among syndicate partners leads to a better chance for clean-tech firms to acquire financing since clean-tech products and processes mostly need a large amount of capital (Knight, 2010). Cumming et al. (2017) argued that the main advantage of mixed GVCs and IVCs is that this can mitigate the deficiencies of GVCs. Moreover, mixed syndication can benefit from the favorable public policies and network contacts of GVCs.

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14 Specifically, horizontal agency costs or principal-principal conflicts between the IVCs and GVCs are likely to arise. Young et al. (2008) defined the principal-principal (PP) model of corporate governance as conflicts between controlling and minority shareholders. These are expected to emerge when there is an unequal distribution of power, so that the value of the minority shareholders is expropriated (Young et al., 2008). Sorenson and Stuart (2008) argue that VCs prefer to syndicate with investors that are similar to themselves and have comparable industry experience. However, they have provided scientific evidence that shows that the diversity of VC syndicates is increasing. They also note that this increasing diversity is necessary for expanding the network in order to get access to future deals and higher financial returns. But on the negative side, this diversity between syndicate members can cause the problem of principal-principal conflicts. Because of the difference in goals and objectives of IVCs compared to GVCs, as well as the expected difference with respect to experience in the clean-tech industry, syndicate members are diverse in this case.

The consequence for the syndicate-backed firm is that the performance may be deteriorated. In the next part, two different views will be explained. The first one is that horizontal agency costs between IVCs and GVCs could be higher in clean-tech, so that the performance of the syndicate-backed firm will be negatively impacted. The second is the other way around, hence, the agency costs between IVCs and GVCs could be lower in clean-tech, so that the performance of the syndicate-backed firm will be positively impacted.

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15 The expectation that the horizontal agency costs between IVCs and GVCs will be higher is derived from the difference in goals and objectives. In addition, there are scholars that categorize public (governmental) investors as bad investors since they do not only use economic motivations for their investment choices (Lerner, 2009). Lerner (2009) also says that there are critics that have doubts whether GVCs could be beneficial for the VC industry because of problems arising from political pressures and other bureaucratic inefficiencies. IVCs and GVCs are heterogeneous in the way they value social, political, and personal motivations, and this likely leads to conflicts of interest between the investors (Chahine et al., 2012). Furthermore, usually IVCs do not want the involvement or the interference of the government because they do not perceive the return on investment as the most important aspect (Knight, 2010). Research by Wright and Lockett (2003) found that the syndication of VC investments leads to longer decision-making, which in turn increases the potential for the occurrence of conflicts of interest. Thus, the heterogeneity between the IVC and the GVC syndicate partners leads to high coordination costs. Du (2009) investigated whether syndicates between similar or different partners perform better. They found that heterogeneity among VC firms leads to more communication costs than to benefits. This is especially the case when the VC firms in the syndicate differ in experience, which is applicable in the case of IVC and GVC because generally IVCs are the more experienced investors. Because of the difference in experience, horizontal agency costs are expected to be higher. Du (2009) argues that more experienced VCs are able to add more value in terms of monitoring and providing advice to the VC-backed firms. Du (2009) adds that the less experienced VC might put in less effort because they are not able to give high quality advice and may free ride on the services that the more experienced VC provides. To conclude, agents with contrasting beliefs have a lower alignment of actions and therefore higher agency costs.

There are also multiple arguments that can explain that the horizontal agency costs between IVCs and GVCs could be lower. The first argument is more long-term oriented. The syndication between heterogeneous partners can lead to the diffusion of knowledge among the partners. Since they contribute with different competencies and perspectives, IVCs and GVCs can learn from one another and hence improve future decision making. Sorensen (2008) argues that the benefits of heterogeneity are likely to be higher when VCs face complex and unconventional problems when they invest in companies that are highly risky. This is the case for the clean-tech industry, hence, co-investing between IVCs and GVCs can be an effective way to learn diverse management skills and monitoring practices.

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16 Second, whether a VC helps in in assembling the board of directors. Third, whether the VC helps to obtain additional financing. The last measurement consists of the frequency of interaction with the VC-backed company. Bottazzi et al. (2008) found that IVC investors are the most active investors, in terms of active involvement in their portfolio companies. Moreover, they found that active involvement positively correlates with exit performance. Syndicates between IVCs and GVCs could be established because the IVCs know that the GVCs are more passive investors. This means that the GVCs provide the financial means, but the decision making is completely delegated to the IVCs. In this scenario, the expected horizontal agency costs will be very small because the IVC and GVC have reached an agreement about how the value-adding resources will be used.

Another argument that can explain why syndicates between IVCs and GVCs are being formed, is the fact that the clean-tech industry is a relatively new industry. There is a lot of investigation and exploration towards new innovative sustainable ideas. For instance, these ideas are being developed and the plan is to arrive at the market in ten years. This timeframe is too long for IVCs because they usually want to exit the company between four and seven years. Hence, the duration of the investment is much longer compared to other (older) industries (Knight, 2010). This means that for these companies, there are no private funds available. Since these sustainable innovative ideas might be important for the future, GVCs are willing to invest. GVCs invest in these startup companies and after a couple of years can provide a credible signal to private investors (Luukkonen et al., 2013). Consequently, the GVC can form a syndicate with an IVC. They will coordinate this process in such a way that their interests are aligned. The reason for this is that both investors know that they need each other in order to get what they want. The IVC knows that the GVC is fundamental for providing the initial funding whereas the GVC knows that the IVC is fundamental for further commercializing the innovation. Moreover, the most important goal of the GVC is to maximize the social welfare, which the clean-tech industry ultimately aims to achieve (Brander et al., 2008). GVCs know that the syndication with an IVC is crucial for the achievement of this goal. In this situation, lower horizontal agency costs between IVCs and GVCs are expected because they need each other to get the most out of their investments.

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17 found that syndicated investments by IVCs and GVCs lead to a higher likelihood of a positive exit (IPO) than IVC investments alone, but this effect was not statistically different.

Since the syndication between IVC and GVC may be an important form of financing for the clean-tech sector, it is interesting to find out whether they positively or negatively influence the performance of the VC-backed firms. Taken the existing literature into account, the arguments in favor and against the syndication of IVC and GVC investments, hypotheses 3a and 3b are the following:

H3a: The syndication of IVC and GVC investments in clean-tech ventures positively impacts the

performance of the VC-backed firm, more than IVC alone

H3b: The syndication of IVC and GVC investments in clean-tech ventures negatively impacts the

performance of the VC-backed firm

Conceptual model

Figure 1: Conceptual model

3. Methodology

3.1 Data collection

In this research, data is collected using multiple commercially available databases, hence this is secondary data. This section comprises of the process of the data collection. The databases Zephyr and Orbis are used to collect the necessary data for this Thesis. Orbis and Zephyr are databases that are owned by Bureau van Dijk, which is a major publisher of business information. Bureau van Dijk is specialized in private company data combined with software to analyze companies. They offer a wide range of products and databases that focus on corporate finance and mergers and acquisitions (M&A). Orbis could be described as a database which contains private company information about more than 200 million companies in over 200 countries. Orbis gives access to all sorts of financial and accounting data including the data needed for this research, which will be specified in the next section. Zephyr

IVC

GVC

IVC and GVC syndication

Performance of VC-backed clean-tech firm

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18 could be described as a database in which M&A, IPO, and venture capital deals could be found. This database is updated daily and contains more than 1,2 million deals.

The collected data about the VC investments in the Zephyr database is found by selecting a number of criteria. First of all, the selected date ranges from 01/01/2010 till 01/01/2017 because the clean-tech industry is relatively new, and therefore recent investments have been chosen. Secondly, a lot of keywords are used in order to find VC-backed clean-tech firms. The keywords that are used are derived from the official Zephyr Cleantech M&A Activity Report established by Bureau van Dijk (Zephyr published by BvD). The keywords are based on the business description and the overview of the target company. The following keywords are used: “alternative energy” “alternative fuel” “alternative power” “biomass” “bioenergy” “bio energy” “bio-energy” “biofuel” “fuel cell” “hydrogen” “photovoltaic” “renewable energy” “reuseable energy” “re-usable energy” “solar” “wind power” “wind farm” “wave power” “geothermal” “geothermal” “hydropower” “hydro-power” “bio-diesel” “biodiesel” “energy resource management” “water purification” “intelligent power” “air quality” “energy efficiency” “thin film energy” “thin-film energy” “energy efficiency software” “energy storage” “water treatment” “waste management” “biogas” “anaerobic digestion” “wastewater” “green construction” “green buildings” “smart meter” “smart grid” “energy monitoring” “marine energy” “solar thermal” “algae” “green energy” “cleantech” “clean tech” “environmental technology” “greentech” “green infrastructure” “clean energy” “tidal power” “tidal energy” “biodegradable” “wind turbine”. The number of keywords is this large because the clean-tech industry is hard to define, since clean-tech firms could be present in almost any industry. There is no specific country selected, hence the deals that are found are worldwide. This search leads to a total number of 1518 deals. Then, the BvD-ID-numbers of the VC-backed companies are entered in Orbis in order to retrieve the financial (performance) data. The number of companies found in Orbis is 549, the reason for this being the fact that there are multiple deals for some companies. Financial data selected includes operating revenue, net income, cashflow, total assets, ROE, ROA, and sales. The reason for the selection of these multiple financial performance measures is that there might be missing data. Hence, the most suitable performance measure can be selected based on available data and the relevance of the indicator. Information about whether a company is listed on the stock exchange or not is included as well, since this data will be used for the exit (IPO) performance. Since for a lot of companies there is no financial data available, these were dropped. The final sample consists of 135 VC-backed clean-tech companies in 25 countries.

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19 backed by GVCs is more difficult because sometimes this could not be deduced from the name of the venture capitalist. When this is the case, further investigation on government websites is being done in order to be fully sure that it is really a governmental venture capitalist. In order to collect the control sample, the following steps are taken. The search criteria on Orbis are almost the same, except not all the keywords are selected. For the control group, the selected keywords are: “tidal power”, “renewable energy”, “biofuel”, “wind power”. These words are randomly picked in order to prevent selection bias. The companies without relevant available financial data were deleted. The final group of non-VC-backed clean-tech companies consists of 1748 companies, in the same 25 countries.

3.2 Measurements

In this section, the selection of the dependent, independent, and control variables will be explained. In order to know which variables are necessary for this analysis, empirical papers that covered similar topics were investigated. The chosen variables are thus inspired from or based on existing literature and research.

3.2.1 Dependent variables

The dependent variable in this research is the performance of the VC-backed companies. In order to measure the performance of these companies, two different variables are used. In fact, these measures can be seen as the long-term performance and the short-term performance of the company. The variable used for the measure of long-term performance is whether the VC-backed firms had an initial public offering (IPO). This is a widely used measure in the VC literature (Botazzi and Da Rin, 2002; Brander et al., 2002; Sorensen, 2007; Botazzi et al., 2008). This is done by creating a dummy variable called IPO that equals 0 if the firm has not been listed, and equals 1 if the company has gone public. The short-term performance is usually measured by the return on assets of the firm (ROA). This is the net profit divided by the total assets, which is used to measure the profitability of the firm. Since ROA data was only available for some sampled firms, Totalassets has been used to measure the performance. Total assets is a measure for short-term performance, as well as a general measure of firm size (Puri and Zarutskie, 2012). Because the distribution of total assets does not follow a Normal distribution (but instead a log-Normal one), the Totalassets_log variable has been created by taking the logarithm of Totalassets.

3.2.2 Independent variables

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20 GVC, and 0 otherwise. Syn is a dummy variable that takes value 1 if the clean-tech firm is backed by a syndicate consisting of both an IVC and a GVC, and 0 otherwise. These variables are equal to 0 in the years before the investment, and take on value 1 in the year of, and all the years after the investment. The variables IVC_pre, GVC_pre, and Syn_pre are created in order to account for a so-called selection effect (Gompers and Lerner, 2001; Croce et al., 2013). The selection effect means that the superior performance of VC-backed companies could be due to the already better performance before the investment. Hence, VCs possess the ability to already select higher quality firms from the beginning. The IVC_pre, GVC_pre, and Syn_pre variables take value 1 in the year before the investment and 0 in all the other years.

3.2.3 Control variables

Multiple control variables will be used in this Thesis in order to eliminate alternative explanations that might have an undesirable effect on the dependent variables. Previous research has shown that multiple variables are able to affect the performance of the VC-backed firm (Botazzi et al., 2008). A control variable used in this study is the age of the firm at the time of investment. This variable is included because older companies might perform better than younger companies because of several reasons. Older companies may have more experience in terms of management and/or more financial resources available. In addition, it is more likely that younger companies need more support, both in terms of financial resources and other value-adding activities. The variable Age is calculated by substracting the investment year from the incorporation year. Next, the Age_log variable is computed by taking the logarithm of the Age. This is done to make the variable less skewed and closer to a Normal distribution. Moreover, dummy variables are created for the countries since different business climates might influence the impact of VC on the clean-tech ventures. Differences between organizational cultures in countries have been widely studied in the academic literature (Hofstede, 1983). Also, different laws and regulations may be able to affect the VC-backed ventures positively or negatively. The last control variable consists of year dummies, this is to account for the fact that the state of the economy is not the same every year. Macroeconomic effects such as recessions or periods of economic growth may influence the performance of the firms under consideration.

3.3 Analysis

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21 the degree of relatedness among pairs of variables. Empirically testing of the hypotheses is the next step. Both a logit and a multiple regression analysis are executed since this research uses two different types of dependent variables. In order to test the dependent variable IPO, a logit model is used because the IPO variable is categorical. In fact, IPO is a binary variable that takes on values 0 and 1. The logit regression is used to estimate the odds of a binary dependent variable on one or more independent variables. Research by Lewis (2007) investigated two different forms of multiple regression; stepwise regression and hierarchical regression. These are two different methods to determine the quality of the predictors. According to Lewis (2007), hierarchical regression is an appropriate method used to analyze the effect of a predictor variable after controlling for other variables. Furthermore, because the predictors in this study are chosen based on theory and past literature, hierarchical regression is more suitable (Lewis, 2007).

3.3.1 Descriptive statistics

The descriptive statistics of the variables of interest can be found in Table 1 in Appendix A. The different measures will be explained very briefly. The mean is the arithmetic average of the values. The standard deviation could be defined as the amount of variation from the mean. As can be seen in Table 1 in the Appendix, the standard deviation of the total assets is quite high. A reasonable explanation for this is that the sizes of the companies are different, which leads to the presence of outliers in the sample. The minimum and maximum do not need any explanation. Skewness and kurtosis are both measures for the symmetry and distribution of a set of variables. In more detail, skewness measures the lack of symmetry compared to a Normal distribution whereas the kurtosis measures the distribution of the tails. Hence, kurtosis describes the “heaviness” or “lightness” of the distribution of the tails compared to a Normal distribution. The skewness is positive for most of the variables, however, it is negative for

Age_log. In terms of the size of the skewness, most of the variables have a skewness that is different

from 0. This means that the data is asymmetric and therefore does not follow a Normal distribution. The kurtosis is positive for all the variables and also large for most of the variables. A kurtosis value of 3 is associated with a Normal distribution, so in this case the distribution of the kurtosis does not follow a Normal distribution.

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22

Table 2. Types of VC

Type of VC No. Percentage

IVC 106 78,52%

GVC 9 6,67%

Syndicate 20 14,81%

Total 135 100%

Table 3. Number of IVC/GVC/Syndicate per country

Country No. firms IVC GVC Syndicate

AT 3 3 0 0 AU 3 3 0 0 BE 2 0 0 2 BR 1 0 1 0 CA 5 5 0 0 CH 2 0 0 2 CN 1 1 0 0 CO 1 1 0 0 DE 9 2 1 6 DK 1 1 0 0 ES 5 4 1 0 FI 6 3 1 2 FR 22 21 0 1 GB 32 30 0 2 IN 6 6 0 0 IT 4 4 0 0 JP 1 1 0 0 LT 1 1 0 0 NL 9 4 2 3 NO 1 1 0 0 RS 1 1 0 0 RU 1 1 0 0 SE 7 2 3 2 SG 1 1 0 0 US 10 10 0 0 Total 135 106 9 20

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3.3.2 Models used to test the hypotheses

In order to estimate the impact of receiving IVC, GVC, or the syndication of IVC and GVC on the performance of clean-tech companies, I performed both a logit regression (where the dependent variable is IPO) and a panel fixed effects regression (where the dependent variable is Totalassets_log), where selection effects are controlled (replicating the procedure suggested by Chemmanur et al., 2011). Multiple models are used, and the differences across models are the different specifications that are used. Model 1 and Model 2 represent the baseline models and are shown in Tables 5 and 6, respectively. Then, to test for a potential selection effect, Model 3 and Model 4 – shown in Tables 7 and 8 – include the variables IVC_pre, GVC_pre, and Syn_pre. The same control variables are included in all the models described here.

4. Results

In this section, the results that have been found will be explained in detail. First of all, the correlation analysis is presented to check whether collinearity issues may be at play. Then, the regression results are provided.

4.1 Correlation analysis Table 4. Correlation analysis

1 2 3 4 5 6 7 8 9 1 IPO 1 2 Totalassets_log 0.23* 1 3 IVC_pre 0.04* -0.08* 1 4 IVC 0.09* -0.12* -0.02 1 5 GVC_pre 0.0001 -0.02 -0.003 -0.005 1 6 GVC -0.003 -0.06* -0.005 -0.01 -0.001 1 7 Syn_pre -0.008 -0.03* 0.02 -0.008 -0.001 -0.002 1 8 Syn -0.03* -0.05* -0.008 -0.02 -0.002 -0.004 -0.003 1 9 Age_log 0.06* 0.27* -0.13* -0.26* -0.03* -0.07* -0.07* -0.15* 1

Note: * Represents statistical significance at 1%

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24 cause for concern. In this study, the mean VIF is 7.4 and therefore there are no issues regarding multicollinearity.

4.2 Testing the hypotheses

As mentioned earlier, the results of Model 1 show the impact of the IVC, GVC, and Syn variables on the dependent IPO variable. The outcomes of this logit regression are shown in Table 5 below.

Table 5. Model 1, IPO as dependent variable (N=11496)

Logit Logit

Variable Coefficients Odds ratio

IVC 1.3387*** _3.8141*** (0.4408) (1.6814) GVC 0.4208 1.5231 (1.8491) (2.8164) Syn -1.1994 0.3014 (0.9010) (0.2715) Age_log 0.6068*** _1.8345*** Year Dummies Country Dummies (0.1576) YES YES (0.2891) YES YES

Marginal effects; Standard errors in parentheses *_{p < 0.10,}**_{p < 0.05,}***_{p < 0.01}

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25 The coefficients in Table 5 are in terms of log odds, this means that for IVC the coefficient of 1.3387 implies that a one-unit change (so going from non-IVC-backed to IVC-backed) leads to a 1.3387 unit change in the log of the odds. By calculating e^1.3387 = 3.8141, the OR is found. Hence, receiving IVC increases the likelihood of going public by 3.8141 times, as compared to a company that does not receive IVC. The coefficient or OR for IVC is significant at 1%. Hence, this positive effect of IVC is consistent with the existing literature (Cumming et al., 2017; Grilli and Murtinu, 2014a; Grilli and Murtinu, 2014b). GVC and Syn are not found to be significant. However, for GVC the coefficient or OR is positive. The standard error is quite high and therefore one can say that there is a high variance of the effect of GVC on the likelihood of IPO. Consequently, the effect is positive but insignificant which could imply that there are a lot of differences with respect to GVC programs. Some GVC investments might be important in influencing the likelihood to become a public company and others do not. The coefficient for Syn is negative which implies that investments by syndicates consisting of IVCs and GVCs negatively influence the likelihood of becoming a public company. The reason for this could be that there are horizontal agency costs between the two investors. However, this result is insignificant and therefore not much can be concluded.

The results of Model 2 show the impact of the IVC, GVC, and Syn variables on the dependent

Totalassets_log variable. The outcomes of this multiple regression are shown in Table 6 below.

Table 6. Model 2, Fixed effects; Totalassets_log as dependent variable (N=11132)

Multiple regression Variables Coefficients IVC 0.6452*** (0.2080) GVC 0.8909 (0.6997) Syn 0.0615 (0.3103) Age_log 0.0000 (.) Year Dummies Country Dummies YES YES

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26 The regression that is used is a panel regression with fixed effects in order to control for endogeneity. Endogeneity occurs when an independent variable is correlated with the error term. The cause of this problem can be measurement error or omitted variables. When an independent variable is correlated with the error term, then the coefficient of the variable is biased and therefore inaccurate. In general, a random effects model is more efficient than a fixed effects model. However, the problem could be that the effect of the unobserved variable will be incorrectly attributed to the coefficient of one of the independent variables in the model. If this occurs, then the random effects model may not consistently estimate the coefficients. For instance, there might be an unobserved variable that is positively correlated with IVC. To illustrate this with an example, imagine that the quality of the clean technology of the IVC-backed company is the unobserved variable. The estimated coefficient is then the effect of IVC plus the unobserved variable that is in the error term of the model. Hence, the coefficient of IVC might be inflated. By estimating the regression with fixed effects, the so-called within transformation of the model is performed. This fixed effects model controls for unobserved variables that are constant over time. This leads to the disappearance of unobserved effects and therefore the effect can be estimated consistently.

This model is also statistically significant because the p-value of the Chi2 test statistic is p<0.000. The interpretation of the coefficients is different from the interpretation of the logit model. In this case, the dependent variable is a logarithmic function and the independent variables are binary variables. Therefore, the coefficient is interpretable as an increasing (or decreasing) percentage. The coefficient of IVC is 0.6452 and statistically significant at 1%. This means that the total assets of the IVC-backed clean-tech firm increase by 64.52 %, compared to firms that do not receive IVC. Hence, IVC has a positive impact of the performance of the IVC-backed firm.

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27 In order to disentangle between a selection effect and a value-adding (treatment) effect, Model 3 and Model 4 are established. As described earlier, these two models are the same as before but now the

IVC_pre, GVC_pre, and Syn_pre variables are added. The results of the logit model are presented in

Table 7, which again shows both the coefficients and the odds ratios.

Table 7. Model 3, IPO as dependent variable (N=11496)

Logit Logit

Variable Coefficients Odds ratios

IVC_pre 1.4061*** _4.0798*** (0.3783) (1.5433) GVC_pre 0.8976 2.4537 (1.7617) (4.3228) Syn_pre -0.2385 0.7878 (1.3782) (1.0858) IVC 1.3876*** _4.0053*** (0.4507) (1.8052) GVC 0.4760 1.6097 (1.8710) (3.0118) Syn -1.1505 0.3165 (0.9091) (0.2877) Age_log 0.6289*** _1.8756*** Year Dummies Country Dummies (0.1572) YES YES (0.2949) YES YES

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28 investment the likelihood was already larger, the linear combination of the two coefficients needs to be computed in order to draw conclusions. The linear combination (IVC - IVC_pre) leads to an odds ratio of 0.9817 with a p-value of 0.927. The odds ratio is very close to one and highly insignificant. The intuition behind this linear combination is that the net effect of the IVC investment, i.e. the value-addition of IVC after accounting for the selection ability of the IVC, is not statistically different from zero. Hence, the real effect of IVC on IPO is a pure selection effect. This is an interesting result because multiple scholars did not find a significant selection effect (Bertoni et al, 2011; Croce et al, 2013). However, in these studies the dependent variable was not the likelihood of IPO, but the growth of employment and sales in the paper of Bertoni et al (2013) and productivity growth in the paper of Croce et al (2013).

The variables GVC_pre, GVC, Syn_pre, and Syn are not found to be significant. Even though the odds ratio for GVC_pre is 2.4537, it is not statistically significant due to the large standard error. Hence, it seems that GVCs also try to select companies that are already performing better before the investment. The same reasoning holds for the odds ratio for GVC. The coefficient for Syn_pre is negative and the accompanying odds ratio for is smaller than zero which implies that the likelihood to become a public company is not already higher before the investment. The coefficient for Syn is also negative. However, both variables are not statistically significant and consequently not much can be concluded.

The results of Model 4 are presented in Table 8.

Table 8. Model 4, Fixed effects; Totalassets_log as dependent variable (N=11132)

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29 Age_log 0.0000 (.) Year Dummies Country Dummies YES YES

As in the previous models, the p-value of the Chi2 test statistic is p<0.000. In this model, the coefficient of IVC_pre is slightly positive and not statistically significant. This is an extremely interesting finding because the coefficient for IVC_pre in the previous model was found to be positive and highly significant. Hence, there is no selection effect and thus IVCs do not invest in companies that are already performing better (i.e. that are larger) before the investment. The coefficient for IVC is 0.7539 and statistically significant at 1%. The interpretation is that the total assets of the IVC-backed clean-tech firms increase by 75.39 % after the investment in comparison with firms that are not IVC-backed. When computing the linear combination (IVC - IVC_pre), the coefficient is 0.4781 with a p-value of 0.035. This implies that it is significant at 5%. Thus, in this case the impact of IVC is not a pure selection effect, but a pure treatment effect. This finding implies that the contribution of the investment are the value-adding and monitoring activities that the IVC provides. Hence, activities such as providing advice, taking a seat on the board, providing complementary assets and networks, are all contributing to an increase in performance. This finding is consistent with existing literature (Cumming et al., 2017; Grilli and Murtinu, 2014a; Grilli and Murtinu, 2014b).

The coefficient for GVC_pre is positive which implies that there is a selection effect regarding GVC. The coefficient for GVC is also positive but a bit smaller than GVC_pre. Since both coefficients are insignificant, the finding is that GVC investments do not significantly impact the performance of the GVC-backed clean-tech firm. The coefficient of Syn_pre is negative and the coefficient for Syn is slightly positive. However, the coefficients for Syn_pre and Syn are both insignificant and thus the mentioned coefficients are not clearly interpretable.

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30 dependent variable is used to test the hypothesis. H2 can be supported since the GVC does not lead to an increase in performance for the GVC-backed clean-tech firm. This finding is consistent with the existing literature about GVC investments (Brander et al., 2008; Cumming et al., 2017; Grilli and Murtinu 2014a). H3a and H3b cannot be supported because the syndication of IVC and GVC does not significantly impact the performance of the syndicate-backed clean-tech firm, either positively or negatively.

4.3 Robustness checks

In order to test the reliability and strength of the previous regression outcomes, it is valuable to perform robustness checks. The term robustness refers to the strength of statistical procedures and is used to check if the previously estimated models are reliable. The first robustness check is the estimation of a different model. Model 2 and Model 4 were estimated by means of a panel regression with fixed effects. Now, the same models are being estimated using a panel regression with random effects in order to check if the results remain consistent.

Model 5 and Model 6 can be found in Table 9 and Table 10 in Appendix A. The results of Model 5 are very similar to the results of Model 2. The variable IVC is still positive and significant at the one percent level while GVC and Syn are positive but insignificant. These results are consistent with the results from the fixed effects model and therefore it can be concluded that the earlier estimated results are robust. The results of Model 6 are also similar to the results of Model 4. The variable IVC_pre and GVC_pre are still positive but insignificant. Syn_pre is still negative and insignificant. The same holds for the variable

GVC, it remains positive but insignificant. The only noticeable differences between the fixed effects

and the random effects models are the variables IVC and Syn. In the fixed effects model the variable

Syn was 0.0077 and now it is -0.0483, so it went from positive to negative, but it is only a very small

difference. The variable IVC is still positive and significant at the one percent level. However, the coefficient decreased from 0.7539 to 0.6084. This means that the increase in total assets is approximately 15 % higher in the model with fixed effects. Since the results are similar to the previously estimated models, the outcomes of the hypotheses remain the same.

5. Discussion and conclusion

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31 the hypotheses supported or not, answers to the research questions can be provided. To answer the first research question, What is the value-added effect of IVC investments in clean-tech ventures?, one can say that this effect depends on the dependent variable used. When distinguishing between a selection effect and a real value-added effect (as done in Models 3 and 4), findings highlight a pure selection effect on IPO and a pure treatment (value-added) effect on the growth in total assets. Hence, the value-added effect of IVC on clean-tech ventures’ total assets growth is represented by the monitoring activities and other valuable resources that the IVC provides. These could be helping writing business plans, providing strategic advice, hiring a competent management team, taking a seat on the board, and providing the clean-tech company with access to networks and other professional relationships. 67

To answer the second research question, RQ2: What is the value-added effect of GVC investments in

clean-tech ventures?, one can conclude that GVC does not significantly add value to the clean-tech

firms. Even though the coefficients are all positive in all the models, the results are not significant due to the relatively large standard error. This implies that some GVC investments add value to the clean-tech companies, but the variance among different GVC programs is large. Consequently, the negligible effect of GVC is likely to be the result of two contradictory forces. The positive impact related to the stimulation of innovation and the creation of jobs might be offset by the low amount of experience and the limited knowledge about adding value to clean-tech firms. GVC could be important for the development of clean-tech firms; however, due to the large standard error, it is not clear which type or sort of GVC adds the most value.

To answer the third research question, RQ3: What is the value-added effect of the syndication of IVC

and GVC investments in clean-tech ventures?, it can be concluded that syndicated investments do not

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5.1 Practical implications

Multiple important implications for practitioners in the field of venture capital and the clean-tech industry have emerged from this research. The clean-tech industry is an upcoming sector that is extremely valuable for a more sustainable business environment and society in general, and therefore the practical implications are important to venture capitalists, governments, and society. For independent venture capitalists, these implications differ in terms of which criteria are more important to them. If the long-term performance is more important, then they should devote more attention towards screening potential ventures to invest in. If the short-term performance is more essential, it would be better if they invest in the value-adding activities that they provide such as monitoring and giving advice.

Since direct investments by the government are found to have an insignificant effect on the performance of clean-tech ventures, it would be wise to re-evaluate their investment motives. Getting more experience in funding and monitoring high-risk ventures might lead to an improvement in providing value-adding activities and thus enhance performance of the backed venture. However, it could be the case that GVCs are crucial in terms of generating innovations and patents. This might lead to spillover effects from which the entire society contributes. Consequently, investments by GVCs could be able to improve the benefits to society instead of enhancing individual firm performance. In addition, in syndicated IVC and GVC investments the goals of IVCs and GVCs need to be more aligned in order to positively contribute to the performance of clean-tech firms. Creating more arrangements and agreements in terms of the investment procedure might lead to more unity and less conflicts and hence a higher contribution towards the performance of clean-tech firms.

5.2 Limitations and future research

There are some limitations regarding this research and these will be highlighted in this section in order to give recommendations for future research. The first part of this section is about limitations regarding the data collection process. Due to incompleteness of the Orbis and Zephyr databases, not all information could be collected. For instance, for the VC-backed clean-tech companies, not all performance indicators that were deemed as relevant for this research were available. In addition, there were many cases of missing data for the years of interest. This means that for some companies only financial performance data in the years before the investment was available while for others there was only performance data available for the years after the investment. Hence, the eventual total panel dataset was unbalanced.

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33 investments by the government because filtering for these investments in the search strategy system is not possible. Therefore, all the venture capital deals were investigated one by one in order to detect whether the investor is an IVC, GVC, or syndicate. A suggestion for future research would thus be to use a much better dataset, like Thomson One, so that more data will be available and the amount of IVCs and GVCs in the sample is similar

The previously mentioned value-adding resources that IVCs provide to their clean-tech portfolio firms could be an area of future research. It would be interesting to investigate in which ways IVCs specifically contribute to a higher performance. Another area of future research could be the investigation into the differences among GVC programs. This might lead to insights into GVC composition and to which GVCs are able to add value to clean-tech companies and which are not. The last area of future research considering direct government investments is the impact on the society as a whole. Since the most important goal of GVCs is to enhance the overall economic value, research into how GVC investments leads to positive externalities is meaningful.

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