Master’s Thesis International Business & Management June 2020

Information Asymmetries in Bank Lending, with the Moderating Effect of : A Driver of Equity

AUTHOR Tiago Lopes (S4015258)

SUPERVISOR Dr. A. Kuiken

CO-ASSESSOR Dr. M.C. Sestu

Word count: 16451 (Excluding title page, table of contents, tables, references, and appendices) Abstract

The exponential adoption of platforms by SMEs had as its catalyst the Global Financial Crisis in 2008, yet detailed research surrounding the conditions that drive SMEs towards these platforms remains sparse. Much prior research has instead focused on the factors that contribute to a successful campaign on these platforms. By drawing on the pecking order and information asymmetry theory, this paper explores the assertion that when SMEs seek bank credit greater information asymmetries will contribute to a higher amount of equity crowdfunding being raised in that country. Additionally, this research assesses the relationship between the drive towards equity crowdfunding platforms and the level of entrepreneurial activity in a country. One of the first quantitative studies in this area is provided by manually constructing a unique sample gathered from the most used equity crowdfunding platforms in the European Union. The sample consists of firms that have successfully obtained funding via these platforms, across 13 European Union countries between 2014 and 2019. In general, the empirical evidence supports the aforementioned theory. I find that an increase in depth of credit information that banks have on SMEs leads to smaller amounts of equity crowdfunding raised. Moreover, the strength of the legal rights of both banks and borrowers, and the existence of a credit registry are found to have a negative effect on the amounts raised. Finally, it is clear that the more entrepreneurial the country and the higher the amounts of equity crowdfunding being raised, the stronger the effect of all the above-mentioned variables. The implications of such findings for theory and practice are outlined in this paper.

Keywords: Bank credit; Entrepreneurial finance; Equity crowdfunding; Information asymmetries; Pecking order; Quantile regression; Regulation; Web scraper

2

Table of contents

INTRODUCTION 4

THEORETICAL FRAMEWORK 7

I. CROWDFUNDING 7 (I) EQUITY CROWDFUNDING 8 II. REGULATION 11 (I) BANKS 11 (II) INFORMATION ASYMMETRIES 15 III. HYPOTHESES DEVELOPMENT 19 (I) STRENGTH OF LEGAL RIGHTS 19 (II) DEPTH OF CREDIT INFORMATION AND CREDIT REGISTRY 20 (III) ENTREPRENEURSHIP 22

METHODOLOGY 24

I. SAMPLE 24 II. DATA COLLECTION PROCESS AND SOURCES 27 (I) DEPENDENT VARIABLE 27 (II) EXPLANATORY VARIABLES 29 (III) CONTROL VARIABLES 30 III. VARIABLES 30 (I) DEPENDENT VARIABLE 30 (II) EXPLANATORY VARIABLES 31 (III) CONTROL VARIABLES 33 IV. METHOD OF ANALYSIS 35

RESULTS 38

I. MAIN RESULTS 38 II. ROBUSTNESS TESTS 43

DISCUSSION AND CONCLUSION 45

REFERENCES 49

APPENDIX 58

3

Introduction

In recent years, the literature began to focus on the topic of crowdfunding (Habla & Broby, 2019: 3-6), as small and medium-sized enterprises (SMEs) have started to employ this new alternative source of financing. The exponential adoption of crowdfunding and the subsequent interest by scholars on the topic had as a catalyst the Global Financial Crisis (GFC) in 2008. In the aftermath of the crisis, the “brand image of banks and their perceived stability had been shaken to the core” (Arner, Barberis, & Buckley, 2015: 1286) and banks were more averse to concede loans to SME borrowers (Fenwick, McCahery, & Vermeulen, 2018). In essence, the vast governmental post-crisis regulatory responses (Sum, 2016) and the tightening of lending standards (Hempell & Sørensen, 2010) further constrained banks’ lending capacity. In these conditions, SMEs are deemed to be the most vulnerable and affected (OECD, 2012). Information asymmetries play a key role in this condition. Essentially, banks lack the necessary information to delineate a loan contract with SMEs, often due to their opaqueness (Meza & Webb, 1987; Stiglitz & Weiss, 1981). Information asymmetries constrain banks’ lending to these firms for two main reasons. Firstly, when banks increase interest rates, to reduce credit risk, they tend to attract high risk borrowers and alienate low risk firms (Huang, When, & Liu, 2014; Steijvers & Voordeckers, 2009: 925). This effect is called the adverse selection. Thus, when increasing interest rates, banks expected return will decrease (Leland & Pyle, 1977). Ultimately, leading them to prefer to ration credit to these opaque firms. Secondly, moral hazard restrains the provision of credit by banks due to the fear of SMEs not complying with their contract (Binks, Ennew, & Reed, 1992) and the difficulty in monitoring their behaviour (Jenses & Meckling, 1976). In order to minimize the effect of both these information asymmetry issues, banks often make use of collateral (Blazy & Weill, 2013). However, banks are frequently still reluctant to provide credit to SMEs, because these firms tend to have limited assets to collateralize and most of them are intangible (Bădulescu, 2010; OECD, 2020: 78). Overall, the GFC further accentuated the information asymmetries between SMEs and banks, which in tandem with the formers’ lack of collateral and their highly uncertain environment, heavily constrains banks’ lending (Bădulescu, 2010).

4

Crowdfunding platforms started to slowly fulfil the underserved need for credit by SMEs. Hence, bridging the gap which had been left opened by banks (Belleflamme, Lambert, & Schwienbacher, 2014; Gradoń & Cichy, 2015). The pecking order theory (Myers & Majluf, 1984) suggests that firms prefer to obtain funding via equity-based deals when the information asymmetries for debt-based deals (i.e., bank credit) are too high (Bharath, Pasquariello, & Wu, 2009). In this context, equity crowdfunding becomes relevant as it is a novel way to reduce transaction costs. These platforms provide standardized information (e.g., pitch decks) for all the companies listed (Blaseg & Koetter, 2015: 7) and rely on the “wisdom of the crowd” to decide which ventures are the most promising ones (Surowiecki, 2004; Yum, Lee, & Chae, 2012). Additionally, equity crowdfunding attracts mainly seeking financial returns, as opposed to more social and artistic oriented platforms (i.e., donation- and reward-based crowdfunding) (Cox & Nguyen, 2018). Thus, it resembles the more traditional financiers, such as banks.

When it comes to the literature, equity crowdfunding has been underrepresented (e.g., Mochkabadi & Volkmann, 2020). Scholars, regardless of the changing lending environment, have largely dedicated their attention to assess how SMEs react to tighter credit conditions when it comes to trade credit (e.g., McGuinness & Hogan, 2016; Ogawa, Sterken, & Tokutsu, 2013), leasing (e.g., Kraemer-Eis & Lang, 2014) and factoring (e.g., Mol-Gómez-Vázquez, Hernández-Cánovas, & Koëter-Kant, 2018). Research has also focused on the use by SMEs of business angels (e.g., Ramadani, 2012) and (VC) (e.g., Gompers & Lerner, 2001) funding. However, despite equity crowdfunding being a substitute to VC funding (D’Ambrosio & Gianfrate, 2016), the research output on equity crowdfunding has been far inferior. Research on the different types of crowdfunding has mainly focused on four streams: (1) determinants of a successful campaign (e.g., Belleflamme, Lambert, & Schwienbacher, 2013; Belleflamme et al., 2014; Mollick, 2014), (2) herding behaviour of capital providers and the influence on companies’ funding amount (e.g., Herzenstein, Dholakia, & Andrews, 2011; Kuppuswamy & Bayus, 2018; Lee & Lee, 2012) (3) companies’ motivations to join equity crowdfunding platforms (e.g., Belleflamme et al., 2013) and (4) capital providers’ motivations to fund these companies (e.g., Lin, Boh, & Goh, 2014). Only a frankly small number of scholars have focused on the role of equity crowdfunding in situations of tighter lending conditions for SMEs (e.g., Hornuf & Schwienbacher, 2017; Klöhn, Hornuf, & Schilling, 2016; Pereira, 2017). In general, scholars have failed to acknowledge the importance of equity crowdfunding and the role of information

5 asymmetries has equally been overlooked. Information asymmetries are detrimental for the formation of loan contracts between SMEs and banks. Thus, especially in a time of potentially stricter banking regulations (Binham, 2019), these can further hinder SME’s ability to obtain bank credit (Deakins & Hussain, 1994: 24), illustrated by the rising adoption of equity crowdfunding by European users (Zhang et al., 2018: 41-42; Ziegler et al., 2017). The growing importance of equity crowdfunding, together with its potential benefits, the absence of data and the existing information asymmetries, highlights the research gap (Esho & Verhoef, 2018: 21-22; Habla & Broby, 2019: 6-9). Thus, I address the following research question: How do countries’ information asymmetries between banks and SMEs influence the amounts of equity crowdfunding being raised?

To test this claim, a web scraper is used to gather data from 15 equity crowdfunding platforms of 13 European Union (EU) countries for firms between 2014 and 2019. This is then combined with data from The World Bank on countries’ information asymmetry indexes. A quantile regression for panel data with bootstrapped standard errors is employed.

This research makes a noteworthy contribution to the pecking order theory and entrepreneurial finance literature. This paper presents novel evidence of the negative impact that a decrease in information asymmetries can have on the amounts of equity crowdfunding raised across different countries. Such an impact results from both an increase in the depth of credit information that banks have on borrowers, and the existence of a credit registry that provides that information. Additionally, quantitative results on the negative impact of an improvement on collateral and bankruptcy laws on equity crowdfunding are provided with different effects across quantiles. Thus, contributing to the existing but sparse crowdfunding literature. Overall, these results aid in understanding how regulation can be either a key driver for or hinderance to SMEs’ growth, if the appropriate alternative financing channels, namely equity crowdfunding, are not in place

The study unfolds as follows. The following chapter explores the existing literature on the emerging equity crowdfunding strand and delves deeper into the information asymmetry literature. Additionally, it provides a background to the banking regulation and develops the hypotheses. The methodology chapter extensively describes the data and the statistical method employed. The chapter that follows reports the results. The final section discusses the theoretical and practical implications. Additionally, it provides an outline of the limitations and possible research directions.

6

Theoretical framework

I. Crowdfunding

Crowdfunding is a phenomenon that has spanned across many centuries, although without the terminology that we can encounter today. On March 16, 1885 Joseph Pulitzer funded the completion of the Statue of Liberty, by appealing to his newspaper’s audience. He was able to collect more than 100,000 thousand dollars in donations from roughly 125,000 thousand people (National Park , 2015). This practice was not as uncommon as one might think. However, the average entrepreneur did not have the means nor the resources to appeal to mass audiences. Posterior to the advent of the internet, these barriers began to progressively disappear. Technological advancements, namely the web 2.0, allowed for the surge of crowdfunding platforms (Ordanini et al., 2011), which, compounded by a more digital society, led individuals towards these platforms. However, only after 2013 did equity crowdfunding truly begin to be more widely adopted by firms and recognized as a valid alternative financing source (Appendix A). Thus, only a study that encapsulates the years that followed can fully provide a comprehensive analysis of this phenomenon.

Belleflamme, Lambert, & Schwienbacher (2010: 5), provide one of the first and most widely cited definitions of crowdfunding, they define it as “(…) an open call, essentially through the Internet, for the provision of financial resources either in form of donation or in exchange for some form of reward and/or voting rights”. This definition encompasses most of the categories distinguished in the literature, thus it is an appropriate one to adopt. There are four main categories: (1) reward, (2) donation, (3) lending and (4) equity crowdfunding. In reward crowdfunding, investors in exchange for their contribution receive perks, such as getting the funded product before being released to the market (Cox & Nguyen, 2018). The incentives received are proportional to the investment made. Donation crowdfunding is probably the most unique out of the four types. In this case, the crowd contributes towards a project – namely, creative ones – without receiving any material returns, but rather socials rewards (e.g., public acknowledgments by the entrepreneur) (Block, Colombo, Cumming, & Vismara, 2018).

7

In regard to the lending model, investors receive an interest rate in exchange for the amount they have lent to the individual borrowers (Lin, Prabhala, & Viswanathan, 2013). Lastly, the equity crowdfunding model, allows for entrepreneurs to sell an equity stake in their business, similarly to the way VC operates. It is an innovative and recent method for SMEs to raise capital and it is paving the way for entrepreneurial finance, namely on early stage financing (Vulkan, Åstebro, & Sierra, 2016).

Overall, the crowdfunding literature has mainly focused on four streams. Firstly, the determinants of a successful campaign. Some of the main findings were that firms which (1) aim at solving a social issue, (2) request for smaller amounts of funding and (3) are geographically closer to their capital providers (i.e., investors), are more likely to obtain funding (Belleflamme et al., 2013, 2014; Goldfarb & Working, 2011; Mollick, 2014). Secondly, the herding behaviour on crowdfunding platforms. It was found that capital providers tend to contribute more towards projects which are closer to obtaining funding or that are being backed by well-established investors. (Herzenstein et al., 2011; Kuppuswamy & Bayus, 2018; Lee & Lee, 2012; Yum et al., 2012; Zhang & Liu, 2012). Thirdly, companies’ motivation to use crowdfunding. The main reasons are to obtain (1) funds, (2) public attention and (3) feedback (Belleflamme et al., 2013; Burtch et al., 2013). Lastly, research has also focused on what brings the capital providers to the platforms. The motives encompass not only financial reasons, but also social reputation and intrinsic values play a key role (Allison et al., 2015; Lin et al., 2014; Ordanini et al., 2011). Current research mainly focuses on the dynamics between implementing new regulation to foster these new forms of financing for SMEs and the extent to which it hinders investors’ protection (Härkönen, 2017; Hornuf & Schwienbacher, 2017; Huang, 2018; Ridley, 2016).

(i) Equity crowdfunding

It is widely acknowledged that mobilizing capital to fund SMEs is of the utmost importance, namely for firms that are too innovative, too complex and/or too risky to be able to obtain funding from banks (Hemer, 2011). This is accentuated by the fact that SMEs contribute towards more than two thirds of employment in the EU (European Commission, 2019a: 17–23) (Appendix B). One of the ways to fund these companies is via equity-based deals. Equity crowdfunding has unique characteristics compared with other forms of crowdfunding. Firstly, the potential investors that use equity crowdfunding are primarily driven by financial reasons, akin to the traditional financiers (e.g., banks) of

8

SMEs (Cholakova & Clarysse, 2015; Cumming & Johan, 2013). Secondly, as opposed to the reward-based and donation-based crowdfunding, projects on equity crowdfunding are by definition related to firms. For instance, reward-based crowdfunding platforms is associated predominantly with artistic and creative ventures (Cox & Nguyen, 2018). Thirdly, equity crowdfunding provides investors with voting rights, improving the attractiveness of the platform for professional investors (Cumming, Meoli, & Vismara, 2019). Moreover, there are plenty of valid reasons which can lead firms to opt for equity crowdfunding instead of bank credit. Firstly, the marginal cost of debt financing progressively increases at a faster pace the more debt a firm has. Hence, potentially leading to financial distress (Carpenter & Petersen, 2002). Thus, firms with excessive debt will be more likely to seek equity crowdfunding (Walthoff-Borm, Schwienbacher, & Vanacker, 2018). Secondly, firms with more intangible assets will seek this source of financing, as they find it difficult to obtain funding from banks (Walthoff-Borm et al., 2018). As we will see in section (ii) of the Regulation chapter, an increasingly higher proportion of SMEs’ assets are intangible (Brassel & Boschmans, 2018), which hinders their ability to attract debt financing (Cassar, 2004). Thirdly, these firms are also attracted towards these platforms as they can be more efficiently exploited and are experts in providing a better, cheaper and faster service (Agrawal, Catalini, & Goldfarb, 2013). Lastly, in the period that followed the GFC, central banks cut interest rates to promote economic activity, thus making debt financing relatively cheap (FSB, 2019). However, this was only provided that the SMEs were low-risk and had enough track record and collateral to comply with the post-crisis measures introduced (Block et al., 2018). As a consequence of the low interest rates, investors were seeking other investment opportunities. Equity crowdfunding platforms benefited from this shift, attracting investors and beginning to established themselves as a valuable source of financing for firms (Kraemer-eis, Botsari, Gvetadze, Lang, & Torfs, 2018). Thus, expanding the financing options of these more innovative and high-risk firms (Block et al., 2018). Overall, there are tangible reasons for firms to opt for this source of financing, which is further illustrated by the increase both in the literature (Mochkabadi & Volkmann, 2020: 1-2; Vulkan, Åstebro, & Sierra, 2016) and in its adoption by users and firms (Hannah Skingle, 2019; Ziegler et al., 2017).

When it comes to literature, equity crowdfunding has been underrepresented (Mochkabadi & Volkmann, 2020; Moritz & Block, 2016; Short, Ketchen, McKenny, Allison, & Ireland, 2017). Thus, little research has been put forward, namely regarding

9 the impact of banking regulation on firms opting for equity crowdfunding. The importance of identifying this relationship is underscored by previous scholars having sought to understand a similar link on other alternative financing sources. For example, scholars focusing on trade credit have shown that SMEs with little access to bank credit or that are credit-rationed (i.e., loan applications are rejected straight away) resort more to trade credit (Casey & O’Toole, 2014; Ogawa et al., 2013). Trade credit is when “a firm pays its suppliers with a lag which creates the equivalent of a loan” (Esho & Verhoef, 2018: 11) and it has been the most or second most popular alternative to bank credit in recent years (Kraemer-eis et al., 2018). Moreover, scholars have also found that the use of trade credit tends to increase during a financial crisis (i.e., banks restrict some firms’ loans) (McGuinness & Hogan, 2016; Nilsen, 2002). Additionally, country-level variables also play a role in the easiness of obtaining such credit (Andrieu, Staglianò, & van der Zwan, 2018). Basically, several papers have been published that sought to understand how SMEs, which are heavily dependent on bank credit, react to a tightening of credit conditions. It is also noteworthy that an abundance of empirical evidence has been geared toward VC funding (for a reflection on the field see Harrison & Mason (2019)). However, equity crowdfunding is a meaningful substitute for traditional VC (D’Ambrosio & Gianfrate, 2016). Thus, the study of its role as a substitute of bank credit is of equal or greater importance, given its increasingly higher adoption.

However, there is a lack of research when it comes to assessing the role of equity crowdfunding in situations of higher difficulties for SMEs to obtain funding. For an in- depth discussion of the equity crowdfunding laws’ impact and a comparison of the legal conditions in the different countries refer to Mochkabadi & Volkmann (2020). A noteworthy study, although, is the one from Blaseg & Koetter (2015), which finds that in Germany new ventures tied to stressed banks are more likely to use crowdfunding. Additionally, they equally find that ventures with fewer tangible assets will also opt more for this financing option. This is one of the few studies that seeks to address the gap already filled for other sources of financing. That is, in what conditions can crowdfunding be a valid substitute to bank credit? Another pioneering study is the one from Walthoff- Borm et al. (2018), they draw on the pecking order theory to provide first-time evidence on how entrepreneurial firms that search for equity crowdfunding diverge from those that do not search for equity crowdfunding. However, both studies focus on the firm-level characteristics that drive firms’ choice for crowdfunding and equity crowdfunding. Moreover, they solely focus on one platform. Drawing further on the paper by Walthoff-

10

Borm et al. (2018), it is possible to verify that they use the information asymmetry theory, but solely the firm’s side. That is, the more debt a firm has, the higher the likelihood to opt for equity crowdfunding. By doing so, it ignores the more prevalent issue that firms encounter, namely SMEs. This pertains with the difficulty to obtain the still very much needed credit from banks (European Commission, 2019b). Thus, ignoring the general unwillingness of banks to provide credit to these firms and how equity crowdfunding can be an alternative to established forms of early-stage venture financing, such as bank loans (Mochkabadi & Volkmann, 2020: 83). In essence, firms’ choice for equity crowdfunding is not only dependent on their characteristics, but also on the existing information asymmetries that hinder banks’ willingness to provide capital to these firms. Such information asymmetries arose largely from the banking regulations imposed during and after the GFC, and these regulations are at risk of being further strengthen in the following years. Thus, further aggravating the existing information asymmetries between banks and SMEs and consequently damaging firms’ ability to capture bank credit. This is the topic covered in the following section.

II. Regulation

(i) Banks

Banks are of the utmost importance as a financial intermediary between savers and borrowers. Financial markets (e.g., stock and bond market), similarly to banks, provide borrowers direct access to savers’ funds. However, in banks’ case they act as an intermediary by accepting the actor’s deposits and providing loans to the debtors (i.e., an entity or person that owes money to another party). Figure 1 provides an overview of the financial system channels between savers and borrowers. In essence, banks have a pivotal role in determining the distribution of credit to the entrepreneurs seeking financing. However, when it comes to these credit markets how well-rated with the bank an entrepreneur or an SME is, affects its likelihood of obtaining funding. In general, one of the main types of information banks seek to take into consideration (Ramasastri & Unnikrishnan, 2006) is the experience the bank has with the borrower. Each bank builds their own information base on the borrowers that it has dealt with in the past. Ultimately, the drawing of the loan is not necessarily dependent on the bank’s available funds, but rather on their assessment of the creditworthiness of the borrower. In 2008, the GFC

11 amplified the general prevalent difficulties that SMEs face when trying to obtain funding (Blaseg & Koetter, 2015: 2).

Figure 1 - Representation of the financial system (Ramasastri & Unnikrishnan, 2006)

In the aftermath of the GFC, banks’ profits declined sharply, and it put severe pressure on their liquidity positions. Ultimately, leading to a tightening of credit supply in the EU, thus impairing the banks’ capacity to provide funding to companies in the non- financial private sector (Hempell & Sørensen, 2010). For instance, Van der Veer and Hoeberichts (2016), show that an increase in the level of tightening of a bank’s lending standards lead to a reduction in the lending to businesses in the Netherlands. Similarly, Lee et al. (2015), also found that UK SMEs’ likelihood of obtaining bank credit worsened in the crisis. During these periods of credit rationing, a bank must assess the probability of default of a firm seeking credit. In situations in which the chances of the firm going into bankruptcy are superior to the return the company is able to potentially provide, the bank will decline conceding the credit needed to the firm (Ramasastri & Unnikrishnan, 2006). In these situations, in which the supply of credit by banks is limited, SMEs traditionally face stronger financing constraints, mostly due to being informationally the most opaque firms (Cassar, 2004). In response to the inadequate regulatory framework prevalent before and during the GFC, regulators on the post-crisis responded with more demanding capital and liquidity standards and stronger supervision (Committee on the Global Financial System, 2018). These measures were applied in the EU via the Basel III framework, in order to improve the banks’ ability to provide credit in periods of downturn

12

(BCBS, 2010a). There are three main principles of the Basel III framework. These principles will now be outlined, in order to illustrate how these measures further constrained SMEs in the process of obtaining credit from banks. 1. Minimum capital requirements – capital reserves act as a buffer to absorb shocks during periods of financial crisis. During the crisis, the total minimum equity requirement increased to 7% of the risk-weighted assets (BCBS, 2010b; CFI, n.d.). In other words, the higher the risk of the total assets held by the bank the greater the requirement to have in possession more easily sold assets (Chen, 2019). This new requirement dampens lending when the economy is in periods of growth and encourages lending in times of crisis (Committee on the Global Financial System, 2018: 64). Moreover, as per Bridges et al. (2014), on the year that follows an increase in capital requirements, banks cut on the loans provided taking as much as three years for these to return to normal levels. This is of particular interest, especially in a period in which further capital requirements are planned to be implemented by 2022 (Binham, 2019). Additionally, and in line with the aforementioned papers, Michael Lever – head of AFME’s1 prudential regulatory division – highlighted the fact that having banks holding more capital can have additional negative consequences on the supply of credit (Binham, 2019).

2. Liquidity requirements – during the beginning of the GFC it became apparent that many banks, although some with adequate capital levels, had insufficient levels of liquidity. When the market conditions changed the shortage of liquidity put the banking system under severe stress, as they could no longer meet their immediate obligations (i.e., repay loans, pay ongoing operational bills) (BCBS, 2010a: 3; Chappelow, 2019). Therefore, to tackle this problem two standards were implemented. Firstly, the Liquidity Coverage Ratio requires banks to maintain and adequate level of assets that can be easily converted to cash, in order for it to meet its short-term (i.e., 30 days) obligations, whilst in a scenario of liquidity impairment (BCBS, 2010a: 5; CFI, n.d.). Secondly, Basel III also implemented the Net Stable Funding Ratio. This ratio has the goal of limiting the banks’ over-

1 The Association for Financial Markets in (AFME) represents the global and European leading banks. The association acts as a bridge between the market participants and policy makers in Europe. The prudential division, analyses the regulations implemented at the EU level and advises on technical matters (AFME, n.d.).

13

reliance on short-term wholesale funding (e.g., deposits). Thus, promoting banks to opt for more stable long-term funding of business activities (BCBS, 2010a: 27). Overall, banks have undergone periods of stress because they did not have (1) enough capital, (2) liquidity or (3) a combination of both. Bank liquidity is a relevant determinant of bank lending (Alper, Hulagu, & Keles, 2012). By drawing further on the topic, it is possible to understand from Hoerova, Mendicino, Nikolov, Schepens, & Van den Heuvel (2018: 27–31) paper, that liquidity requirements make banks safer. However, it also leads to banks being more risk- averse when it comes to the loans conceded. Thus, it hampers banks’ ability to create net liquidity (i.e., use deposits to fund loans), which is one of banks’ raisons d’être.

3. Leverage ratio – one of the underlying reasons of the crisis was the excessive leverage banks had taken (BCBS, 2010b: 61). Put it simply, banks were using too much “borrowed” money from clients’ deposits to finance loans. Therefore, in order to push banks to use more of their own capital for financing purposes, a ratio was implemented to constraint the level of debt that can be used (BCBS, 2010b: 61; Carney, 2013). This ratio builds on the capital requirements implemented. Thus, its benefits and costs are similar to the aforementioned requirement (Gambacorta & Karmakar, 2018).

Overall, banking regulation has been growing immensely for the past one hundred years. Some of the most recent and impactful policies were highlighted previously, which in tandem with extensive supervision, are used throughout the world to ensure banks safety and reduce its systemic risks (Hoerova et al., 2018: 7). These measures may finally be put to the test. As per the IMF (2020) report, the rising anxiety about the economic outlook, derived from the COVID-19 pandemic, has raised concerns amongst investors regarding banks’ health. Therefore, the present sentiment regarding the pandemic can lead to a tightening in the lending conditions, although potential softened by the measures put in place after the GFC. As forementioned, one of the main reasons for the implementation of these measures is to avoid banks’ risky lending. This is of particular relevancy as the regulatory framework highly influences and constrains SMEs financing decisions (Esho & Verhoef, 2018: 15). SMEs are less resistant to regulatory shocks and uncertainties (Lampadarios, Kyriakidou, & Smith, 2017: 16-17) and are highly dependent on financing to grow (Ayyagari, Demirgüç-Kunt, & Maksimovic, 2008). SMEs’ ability

14 to obtain funding from these banks gets hindered, as banks in situations of tighter lending conditions need to screen more accurately the firms which they are willing to finance (Paulet, 2018). Thus, particularly when the banking sector is financially constrained, it is of the utmost importance for EU policymakers to explore alternative lending forms (Sapir & Wolff, 2013), such as equity crowdfunding. All of the above, highlights further the importance of studying SMEs’ alternative lending options. The above-mentioned credit constraints are greatly accentuated by the well-known information asymmetries between banks and SMEs. This is the topic covered in the following section.

(ii) Information asymmetries

The foundation for the information asymmetry theory was laid down when Akerlof (1970) raised the question: how can investors select projects from a group of opaque applicants?

Bank assessment of an SME’s credit worthiness is an example of a decision made under the effect of asymmetric information (Deakins & Hussain, 1994: 24). This occurs due to the opaqueness of SMEs, which has always existed, namely during early stage financing (Meza & Webb, 1987; Stiglitz & Weiss, 1981). In other words, asymmetric information means that one party (i.e., banks) lacks the necessary information to delineate a loan contract, whereas the other (i.e., firms) has access to all the relevant information. Ultimately, leading to adverse impacts on decision-making by banks. The relationship intensity between banks and firms and its longevity can mitigate these information asymmetries (Behr, Entzian, & Güttler, 2011). Financial intermediaries (e.g., credit registries) can generate private information as a by-product of the banks’ interactions with firms (Rajan, 1992; Uchida, Udell, & Yamori, 2012), which are latter on used by banks to support their decisions. However, relationship lending is costly, so banks may be unwilling to fund firms if they cannot cover the cost of collecting the necessary private information (Petersen & Rajan, 1994; Rajan, 1992). Overall, information asymmetries pose two main problems for the provision of debt finance by banks. Firstly, the bank is uncapable to have a complete view ex ante of all the relevant information needed, in order to provide the required funding (Binks et al., 1992). Therefore, banks, in order to reduce the potential risk of credit losses, opt for raising loans’ interest rates. However, higher interest rates will lead to low risk firms to drop out, as they will not be willing to pay for the interest rate premiums. Moreover, it will attract high risk borrowers (i.e., companies with a poor business performance) (Huang et al., 2014; Steijvers & Voordeckers, 2009:

15

925). This is the adverse selection effect. Ultimately, if the bank increases the loan’s interest rate, its expected return will decrease (Leland & Pyle, 1977). Therefore, rather than increasing the interest rate, banks prefer rationing credit to these opaque firms. Secondly, moral hazard may also pose a challenge for the provision of credit by banks. There is an underlying risk that SMEs will not comply with the loan contract, thus needing some sort of monitoring (Binks et al., 1992). Basically, once the loan is granted, the burden is on the borrower’s side, so there is always a risk for the bank that the loan will be misused (Bădulescu, 2010: 26). The main issue is that banks are uncapable of costlessly monitor the business performance of firms, or their behaviour and obtain reliable information regarding borrowers’ willingness to pay back the loans (Binks et al., 1992; Jenses & Meckling, 1976). For banks to mitigate these informational asymmetries, they make use of collateral (Blazy & Weill, 2013). When it comes to solving the adverse selection issue, collateral provides a signal to the bank about the borrower’s quality. High quality firms choose loans with more collateral, in order to obtain a lower interest rate. Collateral provides a credible signal, since it is more costly for low-quality firms to provide that guarantee, due to their higher chance of defaulting. Ultimately, leading them to potentially lose the collateral (Besanko & Thakor, 1987; Bester, 1985; Chan & Kanatas, 1985). Thus, collateral improves banks’ ability to assess the borrower’s quality, hence decreasing the likelihood of the bank rationing credit, due to its inability to price the loan (Stiglitz & Weiss, 1981). Collateral can also diminish the problem of moral hazard. It can prevent high-risk firms from changing from a low- to a high-risk project after the loan has been conceded or from putting little effort towards the project (Boot, Thakor, & Udell, 1991; Hainz, 2003). This occurs because the collateral put forward by the firm to be able to obtain the loan, would impose a great loss on the company. However, SMEs often have limited assets to use as collateral. Furthermore, a considerable share of those assets are intangible, which leads to banks being often reluctant to provide credit to these SMEs (Bădulescu, 2010; OECD, 2020: 78). This occurs as banks are unsure on the value to attribute to these assets and if they will be recoverable in the event of a firm’s default (Brassel & Boschmans, 2018: 16–17). Intangible assets, amongst other characteristics, “lack a physical substance and have a non-monetary nature (…)” (Brassel & Boschmans, 2018: 12) (e.g., patents, brand recognition). On the other hand, equity financiers, such as the ones in equity crowdfunding platforms, take a great interest in this type of assets. As highlighted in Brassell & King (2013) paper, there are three main reasons for this. Firstly, plenty of businesses often do not have significantly valuable

16 tangible assets. Thus, the investors cannot resort to these to assess the firm’s value. Secondly, as highlighted before, equity funding investors are primarily interested in financial returns. The highest returns are often linked with business models and technologies that are disruptive and innovative which is mainly associated with intangible assets. Thirdly, firms must have entry barriers to discourage potential competitors, which is primarily achieved through the use of patents. Overall, firms will less tangible assets will be pushed towards using equity financing (Mac an Bhaird & Lucey, 2010; Vanacker & Manigart, 2010).

Information asymmetries between firms and potential investors results in the pecking order theory, as pioneered by Myers & Majluf (1984). The authors posit that in the presence of information asymmetries, a firm will prefer to obtain financing beginning with (1) internal funds, followed by (2) debt and (3) equity, in order to minimize the costs of adverse selection. This theory is particularly suitable for this study, because of the high information asymmetry that characterizes the entrepreneurial setting (Cassar, 2004). Bharath, Pasquariello, & Wu (2009), find supportive evidence for the pecking order hypothesis, showing that information asymmetries are an important determinant of the level of debt a company takes on. Additionally, this theory is also of extreme relevancy when it comes to firms in the beginning of their life. In the vast majority of cases, this is when firms are not attractive to the types of funding sources discussed in the early-stage financing literature (e.g., VC). Thus, these companies tend to be heavily dependent on outside debt in the beginning of their venture’s life, mostly due to its availability (Robb & Robinson, 2014).

A recent way to reduce transaction costs in entrepreneurial financing is crowdfunding. Amongst the different types of crowdfunding available, the equity-based one can act as a complement or a substitute for entrepreneurial ventures that have difficulties in raising capital from traditional sources (e.g., bank loans, VC).

To reduce investors’ transaction costs, equity-crowdfunding platforms provide standardized information (e.g., pitch decks, financials, valuations) for all the companies listed. All the details supplied are directly sourced from the ventures (Blaseg & Koetter, 2015: 7). Additionally, they rely on the “wisdom of crowds” to, amongst the pool of ventures available, decide which ones are the most promising. According to the “wisdom of crowds” paradigm, the average of crowd guesses is more accurate than the single judgment of an expert (Surowiecki, 2004). In other words, using the internet community

17 helps with problem-solving and decision-making. Thus, the information asymmetries will be minimized as investors will take advantage of the collective intelligence of the marketplace. For example, if a company returns to the platform after being successfully funded, investors will weigh that information more favourably when deciding about their potential investment in the company (Yum et al., 2012). Overall, in recent academic literature, crowdfunding and equity crowdfunding in particular have been mentioned as potential paths for entrepreneurs to take, in order to overcome the financing constraints highlighted throughout the paper (e.g., Blaseg & Koetter, 2015; Hornuf & Schwienbacher, 2015; Vulkan et al., 2016).

Generally, it is widely acknowledged the importance that SMEs have in the EU, especially as a source of employment (European Commission, 2019a: 17–23). Nevertheless, as elucidated before, bankers care mostly about a firm’s creditworthiness. Therefore, they are more risk-averse to provide loans to informationally opaque firms with low-quality or inexistent collateral. In the aftermath of the GFC, the financing challenges that SMEs face were aggravated, namely for young, fast-growing and innovative ventures (OECD, 2020: 77). These challenges vary in terms of their impact across the different jurisdictions in the EU, as they are dependent on the financial systems and macroeconomic conditions of each country (FSB, 2019). All in all, equity crowdfunding appears as suitable alternative of the traditional funding sources, by minimizing the information asymmetries between the and the borrower. While drawing on the pecking order theory, this study will be able to assess how the changes in information asymmetries in bank lending across the different countries push firms to seek other sources of financing, namely equity crowdfunding.

Overall, problems stemming from asymmetric information between banks and firms are important factors for loan contracts (Qian & Strahan, 2007). Thus, fewer information asymmetries should increase banks’ willingness to provide credit (Moro, Fink, & Maresch, 2015; Pagano & Jappelli, 1993), hence leading to a more residual use of equity crowdfunding. Based on these ideas, this study considers the (1) strength of legal rights, (2) depth of credit information, (3) credit registry and (4) entrepreneurship as the country-level explanatory variables. As highlighted and then further proved by Qian & Strahan (2007), the first three variables represent the supply-side factors that can define the total amount and the terms of credit which banks are willing to make available

18 for borrowers. The following section will proceed to explain each of these country-level variables.

III. Hypotheses Development

(i) Strength of legal rights

Previous measures of legal rights (e.g., Haselmann, Pistor, & Vig, 2010; Qian & Strahan, 2007) focused solely on the rights which creditors have. However, for the purpose of this research, it is of the utmost importance to also take into account borrowers’ rights. As outlined by some scholarly papers (Ferrando & Mulier, 2015; Freel, Carter, Tagg, & Mason, 2012; Kon & Storey, 2003), information asymmetries can equally discourage firms to search for bank credit, regardless of their creditworthiness. For example, Vig (2013) demonstrated that a regulatory change in India which strengthen the rights of creditors led to an additional cost for firms. Consequently, contributing to a reduction in the use of credit by these. In essence, borrowers’ rights should also be taken into account.

For now, it is important to distinguish between collateral and bankruptcy. Collateral determines the type and scope of the security that a bank can obtain from a firm (e.g., mortgage land). Apart from what was mentioned in the earlier sub-section (ii), collateral offers two advantages (Haselmann et al., 2010: 556–558). Firstly, it facilitates banks’ enforcement against a firm in a default situation. In other words, collateral protects the bank when the firm falls behind on the payments. Secondly, if a firm becomes insolvent a bank which has collateral against that firm, will have priority against competing claims by other creditors. On the other hand, bankruptcy governs the procedure that takes place upon the insolvency of a firm with multiple creditors, each seeking their respective amount that is owed (Baird, 1992). From banks’ perspective having a good bankruptcy regime in place is far better than not having one. The reason behind it is because the former will not delay as much the enforcement of bank’s claims over an insolvent firm’s assets.

Overall, it is clear the importance of collateral in reducing information asymmetries (moral hazard and adverse selection) and easing conditions of credit rationing that SMEs face (Stiglitz & Weiss, 1981). Additionally, bankruptcy is relevant

19 as lenders with a better legal protection during bankruptcy will become more confident in their investment (Djankov, McLiesh, & Shleifer, 2007). This is of special importance namely for SMEs, due to their riskiness, high failure rates and poor performance levels (Lampadarios et al., 2017). With better legal rights, banks will be able to offset the risk and ease the regulatory burden when lending to SMEs. Moreover, improvements on the banks’ rights benefits small borrowers more than large corporations, especially when accompanied by collateral reforms (Haselmann et al., 2010). Haselmann et al., (2010) find that an improvement in collateral laws leads to firms changing their capital structure to accommodate for more debt. Moreover, as per the rationales highlighted in previous sections there are a sufficient number of reasons that can lead some SMEs to use equity crowdfunding. Firstly, as per the equity crowdfunding sub-section, (1) when firms reach a certain level of debt they need to resort to equity financing, (2) these platforms provide a better and cheaper service than more traditional financiers and (3) high-risk firms have a higher chance of receiving funding in these platforms, as investors have been seeking other investment opportunities due to the prevalent low interest rates. Secondly, in the information asymmetries sub-section, I drew further on the attractiveness of equity crowdfunding platforms due to their ability to reduce information asymmetries. Thirdly, in the same sub-section, it was outlined the role of equity crowdfunding’s investors due to their higher preference for SMEs with more predominant intangible assets. Thus, all in all, and in line with the overall theoretical framework provided so far, I posit the following:

Hypothesis 1. On average, countries with stronger legal rights will be associated with a proportionally lower amount of equity crowdfunding being raised compared with countries with weaker legal rights.

(ii) Depth of credit information and credit registry

Apart from the strength of legal rights, the depth of credit information is also worth highlighting. Credit information is dependent, for example, on the existing credit registries, which are important determinants of the contracting environment between banks and firms (Qian & Strahan, 2007). Thus, have a potential role in reducing information asymmetries.

Credit registries are a vital part of the financial infrastructure, helping greatly in addressing the issue of access to credit (Peria & Singh, 2014). A credit registry is a

20 database managed by the public sector (e.g., central bank), that collects information on the creditworthiness of the different borrowers. Additionally, it facilitates the exchange of information amongst banks and other financial institutions (Jappelli & Pagano, 2002). However, the depth of credit information that these institutions provide is influenced by the country’s environment (Houston, Lin, Lin, & Ma, 2010: 487). Credit information tracks the rules that affect the coverage, scope and accessibility of credit information through a credit registry. The amount of information that is collected by each registry varies. Some agencies only collect information on outstanding loans of large borrowers, whereas other agencies distribute extensive information (e.g., late payments, defaults, demographic data, credit inquiries, court records of the company and its owners) (Djankov et al., 2007). With the information provided by these institutions, when lending, banks can make objective decisions based on past borrowing behaviour (Brown, Jappelli, & Pagano, 2009). Ultimately, increasing small firms’ access to financing (Pagano & Jappelli, 1993). From the borrowers’ side, credit information sharing acts as a disciplinary measure. Borrowers tend to have a greater incentive to repay back the loans when they know that by defaulting on loans from one bank, they may be jeopardising their access to funding from other banks (Padilla & Pagano, 2000). Thus, as per the The World Bank (n.d.), “by sharing credit information, these institutions help to reduce information asymmetries, increase access to credit for small firms, lower interest rates, improve borrower discipline and support bank supervision and credit risk monitoring”. Based on the above and the reasons highlighted so far for the use of equity crowdfunding, I posit the following:

Hypothesis 2a. On average, countries with a better credit information sharing will be associated with a proportionally lower amount of equity crowdfunding being raised compared with countries with worse credit information sharing.

Hypothesis 2b. On average, countries with a credit registry will be associated with a proportionally lower amount of equity crowdfunding being raised compared with countries with worse credit information sharing.

Overall, the structure of the credit information sharing, and the use of collateral can be illustrated by the following.

21

Figure 2 - Credit information sharing and use of collateral (The World Bank, n.d.)

(iii) Entrepreneurship

Entrepreneurship plays a key role in the modern market economy. Countries with a higher percentage of firm registrations see a greater increase of competition and economic growth (Djankov, La Porta, Lopez-de-Silanes, & Shleifer, 2002; Lerner, Schoar, Klapper, Amit, & Guillén, 2007). Entrepreneurship is often defined as “the nexus of two phenomena: the presence of lucrative opportunities and the presence of enterprising individuals” (Venkataraman, 1997). SMEs have a great importance in fostering growth and generating jobs (Musso & Francioni, 2014: 1). Thus, as per definition, a higher amount of firm creation will result in a wider range of products, more competition amongst firms and better technology (Kasseeah, 2016: 897).

However, the GFC had strong repercussions in firms’ ability to obtain funding, as extensively explained in the previous sections. It also contributed to record levels of unemployment in many EU countries (OECD, 2012). A solution to this problem has been the encouragement by the governments for the unemployed to set up their own company and at a later stage to provide jobs for others (Acs & Audretsch, 2013). The main reason for that is due to entrepreneurship’s role in speeding up economic progress (Stel, Carree, & Thurik, 2005). In this area plentiful of cross-country studies have sought to study the influence of the different institutional dimensions and/or macroeconomic indicators (Kasseeah, 2016; Lerner et al., 2007) on firms creation. Urbano & Alvarez (2014), for example, found that a positive regulative dimension (e.g., less bureaucracy to start a business), normative dimension (e.g., social norms) and cultural-cognitive dimension (e.g., less fear of business failure) help in fostering new firms creation. Thus, the rate of firms’ creation heavily changes depending on the country. The intertwined relationship between entrepreneurship and a country’s institutions underlines the importance of

22 studying the former across different countries. Additionally, Ho & Wong (2007) also highlight the influence of the availability of financing sources, namely informal investors (e.g., angle investors) on one’s propensity to become an entrepreneur. Even though the aforementioned study preceded the appearance of equity crowdfunding, this new method of alternative financing fits in the definition of informal investors. Thus, its presence helps in widening the availability of financing sources, hence should lead to the same outcome highlighted by Ho & Wong (2007). Overall, countries with more extensive financing sources will have a higher number of newly registered firms. Consequently, and by definition, firms’ use of financing should equally grow, as firms, namely start-ups, need to raise capital to implement their novel ideas.

Overall, it has been established (1) the importance of a cross-country study of entrepreneurship and (2) that a higher percentage of entrepreneurship leads to a higher demand for funding. Additionally, as per the reasons highlighted in the earlier section, informational asymmetries lead firms to look for other forms of financing, namely equity crowdfunding. Taking everything into account, a higher presence of newly registered firms will only accentuate the difficulties faced by firms and their need for alternative financing sources. Alike other studies (e.g., Kasseeah, 2016), I measure entrepreneurship as the new business density of a country. Against this background, I posit the following:

Hypothesis 3. At the mean level, the level of new business density will positively moderate the relationship between the information asymmetry variables on the equity crowdfunding amount being raised.

23

Methodology

This study analyses how information asymmetries can be positively associated with companies seeking alternative financing sources, namely equity crowdfunding. This is tested at the country-level by using the total equity crowdfunding raised between 2014 and 2019, in each EU country. The hypotheses above mentioned are tested using a quantile regression for panel data with bootstrapped standard errors. In total, the sample consists of 13 EU countries.

I. Sample

The present research relies on a sample of 15 platforms. The focus of this study is in all the countries in Table 1, apart from the UK. The UK represents the vast majority of crowdfunding in Europe. Thus, similarly to other studies (e.g., Buzwani et al., 2020; Zhang et al., 2018), it is more worthwhile to investigate the UK separately given its dimension, as it would also likely skew the results. The initial goal was to create a database which would provide a proxy of the total funding amount raised by firms in each year on the different equity crowdfunding platforms. The study employs a relatively small sample, represented by a total of 13 countries from 2014 to 2019. The small sample size is justified by equity crowdfunding still not being a widely spread phenomenon in the EU. Thus, a variety of countries still do not have (1) equity crowdfunding platforms and/or (2) a significant amount of funding being raised via these equity-based deals. Therefore, a sample with a total 78 observations is employed. The sample was obtained in two main steps.

Firstly, it was important to understand the current state of the equity crowdfunding market in Europe. To identify which countries were making the most use out of this alternative financing source, this study resorted to the most recent publications on the matter. The Cambridge Centre for Alternative Finance (CCAF) has recently introduced two reports which provide the most comprehensive review available on the evolution of equity crowdfunding, as further highlighted by websites specialized on the topic (e.g., Alois, 2020). Both the report on the European (Ziegler et al., 2019) and Global (Buzwani et al., 2020) landscape, obtain their data from a wide array of platforms – more than 250 in Europe. Moreover, most of the platforms surveyed have been the same since the first

24 release of the European report in 2015, thus allowing for a longitudinal analysis. These reports are of extreme relevance mainly due to the lack of (reliable) sources providing a macro perspective on this up and coming financing option. The report on the European landscape (Ziegler et al., 2019) provides a breakdown per country of the total equity crowdfunding amount raised since 2015 up until 2017. Table 1 was built based on the overall amounts reported.

Table 1 – Equity crowdfunding amount raised per country in the EU between 2015 and 2017

Total equity crowdfunding volume Country Total Average 2015 2016 2017 France 75.1 43.3 48.4 166.8 55.6 Germany 23.7 47.4 19.7 90.8 30.3 Finland 0 28.8 50.7 79.5 26.5 Sweden 0 46.0 34.0 80.0 26.7 Netherlands 0 27.2 17.8 45.0 15.0 Spain 0 10.1 21.2 31.3 10.4 Italy 5.4 1.7 4.8 11.9 4.0 Austria 0 4.0 8.7 12.7 4.2 0 0.3 0.5 0.8 0.3 0 0.9 0.8 1.7 0.6 Denmark 0 0 0.1 0.1 0.1 Belgium 0 0.1 1.0 1.1 0.3 0 0 0 0 0 Czech Republic 0 0 0 0 0 Greece 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Portugal 0 0 0 0 0 0 0 0 0 0 Slovenia 0 0 0 0 0 Croatia - - - - - Cyprus - - - - - Ireland - - - - -

25

Luxembourg - - - - - Malta - - - - - - - - - - Note: The table is organized in descending order (i.e., from the country with the highest total amount raised to the least). All the 27 EU countries are included. The countries for which the total amount is 0 did not have any equity financing volume reported. Additionally, for the bottom six countries no information was available in the report regarding the (non)existence of equity crowdfunding volume in the country. All the values displayed are in Millions of euros.

Compared with the scope of the study (i.e., 2014-2019), the report only provides a limited view of the total funding in Europe, but it still provides insights into which countries are important. This results from the fact that countries in which no funding occurred during 2014 and 2017 are extremely likely to not have any equity crowdfunding platform operating in the country. Thus, no information can be extracted. Posterior to producing a similar table to the one above, a number of sources were used. These further aided in understanding, which countries should be included based on their overall volume in the years after the 2017. Basically, to assess if there was enough and meaningful data between 2014 and 2019 the following sources were used: (1) European crowdfunding associations (Crowdfunding Hub, n.d.), (2) crowdfunding directories (Crowdfund Insider, n.d.; Crowdfundmarkt, n.d.; Crunchbase, n.d.; Findcrowdfunding, n.d.; P2PMarketData, n.d.) and (3) crowdfunding studies focused on EU countries were used (e.g., Blaseg & Koetter, 2015; Dushnitsky, Guerini, Piva, & Rossi-Lamastra, 2016; Vulkan et al., 2016; Walthoff-Borm et al., 2018). By triangulating across the different sources, it was possible to assess that the top twelve countries present in Table 1 had meaningful volumes of funding. However, there are three noteworthy comments. Firstly, France despite being the country demonstrating the most volume generated, it was later on not included in the final sample. This results from the fact that none of its major platforms had any of the needed information on past campaigns publicly available. Thus, no data could be retrieved. Secondly, Ireland and Portugal were included in the final sample, regardless of not having any volume reported in Table 1. Regarding the former, Ireland’s situation is surprisingly not addressed in the report. However, by triangulating across the aforementioned sources of information, I was able to find that an equity platform had emerged after 2017. Furthermore, prior to that, Irish companies had obtained funding through equity financing, but via other countries’ platforms, which helps in explaining why they were not accounted for by the CCAF. In regard to Portugal’s case,

26 apart from still not having an equity platform in place, the same reasoning applies. Overall, as already highlighted 13 countries were considered: Austria, Belgium, Denmark, Estonia, Finland, Germany, Ireland, Italy, Netherlands, Poland, Portugal, Spain and Sweden. This leads us to the second step of the construction of this sample.

II. Data collection process and sources

(i) Dependent variable

There was not a main data source, but rather a manual collection of the information from the different equity crowdfunding websites. Similarly, to other crowdfunding- centered studies, the data obtained was continuously pulled from the different platforms (Blaseg & Koetter, 2015; Walthoff-Borm et al., 2018). Moreover, due to equity crowdfunding being a global phenomenon, the data available is often decentralized and dispersed (Stasik & Wilczyńska, 2018: 53). Thus, there is still a need to manually collect one’s required data. In regard to the collection of data per se, most platforms offer the possibility to browse through past (un)successful campaigns. However, due to the extensive time-consuming task, several big data methods have been employed to shorten the time frame needed for obtaining such data. As highlighted by Stasik & Wilczyńska (2018: 60), in their review of the techniques used to study crowdfunding, the vast majority of platform-centered studies have focused on using big data analysis to retrieve data on past campaigns. One of the options available is through a web scraper.

A web scraper accesses websites, finds the required information and saves it in a structured data set. Thus, opening a new world of data to scholars (Boeing & Waddell, 2016). In other words, the web scrapper automates the otherwise onerous process of collecting the data of the individual campaigns from the different platforms. As aforementioned, previous crowdfunding studies have employed similar techniques. Moreover, existing reliable sources of crowdfunding data (i.e., CCAF) have also used web scrapping to complement their research, in order to produce their industry reports on crowdfunding (Zhang et al., 2018; Ziegler et al., 2017, 2019). In Appendix C it is possible to find the tool used for this purpose, along with the code personally written on it to obtain the needed data. The code can be used by other scholars to obtain similar and more up to date data.

27

Subsequently to the research mentioned in the previous sub-section, most of the major equity crowdfunding platforms of each country were scrapped (i.e., 12), in order to obtain the needed information. For the remaining platforms (i.e., 3), the data was manually collected, as they did not have a sufficient number of campaigns that would make it worthwhile to scrap them. For each platform, I collected the following data: (1) name of the company, (2) country where the firm is from, (3) year when it obtained the funding and (4) the total amount of funding raised. One major setback was the fact that not all the equity crowdfunding websites had all the data needed, namely the year of the funding. The missing information was obtained via (1) the companies’ social media accounts (e.g., LinkedIn, Facebook), as firms often promote their campaigns there, (2) news articles, (3) companies financial statements submitted in the platform and (4) crowdfunding directories. Please refer to Appendix D for a more in-depth review of all the challenges encountered and the solutions implemented when dealing with the data extracted.

Overall, these efforts resulted in a comprehensive sample of each country, regarding the total yearly funding obtained from the most used European equity crowdfunding platforms. Therefore, a dataset covering 15 platforms across 13 countries between 2014 and 2019 was employed in this study. Table 2 presents (1) all the platforms used in this study, (2) their country and (3) website where the information was retrieved from.

Table 2 – Equity crowdfunding platforms used

Country Platform Website Austria Green Rocket https://www.greenrocket.com/ Belgium Spreds https://www.spreds.com/en Denmark - - Estonia Funderbeam https://www.funderbeam.com/ Finland https://www.invesdor.com/en Seedmatch https://www.seedmatch.de/ Germany https://www.companisto.com/en Ireland Spark crowdfunding https://www.sparkcrowdfunding.com/ Mamacrowd https://mamacrowd.com/ Italy Crowdfundme https://www.crowdfundme.it/en/ Netherlands https://www.symbid.com/

28

Poland Bessfund https://beesfund.com/ Portugal - - Spain Startupxplore https://startupxplore.com/en Sweden FundedByMe https://www.fundedbyme.com/en/ https://www.seedrs.com/ UK https://www.crowdcube.com/ Note: There are two noteworthy comments. Firstly, in both Portugal’s and Denmark’s case no equity crowdfunding platforms exist. Nevertheless, Danish and Portuguese companies have obtained funding from other countries’ platforms, hence its inclusion as enough data is available. Secondly, UK firms are not included in the study. However, a vast number of European companies use UK’s platforms due to the higher presence of investors. Thus, those websites were equally scrapped to obtain information on the European firms.

(ii) Explanatory variables

Three main independent variables are considered in this study. The data on those variables was retrieved from The World Bank Doing Business Database. For the past 17 years, The World Bank has been measuring the regulations that improve business activity and those that hinder it. This data is harmonized at the country-level. Thus, one is able to compare the covered 12 areas of business regulation across 190 economies. In this study, the focus is on the getting credit indicator, which is decomposed in the three independent variables. However, due to the inherent differences between the variables, distinct collection procedures are applied by The World Bank. Overall, apart from the description of the methodological process provided in the next section, a more extensive explanation can be found in the website of The World Bank (n.d.). In general, the getting credit indicator from the Doing Business database is widely used in research and provides all the needed information, thus is a great fit for the present study. Moreover, its methodology has been supported by the much cited paper from Djankov et al. (2007).

Aside from the above-mentioned independent variables a moderator is also employed in this study. Akin to the above, the data is retrieved from The World Bank. Thus, the moderator variable benefits from an equally reliable methodology. The data is obtained via the Entrepreneurship database. This database equally facilitates cross- country comparability by employing the same unit of measurement, source of information and concept of entrepreneurship. The two main advantages of using this database are that the data only reflects the formal economy, and its longitudinal and cross-country comparability.

29

(iii) Control variables

Although the focus of this work is to study the impact of information asymmetries on firms opting for equity crowdfunding, other factors may also influence such decision. Thus, this study includes several control variables at the country level, to guarantee that the results obtained are not unjustifiably influenced by other factors. The control variables data was either retrieved from the Eurostat database or from The World Bank database. Both those sources compile information from national sources. Regarding the former, the Eurostat is the statistical office of the EU, thus has the goal of providing high quality statistics. Moreover, akin to the previous databases it also allows for comparability across the different countries (European Commission, 2020). The database’s reliability is underlined by the vast publications that have used it (European Commission, n.d.). Regarding the World Bank database, the same reasoning applies as the one of the previous sub-section.

III. Variables

Table 3 reports the summary statistics of the variables used in this analysis. Furthermore, an extensive description of each variable is provided below.

(i) Dependent variable

This variable is measured via the Crowdfunding Total. To test the hypotheses, one has to know the total amount of funding the companies of each country have raised. Thus, by doing so, it will be possible to understand if countries with worse information asymmetries – between banks and companies – will have a stronger use of equity crowdfunding. The Crowdfunding Total quantifies in euros the total amount of money successfully raised by companies of a given country per year. A similar variable is used by other crowdfunding studies (e.g., Blaseg & Koetter, 2015; Walthoff-Borm et al., 2018). However, they simply indicate via a binary variable if a company has sought to use crowdfunding or not in response to supply shocks (e.g., added difficulties in obtaining funding via banks loans; information asymmetries). Thus, they are unable to quantify the impact of such shocks on the amount raised by firms. Moreover, they equally cannot measure if information asymmetries have the same impact across countries with different levels of equity crowdfunding raised. In other words, is the information available to the bank on an SME’s creditworthiness impacting in a similar way countries with smaller and

30 bigger amounts of funding raised? Ultimately, not answering this question only provides a limited view on this issue. This is of special importance as the total amount of funding being raised each year via equity crowdfunding varies drastically depending on the year and the country where it is raised. Moreover, I focus on a range of countries, as institutional factors are likely to influence the equity crowdfunding ecosystem (Kshetri, 2015). Additionally, a cross-country analysis is imperative due to the regulatory differences in the European countries (Hornuf & Schwienbacher, 2017), as regulation at the EU level is still non-existent (Moritz & Block, 2016: 9). Overall, by employing the Crowdfunding Total variable, this study is able to quantify how changes in the independent variables impact the total amount of equity funding raised across the different sample subsets.

(ii) Explanatory variables

Strength of legal rights index – drawing further on what was highlighted on the Hypotheses Development chapter, this index measures to what extent is lending facilitated for SMEs and banks by the country’s collateral and bankruptcy laws. On a yearly basis, the legal rights index tracks the changes in laws and regulations that facilitate or hinder lending (see Appendix E). It will be recognized as a reform: (1) new laws and amendments which improve or restrain the flexibility behind the choice of assets to be used as collateral (e.g., granting collateral over intangible assets) and (2) changes regarding the implementation and improvement of any of the features in appendix E. For example, allowing out-of-court enforcement on default (see point five of appendix E). Such enforcement avoids costly and troublesome court actions. Thus, the bank can enforce his rights to seize the collateral in case of default, by an agreed-upon contract between the bank and the firm. The bank can reduce its dependence on the courts, thus capturing the collateral until the outstanding loan is covered (European Council, 2019). The index also recognizes collateral’s higher importance over bankruptcy laws (Haselmann et al., 2010), which is reflected in a greater number of questions that impact the total index. This is extremely relevant especially due to the importance of collateral. In essence, banks are heavily dependent on collateral to reduce their risk when lending, however SMEs are limited in the collateral that can put forward. Moreover, most of the available assets are intangible, which poses a problem for these firms as a great number of countries do not allow its use as collateral. Thus, this index tracks the changes in law that facilitate its use and consequently improve bank lending to SMEs. Past research (e.g.,

31

Djankov et al., 2007; Haselmann et al., 2010; Qian & Strahan, 2007) has equally used a similar index, which was initially designed by La Porta, Lopez-De-Silanes, Shleifer, & Vishny (1997) and later on adopted by The World Bank. Overall, this index captures not only the impact that certain laws have on SMEs securing bank financing, but also on “the relatively permanent features of the institutional environment, deeply rooted in national legal traditions” (Djankov et al., 2007: 307). This index ranges from 0 (weak legal rights) to 12 (strong legal rights) and the data for it is gathered through a questionnaire given to financial lawyers. The questionnaire data is verified across several follow-up rounds to assess its validity. Furthermore, publicly available laws and regulations on collateral and bankruptcy are analysed. In general, both this index and the depth of credit information one, do not change throughout the years within the same country, as the regulatory environment has remained rather stable. However, their values are distinct amongst the different countries. Thus, these questionnaires mainly aid in understanding the differences across countries. Overall, it allows for a cross-country comparison between all the countries in the sample.

Depth of credit information index – akin to the above index, in the Hypotheses Development chapter it was equally highlighted that the amount of information that is collected by each credit registry varies. Thus, it is important to capture these differences via an index (see Appendix F). The depth of credit information index, will consider as a reform: (1) changes that increase or decrease the coverage of the credit registry for more than 5% of the adult population (e.g., providing online access to information on firms and individuals), (2) impactful legislation (e.g., laws on personal data) and (3) positive or negative modifications in one of the eight components of this indicator (see appendix F) (e.g., credit registry starts sharing data on loans – see point one of appendix F). Past research has equally employed this or a similar index (e.g., Mol-Gómez-Vázquez et al., 2018; Nketcha Nana, 2014), as credit information sharing is a great predictor of the lending activity (Brown et al., 2009; Moro et al., 2015; Pagano & Jappelli, 1993). This index ranges from 0 (low availability of credit information) to 8 (high availability of credit information). This index is built in two stages. Firstly, banking supervision authorities are surveyed to verify the existence of a credit registry. Secondly, a questionnaire is provided to the aforementioned entity, in order to have a better understanding of the reforms that have been applied. Similarly, to the previous variable, questionnaire responses are afterwards verified.

32

Credit registry – the information sharing highlighted above is driven by the existence of the credit registry. Similarly to other studies (e.g., Djankov et al., 2007; Haselmann et al., 2010; Nketcha Nana, 2014; Qian & Strahan, 2007), I define as a dummy equal to one if a country has a private credit registry. The importance of this institution is underscored by the fact that information sharing only occurs due to its existence. Regarding the information collection process by The World Bank, this data follows a similar pattern to the one of the previous two variables.

New business density – as highlighted in the Hypotheses Development chapter, the moderator variable measures the new business density. That is, the number of newly registered corporations per 1000 working-age people (i.e., ages 15-64). The new business density allows the measurements of activity across the different countries and over time. Thus, enabling the understanding between new firm creation and the regulatory environment (The World Bank, n.d.). Studies such as the one by Kasseeah (2016) have found that entrepreneurship has an important impact on economic development and that new business density can be used as a proxy of entrepreneurship. To collect the information required, interviews are done with the national business registries and other governmental statistical offices.

(iii) Control variables

For the following three variables the data was obtained via the Eurostat database, which is compiled from national sources. The greatest advantage of this data source is the fact that all the information is reported in euros. Thus, there is no need to perform a conversion of the values to the needed currency (i.e., euros). I will now begin to explain each variable.

GDP – tracks the gross national product of each country in millions of euros. This study controls for the country’s total GDP, as it has been suggested that stronger economies have larger credit markets (Djankov et al., 2007; La Porta et al., 1997). Credit markets need fixed institutional costs to function. Thus, mainly due to economies of scale, larger economies tend to equally have bigger credit markets. Overall, these countries may be better able to provide funding to SMEs, hence influencing the results.

GDP growth rate – the use of this measure allows for comparisons of the economic development between countries of distinct sizes (European Commission, 2020). It measures the percentage change on the previous year. Alike Djankov et al.

33

(2007) and La Porta et al. (1997), I also control for the GDP growth rate, because faster growing economies tend to also have a greater demand for credit. Thus, possibly leading to fluctuations on the amount of equity crowdfunding raised depending on the country.

GDP per Capita – calculates the ratio of GDP to the average population of a specific year (European Commission, 2020). It can be used as a proxy for the development in a country’s living standards. This measure has been proven to be positively correlated with the independent variables employed in this study (Nketcha Nana, 2014). Thus, it is an excellent measure to capture the differences in private credit, legal rights and information sharing which are reflected by the different levels of economic development.

For the following two variables the information was retrieved from The World Bank database. Similarly, to the above, all of the indicators are compiled from officially recognized international sources. The variables and the reasons for their inclusion are the following.

Inflation – reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services (The World Bank, 2020). As per Djankov et al. (2007), inflation can decrease the value of outstanding debt. In other words, it can hinder the firms use of debt markets, especially when it comes to richer countries (Nketcha Nana, 2014) – such as the ones that can be found in this sample. In simpler terms, firms can mitigate informational frictions when seeking to obtain funding by resorting to internal funds. However, under higher inflation the value of money (e.g., firm’s internal funds) is eroded, thus a firm for any given investment project will be more dependent upon outside financing (Smith & van Egteren, 2005). Overall, inflation promotes investment. Crowdfunding, namely equity crowdfunding has the role of getting new ventures off the ground, which mainly consists in funding investment decisions (Hemer, 2011).

Domestic credit to private sector by banks – this variable captures the financial development of a country. It represents the ratio of credit from deposit taking financial institutions (e.g., banks) to the private sector to GDP (The World Bank, 2020). Financial development improves firms’ access to finance. Thus, facilitating firms’ growth and new firm creation (Kasseeah, 2016). Previous studies (Djankov et al., 2007; Haselmann et al., 2010; Qian & Strahan, 2007) have sought to measure the financial development due to

34

the difficulty in measuring the quality of financial intermediation, which has a first order effect on capital flows (Kasseeah, 2016). Moreover, a higher ratio of domestic credit to private sector by banks implies a higher demand for loans (Qian & Strahan, 2007: 2820), thus it can lead to a more residual use of equity crowdfunding.

Table 3 – Summary statistics: the full sample includes 78 observations across 13 countries between 2014 to 2019

N Mean SD Q 0.25 Q 0.5 Q 0.75 Q 0.90 Independent variable Crowdfunding Total 78 4576762 6367266 342753 1648311 6736824 24549329

Explanatory variables Strength of legal rights index 78 5.205 2.104 4 6 7 8 Depth of credit information index 78 6.641 0.925 6 7 7 8 Credit registry 78 0.474 0.503 0 0 1 1 New business density 78 5.548 5.139 2.559 3.979 7.119 25.448

Control variables Inflation 78 0.912 0.91 0.25 0.853 1.606 3.436 GDP growth rate 78 2.831 3.07 1.6 2.2 3.1 25.2 GDP 78 731128 838110 262833 421628.5 774039 3435760 GDP per Capita 78 32181 12397 23080 35255 40730 60350 Domestic credit to private sector 78 91.874 32.55 66.804 86.314 111.507 173.325

IV. Method of analysis

This study employs a panel data analysis, as the dataset includes observations across multiple years. The dataset is strongly balanced, with every country having the same number of years to analyse. I begin this section with an introduction to quantile regression.

Standard approaches in regression analysis tend to focus on the average values. In other words, how the average effect of a given independent variable impacts the average of the dependent variable. However, this may hide interesting features of the relationship between the dependent and independent variable(s). Thus, an incomplete picture is obtained if one only takes into account the mean of a distribution, as further highlighted by Mosteller & Turkey (1977: 266). Overall, applying a technique such as quantile regression can help in providing a more complete picture of the underlying relationship between information asymmetries and the total equity crowdfunding raised. Thus, it

35 allows us to understand how information asymmetries affect countries that are making the most and the least use out of this alternative financing source. In other words, it enables the investigation of whether the relationship between the total equity crowdfunding raised and the explanatory variables differs throughout the distribution of the dependent variable (Koenker & Bassett, 1978). In the case of this study, employing this type of regression is preferable for a number of reasons.

Firstly, one of the core assumptions behind, for instance, OLS regressions is that the dependent variable and the error term are normally distributed. This does not hold for this study’s database, because the total amounts of equity crowdfunding raised by the different countries is heavily skewed. Figure 3 displays a kernel density estimate for the total equity crowdfunding amount. It is possible to conclude that it does not follow a Gaussian distribution. The equity crowdfunding amounts raised vary depending on a firm’s needs, thus one is expected to find drastically different values. This is compounded by the fact that the data gathered is from firms of different countries. Thus, the total amount raised per country will vary depending on how developed the country’s equity crowdfunding market is along with other macroeconomic factors. Large samples alleviate these concerns. On the other hand, small sample studies – such as the present – might be biased and lead to unreliable findings if an OLS estimation is employed (Nikitina, Paidi, & Furuoka, 2019). Quantile regression does not need a normally distributed error term (Koenker & Bassett, 1978). Moreover, quantile regression results are robust to outliers and heavy-tailed distributions, such as the one in Figure 3. In fact, these results are invariant to the dependent variable’s outliers when these tend to +∞ (Buchinsky, 1998), as in the present case. Another advantage is the quantile regressions’ ability to describe the entire distribution of the dependent variable. Although, it is not the main focus of this research to study the outliers, these are still relevant. The reason for that is because due to the nature of equity crowdfunding, very distinct amounts are raised. Thus, by focusing on subsets of the sample one is able to analyse the effects of information asymmetries when countries have more (less) developed markets (i.e., higher (lower) amounts of equity crowdfunding are being raised). Given the still fragmented nature of equity crowdfunding in the EU this can only be deemed as relevant. By not only focusing on the mean, as the widely popular OLS, a more complete picture can be captured.

36

7

0

-

e

0

0

5

.

1

7

0

-

e

0

0

0

.

1

y

t

i

s

n

e

D

8

0

-

e

0

0

0

.

5 0

0 5000000 10000000 15000000 20000000 25000000 Crowdfunding amount (€)

Kernel density estimate Normal density

Figure 3 - The distribution of the total equity crowdfunding amount

Overall, in order to obtain results which can be replicable, researchers need to be aware of the different issues associated with each statistical method used. A small sample research could suffers from two major methodological problems (Nikitina et al., 2019): (1) violating the normal distribution assumption and (2) the presence of outliers in the data used. To overcome this issue, a quantile regression method is employed. This analysis tends to be more robust against the existence of outliers (Chernozhukov, Hansen, & Jansson, 2009). Additionally, a bootstrapping method is used. This method can be employed for a small sample analysis (McNeish, 2016: 752) without having to comply with the normality assumption of the error terms (Stuckler, Basu, Suhrcke, Coutts, & McKee, 2009). Briefly, bootstrapping is a resampling method. It allows for the existing data to be repeatedly and randomly resampled, in order to allow for inferences to be made regarding the unknown population (Nikitina & Furuoka, 2018: 422). Thus, bootstrapping albeit not solving the problem of having a small sample it greatly improves the ability of this study to make inferences from the sample to the population. Ultimately, improving the quality of the sample and of the inferences made. On another note, quantile regression for panel data has captured the interest of different scholars (Galvao & Montes-Rojas, 2015: 655), thus its use has been transversal across distinct study areas (e.g., Billger & Goel, 2009; Coad & Rao, 2008). Canto-Cuevas, Palacín-Sánchez, & di Pietro (2016) paper is also noteworthy. They employed a similar approach to study the relationship between trade credit and bank credit, in order to account for the heterogeneity of firms.

37

Results

I. Main results

Correlation among the independent variables can pose a problem when interpreting the coefficients that derive from the quantile regression. This is an issue strictly related with data and not the model used (Hair, Black, Babin, Anderson, & Tatham, 2006). The correlation matrix presented in Appendix G indicates the magnitude and direction of the relationships between all the variables employed in this study. Most of the variables did not have a high degree of collinearity, as they do not have values close to the unity. However, the depth of credit information index, strength of legal rights index and new business density showed a high correlation, namely when doing the interaction between them. Thus, for these variables, I mean centered their values and included them in the models. Subsequently, those values were used to calculate the interaction terms. This is done in order to reduce multicollinearity problems, as indicated by Neter, Kutner, Nachtsheim, & Wasserman (1996). Notwithstanding, the lack of high correlation values for certain variables, this does not necessarily guarantee the inexistence of collinearity. One of the conventional measures to assess multicollinearity is the variance inflation factor (VIF). As per the Appendix H, the maximum VIF for the different independent variables is in general below 10 with a mean value of 4.6, which indicates that multicollinearity is not a concern (Neter et al., 1996).

Table 4 presents the main results of the quantile regression for panel data with bootstrapped standard errors. The table has a total of 3 models. Firstly, model 1 presents the regression results with only the control variables. Secondly, model 2 introduces the different measures of information asymmetry. Lastly, model 3 includes all the variables from the previous models and adds the interaction terms. It is worth highlighting that lower quantiles correspond to countries with smaller amounts of equity crowdfunding raised.

38

Table 4 – Determinants of the total amount of equity crowdfunding raised

Q 0.25 Q 0.50 Q 0.75 Q 0.90 Model 1 Inflation -119912.5 72480.7 327742.9 -1025714.6*** (238572.7) (191493.9) (1100017.6) (275852.2)

Domestic credit to private sector 9265.7* -2746.1** 81178.5 -88852.9* (4656.6) (971.1) (63347.4) (35420.4)

GDP per Capita 18.45* -10.46 49.97 394.8*** (8.284) (9.929) (66.25) (25.87)

GDP 2.970*** 3.300*** 8.746* 2.860** (0.513) (0.262) (3.692) (1.028)

GDP growth rate 112826.2 -210832.9*** 507961.4 -795950.3*** (107626.6) (58988.3) (497254.3) (190325.8) Model 2 Depth of credit information index -604108.3*** -4166522.2** -2256184.5*** -51244429.0 (45750.8) (1289062.6) (247622.1) (94030895.3)

Strength of legal rights index 233954.8*** 985520.6* -545606.5** 27411439.0 (33364.1) (399433.0) (197124.5) (62407844.8)

Credit registry 119555.2 -3555288.8*** -3815470.6*** 880392931.7 (226313.4) (914996.0) (1002106.8) (1.86732e+09)

Inflation 288149.9*** 4086539.6** 1434533.5*** -200075579.6 (75585.6) (1296545.3) (344512.1) (412534245.4)

Domestic credit to private sector -8284.2*** -90399.1 -76191.3*** -4521086.0 (1798.2) (52327.9) (9113.4) (8920428.4)

GDP per capita 21.13** -73.31 94.23** -42096.1 (7.246) (46.71) (31.35) (87143.1)

GDP 3.991*** 5.133*** 7.361*** 180.0 (0.0610) (0.396) (0.462) (352.0)

GDP growth rate -36001.2* 606212.0* -225996.5*** 10220266.1 (18167.3) (280931.1) (39763.1) (24611669.0) Model 3 Depth of credit information index -285966.6*** -1061172.0*** -3255453.2*** -7264350.8*** (35094.7) (124500.2) (5451.1) (201043.8)

Strength of legal rights index 606988.3*** 568483.7*** -537982.1*** -307887.4*** (11311.6) (103468.1) (7349.2) (70452.3)

Credit registry 2887794.0*** 2781370.1*** -5738214.6*** -8520239.2*** (81580.4) (290384.6) (46580.7) (465484.5)

New business density 4820269.9*** 5326004.9*** -3365588.6*** -7194401.2*** (93436.7) (337053.7) (51053.1) (240445.2)

39

Inflation 191297.0*** 153564.8 840429.8*** 697962.9*** (46072.9) (379207.1) (9165.7) (62788.9)

Domestic credit to private sector -13565.4*** -51074.9*** -35889.3*** -52850.7*** (850.0) (9906.3) (232.6) (4032.0)

GDP per Capita 5.743* 41.07*** -1.366** -88.49*** (2.489) (5.142) (0.492) (4.218)

GDP 2.300*** 3.195*** 8.927*** 12.82*** (0.0550) (0.167) (0.0148) (0.183)

GDP growth rate 15296.7** -213636.9*** -205373.2*** -238891.6*** (5146.6) (35525.2) (1220.3) (23242.3)

D_C_IXB_D -1641490.6*** -2928059.8*** 2511477.3*** 6567439.3*** (84494.8) (209789.2) (39074.2) (511225.2)

S_L_RXB_D -1706273.8*** -1669503.9*** 339861.2*** 753488.9*** (33823.0) (125111.8) (8941.6) (58685.4)

C_RXB_D -5930731.7*** -4935012.5*** 3977374.9*** 6099987.9*** (129785.3) (440019.0) (59200.3) (259294.4) Observations 78 78 78 78 Note: B_D = New business density; D_C_I = Depth of credit information index; S_L_R = Strength of legal rights index; C_R = Credit registry; X = Interaction between the two variables

Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

As mentioned before, model 1 only includes the country-level control variables. Consistent with the existing literature, the results suggest that country-level factors impact the flow of credit. The results are mostly significant at the median level (i.e., Q 0.5) and are always significant at levels of high equity crowdfunding raised (i.e., Q 0.90). Regarding the former, both the GDP growth rate and domestic credit to private sector have a negative effect on the total crowdfunding amount raised. In other words, countries which are growing faster and have a higher demand for loans tend to raise less funds via equity crowdfunding. On the other hand, the higher a country’s GDP is (i.e., countries with larger credit markets) the more equity crowdfunding is raised. In general, all the three variables are extremely statistically significant (p < 0.01 or better). In regard to the higher quantile (i.e., Q 0.90), the effect of all the above-mentioned variables – apart from GDP – was accentuated. Furthermore, the effect of inflation and GDP per capita become statistically significant (p < 0.001). The GDP per capita being significant shows that different levels of economic development have an impact in the use of equity crowdfunding. In contrast, an increase of inflation has a strong negative effect not only

40 on the debt market, as the literature suggested, but also on the equity crowdfunding raised. Overall, these findings alike the ones in the following models are not only statistically significantly but also economically relevant.

Model 2 introduces the existing independent variables to help in explaining the impact of information asymmetries on the total equity crowdfunding raised. Regarding the strength of legal rights index, this is significant at the median level (p < 0.05). However, contrary to the initial expectations, the results show that an increase on the strength of legal rights index leads to an increase of approximately 985 thousand euros (K€) in the total amount of equity crowdfunding raised. However, this positive impact, albeit statistically significant, is rather economically weak. At Q 0.50, an increase in the strength of legal rights leads to a variation 4 times smaller than the one produced by the depth of credit information. Q 0.25 and Q 0.75 are also noteworthy. Regarding the former, a similar effect to the one of the median levels is present, albeit more statistically significant (p < 0.001) and with a smaller variation – 2.5 times smaller when compared with the effect of the depth of credit information in Q 0.25. In regard to Q 0.75, an increase in the strength of legal rights, has a negative effect on the dependent variable for higher levels of equity crowdfunding raised. Thus, it is consistent with what was discussed in the literature. For Q 0.90, and similarly to the remaining variables of this model, there is no statistical significance. From now onwards, for this model, when referring to “all quantiles” it includes only Q 0.25, Q 0.50 and Q 0.75. Overall, the strength of legal rights index shows contradicting results and is not on average (i.e., at the median level Q 0.50), but at higher values consistent with Hypothesis 1. In regard to the depth of credit information index, at Q 0.50, the results show that an increase from the average to the average +1 standard deviation leads to a reduction of around 4.2 Million euros (M€) on the amount of equity crowdfunding raised. This outcome is consistent across all quantiles with high levels of statistical significance (p < 0.01 or better). The effect is stronger at Q 0.50 when compared with Q 0.25. Thus, countries with larger amounts raised will witness a more sharply decrease in the equity-based deals. However, this effect seems to lose its importance after a certain level, as from Q 0.50 to Q 0.75 the strength of the effect is halved. In essence, when there is an improvement on the information that banks have on SMEs’ creditworthiness the total amount of equity crowdfunding raised plummets, which is consistent with Hypothesis 2a. The third variable in the model is the credit registry. Consistently with Hypothesis 2b this institution has a negative impact on the total amount of equity crowdfunding raised. Q 0.50 and Q 0.75 are the only statistically significant

41 quantiles (p < 0.001). For these quantiles, the existence of a credit registry leads to a reduction of 3.5 M€ and 3.8 M€ on the equity crowdfunding amount raised, respectively. Lastly, in regard to the control variables, there are some results worth highlighting. Inflation, as opposed to model 1, has a positive impact on the dependent variable and is statistically significant across all quantiles (p < 0.01 or better). As highlighted in the variables section, inflation promotes investment. Thus, the results show that in countries with a higher inflation, there is a preference for equity-based deals. The results of the variable domestic credit to private sector were as expected. In the presence of the information asymmetry variables, the effect of a higher demand for bank credit has a negative impact on the funds raised via equity crowdfunding. This effect is statistically significant (p < 0.001). Lastly, the GDP per capita, GDP and GDP growth rate have similar results to the ones of model 1. Notably, is the fact that GDP growth rate now has a positive effect on the equity crowdfunding amount raised at the median level (p < 0.05), which is consistent with what was expected.

The last model is model 3 and it introduces the interaction terms (i.e., the moderator). Both the depth of credit information index and the strength of legal rights index have been mean centered. Thus, a different interpretation is needed, as in interaction terms they are equal to zero when their values equate the average index for a given quantile. Thus, the following results should be interpreted in light of such change. First and foremost, it is important to highlighted that for all the explanatory variables, the results are statistically significant across all quantiles (including Q 0.90) (p < 0.001). Regarding the depth of credit information index, when compared with the previous model the effect remains the same, although more economically meaningful. For the variable strength of legal rights index as we move up the quantiles the impact on the dependent variable becomes negative and less economically significant. Thus, once again the results are rather contrasting and economically weak when compared with the previous measure. In this model, the presence of a credit registry is no longer only having a negative impact on the equity crowdfunding amount raised. Similar to the previous measure, when moving up the quantiles the influence on the dependent variable becomes negative, but, in this case, economically stronger. The new business density measure follows the same structure as the credit registry variable. On average, it has a positive impact on the equity crowdfunding amount raised, as more businesses represent a higher demand for funding. In regard to the control variables, in general everything remains the same as in model 2. Lastly, and more importantly, the interaction terms contribute in a meaningful way to the

42 explanatory power of the model as they all are extremely statistically significant across all quantiles (p < 0.001). Aligned with the Hypothesis developed I will now focus on Q 0.50. The model reveals significant negative interactions between the depth of credit information index and new business density. Thus, higher levels of entrepreneurship amplify the negative effect of the depth of credit information index on the total equity crowdfunding amount raised. Regarding the strength of legal rights index, by its own and when the new business density is equal to 0, its effect on the dependent variable is surprisingly positive (+568K€). However, for higher values of new business density this effect becomes progressively more negative, equating to -1.1M€ (+568K€ - 1.6M€). Similarly, the existence of a credit registry, in this model, also has a positive impact on the equity crowdfunding amount raised. Nevertheless, for higher values of new business density the effect of the existence of a credit registry goes from +2.8M€ to -2.1M€ (+2.8M€ - 4.9M€). Overall, for the average level, the higher the new business density in a given country (i.e., the higher the levels of entrepreneurship), the more negative are the effects of a reduction in information asymmetries on the equity crowdfunding amount raised. Thus, these results are consistent with Hypothesis 3.

II. Robustness tests

As further robustness checks, I replace the variables credit registry and new business density. Regarding the former, and similarly to Nketcha Nana (2014), I use the coverage indicator instead of considering credit registry as a 1 or 0 depending on the existence of the institution in that country. That is, for a given country in each year the coverage indicator tracks the number of individuals and firms registered in that institutions with information from the past 5 years regarding their borrowing history. The number is expressed as a percentage of the adult population. Furthermore, the indicator is provided by the Doing Business database alongside the dummy variable used in the previous models. Thus, it follows the same collection process. The relevancy of this alternative measure is underlined by the fact that a higher coverage by the credit registry is associated with better availability of credit information (Nketcha Nana, 2014). Regarding the new business density and alike Kasseeah (2016), an alternative measure is employed to capture the entrepreneurial spirit of a country. This new measure tracks the absolute number of newly registered firms for every year in a given country. It is equally

43 provided by the Doing Business database and captured in the same way as the new business density variable.

The results reported in Appendix I provide similar results for model 2 to the ones previously found. At Q 0.50, both the depth of credit information index and credit registry coverage show a negative influence on the equity crowdfunding amount raised. Regarding the credit registry coverage, it is noteworthy that the higher the number of individuals and firms covered, the higher the negative impact on the equity crowdfunding amount raised. On the other hand, the strength of legal rights index remains having a positive impact on the amounts raised, and only at Q 0.75 it reports a negative effect on the dependent variable. Thus, showing the same inconsistent conclusions. All of the above-mentioned results are significant (p < 0.001). Overall, the reported results are consistent with the previous findings. Regarding model 3, at the median level, for all the information asymmetry variables, similar findings with the same level of significance are found to the ones reported in the main results. However, it is worth noting that at Q 0.75 all the information asymmetry variables have a negative impact on the dependent variable. Thus, making it clearer that for higher amounts of equity crowdfunding being raised, the negative impact of information asymmetries is more striking. When it comes to the interaction terms, it is clear that a higher number of companies leads to the information asymmetry variables having a negative effect on the equity crowdfunding amount raised. These results are equally statistically significant (p < 0.05) and economically meaningful. Overall, the results of the implemented robustness tests are consistent with the ones found in the earlier section, as well as highlight the statistical significance and economic impact that information asymmetries can have on the amounts being raised in equity crowdfunding platforms.

44

Discussion and conclusion

The residual attention by prior research on the factors that lead to higher amounts of equity crowdfunding being raised by SMEs is unfortunate, considering the prevalent difficulties that SMEs encounter when trying to obtain bank loans (Binks et al., 1992; Jenses & Meckling, 1976). Moreover, information asymmetries are a vital part of banks’ assessment of an SME’s credit worthiness (Deakins & Hussain, 1994: 24). Thus, in theory, with high information asymmetries the expanding equity crowdfunding market can provide a fertile ground for SMEs to obtain financing (Bharath et al., 2009; Myers & Majluf, 1984). Raising some key questions about the measurable role of information asymmetries in helping equity crowdfunding become a valid substitute to bank credit. This study provides novel evidence on this topic, presenting statistically significant and economically meaningful results.

Firstly, this research makes a noteworthy contribution to the pecking order theory and entrepreneurial finance literature. Scholars have long discussed the importance of information asymmetries in banks’ willingness to provide credit to firms (Moro et al., 2015; Pagano & Jappelli, 1993; Qian & Strahan, 2007). However, notwithstanding scholars’ arguments (e.g., Vulkan et al., 2016) in favour of the potential role of equity crowdfunding on early stage financing, little research has been put forward that directly relates it with banking information asymmetries. The traditional pecking order theory focuses on information asymmetries as the main mechanism that drives a pecking order. However, few studies (Blaseg & Koetter, 2015; Walthoff-Borm et al., 2018) have attempted to quantify the circumstances in which firms opt for equity crowdfunding, let alone under the direct influence of information asymmetries. In this vein, this research, provides novel evidence of the negative impact that a decrease in information asymmetries can have on the amounts of equity crowdfunding raised across different countries. Such an impact results from both an increase in the depth of credit information that banks have on borrowers, and the existence of a credit registry that provides that information. Therefore, a country with high information asymmetries will witness an incrementally higher use of equity crowdfunding. This evidence is consistent with the assertions put forward by both the pecking order theory and information asymmetry theory. Potential further research to add to this finding could seek to investigate if an

45 improvement in the banking information asymmetries will lead to companies returning to banks. If companies progressively familiarize themselves with these equity crowdfunding platforms and obtain the needed funding, why would they feel the need to return to the more traditional financing sources, such as banks? Research at firm-level, could assess the extent to which an individual company’s experience on equity crowdfunding influences their decision. At country-level, in light of the ensuing stricter banking regulations (Binham, 2019; IMF, 2020) information asymmetries can potentially worsen. Thus, further research could explore the extent to which specific laws or legal reforms affect the relationship between equity crowdfunding and bank credit, through the lenses of pecking order and information asymmetry theories. A differences-in-differences approach could be appropriate for such a venture. Such research could prove both timely and pertinent, especially in the EU, where most countries have seen banking regulations remain relatively unchanged, as illustrated by the rather stable depth of credit information and strength of legal rights index values in recent years.

Secondly, this study contributes to the crowdfunding literature by shifting away from the traditional qualitative and conceptual studies, and the platform-centered scholarly papers (Mochkabadi & Volkmann, 2020), instead offering a more quantitative analysis. Previous scholars have focused on the influence of the implemented equity crowdfunding regulations on the platforms’ functioning and the shareholders’ behaviour, both in one jurisdiction (e.g., Klöhn, Hornuf, & Schilling, 2016; Pereira, 2017) or across countries (e.g., Hornuf & Schwienbacher, 2017). Additionally, a significant amount of these studies is focused in the more developed United States market and the impact of the JOBS act in recent years. Within this research, quantitative results on the negative impact of an improvement on collateral and bankruptcy laws on equity crowdfunding are provided, captured by the strength of legal rights index. Despite the fact that for smaller equity crowdfunding amounts raised this negative effect does not materialize, it begins to become negative for higher levels of entrepreneurship, whereby this discrepancy may be attributable to the nature of the index. Robb & Robinson, (2014), show that in the US, borrowers in states with higher bankruptcy laws are expected to receive less bank debt. This results from increased bankruptcy protection impairing the collateral value of the assets a company owns. Thus, even though the index is considering an increasing in bankruptcy laws in a positive way, it might not be reflecting the reality. The Doing Business database provides the answers to the questionnaire that makes up the index for each country. As the answers for each question vary greatly across the countries of this

46 sample, it would be useful for future research to disentangle the index into the different questions, providing further clarity. Ultimately, this could facilitate further analysis of the consequences of the individual bankruptcy and collateral laws and the way they interact with each other.

As with any research, this study is not free of limitations, which may in themselves present valuable avenues for future research. Firstly, the potential drawback of the getting credit database is that it is mostly based on survey data, so it is subject to sampling error bounds. Secondly, and perhaps the most important limitation, is the sample size employed. The implementation of a bootstrapping method helped in increasing the ability of this study to make inferences from the sample to the population (Nikitina & Furuoka, 2018: 422). Nevertheless, it still does not entirely succeed in overcoming the problem of having a relatively small sample. This can be explained by the nature of equity crowdfunding, as not many countries in the EU have a sufficiently developed market that allows for its study. Thus, similar to past studies (e.g., Djankov et al., 2007), which sought to investigate the determinants of bank credit across an array of economies in different continents, future research could expand the present study by considering countries outside of the EU. Countries in the Asia-Pacific region such as Australia, Hong Kong and South Korea have well developed equity crowdfunding markets (Buzwani et al., 2020), meaning their inclusion could add value to the sample. Moreover, their platforms (e.g., OnMarket, AngelHub and Wadiz, respectively) can be scraped using a code consistent with that used in this study (see Appendix C). Similar cases can be found in other regions.

This research also has important practical implications at both governmental and individual equity crowdfunding platform levels. The results suggest that an improvement in the information asymmetries has a negative effect on the amounts of equity crowdfunding raised, namely in countries where SMEs are raising larger amounts. Thus, policy makers in countries with high information asymmetries (e.g., low credit registry coverage of firms) can look into equity crowdfunding as a viable alternative to bank financing. Therefore, policy makers should try to both foster the emergence of equity crowdfunding platforms in their country and their use by companies and investors. The framework that should be implemented for this to occur is beyond the scope of this study. However, it is worth noting that for instance, in Germany, by law, investors can only invest up to 1,000€ without having to disclose their income and personal assets. Given that few investors are willing to lose confidentiality of their financial data, this poses a

47 significant obstacle to the growth of this market (Klöhn et al., 2016). This is of the utmost importance for countries with high information asymmetries as firms that cannot obtain bank credit or access an equity crowdfunding platform in their country, may seek other countries’ platforms to fulfil their financing needs. As a result, both the firm and the country where the company is from, become dependent on other countries’ capital flows and naturally their abrupt economical shifts. This is a time sensitive issue given that as of March 2018 platforms can apply for an EU passport which is based on a single set of rules, making it easier for them to offer their services across the EU (European Commission, 2018). Additionally, the results also show a positive correlation between a higher number of companies being set up and the negative effect of information asymmetries on the equity crowdfunding amounts raised, namely in markets with smaller amounts being raised. For this reason, countries with a high business density should place an additional emphasis on fostering the development of equity crowdfunding platforms and their use. The timely significance of such a recommendation is clear, as according to the European Commission (2019a), SMEs in the EU are forecasted to enjoy growing importance in a country’s economy, although likely weakened by the economic implications of COVID-19. Overall, these results aid in understanding how regulation can be either a key driver for or hinderance to SMEs’ growth, if the appropriate alternative financing channels, namely equity crowdfunding, are not in place. This is especially relevant during financial stress periods, in which information asymmetries might be accentuated (Esho & Verhoef, 2018: 15; Paulet, 2018; Sapir & Wolff, 2013).

Lastly, this study also provides valuable insights for equity crowdfunding platforms allowing them to better identify which countries offer more lucrative expansion opportunities, based on the strength of the information asymmetries in those economies. The recent ability for these platforms to offer their services across the EU (European Commission, 2018) highlights the importance of this finding. Moreover, such insights enable equity crowdfunding platforms to better understand how banking regulatory changes may or may not impact their businesses, namely the ones involving collateral and bankruptcy laws. Notably, factors such as a higher inflation and GDP can also have a positive influence on the total amounts being raised in the country. In conclusion, this paper sheds a compelling light on a wide array of factors that can help these platforms in their strategic decisions, as well as on the potential future role of such platforms in today’s global economy.

48

References

Acs, Z. J., & Audretsch, D. B. 2013. The knowledge spillover theory of entrepreneurship. Small Business Economics, 41: 757–774. AFME. n.d. AFME - About us. https://www.afme.eu/About-Us, April 20, 2020. Akerlof, G. 1970. The Market for “Lemons”: Quality Uncertainty and the Market Mechanism. The Quarterly Journal of Economics, 84(3): 488–500. Allison, T. H., Davis, B. C., Short, J. C., & Webb, J. W. 2015. Crowdfunding in a prosocial microlending environment: Examining the role of intrinsic versus extrinsic cues. Entrepreneurship: Theory and Practice, 39(1): 53–73. Alois, J. 2020. Cambridge Centre for Alternative Finance Publishes First Global Report on Alternative Finance: Over $300 Billion in Volume in 2018. Crowdfund Insider. https://www.crowdfundinsider.com/2020/04/160263-cambridge-centre-for- alternative-finance-publishes-first-global-report-on-alternative-finance-over-300- billion-in-volume-in-2018/. Alper, K., Hulagu, T., & Keles, G. 2012. An Empirical Study on Liquidity and Bank Lending. Central Bank of the Republic of Turkey, vol. 12. http://www.tcmb.gov.tr/research/discus/2012/WP1204.pdf. Andrieu, G., Staglianò, R., & van der Zwan, P. 2018. Bank debt and trade credit for SMEs in Europe: firm-, industry-, and country-level determinants. Small Business Economics, 51(1): 245–264. Arner, D. W., Barberis, J., & Buckley, R. P. 2015. The Evolution of Fintech: A New Post- Crisis Paradigm? SSRN Electronic Journal. Ayyagari, M., Demirgüç-Kunt, A., & Maksimovic, V. 2008. How important are financing constraints? The role of finance in the business environment. World Bank Economic Review, 22(3): 483–516. Bădulescu, D. 2010. SMEs Financing: the Extent of Need and the Responses of Different Credit Structures. Theoretical and Applied Economics, 17(7): 25–36. Baird, D. G. 1992. The Elements of Bankruptcy. New York: Foundation Press. BCBS. 2010a. Basel III: International framework for liquidity risk measurement, standards and monitoring. BCBS. 2010b. Basel III: A global regulatory framework for more resilient banks and banking systems. http://www.bis.org/publ/bcbs189.pdf. Behr, P., Entzian, A., & Güttler, A. 2011. How do lending relationships affect access to credit and loan conditions in microlending? Journal of Banking and Finance, 35(8): 2169–2178. Belleflamme, P., Lambert, T., & Schwienbacher, A. 2010. Crowdfunding : An industrial organization perspective. Business, 25–26. Belleflamme, P., Lambert, T., & Schwienbacher, A. 2013. Individual crowdfunding practices. Venture Capital, 15(4): 313–333. Belleflamme, P., Lambert, T., & Schwienbacher, A. 2014. Crowdfunding: Tapping the right crowd. Journal of Business Venturing, 29(5): 585–609. Besanko, D., & Thakor, A. V. 1987. Collateral and Rationing : Sorting Equilibria in Monopolistic and Competitive Credit Markets. International Economic Review, 28(3): 671–689. Bester, H. 1985. Screening vs Rationing in Credit Markets with Imperfect Information. The American Economic Review, 75(4): 850–855. Bharath, S. T., Pasquariello, P., & Wu, G. 2009. Does asymmetric information drive

49

capital structure decisions? Review of Financial Studies, 22(8): 3211–3243. Billger, S. M., & Goel, R. K. 2009. Do existing corruption levels matter in controlling corruption?. Cross-country quantile regression estimates. Journal of Development Economics, 90(2): 299–305. Binham, C. 2019, July 2. EU watchdog warns of €135bn bank capital shortfall. Financial Times. https://www.ft.com/content/c20db49e-9cd2-11e9-9c06-a4640c9feebb. Binks, M. R., Ennew, C. T., & Reed, G. V. 1992. Information Asymmetries and the Provision of Finance to Small Firms. International Small Business Journal, 11(1): 35–46. Blaseg, D., & Koetter, M. 2015. Friend or Foe? Crowdfunding Versus Credit when Banks are Stressed. Blazy, R., & Weill, L. 2013. Why do banks ask for collateral in SME lending? Applied Financial Economics, 23(13): 1109–1122. Block, J. H., Colombo, M. G., Cumming, D. J., & Vismara, S. 2018. New players in entrepreneurial finance and why they are there. Small Business Economics, 50(2): 239–250. Boeing, G., & Waddell, P. 2016. New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings. Journal of Planning Education and Research, 37(4): 457–476. Boot, A. W. A., Thakor, A. V., & Udell, G. F. 1991. Secured Lending and Default Risk: Equilibrium Analysis, Policy Implications and Empirical Results. The Economic Journal, 101: 458–472. Brassel, M., & Boschmans, K. 2018. Fostering the use of Intangibles to strengthen SME access to finance. Brassell, M., & King, K. 2013. Banking on IP? The role of intellectual property and intangible assets in facilitating business finance. Bridges, J., Gregory, D., Nielsen, M., Pezzini, S., Radia, A., et al. 2014. The Impact of Capital Requirements on Bank Lending. Bank of England. Brown, M., Jappelli, T., & Pagano, M. 2009. Information sharing and credit: Firm-level evidence from transition countries. Journal of Financial Intermediation, 18(2): 151–172. Buchinsky, M. 1998. Recent advances in quantile regression models: A practical guideline for empirical research. Journal of Human Resources, 33(1): 88–126. Burtch, G., Ghose, A., & Wattal, S. 2013. An empirical examination of the antecedents and consequences of contribution patterns in crowd-funded markets. Information Systems Research, 24(3): 499–519. Buzwani, M., Cai, W., Forbes, H., Soki, E., Hao, R., et al. 2020. The Global Alternative Finance Market Benchmarking Report. Canto-Cuevas, F. J., Palacín-Sánchez, M. J., & di Pietro, F. 2016. Trade credit in SMEs: a quantile regression approach. Applied Economics Letters, 23(13): 945–948. Carney, J. 2013. Everything you ever wanted to know about bank leverage rules. CNBC. https://www.cnbc.com/id/100880857. Carpenter, R. E., & Petersen, B. C. 2002. imperfections, high-tech investment, and new equity financing. Economic Journal, 112(477). Casey, E., & O’Toole, C. M. 2014. Bank lending constraints, trade credit and alternative financing during the financial crisis: Evidence from European SMEs. Journal of Corporate Finance, 27: 173–193. Cassar, G. 2004. The financing of business start-ups. Journal of Business Venturing, 19(2): 261–283. CFI. n.d. What is Basel III? https://corporatefinanceinstitute.com/resources/knowledge/finance/basel-iii/.

50

Chan, Y., & Kanatas, G. 1985. Asymmetric Valuations and the Role of Collateral in Loan Agreements. Journal of Money, 17(1): 84–95. Chappelow, J. 2019. Liquidity Crisis. Investopedia. https://www.investopedia.com/terms/l/liquidity-crisis.asp. Chen, J. 2019. Capital Requirements. Investopedia. https://www.investopedia.com/terms/c/capitalrequirement.asp. Chernozhukov, V., Hansen, C., & Jansson, M. 2009. Finite sample inference for quantile regression models. Journal of Econometrics, 152(2): 93–103. Cholakova, M., & Clarysse, B. 2015. Does the Possibility to Make Equity Investments in Crowdfunding Projects Crowd Out Reward-Based Investments? Entrepreneurship Theory and Practice, 39(1): 145–172. Coad, A., & Rao, R. 2008. Innovation and firm growth in high-tech sectors: A quantile regression approach. Research Policy, 37(4): 633–648. Committee on the Global Financial System. 2018. Structural changes in banking after the crisis. CGFS Papers. https://www.bis.org/publ/cgfs60.pdf. Cox, J., & Nguyen, T. 2018. Does the crowd mean business? An analysis of rewards- based crowdfunding as a source of finance for start-ups and small businesses. Journal of Small Business and Enterprise Development, 25(1): 147–162. Crowdfund Insider. n.d. Crowdfund Insider. https://www.crowdfundinsider.com/. Crowdfunding Hub. n.d. Crowdfunding Hub. https://www.crowdfundinghub.eu/, May 12, 2020. Crowdfundmarkt. n.d. Crowdfundmarkt. https://www.crowdfundmarkt.nl/. Crunchbase. n.d. Crunchbase. https://www.crunchbase.com/home, May 12, 2020. Cumming, D., & Johan, S. 2013. Demand-driven securities regulation: evidence from crowdfunding. Venture Capital, 15(4): 361–379. Cumming, D., Meoli, M., & Vismara, S. 2019. Investors’ choices between cash and voting rights: Evidence from dual-class equity crowdfunding. Research Policy, 48(8): 1–19. D’Ambrosio, M., & Gianfrate, G. 2016. Crowdfunding and venture capital: Substitutes or complements? Journal of Private Equity, 20(1): 7–20. Deakins, D., & Hussain, G. 1994. Risk assessment with asymmetric information. International Journal of Bank Marketing, 12(1): 24–31. Djankov, S., La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. 2002. The Regulation of Entry. The Quarterly Journal of Economics, 117(1): 1–37. Djankov, S., McLiesh, C., & Shleifer, A. 2007. Private credit in 129 countries. Journal of Financial Economics, 84(2): 299–329. Dushnitsky, G., Guerini, M., Piva, E., & Rossi-Lamastra, C. 2016. Crowdfunding in Europe: Determinants of platform creation across countries. California Management Review, 58(2): 44–71. Esho, E., & Verhoef, G. 2018. The Funding Gap and the Financing of Small and Medium Businesses: An Integrated Literature Review and an Agenda. Munich Personal RePEc Archive. European Commission. 2018. Proposal for regulation on crowdfunding at the EU level. European Comission, vol. 56. https://doi.org/10.1017/CBO9781107415324.004. European Commission. 2019a. Annual Report On European SMEs 2018/2019. https://doi.org/10.2873/742338. European Commission. 2019b. Survey on the access to finance of enterprises (SAFE). https://ec.europa.eu/docsroom/documents/38462. European Commission. 2020. Eurostat. https://ec.europa.eu/eurostat. European Commission. n.d. All publications - Eurostat. https://ec.europa.eu/eurostat/publications/all-publications.

51

European Council. 2019, November 27. Non-performing loans: Council adopts position on a new mechanism for out-of-court enforcement - Consilium. https://www.consilium.europa.eu/en/press/press-releases/2019/11/27/non- performing-loans-council-adopts-position-on-a-new-mechanism-for-out-of-court- enforcement/. Federal reserve. 2019. The Fed - What is the difference between a bank’s liquidity and its capital? https://www.federalreserve.gov/faqs/cat_21427.htm. Fenwick, M., McCahery, J. A., & Vermeulen, E. P. M. 2018. Fintech and the financing of SMEs and entrepreneurs: From crowdfunding to marketplace lending. In D. Cumming & L. Hornuf (Eds.), The Economics of Crowdfunding: Startups, Portals and Investor Behavior: 103–129. Palgrave Macmillan. Ferrando, A., & Mulier, K. 2015. The real effects of credit constraints: evidence from discouraged borrowers in the euro area. Findcrowdfunding. n.d. Findcrowdfunding. https://www.findcrowdfunding.com/en. Freel, M., Carter, S., Tagg, S., & Mason, C. 2012. The latent demand for bank debt: Characterizing “discouraged borrowers.” Small Business Economics, 38(4): 399– 418. FSB. 2019. Evaluation of the effects of financial regulatory reforms on small and medium-sized enterprise (SME) financing. https://www.fsb.org/wp- content/uploads/P070619-1.pdf. Galvao, A. F., & Montes-Rojas, G. 2015. On bootstrap inference for quantile regression panel data: A Monte Carlo study. Econometrics, 3(3): 654–666. Gambacorta, L., & Karmakar, S. 2018. Leverage and Risk-Weighted Capital. International Journal of Central Banking, 14(5): 153–191. Goldfarb, A. K. A. C. C., & Working, A. 2011. The Geography of Crowdfunding. National Bureau Of Economic Research. Gompers, P., & Lerner, J. 2001. The venture capital revolution. Journal of Economic Perspectives, 15(2): 145–168. Gradoń, W., & Cichy, J. 2015. Crowdfunding as a mechanism for financing small and medium-sized enterprises. E-Finance, 12(3): 38–48. Habla, Z. A., & Broby, D. 2019. Equity Crowdfunding. Centre For Financial Regulation and Innovation. https://www.aicpa.org/content/dam/aicpa/interestareas/frc/accountingfinancialrepor ting/downloadabledocuments/crowdfunding/crowdfunding-snapshot.pdf. Hainz, C. 2003. Bank competition and credit markets in transition economies. Journal of Comparative Economics, 31: 223–245. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. 2006. Multivariate Data Analysis. Technometrics. Upper Saddle River, New Jersey: Pearson Prentice Hall. Hannah Skingle. 2019, July 18. Equity investment market. Beauhurst. https://about.beauhurst.com/blog/equity-investment-update-h1-2019/. Härkönen, E. 2017. Crowdfunding and the small offering exemption in european and US prospectus regulation: Striking a balance between investor protection and access to capital? European Company and Financial Law Review, 14(1): 121–148. Harrison, R. T., & Mason, C. M. 2019. Venture Capital 20 years on: reflections on the evolution of a field. Venture Capital, 21(1): 1–34. Haselmann, R., Pistor, K., & Vig, V. 2010. How Law Affects Lending. Review of Financial Studies, 23(2): 549–580. Hemer, J. 2011. A snapshot on crowdfunding. Fraunhofer Institute for Sytems and Innovation Research. Hempell, H. S., & Sørensen, C. K. 2010. The impact of supply constraints on bank lending

52

in the euro area - crisis induced crunching? European Central Bank. Herzenstein, M., Dholakia, U. M., & Andrews, R. L. 2011. Strategic Herding Behavior in Peer-to-Peer Loan Auctions. Journal of Interactive Marketing, 25(1): 27–36. Ho, Y. P., & Wong, P. K. 2007. Financing, regulatory costs and entrepreneurial propensity. Small Business Economics, 28(2–3): 187–204. Hoerova, M., Mendicino, C., Nikolov, K., Schepens, G., & Van den Heuvel, S. 2018. Benefits and costs of liquidity regulation. https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2169.en.pdf. Hornuf, L., & Schwienbacher, A. 2015. Crowdinvesting: Angel investing for the masses? In H. Landström (Ed.), Handbook of Research on Venture Capital: 381–397. Edward Elgar Publishing. Hornuf, L., & Schwienbacher, A. 2017. Should securities regulation promote equity crowdfunding? Small Business Economics, 49(3): 579–593. Houston, J., Lin, C., Lin, P., & Ma, Y. 2010. Creditor rights, information sharing, and bank risk taking. Journal of Financial Economics, 96: 485–512. Huang, C., When, Y., & Liu, Z. 2014. Analysis on Financing Difficulties for SMEs due to Asymmetric Information. Global Disclosure of Economics and Business, 3(1): 77–80. Huang, R. H. 2018. Online P2P Lending and Regulatory Responses in China: Opportunities and Challenges. European Business Organization Law Review, 19(1): 63–92. IMF. 2020. World Economic Outlook - Chapter 1: The Great Lockdown (April 2020). Jappelli, T., & Pagano, M. 2002. Information sharing, lending and defaults: Cross-country evidence. Journal of Banking and Finance, 26(10): 2017–2045. Jenses, M., & Meckling, W. 1976. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3: 305–360. Kasseeah, H. 2016. Investigating the impact of entrepreneurship on economic development: a regional analysis. Small Business and Enterprise Development, 23(3): 896–916. Klöhn, L., Hornuf, L., & Schilling, T. 2016. The Regulation of Crowdfunding in the German Small Investor Protection Act: Content, Consequences, Critique, Suggestions. European Company Law, 13(2): 56–66. Koenker, R., & Bassett, G. 1978. Regression Quantiles. Econometrica, 46(1): 33. Kon, Y., & Storey, D. J. 2003. A Theory of Discouraged Borrowers. Small Business Economics, 21(1): 37–49. Kraemer-eis, H., Botsari, A., Gvetadze, S., Lang, F., & Torfs, W. 2018. European Small Business Finance Outlook - June 2018. Kraemer-Eis, H., & Lang, F. 2014. The importance of leasing for SME finance. Kshetri, N. 2015. Success of Crowd-based Online Technology in Fundraising: An Institutional Perspective. Journal of International Management, 21(2): 100–116. Kuppuswamy, V., & Bayus, B. L. 2018. Crowdfunding creative ideas: The dynamics of project backers. The economics of crowdfunding: 151–182. Palgrave Macmillan. La Porta, R., Lopez-De-Silanes, F., Shleifer, A., & Vishny, R. W. 1997. Legal determinants of external finance. Journal of Finance, 52(3): 1131–1150. Lampadarios, E., Kyriakidou, N., & Smith, G. 2017. Towards a new framework for SMEs success: A literature review. International Journal of Business and Globalisation, 18(2): 194–232. Lee, E., & Lee, B. 2012. Herding behavior in online P2P lending: An empirical investigation. Electronic Commerce Research and Applications, 11(5): 495–503. Lee, N., Sameen, H., & Cowling, M. 2015. Access to finance for innovative SMEs since the financial crisis. Research Policy, 44(2): 370–380.

53

Leland, H., & Pyle, D. 1977. Informational asymmetries, financial structure and financial intermediation. The Journal of Finance, 32(2): 371–387. Lerner, J., Schoar, A., Klapper, L., Amit, R., & Guillén, M. F. 2007. Entrepreneurship and Firm Formation across Countries. Policy Research. Lin, M., Prabhala, N. R., & Viswanathan, S. 2013. Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Science, 59(1): 17–35. Lin, Y., Boh, W., & Goh, K. 2014. How Different are Crowdfunders? Examining Archetypes of Crowdfunders and Their Choice of Projects. Academy of Management Proceedings, 1: 13309. Mac an Bhaird, C., & Lucey, B. 2010. Determinants of capital structure in Irish SMEs. Small Business Economics, 35(3): 357–375. McGuinness, G., & Hogan, T. 2016. Bank credit and trade credit: Evidence from SMEs over the financial crisis. International Small Business Journal: Researching Entrepreneurship, 34(4): 412–445. McNeish, D. 2016. On Using Bayesian Methods to Address Small Sample Problems. Structural Equation Modeling, 23(5): 750–773. Meza, D. De, & Webb, D. C. 1987. Too Much Investment: A Problem of Asymmetric Information. The Quarterly Journal of Economics, 102(2): 281–292. Mochkabadi, K., & Volkmann, C. K. 2020. Equity crowdfunding: a systematic review of the literature. Small Business Economics, 54(1): 75–118. Mol-Gómez-Vázquez, A., Hernández-Cánovas, G., & Koëter-Kant, J. 2018. Legal and Institutional Determinants of Factoring in SMEs: Empirical Analysis across 25 European Countries. Journal of Small Business Management, 56(2): 312–329. Mollick, E. 2014. The dynamics of crowdfunding: An exploratory study. Journal of Business Venturing, 29(1): 1–16. Moritz, A., & Block, J. H. 2016. Crowdfunding: A Literature Review and Research Directions. Crowdfunding in Europe – State of the Art in Theory and Practice: 25–53. Moro, A., Fink, M., & Maresch, D. 2015. Reduction in information asymmetry and credit access for small and medium-sized enterprises. Journal of Financial Research, 38(1): 121–143. Mosteller, F., & Turkey, J. 1977. Data Analysis and Regression. (Addison-Wesley, Ed.). Reading. Musso, F., & Francioni, B. 2014. International strategy for SMEs: Criteria for foreign markets and entry modes selection. Journal of Small Business and Enterprise Development, 21(2): 301–312. Myers, S., & Majluf, N. 1984. Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics, 13(2): 187–221. National Park Service. 2015. Joseph Pulitzer - Statue of liberty national monument. https://www.nps.gov/stli/learn/historyculture/joseph-pulitzer.htm. Neter, J., Kutner, H., Nachtsheim, C., & Wasserman, W. 1996. Applied Linear Statistical. Irwin, Chicago: Norton. Neuberger, D., & Räthke-Döppner, S. 2013. Leasing by small enterprises. Applied Financial Economics, 23(7): 535–549. Nikitina, L., & Furuoka, F. 2018. Expanding the Methodological Arsenal of Applied Linguistics with a Robust Statistical Procedure. Applied Linguistics, 39(3): 422– 428. Nikitina, L., Paidi, R., & Furuoka, F. 2019. Using bootstrapped quantile regression analysis for small sample research in applied linguistics: Some methodological

54

considerations. PLOS ONE, 14(1): 1–19. Nilsen, J. H. . 2002. Trade Credit and the Bank Lending Channel. Journal of Money, Credit and Banking, 34(1): 226–253. Nketcha Nana, P. V. 2014. Legal rights, information sharing, and private credit: New cross-country evidence. Quarterly Review of Economics and Finance, 54(3): 315– 323. OECD. 2012. Financing SMEs and Entrepreneurs 2012: An OECD Scoreboard, OECD Publishing. OECD. 2020. Financing SMEs and entrepreneurs 2020: An OECD Scoreboard. OECD Publishing. Ogawa, K., Sterken, E., & Tokutsu, I. 2013. The trade credit channel revisited: Evidence from micro data of Japanese small firms. Small Business Economics, 40(1): 101– 118. Ordanini, A., Miceli, L., Pizzetti, M., & Parasuraman, A. 2011. Crowd-funding: transforming customers into investors through innovative service platforms. Journal of Service Management, 22(4): 443–470. P2PMarketData. n.d. P2P Equity Platforms of the World. https://p2pmarketdata.com/p2p-equity-platforms-of-the-world/, March 1, 2020. Padilla, A. J., & Pagano, M. 2000. Sharing default information as a borrower discipline device. European Economic Review, 44(10): 1951–1980. Pagano, M., & Jappelli, T. 1993. Information Sharing in Credit Markets. The Journal of Finance, 48(5): 1693–1718. Paulet, E. 2018. Banking liquidity regulation: Impact on their and on entrepreneurial finance in Europe. Strategic Change, 27(4): 339–350. Pereira, C. M. 2017. A Brief Analysis of the New Portuguese Equity Crowdfunding Regime. Peria, M. S. M., & Singh, S. 2014. The Impact of Credit Information Sharing on Interest Rates. World Bank Policy Research. Washington, DC. Petersen, M., & Rajan, R. 1994. The Benefits of Lending Relationships: Evidence from Small Business Data. The Journal of Finance, 49(1): 3–37. Qian, J., & Strahan, P. E. 2007. How Laws and Institutions Shape Financial Contracts: The Case of Bank Loans. The Journal of Finance, 62(6): 2803–2834. Rajan, R. 1992. Insiders and Outsiders: The Choice between Informed and Arm’s‐Length Debt. The Journal of Finance, 47(4): 1367–1400. Ramadani, V. 2012. The Importance of Angel Investors in Financing the Growth of Small and Medium Sized Enterprises. International Journal of Academic Research in Business and Social Sciences, 2(7): 306–322. Ramasastri, A. S., & Unnikrishnan, N. K. 2006. Is the Role of Banks as Financial Intermediaries Decreasing ? Economic and Political Weekly, 41(11): 1063–1068. Ridley, D. 2016. Will new regulation on crowdfunding in the and United States have a positive impact and lead to crowdfunding becoming an established financing technique? Statute Law Review, 37(1): 57–76. Robb, A. M., & Robinson, D. T. 2014. The Capital Structure Decisions of New Firms. Review of Financial Studies, 27(1): 153–179. Sapir, A., & Wolff, G. B. 2013. The neglected side of banking union: reshaping Europe’s financial system. ECOFIN, (September): 1–13. Short, J. C., Ketchen, D. J., McKenny, A. F., Allison, T. H., & Ireland, R. D. 2017. Research on Crowdfunding: Reviewing the (Very Recent) Past and Celebrating the Present. Entrepreneurship: Theory and Practice, 41(2): 149–160. Smith, R. T., & van Egteren, H. 2005. Inflation, investment and economic performance: The role of internal financing. European Economic Review, 49(5): 1283–1303.

55

Stasik, A., & Wilczyńska, E. 2018. How do we study crowdfunding? An overview of methods and introduction to new research agenda. Journal of Management and Business Administration. , 26(1): 49–78. Steijvers, T., & Voordeckers, W. 2009. Collateral and credit rationing: A review of recent empirical studies as a guide for future research. Journal of Economic Surveys, 23(5): 924–946. Stel, A. van, Carree, M., & Thurik, R. 2005. The Effect of Entrepreneurial Activity on National Economic Growth. Small Business Economics, 24(3): 311–321. Stiglitz, J. E., & Weiss, A. 1981. Credit Rationing in Markets with Imperfect Information. American Economic Association, 71(3): 393–410. Stuckler, D., Basu, S., Suhrcke, M., Coutts, A., & McKee, M. 2009. The public health effect of economic crises and alternative policy responses in Europe: an empirical analysis. The Lancet, 374(9686): 315–323. Sum, K. 2016. Banking Regulation and Bank Lending in the EU. Post-Crisis Banking Regulation in the European Union: 209–250. Palgrave Macmillan. Surowiecki, J. 2004. The wisdom of crowds: why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations. New York: Doubleday. The World Bank. 2020. World Bank Group. https://www.worldbank.org/. The World Bank. n.d. Why it matters? https://www.doingbusiness.org/en/data/exploretopics/getting-credit/why- matters#notes. The World Bank. n.d. Getting Credit Methodology. https://www.doingbusiness.org/en/methodology/getting-credit, March 11, 2020b. The World Bank. n.d. Doing Business. https://www.doingbusiness.org/en/doingbusiness, March 1, 2020c. Uchida, H., Udell, G. F., & Yamori, N. 2012. Loan officers and relationship lending to SMEs. Journal of Financial Intermediation, 21(1): 97–122. Urbano, D., & Alvarez, C. 2014. Institutional dimensions and entrepreneurial activity: An international study. Small Business Economics, 42(4): 703–716. Van der Veer, K. J. M., & Hoeberichts, M. M. 2016. The level effect of bank lending standards on business lending. Journal of Banking and Finance, 66: 79–88. Vanacker, T. R., & Manigart, S. 2010. Pecking order and debt capacity considerations for high-growth companies seeking financing. Small Business Economics, 35(1): 53– 69. Venkataraman, S. 1997. The distinctive domain of entrepreneurship research. In J. Katz & R. Brockhouse (Eds.), Advances in entrepreneurship, firm’s emergence and growth: 119–138. Connecticut: JAI Press. Vig, V. 2013. Access to Collateral and Corporate Debt Structure: Evidence from a Natural . Journal of Finance, 68(3): 881–928. Vulkan, N., Åstebro, T., & Sierra, M. F. 2016. Equity crowdfunding: A new phenomena. Journal of Business Venturing Insights, 5: 37–49. Walthoff-Borm, X., Schwienbacher, A., & Vanacker, T. 2018. Equity crowdfunding: First resort or last resort? Journal of Business Venturing, 33(4): 513–533. WebScraper.io. n.d. Web Scraper. https://www.webscraper.io/, May 6, 2020. Yum, H., Lee, B., & Chae, M. 2012. From the wisdom of crowds to my own judgment in microfinance through online peer-to-peer lending platforms. Electronic Commerce Research and Applications, 11(5): 469–483. Zhang, B., Ziegler, T., Mammadova, L., Johanson, D., Gray, M., et al. 2018. The 5th UK Alternative Finance Industry Report. https://www.jbs.cam.ac.uk/fileadmin/user_upload/research/centres/alternative-

56

finance/downloads/2018-5th-uk-alternative-finance-industry-report.pdf. Zhang, J., & Liu, P. 2012. Rational herding in microloan markets. Management Science, 58(5): 892–912. Ziegler, T., Shneor, R., Garvey, K., Wenzlaff, K., Yerolemou, N., et al. 2017. Expanding Horizons - The 3rd European Alternative Finance Industry Report. Cambridge Centre for Alternative Finance. Ziegler, T., Shneor, R., Wenzlaff, K., Odorovic, A., Ryll, L., et al. 2019. Shifting paradigms - The 4th European Alternative Financee Benchmark Report.

57

Appendix

Appendix A – Equity crowdfunding amount raised in Europe (excluding the UK) (in millions of dollars)

2018 278,1

2017 237,9

2016 242

2015 176,9

2014 109,8

2013 63,1

Source: Graph computed based on data provided by the Cambridge Centre for Alternative Finance (Buzwani et al., 2020: 78)

Appendix B – Composition of the European enterprises number, value added and employment in 2018

Source: European Commission, 2019

58

Appendix C – Web scraping sitemaps

Source: WebScraper.io, n.d. UK 1. Seedrs {"_id":"seedrs","startUrl":["https://www.seedrs.com/secondary- market?available_shares=false&sort=trending_desc"],"selectors":[{"id":"Company","ty pe":"SelectorElementScroll","parentSelectors":["_root"],"selector":"div.table- cell__ImageWrapper-wx5enb- 3","multiple":true,"delay":"1000"},{"id":"Link","type":"SelectorLink","parentSelectors ":["_root","Company"],"selector":".ePikxk a","multiple":true,"delay":0},{"id":"Company_Name","type":"SelectorText","parentSel ectors":["Link"],"selector":"h1","multiple":false,"regex":"","delay":0},{"id":"Funding", "type":"SelectorElementClick","parentSelectors":["Link"],"selector":"section.about","m ultiple":false,"delay":"500","clickElementSelector":".is-active a.Tabs- trigger","clickType":"clickOnce","discardInitialElements":"do-not- discard","clickElementUniquenessType":"uniqueText"},{"id":"Table","type":"Selector Element","parentSelectors":["Funding"],"selector":"_parent_","multiple":false,"delay":0 },{"id":"Rows","type":"SelectorElement","parentSelectors":["Table"],"selector":"div.Li stGroup- rowContent","multiple":true,"delay":0},{"id":"Country_Location","type":"SelectorText ","parentSelectors":["Link"],"selector":".location dd","multiple":false,"regex":"","delay":0},{"id":"Total_Raised","type":"SelectorText"," parentSelectors":["Link"],"selector":"dt:contains('\nTotal raised\n') + dd","multiple":false,"regex":"","delay":0},{"id":"Date","type":"SelectorText","parentSe lectors":["Rows"],"selector":"div:nth-of- type(1)","multiple":false,"regex":"","delay":0},{"id":"Amount","type":"SelectorText"," parentSelectors":["Rows"],"selector":"div:nth-of- type(2)","multiple":false,"regex":"","delay":0}]} 2. Crowdcube {"_id":"crowdcube","startUrl":["https://www.crowdcube.com/companies?fund_type=eq uity&funded_date=&raise_amount=&search=§or="],"selectors":[{"id":"Company", "type":"SelectorElementClick","parentSelectors":["_root"],"selector":"section","multipl e":true,"delay":"10000","clickElementSelector":"button.cc- pagination__load","clickType":"clickMore","discardInitialElements":"do-not- discard","clickElementUniquenessType":"uniqueText"},{"id":"Link","type":"SelectorLi nk","parentSelectors":["_root","Company"],"selector":"a.cc- card__link","multiple":true,"delay":0},{"id":"Company_Name","type":"SelectorText"," parentSelectors":["Link"],"selector":"h1","multiple":false,"regex":"","delay":0},{"id":" Total_Raised","type":"SelectorText","parentSelectors":["Link"],"selector":"li:nth-of- type(1) h4","multiple":false,"regex":"","delay":0},{"id":"Funding","type":"SelectorTable","pare ntSelectors":["Link"],"selector":".cc-company__history table","multiple":true,"columns":[{"header":"Date","name":"Date","extract":true},{"hea der":"Amount","name":"Amount","extract":true},{"header":"No. of

59

Investors","name":"123","extract":false}],"delay":0,"tableDataRowSelector":"tbody tr","tableHeaderRowSelector":"thead tr"}]}

Spain 1. Startupxplore {"_id":"startupxplore","startUrl":["https://startupxplore.com/en/investors/syndicates/pre vious"],"selectors":[{"id":"Company_Name","type":"SelectorText","parentSelectors":[" Company"],"selector":"h3","multiple":false,"regex":"","delay":0},{"id":"Total_Raised", "type":"SelectorText","parentSelectors":["Company"],"selector":"p.orange","multiple":f alse,"regex":"","delay":0},{"id":"Funding_Date","type":"SelectorText","parentSelectors ":["Company"],"selector":"li:nth-of-type(1) span.number","multiple":false,"regex":"","delay":0},{"id":"Company","type":"Selector Element","parentSelectors":["_root"],"selector":"div.item","multiple":true,"delay":0}]}

Sweden 1. Fundedbyme {"_id":"","startUrl":["https://www.fundedbyme.com/en/browse?state=close d"],"selectors":[{"id":"Company","type":"SelectorElementScroll","parentSelectors":["_ root"],"selector":"campaign- card","multiple":true,"delay":"2000"},{"id":"Link","type":"SelectorLink","parentSelect ors":["_root","Company"],"selector":"a.card_link","multiple":true,"delay":0},{"id":"Co mpany_Name","type":"SelectorText","parentSelectors":["Link"],"selector":"h1","multip le":false,"regex":"","delay":0},{"id":"Country_Location","type":"SelectorText","parent Selectors":["Link"],"selector":"li:nth-of-type(1) a.colorText","multiple":false,"regex":"","delay":0},{"id":"Total_Raised","type":"Select orText","parentSelectors":["Link"],"selector":"#sticky-card-wrapper div[data- selenium='amount_invested']","multiple":false,"regex":"","delay":0},{"id":"Funding_D ate","type":"SelectorText","parentSelectors":["Link"],"selector":"#sticky-card-wrapper span.funding-stats-note","multiple":false,"regex":"","delay":0}]}

Germany 1. Companisto {"_id":"companisto","startUrl":["https://www.companisto.com/en/investments"],"select ors":[{"id":"Company","type":"SelectorElementClick","parentSelectors":["_root"],"sele ctor":"div.content_info_wrapper","multiple":true,"delay":"10000","clickElementSelecto r":"a.btn-new-layout-wb","clickType":"clickMore","discardInitialElements":"do-not- discard","clickElementUniquenessType":"uniqueText"},{"id":"Company_Name","type ":"SelectorText","parentSelectors":["Company"],"selector":"h2.mb- 5px","multiple":false,"regex":"","delay":0},{"id":"Total_Raised","type":"SelectorText", "parentSelectors":["Company"],"selector":"strong","multiple":false,"regex":"","delay":0 },{"id":"Funding_Date","type":"SelectorText","parentSelectors":["Company"],"selector

60

":"div.col-xs-6:nth-of-type(1) h2","multiple":true,"regex":"","delay":0},{"id":"Country_Location","type":"SelectorTe xt","parentSelectors":["Company"],"selector":".co_financed_text span","multiple":false,"regex":"","delay":0}]} 2. Seedmatch {"_id":"seedmatch","startUrl":["https://www.seedmatch.de/investmentchancen"],"select ors":[{"id":"Company_Name","type":"SelectorText","parentSelectors":["Company"],"s elector":".col-10 h2","multiple":false,"regex":"","delay":0},{"id":"Total_Raised","type":"SelectorText"," parentSelectors":["Company"],"selector":".funding-kpi-desktop div:nth-of-type(2) h2","multiple":false,"regex":"","delay":0},{"id":"Funding_Date","type":"SelectorText", "parentSelectors":["Company"],"selector":".funding-kpi-desktop div:nth-of-type(3) h2","multiple":false,"regex":"","delay":0},{"id":"Company","type":"SelectorLink","par entSelectors":["_root"],"selector":"div:nth-of-type(3) .justify-content-center a","multiple":true,"delay":0}]}

Finland 1. Invesdor {"_id":"invesdor","startUrl":["https://www.invesdor.com/en/pitches/closed?page=[1- 12]"],"selectors":[{"id":"Company","type":"SelectorLink","parentSelectors":["_root"]," selector":"a.grid-box- link","multiple":true,"delay":0},{"id":"Company_Name","type":"SelectorText","parent Selectors":["Company"],"selector":".company-name td","multiple":false,"regex":"","delay":0},{"id":"Funding_Date","type":"SelectorText"," parentSelectors":["Company"],"selector":".start-at td","multiple":false,"regex":"","delay":0},{"id":"Country_Location","type":"SelectorTe xt","parentSelectors":["Company"],"selector":".location td","multiple":false,"regex":"","delay":0},{"id":"Total_Raised","type":"SelectorText"," parentSelectors":["Company"],"selector":".amount-invested span","multiple":false,"regex":"","delay":0}]}

Estonia 1. Fundwise {"_id":"fundwise","startUrl":["https://fundwise.me/en/browse"],"selectors":[{"id":"Link ","type":"SelectorLink","parentSelectors":["_root"],"selector":".panel-body a","multiple":true,"delay":0},{"id":"Company_Name","type":"SelectorText","parentSel ectors":["Link"],"selector":"h1","multiple":false,"regex":"","delay":0},{"id":"Company _Country","type":"SelectorText","parentSelectors":["Link"],"selector":".introduction- header-details div:nth-of- type(1)","multiple":false,"regex":"","delay":0},{"id":"Total_Raised","type":"SelectorTe xt","parentSelectors":["Link"],"selector":"strong.orange","multiple":false,"regex":"","de

61 lay":0},{"id":"Funding_Date","type":"SelectorText","parentSelectors":["Link"],"selecto r":".entry span:nth-of-type(2)","multiple":false,"regex":"","delay":0}]}

Netherlands 1. Symbid {"_id":"symbid","startUrl":["https://www.symbid.com/ideas?locale=en&selection=fund ed"],"selectors":[{"id":"Company","type":"SelectorLink","parentSelectors":["_root"],"s elector":"h3 a","multiple":true,"delay":0},{"id":"Company_Name","type":"SelectorText","parentSel ectors":["Company"],"selector":".invest-info h2","multiple":false,"regex":"","delay":0},{"id":"Total_Raised","type":"SelectorText"," parentSelectors":["Company"],"selector":"strong.value","multiple":false,"regex":"","del ay":0},{"id":"Country_Location","type":"SelectorText","parentSelectors":["Company"] ,"selector":"span.place","multiple":false,"regex":"","delay":0}]}

Belgium 1. Spreds {"_id":"spreds","startUrl":["https://www.spreds.com/en/compartments"],"selectors":[{"i d":"Companies","type":"SelectorElementClick","parentSelectors":["_root"],"selector":". past .product-card a","multiple":true,"delay":"10000","clickElementSelector":"a.btn- secondary","clickType":"clickMore","discardInitialElements":"do-not- discard","clickElementUniquenessType":"uniqueText"},{"id":"Company_Name","type ":"SelectorText","parentSelectors":["Link"],"selector":"h1","multiple":false,"regex":""," delay":0},{"id":"Company_Country","type":"SelectorText","parentSelectors":["Link"], "selector":"small:nth-of- type(2)","multiple":false,"regex":"","delay":0},{"id":"Total_Funding","type":"Selector Text","parentSelectors":["Link"],"selector":"[data-tooltip='Amount of money that has been invested by the crowd during this financing round.'] div.stat- cell_number","multiple":false,"regex":"","delay":0},{"id":"Equity?","type":"SelectorTe xt","parentSelectors":["Link"],"selector":"small:nth-of- type(1)","multiple":false,"regex":"","delay":0},{"id":"Link","type":"SelectorLink","par entSelectors":["_root","Companies"],"selector":".past .product-card a","multiple":true,"delay":0}]}

Italy 1. Mamacrowd {"_id":"mamacrowd","startUrl":["https://mamacrowd.com/projects"],"selectors":[{"id": "Company","type":"SelectorElementScroll","parentSelectors":["_root"],"selector":".jss3 94 div.MuiCardContent- root","multiple":true,"delay":"5000"},{"id":"Company_Name","type":"SelectorText","p arentSelectors":["Wrapper"],"selector":"h3","multiple":false,"regex":"","delay":0},{"id"

62

:"Total_Raised","type":"SelectorText","parentSelectors":["Wrapper"],"selector":"p.Mui Typography- colorPrimary","multiple":false,"regex":"","delay":0},{"id":"Funding_Date","type":"Sel ectorText","parentSelectors":["Wrapper"],"selector":".jss505 p.jss508","multiple":false,"regex":"","delay":0},{"id":"Wrapper","type":"SelectorEleme nt","parentSelectors":["Company"],"selector":"_parent_","multiple":true,"delay":0}]}

Poland 1. {"_id":"beesfund","startUrl":["https://beesfund.com/"],"selectors":[{"id":"Link","type": "SelectorLink","parentSelectors":["Wrapper"],"selector":"_parent_","multiple":false,"de lay":0},{"id":"Close","type":"SelectorElementClick","parentSelectors":["Link"],"select or":".section-details div.col-sm-6:nth-of- type(1)","multiple":true,"delay":0,"clickElementSelector":"div.modal","clickType":"cli ckOnce","discardInitialElements":"do-not- discard","clickElementUniquenessType":"uniqueText"},{"id":"Funding_Date","type":" SelectorText","parentSelectors":["Close"],"selector":"li:nth-of-type(5) span","multiple":false,"regex":"","delay":0},{"id":"Wrapper","type":"SelectorElement", "parentSelectors":["_root"],"selector":".projects-finished a","multiple":true,"delay":0},{"id":"Company_Name","type":"SelectorText","parentSel ectors":["Wrapper"],"selector":"h3","multiple":false,"regex":"","delay":0},{"id":"Total_ Raised","type":"SelectorText","parentSelectors":["Wrapper"],"selector":"div.col-xs- 4:nth-of-type(1) h4","multiple":false,"regex":"","delay":0}]}

Appendix D – Data cleaning actions

Issue Solution Issue 1. Plentiful of excel formatting issues. For example, upon extracting the data to excel, the total funding raised (e.g., €695 168) was often recognized as a string Excel available tools and formulas. and not as a number. Thus, the two had to be separated in order to transform the cell into a number.

Issue 2. Some of the companies extracted did not have any past campaigns, even The companies were deleted. though they were listed in the “past

63 campaigns” section of the different platforms.

Issue 3. Certain websites did not have the Resources such as the company’s country from where the company was LinkedIn and Facebook were used. from and/or the year of the funding. Moreover, a number of crowdfunding Crowdcube, for instance, in regard to the directories (e.g., Crowdfundmarkt, n.d.; location, only had the post code. Crunchbase, n.d.; Findcrowdfunding, n.d.) were equally employed. Finally, news outlets often had information regarding some companies past deals.

Issue 4. Websites such as FundedByMe, A table was built with the conversion rate Seedrs, Invesdor, Beesfund and between the mentioned currencies and Crowdcube did not have the currency of euros for each year between 2014 and the companies listed in euros. Some of the 2019. The conversion rate for each year currencies present were in SEK, USD, represented an average of the year. GBP, PLN. Subsequently, all the non-euro amounts were converted to euros.

Issue 5. Some of the companies listed (1) For the case number (1), these companies had received funding through loans, (2) are outside the scope of the study. In were currently going through the funding regard to case (2) and (3), the firms did not process and (3) did not achieve their successfully close the funding round. funding goal, so were not able to Thus, the companies in these three successfully obtain the funding needed. scenarios could not be included in the final sample. Therefore, they were deleted.

64

Appendix E – Twelve features related to collateral law and bankruptcy law

Source: The World Bank, n.d. 1. The economy has an integrated or unified legal framework for secured transactions that extends to the creation, publicity and enforcement of four functional equivalents to security interests in movable assets: fiduciary transfers of title; financial leases; assignments or transfers of receivables; and sales with retention of title; 2. The law allows a business to grant a nonpossessory security right in a single category of movable assets (such as accounts receivable, tangible movable assets and inventory), without requiring a specific description of the collateral; 3. The law allows a business to grant a nonpossessory security right in substantially all its movable assets, without requiring a specific description of the collateral; 4. A security right can be given over future and after-acquired assets, and extends automatically to the products, proceeds and replacements of the original assets; 5. All types of debts and obligations can be secured between the parties, and a general description of such debts and obligations is permitted in the collateral agreement and in registration documents; 6. A collateral registry or registration institution for security interests granted over movable property by incorporated and nonincorporated entities is in operation, unified geographically and with an electronic database indexed by debtors’ names; 7. The collateral registry is a notice-based registry—a registry that files only a notice of the existence of a security interest (not the underlying documents) and does not perform a legal review of the transaction. The registry also publicizes functional equivalents to security interests; 8. The collateral registry has modern features such as those that allow secured creditors (or their representatives) to register, search, amend or cancel security interests online; 9. Secured creditors are paid first (for example, before tax claims and employee claims) when a debtor defaults outside an insolvency procedure; 10. Secured creditors are paid first (for example, before tax claims and employee claims) when a business is liquidated; 11. Secured creditors are subject to an automatic stay on enforcement proceedings when a debtor enters a court-supervised reorganization procedure, but the law protects secured creditors’ rights by providing clear grounds for relief from the automatic stay (for example, if the movable property is not used for the reorganization or sale of the business as a going concern, or if there is a risk to its existence) and setting a time limit for it; 12. The law allows parties to agree in the collateral agreement that the lender may enforce its security right out of court; the law allows the assets to be sold through public or private auctions and permits the secured creditor to take the asset in satisfaction of the debt.

65

Appendix F – Eight features of the depth of credit information provided by credit registries

Source: The World Bank, n.d. 1. Data on firms and individuals are distributed; 2. Both positive credit information (for example, original loan amounts, outstanding loan amounts and a pattern of on-time repayments) and negative information (for example, late payments and the number and amount of defaults) are distributed; 3. Data from retailers or utility companies are distributed in addition to data from financial institutions; 4. At least two years of historical data are distributed. Credit registries that erase data on defaults as soon as they are repaid or distribute negative information more than 10 years after defaults are repaid receive a score of 0 for this component; 5. Data on loan amounts below 1% of income per capita are distributed; 6. By law, borrowers have the right to access their data in the largest credit registries in the economy. Credit registries that charge more than 1% of income per capita for borrowers to inspect their data receive a score of 0 for this component; 7. Banks and other financial institutions have online access to the credit information (for example, through a web interface, a system-to-system connection or both); 8. Registry credit scores are offered as a value-added service to help banks and other financial institutions assess the creditworthiness of borrowers.

66

Appendix G – Correlation matrix

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (1) Crowdfunding total 1.000 (2) Depth of credit information index 0.114 1.000 (3) Strength of legal rights index 0.005 -0.055 1.000 (4) Credit registry 0.156 0.203 -0.535 1.000 (5) New business density -0.199 -0.397 0.232 -0.539 1.000 (6) Inflation 0.177 -0.063 -0.028 0.041 -0.012 1.000 (7) Domestic credit to private sector -0.059 -0.419 0.023 -0.164 0.270 -0.110 1.000 (8) GDP per Capita 0.122 -0.462 0.204 -0.175 0.086 0.021 0.291 1.000 (9) GDP 0.655 0.430 -0.160 0.477 -0.445 0.016 -0.094 0.015 1.000 (10) GDP growth rate -0.221 0.152 0.305 -0.295 0.198 -0.096 -0.283 0.168 -0.204 1.000 (11) D_C_IXB_D -0.331 -0.266 -0.272 0.027 0.568 -0.002 -0.140 -0.063 -0.483 0.120 1.000 (12) S_L_RXB_D -0.223 -0.258 0.350 -0.116 0.326 0.085 0.170 0.243 -0.286 0.059 0.286 1.000 (13) C_RXB_D -0.194 -0.283 0.101 -0.450 0.744 -0.173 0.129 -0.118 -0.432 0.185 0.402 -0.229 1.000 Note: B_D = New business density; D_C_I = Depth of credit information index; S_L_R = Strength of legal rights index; C_R = Credit registry; X = Interaction between the two variables

Appendix H – Variance inflation factor (VIF)

Variables VIF 1/VIF Variables VIF 1/VIF New business density 11.546 .087 GDP 3.616 .277 B_DXC_R 8.164 .122 Strength of legal rights index 3.238 .309 Depth of credit information index 6.073 .165 Domestic credit to private sector 2.846 .351 B_DXD_C_I 5.362 .186 GDP per Capita 2.176 .46 B_DXS_L_R 5.09 .196 GDP Growth Rate 1.688 .592 Credit registry 4.49 .223 Inflation 1.211 .826 Mean VIF 4.592 .

Appendix I – Robustness test: determinants of the total amount of equity crowdfunding raised

Q 0.25 Q 0.50 Q 0.75 Q 0.90 Model 2 Depth of credit information index 48058500.1 -1889188.7*** -2417771.3*** 9901946.8 (94232597.1) (136312.2) (587880.9) (22515073.8)

Strength of legal rights index 9917773.5 503721.3*** -712139.6*** -2500717.7 (18172585.2) (54730.3) (90142.4) (2715197.3)

Credit registry coverage 479011.1 -22015.2*** -10877.5 -142213.5 (996108.0) (5042.4) (9795.6) (83699.5)

Inflation 15536681.4 1877223.3*** 504071.4 2204726.7 (26806991.0) (237080.2) (317275.8) (4340639.4)

Domestic credit to private sector -159764.5 -27487.0*** -39079.4*** 40403.3 (285927.8) (4066.4) (7630.9) (215045.5)

GDP per capita 2902.1 -62.90*** 168.0** 563.8 (5505.6) (9.269) (59.03) (793.0)

GDP -31.13 4.628*** 8.420*** -4.722 (67.28) (0.209) (0.566) (14.48)

GDP growth rate -3705886.3 45606.9* -36056.4 -348285.4 (6976550.3) (20821.4) (106194.5) (303007.7) Model 3 Depth of credit information index -1348616.9*** -945462.6*** -2503433.0*** 4.60893e+15 (5491.9) (43351.1) (147253.9) (1.26962e+16)

Strength of legal rights index 219771.9*** 321762.3*** -738785.6*** 1.55569e+15 (1685.0) (16122.5) (71233.0) (4.28459e+15)

Credit registry coverage -11816.6*** 15091.5*** -35638.0*** 9.57560e+13 (64.98) (719.4) (5398.3) (2.63774e+14)

Number of new companies -934567.7*** -1123861.0*** 1694137.5*** 1.89564e+11 (30105.84) (753650.1) (410716.8) (5.22022e+11)

Inflation 305103.2*** 796796.9*** 546303.7*** 5.58494e+14 (4692.8) (48409.0) (95066.9) (1.53844e+15)

Domestic credit to private sector -13401.6*** -8752.0*** -11176.9* -1.64390e+13 (74.69) (1045.8) (5071.8) (4.52774e+13)

GDP per Capita -20.67*** -32.26*** -48.05** 4.90006e+10 (0.594) (1.854) (15.50) (1.34894e+11)

GDP 4.798*** 6.377*** 7.240*** 156399853.3 (0.00655) (0.0611) (0.537) (435852024.5)

GDP growth rate -980.1 -27223.4*** 44031.0 -4.18898e+14 (1815.3) (3852.8) (39551.8) (1.15426e+15)

D_C_IXN_C -292335.2*** -187525.4* 1648683.5* -1.33809e+16 (8773.8) (85862.7) (786120.4) (3.68628e+16)

S_L_RXN_C -249930.2*** -384685.0*** -1171687.1*** -1.47035e+15 (7301.2) (12128.8) (116451.6) (4.05159e+15)

C_RXN_C 5368.4*** -6956.6*** -58092.4*** -4.25867e+13 (124.3) (1202.0) (6943.6) (1.17302e+14) N 78 78 78 78 Note: N_C = New companies; D_C_I = Depth of credit information index; S_L_R = Strength of legal rights index; C_R = Credit registry; X = Interaction between the two variables

Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

69