The role of financial constraints in cross-border acquisitions
Evidence from Western-European firms
University of Groningen
Faculty of Economics and Business
Department of Economics, Econometrics and Finance
MSc Business Administration Finance
August 17, 2011
Roelof Gerard Reinier Potgieser
Petrus Campersingel 181 A
9713 AK Groningen
06-46352296
r.g.r.potgieser@student.rug.nl
Student number: 1463888
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stSupervisor: dr. J.H. von Eije
R.G.R. Potgieser University of Groningen 2
- ABSTRACT -
In this thesis, the role of financial constraints in cross-border acquisitions of Western-European firms is investigated using a sample of 802 acquisitions from 2001-2009 in a probit model specification. Evidence is presented that determinants of financial constraints can explain whether an acquisition is cross-border or domestic, and that firms that face a higher probability of being financially constrained are less likely to engage in cross-border acquisitions. However, marginal effect sizes are modest, suggesting that financial constraints do not severely limit firms in their option set. When looking at cross-border acquisitions in more detail, conflicting evidence is found regarding the role of financial constraints determinants in explaining whether cross-border acquisitions are inside or outside Western-Europe.
Key words: Mergers and acquisitions, cross-border, financial constraints, information asymmetry
R.G.R. Potgieser University of Groningen 3
– TABLE OF CONTENTS - 1 Introduction ... 4
2 Background literature ... 7
2.1 Informational asymmetries, incentive problems and the cost of external finance ... 7
2.2 Financial constraints and investments ... 10
2.2.1 Financial constraints at the firm level ... 10
2.2.2 Financial constraints at the country level ... 13
2.3 Financial constraints and (cross-border) acquisitions ... 14
3 Hypotheses ... 18
3.1 Hypotheses and research objectives ... 18
4 Methodology ... 19
4.1 Binary probit model... 19
4.2 Marginal effects ... 20
4.3 Model specification ... 21
4.3.1 Dependent variable ... 21
4.3.2 Independent variables ... 21
4.3.3 Control variables ... 24
4.3.4 Hypothesis testing and robustness check ... 24
5 Data ... 25
5.1 Data selection ... 25
5.2 Descriptive statistics ... 29
6 Multivariate results ... 32
6.1 The effect of financial constraints determinants on domestic and cross-border acquisitions ... 32
6.1.1 Multivariate results ... 32
6.1.2 Model performance and marginal effect sizes ... 34
6.1.3 Robustness test ... 35
6.2 The effect of financial constraints determinants on cross-border acquisitions inside and outside Western-Europe ... 37
6.2.1 Multivariate results ... 37
6.2.2 Model performance and marginal effect sizes ... 39
6.2.3 Robustness test ... 40
7 Conclusion ... 41
7.1 Main findings ... 41
7.2 Limitations and suggestions for future research ... 43
References ... 44
R.G.R. Potgieser University of Groningen 4
1 – INTRODUCTION -
Perfect capital markets, as theorized by Modigliani and Miller (1958), are unlikely to exist in reality. Aside from taxes and transactions costs, frictions can occur because of asymmetric information and incentive problems. Resulting information and agency costs raise the cost of external financing and create an imperfect substitutability between internal and external funds, and may even limit access to external funds (e.g. Myers and Majluf, 1984; Jaffee and Russell, 1976; Myers, 1977; Jensen and Meckling, 1976). Consequently, the availability and cost of capital become relevant in financing decisions. In turn, firms may be constrained in financing their investments and be unable to undertake value enhancing investments (Fazzari and Athey, 1987; Fazzari et al. 1988). So, a firm’s financial position may be relevant for its investment decisions.
Acquisitions tend to be sizeable investments and generally involve imperfect information between the buyer and seller (Hansen, 1987). As such, financial constraints may play a role in acquisitions. Also, they often involve external financing. For example, acquisitions tend to occur in waves. Martynova and Renneboog (2008) state that takeover waves generally start in times of economic recovery, fast credit expansion and expanding stock markets, only to end with declining stock markets and economic recessions. The authors state that a thriving external capital market seems an essential prerequisite for takeover waves to take place, suggesting that external finance is relevant for acquisitions. In addition, acquisitions can be made either domestically or cross-border. Martynova and Renneboog (2008) show that especially the last two takeover waves are characterized by an increasing and high amount of border acquisitions, as a response to globalization processes. So, cross-border acquisitions are increasing in importance for firms operating in an environment that is more and more influenced by globalization.
R.G.R. Potgieser University of Groningen 5 In this thesis, I investigate the role of financial constraints in cross-border acquisitions of Western-European firms using a sample of 802 acquisitions from 2001-2009 in a probit model specification. Further extending the research of Chen et al. (2009), I also differentiate between cross-border acquisitions inside and outside Western-Europe. Since information costs increase with distance (Di Giovanni, 2005), financial constraints are expected to increase in importance for acquisitions that are further away. Finally, I also look at the economic importance of financial constraints in explaining whether an acquisition is cross-border. The main problem statement I want to answer is therefore:
Are financial constraints relevant in explaining whether acquisitions of Western-European listed companies are cross-border and to what extent?
As noted before, cross-border acquisitions are increasing in occurrence and importance for firms operating in a globalizing economy. Providing an answer to the above question is therefore relevant for firms that are more and more engaging in cross-border acquisitions. They may need a stronger financial position to finance these acquisitions than when making acquisitions domestically. In turn, financial constraints may limit a company in its choices or option set.
Consistent with expectations, I find evidence that determinants of financial constraints are significant in explaining whether an acquisition is domestic or cross-border. Smaller firms, with more leverage are less likely to engage in cross-border acquisitions. Also, there is some evidence that firms from a better governance environment are more likely to go cross-border. However, due to high correlation of country variables, this result should be interpreted with caution. A robustness check confirms that firm with a higher probability of being financially constrained are less likely to engage in cross-border acquisitions. However, when looking at the marginal effects of the results, it seems that the importance of financial constraints is modest. This suggests that they do play a role in cross-border acquisitions, but are unlikely to severely limit a company in its option set.
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2 – BACKGROUND LITERATURE -
This section presents an overview of the relevant financial constraints literature. First, paragraph 2.1 discusses the underlying theoretical foundations of the research regarding financial constraints and investments. Second, paragraph 2.2 discusses mainly empirical research related to financial constraints and investments at the firm and country level. Finally, paragraph 2.3 relates financial constraints and (cross-border) acquisitions.
2.1 Informational asymmetries, incentive problems and the cost of external finance
In their well-known paper, Modigliani and Miller (1958) developed a theory stating that a firm’s capital structure is irrelevant for its market value and cost of capital. In this world of perfect capital markets, there is no real difference between internal and external financing for a firm, they are perfect substitutes. Decisions regarding investments are independent from how they are financed. The authors state that, under market value maximization, the only relevant question in undertaking investments is whether the net present value (NPV) of an investment project is positive. If so, the project will be funded. Otherwise, it will not be undertaken. When considering this decision, they state it does not matter whether the project will be funded with retained earnings, equity or debt. They assume markets to be efficient with perfect competition and for example, securities are sold at fair prices, equal to the stream of expected returns.
However, a perfect capital market with full information is unlikely to exist in reality. Apart from taxes and transaction costs, informational asymmetries and incentive problems are likely to create distortions in capital markets. Resulting information and agency costs raise the cost of external financing and have a profound impact on the functioning of capital markets. In a detailed review1, Stein (2003) provides a useful distinction between different models explaining capital market imperfections. He classifies models based on whether managers act in the interests of current shareholders or not.
On the one hand, several models assume managers act in the interest of current shareholders or are owner-manager. These models emphasize that informational asymmetries and incentive problems can lead to underinvestment. Firms can have too little access to resources due to problems with raising external finance, causing underinvestment. If they have enough resources, investment will be efficient in these models (e.g. funds are not squandered since managers act in the interest of current shareholders).
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R.G.R. Potgieser University of Groningen 8 First of all, Myers and Majluf (1984) state that separation of ownership and control naturally leads to informational asymmetries. Managers are better informed than outside investors. They argue that this can lead a firm to situations of passing up positive NPV investments, not wanting to use required external equity financing because of conflicts of interest between old and new shareholders due to manager’s inside information. New investors demand a premium for a new issue, when the manager acts in the interest of the old shareholders, afraid of financing bad projects. Such adverse selection could lead to problems in raising external financing, since it raises the cost of equity financing.
Second, Jaffee and Russell (1976) and Stiglitz and Weiss (1981) show thatinformational asymmetries can lead to an equilibrium situation on the loan market in which there is credit rationing. Imperfect information between investors and the firm could cause credit rationing through moral hazard and because of adverse selection. A higher interest rate reflects the riskiness of the loan contracts, either by inducing firms to engage in projects that are less likely to succeed but with a big pay-off, or by attracting a higher amount of low-quality borrowers. According to Stiglitz and Weiss (1981), an equilibrium interest rate exists that maximizes the expected return of the lender, but the demand for funds is bigger than the supply at this point. Thus, some firms are unable to attract financing, even if they are willing to pay an even higher interest rate, because incentives need to be taken into account in designing contracts. Furthermore, Townsend (1979) and Williamson (1987) show that also with the costly state verification problem, in which the investor has to pay a monitoring cost to reveal the outcome of a project, credit rationing can occur in a market equilibrium.
Third, there could be the problem of debt overhang. Myers (1977) states that in the presence of risky debt, a firm acting in its shareholders’ interest might forgo positive NPV investments that could increase the value of the firm. This occurs when the benefits associated with these investments primarily accrue to debt holders.
Finally, asset substitution could also create underinvestment. Jensen and Meckling (1976) state that in the presence of debt, a firm might engage in excessive risk taking. If successful, the owner-manager gets most of the gains, but when the project fails, creditors bear most of the costs. Anticipating this, debt holders will incorporate this in the price they are willing to pay for a debt claim. This increases the cost of debt financing.
R.G.R. Potgieser University of Groningen 9 The owner-manager will bear these costs. This could lead to underinvestment.2
Continuing this line, Jensen (1986) argues that managers may use available free cash flow (i.e. cash in excess of what is needed to fund positive NPV projects) to spend on non-value maximizing investments. Jensen states managers have an incentive to grow beyond the optimal size to increase their power, because this gives them more resources to control. So, empire-building motives could increase investment spending leading to overinvestment. Alternatively, managers may prefer internal financing to avoid capital market monitoring, which could induce underinvestment.
Stein (2003) also mentions other sources of agency conflicts between managers and shareholders that might influence investment spending, leading to either overinvestment or underinvestment. Managers might be concerned with their reputation and career (Fama, 1980), may be overconfident (Roll, 1986) or prefer the “quiet life” (Bertrand and Mullainathan, 2003) meaning they react to slow to developments or avoid taking difficult decisions.
So, the primary difference with the other sources of market imperfections is that agency problems between managers and shareholders can lead to underinvestment ánd overinvestment. This is an important difference that is relevant for research regarding financial constraints, an observation that will be used in the next section.
In summary, informational asymmetries and various incentive problems could thus lead to capital market imperfections, increasing the cost of external financing. This leads to an imperfect substitutability (a wedge) between internal and external financing. When funding investments, external financing is likely to be more costly than internal financing. So, there could be a direct link between financial conditions and a firm’s investment decisions. A bigger wedge between the cost of internal and external financing would indicate a higher possibility that profitable investment opportunities cannot be financed. Next to the cost of capital, the availability of capital can also be relevant. Financial slack could thus be relevant for investments, since it could be used to avoid situations in which a firm is not able to finance profitable investment opportunities.
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2.2 Financial constraints and investments
2.2.1. Financial constraints at the firm level
Asymmetric information and agency costs, and the resulting wedge between internal and external financing form the theoretical basis for research regarding financial constraints. The bigger the wedge between internal and external financing, the higher the probability that a firm is constrained in its financing (Kaplan and Zingales, 1997). Based on the theoretical considerations about market imperfections, several notions have consistently emerged from empirical literature (Schiantarelli, 1996; Hubbard, 1998; Stein 2003). First, uncollateralized external funding is more costly than internal funding. Second, controlling for investment opportunities, the extra cost of external financing has consistently shown to be an inverse function of a firm´s net worth (being the sum of liquid assets and the collateralizable value of illiquid assets). Third, controlling for investment opportunities, firms with more cash and less debt invest more.
The traditional way to measure the relationship between financial constraints and investment is to see whether the sensitivity of investments to cash flows increases monotonically with how financially constrained a firm is. The most widely used models are q- models of investment (regressing investment on net worth, accounting for investment opportunities with Tobin´s q) or Euler equations.3 Cash-flow is used as a proxy for net worth, since the measurement of net worth through time is difficult. It is influenced by future return expectations (Schiantarelli, 1996). Also, cash-flow provides insight in the amount of investment that can be internally financed. Financially constrained firms are expected to show a high sensitivity of investment to cash flow, a critical assumption. The procedure consists in looking whether firms that are more likely to face information and incentive problems beforehand (e.g. are more likely financially constrained), actually show a significantly higher sensitivity of investment to cash flow compared to firms that are not, controlling for growth opportunities. This is then used as evidence that firms with these a-priori characteristics are financially constrained. As stated by Beck (2006, p. 933), under these circumstances a firm is financially constrained if “…a windfall increase in the supply of internal funds results in a higher level of investment spending”. This is based on the thought that in the absence of information and incentive problems, investment is not that sensitive to cash flow. These unconstrained firms could simply attract external funds when necessary (i.e. in case of positive NPV projects). Investments do not change after increases in cash flow, since all value-enhancing investment opportunities are already financed.
R.G.R. Potgieser University of Groningen 11 Returning to the distinction in capital market imperfections introduced in the previous section, Q- and Euler models of investment, also called costly external finance models, implicitly assume that managers act in the interest of shareholders or that ownership and control is the same (Schiantarelli, 1996). Put differently, financial frictions can only cause underinvestment and investment is assumed to be efficient. But, as argued in the previous section, managers might also engage in, for example, empire building. This could complicate the interpretation of sensitivity of investment to cash flow, since overinvestment is also possible. A high sensitivity could partly be driven by non-value maximizing behavior, instead of information and incentive problems associated with raising new equity or debt (Schiantarelli, 1996). So, it is difficult to pinpoint the exact source of capital market imperfections, as emphasized by Schiantarelli (1996), Stein (2003) and to some extent Hubbard (1998).4 For example, Blanchard et al. (1994) show that low-Q firms use cash windfalls from legal settlements unrelated to their line of business to engage in acquisitions or keep it in the firm. This is inconsistent with models of costly external finance. These models would assume that this would be returned to shareholders, since there are little to no profitable investment opportunities.
Empirical studies have used several a-priori measures at the firm-level to classify a firm as financially constrained (i.e. more likely to face a wedge between internal and external financing due to information and incentive problems). What they have in common is that these companies indeed show a higher sensitivity of investment to cash-flow than firms that are not assumed to be constrained. Fazzari and Athey (1987) establish that under asymmetric information, the investment of a US firm is significantly influenced by internal liquidity and interest expense, both firm-level financial variables. They constrain a firm directly, “…because asymmetric information prevents interest rates and securities prices from fully adjusting to allow firms to undertake all desired investment” (Fazzari and Athey, 1987, p 482), instead of indirectly influencing the cost of capital.
In their seminal paper, Fazzari et al. (1988) show that investment may vary with the availability of funds for a sample of US firms. They classify firms as financially constrained according to their dividend pay-out practices. Firms that do not pay out much dividends, but retain and invest most of their earnings, may not have alternative sources of low-cost financing, making their investments conditional on fluctuations in cash flow (Fazzari et al. 1988). They show that investment of firms that exhaust all their internal funds is more sensitive to fluctuations in cash flow than that of firms which pay out much of their earnings and conclude that capital market imperfections can lead to financial constraints that limit investment.
Devereux and Schiantarelli (1990) use firm size and age as a proxy for financial constraints in the UK, since they seem to be correlated with basic factors that determine whether a company is constrained (Schiantarelli, 1996), such as the development of a creditable track-record. Further,
R.G.R. Potgieser University of Groningen 12 Schiantarelli states that bigger firms may face lower transactions costs in obtaining external finance, and unit bankruptcy costs are likely to decrease with size.
Hoshi et al. (1991) study Japanese firms and use affiliation with business groups and banks to classify firms, arguing close ties with banks and keiretsus mitigate information problems and ease the access to outside capital.
Whited (1992) uses the existence of a bond rating to classify US firms, stating it´s presence means that a firm has undergone more investor screening and thus there is less informational asymmetry. Furthermore, Oliner and Rudebusch (1992) use the concentration of equity ownership of US firms, stating incentives between insiders and outsiders are more aligned and there is closer monitoring, thereby reducing informational asymmetries and the cost of external financing.
The fact that financially constrained firms should have a higher sensitivity of investment to cash flow altogether has since been criticized in more recent literature. Kaplan and Zingales (1997, 2000) are the first to question this relationship. They directly test the assumption upon which the literature is based that higher investment-cash flow sensitivity is associated with problems in financing investments. Employing the same US firms classified by Fazzari et al. (1988) as having a high investment-cash flow sensitivity (and are thus financially constrained according to Fazzari et al. (1988)), they use quantitative (e.g. interest coverage) ánd qualitative information from financial statements, to derive the availability of internal and external funding and corresponding demand of these firms. They empirically show that less financially constrained firms actually show higher investment-cash flow sensitivity than financially constrained companies. They conclude that the sensitivity of investment to cash flow is a weak indicator of financial constraints. In addition, they provide firm-specific indicators of financial constraints that are significant in explaining the probability that a firm is financially constrained, based on their own classification. They show that firms with higher cash holdings and higher dividends are less likely to be financially constrained, and firms with higher leverage are more likely to be financially constrained. Higher growth opportunities produce conflicting results.
Cleary (1999) classifies US firms using financial variables related to financial constraints, following Kaplan and Zingales (1997). He provides similar findings, showing more creditworthy firms exhibit higher investment-cash flow sensitivity than firm that do not possess this characteristic. Also, in general, firm investment tends to be very sensitive to the availability of internal funds.
R.G.R. Potgieser University of Groningen 13 investments are constrained by capital market imperfections manage liquidity to maximize firm value. They argue that liquid asset holdings of financially constrained firms should thus increase when cash flows are higher, and those of unconstrained firms should not. They find that constrained US firms indeed display significantly positive cash to cash-flow sensitivities, contrary to unconstrained firms. However, they find the opposite result when categorizing firms according to an index measure derived from Kaplan and Zingales (1997).
Gomes (2001) provides evidence for the US suggesting cash flow sensitivity cannot be linked to financial constraints when employing q-models of investment, because of methodological difficulties. He shows that even in the absence of financial constraints, significant cash flow effects can occur in investment equations.
More recent, Pál and Kozhan (2009) investigate financial constraints in the euro area. They too find that firms that are more credit rationed exhibit a lower sensitivity of investment to cash flow than firms that are capable of getting short-term external funds.
Moyen (2004) actually provides evidence for both the results of Kaplan and Zingales (1997) and of Fazzari et al. (1988) for US firms. She states it is hard to identify firms that are financially constrained, and dependent on the criteria used, evidence can be found for both studies. So, evidence would not necessarily be inconsistent.
Beck et al. (2006) choose yet another methodology and make use of the 2000 World Business Environment Survey (WBES) to differentiate firms on the basis of the firm’s own perceptions of how financially constrained they are. This avoids the need to base the assessment of financial constraints on financial statements, as is the case with most literature. It allows an alternative way of testing several a priori classifications of previous studies. They find size, age and ownership to be significant in explaining financial obstacles.
In sum, there is a large body of literature that has investigated the relationship between financial constraints and investment. Different methodologies have been used to differentiate firms on their level of financial constraints, based on quantitative and qualitative data. Defining firms that are financially constrained proves to be a difficult task. Literature has identified several firm level variables that may be relevant in classifying firms that are more likely financially constrained. These are related to the availability of internal funds and/or indicative of whether a company is more likely to face information and incentive problems. They include internal liquidity, interest coverage, leverage, dividend pay-out ratio, size, age, ownership, the existence of a bond rating and affiliations with business groups or banks.
2.2.2. Financial constraints at the country level
R.G.R. Potgieser University of Groningen 14 governance environment in which a company operates (Demirgüç-Kunt and Maksimovic 1998, Love 2003, Laeven 2003, di Giovanni 2005). More developed financial markets improve access to financing and could thus mitigate constraints. A more developed governance environment, with better investor protection and enforcement, can have a similar effect.
Demirgüç-Kunt and Maksimovic (1998) develop a financial planning model to estimate the maximum growth rate firms can possibly achieve without access to external financing. They compare this growth rate with actual growth rates of firms in different countries with different degrees of development of their financial and legal system. They find these differences in growth rates to be associated with these two factors. More specific, active stock markets and a high score on an index of respect for legal norms are associated with better access to external financing and higher growth. Love (2003) also looks at the relationship between financial development, the legal environment and financial constraints, using an investment Euler equation. After controlling for firm size and the business cycle, which could also influence financial constraints, she finds that financial development is associated with a reduction in financial constraints. Also, she finds a more efficient legal system to be associated with lower financial constraints.
Laeven (2003) finds that financial liberalization leads to lower financial constraints for small firms, in a sample of firms from 13 developing countries.
Di Giovanni (2005) shows that stock market development is positive and significantly associated with outward foreign investment for both developing and developed countries, arguing deep financial markets provide means to undertake investment projects that would otherwise have been passed up. However, he finds no significant results for the amount of domestic credit provided by the banking sector, suggesting that correlation with GDP and stock market development may cause the low significance levels.
In addition to financial development and the governance environment, Doidge et al. (2004) find that a U.S. cross-listing induces a reduction in agency costs between controlling shareholders and minority shareholders. This could increase the ability of firms to finance growth opportunities. Also, firms could have better access to capital markets and better investor protection. So, a cross-listing could possibly decrease financial constraints.
Lins et al. (2005) emphasize the reduction in credit constraints following a U.S. cross-listing. They show this leads to a significant decrease of sensitivity of investment to free cash flow. However, they show this is only the case for firms from developing countries; it does not change for developed market firms. This suggests that a cross-listing is less important for gaining access to external financing when a firm comes from a country with a developed market.
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2.3 Financial constraints and (cross-border) acquisitions of firms
In general, undertaking an investment depends on its feasibility and the financial state a company is in. If an investment requires more financing, the financial shape the company is in becomes more important. Acquisitions, being domestic or cross-border, are usually considerable investments. So, it could be argued that financial constraints play a role in making acquisitions, as posited by Harford (1999). Beck et al. (2005) show that financial constraints limit firm growth for a sample of companies from 54 countries, because they constrain firms in their ability to finance investments.
Also, evidence has been documented about the importance of financial constraints in the method of payment of acquisitions and the way they are financed (e.g. with internal funds or relying on external financing). Faccio and Masulis (2005) study the determinants of the method of payment of acquisitions in a large sample of listed Western-European companies. They show that financial constraints (e.g. financial leverage (-) and size (+)) are significant in explaining whether a bid is paid for in cash.5 Also, Faccio and Masulis argue most companies have limited cash and liquid assets, and that cash offers generally require external debt financing. So less constrained companies could be more likely to pay with cash instead of stock. Interestingly, they find that cross-border transactions are significantly more likely to be paid in cash than domestic acquisitions.
Martynova and Renneboog (2009) go into more detail and not only look at the method of payment, but also at the source of financing for a sample of listed Western-European corporations. They show that external sources of financing are frequently employed in takeovers involving cash payment. They too provide evidence that (intra-EU) cross-border acquisitions are significantly positively associated with cash payment. Moreover, they show that the cost of capital is relevant for explaining the financing decision in acquisitions. In line with pecking order theory, they show that cash-rich bidders use internal funds to finance an acquisitions, which is least expensive. Firms with less internal funds raise external capital to this end. If they have enough debt capacity (e.g. less constrained) they borrow, otherwise they use equity.
In addition, Martynova and Renneboog (2006) show that cross-border acquisitions of companies within Europe during the fifth take-over wave entailed larger deal values than domestic acquisitions.
Acquisitions generally involve imperfect information between the buyer and seller. Hansen (1987) argues that larger transactions entail more informational asymmetries, due to more uncertainty about a targets’ assets. As targets assets rise relative to the size of the acquirer, informational asymmetries are likely to increase. So, for an acquirer of a given size, cross-border acquisitions (which tend to be larger) may involve more informational asymmetries.
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R.G.R. Potgieser University of Groningen 16 Gordon and Bovenberg (1996) argue there exists asymmetric information between investors in different countries, which causes immobility of capital and this lack of information of investors of the acquiring company could lead to overpayment or higher information costs. Thus, information asymmetry could make acquisitions across countries more costly.
Firms engaging in cross-border investment face a `liability of foreignness´ due to an unfamiliar and uncertain environment, being either cultural, institutional, legal, economic or political (Zaheer, 1995). This could be especially relevant for cross-border acquisitions, since they suffer from higher internal uncertainty and incomplete information (Gatignon and Anderson, 1988).
In turn, more incomplete knowledge in cross-border acquisitions leads to a greater variation in expected outcomes (Lee and Caves, 1998), which in turn causes a higher level of risk.
In addition, Reeb et al. (1998) show that higher systematic risk exists in international business, consistent with their observation that higher discount rates are used in evaluating international projects. They posit higher agency costs and greater informational asymmetries to be among possible explanations.
Di Giovanni (2005) shows a significant and negative relationship between information costs and cross-border acquisitions across specifications in a large sample of both developed and developing countries. He uses distance as a proxy for information costs, since informational asymmetries may increase with distance. 6
The importance of information costs for cross-border asset flows is further emphasized in a more theoretical two country setting by Martin and Rey (2004), who show that information costs have a detrimental effect on cross-border flows. They empirically test their model with data from Portes and Rey (2005). These authors examine cross-border equity flows for 14 large economies and also conclude that there is a significant relationship between information costs, proxied by distance, and portfolio investment flows.
De Menil (1999) also documents a negative significant association between distance and bilateral foreign direct investment (FDI) flows between OECD countries from 1984-1994.
Relating the concepts of financial constraints and cross-border acquisitions, Chen et al. (2009) research the effect of financial constraint determinants on cross-border and domestic acquisition announcements in nine East Asian economies from 1998-2005. Financial development, the governance environment of the acquirer, and a cross-listing increase the likelihood of an acquisition being cross-border. Some evidence is found for firm-level financial constraint determinants, being cash-holdings, leverage, and market-to-book ratio. They also employ a financial constraints index (KZ-index), based on the findings of Kaplan and Zingales (1997), but it is found insignificant. In addition, family- and state-controlled firms are less likely to make cross-border acquisitions.
6 Di Giovanni (2005) argues this means that information costs dominate trade costs when looking at distance. It
R.G.R. Potgieser University of Groningen 17 The authors state that although they may have better access to external financing, they are reluctant to dilute their control. They control for GDP, GDP growth, return on assets and whether a firm is in a high-tech industry.
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3 - HYPOTHESES -
3.1 Hypotheses and research objectives
Based on the literature, I identify two hypotheses to answer the main problem statement. As stated in the literature section, cross-border acquisitions involve higher informational asymmetries, information costs and risk compared to domestic acquisitions. Also, they tend to be bigger and are more often paid in cash. As such, companies that are more likely to be financially constrained may have more difficulty in financing cross-border acquisitions, thus limiting the option set of a firm. This leads to the first hypothesis, which is based on the research of Chen et al. (2009) in East Asia:
Hypothesis 1: Determinants of financial constraints are significant in explaining whether acquisitions are domestic or cross-border.
Further extending the first hypothesis, I posit a second hypothesis. As stated in the literature section, informational asymmetries increase with distance and differences in culture. This leads to the second hypothesis:
Hypothesis 2: Determinants of financial constraints are significant in explaining whether cross-border acquisition occur outside Western-Europe or within Western-Europe.
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4 – METHODOLOGY -
4.1 Binary probit model
In my thesis, I use a limited dependent variable model, namely the binary probit model specification to test the hypotheses. This model is used in comparable articles in the acquisitions literature (e.g. Harford (1999); Faccio and Masulis (2005)). Chen et al. (2009) use a logit model, but these models are very similar in nature and do not provide differing results, as can be seen in Note A1 in the appendix (p. 58), which provides a technical note on qualitative response models. The dependent variable in a probit model can be seen as a qualitative or discrete choice, being either zero or one. It is commonly used to predict the occurrence of an event. The probit model is based on the cumulative normal distribution to adjust the regression equation to ensure that the fitted probabilities will lie between (and are asymptotic to) zero and one. The function is defined as follows (Brooks, 2008):
2 2 12
1
)
(
i z ie
z
F
(1), in which the function F is the cumulative distribution function for a standard normally distributed random variable . The binary probit model estimated will thus be:
2 2 2 1 ... ) ( 2 12
1
)
1
(
i ki k i x x i iP
y
e
P
(2), where
P
i is the probability of an event i occurring and i=1…N. β’s are coefficients, x’s refers to the explanatory variables and is a disturbance term. Alternatively, and more common, the probit model can be represented in the form of latent variable model. In such a model, the outcome of a discrete choice is a reflection of an underlying latent variable (Greene, 2003). This regression with unobserved dependent variabley
*i is defined as:i ki k i i i
x
x
x
y
*
1
2 2
3 3
...
, (3) , with if ifR.G.R. Potgieser University of Groningen 20 After it passes a certain threshold level , which is typically assumed to be 0, becomes 1.7
Greene (2003) shows that from this equation, probabilities can be derived. It is hereby assumed that the probability density function of is symmetric (which is the case when assuming a standard normal distribution). This provides a similar result as in (2):
(4)
Again this provides the probability of an event occurring. is the standard normal cumulative distribution function. ’s are coefficients and x’s are explanatory variables.
Since the model is obviously non-linear, ordinary least squares (OLS) cannot be used to estimate the equation. The model is estimated in Eviews 7.2 with maximum likelihood (ML), which involves jointly maximizing a log-likelihood function through an iterative process. As indicated by Greene (2003), it could be that the normal probability model might be misspecified. In addition, as mentioned in note A1 in the appendix, the error terms are likely to be heteroskedastic. Brooks (2008) states it is essential to use heteroskedasticity-robust standard errors in limited dependent variable models. Therefore, I include QML (Huber/White) consistent standard errors. These ensure some robustness to misspecifications in the underlying distribution (Greene, 2003) and according to Brooks (2008, p. 540): “ensure that the standard error estimates are robust to heteroskedasticity.”
4.2 Marginal effects
The probit model loses the simple interpretation of marginal effects being equal to the estimated parameters. This is obvious from the equations (1) and (2), since the regression equation does not directly estimate the probabilities, but is first adjusted through the cumulative normal distribution function. Interpretation of marginal effects of parameters might be useful for the economic interpretation of the regression function. Fortunately, it turns out that a one unit increase in an explanatory variable k, causes an
kF
(
z
i)
increase in probability. The impacts of incremental changes in an independent variable are usually evaluated by setting each to their mean value (Brooks, 2008). So, the procedure involves estimating the probit model in equation (2), and inserting the mean values of the independent variables to arrive at an estimate ofF
(
z
i)
, or ̂. Marginal effects are then obtained by multiplying coefficient estimates (
k) from the estimated model with this estimate of)
(
z
iF
.87
For example, in the case of whether an acquisition is domestic or cross-border you do not observe the net trade-off, only whether a certain type of acquisition is made. Once this trade-off exceeds a certain threshold, a cross-border acquisition is/can be made, but when it stays below a certain threshold, a domestic acquisition is made.
R.G.R. Potgieser University of Groningen 21
4.3 Model specification
To study the impact of financial constraint determinants on acquisition decisions, I use the general model specified below to test the hypotheses. The variables are detailed in paragraphs 4.3.1 until 4.3.3. Paragraph 4.3.4 details how the model will be used to test the hypotheses, and explains the steps taken. In addition, it explains a robustness check. The basic model is as follows:
(5)
4.3.1 Dependent variable
Acquisition type ( ) is the dependent variable, that denotes the type of acquisition a company announces in a certain year. With respect to the first hypothesis, it equals 1 if the acquisition is cross-border and 0 if the acquisition is domestic, following Chen et al. (2009). To study the second hypothesis, two regressions are run. In the first, the dependent variable equals 0 when the acquisition is domestic and 1 if the acquisition is cross-border and within Western-Europe9. In the second, the dependent variable equals 0 if the acquisition is cross-border and within Western-Europe, and 1 if the acquisition is cross-border and outside Western-Europe.
4.3.2 Independent variables
The model encompasses several independent variables, which determine financial constraints that are firm-specific or at the country level. All independent variables are lagged one year. This is done to reflect that firm´s acquisition decisions are made prior to the announcement date, using past information for their financing decisions (Di Giovanni, 2005). In addition, with respect to the firm-specific variables, it mitigates endogeneity problems. Such a lag is also used in the literature, for both firm-specific and country variables. (Di Giovanni, 2005; Chen et al., 2009; Martynova and Renneboog, 2009). All variables have been converted to a common currency.
At the firm-level, I include Cash holdings, Leverage, Market-to-book, Dividend (yes or no) and
Log(assets) as determinants of financial constraints, mainly based on the findings of Kaplan and
Zingales (1997), Devereux and Schiantarelli (1990), Beck (2006) and partly following Chen et al. (2009). Firms with higher cash holdings have a higher availability of (cheaper) internal funds and are thus less likely to be constrained.10 A highly-levered firm faces more difficulties in raising external debt finance and is more likely to be constrained in its investments. A higher market-to-book ratio indicates more growth opportunities. On the one hand, a firm could be more constrained due to its
9
The following countries are denoted Western-European: Andorra, Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Gibraltar, Greece, Iceland, Ireland, Italy, Liechtenstein, Luxembourg, Malta, Monaco, Netherlands, Norway, Portugal, San Marino, Spain, Sweden, Switzerland and the UK.
10
R.G.R. Potgieser University of Groningen 22 lower internal funds relative to its investment opportunities. One the other hand, it conveys information about future returns, which might induce investors to participate in external financing, so the direction of the effect is not clear a-priori. The payment of dividends is often a residual in corporate finance and points to excess funds, so it would be expected that companies that pay a dividend are less likely to be constrained than companies that do not. Finally, Schiantarelli (1996) states that size is correlated with basic factors that determine whether a company is constrained: bigger companies are older, more likely to have developed a creditable track-record and may face lower transactions costs in obtaining external finance.11, 12
At the country-level, I include Stock-market development, Bank credit, WGI-score, and measures of
Shareholder and Creditor protection, based on Demirgüç-Kunt and Maksimovic (1998), Laeven
(2003), Love (2003), Di Giovanni (2005) and Chen (2009). Variables at the country level are either financial development or governance variables. More developed financial markets improve access to financing and could thus relax external financing constraints. A more developed governance environment, with better investor protection and enforcement, can have a similar effect. Stock-market capitalization and bank credit measure the depth of financial markets. The Worldwide Governance Indicators (WGI) variable measures the efficiency and effectiveness of a country’s governance system. To measure investor protection, I use indicators for shareholder and creditor protection, developed by Martynova and Renneboog (2010).
Based on Doidge et al. (2004), Lins et al. (2005), and Chen et al. (2009), I also include a proxy for a cross-listing, ADR, which may give better access to external finance and is associated with greater investor scrutiny. This proxy entails whether a company had an American depository receipt (ADR) or not in the year prior to acquisition. Although it is also possible to have a direct listing on an American stock exchange or other exchanges, and not all ADRs are cross-listings, this was the best proxy I could collect.
11
Chen et al. (2009) also include a financial constraints index, based on regression coefficients from Kaplan and Zingales (1997). This KZ-index is not included in my model, since the KZ-index is based on American data, whereas I study Western-European firms. Actually, the components of the index are already included individually.
12
R.G.R. Potgieser University of Groningen 23 In sum, the independent variables are defined below. Where applicable, the relevant Datastream code (e.g. WC01234) is given in parentheses.
Firm-specific
Cash holdings: this variable represents cash and equivalents (WC02001) divided by the total assets
(WC02999) of the acquiring firm.
Leverage: this variable represents total debt (WC03255) divided by total assets (WC02999) of the
acquiring firm.
Market-to-book (MtB): this variable represents the market value of assets, which equals book value of
assets (WC02999) minus book value of common equity (WC03501) plus the market value of common equity (WC08001), divided by the book value of assets (WC02999) of the acquiring firm.
Dividend (yes or no): this variable equals 1 if the acquiring company paid a dividend in the year prior
to acquisition and 0 otherwise.
Log (assets): This variable is the logarithm of the total assets in thousand euros (WC02999) of the
acquiring company.
ADR: This variable equals 1 if the acquiring company had an American depository receipt in the year
prior to the acquisition and 0 otherwise.
Country-level
Stock-market development: This variable is defined as the market capitalization of listed companies as
a percentage of its GDP.
Bank credit: This variable is defined as domestic credit provided by the banking sector as a
percentage of its GDP.
WGI-score: This variable is based on scores of the Worldwide Governance Indicators (WGI) project.
Following Chen et al. (2009), it consists of the sum of individual scores (which range from -2,5 to 2,5 and are ) on 6 dimensions: voice and accountability, political instability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of law, and control of corruption. A higher score points to better governance outcomes.
Shareholder protection: This variable represents an index of shareholder protection by Martynova and
Renneboog (2010), reflecting scores on appointment rights, decision rights, trusteeship (efficiency of board to monitor) and transparency, with a maximum of 25. Available for 2000 and 2005, interpolated between these years, assumed constant afterwards. A higher score points to better governance outcomes.
Creditor protection: This variable represents an index of creditor protection by Martynova and
R.G.R. Potgieser University of Groningen 24
4.3.3 Control variables
As control variables I include GDP-growth, Log (GDP), a High-tech dummy, and Return on assets
(ROA), based on the study of Chen et al. (2009). A higher GDP indicates there are more growth
opportunities in the home country of the acquirer. This makes growth by domestic acquisition more likely, negatively affecting a firm’s propensity to go cross-border. GDP-growth is expected to have a similar effect. High-tech companies are very active in making acquisitions. Martynova and Renneboog (2006) state that increasing R&D expenditures led to a boost in cross-border acquisitions from 1993-2001. A large part of the cross-border acquisitions was made by high-tech companies, in number of deals and in value, whereas this was a lot less regarding domestic acquisitions. Furthermore, high-tech companies may be more likely to face financial constraints, since their business generally involves more informational asymmetry. Lastly return on assets is included, to control for the profitability of a firm. For the same reason as the independent variables, consistent with Chen et al. (2009), the control variables are lagged and are defined as follows:
Control
Return on assets (ROA): This variable is defined as earnings before interest, taxes, depreciation and
amortization (EBITDA) (WC18198) divided by total assets (WC02999).
GDP-growth: This variable reflects the annual percentage growth of the gross-domestic product. Log(GDP): This variable reflects the logarithm of the gross domestic product of a country.
High-tech: This is a dummy variable that equals 1 if a firm is from a high-tech industry and zero
otherwise.A firm is classified as high-tech if it has a NACE Rev. 2 code starting with 21, 26, 303, 59-63 or 72, according to EUROSTAT definitions.13
4.3.4 Hypothesis testing and robustness check
The first hypothesis tested is whether the determinants of financial constraints at the firm and country level are more critical for cross-border acquisitions than for domestic acquisition. This hypothesis is tested using the probit model specification, in which the dependent variable equals 1 if the acquisition is cross-border and 0 if the acquisition is domestic. The null hypothesis for the included variables is that they are not significantly different from zero. I test the hypothesis first by pooling all observations over the years, including year dummies to correct for potential systematic effects through the years. Second, I will estimate the probit model for each year separately, to see if the outcomes are consistent throughout the years. I only estimate the years for which a minimum of 100 observations are present. The second hypothesis tested is whether determinants of financial constraints are more important for cross-border acquisitions outside Europe than for cross-border acquisitions within Western-Europe. To this end, I first estimate a regression in which the dependent variable equals 0 when the
13 High-tech classification on www.epp.eurostat.ec.europa.eu, the document is available at
R.G.R. Potgieser University of Groningen 25 acquisition is domestic and 1 if the acquisition is cross-border and within Western-Europe to see if determinants of financing constraints play a role. Next, once an acquisition is cross-border, I run a regression with the dependent variable equal to 0 if the acquisition is cross-border and within Western-Europe, and 1 if the acquisition is cross-border and outside Western-Europe, to see if financial constraints determinants have power in explaining differences between the two. Comparing the outcomes of the two regressions allows me to determine whether, once a company decides to go cross-border, if financial constraints determinants can explain differences in the type of cross-border acquisition.
Finally, I perform a robustness check, following Chen et al. (2009). This is done to check for the relationship between the probability that a company is constrained in its financing and the likelihood of making a cross-border acquisition. They follow a two-step analysis based on the empirical design of Kaplan and Zingales (1997). First, firms are classified based on their likelihood of being financially constrained, according to their interest coverage (EBITDA (WC18198) divided by interest expense (WC01251)). Companies in the highest quartile are classified as less likely financially constrained, and companies in the lowest quartile are classified as more likely financially constrained. Second, the following probit regression is run:
(6)
Variable definitions are similar to (5). The estimated probabilities of a firm being financially constrained are used in the third step, where the following probit regression is run:
̂ (7)
Again, variable definitions are similar to (5) and ̂ denotes the (fitted) probability that a company is more likely financially constrained.
It should be noted that by focusing on companies that engage in acquisitions, a sample selection bias could result. Companies that are more financially constrained may refrain from making acquisition altogether. So, it is possible that the importance of financial constraints determinants is underestimated. Chen et al. (2009) leave this issue unaddressed.Finally, I make the basic assumption that possible differences in cash holdings or leverage between firms that engage in domestic or cross-border acquisitions are not caused by non-value maximizing behaviour of managers, for example, managerial empire building. So, investments are assumed to be value enhancing.14
14
R.G.R. Potgieser University of Groningen 26
5 – DATA -
5.1 Data selection
Inclusion in the dataset is subject to making an acquisition and several further criteria. Chen et al. (2009) study all acquisition announcements with available data in a certain period and geographical area in their study; I choose to make several additional restrictions. Criteria are listed in Table I, and explained onwards. The number of remaining acquisitions is shown in parentheses. In order to collect relevant data about acquisitions, I have made use of Zephyr, a Bureau van Dijk publication, containing elaborate information regarding acquisitions.
Table I
Selection criteria for acquisitions
This table indicates relevant variables, criteria and values for sample selection purposes.
Variable Criterion No. of
acquisitions
1. Deal type - Acquisition (349,337)
2. Time period - Announced and completed between 01/01/2001 and 31/12/2009
(223,954)
3. Current deal status - Completed (166,065)
4. Listing status - Listed acquirer (52,321)
5. Deal value - 5 million euro minimum (20,781)
6. Percentage of stake - Initial stake = 0% - Final stake > 50%
(14,297) 7. Geography - Acquirer is from Western-Europe (ex. UK/Ireland) (3,485) 8. Industry - All industries, except for financial
companies (NACE Rev. 2 64-66) and utility companies (NACE Rev. 2 35-39)
(2,882)
9. Completeness - ISIN code and country available in Zephyr - Check-up Zephyr
- Maximum of 1 year between announcement and completion date
(2,501)
10. Data availability and additional requirements
- Data available on independent variables - Fiscal year-end is December 31
- No multiple acquisitions in the same year
(802)
R.G.R. Potgieser University of Groningen 27 There is actually evidence of a sixth takeover wave, starting mid-2003 in a period of economic recovery and cheap funding, and ending with the credit crisis in 2008 (McCarthy, 2011). This wave is characterized by a high incidence of cross-border activity (Martynova and Renneboog, 2008). Studying the period 2001-2009 allows me to look at a full cycle, going from a period at the end of a takeover wave with relatively little acquisitions (2001-mid 2003) and a consecutive takeover wave (mid 2003-2008) with a lot of cross-border acquisitions.
Regarding (3) the deal has to be completed so only serious acquisition offers are taken into account. Also, when the deal is completed, this means that the financing was successful.
Considering (4) I restrict myself to listed acquirers, primarily motivated because of data availability. The minimum deal value (5) is five million euro, to exclude deals with a minor impact on the acquirer. The acquired stake (6) is required to be a minimum of 50,1% and the initial stake to be 0%. This way, the acquirer wasn’t previously involved in the target (no prior information or knowledge), the deal involves a change in control and intra-group acquisitions are not included.
Concerning (7) I require the acquirer to be from Western-Europe, since my research area is restricted to Western-Europe. I exclude firms from the UK and Ireland for several reasons. Especially UK firms tend to make much more acquisitions than Continental European ones (Martynova and Renneboog, 2008), which could indicate that financing conditions are less important than with Continental European firms. In addition, Faccio and Lang (2002) show that there is a considerable difference in ownership patterns. Widely held firms are most important in UK and Ireland, whereas family controlled firms are most important in Continental European countries, often actively involved in management. Also, Ireland and the UK have a different legal system (common-law) and a more market based financial system whereas Continental European countries have code law legal systems and more bank-based financial systems (La Porta et al. 1998).
As to (8) acquirers from the financial industry (e.g. banking and insurance) and utility companies are excluded, because of their differing financial structure and the regulated industry nature.
Finally (9), companies with missing ISIN and country codes in Zephyr were excluded and the difference between the announcement and completion date was required to be non-negative and a maximum of one year. Also, the dataset was carefully checked to be consistent with the requirements, since occasionally Zephyr mistakenly included acquisitions that do not meet the stated criteria.
A total sample of 2501 acquisitions, made by 617 unique companies, remains. From these companies, data is collected with respect to the independent variables.
R.G.R. Potgieser University of Groningen 28 Information about ADRs is from BNY Mellon´s DR Directory. NACE Rev. 2 codes are used as a basis for industry classification, obtained from EUROSTAT.15 Table AI in the appendix (p. 47) provides an overview of all data sources used.
Finally, firms with missing data and firms making more than one acquisition, or other acquisitions than previously defined, in the same year are also excluded.16 Also, the fiscal year-end is required to be the 31th of December, to ensure consistency and comparability in firm-specific data across firms. The final sample consists of 802 acquisitions during the period 2001-2009, made by 566 unique companies. 498 acquisitions are cross-border, 271 are inside Western-Europe and 227 are outside Western-Europe. 304 acquisitions are domestic.
15 Classification scheme on www.epp.eurostat.ec.europa.eu, the document is available at
http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-07-015/EN/KS-RA-07-015-EN.PDF, accessed 24/04/2011.
16This avoids biasing the results. Chen et al. (2009) do seem to include multiple acquisitions per company. For
R.G.R. Potgieser University of Groningen 29
5.2 Descriptive statistics
Table II presents an overview of the number of domestic and cross-border acquisitions made per country. It also shows the total number of acquisitions made and the percentage of these acquisitions that is cross-border. In my sample, Luxembourg, Austria, Denmark and the Netherlands show the highest relative incidence of cross-border acquisitions. Greece, Portugal, and Italy show the lowest relative incidence of cross-border acquisitions. 62% of the acquisitions in my sample are cross-border. Figure A1 in the appendix (p. 56) gives a graphical representation of the total share of acquisitions per country. It can be seen that France, Germany and Sweden make the most acquisitions in my sample; Luxembourg, Norway, Portugal and Austria the least.
Table II
Overview domestic and cross-border acquisitions per country
Table II shows a division in cross-border and domestic acquisitions per country, including the cross-border acquisitions of a country as a % of the total number of acquisitions of that country.
Figure A2 in the appendix (p. 57) provides an overview of the total number of acquisitions, cross-border and domestic, over the time period studied (2001-2009). It can be seen that there is a clear rise in the number of acquisitions from 2001 onwards, coming to a halt after 2007. The number of acquisitions clearly declines in the crisis years of 2008 and 2009. So, the sample shows a pattern similar to the sixth merger wave (Martynova and Renneboog, 2008; McCarthy 2011). Table AII in the appendix (p. 48) shows a classification by industry of the acquirer.
Country Domestic acquisitions
Cross-border
acquisitions Total Cross-border %
R.G.R. Potgieser University of Groningen 30 Table III gives descriptive statistics and univariate results for the independent variables and deal characteristics. Acquisitions are partitioned in groups on the basis of whether they are domestic or cross-border.
Table III
Descriptive statistics and univariate analysis acquisitions
Table III displays descriptive statistics for the independent variables, partitioned by domestic and cross-border acquisitions. It also displays the Wilcoxon Z-statistic for differences in median between these two groups. *** denotes significance at the 1% level, ** at the 5% level, * at the 10% level. For a definition of the firm-specific,
governance, financial development and control variables, I refer to the description on pages 23 and 24.
The final column shows the Z-statistic of the Wilcoxon rank sum test between two groups.17 The appendix contains several other statistics for the whole sample. Descriptive statistics are given in Table AIII in the appendix (p. 49), Table AIV (p. 50) provides additional deal characteristics, Table AV (p. 51) presents the correlation matrix.
17
A non-parametric test is used for all tests, since almost all variables are not normally distributed (according to the Jarque-Bera statistic for the full sample). Log (assets) and GDP growth could also have been tested with a t-test, but this gives the same results. See Table AIII in the appendix (p. 49).
N=802 (1) Domestic acq. (N=304) (2) Cross-border acq. (N=498) Wilcoxon Z-stat. Mean Median St.dev. Mean Median St.dev. H0: (1)=(2)
Deal characteristics
Log (deal value in th. EUR) 4.61 4.44 0.71 4.80 4.68 0.74 3.95*** All cash payment (N=550) 0.36 0.00 0.48 0.60 1.00 0.49 4.73***
Firm-specific variables
Log (assets in th. EUR) 5.59 5.59 0.95 5.99 5.96 0.85 5.69*** Cash holdings 0.14 0.09 0.16 0.14 0.09 0.15 0.91 Leverage 0.24 0.21 0.22 0.22 0.21 0.15 -0.58
MtB 1.87 1.44 1.87 1.87 1.59 1.03 3.03***
ADR 0.07 0 0.26 0.13 0 0.34 1.38
Dividend (yes or no) 0.66 1 0.47 0.75 1 0.43 2.11** Governance variables Shareholder protection 17.37 17.20 3.65 16.77 17.20 3.46 -1.56 Creditor protection 1.80 1.20 1.01 1.98 2.00 1.02 2.73*** WGI 7.99 7.66 2.17 8.82 9.62 1.97 5.58*** Financial development
