Drivers of Private Equity Activity across Europe: An East-West Comparison

Drivers of Private Equity Activity across Europe: An East-West Comparison | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Drivers of Private Equity Activity across Europe: An East-West Comparison Evžen Kočenda, Shivendra Rai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4125626/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We investigate the key macroeconomic and institutional determinants of fundraising and investment activities and compare them across Europe, covering 13 Central and Eastern European (CEE) and 16 Western European (WE) countries. Five macroeconomic variables and nineteen institutional variables are selected. These variables are studied using panel data analysis with fixed effects and random effects models over an eleven-year observation period (2010–2020). Bayesian Model Averaging (BMA) is applied to select the key variables. Our results suggest that macroeconomic variables have no significant impact on fundraising and investment activity in either region. Investment activity is a significant driver of fundraising across Europe. Similarly, fundraising and divestment activity are significant drivers of investments across Europe. Institutional variables, however, affect fundraising and investment activity differently. While investment freedom has a significant effect on funds raised in the WE and CEE countries, government integrity and trade freedom are both significant determinants of investments in both European regions. In addition, the results demonstrate that, in contrast to the WE region, fundraising in the CEE region is not country specific. JEL Classifications: C11, C23, C52, E22, G15, G24, G28, O16 Private equity (PE) Fundraising Investment Central and Eastern Europe (CEE) Western Europe (WE) Bayesian Model Averaging (BMA) 1. Introduction Public interest in private equity (PE) in Europe has been growing in recent years, and PE has become one of the most significant alternative asset classes. The funds raised and invested by European private equity firms confirm this. According to the most recent data published by Invest Europe (2022), private equity fundraising in Europe reached a record-breaking €118 billion from 841 funds in 2021. In addition, private equity investments totalled €138 billion in European companies, representing 0.76% of the GDP (Invest Europe, 2022a ). Still, the topic of what drives the PE activity across the entire Europe is not sufficiently researched in the literature, and specifically, Central and Eastern Europe (CEE) seems overlooked. Most of the research, as noted by Precup ( 2019 ), Diaconu ( 2017 ), and Bernoth and Colavecchio ( 2014 ), is concentrated on Western Europe (WE) and on venture capital (VC), which can be explained by greater investor interest in the developed VC market of WE as compared to CEE. Further, to the best of our knowledge, there is no analysis that would compare the PE fundraising and investment activities between WE and CEE regions. In our paper, we provide a comprehensive assessment of the key macroeconomic and institutional determinants of fundraising and investment activities in private equity and compare them across Europe. There has been a tremendous increase in European PE investments over the past decade. But the differences in PE activity across the countries are substantial. And these differences become even more prominent when comparing the WE and CEE regions. Admittedly, the PE industry has a far shorter history in the CEE region than in the WE region. Hence, the amount of funds raised and invested as a proportion of GDP in the CEE region is significantly lower than in the rest of Europe, despite recent rapid growth. Recent data from Invest Europe (2022) shows that CEE region fundraising surpassed €1.75 billion in 2021, up 33% from 2020 and the second-best sum since the 2009 financial crisis. And investment in the same year more than doubled, to €4.15 billion, the largest yearly value on record. Evidently, Central and Eastern Europe offers attractive investment opportunities due to its rising economic importance, rapidly expanding economies, and long-term trends of convergence and integration. But despite the growing relevance of private equity as an important asset class in the CEE region, the factors that influence PE activity in the region are little understood. Extensive research has been conducted on the determinants of PE activity. However, previous studies may not be fully applicable to the CEE region, as they may not fully capture the unique characteristics and dynamics of the region’s PE market. Firstly, the economic, political, and social environments in the CEE region are different from those in Western Europe and the United States, where most of the PE research has been conducted. The CEE region has experienced significant political and economic transitions over the past few decades, which influence the stability and predictability of the PE market. Secondly, the legal and regulatory frameworks governing PE activities in the CEE region also differ from those in other regions. These differences can impact the ease of doing PE transactions, the protection of investor rights, and the overall attractiveness of the CEE region for PE activity. Thirdly, the CEE region's PE market is relatively young and less developed compared to more mature markets like the US and Western Europe. Therefore, more region-specific research is needed to better understand and navigate the PE market in the CEE region. There are two perspectives on the private equity industry. One distinguishes it from venture capital, and the other categorizes it as a subset of private equity. This paper considers the broader definition of private equity, inclusive of venture capital, which is also used in Invest Europe (Invest Europe, 2022). Invest Europe represents the European association of private equity investors. In light of private equity's significant contribution to economic growth, there's a wealth of scholarly material on the subject, but much of it is skewed toward the American and Western markets. But only a handful of studies have attempted to understand the drivers of private equity fundraising and investment in the CEE region. However, due to their limited scope, these studies do not allow us to draw generalizations about fundraising and investing activities in the CEE region. Ljumović et al. ( 2020 ) examined the drivers of private equity (PE) investment in the CEE region. However, no quantitative methods were utilized to establish the link between the considered drivers and the investment activity. In the study, the attractiveness of Serbia for private equity investments was evaluated using SWOT analysis. Skalická Dušátková et al. ( 2017 ) conducted qualitative research to determine the institutional factors of the Czech Republic's PE market. Grzywacz and Jagodzińska-Komar ( 2019 ) analyzed the PE industry in Poland and its role in the wider CEE region and highlighted the increasing importance of PE as a source of financing for small and medium-sized enterprises (SMEs). Stefanova ( 2015 ) examined the state of VC investment in the CEE region, with a particular focus on Bulgaria’s economic and political environment as well as the state of its entrepreneurial ecosystem. Wright et al. ( 2004 ) studied the impact of EU accession on the development of the PE industry in the CEE region, as well as the factors that have contributed to its growth, using a qualitative approach in three accession countries (Poland, Hungary, and the Czech Republic). Sato ( 2011 ) also used a qualitative approach to study the key drivers and challenges of the PE industry in the CEE region and concluded that the lack of institutional and regulatory frameworks is a key challenge for the development of the PE industry, along with a lack of a mature capital market and a limited pool of qualified professionals. Precup ( 2017 ) investigated the factors of leveraged buyout and venture capital investment activity in Eastern European nations; however, fundraising activity was not analyzed, and only one institutional determinant was considered. We contribute to the research in this field by considering (i) the CEE region as a whole with its 13 constituent countries; (ii) both macroeconomic and institutional factors; (iii) fundraising and investment activities; and (iv) juxtaposition with Western Europe. To the best of our knowledge, no such study has ever been conducted before. The focus of this study can be summed up by the following research question: How do the factors driving CEE and WE private equity activity differ? In this research, we consider 29 countries (13 in Central and Eastern Europe and 16 in Western Europe) during the 11-year period from 2010 to 2020. First, using the literature on private equity, we identify five macroeconomic variables. Then, from the Index of Economic Freedom, we identify thirteen institutional variables and utilize the six Worldwide Governance Indicators. Next, we use Bayesian Model Averaging to choose only the most relevant variables for the panel data analysis, taking into account the posterior probabilities. Subsequently, we use fixed effects and random effects models to isolate the key determinants of private equity activity in each region. The remainder of the paper is organized as follows: In Section 2 , we discuss the relevant literature and formulate the hypothesis. In Section 3 , we explain the methodology and describe the dataset and variables used to test our hypotheses. In Section 4 , we describe the results, followed by a discussion. And in Section 5 , we summarize our work with concluding remarks. 2. Related Studies and Research Hypotheses In this section, we perform two tasks. We review studies relevant to our analysis and, on its basis, we formulate the hypothesis that we later test. 2.1 Literature Review The most pertinent studies on this topic were conducted by Gompers and Lerner ( 1998 ), Jeng and Wells ( 2000 ), Balboa and Martí ( 2001 ), Balboa and Martí ( 2003 ), Schertler ( 2003 ), Romain and van Pottelsberghe de la Potterie (2004), Cherif and Gazdar ( 2011 ), Kelly ( 2012 ), Félix et al. ( 2013 ), Bernoth and Colavecchia (2014), Precup ( 2015 ), Henchiri ( 2016 ), and Precup ( 2017 ). Gompers and Lerner ( 1998 ) have shown that better GDP growth, higher R&D spending, and a lower capital gains tax led to more venture capital. Jeng and Wells ( 2000 ), on the other hand, found that neither GDP growth nor market capitalization were important venture capital drivers. Balboa and Martí ( 2001 ) claim that, contrary to the conclusions of Gompers and Lerner ( 1998 ), GDP growth was not statistically significant. This result, however, is consistent with Jeng and Wells ( 2000 ). Balboa and Martí ( 2003 ) enhanced earlier research and concluded that GDP growth and gross domestic savings had a statistically favorable effect. Schertler ( 2003 ) found that investment levels are positively correlated with stock market capitalization, the proportion of employees in R&D, and labor market rigidity. Romain and van Pottelsberghe de la Potterie (2004) found evidence that an increase in VC activity was supported by both long-term and short-term interest rates and claimed that venture capital financing has become more appealing due to rising interest rates. Concurrently, a positive impact of technological potential (as evaluated by patents, knowledge stock, and R&D growth) was confirmed. In contrast to Jeng and Wells ( 2000 ), it was discovered that GDP growth is a significant driver of VC activity, validating Gompers and Lerner's findings (1998). According to Romain and van Pottelsberghe de la Potterie (2004), increased GDP growth leads to greater venture capital activity. In line with Gompers and Lerner ( 1998 ) and Romain and van Pottelsberghe de la Potterie (2004), Cherif and Gazdar ( 2011 ) found that market capitalization has a positive effect on VC investments. Their results supported Gompers and Lerner ( 1998 ) by demonstrating a positive and statistically significant impact of R&D expenditures on venture capital investments and funds raised. Kelly ( 2012 ) showed that employment protection and R&D spending had little effect on PE activity, which is contrary to the results of Gompers and Lerner ( 1998 ), van Pottelsberghe Romain and van Pottelsberghe de la Potterie (2004), and Félix et al. ( 2013 ). Kelly cited employment protection, market capitalization, IPO exits, and R&D as drivers for buyout activity. But VC activity was unaffected by market capitalization or IPO exits. Félix et al. ( 2013 ) demonstrated that R&D has a beneficial impact on VC activity. This finding is consistent with that of Gompers and Lerner ( 1998 ) and van (2004). The correlation between VC activity and market capitalization was negative. Bernoth and Colavecchio ( 2014 ) found a positive effect of equity market capitalization. Reduced corporate tax rates (particularly in CEE) boosted PE flow, supporting Gompers and Lerner ( 1998 ). But no evidence suggests that short-term interest rates affect private equity investment. Economic growth had no effect on PE in the CEE region. Western European companies, on the other hand, attracted investment due to real GDP growth, inflation, and market capitalization. Precup ( 2015 ) found that market capitalization and the unemployment rate were statistically significant determinants of PE investment, but R&D expenditures were statistically unimportant. Henchiri ( 2016 ) showed that IPOs are the most important factor that positively influences LBO investment, but GDP growth does not show any significant impact. However, the interest rate and the unemployment rate negatively affect the growth of LBO investments. Later, Precup ( 2017 ) showed a positive effect of economic growth on VC activity, which supported Gompers and Lerner ( 1998 ), Romain and van Pottelsberghe de la Potterie (2004), and Bernoth and Colavecchio ( 2014 ). Precup ( 2017 ) validated the positive effect of long-term interest rates on VC investments, validating Romain and van Pottelsberghe de la Potterie’s findings (2004). Market capitalization was statistically insignificant for VC but substantial for LBO. Neither GDP growth, long-term interest rates, the unemployment rate, nor market capitalization affected LBO. Precup ( 2017 ) showed that R&D expenditures positively and dramatically affect VC investments but negatively affect LBOs. It is evident that there is no consensus among the researchers about the impact of macroeconomic determinants on VC and PE activities. The majority of the research is focused on Western European countries, which consider VC and PE separately and do not study investment and fundraising activities holistically. Hence, these results cannot be used to draw conclusions about PE activity in the CEE region. There are five criteria that can be used to evaluate the existing literature on private equity and venture capital. Firstly, type of private equity strategy, either venture capital, leveraged buyout, or private equity holistically. Secondly, the type of private equity activity, namely fundraising, investment, and divestment. Thirdly, geographical regions or the countries chosen as the focus of research. Fourthly, types of dependent variables, such as macroeconomic, institutional, structural, and those directly related to PE. And lastly, the methodology of selecting the variables and the estimation model. A lot of research is focused on venture capital. Gompers and Lerner ( 1998 ) studied venture capital fundraising in the US using multivariate and fixed-effects regression models. Jeng and Wells ( 2000 ), Schertler ( 2003 ), Romain and van Pottelsberghe de la Potterie (2004), Cherif and Gazdar ( 2011 ), and Felix et al. (2013) were also focused on venture capital only. They considered venture capital a separate activity from private equity. This paper, on the other hand, considers VC and PE activities cumulatively. Secondly, no attempt has been made in the past to compare PE fundraising and investment activities between the WE and CEE regions of Europe. Most of the previous studies focused on analyzing the determinants of venture capital or separately analyzing the determinants of LBOs. Very few studies have tried to use the same methodology to analyze both the VC and the LBO at the same time in order to understand the motivations behind each type of investor. Furthermore, very few studies cover Eastern European countries. The majority of the research is focused on Western European countries. Only a few other authors considered the countries from the CEE region and only the ones from the European Union: The Czech Republic, Slovakia, Poland, and Hungary. And they are bulked into one European segment. The CEE region is much broader, and this paper fills this gap by comparing 13 CEE countries with 16 WE countries. Nearly all authors are focused mainly on macroeconomic variables, with a few authors, such as Jeng and Wells ( 2000 ) and Schertler ( 2003 ), including institutional variables. Only Balboa and Martí ( 2001 ) have included variables related directly to the private equity process but focused on WE countries. For the first time, variables related directly to the private equity process are considered for comparing fundraising and investment activity across Europe. Lastly, no variable selection methodology has been used in the previous studies. We use Bayesian model averaging (BMA), which has never been employed, to select the determinants. It provides a coherent mechanism for accounting for this model uncertainty when deriving parameter estimates. It is also important to note that almost all past research used a panel data estimation technique (fixed and random effects specifications) to account for time-invariant country characteristics and time trends, and this paper follows suit. 2.2 Hypotheses Development The following hypotheses are formulated based on the literature review and the theoretical framework: Hypothesis 1 (H-Macro): Favourable macroeconomic conditions, characterized by higher GDP growth, lower unemployment rates, and a stable labor market, higher by higher market capitalization and lower interest rates positively influence private equity investment and fundraising, fostering a conducive environment for increased economic activity. Hypothesis 2 (H-PE): Private equity-related variables drive fundraising and investment. There is a causal relationship between funds raised and investments made, and it is bidirectional. The investments made in each of these regions have a direct impact on the success of fundraising initiatives in those regions and vice-versa. Hypothesis 3 (H-Institutional): Strong institutional foundations, including control of corruption, government effectiveness, political stability, regulatory quality, rule of law, and levels of voice and accountability, collectively positively impact private equity investment and fundraising, ensuring transparency, security, and a reliable regulatory framework for investors. 3. Methodology and Data We follow the framework described in Woolridge ( 2013 ) and Greene (2003) to estimate a panel data model with fixed and random effects, which has also been used by Gompers and Lerner ( 1998 ), Precup ( 2019 ), Precup ( 2017 ), Cherif and Gazdar ( 2011 ), and Leachman (2002). Thus, we use panel data analysis in our research. Panel data permits us to analyze the factors of private equity activities (fundraising and investing) using both spatial and temporal features of the data. Ideally, only those regressors should be included that are robust to the inclusion or exclusion of other regressors. Hence, we use BMA to examine if the variables provided in the existing literature are truly robust drivers of private equity fundraising and investments. Then, we apply regression models with fixed effects (FE) and random effects (RE). The fixed effects model implies that all panel members have the same variance and that there is no correlation over time, neither between nor among panel members. The random effects model implies that the unobserved effect is independent of the explanatory variables and that both the unobserved effect and the explanatory variables may fluctuate randomly over time and across countries. According to Jeng and Wells ( 2000 ), FE estimation provided a better explanation of the evolution of private equity across countries and RE estimation provides a better explanation of the evolution of PE over time. We then apply a panel data analysis with both horizontal dimension (i) and temporal dimension (t) in this research paper. We can then construct the model as follows: $${\varvec{y}}_{\varvec{i}\varvec{t}}={\varvec{\beta }}_{0}+\sum _{\varvec{j}=1}^{\varvec{k}}{\varvec{\beta }}_{\varvec{j}}{\varvec{x}}_{\varvec{i}\varvec{t}\varvec{j}}+{\varvec{v}}_{\varvec{i}\varvec{t}}$$ 1 where i = 1...N represents the number of countries and t = 1...T represents the number of years for which empirical simulations are run. As a quality check, we run the Hausman specification test to compare the consistency of FE and RE models in explaining the behaviour of the private equity market in the European countries. 3.1 Data Sources Private equity activity (fundraising, investments, and divestments) data for this research was supplied by Invest Europe, a trade association representing private equity and venture capital firms and investors in Europe. However, the data comes from the European Data Cooperative (EDC). EDC is a joint initiative developed by Invest Europe and its national association partners to collect Europe-wide industry data on PE activity. The EDC platform acts as a central hub for private equity and venture capital groups across Europe. Based on the provided data, a balanced panel dataset was constructed. Our dataset consists of annual data spanning from 2010 to 2020 from the following 16 WE countries: Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, Norway, Switzerland, United Kingdom, and the following 13 CEE countries: Bulgaria, Croatia, Czech Republic, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Serbia, Slovakia, and Slovenia. 1 All variables associated with the private equity industry are normalized by the GDP in order to make the data more comparable. This modification is necessary for at least two reasons. Firstly, as countries have varying economic levels and economic growth rates, the problem of heteroscedasticity may arise, which states that the higher the economic level, the larger the observed variability. Consequently, normalizing data by GDP permits us to address this issue. Secondly, because all variables are initially stated in nominal terms, an observed increase in a variable over time may be solely attributable to a change in price levels. So, varying inflation rates among countries could affect the estimation of parameters. Normalizing variables by GDP circumvents this issue because GDP includes the effect of inflation in each country. The discrete nature of the PE industry poses a unique analysis issue. Because the database only contains information from private equity firms that opted to submit it willingly, the data may be skewed. A portion of the data may be missing, and its correctness and reliability are unclear; therefore, it may be biased. In addition, the data for the independent variables were gathered from a wide range of sources, including Eurostat, the International Monetary Fund (IMF), the World Bank, the OECD National Accounts, and the Heritage Foundation. It is essential to highlight the potential limitations of the Heritage Foundation’s index of economic freedom as a data source. Key criticisms include political bias, limited transparency, and subjectivity in measurement. Despite these limitations, the index's quantitative nature, wide coverage, clear methodology and standardized format makes it a useful tool for conducting comparative research and analysis. This paper addresses the aforementioned biases by combining data from the World Bank (worldwide governance indicators) with the data from the Heritage Foundation (index of economic freedom) to gain a more balanced perspective. As the market capitalization data for several countries was missing in the above-mentioned data sources, it was manually collected by perusing the websites and monthly and annual reports of the respective stock exchanges. Among these are Nasdaq Nordic (Sweden, Finland, and Denmark), Nasdaq Baltic (Estonia, Latvia, and Lithuania), Belgrade Stock Exchange (Serbia), Zagreb Stock Exchange (Croatia), Prague Stock Exchange (Czech Republic), Bucharest Stock Exchange (Romania), Bratislava Stock Exchange (Slovakia), London Stock Exchange (United Kingdom), and Borsa Italiana (Italy). 3.2 Target Variables Fundraising is the amount of money raised by PE funds as a percentage of GDP. And investments are the amount of money invested in private companies based in Europe as a percentage of GDP. Fundraising and investments are commonly used as key indicators of private equity activity because they are strong measures of the PE industry's health and growth. As demonstrated by Balboa and Martí (2001), Schertler (2003), Kelly (2012), Bernoth and Colavecchio (2014), and Henchiri (2016), most of the research examining the determinants and drivers of PE activity uses funds raised and invested as the target variables. And thus, to study the drivers of private equity, we employ these two target variables as well: Fundraising & Investments . Fundraising represents investor confidence in PE firms. Investments, on the other hand, represent the PE firms’ strategies and decisions for the deployment of capital into private companies. 3.3 Explanatory Variables Furthermore, we employ the following factors as explanatory variables: Divestments : the amount of money divested as a percentage of GDP. This variable is directly related to the PE process 2 . The authors Balboa and Martí (2001) and Félix et al. (2007) standardize this variable to the corresponding GDP. And the research conducted by Jeng and Wells (2000) and Félix et al. (2007) indicates a positive relationship between investments and divestments. The macroeconomic factors described below are defined in Appendix Table (7). Several authors, including Gompers and Lerner (1998), van Pottelsberghe de la Potterie and Romain (2004), Cherif and Gazdar (2011), and Félix et al. (2012), have concluded that GDP growth is indicative of economic expansion and thus has a positive impact on PE activity. The short-term interest rates, at which financial organizations can borrow funds from one another for a short period of time, are obtained from the OECD, with the exception of Serbia, Romania, Bulgaria, and Croatia due to a lack of availability. The money market rates for these countries are obtained from the IMF. Short-term interest rates are the rates at which short-term borrowings are affected between financial institutions or the rate at which short-term government paper is issued or traded in the market. Gompers and Lerner (1998) and van Pottelsberghe de la Potterie and Romain (2004) show that a higher interest rate results in higher fundraising and investment activity. Cherif and Gazdar (2011) and Félix et al. (2013) have shown a negative correlation between PE activity and unemployment rate. According to the findings of Félix et al. (2013), market capitalization acts as a proxy for the liquidity of the stock market, and a positive association between PE activity and fundraising and investment might be anticipated. This variable, however, has been shown to be statistically insignificant by Jeng and Wells (2000) and Balboa and Martí (2003). Research and development (R&D) expenditure acts as a proxy for innovation and technological advancement. According to research by Gompers and Lerner (1998), the demand and supply of venture capital investments in the United States increased during the 1990s as a result of the country's increased spending on research and development and the resulting technological advancements. It was also proven by Romain and de la Potterie (2004) that technological advancements have a beneficial effect on the development of venture capital investments. The Heritage Foundation's index (Beach and Kane, 2007) reflects the degree of economic freedom annually in countries as a measure of institutional quality. The index takes into account the following aspects scored on a scale from 0 to 100 and weighted equally: (1) rule of law (property rights, judicial effectiveness, and government integrity); (2) government size (tax burden, government spending, and fiscal health); (3) regulatory efficiency (business freedom, labor freedom, and monetary freedom); and (4) market openness (trade freedom, investment freedom, and financial freedom). The 12 Economic Freedoms, defined by Beach and Kane (2007), are described in Appendix Table (8). Worldwide governance indicators (WGI) indicators measure how well countries run their governments. It is a World Bank research initiative and is based on surveys of public and private sector specialists, non-governmental organizations, and other international organizations. WGI is composed of the following six indicators: (1) control of corruption; (2) government effectiveness; (3) political stability and absence of violence; (4) regulatory quality; (5) rule of law; and (6) voice and accountability. These indicators determine the effectiveness of governance systems in promoting economic growth, eliminating poverty, and promoting social welfare. They are described in Appendix Table (9). 3.4 Descriptive Statistics The summary of the descriptive statistics for all the variables (target and explanatory) is presented in Table (1). Given that more than 60% of the data is missing for judicial effectiveness and fiscal health, we eliminated these institutional variables from our study. Similar data gaps exist for interest rates, market capitalization, and research and development expenditure (highlighted in gray). We impute these values using a predictive mean-matching algorithm. 3 3.5 Stationarity Tests The stationarity of the series data is analyzed using the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test. The KPSS test is a unit root test that checks whether a certain series is stationary. The outcomes of the stationarity tests are displayed in Appendix Table (10). According to the KPSS test, the variables market capitalization, R&D expenditure, and financial freedom are non-stationary. Therefore, using differencing, these variables are transformed into stationary series. The temporal component of the panel data is shortened from 11 years (2010–2020) to 10 years (2011–2020) due to differencing. Only stationary series are considered in this research. 3.6 Correlation We examine correlations among potential private equity fundraising and investing determinants. Table (2) shows the correlation matrix. By observing the correlation matrix, we notice several strong correlations (greater than 0.7), which are highlighted in gray. And to account for multicollinearity, we exclude the following variables from our analysis: economic freedom index, property rights, government spending, control of corruption, government effectiveness, regulatory quality, rule of law, and voice and accountability. 3.7 Bayesian Model Averaging We employ a Bayesian model averaging (BMA) approach to decrease the model uncertainty associated with the selection of variables. BMA is a robust statistical technique with a solid theoretical background. To account for model uncertainty, BMA performs a marginalization over models to derive posterior densities on model parameters (Hoeting et al., 1999). However, the empirical outcomes of such processes might be highly sensitive to prior assumptions. Five macroeconomic factors and nineteen institutional variables are used as a starting point for our analysis. Because of the lack of data indicated in subsection 3.1, we eliminate two of the institutional variables (judicial effectiveness and fiscal health). Now we apply BMA to six subsets of our panel data to find the best explanatory variable for each region and PE activity combination: CEE fundraising, WE fundraising, Europe fundraising, CEE investments, WE investments, and Europe investments. In our research, we treat the combined CEE and WE regions as a single European one. A summary of the BMA results is shown in Appendix Table (11), with X denoting the variables with a Posterior Inclusion Probability (PIP) of more than 0.8. The Appendix Tables (12) – (13) present the complete results of the BMA. And since the economic freedom index, property rights, and government spending, control of corruption, government effectiveness, regulatory quality, rule of law, and voice and accountability all have low PIP, removing them from our analysis to eliminate multicollinearity has no major effect on our results. 4. Results Fixed-effects regression is used to control for unobserved heterogeneity in panel data analysis. This allows for the estimation of within-group effects while controlling for time-invariant factors. This approach is used to investigate the impact of country-specific events. Random effects regression, on the other hand, assumes that the country-specific effects are randomly generated from a normal distribution. This allows for the estimation of between-group effects. This approach is used to investigate the variance of country-specific effects. We run FE and RE regressions for the CEE, WE, and European regions. And the dependent variables selected for the regressions are determined by the BMA results presented in Appendix Tables (11) – (13). It is important to note that based on the results of the BMA, only a subset of the different dependent variables is chosen for the estimations of fundraising and investments, respectively. The Hausman (1978) specification test compares FE and RE under the null hypothesis that unobserved heterogeneity (individual effects) has no correlation with any explanatory variable. 4 WE and Europe Fundraising are the only regressions where the Hausman test rejects the null hypothesis. Hence, FE estimators are consistent. RE estimators are consistent and efficient for the other 4 cases: CEE Fundraising, CEE Investments, WE Investments, and Europe Investments. The results of Hausman specification test are shown in Appendix Table (14). 4.1 Fundraising For all 3 regions considered (CEE, WE, and Europe), the FE estimation results for fundraising are presented in Table (3) and the RE estimation results are presented in Table (4). The target variable of fundraising is regressed against the following dependent variables: investments and investment freedom. Based on the results of the Hausman test, the RE estimator is consistent for fundraising in the CEE region. And FE estimators are consistent for fundraising in the WE and European regions. As presented in Table (4), for the CEE region, investments and investment freedom are both statistically significant variables. Similarly, it is shown in Table (4) that for the WE and European regions, investments and investment freedom are also statistically significant. As anticipated, both coefficients are positive, which means that high levels of investment and higher investment freedom result in more fundraising. The importance of investment is greater in the WE region than in the CEE region. But the importance of investment freedom is greater in the CEE region in comparison to the WE region. It is clear that the institutional factor that plays an important role in raising funds in both regions is investment freedom. But the ability of a PE firm to deploy funds is a stronger determinant of its ability to raise funds. Thus, a PE firm’s investments and investment freedom in the country are key factors that investors consider when deciding whether to commit capital to the PE firm's fund. 4.2 Investments For all 3 regions considered (CEE, WE, and Europe), the FE estimation results for investments are presented in Table (5), and the RE estimation results are presented in Table (6). The target variable is regressed against the dependent variables: fundraising, divestments, government integrity, and trade freedom. Based on the results of the Hausman test, the RE estimator is consistent and efficient for investments in all three regions: CEE, WE, and Europe. As presented in Table (6), fundraising, divestments, government integrity, and trade freedom are all robustly associated with investment and are statistically significant at the 1% level. As expected, the variables directly related to PE activity, i.e., fundraising and divestments, have a positive relationship with investments. The amount of funds raised by PE firms can influence their investment decisions. If PE firms can raise more capital, they can pursue larger deals or invest in more companies. Conversely, if fundraising is limited, firms may need to be more selective in their investment choices. Divestments provide evidence of a PE firm's ability to generate returns for its investors. If a PE firm can sell its portfolio companies at a significant profit, it can help build investor confidence and increase the likelihood of raising funds. In comparison to fundraising, different institutional factors play an important role in determining the funds invested in both regions. It is evident that government integrity is more important in the CEE region than in the WE region. Trade freedom, on the other hand, is a negative determinant of investment activity in both regions. Although government integrity has a stronger influence in the CEE region in comparison to the WE region, the impact of trade freedom is stronger in the WE region in comparison to the CEE region. 4.3 Discussion Based on the above results, there are three important findings from this study that provide the answer to our research question: “How do the factors driving CEE and WE private equity activity differ?” Firstly, contrary to all prior research, macroeconomic factors, including GDP growth rate, unemployment rate, interest rate, market capitalization, and R&D expenditure, have no statistically significant effect on the funds raised and invested in Europe (both CEE and WE regions) by private equity firms. This disproves our first hypothesis. Our findings support Cherif and Gazdar’s (2011) finding that interest rates have no impact on the amount of funds deployed. Our results are also consistent with Kelly’s (2012) conclusion that R&D expenditures are insignificant. We also confirm Jeng and Wells’ (2000) conclusion that GDP growth rate and market capitalization have no significant influence on the amount of funds raised. Our results contradict the findings of Gompers and Lerner (1998), Romain and de la Potterie (2016), and Bernoth and Colavecchio (2014) about GDP growth rates. Our findings agree with Precup (2017) on the insignificance of the unemployment rate but disagree on the positive impact of R&D expenditure on investments. Secondly, variables directly related to the private equity process are statistically significant drivers of fundraising and investment activities. According to the results, the funds raised in both the WE and CEE regions are dependent on investments made in the respective regions. This is in line with Balboa and Martí (2001) and Balboa and Martí (2003). Similarly, investment in both the WE and CEE regions is dependent on funds raised and divestment in the respective regions. Thus, validating our second hypothesis. In addition to the similarities stated above, there are differences in the drivers of PE activity in the two European regions as well. While investment freedom (positive effect) is the only significant institutional determinant of funds raised in the WE and CEE countries, government integrity (positive effect) and trade freedom (negative effect) are both significant determinants of investments in both European regions. This asymmetric effect of institutional variables can be explained by the investors’ sensitivity to protection and the institutional environment that “guarantees” investor protection via law and its enforcement. The surprising finding that trade restrictions promote PE investment, can be explained by the fact that market inefficiencies can create opportunities for PE firms to invest in, restructure and capitalize on domestic market opportunities. Ghodsi (2020) argues that lack of trade freedom can have a positive impact on investments under certain conditions, based on the data from Central, East and Southeast Europe. Though not very strongly, the findings do support our third hypothesis. And the last notable finding of this paper is the distinction in fundraising activity between the WE and CEE regions. The FE estimator is consistent for WE fundraising, implying that the funds raised differ across the countries in Western Europe. However, fundraising in the CEE region is not country-specific, as demonstrated by the consistency of the RE estimator. This conclusion is backed by the fact that the vast majority of PE funds intended for the CEE region are raised outside the region. In the last eleven years, only 30.72% of the money raised for the CEE area was raised in CEE countries (Invest Europe, 2022b). 5. Conclusion Availability of data in private equity is relatively limited due to the confidential nature of the transactions. Private sources provide very little information regarding the actions of fund managers, and even that information is updated only once a year. Nevertheless, Kaplan and Lerner (2016) argue that the quality of venture capital data (but not overall private equity) available has improved recently and is likely to do so in the future. However, the databases used to support this assertion are from the United States. Furthermore, the fund managers may choose not to share some information, or the information they do give may not be independently verified. Consequently, even the researchers focused on Western Europe and the United States faced challenges in finding relevant factors for which trustworthy data is readily available. In general, they focused on macroeconomic and structural factors such as GDP growth, market capitalization, interest rate, capital gains tax, level of initial public offerings, labor market rigidity, and productivity, among others. But most of the past researchers did not consider the variables that are directly related to the PE process, which are included in this research. And our results demonstrate a strong and positive relationship between PE activity and the variables directly related to PE. Funds invested in both the CEE and WE regions are positively related to funds divested and funds raised in those regions. Likewise, funds raised in both regions are positively related to funds divested. In addition, institutional factors have no influence on the funds raised in both regions. But government integrity and trade freedom are important drivers of the funds invested. Interestingly, contrary to the majority of the previous studies, we discovered that the most researched macroeconomic indicators, including GDP growth, interest rate, unemployment rate, market capitalization, and R&D expenditure, have no significant influence on funds raised and invested in both regions. However, an important observation is that there is still no widespread consensus on the macroeconomic determinants of PE fundraising and investments. The results offer direct policy implications for three parties involved in European PE: (1) general partners (GPs) of PE firms; (2) limited partners (LPs) investing in the funds offered by such firms; and (3) the government. GPs seeking to raise funds in Europe must demonstrate a successful track record of investments. LPs interested in investing in PE firms must critically assess the amount of funds raised and divested by the prospective firms. Lastly, to encourage private investments in their respective countries, governments must engage in activities aimed at enhancing integrity, reducing corruption, and eliminating constraints on the movement and usage of investment money within and beyond the national borders. Current research considers a country-level cross-section and can be extended by conducting a similar study on a firm-level dataset to get a deeper understanding of the determinants of PE activity. The analysis can also be further developed to incorporate the track record of the PE firms in these regions by employing lagged variables of fundraising and investment activity. Furthermore, the impact of the COVID-19 crisis on fundraising and investment activities in the two regions can be examined and compared. Declarations Author Contribution S.R. wrote the main manuscript and E.K. provided guidance and review of the work, as the supervisor of S.R. as part the PhD program. Acknowledgement We are grateful to Sofian Giuroiu for the data on country-level PE fundraising, investment, and divestment activities provided by Invest Europe. The usual disclaimer applies. Availability of data in private equity is relatively limited due to the confidential nature of the transactions. Private sources provide very little information regarding the actions of fund managers, and even that information is updated only once a year. Nevertheless, Kaplan and Lerner ( 2016 ) argue that the quality of venture capital data (but not overall private equity) available has improved recently and is likely to do so in the future. However, the databases used to support this assertion are from the United States. Furthermore, the fund managers may choose not to share some information, or the information they do give may not be independently verified. References Bernoth, K. and Colavecchio, R. (2014). The Macroeconomic Determinants of Private Equity Investment: A European Comparison. Applied Economics , 46(11), 1170–1183. https://doi.org/10.1080/00036846.2013.866306 Balboa, M. and Martí, J. (2001). Determinants of Private Equity Fundraising in Western Europe. https://dx.doi.org/10.2139/ssrn.269789 Balboa, M. and Martí, J. (2003). An Integrative Approach to the Determinants of Private Equity Fundraising. https://dx.doi.org/10.2139/ssrn.493344 Beach, W.W. and Kane, T. (2007). Methodology: Measuring the 10 Economic Freedoms. Index of Economic Freedom. www.heritage.org/index Cherif, M. and Gazdar, K. (2011). What drives Venture Capital Investments in Europe? New Results from a Panel Data Analysis. Journal of Applied Business and Economics , 12, 3. Diaconu, M. (2017). Private equity market developments in central and Eastern Europe. Theoretical and Applied Economics , 34, 2(611), 131-146. Félix, E., Gulamhussen, M. and Pires, C. (2013). The Determinants of Venture Capital in Europe — Evidence Across Countries. Journal of Financial Services Research , 44, (3), 259-279. https://doi.org/10.1007/s10693-012-0146-y Ghodsi, M. (2020). How do technical barriers to trade affect foreign direct investment? Tariff jumping versus regulation haven hypotheses. Structural Change and Economic Dynamics , 52, 269-278. https://doi.org/10.1016/j.strueco.2019.11.008 Green, W. H. (2003). Econometric Analysis . Pearson Education India. Gompers, P. and Lerner, J. (1998). What Drives Venture Capital Fundraising? Brooking Papers on Economic Activity. Macroeconomics , 149-192. https://dx.doi.org/10.2139/ssrn.57935 Grzywacz, J., and Jagodzińska-Komar, E. (2019). The Role of the Polish Private Equity Sector in the CEE Region. Journal of Management and Financial Sciences , 29, 131-142. Hausman, J. (1978). Specification Tests in Econometrics. Econometrica , 46, 6, 1251-1271. Henchiri, B. (2016). The Impact of the Macroeconomic and Institutional Environment on LBO Fundraising. Global Journal of Management and Business Research , ISSN 2249-4588. Hoeting, J. A., Madigan, D., Raftery, A. E., Volinsky C. T., (1999). Bayesian model averaging: a tutorial. Statistical Science , 14, No. 4, 382-417. Invest Europe. (2022a). Investing in Europe: Private Equity Activity 2021. Report. Invest Europe. (2022b). 2021 Central & Eastern Europe Private Equity Statistics. Report. Jeng, L. A. and Wells, P. C. (2000). The Determinants of Venture Capital Funding: Evidence Across Countries. Journal of Corporate Finance , 6, No. 3, 241-289. Kaplan, S. N., and Lerner, J. (2016). Venture Capital Data: Opportunities and Challenges. NBER Working Paper No. w22500. Kelly, R. (2012). Drivers of Private Equity Investment Activity: Are Buyout and Venture Investors Really So Different? Venture Capital , 14, 4, 309-330. https://doi.org/10.1080/13691066.2012.688494 Skalická Dušátková, M., Zinecker, M., Meluzín, T. (2017). Institutional Determinants of Private Equity Market in Czech Republic. Economics and Sociology , 10, 4, 83-98. Leachman, L., Kumar, V., and Orleck, S. (2002). Explaining Variations in Private Equity: A Panel Approach. Duke University. Department of Economics, Working Papers. Ljumović, I., Milojkić, I. L., and Obradović V. (2020). What Drives Private Equity and Venture Capital in Central and Eastern Europe Countries: Focus on Serbia. Economic Analysis: Applied Research in Emerging Markets , 53, 1, 133-148. https://doi.org/10.28934/ea.20.53.1.pp133-148 Precup, M. (2015). The Future of Private Equity in Europe – The Determinants Across Countries. Romanian Journal of European Affairs , 15, 72-92. Precup, M. (2017). Venture Capital and Leveraged Buyout: What is the Difference in Eastern Europe? – A Cross-Country Panel Data Analysis. Romanian Journal of European Affairs , 17, 2. Precup, M. (2019). Challenges to Scaling Sustainable Private Equity Markets in Emerging Europe. Sustainability , 11, 15, 4080. https://doi.org/10.3390/su11154080 Romain, A., and van Pottelsberghe de la Potterie, B. (2004). The determinants of venture capital: a panel analysis of 16 OECD countries. Universite Libre de Bruxelles Working Paper no. WP-CEB 04/015. Sato, A. (2011). Private Equity Investment in Central and Eastern Europe. International Journal of Management Cases , 13, 4, 199-206. https://doi.org/10.3905/joi.2003.319563 Schertler, A. (2003). Driving Forces of Venture Capital Investments in Europe: A Dynamic Panel Data Analysis. Kiel Institute for World Economics, Kiel Working Paper no 27. Stefanova, J. (2015). Venture Capital in Central and Eastern Europe: A Comparative Analysis and Implications for Bulgaria. Journal of US-China Public Administration , 12, 1, 51-59. https://doi.org/10.17265/1548-6591/2015.01.006 Woolridge, J. M. (2013). Introductory Econometrics: A Modern Approach . Southwestern, Cengage Learning. Wright, M., Kissane, J., and Burrows, A. (2004). Private Equity in EU Accession Countries of Central and Eastern Europe. The Journal of Private Equity , 7, 3, 32-46. https://doi.org/10.3905/jpe.2004.412333 Footnotes Due to the unavailability of data in the Invest Europe databases, we exclude some CEE countries: Bosnia and Herzegovina, Montenegro, Albania, Kosovo, and North Macedonia. Divestments can be categorized into public offering (IPO), write-off, and trade sale. These sub-categories were considered but were not pursued due to unavailability of the data. Predictive mean matching (PMM) is a technique used to address missing data. The PMM method is especially helpful when the missing data are neither missing completely at random (MCAR) nor missing at random (MAR), but rather missing not at random (MNAR). When the Hausman test fails to reject the null hypothesis that the individual effects are uncorrelated with the explanatory factors, the RE model is the most appropriate estimation. But when the null hypothesis is rejected, the FE model is the most suitable. Tables TABLE 1: DESCRIPTIVE STATISTICS Statistic N Mean St. Dev. Min Max Investments 319 0.299 0.303 0.000 2.540 Fundraising 319 0.292 0.666 0.000 8.130 Divestments 319 0.175 0.206 0.000 1.470 GDP Growth Rate 319 1.480 3.254 -10.820 25.180 Interest Rate (m) 311 0.691 1.747 -0.820 12.880 Unemployment Rate 319 8.901 4.997 2.010 27.470 Market Capitalization (m) 306 59.015 63.149 0.520 393.040 R&D Expenditures (m) 312 1.680 0.853 0.380 3.710 Property Rights 319 72.379 17.471 30.000 95.000 Government Integrity 319 64.322 19.015 33.000 96.100 Judicial Effectiveness (m) 116 64.522 14.855 37.200 93.800 Tax Burden 319 66.466 14.691 35.900 94.000 Government Spending 319 37.667 18.294 0.000 78.800 Fiscal Health (m) 116 82.124 17.963 6.100 99.900 Business Freedom 319 78.020 10.122 53.600 99.700 Labor Freedom 319 60.497 13.243 31.000 93.700 Monetary Freedom 319 80.691 4.281 64.500 91.700 Trade Freedom 319 86.635 2.310 75.200 90.000 Investment Freedom 319 79.091 9.990 50.000 95.000 Financial Freedom 319 68.339 11.357 40.000 90.000 Economic Freedom Index 319 69.610 6.218 53.200 82.000 Control of corruption 319 78.646 17.313 38.460 100.000 Government effectiveness 319 81.657 14.220 42.310 100.000 Political stability and absence of violence 319 72.125 15.370 31.280 99.050 Regulatory quality 319 83.605 11.629 50.710 99.530 Rule of law 319 81.693 15.213 41.230 100.000 Voice and accountability 319 83.070 13.751 40.580 100.000 Note: This table provides descriptive statistics for the target and explanatory variables analyzed in this study. Variables with missing values are marked with superscript (m). TABLE 2: CORRELATION MATRIX *p<0.1; **p<0.05; ***p<0.01 Note: This table displays the correlation matrix between the target and explanatory variables (except for judicial effectiveness and fiscal health). Correlation values higher than 0.7 are highlighted in gray. TABLE 3: FE MODEL - FUNDRAISING Statistic CEE Fundraising WE Fundraising Europe Fundraising Investments 0.089*** (0.034) 0.156** (0.264) 0.118** (0.130) Investment freedom 0.002 (0.002) 0.002* (0.017) 0.002* (0.009) Observations 130 160 290 R 2 0.065 0.003 0.004 Adjusted R 2 -0.049 -0.117 -0.112 F Statistic 3.986** (df = 2; 115) 0.190* (df = 2; 142) 0.456* (df = 2; 259) *p<0.1; **p<0.05; ***p<0.01 (Standard errors in parentheses) Note: This table presents the results of the country-fixed effects estimation for the target variable, fundraising, for each of the three regions. TABLE 4: RE MODEL - FUNDRAISING Statistic CEE Fundraising WE Fundraising Europe Fundraising Investments 0.104*** (0.033) 0.502** (0.254) 0.281** (0.127) Investment freedom 0.003** (0.001) 0.017 (0.012) 0.013** (0.006) Constant -0.164 (0.101) -1.143 (0.964) '-0.803* (0.475) Observations 130 160 290 R 2 0.126 0.042 0.04 Adjusted R 2 0.113 0.03 0.033 F Statistic 18.358*** 6.892** 12.003*** *p<0.1; **p<0.05; ***p<0.01 (Standard errors in parentheses) Note: This table presents the results of the country-random effects estimation for the target variable, fundraising, for each of the three regions. TABLE 5: FE MODEL - INVESTMENTS Statistic CEE Investments WE Investments Europe Investments Divestments 0.591*** (0.201) -0.005 (0.111) 0.173* (0.104) Fundraising 0.758*** (0.233) 0.009 (0.029) 0.043 (0.031) Government integrity 0.011*** (0.004) -0.005 (0.005) 0.005* (0.003) Trade freedom -0.039* (0.021) -0.058*** (0.020) -0.046*** (0.014) Observations 130 160 290 R 2 0.199 0.059 0.064 Adjusted R 2 0.085 -0.069 -0.052 F Statistic 7.004*** (df = 4; 113) 2.183* (df = 4; 140) 4.403*** (df = 4; 257) *p<0.1; **p<0.05; ***p<0.01 (Standard errors in parentheses) Note: This table presents the results of the country-fixed effects estimation for the target variable, investments, for each of the three regions. TABLE 6: RE MODEL - INVESTMENTS Statistic CEE Investments WE Investments Europe Investments Divestments 0.538*** (0.179) 0.284*** (0.081) 0.296*** (0.078) Fundraising 0.837*** (0.192) 0.078*** (0.021) 0.083*** (0.023) Government integrity 0.010*** (0.002) 0.004*** (0.001) 0.007*** (0.001) Trade freedom -0.028*** (0.008) -0.033*** (0.012) -0.026*** (0.007) Constant 2.012*** (0.672) 2.902*** (0.993) 2.070*** (0.607) Observations 130 160 290 R 2 0.315 0.27 0.338 Adjusted R 2 0.293 0.252 0.329 F Statistic 57.411*** 57.465*** 145.682*** *p<0.1; **p<0.05; ***p<0.01 (Standard errors in parentheses) Note: This table presents the results of the country-random effects estimation for the target variable, investments, for each of the three regions. Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003ePublic interest in private equity (PE) in Europe has been growing in recent years, and PE has become one of the most significant alternative asset classes. The funds raised and invested by European private equity firms confirm this. According to the most recent data published by Invest Europe (2022), private equity fundraising in Europe reached a record-breaking \u0026euro;118\u0026nbsp;billion from 841 funds in 2021. In addition, private equity investments totalled \u0026euro;138\u0026nbsp;billion in European companies, representing 0.76% of the GDP (Invest Europe, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Still, the topic of what drives the PE activity across the entire Europe is not sufficiently researched in the literature, and specifically, Central and Eastern Europe (CEE) seems overlooked. Most of the research, as noted by Precup (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Diaconu (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Bernoth and Colavecchio (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), is concentrated on Western Europe (WE) and on venture capital (VC), which can be explained by greater investor interest in the developed VC market of WE as compared to CEE. Further, to the best of our knowledge, there is no analysis that would compare the PE fundraising and investment activities between WE and CEE regions. In our paper, we provide a comprehensive assessment of the key macroeconomic and institutional determinants of fundraising and investment activities in private equity and compare them across Europe.\u003c/p\u003e \u003cp\u003eThere has been a tremendous increase in European PE investments over the past decade. But the differences in PE activity across the countries are substantial. And these differences become even more prominent when comparing the WE and CEE regions. Admittedly, the PE industry has a far shorter history in the CEE region than in the WE region. Hence, the amount of funds raised and invested as a proportion of GDP in the CEE region is significantly lower than in the rest of Europe, despite recent rapid growth. Recent data from Invest Europe (2022) shows that CEE region fundraising surpassed \u0026euro;1.75\u0026nbsp;billion in 2021, up 33% from 2020 and the second-best sum since the 2009 financial crisis. And investment in the same year more than doubled, to \u0026euro;4.15\u0026nbsp;billion, the largest yearly value on record.\u003c/p\u003e \u003cp\u003eEvidently, Central and Eastern Europe offers attractive investment opportunities due to its rising economic importance, rapidly expanding economies, and long-term trends of convergence and integration. But despite the growing relevance of private equity as an important asset class in the CEE region, the factors that influence PE activity in the region are little understood.\u003c/p\u003e \u003cp\u003eExtensive research has been conducted on the determinants of PE activity. However, previous studies may not be fully applicable to the CEE region, as they may not fully capture the unique characteristics and dynamics of the region\u0026rsquo;s PE market. Firstly, the economic, political, and social environments in the CEE region are different from those in Western Europe and the United States, where most of the PE research has been conducted. The CEE region has experienced significant political and economic transitions over the past few decades, which influence the stability and predictability of the PE market. Secondly, the legal and regulatory frameworks governing PE activities in the CEE region also differ from those in other regions. These differences can impact the ease of doing PE transactions, the protection of investor rights, and the overall attractiveness of the CEE region for PE activity. Thirdly, the CEE region's PE market is relatively young and less developed compared to more mature markets like the US and Western Europe. Therefore, more region-specific research is needed to better understand and navigate the PE market in the CEE region.\u003c/p\u003e \u003cp\u003eThere are two perspectives on the private equity industry. One distinguishes it from venture capital, and the other categorizes it as a subset of private equity. This paper considers the broader definition of private equity, inclusive of venture capital, which is also used in Invest Europe (Invest Europe, 2022). Invest Europe represents the European association of private equity investors.\u003c/p\u003e \u003cp\u003eIn light of private equity's significant contribution to economic growth, there's a wealth of scholarly material on the subject, but much of it is skewed toward the American and Western markets. But only a handful of studies have attempted to understand the drivers of private equity fundraising and investment in the CEE region. However, due to their limited scope, these studies do not allow us to draw generalizations about fundraising and investing activities in the CEE region.\u003c/p\u003e \u003cp\u003eLjumović et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) examined the drivers of private equity (PE) investment in the CEE region. However, no quantitative methods were utilized to establish the link between the considered drivers and the investment activity. In the study, the attractiveness of Serbia for private equity investments was evaluated using SWOT analysis. Skalick\u0026aacute; Duš\u0026aacute;tkov\u0026aacute; et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) conducted qualitative research to determine the institutional factors of the Czech Republic's PE market. Grzywacz and Jagodzińska-Komar (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) analyzed the PE industry in Poland and its role in the wider CEE region and highlighted the increasing importance of PE as a source of financing for small and medium-sized enterprises (SMEs). Stefanova (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) examined the state of VC investment in the CEE region, with a particular focus on Bulgaria\u0026rsquo;s economic and political environment as well as the state of its entrepreneurial ecosystem.\u003c/p\u003e \u003cp\u003eWright et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) studied the impact of EU accession on the development of the PE industry in the CEE region, as well as the factors that have contributed to its growth, using a qualitative approach in three accession countries (Poland, Hungary, and the Czech Republic). Sato (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) also used a qualitative approach to study the key drivers and challenges of the PE industry in the CEE region and concluded that the lack of institutional and regulatory frameworks is a key challenge for the development of the PE industry, along with a lack of a mature capital market and a limited pool of qualified professionals. Precup (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) investigated the factors of leveraged buyout and venture capital investment activity in Eastern European nations; however, fundraising activity was not analyzed, and only one institutional determinant was considered.\u003c/p\u003e \u003cp\u003eWe contribute to the research in this field by considering (i) the CEE region as a whole with its 13 constituent countries; (ii) both macroeconomic and institutional factors; (iii) fundraising and investment activities; and (iv) juxtaposition with Western Europe. To the best of our knowledge, no such study has ever been conducted before. The focus of this study can be summed up by the following research question: How do the factors driving CEE and WE private equity activity differ?\u003c/p\u003e \u003cp\u003eIn this research, we consider 29 countries (13 in Central and Eastern Europe and 16 in Western Europe) during the 11-year period from 2010 to 2020. First, using the literature on private equity, we identify five macroeconomic variables. Then, from the Index of Economic Freedom, we identify thirteen institutional variables and utilize the six Worldwide Governance Indicators. Next, we use Bayesian Model Averaging to choose only the most relevant variables for the panel data analysis, taking into account the posterior probabilities. Subsequently, we use fixed effects and random effects models to isolate the key determinants of private equity activity in each region.\u003c/p\u003e \u003cp\u003eThe remainder of the paper is organized as follows: In Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we discuss the relevant literature and formulate the hypothesis. In Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we explain the methodology and describe the dataset and variables used to test our hypotheses. In Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e4\u003c/span\u003e, we describe the results, followed by a discussion. And in Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e5\u003c/span\u003e, we summarize our work with concluding remarks.\u003c/p\u003e"},{"header":"2. Related Studies and Research Hypotheses","content":"\u003cp\u003eIn this section, we perform two tasks. We review studies relevant to our analysis and, on its basis, we formulate the hypothesis that we later test.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Literature Review\u003c/h2\u003e \u003cp\u003eThe most pertinent studies on this topic were conducted by Gompers and Lerner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), Jeng and Wells (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), Balboa and Mart\u0026iacute; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), Balboa and Mart\u0026iacute; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), Schertler (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), Romain and van Pottelsberghe de la Potterie (2004), Cherif and Gazdar (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), Kelly (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), F\u0026eacute;lix et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Bernoth and Colavecchia (2014), Precup (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Henchiri (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and Precup (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGompers and Lerner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) have shown that better GDP growth, higher R\u0026amp;D spending, and a lower capital gains tax led to more venture capital. Jeng and Wells (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), on the other hand, found that neither GDP growth nor market capitalization were important venture capital drivers. Balboa and Mart\u0026iacute; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) claim that, contrary to the conclusions of Gompers and Lerner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), GDP growth was not statistically significant. This result, however, is consistent with Jeng and Wells (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Balboa and Mart\u0026iacute; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) enhanced earlier research and concluded that GDP growth and gross domestic savings had a statistically favorable effect.\u003c/p\u003e \u003cp\u003eSchertler (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) found that investment levels are positively correlated with stock market capitalization, the proportion of employees in R\u0026amp;D, and labor market rigidity. Romain and van Pottelsberghe de la Potterie (2004) found evidence that an increase in VC activity was supported by both long-term and short-term interest rates and claimed that venture capital financing has become more appealing due to rising interest rates. Concurrently, a positive impact of technological potential (as evaluated by patents, knowledge stock, and R\u0026amp;D growth) was confirmed. In contrast to Jeng and Wells (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), it was discovered that GDP growth is a significant driver of VC activity, validating Gompers and Lerner's findings (1998).\u003c/p\u003e \u003cp\u003eAccording to Romain and van Pottelsberghe de la Potterie (2004), increased GDP growth leads to greater venture capital activity. In line with Gompers and Lerner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and Romain and van Pottelsberghe de la Potterie (2004), Cherif and Gazdar (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) found that market capitalization has a positive effect on VC investments. Their results supported Gompers and Lerner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) by demonstrating a positive and statistically significant impact of R\u0026amp;D expenditures on venture capital investments and funds raised.\u003c/p\u003e \u003cp\u003eKelly (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) showed that employment protection and R\u0026amp;D spending had little effect on PE activity, which is contrary to the results of Gompers and Lerner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), van Pottelsberghe Romain and van Pottelsberghe de la Potterie (2004), and F\u0026eacute;lix et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Kelly cited employment protection, market capitalization, IPO exits, and R\u0026amp;D as drivers for buyout activity. But VC activity was unaffected by market capitalization or IPO exits. F\u0026eacute;lix et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) demonstrated that R\u0026amp;D has a beneficial impact on VC activity. This finding is consistent with that of Gompers and Lerner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and van (2004). The correlation between VC activity and market capitalization was negative.\u003c/p\u003e \u003cp\u003eBernoth and Colavecchio (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found a positive effect of equity market capitalization. Reduced corporate tax rates (particularly in CEE) boosted PE flow, supporting Gompers and Lerner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). But no evidence suggests that short-term interest rates affect private equity investment. Economic growth had no effect on PE in the CEE region. Western European companies, on the other hand, attracted investment due to real GDP growth, inflation, and market capitalization.\u003c/p\u003e \u003cp\u003ePrecup (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that market capitalization and the unemployment rate were statistically significant determinants of PE investment, but R\u0026amp;D expenditures were statistically unimportant. Henchiri (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) showed that IPOs are the most important factor that positively influences LBO investment, but GDP growth does not show any significant impact. However, the interest rate and the unemployment rate negatively affect the growth of LBO investments. Later, Precup (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) showed a positive effect of economic growth on VC activity, which supported Gompers and Lerner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), Romain and van Pottelsberghe de la Potterie (2004), and Bernoth and Colavecchio (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Precup (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) validated the positive effect of long-term interest rates on VC investments, validating Romain and van Pottelsberghe de la Potterie\u0026rsquo;s findings (2004). Market capitalization was statistically insignificant for VC but substantial for LBO. Neither GDP growth, long-term interest rates, the unemployment rate, nor market capitalization affected LBO. Precup (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) showed that R\u0026amp;D expenditures positively and dramatically affect VC investments but negatively affect LBOs.\u003c/p\u003e \u003cp\u003eIt is evident that there is no consensus among the researchers about the impact of macroeconomic determinants on VC and PE activities. The majority of the research is focused on Western European countries, which consider VC and PE separately and do not study investment and fundraising activities holistically. Hence, these results cannot be used to draw conclusions about PE activity in the CEE region.\u003c/p\u003e \u003cp\u003eThere are five criteria that can be used to evaluate the existing literature on private equity and venture capital. Firstly, type of private equity strategy, either venture capital, leveraged buyout, or private equity holistically. Secondly, the type of private equity activity, namely fundraising, investment, and divestment. Thirdly, geographical regions or the countries chosen as the focus of research. Fourthly, types of dependent variables, such as macroeconomic, institutional, structural, and those directly related to PE. And lastly, the methodology of selecting the variables and the estimation model.\u003c/p\u003e \u003cp\u003eA lot of research is focused on venture capital. Gompers and Lerner (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) studied venture capital fundraising in the US using multivariate and fixed-effects regression models. Jeng and Wells (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), Schertler (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), Romain and van Pottelsberghe de la Potterie (2004), Cherif and Gazdar (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and Felix et al. (2013) were also focused on venture capital only. They considered venture capital a separate activity from private equity. This paper, on the other hand, considers VC and PE activities cumulatively.\u003c/p\u003e \u003cp\u003eSecondly, no attempt has been made in the past to compare PE fundraising and investment activities between the WE and CEE regions of Europe. Most of the previous studies focused on analyzing the determinants of venture capital or separately analyzing the determinants of LBOs. Very few studies have tried to use the same methodology to analyze both the VC and the LBO at the same time in order to understand the motivations behind each type of investor.\u003c/p\u003e \u003cp\u003eFurthermore, very few studies cover Eastern European countries. The majority of the research is focused on Western European countries. Only a few other authors considered the countries from the CEE region and only the ones from the European Union: The Czech Republic, Slovakia, Poland, and Hungary. And they are bulked into one European segment. The CEE region is much broader, and this paper fills this gap by comparing 13 CEE countries with 16 WE countries.\u003c/p\u003e \u003cp\u003eNearly all authors are focused mainly on macroeconomic variables, with a few authors, such as Jeng and Wells (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and Schertler (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), including institutional variables. Only Balboa and Mart\u0026iacute; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) have included variables related directly to the private equity process but focused on WE countries. For the first time, variables related directly to the private equity process are considered for comparing fundraising and investment activity across Europe.\u003c/p\u003e \u003cp\u003eLastly, no variable selection methodology has been used in the previous studies. We use Bayesian model averaging (BMA), which has never been employed, to select the determinants. It provides a coherent mechanism for accounting for this model uncertainty when deriving parameter estimates.\u003c/p\u003e \u003cp\u003eIt is also important to note that almost all past research used a panel data estimation technique (fixed and random effects specifications) to account for time-invariant country characteristics and time trends, and this paper follows suit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Hypotheses Development\u003c/h2\u003e \u003cp\u003eThe following hypotheses are formulated based on the literature review and the theoretical framework:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003e(H-Macro): Favourable macroeconomic conditions, characterized by higher GDP growth, lower unemployment rates, and a stable labor market, higher by higher market capitalization and lower interest rates positively influence private equity investment and fundraising, fostering a conducive environment for increased economic activity.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003e(H-PE): Private equity-related variables drive fundraising and investment. There is a causal relationship between funds raised and investments made, and it is bidirectional. The investments made in each of these regions have a direct impact on the success of fundraising initiatives in those regions and vice-versa.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003e(H-Institutional): Strong institutional foundations, including control of corruption, government effectiveness, political stability, regulatory quality, rule of law, and levels of voice and accountability, collectively positively impact private equity investment and fundraising, ensuring transparency, security, and a reliable regulatory framework for investors.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology and Data","content":"\u003cp\u003eWe follow the framework described in Woolridge (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Greene (2003) to estimate a panel data model with fixed and random effects, which has also been used by Gompers and Lerner (\u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e), Precup (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), Precup (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), Cherif and Gazdar (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), and Leachman (2002).\u003c/p\u003e\n\u003cp\u003eThus, we use panel data analysis in our research. Panel data permits us to analyze the factors of private equity activities (fundraising and investing) using both spatial and temporal features of the data. Ideally, only those regressors should be included that are robust to the inclusion or exclusion of other regressors. Hence, we use BMA to examine if the variables provided in the existing literature are truly robust drivers of private equity fundraising and investments.\u003c/p\u003e\n\u003cp\u003eThen, we apply regression models with fixed effects (FE) and random effects (RE). The fixed effects model implies that all panel members have the same variance and that there is no correlation over time, neither between nor among panel members. The random effects model implies that the unobserved effect is independent of the explanatory variables and that both the unobserved effect and the explanatory variables may fluctuate randomly over time and across countries. According to Jeng and Wells (\u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e), FE estimation provided a better explanation of the evolution of private equity across countries and RE estimation provides a better explanation of the evolution of PE over time.\u003c/p\u003e\n\u003cp\u003eWe then apply a panel data analysis with both horizontal dimension (i) and temporal dimension (t) in this research paper. We can then construct the model as follows:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$${\\varvec{y}}_{\\varvec{i}\\varvec{t}}={\\varvec{\\beta }}_{0}+\\sum _{\\varvec{j}=1}^{\\varvec{k}}{\\varvec{\\beta }}_{\\varvec{j}}{\\varvec{x}}_{\\varvec{i}\\varvec{t}\\varvec{j}}+{\\varvec{v}}_{\\varvec{i}\\varvec{t}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere i\u0026thinsp;=\u0026thinsp;1...N represents the number of countries and t\u0026thinsp;=\u0026thinsp;1...T represents the number of years for which empirical simulations are run.\u003c/p\u003e\n\u003cp\u003eAs a quality check, we run the Hausman specification test to compare the consistency of FE and RE models in explaining the behaviour of the private equity market in the European countries.\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cem\u003e3.1 Data Sources\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003ePrivate equity activity (fundraising, investments, and divestments) data for this research was supplied by Invest Europe, a trade association representing private equity and venture capital firms and investors in Europe. However, the data comes from the European Data Cooperative (EDC). EDC is a joint initiative developed by Invest Europe and its national association partners to collect Europe-wide industry data on PE activity. The EDC platform acts as a central hub for private equity and venture capital groups across Europe.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBased on the provided data, a balanced panel dataset was constructed. Our dataset consists of annual data spanning from 2010 to 2020 from the following 16 WE countries: Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, Norway, Switzerland, United Kingdom, and the following 13 CEE countries: Bulgaria, Croatia, Czech Republic, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Serbia, Slovakia, and Slovenia.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eAll variables associated with the private equity industry are\u0026nbsp;normalized\u0026nbsp;by the GDP in order to make the data more comparable. This modification is necessary for at least two reasons. Firstly, as countries have varying economic levels and economic growth rates, the problem of\u0026nbsp;heteroscedasticity\u0026nbsp;may arise, which states that the higher the economic level, the larger the observed variability. Consequently,\u0026nbsp;normalizing\u0026nbsp;data by GDP permits us to address this issue. Secondly, because all variables are initially stated in nominal terms, an observed increase in a variable over time may be solely attributable to a change in price levels. So, varying inflation rates among countries could affect the estimation of parameters.\u0026nbsp;Normalizing\u0026nbsp;variables by GDP circumvents this issue because GDP includes the effect of inflation in each country.\u003c/p\u003e\n \u003cp\u003eThe discrete nature of the PE industry poses a unique analysis issue. Because the database only contains information from private equity firms that opted to submit it willingly, the data may be skewed. A portion of the data may be missing, and its correctness and reliability are unclear; therefore, it may be biased.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIn addition, the data for the independent variables were gathered from a wide range of sources, including Eurostat, the International Monetary Fund (IMF), the World Bank, the OECD National Accounts, and the Heritage Foundation.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIt is essential to highlight the potential limitations of the Heritage Foundation\u0026rsquo;s index of economic freedom as a data source. Key criticisms include political bias, limited transparency, and subjectivity in measurement. Despite these limitations, the index\u0026apos;s quantitative nature, wide coverage, clear methodology and standardized format makes it a useful tool for conducting comparative research and analysis. This paper addresses the aforementioned biases by combining data from the World Bank (worldwide governance indicators) with the data from the Heritage Foundation (index of economic freedom) to gain a more balanced perspective.\u003c/p\u003e\n \u003cp\u003eAs the market capitalization data for several countries was missing in the above-mentioned data sources, it was manually collected by perusing the websites and monthly and annual reports of the respective stock exchanges. Among these are Nasdaq Nordic (Sweden, Finland, and Denmark), Nasdaq Baltic (Estonia, Latvia, and Lithuania), Belgrade Stock Exchange (Serbia), Zagreb Stock Exchange (Croatia), Prague Stock Exchange (Czech Republic), Bucharest Stock Exchange (Romania), Bratislava Stock Exchange (Slovakia), London Stock Exchange (United Kingdom), and Borsa Italiana (Italy).\u0026nbsp;\u003c/p\u003e\n \u003ch2\u003e\u003cem\u003e3.2 Target Variables\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eFundraising is the amount of money raised by PE funds as a percentage of GDP. And investments are the amount of money invested in private companies based in Europe as a percentage of GDP. Fundraising and investments are commonly used as key indicators of private equity activity because they are strong measures of the PE industry\u0026apos;s health and growth.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAs demonstrated by Balboa and Mart\u0026iacute; (2001), Schertler (2003), Kelly (2012), Bernoth and Colavecchio (2014), and Henchiri (2016), most of the research examining the determinants and drivers of PE activity uses funds raised and invested as the target variables. And thus, to study the drivers of private equity, we employ these two target variables as well: \u003cstrong\u003eFundraising\u003c/strong\u003e \u0026amp; \u003cstrong\u003eInvestments\u003c/strong\u003e. Fundraising represents investor confidence in PE firms. Investments, on the other hand, represent the PE firms\u0026rsquo; strategies and decisions for the deployment of capital into private companies.\u0026nbsp;\u003c/p\u003e\n \u003ch2\u003e\u003cem\u003e3.3 Explanatory Variables\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eFurthermore, we employ the following factors as explanatory variables:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDivestments\u003c/strong\u003e: the amount of money divested as a percentage of GDP. This variable is directly related to the PE process\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e. The authors Balboa and Mart\u0026iacute; (2001) and F\u0026eacute;lix et al. (2007) standardize this variable to the corresponding GDP. And the research conducted by Jeng and Wells (2000) and F\u0026eacute;lix et al. (2007) indicates a positive relationship between investments and divestments.\u003c/p\u003e\n \u003cp\u003eThe macroeconomic factors described below are defined in Appendix Table (7). Several authors, including Gompers and Lerner (1998), van Pottelsberghe de la Potterie and Romain (2004), Cherif and Gazdar (2011), and F\u0026eacute;lix et al. (2012), have concluded that GDP growth is indicative of economic expansion and thus has a positive impact on PE activity.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe short-term interest rates, at which financial organizations can borrow funds from one another for a short period of time, are obtained from the OECD, with the exception of Serbia, Romania, Bulgaria, and Croatia due to a lack of availability. The money market rates for these countries are obtained from the IMF. Short-term interest rates are the rates at which short-term borrowings are affected between financial institutions or the rate at which short-term government paper is issued or traded in the market. Gompers and Lerner (1998) and van Pottelsberghe de la Potterie and Romain (2004) show that a higher interest rate results in higher fundraising and investment activity. Cherif and Gazdar (2011) and F\u0026eacute;lix et al. (2013) have shown a negative correlation between PE activity and unemployment rate.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAccording to the findings of F\u0026eacute;lix et al. (2013), market capitalization acts as a proxy for the liquidity of the stock market, and a positive association between PE activity and fundraising and investment might be anticipated. This variable, however, has been shown to be statistically insignificant by Jeng and Wells (2000) and Balboa and Mart\u0026iacute; (2003).\u0026nbsp;Research and development (R\u0026amp;D) expenditure acts as a proxy for innovation and technological advancement. According to research by Gompers and Lerner (1998), the demand and supply of venture capital investments in the United States increased during the 1990s as a result of the country\u0026apos;s increased spending on research and development and the resulting technological advancements. It was also proven by Romain and de la Potterie (2004) that technological advancements have a beneficial effect on the development of venture capital investments.\u003c/p\u003e\n \u003cp\u003eThe Heritage Foundation\u0026apos;s index\u0026nbsp;(Beach and Kane, 2007)\u0026nbsp;reflects\u0026nbsp;the degree of economic freedom annually in countries as a measure of institutional quality. The index takes into account the following aspects scored on a scale from 0 to 100 and weighted equally: (1) rule of law (property rights, judicial effectiveness, and government integrity); (2) government size (tax burden, government spending, and fiscal health); (3) regulatory efficiency (business freedom, labor freedom, and monetary freedom); and (4) market openness (trade freedom, investment freedom, and financial freedom). The 12 Economic Freedoms, defined by Beach and Kane (2007), are described in Appendix Table (8).\u003c/p\u003e\n \u003cp\u003eWorldwide governance indicators (WGI) indicators measure how well countries run their governments. It is a World Bank research initiative and is based on surveys of public and private sector specialists, non-governmental organizations, and other international organizations. WGI is composed of the following six indicators: (1) control of corruption; (2) government effectiveness; (3) political stability and absence of violence; (4) regulatory quality; (5) rule of law; and (6) voice and accountability. These indicators determine the effectiveness of governance systems in promoting economic growth, eliminating poverty, and promoting social welfare. They are described in Appendix Table (9).\u003c/p\u003e\n \u003ch2\u003e\u003cem\u003e3.4 Descriptive Statistics\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eThe summary of the descriptive statistics for all the variables (target and explanatory) is presented in Table (1). Given that more than 60% of the data is missing for judicial effectiveness and fiscal health, we eliminated these institutional variables from our study. Similar data gaps exist for interest rates, market capitalization, and research and development expenditure (highlighted in gray). We impute these values using a predictive mean-matching algorithm.\u003ca href=\"#_ftn3\" name=\"_ftnref3\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003ch2\u003e\u003cem\u003e3.5 Stationarity Tests\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eThe stationarity of the series data is analyzed using the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test. The KPSS test is a unit root test that checks whether a certain series is stationary.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe outcomes of the stationarity tests are displayed in Appendix Table (10). According to the KPSS test, the variables market capitalization, R\u0026amp;D expenditure, and financial freedom are non-stationary. Therefore, using differencing, these variables are transformed into stationary series. The temporal component of the panel data is shortened from 11 years (2010\u0026ndash;2020) to 10 years (2011\u0026ndash;2020) due to differencing. Only stationary series are considered in this research.\u0026nbsp;\u003c/p\u003e\n \u003ch2\u003e\u003cem\u003e3.6 Correlation\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eWe examine correlations among potential private equity fundraising and investing determinants. Table (2) shows the correlation matrix. By observing the correlation matrix, we notice several strong correlations (greater than 0.7), which are highlighted in gray. And to account for multicollinearity, we exclude the following variables from our analysis: economic freedom index, property rights, government spending, control of corruption, government effectiveness, regulatory quality, rule of law, and voice and accountability.\u003c/p\u003e\n \u003ch2\u003e\u003cem\u003e3.7 Bayesian Model Averaging\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eWe employ a Bayesian model averaging (BMA) approach to decrease the model uncertainty associated with the selection of variables. BMA is a robust statistical technique with a solid theoretical background. To account for model uncertainty, BMA performs a marginalization over models to derive posterior densities on model parameters (Hoeting et al., 1999).\u003c/p\u003e\n \u003cp\u003eHowever, the empirical outcomes of such processes might be highly sensitive to prior assumptions. Five macroeconomic factors and nineteen institutional variables are used as a starting point for our analysis. Because of the lack of data indicated in subsection 3.1, we eliminate two of the institutional variables (judicial effectiveness and fiscal health). Now we apply BMA to six subsets of our panel data to find the best explanatory variable for each region and PE activity combination: CEE fundraising, WE fundraising, Europe fundraising, CEE investments, WE investments, and Europe investments. In our research, we treat the combined CEE and WE regions as a single European one.\u003c/p\u003e\n \u003cp\u003eA summary of the BMA results is shown in Appendix Table (11), with \u003cstrong\u003eX\u003c/strong\u003e denoting the variables with a Posterior Inclusion Probability (PIP) of more than 0.8. The Appendix Tables (12) \u0026ndash; (13) present the complete results of the BMA. And since the economic freedom index, property rights, and government spending, control of corruption, government effectiveness, regulatory quality, rule of law, and voice and accountability all have low PIP, removing them from our analysis to eliminate multicollinearity has no major effect on our results.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003cbr\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eFixed-effects regression is used to control for unobserved heterogeneity in panel data analysis. This allows for the estimation of within-group effects while controlling for time-invariant factors. This approach is used to investigate the impact of country-specific events. Random effects regression, on the other hand, assumes that the country-specific effects are randomly generated from a normal distribution. This allows for the estimation of between-group effects. This approach is used to investigate the variance of country-specific effects. We run FE and RE regressions for the CEE, WE, and European regions. And the dependent variables selected for the regressions are determined by the BMA results presented in Appendix Tables (11) \u0026ndash; (13). It is important to note that based on the results of the BMA, only a subset of the different dependent variables is chosen for the estimations of fundraising and investments, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Hausman (1978) specification test compares FE and RE under the null hypothesis that unobserved heterogeneity (individual effects) has no correlation with any explanatory variable.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e4\u003c/sup\u003e WE and Europe Fundraising are the only regressions where the Hausman test rejects the null hypothesis. Hence, FE estimators are consistent. RE estimators are consistent and efficient for the other 4 cases: CEE Fundraising, CEE Investments, WE Investments, and Europe Investments. The results of Hausman specification test are shown in Appendix Table (14).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.1 Fundraising\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eFor all 3 regions considered (CEE, WE, and Europe), the FE estimation results for fundraising are presented in Table (3) and the RE estimation results are presented in Table (4). The target variable of fundraising is regressed against the following dependent variables: investments and investment freedom. Based on the results of the Hausman test, the RE estimator is consistent for fundraising in the CEE region. And FE estimators are consistent for fundraising in the WE and European regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs presented in Table (4), for the CEE region, investments and investment freedom are both statistically significant variables. Similarly, it is shown in Table (4) that for the WE and European regions, investments and investment freedom are also statistically significant. As anticipated, both coefficients are positive, which means that high levels of investment and higher investment freedom result in more fundraising. The importance of investment is greater in the WE region than in the CEE region. But the importance of investment freedom is greater in the CEE region in comparison to the WE region.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt is clear that the institutional factor that plays an important role in raising funds in both regions is investment freedom. But the ability of a PE firm to deploy funds is a stronger determinant of its ability to raise funds. Thus, a PE firm\u0026rsquo;s investments and investment freedom in the country are key factors that investors consider when deciding whether to commit capital to the PE firm\u0026apos;s fund.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.2 Investments\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eFor all 3 regions considered (CEE, WE, and Europe), the FE estimation results for investments are presented in Table (5), and the RE estimation results are presented in Table (6). The target variable is regressed against the dependent variables: fundraising, divestments, government integrity, and trade freedom. Based on the results of the Hausman test, the RE estimator is consistent and efficient for investments in all three regions: CEE, WE, and Europe.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs presented in Table (6), fundraising, divestments, government integrity, and trade freedom are all robustly associated with investment and are statistically significant at the 1% level. As expected, the variables directly related to PE activity, i.e., fundraising and divestments, have a positive relationship with investments. The amount of funds raised by PE firms can influence their investment decisions. If PE firms can raise more capital, they can pursue larger deals or invest in more companies. Conversely, if fundraising is limited, firms may need to be more selective in their investment choices. Divestments provide evidence of a PE firm\u0026apos;s ability to generate returns for its investors. If a PE firm can sell its portfolio companies at a significant profit, it can help build investor confidence and increase the likelihood of raising funds.\u003c/p\u003e\n\u003cp\u003eIn comparison to fundraising, different institutional factors play an important role in determining the funds invested in both regions. It is evident that government integrity is more important in the CEE region than in the WE region. Trade freedom, on the other hand, is a negative determinant of investment activity in both regions. Although government integrity has a stronger influence in the CEE region in comparison to the WE region, the impact of trade freedom is stronger in the WE region in comparison to the CEE region.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.3 Discussion\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eBased on the above results, there are three important findings from this study that provide the answer to our research question: \u0026ldquo;How do the factors driving CEE and WE private equity activity differ?\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eFirstly, contrary to all prior research, macroeconomic factors, including GDP growth rate, unemployment rate, interest rate, market capitalization, and R\u0026amp;D expenditure, have no statistically significant effect on the funds raised and invested in Europe (both CEE and WE regions) by private equity firms. This disproves our first hypothesis. Our findings support Cherif and Gazdar\u0026rsquo;s (2011) finding that interest rates have no impact on the amount of funds deployed. Our results are also consistent with Kelly\u0026rsquo;s (2012) conclusion that R\u0026amp;D expenditures are insignificant. We also confirm Jeng and Wells\u0026rsquo; (2000) conclusion that GDP growth rate and market capitalization have no significant influence on the amount of funds raised. Our results contradict the findings of Gompers and Lerner (1998), Romain and de la Potterie (2016), and Bernoth and Colavecchio (2014) about GDP growth rates. Our findings agree with Precup (2017) on the insignificance of the unemployment rate but disagree on the positive impact of R\u0026amp;D expenditure on investments.\u003c/p\u003e\n\u003cp\u003eSecondly, variables directly related to the private equity process are statistically significant drivers of fundraising and investment activities. According to the results, the funds raised in both the WE and CEE regions are dependent on investments made in the respective regions. This is in line with Balboa and Mart\u0026iacute; (2001) and Balboa and Mart\u0026iacute; (2003). Similarly, investment in both the WE and CEE regions is dependent on funds raised and divestment in the respective regions. Thus, validating our second hypothesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to the similarities stated above, there are differences in the drivers of PE activity in the two European regions as well. While investment freedom (positive effect) is the only significant institutional determinant of funds raised in the WE and CEE countries, government integrity (positive effect) and trade freedom (negative effect) are both significant determinants of investments in both European regions. This asymmetric effect of institutional variables can be explained by the investors\u0026rsquo; sensitivity to protection and the institutional environment that \u0026ldquo;guarantees\u0026rdquo; investor protection via law and its enforcement. The surprising finding that trade restrictions promote PE investment, can be explained by the fact that market inefficiencies can create opportunities for PE firms to invest in, restructure and capitalize on domestic market opportunities. Ghodsi (2020) argues that lack of trade freedom can have a positive impact on investments under certain conditions, based on the data from Central, East and Southeast Europe. Though not very strongly, the findings do support our third hypothesis.\u003c/p\u003e\n\u003cp\u003eAnd the last notable finding of this paper is the distinction in fundraising activity between the WE and CEE regions. The FE estimator is consistent for WE fundraising, implying that the funds raised differ across the countries in Western Europe. However, fundraising in the CEE region is not country-specific, as demonstrated by the consistency of the RE estimator. This conclusion is backed by the fact that the vast majority of PE funds intended for the CEE region are raised outside the region. In the last eleven years, only 30.72% of the money raised for the CEE area was raised in CEE countries (Invest Europe, 2022b).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eAvailability of data in private equity is relatively limited due to the confidential nature of the transactions. Private sources provide very little information regarding the actions of fund managers, and even that information is updated only once a year. Nevertheless, Kaplan and Lerner (2016) argue that the quality of venture capital data (but not overall private equity) available has improved recently and is likely to do so in the future. However, the databases used to support this assertion are from the United States. Furthermore, the fund managers may choose not to share some information, or the information they do give may not be independently verified.\u003c/p\u003e\n\u003cp\u003eConsequently, even the researchers focused on Western Europe and the United States faced challenges in finding relevant factors for which trustworthy data is readily available. In general, they focused on macroeconomic and structural factors such as GDP growth, market capitalization, interest rate, capital gains tax, level of initial public offerings, labor market rigidity, and productivity, among others. But most of the past researchers did not consider the variables that are directly related to the PE process, which are included in this research. And our results demonstrate a strong and positive relationship between PE activity and the variables directly related to PE. Funds invested in both the CEE and WE regions are positively related to funds divested and funds raised in those regions. Likewise, funds raised in both regions are positively related to funds divested. In addition, institutional factors have no influence on the funds raised in both regions. But government integrity and trade freedom are important drivers of the funds invested.\u003c/p\u003e\n\u003cp\u003eInterestingly, contrary to the majority of the previous studies, we discovered that the most researched macroeconomic indicators, including GDP growth, interest rate, unemployment rate, market capitalization, and R\u0026amp;D expenditure, have no significant influence on funds raised and invested in both regions. However, an important observation is that there is still no widespread consensus on the macroeconomic determinants of PE fundraising and investments.\u003c/p\u003e\n\u003cp\u003eThe results offer direct policy implications for three parties involved in European PE: (1) general partners (GPs) of PE firms; (2) limited partners (LPs) investing in the funds offered by such firms; and (3) the government. GPs seeking to raise funds in Europe must demonstrate a successful track record of investments. LPs interested in investing in PE firms must critically assess the amount of funds raised and divested by the prospective firms. Lastly, to encourage private investments in their respective countries, governments must engage in activities aimed at enhancing integrity, reducing corruption, and eliminating constraints on the movement and usage of investment money within and beyond the national borders.\u003c/p\u003e\n\u003cp\u003eCurrent research considers a country-level cross-section and can be extended by conducting a similar study on a firm-level dataset to get a deeper understanding of the determinants of PE activity. The analysis can also be further developed to incorporate the track record of the PE firms in these regions by employing lagged variables of fundraising and investment activity. Furthermore, the impact of the COVID-19 crisis on fundraising and investment activities in the two regions can be examined and compared. \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.R. wrote the main manuscript and E.K. provided guidance and review of the work, as the supervisor of S.R. as part the PhD program.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eWe are grateful to Sofian Giuroiu for the data on country-level PE fundraising, investment, and divestment activities provided by Invest Europe. The usual disclaimer applies.\u003c/p\u003e\u003ch2\u003eAvailability of data\u003c/h2\u003e \u003cp\u003ein private equity is relatively limited due to the confidential nature of the transactions. Private sources provide very little information regarding the actions of fund managers, and even that information is updated only once a year. Nevertheless, Kaplan and Lerner (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) argue that the quality of venture capital data (but not overall private equity) available has improved recently and is likely to do so in the future. However, the databases used to support this assertion are from the United States. Furthermore, the fund managers may choose not to share some information, or the information they do give may not be independently verified.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBernoth, K. and Colavecchio, R. (2014). The Macroeconomic Determinants of Private Equity Investment: A European Comparison. \u003cem\u003eApplied Economics\u003c/em\u003e, 46(11), 1170\u0026ndash;1183. https://doi.org/10.1080/00036846.2013.866306\u003c/li\u003e\n \u003cli\u003eBalboa, M. and Mart\u0026iacute;, J. (2001). Determinants of Private Equity Fundraising in Western Europe. https://dx.doi.org/10.2139/ssrn.269789\u003c/li\u003e\n \u003cli\u003eBalboa, M. and Mart\u0026iacute;, J. (2003). An Integrative Approach to the Determinants of Private Equity Fundraising. https://dx.doi.org/10.2139/ssrn.493344\u003c/li\u003e\n \u003cli\u003eBeach, W.W. and Kane, T. (2007). Methodology: Measuring the 10 Economic Freedoms. Index of Economic Freedom. www.heritage.org/index\u003c/li\u003e\n \u003cli\u003eCherif, M. and Gazdar, K. (2011). What drives Venture Capital Investments in Europe? New Results from a Panel Data Analysis. \u003cem\u003eJournal of Applied Business and Economics\u003c/em\u003e, 12, 3.\u003c/li\u003e\n \u003cli\u003eDiaconu, M. (2017). Private equity market developments in central and Eastern Europe. \u003cem\u003eTheoretical and Applied Economics\u003c/em\u003e, 34, 2(611), 131-146.\u003c/li\u003e\n \u003cli\u003eF\u0026eacute;lix, E., Gulamhussen, M. and Pires, C. (2013). The Determinants of Venture Capital in Europe \u0026mdash; Evidence Across Countries. \u003cem\u003eJournal of Financial Services Research\u003c/em\u003e, 44, (3), 259-279. https://doi.org/10.1007/s10693-012-0146-y\u003c/li\u003e\n \u003cli\u003eGhodsi, M. (2020). How do technical barriers to trade affect foreign direct investment? Tariff jumping versus regulation haven hypotheses. \u003cem\u003eStructural Change and Economic Dynamics\u003c/em\u003e, 52, 269-278. https://doi.org/10.1016/j.strueco.2019.11.008\u003c/li\u003e\n \u003cli\u003eGreen, W. H. (2003). \u003cem\u003eEconometric Analysis\u003c/em\u003e. Pearson Education India.\u003c/li\u003e\n \u003cli\u003eGompers, P. and Lerner, J. (1998). What Drives Venture Capital Fundraising? Brooking Papers on Economic Activity. \u003cem\u003eMacroeconomics\u003c/em\u003e, 149-192. https://dx.doi.org/10.2139/ssrn.57935\u003c/li\u003e\n \u003cli\u003eGrzywacz, J., and Jagodzińska-Komar, E. (2019). The Role of the Polish Private Equity Sector in the CEE Region. \u003cem\u003eJournal of Management and Financial Sciences\u003c/em\u003e, 29, 131-142.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHausman, J. (1978). Specification Tests in Econometrics. \u003cem\u003eEconometrica\u003c/em\u003e, 46, 6, 1251-1271.\u003c/li\u003e\n \u003cli\u003eHenchiri, B. (2016). The Impact of the Macroeconomic and Institutional Environment on LBO Fundraising. \u003cem\u003eGlobal Journal of Management and Business Research\u003c/em\u003e, ISSN 2249-4588.\u003c/li\u003e\n \u003cli\u003eHoeting, J. A., Madigan, D., Raftery, A. E., Volinsky C. T., (1999). Bayesian model averaging: a tutorial. \u003cem\u003eStatistical Science\u003c/em\u003e, 14, No. 4, 382-417.\u003c/li\u003e\n \u003cli\u003eInvest Europe. (2022a). Investing in Europe: Private Equity Activity 2021. Report.\u003c/li\u003e\n \u003cli\u003eInvest Europe. (2022b). 2021 Central \u0026amp; Eastern Europe Private Equity Statistics. Report.\u003c/li\u003e\n \u003cli\u003eJeng, L. A. and Wells, P. C. (2000). The Determinants of Venture Capital Funding: Evidence Across Countries. \u003cem\u003eJournal of Corporate Finance\u003c/em\u003e, 6, No. 3, 241-289.\u003c/li\u003e\n \u003cli\u003eKaplan, S. N., and Lerner, J. (2016). Venture Capital Data: Opportunities and Challenges. NBER Working Paper No. w22500.\u003c/li\u003e\n \u003cli\u003eKelly, R. (2012). Drivers of Private Equity Investment Activity: Are Buyout and Venture Investors Really So Different? \u003cem\u003eVenture Capital\u003c/em\u003e, 14, 4, 309-330. https://doi.org/10.1080/13691066.2012.688494\u003c/li\u003e\n \u003cli\u003eSkalick\u0026aacute; Du\u0026scaron;\u0026aacute;tkov\u0026aacute;, M., Zinecker, M., Meluz\u0026iacute;n, T. (2017). Institutional Determinants of Private Equity Market in Czech Republic. \u003cem\u003eEconomics and Sociology\u003c/em\u003e, 10, 4, 83-98.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLeachman, L., Kumar, V., and Orleck, S. (2002). Explaining Variations in Private Equity: A Panel Approach. Duke University. Department of Economics, Working Papers.\u003c/li\u003e\n \u003cli\u003eLjumović, I., Milojkić, I. L., and Obradović V. (2020). What Drives Private Equity and Venture Capital in Central and Eastern Europe Countries: Focus on Serbia. \u003cem\u003eEconomic Analysis: Applied Research in Emerging Markets\u003c/em\u003e, 53, 1, 133-148. https://doi.org/10.28934/ea.20.53.1.pp133-148\u003c/li\u003e\n \u003cli\u003ePrecup, M. (2015). The Future of Private Equity in Europe \u0026ndash; The Determinants Across Countries. \u003cem\u003eRomanian Journal of European Affairs\u003c/em\u003e, 15, 72-92.\u003c/li\u003e\n \u003cli\u003ePrecup, M. (2017). Venture Capital and Leveraged Buyout: What is the Difference in Eastern Europe? \u0026ndash; A Cross-Country Panel Data Analysis. \u003cem\u003eRomanian Journal of European Affairs\u003c/em\u003e, 17, 2.\u003c/li\u003e\n \u003cli\u003ePrecup, M. (2019). Challenges to Scaling Sustainable Private Equity Markets in Emerging Europe. \u003cem\u003eSustainability\u003c/em\u003e, 11, 15, 4080. https://doi.org/10.3390/su11154080\u003c/li\u003e\n \u003cli\u003eRomain, A., and van Pottelsberghe de la Potterie, B. (2004). The determinants of venture capital: a panel analysis of 16 OECD countries. Universite Libre de Bruxelles Working Paper no. WP-CEB 04/015.\u003c/li\u003e\n \u003cli\u003eSato, A. (2011). Private Equity Investment in Central and Eastern Europe. \u003cem\u003eInternational Journal of Management Cases\u003c/em\u003e, 13, 4, 199-206. https://doi.org/10.3905/joi.2003.319563\u003c/li\u003e\n \u003cli\u003eSchertler, A. (2003). Driving Forces of Venture Capital Investments in Europe: A Dynamic Panel Data Analysis. Kiel Institute for World Economics, Kiel Working Paper no 27.\u003c/li\u003e\n \u003cli\u003eStefanova, J. (2015). Venture Capital in Central and Eastern Europe: A Comparative Analysis and Implications for Bulgaria. \u003cem\u003eJournal of US-China Public Administration\u003c/em\u003e, 12, 1, 51-59. https://doi.org/10.17265/1548-6591/2015.01.006\u003c/li\u003e\n \u003cli\u003eWoolridge, J. M. (2013). \u003cem\u003eIntroductory Econometrics: A Modern Approach\u003c/em\u003e. Southwestern, Cengage Learning.\u003c/li\u003e\n \u003cli\u003eWright, M., Kissane, J., and Burrows, A. (2004). Private Equity in EU Accession Countries of Central and Eastern Europe. \u003cem\u003eThe Journal of Private Equity\u003c/em\u003e, 7, 3, 32-46. https://doi.org/10.3905/jpe.2004.412333\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Due to the unavailability of data in the Invest Europe databases, we exclude some CEE countries: Bosnia and Herzegovina, Montenegro, Albania, Kosovo, and North Macedonia.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Divestments can be categorized into public offering (IPO), write-off, and trade sale. These sub-categories were considered but were not pursued due to unavailability of the data.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Predictive mean matching (PMM) is a technique used to address missing data. The PMM method is especially helpful when the missing data are neither missing completely at random (MCAR) nor missing at random (MAR), but rather missing not at random (MNAR).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e When the Hausman test fails to reject the null hypothesis that the individual effects are uncorrelated with the explanatory factors, the RE model is the most appropriate estimation. But when the null hypothesis is rejected, the FE model is the most suitable.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTABLE 1: DESCRIPTIVE STATISTICS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"545\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSt. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInvestments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e2.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFundraising\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e8.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDivestments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e1.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDP Growth Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e1.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e3.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e-10.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e25.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterest Rate\u003c/strong\u003e\u003csup\u003e(m)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e1.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e-0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e12.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnemployment Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e8.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e4.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e2.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e27.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarket Capitalization\u003c/strong\u003e\u003csup\u003e(m)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e59.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e63.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e393.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026amp;D Expenditures\u003c/strong\u003e\u003csup\u003e(m)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e1.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e3.710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProperty Rights\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e72.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e17.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e30.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e95.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovernment Integrity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e64.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e19.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e33.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e96.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eJudicial Effectiveness\u003c/strong\u003e\u003csup\u003e(m)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e64.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e14.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e37.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e93.800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTax Burden\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e66.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e14.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e35.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e94.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovernment Spending\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e37.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e18.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e78.800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFiscal Health\u003c/strong\u003e\u003csup\u003e(m)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e82.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e17.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e6.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e99.900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBusiness Freedom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e78.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e10.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e53.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e99.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLabor Freedom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e60.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e13.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e31.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e93.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonetary Freedom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e80.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e4.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e64.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e91.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrade Freedom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e86.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e2.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e75.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e90.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInvestment Freedom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e79.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e9.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e50.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e95.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinancial Freedom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e68.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e11.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e40.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e90.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomic Freedom Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e69.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e6.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e53.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e82.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl of corruption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e78.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e17.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e38.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e100.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovernment effectiveness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e81.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e14.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e42.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e100.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolitical stability and absence of violence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e72.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e15.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e31.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e99.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulatory quality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e83.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e11.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e50.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e99.530\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRule of law\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e81.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e15.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e41.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e100.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.28440366972477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVoice and accountability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e83.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e13.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e40.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.743119266055047%\"\u003e\n \u003cp\u003e100.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote: This table provides descriptive statistics for the target and explanatory variables analyzed in this study. Variables with missing values are marked with superscript (m).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2: CORRELATION MATRIX\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"image\" width=\"2016\" height=\"648\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003cem\u003e*p\u0026lt;0.1; **p\u0026lt;0.05; ***p\u0026lt;0.01\u003c/em\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003cem\u003eNote: This table displays the correlation matrix between the target and explanatory variables (except for judicial effectiveness and fiscal health). Correlation values higher than 0.7 are highlighted in gray.\u003c/em\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3: FE MODEL - FUNDRAISING\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"433\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCEE Fundraising\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWE Fundraising\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEurope Fundraising\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInvestments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e\u0026nbsp;0.089*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e\u0026nbsp;0.156** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.264)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e0.118** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.130)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInvestment freedom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e\u0026nbsp;0.002 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e0.002* \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.017)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e0.002* \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eObservations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e-0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e3.986**\u0026nbsp;\u003cbr\u003e\u0026nbsp;(df = 2; 115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e0.190*\u003cbr\u003e\u0026nbsp;(df = 2; 142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e0.456*\u003cbr\u003e\u0026nbsp;(df = 2; 259)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e*p\u0026lt;0.1;\u0026nbsp;**p\u0026lt;0.05;\u0026nbsp;***p\u0026lt;0.01\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Standard errors in parentheses)\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eNote: This table presents the results of the country-fixed effects estimation for the target variable, fundraising, for each of the three regions.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4: RE MODEL - FUNDRAISING\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"437\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCEE Fundraising\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWE Fundraising\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEurope Fundraising\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInvestments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.104*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.502** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.254)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.281** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInvestment freedom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.003** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.017 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;0.013** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e-0.164 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;-1.143 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.964)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026apos;-0.803* \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.475)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eObservations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e18.358***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;6.892**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e12.003***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e*p\u0026lt;0.1;\u0026nbsp;**p\u0026lt;0.05;\u0026nbsp;***p\u0026lt;0.01\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Standard errors in parentheses)\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eNote: This table presents the results of the country-random effects estimation for the target variable, fundraising, for each of the three regions.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 5: FE MODEL - INVESTMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"433\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCEE Investments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWE Investments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEurope Investments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDivestments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e0.591***\u003cbr\u003e\u0026nbsp;(0.201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e-0.005 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e\u0026nbsp;0.173* \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.104)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFundraising\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e0.758*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.233)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e0.009 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e0.043 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovernment integrity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e\u0026nbsp;0.011*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e-0.005 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e\u0026nbsp;0.005* \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrade freedom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e\u0026nbsp;-0.039* \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e-0.058*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e-0.046*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eObservations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e\u0026nbsp;-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.1824480369515%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.247113163972287%\"\u003e\n \u003cp\u003e7.004***\u0026nbsp;\u003cbr\u003e\u0026nbsp;(df = 4; 113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.016166281755197%\"\u003e\n \u003cp\u003e2.183*\u0026nbsp;\u003cbr\u003e\u0026nbsp;(df = 4; 140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.554272517321017%\"\u003e\n \u003cp\u003e4.403***\u0026nbsp;\u003cbr\u003e\u0026nbsp;(df = 4; 257)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e*p\u0026lt;0.1;\u0026nbsp;**p\u0026lt;0.05;\u0026nbsp;***p\u0026lt;0.01\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Standard errors in parentheses)\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eNote: This table presents the results of the country-fixed effects estimation for the target variable, investments, for each of the three regions.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 6: RE MODEL - INVESTMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"437\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCEE Investments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWE Investments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEurope Investments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDivestments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.538*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.179)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;0.284*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.296*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.078)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFundraising\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;0.837*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.078*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;0.083*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovernment integrity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.010*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;0.004*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;0.007*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrade freedom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e-0.028*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e-0.033*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e-0.026*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;2.012*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.672)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e2.902*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;2.070*** \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.607)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eObservations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e57.411***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e57.465***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;145.682***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e*p\u0026lt;0.1;\u0026nbsp;**p\u0026lt;0.05;\u0026nbsp;***p\u0026lt;0.01\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Standard errors in parentheses)\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eNote: This table presents the results of the country-random effects estimation for the target variable, investments, for each of the three regions.\u003c/em\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Private equity (PE), Fundraising, Investment, Central and Eastern Europe (CEE), Western Europe (WE), Bayesian Model Averaging (BMA)","lastPublishedDoi":"10.21203/rs.3.rs-4125626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4125626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe investigate the key macroeconomic and institutional determinants of fundraising and investment activities and compare them across Europe, covering 13 Central and Eastern European (CEE) and 16 Western European (WE) countries. Five macroeconomic variables and nineteen institutional variables are selected. These variables are studied using panel data analysis with fixed effects and random effects models over an eleven-year observation period (2010–2020). Bayesian Model Averaging (BMA) is applied to select the key variables. Our results suggest that macroeconomic variables have no significant impact on fundraising and investment activity in either region. Investment activity is a significant driver of fundraising across Europe. Similarly, fundraising and divestment activity are significant drivers of investments across Europe. Institutional variables, however, affect fundraising and investment activity differently. While investment freedom has a significant effect on funds raised in the WE and CEE countries, government integrity and trade freedom are both significant determinants of investments in both European regions. In addition, the results demonstrate that, in contrast to the WE region, fundraising in the CEE region is not country specific.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classifications: \u003c/strong\u003eC11, C23, C52, E22, G15, G24, G28, O16\u003c/p\u003e","manuscriptTitle":"Drivers of Private Equity Activity across Europe: An East-West Comparison","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 07:00:20","doi":"10.21203/rs.3.rs-4125626/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fcd65032-89fd-4cb6-bc89-21f8d25ab55f","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-26T10:50:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-21 07:00:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4125626","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4125626","identity":"rs-4125626","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}