The signalling effect of education for entrepreneurs

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Master Thesis

The Signalling Effect of

Education for Entrepreneurs

Vincent Vos

Supervisor:

UVA#: 10471405

Prof. Y. Song

VU#: 2587047

Abstract

In this master thesis, the effect of educational signalling in equity-based crowdfunding

has been quantitatively analysed. The literature about educational signalling for entrepreneurs

is still growing, but the research has not yet expanded to the relatively new field of

equity-based crowdfunding. Sixty-eight crowdfunding projects from an equity equity-based crowdfunding

platform have been analysed. Statistical analysis revealed that: 1) higher levels of education

within the team increase the amount of funding received, and 2) higher educational

heterogeneity within the team increases the amount of funding received. The data provides a

unique insight into the equity-based crowdfunding community in the Netherlands, and the

results enrich the existing literature about educational signalling for entrepreneurs.

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Introduction

Entrepreneurship contributes to GDP, a less volatile labour market and to innovation, effectively creating more value than the private gains captured by the entrepreneur (see van Praag & Versloot, 2007 for an overview). There are many factors contributing to entrepreneurial success, but one that is most easily influenced by policy makers is education (van der Sluis et al., 2008). The returns to education have been studied well in the context of employment, and added years of education have a significant effect on productivity (Harmon et al., 2003). In addition, education has significant value as a signal of this productivity in a labor market with incomplete information (Riley, 2002). Entrepreneurs may use the same signaling effect in another market with incomplete information; the selection process by clients, stakeholders or business relations (van der Sluis et al., 2008). Backes-Geller & Werner (2006) have analyzed how innovative start-ups specifically use signaling to reduce their labor market and credit market problems. They recognize an information asymmetry gap between the founder and potential investors, and conclude that educational signaling is ‘a powerful instrument in overcoming typical problems of asymmetric information for innovative

start-ups, an aspect which has rarely been analyzed. The dearth of studies is largely due to a lack of adequate data.’ (Backes-Gellner & Werner, 2006, pp. 15-16). This research project aims to further

analyze the effect of educational signaling. Different insights have been gathered about the effectiveness of signals that entrepreneurs use to induce (small) investors to commit financial resources in a new field; equity based crowdfunding (Ahlers et al., 2015). Since crowdfunding has become an increasingly dominant financing method in the early stage of start-ups (Hornuf & Schwienbacher, 2014), it provides an abundance of information overcoming the aforementioned research constraint mentioned by Backes-Geller & Werner.

This research project attempts to analyse the effect of education signalling in financing success for entrepreneurs. More specifically, the research project focuses on web-based, equity-based crowdfunding, as it shows the greatest need for education signalling; this is because the investor’s returns are more reliant on the founder’s productivity as compared to other forms of crowdfunding. The presence and presentation of the founder’s level of education will be taken into account, as well as many control factors and demographic variables, in order to find an answer to the following research question: What is the effect of education signalling on the investor’s decision making in equity based crowdfunding? Since there are many factors involved in the investor’s decision on crowdfunding, the effect is expected to be smaller on crowdfunding platforms than in other situations. However, as entrepreneurs can signal their skill level to the potential investors, the effect is still expected to be positive.

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5 The paper aims to make a valuable contribution to the existing literature on education signalling. The next section will start with a review of the existing literature in the field, followed by a concise build-up of the data acquisition method used, which will result in a clear framing of the sample. This will be followed by careful analysis of the data, a conclusion and a discussion of the possible implications and limitations of the research.

Literature Review

Factors influencing investor's decision

A number of different criteria venture capitalists use to base their investment decision on have been studied in the last decades (Wells, 1974; Poindexter, 1976; Tyebjee & Bruno, 1984; Macmillan, Siegel & Narasimha, 1986; MacMillan, Zemann & Narasimha, 1987; Hisrich & Jankowicz, 1990; Bygrave & Timmons, 1992; Fried & Hisrich, 1994; Muzyka, Birley, & Leleux, 1996; Wright, Robbie & Ennew, 1997; Zacharakis & Meyer, 1998; Zacharakis & Shepherd, 2005; Zacharakis, McMullen & Shepherd, 2007; Sudek, Mitteness & Baucus, 2008). According to this body of literature, a list of criteria emerges that are dominantly required by investors. In the concept of the new venture, there should be: a significant potential for earnings growth; a business idea that can be brought to market within two to three years and it must offer a substantial competitive advantage or be in a relatively non-competitive industry. Next, the overall capital requirements should be reasonable. To continue, there should be an exit opportunity that allows the investors to grab a return, which should have a potential to have a high rate of return as well as a high absolute return, in order to make the investment a feasible undertaking for the investor.

In addition, the management should have certain characteristics: the management must display a certain personal integrity, the management should have performed well at prior jobs, they should be able to identify risks and therefore be realistic, they should be hardworking yet flexible, have a thorough understanding of the business and industry, should be able to show leadership, and should have general management experience.

While this list may represent quite accurately what it is that investors are searching for, it gives no explanation on how investors should proceed in assessing these criteria. Experience and expertise may be beneficial in the assessment of the concept and the potential return, but an objective assessment of the management remains difficult. The attitudes and capabilities of the founders have a significant effect on the performance of the new venture (Rock, 1987; Hart, Stevenson, & Dial, 1995; Greene, Brush, & Hart, 1999), but judging the quality of entrepreneurs remains hard for venture capitalists (Kozmetsky, Gill & Smilor, 1985; Bygrave & Timmons, 1992; Zacharakis & Meyer, 1998;

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6 Smart, 1999). Especially the entrepreneur’s human capital remains hard to assess for investors (Barney, Wright & Ketchen, 2001; Riquelme & Watson, 2002).

A possible reason for why the investors struggle so much in judging their human capital is the use of heuristics: as Tversky & Kahneman (1974) indicated, there are three global heuristics that may lead to biases when making judgments under uncertainty: representativeness, which may falsely accredit aspects to someone because they appear to belong to a certain category (for example, an entrepreneur may appear more potent due to a posh or international family name, availability of instances or scenarios, which may lead to overestimate the presence of a phenomenon because one has observed that certain phenomenon more often than the other (for example, an entrepreneur may appear more potent when the investors encounters mostly positive referrals) and finally, the anchoring effect, which in this situation may cause the investor to always have a bias of an entrepreneur towards the investor’s first impression.

Signalling

A second reason may be found in signalling (Merton, 1968; Spence, 1974; Levy & Lazarovich-Porat, 1995; Podolny, 2005). For a review about signalling theory, see Connelly et al. (2011). Founders actively use signalling to communicate their human capital in order to attract funds for their ventures (Busenitz, Fiet & Moesel, 2005). Venture capitalists similarly attempt to solve information asymmetry issues during investment decisions (Mason & Stark, 2004). One method of estimating risk is by working predominantly with serial entrepreneurs (MacMillan, Siegel & Narasimha, 1986; Wright, Robbie & Ennew, 1997), but this leaves a large potential market untapped, especially because serial entrepreneurs do not necessarily perform better than novice entrepreneurs (Birley & Westhead, 1993; Wright, Robbie & Ennew, 1997; Westhead, Ucbasaran & Wright, 2005). Another method is to only invest in ventures of entrepreneurs you know (Shane & Stuart, 2002; Hsu, 2007), which fits to sociology based signalling theory, emphasizing the social character of economic exchange under conditions of market uncertainty (Podolny, 1994). This is however another method that leaves a large portion of the market untapped: venture capitalists cannot limit their investments to only people they know.

Due to the absence of direct measurements of risk for new ventures, venture capitalists are forced to use indirect measurements of risks as a signal of the actual risk (Levy & Lazarovich-Porat, 1995). This is where signalling comes in. Entrepreneurs cannot convince potential investors of their skills by simply claiming they are good at it. They need a signal that only high quality candidates are able to give. A higher level of education is presumed a reliable signal as lower quality candidates will struggle too much with obtaining such levels of education, making the (time) investment too costly to benefit from the signal (Spence, 1974). This is a different perspective on the benefits of education than

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7 the traditional approach to education, referring to the beneficial effect of education on the human capital of the individual (Weiss, 1995; Connelly et al. 2011).

Education

A person’s formal educational background may yield rich but complex information. It has effects on a person’s knowledge and skills, but exactly which degree has been obtained is expected to have quite an effect on which knowledge is obtained. An individual who has completed a language degree is expected to have different qualities than one who studied business. An investor may search for someone with an educational degree in which matters have been studied that effect business and entrepreneurship, especially since it has been shown that components of entrepreneurship can be taught (Gorman et al., 1997; Kirby 2005). Entrepreneurship education “is a process through which

such education is provided to people ‘with the ability to recognize commercial opportunities and have the insight, self-esteem, knowledge and skills to act on them’. It includes instruction in opportunity recognition, commercializing a concept, marshalling resources in the face of risk and initiating a business venture, as well as instruction in traditional business disciplines such as management, marketing, information systems, and finance“(Jones and English, 2004, p.416). Educational programs

focussed on entrepreneurship are now being introduced everywhere in the world (Schwartz & Malach-Pines, 2009), but have not existed for so long that such degrees are common among entrepreneurs. Investors may still look for educational degrees focussing on the aforementioned topics, especially since most people pick their education quite carefully, causing the chosen degree to carry quite some information regarding a person’s values and cognitive preferences (Hambrick & Mason, 1984). In other words, an educational level reflects an individual’s cognitive ability and skills (Wiersema & Bantel, 1992).

The effect of education on the performance of firms has been studied well, finding that greater levels of innovation are achieved with higher levels of management team education (Kimberly & Evanisko, 1981; Wiersema & Bantel, 1992; Bantel, 1993). Having a broad and diverse educational base may also allow management teams to more adequately deal with a wide range of issues (Tihanyi et al., 2000). Having a greater heterogeneity in education may lead to greater diversity of information sources as well as a varied base of perspectives, leading to greater performance (Milliken & Martins 1996).

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Educational Signalling

The effect of educational signalling in the labour market has been extensively studied (Riley 1979; Albrecht, 1981; Elliot, 1991; Breen, 2005; Weiss, Klein & Grauenhorst, 2014), but these results cannot be readily extended to the investment market. There are some clear parallels with funding of entrepreneurial ventures however, and signalling theory has been employed in this domain (Elitzur & Gavious, 2003; Janney & Folta, 2003; Busenitz et al., 2005; Higgins and Gulati, 2006; Hsu, 2007; Kleer, 2010, Gimmon & Levie, 2010). Its effect has also been studied in some specific sectors. Deeds et al. (2004) suggest that the quality of the business education of a venture’s management team is positively related to the legitimacy of a venture in an emerging sector of the financial industry. Some research has focussed on the effect of education signalling during IPO’s (Bruton et al., 2010). Zimmerman (2008) suggests that higher levels of heterogeneity in the venture management team’s functional and educational background increases the amount of capital raised during IPO’s. Similarly, Certo (2003) and Filatotchev & Bishop (2002) indicate that leaders of starting firms try and put many diverse board members in their board to increase legitimacy of the firm.

All these findings are consistent with research suggesting the decreasing relevance of financial information such as earnings and book values and the increasing importance of nonfinancial information (for a recent review, see Modi, 2016); especially since financial values are often hard to predict and not available for new firms. This suggests that signalling is becoming even more important. Signalling theories however, tend to be difficult, if not impossible, to test empirically (Levy and Lazarovich-Porat, 1995, p.39). One of the most important issues is the difficulty in holding all other parameters constant. That is, in the case of educational signalling, it is obviously difficult to keep the level of knowledge and access to valuable networks constant.

Crowdfunding

Over the last few years, crowdfunding has seen an increase in popularity. “Crowdfunding is a

novel method for funding a variety of new ventures, allowing individual founders of for-profit, cultural, or social projects to request funding from many individuals, often in return for future products or equity” (Mollick, 2014, p.1). The concept is becoming increasingly popular and

successful, and the body of peer-reviewed literature is slowly growing. One of the first descriptions has been offered by Schwienbacher and Larralde (2010), discussing a small music crowdfunding start-up. Agrawal et al. (2011) studied the phenomenon from an interest in the geographical dispersion of the investors. Attempts have been made to include it in a theoretical framework of when individuals will choose to crowdfund (Belleflamme et al., 2012), and from the other perspective; on the role of investors within crowdfunding (Kuppuswamy and Bayus, 2013-2015).

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As crowdfunding and its academic research are becoming more mature, it is slowly being absorbed into recent practise and research. For this reason it is important to understand whether crowdfunding successes and failures are driven by the same dynamics as other forms of funding (Mollick, 2014). These dynamics are also likely to be different for the different kinds of crowdfunding. Four different methods are currently being distinguished: Donation-based crowdfunding, focussing on projects with a charitable cause that can only be made possible with the shared help of the crowd; reward-based crowdfunding, in which the funder will receive a reward such as the first product or gift certificates in reward for their backing; loan-based crowdfunding, in which the crowd makes funds available to the entrepreneur which will be paid back with interest after a given time and finally: equity-based crowdfunding, sometimes also referred to as crowdinvesting, in which the crowd provides funds in return for equity of the project. It is this latter kind of crowdfunding that is receiving an increasing amount of attention from the financial world as well as the academic world. Since the potential rewards are becoming real, and billions of dollars have already been transferred through this new system, the funding method is really becoming part of the financial world. The United States has very recently released the long anticipated Jobs Act Title III, bringing in real financial rules for crowdfunding which are likely to be adopted by other countries in the near future.

Two papers have significantly contributed to knowledge about equity-based crowdfunding: Hornuf & Schwienbacher (2014) suggest that equity-based crowdfunding is quite likely to act as a complement to angel investment in the lower segment of financing. The second paper is the paper by Ahlers et al. (2015), which focuses on signalling within equity crowdfunding. The authors analysed 104 equity-based crowdfunding projects in Australia in order to analyse how the information that entrepreneurs choose to display affects the odds for success of the projects. They find that the more information that is being disclosed by entrepreneur, the more successful the campaigns are.

As Ahlers et al. (2015) have shown equity-based crowdfunding can be used to distinguish and measure important factors of funding success, possibly providing an answer tothe struggle of testing signalling theories as mentioned by Levy and Lazarovich-Porat. Similarly, it is a good platform from which projects can be extracted that have been funded in a very similar fashion, following similar formats and rules, addressing a crowd which is unknown to them. Because of this, equity-based crowdfunding may be a good platform to research the effect of education signalling.

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Propositions

From the literature, two propositions arise that will be tested:

Proposition 1:

The higher the level of education of the team that is presented, the higher the obtained investment will be in relation to the required investment.

Proposition 1 will be divided into three subpropositions:

Since some authors specifically mention the positive effects of a doctorate level degree (Hsu, 2007; Gimmon & Levie, 2010), this specific proposition will also be tested:

Since the heterogeneity of the team member’s education is also specifically mentioned, this will also be tested.

Proposition 2:

Proposition 1b:

Between teams that have members mentioning their educational degree, a team with a higher level of education will obtain a greater investment in relation to the required investment than teams with lower levels of education.

Proposition 1a:

A team with members mentioning an educational degree will obtain a higher investment in relation to the required investment than teams without any members mentioning an educational degree.

Proposition 1c:

If team members own an MBA or PhD, the obtained investment will be higher in relation to the required investment than teams without any MBA or PhD holders.

Proposition 2:

The higher the variety of education that is presented, the higher the obtained investment will be in relation to the required investment.

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Method

In order to adequately analyse the signalling effect of education in crowdfunding, one needs a considerable amount of comparable crowdfunding projects. Since the popularity of crowdfunding has increased exponentially in the last decade, crowdfunding projects are not hard to find. Finding projects that are comparable however, is a bit more difficult. Different crowdfunding platforms use different methods to reward investors, different layouts of the project descriptions, and often have unique systems in their websites which will provide an advantage to one project over another. Some platforms focus on niche sectors such as the arts or charity projects. This means that projects from different platforms cannot easily be compared. One of the fairest methods to make the projects comparable is by only comparing projects from one platform, especially one that makes no difference between so called ‘premium’ projects with featured advertisements on the website. As mentioned before, equity-based crowdfunding is the most eligible1 for this research as the investor’s decision is guided by a monetary incentive in this method. This already reduces the amount of eligible crowdfunding platforms to a very select group. Last but not least, there is the operational consideration of how accessible data is. Some platforms require a considerable amount of personal information to verify that the person is eligible to be an investor, especially within equity-based crowdfunding, before one can view the projects that are open for investors.

One crowdfunding platform that meets all these requirements is the Dutch crowdfunding platform Symbid. It was founded in 2011 as one of the first equity- based crowdfunding platforms in the world. It has quickly grown to be the biggest crowdfunding platform in the Netherlands and is aiming to expand to 8 other countries by 2018. It has already realized the funding of hundreds of projects over the past few years. The platform gives entrepreneurs the opportunity to give a detailed description of the project to convince investors. The description is divided into 6 parts: a summary, a description of the team, an explanation of the product, an analysis of the market, a projection of the sales and a clarification of the financing. This division of the description has been in effect since March 2015, before which the team section was only accessible by selecting the entrepreneur’s profile. In order to keep the projects comparable, only projects after this change have been taken in account in the research.

This research focuses on the section that gives a description of the team members. In this section, the entrepreneur may put up to 7 team members in the spotlight. There is space for a team member’s name, function within the venture, a picture and a description of up to 60 words. The entrepreneur may choose freely which members are shown here and how they are represented. There is no standard selection of job titles, no standard method to display years of experience or education

1_{Loan-based equity could be eligible to some extent as well, considering there is still a monetary incentive to}

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12 and no slot to display someone’s age. This means that all the information that the entrepreneur chooses to display is likely to be a meaningful attempt to signal some information with that information.

This aspect does increase the difficulty of the operationalization however. For this section, each individual team member has been taken into account with four variables each: education, work experience, age and gender. For education, the variable is recorded only when there is an explicit mention of a completed educational degree. This means that mentions such as ‘a background in finance’ are ignored as they are too vague to be regarded as proper educational signalling. Since there is no mention of any educational level in this case, such as an MBA or an MSc, no distinction can be made, undermining a key aspect of the concept of signalling. Similarly, for the variable of work experience, it is only entered into the dataset when there is an explicit mention of years of experience. Some entrepreneurs choose to refer to someone’s relevant work experience by subjective terms such as ‘a lot of experience’ or ‘very experienced’: these have been ignored for the same reason. The individual’s age was taken into account only when a year of birth or an actual age was given.

The individual variables have been converted into composite team variables for proper comparison between the teams. For education, a dummy is constructed that represents whether the team has an individual who obtained either an MBA or a PhD. Furthermore, the average level of education is measured2 as well as the variation in educational degrees, represented by the total amount of unique study fields in which degrees are obtained and presented.

Since the entrepreneur is so free in deciding which information to provide about the individuals, there is a possibility to express a signal about the team. For example, one might decide to present a team as a very skilled team, by focussing on education and awards obtained by the team members in the past, or to discuss the years of experience and all the companies that a person has been working for. Others choose to focus on the international dimension of the team, where someone’s international background or network may serve as a hint to potential of international expansion of the project, or the presence of multicultural values within the team. Some focus on the motivation of the team members, expressing that the project’s driving force is the passion of the team, and some simply choose to give a social introduction of someone’s hobbies and interests, leaving the capabilities up to the imagination of the reader. This information has been operationalized in the team focus variable,

2_{This variable is constructed by dividing the sum of levels of education by the total amount of team members.}

It could also be divided by the total amount of members mentioning education, but this would unfairly judge a team with one person mentioning a PhD and the rest mentioning nothing, as a team full of academicians. In addition, if a team chooses to mention the education of person A and B but not of person C, one might argue that the chance is high that the signalling value of the educational degree of person C is low.

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13 which is incorporated in the skill dummy, which represents whether or not the focus of the team description lies on convincing the reader of the team being very skilled3.

A number of general variables have been recorded regarding the projects: the project name, the type of product that the project is intending to sell, and the sector this product belongs to. Considering the sample, six broad sectors have been identified: apparel, entertainment, financial, food, software & online and tech &accessories. These dummy variables will be used to assess different tactics employed for different sectors. Within the models, all dummies are analysed as compared to the tech & accessories dummy, which is the largest sector in the sample.

A number of financial variables have also been recorded. Firstly the required investment, an amount which is selected by the entrepreneur reflecting the target that the crowdfunding campaign is aiming for, which equals the amount of financing that the venture needs from crowdfunding in that financing round. This value is often clarified in the Finance section of the project. The targeted amount of a crowdfunding campaign is often calculated carefully4, reflecting the notion that an entrepreneur should not attract more investment than he knows how to spend, as well as a perception of how much the entrepreneur is able to raise during such a campaign. The second financial variable is the valuation5 of the firm. Another variable can be calculated from these variables, which is the total percentage of equity that is being traded in this crowdfunding round. This percentage is also shown in the presentation and is an important variable for potential investors, as it represents what percentage of the company they are acquiring, which influences their potential return. More importantly, it determines the percentage of shares that remains in the hands of the entrepreneur, which provides a strong signal to potential investors of how confident the entrepreneur is of his own project. The third

3

Even though the team focus is often very clear to recognize, this operationalization has a weakness in its subjective element.

4_{During the course of a crowdfunding campaign the target of a project may be altered to reflect the performance}

of a campaign. In some situations, the campaign is very successful and the entrepreneurs are also able to spend more money than the original target; the target is then increased to stimulate more willing investors to also invest. In some situations, the campaign is underperforming, but the entrepreneur is willing to adjust his strategy and go ahead with the project with a smaller amount of crowdfunding capital. Even though this may be a sensible option in some situations, not all crowdfunding platforms allow this practice, as some of the values which the investors used when making the decision are altered after the decision making. Symbid allows this practise on its platform however, which is combined with an option to withdraw an investment during the campaign. Due to this issue, all projects on the platform are recorded as being a 100% financed, even though the original target may not have been achieved or oppositely, when the target has been superseded and increased. In the latter situation, the crowdfunding platform proudly attaches a ribbon to the project’s picture awarding its overfunding, revealing the original target. The former situation however, is resolved quietly with a lowered target, removing any trace of its underperformance. This matter may affect the results of this research, which is why all projects have been checked thoroughly for their initial target in their description. However, not all descriptions mentioned their initial target, which is why a second (very arduous) check has been performed: by checking all projects in archived versions of the website, the original target of most of these campaigns became revealed. See the discussion section for further information about the effects of this implication.

5_{This is the post-money evaluation of the project, which is the valuation the venture will have after the current}

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14 financial variable is the investment obtained. This is the actual outcome of the crowdfunding round and can be higher, or in some cases, lower than the targeted amount. The ratio of the required investment to the obtained investment is an indication of the performance of the crowdfunding campaign. This allows for an accurate comparison of the performance of the crowdfunding campaign, which is important for analysing the effect of the education signalling by the entrepreneur.

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Table 1: Variables

Main variables

%Obtained The percentage of the required investment that is obtained, reflecting the performance of the crowdfunding campaign

Members_Mentioning_Educ The amount of team members that specifically mention an obtained educational degree

Educ_MBA_plus A dummy that represents whether the team has an individual who obtained either an MBA or a PhD

Educ_level The average level of education of the team members

Educ_variation The variation in educational degrees, represented by the total amount of unique study fields in which degrees are obtained

Control Variables

%Share The percentage of equity that is being sold in the current crowdfunding campaign

Team_Members The amount of team members that the entrepreneur has chosen to present in the project description

Team_Gender A dummy that represents whether the team has mixed genders or only male members

Members_Mentioning_Age The amount of team members that specifically mention their age Members_Mentioning_Exp The amount of team members that specifically mention their

amount of work experience in a related field

Skill_Dummy A dummy that represents whether the team description focuses on convincing the reader of being skilled

Apparel_Dummy A dummy that represents whether the project belongs to the apparel sector

Entertainment_Dummy A dummy that represents whether the project belongs to the entertainment sector

Financial_Dummy A dummy that represents whether the project belongs to the financial sector

Food_Dummy A dummy that represents whether the project belongs to the food sector

Tech_Accessories_Dummy A dummy that represents whether the project belongs to the tech & accessories sector

Software_Online_Dummy A dummy that represents whether the project belongs to the software & online sector

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Results

Descriptive Statistics

Of the 99 projects that have been analysed, 68 projects show information about the team members, in which there are 212 individuals with an individual description. The other 31 projects did not deliberately choose to skip the team description, but have been initiated before March 2015, when the crowdfunding platform did not provide a specific section for team descriptions, and are therefore exempt from the analysis. Of these 212 individual descriptions, 67 (31.6%) contain an explicit educational degree (see table 3 for details), whereas 57 (26.9%) of the individual descriptions contain a numerical mention of years of work experience. 16 (7.5%) of the individual descriptions contained both an explicit educational degree as well as a numerical mention of years of work experience. The age or year of birth was indicated in 24 (11.3%) of the cases, ranging from 28 to 51 years old, with a mean of 38.6 and a median of 36.5. Of all the individuals, 168 (79.2%) are male and 43 (20.3%) are female, leaving 1 person that was introduced by title and initials without a picture.

Table 2: Individual Level Descriptive Statistics

N

% of

Total Minimum Maximum Mean Median

Mentioned Education 67.00 31.60% MBO PhD - MSc

Mentioned Experience 57.00 26.89% 2.00 45.00 16.93 15.00

Mentioned Age 24.00 11.32% 28.00 59.00 38.63 36.50

Male 168.00 79.25% - - - -

Female 43.00 20.28% - - - -

Table 3: Frequencies of Mentioned Educational Degrees Level of Mentioned

Education Frequency Percentage

Cumulative Percentage Not mentioned 145.00 68.40% 68% MBO 2.00 0.94% 69% HBO 15.00 7.08% 76% BSc 9.00 4.25% 81% MSc 33.00 15.57% 96% MBA 3.00 1.42% 98% PhD 5.00 2.36% 100%

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17 On a team level, on average, a project displayed 3.26 team members (median = 4). In 34 cases (50%), at least one person specifically mentioned an educational degree, in which case the education level and the educational variation are included. 30 of the projects (43.5%) had a mixed gender team, and none of the projects was presented by females only. Table 4 summarizes all team level descriptive statistics.

Table 4: Team Level Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

% Obtained 68.00 14% 384% 120% 56% Members_Mentioning_Educ 68.00 0.00 5.00 0.99 1.34 Educ_MBA_Plus 68.00 0.00 1.00 0.12 0.32 Educ_level 34.00 0.50 4.00 2.04 1.14 Educ_variation 34.00 1.00 4.00 1.71 0.91 % Share 68.00 1% 83% 10% 12% Team_Members 68.00 1.00 6.00 3.26 1.72 Team_Gender 68.00 0.00 1.00 0.44 0.50 Members_Mentioning_Age 68.00 0.00 5.00 0.35 0.97 Members_Mentioning_Exp 68.00 0.00 4.00 0.84 1.11 Skill_Dummy 68.00 0.00 1.00 0.62 0.49 Apparel_Dummy 68.00 0.00 1.00 0.06 0.24 Entertainment_Dummy 68.00 0.00 1.00 0.10 0.31 Financial_Dummy 68.00 0.00 1.00 0.07 0.26 Food_Dummy 68.00 0.00 1.00 0.10 0.31 Tech_Accessories_Dummy 68.00 0.00 1.00 0.34 0.48 Software_Online_Dummy 68.00 0.00 1.00 0.32 0.47

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Correlations

The correlations of the variables are shown in table 5. Many variables show significant correlations with the amount of team members, which is to be expected considering the nature of these variables, with two exceptions: there tend to be less team members in the entertainment sector and more team members in the tech & accessories sector. Positive correlations also appear between the variables regarding how often a certain aspect (age, experience or education) is mentioned, reflecting how teams sometimes choose to present as much general information as possible, where others choose to focus on motivation or other matters, without presenting any data. Some extra correlations can be found, such as more MBA or PhD holders in the financial sector projects, and significantly higher shares of the venture being sold in the entertainment sector, which is caused by movies that are financed for the greatest part by crowdfunding projects. Mixed gender teams appear to rather present years of experience than education, and are less common in the software & online sector. No significant correlation is found between the level of education and the percentage of funding obtained, but a strong positive correlation is present between the percentage of funding obtained and the variation in educational degrees of the team members.

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19 Table 5: Correlations % Obtaine d Member s Mentioni ng Educ Educ_M BA_Plu s EducLe vel EducVa riation % Share Team Member s Team Gender Member s Mentioni ng Age Member s Mentioni ng Exp Skill_Du mmy Apparel _Dumm y Entertai nment_ Dummy Financia l_Dum my Food_D ummy Tech_A ccessoi ries_Du mmy Softwar e_Onlin e_Dum my % Obtained 1 0.122 -0.048 0.222 ,475** _-0.211 _-0.133 _-0.194 _0.033 _-0.133 _0.176 _-0.007 _-0.025 _-0.066 _0.044 _-0.221 _.251* Members Mentioning Educ 0.122 1 0.107 ,562 ** _0.083 _-0.178 _,330** _0.032 _,289* _-0.041 _0.150 _0.003 _-0.069 _-0.081 _0.040 _0.101 _-0.040 Educ_MBA_Plus -0.048 0.107 1 0.026 -0.060 -0.089 0.077 0.227 -0.133 ,260* _-0.088 _-0.091 _0.027 _,247* _-0.124 _0.028 _-0.057 EducLevel 0.222 ,562** _0.026 ₁ _-0.027 _-0.335 _-,344* _-,342* _0.144 _-0.248 _0.163 _-0.176 _0.308 _-0.011 _0.035 _-0.092 _0.013 EducVariation ,475** _0.083 _-0.060 _-0.027 ₁ _-0.285 _,853** _0.121 .c _-0.016 _0.170 _0.077 _0.054 _-0.139 _-0.248 _-0.012 _-0.032 % Share -0.211 -0.178 -0.089 -0.335 -0.285 1 -,251* _-0.159 _0.006 _-0.171 _-0.148 _0.017 _,427** _-0.070 _-0.021 _-0.039 _-0.194 Team Members -0.133 ,330** _0.077 _-,344* _,853** -,251* ₁ _,537** _,255* _,325** _0.104 _-0.002 _-,250* _-0.109 _-0.052 _,253* _0.003 Team Gender -0.194 0.032 0.227 -,342* _0.121 _-0.159 _,537** ₁ _-0.110 _,371** _-0.032 _0.155 _-0.009 _-0.137 _0.186 _0.179 _-.298* Members Mentioning Age 0.033 ,289 * _-0.133 _0.144 .c _0.006 _,255* _-0.110 ₁ _-0.043 _0.068 _-0.027 _-0.124 _-0.103 _-0.124 _0.093 _0.138 Members Mentioning Exp -0.133 -0.041 ,260 * _-0.248 _-0.016 _-0.171 ,325** ,371** -0.043 1 0.158 -0.189 -0.082 0.143 -0.082 0.105 0.016 Skill_Dummy 0.176 0.150 -0.088 0.163 0.170 -0.148 0.104 -0.032 0.068 0.158 1 -0.189 -0.132 -0.010 0.067 0.051 0.091 Apparel_Dummy -0.007 0.003 -0.091 -0.176 0.077 0.017 -0.002 0.155 -0.027 -0.189 -0.189 1 -0.085 -0.070 -0.085 -0.179 -0.173 Entertainment_Dummy -0.025 -0.069 0.027 0.308 0.054 ,427** _-,250* _-0.009 _-0.124 _-0.082 _-0.132 _-0.085 ₁ _-0.095 _-0.115 _-,242* _-0.234 Financial_Dummy -0.066 -0.081 ,247* _-0.011 _-0.139 _-0.070 _-0.109 _-0.137 _-0.103 _0.143 _-0.010 _-0.070 _-0.095 ₁ _-0.095 _-0.201 _-0.195 Food_Dummy 0.044 0.040 -0.124 0.035 -0.248 -0.021 -0.052 0.186 -0.124 -0.082 0.067 -0.085 -0.115 -0.095 1 -,242* _-0.234 Tech_Accessoiries_Du mmy -0.221 0.101 0.028 -0.092 -0.012 -0.039 ,253 * _0.179 _0.093 _0.105 _0.051 _-0.179 _-,242* _-0.201 _-,242* ₁ _-0.494** Software_Online_Dum my .251* -0.040 -0.057 0.013 -0.032 -0.194 0.003 -.298* 0.138 0.016 0.091 -0.173 -0.234 -0.195 -0.234 -0.494** 1 **. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

c. Cannot be computed because at least one of the variables is constant.

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20

Regressions

The first analysis will be a bivariate analysis testing proposition 1a. Through linear regression analysis,

β

ˆ

_MMEwill be estimated for the model (1):

i

ˆ

0

ˆ

MME i

ˆ

Model(1):%obtained =

β + β

* Members_Mentioning_Educ

ˆ

MME

β

_{then represents the difference in percentage of the required investment that is obtained,} measured in percentage points, when a team has a member mentioning an educational degree or not. Linear regression results in the following results:

Table 6: Members mentioning their educational degrees to % obtained. (n=68)

Model Coefficient _B _{Std. Error} t Sig.

Constant 115.210 8.420 8.701 0.000

Members_Mentioning_Educ 5.085 5.077 1.002 0.320

No significant results are found for

β

ˆ

_MME in this model. The next model, model (2), will add a number of control variables.

i 0 i teammembers i teamgender i

skill i share i appa

ˆ ˆ ˆ ˆ

ˆ

Model(2):%obtained = β +β * Members_mentioning_educ + β * Team_members + β * Team_gender +

ˆ ˆ ˆ

β * Skill_dummy + β *%Share + β

MME

rel i entertainment i

financial i food i softwareonline i

ˆ

*Apparel_dummy + β *Entertainment_dummy +

ˆ ˆ ˆ

β *Financial_dummy + β *Food_dummy + β * Software_online_dummy

Table 7: Members mentioning their educational degrees to % obtained with control variables. (n=68)

Constant 122.945 23.158 5.309 _0.000 Members_Mentioning_Educ 5.002 5.529 0.905 _0.369 Team_Members -4.542 5.436 -0.836 0.407 Team_Gender .14.271 18.616 -0.767 0.447 Skill_Dummy 16.141 14.187 1.138 0.26 % Share -1.115 0.645 -1.729 0.089 Apparel_Dummy 24.496 30.485 0.804 0.425 Entertainment_Dummy 26.098 26.616 0.981 0.331 Financial_Dummy -5.741 27.727 -0.207 0.837 Food_Dummy 21.568 24.419 0.883 0.381 Software_Online_Dummy 27.575 17.313 1.593 0.117

No significant effect could be found for

β

ˆ

MME in this model either, meaning there is no

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21 members mentioning an educational degree will obtain a higher investment in relation to the required effect than teams without any members mentioning an educational degree.

The third model will be a bivariate analysis testing proposition 1b. Through linear regression analysis,

β

ˆ

_educlevelwill be estimated for the model (3):

0

ˆ

(3) : %

_i _educlevel

*

_

_i

Model

obtained

=

β β

+

Educ level

ˆ

educlevel

β

then represents the change in percentage of the required investment that is obtained, measured in percentage points, when the level of education increases by 1 level. Linear regression results in the following results:

Table 8: Average level of education to % obtained. (n=34)

Constant 100.698 11.573 8.701 0.000

Educ_Level -0.263 5.750 -0.046 0.964

β

ˆ

_educlevel in this model. The next model will also control for a number of control variables.

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22

i 0 _ i teammembers i teamgender i

skill i share i apparel

ˆ ˆ ˆ ˆ

ˆ

Model(4):%obtained = β +β * Educ_level + β * Team_members + β * Team_gender +

ˆ ˆ ˆ

β * Skill_dummy + β *%Share + β *Ap

educ level

i entertainment i

ˆ

parel_dummy + β *Entertainment_dummy +

ˆ ˆ ˆ

Table 9: Average level of education to % obtained with control variables. (n=34)

Constant 65.184 35.564 1.833 _0.080 Educ_Level 10.965 6.386 1.717 _0.099 Team_Members 9.184 4.557 2.015 0.056 Team_Gender -8.569 15.812 -0.542 0.593 Skill_Dummy -8.642 13.533 -0.639 0.529 % Share 0.168 1.549 0.108 0.915 Apparel_Dummy 28.265 28.364 0.996 0.329 Entertainment_Dummy -6.971 28.477 -0.245 0.809 Financial_Dummy -6.323 23.306 -0.271 0.789 Food_Dummy 28.346 23.319 1.216 0.236 Software_Online_Dummy 14.036 15.745 0.891 0.382

This analysis finds a significant effect for the level of education with 90% confidence, holding the other parameters constant. This means that these results provide evidence that supports proposition 1b. In other words, the notion that higher levels of education lead to increased amounts of funding obtained in equity-based crowdfunding cannot be rejected, supporting proposition 1b.

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23 The next model will test proposition 1c, if the effect will be different when only MBA or PhD holders are included. A dummy is constructed representing if an MBA or PhD holder is present in the team.

0

ˆ

(5) : %

_i _MBA

*

_

_i

Model

obtained

=

β β

+

₊

Educ MBA plus

ˆ

MBA

β

₊then represents the change in percentage of the required investment that is obtained, measured in percentage points, when the team has a member with an MBA/PhD or not. Linear regression results in the following results:

Table 10: Dummy for having an MBA or PhD to % obtained. (n=68)

Constant 121.183 7.256 16.701 0.000

Educ_MBA_Plus -8.183 21.155 -0.387 0.700

β

ˆ

_MBA₊ in this model. The next model will also include the control variables.

i 0 i teammembers i teamgender i

skill i share i appare

ˆ ˆ ˆ ˆ

ˆ

Model(6):%obtained = β +β * Educ_MBA_plus + β * Team_members + β * Team_gender +

ˆ ˆ ˆ

β * Skill_dummy + β *%Share + β

educmbaplus

l i entertainment i

ˆ

ˆ ˆ ˆ

Table 11: Dummy for having an MBA or PhD to % obtained with control variables. (n=68)

Model Coefficient t Sig.

B Std. Error Constant 123.438 23.425 5.270 0.000 Educ_MBA_Plus 6.516 22.847 0.285 0.777 Team_Members -2.557 5.087 -0.503 0.617 Team_Gender -20.295 18.928 -1.072 0.288 Skill_Dummy 17.844 140267 1.251 0.216 % Share -1.205 0.64 -1.882 0.065 Apparel_Dummy 27.637 30.991 0.892 0.376 Entertainment_Dummy 28.407 26.665 1.066 0.291 Financial_Dummy -10.168 28.780 -0.353 0.725 Food_Dummy 24.773 24.819 0.998 0.322 Software_Online_Dummy 25.363 17.293 1.467 0.148

The effect is insignificant for the current sample, providing no evidence to support proposition 1c. In other words, no evidence is found to support the idea that having a team member with an MBA or PhD will lead to increased amounts of funding obtained in equity-based crowdfunding.

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24 The next model will test whether a higher variety in educational degrees will lead to more investment obtained.

i

ˆ

0

ˆ

educvariation i

ˆ

Model(7): %obtained =

β + β

* educvariation

educvaration+

ˆ

β

then represents the change in percentage of the required investment that is obtained, measured in percentage points, with an increase of 1 unique study field in the educational degrees of the team members. Linear regression results in the following results:

Table 12: Variation in educational degrees to % obtained. (n=34)

Constant 91.389 10.897 8.387 0.000

Educ_Variation 17.289 5.660 3.054 0.005

The results suggest that an increased variety in educational degrees of the team members significantly increases the amount of funding obtained in equity-based crowdfunding, with 95% confidence.

The next model will test the same effect, adding the control variables.

i 0 educvariation i teammembers i teamgender i

skill i share i app

ˆ ˆ ˆ ˆ

ˆ

Model(8):%obtained = β +β * Educ_variation + β * Team_members + β * Team_gender +

ˆ ˆ ˆ

β * Skill_dummy + β *%Share + β arel i entertainment i

ˆ

ˆ ˆ ˆ

Table 13: Variation in educational degrees to % obtained, with control variables. (n=34)

Constant 85.960 28.378 3.029 0.006 Educ_Variation 15.117 7.805 1.937 0.065 Team_Members 3.160 4.910 0.644 0.526 Team_Gender -6.399 15.780 -0.406 0.689 Skill_Dummy -9.584 13.353 -0.718 0.480 % Share -0.122 1.480 -0.083 0.935 Apparel_Dummy 24.217 28.080 0.862 0.397 Entertainment_Dummy -5.850 27.603 -0.212 0.834 Financial_Dummy -0.786 23.379 -0.034 0.973 Food_Dummy 21.097 23.790 0.887 0.384 Software_Online_Dummy 14.161 15.485 0.915 0.370

The results suggest that an increased variety in educational degrees of the team members significantly increases the amount of funding obtained in equity-based crowdfunding, with 90% confidence, ceteris paribus.

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25 With a value of 15.117,

β

ˆ

_{educvariation} can be interpreted: an increase of 1 unique study field in the educational degrees of the team members leads to 15.117 percentage points more of the required funding obtained, holding the other parameters constant.

With these results, no evidence is found to reject the hypothesis of proposition 2, meaning that the results suggest that the higher the variety of education that is presented, the higher the obtained investment will be in relation to the required investment.

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26

Conclusion

In this research project, the signalling effect of education for equity-based crowdfunding has been tested. More specifically, the effect of increased levels of education on the investment obtained has been analysed, as well as the effect of a bigger variety in educational degrees of the team members. Through statistical analysis of 68 Dutch equity-crowdfunding projects held in 2015 and 2016, the effects could be analysed quantitatively holding other variables constant, overcoming some of the constraints mentioned by the existing body of literature in testing the existing theory about education signalling. The gathered data also shows new insights about the equity-based crowdfunding community, a community that is becoming increasingly important in the financing market.

A first observation already revealed an interesting insight: 67 of the 212 descriptions (31.6%) contain an explicitly mentioned educational degree. This means that the majority of the entrepreneurs in this sample did not present an educational degree. According to CBS (Bierings, 2013), 28% of the entire working population of the Netherlands has an HBO or University degree in 2012, and according to GEM/Panteia (Span et al., 2014) 36% of the early stage entrepreneurs possesses either an HBO or University degree in 2014. Assuming that the sample has a similar percentage of educational degree holders, a great majority of the educational degree holders chose to present their degree in the description. This is an indication that the entrepreneurs believe in the signalling power of their educational degree.

Further analysis showed whether or not mentioning an educational degree has any effect. The results suggest that there is no significant effect on the amount of funding obtained by mentioning an educational degree or not, not even when an MBA is set as the benchmark. However, different levels in education amongst those who mentioned their education did appear to have a significant effect on the amount of funding obtained. This is an indication that investors do value levels of education, but not enough to have a significant effect as compared to projects without any educational degree holders.

The second proposition that was tested focuses not on the level of education but the variety in educational degrees possessed by the different team members. The results suggest that there is a significant positive effect of having more variation in the educational degrees of the team members. According to the existing body of literature, this effect is present in other funding mechanisms and the current results provide no evidence the reject the opposite for equity-based crowdfunding. Investors believe that heterogeneity in educational degrees will help the team to make the venture more successful, as the team may approach issues from multidisciplinary perspectives. Yet when teams possess a high level of heterogeneity in educational degrees, they do not emphasize this matter in their campaigns. This is an interesting final conclusion: it is the educational level that entrepreneurs focus

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27 on in their campaigns, but it is the variation in educational degrees that attracts more funding for ventures in equity-based crowdfunding. Entrepreneurs should adjust their strategies accordingly.

Discussion

This research project has been able to provide new contributions to the existing literature, yet some important remarks have to be made. A number of matters that could increase the reliability of this research were out of the scope of this research. For example, the amount of projects that could be analysed remained limited to one website in one country; therefore the results might not fully extend to other countries and platforms. Future research might verify these results for other countries and platforms, including those focused on loan-based equity. Furthermore, the data has been gathered after the funding rounds were finished, meaning that the progress of the funding could not be tracked during the campaign. This would reveal information such as how quickly the funding target was reached and which information was edited during the campaign, all of which might capture important information. The most important implication of this matter is that projects that have been cancelled before the funding round was finished may not all be included in the analysis. This may lead to a bias of the results, as the compositions of teams of ventures that did not succeed in their funding round may have been different, possibly leading to more significant results if this data was included. This is an important matter to consider in future research.

The variables for age and experience could not be included in the model because the part of the sample that chose to present information about all these variables was too small to perform statistical analysis on. Especially the effect of experience could affect the outcome of this research, possibly acting as a partial substitute for education. This problem could be tackled by analysing a greater number of crowdfunding projects.

Another important issue is the extent to which education is relevant for a certain position in the venture. Some of the teams pointed at decades of relevant experience per member without mentioning education, and some projects such as funding projects for a new movie, only referred to some famous movie stars as their team, which might convince investors in a different fashion. Similarly some team descriptions were focussed on the motivation of the team members or on the international aspect of the team composition. These effects are largely captured by the various dummies for team focus and sectors, but the final effect might be more complex and subjective.

Something that cannot be excluded for this research is the possibility of reversed causality. Teams with higher levels of education and teams with more educational heterogeneity appear to be more successful, but one might argue that these are actually both determined by a third factor: the quality of the idea. Projects with a very good idea might be more successful in attracting highly educated team members. Since the team members presented in the platform are often all part of the

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28 founding team, the effect is likely to be limited, yet future research should control for this matter. One option would be to interview both the team of entrepreneurs as well as a panel of entrepreneurs in the course of multiple crowdfunding campaigns. This is likely to reveal a treasure of information: unfortunately, this was out of the scope of the current research project.

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The signalling effect of education for entrepreneurs

Master Thesis