Key factors of venture capital investments in Europe

KEY FACTORS OF VENTURE CAPITAL INVESTMENTS IN EUROPE Jyväskylä University School of Business and Economics Master’s Thesis 2024 Author: Eeli Leppälä Subject: Banking and International Finance Supervisor: Kari Heimonen 3 ABSTRACT Author Eeli Leppälä Title Key factors of venture capital investments in Europe Subject Banking and International Finance Type of work Master’s thesis Date 30.10.2024 Number of pages 59 Abstract The purpose of this master’s thesis is to identify key factors that influence venture capital investments in Europe. European venture capital investments have increased significantly especially between the years 2016-2021. However, current literature has mainly focused on the turn of the 2000s and hence does not consider the period of rapid growth. This study examines key macroeconomic factors quantitatively by implementing panel OLS regression analysis. The panel data consist of 24 different European countries which observation points have been collected between 2012 and 2022. As a result of this study, it was found that shadow interest rate has significant and strong positive impact on venture capital investments especially when observing seed -and start-phase investments. Moreover, it is found that both hightech exports and patents by origin county has small but statistically significant positive correlation on venture capital investments. The last significant robust observation is the significant positive effect between graduates in science and engineering and venture capital investments, especially when observing start and later -phase venture capital investments. Keywords Venture capital, shadow interest rate, Europe, panel regression Place of storage Jyväskylä University Library 4 TIIVISTELMÄ Tekijä Eeli Leppälä Työn nimi Tärkeimmät tekijät venture capital sijoituksissa Euroopassa Oppiaine Banking and International Finance Työn laji Pro Gradututkielma Päivämäärä 30.10.2024 Sivumäärä 59 Tiivistelmä Tämän Pro Gradu -tutkielman tarkoituksena on tunnistaa tärkeitä muuttujia, jotka vaikuttavat Euroopan riskipääomasijoituksiin (venture capital). Venture capital sijoitukset ovat Euroopassa kasvaneet vuosina 2016-2021 merkittävästi, mutta nykyinen kirjallisuus on keskittynyt pääsääntöisesti 2000 luvun vaihteeseen eikä näin ollen huomioi kasvun aikakautta. Tämä tutkimus tarkastelee makrotaloudellisten muuttujien vaikutuksia kvantitatiivisesti hyödyntäen paneeli OLS regressiomenetelmää. Paneeliaineisto koostu 24 eri Euroopan maasta, joista havaintopisteet on kerätty vuosina 2012– 2022. Tutkimuksen lopputuloksena havaittiin varjokorolla olevan voimakas positiivinen korrelaatio venture capital sijoitusten kanssa, etenkin tarkastellessa sijoituksia siemenja alkuvaiheessa. Tämän löydöksen lisäksi havaittiin, että sekä maan korkean teknologian viennillä ja patenteilla on pieni, mutta tilastollisesti merkittävä positiivinen yhteys riskipääomasijoituksiin. Tämän tutkimuksen viimeinen merkittävä pysyvä löydös on, että tieteen ja tekniikan aloilta valmistuneiden ja venture capital sijoitusten välillä on merkittävä positiivinen vaikutus etenkin, kun tarkastellaan ajanjaksoa alku -ja loppuvaiheen sijoituksia. Asiasanat Venture capital, varjokorko, Eurooppa, paneeliregressio Säilytyspaikka Jyväskylän Yliopiston Kirjasto 5 CONTENTS 1 INTRODUCTION ................................................................................................ 7 1.1 Motivation and background ..................................................................... 7 1.2 Research questions ..................................................................................... 8 1.3 Research methods ....................................................................................... 8 1.4 Structure of the thesis ................................................................................. 9 2 CHARACTERIZING VENTURE CAPITAL INVESTMENTS ..................... 10 2.1 Entrepreneur and venture capital .......................................................... 11 2.2 Fund manager and venture capital ........................................................ 12 2.3 Venture capital investment process ....................................................... 13 2.4 The venture market in Europe ................................................................ 15 2.5 Banking and ventures: ............................................................................. 17 2.6 Overview of the European Venture capital market ............................. 18 3 FACTORS AFFECTING VENTURE CAPITAL ............................................. 23 3.1 Economic activity ...................................................................................... 23 3.2 Capital markets ......................................................................................... 24 3.3 Legislation and investor protection ....................................................... 25 3.4 Entrepreneurial culture ............................................................................ 26 4 DATA AND METHODOLOGY ....................................................................... 27 4.1 Data ............................................................................................................. 27 4.2 Methodology ............................................................................................. 33 4.2.1 Panel Regression ........................................................................... 34 4.2.2 Fixed effects and Random effects panel regression ................. 34 4.2.3 Hausman test ................................................................................. 36 4.2.4 Collinearity ..................................................................................... 36 4.2.5 Heteroskedasticity ........................................................................ 37 4.3 Applications to this thesis ....................................................................... 37 5 RESULTS ............................................................................................................. 39 5.1 Subsample largest countries by stages (2012 – 2022) ........................... 42 5.2 Robustness tests ........................................................................................ 44 5.3 Summary of results ................................................................................... 47 5.4 Discussion with previous literature ....................................................... 49 6 CONCLUSIONS, LIMITATIONS AND IMPLICATIONS ........................... 53 REFERENCES ............................................................................................................... 55 APPENDICES ............................................................................................................... 58 6 TABLES: TABLE 1 VARIABLES FOR PANEL OLS MODEL (SOURCE: WIPO / GII) ..................................................... 28 TABLE 2 PANEL OLS RESULTS FROM ALL COUNTRIES .............................................................................. 40 TABLE 3 RESULTS OF LARGEST COUNTRIES SUBSAMPLE (2012 - 2022) ..................................................... 43 TABLE 4 RESULTS OF ROBUSTNESS TESTS ................................................................................................... 46 FIGURES: FIGURE 1 BUSINESS FINANCIAL PHASES .................................................................................................... 14 FIGURE 2 MAP OF VENTURE CAPITAL INVESTMENTS IN EUROPE 2007 (OECD) ...................................... 18 FIGURE 3 MAP OF VENTURE CAPITAL INVESTMENTS IN EUROPE 2022 (OECD) ...................................... 19 FIGURE 4 VENTURE CAPITAL INVESTMENTS IN NOMINAL VALUES IN EUROPE 2007 (OECD) ................ 20 FIGURE 5 VENTURE CAPITAL INVESTMENTS IN NOMINAL VALUES IN EUROPE 2022 (OECD) ................ 20 FIGURE 6 VENTURE CAPITAL MARKET SIZE IN EUROPE (OECD) ............................................................. 21 FIGURE 7 VC -INVESTMENT DIVISION BY STAGES (TOTAL) ........................................................................ 22 FIGURE 8 VC -INVESTMENTS PER COUNTRY .............................................................................................. 29 FIGURE 9 DATA ON NORWAY .................................................................................................................... 31 FIGURE 10 DATA ON ESTONIA ................................................................................................................... 32 FIGURE 11 NORWAY VS. ESTONIA 2022 .................................................................................................... 33 FIGURE 12 VC -INVESTMENTS AND SHADOW INTEREST RATE .................................................................. 51 EQUATIONS: EQUATION 1 PANEL OLS MODEL .............................................................................................................. 30 EQUATION 2 FIXED EFFETS…………………………………. .................................................................... 35 EQUATION 3 RANDOM EFFECTS… ............................................................................................................ 35 EQUATION 4 VIF ....................................................................................................................................... 37 APPENDICES: APPENDIX 1 VIF ......................................................................................................................................... 58 APPENDIX 2: CORRELATION MATRIX ........................................................................................................ 58 APPENDIX 3 USE OF AI-BASED TOOLS ....................................................................................................... 59 7 1.1 Motivation and background Venture capital (VC) investments are important in stemming innovation in Europe. This thesis studies key macroeconomic factors affecting venture capital investments from 2012 to 2022. Venture capital typically involves investing in early-stage companies. These firms are often characterized by their focus on high technology or the introduction of innovative ideas. From an investor's perspective, these young companies generally lack the cash flow to operate independently. Once profitable, they rarely pay dividends, opting to reinvest profits into growth. As a result, returns on venture capital investments are usually realized through exits, such as initial public offerings (IPOs) or acquisitions by other private equity (PE) firms. (Cumming, 2012). Also, venture capital investments are high-risk investments with significant risk that these companies fail to become profitable. It has been noted, that investing in venture capital funds does not create any larger profits than investing in standard and poor 500 (SP500) index funds. However, in most cases VC -investments are equity-based which especially is beneficial for entrepreneurs, since investors and entrepreneurs share the same incentives. From a fund manager's perspective, VC -investments are highly profitable since they receive high commissions and underwriting fees. ( Cumming, 2012; Cumming et al., 2017; Lerner & Nanda, 2020) The aim of this thesis is to find key factors affecting venture capital investments between the years 2012-2022 from 24 different European countries, using panel OLS model. The results of this thesis indicate that market sophistication does not significantly impact venture capital investments. In contrast, hightech exports from the origin country, along with graduates in science and engineering and intangible assets, exhibit statistically significant positive effects on venture capital investments. Additionally, the thesis found that seedand start1 INTRODUCTION 8 phase investments positively correlate with Wu-Xia shadow interest rates, which strongly impact venture capital investments. Furthermore, it was observed that in 2021, fund managers rapidly increased later-stage investments in high-tech businesses that benefitted from social distancing during the COVID-19 pandemic. 1.2 Research questions This thesis explores which macroeconomic factors directly affect venture capital investments by analyzing correlations with regression analysis. The main research question is: Which factors make venture capital investments thrive in Europe? The main research question can be refined as: 1. What caused the rapid increase in venture capital investments in Europe in 2021? 2. What macroeconomic conditions are most effective for raising seed or earlyphase VC -funding? 1.3 Research methods The primary aim of this thesis is to analyze various macroeconomic factors that could impact venture capital investments using Panel Ordinary Least Squares (Panel OLS) as an analytical approach. This study uses OECD data as the dependent variable, which also contains different stages of VCinvestments. Independent variables include data from the World Intellectual Property Organization (WIPO) which has conducted the Global Innovativeness Index. By using this comprehensive dataset and improving with several other macroeconomic factors such as GDP growth, unemployment rate, and Wu-Xia shadow interest rate, which was found from previous literature. A panel dataset was conducted to examine individual and country-level effects across countries. Furthermore, to ensure the reliability of results robustness tests were performed for each regression. The robustness test allowed to examine whether any other variables do not impact results. 9 1.4 Structure of the thesis This study has been divided into 6 different chapters. Chapter 2 aim is to create a comprehensive characterization of venture capital and how various entities impact it. Chapter 3 mainly focuses on previous studies, which they have found to be critical factors impacting venture capital. Moreover, chapter 4 contains the data and methodology section, and Chapter 5 includes results, robustness tests, and a discussion of previous studies. Lastly, all the most important results have been gathered in Chapter 6. 16 structured fees. On the contrary, governmental fund managers typically receive a monthly based steady salary, which may affect work morale and the effort trying to grow the company (Cumming et al., 2017) The third issue that Cumming et al. (2017) noticed is the lack of independence because governmental fund managers also encounter pressure to progress other things than financial growth i.e., employment maximation or geographical-specific investments which benefits policymakers’ interest. Regardless the issues that governmental fund managers encounter, Cumming et al. (2017) also stated that the syndication between independent venture capital funds and governmental-backed venture capital funds also generates synergies. The reason is that governmental venture capitalists can benefit the structural advantages of independent venture capital investors e.g. financial agent issues and the fact that they have greater incentive to grow company financially. Moreover, independent venture capitalists also enjoy synergies that governmental venture capital investors contribute. For instance, Guerini & Quas (2016) studied that governmental investors certify companies that are valid investments. Also, Cumming et al. (2017) mentioned that governmental VC investors could also bring customers from the public sector. Even though governmental venture capital investments have positive and negative impacts, Lerner (2002) added some criticism to the discussion. His research showed that when government assistance was introduced into venture capital investments with the goal of boosting innovation, it actually intensified an already overheated venture capital market. However, the study noted that governmental assistance did increase VC -investments but investments should focus more on industries where private investors do not invest in. This could balance the VC market instead of “throwing gas on a fire” (Lerner, 2002). Croce et al. (2013) studied productivity differences between venture capital-backed firms and non-VC-backed firms. As mentioned above, they studied 696 companies of which 267 firms were VC-backed they did not notice difference between VC-backed and non-VC-backed firms. These results were not in line with previous studies from the US. They pointed out two significant reasons for the divergence. First, the US VC market is more developed than in Europe, and second, they found out that the reputation of a firm makes a difference in the productivity of entrepreneurial firms. Even though Europe has generated multiple unicorns in the past years, Croche et al. (2013) criticized the business environment in Europe. They pointed out that the climate in Europe makes it difficult for VC -investors to develop and make exits. To make this statement they referred to Colombo et al. (2011) book which captured ongoing reforms in Europe making it more attractive. Colombo et al. (2013) created the so-called reform index, which calculated simple steps that different European countries must take toward the European Commission goal in 2020, which contained public policies to develop an economy based on knowledge and innovation. The main idea of this program is to remove the cost of market and other types of friction that regulation needs. According to Croche et al. (2013) study mentioned that Europe has taken a 17 significant step in increasing entrepreneurship but there is still a lot of underdevelopment in financial markets compared to the U.S.(Colombo et al., 2011) One crucial factor for venture capital is patents because it creates security for venture capital investors. Ueda (2004) argued that the U.S. is much more attractive for venture capitalists than Europe because the U.S. has committed to protecting intellectual property rights, e.g., patents for software and business model patents. Furthermore, if the U.S. offers better circumstances for startups, the question arises that why European startups do not migrate to the U.S. Weik, (2023) studied over 11 066 different European startup businesses during the observation period 2000-2014 when received the first VC -investment. This study examined the exits of 555 companies from 2015 to 2021. According to the survey, they noticed that these companies typically migrated 1.6 years later when businesses received the first VC investment. Furthermore, he saw that typically in the US, startups receive more funding than in Europe. For example, 12 million funding round in Europe is doubled in the US. This led to increasing performance in these companies. It was noted that companies relocating from Europe to the U.S. enhanced support from the entrepreneurial environment, such as more vital protection through more accessible patenting opportunities. However, the study indicated that migrating business to the U.S. does not improve the likelihood of exit or success. 2.5 Banking and ventures: Even though entrepreneurs currently have a lot of different options to get finance e.g., via crowdfunding, business angels, PE firms, and VC investors, banking is still an essential source of start-up funding. Ueda (2004) studied why some entrepreneurs prefer venture capital as the primary source of financing, and some prefer traditional banking. If a venture firm has limited collateral, along with high growth, high returns, and high risk, the level of information asymmetry increases, leading to higher costs for bank loans. Hence, entrepreneurs prefer venture capital rather than standard bank loans. However, in most cases, the due diligence process that VC investors prefer before making an investing decision can be long and frustrating. Entrepreneurs have to hand over all the business models etc. which are kept as confidential information. (Ueda, 2004) In most cases, startups are riskier than traditional companies, because of their lack of track record and collaterals required by banks. This inherent risk is significant, especially in the financial sector, where stability and trust are the key factors. The risks became evident in 2022 when Silicon Valley bank surprised investors by informing considerable losses in their balance sheet. Silicon Valley Bank was kept as one of the most long-standing and best banks in the US. The Silicon Valley Bank had an excellent reputation for having good relationships with venture capitalists and other private equity investors (Vo & Le, 2023). 18 As suggested by Vo & Le (2023), banking failure is most likely a consequence of multiple factors. They pointed out that reasons for bank failures could be poor risk management, which could increase the odds for realized losses, undiversified or small number of depositors, which increases bank run likelihood, and rapid interest rate changes. They also noted that typically before bank failure there is increasing activity in other operations or other than standard bank activities. Since the Silicon Valley bank had loaned during a low-interest rate period they started to struggle. When investors noticed the occurrence of possible bank losses, they began to withdraw their money from the bank which also increased the risk of banking failure. As Vo & Le (2023) pointed out for Silicon Valley banks this was catastrophic because they had a small number of depositors and those depositors were the ones who took out the money. 2.6 Overview of the European Venture capital market Figure 2 Map of venture capital investments in Europe 2007 (OECD). The percentage is venture capital investments divided by GDP. 19 Figure 3 Map of venture capital investments in Europe 2022 (OECD). The percentage indicates venture capital investments divided by GDP. Figure 2 and Figure 3 present a comparative analysis of venture capital investments across various countries, depicted with their Gross Domestic Product (GDP), with the results expressed as percentages. The Figure 3 reveals a concentration of VC investments primarily in Western Europe and to the Nordic countries. Additionally, it's noteworthy that among the former Soviet states, only the Baltic states have successfully attracted venture capital significantly. Figures 2 and 3 illustrate the progression of venture capital investments from a country-specific perspective. The overall trend is clear: VC investments have risen in almost every country, except for Norway and Portugal, which interestingly, have seen a reverse trend despite being in regions that typically have attracted venture capital investors. A notable observation is the significant increase in VC investments in Estonia, possibly linked to the country's high number of unicorns per capita, including prominent examples like Bolt, Skype, and Veriff (Kütt, 2022) On average, the VC investment rate for these countries was 0.0371% with a mean of 0.0287% in 2007, which rose to 0.0885% with a mean of 5% in 2022. However, it's important to mention that the lack of data points for the newer EU countries may have led to a lower mean value. Figures 2 and Figure 3 collectively suggest that venture capital investment is on an upward trend. 20 Figure 4 Venture capital investments in nominal values in Europe 2007 (OECD). Data are presented in millions. Figure 5 Venture capital investments in nominal values in Europe 2022 (OECD). Data are presented in millions. From Figure 4 and Figure 5, notably, the most significant amounts of money flow to Western Europe and the Nordic countries. It is noteworthy that from a VC investment perspective, Norway lags other Nordic countries. Contrary to the previous Figure 2, we note that even though Estonia was the leading country in 21 VC investments to GDP, the nominal amount of invested money is relatively small. However, this is understandable given the small population in Estonia. What stands out is the significant increase in formed soviet countries like Estonia, Check Republic, and Slovak Republic. From a development perspective, notably, only two countries Norway and Romania had an adverse change in venture capital investments when comparing the years 2007 and 2022. The development in the past 15 years has been significant from overall development. The median development has been 190% between the years 2007 and 2022. Figure 6 Venture capital market size in Europe (OECD). Data are presented in millions. Figure 6 illustrates the significant growth of the venture capital VC -market over the past 15 years, with one of the most notable increases occurring between 2016 and 2017, and continued growth through 2021. One factor contributing to this surge was the European Central Bank (ECB) lowering its interest rates below zero, which, in theory, could have driven more funds toward higher-risk investments as investors sought greater returns. Another possible explanation for this rise is the monetary easing programs initiated by the European Council in 2016 as part of their interest rate reduction efforts. The sharp increase between 2020 and 2021 could be attributed to the monetary easing programs implemented during the COVID-19 pandemic. According to the ECB, they began their Asset Purchase Programs (APP) in 2015, with the latest net purchases recorded in June 2022. Notably, during periods of significant growth, the ECB ramped up its net purchases. From April 2016 to March 2017, there was a record-high net purchase of 80 billion euros. In 2020, the ECB introduced a temporary 120 billion euro net purchase 0 5000 10000 15000 20000 25000 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Venture capital market in Europe million USD 22 program, in addition to the regular 20 billion euros of net purchases (European Central Bank, 2023). Similarly, the U.S. government established multiple economic aid packages during the COVID-19 crisis. In March 2020 they added a 2,2 trillion USD relief package, the CARES Act, to the market to slow down weakening market conditions, which the pandemic created. Consequently, they added another relief package of 1,9 trillion USD, the American Rescue Plan Act, for the same reason (Vo & Le, 2023). Periods of weakness in the venture capital market are only observed during economic shocks. As shown in Figure 6, declines occur exclusively during these periods. The first was during the financial crisis of 2007–2008, the second during the euro crisis in 2011–2012, and the most recent in 2022 amid geopolitical tensions. Despite rising inflation during the increasing geopolitical tensions, the European Central Bank (ECB) continued its net asset purchases into 2022 but ceased them by the end of June of that year (European Central Bank, 2023). Figure 7 VC -investment division by stages (total) Figure 7 VC -investment division by stages illustrates the division of venture capital VC-investments by stages, and clearly shows how investments are distributed across different funding stages. This data, sourced from the OECD, (2019), demonstrates the typical pattern where investments tend to be more prominent in later stages. However, European venture capital investments appear to be primarily concentrated in seed and start-phase companies. From this, it can be concluded that the number of startup investments is likely higher in start-phase companies compared to later-phase companies. 23 This chapter is divided into four different sections. The section 3.1 focuses on Economic activity, which mainly covers studies examining the impact of macroeconomic factors on venture capital. Section 3.2 focuses on capital markets, Section 3.3 on legislation and investor protection, and the fourth Section 3.4 on entrepreneurial culture. This structure has been used by Croce et al. (2013) who studied the attractiveness of various European countries. 3.1 Economic activity Logically, a more vibrant economy should attract more venture capital investments, and this conclusion is supported by many studies. Gompers et al. (1998) examined various venture capital firms between 1972 and 1994, aiming to identify factors influencing venture capital investments. The study found that certain elements, such as research and development (R&D) increase venture capital investments. Interestingly, the research also revealed that general economic growth positively affects venture capital investments. The authors proposed that this correlation is likely because economic growth stimulates a broader demand for finance. A couple of years later Gompers & Lerner (2000) studied valuations in the private equity market. The study concluded that valuations rise during “hot” periods, referred to as times when overall economic conditions are good. This rise is primarily driven by demand, which pushes private equity investment prices higher. This indicates that the growth of GDP increases the prices of underlying companies which increases the profits of the venture capital fund but simultaneously the amount of money increases during the funding period. Moreover, Lerner (2002) argued that the impact of venture capital investments on innovation diminishes during rapid growth, indicating that when the venture capital market becomes overheated, these investments have a reduced effect on driving innovation. 3 FACTORS AFFECTING VENTURE CAPITAL 24 It is reasonable to assume that high tax rates could also be one potential barrier for startups. For instance, if the startup cannot invest fully its profits into growth it could be one factor that slows venture capital investments. Intuitive thinking seems to be partly true, because Gompers et al. (1998) examined that reductions in capital gains tax increase venture capital investments. Also, a more recent study by Guzman (2018) found that personal income tax does not have any statistical significance on the performance of startups. Jeng & Wells (2000) studied venture capital investments in 21 countries. The authors created a panel OLS model to study how investor protection, IPOs, labor market, and GDP growth have affected venture capital markets. Researchers noticed that the rule of law and GDP growth did not affect venture capital markets. However, they highlighted that venture capitalists typically invest during economic booms, as the demand for venture capital funds tends to rise in such periods. Several studies (Félix et al. 2013; Gompers et al. 1998) have found a positive correlation between long-term interest rates and venture capital investments. Both studies noted that this is primarily driven by the demand side, where wealthy private investors and institutions tend to allocate more capital toward riskier investments when the overall economy is performing well and growing. Félix et al. (2013) investigated the factors driving venture capital investments across 23 European countries between 1998 and 2003. Their study was the first to examine the impact of unemployment on venture capital investments and found that unemployment has a significant negative effect on all phases of venture capital investments, from early to later stages. 3.2 Capital markets Based on previous literature, the depth of capital markets appears to be a debated topic with venture capital. Croce et al. (2013) examined which European countries create the most attractive environments for venture capital investors. Their study concluded that the United Kingdom provided the best environment, primarily due to the depth of its capital markets and strong investor protection. Similarly, Schertler (2003) conducted a panel OLS model study covering 14 European countries from 1988 to 2000 and finding that stock market capitalization was a key factor driving venture capital investments. Moreover Gompers et al. (1998) came to a similar conclusion that the depth of capital markets has a positive effect on attracting venture capital investments. However Jeng & Wells (2000) noted that market capitalization does not have a significant impact on venture capital investments. They did, however, come to the same conclusion as Gompers et al. (1998) and Schertler (2003) identifying that the number of IPOs has the most significant impact on venture capital investments. By distinguishing the different stages of venture capital investments, they concluded that the positive significance vanished when observing early-stage venture capital investments and IPOs. 25 Interestingly, since the year 2000, Initial Public Offerings (IPOs) have proven to be a significant factor in the venture capital markets. A notable trend has been the listing of European start-ups on U.S. stock exchanges. This is primarily attributed to the higher valuations, awarded by the U.S. market. Pisoni & Onetti (2018) utilized Crunchbase, one of the largest start-up funding platforms, to gather data on mergers and acquisitions (M&A) and explore exit strategies employed by start-ups in the U.S. and Europe. Their research, covering the period from 2012 to 2016, revealed that U.S. companies acquired 44% of European start-ups. Moreover, they highlighted a growing trend in the latter years of the observation period, with U.S. companies increasingly acquiring European startups. However, as Gompers & Lerner (2000) pointed out, in regions where the venture capital scene is particularly active, the demand side drives venture capital investment prices higher. This may be one of the reasons why European startups are increasingly looking to relocate to the U.S. for better opportunities. 3.3 Legislation and investor protection As mentioned in Chapter 2 one of the critical factors venture capitalists seek is patents which assumably generate safety for investors. One of the most significant issues that Europe lacks is that intangible assets are almost impossible to patent. Compared to the U.S Europe cannot patent intangible assets (Ueda, 2004). Nahata et al. (2014) studied over 10000 companies across 30 countries and examined how cultural differences affect to the success of venture capital investments. They found two key factors in their study. First, countries that rank higher in legal indices, accounting for investor protection and the rule of law, experience a positive impact on VC -investment success. Second, they found that when VC -investors are unfamiliar with the local business culture and legal system, it introduces an additional risk factor due to information asymmetry. As a result, cultural differences can also manifest in trust issues and challenges related to contracting. However, they also concluded that more significant cultural differences lead to better company performance. Nahata et al. (2014) explained this by noting that VC -investors are cautious and spend more time screening during the investment-making process when cultural differences are significant. Fascinatingly, when comparing different continents, the aspect of legality yields varied outcomes. Cumming et al. (2006) suggest that in the context of homogeneous countries, like those in Europe, legality or the rule of law is not a significant factor. However, in the case of heterogeneous countries, such as those around the Pacific Ocean, the presence of a solid legal system significantly enhances the likelihood of successful business exits. Jia et al. (2021) conducted a study on the impact of GDPR on European enterprises, employing a differences-in-differences methodology across various age groups, industries, and business models (B2B, B2C). Their findings revealed an immediate decrease in venture capital activity following the rule of the law, particularly affecting data-centric and consumer-focused ventures. 32 deals have increased. These changes suggest that Norway has identified alternative sources of financing. Figure 10 GII data on Estonia From Figure 10 it can be seen that all study-related metrics have declined except for a significant increase in graduates in science and engineering. At the same time, Estonia has also seen a substantial rise in its number of science and engineering graduates, enhancing its potential to become a world-class player in the technology sector. Additionally, high-tech exports have increased in recent years. 33 Figure 11 GII data Norway vs. Estonia 2022 The comparative spiderweb chart from 2022 (Figure 11) clearly illustrates the period, indicating notable advancements in technological capabilities in Estonia. Moreover, it suggests that Norway has prioritized research and development (R&D) initiatives more than Estonia. Conversely, Estonians exhibit a higher average enrolment rate in tertiary education, particularly in science and engineering. 4.2 Methodology After consideration, the panel OLS (Ordinary Least Squares) method was selected. The choice to favor using panel OLS was that panel OLS can account for both time series and cross-sectional data. However, pooled OLS does have the same capabilities as panel OLS, but the main difference between these methods is that pooled OLS is more suitable for data where the population may vary 34 across time and when individual-specific effects can be ignored (Hill et al., 2011, p.540-543). Due to this reason, pooled OLS regression may not be ideal for this thesis. In other words, the model would assume that there are no country differences, which does not make sense, and the right-hand side variables are always standard and do not change. Additionally, studies with a similar approach, like this thesis, have used the panel OLS method with either a fixed effects model or random effects (Félix et al., 2013; Jeng & Wells, 2000). This thesis aims to use the balanced panel OLS -method and fill in possibly missing values with each country's mean. In this essence, the entire year will not be lost in the panel OLS model. (Hill et al., 2011, p.567) 4.2.1 Panel Regression Panel OLS model includes quantitative comparisons to determine countries’ factors and their ability to attract venture capital investments. Moreover, to create a statistical framework that takes account of both country and time-specific factors and possible unobserved heterogeneity, the panel OLS model is the most suitable. This statistical method is particularly suited for data that spans several time periods (time-series) and includes multiple entities (cross-sectional), such as different countries, allowing for a multifaceted exploration of the factors influencing venture capital flow. (Pedace, 2013, p.136-143) Typically, OLS regressions have some restrictions to work appropriately, which we must consider. According to Berry (1993) there are a few assumptions to consider: The assumption is that the error term is normally distributed. The assumptions of homoscedasticity and without autocorrelation The assumptions of linearity and additivity The assumptions that collinearity does not exists. 4.2.2 Fixed effects and Random effects panel regression The typical question when using panel OLS regression is which method to use, Fixed effects or random effects panel regression. The primary difference between these methods is how they treat the error term. Fixed effects panel regression is typically used when the dataset has more time observations, indicating that the time-series data are longer than the cross-sectional data. The fixed effect model assumes that the regression captures every entity's unique characteristic. The fixed model assumes that individual effects are constant but not connected with other entities. Fixed effects regression treats the error term in a way that isolates and captures the variance specific to each entity, like in this thesis, countries. The formula of the fixed model is following: 35 𝑦𝑖𝑡 = 𝛽0+ 𝛽1𝑥1,𝑖𝑡 + 𝛽2𝑥2,𝑖𝑡 … + 𝛽𝑛,𝑖𝑡𝑥𝑛,𝑖𝑡 + 𝜖𝑖𝑡 𝑦𝑖𝑡 ,Dependent variable in unit i at time t 𝛽0 , Coefficients of the model which represents effects by explanatory variables (intercept) 𝛽1 , Coefficients of the model which represents effects by explanatory variables 𝑥𝑛𝑡 , Explanatory variables at specific time t 𝜖𝑖𝑡 , Error term which includes the residual at time t (Hill et al., 2011, p. 543-549) (2) The random effects model is slightly different from the fixed effects model. The most significant difference is how these models handle error terms. The random effects model assumes that the variance of random estimates is constant among the entities. The random effects model can be denoted as follows: 𝑦𝑖𝑡 = 𝛽0+ 𝛽1𝑥1,𝑖𝑡 + 𝛽2𝑥2,𝑖𝑡 … + 𝛽𝑛,𝑖𝑡𝑥𝑛,𝑖𝑡 + ( 𝑢𝑖𝑡 + 𝜖𝑖𝑡) 𝑦𝑖𝑡 , Dependent variable in unit i at time t 𝛽0 , fixed population parameter variables 𝛽1 , Coefficients of the model which represents effects by explanatory variables 𝑥𝑛𝑡 , Explanatory variables at specific time t 𝜖𝑖𝑡 , Error term which includes the residual at time t 𝑢𝑖𝑡 , random effect (Hill et al., 2011, p.552) (3) Observing the formula of the random effects model we can notice that (𝑢𝑖𝑡) as an error term within and across cluster captures data within and across the cluster. This is partly its advantage and simultaneously its weakness. The advantage of the random effect model is that including country-specific effects enables the model to account for unobserved factors that may be common to all observations within a particular country. This capability enhances the model's accuracy by incorporating country-specific characteristics that could influence the dependent variable. Simultaneously if there is no belief that the countries have anything in common, alerts should arise for a biased model. However, in this case, when handling homogeneous countries the difference could be minimal (Cumming et al., 2006). (Bartels, 2009) Also, one absolute advantage of the random effects model is that it can efficiently capture time-invariant data due to the ability of the(𝑎𝑖𝑡 ) term which shares variance across the cluster. 36 4.2.3 Hausman test Typically, the Hausman test is used to decide which of the models, fixed effects or random effects model is used. If there is no economic reasoning behind it, the Hausman test should be employed. The critical point of the Hausman or DurbinWu-Hausman test is to decide whether there are differences between random and fixed effect models. The basic principle is following, if the null hypothesis is rejected, the random effects model is favoured, and vice versa. If the Hausman test has significant value it describes that those fixed effects have more information than to random effects. (Hill et al., 2011, p. 420-421) Moreover, the reason why the Hausman test is typically employed when comparing fixed effects and random effects is its simplicity. However, (Bartels, 2009) added some criticism to this approach. According to the article, unobserved heterogeneity includes the unmeasured disparities in the dependent variable, both within each cluster and across different clusters, and if heteroskedasticity is present Hausman test can lead to misleading interpretations. 4.2.4 Collinearity Collinearity is a condition where some variables in a regression model are highly correlated with each other and can weaken the performance of the model, leading to inaccurate results. According to Mela & Kopalle (2002) the main issue is that collinearity weakens parameter estimation power by reducing variance. To address this concern, it is suggested to observe correlations via correlation matrixes. Typically 0,8 by its absolute value is kept as a rule of thumb when observing high correlations (Pedace, 2013, p.321). However, a whole sample correlation matrix describes the linear relationship between two variables. However, one of the OLS regression assumptions was that there should not be multicollinearity between variables. The aim is to implement a VIF (Variance inflation factor) panel to address this point. VIF measures the linear relationship between independent variables. VIF has a relatively simple mechanism to interpret, VIF value at level 1 means that no multicollinearity exists, and 10 means that there is a potential issue with multicollinearity. When using VIF, it's crucial to consider how variables should be managed or potentially eliminated. Throughout the interpretation process, it's vital to adjust changes with economic reasoning. (Pedace, 2013, p.321-322) 𝑉𝐼𝐹𝑘= 1 1 − 𝑅2 37 (4) 4.2.5 Heteroskedasticity One of the regressions estimate assumptions is that data are not heteroskedastic. This means that the error terms in the regression model have constant variance across all levels of the independent variables. When the assumption of homoskedasticity is violated, and the error variance varies systematically with the values of the independent variables, it is referred to as heteroskedasticity. In the essence of ordinary least squares, this is crucial because the regression line is calculated by the squared distance from the error terms. Results for VIF -analysis can be observed in Appendix 1 and similarly results for the correlation matrix can be observed in Appendix 2. Typically, heteroskedasticity is handled or minimized with statistical tests. The two most used tests are the Whites test and the Breusch-Pagan test. Both test measures and their null hypothesis is that data are homoscedastic. If the null hypothesis is rejected the data are below 5% significance level. The main difference between these methods is that White’s test allows error terms to have nonlinear can be applied to the predicted model, hence using it for optimizing purposes. (Pedace, 2013, p.336-356) 4.3 Applications to this thesis To identify the correct variables without violating the assumptions of the regression model, the first step is to conduct a VIF (Variance Inflation Factor) analysis to address potential collinearity issues among the variables. The goal is to maintain a VIF below the 2.0 threshold, where a value of 1 indicates no multicollinearity and a value of 10 suggests high collinearity (Pedace, 2013, p.324-328), The second step is to implement a correlation matrix and keep values below the 0,5 threshold. To find the optimal model for each regression, the aim is to implement the Hausman test for each regression model. Hausman test indicates the possible differences between the random and fixed effects model. If the p-value is five percent or less, indicating statistical significance, a random effects model will be used; otherwise, a fixed effects model will be applied. However, given that the focus of this thesis is primarily on countries, fixed effects should be applied exclusively to the country entities. The model selection begins by running both random and fixed effects panel OLS regressions to assess the significance of these models using the Hausman test.If 𝐻0 is rejected the fixed effects model will be used. In the case where 𝐻0 is rejected by the Hausman test, the Breusch-Pagan test is conducted to check for heteroscedasticity. If the Breusch-Pagan test shows significance, a robust covariance estimator, as suggested by the author of linearmodels Kevin 38 Sheppard et al. (2024). The method cov_type=”robust” allows the linearmodels program to adjust robust standard errors using White’s estimator automatically. 39 To address research questions, six different panel OLS models will be conducted, to understand which variables have a significant impact on venture capital investments in Europe. Firstly, the aim is to implement full sample panel OLS estimation and then create two subsets. These subsets have different observation periods, allowing factors with time-varying effects to be examined. As noted in Figure 6, 2016 was a turning point in European Union venture capital investments for two reasons. At first, this was the period when negative interest rates were observed, and a significant amount of money flowed from the European Union to startups. Based on these developments, it can be assumed that these critical events may have impacted venture capital fundraising across European countries. Additionally, in 2016 European Union created a vast change towards more consumer-friendly data protection laws, launching new GDPR (General Data Protection Laws) laws (European Central Bank, 2023; Jia et al., 2021). By extending the observation period for the subsample to 2018, that model should also identify events before COVID-19 and geopolitical tensions. For these reasons, the aim is to conduct two subsets one for the years 2012-2018 and the second for 20162022. One of this thesis's critical points is to observe the effects of different phases. As mentioned in many previous studies the effects might change depending on which stage the business is at (Félix et al., 2013; Gompers et al., 1998). Luckily, OECD data captures also data from seed, start, later, and total. By comparing these stages, this paper can also capture similar changes. To receive reliable results for this model the aim is to remove countries that have received less than 150 million USD in venture capital investments in 2022. This reduces potential outlier issues while conducting the model. After conducting panel OLS regression analyses the next step is to perform a robustness test for each significant variable. These tests, a group of variables that have shown a significant impact on venture capital investments is analyzed. This allows us to create conclusions confidently. The final step of this chapter is to create a comprehensive summary of all the findings. 5 RESULTS 40 In Appendix 1, it was observed that all VIF values are below 2, indicating no multicollinearity issues among variables. However, Appendix 2 showed that the GDP growth index presented moderate values. For instance, the GDP growth index had a moderately high correlation with market sophistication (0.35) and unemployment (0.4). These values, however, are still below the 0.9 threshold generally considered indicative of high correlation. While VIF provides a more advanced method for analyzing potential multicollinearity issues, these moderate correlations suggest that multicollinearity may not be a significant concern. While VIF is a more sophisticated method to analyse possible multicollinearity issue. Furthermore, multiple studies (Félix et al., 2013; Gompers et al., 1998; Jeng & Wells, 2000) have used GDP growth in the literature and hence is a critical part of this analysis. Table 2 presents results from three different panel OLS regressions, each covering a distinct observation period and including data from all 24 countries. To determine whether to use a fixed effects or random effects model, the Hausman test is applied. Table 2 Panel OLS results from all countries VARIABLE 2012-2022 (RE) 2016-2022 (FE) 2012-2018 (RE) INTERCEPT 2.561* (0.068) 1.622 (0.381) 2.33 (0.173) GDP_GROWTH_IDX 0.01* (0.10) 0.010 (0.215) 0.001 (0.910) WU_XIA_SHADOW_INTEREST_RATE 0.003 (0.925) -0.075 (0.119) 0.033 (0.33) UNEMPLOYMENT -0.032 (0.12) -0.033 (0.442) -0.009 (0.740) MARKET_SOPHISTICATION -0.013 (0.25) -0.002 (0.885) 0.004 (0.79) GRADUATES_IN_SCIENCE_ AND_ENGINEERING 0.025*** (0.00) 0.035*** (0.001) 0.004 (0.581) PCT_PATENTS_BY_ORIGIN 0.018*** (0.002) 0.018*** (0.024) 0.009* (0.069) HIGH_TECH_EXPORTS 0.014*** (0.001) 0.018*** (0.002) 0.01*** (0.034) INTANGIBLE_ASSETS -0.008 (0.35) -0.009 (0.335) 0.016* (0.066) R-SQUARED 0.35 0.34 0.11 NO. OBSERVATIONS 264 168 168 HAUSMAN P-VALUE 1.00 0.04 1.00 P-VALUE REGRESSION 0.00 0.00 0.00 Notes: P-values are in parentheses. * Indicates statistical significance at 10% level and *** at 5% level. Results of this table contains all the 24 countries. P-values are in parentheses. 41 Table 2 highlights key factors influencing European venture capital investments across different observation periods. For the full period from 2012 to 2022, education in science and engineering shows a significant positive effect on venture capital investments, as indicated by its low p-value. This suggests that venture capital investors are attracted to high-tech startups that rely on skilled professionals and robust export capabilities. Specifically, a one-point increase in the Global Innovation Index (GII) score for graduates in science and engineering is associated with a 2.5% increase in venture capital investments from 2012 to 2022, and a 3.5% increase from 2016 to 2022. However, for the period 2012-2018, graduates in science and engineering no longer show a statistically significant impact. Investor protection also stands out as a critical factor in venture capital investment. In this panel OLS model, both patents originating within a country and intangible assets are evaluated. A one-point increase in the GII score for patents by origin is associated with a 1.8% increase in VC investments in both the full-period regression and the 2016-2022 period. Similarly, for 2012-2022, patents show a positive impact on VC investments, contributing a 0.9% increase. Notably, the coefficient for intangible assets is negative for both the 2012-2022 and 2016-2022 periods, though this result is not statistically significant. However, during 2012-2018, intangible assets display weak statistical significance at the 7% level with a 1.6% impact on venture capital investments. Table 2 reveals that, over the 2012-2022 period, the Wu-Xia shadow interest rate, unemployment, and market sophistication do not significantly impact venture capital investments. Interestingly, the coefficient for market sophistication is negative, suggesting a 1.3% decline in VC investments with each onepoint increase in GII data. This may indicate that VC investors are more likely to step in when domestic financial markets face challenges in supporting startups, although this result is not statistically significant. After 2016, the Wu-Xia shadow interest rate shows a significant negative impact on venture capital investments, suggesting that investors prefer to fund startups when monetary policy eases. Specifically, a one-percent increase in the shadow interest rate corresponds to an approximate 7.6% decrease in VC investments. However, this effect lies outside the 5% and 10% significance levels, with a p-value of 0.1199. The GDP growth index shows a modest 1% impact on venture capital investments, implying that VC investments rise in line with general economic growth. This effect, however, disappears over shorter time frames. For the 20122018 and 2016-2022 periods, GDP growth would only contribute a 0.1% increase in VC investments, with these results lacking statistical significance. Across all observation periods, there is no statistical evidence that unemployment rates or market sophistication significantly affect VC investments. However, unemployment does show a small negative coefficient, though it is not statistically significant. Consistent with the characteristics of venture capital, VC investors tend to favour countries with strong high-tech exports. This subsample indicates a statistically significant positive impact of high-tech exports on VC investments, although in recent years this effect has weakened, suggesting an increased focus 48 stage phases. However, this effect was not observed in other samples, suggesting that shadow interest rates are more beneficial to VC investments during stable economic conditions, primarily driving investments in the seed and start phases. Later in the period, the Wu-Xia shadow interest rate showed weak statistical significance but had a strong negative effect on VC investments. These results suggest a positive correlation between the Wu-Xia shadow interest rate and VC investments during seed and start phases, with the strongest correlation occurring in steady economic environments. Given the size of the parameter, the Wu-Xia shadow rate appears to be a key factor influencing the direction of VC investments. The panel OLS analysis also found a significant negative impact of the unemployment rate on venture capital investments in the start phase. While unemployment showed a negative impact across all regressions, the results did not reach statistical significance except for the start phase, where a growth in unemployment was associated with an approximate 10% reduction in VC investments, based on robustness tests. This suggests that, although unemployment may not directly influence overall VC investments, it does have an impact during the start phase, where economic challenges may deter investment. The study’s findings align with the typical characteristics of venturebacked startups, revealing a statistically significant positive impact of high-tech exports and graduates in science and engineering on VC investments. These results are consistent with expectations, as startups often require advanced technical expertise and scalability in foreign markets. This underscores the importance of innovation-driven industries in attracting venture capital. Graduates in science and engineering have a moderate impact on VC investments, while high-tech exports show a smaller but positive effect. However, statistical significance for these factors was absent in the seed-phase and 2012-2018 subsample, which is likely due to the reduced demand for technical expertise in seed-stage funding. Another key finding is that GDP growth did not have a significant impact on VC investments, though there was a weak positive trend in the full sample. This suggests that while GDP growth correlates positively with VC investments, it may not be a decisive factor in investment decisions. Although GDP growth had a positive coefficient in every regression, none of these results were statistically significant. Patents by origin country appear to have a small but positive influence on venture capital investments, contributing about 1-2% to each regression, depending on the model. Only the 2012-2018 and seed-phase samples did not show significant impacts from patents, possibly due to sectoral preferences during that period, such as software, which is less reliant on patents. This is supported by the significance of intangible assets during that time, which had a 23% effect on VC investments. Additionally, delayed effects could be a factor, as the development and application of patent-worthy innovations often take time. An unexpected result is that market sophistication did not show robust significance in any model. While it initially appeared to have a negative 49 impact on the full sample, this result was not consistent across other regressions. This may indicate that, in the European context, venture capital investors no longer prioritize market sophistication when making investment decisions. One possible explanation is that changing a startup’s country of origin has become easier, and software-related startups can scale internationally with relative ease, supported by cloud infrastructure scalability. 5.4 Discussion with previous literature Cumming (2012) characterized venture capital that it invests early-stage companies which typically acquire stakes from companies that are focused on high-tech businesses or other innovative business ideas. He also mentioned that from a venture capital investor point of view, this always contains more risk. High-tech businesses need professionals which this thesis supports. In every panel regression, it seems that graduates in science and engineering and high-tech exports are statistically significant. This study confirms Jeng & Wells (2000) study that startups need high-tech professionals. However, it seems that their research did not find that high-tech professionals have a statistically significant impact on businesses at early stages. This study found that high-tech exports have a statistically significant positive effect on venture capital investments across various samples. Additionally, graduates in science and engineering were shown to impact venture capital investments in the full sample, as well as in the start and later-stage investments. On the other hand, these results are not fully comparable because in this thesis, graduates were used, while they focused on jobs in hightech business. Investor protection seems to play a crucial part in venture capital investments. According to this study, patents seem essential for venture capital investors. Positive and statistically significant results were obtained from every panel analysis except from seed and later -phase VC -investments. As Ueda (2004) and Weik (2023) mentioned, Europe is not as attractive compared to the US from a venture capitalist point of view, because investors cannot patent their intangible assets as well as in the US. According to this study, it seems that patents do increase the likelihood to receive venture capital investments, but this effect is approximately 1-2%. This study generally found no evidence that investors prioritize other intangible assets for protection, as they were not statistically significant in most analyses. However, intangible assets showed statistical significance in the 2012-2018 subsample, a period during which the impact of patents decreased. Multiple articles (Groh 2010; Schertler 2003) suggested that the number of IPOs significantly impact venture capital investments. Moreover, market capitalization seems to be a more argued topic because Félix et al. (2013) and Jeng & Wells (2000) both argued that market capitalization does not positively impact venture capital investments. On the contrary Groh (2010) suggested that United Kingdom attracts venture capital investments most due to the depth of its capital 50 markets. However, it seems that the results of this thesis are more aligned with Félix et al. (2013) which concluded that venture capital market can develop even though capital markets are less developed. According to results of this paper there is no evidence that market sophistication positively impacts venture capital investments. Moreover, according to Weik (2023) nowadays migrating startup is quite common and seems that geographic location has no impact and in this essence the actual location of business seems to have no difference. It has been argued in the literature whether GDP growth impacts venture capital investments or not. According to Jeng & Wells (2000) study they argued that GDP growth does not impact venture capital. However, Félix et al. (2013) were able to detect that GDP has positive impact on venture capital investments. They also noted that typically when GDP is growing, the number of businesses increase, which leads to increase in VC investments. However, this study came into same conclusions like Jeng & Wells (2000) since only a minor statistically significant impact was observed when analyzing the full sample. It seems that there can be timing differences which causes the differences between studies. According to Gompers et al. (1998) the effects of venture capital investments and GDP growth are lagged. They noted that higher GDP growth increases spending on research and development, which eventually become an opportunity for venture capital investors. In other words, the impacts of GDP growth are not direct as studied in this thesis. This thesis observed that shadow interest rate has statistically significant positive impact on venture capital investments especially when controlling the received venture capital investments and time. In the biggest countries, seed and -start phase companies seem to have significant positive effects on venture capital investments. Also, during steady economic conditions positive effect caused by shadow interest can be noted. These results seem to align with previous studies (Félix et al., 2013; Gompers et al., 1998). However, it is worth mentioning that both of these studies used real interest rates instead of shadow interest rates like in this thesis. Gompers et al. (1998) proposed that interest rates could negatively affect venture capital investments, as lower risk-free rates make venture capital funds more attractive to investors, thereby increasing demand for these funds. However, their empirical results indicated the opposite: interest rates had a positive impact on venture capital investments. Both studies explained that, under normal economic conditions, rising interest rates typically accompany economic growth. Nonetheless, this finding appears to contradict their earlier literature review. Additionally, alternative explanations referenced the late 1980s and early 1990s, when the assumption of a positive correlation between venture capital commitments and interest rates was questioned, as VC commitments declined during that period. The theory that venture capital investments increase during economic booms did not hold during the COVID-19 pandemic, when nearly every asset class saw declines, and consumption dropped significantly—except for venture capital investments in Europe, which remained strong. 51 According to this thesis results, statement that interest rates positively impact venture capital cannot hold. The subsample from 2016-2022 indicates have negative impact of shadow interest rates on venture capital investments at 12% level. Hence, the alternative explanation is that when economy is rapidly booming the demand from VC -fund investors increase. Suggested by Lerner, (2002a) VC fund managers instead reinvest these funds to same companies in portfolio than investing to new startups. However, this does not explain fully why venture capital investments increased during post-covid-19. Bellucci et al., (2023), observed that VC investors rapidly invested in digital services and high-tech startups due to increased demand during the pandemic. Reinvesting in these companies was advantageous as it required less effort compared to onboarding new ventures. This trend is reflected in the Figure 12. It also makes sense from a VC -fund manager's point of view to reinvest in these companies for two reasons. First, the market is growing, and business has the potential to grow. Secondly, reinvesting is cost-efficient from fund managers' perspective because they do not have to do the screening, due diligence, and background check processes for these companies since they have already done that. This effect can be noted in the Figure 12. Figure 12 VC -investments and shadow interest rate. VC -investments are measured in millions of dollars Interestingly, this study found that unemployment has negative impact on all regressions, but only at start -phase this was statistically significant and robust. The impact of it was relatively large approximately 7%. This could indicate that when a startup is ready to enter the market, high unemployment levels deter VC investments due to economic uncertainties. Moreover, Félix et al. (2013) 52 conducted a similar panel OLS model with fixed effects and random effects for European countries and noticed that unemployment had a statistically significant negative impact on venture capital investments. According to my knowledge, it seems that they are the only ones who have studied unemployment's impact on venture capital. However, there were several articles that studied unemployment insurance and its effect on venture capital investments. 53 This master’s thesis investigates the factors that influence European venture capital investments using quantitative panel OLS, using both fixed effects and random effects models. The dataset contains 24 European countries, with a focus on the period from 2012 to 2022. Different subsamples allowed for the analysis of time-variant effects on venture capital investments in Europe. This thesis contributes to the existing literature, primarily since many earlier studies were conducted in the late 1990s or early 2000s, meaning this thesis introduces more upto-date data. This study has great implications for policymakers in understanding how to feed innovations. Also, this thesis has great insights for fund managers to understand how macroeconomic changes affect venture capital investments. The study further supports previous research on the importance of sophisticated capital markets for venture capital. However, the increasing integration of global markets appears to have reduced the need for highly developed capital markets within Europe. A notable finding is the strong positive impact of the Wu-Xia shadow interest rate on venture capital investments, particularly in the seed and start stages. This effect likely occurs because the shadow interest rate tends to rise during economic booms, elevating valuations in other asset classes and channelling excess funds toward riskier assets. These results indicate that shadow interest rate is crucial factor influencing VC -investments in Europe. Growth in high-tech exports significantly attracts venture capital investments. The study also highlights that an increase in science graduates has a substantial positive effect on VC investments, particularly in later-stage investments, while high-tech exports remain important across all stages. Additionally, this thesis finds that unemployment has a dampening effect on venture capital investments. A negative relationship was observed in all regressions, although it was statistically significant and stronger only at the start phase. This suggests that while the overall unemployment rate may not directly 6 CONCLUSIONS, LIMITATIONS AND IMPLICATIONS 54 affect VC investments, it does influence early-stage investments, where economic challenges can deter funding. When examining overall trends in VC investments in Europe, patents show a statistically significant, though modest, positive impact of approximately 1-2% on VC investments. During the years 2012-2018, this effect may have been influenced by broader investment trends, as intangible assets also exhibited a statistically weak impact on venture capital investments. These findings suggest that such investments are primarily concentrated in start-phase businesses, as there is no evidence of significant patent-related effects in seed or later-stage investments. This could be because patents are not typically prioritized at the seed stage, when the business is still in the idea phase, nor at the later stage, when the startup has already entered the market and patents are less essential. Based on the results of this thesis, maximizing potential VC investments for seedand start-phase startups appears to require an increase in WuXia shadow interest rates alongside an accelerating economy. Additionally, a strong high-tech export sector is crucial, as it fosters a high-tech environment that supports the development of new products and knowledge. Furthermore, a country with efficient patenting processes and a low unemployment rate would be well-positioned to attract greater VC investments. Based on previous studies and the findings of this thesis, the reason behind the rapid increase in venture capital funding in 2021 was identified. Under normal conditions, the Wu-Xia shadow interest rate positively influences venture capital funding. However, this trend did not hold in the 2016-2022 subsample since there was a weak statistical significance at 12% level. In 2020 and 2021, social distancing significantly boosted IT-related high-tech companies, which are typical targets for venture capital investments. As these businesses experienced rapid growth, venture capitalists increased funding, particularly in later-stage investments. However, since this study primarily focuses on the receipt of venture capital investments, it remains unclear why the demand side contributed to the surge in VC funds. This could be an area for further research. While this thesis primarily uses annualized venture capital data, it leaves several unanswered questions, particularly regarding the economic shocks of 2019–2022. One critical question is whether the negative impact of rising interest rates is temporary or indicative of a broader trend, which would be a valuable subject for future research. Another key issue that this thesis could have been that the observed period is relatively small, which makes it especially hard to fit error terms into the panel OLS model. Also, according to OECD ( 2019) there are no standardized definitions for each of the stages. This could mean that some investments have been categorized falsely, which could lead to misleading results. Moreover, there can be other variables that could affect venture capital investments, which were not found while conducting this thesis literature review. 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