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Private equity and venture capital funds: what drives the demand and supply?

Introduction

The Private Equity/Venture Capital (PE/VC) industry has grown in recent years especially in developing economies, where a considerable increase in financing activities has been observed. One possible reason could be the search for different paybacks in economies that go through an economic and institutional maturity, given that developed markets have been evidencing a decrease in profitability levels, since the 1990s (Comodo, 2009).

Despite it being widely disseminated around the world, the PE/VC activity is mostly concentrated in the United States of America and the United Kingdom, which together account for approximately 60% of the raised capital, although there has been a continuous reduction in the difference, compared to other countries. Brazil, China and India, for example, have created conditions for the development of the PE/VC activity, hence, they are the ones that have been successful in fundraising (Bain & Company Inc., 2013).

The fundraising and resource allocation process by PE/VC funds can boost the internal market of a country. So, it is intuitively possible to assert that countries will battle for international investments of PE/VC in an attempt to bring these resources to their market by creating attractive conditions for PE/VC managers. Therefore, it is the theoretical gap that this study seeks to fill: through the investigation of the factors influencing supply and demand PE/VC, it is possible to understand what makes a country more interesting for the attraction of international capital.

The main goal of this paper is to understand which factors influence the PE/VC market. To achieve it, we separately analyzed the supply and demand sides of transactions. Twenty-five variables were chosen consistent with the existing literature. Using Factor Analysis, we modeled variables that possibly affect the demand of the PE/VC. These factors include macroeconomic, financial, corporate governance, entrepreneurship, social and environmental development variables. We then exploited regressions made up of 25 countries over a six-year period (2006-2011).

The results indicate that investments are adversely affected by the depth of the capital market: PE/VC funds search for an exit strategy that the stock market can offer by means of IPOs (Initial Public Offering). Other significant factors were the protection of investors, environmental development and the level of entrepreneurship. Different from expected, the economic activity was negative significant to demand side.

The paper is structured as follows: The next section explains the market functioning and summarizes the findings of the existing evaluation of key drivers of the PE/VC. Third section shows the methodology aspects concerning dataset, multivariate data analysis and the models. The empirical results are interpreted in fourth section. And final section presents the conclusions.

Market functioning and literature review

Private Equity (PE) can be defined as business investments in companies that are not listed in the stock market. This type of acquisition has natural characteristics such as low liquidity, expectation of higher returns in the long-term and higher risk. Investments like this have also invariably been characterized by the informational asymmetry, since managers of PEs are seeking business opportunities that have not been priced by the market, which can be acquired at an attractive discount for a fair price and that have a valuation perspective (Agencia Brasileira de Desenvolvimento Industrial [ABDI], 2011; Almeida, 2013; Gionelli, 2008; Lopes & Furtado, 2006).

Investments in PE/VC are temporary and they usually take more than five years, thus being considered long-term investments. Such opportunities are identified by the company's stage of development. Despite it has been separately specified, it is possible to infer that Venture Capital is a type of Private Equity, so that companies receiving investments in their early stage of development (such as startups), and the term Private Equity are used to designate companies already established in the market.

The PE/VC market has three agents: management organizations, investors, and invested companies. Simplifying the dynamic market, investors apply their capital in investment vehicles that are driven by management organizations, which in turn, buy participation in portfolio companies for a specified period of time. At the end of this period, managers undo the long positions and assign the appropriate parties to investors, leaving a residual portion of that amount to pay for the service provided.

Figure 1 illustrates the agents and their interactions below:

[FIGURE 1 OMITTED]

Some studies discuss the determinant attributes to the activity of the PE/VC market, but most of them deal with Private Equity markets and Venture Capital markets separately. The reasons for this are obvious: while some attributes have the greatest impact on early-stage companies, there are other attributes that directly affect mature companies already established in the market (Jeng & Wells, 2000). However, the intention is not to discuss these differences, and as consequence, both segments will be treated as a single element.

The Private Equity and Venture Capital market is an important object of study in academic research. Some authors have focused on the economic impact of PE/VC funds (Engel, 2002; Hellmann & Puri, 2002; Kortum & Lerner, 2000), while other studies have focused on fund performance and management skills (Kaplan & Schoar, 2005).

Considering performance and value creation, Jensen (1989) argues that public companies suffer the entrenchment of management, allowing possible cash flow deviations, thus decreasing efficiency. Therefore, Leveraged Buyouts generate value through significant improvement of operational processes.

In their research, Muscarella and Vetsuypens (1990) analyzed 72 companies that have undergone Reverse Leveraged Buyout (RLBO) and shown an improvement in profitability, while as a private company, the sample achieved 34% increase in its value. This result is due to organizational restructuring, forced by the entry of a PE/VC fund in a management that enabled the reduction of costs resulting in greater operational efficiency. Similar results can be found in Kaplan (1989), Kaplan and Schoar (2005) and Phalippou and Gottschalg (2007).

Contrary to expectations, only few of those papers attempted to understand the determinants of PE/VC. Studying the US market, Gompers and Lerner (1998) found out that performance, size and the fund age are important factors to raise more capital, additionally that PE/VC fundraising reacts positively to GDP growth and increases R&D expenditure. Lee and Peterson (2000) and Baughn and Neupert (2003) explored similar results and also argued that the national culture shapes individual orientation and consequently the environmental condition that ultimately leads to different entrepreneurship levels.

Romain and Pottelsberghe de la Potterie (2004) investigated the intensity determinants of the VC market in sixteen countries using panel regressions and found evidence that the market reacts positively and significantly to GDP growth. They also concluded that technological opportunity indicators (such as increased investment in research and development and number of patents) significantly influence the VC market.

Jeng and Wells (2000) argue that the PE/VC market evidences strong fluctuations over time and that the driving force of these fluctuations is the IPOs, making the development of the capital market one of the determining factors. Despite this result, fund managers tend not to take the companies they invest in, in countries where the most developed capital markets seek more IPO opportunities, as Israeli technology companies have done in NASDAQ. The increased costs and monitoring efforts of geographically distant companies partially explain this phenomenon.

Black and Gilson (1998) found similar results; however, they divided countries into two types: countries with centralization in the capital markets and countries with centralization in the banking system. This division will be the core of the development of the PE/VC market, since the centralization in the capital market is a precondition for the existence of a PE/VC vibrant market, given that a well-developed capital market makes an exit strategy possible through a public offer. In the same way, Balboa and Marti (2004) showed dependence between the growth of fundraising volume by PE/VC funds and the market liquidity in the previous year.

The legal environment also affects significantly as demonstrated by La Porta, Lopez-de-Silanes, Shleifer and Vishny (1997): a good legal environment protects potential financiers against expropriation by entrepreneurs increasing their willingness to supply resources to the funds in exchange for securities. Therefore, it extends the market range. The study evaluated laws protecting investors in 49 countries and showed that Common Law countries provide greater protection to investors than Civil Law countries. Some other evidences were gathered in the table below:

As described, there are several determinants for PE/VC. Some of them can be observed at the macroeconomic level while others are microeconomic factors. Groh et al. (2010) identified what was most attractive for financiers, for it used 42 parameters that made up six factors: economic activity, taxes, investor protection, entrepreneurial culture, social and environmental development, depth of capital market.

The following section presents these variables, how they were measured and grouped by factor analysis. We then developed a theoretical model that aimed at understanding the impact of these factors in the PE/VC market, especially on the demand side.

Methodology Aspects

Table 2 summarizes the variables that will be examined for construction of factors to best represent the constructs mentioned. It also shows the source of each variable, as follows:

First, it is important to state that our data series is driven by previous literature findings. Our data covers the 2006 to 2011 period, and was composed of 25 countries: Argentina, South Africa, Australia, Brazil, Canada, Chile, Colombia, South Korea, Spain, USA, Finland, France, Holland, India, Israel, Italy, Japan, Malaysia, Mexico, Norway, UK, Russia, Germany, Sweden and Switzerland.

The selection of countries was also imposed by data availability. The major task at that point was to find appropriate variables that reveal the characteristics of the constructs. Table 2 shows the constructs, correlate variables and their sources.

Table 2 shows the constructs and variables we used to describe them; a total of 25 selected variables divided in 6 constructs. The construct Social Development was divided into 3 sub-constructs: education, crime and communication. The crime and education variables were used in previous research to describe the construct under discussion. Communication variables were added since weak telecommunication structures disturb the development of the economy and market competitiveness (Frieden, 2005) can have a strong impact on businesses related to technology: a common characteristic of startups financed by venture capital.

The study aims to understand the forces that influence the fundraising (supply side) and resource allocation process (demand side). Thus, the formed factors were used in econometric models taking the sum of the resources raised to measure its impact on the supply side, as dependent variables. Next, we tested a regression model using the same factors against the sum of the resources invested by PE/VC funds in companies as the dependent variable. A restrictive factor in obtaining this kind of data was the fact that those negotiations between PE/VC funds and firms are private, making its disclosure optional.

For this reason, the study was limited, in investigation, to the use of existing public information, which in this case was obtained from the Thomson Reuters database. The next dependent variables were formally defined as follow:

* Fundraising (FUNDR): Total funds raised by PE/VC funds coming from investors. Gompers and Lerner (1998) define these resources as the desire of investors to deposit their capital excess in PE / VC funds. Quantitatively, it is measured by the sum of the resources of all resident funds in each country.

* Invested Resources (INVM): the amount of resources invested on the demand side of the PE/VC market. The demand comes from entrepreneurs interested in obtaining resources from PE/VC funds. Numerically, it is the amount of resources invested by the PE/VC funds of each country in the sample.

Factor Analysis

Factor analysis is a technique that aims to synthesize a set of interrelated variables in order to find common factors. It allows the reduction of data into a smaller set of hypothetical variables that can compress what is common among the initial variables (Favero, Belfiore, Silva, & Chan, 2009; Kim & Mueller, 1978).

Favero, Belfiore, Silva and Chan (2009) divides the factor analysis into confirmatory and exploratory, the first being performed when there is solid prior knowledge of how the variables are related and, therefore, it is assumed that the factor structure is known. In the exploratory factor analysis there is little or no prior knowledge about the behavior of variables.

Due to the characteristics of the study, we used exploratory factor analysis since there are previous studies that report the use of these attributes (Groh, Liechtenstein, & Lieser, 2010). Nonetheless, there is disagreement in the literature about the variables used for the formation of factors. Since the purpose is to summarize variables, we used the Principal Component Analysis (PCA) and R- Type that, according to Hair, Black, Babin, Anderson and Tatham (2005), applies to a correlation matrix of variables to identify the latent dimensions.

Factor analysis becomes crucial due to the number of variables used (25), which would cause an impact on the parsimony of the econometric model since the aim is to evaluate a model that best describes the relationship among the variables and, at the same time, to be as simple as possible. Thus, for each construct, a Factor analysis was used in order to extract a smaller data set, where the priority was the generation of a single factor for each construct.

Figure 2 demonstrates the use of this method in the study, where the ellipses represent the constructs, the rectangles represent the variables used to describe the overlying construct and the triangles represent the factors resulting from the use of the technique:

[FIGURE 2 OMITTED]

Econometric Models

Considering the previous studies and our proposal, we modeled a regression using the resulting factors to examine their impact on the dependent variable, as explanatory variables, Pooled regressions were used to describe the following linear relationship model between the variables:

[FUNDR.sub.i] = [[beta].sub.0] + [[beta].sub.1] [ECO.sub.i] + [[beta].sub.2][MCAP.sub.i] + [[beta].sub.3][INVPROT.sub.i] + [[beta].sub.4][DSO.sub.i] + [[beta].sub.5][EMP.sub.i] + [[beta].sub.6][CORPTX.sub.i] + [[epsilon].sub.i] (E1)

[INVM.sub.i] = [[beta].sub.0] + [[beta].sub.1] [ECO.sub.i] + [[beta].sub.2][MCAP.sub.i] + [[beta].sub.3][INVPROT.sub.i] + [[beta].sub.4][DSO.sub.i] + [[beta].sub.5][EMP.sub.i] + [[beta].sub.6][CORPTX.sub.i] (E2)

In both cases, we reported fixed effects as robustness check (Hausman's test showed fixed effects instead of random effects). In the E1 model, FUNDR is the number of resources raised by PE/VC funds. In the E2 model, INVM is the amount invested by PE/VC funds in firms. The independent variables are the same for both regressions, where: ECO is Economic Activity factor, MCAP is factor of capital market, INVPROT is factor of investor protection, DSO is social development factor, EMP is entrepreneurial culture factor, and CORPTX is a variable for taxes, measuring the Corporate Tax. The models were preliminarily tested using the natural logarithm of the dependent variables, but the results remained unchanged and therefore were not reported.

A positive and significant relationship with the economic activity is expected ([[beta].sub.1] since all the environmental performance also stems from the economic behavior of the country). Groh et al. (2010) argue that the size of the economy is an indicator of the number of organizations and general opportunities flow.

The previous literature reveals that the depth of capital market ([[beta].sub.2] has a strong impact on the market PE/VC and a positive and significant relationship is expected). Consequently, a positive and significant relationship with [[beta].sub.3], as described by La Porta et al. (1997), is expected. [[beta].sub.4] reflects the impact of environmental structure and a positive relationship is expected. [[beta].sub.5] reflects the influence of entrepreneurial culture and it is expected to have a positive relationship: more available projects allow managers to choose those with the highest potential growth. Corporate taxation ([[beta].sub.6]) has a negative impact on the volume of PE/VC as described by La Porta et al. (1997) and for this reason, a negative relationship is foreseen.

Empirical Results

Table 3 shows the descriptive statistics of the variables commented on the theoretical framework and methodology divided by construct. Looking at the table below, the mean and median have values similar to the GDPPC, GDPGROW, PRICELVL, BANK, CREDPRIV, DISCINDEX, SHAREHD, LEGAL, PROCEDE, GASTOPRIM GASTOSEC, CPI and CORPTX variables. This shows possible evidence of normal distribution, but this analysis is not conclusive and a formal test is required. For this purpose, the Shapiro-Wilk test was executed. Only six variables did not reject the null hypothesis of normality (SHAREHD, LEGAL, PROCEDE, GASTOPRI, GASTOSEC, and CORPTX):

Favero et al. (2009) state that an assessment of the technical suitability is essential by means of some tests, which are presented below. The first one is the Correlation Matrix Analysis, seeking to identify significant relation, greater than 0.3, to justify the use of this method. The next step is the analysis of the KMO statistic, which needs to be greater than 0.5 in a range between 0 and 1; Bartlett's test, in turn, examines the null hypothesis of the correlation matrix to be an identity matrix with the determinant equal to one. Once rejected, it means that the variables are correlated. After that, an Anti-image matrix has to be analyzed investigating whether any specific variable have to be dropped, so that values below and above the main diagonal reveal the inadequacy of the method.

Table 4 presents the total of the explained variance, revealing the number of factors of each construct. Furthermore, it shows the KMO statistic and also, for the Bartlett's test, presents the Chi-Squared with significance represented by the number of stars (*).

Panel E (Social and Environmental Development) presented the biggest explained variance, with a 67.04% total. The results of Table 3 show that all the constructs have formed a single factor, except for the Capital Market. This one was split in two factors to explain 77.98% of the variables variance. After evaluating the structure of these factors, it was possible to differentiate them through the variables that composed these factors: the first factor has grouped variables related to the capital market and the second factor has added variables of financial institutions and banks.

Table 5 below summarizes the number of factors formed in each construct, as well as their names. It is worthy to note that Capital Market was divided in MCAP and BANK.

The results of the factor analysis changed the econometric models because they were increased by a new variable once the construct Capital Markets resulted in two factors: the first variable with the capital market characteristics (Label: MCAP) and the second factor variable with banking system characteristics (Label: BANK). Thus, the econometric models can be described by the following equations:

[FUNDR.sub.i] = [[beta].sub.0] + [[beta].sub.1] [ECO.sub.i] + [[beta].sub.2][MCAP.sub.i] + [[beta].sub.3][BANK.sub.i] + [[beta].sub.4][INVPROT.sub.i] + [[beta].sub.5][DSO.sub.i] + [[beta].sub.6][EMP.sub.i] + [[beta].sub.7][CORPTX.sub.i] + [[epsilon].sub.i] (E3)

[INVM.sub.i] = [[beta].sub.0] + [[beta].sub.1] [ECO.sub.i] + [[beta].sub.2][MCAP.sub.i] + [[beta].sub.3][BANK.sub.i] + [[beta].sub.4][INVPROT.sub.i] + [[beta].sub.5][DSO.sub.i] + [[beta].sub.6][EMP.sub.i] + [[beta].sub.7][CORPTX.sub.i] + [[epsilon].sub.i] (E4)

This division of the construct corresponds to the Black and Gilson (1998) proposition and, therefore, [[beta].sub.2] is expected to be positive, while is expected to signalize a negative relationship with the dependent variable. In the next subsections we estimate the correlation matrix and regression models to analyze the coefficients.

Correlation Matrix

We evaluated existence of almost exact linear dependencies among the independent variables, which would result in the instability to estimated coefficients and models as a whole. There is some complexity in an attempt to accurately assess the individual effects of explanatory variables on the dependent variable, since they can be naturally related.

Because of this possible natural relationship, it is important to evaluate among the regressors what level and type of relationship exists among them. Possible evidence of these relationships can be found in the correlation matrix (Table 6):

It is possible to infer that absolute correlations higher than 0.80 indicate strong linear association and relationship collinear detrimental to the model (Hill, Griffiths, & Judge, 2010). By visual inspection, none of the variables in the correlation table exceeded the acceptable level, but three pairs of variables (ECO-EMP, ECO-DSO and DSO-EMP) obtained correlation values that were near to the critical point.

For this reason, we modeled auxiliary regressions (not reported) among the independent variables searching for multicollinearity problems. When variables ECO, DSO and EMP were used as dependent variables, these obtained [r.sup.2] coefficient of 0.669, 0.579 and 0.716, respectively. We conclude that none of the independent variables is highly collinear with any other because the [R.sup.2] coefficients of the auxiliary regressions were not greater than 0.80 (Hill et al., 2010).

Before reporting the results of the regressions we tested some assumptions:

* Homoscedasticity: The Breusch-Pagan test for E3 equation resulted in Chi- Square of 420.74 (p-value < 0.0001), and 383.72 (p-value < 0.0001) for E4 equation, rejecting the null hypothesis of homoscedasticity of residues. As a corrective measure we used the White's robust correction.

* Normality: The Asymmetry/Kurtosis test for Normality was used where the null hypothesis is normality of residues. This test resulted in statistical chi-square of 24.97 (p- value < 0.0001) for the E3 model and chi-square statistic of 18.34 (p-value < 0.0001) for the E4 model, rejecting the null hypothesis of normality. Despite the results, we used the recommendations of Brooks (2008) and Levine, Berenson and Stephan (2005) which emphasize that for sample sizes that are sufficiently large, violation of the normality assumption is virtually inconsequential. Based on the central limit theorem, the authors have emphasized that the test statistics will asymptotically follow the appropriate distributions even in the absence of error normality.

Econometric Results

In this topic, we report the econometric results of demand and supply sides together:

On the demand side (VAR: INVM), the explanatory variables presented statistically significant coefficients for the variables MCAP and EMP at 5% of significance level. The variables BANK and INVPROT have presented significant coefficients at 10% of significance, and DSO at 1% of significance level. CORPTX and ECO were not significant. However, from the fixed effects regression it shows the variable ECO as negative and significant.

One possible explanation is the pursuit of differentiated returns by PE / VC fund managers in countries whose economic activity is still in a maturing process. In developed markets the scenario is reversed since they have difficulty in maintenance of profitability of the early 90s (Comodo, 2009). The result seems controversial but its lack of significance highlights the importance of the capital market (through IPOs as a way of disinvestment) as key drivers of PE/VC market. Similar results can be found in Jeng and Wells (2000, p. 32) who claim that "the absence of significance on our macroeconomic variable, GDP growth, underscores the importance of IPOs as the main explanatory factor for venture capital and private equity investments".

The coefficients of the factors MCAP and BANK corroborate international literature having a positive relationship with the first variable (Gompers & Lerner, 1998; Jeng & Wells, 2000) and a negative one with the second (Black & Gilson, 1998). Through relationship, it is possible to infer that the capital market positively influences the generation and maintenance of a vibrant PE/VC market. Inversely, the banking system has a negative effect, which weakens the PE/VC market. One possible explanation is the necessity PE/VC funds have for an exit strategy. Among the possibilities, the capital market provides an efficient and widely used way out for companies financed by PE/VC funds: the IPO.

The investor protection factor (INVPROT) resulted in positive and significant relationship (significance level: 10%). The result confirms, on the demand side, those found out by La Porta et al. (1997) who state that a structured legal environment protects potential financiers of being expropriated, which increases the willingness to allocate their financial resources on riskier investments such as stock, which in turn enhances the participation of PE/VC funds.

The variable of entrepreneurial culture (EMP) resulted in a positive and significant coefficient, confirming the findings of previous studies (Gompers & Lerner, 1998; Romain & Pottelsberghe de la Potterie, 2004). The result of this variable relates to the findings by Gompers and Lerner (1998, p. 188) which concluded that "the greater the number of good firms, the greater the demand for Venture Capital".

The corporate tax value was not significant. One possible explanation is that the levels of taxation in the sample are too close and did not change over time and therefore do not impact the PE/VC market. This means that for a government to influence the local PE/VC market, it should offer discount rates or high tax incentives, enough to mitigate the effect of other barriers faced by PE/VC market.

The Social Development factor (DSO) has a significant impact on the demand side: the sub-levels of the construct help to explain the investments of PE/ VC funds. Given the construct characteristics, the most difficult task was to find variables that could properly identify it. Besides variables like education and crime, communication is also important, since it impacts startups linked to the technology sector (app companies for example). This could be crucial in an investment decision due to the dependence of this market on technological infrastructure for dissemination and functioning of products.

On the supply side the variable for economic activity was significant only at 10%, contrary to the difference found by Jeng and Wells (2000) but in accordance with the evidence of Gompers and Lerner (1998) . Again the MCAP and BANK variables were significant and their coefficients had their signs as expected and proposed in the literature.

The INVPROT variable resulted in a negative and significant coefficient which is contrary to the results found by La Porta et al. (1997). This result obtained a different signal from that obtained on the demand side, a condition that evidences that the motivations of agents change according to the financial flow: while on the supply side, fundraising is adversely affected when there is greater protection to investors, funds seek better protective conditions to put money on the demand.

The level of entrepreneurship resulted in a sign as expected: the positive impact shows that the supply side is also sensitive to the number of new businesses. As well as on the demand side, the Social Development factor was significant and positive. This means that for solid investments by funds to become effective, the environment must provide qualified persons, low criminality, and quality communication structure.

Taxation has a negative and weak significant impact (significant at 10% level only) on the supply side and shows that, in comparison with other factors, this is the one of least impact. It reinforces the idea that for greater impact, higher discount rates or incentives should be provided for both the demand and the supply sides.

In order to check the robustness of the results additional regressions were made applying natural logarithm in the dependent variable for each model. This change in the dependent variable aims to reduce the difference in magnitude between it and the independent variables. The results presented in the Appendix show that there are few changes from the main results reported, and these alterations have low impact on the analysis and conclusions. Since there was no sign of change, only small changes in levels of significance, these do not impact the analyses substantially.

Discussions

Despite the increasing growth in recent years, the PE/VC market faces challenges to its stabilization, since it is influenced by regional characteristics. One is the cultural differences in willingness to make riskier investments as pointed out by Black and Gilson (1998). A possible solution for investors in conservative cultures would be investing in geographically distant companies, but the cost of monitoring can explain why this solution does not occur in practice (despite the Israeli case, see Jeng & Wells, 2000).

The results show that there is dependence between the capital market and the PE/VC market, but also suggest that the shrinkage of the PE/VC market may occur due to the lack of secondary institutions that support fundraising and investment activities. Thus, to attract investors, a local infrastructure with ability to find lucrative opportunities, smooths the bureaucratic issues and ensure compliance with the contracts is required.

Another possible cause is that investment in PE/VC activities often goes beyond the allocation of financial resources, given that a non-financial contribution (management activities) is common. However, the latter is an uncommon fact among investors in the banking system.

Overall, it was understood that stimulating the analyzed constructs singly, may be an ineffective way to strengthen the PE/VC market. For example, countries which reduce taxes in PE/VC transactions as incentive to this market cannot attract the attention of the PE/VC managers if they have high rates of unemployment, corruption and bribery.

Concluding Remarks

Our methodological structure consisted of a descriptive analysis, a factor analysis and multiple regressions. The results were achieved by econometric models with a sample of six years (2006-2011) and twenty-five countries and we employed factor analysis in order to condense the twenty-five variables that characterize the six constructions. Among the results, our greatest contribution consists of the evidence concerning the importance of the capital market to a higher level of fundraising: dynamic stock markets, with many public offerings guarantee liquidity to the PE/VC market, which allows funds a way of safe divestment.

Our results reinforce the findings in the literature: a positive relationship between the levels of financing generated by PE/VC funds and the depth of the capital market. The demand and supply side of PE/VC market proved strongly sensitive to the volume traded in the stock market, the number of IPOs and the number of M&A, for example. This explains, for example, the reason for the US being the biggest PE/VC market. Obviously this is not the only factor, but it is the most relevant. In general, there is a dependency between the depth of the capital market and the amount of funds raised.

The evidences confirm the impacts of economic activity on the supply side as proposed by Romain and Pottelsbergue de la Potterie (2004). But some prudence is necessary regarding that result: lack of strength on the significance should be further analyzed using larger samples or longer periods. The protection of investors proved controversial: while it proved to be negative on the supply side, its signal was inverted on the demand side. Possibly, PE/VC funds seek greater protections in their investments than they that are willing to deliver to its investors.

The PE/VC market is positively affected by the Social and Environmental Development factor at both ends. This shows that a PE/VC market growth occurs when the environment provides resources and ideal conditions. Reversing the idea, it could be argued that the PE/VC market tends to start with a greater chance of success in locations where there is low crime, qualified people available, and a quality communication structure.

The results reveal the importance of these constructs to PE/VC market, but it is clear, for example, that the depth of the capital market has greater impact than taxation. Conclusions as the one discussed in this study can impact recent research as that of Groh et al. (2010) which, for the construction of attractiveness indices, considered these constructs equally.

Our research has a variety of practical implications for those interested in stimulating the PE/VC market: in fundraising, the evidences indicates that specific policies are necessary to the greater commitment in the PE/VC market, or rather; current policies have not had the desired effect, at least in the sampled countries.

http://dx.doi.org/10.1590/1807-7692bar2016150058

Acknowledgements

The authors gratefully acknowledge the contributions of Paul Victor Gomes Novaes, Luiz Claudio Louzada (UFES) and Alfredo Sarlo Neto (UFES).

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Authors' Profiles

Ricardo dos Santos Dias Av. Fernando Ferrari, 514, Goiabeiras, 29075-910, Vitoria, ES, Brazil. E-mail address: ricardodossantosdias@gmail.com

Marcelo Alvaro da Silva Macedo Av. Pasteur, 250, sala 249, 22290-902, Rio de Janeiro, RJ, Brazil. E-mail address: malvaro@facc.ufrj.br
APPENDIX Additional Econometric Results: BotDependent Variable

                         (LnFUNDR)                   (LnINVM)

Variables        Robust     Fixed Effects    Robust     Fixed Effects

ECO              0.235        0.624 **      -0.497 *      -0.629 **
                (0.179)        (0.264)       (0.167)       (0.285)
                1.270 *        1.370 *      0.238 ***     0.531 ***
MCAP
                (0.121)        (0.453)       (0.139)       (0.286)
               -0 177 ***    -0.633 ***     -0.504 *     -0.192 ***
BANK
                (0.104)        (0.358)       (0.135)       (0.114)
               -0.185 **     -0.585 ***     0.444 **        0.290
GC
                (0.109)        (0.352)       (0.174)       (0.605)
                0.439 *         0.327         0.167         0.438
EMP
                (0.169)        (0.708)       (0.224)       (0.447)
                0.704 *         0.226        1.956 *       -0.577
DSO
                (0.169)        (0.610)       (0.306)       (0.385)
               -0.0815 *      -0.145 **      -0.0331       -0.0100
CORPTX
                (0.0208)      (0.0669)      (0.0291)      (0.0423)
                4.834 *         2.933        5.010 *       5.726 *
CONSTANT
                (0.647)        (2.029)       (0.854)       (1.282)
Observations      150            150           150           150
R-squared        0.612          0.166         0.558         0.123
F-stat          33.71 *        3.35 *        25.12 *       2.37 **

Note. Robust standard errors in parentheses.

* p < 0.01, ** p < 0.05, *** p < 0.1.


Ricardo dos Santos Dias (1)

Marcelo Alvaro da Silva Macedo (2)

Universidade Federal do Espirito Santo (1)

Universidade Federal do Rio de Janeiro (2)

Received 29 September 2015; received in revised form in 27 April 2016 (this paper has been with the authors for three revisions); accepted in 1 May 2016; published online 13 June 2016.
Table 1
Previous Evidence

Authors                Results

Black and Gilson       Economies with more developed capital
(1998)                 market are more active in private equity
                       than economy centered on banks.

Jeng and Wells         1) IPO as driving force for Private
(2000)                 Equity investments;

                       2) Government policies can have a strong
                       impact in Venture Capital market.

Balboa and Martin      The volume growth in t of investments in
(2004)                 Private Equity is partially explained by
                       the market liquidity in t-1

Gompers and Lerner     The level of the market liquidity
(2001)                 influences the level of investments in
                       Private Equity

Gompers and Lerner     1) The annual growth in Private Equity
(1998)                 is affected by regulatory changes
                       relating to pension funds, the growth of
                       the economy as a whole, the fund's
                       performance in question and its
                       reputation;

                       2) The tax on capital gains also
                       influence the Private Equity activity

Cumming, Fleming and   The quality of the legal system is more
Schwienbacher (2006)   likely to facilitate the activities of
                       private equity than the size of the
                       stock market. The legal origin and
                       accounting standards have significant
                       impact on the governance of private
                       equity investments.

Armour and Cumming     Government programs hinder more, rather
(2008)                 than help the development of private
                       equity markets.

Cullen and Gordon      Taxes affect the entry and exit of
(2002)                 business.

Megginsson (2002)      Countries with growing R&D, especially
                       in the national universities and
                       laboratories, are important for the
                       capital industry risk.

Schertler (2003)       The number of employees in R&D and the
                       number of patents as a proxy of human
                       capital has strong significance in PE.

Groh, Liechtenstein    Investor protection and capital market
and Lieser (2010)      are very important determinants for
                       attractiveness of PE/VC market.

Table 2
Constructs and Variables Used

Construct              Variable               Source

Intensity of Capital   IPO volume             World Bank
Market activity        (% of GDP)

                       IPO numbers

                       Private credit
                       available for banks
                       and financial
                       institutions (% of
                       GDP)

                       Number of banks
                       (total of agencies
                       per 100,000
                       habitants)

                       Numbers of listed
                       companies

                       Merge and              Thomson One
                       Acquisition (% of
                       GDP)

                       Merge and
                       Acquisition (numbers
                       of negotiation)

Friendly environment   Number of procedures   World Bank
for entrepreneurship   to open a new
                       business

                       Number of trademarks
                       and patents

                       Percentage of per
                       capita income for
                       the payment of fees
                       and other expenses
                       for compliance with
                       legal records

Social Development

Economic Activity      GDP per capita         World Bank

                       GDP grow (%)

                       Price Level
                       (2005 = 100)

Education              Number of              World Bank
                       researchers per
                       million inhabitants
                       Expenditure per
                       student, primary (%
                       of GDP per capita)

                       Expenditure per
                       student, secondary
                       (% of GDP per
                       capita)

Communication          Internet users (per    World Bank
                       100 people)

                       Fixed broadband
                       Internet subscribers
                       (per 100 people)

                       Telephone lines (per
                       100 people)

Crime                  Intentional            World Bank
                       homicides (per
                       100,000 people)

                       Corruption             Transparency.org
                       Perception Index

Investor Protection    Business extent of     World Bank
                       disclosure index (a)

                       Ease of Shareholder
                       Suits indexb

                       Strength of legal
                       rights index (c)

Taxes                  Corporate Tax          Thomson One

Note. (a) Business extent of disclosure index is the World
Bank disclosure index that measures the extent to which
investors are protected through disclosure of ownership and
financial information. Ranges from 0 (less disclosure) to 10
(more disclosure); (b) Ease of Shareholder Suits index is
the World Bank index that measures the shareholders' ability
to sue officers and directors for misconduct. The index
ranges from 0 (little to no ability to file suit) to 10
(greater ability to file suit); (c) Strength of legal rights
index is the strength of legal rights index that measures
the level to which collateral and bankruptcy laws protect
the rights of borrowers and lenders and thus facilitate
lending. Ranges from 0 (weak) to 12 (strong).

Table 3
Descriptive Analysis

Const.    Variables          Mean        Med    St. Dev.    Kurt

ECO       GDPPC           31199.16   34673.83   21811.06    0.02
          GDPGROW            2.47       2.71        3.45    0.79
          PRICELVL         113.55     109.57       14.26    5.40

CAP.      IPO             6129.77    2041.02    13376.55    23.07
MARK      IPON              46.05      13.00       85.63    14.32
          BANK              28.52      22.52       20.47    4.23
          MA              99306.49   34766.21   228408.90   25.52
          MANEG           1183.43     678.00     1771.96    13.39
          BOLSAN          1303.80     411.50     1551.28    0.22
          CREDPRIV         112.77     111.26       58.07    -1.08

INVEST.   DISCINDEX          6.74       7.00        2.13    2.49
PROTEC    SHAREHD            6.49       7.00        1.64    -0.65
          LEGAL              6.68       7.00        2.08    -0.81
          PROCEDE            7.37       7.50        3.29    -0.19
          CUSTO             10.00       5.90       13.82    11.30
          MARCA           67331.83   47717.50   61666.71    4.94

SDE       PESQUI          2971.81    3185.78     2133.56    -0.88
          GASTOPRIM         18.64      18.43        4.20    0.08
          GASTOSEC          22.46      23.77        6.11    -0.78
          NETUSERS          59.01      68.75       25.69    -0.89
          TELFIXO           38.00      43.41       17.82    -1.11
          NETSUBS           19.59      23.11       11.92    -1.38
          CPI                6.32       6.90        2.27    -1.36
          HOM                6.63       1.80       10.43    2.95

TAX       CORPTX            29.91      30.00        5.79    -0.27

Const.    Variables        Asm       Min          Max    Num.

ECO       GDPPC           0.56    830.16     99091.09    150
          GDPGROW         -0.54    -8.54        10.26    150
          PRICELVL        2.21     99.30       176.85    150

CAP.      IPO             4.48      0.00     96877.73    144
MARK      IPON            3.55      0.00       508.00    144
          BANK            1.97      6.06       105.25    149
          MA              4.81    134.32    1675895.24   135
          MANEG           3.49     51.00     10571.00    135
          BOLSAN          1.26     79.00      5603.00    150
          CREDPRIV        0.00     13.03       215.06    142

INVEST.   DISCINDEX       -1.16     0.00        10.00    150
PROTEC    SHAREHD         -0.36     3.00         9.00    150
          LEGAL           -0.33     3.00        10.00    150
          PROCEDE         0.38      1.00        16.00    150
          CUSTO           3.13      0.40        78.40    150
          MARCA           2.07    5447.00   306049.00    144

SDE       PESQUI          0.25    154.00      7717.48    122
          GASTOPRIM       -0.30     7.07        28.35    125
          GASTOSEC        0.06      9.97        36.53    127
          NETUSERS        -0.59     2.81        93.49    150
          TELFIXO         -0.24     2.68        67.12    150
          NETSUBS         -0.25     0.20        38.99    149
          CPI             -0.28     2.10         9.60    150
          HOM             2.05      0.40        40.00    135

TAX       CORPTX          -0.19    17.00        40.69    150

Note. Constructs: ECO is the 'Economy Construct'; CAP. MARK is the
'Depth of Capital Market Construct'; INVEST. PROTEC is the
'Investor Protection Construct'; ENTERP is the 'Entrepreneurship
Construct'; HSE is the 'Social and Environmental Development
Construct'; and TAX is the 'Taxation Construct'. Variables: GDPPC
is Gross Domestic Product per Capita; GDPGROW is the yearly grow of
Gross Domestic Product (%); PRICELVL is the increase in the general
price level of goods and services; IPO is IPO volume in % of GDP;
IPON is the number of IPOs; BANK is the number of banks per 100,000
inhabitants; MA is the amount negotiated in Merger and
Acquisitions; MANEG is the number of Merger and Acquisitions;
BOLSAN is the number of listed companies; CREDPRIV is the amount of
private credit by deposit banks and others financial institutions (
% of GDP); DISCINDEX is the World Bank disclosure index that
measures the extent to which investors are protected through
disclosure of ownership and financial information. Ranges from 0
(less disclosure) to 10 (more disclosure); SHAREHD is the World
Bank index that measures the shareholders' ability to sue officers
and directors for misconduct. The index ranges from 0 (little to no
ability to file suit) to 10 (greater ability to file suit); LEGAL
is the strength of legal rights index that measures the level to
which collateral and bankruptcy laws protect the rights of
borrowers and lenders and thus facilitate lending. Ranges from 0
(weak) to 12 (strong); PROCEDE is the number of procedures to start
a new business; CUSTO is the cost to start a new business (% of GDP
per capita); MARCA is the number of new patents; PESQUI is the
number of full-time researchers (per 1,000,000 habitants);
GASTOPRIM is the government expenditure on primary education (% of
GDP); GASTOSEC is the government expenditure on secondary education
(% of GDP); NETUSErS is the number of internet users (per 100
habitants); TELFIXO is the number of telephones (per 100
habitants); NETSUBS is the number of internet subscriptions (per
100 inhabitants); CPI is the Corruption Perception Index. Ranges
from 0 (more perception of corruption) to 10 (less perception of
corruption); HOM is the number of homicides (per 100,000
habitants); CORPTX is the Corporative Tax (%).

Table 4 Total of Explained Variance, KMO and Bartlett's Test

                     Eigenvalues

                      % of      Cumulative
Comp.        Total   Variance       %

Panel A--Economy
1            1.697    56.578      56.578
2            0.843    28.087      84.665

Panel B--Intensity of Capital Market activity
1            4.193    59.902      59.902

2            1.266    18.086      77.987
3            0.612    8.747       86.734

Panel C--Investor Protection
1            1.592    53.062      53.062
2            0.894    29.806      82.868

Panel D--Friendly environment for entrepreneurship

1            1.648    54.928      54.928
2            0.962    32.055      86.983

Panel E--Social Development

1            5.364    67.044      67.044
2            0.829    10.358      77.402

                Extractions Sums of Squared
                        Loadings

                      % of      Cumulative
Comp.        Total   Variance       %

Panel A--Economy
1            1.697    56.578      56.578
2

Panel B--Intensity of Capital Market activity
1            4.193    59.902      59.902

2            1.266    18.086      77.987
3

Panel C--Investor Protection
1            1.592    53.062      53.062
2

Panel D--Friendly environment for entrepreneurship

1            1.648    54.928      54.928
2

Panel E--Social Development

1            5.364    67.044      67.044
2

Comp.        KMO     Bartlett

Panel A--Economy
1            0.542   64.118 *
2

Panel B--Intensity of Capital Market activity
1
             0.775   773.873 *
2
3

Panel C--Investor Protection
1            0,541   47.875 *
2

Panel D--Friendly environment for entrepreneurship

1            0,548   47.875 *
2

Panel E--Social Development

1            0,883   728.324 *
2

Note. We choose to highlight the extracted factors in each construct.

Significance Level: * Significant at 1%

Table 5
Factors Summary

Construct              Number of    Factor
                        Factors      Name

Economy Activity           1          ECO

Intensity of Capital       2         MCAP
Market activity                      BANK

Investor Protection        1        INVPROT

Friendly Environment       1          EMP
for Entrepreneurship

Social Development         1          DSO

Table 6
Correlation Matrix

             ECO         MCAP         BANK        INVPROT

ECO           1
MCAP      0.3374 *        1
BANK      0.4616 *      0.1117          1
INVPROT    0.1200      0.3634 *    -0.1766 **        1
EMP       0.6137 *      0.0922      0.2508 *      0.2630 *
DSO       -0.7899 *   -0.3645 *    -0.4774 *     -0.1649 **
CORPTX     -0.0225    0.1383 ***     0.0064       0.2631 *

             EMP        DSO     CORPTX

ECO
MCAP
BANK
INVPROT
EMP           1
DSO       -0.6593 *      1
CORPTX    -0.2121 *    0.0995     1

Note. Table 6 shows the correlation matrix of the variables
used in the study for indication of multicollinearity
problems. We choose to highlight the variable pairs that
could cause multicollinearity. Variables: ECO is a variable
of 'Economy Construct'; MCAP is a variable of Capital Market
in 'Intensity of Capital Market activity Construct'; BANK is
a variable of bank system in 'Intensity of Capital Market
activity Construct'; INVPROT is a variable of 'Investor
Protection Construct'; EMP is variable of 'Entrepreneurship
Construct'; DSO is a variable of 'Social Development
Construct'; and CORPTX is a variable to Taxation.

Significance Level: * Significant at 1%; ** Significant at
5%; *** Significant at 10%.

Table 7
Econometric Results: Both Sides

                    (FUNDR)
Variables           Robust         Fixed Effects

ECO               4100.94 ***       7890.72 **
                   (2180.46)         (3807.05)
MCAP              49431.28 *         75230.7 *
                   (5997.06)         (6544.54)
RANT^              -17159 *         -18004.37 *
                   (2921.18)         (5166.63)
GC                -5546.53 *        -17858.56 *
                   (1768.25)         (3381.85)
EMP                 6499 *             2735
                    (2103)          (10218.07)
DSO                8890.16 *         -11431.03
                   (2475.67)         (8801.96)
                  -510.01 ***         168.48
                   (282.43)          (965.40)
r^rwrcT a mt      30973.39 *           10314
                   (8842.03)        (29291.73)
Observations          150               150
R-squared            0.848             0.644
Adj R-squared        0.840             0.478
F Stat              10.59 *           23.96 *

                                      (INVM)
Variables           Robust         Fixed Effects

ECO                -2788.25         -1990.01 **
                   (1917.81)         (799.19)
MCAP              3123.99 **        2705.25 ***
                   (1300.69)         (1373.86)
RANT^            -2198.53 ***      -2043.62 ***
                   (1172.76)         (1084.60)
GC                1414.35 ***         1335.97
                   (7353.35)         (2900.85)
EMP               2377.53 **          -675.23
                   (1161.33)         (2145.02)
DSO                7398.28 *          518.90
                   (2685.65)         (1847.75)
                    -12.665           -127.15
                   (108.42)          (202.66)
r^rwrcT a mt        4816.99           8845.59
                   (3942.40)         (6149.06)
Observations          150               150
R-squared            0.165             0.175
Adj R-squared        0.124             0.139
F Stat              3.70 *            4.18 *

Note. Variables: ECO is a variable of 'Economy Construct';
MCAP is a variable of Capital Market in 'Depth of Capital
Market Construct'; BANK is a variable of bank system in
'Depth of Capital Market Construct'; INVPROT is a variable
of 'Investor Protection Construct'; EMP is variable of
'Entrepreneurship Construct'; DSO is a variable of 'Social
and Environmental Development Construct'; and CORPTX is a
variable to Taxation. Significance Level:

* Significant at 1%; ** Significant at 5%; *** Significant at 10%.
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Geographic Code:3BRAZ
Date:Apr 1, 2016
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